This article studies the morphological parameters of vegetative and generative organs of different age groups of Crataegus ambigua from four populations in Western Karatau (Mangistau region, Kazakhstan). The study examined four populations Pop 1 Sultan Epe, Pop 2 Karakozaiym, Pop 3 Emdikorgan, and Pop 4 Samal located in various gorges of western Karatau. Some phylogenetic inference methods were applied using six genetic markers atpF-atpH, ITS, matK, psbK-psbI, rbcL, and trnH-psbA. We also used a statistical analysis of the vegetative and generative organs of plants for three age groups of plants (virgin, young generative, and adult generative). According to the age structure, Samal populations have a high concentration of young generative plants - 42.3% and adult generative plants - 30.9%. Morphological analysis showed the significance of the parameters of the generative organs and separated the Samal population into a separate group according to the main PCoA coordinates. The results of the floristic analysis showed that the populations in the Samal have a high concentration of species diversity. The comparative dendrogram of UPGMA shows the informativeness of the use of genetic markers and the psbK-psbI region can be used to judge the difference between the fourth Samal population and the other three.

Interior permanent magnets motors are widely used in applications requiring high power den-sity and high efficiency due to their high torque generating capabilities. Recently, given the re-cent price fluctuations and unstable supply of rare-earth permanent magnets, alternative config-urations with reduced use of permanent magnets are being sought. Among the various candi-dates related to this, the consequent-pole type rotor structure can halve the number of perma-nent magnets used compared with conventional structures. However, in the no-load analysis, the waveform of the back electromotive force becomes asymmetric, generating a harmonic component. As a result, there is a disadvantage that the torque ripple increases. To overcome these shortcomings, we propose a novel rotor structure that applies a consequent-pole structure to an embedded permanent-magnet motor structure, wherein a number of permanent magnets are arranged in a flared structure to constitute a single polarity. In the proposed flared-structured magnet arrangement, it is possible to adjust the angle of the permanent magnet and the polar angle to mitigate the asymmetry of the back EMF waveform. The proposed struc-ture was optimized with a genetic algorithm and a prototype of the optimal model was con-structed and experimentally evaluated to verify its validity. Finally, the performance improve-ment and validity of the proposed structure were verified by comparing the analysis results of the optimal model with the experimental results.

Gut luminal dysbiosis and pathobiosis result in compositional and biodiversify alterations in the microbial and host co-metabolites. The main mechanism of bacterial evolution is horizontal gene transfer (HGT) and acquisition of new traits can be achieved through mobile genetic element (MGEs) exchange. Introducing genetically engineered microbes (GEMs) might break the harmonized balance in the intestinal compartment. The present objectives are: 1. To reveal the role played by GEMs' horizontal gene transfers in changing the landscape of the enteric microbiome eubiosis 2. To expand on the potential detrimental effects of those changes on human genome and health. A search of articles published in PubMed/MEDLINE, EMBASE, and Scielo from 2000 to august 2023, using appropriate MeSH entry terms were performed. GEMs' horizontal gene exchanges might induce multiple human diseases. The new GEMs can change the long-term natural evolution of the enteric pro- or eukaryotic cells inhabitants. The worldwide regulatory authority's safety control of GEMs is not enough to protect public health. Viability and biocontainment and many other aspects are only partially controlled and harmful consequences for public health should be avoided. Always remember that prevention is the most cost- effective strategy and "primum non nocere" is the name of the game.

The oxidative coupling of methane (OCM) to produce ethane and ethylene (C2 compounds) as platform chemicals involves complex chemistry with reactions both in the gas phase and on the catalyst surface, resulting in a distribution of products at the expense of C2 selectivity. This work uses experimental data from a variety of mixed metal oxides on supports at different reaction conditions (temperature, contact time, and reactant flow rates) to train a random forest regressor that predicts methane conversion and C2 selectivity (key performance indicators (KPIs)), and deploys it to locate optimal conditions that maximize C2 yield for a catalyst. Investigating the regressor interpretability via feature importance reveals that the choice of metals and support are crucial to C2 selectivity predictions, while the predictions of methane conversion are driven by the reaction conditions. The machine learning (ML) regressor is used as a surrogate to develop performance curves for each of the catalysts via a multi-objective optimization routine that seeks to maximize the KPIs in the decision space of reaction conditions, is seen to locate optimal conditions at which the maximum C2 yields for catalysts are predicted to be 15%, higher on average. Analyzing the catalysts in the space of their performance curves with respect to a popular OCM catalyst, Mn-Na2WO4/SiO2, reveals distinct patterns based on intrinsic properties of metals and supports. Further, the decision space with catalyst descriptors and reaction conditions is optimized for high C2 yields using the ML surrogate, in a static multi-objective optimization routine, and an adaptive Bayesian routine, where the latter was found to have a wider field focus in proposing catalyst formulations and conditions. Transition metal oxides on a variety of supports were proposed but not their lanthanide oxide counterparts.

Achieving high-yield potential is always the ultimate objective of any breeding program. However, various abiotic stresses like salinity, drought, cold, flood, and heat hampered rice productivity tremendously. Salinity is one of the most important abiotic stresses that adversely affect rice grain yield. The present investigation was undertaken to dissect new genetic loci, which are responsible for salt tolerance at the early seedling stage in rice. A bi-parental mapping population (F2:3) was developed from the cross between BRRI dhan28/Akundi, where BRRI dhan28 (BR28) is a salt-sensitive irrigated (boro) rice mega variety and Akundi is a highly salinity tolerant Bangladeshi origin indica rice landrace that is utilized as a donor parent. We report stable QTLs for salt tolerance from a common donor (Akundi) irrespective of two different genetic backgrounds (BRRI dhan49/Akundi and BRRI dhan28/Akundi). A robust 1k-Rice Custom Amplicon (1k-RiCA) SNP marker genotyping platform was used to genotype this bi-parental population. After eliminating markers with high segregation distortion, 886 polymorphic SNPs built a genetic linkage map covering 1526.5 cM of whole rice genome with an average SNP density of 1.72 cM for the 12 genetic linkage groups. A total of 12 QTLs for nine different salt tolerance-related traits were identified using QGene and inclusive composite interval mapping of additive and dominant QTL (ICIM-ADD) under salt stress on seven different chromosomes. All of these 12 new QTLs found unique, as no other map from the previous study has reported these QTLs in the similar chromosomal location and found them different from extensively studied Saltol, SKC1, OsSalT and salT locus. Twenty-eight significant digenic/epistatic interactions identified between chromosomal regions linked to or unlinked to QTLs. Akundi acts like a new alternate donor source of salt tolerance except for other usually known donors like Nona Bokra, Pokkali, Capsule, and Hasawi used in salt tolerance genetic analysis and breeding programs worldwide, including Bangladesh. Integration of the seven novel and stable QTLs (qSES1, qSL1, qRL1, qSUR1, qSL8, qK8, qK1) reported in this investigation will expedite the cultivar development that is highly tolerant to salt stress.

Increased mitotic activity is associated with the genesis and aggressiveness of many cancers. To assess the clinical value of mitotic activity as prognostic biomarker, we performed a pan-cancer study on the mitotic network activity index (MNAI) constructed based on 54-gene mitotic apparatus network. Our pan-cancer assessment on TCGA (33 tumor types, 10,061 patients) and validation on other publicly available cohorts (23 tumor types, 9,209 patients) confirmed the significant association of MNAI with overall survival, progression-free survival and other prognostic endpoints in multiple cancer types, including Lower-Grade Gliomas (LGG), Breast Invasive Carcinoma (BRCA) and many others. We also showed significant association of MNAI with genetic instability, which provides a biological explanation of its prognostic impact at pan-cancer landscape. Futhermore, we found that patients with high MNAI benefit more from anti-PD-1 and Anti-CTLA-4 treatment. In addition, we demonstrated on LGG and BRCA that the multimodal integration of MNAI and the AI-empowerd Cellular Morphometric Subtypes (CMS) significantly improved the predictive power of prognosis compared to MNAI and CMS alone. Our results suggest that MNAI can be used as a potential prognostic biomarker for different tumor types toward different clinical endpoints, and multimodal integration of MNAI and CMS exceeds individual biomarker for precision prognosis.

Ventricina del Vastese is a traditional dry fermented sausage from Central Italy not yet charac-terized for occurrence, identity and safety of coagulase negative staphylococci (CNS), a bacterial group technologically important for this kind of products. Therefore, in this study, 98 CNS isolates from four manufacturers were differentiated by Repetitive element palindromic PCR (Rep-PCR) and identified by 16S rRNA gene sequencing. These were examined for genes encoding biogenic amine (BA) production, resistance to aminoglycosides, β-lactams and tetracyclines and staphylococcal enterotoxins (SEs). Staphylococcus succinus (55%) predominated, followed by S. xylosus (30%), S. epidermidis (7.4%), S. equorum (3.1%), S. saprophyticus (3.1%) and S. warneri (1%). One S. succinus subsp. casei isolate was slightly β-hemolytic. SEs and the histidine decarboxylase gene hdcA were not detected, while the tyrosine decarboxylase gene tdcA was detected in four S. xylosus isolates. A blaZ beta-lactamase gene and tetracycline resistance genes tetK (six isolates) and tetA (one isolate also bearing tetK) were found, respectively. However, fewer hazardous and AR traits compared to CNS examined in other studies were found, as a probable consequence of using meat from animals reared in conditions that allow to minimize pathogen circulation and antibiotic use. Therefore, appropriate production conditions can reduce these threats.

The ability to directly look into genome sequences has opened great opportunities in plant breeding. Yet, the assembly of full-length chromosomes remains one of the most difficult problems in modern genomics. Genetic maps are commonly used in de novo genome assembly and are constructed on the basis of a statistical analysis of the number of recombination rather than physical distance in base pairs. This may affect the accuracy of the ordering and orientation of scaffolds within the chromosome, especially in the region of recombination suppression. Here we report the use of Tyr-FISH for validation of genetic and pseudochromosome maps. For probe design we developed a pipeline that is based on selection of a unique sequence with minimal potential fluorescent background arising from non-specific in situ hybridization. In total of 24 unique genes were located on physical chromosomes 2 and 6. The order of markers has been corrected by integration genetic and cytogenetic maps. Tyr-FISH mapping showed that the order of 23.1% (chromosome 2) and 27.3% (chromosome 6) of the tested genes differed between physical chromosomes and pseudochromosomes. Also the position of the mlh1 gene, which was not on the genetic map, was defined on physical chromosome 2. The results suggest the possibility of Tyr-FISH mapping of any gene that may not be neither on the genetic map nor in the assembly. Hence, Tyr-FISH provides valuable information for the improvement of the genome assembly.

Externalizing behavior in its more extreme form is often considered a problem to the individual, their families, teachers and society as a whole. Several brain structures have been linked to externalizing behavior and such associations may arise if the (co)development of externalizing behavior and brain structures share the same genetic and/or environmental factor(s). We assessed externalizing behavior with the Child Behavior Checklist and Youth Self Report, and brain volumes and white matter integrity (FA and MD) with magnetic resonance imaging in the BrainSCALE cohort, consisting of twins and their older siblings from 112 families measured longitudinally at ages 10, 13, and 18 years of the twins. Genes influenced externalizing behavior and changes therein (h2 up to 88%). More pronounced externalizing behavior was associated with higher FA (observed correlation rph up to +0.20) and lower MD (rph up to –0.20); with sizeable genetic correlations (FA ra up to +0.42; MD ra up to –0.33). Cortical gray matter (CGM; rph up to –0.20) and cerebral white matter (CWM; rph up to +0.20) volume were phenotypically but not genotypically associated with externalizing behavior. These results suggest a potential mediating role for global brain structures in the display of externalizing behavior during adolescence that are both partially explained by the influence from the same genetic factor.

Wheat (Triticum aestivum L.) breeding programs can take over a decade to release a new variety. However, new methods of selection such as genomic selection (GS) must be integrated to decrease the time it takes to release new varieties to meet the demand of a growing population. The implementation of GS into breeding programs is still being explored, with many studies showing its potential to change wheat breeding through achieving higher genetic gain. In this review, we explore the integration of GS for a wheat breeding program by redesigning the traditional breeding pipeline to implement GS. We propose implementing a two-part breeding strategy by differentiating between population improvement and product development. The implementation of GS in the product development pipeline can be integrated into most stages and can predict within and across breeding cycles. Additionally, we explore optimizing the population improvement strategy through GS recurrent selection schemes to reduce crossing cycle time and significantly increase genetic gain. The recurrent selection schemes can be optimized for parental selection, maintenance of genetic variation, and optimal cross-prediction. Overall, we outline the ability to increase the genetic gain of a breeding program by implementing GS and a two-part breeding strategy.

A protocol was designed for plasmid curing using a novel counter-selectable marker, named pylSZK-pylT, in Escherichia coli. The pylSZK-pylT marker consists of the archaeal pyrrolysyl-tRNA synthetase (PylRS) and its cognate tRNA (tRNApyl) with modification, and incorporates an unnatural amino acid (Uaa), Nε-benzyloxycarbonyl-l-lysine (ZK), at a sense codon in ribosomally synthesized proteins, resulting in bacterial growth inhibition or killing. Plasmid curing is performed by exerting toxicity on pylSZK-pylT located on the target plasmid, and selecting only proliferative bacteria. All tested bacteria obtained using this protocol had lost the target plasmid (64/64), suggesting that plasmid curing was successful. Next, we attempted to exchange plasmids with the identical replication origin and an antibiotic resistance gene without plasmid curing using a modified protocol, assuming substitution of plasmids complementing genomic essential genes. All randomly selected bacteria after screening had only the substitute plasmid and no target plasmid (25/25), suggesting that plasmid exchange was also accomplished. Counter-selectable markers based on PylRS-tRNApyl, such as pylSZK-pylT, may be scalable in application due to their independence from the host genotype, applicability to a wide range of species, and high tunability due to the freedom of choice of target codons and Uaa’s to be incorporated.

Takotsubo syndrome (TTS), recognized as stress’s cardiomyopathy, or better as left ventricular apical balloon syndrome in the recent years, is a rare pathology, described for the first time by Japanese researchers in 1990. TTS is characterized by an interindividual heterogeneity in onset and progression, and by the strong predominance in postmenopausal women. The clear causes of these TTS features are uncertain, given the reduced understanding of this intriguing syndrome until now. However, the increasing frequency of TTS cases in the last years, and particularly correlated to SARS-CoV-2 pandemic, leads us to imperative necessity both of a complete knowledge of TTS pathophysiology for identifying biomarkers facilitating its management, and targets for specific and effective treatments. The suspect of a genetic basis in TTS pathogenesis has been evidenced. Accordingly, familial form of TTS has been described. But a systematic and comprehensive characterization of the genetic or epigenetic factors significantly associated with TTS is lacking. Thus, we, here, conducted a systematic review of literature before June 2021, to contribute to identify potential genetic and epigenetic factors associated with TTS. Interesting data were evidenced, but of reduced number and with diverse limitations. Consequently, we concluded further work is needed to address the gaps discussed, and probably a clear evidence may arrive using multi-omics investigations.

Every protein consists of a linear sequence over an alphabet of $20$ letters/amino acids. The sequence unfolds in the $3$-dimensional space through secondary (local foldings), tertiary (bonds) and quaternary (disjoint multiple) structures. The mere existence of the genetic code for the $20$ letters of the linear chain could be predicted with the (informationally complete) irreducible characters of the finite group $G_n:=\mathbb{Z}_n \rtimes 2O$ (with $n=5$ or $7$ and $2O$ the binary octahedral group) in our previous two papers. It turns out that some quaternary structures of protein complexes display $n$-fold symmetries. We propose an approach of secondary structures based on free group theory. Our results are compared to other approaches of predicting secondary structures of proteins in terms of $\alpha$ helices, $\beta$ sheets and coils, or more refined techniques. It is shown that the secondary structure of proteins shows similarities to the structure of some hyperbolic $3$-manifolds. The hyperbolic $3$-manifold of smallest volume --Gieseking manifold--, some other $3$ manifolds and the oriented hypercartographic group are singled out as tentative models of such secondary structures. For the quaternary structure, there are links to the Kummer surface.

Acute leukemias constitute some of the most common malignant disorders. Despite significant progress made in the treatment of these disorders, their etiology remains unknown. A large and diverse group of genetic and environmental variables have been proposed. The role of a variety of factors, including pre-existing and acquired genetic mutations, exposure to radiation and various chemicals during pre-conception, pregnancy and throughout life have been explored. The effects of inherited genetic variations and disorders, pre-existing diseases, infectious agents, hobbies, occupations, prior treatments and a host of other factors have been proposed, but none is universally applicable to all cases. Variation in the incidence and prognosis based on the age, sex, race, type of the disease, geographic area of residence and other factors are intriguing, but remains unexplained. Advances in genomic profiling, including genome‐wide gene expression, DNA copy number, and single nucleotide polymorphism [SNP] genotype may shed some light on the role of genetics in these disparities. Separate two-hit hypothesis for the development of acute myeloblastic and lymphoblastic leukemia have been proposed. The latter combines genetics and infection factors resulting in leukemogenesis. A number of pre- and post-natal environmental conditions and exposure to infections, including a mycovirus infected Aspergillus flavus, have been suggested. The exact nature, timing, sequence of the events and mechanisms resulting in occurrence of leukemia requires further investigations. This review summarizes some of the above factors and the direction for future research on the etiology of acute leukemias.

Cardiovascular disease (CVD) comprises a group of heart and circulatory disorders, which are regarded as a global medical issue with high prevalence and mortality rates. Currently, vascular regenerative surgery represents the most employed therapeutic option to treat ischemic disorders, even though not all the patients are amenable to surgical revascularization. Therefore, more efficient therapeutic approaches are urgently required to promote neovascularization. Therapeutic angiogenesis represents an emerging strategy that aims at reconstructing the damaged vascular network by stimulating local angiogenesis and/or promoting de novo blood vessel formation according to a process known as vasculogenesis. Circulating endothelial colony forming cells (ECFCs), in turn, represent truly endothelial precursors able to aggregate into bidimensional tube networks and to originate patent vessels. Accordingly, ECFCs provide the most rationale and promising cellular candidate for therapeutic purposes. The current review provides a brief outline on the origin and characterization of ECFCs and a summary of the progress in preclinical studies aiming at assessing their efficacy in a variety of ischemic disorders, including AMI, PAD, ischemic brain disease and retinopathy. We also describe how to enhance the vasoreparative potential of ECFCs by boosting specific pro-angiogenic signalling pathways either pharmacologically or through gene manipulation. Taken together, these observations suggest that ECFCs represent a useful strategy to treat ischemic diseases.

Bus operators around the world are facing the transformation of their fleets from fossil-fuelled to electric buses. Two technologies prevail: Depot charging and opportunity charging at terminal stops. Total cost of ownership (TCO) is an important metric for the decision between the two technologies, however, most TCO studies for electric bus systems rely on generalised route data and simplifying assumptions that may not reflect local conditions. In particular, the need to re-schedule vehicle operations to satisfy electric buses’ range and charging time constraints is commonly disregarded. We present a simulation tool based on discrete-event simulation to determine the vehicle, charging infrastructure, energy and staff demand required to electrify real-world bus networks. These results are then passed to a TCO model. A greedy scheduling algorithm is developed to plan vehicle schedules suitable for electric buses. Scheduling and simulation are coupled with a genetic algorithm to determine cost-optimised charging locations for opportunity charging. A case study is carried out in which we analyse the electrification of a metropolitan bus network consisting of 39 lines with 4748 passenger trips per day. The results generally favour opportunity charging over depot charging in terms of TCO, however, under some circumstances, the technologies are on par. This emphasises the need for detailed analysis of the local bus network in order to make an informed procurement decision.

Saccharomyces cerevisiae are a phenotypically diverse species that adapt to a wide variety of environments by exploiting standing genetic diversity and selecting for advantageous mutations. Glyphosate and copper-based herbicides/ fungicides affect non-target organisms, these incidental exposures can impact microbial populations. In this study, glyphosate resistance was found in the historical collection of yeast which was collected over the last century, but only in yeast isolated after the introduction of glyphosate. The highest glyphosate-resistant yeasts were isolated from agricultural sites. However, herbicide application at these sites was not recorded. In an effort to assess glyphosate resistance and impact on non-target microorganisms, yeast were harvested from 15 areas with known herbicidal histories, including an organic farm, conventional farm, remediated coal mine, suburban locations, state park, and a national forest. Yeast representing 23 genera were isolated from 237 samples of plant, soil, spontaneous fermentation, nut, flower, fruit, feces, and tree material samples. Saccharomyces, Candida, Metschnikowia, Klyveromyces, Hanseniaspora, and Pichia were other genera commonly found across our sampled environments. Managed areas had less species diversity and at the brewery, only Saccharomyces and Pichia were isolated. A conventional farm growing RoundUp Ready™ corn had the lowest phylogenetic diversity and the highest glyphosate resistance. The mine was sprayed with multiple herbicides including a commercial formulation of glyphosate; however, the yeast did not have elevated glyphosate resistance. In contrast to the conventional farm, the mine was exposed to glyphosate only one year prior to sample isolation. Glyphosate resistance is an example of the anthropogenic selection of nontarget organisms.

Evaluation of agronomic practices, genetic diversity and population structure of Moringa oleifera (Lam.) is crucial to its sustainable utilization to ensure food and nutritional security. Four agronomic practices of field preparation and soil analysis, regeneration, weeding and pruning, as well as harvesting, were adopted. Genetic diversity and population structure of 20 populations were evaluated using amplified fragment length polymorphism (AFLP) primer pairs (E-ACC/M-CAC and E-ACA/M-CAG). The effects of agronomic practices on leaf quantity production were evaluated using standard procedures. The physico-chemical and morphological data were analyzed using descriptive statistics, while genetic diversity, population structure, dendrogram reconstruction, and Principal Component Analysis (PCA) were analyzed from the AFLP genetic data. Significant effects (P ≤ 0.05) of agronomic practices on the quantity of leaf production were recorded across the accessions. The two primer pairs generated a total of 80 alleles with a mean major allele frequency of 0.0250. Gene diversity and polymorphic information content (PIC) values were high with a mean of 0.98 and 0.974, respectively. The Gst value of 0.0490 indicated that 5 % of the total genetic divergence was among the population while 95 % within the population. Dendrogram reconstruction with genetic distance ranging from 0.87 to 0.96 segregated the accessions into eleven clusters while PCA generated six cluster groups. Field preparation and physico-chemical soil properties, regeneration, weeding and pruning, and stand development, as well as harvesting, influenced the leaf yield and genetic diversity observed in this study. The genetic data revealed that some accessions were clustered along eco-geographical lines while others grouped disparately. Identified potential parent genotypes with valuable and desirable genetic traits can be exploited for commercial, breeding and conservation purposes to ensure sustainable utilization of the species in Nigeria.

Akt1 is a proto-oncogene that is over active in most cancers. Akt1 activation requires phosphorylation at Thr308; phosphorylation at Ser473 further enhances catalytic activity. Akt1 activity is also regulated via interactions between the kinase domain and the N-terminal auto-inhibitory pleckstrin homology (PH) domain. As it was previously difficult to produce Akt1 in site-specifically phosphorylated forms, the contribution of each activating phosphorylation site to auto-inhibition was unknown. Using a combination of genetic code expansion and in vivo enzymatic phosphorylation, we produced Akt1 variants containing programmed phosphorylation to probe the interplay between Akt1 phosphorylation status and the auto-inhibitory function of the PH domain. Deletion of the PH domain increased the enzyme activity for all three phosphorylated Akt1 variants. For the doubly phosphorylated enzyme, deletion of the PH domain relieved auto-inhibition by 295-fold. We next found that phosphorylation at Ser473 provided resistance to chemical inhibition by Akti-1/2 inhibitor VIII. The Akti-1/2 inhibitor was most effective against pAkt1T308 and showed 4-fold decreased potency with Akt1 variants phosphorylated at Ser473. The data highlight the need to design more potent Akt1 inhibitors that are effective against the doubly phosphorylated and most pathogenic form of Akt1.

The Late Heavy Bombardment Period (4.1 to 3.8 billion years ago) of heightened impact cratering activity on young Earth is likely the driving force for the origin of life. During the Eoarchean, asteroids such as carbonaceous chondrites delivered the building blocks of life and water to early Earth. Asteroid collisions created innumerable hydrothermal crater lakes in the Eoarchean crust which inadvertently became the perfect cradle for prebiotic chemistry. These hydrothermal crater lakes were filled with cosmic water and the building blocks of life. forming a thick prebiotic soup. The unique combination of exogenous delivery of extraterrestrial building blocks of life, and the endogenous biosynthesis in hydrothermal impact crater lakes very likely gave rise to life. A new symbiotic model for the origin of life within the hydrothermal crater lakes is here proposed. In this scenario, life arose around four billion years ago through five hierarchical stages of increasing molecular complexity: cosmic, geologic, chemical, information, and biological. During the prebiotic synthesis, membranes first appeared in the hydrothermal crater lakes, followed by the simultaneous origin of RNA and protein molecules, creating the RNA/protein world. These proteins were noncoded protein enzymes that facilitated chemical reactions. RNA molecules formed in the hydrothermal crater basin by polymerization of the nucleotides on the montmorillonite mineral substrate. Similarly, the initial synthesis of abiotic protein enzymes was mediated by the condensation of amino acids on pyrite surfaces. The regular wet-dry cycles within the crater lakes assisted further concentration, condensation, and polymerization of the RNAs and proteins. Lipid membranes randomly encapsulated amino acids, RNA, and protein molecules from the prebiotic soup to initiate a molecular symbiosis inside the protocells, this led to the hierarchical emergence of several cell components. As the role of protein enzymes became essential for catalytic process in the RNA/protein world, Darwinian selection from noncoded to coded protein synthesis led to translation systems and the genetic code, heralding the information stage. In this stage, the biochemical pathways suggest the successive emergence of translation machineries such as tRNAs, aaRS, mRNAs, and of ribosomes for protein synthesis. The molecular attraction between tRNA and amino acid led to the emergence of translation machinery and the genetic code. tRNA is an ancient molecule that created mRNA for the purpose of storing amino acid information like a digital strip. Each mRNA strand became the storage device for genetic information that encoded the amino acid sequences in triplet nucleotides. As information became available in the digital languages of the codon within mRNA, biosynthesis became less random and more organized and directional. The original translation machinery was simpler before the emergence of the ribosome than that of today. We review three main concepts on the origin and evolution of the genetic code: the stereochemical theory, the coevolution theory, and adaptive theory. We believe that these three theories are not mutually exclusive, but are compatible with our coevolution model of translations machines and the genetic code. We suggest biosynthetic pathways as the origin of the translation machine that provided the framework for the origin of the genetic code. During translation, the genetic code developed in three stages coincident with the refinement of the translation machinery: GNC code with four codons and four amino acids during interactions of pre-tRNA/pre-aaRS /pre-mRNA, SNS code consisting of 16 codons and 10 amino acids appeared during the tRNA/aaRS/mRNA interaction, and finally the universal genetic code evolved with the emergence of the tRNA/aaRS/mRNA/ribosome machine. The universal code consists of 64 codons and 20 amino acids, with a redundancy that minimizes errors in translation. To address the question of the origin of the biological information system in the RNA/protein world, we converted letter codons into numerical codons in the Universal Genetic Code Table. We developed a software called CATI (Codon-Amino Acid-Translator-Imitator) to translate randomly chosen numerical codons into corresponding amino acids and vice versa, gaining insight into how translation might have worked in the RNA/protein world. We simulated the likely biochemical pathways for the origin of translation and the genetic code using the Model-View-Controller (MVC) software framework, and the translation machinery step-by-step. We used AnyLogic software to simulate and visualize the evolution of the translation machines and the genetic code. We conclude that the emergence of the information age from the RNA/protein world was a watershed event in the origin of life about four billion years ago.

Grass pea (Lathyrus sativus L.) is a nutritious legume crop well-adapted to fragile agro-ecosystems that can survive under challenging climatic conditions. The cultivation of grass pea faces stigma primarily due to the presence of β-N-Oxalyl-l-α, β-diaminopropionic Acid (β-ODAP), which is associated with a risk of inducing neurolathyrism upon prolonged consumption of its grains as a staple diet. The grass pea improvement program of International Center for Agricultural Research in the Dry Areas (ICARDA) aims to reduce β-ODAP content to a safe level along with improving yield potential and nutritional quality of grass pea. In this study, 183 germplasm accessions representing 13 different Lathyrus species and 11 L. sativus breeding lines were evaluated for β-ODAP content based on Rao protocol and mineral concentration using ICP-OES. Significant variability was observed among the accessions for the studied traits. The results showed low β-ODAP content and high mineral concentration in 25 accessions of crop wild relatives (CWR) which included L. cicera, L. ochrus and L. cassius with one accession IG65277 of L. cassius, in addition to two lines IG117034 and ACC1335 of L. sativus having very low ODAP content. Furthermore, some accessions of L. pseudocicera, L. aphaca, L. cicera, L. marmoratus, L. gorgoni and L. tingitanus also showed low β-ODAP content. The results showed significant positive correlations between different trait combinations viz., K and P (r =0.193***), K and Fe (r =0.177***), Mn and Fe (r = 0.210***), Mn and Se (r = 0.137***), ODAP and Mg (r = 0.158**), and ODAP and Ca (r = 0.140**). L. cicera, L. ochrus, and L. cassius were identified as a great source for improving the mineral concentration and reducing ODAP content in the cultivated grass pea.

Quorum sensing (QS) is a sophisticated bacterial cell-to-cell communication mechanism that allows bacteria to sense population density through the secretion and detection of diffusible small molecular signals. This process leads to the coordinated expression of specific genes at the transcriptional level. Over time, continuous research has elucidated the genetic elements and regulatory principles of QS. Recently, synthetic biology has leveraged these insights to construct genetic circuits incorporating QS components, enabling both intra-species and inter-species artificial communication. These QS-based genetic circuits hold significant potential for applications in biotechnology and biomedicine. This paper reviews several well-characterized microbial QS systems and their functional roles, while also introducing the application of QS-based genetic circuits in cellular communication across species. The paper further discusses the role of QS in the development of biological computing tools, population density regulation, and metabolic flux control, offering a forward-looking perspective on future advancements. For intra-species communication, the focus is on the use of QS systems in constructing biological computing tools, including toggle switches, biosensors, and logic gates in synthetic biology. These tools, designed on the QS mechanism, can more precisely coordinate cellular behavior by integrating biological control circuits to achieve spatial, temporal, and population-level regulation. In the context of inter-species communication, the introduction of QS systems plays a pivotal role in population density control and metabolic flow regulation. By recombining metabolic networks, QS enables the redistribution of metabolic flux in desired pathways, facilitating the regulation of population density and the co-culture of mixed strains. Moreover, combining QS with oscillator models has shown great potential in synchronizing microbial communities. In summary, in-depth research into QS mechanisms and their applications not only lays a solid foundation for understanding microbial ecological competition and dynamic balance, but also offers promising avenues for regulating pathogenic bacteria and developing innovative disease control strategies.

Introduction: Situated within the broad spectrum of lymphoproliferative diseases, Hodgkin (HL) and non-Hodgkin lymphomas (NHL) are neoplasms renowned for their complexity and heterogeneity, insidious onset, and frequent detectability at advanced, even terminal, stages worldwide.Objectives: The study aimed to compile conclusive statistics, identify genetic factors, and establish correlations with environmental carcinogens, while simultaneously highlighting the importance of developing preventive informational programs for the population.Materials and Methods: The retrospective study encompassed individuals admitted to the Hematology Clinic of "Sfântul Spiridon" Hospital in Iași between January 1, 2010 and December 31, 2019. Out of 1916 patients hospitalized with hematologic neoplasms, 392 were diagnosed with lymphoma. Eighty-four cases exhibiting genetic predisposition (21.42%) were selected, among which 20 displayed a heightened genetic burden (5.1%). These cases were thoroughly analyzed based on medical records.Results and discussion: The study underscores/highlights/emphasizes the significant position of malignant lymphomas among hematologic neoplasms (20.45%). Although the majority do not present malignant heredocolateral antecedents(78.58%), with an estimated empirical recurrence risk(2-4%), predisposing diseases, along with exposure to environmental carcinogens, as well as the region of residence, play fundamental roles in the determinism. It is observed that older individuals from urban agglomerations, especially males (54-70%), have a higher probability of developing these conditions and are therefore recommended to undergo regular check-ups.Conclusion: Unequivocally characterized by a vast etiology, multifactorial diseases result from the bilateral interaction between environmental and hereditary factors.

Accurate, rapid, and stable prediction of electrical energy consumption is essential for decision-making, energy management, efficient planning, and reliable power system operation. Errors in forecasting can lead to electricity shortages, wasted resources, power supply interruptions, and even grid failures. Accurate forecasting enables timely decisions for secure energy management. However, predicting future consumption is challenging due to the variable behavior of customers, requiring flexible models that capture random and complex patterns. Existing forecasting methods, both traditional and modern, have limitations and do not fully meet accuracy expectations. To address these issues, this research introduces a hybrid models that combine FCRBM based forecaster, and GWDO based optimizer, namely FS-FCRBM-GWDO approach to enhance the model performance in STLF have been developed. While some models excel in accuracy and others in convergence rate, both aspects are crucial. The main objective is to create a forecasting model that provides reliable, consistent, and precise predictions for effective energy management. This led to the development of a novel two-stage hybrid model. The first stage predicts electrical energy usage through four modules using deep learning, support vector machines, and optimization algorithms. The second stage optimizes energy management based on predicted consumption, focusing on reducing costs, managing demand surges, and balancing electricity expenses with customer inconvenience. This approach benefits both consumers and electricity corporations by lowering bills and enhancing power system stability. Simulation results validate the proposed model's efficacy and efficiency compared to benchmark models.

Facioscapulohumeral muscular dystrophy (FSHD) is an inherited myopathy, characterized by progressive and asymmetric muscle atrophy, primarily affecting muscles of the face, shoulder girdle, and upper arms before affecting muscles of the lower extremities with age and greater disease severity. FSHD is a disabling condition, and patients may also present with various extramuscular symptoms. FSHD is caused by the aberrant expression of double homeobox 4 (DUX4) in adult skeletal muscle, arising from compromised epigenetic repression of the D4Z4 array. DUX4 encodes the DUX4 protein, a transcription factor that activates myotoxic gene programs to produce the FSHD pathology. Therefore, sequence-specific oligonucleotides aimed at reducing DUX4 levels in patients is a compelling therapeutic approach, and one that has received considerable research interest over the last decade. This review aims to describe the current preclinical landscape of oligonucleotide therapies for FSHD. This includes outlining the mechanism of action of each therapy and summarizing the preclinical results obtained regarding their efficacy in cellular and/or murine disease models. The scope of this review is limited to oligonucleotide-based therapies that inhibit the DUX4 gene, mRNA, or protein in a way that does not involve gene editing.

A significant challenge encountered in mmWave and Sub-terahertz systems used in 5G and 1 the upcoming 6G networks is the rapid fluctuation in signal quality across various beam directions. Extremely high-frequency waves are highly vulnerable to obstruction, making even slight adjustments in device orientation or the presence of blockers capable of causing substantial fluctuations in link quality along a designated path. This issue poses a major obstacle because numerous applications with low-latency requirements necessitate precise forecasting of network quality from many directions and cells. The method that is proposed in this research demonstrates an avant-garde approach for assessing the quality of multi-directional connections in mmWave systems by utilizing the Liquid Time Constant network (LTC) instead of the conventionally used Long Short-Term Memory (LSTM) technique. The method’s validity was tested through an optimistic simulation involving the monitoring of multi-cell connections at 28 GHz in a scenario where humans and various obstructions were moving arbitrarily. The results with LTC are significantly better than those obtained by conventional approaches such as LSTM. The latter resulted in a test Root Mean Squared Error (RMSE) of 3.44 dB, while the former, 0.25 dB, demonstrating a 13-fold improvement. For better interpretability and to illustrate the complexity of prediction, an approximate mathematical expression is also fitted to the simulated signal data using Symbolic Regression.

Prokaryotic genomes are dynamic tapestries that are strongly influenced by mobile genetic elements (MGEs), including transposons (Tn’s), plasmids, and bacteriophages. Of these, miniature inverted-repeat transposable elements (MITEs) are undoubtedly the least studied MGEs in bacteria and archaea. This review explores the diversity and distribution of MITEs in prokaryotes and describes what is known about their functional roles in the host and involvement in genomic plasticity and evolution.

: The primary objective of this study was to investigate the association of certain genetic alterations and intraoperative fluorescent activity of 5-aminolevulinic acid (ALA) in brain metastasis (BM) of lung adenocarcinoma. A retrospective cohort study was conducted among 72 patients who underwent surgical resection of BM from lung adenocarcinoma at our institute for 5 years. Cancer cell infiltration was estimated by the intraoperative fluorescent activity of 5-ALA, and genetic alterations were analyzed by next-generation sequencing (NGS). The sensitivity and specificity for detecting cancer cell infiltration using 5-ALA were 87.5% and 96.4%, respectively. Genes associated with cell cycle regulation (p = 0.003) and cell proliferation (p = 0.044) were significantly associated with positive fluorescence activity of 5-ALA in the adjacent brain tissue. Genetic alterations in cell cycle regulation and cell proliferation were also associated with shorter recurrence-free survival (p = 0.013 and p = 0.042, respectively) and overall survival (p = 0.026 and p = 0.042, respectively) in the multivariate analysis. The results suggest that genetic alterations in cell cycle regulation and cell proliferation are associated with positive fluorescence activity of 5-ALA in the adjacent infiltrative brain tissue and influence the clinical outcome of BM of lung adenocarcinoma.

Familial dysbetalipoproteinemia (FD) is a highly atherogenic genetically-based lipid disorder with the underestimated actual prevalence. In the recent years, several biochemical algorithms have been developed to diagnose FD using available laboratory tests. However, there is not enough data on their use in real-world clinical implementation. We studied the applicability of the most accessible biochemical algorithms to diagnose FD in clinical practice. We also investi-gated the prevalence of FD in one of the European regions of Russia based on a population sample. In this study there was detected a high prevalence of FD: 1 in 151. We demonstrated that the diagnostic algorithms of FD including a diagnostic apoB levels require correction, taking into account the characteristics of the distribution of apoB levels in the population. At the same time a triglycerides cutoff ≥1.5 mmol/L may be a useful tool in identifying subjects with FD. We also analyzed the presence and pathogenicity of APOE variants associated with the autosomal dominant FD in a large research sample.

Traveling salesman problems (TSPs) are well-known combinatorial optimization problems, and most existing algorithms are challenging for solving TSPs when its scale is large. To improve the efficiency of solving large-scale TSPs, this work presents a novel adaptive layered clustering framework with improved genetic algorithm (ALC\_IGA). The primary idea behind ALC\_IGA is to break down a large-scale problem into a series of small-scale problems. First, the $k$-means and improved genetic algorithm are used to segment the large-scale TSPs layer by layer and generate the initial solution. Then, the developed two phases simplified $2$-opt algorithm is applied to further improve the quality of the initial solution. The analysis reveals that the computational complexity of the ALC\_IGA is between $O(n\log n)$ and $O(n^2)$. The results of numerical experiments on various TSP instances indicate that, in most situations, the ALC\_IGA surpasses the state-of-the-art algorithms in convergence speed, stability, and solution quality. Specifically, the ALC\_IGA can solve instances with $2 \times 10^5$ nodes within 0.15h, $1.4 \times 10^6$ nodes within 1h, and $2 \times 10^6$ nodes in three dimensions within 1.5h.

The genetic code evolved by parallel tracks of chaotic and ordered processes. Liquid-liquid phase separation (hydrogels), a chaotic process, constructs diverse membraneless compartments within cells, resulting in regulated hydration and sequestration and concentration of reaction components. Hydrogels relate to chaotic amyloid fiber production. We propose that polyglycine and related hydrogels (i.e. GADV; G is glycine), phase separations, membraneless droplets and amyloid accretions organized protocell domains to drive the earliest evolution of the genetic code and the pre-life to cellular life transition. By contrast, evolution of tRNA, tRNAomes, aminoacyl-tRNA synthetases and translation systems followed highly ordered and systematic pathways, described by well-defined mechanisms and rules. The pathway of evolution of aminoacyl-tRNA synthetases, which tracked evolution of the genetic code, is clarified. Hydrogels and amyloids form a chaotic component, therefore, that complemented otherwise systematic processes. We describe with detail a pre-life world in which hydrogels and amyloids provided the selections of the first life.

Breast cancer is a significant health issue across the world. Breast cancer is the most widely-diagnosed cancer in women; early-stage diagnosis of disease and therapies increase patient safety. This paper proposes a synthetic model set of features focused on the optimization of the genetic algorithm (CHFS-BOGA) to forecast breast cancer. This hybrid feature selection approach combines the advantages of three filter feature selection approaches with an optimize Genetic Algorithm (OGA) to select the best features to improve the performance of the classification process and scalability. We propose OGA by improving the initial population generating and genetic operators using the results of filter approaches as some prior information with using the C4.5 decision tree classifier as a fitness function instead of probability and random selection. The authors collected available updated data from Wisconsin UCI machine learning with a total of 569 rows and 32 columns. The dataset evaluated using an explorer set of weka data mining open-source software for the analysis purpose. The results show that the proposed hybrid feature selection approach significantly outperforms the single filter approaches and principal component analysis (PCA) for optimum feature selection. These characteristics are good indicators for the return prediction. The highest accuracy achieved with the proposed system before (CHFS-BOGA) using the support vector machine (SVM) classifiers was 97.3%. The highest accuracy after (CHFS-BOGA-SVM) was 98.25% on split 70.0% train, remainder test, and 100% on the full training set. Moreover, the receiver operating characteristic (ROC) curve was equal to 1.0. The results showed that the proposed (CHFS-BOGA-SVM) system was able to accurately classify the type of breast tumor, whether malignant or benign.

Promoter is a small region of DNA sequence in response to various transcription factors, which initiates a particular gene expression. The promoter-engineered bio-sensor can activate or repress gene expression through transcription factor recognizing specific molecules, such as polyamine, sugars, lactams, amino acids, organic acids or redox molecule, however, the reported applications of promoter enhanced bio-sensor are not too much. This review paper highlights the strategies of construction of promoter-gene engineered bio-sensor with human and bacteria’s genetic promoter array for high-throughput screening (HTS) molecular drugs, study of membrane protein’s localization and nucleocytoplasmic shuttling mechanism of regulating factor, enzyme activity, detection of the toxicity of intermediate chemicals, and probing bacteria density to improve value-added product titer. These bio-sensors’s sensitivity and specificity can be further improved by proposed approaches of Mn²⁺ and Mg²⁺ added random Error-prone PCR and site-directed mutagenesis which is applied for construction of bacteria’s “mutant library”. It is expected to establish flexible HTS platform (Bio-sensor array) to large-scale screen transcription factor-acting drugs, reducing the toxicity of intermediate compounds, and constructing gene dynamic regulatory system in “push and pull” mode to effectively regulate the valuable medicinal product production. This proposed novel promoter-engineered biosensors aided synthetic genetic circuit construction will maximize the efficiency of bio-synthesis of medicinal compound, which will greatly promote the development of microbial metabolic engineering and biomedical science.

It is documented that maternal diseases or treatments influence a newborn’s clinical status at birth; autoimmune diseases with circulating antibodies are often clinically expressed transiently in newborns due to the crossover of the specific IgG through the placenta during pregnancy. The offspring can inherit maternal genetic anomalies, but clinical expression usually becomes patent later in life. If prenatal medical history is not available or if signs or symptoms of a mother’s disease are revealed for the first time during pregnancy or postpartum, their effects on the newborn may be misattributed. In this article, we present three cases of pregnant women, without any known pathology before or during pregnancy, who gave birth to very sick preterm newborns that required admission to the Neonatal Intensive Care Unit (NICU). The neonates’ complications were considered initially as consequences of prematurity or infection, but later investigations revealed transient autoimmune disease in two of the cases (myasthenia gravis and hyperthyroidism) and a severe form of thrombophilia in the third case. In all cases, mothers were asymptomatic and unaware of their subclinical disease. Preemies’ diagnosis has contributed to the identification of their mothers’ pathology and adequate treatment was prescribed with favourable short- and long-term outcomes. In one of the cases (myasthenia gravis) both mother and child had associated infectious diseases in the perinatal period that complicated the clinical picture and created problems of diagnosis. This article intends to emphasize the paramount importance of prenatal care, for both mother and newborn. Repeated medical visits and investigations throughout the pregnancy are a good opportunity to detect subclinical diseases or predispositions. As newborns usually develop non-specific signs, one should have experience and pay attention to differentiate among etiologies. Our paper takes a reversed approach to the usual medical diagnosis pathway: from infant to mother instead of mother towards the infant, proving that inter-speciality collaboration can work bi-directionally.

In many parts of the Asia, particularly in the arid regions of the middle east the date palm i.e. Phoneix dactylifera L. is considered a significant plant both culturally and economically. Over the past decade numerous biotechnological tools have been applied to revolutionize the date palm research and its cultivation process. In this comprehensive review ,we provided the in depth overview of the cutting edge developments in the date palm biotechnology, mentioning the important areas such as genomics, genetic engineering, in vitro propagation, omics technologies, and the integration of the artificial intelligence and machine learning (AI-ML).Due to these advancements ,in the date palm production how the date palm production lead the production of superior date palm cultivars with the improved yield ,fruit quality and resilience to biotic and abiotic stresses. Also it explores the application of the biotech tools in the enhancing pest and disease management strategies, increasing date palm productivity and developing the date palm based bio-factories for the production of high value compounds. This review highlights the current challenges faced by the date palm industries ,including the limited water resources ,genetic erosion , pests and disease and the need for improved postharvest handling and processing. It examines how these tools coupled with AI-based approaches can be leveraged to address these challenges and ensure the long term sustainability of date palm cultivation.

We evolve our past models PYR, Loukeris, et al. (2016) to provide a competitive system whose infiltration of categorical information and fundamentals into advanced higher moments, supports a more objective Portfolio Selection, aided by Intelligent Computing. The system of Portfolia Yield Reactives (PYR) searches for hidden prototypes into big data of accounting and financial statements, restricting malicious patterns such as hoax, noise, manipulation, incorporated to a novel optimal portfolio selection method.

The incorporation of renewable energy systems in the world energy system has been steadily increasing during the last years. In terms of the building sector, the usual consumers are becoming increasingly prosumers, and the trend is that communities of energy, whose households share produced electricity, will increase in number in the future. Another observed tendency is that the aggregator (the entity that manages the community) trades the net community energy in public energy markets. To accomplish economically good transactions, accurate and reliable forecasts of the day-ahead net energy community must be available. These can be obtained using an ensemble of multi-step shallow artificial neural networks, with prediction intervals obtained by the covariance algorithm. Using real data obtained by a small energy community of four houses located in the South region of Portugal, one can verify that the deterministic and probabilistic performance of the proposed approach is at least similar, typically better than using complex, deep models.

Abstract The relationship between cancer and neurodegeneration has spurred extensive scientific discourse, challenging their historical classification as distinct disorders. While cancer signifies uncontrolled cellular proliferation and neurodegeneration marks progressive neuronal loss, recent insights highlight intriguing interconnections between these seemingly disparate conditions. Cancer entails perpetual signaling for growth, evasion of growth inhibitors, resistance to cell death, acquisition of replicative immortality, stimulation of blood vessel growth (angiogenesis), and initiation of invasion and metastasis. Conversely, investigations emphasize disrupted cellular energy regulation and evasion of immune surveillance as key traits arising from genome instability, mutations, and inflammation driving tumorigenesis. Neurodegeneration involves neuronal malfunction and depletion, synaptic impairment, and protein abnormalities aggregations contributing to muscle atrophy and cognitive deficits. Varied clinical studies underscore contrasting correlations between cancer and neurodegeneration, hinting at mirrored molecular pathways that foster cell resilience or vulnerability. Research explores cellular signaling in tumorigenesis, shared with neurodegenerative disorders. Aberrant expression or mutations in crucial genes implicated in neurodegeneration also surface in cancer contexts. Debates persist on the nature of the relationship between cancer and neurodegeneration, contemplating inverse associations or shared pathways. Understanding their complex interplay, encompassing genetics, cellular mechanisms, and environmental influences, remains pivotal in unraveling their connections. This review explores the intriguing perspective that neurodegeneration and cancer might share fundamental genetic links, delving into potential implications for their onset and progression.

Egg activation is a cellular transition of an arrested mature oocyte into a developing embryo through a coordinated series of events. Previous studies in Hymenoptera have indicated that mechanical pressure can induce egg activation. In this study, we developed the first egg activation protocol for the haplodiploid insect pest, Sirex noctilio (Hymenoptera: Siricidae), from two climatically different regions in South Africa. In addition, activated eggs were exposed to three treatments involving water, pine sawdust, and the fungal symbiont of S. noctilio, Amylostereum areolatum (Russulales: Amylostereaceae), to confirm the hypothesised prerequisite of the symbiotic fungus for egg development. A rearing protocol was developed for emerged first instar larvae using a modified Anoplophora glabripennis (Coleoptera: Cerambycidae) artificial diet. A significant difference between mean survival rates of activated eggs from the two different regions was observed. Amylostereum areolatum was shown to be unnecessary for egg survival and adversely affected egg eclosion. Maximum larval survival duration on the artificial diet was 92 days. The egg activation and rearing protocol developed in this study enables opportunities for research on the physiology, ecology, symbioses, and genetics of S. noctilio, which can be exploited for new genetic pest management strategies.

As the core components of water jet propulsion system, water jet pump, directly affect the ship's propulsion performance. 3D reverse design method can effectively suppress the impeller secondary flow to get better performing hydraulic models. Aiming at fast and efficient optimization of the impellers, we parameterize the impellers and runner, and coupled with optimization algorithms and numerical calculations. It is an efficient and feasible way to coupling optimization based on multi-parameter, multi-objective and optimization algorithms, and parameterizing the impellers and guide vane geometry based on the 3D model designed by 3D reverse design method. In this paper, the waterjet propulsion pump is taken as the research object. According to the design method of the waterjet propulsion pump hydraulic model, the traditional axial flow blade design method, the modern ternary design theory of the blades, and the parametric three-dimensional reverse design method are selected to constructing an optimal design optimization strategy for the impeller of water-jet propulsion pump based on multi-parameter and multi-object genetic algorithm. Under the design conditions, the optimized pump efficiency reaches 89.28%, which is 1.24% higher than the original hydraulic model. The optimized pump head is 13.51 m, which is 0.39m more than the original hydraulic model. Under the design condition, the test measured head is 13.26m, the numerical calculation result is 1.88% higher than the test result, the test measured efficiency is 87.1%, the numerical calculation result is 2.5% higher than the test result. From the test results, the optimized impeller has higher hydraulic performance. Experiments have shown that it is feasible to optimize the design strategy.

Cyclic vomiting syndrome (CVS) is a frequently disabling condition defined by severe, discrete, stereotypical episodes of nausea and vomiting. In our previous report [1], a candidate gene list was evaluated in a group of 80 unrelated participants with CVS with extensive genetic sequencing (whole exome/genome). Twenty-two good candidate genes for CVS association were identified, from which a cellular model of aberrant cation transport and energy metabolism was proposed. Herein, we present additional clinical information on those 80 participants, including eight case reports to highlight general principles and clinical practices. Our participants demonstrate substantial phenotypic and genotypic heterogeneity in CVS, including a propensity for specific co-morbidities in the participants and relatives, evolution towards episodes in which vomiting is less apparent, labeling with “non-organic” diagnoses, multiple genes, polygenic inheritance, and association with variants not designated as Pathogenic. Our case reports illustrate how genetic information can guide clinical management and argue to the high clinical utility of appropriate genetic testing and analysis in CVS. Expression profiling of our 22 candidate genes suggests an anatomical model in a unit defined by vagal afferents and adjacent cells. Our models are consistent with multiple current hypotheses of CVS and the efficacy of commonly-recommended treatments.

The genetic pool of valuable old ornamental cultivars and their in situ maintenance may be threated by climate change. Meanwhile, the ornamental plants like roses make up an important share of both gardens and urban green spaces, where they are particularly vulnerable to multistress growth conditions. The aim of this research was to evaluate the effect of changing climatic conditions on growth and flowering of 11 historic climber roses through long-term studies (2000-2017) conducted in Central Europe. The evaluation of plants consisted of assessment of frost damage and the timing of early phenological stages (starting of bud break, leaf unfolding) as well as gathering data on beginning, fullness and end of flowering and its abundance. Frost damage was not recorded in any year only in ‘Mme Plantier’, and did not occur for any cultivar after the winter in the years 2007, 2008, and 2014. Only a little damage to one-year shoots was recorded after the winter in the years 2015-2017. Frost damage to ‘Alberic Barbier’, ‘Albertine’, ‘Chaplin's Pink Climber’, ‘Orange Triumph clg’ and ‘Venusta Pendula’ led to pruning to ground level in every year excluding those listed above. Frost damage of once blooming roses limited their flowering; however, the many-year data-sets showed a trend for decreased frost damage and improved abundance of flowering, and these results can be interpreted as a response to the increase of average air temperature. The timing of bud breaking and leaf development in all climber roses was strictly correlated with average air temperature in the dormancy period. The reactions of climber roses to weather conditions confirmed the influence of climatic changes on ornamental crop plants in Central Europe, introducing the potential possibility for the wider application of climber roses, but without certainty of flowering every year.

Background: Extremely rare progressive diseases like Sedaghatian-type Spondylometaphyseal Dysplasia (SSMD) can be neonatally lethal and therefore go undiagnosed or are difficult to treat. Recent sequencing efforts have linked this disease to mutations in GPX4, with consequences in the resulting enzyme, glutathione peroxidase 4. This offers potential diagnostic and therapeutic avenues for those suffering from this disease, though the steps toward these treatments is often convoluted, expensive, and time-consuming. Main body: The CureGPX4 organization was developed to promote awareness of GPX4-related diseases like SSMD, as well as support research that could lead to essential therapeutics for patients. We provide an overview of the 21 published SSMD cases and have compiled additional sequencing data for four previously unpublished individuals to illustrate the genetic component of SSMD, and the role of sequencing data in diagnosis. We outline in detail the steps CureGPX4 has taken to reach milestones of team creation, disease understanding, drug repurposing, and design of future studies. Conclusion: The primary aim of this review is to provide a roadmap for therapy development for rare, ultra-rare, and difficult to diagnose diseases, as well as increase awareness of the genetic component of SSMD. This work will offer a better understanding of GPx4-related diseases, and help guide researchers, clinicians, and patients interested in other rare diseases find a path towards treatments.

Bioenergetics, genetic coding, and catalysis are all difficult to imagine emerging without pre-existing historical context. That context is often posed as a “Chicken and Egg” problem; its resolution is concisely described by de Grasse Tyson: “the egg was laid by a bird that was not a chicken”. The concision and generality of that answer furnish no details—only an appropriate framework from which to examine detailed paradigms that might illuminate paradoxes underlying these three life-defining biomolecular processes. We examine experimental aspects here of five examples that all conform to the same paradigm. The paradox in each example is resolved by coupling if, and only if, conditions for two related transitions between levels. One drives, and each restricts fluxes through, or “gates” the other. That reciprocally-coupled gating, in which two gated processes constrain one another, maps onto the formal structure of “strange loops”. That mapping may help unite the axiomatic foundations of genetics, bioenergetics, and catalysis. As a physical analog for Gödel’s logic, biomolecular strange-loops provide a natural metaphor around which to organize these data, linking biology to the physics of information, free energy, and the second law of thermodynamics.

Dalbergia sissoo (shisham), an important timber yielding multipurpose tree species of the Indian subcontinent, has been afflicted with large scale mortality due to wilt in natural forests and plantations, causing huge economic losses. Fusarium solani f. sp. dalbergiae (Fsd) has been identified as one of the causal organisms for wilt disease in D. sissoo. Present study comprises in vitro screening of ten selected genotypes of D. sissoo against two strains of Fsd in a dual culture set up under axenic condition. Callus and plantlets of ten genotypes of host plant were multiplied in vitro and were inoculated with conidial suspension of two strains of Fsd at three concentrations; 1× 10¹, 1× 10³, and 1× 10⁵ conidia/ml. Gnotobiotic evaluation of dual culture set up shows variations among D. sissoo genotypes in their response towards in vitro Fsd infection; and two genotypes (14 and 66) exhibited resistance against the pathogen strains. Callus of genotypes 14 and 66 significantly restricted the fungal mycelium growth whereas callus of remaining eight genotypes were completely infested by Fsd mycelium within 9 days. Similarly, plantlets of genotype 14 and 66, had lesser disease severity and remained green, and had fewer necrotic lesions in the roots whereas plantlets of remaining eight genotypes died within 15 days.

The article shows materials to the question about algebraic features of the genetic code and about the dictatorial influence of the DNA and RNA molecules on the whole organism. Presented results testify in favor that the genetic code is an algebraic code related with a wide class of algebraic codes, which are a basis of noise-immune coding of information in communication technologies. Structural features of the genetic systems are associated with hypercomplex double (or hyperbolic) numbers and with bisymmetric doubly stochastic matrices. The received results confirm that represented matrix approaches are effective for modeling genetic phenomena and revealing the interconnections of structures of biological bodies at various levels of their organization. This allows one to think that living organisms are algebraically encoded entities where structures of genetic molecules have the dictatorial influence on inherited structures of the whole organism. New described algebraic approaches and results are discussed.

African locust bean (Parkia biglobosa (Jacq.) is a multi-purpose economic tree with genetic potentials in sub-Saharan Africa. Its cultivation and production is declining with increased aging and genetically threatened throughout its natural ranges. Research efforts are needed to change the present scenario to sustainable cultivation and utilization, hence this present study. This study was aimed at evaluating genetic diversity and geographical spread relationships of twenty landraces collected from different ecological zones of Nigeria using simple sequence repeat (SSR) markers. Ten SSR markers were screened and five primers (PbL02, PbL03, PbL04, PbL05 and PbL09) were selected based on clear amplification products and reproducible scorable bands. The SSR primers detected a total of 55 alleles ranged from 10 to 14 alleles with a mean of 11. The percentage polymorphisms were high and ranged from 68.75 % in PbL04 to 84.21 % in PbL05 with a mean of 74.16 %. The polymorphic information content (PIC) was in the range of 0.31 in PbL02 to 0.37 in PbL09. The genetic diversity and heterozygosity values ranged from 0.39 to 0.50 and 0.00 to 0.68 while the average genetic distance for all pair wise comparisons was 0.31.The first five Principal Component (PC) accounted for 70.20 % of the total variation out of which PC1 (31.50%) and PC2 (19.20%) extracted 49.70% molecular similarity. The dendrogram resulted in separation of the 19 landraces into three major clusters based on unweighted pair group method with arithmetic average. Cluster I comprised of five landraces: ABNo130 and BENo023; OYNo11, KANo125 and NiNo262 while cluster II had only one (BANo116). Cluster III was diverse comprising 13 landraces: ZANo188, KNNo162, KENo220, GMNo076 and EbNo260, ADNo64, EdNo164, KANo137, KENo217, KwNo270, NiNo241, OsNo206 and PLNo120. The homogeneity of alleles among the studied landraces suggested suspicion of loss of genetic intra-specific variation among the landraces of P. biglobosa which calls for concerted efforts toward better cultivation, conservation, management, utilization and genetic improvement of the species in Nigeria.

The study presents a comparative approach between response surface methodology (RSM) and hybridized, genetic algorithm artificial neural network (GA-ANN) in predicting the water absorption, compressive strength, flexural strength split tensile strength and slump for steel fiber reinforced concrete. The effect of process variables such as aspect ratio, water cement ratio and cement content were investigated using the central composite design of response surface methodology. This same experimental design was used in training the hybrid-training approach of artificial neural network. The predicting ability of both methodologies were compared using the root mean sqaured error (RMSE), mean absolute error (MAE), model predictive error (MPE) and absolute average deviation (AAD). The RSM model was found more accurate in prediction compared to hybrid GA-ANN.

The challenge posed by multi-drug resistance (MDR) of pathogenic organisms, spectacularly manifested in the 6 “ESKAPE” bacterium (two Gram-positive, four Gram-negative) species, should invoke new comprehensive strategies, and needs cooperation of scientists with medical, veterinary and natural science background. This review is aimed at informing newcomers, coming from the field of biology and genetics, about problems related to rapidly emerging, new multi-drug resistant, pathogenic, bacteria. Unlike persistence, the antibiotic resistance is inherited. A functioning “resistance gene” makes a susceptible organism resistant to a given antibiotic, encoding for polypeptides capable of acting either as decomposing enzymes, or acting as trans-membrane pumps, or membrane structure components capable of modifying the permeability implementing a «by pass» mechanism enabling the antibiotic molecule to reach its cellular target(s). A functioning “sensitivity gene” encode for a polypeptide, capable (directly or indirectly) of transferring toxic molecules into target cells, or of metabolizing non-transferable to transferable, or non-toxic molecules to toxic derivatives. A gene of a normal function could act as a “sensitivity” gene in the presence of antibiotics of chemical structures similar to the natural substrate of the gene product, (enzyme or binding/ trans-membrane protein). The Agrocin 84 story is a good example. Multi-drug resistance is a phenotypic consequence of the sequential accumulation of mutations, and/or up-take of plasmids or genomic islands carrying resistance genes from the environment via horizontal gene transfer, mediated by conjugative plasmid or bacteriophage carrying mobile genetic elements. Both multi-drug resistance and collateral sensitivity are evolutionary products. Some revealed evolutionary process and their Lamarckian and Darwinian interpretations are discussed. Toolkits of comparative full-genome sequencing, genomics, experimental evolution and population genetics may provide perspectives for overcoming the invincibility of multi-drug panresistance. The status of some recently emerging pathogenic bacterium species with zoonic features and of veterinary background is also discussed.

Mobile Edge Computing (MEC) is a growing concept that expands on cloud computing technology. It leverages edge infrastructure to efficiently manage computationally intensive and time-sensitive tasks. By tapping into the capabilities of 7G network infrastructure, MEC can effectively reduce latency by transferring computing tasks from edge devices to edge servers. As part of Industrial 4.0 revolution, the use of large number of IOT sensors, edge devices, and edge servers are increasing at rapid phase. To effectively process these sensor and device data with less power and low latency is a key challenge. Here comes the need for an effective task offloading strategy to edge servers. This strategy must meet the industrial automation requirements of reduced energy usage and faster processing. A solution based on an Improved Particle Swarm Optimization with Genetic Algorithm (IPSOGA) is designed. It harnesses the power of effective exploration using Particle Swarm Optimization (PSO) and maintain genetic diversity within the population using Genetic Algorithm (GA). This hybrid approach is powerful in solving complex optimization problems with the aims to effective resource allocation, faster task offloading decisions, and consequently reduce processing delays. The proposed IPSOGA is compared with popular metaheuristic algorithms like Genetic Algorithm (GA) and Simulated Annealing (SA), and it has been proven to be superior in task offloading strategy by effectively reducing processing delays, energy consumption, and optimizing resource allocation efficiently.

(1) Background: In most cases, passive isolation control methods are commonly used for the seismic isolation design of bridge engineering. However, for passive seismic isolation devices, due to their non-adjustable performance parameters, it is challenging to achieve a good seismic isolation effect for structures under random and variable seismic loads in the wide frequency range of 0 Hz to 20 Hz; (2) Methods: the sensitivity of the design parameters of the seismic isolation bearing was analyzed using the optimization center gradient method, and an improved genetic algorithm was employed to quickly optimize and obtain the optimal design parameters; (3) Results: the effectiveness of the three-dimensional seismic isolation bearing was validated through experiments. (4) Conclusions: The multi-factor sensitivity analysis approach used in this study for designing novel isolation bearings is applicable not only to seismic design in bridges but also serves as a reference for parameter design in isolation bearings requiring medium to high precision in seismic performance.

This paper addresses the challenges faced by icebreakers in polar environments, particularly the difficulty in sensing underwater ice formations when navigating through thick ice layers, which often results in suboptimal icebreaking effectiveness. To overcome these challenges, this paper introduces a novel underwater robot equipped with both sensing and ice-breaking capabilities. We propose a path planning method for icebreaking that leverages the synergistic capabilities of the Safe Particle Swarm Optimization and Genetic Algorithm (GA-SPSO). The GA-SPSO algorithm integrates the global search prowess of the particle swarm optimization with the local optimization strength of the genetic algorithm, enabling efficient and adaptive path planning in complex ice environments. The underwater unmanned vehicle (UUV)-assisted icebreaking approach developed here utilizes the UUV's flexibility and high-precision environmental sensing to provide real-time optimization suggestions for icebreaker navigation paths. Simulation results demonstrate that the GA-SPSO algorithm not only effectively circumvents hazardous areas but also significantly reduces the energy consumption and operational time of icebreakers, thereby enhancing the safety and stability of navigation. When compared to the conventional Safe Particle Swarm Optimization (SPSO), our approach shows marked improvements in path length, convergence speed, and obstacle avoidance capabilities, significantly enhancing the success and efficiency of polar navigation missions.

Autism Spectrum Disorder (ASD), a neurodevelopmental disorder, is increasingly diagnosed and significantly affects communication, behavior, and social interaction. The current diagnostic framework, guided by the DSM-5, has limitations that may lead to clinical gaps due to overlapping symptoms of ASD and other disorders. This study proposes a comprehensive approach to enhance ASD diagnosis through clinical stratification based on comorbidities. We applied precision medicine techniques to identify and categorize ASD into ten clinical profiles. Our findings contribute to a more profound understanding of ASD, including its varied clinical manifestations and comorbidities, and facilitate the development of personalized therapeutic strategies.

Anthracnose stands as the primary obstacle to lupin cultivation, impeding development despite the considerable agronomic, ecological, and economic potential of such legume crops. This review explores recent efforts to unravel the complexities of anthracnose in domesticated lupins, focusing on both the plant perspective and the causative pathogenic agent, Colletotrichum lupini. Leveraging cutting-edge technologies has yielded crucial insights into various facets of this devastating disease, encompassing plant and pathogen biology, genetic and molecular regulations of the interaction, fungal diversity and population dynamics, and screening of plant genetic resources for anthracnose resistance. The lack of effective disease control measures, relying primarily on the use of disease-free seeds, highlights the need to develop anthracnose-resistant varieties. However, challenges arise from the intricacy of lupin's response to the disease, influenced by polygenic inheritance, in spite of loci with major effects, and environmental factors. The slow pace of genetic improvement underscores the need for more efficient breeding processes, including biotechnological approaches. This review offers a comprehensive overview of current progress and knowledge gaps, stressing the urgent need to further enhance understanding of C. lupini pathogenic mechanisms and lupin‘s resistance. Integrating advanced technologies and accelerated research efforts is paramount for achieving efficient disease management and sustainable lupin cultivation in the face of anthracnose challenges.

Radiotherapy and cisplatin-based chemotherapy belong to the main treatment modalities for head and neck squamous cell carcinoma (HNSCC), and induce cancer cell death by generating DNA damage, including the most severe double strand breaks (DSBs). Alterations in DSB response and repair genes may affect individual DNA repair capacity and treatment sensitivity, contributing to therapy resistance and poor prognosis often observed in HNSCC. In this study, we investigated the association of a panel of single nucleotide polymorphisms (SNPs) in 20 DSB signaling and repair genes with therapy results and prognosis in 505 HNSCC patients treated non-surgically with DNA damage-inducing therapies. In the multivariate analysis, there were a total of 14 variants associated with overall, locoregional recurrence-free or metastasis-free survival. Moreover, we identified 10 of these SNPs as independent predictors of therapy failure and unfavorable prognosis in the whole group or in two treatment subgroups. They were MRE11A rs2155209, XRCC5 rs828907, RAD51 rs1801321, rs12593359, LIG4 rs1805388, CHEK1 rs558351, TP53 rs1042522, ATM rs1801516, XRCC6 rs2267437 and NBS1 rs2735383. Only CHEK1 rs558351 remained statistically significant after correction for multiple testing. These results suggest that specific germline variants related to DSB response and repair may be potential genetic modifiers of therapy effects and disease progression in HNSCC treated with radiotherapy and cisplatin-based chemoradiation.

Sources of resistance to powdery mildew in wild barley (Hordeum vulgare subsp. spontaneum) collected in Jordan, Lebanon, and Libya Jerzy H. Czembor 1, *, Elżbieta Czembor 1 1 Plant Breeding and Acclimatization Institute – National Research Institute (IHAR-PIB), Radzikow, 05-870 Błonie, Poland; e.czembor@ihar.edu.pl (E.C.) *Correspondence: j.h.czembor@ihar.edu.pl) Abstract Barley powdery mildew (BPM) is caused by the pathogen Blumeria graminis f.sp. hordei (Bgh). It is an economically important disease and plant pathologists are looking for new sources of resistance to BPM. Barley genetic resources present in gene banks are often a rich source of disease resistance to be used by breeders. These new sources of resistance to BPM are often used in combination (pyramiding) with those that are already used in modern cultivars. Barley accessions, including the wild subspecies Hordeum vulgare subsp. spontaneum (Hvs), are stored in many gene banks and often are a valuable source of economically important characteristics. This source of biodiversity should be more efficiently used to improve barley in the process of plant breeding. However, their proper characterization and availability are urgently needed. The resistance to BPM in 81 accessions of wild barley (Hvs) collected in Jordan (47), Lebanon (23), and Libya (11) was investigated. The seed samples of these accessions were obtained from the ICARDA gene bank and collected in 10 expeditions from 1981 to 1995. Twenty European differential isolates of BPM were used to select accessions with efficient resistance. Thirty-one resistant single plant lines were selected from 15 accessions from Jordan and Libya based on tests performed with the most avirulent isolate of Bgh available. These resistant single plant lines were tested for the presence of specific resistance genes using a differential set of Bgh isolates. After analysis of obtained results, it was concluded that all tested 31 single plant lines of wild barley have genes for resistance that are not represented in the Pallas isolines differential s. Twenty-six lines of Hvs selected from accessions originated in Jordan and Libya showed resistance reaction to all isolates used. Identified new sources of effective resistance to BPM in single plant lines of Hvs will be further tested and used in barley pre-breeding programs. Keywords: Blumeria graminis; resistance genes; resistance; germplasm; gene bank;

FACTS devices provide significance to enhance the functioning of both static and dynamic power systems. The type, size, and location of FACTS devices determine how well they achieve the goals of enhancing voltage stability, loadability, and loss minimization simultaneously. The aim of the function in this study includes voltage stability, line loadings, and loss minimization, which are expected to obtain the most benefits from their installation and other weights allocated to them. It has been determined how installing TCSC, and SVC will improve loadability under situations of continuously increasing load. In this study, a novel optimization technique known as differential evolution invasive weed optimization (DEIWO) is utilized to locate the optimal location for FACTS devices within the IEEE 14 bus system by considering valve point effects. DEIWO was a recently developed, dominant, and simple metaheuristic algorithm for genuine parameter optimization. DEIWO was inspired by the natural process of weed colonization and dissemination, and it is highly effective at addressing broad multi-dimensional, linear and nonlinear optimization problems. The proposed method was implemented in the IEEE 14 bus testing system. Different methods from the research papers have been used to compare and analyze the results. The results demonstrate the DEIWO algorithm's capability and show how it may be used in practice to locate FACTS devices in deregulated electricity networks in the most advantageous place. A comparison of the proposed process to existing algorithms found in the literature and implemented using traditional techniques reveals that it performs better in terms of precision and convergence

This study presents a detailed assessment on the impact of the Brazilian legal framework related to the access and benefit sharing provisions of the UN Convention on Biological Diversity. The research is based on a comprehensive dataset that was systematically collected over several years, directly from the official government bodies responsible for its implementation. The aim of the study is to contribute to a fact-based discussion on the effectiveness of national ABS laws, focusing on the Brazilian legal framework, first established in the year 2000 and revised in 2015, as a case example. This study balances the costs and benefits of the Brazilian ABS system and assesses the regulatory challenges it poses to individuals, companies, and institutions that perform research, share knowledge, develop, manufacture or market products derived from Brazilian biodiversity. The study indicates that, after over 22 years of operation, the regulatory challenges are still real and relevant, and that the significant volume of data collected from users on access, prior commercialization and shipment of genetic materials abroad was never systematically assessed by the agencies in charge. Besides, it shows that the costs incurred by the government in managing the policy itself have been higher than the economic benefits it has made possible, and that the institutional mechanisms in place since 2015 have not been able to allocate the monetary benefits contributed by users to the National Benefit Sharing Fund to any projects aimed at developing sustainable uses or preserving Brazilian biodiversity.

Plant geneticists and breeders have used marker technology since the 1980s in quantitative trait locus (QTL) identification. Marker-assisted selection is effective for large-effect QTL but has been challenging to use with quantitative traits controlled by multiple minor effect alleles. Therefore, genomic selection (GS) was proposed to estimate all markers simultaneously, thereby capturing all their effects. However, breeding programs are still struggling to identify the best strategy to implement it into their programs. Traditional breeding programs need to be optimized to implement GS effectively. This review explores the optimization of breeding programs for variety release based on aspects of the breeder’s equation. Optimizations include reorganizing field designs, training populations, increasing the number of lines evaluated, and leveraging the large amount of genomic and phenotypic data collected across different growing seasons and environments to increase heritability estimates, selection intensity, and selection accuracy. Breeding programs can leverage their phenotypic and genotypic data to maximize genetic gain and selection accuracy through GS methods utilizing multi-trait and, multi-environment models, high-throughput phenotyping, and deep learning approaches. Overall, this review describes various methods that plant breeders can utilize to increase genetic gains and effectively implement GS in breeding .

The article studies the Venetsiansky (Hydropark) Island (the Dnipro River, Kyiv, Ukraine) landscape structure applying the genetic landscape science method and the European landscape classification (LANMAP) approach. The aim of the article is to determine the best way to study the river islands landscapes analyzing the Venetsiansky Island landscape structure by the both methods. Methodology. The genetic landscape science method consists in the next steps: 1) the island’s territory information collecting; 2) the preliminary landscape map creating; 3) field study; 4) the final landscape map creating. The LANMAP method consists on: 1) the climate study; 2) the altitude study; 3) the parent material study; 4) the land cover study. The results. The genetic landscape science method demonstrates that the Venetsiansky Island is not a landscape, but a structural part of the landscape – the tract. The LANMAP study reveals that the Venetsiansky Island is naturally homogeneous, but land cover differs. The conclusion. The both methods’ study shows that the river island cannot be the entire landscape by the natural criteria, but only the part of it. The genetic landscape science explores the landscape forming while the LANMAP reveals the current state of the landscape.

Due to the rapid developments in aquaculture industry, Artemia franciscana, originally an American species, has been intentionally introduced to the Eurasia, Africa and Australia. In the present study, we used a partial sequence of the mitochondrial DNA Cytochrome Oxidase subunit I (mt-DNA COI) gene and genomic fingerprinting by Inter-Simple Sequence Repeats (ISSRs) to determine the genetic variability and population structure of Artemia populations (indigenous and introduced) from 14 different geographical locations in Western Asia. Based on the haplotype spanning network, Artemia urmiana has exhibited higher genetic variation than native parthenogenetic populations. Although A. urmiana represented a completely private haplotype distribution, no apparent genetic structure was recognized among the native parthenogenetic and invasive A. franciscana populations. Our ISSR findings have documented that despite invasive populations have lower variation than source population in Great Salt Lake (Utah, USA), they have significantly revealed higher genetic variability compare to the native populations in Western Asia. According to the ISSR results, the native populations were not fully differentiated by the PCoA analysis, but the exotic A. franciscana populations were geographically divided in four genetic groups. We believe that during the colonization, invasive populations have experienced substantial genetic divergences, under new ecological conditions in the non-indigenous regions.

The greenhouse gases and air pollution generated by extensive energy use have exacerbated climate change. An electric-bus (e-bus) transportation system favors reducing pollution and carbon emissions. This study analyzed the minimization of construction costs for an all battery-swapping public e-bus transportation system. A simulation was conducted according to existing timetables and routes. Daytime charging was incorporated during the hours of operation; the two parameters of the daytime charging scheme were the residual battery capacity and battery-charging energy during various intervals of daytime peak electricity hours. The parameters were optimized using three algorithms: particle swarm optimization (PSO), a genetic algorithm (GA), and a PSO–GA. This study observed the effects of optimization on cost changes (e.g., number of e-buses, on-board battery capacity, number of extra batteries, charging facilities, and energy consumption) and compared the plug-in and battery-swapping e-bus systems. The results revealed that daytime charging can reduce the construction costs of both systems. In contrast to the other two algorithms, the PSO–GA yielded the most favorable optimization results for the charging scheme. Finally, according to the cases investigated and the parameters of this study, the construction cost of the plug-in e-bus system was lower than that of the battery-swapping e-bus system.

Today, Colebrook’s equation is mostly accepted as an informal standard for modeling of turbulent flow in hydraulically smooth and rough pipes including transient zone in between. The empirical Colebrook’s equation relates the unknown flow friction factor (λ) with the known Reynolds number (R) and the known relative roughness of inner pipe surface (ε/D). It is implicit in unknown friction factor (λ). Implicit Colebrook’s equation cannot be rearranged to derive friction factor (λ) directly and therefore it can be solved only iteratively [λ=f(λ, R, ε/D)] or using its explicit approximations [λ≈f(R, ε/D)]. Of course, approximations carry in certain error compared with the iterative solution where the highest level of accuracy can be reached after enough number of iterations. The explicit approximations give a relatively good prediction of the friction factor (λ) and can reproduce accurately Colebrook’s equation and its Moody’s plot. Usually, more complex models of approximations are more accurate and vice versa. In this paper, numerical values of parameters in various existing approximations are changed (optimized) using genetic algorithms to reduce maximal relative error. After this improvement computational burden stays unchanged while accuracy of approximations increases in some of the cases very significantly.

Malignant melanoma and urological cancers originate from different tissues and organs, yet several studies highlight connections between these malignancies, including common risk factors, genetic predispositions, and immunological pathways. Research indicates that individuals with a history of melanoma have an elevated risk of developing renal cell carcinoma (RCC), while those with RCC are more likely to develop melanoma. Shared factors such as a personal or family history of cancer, UV radiation exposure, smoking, and obesity have all been linked to an increased incidence of various cancer types. A major link between malignant melanoma and urological cancers is the presence of shared genetic mutations and familial cancer syndromes. Key mutations, including germline mutations in BRCA1, MITF, CDKN2A, TP53, and alterations in the PI3K/AKT pathway, significantly contribute to the risk of both types of malignancies. Personalized medicine, which tailors prevention and treatment strategies to an individual’s genetic, environmental, and lifestyle factors, has significantly improved cancer care. The primary aim is to select the most effective treatment for each patient, maximizing therapeutic outcomes, reducing side effects, and minimizing the risk of drug resistance. Advances in genomics and immunology are driving the development of personalized therapies that target specific molecular pathways and immune responses common to both melanoma and urological cancers. Angiogenesis inhibitors and checkpoint inhibitors have demonstrated notable success in treating these cancers, with tumor mutational burden serving as a valuable biomarker for predicting the efficacy of immune checkpoint inhibitors.

Recent data has shown a continued rise in the worldwide annual incidence and mortality rates of head and neck cancers. The present standard for diagnosis and monitoring for disease recurrence or progression involves clinical examination, imaging, and invasive biopsy techniques of lesions suspected of being malignant. In addition to limitations relating to cost, time, and patient discomfort, these methodologies have inherent inaccuracies for detecting recurrence. In view of these limitations, the analysis of patient bodily fluid samples via liquid biopsy proposes a cost-effective and convenient alternative, which provides insight on the biogenetic and biomolecular underpinnings of oncologic disease processes. Liquid biopsy’s monitoring for biomarkers of head and neck cancer, including circulating tumor DNA, circulating tumor cells, and circulating cell-free RNA, has shown clinical utility in the screening, diagnosis, prognostication, and monitoring of patients with various forms of head and neck cancer. The present review will provide an update on the current literature examining the use of liquid biopsy in head and neck cancer care and the clinical applicability of potential biomarkers, with a focus on viral and non-viral circulating tumor DNA. Possible future avenues for research to address specific shortcomings of liquid biopsy will be discussed.

Goldenhar Syndrome (GS), also known as oculoauriculovertebral spectrum (OVAS), is a rare congenital condition characterized by impaired development of structures such as ears, eyes, nose, palate, lip, mandible, maxilla, and teeth. The etiopathogenesis is multifactorial and dependent on genetic and environmental factors, yet the syndrome has many unknowns. We report a case of a 31-year-old male diagnosed clinically with GS as an infant with an absent left ear, hearing loss, absent left lung lobe, reduced growth of left upper limb, decreased size of left face, absent left kidney, and a missing left thumb, who was seen in the genetic clinic for prenatal genetic counseling. A microarray comparative genome hybridization (aCGH) was performed for copy number abnormalities and targeted NGS panels were performed for hereditary hearing loss and limb/digital malformations. aCGH revealed a 471kb loss at 16p12.2 and a mosaic 7.3Mb gain at 19p12q12. Metaphase FISH confirmed the orientation of 19p12q12 gain as small supernumerary ring chromosome 19. NGS revealed multiple variants (MYH14 c.4001C>T, PDE1C c.8C>T, ADGRV1 c.15502A>G, CDH23 c.8772C>A, MYO3A c.484G>A, ADAMTS10 c.506G>A, CHUK c.1294C>T, CSPP1 c.1972A>G, and TCTN2 c.160G>A). Due to the heterogeneous clinical manifestation and genetic heterogeneity of GS, a comprehensive genetic testing strategy is needed for timely diagnosis and better patient care.

Abstract: This study aimed to assess the agronomic performance and yield stability of elite white yam (Dioscorea rotundata) genotypes across diverse Nigerian environments. Twenty-five genotypes were evaluated at three locations in two consecutive growing seasons, 2022 and 2023. The genotypes were assessed for their agronomic performance, tuber yield and related traits using various analytical tools such as AMMI (Additive main effects and multiplicative interaction) and MTSI (Multi-trait stability index). The AMMI analysis revealed significant differences among the genotypes and across the environ-ments for all traits (p

Ovarian cancer (OC) represents a significant challenge in the realm of gynecological cancers, characterized by poor survival rates and complex etiology. In this review, we delve into the se-lected genetic, environmental, and hormonal factors underpinning OC development. We have reviewed scientific databases searching for ovarian cancer genetic and epigenetic factors. We have included studies based on their relevancy. As a result of exploring ovarian carcinogen-esis, this systematic review contains data collected from 126 various works. The role of prominent genetic players such as BRCA mutations, DNA repair mechanisms, un-derscores the intricate landscape of OC susceptibility. We explore Li-Fraumeni and Lynch syn-drome, which impart a heightened predisposition to OC development. Hormonal factors such as estrogen, progesterone, and androgens are also discussed. Environmental alterations, ranging from lifestyle influences on OC to microbiome dysbiosis and lifestyle factors such as obesity, al-cohol consumption, and physical activity intersect with genetic and epigenetic pathways, shaping the risk landscape for OC. Through a meticulous examination of current literature, this review provides a nuanced under-standing of the multifactorial nature of OC, emphasizing the need for holistic approaches to prevention, diagnosis, and treatment.

This comprehensive review traces the transformative journey of genomic mapping, ushering in the era of "Genomic Atlas 2.0," characterized by advanced strategies and cutting-edge technologies. Beginning with a historical overview, it highlights the evolution from early linkage analysis to the current landscape shaped by high-throughput sequencing. Key breakthroughs are emphasized, showcasing the pivotal role of platforms such as Illumina and Oxford Nanopore in accelerating the identification of genetic variations. Long-read sequencing technologies, including PacBio and Oxford Nanopore, are explored as transformative elements overcoming limitations associated with short-read sequencing. The review delves into spatial genomics and chromatin conformation capture techniques, revealing the three-dimensional intricacies of the genome, with a particular focus on the revolutionary impact of Hi-C in understanding chromatin interactions. CRISPR technologies emerge as indispensable tools in Genomic Atlas 2.0, enabling targeted genome editing and the generation of precise genetic models. Functional genomics screens, utilizing RNA interference and CRISPR-based techniques, are discussed for their scalable precision in deciphering genome functions. Navigating challenges, the review addresses complexities in data integration and ethical considerations, offering strategies to ensure the robustness and ethical conduct of genetic mapping. Looking optimistically towards the future, it explores opportunities such as improved sequencing accuracy and novel applications of CRISPR technologies. Envisioning the expansion of genomics into personalized medicine, agriculture, and conservation biology, the review serves as a guiding resource for researchers, scientists, and students navigating the intricate terrain of Genomic Atlas 2.0.

Introduction: The repertoire of microRNAs (miRNAs) in thyroid carcinomas starts to be elucidated. Among differentiated thyroid carcinomas (DTC), papillary thyroid carcinoma (PTC) is the most common. The assessment of miRNAs expression may contribute to refine the pre-surgical diagnosis, in order to obtain a personalized and more effective treatment of patients. Aims: This study aims to evaluated 1) miRNAs expression in a series of DTC, and the association with the presence of selected genetic mutations, in order to improve diagnosis and predict biologic behavior of DTC/PTC. 2) the reliability of molecular tests in Ultrasound-guided Fine Needle Aspiration Cytology (US-FNAC) for a more precise preoperative diagnosis. Material and methods: This series includes 176 samples (98 cytology samples and 78 formalin-fixed paraffin embedded (FFPE) tissues) obtained from 106 patients submitted to surgery, being 13 benign lesions (controls) and 93 DTCs (cases). The microRNA expression was assessed for miR-146b, miR-221, miR-222, and miR-15a by quantitative reverse transcriptase-polymerase chain reaction (qRT-PCR) and the results were analyzed using the 2- ΔΔCT method, using miR16 as an endogenous control. The discriminative ability of miRNAs expression regarding PTC diagnosis was evaluated using the area under the Receiver Operating Characteristic Curve (AUC). The association of miRNAs expression, clinicopatological features and genetic alterations (BRAF, RAS and TERTp) was evaluated in PTCs. Results/Discussion: All the analyzed miRNAs showed a tendency to be over expressed in DTCs/PTCs when compared with benign lesions, both in cytology and histology samples. In cytology, miRNAs expression levels were higher in malignant than in benign tumors. In histology, corresponding discriminative abilities regarding PTC diagnosis were: miR-146b (AUC0.94, 95%CI 0.87-1), miR-221 (AUC0.79, 95%CI 0.68-0.9), miR-222 (AUC0.76, 95%CI 0.63-0.89) and miR-15a (AUC0.85, 95%CI 0.74-0.97). miR-146b showed 89% sensitivity (se) and 87% specificity (sp); mi-R221 se=68.4, sp=90; miR222 se=73, sp=70, and mi-R15a se=72, sp=80. MicroRNAs were associated with aggressive clinicopathological features and tumor progression in PTCs (p < 0.05), particularly for miR-222. Our data reveals a significant association between higher expression levels of miR-146b, miR-221 and miR-222, with the presence of BRAF mutation (p<0.001), and miR-146b (p=0.016) and miR-221 (p=0.010) with the presence of RAS mutation, suggesting an interplay of these mutations with miRNAs expression. Although this study has a relatively small sample size, over expression of miRNAs in cytology may contribute to a more precise preoperative diagnosis. The miRNAs showed a good discriminative ability in PTC diagnosis. The association between the miRNAs expression profile and genetic alterations results could be useful for an accurate diagnosis of DTCs/PTCs in FNAC.

The aim of this pilot study was to investigate whether single nucleotide polymorphisms (SNP) in the gene encoding the catalytic subunit of glutamate cysteine ligase (GCLC) are associated with the risk and clinical features of psoriasis. A total of 944 unrelated individuals, including 474 patients with a diagnosis of psoriasis and 470 healthy controls, were recruited for the study. Six common SNPs in the GCLC gene were genotyped using the MassArray-4 system. Polymorphisms rs648595 (OR=0.56 95%CI 0.35-0.90, Pperm=0.017) and rs2397147 (OR=0.54 95%CI 0.30-0.98, Pperm=0.05) were associated with susceptibility to psoriasis in males. In the male group, diplotype rs2397147-C/C×rs17883901-G/G was associated with decreased risk of psoriasis (FDR-adjusted P=0.014), whereas diplotype rs6933870-G/G×rs17883901-G/G (FDR-adjusted P=0.045) showed association with increased disease risk in females. Joint effects of SNPs with tobacco smoking (rs648595 and rs17883901) and alcohol abuse (rs648595 and rs542914) on the risk of psoriasis were observed (Pperm≤0.05). Furthermore, we found multiple sex-independent associations between GCLC gene polymorphisms and various clinical features such as earlier disease onset, the psoriatic triad, and specific localizations of skin lesions. The present study is the first to show that polymorphisms of the GCLC gene are significantly associated with the risk of psoriasis and related to its clinical features.

Antisense oligonucleotide (ASO)-mediated exon skipping has become a valuable tool for investigating gene function and developing gene therapy. Machine learning-based computational methods such as eSkip-Finder have been developed to predict the efficacy of ASOs via exon skipping. However, these methods are computationally demanding, and the accuracy of predictions remains suboptimal. In this study, we propose a new approach to reduce computational burden and improve prediction performance by using feature selection within machine learning algorithms and ensemble learning techniques. We evaluated our approach using a dataset of experimentally validated exon skipping events, dividing it into training and testing sets. Our results demonstrate that using a 3-way voting approach with random forest, gradient boosting, and XGBoost can significantly reduce computation time to under ten seconds while improving prediction performance, as measured by R2 for both 2’-O-methyl nucleotides (2OMe) and phosphorodiamidate morpholino oligomers (PMOs). Additionally, the feature importance ranking derived from our approach is in good agreement with previously published results. Our findings suggest that our approach has the potential to enhance the accuracy and efficiency of predicting ASO efficacy via exon skipping. It could also facilitate the development of novel therapeutic strategies. This study could contribute to the ongoing efforts to improve ASO design and optimize gene therapy approaches.

Antisense oligonucleotide (ASO)-mediated exon skipping has emerged as a powerful tool for examining the function of genes and exons in basic research, as well as gene therapy. Computational methods, such as eSkip-Finder, have been developed to predict the efficacy of ASOs via exon skipping using machine learning. However, these methods can be computationally demanding and the prediction accuracy of the tool is not yet optimal. In this study, we propose an approach to reduce computational burden and improve prediction performance by utilizing feature selection within machine learning algorithms and employing ensemble learning techniques. The method was evaluated using a dataset of genes with experimentally validated exon skipping events. The dataset was divided into training and testing sets to assess the accuracy of the algorithm. Our results demonstrate that using a 3-way voting approach with random forest, gradient boosting, and XGBoost can significantly reduce computation time to under ten seconds while improving prediction performance, as measured by R2 for both 2’-O-methyl nucleotides (2OMe) and phosphorodiamidate morpholino oligomers (PMOs). Additionally, the feature importance ranking derived from our approach is in good agreement with previously published results. These findings suggest that this approach has the potential to enhance the efficiency and accuracy of predicting ASO efficacy via exon skipping, facilitating the development of novel therapeutic strategies.

We are all similar, but a bit different. These differences are partially due to variations in our genomes and are related to the heterogeneity of symptoms and responses to treatments that patients exhibit. Most animal studies are performed in one single strain with one manipulation. However, due to the lack of variability, therapies are not always reproducible when treatments are translated to humans. Panels of already sequenced organisms are valuable tools for mimicking human phenotypic heterogeneities and gene mapping. This review summarizes the current knowledge of mouse, fly and yeast panels with insightful applications for translational research.

Germplasm is a long-term resource management mission and investment for civilization. For both food and nutritional health, the present changing environmental scenario has become an urgent universal concern. Multiple excellent studies have been previously performed, although the advancement and innovation of practices will require the exploration of the potentiality of crop germplasm. In this study, we emphasized (i) germplasm activates, current challenges and ongoing trends of the crop germplasm, and (ii) how the system biology will be helpful to understand the complex traits such as water use efficiency (WUE), and nitrogen use efficiency (NUE) to mitigate challenges for sustainable development under growing food requirement and climate change conditions. We focused on a vision for transforming PGR into a bio-digital resource system, for the development of climate-smart crops for sustainable food production. Moreover, this review attempted to address current challenges, research gaps and describe the advanced integrated strategies that could provide a platform for future crop improvement research.

In interepidemic periods, a sapronoses typically employs a variety of mechanisms for maintaining viability of its causative agent in terrestrial parasitic systems, associated with different adaptive strategies utilized by its populations to survive. Unlike spore-forming bacteria, causative agents of sapronoses use resistant cell forms, which is a viable but nonculturable (VBNC) state, and persistence. Implementation of these strategies is conditioned by effects of various stress factors of the habitat and is characterized by decreased metabolism, alteration of the morphology and physiology of bacterial cell, and cessation of its replication. It is important that the resistant forms of cells retain virulence and, as favorable conditions come, turn back into the active vegetative form again. The discovery of the genetic modules of bacterial toxin–antitoxin systems in recent years has made it possible to identify a number of complicated regulatory molecular mechanisms responsible for maintaining the pathogenic potential of resistant forms of causative agents of natural-focus sapronoses in interepidemic periods.

Chronic granulomatous disease (CGD) is a group of rare primary inborn errors of immunity characterised by a defect in the phagocyte respiratory burst, which leads to severe and life-threatening infective and inflammatory complications. Despite recent advances in our understanding of the genetic and molecular pathophysiology of X-linked and autosomal recessive CGD, and growth in the availability of functional and genetic testing, there remain significant barriers to early and accurate diagnosis. In the current review, we provide an up-to-date summary of CGD pathophysiology, which underpins methods of diagnostic testing for CGD and closely related disorders. We present an overview of the benefits of early diagnosis and when to suspect and test for CGD. We discuss current and historical methods for functional testing of NADPH oxidase activity, as well as assays for measuring protein expression of NADPH oxidase subunits. Lastly, we focus on genetic and genomic methods employed to diagnose CGD, including gene-targeted panels, comprehensive genomic testing and ancillary methods. Throughout, we highlight general limitations of testing, and caveats specific to interpretation of results in the context of CGD and related disorders, and provide an outlook for newborn screening and the future.

Acute Myeloid Leukemia (AML) is a diverse malignancy originating from myeloid progenitor cells, with significant genetic and clinical variability. Modern classification systems like those from the World Health Organization (WHO) and European LeukemiaNet use immunophenotyping, molecular genetics, and clinical features to categorize AML subtypes. This classification highlights crucial genetic markers such as FLT3, NPM1 mutations, and the MLL-AF9 fusion, which are essential for prognosis and directing targeted therapies. The MLL-AF9 fusion protein is often linked with therapy-resistant AML due, highlighting the risk of relapse due standard chemotherapeutic regimes. In this sense, factors like the ZEB, SNAI, and TWIST gene families, known for their roles in epithelial-mesenchymal transition (EMT) and cancer metastasis, also regulate hematopoiesis and may serve as effective therapeutic targets in AML. These genes contribute to cell proliferation, differentiation, and extramedullary hematopoiesis, suggesting new possibilities for treatment. Advancing our understanding of the molecular mechanisms that promote AML, especially how the bone marrow microenvironment affects invasion and drug resistance, is crucial. This comprehensive insight into the molecular and environmental interactions in AML emphasizes the need for ongoing research and more effective treatments.

Hypervirulent Klebsiella pneumoniae (hvKp) is a variant that has been increasingly linked to severe, life threatening infections including pyogenic liver abscess and bloodstream infections. HvKp belonging to the capsular serotypes K1 and K2 have been reported worldwide, however very scarce studies are available on the their genomics and virulence. In the current study we report four hypermucoviscous hvKp ESBL clinical strains of capsular serotype K1 and K2 isolated from pus and urine of critically ill patients in tertiary care hospitals in Oman. These strains belong to diverse sequence types (STs) namely ST-23(K1), ST-231(K2), ST-881(K2), and ST-14(K2). To study their virulence, Galleria Mellonella model and resistance to human serum killing were used. The G. mellonella model revealed that the K1/ST-23 isolate is the most virulent as 50% of the larvae died in the first day, followed by isolate K2/ST-231 for which 75% of the larvae died in the second day and isolate K2/ST-14 for which 50% of the larvae died in the second day. Resistance to human serum killing showed there was complete inhibition of bacterial growth of all four isolates by the end of the first hour and up to the third hour. For ST-23(K1), ST-231(K2), ST-881(K2), and ST-14(K2), there was a gradual decrease in the number of colonies up to the second to third hour, which indicates neutralization of bacterial cells by the serum components. However, this was followed by sudden increase of bacterial growth, indicating possible resistance of bacteria against human serum bactericidal activity. Whole genome sequencing (WGS) revealed that hvKp strains display unique genetic arrangement of k-loci. Whole genome SNP-based phylogenetic analysis revealed that these hvKp isolates are phylogenetically distinct, belonging to diverse clades and belong to different STs in comparison to global isolates. This is the first report from Oman detailing the WGS of hvKp clinical isolates and assessing their resistance and virulence genomics, which reinforce our understanding of their epidemiology and dissemination in clinical settings.

In photolithography process, nanometer level precise, wavefront aberration models enable the machine to be able to meet the overlay (OVL) drift and critical dimension (CD) specifications. Software control algorithms take as input these models and correct any expected wavefront imperfections before reaching the wafer. In such way a near optimal image is exposed on the wafer surface. Optimizing the parameters of these models though, involves several time costly sensor measurements which reduce the throughput performance, in terms of exposed wafers per hour, of the machine. In that case, photolithography machines come across the trade-off between throughput and quality. Therefore one of the most common Optimal Experimental Design (OED) problems in photolithography machines (and not only) is how to choose the minimum amount of sensor measurements that will provide the maximum amount of information. Additionally, each sensor measurement corresponds to a point on the wafer surface and therefore we must measure uniformly around the wafer surface as well. In order to solve this problem, we propose a Sensor Marks Selection Algorithm which exploits Genetic Algorithms. The proposed solution first selects a pool of points that qualify as candidates to be selected in order to meet the uniformity constraint. Then, the point that provides the maximum amount of information, quantified by the Fisher based criteria of G, D and A-Optimality, is selected and added to the measurement scheme. This process though is considered "greedy", and for this reason Genetic Algorithms (GA) are exploited to further improve the solution. By repeating in parallel the "greedy" part several times we get an initial population that will be the input to our GA. This meta-heuristic approach outperforms the "greedy" approach significantly. The proposed solution is applied in a real life semiconductors industry use case and achieves interesting industry and academical results as well.

Advancements in Parametric Design, Generative Design, and automation in Building Information Modelling (BIM) have opened new opportunities for architects and engineers working on complex buildings, such as offices. These developments allow designers to enhance their designs, increase project efficiency, improve performance, and reduce project time and costs. To address conflicting objectives that arise during the design process, genetic algorithms with multi-objective optimization (MOO) have been employed in architectural design. The GENIUS project aims to optimize building shape, apertures, and shading systems in the concept and design stages, with a focus on energy and daylight performance. The project integrates BIM and visual programming tools with Artificial Intelligence techniques like Genetic Algorithms and RBFOpt model-based optimization. Optimization algorithms are used to identify the best solutions that meet all design objectives, helping architects optimize their designs and achieve desired outcomes. The workflow was tested on a case study of a large office building, with MOO focused on maximizing daylight performance using Spatial Daylight Autonomy metric and minimizing energy consumption using Energy Use Intensity metric. The project provides architects with a method to improve their designs using Algorithm Aided Design tools, which help identify the best solutions for complex design problems.

Background: Studies have demonstrated the link between vitamin D-related genetic variations and non-skeletal outcomes. We aimed to identify all available data on the association of vitamin D-related genetic variations with non-alcoholic fatty liver disease (NAFLD). Methods: Potentially eligible studies were identified from Embase and Medline databases from inception to June 2022 using search strategy that comprised terms for “Vitamin D” and “NAFLD”. Eligible study must report the association between vitamin D-related genetic variations and presence, severity or response to treatment of NAFLD. Data were extracted from each eligible study. Results: A total of 3,495 articles were identified. After systematic review, twelve studies were in-cluded. A total of 26 genetic variations were identified. Presence of NAFLD was associated with variations of GC (rs222054, rs222020, rs10011000, rs7041), VDR (rs2228570, rs11168287, rs10783219, rs4752), CYP24A1 (rs3787557, rs6068816, rs2296241, rs2248359) and CYP27B1 (rs4646536). Severity of NAFLD was associated with variations of GC (rs4588), VDR (rs2228570, rs4334089), CYP2R1 (rs10741657), DHCR7 (rs1544410, rs3829251, rs12785878) and CYP24A1 (rs3787557, rs6068816, rs6097809, rs6127119, rs2248359, rs3787554, rs4809960, rs6022999). Response to calcitriol treatment was associated with variation of VDR (rs10735810). Conclusions: Multiple vitamin D-related genetic variations were associated with NAFLD, indi-cating the role of vitamin D in the pathogenesis of NAFLD.

Satellite communication (Satcom) is an artificial geostationary satellite that facilitates a wide range of telecommunications. Considering its quality of service (QoS) and security is crucial in government/military applications. The most challenging situation for efficient Satcom is radio frequency interference (RFI) environment. Thus, it is necessary to ensure that transmissions are incorruptible or at least sense the quality of its spectrum. This paper presents a new method to recognize received signal characteristics using a hierarchical classification in a multi-layer perceptron neural network. We consider signal modulation and the type of RFI as the characteristics of a real-time video stream transmitted in the direct broadcast satellite. Four different modulation types are investigated in this study. Moreover, the combination of the communication signal with various kinds of interference and their effects on the classification method widely have been analyzed. Besides, two robust feature selection techniques have been developed to reduce the data-set dimensional, which leads to optimizing the classification process. The results show that the Genetic Algorithm (GA) slightly outperforms Principal Component Analysis (PCA) for feature selection. Furthermore, the robustness of the proposed techniques is assessed to detect unknown signals at different signal to noise ratios.

Plant-associated yeasts can also be part of soils’ and pollinating-insects’ microbiomes, where they play significant ecological roles. Indeed, in previous studies, we have shown that yeasts may help pollinators find nectar, which is crucial for their nutrition and for the reproduction of many angiosperms, and, in the soil, can act as plant-growth promoters. Given the importance of yeasts for plant development, in this review, we first delve deep into the biochemical processes behind the ecological role of these microorganisms in soil, insects, and in direct association with plants. Based on this premise, we discuss the influence of this relationship on agricultural production and the possible effects of pesticide use on the yeast microbiota. Finally, we address the most relevant studies in the literature that support the potential of these microorganisms (either indigenous or genetically engineered) as bioremediation agents for soils and foods contaminated by pesticides. Our review indicates that yeasts can be satisfactorily employed in organic agriculture to increase plant growth or bioremediate contaminated soil or food.

Determining the genetic contribution of susceptibility to severe SARS-CoV-2 infection outcomes is important for public health measures and individualized treatment. Through intense research on this topic, several hundred genes have been implicated as possibly contributing to the severe infection phenotype(s); however, the findings are complex and appear to be population-dependent. We aimed to determine the contribution of human rare genetic variants associated with a severe outcome of SARS-CoV-2 infections and their burden in the Slovenian population. A panel of 517 genes associated with severe SARS-CoV-2 infection were obtained through an extensive review of the literature, and additionally included target genes identified by the COVID-19 Host Genetic Initiative, as well as the curated Research COVID-19 associated genes from PanelApp, England Genomics. Whole genome sequencing was performed using PCR-free WGS on DNA from 60 patients hospitalized due to severe COVID-19 disease, and the identified rare genomic variants were analyzed and classified according to the ACMG criteria. Background prevalence in the general Slovenian population was determined by comparison with sequencing data from 8025 individuals included in the Slovenian genomic database (SGDB). Results show that several rare pathogenic/likely pathogenic genomic variants in genes CFTR, MASP2, MEFV, TNFRSF13B, and RNASEL likely contribute to the severe infection outcomes in our patient cohort. These results represent an insight into the Slovenian genomic diversity associated with severe COVID-19 outcome.

Building electric energy is characterized by a significant increase of its uses (e.g. vehicle charging), a rapidly declining cost of all related data collection and a proliferation of smart grid concepts, including diverse and flexible electricity pricing schemes. Not surprisingly, an increased number of approaches have been proposed for its modeling and forecasting. In this work, we place our emphasis on three forecasting related issues. First, on the forecasting explainability, i.e. the ability to understand and explain to the user what shapes the forecast. To this extent we rely on concepts and approaches that are inherently explainable, such as the evolutionary approach of genetic programming (GP) and its associated symbolic expressions, as well as the so-called SHAP (SHapley Additive eXplanations) values, which is a well established model agnostic approach for explainability, especially in terms of feature importance. Second, we investigate the impact of the training timeframe on the forecasting accuracy; this is driven by the realization that a fast training would allow for faster deployment of forecasting in real life solutions. And third, we explore the concept of counterfactual analysis on actionable features, i.e. features that the user can really act upon and which therefore present an inherent advantage when it comes to decision support. We have found that SHAP values can provide important insights into the model explainability. As for GP models, we have found comparable and in some cases superior accuracy when compared to its neural-network and time-series counterparts but a rather questionable potential to produce crisp and insightful symbolic expressions, allowing a better insight into the model performance. We have also found, and report here on an important potential especially for practical, decision support, solutions of counterfactuals built on actionable features and short training timeframes.

Repeat-Induced Point mutations (RIP) serves as a genome defence mechanism that impedes the deleterious consequences of repeated motifs such as transposable elements in fungi. Genomic regions with RIP are biased for adenosine and thymine transitions and the cumulative influence of RIP is thought to have a considerable impact on genome composition. We investigated the impact of RIP on localized genomic regions and whole-genome sequences for representatives of the pine pathogen, Fusarium circinatum. We set out to determine the intraspecific variation in acquired RIP and the role of RIP in the development of diverse F. circinatum sub-genomic compartments. The results of the study show that the AT-enriched sub-genomic compartment accounts for ca. 97% of the calculated RIP and was further prominent in both core and accessory genomic regions. However, more extensive RIP was observed in the accessory sub-compartment and more variable regions of the genome. Regions with RIP indicated increased intrinsic curvature of the DNA which may influence DNA-protein interactions and may promote constitutive heterochromatin formation. The results show that RIP is an important source of functional novelty and genome variation. RIP contributes to the evolution of the genetic landscape and differentiation of diverse sub-genomic compartments of this important fungal pathogen.

Introduction: A plethora of biological molecules regulate chondrogenesis in the epiphyseal growth plate. Disruptions of quantity and function of these molecules can manifest clinically as stature abnormalities of various etiologies. Traditionally, the growth hormone/insulin-like growth factor 1 (IGF1) axis represents the etiological center of final stature attainment. Of note, little is known about the molecular events that dominate the growth of the craniofacial complex and its correlation with somatic stature. Aim: Given the paucity of the relevant data, this review discusses available information regarding potential applications of lateral cephalometric radiography as a potential clinical indicator of genetic short stature in children. Materials and Methods: A literature search was conducted in the PubMed electronic database using the key words: cephalometric analysis and short stature; cephalometric analysis and achondroplasia; cephalometric analysis and hypochondroplasia; cephalometric analysis and skeletal abnormalities; cephalometr* and SHOX; cephalometr* and CNP; cephalometr* and ACAN; cephalometr* and CNVs; cephalometr* and IHH; cephalometr* and FGFR3; cephalometr* and Noonan syndrome; cephalometr* and “Turner syndrome”; cephalometr* and achondroplasia. Results: In individuals with genetic syndromes causing short stature, linear growth of the craniofacial complex is confined, following the pattern of somatic short stature regardless of its etiology. The angular cephalometric measurements differ from the average height normal individuals and are suggestive of a posterior placement of the jaws and a vertical growth pattern of the face. Conclusion: The greater part of the existing literature regarding cephalometric measurements in short statured children with genetic syndromes provides qualitative data. Furthermore, cephalometric data for individuals affected with specific rare genetic conditions causing short stature, should be the focus of future studies. These quantitative data are required to potentially establish cut-off values to refer for genetic testing based on craniofacial phenotypes.

The World Health Organization declared the coronavirus disease 2019 (COVID-19) pandemic in 2020, following which a global genetic vaccination program has been rapidly implemented as a fundamental solution. However, it has been reported worldwide that the modified mRNAs encoding spike proteins and lipid nanoparticles, which are used as drug delivery systems, not only cause thrombosis and cardiovascular disorders post vaccination, but might also cause diverse diseases involving all organs and systems, including the nervous system. Furthermore, the toxicity and pathogenicity of spike proteins may necessitate defining these proteins as nonbiological infective material. Based on these reports and the abundant evidence that has come to light in the past few years, this paper aims to draw the attention of medical professionals to the various risks associated with transfusion using blood products derived from long COVID patients or from genetic vaccine recipients, and to make proposals regarding specific inspection items, testing methods, regulations, etc. This paper provides insights to address the post-vaccination syndrome and its consequences following such genetic vaccination programs.

The exploration of three-dimensional (3D) DNA structures marks a pivotal shift in genomics, offering unprecedented insights into the intricacies of cellular regulation, disease genesis, and the potential for novel diagnostic and therapeutic strategies. This study delves into the dynamic nature of 3D genome organization, investigating how these structures influence cellular functions in response to environmental changes and over time. Through a combination of real-time imaging and advanced computational models, we aim to unravel the complex interplay between 3D DNA structures, non-coding RNAs, and chromatin organization, and their collective impact on gene expression and cellular differentiation. A significant focus is placed on understanding the role of 3D DNA structures in the development and progression of genetic disorders. By employing comprehensive genomic and epigenomic profiling, this research seeks to identify structural aberrations linked to diseases, paving the way for the development of targeted diagnostics and therapies. Additionally, the study explores the potential of genome editing technologies, such as CRISPR-Cas systems, in correcting these structural abnormalities, while also addressing the ethical, legal, and social implications (ELSI) of such interventions. Integrating data from various genomic analyses, we propose the development of advanced computational models to predict the effects of 3D DNA structures on gene expression and cellular function. This integrative approach not only promises to enhance our understanding of genomic regulation but also sets the stage for groundbreaking advances in genomics-based diagnostics and therapeutics. The findings of this study have the potential to revolutionize our approach to treating genetic disorders, offering a new frontier in personalized medicine.

Toxoplasmosis is a zoonosis of warm-blooded animals, affecting also humans, caused by Toxo-plasma gondii, an obligate intracellular parasite with a complex biological cycle. Toxoplasmosis is considered a re-emerging zoonosis and an important public health threat, also for the absence, up to now, of efficient vaccines and preventive measures. Sources of transmission to humans are environmental or food-borne, mainly through the consumption of raw and undercooked meat, predominantly of Sus scrofa, considered highly susceptible to T. gondii. Mainly in the last dec-ades, an effective strategy to counter animal infectious diseases was based on the discovery of resistance immuno-genetic markers, possibly to be used for association studies between geno-typic and phenotypic traits and consequently in Marker Assisted Selection. Until now, this gen-otyping approach has never been adopted for T. gondii infection, so this study aimed, for the first time, to fill this gap. In particular, pigs, reared in different farms of Central Italy, were serologically characterized by an ELISA assay performed on diaphragm meat juice. Out of 179 tested animals, 98 resulted seropositive (54.74%), 57 seronegative (31.84%) and 24 doubtful (10.6%), underlining a possible re-emerging diffusion of this protozoan in the investigated areas. Atten-tion was firstly focused on IL-18 gene, encoding for a pro-inflammatory cytokine that plays a key role starting from the upstream steps of the immunity pathway towards T. gondii. A RT-PCR assay followed by Sanger sequencing of IL-18 cDNA was de novo developed. For this interleukin, at this stage, genetic analysis has not highlighted significant polymorphic variations compared to the reference sequence, except for a seropositive animal. However, the outcomes of this pre-liminary innovative study will be investigated in depth analyzing other relevant genetic re-gions and other target genes.

Reversed Flow Gas Chromatography (R.F.G.C.) was employed to assess the impact of genetic modification on Saccharomyces cerevisiae yeast strains during the process of alcoholic fermentation, utilizing fig syrup. Multiple fermentations were carried out at various temperatures to evaluate the influence of genetic modifications on yeast strain efficiency. The study involved a wild-type yeast strain, W303, as a control, and two genetically modified strains, W_M4_533 and W_M4_558, sharing the same genetic background as the wild type. Notably, the genetic modifications in the Msn4p transcription factor involved the substitution of serine residues with alanine at positions 533 and 558, resulting in the development of psychrophilic or ethanol-resistant strains. Utilizing the R.F.G.C. method enabled the differentiation of the duration of alcoholic fermentation phases, providing insights correlated to the yeast cell life cycle. The values of rate constants (k) for each phase, conducted with both wild-type and genetically modified cells using RFGC, aligned with existing literature. Additionally, the calculation of activation energies for distinct phases revealed lower values for genetically modified strains compared to wild-type strains. This decrease in activation energies suggests enhanced efficiency in the alcoholic fermentation process for the genetically modified strains.

This study investigates the emergence of ertapenem-resistant, meropenem-susceptible (ErMs) among non-carbapenemase producing (NCP) and carbapenemase producing (CP) Escherichia coli and Klebsiella pneumoniae. As mutations for ertapenem resistance establish the genetic background for non-carbapenemase meropenem resistance, there is a great need for antibiotic stewards and researchers to understand the determinants of a strain’s propensity to become resistant. Whole genome sequencing was conducted on clinical carbapenem-resistant E. coli (CREC) and K. pneumoniae (CRKP) across 5 hospitals in San Antonio, U.S. from 2012-2018. The majority of carbapenem resistant Enterobacterales (CRE) were NCP (54%; 41/76). The blaCTX-M was found to be most prevalent among NCP isolates (p = 0.02). LC-MS/MS analysis of carbapenem hydrolysis revealed that blaCTX-M-mediated carbapenem hydrolysis, indicating the need to reappraise the term, “non-carbapenemase (NCP)” for quantitatively uncharacterized CRE strains harboring blaCTX-M. Antimicrobial susceptibility results showed that 56% of all NCPE isolates had an ErMs phenotype (NCPE vs. CPE, p < 0.001), with E. coli driving the phenotype (E. coli vs. K. pneumoniae, p < 0.001). ErMs strains carrying blaCTX-M, had approximately 4-fold more copies of blaCTX-M than ceftriaxone-resistant but ertapenem and meropenem susceptible (EsMs) isolates (3.7 v. 0.9, p < 0.001). ErMs also carried more mobile genetic elements (MGEs), particularly IS26 composite transposons, than EsMs (37 vs. 0.2, p < 0.0001). Immunoblot analysis demonstrated the absence of OmpC expression in NCP-ErMs E. coli, with 92% of strains lacking full contig coverage of ompC. Overall, this work provides evidence of a collaborative effort between blaCTX-M and OmpC in NCP strains that confer resistance to ertapenem but not meropenem. To thwart potential mismanagement of CRE infected patients, future efforts should focus on understanding the mechanism(s) underlying OmpC loss, developing rapid methods to detect blaCTX-M copy number variants, and targeted antimicrobials for NCPE and ErMs strains.

Human T-cell leukemia virus type-1 (HTLV-1) is the causative agent of adult T-cell leukemia/lymphoma (ATL). HTLV-1 carriers have a life-long asymptomatic balance between infected cells and host antiviral immunity, but 5–10% of carriers lose this balance and develop ATL. Coinfection with Strongyloides also promotes ATL development, suggesting that the immunological status of infected individuals is a determinant of viral pathogenicity. As CD4+ T cells play a central role in host immunity, deregulation of their function and differentiation by HTLV-1 promotes the immune evasion of infected T cells. During ATL development, the accumulation of genetic and epigenetic alterations in key host immunity-related genes further disturbs immunological conditions. Various therapeutic approaches have been developed to treat these abnormalities. Allogeneic hematopoietic stem cell transplantation is currently the only treatment with the potential to cure ATL; however, the patient's immune state may also contribute to its outcome. Additionally, the activity of the anti-CC chemokine receptor 4 antibody, mogamulizumab, also depends on immune functions, such as antibody-dependent cytotoxicity. In this review, we comprehensively summarize the immunological pathogenesis of HTLV-1 infection in ATL and integrate clinical findings to determine the factors to be considered for developing treatment strategies for ATL.

Translation of the genetic code into proteins is the main process across all life and ribosomes are ancient cellular machines uniquely enabling this information transformation. We provide a brief overview of the recent advances in linking the ribosomal structure and evolution. Based on these insights into ribosomal organisation across time, we propose that early replication and protein biosynthesis functions were inseparable and in fact were performed by the same ancient RNA molecule, the riboreplisome. Riboreplisome hypothesis helps to address issues of non-Darwinian evolution and complicated starting point that are characteristic to the RNA world, protein world and RNA:protein mixed co-development theories. We suggest that the riboreplisome is the missing link and a molecular machine connecting chemical and biological evolution paths, by being capable of basic genetic and feature selection functions in a cell- or cell-free setting. The riboreplisome hypothesis allows ease of sequential, genetically uninterrupted emergence and sophistication of the genetic code and its decoding machinery, and provides plausible explanations to the origins of the three main RNA types involved in the decoding: the ribosomal, transfer and messenger RNA. Furthermore, riboreplisome can help explaining the co-evolution of aminoacylation machinery, the driving force behind selective gene transcription and expression, and the cell-like compartmentalisation. While we may never find the original riboreplisome again, we might continue to discover different molecular remnants of its prior existence across the existing biological RNA, which, once identified or resurrected, can be useful in synthetic biology applications.

: Seasonal influenza (a.k.a flu) is responsible for considerable morbidity and mortality across the globe. The three recognized pathogens that cause epidemics during the winter season are influenza A, B and C. The influenza virus is particularly dangerous due to its mutability. Vaccines are an effective tool in preventing seasonal influenza, and their formulas are updated yearly according to WHO recommendations. However, in order to facilitate decision-making in the planning of the intervention, policymakers need information on the projected costs and quantities related to introducing the influenza vaccine, in order to help governments obtain an optimal allocation of the vaccine each year. In this paper, an approach based on a Controlled Elitism Non-Dominated Sorting Genetic Algorithm (CENSGA) model is introduced to optimize the allocation of influenza vaccination. A bi-objective model is formulated to control the infection volume, and reduce the unit cost of the vaccination campaign. An SIR (Susceptible–Infected–Recovered) model is employed for representing a potential epidemic. The model constraints are based on the epidemiological model, time management, and vaccine quantity. A two-phase optimization process is proposed: guardian control followed by contingent controls. The proposed approach is an evolutionary metaheuristic multi-objective optimization algorithm with a local search procedure based on a hash table. Moreover, in order to optimize the scheduling of a set of policies over a predetermined time to form a complete campaign, an extended CENSGA is introduced with a variable-length chromosome (VLC) along with mutation and crossover operations. To validate the applicability of the proposed CENSGA, it is compared with the classical Non-Dominated Sorting Genetic Algorithm (NSGA-II). The results are analyzed using graphical and statistical comparisons in terms of cardinality, convergence, distribution and spread quality metrics, illustrating that the proposed CENSGA is effective and useful for determining the optimal vaccination allocation campaigns.

We present a novel method for interpolating univariate time series data. The proposed method combines multi-point fractional Brownian bridges, a genetic algorithm, and Takens’ theorem for reconstructing a phase space from univariate time series data. The basic idea is to first generate a population of different stochastically interpolated time series data, and secondly, to use a genetic algorithm to find the pieces in the population which generate the smoothest reconstructed phase space trajectory. A smooth trajectory curve is hereby found to have a low variance of second derivatives along the curve. For simplicity, we refer to the developed method as PhaSpaSto-interpolation, which is an abbreviation for phase-space-trajectory-smoothing stochastic interpolation. The proposed approach is tested and validated with a univariate time series of the Lorenz system, five non-model data sets and tested against a cubic spline interpolation and a linear linear interpolation. We find that the criterion for smoothness guarantees low errors on known model and non-model data. Finally, we interpolate the discussed non-model data sets, and show the corresponding improved phase space portraits. The proposed method is useful for interpolating low-sampled time series data sets for, e.g., machine learning, regression analysis, or time series prediction approaches.

This paper presents a study on optimization of Overcurrent relay (OCR) coordination protection scheme for Sustainable Standalone Hydrokinetic Renewable Energy (SHRE) distribution network at Batang Rajang river, located at Kapit Sarawak, Malaysia by turning river stream into power generation source. The purpose of the project is to develop rural electrification system for native long houses along the river. The research study is tested on a DigSILENT develop model of the SHRE distribution network and in accordance with all respectively unique parameters and relevant standards. Since this is a new standalone distribution system, an efficient and properly coordinated overcurrent protection system must be provided. Improper and miscoordination among OCRs result in maloperation of the protection system that can lead to false tripping and an unnecessary outage and power system instability. Thus, the objective of this work is to employ Genetic Algorithm (GA) technique in Matlab/Simulink for optimal overcurrent coordination and settings among all OCRs in the proposed distribution network in order to improve the speed of OCR tripping operation. The GA is used because the project is fast track and requiring the simplest method available. In this strategy, time dial setting (TDS) is optimized by using plug setting multiplier (PSM) as the constraint. The obtained results show a significant improvement of the relay operating time of 36.01% faster than that of conventional numerical technique during fault occurrence. Thus, an efficient and reliable overcurrent protection scheme has been achieved for the SHRE distribution network.

This paper examines the correlation between numbers of computer cores in parallel genetic algorithms. The objective to determine the linear polynomial complementary equation in order represent the relation between number of parallel processing and optimum solutions. Model this relation as optimization function (f(x)) which able to produce many simulation results. F(x) performance is outperform genetic algorithms. Compression results between genetic algorithm and optimization function is done. Also the optimization function give model to speed up genetic algorithm. Optimization function is a complementary transformation which maps a TSP given to linear without changing the roots of the polynomials.

Acute lower respiratory tract infection is a major health problem that affects more than 15% of the total population of Saudi Arabia each year. Epidemiological studies conducted over the last three decades have indicated that viruses are responsible for the majority of these infections. The epidemiology of respiratory viruses in Saudi Arabia is believed to be affected mainly by the presence and mobility of large numbers of foreign workers and the gathering of millions of Muslims in Mecca during the Hajj and Umrah seasons. Knowledge concerning the epidemiology, circulation pattern, and evolutionary kinetics of respiratory viruses in Saudi Arabia are scant, with the available literature being inconsistent. After reviewing the available data on epidemiology and evolution of respiratory viruses, this study examines the effect of varying vaccination rates across different age groups during the 2019/2020 influenza season in Saudi Arabia. Utilizing an age-structured SEIR (Susceptible–Exposed–Infectious–Recovered) model, it develops customized vaccination policies by considering age-specific incidence rates. This pioneering research segments the population into five age groups—infants and school-aged children, junior and older youth, young adults and adults, middle-aged, and seniors—across five regional areas in Saudi Arabis: Eastern, Central, Northern, Western, and Southern. A contact matrix, compiled from multiple sources, is used to delineate the dynamics of influenza transmission. The study undertakes a thorough comparison of three distinct vaccination scenarios: no vaccination, national vaccination, and optimized vaccine allocation policy, under various potential pandemic situations. It examines their impact on vaccination campaigns in terms of the total number of infections, mortality, and morbidity rates. The results emphasize the crucial impact of population demographics on disease transmission and vaccine distribution. According to the findings, the optimized vaccination scenario proved to be the most effective strategy in all scenarios.