The integration of Genetic Algorithms (GAs) into Serious Games (SGs) has gained traction as a method to optimize game mechanics and personalize user experiences. While GAs are well-known for their effectiveness in solving complex optimization problems, their application in SGs remains relatively under-explored. This review aims to address this gap by systematically mapping the existing literature on the intersection of these two fields. The primary objective of this scoping review is to identify and synthesize the existing research on the application of GAs in SGs, with a focus on understanding the current state of the field, identifying common applications, challenges, and potential future directions. The review includes studies published in English that focus on the use of GAs within SGs. The search encompassed articles from academic journals and conference proceedings without restrictions on publication dates. Exclusion criteria included studies that did not specifically address the integration of GAs in SGs. A systematic search was conducted in databases such as ACM Digital Library, Web of Science, Scopus and Inspec in July 2024. The search terms used included "genetic algorithm," "evolutionary algorithm," "computational algorithm," and "serious games." Unpublished data and articles were not considered. Data were charted using a PRISMA flowchart developed by the author VV. Key categories for data extraction included study objectives, methods, outcomes, type of study and GA used and the specific applications of GAs in SGs. Data charting was conducted independently by two reviewers, with disagreements resolved through discussion and consensus. The search identified 154 studies, of which 23 met the inclusion criteria. The included studies highlight the diverse applications of GAs in SGs, ranging from optimizing game scenarios to personalizing learning experiences. Challenges identified include the computational complexity of GAs and difficulties in their integration into existing game frameworks. The findings of this review suggest that while the use of GAs in SGs is a promising area of research, it remains in its nascent stages. Future research should focus on addressing the technical challenges of integrating GAs into SGs and exploring their application across a wider range of game genres and educational contexts. Limitations of the review include the potential for publication bias and the exclusion of non-English language studies.

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.

Myasthenia gravis (MG) is a disease characterized by typical clinical and immunopathogenic pe-culiarities, which correlate with age and sex/gender. Accordingly, MG patients show thymic al-terations and auto-antibodies productions, that can be the expression of a different pathogenetic mechanism related to age and gender. This determines, in relation to age at and sex/gender, a dif-ferent MG onset, prognosis, and outcome. Therefore, diverse MG forms can be identified, classi-fied, and characterized in relation to their different features, and precisely there are early onset and late-onset forms of MG. It has been hypothesized that the cause of appearance the two diverse forms of MG can be ascribed mainly to the diverse genetic and epigenetic factors. In this narrative review, we will analyze and discuss about the interplay of age- and sex-related factors (i.e. genet-ic and epigenetic factors) related to two different MG forms and how they contribute to their di-verse pathogenesis.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Information is the currency of life, but the origin of prebiotic information remains a mystery. We propose transitional pathways from the cosmic building blocks of life to the complex prebiotic organic chemistry that led to the origin of information systems. The prebiotic information system, specifically the genetic code, is segregated, linear, and digital and probably appeared during biogenesis four billion years ago. In the peptide/RNA world, lipid membranes randomly encapsulated amino acids, RNA, and protein molecules, drawn from the prebiotic soup, to initiate a molecular symbiosis inside the protocells. This endosymbiosis led to the hierarchical emergence of several requisite components of the translation machine: tRNAs, aaRS, mRNAs, and ribosomes. When assembled in the right order, the translation machine created biosynthetic polypeptides, a process that transferred information from mRNAs to proteins. This was the beginning of the prebiotic information age. The molecular attraction between tRNA and amino acids led to different stages of the translation machines and the genetic code. tRNA is an ancient molecule that designed and built mRNA for storing the information of its cognate amino acid. Each mRNA strand became the storage device for the genetic information that encoded the amino acid sequences in triplet nucleotides. As information appeared in the digital languages of the codon within mRNA, and the genetic code for protein synthesis evolved, the prebiotic chemistry then became more organized and directional. The origin of the genetic code is enigmatic; herein we propose an evolutionary explanation: the demand for a wide range of specific enzymes in the peptide/RNA world was the main selective pressure for the origin of information-directed protein synthesis. We review three main concepts on the origin and evolution of the genetic code: the stereochemical theory, the coevolution theory, and the adaptive theory. These three theories are compatible with our coevolution model of the translation machines and the genetic code. We suggest biosynthetic pathways as the origin of the specific translation machines which 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 machines: GNC code developed by the pre-tRNA/pre-aaRS /pre-mRNA machine, SNS code by the tRNA/aaRS/mRNA machine, and finally the universal genetic code by the tRNA/aaRS/mRNA/ribosome machine. Our hypothesis provides the logical and incremental steps for the origin of the programmed protein synthesis. In order to understand the prebiotic information system better, we converted letter codons into numerical codons in the Universal Genetic Code Table. We have developed a software called CATI (Codon-Amino Acid-Translator-Imitator) to translate randomly chosen numerical codons into corresponding amino acids and vice versa. This conversion has granted us insight into how the translation might have worked in the peptide/RNA world. There is great potential in the application of numerical codons to bioinformatics such as barcoding, DNA mining, or DNA fingerprinting. We constructed 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. Using AnyLogic software we were able to simulate and visualize the entire evolution of the translation machines and the genetic code. The results indicate that the emergence of the information age from the peptide/RNA world was a watershed event in the origin of life about four billion years ago.

This study aimed to determine gene pool structure in the tomato (Solanum lycopersicum) along with the relationships of populations in different regions. To do so, it leveraged 276 diverse cultivated accessions representing 35 countries, obtained via the USDA-GRIN tomato germplasm collection. Single nucleotide polymorphisms (SNPs) in these accessions were detected by sequencing, and genetic diversity, population structure, and phylogenetic relationships were estimated from the SNP alleles. The results of model-based ancestry analysis, phylogenetic tree construction, and principal component analysis supported these accessions as representing three well-differentiated genetic populations. Phylogenetic analyses traced the accessions back to their regions of origin, highlighting Central and South America as the primary center of domestication, with secondary centers in Europe and North America. This study provides a comprehensive analysis of the genetic variation in and relationships among cultivated tomato genotypes. The findings are of benefit to curators, researchers, and breeders in understanding, utilizing, conserving, and managing the collection, and in thereby contributing more efficiently to international tomato research.

Chronic urticaria (CU) is a debilitating skin condition affecting up to 20% of the global popula-tion, characterized by erythematous, maculo-papular lesions and significant quality of life im-pairment. This study focused on the role of interleukin 33 (IL-33) and its polymorphisms, partic-ularly SNP rs1929992, in chronic spontaneous urticaria (CSU). Using demographic, clinical, and laboratory data from CU patients and controls, we aestimated allele and genotype frequencies, Hardy-Weinberg equilibrium condition, and serum IL-33 levels, using unconditional binomial logistic regression for association analysis. Results revealed that CU patients had significantly higher frequencies of the minor allele of IL33 rs1929992 compared to controls (31.25% vs. 17.35%, p = 0.024), and carriers of the AG genotype exhibited increased odds of CU (adjusted OR = 2.208, p ≤ 0.001). Additionally, serum IL-33 levels were markedly elevated in CU patients, par-ticularly those with the GA genotype. The findings suggest that the IL-33 SNP rs1929992 is asso-ciated with an increased susceptibility to CSU, emphasizing its potential as a diagnostic and therapeutic biomarker. This study underscores the genetic and immunological underpinnings of CSU, paving the way for personalized treatment approaches.

Species identification and phylogenetic relationships clarification are fundamental goals in species delimitation. Yet these tasks pose challenges when based on morphologies, geographic distribution, and genomic data. Previously, two new species of fern genus Bolbitis, B. × multipinna and B. longiaurita, were described based on morphological traits, are phylogenetically intertwined with B. sinensis and fail to form monophyletic groups. To address the unclear phylogenetic relationships within the B. sinensis species complex, RAD sequencing was performed on 65 individuals from five populations. Our integrated analysis of phylogenetic tree, neighbor net, and genetic structure, indicate that the B. sinensis species complex should not be considered as separate species. Moreover, our findings reveal differences in the degree of genetic differentiation among five populations, ranged from low to moderate, which might be influenced by geographical distance and gene flow. The Fst values also confirmed that genetic differentiation intensifies with increasing geographic distance. Collectively, this study clarifies the complex phylogenetic relationships within the B. sinensis species complex, elucidates the genetic diversity and differentiation across the studied populations, and offers valuable genetic insights that contribute to the broader study of evolutionary relationships and population genetics within the Bolbitis species.

Multiple sclerosis is an autoinflammatory condition that results in damage of myelinated neurons in affected patients. While disease-modifying treatments have been successful in slowing the progression of relapsing remittent disease, most patients still progress to secondary progressive disease that is largely unresponsive to disease-modifying treatments. Similarly, there is currently no effective treatment for patients with primary progressive MS. Innate and adaptive immune cells in the CNS play a critical role in initiating an autoimmune attack and in maintaining the chronic inflammation that drives disease progression. In this review, we will focus on recent insights into the role of T cells with regulatory function in suppressing the progression of MS, and, more importantly, in promoting the remyelination and repair of MS lesions in the CNS. We will discuss the exciting opportunity to genetically reprogram regulatory T cells to achieve immune suppression and enhance repair locally at sites of tissue damage, while retaining a fully competent immune system outside the CNS. In the future, reprogramed regulatory T cells with defined specificity and function may provide life medicines that can persist in patients and achieve lasting disease suppression after one cycle of treatment.

This paper proposes a novel formulation of the fuzzy newsvendor problem for inventory management applications. This new formulation allows the use of any profit function. A new credibility estimation is proposed to explore the neighborhood around the most impactful demand scenarios. Further, a simulation procedure was designed for the different demand scenarios, which allows the comparison of the proposed approach with probabilistic demand curves. The fuzzy newsvendor problem is solved using a modified genetic algorithm (GA), where an initialization mechanism with null values is proposed. The new formulation of the fuzzy newsvendor problem together with the modified GA have shown to improve the average profit up to 55% in problems with low-budget scenarios.

Corrugated board, widely used in the packing industry, is a recyclable and durable material. Its strength and cushioning, influenced by geometry, environmental conditions like humidity and temperature, and paper quality, make it versatile. Double-walled (or five-ply) corrugated board, comprising two flutes and three liners, enhances these properties. This study introduces a novel approach to analyze 5-layered corrugated board, extending a previously published algorithm for single-walled boards. Our method focuses on measuring layer and overall board thickness, flute height, and center lines of each layer. Integrating image processing and genetic algorithms, the research successfully developed an algorithm for precise geometric feature identification of double-walled boards. Images were recorded using a special device with a sophisticated camera and image sensor for detailed corrugated board cross-sections. Demonstrating high accuracy, the method faced limitations only with very deformed or damaged samples. This research contributes significantly to quality control in the packaging industry and paves the way for further automated material analysis using advanced machine learning and image sensors. It emphasizes the importance of sample quality and suggests areas for algorithm refinement to enhance robustness and accuracy.

Genetic diversity is very important in crop improvement. This study was carried out to assess the genetic diversity and the number of unique multilocus genotypes (MLGs) in cassava collection in Burkina Faso. To achieve this objective 130 cassava accessions were genotyped using 32 simple sequence repeat (SSR) markers. The results revealed that among these markers, twelve (12) were highly informative, with the PIC values greater than 0.50, twelve (12) were moderately informative, with the PIC values ranged between 0.25 and 0.50, and eight (8) were not very informative, with the PIC values lower than 0.25. A moderate level of genetic diversity was found for the population, indicated by the average expected heterozygosity (0.45) and the observed heterozygosity (0.48). About 83.8% of unique multilocus genotypes were found in the cassava collection indicating that SSR markers seem to be most appropriate for MLGs identification. Population structure analysis based on hierarchical clustering identified two subpopulations and Bayesian approach suggested five clusters. Additionally, DAPC analysis separated the cassava accessions into 13 subpopulations. The comparison of these results and the previous study using SNP, suggest that each type of markers can be used to assessment the genetic structure of cassava grown in Burkina Faso.

Nonsense mutations cause several genetic diseases such as Cystic fibrosis, Duchenne muscular dystrophy, β-thalassemia, and Shwachman-Diamond syndrome. These mutations induce the formation of a premature termination codon (PTC) inside the mRNA sequence that leads to the aberrant translation, resulting in the synthesis of truncated polypeptides. Nonsense suppression therapy mediated by translational readthrough-inducing drugs (TRIDs) is a promising approach to correct these genetic defects. TRIDs generate a ribosome miscoding of the PTC named “translational readthrough” and restore the synthesis of full-length and potentially functional protein. The new oxadiazole-core TRIDs NV848, NV914, and NV930 showed translational readthrough activity in nonsense-related in vitro systems. In this work, the possible off-target effect of NV molecules on natural termination codons (NTCs) was investigated. Two different in vitro approaches were used to assess if the NV molecules treatment induce NTCs readthrough: 1) A study of translational induced p53 molecular weight and functionality; 2) the evaluation of two housekeeping proteins (Cys-C and β2M) molecular weights. Our results showed that the treatment with NV848, NV914, or NV930 do not induce any translation alterations in both experimental systems. Data suggested that NV molecules have a specific action for the PTCs and an undetectable effect on the NTCs.

The identification of an emerging pathogen in humans can remain difficult by conventional methods such as enrichment culture assays that remain highly selective, require appropriate medium and cannot avoid misidentifications, or serological tests that use surrogate antigens and are often hampered by the level of detectable antibodies. Although not originally designed for this purpose, the implementation of polymerase-chain-reaction (PCR) has resulted in an increasing number of diagnostic tests for many diseases. However, the design of specific molecular assays relies on the availability and reliability of published genetic sequences for the target pathogen as well as enough knowledge on the genetic diversity of species and/or variants giving rise to the same disease symptoms. Usually designed for clinical isolates, molecular tests are often not suitable for environmental samples in which the target DNA is mixed with a mixture of environmental DNA. A key challenge of such molecular assays is thus to ensure high specificity of the target genetic markers when focusing on clinical and environmental samples in order to follow the dynamics of disease transmission and emergence in humans. Here we focus on Buruli ulcer (BU), a human necrotizing skin disease mainly affecting tropical and subtropical areas, commonly admitted to be caused by Mycobacterium ulcerans worldwide although other mycolactone-producing mycobacteria and even mycobacterium species were found associated with BU or BU-like cases. By revisiting the literature, we show that many studies have used non-specific molecular markers (IS2404, IS2606, KR) to identify M. ulcerans from clinical and environmental samples and propose that all mycolactone-producing mycobacteria should be definitively considered as variants from the same group rather than different species. Importantly, we provide evidence that the diversity of mycolactone-producing mycobacteria variants as well as mycobacterium species potentially involved in BU or BU-like skin ulcerations might have been underestimated. We also suggest that the specific variants/species involved in each BU or BU-like case should be carefully identified during the diagnosis phase, either via the key of genetic identification proposed here or by broader metabarcoding approaches, in order to guide the medical community in the choice for the most appropriate antibiotic therapy.

Waste management is an unavoidable technological operation in the process of raw material extraction. The main characteristic of this technological operation is the handling of large quantities of waste material, which can amount to several hundred million cubic metres. Working with this amount of material usually requires high-capacity systems for excavation and loading, a large fleet of trucks for haulage, construction, and maintenance of a complex roads network, use of a significant area of land in order to achieve the required capacities, etc. At the same time, this operation must comply with all administrative and environmental standards. Therefore, optimising waste rock management (particularly haulage and dumping) has the potential to significantly improve the overall value of the project. This paper presents a hybrid model for the optimisation of waste dump design and site selection. The model is based on different mathematical methods (genetic algorithm, analytic hierarchy process and heuristic methods) adapted to different aspects of the problem. The main objective of the model is to provide a solution (in analytical and graphical form) for the draft waste dump design, on the basis of which the final waste dump design can be defined.

The production of plants and crops is influenced by environmental stress, which is a serious scientific issue. Cotton is an essential crop for producing natural fibers that are used to make biofuel and edible oils. Salinity is the main element that influences cotton growth during the beginning of germination. The type of salt and the growth stage affects how sensitive a plant is to salt stress. Developing ways to enhance cotton performance in salty circumstances can be aided by an understanding of the response of cotton to salt, its mechanism of resistance, and its management methods. Osmotic and ionic imbalances originate due to the deposition of soluble salts under salinity stress in the plant's root zone. Soil salinity significantly reduces plant growth due to several factors, such as nutritional ion imbalance, which reduces K+, PO4-, and NO- absorption, excessive salt chloride concentrations, and osmotic stress, which hinders water availability. Research has revealed that compared to subsequent stages, the germination, emergence, and seedling phases are more vulnerable to salinity stress, which ultimately affects the seed cotton yield by delaying blooming, reducing fruiting positions, shedding fewer fruits, and reducing boll weight. The morphology, growth of cotton roots, shoots, yield, and fiber quality are all strongly impacted by salinity stress. It slows down plant feeding, cellular metabolism, and photosynthesis. The soil, water, and climate all affect how the plant responds to salinity stress. During salt stress, excessive exclusion of sodium or its compartmentalization is the key adaptation process in cotton. A major adaptive potential to create cotton types that can withstand salt is provided by the up-regulation of both physicochemical and non-enzymatic antioxidant genes. A successful strategy to increase cotton germination in saline soils is seed priming. Moreover, the transgenic strategy might be a viable choice for improving cotton yield in saline environments. Our review focuses on the impacts of salinity on cotton productivity as well as how plants react to salt stress. It also clarifies recent genetic advancements and molecular breeding for cotton's resistance to soil salt. To create salt-tolerant cultivars, a combination of traditional breeding and novel molecular approaches will be useful.

Plasmodium vivax causes the second highest number of malaria morbidity and mortality cases in humans. Several biological traits of this parasite species, including the formation of dormant stages (hypnozoites) that persist inside the liver for prolonged periods of time, present an obstacle for intervention measures and create a barrier for the elimination of malaria. Research into the biology of hypnozoites requires efficient systems for parasite transmission, liver stage cultivation and genetic modification. However, P. vivax research is hampered by the lack of an in vitro blood stage culture system, rendering it reliant on in vivo derived, mainly patient material, for transmission and liver stage culture. This has also resulted in a limited capability for genetic modification, creating a bottleneck in investigations into the mechanisms underlying the persistence of the parasite inside the liver. This bottleneck can be overcome through optimal use of the closely related and experimentally more amenable nonhuman primate (NHP) parasite Plasmodium cynomolgi as model system. In this review we discuss the genetic modification tools and liver stage cultivation platforms available for studying P. vivax persistent stages and highlight how their combined use may advance our understanding of hypnozoite biology.

Crop yield enhancement is crucial for ensuring food security and meeting the growing global demand for agricultural products. The development of innovative technologies, such as CRISPR (Clustered Regularly Interspaced Short Palindromic Repeats), has provided new avenues for precise genetic modifications in crops. In this research article, we explore the application of CRISPR technology for crop yield improvement. We discuss the methodology, present recent results, and engage in a comprehensive discussion of the potential benefits, challenges, and future prospects of CRISPR-mediated crop yield enhancement.

Missing observation is a common problem in scientific and industrial experiments, particularly in a small-scale experiment. They often present significant challenges when experiment repetition is infeasible. In this research, we propose a multi-objective genetic algorithm as a practical alternative for generating optimal mixture designs that remain robust in the face of missing observation. Our algorithm prioritizes designs that exhibit superior D-efficiency while maintaining a high minimum D-efficiency due to missing observation. The focus on D-efficiency stems from its ability to minimize the impact of missing observations on parameter estimates, ensure reliability across the experimental space, and maximize the utility of available data. We study problems with three mixture components where the experimental region is an irregularly shaped polyhedral within the simplex. Our designs have proven to be D-optimal designs, demonstrating exceptional performance in terms of D-efficiency and robustness against missing observations. We provide a well-distributed set of optimal designs derived from the Pareto front, enabling experimenters to select the most suitable design based on their priorities using the desirability function.

Alum sludge is a byproduct of water treatment plants and its use as a soil stabilizer has gained increasing attention due to its economic and environmental benefits. Its application has been shown to improve the strength and stability of soil, making it suitable for various engineering applications. However, to go beyond just measuring the effects of alum sludge as a soil stabilizer, this paper explores the use of artificial intelligence (AI) methods to predict the California bearing ratio (CBR) of soils stabilized with alum sludge. Three AI methods, including two black box methods (artificial neural network and support vector machines) and one grey box method (genetic programming), were used to predict CBR based on a database with nine input parameters. The results showed that all three AI models were able to predict CBR with good accuracy, with coefficient of determination (R2) values ranging from 0.94 to 0.99 and mean absolute error (MAE) values ranging from 0.30 to 0.51. In a novel approach, the genetic programming method was used to produce an equation to estimate CBR, which included seven inputs and accurately predicted CBR. The analysis of sensitivity and importance of parameters showed that the number of hammer blows for compaction was the most important parameter, while the parameters for maximum dry density of soil and mixture were the least important. This study suggests that AI methods can effectively predict the performance of alum sludge as a soil stabilizer, and the proposed equation using genetic programming can be a useful tool for predicting CBR.

Among the various complex systems that we experience each day, there is the physical phenomenon of infiltration. Infiltration is considered as the dynamics of water flow in the subsurface soil that in this work is framed for the context of civil construction. This issue is approached with the use of mathematical models and stochastic techniques, however, there are hardships in collecting the samples in the field, the adoption of a scale type, the influence in soil layers interfaces, the effects of soil anisotropic characteristics and so on. In this study, methodology is proposed to deal with the soil sampling input such as the retro model, constitutive model and optimization algorithms. Additionally, an automatic calibration is set forth to fix parameters from the mathematical model submitted. Especially among the existing Optimization Algorithms there are Genetic Algorithms and the Generalized Simulated Annealing Algorithm (GSA). This work presents an overview of these optimization methods and a proposal for a more efficient and faster algorithm called GSA-HYBRID based on convergence gradient technique. The applied strategy depends on the type of case study considering its physical properties and constraints. In this sense, it is found that while Genetic Algorithms are able to replicate the optimization surface, Generalized Simulated Annealing is much more adequate in characterizing the system at an extremely low computational cost. Nevertheless, the hybrid technique GSA-HYBRID performed the fastest. Further research is necessary to implement the novel GSA-HYBRID algorithm due to its flexibility and higher speed, also, studying its application at different case studies.

Shewanella spp. are gram-negative bacteria that thrive in aquatic niches and also cause infectious diseases as opportunistic pathogens. Chromosomal integrons (CI) and mobile integrons (MI) were previously described in some Shewanella isolates. Here, we evaluated the occurrence of integrase genes, the integron systems and their genetic surroundings in the genus. We identified 22 integrase gene types, 17 of which were newly described, in different Shewanella species, evidencing multiple gain and loss events. Phylogenetic analysis showed that most of them were strain-specific, except for Shewanella algae, which seem to have co-evolve within the host as typical CIs. Noteworthy, co-existence of up to 5 different integrase genes, as well as their wide dissemination to Alteromonadales, Vibronales, Chromatiales, Oceanospirillales and Enterobacterales was observed. Identification of novel MIs suggests that lateral genetic transfer may have occurred resembling the behavior of class 1 integrons. The constant emergence of determinants associated to antimicrobial resistance worldwide, concomitantly with novel MIs in strains capable to harbor several types of integrons may be an alarming threat for the recruitment of novel antimicrobial resistance gene cassettes in the genus Shewanella, and its evolution towards the multidrug resistance.

Computer network attacks are evolving in parallel with the evolution of hardware and neural network architecture. Despite major advancements in Network Intrusion Detection System (NIDS) technology, most implementations still depend on signature-based intrusion detection systems, which can’t identify unknown attacks. Deep learning can help NIDS to detect novel threats since it has a strong generalization ability. The deep neural network’s architecture has a significant impact on the model’s results. We propose a genetic algorithm based model to find the optimal number of hidden layers and the number of neurons in each layer of the deep neural network (DNN) architecture for the network intrusion detection binary classification problem. Experimental results demonstrate that the proposed DNN architecture shows better performance than classical machine learning algorithms at a lower computational cost.

CRISPR-Cas9 technology allows the modification of DNA sequences in vivo at the location of interest. Although CRISPR-Cas9 can produce genomic changes that do not require DNA vector carriers, the use of transgenesis for stable integration of DNA coding for gene-editing tools into plant genomes is still the most used approach and it can generate unintended transgenic integrations, while Cas9 prolonged expression can increase cleavage at off-target sites. In addition, the selection of genetically modified cells from millions of treated cells, especially plant cells, is still challenging. These downfalls can be avoided with the delivery of preassembled ribonucleoprotein complexes (RNPs) composed of purified recombinant enzyme Cas9 and in vitro- transcribed guide RNA (gRNA) molecules in a protoplast system. We therefore aimed to develop the first DNA-free protocol for gene-editing in maize and introduced RNPs into their protoplasts with PEG 4000. We performed effective transformation of maize protoplasts using different gRNAs sequences targeting the inositol phosphate kinase gene and applying two different exposure times to RNPs. Using low-cost Sanger sequencing protocol, we observed an efficiency rate of 0.85 up to 5.85%, which is equivalent to DNA-free protocols used in other plant species. A positive correlation was displayed between exposure time and mutation frequency. Mutation frequency was gRNA sequence- and exposure time-dependent. In summary, we demonstrated the suitability of RNP transfection as an effective screening platform for gene-editing in maize. This efficient and relatively easy assay method for selection of gRNA suitable for editing of gene of interest will be highly useful for genome editing in maize, since genome size and GC-content are large and high in maize genome, respectively. Nevertheless, the large amplitude of mutations at target site requires scrutiny when checking mutations at off-target sites and potential safety concerns.

Photogrammetry involves aerial photography of the earth’s surface and subsequently processing the images to provide a more accurate depiction of the area (Orthophotography). It’s used by the Spanish Instituto Geográfico Nacional to update road cartography but requires a significant amount of manual labor due to the need to perform visual inspection of all tiled images. Deep Learning techniques (artificial neural networks with more than one hidden layer) can perform road detection but it is still unclear how to find the optimal network architecture. Our system applies grammar guided genetic programming to the search of deep neural network architectures. In this kind of evolutive algorithm all the population individuals (here candidate network architectures) are constrained to rules specified by a grammar that defines valid and useful structural patterns to guide the search process. Grammar used includes well-known complex structures (e.g. Inception-like modules) combined with a custom designed mutation operator (dynamically links the mutation probability to structural diversity). Pilot results show that the system is able to design models for road detection that obtain test accuracies similar to that reached by state of the art models when evaluated over a dataset from the Spanish National Aerial Orthophotography Plan.

Bitter gourd is an important vegetable of the family Cucurbitaceae, cultivated mainly in humid and subtropical Asia. Bitter gourd is vegetable with immense health benefits due to the presence of medicinal compounds such as charatin, vicine, and polypeptide-p, which play an essential role in lessening the blood glucose levels. Moreover, bitter gourd fruits are particularly rich in vitamin C, minerals, and carotenes. Here, an effort has been made to critically evaluate the extent of achievements during the enhancement and enactment of bitter gourd breeding programs with the use of latest technologies. Broadening of the genetic base of cultivated bitter groud varieties as a result of enrichment of the existing resources by using the wild species in the breeding programs. Practical seed production technological know-how along with the use of the MS system (male sterility)/chemical-induced sterility procedure is nonetheless vital to cope up with the market demands. Superior yielding bitter gourd hybrids combining early maturity and resistance to biotic and abiotic stresses are regularly needed to cope up with the challenge of bitter gourd production.

The ASCE-EWRI reference evapotranspiration (ETo) equation is recommended as a standardized method for reference crop ETo estimation. However, various climate data as input variables to the standardized ETo method are considered limiting factors in most cases and restrict the ETo estimation. This paper assessed the potential of different machine learning (ML) models for ETo estimation using limited meteorological data. The ML models used to estimate daily ETo included Gene Expression Programming (GEP), Support Vector Machine (SVM), Multiple Linear Regression (LR), and Random Forest (RF). Three input combinations of daily maximum and minimum temperature (Tmax and Tmin), wind speed (W) with Tmax and Tmin, and solar radiation (Rs) with Tmax and Tmin were considered using meteorological data during 2003–2016 from six weather stations in the Red River Valley. To understand the performance of the applied models with the various combinations, station, and yearly based tests were assessed with local and spatial approaches. Considering the local and spatial approaches analysis, the LR and RF models illustrated the lowest rate of improvement compared to GEP and SVM. The spatial RF and SVM approaches showed the lowest and highest values of the scatter index as 0.333 and 0.457, respectively. As a result, the radiation-based combination and the RF model showed the best performance with higher accuracy for all stations either locally or spatially, and the spatial SVM and GEP illustrated the lowest performance among models and approaches.

Oats represents a promising alternative to small-grain cereals from Triticeae group (wheat, barley, rye) for persons suffering from any form of gluten intolerance, especially celiac disease (CD), since oat-specific prolamins avenins reveal generally lower gluten content and immunoreactivity. Recent studies on avenin molecular structure revealed large genetic variability in avenin sequences affecting the spectrum of gluten peptides produced by hydrolases in human digestive tract. The aim of the present review is to summarise recent knowledge obtained in laboratory in vitro studies focused on the effect of avenin-derived peptides on reactivity of crucial components of human immune system such as dendritic cells (DC) and T-cells. The other part of the review summarises the results of clinical studies with CD patients including oat products in their diet. Since different clinical studies revealed contradictory results regarding potential safety of oats for CD patients, the focus has to be directed at genetic variability in oat avenins. Identification of avenin isoforms with minimum CD immunoreactivity will open up ways leading to designing novel oat cultivars suitable for CD patients. Knowledge on immunoreactivity of gluten peptides together with breeding new oat cultivars revealing minimum avenin immunoreactivity with respect to CD as well as application of food processing technologies leading to gluten content reduction should result in development of gluten-free oats safe for celiacs.

In the past, Unmanned Aerial Vehicles (UAVs) were mostly used in the military operations to prevent pilot losses. Nowadays, the fast technological evolution enables the production of a class of cost-effective UAVs which can service a plethora of public and civilian applications, specially when configured to work cooperatively to accomplish a task. However, designing a communication network among the UAVs is challenging task. In this article, we propose a centralized UAV placement strategy, where UAVs are used as flying access points forming a mesh network, providing connectivity to ground nodes deployed in a target area. The geographical placement of UAVs is optimized based on a Multi-Objective Evolutionary Algorithm (MOEA). The goal of the proposed scheme is to cover all ground nodes using a minimum number of UAVs, while maximizing the fulfillment of their data rate requirements. The UAVs can employ different data rates depending on the channel conditions, which are expressed by the Signal-to-Noise-Ratio (SNR). In this work, elitist Non-Dominated Sorting Genetic Algorithm II (NSGA-II) is used to find a set of optimal positions to place UAVs, given the positions of the ground nodes. We evaluate the trade-off between the number of UAVs used to cover the target area and the data rate requirement of the ground nodes. Simulation results show that the proposed algorithm can optimize the UAV placement given the requirement and the positions of the ground nodes in the geographical area.

This mini-review provides an overview of traditional, emerging, and future applications of lactic acid bacteria (LAB) and discusses how genome editing tools can be used to overcome current challenges in all these applications. It also describes currently available tools and how these can be further developed, and takes current legislation into account. Genome editing tools are necessary for the construction of strains for new applications and products, but can also play a crucial role in traditional ones, such as food and probiotics, as a research tool for understanding mechanistic insights and discovering new properties. Traditionally, recombinant DNA techniques for LAB have strongly focused on being food-grade, but they lack throughput and the number of genetically tractable strains is still rather limited. Further tool development in this direction will enable rapid construction of multiple mutants or mutant libraries on a genomic level in a wide variety of LAB strains. We also propose an iterative Design-Build-Test-Learn workflow cycle for LAB cell factory development based on systems biology, with “cell factory” expanding beyond its traditional meaning of production strains and making use of high-throughput genome editing tools to advance LAB understanding, applications and strain development.

This paper presents an alternative constraint handling approach within a specialized genetic algorithm (SGA) for the optimal reactive power dispatch (ORPD) problem. The ORPD is formulated as a nonlinear single-objective optimization problem aiming to minimize power losses while keeping network constraints. The proposed constraint handling approach is based on a product of sub-functions that represents permissible limits on system variables and that includes a specific goal on power loss reduction. The main advantage of this approach is the fact that it allows a straightforward verification of both feasibility and optimality. The SGA is examined and tested with the proposed constraint handling approach and the traditional penalization of deviations from feasible solutions. Several tests are run in the IEEE 30, 57, 118 and 300 bus test power systems. The results obtained with the proposed approach are compared to those offered by other metaheuristic techniques reported in the specialized literature. Simulation results indicate that the proposed genetic algorithm with the alternative constraint handling approach yields superior solutions when compared to other recently reported techniques.

Nowadays, we live in a world in which people are facing with a lot of data that should be stored or displayed. One of the key methods to control and manage this data refers to grouping and classifying them in clusters. Today, clustering has a critical role in information retrieval methods for organizing large collections inside a few significant clusters. One of the main motivations for the use of clustering is to determine and reveal the hidden and inherent structure of a set of data. Ensemble clustering algorithms combine multiple clustering algorithms to finally reach an overall clustering system. Ensemble clustering methods by lack of information fusing utilize several primary partitions of data to find better ways. Since various clustering algorithms look at the different data points, they can produce various partitions from such data. It is possible to create a partition with high performance by combining the partitions obtained from different algorithms, even if the clusters to be very dense from each other. Most studies in this area have examined all the initial clusters. In this study, a new method is used in which the most sustainable clusters are utilized instead of all primary produced clusters. Consensus function based on co-association matrixes used to select more stable clusters. The most stable clusters selection method is done by cluster stability criterion based on F-measure. Optimization functions are used to optimize the obtained final clusters. The genetic algorithm is the optimizer used in this article to find the ultimate clusters participated in a consensus. Experimental results on several datasets show that the output of proposed method is various clusters with high stability.

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;

This research aimed to figure out the applications of HbA1c protein and HbA1 gene as the prognostic biomarkers for assessing the risks of different cancers among male T2D patients in Bangladesh considering their serological and oncoinformatic parameters. Depending on the concentrations of HbA1c (%) of the T2D patients (n=300), their individual FBS (mmol/L); THABF (mmol/L); creatinine (mg/dl); SC (mg/dl); STGs (mg/dl); HDLC (mg/dl); and LDLC (mg/dl) were estimated. The values of the patients were compared with the control (n=60) group as the serological analysis. Besides, HbA1 gene (encoding hBA1c protein) overexpression and promotor methylation responsible for BLCA, BRCA, CHOL, COAD, LUAD, LUSC, PAAD, and PRAD cancers in the male T2D patients were profiled as the oncoinformatic parameters based on the sample types; caner stages; racial footprints; gender; age; nodal metastasis; p53 methylations; pancreatitis; diabetes status; smoking behaviors; and overall/disease-free survivability. Finally, the ‘HbA1 gene strings’ responsible for genetic coexpression; endophytic vesicle regulation; antioxidant regulation; oxygen species metabolic regulation; and gene-mediated response to the reactive oxygen molecules were studied comprehensively. A strong correlation between BMI and FBS was observed in both the patients and the control (P<0.0001). Similarly, the values of FBS, THABF, and creatinine resulted in equal significance (P<0.0001) as compared to the HbA1c concentrations of all the T2D and control individuals. The SC, STGs, HDLC, and LDLC concentrations regulated ardently in both the control (P<0.0001), and patients group (P<0.0001), while HbA1c ranged from 3.8-5.8%, and 5.11-15.8% respectively. HbA1 gene is found downregulating with cancer progressed in most of the oncoinformatic parameters. According to the DA, CS, EI, CE, PC, NC, GF, H, and AT profiles; the HbA1 gene interacts with 8 other genes responsible for creating a protein cluster comprising- AHSP, HBA1, HBA2, HBB, HBD, HBE1, HBG2, RPS12, and RPS19 proteins for cancer formation. To recapitulate, HbA1c protein and HbA1 gene can be used as the prognostic serological and molecular biomarkers respectively for determining the risks of cancers among male chronic T2D patients.

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.

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

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.

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.

Coxsackievirus A6 (CVA6) has become increasingly clinically relevant as a cause of Hand, Foot and Mouth Disease (HFMD) globally since 2008. However, most laboratories do not routinely determine the enteroviral type of positive samples. The non-pharmaceutical measures introduced to curb transmission during the SARS-CoV-2 pandemic may also have been perturbed CVA6 epidemiology. We thus aimed to determine the prevalence, clinical presentation and genetic relationship of CVA6 across three complete epidemic seasons: one pre- and two post-SARS-CoV-2-emergence in our regional healthcare setting. Surplus diagnostic nucleic acid from diagnosed enteroviral positives diagnosed between September to December 2018 and May 2021 to April 2023 was subject to VP1 gene sequencing to determine CVA6 cases and interrogate their phylogenetic relationship. Confirmed CVA6 cases were also retrospectively clinically audited. CVA6 infections were identified in 33 and 69 individuals pre- and post-pandemic respectively, with cases peaking in November of 2018 and 2022, but in October of 2021. HFMD was the primary diagnosis in 85.5% of post-pandemic cases, but only 69.7% pre-pandemic, where respiratory and neurological symptoms (45.5% and 12.1% respectively) were significantly elevated. Complete VP1 sequence was retrieved for 94% of CVA6 cases, revealing study infections were genetically diverse and suggestive of multiple local and international transmission chains. CVA6 presented a significant clinical burden in our regional UK hospital setting both pre- and post-pandemic and was subject to dynamic clinical and genetic epidemiology.

High purity hydrogen is a necessary need for fuel cell. Pressure swing adsorption (PSA) technology is one of the effective methods for hydrogen purification and separation. A layered adsorption bed packed with activated carbon and zeolite 5A for five-component gas mixture (H2/CH4/CO/N2/CO2=56.4/26.6/8.4/5.5/3.1 mol%) PSA model was built. The model was validated by breakthrough curves and comparing the results with the experimental data. The purification performance of six-step layered bed PSA cycle was studied using the model. In order to optimize the cycle, the Box-Behnken design (BBD) method was used, as implemented in Design Expert™. In addition to adsorption time, the pressure equalization time and the feed flow rate were considered as independent optimization parameters. Quadratic regression equations were then obtained for three responses of the system, namely purity, recovery, and productivity. To explore a better optimization solution, a novel optimization method of machine learning with back propagation neural network (BPNN) was proposed in this work, a kind of heuristic algorithm of genetic algorithm (GA) is introduced to optimize the structure of BPNN. The predicted outputs of hydrogen production using two kinds of models based on back propagation neural network-genetic algorithm (BPNN-GA) and BBD method integrated with BPNN-GA (BBD-BPNN-GA) models. The results showed that the BBD-BPNN-GA model have a better performance with the mean square error (MSE) of 0.0005, while the MSE of BPNN-GA model was 0.0035. And the correlation coefficient of R-values were much closer to 1 of the BBD-BPNN-GA model, which was illustrated that the BBD-BPNN-GA model can be effectively applied to the prediction and optimization of PSA process.

Localized protein translation occurs through trafficking of mRNAs and protein translation machineries to different compartments of the cell, leading to rapid on-site synthesis of proteins in response to signaling cues. The spatiotemporally precise nature of the local translation process necessitates continual developments of technologies reviewed herein to visualize and map biomolecular components and the translation process with better spatial and temporal resolution, and with fewer artifacts. We also discuss approaches to control local translation, which can serve as a design paradigm for subcellular genetic devices for eukaryotic synthetic biology.

Infantile Neuroaxonal Dystrophy (INAD) is a rare neurodegenerative disorder, affecting 1:1,000,000 population. It results from pathogenic variants in the PLA2G6 gene located on chromosome (22q13.1). The onset of symptoms usually occurs between 6 to 18 months, causing developmental regression leading to debilitating symptoms such as muscle weakness, dementia, and loss of basic skills. Eventually, it progresses to life-threatening symptoms including breathing difficulties, which limits the life expectancy to 5-10 years. While potential genetic therapy for treatment is are being developed, they are yet to be approved for use, and management remains essentially supportive. This case report is of a 9-year-old Pakistani girl with INAD. She presented with recurrent chest infections, developmental regression, loss of speech, paralysis, hypertension, and eventually breathing difficulties. Brain magnetic resonance imaging and genetic testing con-firmed the diagnosis. This case posed diagnostic challenges in view of its overlapping clinical presentation. Through this report, we aim to raise awareness about the condition among practitioners, outline the importance of genetic counseling in susceptible couples and suggest potential areas of further research.

Juvenile Polyposis Syndrome (JPS) is a rare autosomal dominant disorder characterized by multiple juvenile polyps in the gastrointestinal tract, often associated with mutations in genes such as Smad4 and BMPR1A. This study explores the impact of Smad4 knockout on the development of intestinal polyps using Collaborative Cross (CC) mice, a genetically diverse model. Our results reveal a significant increase in intestinal polyps in Smad4 knockout mice across the entire population, emphasizing the broad influence of Smad4 on polyposis. Sex-specific analyses demonstrate higher polyp counts in knockout males and females compared to their WT counterparts, with distinct correlation patterns. Line-specific effects highlight the nuanced response to Smad4 knockout, underscoring the importance of genetic variability. Multimorbidity heat maps offer insights into complex relationships between polyp counts, locations, and sizes. Heritability analysis reveals a significant genetic basis for polyp counts and sizes, while machine learning models, including k-Nearest Neighbours and Linear Regression, identify key predictors, enhancing our understanding of juvenile polyposis genetics. Overall, this study provides a comprehensive understanding of the intricate genetic interplay in the context of Smad4 knockout, offering valuable insights into potential therapeutic targets for juvenile polyposis and related diseases.

Advancements in genomics and molecular biology have propelled our understanding of genetic information far beyond the iconic double helix structure of DNA. This comprehensive review delves into the intricate world of DNA and RNA networks, exploring the complexities that extend beyond the classical model. We investigate the dynamic interactions, regulatory mechanisms, and functional implications of the broader genetic landscape, encompassing both coding and non-coding elements. From chromatin architecture to the diverse array of non-coding RNAs, this exploration seeks to provide a holistic perspective on the multifaceted roles played by DNA and RNA in cellular processes. The three-dimensional organization of genetic material within the cell has emerged as a crucial determinant of cellular function. Chromatin architecture, comprising DNA, histones, and other associated proteins, plays a dynamic role in regulating gene expression. We delve into the intricate choreography of chromatin remodeling, deciphering the interplay between transcription factors, epigenetic modifications, and higher-order structures that modulate gene activity. The exploration of chromatin dynamics unveils a nuanced understanding of how the genome responds to environmental cues, cellular signals, and developmental processes. Moreover, the once-dismissed non-coding portion of the genome has emerged as a key regulator of cellular function. Various classes of non-coding RNAs, such as microRNAs, long non-coding RNAs, and circular RNAs, have been implicated in diverse cellular processes, ranging from fine-tuning gene expression to modulating chromatin structure. The review critically examines the functional roles of these non-coding RNAs, shedding light on their involvement in cellular homeostasis, development, and disease. As I traverse this landscape beyond the double helix, our review synthesizes current knowledge on the intricacies of DNA and RNA networks. By exploring the regulatory mechanisms, functional implications, and emerging paradigms, we aim to inspire a deeper appreciation for the molecular choreography that underlies the cellular symphony of life. Furthermore, we discuss the implications of these findings in the context of disease, highlighting potential diagnostic and therapeutic avenues that arise from a comprehensive understanding of DNA and RNA networks. In doing so, this exploration not only broadens our theoretical understanding but also opens new frontiers for translational research in genetics and molecular medicine.

Certain Trichoderma isolates provide biofertilizer, biocontrol, and other plant-beneficial activities while inhabiting the soil or internal plant issues; and their use in agricultural systems could contribute to sustainable food production. It is thought that this colonization of soil and internal plant tissues is fundamental for biocontrol and biofertilizer applications. Our collective analyses of prior surveys, where the tef1 sequence was almost exclusively used to identify Trichoderma species, showed that isolates from the Harzianum Complex Clade, the T. asperellum/T. asperelloides group, T. virens, T. hamatum, and T. atroviride were prevalent in soil and/or as endophytes. Population structure and genetic diversity of these species were investigated, and new lineages with geo-graphic significance within T. atroviride, T. asperellum, T. hamatum, and T. virens populations were found. The nearest relatives of some species were also revealed. Choosing isolates from among more than 500 known Trichoderma species for use in non-targeted evaluation screens for biocontrol or biofertilizer applications is time-consuming and expensive. Preferentially selecting species from T. atroviride, T. asperellum/T. asperelloides, T. hamatum, species from the T. harzianum Complex Clade, T. virens, and possibly nearest relatives, may speed the identification of candidates for commercialization due to the demonstrated ability of these species to successfully inhabit the soil and endorhizosphere. To our knowledge, this is the first report where dominant soil- and en-dophyte-inhabiting Trichoderma species were identified from past survey data and population structure and genetic diversity analyses conducted.

The escalating reliance of modern society on information and communication technology has rendered it vulnerable to an array of cyber-attacks, with distributed denial-of-service (DDoS) at-tacks emerging as one of the most prevalent threats. This paper delves into the intricacies of DDoS attacks, which exploit compromised machines numbering in the thousands or more to disrupt data services and online commercial platforms, resulting in significant downtime and financial losses. Recognizing the gravity of this issue, various detection techniques have been explored, yet the quantitative and prior detection of DDoS attacks has seen a decline in recent methods. This research introduces an innovative approach by integrating evolutionary optimization algorithms and machine learning techniques. Specifically, the study proposes XGB-GA Optimization, RF-GA Optimization, and SVM-GA Optimization methods, employing Evolutionary Algorithms Opti-mization with TOPOT-Genetic Programming. Datasets pertaining to DDoS attacks were utilized to train machine learning models based on XGB, RF, and SVM algorithms, and 10-fold cross-validation was employed. The models were further optimized using evolutionary algo-rithms, achieving remarkable accuracy scores: 99.99% with the XGB-GA method, 99.50% with RF-GA, and 99.99% with SVM-GA. Furthermore, the study employed TPOT to identify the optimal algorithm for constructing a machine learning model, with the genetic algorithm pinpointing XGB-GA as the most effective choice. This research significantly advances the field of DDoS attack detection by presenting a robust and accurate methodology, thereby enhancing the cybersecurity landscape and fortifying digital infrastructures against these pervasive threats.

In this work, we propose a Fog-enabled UAV-as-a-Service (FU-Serve) architecture to address the issues of data transmission delay for serving time-critical Internet of Things (IoT) applications. Traditionally, in a UAV-as-a-Service (UaaS) platform, different UAVs host heterogeneous sensors, which sense the physical phenomenon and transmit the sensed data to a centralized entity. Transmission of data from the sensors to the centralized entity and making any decision for an application consumes a significant amount of time. Consequently, the traditional UaaS architecture is unsuitable for serving time-critical IoT applications such as transportation, healthcare, and industries. To address these issues of service latency for time-critical IoT applications, we present the FU-Serve architecture by introducing the concept of fog computing in the UaaS platform. We discuss all the components of FU-Serve elaborately in this paper. Additionally, we architect optimal and dynamic fog node selection mechanisms for FU-Serve, which reduce the transmission delay in the networks. The simulation results show that the FU-Serve outperforms by $75\%$ compared to the traditional UaaS platform.

As the COVID-19 pandemic has caused enormous damage to society, economy and public health security, it is crucial to combat the epidemic and allocate social and health resources effectively. Global optimization algorithms are extensively used in many domains to find optimal solutions to complex problems. Thus, a survey of three typical global optimizations applied on the COVID-19 pandemic is timely. This article is structured into three typical global optimization algorithms - Genetic Algorithm, Particle Swarm Optimization, and Cat Swarm Optimization - and summarizes the framework models and practical applications, as well as other optimization methods that may be applied to COVID-19. Finally, current unresolved challenges and future research opportunities to be explored are summarized.

Ornamental kale (Brassica oleracea var. acephala) is an attractive ornamental plant with a range of leaf colors and shapes. Breeding new varieties of ornamental kale has proven challenging due to its lengthy breeding cycle and the limited availability of genetic markers. Microspore culture is an effective approach to generate new materials and genetic maps are a prerequisite for quantitative trait loci analysis, MAS, fine gene mapping, and genome sequence assembly. In this study, a F1DH ornamental kale population comprising 300 DH lines was constructed using microspore culture. A high-density genetic map was developed by conducting whole-genome sequencing on 150 individuals from the F1DH population. The genetic map contained 1,696 bin-markers with 982,642 single-nucleotide polymorphisms (SNPs) spanning a total distance of 775.81 cM on all nine chromosomes with an average distance between markers of 0.46 cM. The ornamental kale genetic map contained substantially more SNP markers compared with published genetic maps for other B. oleracea crops. The F1DH progenies provide an excellent resource for germplasm innovation and breeding new varieties of ornamental kale. The high-density genetic map provides crucial insights for gene mapping and unraveling the molecular mechanisms behind important agronomic traits in ornamental kale.

Hospital readmission and length of stay prediction provide info to manage hospitals’ bed capacity and the number of required staff, especially during pandemics. We present a hybrid deep model called Genetic Algorithm-Optimized Convolutional Neural Network (GAOCNN) with a unique preprocessing method to predict hospital readmission and the length of stay in patients having various conditions. GAOCNN uses one-dimensional convolutional layers to predict hospital readmission and length of stay. The parameters of the layers are optimized using a genetic algorithm. To show the performance of the proposed model in patients with various conditions, we evaluate the model under three healthcare datasets; the Diabetes 130-US hospitals dataset, the COVID-19 dataset, and the MIMIC-III dataset. The diabetes 130-US hospitals dataset has information on both readmission and the length of stay, while COVID-19 and MIMIC-III datasets just include information on the length of stay. Experimental results show that the proposed model’s accuracy for hospital readmission is 97.2% for diabetic patients. Also, the accuracy of the length of stay prediction is 89%, 99.4%, and 94.1% for diabetic, COVID-19, and ICU patients, respectively. These results confirm the superiority of the proposed model compared to existing methods. Our findings offer a platform for managing healthcare funds and resources for patients with various diseases.

Distributed electric propulsion technology has great potential and advantage in the development of drones. In this paper, to study the slipstream effect of distributed propeller, the actuator disk method was used to verify a single propeller, and the calculated thrust was in good agreement with the test results. Then, based on the actuator disk method, the influence of different installation positions on the slipstream effect was studied, and the distributed propeller layout was optimized by genetic algorithm. The analysis results showed that lift of the wing will be larger when the propellers are higher than the wing. When the relative height between the propeller and the wing is zero, the drag is the lowest. The influence of disk diameter on the slipstream is that the larger the diameter is, the higher the lift force and the drag force are. The slipstream effect of the optimized propeller distribution improves the lift-drag ratio of the wing, by 108.5% in the initial lift-drag ratio.

Demyelinating diseases alter myelin or the coating surrounding most nerve fibers in the central and peripheral nervous systems. The grouping of human central nervous system demyelinating disorders today includes Multiple Sclerosis (MS) and Neuromyelitis Optica Spectrum Disorders (NMOSD) as distinct disease categories. Each disease is caused by a complex combination of genetic and environmental variables, many involving an autoimmune response. Even though these conditions are fundamentally similar, research into genetic factors, their unique clinical manifestations, and lesion pathology has helped with differential diagnosis and disease pathogenesis knowledge. This review aims to synthesize the genetic approaches that explain the differential susceptibility between these diseases, explore the overlapping clinical features, and pathological findings, discuss existing and emerging hypotheses on the etiology of demyelination, and assess recent pathogenicity studies and their implications for human demyelination. This review presents critical information from previous studies on the disease, which asks several questions to understand the gaps in research in this field.

In recent years, the ecological and economic values of forest plants have been gradually recognized worldwide. However, the growing global demand for new forest plant varieties with higher wood production capacity and better stress tolerance cannot be satisfied by conventional phenotype-based breeding, marker-assisted selection, and genomic selection. In the recent past, diverse omics technologies, including genomics, transcriptomics, epigenomics, proteomics, and metabolomics, have been developed rapidly, providing powerful tools for the precision genetic breeding of forest plants. Genomics lays a solid foundation for understanding complex biological regulatory networks, while other omics technologies provide different perspectives at different levels. Multi-omics integration has combined the different omics technologies, becoming a powerful tool for genome-wide functional element identification in forest plant breeding. This review summarizes the recent progress of omics technologies and their applications in the genetic studies of forest plants. It will provide forest plant breeders with an elementary knowledge of multi-omics techniques for future breeding programs.

Meier–Gorlin syndrome (MGS) is a rare genetic developmental disorder that causes primordial proportional dwarfism, microtia, absent or hypoplastic patellae and other skeletal anomalies. Overlapping skeletal symptoms make MGS difficult to diagnose clinically. We describe a 3-year-old boy with short stature, recurrent respiratory infections, short-rib dysplasia, tower head and facial dysmorphisms who was admitted to the Tomsk Genetic Clinic to verify a clinical diagnosis of Jeune syndrome. Clinical Exome sequencing revealed two variants (compound heterozygosity) in the ORC6 gene: c.2T&gt;C(p.Met1Thr) and c.449+5G&gt;A. In silico analysis showed the pathogenicity of these two mutations and predicted a decrease in donor splicing site strength for c.449+5G&gt;A. An in vitro minigene assay demonstrated that variant c.449+5G&gt;A causes a complete skipping of exon 4 in ORC6 gene. The parents asked for urgent prenatal testing for MGS for the next pregnancy, but it ended in a miscarriage. Thus, this case report may help to prevent MGS misdiagnosis in the future. We also performed in silico and functional analyses of ORC6 mutations and developed a restriction fragment length polymorphism and haplotype-based short-tandem-repeat assay for prenatal genetic testing for MGS. These findings should elucidate MGS aetiology and improve the quality of genetic counselling for affected families.

Arthropods are the most abundant and diverse animals on earth. Among them, pancrustaceans are an ancient and morphologically diverse group, comprising a wide range of aquatic and semi-aquatic crustaceans as well as the insects, which emerged from crustacean ancestors to colonise most terrestrial habitats. Within insects, Drosophila stands out as one of the most powerful animal models, making major contributions to our understanding of development, physiology and behaviour. Given these attributes, crustaceans provide a fertile ground for exploring biological diversity through comparative studies. However, beyond insects, few crustaceans are developed sufficiently as experimental models to enable such studies. The marine amphipod Parhyale hawaiensis is currently the best established crustacean system, offering year-round accessibility to developmental stages, transgenic tools, genomic resources, and established genetics and imaging approaches. The Parhyale research community is small but diverse, investigating the evolution of development, regeneration, aspects of sensory biology, chronobiology, bioprocessing and ecotoxicology.

One of the major challenges in the post-genomic era is elucidating the genetic basis of human diseases. In recent years, studies have shown that polygenic risk scores (PRS), based on aggregated information from millions of variants across the human genome, can estimate individual risk for common diseases. In practice, the current medical practice still predominantly relies on physiological and clinical indicators to assess personal disease risk. For example, caregivers mark individuals with high body mass index (BMI) as having an increased risk to develop type 2 diabetes (T2D). An important question is whether combining PRS with clinical metrics can increase the power of disease prediction in particular from early life. In this work we examined this question, focusing on T2D. We show that an integrated approach combining adult BMI and PRS achieves considerably better prediction than each of the measures on unrelated Caucasians in the UK Biobank (UKB, n=290,584). Likewise, integrating PRS with self-reports on birth weight (n=172,239) and comparative body size at age ten (n=287,203) also substantially enhance prediction as compared to each of its components. While the integration of PRS with BMI achieved better results as compared to the other measurements, the latter are early-life measurements that can be integrated already at childhood, to allow preemptive intervention for those at high risk to develop T2D. Our integrated approach can be easily generalized to other diseases, with the relevant early-life measurements.

In December 2019, the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) emerged in the province of Wuhan, China. Since then, it has spread worldwide with new mutations being reported. We performed genomic analysis to identify the changes in genetic diversity of SARS-CoV-2 between December 2019 and November 2020, and through molecular surveillance, we monitored the mutations that could be involved in viral fitness. We analyzed 2,213 complete genomes from 6 geographical regions worldwide, which were downloaded from GenBank and GISAID databases. Although SARS-CoV-2 presented low genetic diversity, there has been an increase over time, with the presence of several hotspot mutations throughout its genome. We identified 7 frequent mutations that resulted in non-synonymous substitutions (dN). Two of them, C14408T>P323L and A23403G>D614G, located in the nsp12 and Spike protein, respectively, emerged early in the pandemic and showed a considerable increase in frequency over time. Two other mutations, A1163T>I120F in nsp2 and G22992A>S477N in the Spike protein emerged recently and have spread in Oceania and Europe. Continuous molecular surveillance of SARS-CoV-2 will be necessary to detect and describe the transmission dynamics of new variants of the virus with clinical relevance. This information is important to improve programs to control the virus.

Temperature is one of the most important range-limiting factors for many seaweeds. Driven by the recent climatic changes, rapid northward shifts of species’ distribution ranges can potentially modify the phylogeographic signature of Last Glacial Maximum such as increased genetic diversity at lower latitudes. To explore this question in detail in the kelp Saccharina latissima, we used microsatellites and double digest RAD-sequencing derived SNPs on S. latissima sporophytes sampled within 11 sites spanning the entire European Atlantic latitudinal range of this species. In addition, we checked for statistical correlation between genetic marker allele frequencies and three environmental proxies (sea surface temperature, salinity, and water turbidity). Our findings revealed that genetic diversity was significantly higher for the northern localities compared to the southern ones in contrast to the expected phylogeographic pattern. This suggests that the southernmost S. latissima populations are negatively affected by the recent climatic changes but also that the recolonization of S. latissima range following the LGM may have occurred from northerly refugia. Seven SNPs and 12 microsatellite alleles were found to be significantly associated with at least one of the three environmental variables. We discuss the potential adaptive functions of the genes associated with the outlier markers and the importance of these markers for successful conservation and aquaculture strategies for S. latissima in this age of rapid global change.

Upon inbreeding, the architecture of the inbreeding load shifts as selection purges strongly deleterious recessive mutations and drift fixes many milder ones. Most small inbred populations show limited genetic variation while crosses between such populations commonly express pronounced heterosis, confirming fixation. In contrast, purging appears to be limited in that inbred populations often retain substantial inbreeding depression. In addition we have the enigma Darwin noted: purely selfing taxa are unknown. Because both purging and fixation reduce inbreeding depression and load, another mechanism must exist to sustain these. Background selection and the associations that develop among alleles in small inbred populations will shift the architecture of the load potentially creating blocks of recessive mutations linked in repulsion. This would generate pseudo-overdominance that could sustain these “PODs” and inbreeding load. Recombination and crosses between lineages could erode PODs. Crosses between populations fixed for different mutations would generate high pseudo-overdominance, enhancing heterosis and potentially POD formation. New recessive mutations arising within PODs would reinforce overdominance. PODs should generate clear genetic signatures including genomic hotspots of heterozygosity and linkage disequilibrium containing alleles at intermediate frequency generating segregating load. Results from several simulation and empirical studies match these predictions. Further simulations and comparative genomic analyses are needed to rigorously test whether PODs exist in sufficient strength and number to generate persistent inbreeding depression and load in inbred lineages.

Genomic selection (GS) is a predictive approach that was build up to increase the rate of genetic gain per unit of time in breeding programs. It has emerged as a valuable method for improving complex traits that are controlled by many genes with small effect. GS enables the prediction of breeding value of candidate genotypes for selection. In this work we address important issues related to GS and its implementation in tomato breeding context. Genomic constrains and critical parameters affecting the accuracy of prediction in such crop such as phenotyping, genotyping training population composition and size and statistical method should be carefully evaluated. Comparison of GS approaches for facilitating the selection of tomato superior genotypes during breeding program are also discussed. GS applied to tomato breeding has already shown to be feasible. We illustrated how GS can improve the rate of gain in elite lines selection, descendent and in backcross schemes. The GS schemes begin to be delineated and computer science can provide support for future selection strategies. A new breeding framework is beginning to emerge for optimizing tomato improvement procedures.

PD-1/PD-L1 axis is one key therapeutic target against tumor cell immune escape. Structurally essential to the PD-1/PD-L1-linked immune escape is the binding interface of the PD-1/PD-L1 complex structure. Incorporating currently available PD-1/PD-L1-related experimental structures, this article unveils two sets of experimentally observed inter-molecular electrostatic interactions which stabilize the binding interface of the PD-1/PD-L1 complex structure. For the first time, this article proposes an evolutionary structural hypothesis that, as a result of natural selection, PD-1 is able to genetically mutate itself to structurally disrupt the PD-1/PD-L1 axis towards the restoration of T cell-mediated anti-tumor immunity.

Dendritic cells (DCs) are the professional antigen-presenting cells that recognize and present antigens to naïve T cells to induce antigen-specific adaptive immunity. Among the T-cell subsets, T helper type 2 (Th2) cells produce the humoral immune responses required for protection against helminthic disease by activating B cells. DCs induce a Th2 immune response at a certain immune environment. Basophil, eosinophil, mast cells, and type 2 innate lymphoid cells also induce Th2 immunity. However, in the case of DCs, controversy remains regarding which subsets of DCs induce Th2 immunity, which genes in DCs are directly or indirectly involved in inducing Th2 immunity, and the detailed mechanisms underlying induction, regulation, or maintenance of the DC-mediated Th2 immunity against allergic environments and parasite infection. A recent study has shown that a genetic defect in DCs causes an enhanced Th2 immunity leading to a severe atopic dermatitis. We summarize the Th2 immune-inducing DC subsets, the genetic and environmental factors which involved in DC-mediated Th2 immunity, and current therapeutic approaches for Th2-mediated immune disorders. This review is to provide an improved understanding of DC-mediated Th2 immunity and Th1/Th2 immune balancing, leading to control over their adverse consequences.

Yeasts belonging to the Metschnikowia genus are particularly interesting for the unusual formation of only two needle-shaped ascospores during their mating cycle. Presently, the meiotic process that can lead to only two spores from a diploid zygote is poorly understood. The expression of fluorescent nuclear proteins should allow the meiotic process to be visualized in vivo; however, no large-spored species of Metschnikowia has ever been transformed. Accordingly, we aimed to develop a transformation method for Metschnikowia borealis, a particularly large-spored species of Metschnikowia, with the goal of enabling the genetic manipulations required to study biological processes in detail. Genetic analyses confirmed that M. borealis, and many other Metchnikowiacea, are CUG-Ser yeasts. Codon-optimized selectable markers lacking CUG codons were used to successfully transform M. borealis by electroporation and lithium acetate, and transformants appeared to be the result of random integration. Mating experiments confirmed that transformed-strains were capable of generating large asci and undergoing recombination. Finally, random integration was used to transform an additional 18 yeast strains, and all attempts successfully generated transformants. The results provide a simple method to transform many yeasts from an array of different clades and can be used to study or develop many species for various applications.

The current clinical model for genetic-testing is based on clinical-criteria/family-history(FH) and a pre-defined mutation probability threshold. It requires people to develop cancer before identifying unaffected individuals in the family to target prevention. This process is inefficient, resource intense and misses >50% of individuals/mutation carriers at risk. Population genetic-testing can overcome these limitations. It is technically feasible to test populations on a large scale; genetic-testing costs are falling and the acceptability/awareness is rising. MEDLINE/EMBASE/Pubmed/CINAHL/PsychINFO databases were searched using a free-text and MeSH terms; reference lists of publications retrieved screened; additionally web-based platforms, Google, and clinical-trial registries were searched. Quality of studies were evaluated using appropriate check-lists. A number of studies have evaluated population-based BRCA-testing in the Jewish-population. This has been found to be acceptable, feasible, clinically-effective, safe, associated with high satisfaction rates and extremely cost-effective. Data support change in guidelines to population-based BRCA-testing in the Jewish-population. Population panel-testing for BRCA1/BRCA2/RAD51C/RAD51D/BRIP1/PALB2 gene mutations is the most cost-effective genetic-testing strategy in general-population women and can prevent thousands more breast/ovarian cancers than current clinical-criteria based approaches. A few ongoing studies are evaluating population-based genetic-testing for multiple cancer susceptibility genes in the general-population but more implementation studies are needed. A future population-testing programme could also target other chronic diseases.

Widely used in hydraulics, the Colebrook equation for flow friction relates implicitly to the input parameters; the Reynolds number, Re and the relative roughness of inner pipe surface, ε/D with the output unknown parameter; the flow friction factor, λ; λ=f(λ, Re, ε/D). In this paper, a few explicit approximations to the Colebrook equation; λ≈f(Re, ε/D), are generated using the ability of artificial intelligence to make inner patterns to connect input and output parameters in explicit way not knowing their nature or the physical law that connects them, but only knowing raw numbers, {Re, ε/D}→{λ}. The fact that the used genetic programming tool does not know the structure of the Colebrook equation which is based on computationally expensive logarithmic law, is used to obtain better structure of the approximations which is less demanding for calculation but also enough accurate. All generated approximations are with low computational cost because they contain a limited number of logarithmic forms used although for normalization of input parameters or for acceleration, but they are also sufficiently accurate. The relative error regarding the friction factor λ, in best case is up to 0.13% with only two logarithmic forms used. As the second logarithm can be accurately approximated by the Padé approximation, practically the same error is obtained also using only one logarithm.

This paper proposes a method based on genetic algorithm (GA) for the security-constrained optimal dispatch of integrated natural gas and electricity networks, considering operating scenarios in both energy systems. The mathematical formulation of the optimization problem consists of a multi-objective function which aims to minimize both cost of thermal generation (diesel and natural gas) as well as the production and transportation of natural gas. The joint gas-electricity system is modeled by two separate groups of nonlinear equation, which are solved by the combination of Newton's method with the GA. The applicability of the proposed method is tested in the Belgian gas network integrated with the IEEE 14-bus test system and a 15-node natural gas network integrated with the IEEE 118-bus test system. The results demonstrate that the proposed method provides efficient and secure solutions for different operating scenarios in both energy systems.

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.

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.

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.

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 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.

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.

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.

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.

To improve the current density distribution and electromagnetic performance of air-core inductors, a structural optimization method combining BP neural network and genetic algorithm was proposed for the study of axial and radial spiral multi-winding inductors. The Monte Carlo method was used to extract the structural size samples of the inductors, and the training dataset was obtained through finite element calculation of electromagnetic fields. Based on BP neural networks, nonlinear mapping models between the inductance value, volumetric inductance density, current distribution non-uniformity coefficient, and inductor structural parameters were constructed. The sensitivity analysis of the inductor inductance value affected by structural parameters was conducted through the Sobol index calculation. Using the current distribution non-uniformity coefficient as the fitness function and the volumetric inductance density as the constraint condition, the genetic algorithm was applied to optimize the structural parameters of the inductor globally. The optimization results were verified through finite element comparison. The results show that, under the requirement of satisfying the volumetric inductance density, the current distribution non-uniformity coefficient of AHI type inductor was reduced by 4.57% compared with the best sample in the sampling, while that of RHI type inductor was reduced by 5.33%, demonstrating the practicality of the BP-GA joint algorithm in the structural optimization design of inductors.

In genetic conflicts, driver and killer elements achieve biased survival, replication, or transmission over sensitive and targeted elements through a wide range of molecular mechanisms, including mimicry. Driving mechanisms manifest at all organismal levels, from the biased propagation of individual genes, as demonstrated by transposable elements, to the biased transmission of genomes, as illustrated by viruses, to the biased transmission of cell lineages, as in cancer. Targeted genomes are vulnerable to molecular mimicry through the conserved motifs they use for their own signaling and regulation. Mimicking these motifs enables a selfish element to control core target processes, and can occur at the sequence, structure, or functional level. Molecular mimicry was first appreciated as an important phenomenon more than twenty years ago. Modern genomics technologies, databases, and machine learning approaches offer tremendous potential to study the distribution of molecular mimicry across genetic conflicts in nature. Here, we explore the theoretical expectations for molecular mimicry between conflicting genomes, the trends in molecular mimicry mechanisms across known genetic conflicts, and outline how new examples can be gleaned from population genomic datasets. We discuss how mimics involving short sequence-based motifs or gene duplications can evolve convergently from new mutations. Whereas, processes that involve divergent domains or fully-folded structures occur among genomes by horizontal gene transfer. These trends are largely based on a small number of organisms and should be reevaluated in a general, phylogenetically independent framework. Currently, publicly available databases can be mined for genotypes driving non-mendelian inheritance patterns, epistatic interactions, and convergent protein structures. A subset of these conflicting elements may be molecular mimics. We propose approaches for detecting genetic conflict and molecular mimicry from these datasets.

The study titled "Molecular Choreography of DNA: Insights and Innovations" explores the intricate dance of DNA, shedding light on its structure, function, and regulation. This comprehensive review delves into the historical significance of DNA research, from Watson and Crick's double helix discovery to recent innovations in genomics and molecular biology. The review highlights DNA's roles in replication, repair, gene regulation, and chromatin dynamics, emphasizing the precision and complexity of its molecular choreography. Innovations like CRISPR-Cas9 gene editing and advanced sequencing technologies have revolutionized our understanding of DNA, opening new avenues for scientific exploration. This study aims to provide readers with a deeper appreciation of DNA's central role in life's choreographed processes.

The utilization of wild genera in sugarcane has been recognized as an important strategy to enhance the genetic base to impart biotic and abiotic stress tolerance in hybrid cultivars. A set of 15 Erianthus procerus derived hybrids along with five popular check varieties were evaluated for two years (2018–19 and 2019–20) for agro-morphological, quality, physiological traits and three years (2018–19, 2019–2020, and 2020–21 for winter sprouting potential. The E. procerus hybrid GU 12-19 recorded the highest winter sprouting potential with a winter sprouting index (WSI) of 10.6 and a percent superiority of 279 over best check (Co 0238) followed by GU 12-22 with WSI: 8.5 and % superiority over best check was 204. The other top-performing hybrids, GU 12-21 and GU 12-29, recorded the excellent winter sprouting potential with WSI of 7.2 and 6.9 respectively. The four E. procerus hybrids identified with higher WSI were also resistant to the prevalent races of red rot in both tropical and subtropical conditions. The association analysis revealed significant correlations between various traits, particularly fibre content with a maximum number of associations, which indicates its multifaceted impact on sugarcane characteristics. Principal component analysis (PCA) summarized the data, explaining 57.6% of the total variation for measured traits and genotypes, providing valuable insights into the performance and characteristics of the E. procerus-derived hybrids under subtropical climate. The anthocyanin content of E. procerus hybrids was better than the check varieties with a mean of 0.147 (2018–19) and 0.144 (2019–20) and range of 0.123–0.179 (2018–19) and 0.111–0.172 (2019–20), anthocyanin plays a vital role in mitigating the cold injury, acting as an antioxidant in cool weather conditions, particularly in sugarcane. Further, seven hybrids recorded more than 22% fibre threshold, indicating their industrial potential. These hybrids would be potential donors for cold tolerance and high ratooning ability along with red rot resistance under subtropical climate.

The article presents a hybrid method for calculating the chemical composition of steel with the required hardness after cooling from the austenitizing temperature. Artificial neural networks and genetic algorithms were used to develop the model. Based on 550 diagrams of continuous cooling transformation (CCT) of structural steels available in the literature, a dataset of experimental data was created. Artificial neural networks were used to develop a hardness model describing the relationship between the chemical composition of the steel, the austenitizing temperature, and the hardness of the steel after cooling. A genetic algorithm was used to identify the chemical composition of the steel with the required hardness. The value of the objective function was calculated using the neural network model. The developed method for identifying the chemical composition was implemented in a computer application. Examples of calculations of mass concentrations of steel elements with the required hardness after cooling from the austenitizing temperature are presented. The model proposed in this study can be a valuable tool to support chemical composition design by reducing the number of experiments and minimizing research costs.

Curdlan is a water-insoluble polymer that has structure and gelling properties useful in a wide variety of applications such as in medicine, cosmetics, packaging and the food and building industries. The capacity to produce curdlan has been detected in certain soil-dwelling bacteria of various phyla, although the role of curdlan in their survival remains unclear. One of the major limitations of the extensive use of curdlan in industry is the high cost of production during fermentation, partly because production involves specific nutritional requirements such as nitrogen limitation. Engineering of the industrially relevant curdlan-producing strain Agrobacterium sp. ATTC31749 is a promising approach that could decrease the cost of production. Here, during investigations on curdlan production, I found that curdlan was deposited as a capsule. Curiously, only a part of the bacterial population produced a curdlan capsule. This heterogeneous distribution appeared to be due to the activity of Pcrd, the native promoter responsible for the expression of the crdASC biosynthetic gene cluster. To improve curdlan production, Pcrd was replaced by a promoter (PphaP) from another Alphaproteobacterium, Rhodobacter sphaeroides. Compared to Pcrd, PphaP was stronger and only mildly affected by nitrogen levels. Consequently, PphaP dramatically boosted crdASC gene expression and curdlan production. Importantly, the genetic modification overrode the strict nitrogen depletion regulation that presents a hindrance for maximal curdlan production and from nitrogen rich, complex media, demonstrating excellent commercial potential for achieving high yields using cheap substrates under relaxed fermentation conditions.

When using meta-heuristic optimization approaches for optimization, a large number of samples are required. In particular, when generating subgeneration, the utilization of existing samples is low and the number of individuals is high. Therefore, surrogate-based optimization has been developed, which greatly reduces the number of individuals in the subgeneration and the cost of optimization. In complex air supply scenarios, single-objective optimization results may not be comprehensive; therefore, this paper developed a double-objective air supply optimization method based on the Kriging surrogate model and Non-dominated Sorting Genetic Algorithms-II. And proposed the infill criteria based on clustering to advance the Pareto Frontier. The method was validated by an inverse prediction case, and in particular, the problems when based on 3D steady-state simulations were analyzed. The results showed that the method can quickly achieve an approximate prediction of the boundary conditions (when prediction were made based on experimental data, the number of simulations was 82 and the average error was 6.8%). Finally, the method was used to optimize the air supply parameters of a dual-aisle, single-row cabin. The Pareto set suggested that an airflow organization with dual circulation may be optimal.

The current classification of acute myeloid leukemia (AML) relies largely on genomic alterations. AML with mutated Nucleophosmin1 (NPM1-mut) is the largest of the genetically defined groups, involving about 30% of adult AMLs and is currently recognized as a distinct entity in the actual AML classifications. NPM1-mut AML usually occurs in de novo AML and is associated predominantly with a normal karyotype and relatively favorable prognosis. However, NPM1-mut AMLs are genetically, transcriptionally, and phenotypically heterogeneous. Furthermore, NPM1-mut is a clinically heterogenous group. Recent studies have in part clarified the consistent heterogeneities of these AMLs and have strongly supported the need for an additional stratification aiming to improve the therapeutic response of the different subgroups of NPM1-mut AML patients.

We have performed the first comparative analysis of the potential of two physiologically-diverse model cyanobacteria, Synechococcus PCC 7002 (S.7002) and Synechococcus PCC 7942 (S.7942), for the photosynthetic production of four chemically-different high-value terpenes: two monoterpenes limonene and pinene, and two sesquiterpenes bisabolene and farnesene. We showed, for the first time, that S.7002 and S.7942 can produce farnesene and bisabolene, respectively. Both cyanobac-teria produced farnesene (S.7942 better than S.7002) more efficiently than the other tested terpenes (especially pinene the weakest produced terpene). S.7002 produced limonene better than bisabo-lene, whereas S.7942 produced bisabolene better than limonene. These findings suggest that S.7942 is better suited to produce sesquiterpenes than monoterpenes. Also interestingly, higher levels of terpenes were produced by S.7942 and S.7002 expressing a terpene-synthase gene from both a RSF1010-derived replicating plasmid and a neutral chromosomal site, as compared to ei-ther the plasmid alone or the chromosome alone. These results suggest that in both cyanobacteria the production of terpenes is more limited by the activity of terpene synthases than the abondance of terpene precursors. Finally, higher levels of terpenes were produced by S.7002 growing on urea (a frequent pollutant) as compared to nitrate (or ammonium) the standard nitrogen sources for cyanobacteria (more expensive than urea).

Inteins, often referred to as protein introns, are highly mobile genetic elements that invade conserved genes throughout the tree of life. Inteins have been found to invade a wide variety of key genes within actinophages. While in the process of conducting a survey of these inteins in actinophages we discovered that one protein family of methylases contained a putative intein, and two other unique insertion elements. These methylases are known to occur commonly in phages as orphan methylases (possibly as a form of resistance to restriction-modification systems). We found that the methylase family is not conserved within phage clusters and has a disparate distribution across divergent phage groups. We determined that two of the three insertion elements have a patchy distribution within the methylase protein family. We also found that the third insertion element is likely a second homing endonuclease, and that all three elements (the intein, the homing endonuclease, and what we refer to as the ShiLan domain) all have different insertion sites that are conserved in the methylase gene family. Furthermore, we find strong evidence that both the intein and ShiLan domain are partaking in long distance horizontal gene transfer events between divergent methylases in disparate phage hosts within the already dispersed methylase distribution. The reticulate evolutionary history of methylases and their insertion elements reveals high rates of gene transfer and within-gene recombination in actinophages.

Enteroaggregative Escherichia coli (EAEC) is an emerging pathogen frequently associated with acute diarrhea in children and travelers to endemic regions. EAEC was found the most prevalent bacterial diarrheal pathogen from hospitalized Bolivian children less than five years of age with acute diarrhea from 2007 to 2010. Here, we further characterized the epidemiology of EAEC infection, virulence genes, and antimicrobial susceptibility of EAEC isolated from 414 diarrheal and 74 non-diarrheal cases. EAEC isolates were collected and subjected to a PCR-based virulence gene screening of seven virulence genes and a phenotypic resistance test to nine different antimicrobials. Our results showed that atypical EAEC (a-EAEC, AggR-negative) was significantly associated with diarrhea (OR, 1.62, 95% CI, 1.25 to 2.09, P < 0.001) in contrast to typical EAEC (t-EAEC, AggR-positive). EAEC infection was most prevalent among children between 7 - 12 months of age. The number of cases exhibited a biannual cycle with a major peak during the transition from warm to cold season (April – June). Both typical and a-EAEC infections were graded as equally severe; however, t-EAEC harbored more virulence genes. aap, irp2, and pic were the most prevalent genes. Surprisingly, we detected 60% and 52.6% of multidrug resistance (MDR) EAEC among diarrheal and non-diarrheal cases. Resistance to ampicillin, sulfonamides, and tetracyclines was most common, being the corresponding antibiotics, the ones that are frequently used in Bolivia. Our work is the first study that provides comprehensive information on the high heterogenicity of virulence genes in t-EAEC and a- EAEC and the large prevalence of MDR EAEC in Bolivia.

Studies on evolution have made significant progress in multiple disciplines, but evolutionary theories remain scattered, complicated, elusive, and controversial. To address this issue, a novel evolutionary theory is deduced from thermodynamics in this article. As per the formula of Gibbs free energy, carbon-based entities (CBEs) on Earth tend to absorb more energy. This is the evolutionary driving force leading to organic synthesis of higher-hierarchy CBEs (HHCBEs). The organic synthesis raises the amount of HHCBEs and increases the structural complexity and hierarchy of CBEs. Increased structural complexity and hierarchy spontaneously offer complicated functions to HHCBEs. Genetic mutations, epigenetic changes, and uninheritable variations provide diversified HHCBEs for natural selection which is redefined as survival of the fit and elimination of the unfit, leading to increase of diversity and fitness of HHCBEs. Order in biology resulting from permanent natural selection is largely contrary to order in physics. Natural selection acts on the overall fitness involving all traits through the co-action of positive selection and negative selection. Natural selection can establish biological traits in short geological periods. Different combinations of traits can lead to sympatric speciation targeting the same niche. Altruism, collaboration, and obeying rules with balanced freedom are all important throughout the CBE evolution, which harbors three overlapping phases including chemical evolution (abiogenesis), biological evolution, and group evolution. Altogether, this theory termed the CBE evolutionary theory (CBEET) suggests that evolution which favors fitness and diversity is driven hierarchy-wise by energy. It reveals the driving force of evolution and reestablishes the key role of natural selection. It integrates with advances from multiple disciplines and provides simple and rational answers to some evolutionary conundrums. It removes several elusive or erroneous views including the one regarding negative entropy. It bridges natural sciences and social sciences and sheds novel insights into harmonious development of human society.

Owing to the time-varying characteristics and nonlinearities of industrial processes, control has higher difficulties and results in challenges for advanced technology. In this paper, a self-tuning controller that includes a nonlinear proportional-integral-derivative (NPID) control function as well as a self-tuning function is proposed for first-order plus time delay (FOPTD) process control. The NPID control function is implemented using the nonlinear PID controller whose optimum parameters are adapted by a neural network (NN). The self-tuning function is able to identify the process dynamics using a short period of process behavior and tune NPID parameters based on the identified parameters. The advantage of the proposed method is validated with a set of simulation works on three processes and the comparison results are presented.

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.

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.

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.

The integration of genetic technologies into neurological diagnostics has significantly enhanced our ability to understand and manage neurological disorders. This article discusses the functional analysis of key genetic tests, including microarray analysis, variant sequencing, exome sequencing, RNA sequencing, and whole genome sequencing. Each of these technologies offers unique advantages and challenges, providing a comprehensive toolkit for diagnosing and understanding the genetic basis of neurological conditions. The functional insights gained from these tests not only detect genetic abnormalities but also elucidate their functional consequences, aiding in the diagnosis, prognosis, and management of neurological disorders. As these technologies continue to evolve, they are expected to play an increasingly important role in the field of neurological diagnostics, paving the way for personalized medicine and improved patient outcomes.