Efficient alternatives in energy production and consumption are constantly investigated by increasingly strict policies. In this way, the pollutant emissions that contribute to the greenhouse effect reduce and sustainability of the electricity sector increase. With more than a third of the world's energy consumption, buildings have great potential to contribute these sustainability goals. Additionally, with growing incentives in the Distributed Generation (DG) and Electric Vehicle (EV) industry, it is believed that Smart Buildings (SBs) can be a key in the field of residential energy sustainability in the future. In this work, an energy management system in SBs are developed to reduce the power demanded of a residential building. In order to balance the demand and power provided by the grid, microgrids such as Battery Energy Storage System (BESS), EVs and Photovoltaic Generation panels (PV) are used. Here, a Mixed Binary Linear Programming formulation (MBLP) is proposed to optimize the charge and discharge scheduling of EVs and also BESS. In order to show the efficiency of the model, a case study involving three scenarios and an economic analysis is considered. The results point a 65% reduction in peak load consumption supplied by grid and a 28.4% reduction in electricity consumption costs.

We report on the field testing datasets and performance evaluation results obtained from a commercial property-based visually-clear solar window installation site in Perth-Australia. This installation was fitted into a refurbished shopping centre entrance porch, and showcases the potential of glass curtain wall-based solar energy harvesting in built environments. In particular, we focus on photovoltaic (PV) performance characteristics such as the electric power output, specific yield, day-to-day consistency of peak output power, and the amounts of energy generated and stored daily. The dependencies of the generated electric power and stored energy on multiple environmental and geometric parameters are also studied. An overview of the current and future application potential of high-transparency, visually-clear solar window-based curtain wall installations suitable for practical building integration is provided.

The Medi-test system we developed was motivated by the large number of resources available for the medical domain, as well as the number of tests needed in this field (during and after the medical school) for evaluation, promotion, certification, etc. Generating questions to support learning and user interactivity has been an interesting and dynamic topic in NLP since the availability of e-book curricula and e-learning platforms. Current e-learning platforms offer increased support for student evaluation, with an emphasis in exploiting automation in both test generation and evaluation. In this context, our system is able to evaluate a student’s academic performance for the medical domain. Using as input medical reference texts and supported by a specially designed medical ontology, Medi-test generates different types of questionnaires for Romanian language. The evaluation includes 4 types of questions (multiple-choice, fill in the blanks, true/false and match), can have customizable length and difficulty and can be automatically graded. A recent extension of our system also allows for the generation of tests which include images. We evaluated our system with a local testing team, but also with a set of medicine students, and user satisfaction questionnaires showed that the system can be used to enhance learning.

The radial distribution networks are designed for unidirectional power flows and are passive in nature. However, with the penetration of Distributed Generation (DG), the power flow becomes bidirectional and the network becomes active. The integration of DGs into distribution network creates many issues with: system stability, protection coordination, power quality, islanding, proper placement and sizing etc. Among these issues, the two most significant are optimal sizing and placement of DGs and their protection coordination in utility network. The proper coordination of relays with high penetration of DGs placed at optimal location increases the availability and reliability of the network during abnormal operating conditions.This research addresses most of the available methods for efficient sizing and placement of DGs in distribution system (numerical, analytical and heuristic) as well as the developed protection coordination techniques for utility networks in the presence of DGs (Artificial Intelligence (AI), adaptive and non-adaptive, multi-agent, hybrid). This paper indicates the possible research gaps and highlights the applications possibilities and methods’ limitations in the area of DGs.

Urban stormwater runoff from a medium-density residential development in southeast Queensland has been monitored in the field since November 2013. A treatment train installed on the site includes rainwater tanks collecting roofwater, 200-micron mesh baskets installed in grated gully pits and two 850 mm high media filtration cartridges installed in an underground 4 m³ vault. A monitoring protocol developed by research partners, Queensland University of Technology (QUT), guided the monitoring process over a 4.5-year period. Heavy metals were included in the list of analytes during the monitoring period as the catchment is within 1 km of the environmentally-sensitive Moreton Bay, Queensland. Removal efficiencies observed at this site for the regulated pollutants; total suspended solids (TSS), total phosphorus (TP) and total nitrogen (TN) for the pit baskets were 61%, 28% and 45% respectively. The cartridge filters removed 78% TSS, 59% TP, 42% TN, 40% total copper and 51% total zinc. As the measured influent concentrations to the cartridge filters were low when compared to industry guidelines, the dataset was merged with international field results for TSS (n=39) and TP (n=32) but truncated within anticipated guideline levels. The combined dataset for the media filter demonstrates performance at 89% TSS, 66% TP and 42% TN. The total gross pollutant generation rate from the medium-density residential catchment was observed to be 0.24 m³/Ha/year, with a corresponding air-dried mass of 142.5 kg/Ha/year. Less than 2% of the gross pollutant mass was anthropogenic. The findings of this research suggest that the treatment train, and in particular the media filter, holds promise for the removal of total copper and total zinc, in addition to TSS, TP and TN, from urban stormwater runoff. Based on a maximum, low risk trigger TN concentration of 1.5 mg/L, the field test data from 4.5 years of operation and standard maintenance, suggests a 5.5-year replacement interval for the media filters.

We demonstrate for the first time efficient mechanical to electrical energy conversion using InGaN/GaN nanowires (NWs). Using an atomic force microscope equipped with a modified Resiscope module, we analyse the piezoelectric energy generation of GaN NWs and demonstrate an important enhancement when integrating in their volume a thick In-rich InGaN insertion. The piezoelectric response of InGaN/GaN NWs can be tuned as a function of the InGaN insertion thickness and position in the NW volume. The energy harvesting is favoured by the presence of a PtSi/GaN Schottky diode which allows to efficiently collect the piezo-charges generated by InGaN/GaN NWs. Average output voltages up to 330 ± 70 mV and a maximum value of 470 mV per NW has been measured for nanostructures integrating 70 nm-thick InGaN insertion capped with a thin GaN top layer. This latter value establishes an increase of about 35% of the piezo-conversion capacity in comparison with binary p-doped GaN NWs. By considering these output signals, we estimate a maximum power density generated by one layer of dense InGaN/GaN-based NW of about 3.3 W/cm². These results settle the new state-of-the-art for piezo-generation from GaN-based NWs and offer a promising perspective for extending the performances of the piezoelectric sources.

This paper proposes two fully-automatic machine-extracted linguistic features from an unlimited text input for Mandarin prosody generation. One is the punctuation confidence (PC) which measures the likelihood of inserting a major punctuation mark (PM) at a word boundary. Another is the quotation confidence (QC) which measures the likelihood of a word string to be quoted as a meaningful or emphasized unit in text. Because a major PM in a text is highly correlated with a prosodic break, and a quoted word string plays an important role in human language understanding, the two features potentially could provide useful information for prosody generation. The idea is first realized by employing conditional random field (CRF)-based models to predict major PMs, quoted word string locations, and their associated confidences, i.e., the PC and the QC, for each word boundary. Then, the predicted punctuations and their confidences are combined with traditional contextual linguistic features to predict prosodic-acoustic features. Both objective and subjective tests showed that the prosody generation with the proposed linguistic features performed better than the one without the proposed features. So, the proposed PC and QC are promising features for Mandarin prosody generation.

During the COVID-19 pandemic, wastewater surveillance was used to monitor community transmission of SARS-CoV-2. As new genetic variants emerged, the need for timely identification of these variants in wastewater became an important focus. In response to increased reports of Omicron transmission across the United States, the Oklahoma Wastewater Surveillance team utilized allele-specific RT-qPCR assays to detect and differentiate variants, such as Omicron, from other variants found in wastewater in Oklahoma. The PCR assays showed presence of the Omicron variant in Oklahoma on average two weeks before official reports, which was confirmed through genomic sequencing of selected wastewater samples. Through continued surveillance from November 2021 to January 2022, we also demonstrated the transition from prevalence of the Delta variant to prevalence of the Omicron variant in local communities. We further assessed how this transition correlated with certain demographic factors characterizing each community. Our results highlight RT-qPCR assays as a rapid, simple and cost-effective method for monitoring community spread of SARS-CoV-2 genetic variants in wastewater. Additionally, they demonstrate that specific demographic factors such as ethnic composition and household income can correlate with the timing of SARS-CoV-2 variant introduction and spread.

To investigate the composition of caproic acid-producing bacterial communities in sub-high-temperature Daqu, we analyzed four different Daqu samples from Sichuan. After enriching the samples with a specialized medium, we performed high-throughput sequencing of 16S rRNA gene amplicons on the metagenomic DNA from both the Daqu samples and the enrichment cultures. Our analysis focused on identifying and characterizing the caproic acid-producing bacterial communities present in Daqu. The results revealed that enrichment increased both the diversity and abundance of potential caproic acid-producing bacteria in Daqu. The predominant caproic acid-producing bacteria, with a relative abundance greater than 1%, included Clostridium sensu stricto 18 (2.19% to 69.00%), Clostridium sensu stricto 15 (1.76% to 7.65%), as well as smaller amounts of Clostridium sensu stricto 12, Clostridium sensu stricto 1, Clostridium sensu stricto 10, Clostridium sensu stricto 7, and Caproiciproducens. Specifically, Q1 Daqu was characterized by the presence of Clostridium sensu stricto 18 and Clostridium sensu stricto 15. Q2 Daqu featured Clostridium sensu stricto 1, Caproiciproducens, and Clostridium sensu stricto 18. Q3 Daqu included Clostridium sensu stricto 18 and Caproiciproducens. Q4 Daqu contained Clostridium sensu stricto 18 and Clostridium sensu stricto 1. These findings indicate that there are variations in the composition of caproic acid-producing bacterial communities among different sub-high-temperature Daqu samples from Sichuan. This preliminary analysis provides a theoretical basis for further studies on caproic acid bacteria in Daqu and offers insights into the development of high-quality Daqu, which may enhance the quality of Nongxiangxing liquor.

Type 2 Diabetes (T2D) is a major public health concern. We have previously shown that the biochemical functions of the gut microbiome contribute to the postprandial glucose response and built a machine learned (ML) model that can personalize dietary choices using a stool metatranscriptomic test. We integrated this data-driven personalized nutrition process into the Viome Precision Nutrition Program (VPNP), which includes sample collection kits (capillary blood and stool), an AI/ML nutritional recommendation engine, and an app for digital delivery of dietary recommendations. To test the efficacy of VPNP, we conducted a decentralized, blinded, randomized, placebo-controlled trial in a prediabetic and diabetic adult population in the USA, with HbA1c as the primary endpoint. For this pilot trial, we recruited 27 participants and randomized them into two arms. The placebo arm included sample collection, digital delivery of the USDA dietary recommendations, and physical delivery of supplements filled with inert material. The VPNP (interventional) arm included digital delivery of data-driven and personalized dietary recommendations, and physical delivery of precision supplements. The nutritional intervention lasted ~90 days. The ANCOVA analysis method was employed to control for potential confounders measured at baseline. VPNP intervention (n=12) reduced HbA1c more than the placebo (n=15) in a clinically significant manner (HbA1c difference of 0.42%, p = 0.028). We demonstrate that a precision nutrition program based on stool and blood metatranscriptomic data and AI/ML analyses can significantly reduce HbA1c relative to USDA recommended nutrition in a prediabetic and diabetic population. These results suggest that integrating data-driven precision nutrition into the healthcare system could reduce the burden of T2D.

The aim of this study was to investigate the differentially expressed genes associated with intramuscular fat deposition in the longissimus dorsi muscle of Xinjiang Brown Bulls. The longissimus dorsi muscles of 42 Xinjiang Brown Bulls were selected under the same feeding conditions. The intramuscular fat content of muscle samples were determined by Soxhlet extraction method, from which 5 samples with high intramuscular fat content (HIMF group) and 5 samples with low intramuscular fat content (LIMF group) were selected. It was found that the intramuscular fat content of HIMF group was 46.054% higher than that of LIMF group. Muscle samples produced by paraffin section for morphological observation. It was found that the fat richness of the HIMF group was better than that of the LIMF group. Transcriptome sequencing technology was used to analyze the gene expression differences of longissimus dorsi muscle. Through in-depth analysis of the longissimus dorsi muscle by transcriptome sequencing technology, we screened a total of 165 differentially expressed genes. The results of Gene Ontology (GO) enrichment analysis showed that the differentially expressed genes in the two groups were mainly clustered in biological pathways related to carbohydrate metabolic processes, redox processes and oxidoreductase activities. Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis showed that the differentially expressed genes were significantly clustered in 15 metabolic pathways, which mainly covered fatty acid metabolism (related to lipid metabolism and glucose metabolism), pentose phosphate pathway, Peroxisome Proliferator-Activated Receptors (PPAR) signalling pathway and other important metabolic processes. The three genes that were predominantly enriched in the glycolipid metabolic pathway by analysis were SCD5, CPT1C, and FBP2, all of which directly or indirectly affect intramuscular fat deposition. In summary, the present study investigated the differences in gene expression between high and low intramuscular fat content in the longissimus dorsi muscle of Xinjiang Brown Bulls by transcriptome sequencing technology, and revealed the related signalling pathways. Therefore, we hypothesized that SCD5, CPT1C, and FBP2 were the key genes responsible for the significant differences in intramuscular fat content of the longissimus dorsi muscles in a population of Xinjiang Brown Bulls. We expect that these findings will provide fundamental support for subsequent studies exploring key genes affecting fat deposition characteristics in Xinjiang Brown Bulls.

Lecanicillium lecanii KMZW-1, is recognized for its potential as a bio-control agent against agricultural and forestry pests, particularly due to its compatibility with integrated pest management (IPM) strategies. This study aimed to investigate the complete genome of L. lecanii KMZW-1 and assess its pathogenicity against Bactrocera dorsalis. Whole genome sequencing revealed a genome size of 47,239,278 bp, comprising 27 contigs, with a GC content of 51.16%. The genome completeness was assessed as 97.93% using BUSCO analysis, AntiSMASH analysis identified 29 gene clusters associated with secondary metabolite biosynthesis providing insights into the genetic basis of its pathogenic mechanisms and biocontrol potential. The pathogenicity of L. lecanii KMZW-1 was assessed using concentrations ranging from 1×10^4 to 1×10^11 spores/mL. Higher-concentrations (1×10^10 to 1×10^11 spores/mL), resulted in significantly increased cumulative mortality rates of B. dorsalis adults compared to the control group. Notably, the pathogenicity was higher in male adults than in females. Probit regression analysis yielded LogLC50 values as 5.662 for female and 4.486 for male B. dorsalis adults. In summary, L. lecanii KMZW-1 exhibits strong insecticidal activity against B. dorsalis and shows potential as a biocontrol agent with IPM strategies. These findings provide robust genomic evidence supporting the use of L. lecanii KMZW-1in managing against B. dorsalis populations.

The detection, characterization, and monitoring of SARS-CoV-2 recombinant variants is a challenge to public health authorities worldwide. Recombinant variants, composed of two or more SARS-CoV-2 lineages, often have unknown impacts on transmission, immune escape, and virulence in the early stages of emergence. We examined 4,213 SARS-CoV-2 recombinant SARS-CoV-2 genomes collected between 2020 and 2022 from California to describe regional and statewide trends in prevalence. Many of these recombinant genomes, such as those belonging to the XZ lineage, or novel recombinant lineages, likely originated within the state of California. We discuss the challenges and limitations surrounding Pango lineage assignments, the use of publicly available sequence data, and adequate sample sizes for epidemiologic analyses. Although these challenges will continue as SARS-CoV-2 sequencing volumes decrease globally, this study enhances our understanding of SARS-CoV-2 recombinant genomes to date while providing a foundation for future insights into emerging recombinant lineages.

Since the first reported case of COVID-19 in December 2019, several SARS-CoV-2 variants has evolved and some of them have shown higher transmissibility becoming the prevalent strains. Genomic epidemiological investigations into strains from different time points including the early stages of the pandemic are very crucial for understanding the evolution and transmission patterns. Using whole genome sequences, our study describes the early landscape of SARS-CoV-2 variants in central India retrospectively (including the first known occurrence of SARS-CoV-2 in Madhya Pradesh). We performed amplicon based whole genome sequencing of randomly selected SARS-CoV-2 isolates (n=38) collected between 2020-2022 at state level VRDL, ICMR-NIRTH, Jabalpur from 11899 RT-qPCR positive samples. We observed the presence of five lineages namely B.1, B.1.1, B.1.36.8, B.1.195 and B.6 in 19 genomes from the first wave cases and variants of concern (VOC) lineages i.e. B.1.617.2 (Delta), and BA.2.10 (Omicron) in the second wave cases. There was a shift in mutational pattern of SRAS-CoV-2 strains from the second wave in contrast to the first wave. We have identified five immune escape variants in the S gene: P681R, P681H, L452R, Q57H, and N501Y in the isolates collected during the second wave. Furthermore, these genomes were compared with 2160 complete genome sequences reported from central India that encompass 109 different SARS-CoV-2 lineages. Among them, VOC lineages Delta (28.93%), and Omicron (56.11%) were circulating predominantly in this region. This study provides useful insights into the genetic diversity of SARS-CoV-2 strains over the initial course of the COVID-19 pandemic in central India.

Reconstructing the Tree of Life remains a central goal in biology. Early methods, relying on small numbers of morphological or genetic characters, often yielded conflicting evolutionary histories, undermining confidence in the results. Investigations based on phylogenomics, which use hundreds to thousands of loci for phylogenetic inquiry, have provided a clearer picture of life's history, but certain branches remain problematic. To resolve difficult nodes on the Tree of Life, two recent studies tested the utility of synteny, the conserved collinearity of orthologous genetic loci in two or more organisms, for phylogenetics. Synteny exhibits compelling phylogenomic potential while also raising new challenges. We identify and discuss specific opportunities and challenges that bear on the value of synteny data and other rare genomic changes for phylogenomic studies. Synteny-based analyses of highly contiguous genome assemblies mark a new chapter in the phylogenomic era and the quest to reconstruct the Tree of Life.

Fusarium spp. are commonly associated with the root rot complex of soybean (Glycine max). Previous surveys identified six common Fusarium species from Manitoba, including F. oxysporum, F. redolens, F. graminearum, F. solani, F. avenaceum and F. acuminatum. This study aimed to determine their pathogenicity, assess host resistance and evaluate the genetic diversity of Fusarium spp. isolated from Canada. Pathogenicity was tested on two cultivars, ‘Akras’ (moderate resistance) and ‘B150Y1’ (susceptible), under greenhouse conditions. The virulence of the fungal isolates varied, with root rot severities ranging from 1.5 to 3.3 on a 0-4 scale. The six species were used to screen a panel of 20 Canadian soybean cultivars for resistance in a greenhouse. Cluster and principal component analyses were conducted based on the same traits as for the pathogenicity study. Two of the cultivars, ‘P15T46R2’ and ‘B150Y1’, were consistently found to be tolerant to F. oxysporum, F. redolens, F. graminearum and F. solani. To investigate the incidence and prevalence of Fusarium spp. in Canada, fungi were isolated from 106 soybean fields surveyed across Manitoba, Saskatchewan, Ontario and Quebec. Two-hundred twenty-one Fusarium isolates were identified, with phylogenetic analyses indicating diversity of the isolates in the major soybean production regions of Canada.

Background: This study aimed to investigate the molecular profiles of stage III CRC patients from the international IDEA study. It also sought to correlate these profiles with Toll-like and vitamin D receptor polymorphisms, clinicopathological and epidemiological characteristics, and patient outcomes. Methods: Whole Exome Sequencing and PCR-RFLP on surgical specimens and blood samples, respectively, were performed to identify molecular profiling and the presence of Toll-like and vitamin D polymorphisms. Bioinformatic analysis revealed mutational status. Results: Among the enrolled patients, 63.7% were male, 66.7% had left-sided tumors, and 55.7% received CAPOX as adjuvant chemotherapy. Whole exome sequencing identified 59 mutated genes in 11 different signaling pathways from the Kyoto Encyclopedia of Genes and Genomes (KEGG) CRC panel. On average, patients had 8 mutated genes (range, 2-21 genes). Mutations in ARAF and MAPK10 emerged as independent prognostic factors for reduced DFS (p=0.027 and p<0.001, respectively), while RAC3 and RHOA genes emerged as independent prognostic factors for reduced OS (p=0.029 and p=0.006, respectively). Right-sided tumors were also identified as independent prognostic factors for reduced DFS (p=0.019) and OS (p=0.043). Additionally, patients with tumors in the transverse colon had mutations in genes related to apoptosis, PIK3-Akt, Wnt, and MAPK signaling pathways. Conclusions: Molecular characterization of tumor cells can enhance our understanding of the disease course. Mutations may serve as promising prognostic biomarkers, offering improved treatment options. Confirming these findings with require larger patient cohorts and international collaborations to establish correlations between molecular profiling, clinicopathological and epidemiological characteristics and clinical outcomes.

Hereditary hemochromatosis (HH) is an inherited iron metabolism disorder. It is caused by an autosomal recessive disorder resulting from a C28Y8 HFE gene mutation. Mutations in the HFE gene may result in iron accumulation and oxidative stress in the retina, resulting in macular degeneration. This article describes two patients with HH who developed vision difficulties. Both patients were subjected to a retinal exam, multimodal imaging, and electrodiagnostic techniques, which revealed structural and functional degeneration of the central macula. Fundus photography, fluorescein angiography (FA), and fundus autofluorescence (FAF) revealed changes at the level of the retinal pigment epithelium (RPE) in the center of the macula, while optical coherence tomography (OCT) revealed subfoveal accumulation of hyperreflective material at and below the RPE. The multifocal electroretinogram (mfERG) confirmed a decreased cone response, whereas the full-field electroretinogram (ERG) revealed no pathological findings. In addition, patients underwent genetic testing, which ruled out the possibility of hereditary macular dystrophy. Considering macula findings and the nature of the patients' primary illness, we believe that the higher concentration and accumulation of iron and photoreceptor metabolic products have led to dysfunction in the RPE, which has led to morphological and functional changes in the macula.

Urinary extracellular vesicles (uEV) hold non-invasive RNA biomarkers for genitourinary tract diseases. However, missing knowledge about reference genes and effects of pre-analytical choices hinder biomarker studies. We aimed to assess how pre-analytical variables (urine storage temperature, isolation workflow) affect diabetic kidney disease (DKD) -linked miRNAs or kidney -linked miRNAs and mRNAs (kidney-RNAs) in uEV isolates and to discover stable reference mRNAs across diverse uEV datasets. We studied nine raw and normalized sequencing datasets including healthy controls and individuals with prostate cancer or type 1 diabetes with or without albuminuria. We focused on kidney-RNAs reviewing literature for DKD-linked miRNAs from kidney tissue, cell culture and uEV/urine experiments. RNAs were analyzed by expression heatmaps, hierarchical clustering and selecting stable mRNAs with normalized counts (>200) and minimal coefficient of variation. Kidney-RNAs were decreased after urine storage at -20°C vs -80°C. Isolation workflows captured kidney-RNAs with different efficiencies. Ultracentrifugation captured DKD -linked miRNAs that separated healthy and diabetic macroalbuminuria groups. Eleven mRNAs were stably expressed across the datasets. Hence, preanalytical choices had variable effects on kidney-RNAs – analyzing kidney-RNAs complemented global correlation, which could fade differences in some relevant RNAs. Replicating prior DKD-marker results and discovery of candidate reference mRNAs encourages further uEV biomarker studies.

Spatial biology is a rapidly growing research field which focuses on the transcriptomic or proteomic profiling of single cells within tissues with preserved spatial information. Imaging-based spatial transcriptomics uses epifluorescence microscopy, which has shown remarkable results for identification of multiple targets in situ. Nonetheless, the number of genes that can be reliably visualized is limited by the diffraction of light. Here, we investigate the effect of structured illumination (SIM), a super-resolution microscopy approach, over the performance of single gene transcript detection in spatial transcriptomics experiments. We performed direct mRNA-targeted hybridization in situ sequencing for multiple genes in mouse coronal brain tissue sections. We evaluated spot detection performance in widefield and confocal images versus those with SIM in combination with 20X, 25X and 60X objectives. In general, SIM increases the detection efficiency of gene transcripts spots compared to widefield and confocal modes. For each case, the specific fold increase in localizations is dependent on gene transcripts density and the numerical aperture of the objective used, which showed to play an important role especially for densely clustered spots. Taken together, our results suggest that SIM has the capacity to improve spot detection and overall data quality in spatial transcriptomics.

UPF3B is a constituent of the classical nonsense-mediated mRNA decay (NMD) pathway that degrades both the aberrant transcripts and a set of physiological transcripts. In higher eukaryotes, UPF3B have significant biochemical functions in diverse cellular processes including NMD and translation. UPF3B plays a crucial role in neuronal development and differentiation. Next-generation sequencing technologies identified several loss-of-function mutations in the UPF3B gene that results in neuro-developmental disorders in humans. To uncover the mechanistic role of UPF3B in neuronal functions, we have generated the UPF3B-knockout mammalian cell line model system using CRISPR-Cas9 gene editing method. RNA-Sequencing Analysis of cellular transcriptome from UPF3B-KO cells identified specific genes involved in cell growth and neuronal functions. Altered expression of genes related to the axon guidance pathway delineated the UPF3B function to regulate the neuron-specific genes. Functional enrichment analysis identified the genes involved in the disorders related to mental health and intellectual disability. Our study has the potential to identify the direct players of intellectual disability and will have broader implications.

Meloidogyne javanica causing root-knot nematode in soybean is an important problem in soybean areas, leading to several yield losses. Some accessions have been identified carrying resistance loci to this nematode specie. In this study, a set of 317 soybean accessions were characterized for resistance to M. javanica. Genome-wide association study (GWAS) was performed using SNPs from genotyping-by-sequencing (GBS), and a region of 29.2 Kbp on chromosome 13 was identified. The haplotype analysis showed that SNPs were able to discriminate susceptible and resistant accessions, leading to 25 accessions sharing the resistance locus. Furthermore, 5 accessions may be new M. javanica resistance sources. The screening of the SNPs in the USDA soybean germplasm showed that several accessions previous reported as resistance to other nematodes also showed the resistance haplotype on chromosome 13. High levels of concordance among the phenotypes of Brazilian cultivars and the SNPs in chromosome 13 were observed. A in silico analysis of the mapped region on soybean genome revealed a presence of 5 genes with structural similarity with major resistance genes. The expression levels of the candidate genes in the interval demonstrated a potential pseudogene, and other two model genes up-regulated in the resistance source after pathogen infection. The SNPs associated to the region conferring resistance is a important tool for introgression of the resistance by marker-assisted selection in soybean breeding programs.

Background: Studies employing next-generation sequencing (NGS) show that the oral fungal community (mycobiome) is far more complex than hitherto thought. However, the role of the oral mycobiome in health and disease, including oral carcinogenesis, has not been explored. Objective: To characterize the mycobiome associated with oral squamous cell carcinoma (OSCC). Methods: Tissue biopsies [cases: 25 OSCC; controls: 27 intra-oral fibro-epithelial polyp (FEP)] were collected from oral and maxillofacial units in Sri Lanka. Total DNA was extracted and subjected to sequencing of the fungal ITS2 region using Illumina’s 2x300 bp chemistry. High quality, non-chimeric merged reads were classified to species level using a BLASTN-algorithm with UNITE’s named species sequences as reference. Downstream analyses were performed using QIIME and LEfSe. Results: 364 species representing 160 genera and 2 phyla (Ascomycota and Basidiomycota) were identified, with Candida and Malassezia making up 48% and 11% of the average mycobiome, respectively. However, only 5 species and 4 genera were detected in ≥50% of the samples. The species richness and diversity were significantly lower in OSCC. At the genus level, Candida, Hannaella and Gibberella were overrepresented in OSCC while Alternaria and Trametes were more abundant in FEP. Species-wise, C. albicans, C. etchellsii and Hannaella luteola-like species were enriched in OSCC while Malassezia restricta, Aspergillus tamarii, Alternaria alternate, Cladosporium halotolerans, and Hanseniaspora uvarum-like species were the most significantly abundant in FEP. Conclusions: A dysbiotic mycobiome dominated by C. albicans was found in association with OSCC. Whether this dysbiosis plays a role in oral carcinogenesis warrants further investigation.

Studies on the possible association between bacteria and oral squamous cell carcinoma (OSCC) remain inconclusive, largely due to methodological variations/limitations. The objective of this study was to characterize the species composition as well as functional attributes of the bacteriome associated with OSCC. DNA obtained from 20 fresh OSCC biopsies (cases) and 20 deep-epithelium swabs (matched control subjects) were sequenced for the V1-V3 region using Illumina’s 2x300 bp chemistry. High quality, non-chimeric merged reads were classified to species level using a prioritized BLASTN-algorithm. Downstream analyses were performed using QIIME, PICRUSt, and LEfSe. Fusobacterium nucleatum subsp. polymorphum was the most significantly overrepresented species in the tumors followed by Pseudomonas aeruginosa and Campylobacter sp. Oral taxon 44, while Streptococcus mitis, Rothia mucilaginosa and Haemophilus parainfluenzae were the most significantly abundant in the controls. Functionally, genes involved in bacterial mobility, flagellar assembly, bacterial chemotaxis and LPS synthesis were enriched in the tumors while those responsible for DNA repair and combination, purine metabolism, phenylalanine, tyrosine and tryptophan biosynthesis, ribosome biogenesis and glycolysis/gluconeogenesis were significantly associated with the controls. This is the first epidemiological evidence for association of F. nucleatum and P. aeruginosa with OSCC. Functionally, an “inflammatory bacteriome” is enriched in OSSC.

Keywords: Requirements prioritization, Next release plan, Software product planning, Decision support, MoSCoW, AHP, K-Menas, GMM, BIRCH, PAM, Hierarchical, Clustering, Clusters Evaluation

To maximize resource usage, minimize disputes, and improve academic experience, professors must schedule teaching activities efficiently. This study provides an optimized automated schedule creation technique. The system generates conflict-free, efficient schedules using evolutionary algorithms and multi-objective optimization. Resource usage, scheduling prob-lems, and faculty/student satisfaction are the goals of the research. The system optimizes scheduling based on room limitations, instructor availability, and student preferences. The project uses system design, model creation, algorithm implementation, and performance analysis to solve the difficult timetable-generating problem. This research should save administrators time, improve academic operations, and improve staff and student academic experiences. Scalability and flexibility allow the system to be used in multiple faculties and incorporate new limits and requirements. This paper presents a complete approach to faculty scheduling, including insights and recommendations for future study and application in educational institutions.

The bulk LiNa5Mo9O30 (LNM) crystals were successfully grown in the [010] and [001] directions without internal inclusions and cracks by the Czochralskii method with low temperature gradient. The crystal grown in the [010] direction showed a tendency to twinning. The crystal grown in the [001] direction demonstrated high structural perfection (FWHM=13'') for the (001) plane and high optical quality Δn≈210-5. The laser induced damage threshold was measured along a, b and c axes and was 15.7, 27.0 and 27.5 J/cm2 respectively. The thermo-optical coefficient dn/dT was measured for the main crystallographic axes, which was -5.7510-6, -2.0210-5 and 3.6510-6 K-1 along the a, b and c axes, respectively. The second harmonic generation (SHG) is conducted in crystalline LNM sample. The maximum efficiency value of 3.5% at pump power of 12 W had been achieved.

The solar energy can mitigate the power supply shortage in remote regions for portable irrigation systems. The accurate prediction of solar irradiance is crucial for determining the quantity of solar panels and the power capacity of photovoltaic power generation (PVPG) systems for mobile sprinkler machines. In this study, a prediction method is proposed to forecast the solar irradiance of typical irrigation areas. The impact of the meteorological parameters on the solar irradiance is studied, and four different parameter combinations are formed and considered as input to the prediction model. Based on meteorological data provided by ten typical radiation stations uniformly distributed nationwide, an Extreme Gradient Boosting (XGBoost) model optimized using the Whale Optimization Algorithm (WOA) is developed to predict the solar radiation. The prediction accuracy and stability of the proposed method are then evaluated for different input parameters through training and testing. The differences between the prediction performances of models trained based on single-station data and mixed data from multiple stations are also compared. The obtained results show that the proposed model achieves the highest prediction accuracy when the maximum temperature, minimum temperature, sunshine hours ratio, relative humidity, wind speed, and extraterrestrial radiation are used as the input parameters. The prediction effectiveness is also verified based on measured data. The WOA-XGBoost model has higher prediction accuracy than the XGBoost model, with predicted values falling within acceptable ranges. The model developed using mixed data of multiple stations can be applied for forecasting solar irradiance in different regions. This study provides a foundation for the optimization of the configuration of PVPG systems for mobile sprinkler machines.

A two-qubit Hamiltonian model exhibiting an SU(2) symmetry is considered. The related dynamical problem results to be then exactly solvable both in the time-independent and in the time-dependent case. Basing on the formal, general form of the related time evolution operator, the time-dependence of the level of entanglement of some initial conditions is studied within both the Rabi and the Landau-Majorana-Stückelber-Zener scenarios.

Ensuring precise segmentation of colorectal polyps holds critical importance in the early diagnosis and treatment of colorectal cancer. Nevertheless, existing deep learning-based segmentation methods are fully-supervised, requiring extensive precise manual pixel-level annotation data, which leads to high annotation costs. Additionally, it remains challenging to train large-scale segmentation models when confronted with limited colonoscopy data. To address these issues, we introduce the general segmentation foundation model—Segment Anything Model (SAM) into the field of medical image segmentation. Fine-tuning the foundation model is an effective approach to tackle sample scarcity. However, current SAM fine-tuning techniques still rely on precise annotations. To overcome this limitation, we propose WSPoly-SAM, a novel weakly-supervised approach for colonoscopy polyp segmentation. WSPoly-SAM utilizes weak annotations to guide SAM in generating segmentation masks, which are then treated as pseudo-labels to guide the fine-tuning of SAM, thereby reducing the dependence on precise annotations data. To improve the reliability and accuracy of pseudo-labels, we have designed a series of enhancement strategies to improve the quality of pseudo-labels and mitigate the negative impact of low-quality pseudo-labels. Experimental results on five medical image datasets demonstrate that WSPoly-SAM outperforms current fully-supervised mainstream polyp segmentation networks on the Kvasir-SEG, ColonDB, CVC-300, and ETIS datasets. Furthermore, by using different amounts of training data in weakly-supervised and fully-supervised experiments, it is found that weakly-supervised fine-tuning can save 70% to 73% of annotation time costs compared to fully-supervised fine-tuning. This study provides a new perspective on the combination of weakly-supervised learning and SAM models, significantly reducing annotation time and offering insights for further development in the field of colonoscopy polyp segmentation.

There are important challenges for modeling large electrical distribution circuits, even more with the presence of distributed renewable generation. Constructing simulations for assessing the effect of the penetration of distributed generation on electrical distribution networks has become of great importance for Distribution Network Operators (DNOs). This paper proposes a simulation strategy based on open-source platforms and the integration of scripting tools for the rapid modeling of large-scale electrical distribution circuits with distributed renewable generation. The implementation is based on the adaptation of a tool called QGIS2OpenDSS, which creates OpenDSS distribution network models directly from an open-source geographic information system, QGIS. The plugin’s capabilities are demonstrated using a real distribution feeder with more than 60% penetration of renewable generation based on photovoltaic systems. These simulations are carried out using real data from a circuit provided by a DNO in the Dominican Republic, which is used to demonstrate how this approach provides a more accessible and flexible way to simulate and assess the effect of Distributed Energy Resources (DERs) in Medium Voltage (MV) and Low Voltage (LV) networks, enabling utilities to evaluate system performance and identify potential issues.

Natural products contribute substantially to anticancer therapy; the plant kingdom provides an important source of molecules. Conofolidine is a novel Aspidosperma-Aspidosperma bisindole alkaloid isolated from the Malayan plant Tabernaemontana corymbosa. Herein, we report conofolidine’s anticancer activity together with that of three other bisindoles - conophylline, leucophyllidine and bipleiophylline against human-derived carcinoma cell lines. Remarkably, conofolidine was able to induce apoptosis (as observed in MDA-MB-468 breast cancer cells) or senescence (as detected in HT-29 colorectal carcinoma cells). Annexin V-FITC/PI, caspase activation and PARP cleavage confirmed the former while positive β-gal staining corroborated the latter. Evident cell cycle perturbations were observed comprising S-phase depletion, accompanied by downregulated CDK2, and cyclins (A2, D1) with p21 upregulation. Confocal imaging of HCT-116 cells revealed induction of aberrant mitotic phenotypes - multi-nucleation, membrane blebbing and DNA-fragmentation. The DNA integrity assessment of HCT-116 and MDA-MB-468 showed irreparable damage identified by increased fluorescent γ-H2AX during the G1 cell cycle phase. Furthermore, γ-H2AX foci were visually validated in HCT-116 and MDA-MB-468 cells using confocal microscopy. Conofolidine increased oxidative stress, preceding apoptosis- and senescence-induction in most carcinoma cell lines as seen by enhanced ROS levels accompanied by NQO1 expression. Collectively, we present conofolidine as a potential anticancer candidate capable of inducing heterogeneous modes of cancer cell death in vitro, encouraging further preclinical evaluation of this natural product.

Industrial Control Systems (ICS) are integral to managing and optimizing processes in various industries, including manufacturing, power generation, and more. However, the scarcity of widely adopted ICS datasets hampers research efforts in areas like optimization and security. This scarcity arises due to the substantial cost and technical expertise required to create physical ICS environments. In response to these challenges, this paper presents a groundbreaking approach to generating synthetic ICS data through a data-driven ICS network simulation. We circumvent the need for expensive hardware by recreating the entire ICS environment in software. Moreover, rather than manually replicating the control logic of ICS components, we leverage existing data to autonomously generate control logic. The core of our method involves the stochastic setting of setpoints, which introduces randomness into the generated data. Setpoints serve as target values for controlling the operation of the ICS process. This approach enables us to augment existing ICS datasets and cater to the data requirements of machine learning-based ICS intrusion detection systems and other data-driven applications. Our simulated ICS environment employs virtualized containers to mimic the behavior of real-world PLCs and SCADA systems, while control logic is deduced from publicly available ICS datasets. Setpoints are generated probabilistically to ensure data diversity. Experimental results validate the fidelity of our synthetic data, emphasizing its ability to closely replicate temporal and statistical characteristics of real-world ICS networks. In conclusion, this innovative data-driven ICS network simulation offers a cost-effective and scalable solution for generating synthetic ICS data. It empowers researchers in the field of ICS optimization and security with diverse, realistic datasets, furthering advancements in this critical domain. Future work may involve refining the simulation model and exploring additional applications for synthetic ICS data.

In this paper, we propose a method for the generation of simulation test scenarios for autonomous driving. Based on the requirements of standard regulatory test scenarios, we can generate virtual simulation scenarios and functional scenario libraries for autonomous driving, which can be used for simulation verification of different ADAS functions. Firstly, the design operation domain (ODD) of the functional scenario is selected and the weight values of the ODD elements taken are calculated; then a combination test algorithm based on parameter weights is improved to generate autonomous driving virtual simulation test cases for the ODD elements, which can effectively reduce the number of generated test cases compared with the traditional combination test algorithm; then the traffic participant elements under each test case are Then, the values of the subelements under the traffic participant element in each test case are sampled and clustered to obtain hazard specific scenarios. Finally, the specific scenarios are applied to the automatic emergency braking (AEB) system on the model-in-the-loop (MIL) testbed to verify the effectiveness of this scenario generation method.

A novel approach was developed that combined LP-based row generation with optimization-based sorting to tackle computational challenges posed by budget allocation problems with combinatorial constraints. The proposed approach dynamically generated constraints using row generation and prioritized them using optimization-based sorting to ensure a high-quality solution. Computational experiments and case studies revealed that as the problem size increased, the proposed approach outperformed simplex solutions in terms of solution search time. Specifically, for a problem with 50 projects (N = 50) and 2,251,799,813,685,250 constraints, the proposed approach found a solution in just 1.4 seconds, while LP failed due to the problem size. The proposed approach demonstrated enhanced computational efficiency and solution quality compared to traditional LP methods.

The optimal regime of three-photon resonant excitation of a helium atom by a femtosecond ul-traviolet (UV) pulse was discovered and numerically studied, at which the maximum power of the third harmonic of the UV field is achieved in the spectrum of dipole acceleration (the second time derivative of the induced dipole moment) of the atom. It is shown that the optimal frequency of the UV field nearly coincides with the frequency of the three-photon transition 1s2 – 1s2p, taking into account its shift as a result of the dynamic Stark effect, and the intensity of the UV field is dictated by the condition of maximizing the product of the populations of the 1s2 and 1s2p states, averaged over the time interval during which the UV field is non-zero. For the considered UV field durations, from 10 to 100 cycles of the carrier frequency (from units to tens of femtoseconds), the optimal intensity lies in the range from 10^14 W/cm^2 to several units of 10^14 W/cm^2. It is shown that with an optimal choice of the frequency and intensity of the UV field, the dynamics of excitation of bound and continuum states, as well as the shape of the time envelope of the dipole acceleration of the atom, weakly depend on the duration of the UV field envelope; only their time scale changes significantly. In addition, under the optimal conditions the average power of the third harmonic signal in the dipole acceleration spectrum is practically independent of the duration of the UV field envelope.

We present results of the detected voltage distribution of a quantum random number generator based on the photonic integration of semiconductor laser, delay interferometer and photodetector. We find that the integrated system behaves as expected for random number generation from gain-switched laser sources. The biggest advantage is that only electrical connections are needed to operate the system without the need for tricky and expensive optical alignment to external circuitry. We supply results showing that random bit stream created from the random numbers passes the NISTS statistical test suite tests; thus demonstrating the feasibility to generate random number via quantum means at gigabit/s rates from a single photonic integrated circuit. The results are backed by numerical simulations.

This study presents a comprehensive investigation of entropy generation during natural convection in a porous medium with Casson fluid. The governing equations, including the momentum, energy, and entropy balance equations, are solved numerically using finite element method. Through the analysis, various parameters affecting entropy generation are investigated, such as the Casson fluid parameter, Radiation, Prandtl number, and Rayleigh number. The results indicate that the Casson fluid parameter significantly influences the flow and heat transfer characteristics, while the Darcy number and Rayleigh number control the intensity of natural convection. Moreover, the Prandtl number determines the relative significance of heat transfer compared to viscous effects.

- Power grids of the future will likely incorporate more renewable energy distributed generation (REDG), also known as alternative energy systems. REDG units are increasingly being used in electrical transmission networks because to the overall positive effects they have on power networks. REDG systems are the backbone of smart electric networks and are essential to the operation of the smart grid. These REDG systems can additionally improve system reliability by providing some customers with a backup generator in the event of power interruptions. The report offers a thorough evaluation of the existing body of information on the topic of electric vehicles' (EVs') future interactions with the smart grid. The combination of the EVs' potential deployment and the smart grid's conceptual goal presents challenges in electric grid infra-structure, communication, and control. The proposal for connecting EVs to the grid is based on research into cutting-edge smart metering and communication systems. In the context of the vehicle-to-grid (V2G) phenomenon, the possibilities, benefits, and limitations of various EV smart charging systems are also fully examined. There is a fast growing percentage of distributed energy that is wind and solar photovoltaic. Their variable power output, however, introduces fresh challenges for those responsible for organizing, operating, and controlling the power grid. While fluctuations in the electric grid are problematic, they may be mitigated by the entry of EVs into the energy market. As such, we do a comprehensive literature search to learn more about the exciting research gap that needs to be filled and the most recent possible answer that involves EVs. Additionally, we take a close look at the practicality of the sophisticated V2G technology. The smart grid is a developing concept for the world's energy infrastructure, and this study analyzes in depth how EVs interact with it.

Nowadays, energy decarbonization due to the integration of renewable energy sources presents important challenges to overcome. The intermittent nature of photovoltaic systems reduces power quality by producing voltage variations and frequency deviations in electrical systems networks, especially, in weak and isolated distribution systems in developing countries. This paper presents a power smoothing method improving the low pass filter and moving average for grid-connected photovoltaic system, the novel method includes state-of-charge monitoring control of the supercapacitors energy storage system to reduce the fluctuations of photovoltaic power at the point of common coupling. A case study for a microgrid in a high-altitude city in Ecuador is presented with exhaustive laboratory tests using real data. The ultimate goal of this research is to improve energy power quality in electrical distribution systems to cope with the growth of renewable penetration.

The battery energy storage systems (BESS) are most promising solution for increasing efficiency and flexibility of distribution networks (DNs) with significant penetration level of photovoltaic (PV) systems. There are various issues related to the optimal operation of DNs with integrated PV sys-tems and BESS that need to be addressed to maximize DN performance. This paper deals with the day-ahead optimal active-reactive power dispatching in unbalanced DNs with integrated sin-gle-phase PV generation and BESSs. The objectives are the minimization of cost for electricity, en-ergy losses in the DN and voltage unbalance at three-phase load buses by optimal managing of active and reactive power flows. To solve this highly constrained nonlinear optimization problem, a hybrid particle swarm optimization with sigmoid-based acceleration coefficients (PSOS) and cha-otic gravitational search algorithm (CGSA), called PSOS-CGSA algorithm, is proposed. A scenario based approach encompassing the Monte Carlo simulation (MCS) method with the simultaneous backward reduction algorithm is used for the probabilistic assessment of the uncertainty of PV generation and power of loads. The effectiveness of the proposed procedure is evaluated through the series test cases in a modified IEEE 13-bus feeder.

“A Picture is worth a thousand words”. Given an image, humans are able to deduce various cause-and-effect captions of past, current, and future events beyond the image. The task of visual commonsense generation aims at generating three cause-and-effect captions (1) what needed to happen before, (2) what is the current intent, and (3) what will happen after for a given image. However, such a task is challenging for machines owing to two limitations: existing approaches (1) directly utilize conventional vision-language transformers to learn relationships between input modalities, and (2) ignore relations among target cause-and-effect captions but consider each caption independently. We propose Cause-and-Effect BART (CE-BART) which is based on (1) Structured Graph Reasoner that captures intra- and inter-modality relationships among visual and textual representations, and (2) Cause-and-Effect Generator that generates cause-and-effect captions by considering the causal relations among inferences. We demonstrate the validity of CE-BART on VisualCOMET and AVSD benchmarks. CE-BART achieves SOTA performances on both benchmarks, while extensive ablation study and qualitative analysis demonstrate the performance gain and improved interpretability.

Generation of unique identifiers extracted from the physical characteristics of the underlying hardware ensures the protection of electronic devices against counterfeiting and provides security to the data they store and process. This work describes the design of an efficient Physical Unclonable Function (PUF) based on the differences in the frequency of Ring Oscillators (ROs) with identical layout due to variations in technological processes involved in the manufacture of the integrated circuit. The logic resources available in the Xilinx Series-7 programmable devices are exploited in the design to make it more compact and achieve an optimal bit-per-area rate. On the other hand, the design parameters can also be adjusted to provide a high bit-per-time rate for a particular target device. The PUF has been encapsulated as a configurable Intellectual Property (IP) module, providing it with an AXI4-Lite interface to ease its incorporation into embedded systems in combination with soft- or hard-core implementations of general-purpose processors. The capability of the proposed RO-PUF to generate implementation-dependent identifiers has been extensively tested, using a series of metrics to evaluate its reliability and robustness for different configuration options. Finally, in order to demonstrate its utility to improve system security, the identifiers provided by RO-PUFs implemented on different devices have been used in a Helper Data Algorithm (HDA) to obfuscate and retrieve a secret key.

Ayurveda as a healthcare system has survived for thousands of years but continues to be dogged by reported lack of efficacy of the treatments in clinical trials. The reported lack of efficacy could be due to a real lack of efficacy (which then contradicts the survival of Ayurveda as a functional medical system enjoying considerable public patronage) or could be attributed to inadequacies in the efforts towards evidence generation or in a larger context the overall scientific conduct of research in Ayurveda. In an effort towards better evidence generation, there is an immediate need for standardizing the design, conduct and reporting of clinical trials of Ayurveda but it is a daunting task. For this effort to benefit the scientific endeavors of Ayurveda researchers, it should allow the researchers to be able to apply Ayurveda’s multi-component, individualized and inherently holistic approach. Statistical principles can benefit this effort. Statistical hypothesis testing (SHT) is central to these statistical principles and also aligns well with conventional scientific principles of evidence generation. Although there are challenges with SHT, good practitioners engaged in it do much more than just apply the mathematical theory behind it. As a particular example, lot of time in clinical trial designing is spent in addressing biases and designing trials prudently by minimizing the effect of such biases. SHT can benefit such an effort objectively. There is a need for Ayurveda researchers to engage deeply and mindfully about biases in study design in order to gain scientific validity and acceptability. The article highlights issues that arise in Ayurveda research, and discusses few ways of dealing with these issues using statistical principles.

Photovoltaic power generation has high variability and uncertainty because it is affected by uncertain factors such as weather conditions. Therefore, probabilistic forecasting is useful for optimal operation and risk hedging in power systems with large amounts of photovoltaic power generation. However, deterministic forecasting is the mainstay of photovoltaic generation forecasting; there are few studies on probabilistic forecasting and feature selection from weather or time-oriented features in such forecasting. In this study, prediction intervals were generated by the lower upper bound estimation using neural networks with two outputs to make probabilistic predictions. The objective was to improve prediction interval coverage probability (PICP), mean prediction interval width (MPIW), and loss, which is the integration of these two metrics, by removing unnecessary features through feature selection. When features with high gain were selected by random forests (RF), in the forecast of 14.7-kW PV systems, loss improved by 1.57 kW, PICP by 0.057, and MPIW by 0.12 kW on average over two weeks compared to the case where all features were used without feature selection. Therefore, the low gain features from RF act as noise in LUBE and reduce the prediction accuracy.

The fifth Generation (5G) mobile networks use millimeter Waves (mmWaves) to offer giga bit data rates. However, unlike microwaves, mmWave links are prone to user and topographic dynamics. They easily get blocked and end up forming irregular cell patterns for 5G. This in turn cause too early, too late, or wrong handoffs (HOs). To mitigate HO challenges, sustain connectivity and avert unnecessary HO, we propose a HO scheme based on Jump Markov Linear System (JMLS) and Deep Reinforcement Learning (DRL). JMLS is widely known to account for abrupt changes in system dynamics. DRL likewise emerges as an artificial intelligence technique for learning highly dimensional and time-varying behaviors. We combine the two techniques to account for time-varying, abrupt, and irregular changes in mmWave link behaviour by predicting likely deterioration patterns of target links. The prediction is optimized by meta training techniques that also reduces training sample size. Thus, the JMLS-DRL platform formulates intelligent and versatile HO policies for 5G. Results show our proposed prediction scheme about target link behavior post HO to be highly reliable. The scheme also averts unnecessary HOs thus ably supports longer dew time.

This paper provides a methodology for the production of synthetic images for training neural networks to recognise shapes and objects. There are many scenarios in which it is difficult, expensive and even dangerous to produce a set of images that is satisfactory for the training of a neural network. The development of 3D modelling software has nowadays reached such a level of realism and ease of use that it seemed natural to explore this innovative path and to give an answer regarding the reliability of this method that bases the training of the neural network on synthetic images. The results obtained in the two proposed use cases, that of the recognition of a pictorial style and that of the recognition of migrants at sea, leads us to support the validity of the approach, provided that the work is conducted in a very scrupulous and rigorous manner, exploiting the full potential of the modelling software. The code produced, which automatically generates the transformations necessary for the data augmentation of each image, and the generation of random environmental conditions in the case of Blender and Unity3D software, is available under the GPL licence on GitHub. The results obtained lead us to affirm that through the good practices presented in the article, we have defined a simple, reliable, economic and safe method to feed the training phase of a neural network dedicated to the recognition of objects and features, to be applied to various contexts.

In this paper we propose the generation of synthetic small and more sophisticated molecule structures that optimize the binding affinity to a target (ASYNT-GAN). To achieve this we leverage on three important achievements in A.I.: Attention, Deep Learning on Graphs and Generative Adversarial Networks. Similar to text generation based on parts of text we are able to generate a molecule architecture based on an existing target. By adopting this approach, we propose a novel way of searching for existing compounds that are suitable candidates. Similar to question and answer Natural Language solutions we are able to find drugs with highest relevance to a target. We are able to identify substructures of the molecular structure that are the most suitable for binding. In addition, we are proposing a novel way of generating the molecule in 3D space in such a way that the binding is optimized. We show that we are able to generate compound structures and protein structures that are optimised for binding to a target.

The deregulated markets have replaced the traditional way of trading electricity from the producers to the consumer, via government-owned organizations and regulated tariffs. Nowadays, electricity prices are determined by the offer-demand mechanism and consumers can negotiate tariffs with their supplier of choice. For classic wholesale suppliers, the tariffs are a result of the transactions performed on the wholesale market and the energy mix available in certain geographical regions. In parallel with becoming eligible and participating in retail electricity markets, the consumers use increasingly local generation sources based mostly on renewable energy technologies such as PV panels, and become prosumers. They want to be able to sell back to the market the generation surplus, in order to obtain the maximum benefits from their initial investment. Currently, several trading mechanisms for prosumers are available, ranging from the simplest, selling back the surplus to an aggregator at fixed tariffs, to more complex market schemes. This paper proposed a two-tier local market model for prosumers and consumers connected in microgrids, based on the blockchain technologies and other technologies and concepts such as remote sensing, smart grids, crowdsourcing and energy poverty.

The deregulated markets have replaced the traditional way of trading electricity from the producers to the consumer, via government-owned organizations and regulated tariffs. Nowadays, electricity prices are determined by the offer-demand mechanism and consumers can negotiate tariffs with their supplier of choice. For classic wholesale suppliers, the tariffs are a result of the transactions performed on the wholesale market and the energy mix available in certain geographical regions. In parallel with becoming eligible and participating in retail electricity markets, the consumers use increasingly local generation sources based mostly on renewable energy technologies such as PV panels, and become prosumers. They want to be able to sell back to the market the generation surplus, in order to obtain the maximum benefits from their initial investment. Currently, several trading mechanisms for prosumers are available, ranging from the simplest, selling back the surplus to an aggregator at fixed tariffs, to more complex market schemes. This paper proposed a two-tier local market model for prosumers and consumers connected in microgrids, based on the blockchain technologies and other technologies and concepts such as remote sensing, smart grids, crowdsourcing and energy poverty.

Fossil fuels and carbon origin resources are affecting our environment. Therefore, alternative energy sources have to be established to co-produce energy along with fossil fuels and carbon origin resources until it is the right time to replace them. Microbial Fuel Cell (MFC) is a promising technology in the field of energy production. Compared to the conventional power sources it is more efficient and not controlled by the Carnot cycle. Its high efficiencies, low noise, and less pollutant output could make it revolutionize in the power generation industry with a shift from centrally located generating stations and long-distance transmission lines to dispersed power generation at load sites. In this review, several characteristics of the MFC technology will be highlighted. First, a brief history of abiotic to biological fuel cells and subsequently, microbial fuel cells is presented. Second, the focus is then shifted to elements responsible for the making MFC working with efficiency. Setup of the MFC system for every element and their assembly is then introduced, followed by an explanation of the working machinery principle. Finally, microbial fuel cell designs and types of main configurations used are presented along with scalability of the technology for the proper application.

Plasma being the fourth and most abundant form of matter extensively exists in the universe in the inter-galactic regions. It provides an electrically neutral medium of unbound negative and positive charged particles, which has been produced by subjecting air and various other gaseous mixtures to strengthen the electromagnetic field and by heating compressed air or inert gasses for creating negative and positive charged particles known as ions. Nowadays, many researchers are paying attention to the formation of artificial Plasma and its potential benefits for mankind. The literature is sparsely populated with the applications of Plasma. This paper presents specific methods of generation and applications of Plasma, which benefits humankind in various fields, such as in electrical, mechanical, chemical and medical fields. These applications include hydrogen production from alcohol, copper bonding, semiconductor processing, surface treatment, Plasma polymerization, coating, Plasma display panels, antenna beam forming, nanotechnology, Plasma Torch, Plasma pencils, low-current non-thermal Plasmatron, treatment of prostate cancer, Plasma source ion implantation, cutting by Plasma, Plasma etching, pollution control, neutralization of liquid radioactive waste, etc. Resultantly, worth of Plasma technology in the medical industry is increasing exponentially that is closing the gap between its benefits and cost of equipment used for generating and controlling it.

In this work we present the results obtained with an artificial neural network (ANN) which we trained to predict the expected output of high-order harmonic generation (HHG) process, while exploring a multi-dimensional parameter space. We argue on the utility and efficiency of the ANN model and demonstrate its ability to predict the outcome of HHG simulations. In this case study we present the results for a loose focusing HHG beamline, where the changing parameters are: the laser pulse energy, gas pressure, gas cell position relative to focus and gas cell length. The physical quantity which we predict here using ANN is directly related to the total harmonic yield in a specified spectral domain (20-40 eV). We discuss the versatility and adaptability of the presented method.

Agricultural product like cassava produces huge amounts of waste when processed to consumable goods. The waste generated is generally considered to contribute largely to environmental pollution. This study therefore investigates the waste management practice that is adopted by cassava processors in Ogun State, Nigeria. Five (5) Local Government Areas (LGAs) dominant in processing cassava were selected for the study on the basis of spatial location distribution; landmass and population. The survey involved the use of structured questionnaires administered to cassava processors of the selected LGAs. The Statistical Package for Social Sciences (SPSS) software application and descriptive statistics were used for data analysis. Results of the analysis show that majority (70%) of the cassava processors are females. Cassava peel constitutes 10% of the waste produced, of which 91% are heaped at refuse dump in most communities. Results also reveal that 86.3% of cassava residues are used for animal feeds. Other findings show that the peels when dried are used as bio-fuel for cooking and there is a significant potential for biogas production. From the data captured from respondent during the study, most processors are willing to pay for an improved waste management system. The study therefore suggests proper waste management of cassava waste to minimize environmental pollution.

The numerical simulation of entropy generation at mixed natural free and forced convection flow in a square lid-driven cavity filled with an eletrically conducting binary fluids saturated porous media in the presence of a magnetic field is performed in this investigation. Both the top and bottom horizontal walls of the cavity are kept at constant and different temperatures while the left and right vertical walls are adiabatic and insulated. The magnetic field is applied in normal direction to the cavity. The Darcy model, including Brinkman term relative to viscous effects and Forchheimer term due to inertial forces, is used for the momentum equations, and the SIMPLER algorithm, based on the control volume finite-element method approach is used to solve the pressure –velocity coupling. The flow pattern and the heat transfer characteristics inside the cavity are presented in the form of isotherms, streamlines and isentropic lines and average Nusselt number trend. The entropy generation rate is determined for various values of Darcy number (10^-3 ≤ Da ≤ 1) and for a range of Hartmann number (0 ≤ Ha ≤ 10²). This study is limited to Pr = 7 which corresponds to water. It is observed that entropy generation is very affected by the variations of dimensionless control parameters considered.

This study explores the integration of genome sequencing and polymerase chain reaction (PCR) based methods for tracking the diversity of Covid-19 variants in wastewater. The research focuses on monitoring various Omicron subvariants during a period of significant viral evolution. Genome sequencing, particularly using Oxford Nanopore Technology (ONT), provides a detailed view of emerging variants, surpassing the limitations of PCR-based detection kits that rely on known sequences. This study highlights the effectiveness of combining these methodologies to enhance early detection and inform public health strategies, especially in regions with limited clinical sequencing capabilities.

Glioma is the most common primary malignant brain tumors. Despite advances in surgical techniques and treatment regimens, the therapeutic effects remain unsatisfactory. Immunotherapy has brought new hope to glioma patients, but the immunosuppressive nature of the tumor microenvironment (TME) has limited the therapeutic outcomes. This study aims to reveal the subpopulations and functional characteristics of tumor-associated macrophages (TAMs), to explore the regulatory effects of chlorogenic acid on the immune microenvironment, and investigate its potential for clinical application. In this study, Chlorogenic acid slowed tumor growth in the mouse model and extended the survival time of the mice. ScRNA-seq analysis elucidated the differentiation trajectories and functional characteristics of BMDMs and microglia, revealing the regulatory effects of chlorogenic acid on these two populations. It enhanced the antigen-presenting function of macrophages and the gene expression related to T-cell immune activation, activated microglia through the JAK-STAT pathway, and improved antitumor functions. Using the target prediction database, a PPI and molecular docking model were constructed, confirming the good affinity of chlorogenic acid with STAT1. A case of a patient treated with chlorogenic acid was reviewed, who survived for 5 years and 6 months, achieved partial remission (PR) after 9 months of treatment, and remains alive with no new symptoms or toxic side effects. Our study revealed the subtypes and differentiation trajectories of tumor-associated macrophages. Chlorogenic acid significantly improved the immune microenvironment of glioma by modulating the function of bone marrow-derived mono-macrophages (BMDMs) and microglia, demonstrating good antitumor effects. This study provides new insights for targeting the regulation of the TME and offers theoretical and practical support for the clinical application of chlorogenic acid.

Single-cell RNA sequencing (scRNA-seq) technique has enabled detailed analysis of gene expression at the single cell level, enhancing the understanding of subtle mechanisms that underly pathologies and drug resistance. To derive such biological meaning from sequencing data in oncology, some critical processing must be performed, including identification of the tumor cells by markers and algorithms that infer copy number variations (CNVs). We compared the performance of sciCNV, InferCNV, CopyKAT and SCEVAN tools that identify tumour cells by inferring CNVs from scRNA-seq data. Sequencing data from Pancreatic Ductal Adenocarcinoma (PDAC) patients, adjacent and healthy tissues were analysed, and the predicted tumor cells were compared to those identified by well-assessed PDAC markers. Results from InferCNV, CopyKAT and SCEVAN overlapped by less than 30% with InferCNV showing the highest sensitivity (0.72) and SCEVAN the highest specificity (0.75). We show that the predictions are highly dependent on the sample and the software used, and that they return so many false positives hence are of little use in verifying or filtering predictions made via tumor biomarkers. We highlight how critical this processing can be, warn against the blind use of these software and point out the great need for more reliable algorithms.

A 23-month-old neutered male dog of unknown ancestry presented with a history of progressive neurological signs that included anxiety, cognitive impairment, tremors, seizure activity, ataxia, and pronounced visual impairment. The clinical signs were accompanied by global brain atrophy. Due to progression in the severity of disease signs, the dog was euthanized at 26 months of age. Examination of tissues collected at necropsy revealed dramatic intracellular accumulations of autofluorescent inclusions in the brain, retina, and cardiac muscle. The inclusions were immunopositive for subunit c of mitochondrial ATP synthase, and their ultrastructural appearances were similar to those of lysosomal storage bodies that accumulate in some of the neuronal ceroid lipofuscinosis (NCL) diseases. The also dog exhibited widespread neuroinflammation. Based on these findings, the dog was deemed likely to have suffered from a form of NCL. A whole genome sequence analysis of the proband’s DNA revealed a homozygous C to T substitution that altered the intron 3 – exon 4 splice site of CLN6. Other mutations in CLN6 cause NCL diseases in humans and animals including dogs. CLN6 protein was undetectable with immunolabeling in the tissues of the proband. Based on the clinical history, fluorescence and electron-microscopy, immunohistochemistry, and molecular genetic findings, the disorder in this dog is classified as an NCL resulting from the absence of CLN6 protein. Screening the dog’s genome for a panel of breed-specific polymorphisms indicated that its ancestry included numerous breeds, with no single breed predominating. This suggests that the CLN6 disease variant is likely to be present in other mixed breed dogs and at least some of the ancestral breeds.

The present study aimed to determine the genetic diversity of isolates of Mycobacterium tuberculosis (Mtb) from presumed drug resistant tuberculosis patients from several states of Brazil. The isolates had been submitted to conventional drug susceptibility testing for first- and second-line drugs. Multidrug-resistant (MDR-TB) (54.8%) was the most frequent phenotypic resistance profile, in addition to an important high frequency of pre-extensively resistant (p-XDR-TB) (9.2%). Using whole-genome sequencing (WGS), we characterized 298 Mtb isolates from Brazil. Besides analysis of genotype distribution and possible correlations between molecular and clinical data, we determined the performance of an in-house WGS pipeline with other online pipelines for Mtb lineages and drug resistance profile definition. Sub-lineage 4.3 (52%) was the most frequent genotype and the genomic approach revealed a p-XDR-TB level of 22.5%. We detected 20 novel mutations in three resistance genes, and six of these were observed in eight phenotypically resistant isolates. Cluster analysis of 170 isolates showed that 43.5% of the TB patients belonged to 24 genomic clusters, suggesting considerable ongoing transmission of DR-TB, including two interstate transmissions. The in-house WGS pipeline showed the best overall performance in drug-resistance prediction, presenting the best accuracy values to five of nine drugs tested. Significant associations were observed between suffering from fatal disease and genotypic p-XDR-TB (p = 0.03) and either phenotypic (p = 0.006) and genotypic (p = 0.0007) ethambutol resistance. The use of WGS analysis improved our understanding of the population structure of MTBC in Brazil, genetic and clinical data correlations and demonstrating its utility for surveillance efforts for spread of DR-TB, hopefully helping to avoid the emergence of even more resistant strains and to reduce TB incidence and mortality rates.

Circulating tumor cells (CTCs) are cells released from the primary tumor and intravasate into the blood or lymphatic vessels, where they are transported to distant sites and act as seeds that initiate cancer metastases. Recent advances in CTC research have shown their relevance as prognostic markers for early and metastatic disease detection, predictive biomarkers for relapse, and response to medical intervention or therapy. The rapidly evolving landscape of CTC biology has opened new avenues for understanding cancer progression, metastasis, and treatment response. Additionally, translating these findings into clinical applications holds promise for improving cancer diagnostics, prognosis, and personalized therapeutic strategies. This review discusses the significance of CTCs in cancer research and their associated challenges. We explore recent developments in the detection and characterization of CTCs and their implications in cancer research and clinical practice.

Colletotrichum lini is a flax fungal pathogen, comprising differently virulent strains. In this work, we sequenced the genomes of three C. lini strains of different virulence: high virulent #390-1, medium virulent #757, and low virulent #771. We obtained more than 100× genome coverage with Oxford Nanopore Technologies reads and more than 50× genome coverage with Illumina data. To construct high-quality genomes, several assembly strategies were tested. The final assemblies were obtained using the Canu – Racon ×2 – Medaka – Polca scheme and were 54.0-55.3 Mb in length, contained 26-32 contigs, had an N50 > 5 Mb and the BUSCO completeness > 96%. Comparative analysis of the constructed genomes showed high similarity between mitochondrial and nuclear genomes. However, a rearrangement event and the loss of a 0.9 Mb contig were revealed. The candidate effectors were predicted in sets of proteins received after genome annotation. The obtained genome sequences of C. lini strains extend the knowledge on the genetic diversity of Colletotrichum species and are the basis for establishing molecular mechanisms of pathogenicity of the flax pathogen and effective control of flax anthracnose.

Methicillin-resistant Staphylococcus aureus (MRSA) constitutes a serious public health concern with a considerable impact on patients' health and substantial healthcare costs. In this study, patients, and healthcare workers (HCWs)from six public hospitals in Benin were screened for MRSA. Strains were identified as MRSA using conventional microbiological methods in Benin and confirmed using Matrix-Assisted Laser Desorption/Ionization Time of Flight (MALDI-TOF) mass spectrometry in Belgium. Whole genome sequencing (WGS) was used on confirmed MRSA isolates to characterize their genomic content and study their relatedness. In total,304 and 61 samples were collected from patients and HCWs, respectively. The mecA gene was detected in all isolates. The isolates were assigned to five sequence types (STs), with ST8 (55.6%, n=15), ST152 (18.52%, n=5), and ST121 (18.52%, n=5) being the most common. All isolates carried multiple virulence genes, including genes encoding the Panton-Valentine leukocidin toxin (48.15%, n=13) and the test gene (29.63%, n=8) associated with toxic shock syndrome. The presence of strains carrying numerous genomic features associated with antibiotic resistance and virulence in HCWs is a major public health concern. This study highlights the need to implement a multimodal strategy for reducing the risk of cross-transmission of MRSA in hospitals.

Background: After its initial detection in Wuhan, China, in December 2019, SARS-CoV-2 has spread rapidly, causing successive epidemic waves worldwide. This study aims to provide a genomic epidemiology of SARS-CoV-2 in Burkina Faso. Methods: Three hundred and seventy-seven SARS-CoV-2 genomes obtained from PCR-positive nasopharyngeal samples (PCR cycle threshold score <35) collected between May 5, 2020, and January 31, 2022 were analysed. Genomic sequences were assigned to phylogenetic clades using NextClade and to Pango lineages using pangolin. Phylogenetic and phylogeographic analyses were performed to determine the geographical sources and time of virus introduction in Burkina Faso. Results: The analyzed SARS-CoV-2 genomes could be assigned to 10 phylogenetic clades and 27 Pango lineages already described worldwide. Our analyses revealed the important role of cross-border human mobility in the successive SARS-CoV-2 introductions in Burkina Faso from neighboring countries. Conclusion: This study provides additional insights into the genomic epidemiology of SARS-CoV-2 in West Africa. It highlights the importance of land travel in the spread of the virus and the need to rapidly implement preventive policies. Regional cross-border collaborations and the adherence of the general population to government policies are key to prevent new epidemic waves.

Intercropping systems can flexibly use resources such as sunlight, heat, water, and nutrients in time and space, improve crop yield and land utilization rates, effectively reduce continuous cropping obstacles and the occurrence of diseases and insect pests, and control the growth of weeds. Thus, intercropping is a safe and efficient ecological planting mode. The legume–cereal intercropping system is the most common planting combination. Legume crops fix nitrogen from the atmosphere through their symbiotic nitrogen fixation abilities, and the fixed nitrogen can be transferred to and utilized by cereal crops in various ways. The symbiotic nitrogen fixation efficiency of legume crops was improved by reducing the inhibition of soil nitrogen on nitrogenase activity through competitive absorption of soil nitrogen. However, the effects of nitrogen transformation and distribution in intercropping systems, and microbial community structure characteristics on nitrogen transfer need to be further explored. In this review, ⅰ) we present the transformation and distribution of nitrogen in the legume–cereal intercropping system; ⅱ) we describe the soil microbial community characteristics in intercropping systems; and ⅲ) we discuss the advantages of using modern biological molecular techniques to study soil microorganisms. We conclude that intercropping can increase the diversity of soil microorganisms, and the interaction between different plants has an important impact on the diversity and composition of the bacterial and fungal communities. The extensive application of modern biological molecular techniques in soil microbial research and the great contribution of intercropping systems to sustainable agriculture are particularly emphasized in this review.

Atrial fibrillation (AF) is a form of sustained cardiac arrhythmia and microRNAs (miRs) play crucial roles in pathophysiology of AF. To identify novel miR-mRNA pairs, we performed RNAseq from atrial biopsies of AF and non-AF patients. Differentially expressed miRs (11-down and 9-up) and mRNAs (95-up and 82-down) were identified and hierarchically clustered in a heat-map. Subsequently, GO, KEGG, and GSEA analyses were run to identify deregulated pathways. Then, miR targets were predicted in miRDB database, and a regulatory network of negatively correlated miR-mRNA pairs was constructed using Cytoscape. To select potential candidate genes from GSEA analysis, top-50 enriched genes in GSEA were overlaid with predicted targets of differentially deregulated miRs. Besides, protein-protein-interaction (PPI) network of enriched genes in GSEA was constructed, and subsequently GO and canonical pathway analyses were run for genes in PPI network. Our analyses showed that TNF-α, p53, EMT, and SYDECAN1 signaling were among the highly affected pathways in AF samples. SDC-1 (syndecan-1) was the top-enriched gene in p53, EMT, and SYDECAN1 signaling. Consistently, SDC-1 mRNA and protein levels were significantly higher in atrial samples of AF patients. Among negatively correlated miRs, miR-302b-3p was experimentally validated to suppress SDC-1 transcript levels. Overall, our results suggested that miR-302b-3p/SDC-1 axis may involve in pathogenesis of AF.

Soil bacteria drive key ecosystem functions, including nutrient mobilization, soil aggregation and crop bioprotection against pathogens. Bacterial diversity is thus considered a key component of soil health. Conventional agriculture reduces bacterial diversity in many ways. Compost tea has been suggested as a bioinoculant that may restore bacterial community diversity and promote crop performance under conventional agriculture. Here, we conducted a field experiment to test this hypothesis in a soybean-maize rotation. Compost tea application had no influence on bacterial diversity or community structure. Plant growth and yield were also unresponsive to compost tea application. Combined, our results suggest that our compost tea bacteria did not thrive in the soil, and that the positive impacts of compost tea applications reported elsewhere may be caused by different microbial groups (e.g., fungi, protists, nematodes) or by abiotic effects on soil (e.g., contribution of nutrients and dissolved organic matter). Further investigations are needed to elucidate the mechanisms through which compost tea influences crop performance.

Cancer immunotherapy reaches a remarkable achievement in various cancer types and brings new possibilities to improve cancer patients’ long-term survival. However, outcomes vary from case to case, and the present protocol benefits a small fraction of patients. One notable factor is the tumor microenvironment, especially the immune cell components, that may reflect the immune response's status quo on site. Thus, understanding the content of infiltrating immune cells in tumors is not only for research interesting but also a crucial subject toward precision medicine. We implement an algorithm for resolving relative proportions of twenty-one immune cell subclasses from a human tissue profiled transcriptome by microarray technology to reach the goal above. By selecting gene features and then adopting ?-Support Vector Regression, we can construct a deconvolution model and resolve the immune cell context. The excellent consistency between the estimated values and the correct immune-cell composition further demonstrates this approach provides a more natural alternative to revealing samples' immune cell content and reliable results like recent single-cell technologies. Based on this algorithm, the web-based deconvolution tool implemented named mySORT provides a user-friendly interface for estimating the immune cell content by uploading gene expression profiling. We also present comprehensive visualization 2D/3D plots in mySORT so that users can easily make a comparison between different samples. Finally, we synthesized pseudo-bulk expression data from single-cell transcriptomic datasets of 17 melanoma and 16 head and neck cancer patients. The deconvolution results of microarray-based data in the previous study and synthetic pseudo-bulk data all proved the excellent performance of mySORT. We believe that mySORT can help researchers in all fields easily understand complex immune microenvironment. The website of mySORT is freely accessible on https://symbiosis.iis.sinica.edu.tw/mySORT/.

Kersting’s groundnut is an important source of protein and essential nutrients that contribute to food security in West Africa. However, the crop is still underexploited by the populations and under-researched by the scientific community. This study aimed to investigate the genetic diversity and population structure of 217 Kersting’s groundnut accessions from five origins using 886 DArTseq markers. Gene diversity was low and ranged from 0.049 to 0.064. The number of private alleles greatly varied among populations (42–192) and morphotypes (40–339). Moderate to very high levels of selfing and inbreeding were observed among populations (s=56–85%, F_IS=0.389–0.736) and morphotypes (s=57–82%, F_IS=0.400–0.691). Moreover, little to very high genetic differentiations were observed among populations (0.006≤F_IS≤0.371) and morphotypes (0.029≤F_IS≤0.307). Analysis of molecular variance partitioned 38.5% of the genetic variation among and 48.7% within populations (P<0.001). Significant isolations by distance were detected between populations (R²=0.612, P=0.011) and accessions (R²=0.499, P<0.001). Discriminant analysis of principal components and neighbour joining consistently distinguished eight distinct clusters. These data provide a global picture of the existing genetic diversity for Kersting’s groundnut and will guide the choice of breeding strategies to increase production.

The rhizosphere fungal community can play an important role in determining plant growth and health. In this study, using high-throughput sequencing, we investigated the fungal diversity and community composition in the roots and rhizosphere soil of 21 potato (Solanum tuberosum L.) cultivars. The samples were collected at three different sampling points. Furthermore, we assessed the differences in both diversity and composition of pathogen and saprotroph communities. In soil and roots, the fungal richness and relative abundance of pathogens and saprotrophs were mainly affected by sampling time. However, root fungal communities were also significantly affected by cultivar. The most substantial effect of cultivar was on root pathogen diversity. Moreover, the occurrence of most pathogens strongly varied among cultivars. Soil fungal community composition was primarily determined by sampling time; whereas in roots, the primary determinant was cultivar. Our results demonstrate changes in fungal communities over the potato growing season, as well as highlight the importance of potato cultivar on root fungal communities, and emphasise their importance in plant breeding.

Bacterial wilt (BW), caused by Ralstonia solanacearum is one of the major biotic factors limiting tomato production in the humid tropics. Pyramiding of resistance genes through marker-assisted selection is an efficient way to develop durable BW resistant cultivars. Tomato line ‘Hawaii 7996’ (H7996) is a stable and robust resistance source against various R. solanacearum strains. Major BW resistance quantitative trait loci (QTLs) Bwr-12 and Bwr-6, and several minor or strain specific QTLs have been coarse-mapped in this line, but none has been fine-mapped and validated. The objective of the current study was to construct a high density genetic map using single-nucleotide polymorphism (SNP) markers derived from genotyping-by-sequencing, fine-map Bwr-12 and Bwr-6 and determine the effects of these QTLs using a near isogenic line (NIL) population. A high density genetic map using 1,604 SNP markers with an average distance of 0.82 cM was developed for 188 F9 recombinant inbred lines derived from the cross H7996 × WVa700. A total of seven QTLs associated with BW resistance to race 1-phylotype I or/and race 3-phylotype II strains were located on chromosomes 6 (Bwr-6.1, 6.2, 6.3 and 6.4) and 12 (Bwr-12.1, Bwr-12.2 and Bwr-12.3) with logarithm of odds (LOD) scores of 6.2-15.6 and 6.2-31.1, explaining 14.2-33.4% and 15.9-53.9% of the total phenotypic variation contributed from H7996, respectively. To validate the genetic effects of the two QTL regions, a set of 80 BC3F3 NILs containing different sections of Bwr-6 with or without Bwr-12 was phenotyped for disease severity after challenge with either race 1-phylotype I Pss4 or race 3-phylotype II Pss1632 BW strains over two seasons. Bwr-6.1 specific to Pss4 and Bwr-6.3 specific to Pss1632 were mapped to an interval of 5.0 cM (P < 0.05) between 6_33,444,000_SLM6-47 and 6_33,868,000_SLM6-124 SNP marker, and to 2.7 cM (P < 0.01) between positions 6_35,949,000 _SLM6-107 to 6_36,750,000_SLM6-82 marker, respectively. In addition, the specific effect of Bwr-12 for resistance to Pss4 (LOD score of 5.8-16.1, P < 0.01) was confirmed and markers for this QTL have already been made available previously.

Congenital Pouch Colon (CPC) is a rare anorectal anomaly common to North Western India specifically Rajasthan. Despite efforts to understand the clinical genetic makeup of CPC, no attempt on identifying non-coding RNAs was done. We have earlier reported CPC's rare variants from whole exome sequencing across 18 affected samples in a total of 64 subjects. A Smith-Waterman algorithm was used to infer a couple of lncRNAs from WES samples of CPC with predictions from the Noncode database. Further screening and quantification using PCR, we ascertained interactions using Micro Scale Thermophoresis (MST). We report the role of lnc-EPB41-1-1 shown to be promiscuously interacting with KIF13A substantiating their role in regulation.

Background: Gut-Brain-Axis provides bidirectional communicational route; imbalance of which can have pathophysiological consequences. It is a frontier in autism research, affects 85% of autistic children (NIH report). Their microbiome has few overall microbes and smaller number of health promising microbes than their neurotypical peers. We hypothesize autism gut might play a role in manifestation of autism behaviours and on treatment, can revert back to normal behaviour considerably. The aim is to better understand to what degree gut microbiota of autism subjects differs from controls and identify bacterial species present exclusively in autism. Materials and Methods: 16s-rRNA-sequence of autism-subjects were retrieved from the American Gut Project Archive. Taxonomic assignment was inferred by similarity based methods using Quantitative Insights Into Microbial Ecology (QIIME). Species abundance was characterized and co-occurrence network was built to infer species interaction using measures of diversity. Statistical parameters were considered to validate the findings. Result: A total of 206 (1.8%) of American Gut Project datasets onstituted of autistic samples. Various bacteria such as Akkermansia sp., and Prevotella sp., were harboured in higher abundance in autistic children with statistical significance than in controls. Conclusion: These findings indicate connecting-link between gut-microbiome-brain-axis and autistic behaviour which can result in improved management

This paper proposes a novel approach called Multi-strategy Improved Chaotic Whale fused with Perturbation Observation method (IWOA-PO) for Maximum Power Point Tracking (MPPT) con-trol, to address the challenge of low efficiency in photovoltaic (PV) power generation under local shadows, according to the features of whale optimization algorithm (WOA) and perturbation observation (P&O) method. In the early stage of control algorithm, IWOA is used for global search to quickly locate the position of the Maximum Power Point (MPP). In the later stage of control algorithm, P&O is used for fine-grained local search to quickly, accurately and low oscillation track the position of the global maximum power point (GMPP). In order to verify the correctness, the tracking performance of IWOA-PO is comprehensively compared with WOA-PO、WOA、PSO under four conditions including uniform illumination, static partial shade, dynamic irradiance mutation and sudden changes in temperature and irradiance. In the above four conditions, the IWOA-PO algorithm has faster convergence and less power oscillations compared to the re-maining three algorithms, indicating that the IWOA-PO algorithm is effective. Finally, a physical verification experiment process is built to verify the correctness and effectiveness of the algorithm proposed in this paper.

A suitable waste management strategy is crucial for a sustainable and efficient circular economy in the construction sector, and requires precise data on the volume of demolition waste (DW) gen-erated. Therefore, we developed an optimal machine learning (ML) model to forecast the quantity of recycling and landfill waste based on the characteristics of DW. A dataset comprising infor-mation on the characteristics of 150 buildings, demolition equipment utilized, and volume of five waste types generated (i.e., recyclable mineral, recyclable combustible, landfill specified, and landfill mix waste, and recyclable minerals) was constructed. ML models were developed to predict the quantities of such waste. Artificial neural network, decision tree, gradient boosting machine, k-nearest neighbors, linear regression, random forest (RF), and support vector regression were applied, and optimal models were derived via hyperparameter tuning. The RF model demonstrated superior performance. In both validation and test phases, the “recyclable mineral waste” and “recyclable combustible waste” models achieved accuracies of of 0.987 and 0.972, re-spectively. The “recyclable metals” and “landfill specified waste” models achieved accuracies of 0.953 and 0.858 or higher, respectively. Moreover, the “landfill mix waste” model exhibited an accuracy of 0.984 or higher. SHapley Additive exPlanations analysis highlighted floor area as the primary input variable influencing model performance. The type of equipment employed in demolition emerged as another crucial input variable impacting the volume of recycling and landfill wastes generated. The developed model can provide precise data on waste management, thereby facilitating the decision-making process for industry professionals.

The paper is concerned with the concept of elementary observations (EO). EOs are experimental answers to the question whether a matter-instrument interaction had taken place at the micro-scale of quantum phenomena. Focusing on the specific case of photon detection, we show that during their lifetimes EOs proceed through the four phases of initiation, detection, erasure and reset. Once generated, the observational value of EOs can be measured in units of the Planck quantum of physical action h=4.136×10^(-15) eVs. Once terminated, each unit of entropy of size k_B=8.617×10^(-5) eV⁄K, which had been created in the instrument during the observational phase, needs to be removed from the instrument to ready it for a new round of photon detection. This withdrawal of entropy takes place at an energetic cost of at least two units of the Landauer minimum energy bound of E_La=ln⁡〖(2) k_B T_D 〗 for each unit of entropy of size k_B.

Visualising textual content could be helpful to professionals as well as amateurs across several fields. However, training a text-to-image generator in the mainstream domain requires large amounts of paired text-image and data, which is too expensive to collect since labeling millions of images and videos can be tiresome. GANs like StackGAN and StyleGAN can be considered as solutions to generate images from text. But the images generated may be of low accuracy and resolution, and the entire processing can be highly time-consuming. Moreover, image generation is a notion that is still being researched. Hence, the process of developing a Video Generation model necessitates substantial research. Despite the need for such a model, modern technology has lagged behind the solutions to this problem. This proposal suggests combining two methods, Text modification for Action Definition (TexAD) and SeQuential Image Generation for Video Synthesis (SQIGen). The proposed solution synthesises a sequence of images from textual information feeds and combines these images to create a video. TexAD uses Natural Language Processing and Deep Learning techniques to process, classify and modify text data. SQIGen is an extension of the VQGAN+CLIP neural network architecture that generates a sequence of images from the modified text data

The global increase in population, the phenomenon of climate change, the issue of water pollution and contamination, and the inadequate management of water resources all exert heightened strain on freshwater reserves. The potential utilization of the interfacial solar steam generation (ISSG) system, which utilizes photothermal conversion to generate heat on material surfaces for wastewater purification and desalination purposes, has been successfully demonstrated. Textile materials based ISSG devices, including (woven, nonwoven and knitting) fabrics and electrospinning membranes, exhibit distinct properties such as rough surface texture, high porosity, significant surface area, exceptional flexibility, and robust mechanical strength. These characteristics, combined with their affordability, accessibility, and economic viability for widespread implementation, make them extremely attractive for applications in SSG. In this review, a comprehensive analysis of emerging concepts, advancements and applications of textile materials (woven, nonwoven, and knitting) fabrics and electro-spun membranes in the ISSG for wastewater purification and desalination is highlighted. We also emphasize significant obstacles and potential prospects in both theoretical investigations and real-world implementations, aiming to contribute to future advancements in the domain of textile based materials interfacial evaporation in wastewater purification and desalination. Furthermore, the drawbacks and the challenges of ISSG systems are also highlighted.

It is argued that all physical knowledge ultimately stems from observation and that the simplest possible observation is that an event has happened at a certain space-time location . Considering historic experiments, which had been groundbreaking in the evolution of our modern ideas of matter on the atomic, nuclear and elementary particle scales, it is shown that such experiments produce as outputs macroscopically observable events which represent binary decisions with regard to simple alternatives and which accumulate in the course of time into spatio-temporal patterns whose forms allow decisions to be taken concerning conceivable alternatives of explanation. Working towards elucidating the physical and information-theoretic characteristics of those elementary pieces of information we arrive at the conclusion that - unlike the events of relativity which are mere 4-tuples of spacetime coordinates - the events of observation are pieces of information endowed with the properties of physical action, spacetime extension, and Lorentz invariance. Another important aspect is that such events are produced at the expense of irreversibly dissipating energy which can be stated in Landauer diction in the form that more energy needs to be spent in the erasure of events than had previously been invested in their initiation.

To protect power lines, distance relays are used which can rely on a modified distance resistance. But usually the operating range of these relays changes as the network conditions change (network topology, load value, output value, etc.) and leads to false trips, By using methods that can process information and recognize patterns, for example, the use of micro and intelligent processor algorithms can use the new relays with high precision and thus provide adequate protection. In this study, a distance relay was modeled using a neural network, and it was observed that the neural relay had higher accuracy than the conventional relay. In addition to detecting the fault and its location, type and phase of the fault, three-stage simultaneous protection can be performed. As a result, the number of linear relays can be reduced by using relays based on neural technologies. An MLP (multilayer perceptron) neural network is used to model a sequence of distances.

Soiling accumulated on a photovoltaic (PV) module can significantly reduce the transmittance of the cover glass, resulting in power losses and consequent economic losses. Natural atmospheric parameters influence the accumulation of soiling at the various geographic locations. In this paper, the approaches and outcomes of the research studies on either indoor (simulator-based) or outdoor (field-based) PV soiling have been thoroughly reviewed. Different parameters depicted for the power loss of PV modules are analyzed individually and presented. Moreover, this study delves into a detailed examination of the key factors influencing dust depositions on PV modules in various geographical regions, with a particular focus on their relationship with climatic conditions. This way, probable future research directions to quantify soiling losses are identified. In addition, different loss prevention and mitigation techniques are also reviewed. This makes it possible to highlight effective strategies and pinpoint potential future research lines in these areas.

In recent years, the genesis of natural gas and the definition of major gas source rocks in the Baiyun Sag of the Pearl River Mouth Basin have attracted extensive attention from scholars. There is still considerable controversy over the genesis and the main gas source rocks of natural gas. In this study, the chemical kinetics method of natural gas generation and carbon isotope fractionation was used to study the generation history and isotope evolution history of Paleogene natural gas in the Y5 well area located on the north slope of Baiyun Sag. And other geochemical discriminant methods were applied simultaneously to determine the type and source rocks of natural gas. The results show that the natural gas in the Y5 well area on the northern slope of Baiyun Sag is mainly kerogen degradation gas, mixed with a small amount of oil cracking gas. The mixture of kerogen degradation gas and a small amount of crude oil cracking gas caused the inversion of the carbon isotope sequence of ethane and propane. The natural gas in the Y5 well area mainly comes from delta–shallow lacustrine facies source rock. The high–quality reservoirs in the shallower strata accumulate natural gas generated in the early stage, and the tight reservoirs in the deeper strata mainly accumulate natural gas generated in the late stage.

One of the rapidly developing research areas in the power system is the integration of distributed generation (DG) with the distribution system. The size and location of DG sources had a considerable impact on power system networks. Artificial intelligence (AI) techniques can be used to address multidimensional problems relating to DG size and location in distribution systems. Heuristic optimization offers a reliable and effective method for solving complicated real-world problems. This work focuses on a hybrid approach that combines the two heuristic optimization methods i.e., Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) for the optimal siting and sizing of DG in distribution systems. This hybrid approach combines ideas from GA and PSO and generates individuals in a new generation using both PSO mechanisms and the procedures found in GA. An extensive performance analysis of the IEEE 14 busbar standard test system is conducted to demonstrate the viability of the suggested methodologies. In the designated locations, DG is placed, and the outcomes have been verified. The results indicated that the right placement of DG injection enhances the voltage profile and lowers the distributed system’s power losses. These techniques offer unique methods for determining the location of the DG unit, demonstrating the potential of such a computational techniques to reduce computing time and complexity while simultaneously reducing human errors associated with hit-and-trail methods.

Abstract: The diffusion of competition under the coexistence of multi-generation products has become one of the important challenges faced by enterprises in their daily and sustainable operations. At the same time, the competition between different brands has intensified the difficulty and complexity of decision-making in the process of multi-generation product operation, Therefore, based on the Norton-Bass model diffusion process, this paper introduces two marketing variables, dynamic price and quality level, builds a multi-generation product diffusion model under the dual-brand competition, and analyzes the impact of the company's revenue on the launch time to market, pricing, quality or technical level. By using system dynamics (SD) method, From the perspective of strong brand and weak brand enterprises, the competition diffusion model is built and simulated. The simulation shows that: i) When enterprises have the same brand competitiveness, reducing the pricing level cannot obtain more revenue, but diminish the overall revenue of the industry; Raising the pricing level can get more revenue, but also improve the revenue of competitors. ii) When the competitive strength of enterprises is different, the strong brand tends to maintain a stable pricing on the basis of improving the quality level, or slightly raise the price. Weak brands tend to raise the pricing of new products significantly on the basis of improving the quality level. iii) The launch time to market decision of new product is influenced by the degree of product quality upgrade. Therefore, the frequency of releasing new products should trade-off against the degree of technological upgrading of product quality. This paper provides the theoretical basis and new insights for the new product launch and operation decision of enterprises.

The crystal structure of a polymorph of Glycyl-L-alanine HI.H2O was synthesized from chiral cyclic Glycyl-L-alanine dipeptide, known to show molecular flexibility in different environments. The space group is polar (P21) and therefore pyroelectricity and optical second harmonic generation properties are allowed by symmetry. We report a pyroelectric coefficient as high as 45 µC/m2K occurring at 345 K, one order of magnitude smaller than the semi-organic ferroelectric Triglycine Sulphate (TGS) crystal. Furthermore, the polymorph displays a nonlinear optical effective coefficient of 0.14 pm/V, around 14 times smaller than the value from a phase-matched inorganic Barium Borate (BBO) single crystal.

The article analyzes existing materials and structures with quadratic-nonlinear optical properties that can be used to generate a difference frequency in the terahertz and sub-terahertz frequency ranges. The principle of constructing a nonlinear optical-radio converter, based on an optical focon (a focusing cone), is proposed. Based on the assumption that this focon can be implemented from the metal-organic framework (MOF), we propose a technique for modeling its parameters. The mathematical model of the process of propagation and nonlinear interaction of waves inside the focon is based on a simplification of the nonlinear wave equation. Within the framework of the developed model, the following parameters are approximately determined: the 3D gradient of the linear refractive index and the function determining the geometric profile of the focon, which provide a few-mode-based generation of the difference frequency.

The global climate change mitigation efforts have increased the efforts of national government to incentivize local households in adopting individual renewable energy as a mean to help reduce the usage of electricity generated using fossil fuels and to gain independence from the grid. Since the majority of residential generation is made by PV panels that generate electricity at off-peak hours, the optimal management of such installations often considers local storage that can defer the use of locally generated electricity at later times. On the other hand, the presence of distributed generation can affect negatively the operating conditions of low-voltage distribution networks. The energy stored in batteries located in optimal places in the network can be used by the utility to improve the operation of the network. This paper proposes a metaheuristic approach based on a Genetic Algorithm that considers three different scenarios of using energy storage for reducing the losses in the network. Prosumer and network operator priorities can be considered in different scenarios inside the same algorithm, to provide a comparative study of different priorities in storage placement. A case study performed on a real distribution network provides insightful results.

We report on the application of femtosecond laser micromachining to the fabrication of complex glass microdevices, for high-order harmonic generation in gas. The three-dimensional capabilities and extreme flexibility of femtosecond laser micromachining allow us to achieve accurate control of gas density inside the micrometer interaction channel. This device gives a considerable increase in harmonics generation efficiency if compared with traditional harmonic generation in gas jets. We propose different chip geometries that allow to control the gas density and driving field intensity inside the interaction channel to achieve quasi-phase matching conditions in the harmonic generation process. We believe that these glass micro-devices will pave the way to future downscaling of High-order Harmonic Generation beamlines.

A numerical approach for generating a limited number of water demand scenarios and estimating their occurrence probabilities in a Water Distribution Network (WDN) is proposed. This approach makes use of the demand scaling laws in order to consider the natural variability and spatial correlation of nodal consumptions. The scaling laws are employed to determine the statistics of nodal consumption as a function of the number of users and the main statistical features of the unitary user's demand. Besides, consumption at each node is considered to follow a Gamma probability distribution. A high number of groups of cross-correlated demands, i.e., scenarios, for the entire network were generated using Latin Hypercube Sampling (LHS) and the numerical procedure proposed by Iman and Conover. The Kantorovich distance is used to reduce the number of scenarios and estimate their corresponding probabilities, while keeping the statistical information on nodal consumptions. By hydraulic simulation, the whole number of generated demand scenarios was used to obtain a corresponding number of pressure scenarios on which the same reduction procedure was applied. The probabilities of the reduced scenarios of pressure were compared with the corresponding probabilities of demand.

Although microgrids facilitate the increased penetration of distributed generations (DGs) and improve the security of power supplies, they have some issues that need to be better understood and addressed before realising the full potential of microgrids. This paper presents a comprehensive list of challenges and opportunities supported by a literature review on the evolution of converter-based microgrids. The discussion in this paper presented with a view to establishing microgrids as distinct from the existing distribution systems. This is accomplished by, firstly, describing the challenges and benefits of using DG units in a distribution network and then those of microgrid ones. Also, the definitions, classifications and characteristics of microgrids are summarised to provide a sound basis for novice researchers to undertake ongoing research on microgrids.

The model proposed in this study was based on the assumption that the biomass attached to the anode served as biocatalysts for MFC exoelectrogenesis, and this catalytic effect was quantified by the exchange current density of anode. By modifying the Freter model and combining it with the Butler-Volmer equation, this model could adequately describe the processes of electricity generation, substrate utilization, and the suspended and attached biomass concentrations, at both batch and continuous operating modes. MFC performance is affected by the operating variables such as initial substrate concentration, external resistor, influent substrate concentration, and dilution rate, and these variables were revealed to have complex interactions by data simulation. The external power generation and energy efficiency were considered as indices for MFC performance. The simulated results explained that an intermediate initial substrate concentration (about 100 mg/L under this reactor configuration) needed to be chosen to achieve maximum overall energy efficiency from substrate in the batch mode. An external resistor with the value about that of the internal resistance boosted the power generation, and a resistor with several times of that of the internal resistance achieved better overall energy efficiency. At continuous mode, dilution rate significantly impacted the steady-state substrate concentration level (thus substrate removal efficiency and rate), and attached biomass could be fully developed when the influent substrate concentration was equal to or higher than 100 mg/L at any dilution rate of the tested range. Overall, this relatively simple model provided a convenient way for evaluating and optimizing the performance of MFC reactors by regulating operating parameters.

This study was undertaken to forecast waste generation rates of accommodation sector of North Cyprus as a case. Three predictor models including multiple linear regression (MLR), artificial neural networks (ANNs) and central composite design (CCD) were applied to predict the waste generation rate during the lean and peak seasons. ANN showed highest prediction performance, specifically, lowest values of the standard error of prediction (SEP = 2.153), mean absolute error (MAE=1.378) and highest R2 value (0.998) confirmed the accuracy of the model. The analysed wastes were categorised into recyclable, general waste and food residues. The authors estimated the total waste generated during the lean season as 2010.5 kg/day, in which large-sized hotel accounted for largest fraction (66.7%), followed by the medium hotels (19.4%) and guesthouse accounted for smallest part (2.6%). During the peak season, about 49.6% increases in the waste generation rates were obtained. Interestingly, 45% of the wastes were generated by the British tourists while the least waste was generated by African tourists (7.5%). The ANN predicted that the small and large hotels would produce 5.45 and 22.24 tons of waste by the year 2020, respectively. The findings herein are promising and useful in establishing a sustainable waste management system.

Common variable immunodeficiency (CVID) is the most common symptomatic antibody deficiency, characterized by heterogeneous genetic, immunological, and clinical phenotypes. It has no longer been conceived as a sole disease but as an umbrella diagnosis comprising a spectrum of clinical conditions with defects in antibody biosynthesis as their common denominator and complex pathways determining B and T cell developmental impairments due to genetic defects of many receptors and ligands, activating and co-stimulatory molecules, and intracellular signaling molecules. Consequently, these genetic variants may affect crucial immunological processes of antigen presentation, antibody class switch recombination, antibody affinity maturation, and somatic hypermutation. While infections are the most common features of pediatric CVID, variants in genes linked to antibody production defects play a role in pathomechanisms of immune dysregulation with autoimmunity, allergy, and lymphoproliferation reflecting the diversity of the immunogenetic underpinnings of CVID. Herein, we have reviewed the aspects of genetics in CVID, including the monogenic, digenic, and polygenic models of inheritance exemplified by a scope of genes relevant to CVID pathophysiology. We have also briefly discussed the epigenetic mechanisms associated with micro RNA, DNA methylation, chromatin reorganization, and histone protein modification processes as background for CVID development.

Allopolyploidy in plants involves the merging of two or more distinct parental genomes into a single nucleus, a significant evolutionary process in the plant kingdom. Transcriptomic analysis provides valuable insights into the fate of duplicated genes, evolutionary novelties, and environmental adaptations exhibited by allopolyploid plants. However, transcriptome profiling is challenging due to genomic redundancy, which is further complicated by the presence of multiple chromosomes sets and the variations among homoeologs and allelic genes. Prior to transcriptome analysis, sub-genome phasing and homoeology inference are essential for obtaining a comprehensive view of gene expression. This review aims to clarify the terminology in this field, identify the most challenging aspects of transcriptome analysis, explain their inherent difficulties, and suggest reliable analytic strategies. Furthermore, bulk RNA-seq is highlighted as a primary method for studying allopolyploid expression, with an emphasis on the critical steps of read mapping and normalization in differential gene expression analysis.

Monoclonal antibodies (mAbs) are an important treatment option for COVID-19 caused by SARS-CoV-2, especially in immunosuppressed patients. However, this treatment option can become ineffective due to mutations in the SARS-CoV-2 genome, mainly in the receptor binding domain (RBD) of the spike (S) protein. In the present study 7950 SARS-CoV-2 positive samples from the Uppsala and Örebro regions of central Sweden collected between March 2022 and May 2023 were whole-genome sequenced using next-generation sequencing, mainly with the Nanopore sequencing method. Pango lineages were determined and all single nucleotide polymorphism (SNP) mutations that occurred in these samples were identified. The dominant sublineages changed over time and mutations conferring resistance to currently available mAbs became common. Notable ones are R346T and K444T mutations in the RBD that confer significant resistance against tixagevimab and cilgavimab mAbs. Further, mutations conferring a high-fold resistance to bebtelovimab, such as the K444T and V445P mutations, were also observed in the samples. This study highlights that resistance mutations have over time rendered currently available mAbs ineffective against SARS-CoV-2 in most patients. Therefore, there is a need for continued surveillance of resistance mutations and the development of new mAbs that target more conserved regions of the RBD.

Investigation of bacterial communities is on the rise both in human and veterinary medicine. Their role in health maintenance and pathogenic mechanisms is in the limelight of infectious, metabolic, and cancer research. Among the most considered, gut bacterial communities takes the cake. Their part in animals was assessed mainly to improve animal production, public health, and pet management. In this regard, canaries deserve attention, being a popular pet and source of economic income for bird-keepers, for whom breeding represents a pivotal point. Thus, the aim of the present work was to follow gut bacterial communities’ evolution along on whole reproductive cycle of 12 healthy female canaries. Feces were collected during parental care, molting, and resting phase, and submitted for 16S rRNA sequencing. Data analysis a substantial presence of Lactobacillus aviarius along all the phases, and a relevant shift of microbiota during molting and rest due to an abrupt decrease of Vermiphilaceae family. Although the meaning of such change is not clear, future research may highlight unforeseen scenarios. Moreover, Lactobacillus aviarius may be deemed for normal bacteria flora restoration in debilitated birds, perhaps improving their health and productivity.

New Jersey was among the first states impacted by the COVID-19 pandemic, with one of the highest overall death rates in the nation. Nevertheless, relatively few reports have been published focusing specifically on New Jersey. Here we report on molecular, clinical, and epidemiologic observations from the largest healthcare network in the state, in a cohort of vaccinated and unvaccinated individuals with laboratory-confirmed SARS-CoV-2 infection. We conducted molecular surveillance of SARS-CoV-2-positive nasopharyngeal swabs collected in nine hospitals from December 2020 through June 2022, using both whole genome sequencing (WGS) and a real-time RT-PCR screening assay targeting spike protein mutations found in variants of concern (VOC) within our region. De-identified clinical data were obtained retrospectively, including demographics, COVID-19 vaccination status, ICU admission, ventilator support, mortality, and medical history. Statistical analyses were performed to identify associations between SARS-CoV-2 variants, vaccination status, clinical outcomes, and medical risk factors. A total of 5,007 SARS-CoV-2-positive nasopharyngeal swabs were successfully screened and/or sequenced. Variant screening identified three predominant VOC, including Alpha (n =714), Delta (n =1,877), and Omicron (n =1,802). Omicron isolates were further sub-typed as BA.1 (n =899), BA.2 (n =853), and BA.4/BA.5 (n =50); the remaining 614 isolates were classified as “Other”. Approximately 31.5% (1,577/5,007) of the samples were associated with vaccine breakthrough infections, which increased in frequency following the emergence of Delta and Omicron. Severe clinical outcomes included ICU admission (336/5007 = 6.7%), ventilator support (236/5007 = 4.7%), and mortality (430/5007 = 8.6%), with increasing age being the most significant contributor to each (p &lt;0.001). Unvaccinated individuals accounted for 79.7% (268/336) of ICU admissions, 78.3% (185/236) of ventilator cases, and 74.4% (320/430) of deaths. Highly significant (p &lt;0.001) increases in mortality were observed in individuals with cardiovascular disease, hypertension, cancer, diabetes, and hyperlipidemia, but not with obesity, thyroid disease, or respiratory disease. Significant differences (p &lt;0.001) in clinical outcomes were also noted between SARS-CoV-2 variants, including Delta, Omicron BA.1, and Omicron BA.2. Vaccination was associated with significantly improved clinical outcomes in our study, despite an increase in breakthrough infections associated with waning immunity, greater antigenic variability, or both. Underlying comorbidities contributed significantly to mortality in both vaccinated and unvaccinated individuals, with increasing risk based on the total number of comorbidities. Real-time RT-PCR-based screening facilitated timely identification of predominant variants using a minimal number of spike protein mutations, with faster turnaround time and reduced cost compared to WGS. Continued evolution of SARS-CoV-2 variants will likely require ongoing surveillance for new VOCs, with real-time assessment of clinical impact.

Hypertrophic Cardiomyopathy (HCM) is a common inherited disorder that can lead to heart failure and sudden cardiac death, characterized at the histological level by focal areas of myocyte disarray, hypertrophy and fibrosis, and few disease-targeted therapies exist. To identify, focal, spatially restricted alterations in transcriptional pathways and reveal novel therapeutic targets, we performed a spatial transcriptomic analysis of areas of focal myocyte disarray compared to areas of normal tissue, using a commercially available platform (GeoMx, nanoString). We analyzed surgical myectomy tissue from four patients with HCM and control interventricular septum tissue from two unused organ donor hearts that were free of cardiovascular disease. Histological sections were reviewed by an expert pathologist and 72 focal areas with varying degrees of myocyte disarray (normal, mild, moderate, severe) were chosen for analysis. Areas of interest were interrogated with the Human Cancer Transcriptome Atlas designed to profile 1800 transcripts. Differential expression analysis revealed significant changes in gene expression between HCM and Control tissue, and functional enrichment analysis indicated these genes were primarily involved in interferon production and mitochondrial energetics. Within HCM tissue, differentially expressed genes between areas of mild and moderate disarray were enriched for genes related to mitochondrial energetics (moderate disarray) and response to oxygen/cytokine levels (mild disarray). The comparison between areas of moderate and severe disarray were enriched for genes related to the c-Jun N-terminal kinase (JNK) cascade in severe disarray. Analysis of ligand-receptor pair gene expression revealed that HCM tissue exhibited downregulation of platelet-derived growth factor (PDGF), NOTCH, junctional adhesion molecule, and CD46 signaling, while showing upregulation of fibronectin, CD99, cadherin, and amyloid precursor protein signaling. A deconvolution analysis utilizing the matched single nuclei RNA-sequencing (snRNA-seq) data to determine cell type composition in areas of interest revealed significant differences in fibroblast and vascular cell composition in areas of severe disarray when compared to normal areas in HCM samples. Cell composition in normal areas from control tissue was also divergent from normal areas in HCM samples, which was consistent with the differential expression results. Overall, our data identify novel and potential disease-modifying targets for therapy in HCM.

Background: Carbapenem-resistant Klebsiella pneumoniae (CRKP) is a serious public health issue. Aim of the study was to identify the antimicrobial resistance and accessory genes, the clonal relatedness and the evolution dynamics of selected CRKP isolates recovered in an adult and a pediatric intensive care unit of a tertiary hospital in Greece. Methods: Twenty-four CRKP isolates recovered during 2018-2022 were included in the study. Next-generation sequencing was performed using the Ion Torrent PGM Platform. The identification of the plasmid content, MLST and antimicrobial resistance genes, as well as the comparison of multiple genome alignments and the identification of core genome single-nucleotide polymorphism sites, were performed applying various bioinformatics software. Results: The isolates belonged to eight sequence types 11, 15, 30, 35, 39, 307, 323, and 512. A variety of carbapenemases (KPC, VIM, NDM and OXA-48) and resistance genes were detected. CRKP strains shared visually common genomic regions with the reference strain (NTUH-K2044). ST15, ST323, ST39, and ST11 CRKP isolates presented on average 17, 6, 16 and 866 recombined SNPs, respectively. All isolates belonging to ST15, ST323 and ST39 were classified in distinct phylogenetic branches, while ST11 isolates were assigned to a two-subclade branch. For large CRKP sets, the phylogeny seems to change approximately every seven SNPs. Conclusion: The current study provides insight into the genetic characterization of CRKP isolates in the ICUs of a tertiary hospital. Our results indicate clonal dispersion of ST15, ST323, and ST39 and highly diverged ST11 isolates.

The soil and groundwater of Bhagobangola I block of Murshidabad district, West Bengal, India is severely arsenic contaminated. A bacterium was isolated from the garden soil of Mahishasthali village, which could tolerate 36.49 mM arsenic (III), 280.44 mM arsenic (V) and 63 mM chromium (III), which makes it arsenic (III & V) and chromium (III) hyper-tolerant bacterium. The growth pattern of this bacterium does not show much alteration in presence of 10 mM arsenic (III) and chromium (III), emphasizing its resistance to these heavy metals. Scanning electron microscopy depicted the size of this bacterium to be ~1.45 µm. 16S rDNA sequencing followed by subsequent phylogenetic analysis established the identity of this bacterium as Microbacterium paraoxydans. The bacterium is capable of bioremediation of arsenic and showed 30.8% & 22.6%, and 35.2% and 30.5% of bioremediation over a period of 24 and 48 hours in 1 mM and 4 mM arsenite, respectively. Microbacterium paraoxydans also exhibit plant growth promoting properties like nitrogen fixation, phosphate solubilization, indole-3-acetic acid production and production of siderophores. Therefore, the heavy metal resistance, bioremediation potential and plant growth promoting potential of the bacterium could be utilized not only for reduction of arsenic toxicity in soil and groundwater but also for plant growth promotion by using it as a biofertilizer.