Non-homologous end-joining (NHEJ) is a major DNA repair pathway in mammalian cells that recognizes, processes and fixes DNA damages throughout the cell cycle, and is specifically important for homeostasis of post-mitotic neurons and developing lymphocytes. Neuronal apoptosis increases in the mice lacking NHEJ factors Ku70 and Ku80. Inactivation of other NHEJ genes, either Xrcc4 or Lig4, leads to massive neuronal apoptosis in the central nervous system (CNS) that correlates with embryonic lethality in mice. Inactivation of either Paxx, Mri or Dna-pkcs NHEJ gene results in normal CNS development due to compensatory effects of Xlf. Combined inactivation of Xlf/Paxx, Xlf/Mri and Xlf/Dna-pkcs, however, results in late embryonic lethality and high levels of apoptosis in CNS. To determine the impact of NHEJ factors on early stages of neurodevelopment, we isolated neural stem and progenitor cells from mouse embryos and investigated proliferation, self-renewal and differentiation capacity of these cells lacking either Xlf, Paxx, Dna-pkcs, Xlf/Paxx or Xlf/Dna-pkcs. We found that XLF, DNA-PKcs and PAXX maintain the neural stem and progenitor cell populations and neurodevelopment in mammals, which is particularly evident in the double knockout models.

To meet an ever global population's food demand, crop yields must be sustained and increased. Drought, which is getting harsher as a result of global warming, is largely impeding the agricultural productivity. Maize is widely used as food and animal feed in many regions of the world, but its yields are largely effected by drought and heat stress. Historical data on climate change predicts that drought and heat stress becoming major threat for maize cultivation in coming years, which will have huge impact on food security of the world especially in Africa and Asia. Thus there is an immense necessary to develop drought tolerant and climate resilient maize to feed the predicted population of the world. Availability and accessibility of crop genetic resources plays a huge role in development of drought-tolerant maize cultivars. A huge genetic resources of maize, including its landraces and crop wild relatives (CWR) have been reported naturally and many of them have stored in National and International gene banks globally. Conventional breeding methods have been tremendously increased maize yields, but these methods frequently fall short of achieving the demand for improved drought stress resistance. In this article, we have briefly discussed about impact of climate variability on crop production, maize yield losses due to drought, drought tolerance in maize landraces and CWR, and origin and evolution of Mexican landraces. This information may help in utilization of these potential resources in various pre-breeding programs.

In order to explore the genetic diversity of the extremely small population wild plant Habenaria dentata and protect and optimize its wild resources. The genetic diversity of 133 samples from 6 wild populations of H.dentata was analyzed by ISSR molecular marker technique to provide a scientific basis for the screening of improved germplasm resources. The results show that the average number of alleles (Na) was 1.765, the average number of effective alleles (Ne) was 1.424, the average Nei 's gene diversity index (He) was 0.252, the average Shannon information diversity index (I) was 0.381, and the average percentage of polymorphic loci (PPL) was 76.499%. The variation within populations was 77.34%, and the variation between populations was 22.66%. Gene flow (Nm) was 1.705 8, greater than 1. The results of cluster analysis showed that the six populations were mainly divided into four branches and not classified according to geographical location. There was no significant correlation between geographical location and genetic distance between populations (r=0.557, P>0.05). The genetic diversity of H.dentata is high. Among the six wild populations, the genetic diversity of Mulun population is the highest and this population can be used as a key protection unit.

leaf beetle (BLB) (Ootheca mutabilis) has emerged as an important bean pest in Uganda, leading to devastating crop losses. There is limited information on the population genetic structure of BLB despite their importance. In this study, novel microsatellite markers and the partial mitochondrial cytochrome oxidase subunit I (mtCOI) gene sequences were used to analyze the spatial population genetic structure, genetic differentiation, gene flow and haplotype diversity of 87 O. mutabilis samples from five populations. We identified 19,356 simple sequence repeats (SSRs) (mono, di, tri, tetra, penta, and hexa-nucleotides) of which 81 di, tri and tetra-nucleotides were selected for primer synthesis. Five highly polymorphic SSR markers (4-21 alleles, heterozygosi-ty 0.59-0.84, polymorphic information content (PIC) 50.13-83.14%) were used for this study. Analyses of the five O. mutabilis populations with these five novel SSRs found 89% of genetic variation occurring within individuals, 9% among individuals and 2% among populations. Genetic differentiation was low but significant for SSR and insignificant for mtCOI partial sequence data while gene flow was high across the populations. There was no evidence of isolation by distance between geographical and genetic distances. Bayesian clustering identified signature of admixture that suggests genetic contributions from two ancestral genetic lineages, and the median-joining haplotype network showed low differentiation of many different haplotypes from the most common haplotype. Low genetic differentiation and high gene flow indicates unrestricted migrations between populations. This information will contribute to the design of BLB control strategies.

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

Optimization using Genetic Algorithms (GA) is a well-known strategy in several scientific disciplines. The crossover is an essential operator of the Genetic Algorithm. It has been an active area of research to develop sustainable forms for this operand. In this work, a new crossover operand is proposed. This operand depends on giving an elicited description for the chromosome with a new structure for alleles of the parents. It is suggested that each allele has two attitudes, one attitude differs contrastingly with the other, and both of them complement the allele. Thus, in case of one attitude is good, the other should be bad. This is suitable for many systems which contain admired parameters and unadmired parameters. The proposed crossover would improve the desired attitudes and damp the undesired attitudes. The proposed crossover can be achieved in two stages: the first stage is a mating method for both attitudes in one parent to improving one attitude at the expense of the other. The second stage comes after the first improvement stage for mating between different parents. Hence, two concurrent steps for improvement would be applied. Simulation experiments for the system shows improvement in the fitness function. The proposed crossover could be helpful in different fields, especially to optimize routing algorithms and network protocols, an application that has been tested as a case study in this work.

Bartonella species, intracellular parasitic zoonotic pathogens, has drawn increasing attention for their diverse host range and associated human diseases. This study investigates Bartonella infection in small mammals in Yunnan Province, China, focusing on tissue tropism. 333 small mammals were sampled from 13 species, 3 orders, 4 families, and 4 genera in Heqing and Gongshan Counties. Conventional PCR and real-time quantitative PCR (qPCR) were utilized for detection and quantification, followed by bioinformatic analysis of obtained DNA sequences. Results show a 31.5% detection rate, varying across species. Notably, Apodemus chevrieri, Eothenomys eleusis, Niviventer fulvescens, Rattus tanezumi, Episoriculus leucops, Anourosorex squamipes, and Ochotona Thibetana exhibited infection rates of 44.4%, 27.7%, 100.0%, 6.3%, 60.0%, 23.5%, and 22.2%, respectively. Genetic analysis identified 30, 10, and 5 strains based on ssrA, rpoB, and gltA genes, with nucleotide identities ranging from 92.1% to 100.0%. Bartonella strains were assigned to B. grahamii, B. rochalimae, B. sendai, B. koshimizu, B. phoceensis, B. taylorii, and a new species identified in the Episoriculus leucops (GS136). Tissue distribution analysis revealed varied copy numbers across different tissues, with the highest load in spleen. These findings underscore Bartonella's diverse species and host range in Yunnan Province, highlighting its broad tissue tropism in small mammals.

To achieve an acceptable level of security on the web, the Completely Automatic Public Turing test to tell Computer and Human Apart (CAPTCHA) was introduced as a tool to prevent bots from doing destructive actions such as downloading or signing up. Mobile devices have small screens, and therefore, using the common CAPTCHA methods (e.g. text CAPTCHAs) in these devices raises usability issues. To introduce a reliable, secure, and usable CAPTCHA that is suitable for mobile devices, this paper introduces a hand gesture recognition CAPTCHA based on applying Genetic Algorithm (GA) principles on Multi-Layer Perceptron (MLP). The proposed method improves the performance of MLP-based hand gesture recognition. It has been trained and evaluated on 2201 videos of the IPN Hand dataset, and MSE and RMSE benchmarks report the index values of 0.0018 and 0.0424, respectively. Comparison with the related works shows a minimum of 1.79% fewer errors, and experiments produced a sensitivity of 93.42% and accuracy of 92.27% – 10.25% and 6.65% improvement compared to the MLP implementation.

Adipor1^tm1Dgen and Mfrp^rd6 mutant mice share similar eye disease characteristics. Previously, studies established a functional relationship of ADIPOR1 and MFRP proteins in maintaining retinal lipidome homeostasis and visual function. However, the independent and/or interactive contribution of both the genes to similar disease phenotypes, including fundus spots, decreased axial length and photoreceptor degeneration has yet to be examined. We performed a gene-interaction study where homozygous Adipor1^tm1Dgen and Mfrp^rd6 mice were bred together and the resulting doubly heterozygous F1 offspring were intercrossed to produce 210 F2 progeny. Four-month-old mice from all nine genotypic combinations obtained in the F2 generation were assessed for white spots by fundus photo documentation, for axial length by caliper measurements, and for photoreceptor degeneration by histology. Two-way factorial ANOVA was performed to study individual as well as gene interaction effects on each phenotype. Here, we report the first observation of reduced axial length in Adipor1^tmlDgen homozygotes. We show that while Adipor1 and Mfrp interact to affect spotting and degeneration, they act independently to control axial length, highlighting the complex functional association between these two genes. Further examination of the molecular basis of this interaction may help in uncovering mechanisms by which these genes perturb ocular homeostasis.

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

This review article delves into the pivotal role of DNA in advancing criminal investigations, presenting a comprehensive analysis of its impact on modern justice systems. DNA, as the genetic blueprint of individuals, has revolutionized forensic science, augmenting the accuracy and reliability of criminal investigations. This study explores the historical evolution of DNA analysis, from its initial discovery to the development of sophisticated techniques like polymerase chain reaction (PCR), short tandem repeat (STR) profiling, and next-generation sequencing (NGS). The article elucidates the intricate process of DNA collection, preservation, and analysis, highlighting its significance in establishing identity, proving guilt or innocence, and resolving cold cases. The utilization of DNA databases and international cooperation in cross-border investigations are also examined, emphasizing their contributions to solving complex criminal cases. Moreover, the ethical, legal, and privacy considerations surrounding DNA usage in criminal justice are critically evaluated. Through a comprehensive literature review, this article synthesizes empirical evidence showcasing how DNA evidence has exonerated wrongfully convicted individuals and provided closure to victims' families. It further elucidates how technological advancements have extended DNA analysis to trace evidence, touch DNA, and forensic genealogy, thereby broadening its application in criminal investigations.

Information molecules of DNA and RNA should obey principles of quantum mechanics where unitary operators in form of unitary matrices have key meanings. Unitary matrices are the basis of calculations in quantum computers. This article presents some author's results, which show that matrix forms of the representation of structured systems of molecular-genetic alphabets can be considered as sets of sparse unitary matrices related with phenomenologic features of the degeneracy of the genetic code. These sparse unitary matrices have orthogonal systems of functions in their rows and columns. A complementarity exists among some unitary genetic matrices in relation each other. Decompositions of numeric genetic matrices into sets of sparse unitary matrices are connected with the logical operation of modulo-2 addition used in quantum computers as well. Tensor (or Kronecker) families of unitary genetic matrices with their fractal-like properties are also considered. The described results are discussed in the frame of development of quantum-information approaches for modeling genetic systems.

Acid lime [Citrus aurantifolia (Christm.) Swingle] is a fruit crop, enriched with high commercial value and is cultivated in 60 out of 75 districts representing all geographical landscapes of Nepal. Lack of high yielding cultivars is probably one of the main reason for its extremely reduced productivity which warrants a deep understanding of genetic diversity in existing germplasm. Hereby, we aim to access the genetic diversity of acid lime germplasm cultivated at 3-different ecological gradients of eastern Nepal employing PCR-based Inter-Simple Sequence Repeats markers (ISSR). Altogether, 21 polymorphic ISSR markers were used to assess the genetic diversity in 60 acid lime cultivars sampled from different geographical locations. Analysis of binary data matrix was performed on the basis of bands obtained, scoring of the data was done accordingly, and principal coordinate analysis and phenogram were constructed using different computer algorithms. ISSR profiling yielded 234 amplicons, of which 87.18% were found to be polymorphic. The number of amplified fragments ranged from 7-18 with amplicon size ranging from 250-3200 bp. The NTSYS based Cluster analysis using UPGMA algorithm taking Dice Similarity coefficient separated 60 accessions into 2-major and 3-minor clusters. The genetic diversity analysis revealed the highest for Terai and the lowest for High-hill zone. Cluster I comprised of accessions from High-hill and Mid-hill regions revealing the close genetic relationship, whereas cluster II comprised of accessions from all three agro-ecological zones and the exotic varieties. Furthermore, our results revealed the accessions harvested from different geographical gradients were not genetically distinct, but highest diversity was observed in Terai accessions in comparison to the regions belonging to the High and Mid-hills. Thus, our data indicate that the ISSR provides a better option for evaluating the genetic diversity of Nepalese Acid Lime cultivars and furnished significant information, assisting parental selection in current and future breeding programs and germplasm conservation which ultimately may help to provide a technological breakthrough for the farmers of the developing country like Nepal.

Crimean Congo hemorrhagic fever virus (CCHFV) is the causative agent of a globally-spread tick-borne zoonotic infection with an eminent risk of fatal human disease. Imminent public health threat posed by disseminated virus activity and lack of an approved therapeutic make CCHFV an urgent target for vaccine development. We described the construction of a DNA vector expressing nucleocapsid protein (N) of CCHFV (pV-N13) and investigated its potential to stimulate cytokine and total/specific antibody responses in BALB/c and challenge experiment in IFNAR-/- mice. Due to lack of sufficient antibody stimulation towards N protein, we have selected CD24 protein as a potential adjuvant which has proliferative effect on B and T cells. Overall, our N expressing construct when administered solely or in combination with pCD24 vector elicited significant cellular and humoral responses in BALB/c, despite variations in particular cytokines and total antibodies. However, the stimulated antibodies produced due to expression of N protein have shown no neutralizing ability in VNA. Furthermore, challenge experiments were revealed protection potential of N expressing construct in IFNAR -/- mice model. In conclusion, we have shown that CD24 has prominent effect as a genetic adjuvant when co-delivers with a synergic foreign gene expressing vector. Besides, targeting of S segment of CCHFV can be considered as a practical way in developing vaccine against this virus due to its ability to induce immune response which leads to protection in challenge assays in IFN-gamma defective mice models.

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

Turnip mosaic virus (TuMV) is one of the most widespread and economically important virus infecting both crop and ornamental species of the family Brassicaceae. TuMV isolates can be classified to five phylogenetic lineages, basal-B, basal-BR, Asian-BR, world-B and Orchis. To understand the genetic structure of TuMV from radish in China, the 3′-terminal genome of 90 TuMV isolates were determined and analyzed with other Chinese isolates available. The results showed that the Chinese TuMV isolates from radish formed three groups: Asian-BR, basal-BR and world-B. More than half of these isolates (52.54%) were clustered to basal-BR group, and could be further divided into three sub-groups. The TuMV basal-BR isolates in the sub-groups I and II were genetically homologous with Japanese ones, while those in sub-group III formed a distinct lineage. Sub-populations of TuMV basal-BR II and III were new emergent and in a state of expansion. The Chinese TuMV radish populations were under negative selection. Gene flow between TuMV populations from Tai’an, Weifang and Changchun was frequent.

Proso millet is an important allotetraploid cereal crop, however, it is the least studied species of the Poaceae family as it is an under-utilized crop. Genomic resources of proso millet are very limited compared to the major crops. Understanding of genetic relationships among germplasm resources is important for future breeding endeavors. In the present study, the SSR markers were employed to assess the polymorphism and genetic diversity of 100 millet accessions from different countries, which were tested in the dry steppe zone conditions of the Akmola region from 2020 to 2022. The use of 20 SSR markers detected a total of 47 alleles with an average allele number of 2.35 per locus among these proso accessions. Nine of them were polymorphic among genotypes, which suggests that these SSR markers can be used for genetic studies. The results showed a moderate level of polymorphism information content (PIC) averaged at 0.424, ranging from 0.125 to 0.795. The markers SSR 67, SSR-82, SSR-85 and SSR-109 showed high PIC values, 0.536, 0.756, 0.795 and 0.758 respectively. Markers SSR 85 and SSR 86 significantly correlated to agronomic traits, such as productive tillering (PT) and grain yield (GY). The genetic structure, UPGMA cluster and PCoA assay indicated that the accessions originated from Central Asia had higher genetic diversity. Based on Structure (K=3), all the accessions were divided into three groups, whereas the gene pool originated from Central Asia were detected in all the three clusters. Based on principal component analysis (PCA), the accessions of Central Asian origin were genetically closer to the North Asian group.

The relationship between the host and the microbiome, or the assemblage of microorganisms (including bacteria, archaea, fungi, and viruses), has been proven crucial for its health and disease development. The high dimensionality of microbiome datasets has often been addressed as a major difficulty for data analysis, such as the use of Machine Learning (ML) and Deep Learning (DL) models. Here we present BiGAMi, a bi-objective genetic algorithm fitness function for feature selection in microbial datasets to train high-performing phenotype classifiers. The proposed fitness function allowed us to build classifiers that outperformed the baseline performance estimated by the original studies by using as few as 0.04% to 2.32% features of the original dataset. In 19 out of 21 classification exercises, BiGAMi achieved its results by selecting 6-68% fewer features than the highest performance of a Sequential Forward Feature Selection algorithm. This study showed that the application of a bi-objective GA fitness function against microbiome datasets succeeded in selecting small subsets of bacteria whose contribution to understood diseases and the host state was already experimentally proven. Applying this feature selection approach to novel diseases is expected to quickly reveal the microbes most relevant to a specific condition.

Intrusion Detection Systems play a crucial role in a network. They can detect different network attacks and raise warnings on them. Machine learning-based IDSs are trained on datasets that, due to the context, are inherently large, since they can contain network traffic from different time periods and often include a large number of features. In this paper, we present two contributions: the study of the importance of feature selection when using an IDS dataset, while striking a balance between performance and the number of features; and the study of the feasibility of using a low-capacity device, the Nvidia Jetson Nano, to implement an IDS in a low-capacity network. The results, comparing the GA with other well-known techniques in feature selection and reduction, show that the GA has a higher F1-score of 76%, although the time to find the optimal set of features surpasses other methods; the reduction of the number of features reduces the processing time without a significant impact in f1-score. The Jetson Nano allows the classification of network traffic with an overhead of 10 times in comparison to a traditional server.

Winged bean productivity and the potential to enhance food and nutrition security in sub-Saharan Africa are recurrently affected by several constraints, including but not limited to the lack of genetic improvement. The dearth of adequate information on the genetic diversity that guides the choice of progenitors’ selection among other advantages has been a major setback in planning appropriate improvement programs. This study assessed 15 winged bean accessions for genetic diversity using 10 quantitative traits and 10 microsatellites (SSRs) markers. These accessions were evaluated using RCBD with three replicates for two growing seasons. Ten plants constitute each accession during evaluation from where leaf samples were obtained for SSR marker genotyping. Phenotypic results revealed significant variation (p < 0.05) in the performances of the accessions for the measured traits. H2 estimates varied from 18.92% for seed length to 72.67% for seed weight per plant. Pod weight had a positive and significant correlation with pod length (0.53), pod width (0.70), and number of seeds per pod (0.64). However, the number of seeds per pod negatively correlated with days to maturity (-0.71). The number of seeds per pod was positively predicted by pod weight, seed thickness, and days to maturity. Cluster analysis revealed two genetic groups characterized by different traits. The ten SSRs revealed an average allele count of 4.2, gene diversity of 0.25, and polymorphic information content of 0.22. Analysis of molecular variance revealed within the population of 95% as compared to between population variance of 5%. Phylogeny analysis revealed two primary genetic groups from where five secondary genetic subgroups were identified and only three accessions (TPt-6, TPt-126, and TPt-48) showed genetic purity. This study provides the basis for further studies aimed at exploiting existing variations in winged bean germplasm for its improvement.

Background: Currently, many detection methods have high sensitivity to the diagnosis of lung cancer. However, some postoperative patients with pulmonary nodule were eventually diagnosed as benign nodules. The ideal evaluation of an individual with a pulmonary nodule would expedite therapy for a malignant nodule and minimize testing for those with a benign nodule.Methods: This case-control study is designed to explore the relationship between ACE1 rs4646994 polymorphism and the risk of lung cancer in patients with pulmonary nodules, 400 individuals with lung cancer and benign pulmonary nodules were included. A DNA extraction kit was used to extract plasm DNA from peripheral blood. The relationship between ACE1 rs4646994 and the risk of lung cancer in patients with pulmonary nodules was determined by chi-square test, logistic regression analysis and cross analysis. Results: The results showed that the DD genotype of ACE1 rs4646994 may increase the risk of lung cancer in patients with pulmonary nodules, and this correlation was more significant in the female subgroup. In the age stratification analysis, it was found that the risk of lung cancer was significantly increased in the DD genotype of ACE1 rs4646994 in the older subgroup (&gt; 45 years). In addition, the possibility of EGFR mutation in lung adenocarcinoma patients with ACE1 rs4646994 DD genotype was lower than that of II or ID genotype carriers. Conclusions: Our study indicated that ACE1 rs4646994 polymorphism increases the risk of lung cancer in patients with pulmonary nodules from China.

Ningxiang pig is renowned breed for its exceptional meat quality, but it possesses suboptimal carcass traits. To elucidate the genetic architecture of meat quality and carcass traits in Ningxiang pigs, we assessed heritability and executed a genome-wide association study (GWAS) concerning carcass length, backfat thickness, meat color parameters (L.LD, a.LD, b.LD), and pH at two postmortem intervals (45 minutes and 24 hours) within a Ningxiang pig population. Heritability estimates ranged from moderate to high (0.30 ~ 0.80) for carcass traits and from low to high (0.11 ~ 0.48) for meat quality traits. We identified 21 significant SNPs, the majority of which were situated within previously documented QTL regions. Furthermore, the HMGA1 gene emerged as a pleiotropic gene correlated with carcass length and backfat thickness. The ADGRF1, FKBP5, and PRIM2 genes were associated with carcass length, while the NIPBL gene was linked to backfat thickness. These genes hold potential for use in selective breeding programs targeting carcass traits in Ningxiang pigs.

Accurate and efficient battery modeling is essential to maximize the performance of isolated energy systems and to extend battery lifetime. This paper proposes a battery model that represents the charging and discharging process of a lead-acid battery bank. This model is validated over real measures taken from a battery bank installed in a research center placed at “El Chocó”, Colombia. In order to fit the model, three optimization algorithms (Particle Swarm Optimization, Cuckoo Search, and Particle Swarm Optimization+Perturbation) are implemented and compared, being the last one a new proposal. This research shows that the model with the proposed algorithm is able to estimate and manage the real battery characteristics as SOC and charging/discharging voltage. The comparison between simulations and real measures shows that the model is able to absorb reading problems, signal delays, and scaling errors. The approach we present can be implemented in other types of batteries especially those used in stand-alone systems.

Dental implants provide a predictable treatment option for partial and complete edentulism via the placement of fixed permanent artificial root to support prosthetic dental crowns. Despite the high survival rates, long-term implant failures are still reported leading to implant removal and additional financial and health burdens. While extrinsic factors that improve survival rate of implants have been well explored, the impact of genetic factors on this matter is poorly understood. A systematic review and meta-analysis study was conducted to determine whether genetic factors contribute to increase the risk of dental implant failure. A comprehensive search for peer-reviewed articles on dental implants and genetics was performed using various literature database libraries. The study design was conducted according to PRISMA guidelines, and the obtained records were registered in PROSPERO database. According to the exclusion/inclusion criteria, 11 studies were eligible for this study out of 808 articles. The meta-analysis of the combined association studies of DNA variations and dental implants did not indicate an increase risk for implant failure due to DNA variations in IL-1B, IL-10 and TNF-α. This study emphasizes the need for larger randomized controlled clinical trails to inform clinicians and patients about the role of genetic factors on dental implant survival and success rate in healthy and compromised patients.

The aim of this paper is to optimize thickness variation function of simply supported and cantilever beams, in terms of maximizing gaps between chosen neighboring frequencies and analyze obtained results. The optimization results are examined in terms of achieving the objective function (related to eigenproblem), but also in terms of their dynamics stiffness (forced vibrations excited by point harmonic load). In the optimization process, a Genetic Algorithm was used. Problems related to structural dynamics were solved by FEM implementation into the algorithm. Sample results were presented, and the developed algorithm was analyzed in terms of result convergence by examining several variable parameters. The authors demonstrated the validity of applying the described optimization tool to the presented problems. Conclusions were drawn regarding the correlation between stiffness and mass distribution in the optimized beams and the natural frequency modes in terms of which they were optimized.

Desert ecosystem occupies an important position in the composition of global biodiversity. The Tarim Basin locates in south Xinjiang of China and has the world 's second largest mobile desert, the Taklamakan Desert. As an endemic species in this region, Phrynocephalus forsythii has been demonstrated with potential high extinction risk to climate change. In order to understand the overall genetic status and provide accordant conservation strategies of the species, we investi-gated the genetic diversity and population structure of P. forsythii from 15 sites in the Tarim Basin using 21 highly polymorphic microsatellite markers. We found significant genetic structure across the study region. We also revealed generally low levels of gene flow between the 25 sites, sug-gesting individual dispersal and migration may be restricted within populations. In addition, geographical distance and ambient temperature might be important factors in explaining the observed genetic structure. Our results will provide scientific basis for the future protection of P. forsythii in this area, as well as an important reference for the conservation and management of biodiversity in desert ecosystems.

Peripheral arterial diseases (PAD) are complex cardiovascular conditions influenced by environmental factors and somatic mutations in multiple genes involved in hematopoiesis and inflammation. While traditional risk factors such as smoking, hypercholesterolemia, and hypertension have been extensively studied, the role of somatic mutations in PAD progression remains underexplored. This article aims to provide a comprehensive review of the molecular mechanisms, genetic landscape, prognostic significance, and clinical implications of somatic mutations in PAD. The expansion of clonal hematopoiesis of indeterminate potential (CHIP) clones in the circulating blood, named clonal hematopoiesis (CH), leads to the infiltration of these clones into atherosclerotic plaques and the production of inflammatory cytokines, increasing the risk of cardiovascular diseases including PAD. Furthermore, recent experimental evidence has demonstrated the involvement of somatically mutated TP53 genes with a high variant allele frequency (VAF) in PAD development and prognosis. The review delves into the relationship between CH and PAD, elucidating the prevalence, impact, and underlying mechanisms of this association. This understanding paves the way for novel therapeutic approaches targeting CHIP to promote tissue regeneration and improve outcomes in PAD patients. It emphasizes the need for further re-search to fully unravel the genetic footprint of the disease and highlights potential clinical implications. The findings presented in this article lay the foundation for personalized medicine approaches and open avenues for the development of targeted therapies based on somatic mutation profiling.

The KCNA1 gene encodes Kv1.1 voltage-gated potassium channel α subunits, which are crucial for maintaining healthy neuronal firing and preventing hyperexcitability. Mutations in the KCNA1 gene can cause several neurological diseases and symptoms, such as episodic ataxia and epilepsy, which may occur alone or in combination, making it challenging to establish simple genotype-phenotype correlations. Previous analyses of human KCNA1 variants have shown that epilepsy-linked mutations tend to cluster in regions critical for the channel’s pore, whereas EA1-associated mutations are evenly distributed across the length of the protein. In this review, we examine 17 recently discovered pathogenic or likely pathogenic KCNA1 variants to gain new insights into the molecular genetic basis of KCNA1 channelopathy. We provide the first systematic breakdown of disease rates for KCNA1 variants in different protein domains, uncovering potential location biases that influence genotype-phenotype correlations. Our examination of the new mutations strengthens the proposed link between the pore region and epilepsy and reveals new connections between epilepsy-related variants, genetic modifiers, and respiratory dysfunction. Additionally, the new variants include the first two gain-of-function mutations ever discovered for KCNA1, the first frameshift mutation, and the first mutations located in the cytoplasmic N-terminal domain, broadening the functional and molecular scope of KCNA1 channelopathy. Moreover, the recently identified variants highlight emerging links between KCNA1 and musculoskeletal abnormalities and nystagmus, conditions not typically associated with KCNA1. These findings improve our understanding of KCNA1 channelopathy and promise to enhance personalized diagnosis and treatment for individuals with KCNA1-linked disorders.

This study genetically identified the coat color in a small wild horses’ population and compared the correspondence with the visual coat color registrations. Individual DNA from 90 Sarcidano horses was used for the genetic assignment of the coat color, to explore the correspondence with the individual form containing phenotypical traits. By genotyping and sequencing analyses of the MC1R exon 1 and ASIP exon 3, e real image of the coat color distribution in this population has been obtained. Surprisingly, once we compared the genetic results with the individual forms reporting the phenotypic data for each subject, a certain degree of non-correspondence between the phenotypic and genetic data, in relation to coat color emerged. Genetic results showed a clear prevalence of the Chestnuts (n=58), a quite large number of Blacks (n=28) and a very small presence of Bays (n=4), whereas phenotypic distribution resulted in 38 Chestnuts, 40 Bays, only 2 Blacks, and 10 Greys (without the possibility of recognizing the true color they carried). Chestnut resulted a very representative coat color, while many horses visually identified as Bays, were genetically Blacks. This discrepancy suggests the importance of a sure coat color identification to ensure an adequate features registration, reliable also to predict the offspring’s coat color.

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

Lightning discharges in electric power networks generate voltage and current surges that are propagated through the electrical network causing damage and shutdowns in the electrical system. To protect the system against these phenomena, surge arresters are very effective and widely used by electrical utilities in their electric grids. This paper presents a methodology for optimized surge arrester allocation based on genetic algorithm (GA), creating a simulation environment in the software ATP (Alternative Transients Program) to implement the proposed methodology. The optimized allocation procedure is based on a fitness function that minimizes the cost of surge arresters and maximizes the number of protected equipment. To carry out this optimized arrester allocation procedure using ATP may demand too much processing time when running large distribution grids. To overcome this difficulty a procedure is proposed to obtain an overvoltage severity description of the grid and select the most critical electric nodes for the incidence of lightning discharges, in the GA allocation procedure. The case study is applied to the IEEE 123-bus electrical feeder to demonstrate the effectiveness of the proposed methodology.

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

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

The angiotensin-converting enzyme (ACE) is a peptidase that is involved in the synthesis of Angiotensin II, the bioactive component of the renin-angiotensin system. A growing body of literature argues for a beneficial impact of ACE inhibitors (ACEi) on age-associated metabolic disorders, mediated by cellular changes in reactive oxygen species (ROS) that improve mitochondrial function. Yet, our understanding of the relationship between ACEi therapy and metabolic parameters is limited. Here, we used three genetically diverse strains of Drosophila melanogaster to show that Lisinopril treatment reduces thoracic ROS levels and mitochondrial respiration in young flies, and increases mitochondrial content in middle-aged flies. Using untargeted metabolomics analysis, we also showed that Lisinopril perturbs the thoracic metabolic network structure by affecting metabolic pathways involved in glycogen degradation, glycolysis, and mevalonate metabolism. The Lisinopril-induced effects on mitochondrial and metabolic parameters, however, are genotype-specific and likely reflect the drug’s impact on nutrient-dependent fitness traits. Accordingly, we found that Lisinopril negatively affects survival under nutrient starvation, an effect that can be blunted by genotype and age in a manner that partially mirrors the drug-induced changes in mitochondrial respiration. In conclusion, our results provide novel and important insights into the role of ACEi in cellular metabolism.

Prunus tenella is a rare and precious relict plant in China. It is an important genetic resource for almond improvement and an indispensable material in ecological protection and landscaping. However, the research of molecular breeding and genetic evolution has been severely restricted, due to the lack of genome information. In this investigation, we created a chromosome-level genomic pattern of P. tenella, 231Mb in length with a contig N50 of 18.1 Mb by Hi-C techniques and high-accuracy PacBio HiFi sequencing. The present assembly predicted 32088 protein-coding genes, and an examination of the genome assembly indicated that 94.7% among all assembled transcripts were alignable to the genome assembly; most (97.24%) were functionally annotated. By phylogenomic genome comparison, we found that P. tenella is an ancient group that diverged approximately 13.4 million years ago (Mya) from 13 additional closely related species and about 6.5 Mya from the cultivated almond. Collinearity analysis revealed that P. tenella is highly syntenic and has high sequence conservation with almond and peach. However, this species also exhibit many presence/absence variants. Moreover, a large inversion at the 7,588 kb position of chromosome 5 was observed, which may have a significant association with phenotypic traits. Lastly, population genetic structure analysis in eight different populations indicated a high genetic differentiation among the natural distribution of P. tenella. This high-quality genome assembly provides critical clues and comprehensive information for the systematic evolution, genetic characteristics, and functional gene research of P. tenella. Moreover, it provides a valuable genomic resource for in-depth study in protecting, developing, and utilizing P. tenella germplasm resources.

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

The populations of clearwing moth borers in Korea have recently exploited and severe damage on pin oaks (Quercus palustris Munchh.). We conducted the field monitoring and the molecular analyses to identify them in an integrated manner. Morphological examination and molecular analyses on COI-gene based intra-and inter-specific genetic divergences (GD) were revealed that the borers were recognized as two invasive species, Sphecodoptera sheni and Paranthrenella pinoakula sp. nov.. The maximum intraspecific GD was found to be 1.9%, whereas the minimum interspecific GD was confirmed as 8.1%, with a distinct barcoding gap. Both MJ network and NJ tree were also showed that 18 haplotypes (HAPs) were detected from the 52 COI sequences. The borers were revealed total 17 HAPs: i) H1-H7 were detected from all seven regions with S. sheni; ii) Wonju and Goyang populations of S. sheni were revealed as more than three HAPs; iii) H7 was closely connected with H8 of Chinese population of S. sheni; iv) H9-H10 were detected as the other samples from Wonju population with P. pinoakula sp. n., and closely located with congeneric species. Maximum likelihood tree also revealed that P. pinoacula sp. n. nested within the congeneric species by genetically separating from the S. sheni.

The Complete Coverage Path Planning (CCPP) is a key technology in the field of agricultural robot, and has great significance for improving the efficiency and quality of tillage, fertilization, harvesting and other agricultural robot operations, as well as reducing the operation energy consumption. The traditional boustrophedon or heuristic search algorithm based CCPP methods, when coping with the field with irregular boundaries, obstacles and other complex environments, still face many problems and challenges, such as large repeated work areas, multiple turns or U-turns, low operation efficiency and prone to local optimum. In order to solve the above problems, an improved-genetic-algorithm-based CCPP method was proposed in this paper, which introduces the idea of chromosome pairs and multi-points mutation to improve its global optimization ability in complex environments, reduce the repeated work areas and the number of turns and U-turns, and thereby improve the operation efficiency. The simulation and experimental results on simple regular fields showed that the proposed improved genetic algorithm-based CCPP method achieved the comparable performance with the traditional boustrophedon-based CCPP method. However, on the complex irregular fields, although the proposed CCPP method increased the number of turns by 13.76%, the area of repeated operation and the number of U-turns were decreased by 38.54% and 35.00% respectively, and saves the battery voltage by 7.82% on average compared to the boustrophedon-based CCPP method. This proved that the proposed CCPP method has strong adaptive capacity to the environment, and has practical application value in improving the efficiency and quality of agricultural machinery operations, and reducing the energy consumption.

Service pricing is a bottleneck in the development of innovation services, as it is the issue of most concern between the suppliers and demanders. In this paper, a negotiation pricing model that is based on the multiobjective genetic algorithm is developed for innovation service pricing. Regarding the service pricing process as a multiobjective problem, the objective functions, which include the service price, service efficiency, and service quality, for suppliers and demanders are constructed. As the solution of a multiobjective problem is typically a series of alternatives, another negotiation process is necessary for determining the final decision. A learning strategy is adopted during the negotiation process to simulate reality. Finally, the model is implemented for an innovation service transaction, the objective of which is to identify the optimal price plan. The results demonstrate that the model can provide quantitative decision support for the pricing of an innovation service and ultimately yield a win-win result for both the supplier and demander of the innovation service. Furthermore, the influence of the parameters during the negotiation process is analyzed in detail. The effects of the learning strategy on accelerating the negotiation process, as well as the chosen of reasonable parameters are given.

Keyhole neurosurgery implies reaching a target area inside the brain through an entry point specified by the neurosurgeon. In order to avoid complications, a risk assessment procedure must be done to establish the minimum risk trajectory from the entry point to the target area. The neurosurgeon establishes the risk values for the brain structure according to the type of intervention. The preset brain structure risk value is used to assess the risk value for each voxel of the brain. This paper proposes an improved risk assessment methodology based on the sum of N maximum risk values for each voxel. Then, risk assessment for a trajectory is done by adding the risk of all voxels that are part of the path. The safest trajectory is defined as the trajectory with the lower risk. Our proposed search trajectory methodology includes a Genetic Algorithm (GA) for finding the safest trajectories. The use of a GA drastically reduces the number of trajectories to analyze, speeding up the planning procedure. The achieved results were qualified by expert neurosurgeons as satisfactory. Our proposed method allows neurosurgeons to calibrate the surgical planning system by allowing them to establish the risk brain structure and the risk value for each structure.

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.

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

Flood routing in flood forecasting issue, calculation the height of flood bands, determining the river boundaries, and estimation of protective facilities for flood –exposed building is applicable. In many cases, due to the lack of measuring stations, the status of the upstream flood generating hydrograph is not known. The purpose of this study is to present an integrated method comprising of an optimization model and a hydrodynamic numerical model for flood modeling to determine the upstream hydrograph using the provided hydrograph at the downstream measuring station of a river. The routing procedure consists of three steps: (1) generating a hypothetical upstream hydrograph using genetic algorithm method; (2) hydrodynamic modeling using a numerical simulation model for flood routing according to the hypothetical hydrograph which is generated in the first step; (3) compare the calculated and observed hydrograph in downstream by using a fitness function. This recommended procedure was named as Reverse Flood Routing Method (RFRM) and was then applied to Karun River, the largest river in Iran. Comparing the generated upstream hydrograph by the RFRM model with the corresponding measured hydrograph at Ahvaz hydrometric station, as an ungauged river location, shows the high accuracy of the recommended model in this study.

The Huichang fault Basin in Jiangxi Province exhibits high terrestrial heat flow values and a dense concentration of hot springs. However, existing research is limited to individual geothermal fields without a comprehensive basin-wide analysis of geothermal water origins. This study investigates the source of geothermal water across five representative geothermal fields using hydrochemical components and isotopic analyses. The findings indicate: (1) The hydrochemical type transitions from bicarbonate sulphate-sodium-calcium in the north to bicarbonate sodium-calcium in the south; (2) Major chemical components of the geothermal water derive from evaporative salt rocks (Cretaceous sand conglomerate cover) and siliceous formations (granite/metamorphic rock basement); (3) The recharge elevation for geothermal water is approximately 1 km, sourced from adjacent hills. The central basin's geothermal water has an apparent age of about 26,000 years, with δ¹³C indicating participation in deep circulation; (4) Thermal reservoir temperatures range from 74 to 113℃, with the lowest temperatures at the basin's edges (Anzishan and Shangjin geothermal fields) and higher temperatures towards the center, suggesting increased warming and a circulation depth of 1.5 to 2.5 km; (5) Simulation results of reverse water-rock interaction indicate that mineral precipitation-dissolution processes are governed by the basin's main thermal control structures. The geothermal fields are predominantly influenced by reduction alterations associated with granite and are closely linked to paleo-geothermal waters in the enclosed basin, with the formation of these fields heavily reliant on the heat production or conduction from radioactive elements in granite.

Capirona (Calycophyllum spruceanum Benth) is a tree species of commercial importance widely distributed in South American forests and is traditionally used for its medicinal properties and wood quality. Studies on this tree species have been focused mainly on wood properties, propagation and growth. Genetic studies on capirona are very limited to date. Today it is possible to explore genetic diversity and population structure in a fast and reliable manner by using molecular markers. We here used 10 Random Amplified Polymorphic DNAs (RAPDs) markers to analyze genetic diversity and population structure of 59 samples of capirona that were sampled from four provinces located in the eastern region of the Peruvian amazon. A total of 186 bands were manually scored, generating a 59 x 186 presence/absence matrix. We used R software to calculate genetic distances based on provesti coefficient. A dendrogram was generated using the UPGMA clustering algorithm and showed four groups that correspond to the geographic origin of the capirona samples. Similarly, a discriminant analysis of principal components (DAPC) confirmed that capirona is grouped into four clusters. However, we also noticed few accessions are intermingled. Genetic diversity estimation was conducted considering the four groups (populations) identified by adegenet package in R. Nei's genetic diversity estimate varied from 0.26 to 0.39 and Shannon index ranged from 2.48 to 2.83. AMOVA analysis revealed the greatest variation exist within populations (69.7%) and indicated that variability among populations is 31.5%. To our best knowledge, this is the first investigation employing molecular markers in capirona in Peru considering their natural distribution, and sheds light towards its modern genetic improvement and for the sustainable management of forests in Peru.

Sickle cell disease (SCD) is a blood disorder caused by a point mutation on the beta globin gene resulting in the synthesis of abnormal hemoglobin. Fetal hemoglobin (HbF) reduces disease severity, but the levels vary from one individual to another. Most research has focused on common variants which differ across populations and hence do not fully account for HbF variation. To investigate rare and common variants influencing HbF levels in SCD, we performed targeted next generation sequencing covering exonic and other significant fetal hemoglobin-associated loci, including BCL11A, MYB, HOXA9, HBB, HBG1, HBG2, CHD4, KLF1, MBD3, ZBTB7A and PGLYRP1. Results revealed a range of functionally relevant genetic variants. Notably, there were significantly more deletions in individuals with high HbF levels (11% vs 0.9%). We identified frameshift deletion in individuals with high HbF levels and frameshift insertions in individuals with low HbF. CHD4 and MBD3 genes, interacting in the same sub-network, were identified to have a significant number of pathogenic or non-synonymous mutations in individuals with low HbF levels, suggesting an important role of epigenetic pathways in the regulation of HbF synthesis. This study provides new insights in selecting essential variants associated with extreme HbF levels in SCD.

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

Reservoirs are recognized as one of the most efficient infrastructure components in integrated water resources management and development. At present, with the ongoing advancement of social economy and requirement of water, the water resources shortage problem has worsened, and the operation of reservoirs, in terms of consumption of flood water, has become significantly important. Reservoirs perform both regulation of flood and integrated water resources management, in which the flood limited water level is considered as the most important parameter for trade-off between regulation of flood and conservation. To achieve optimal operating policies for reservoirs, large numbers of simulation and optimization models have been developed in the course of recent decades, which vary notably in their applications and working. Since each model has their own limitations, the determination of fitting model for derivation of reservoir operating policies is challenging and most often there is always a scope for further improvement as the selection of model depends on availability of data. Subsequently, assessment and evaluation associated with the operation of reservoir stays conventional. In the present study, the Soil and Water Assessment Tool (SWAT) models and a Genetic Algorithm model has been developed and applied to two reservoirs in Ganga River basin, India to derive the optimal operational policies. The objective function is set to minimize the annual sum of squared deviation form desired irrigation release and desired storage volume. The decision variables are release for irrigation and other demands (industrial and municipal demands), from the reservoir. As a result, a simulation-based optimization model was recommended for optimal reservoir operation, such as allocation of water, flood regulation, hydropower generation, irrigation demands and navigation and e-flows using a definite combination of decision variables. Since the rule curves are derived through random search it is found that the releases are same as that of demand requirements. Hence based on simulated result, in the present case study it is concluded that GA-derived policies are promising and competitive and can be effectively used operation of the reservoir.

The study attempts, above all, to provide a summary, with a strictly scientific basis, about the strategies of conservation of autochthonous agrobiodiversity followed in Italy. A special focus is dedicated on vegetables and, therefore, could represent a contribution to improve the national strategy for the safeguarding of its agrobiodiversity in general. The paper offers also an outlook on the most critical factors of the ex situ conservation and some actions which need to be taken. Some examples of ‘novel’ recovered neglected crops are also given. Finally a case study is proposed: ‘Mugnolicchio’, a neglected race of Brassica oleracea L., cultivated in Altamura (Ba) in southern Italy. ‘Mugnolicchio’ might be considered as an early step in the evolution of broccoli (B. oleracea L. var. italica Plenck) like ‘Mugnoli’ another neglected race described from Salento (Apulia).

Preventive Maintenance (PM) is a periodic maintenance strategy, which has great results for devices in extending their life, increasing productivity, and most importantly, help avoid unexpected breakdowns and their costly consequence. A preventive maintenance scheduling (PMS) is determining the time for carrying out PM, and it represents a sensitive issue in terms of impact on production if the time for the PM process is not optimally distributed. In this study, hybrid heuristic methods were used to solve the PMS problem, as genetic algorithm and Tabu research were adopted. The optimal solution was reached in a short time compared to previous studies in which optimal methods were used, integer programming and nonlinear integer programming. Also, sensitivity analysis was applied to measure the robustness and strength of the method used, where an optimal solution was obtained for all experiments and in record time. This method can be used for power plants in privet or public sectors, to generate an optimal PMS, to save money, and to avoid water or electricity cuts.

Powdery mildew is a severe disease causing substantial yield losses in wheat production worldwide. The Chinese wheat landrace Changanhongmai (CAHM) exhibits high resistance to the physiological race E09 of powdery mildew. This study aims to characterize the powdery mildew resistance gene in CAHM, and develop molecular markers for marker-assisted selection of wheat. To investigate the genetic characteristics of this resistant gene, we developed F1 plants, F2 generation population, and F2:3 families from a cross between CAHM and SY225. Genetic analysis demonstrated that all F1 plants were resistant to the disease, while the ratio of resistant to susceptible plants was 3: 1 in both the F2 population and F2:3 families, indicating that CAHM is inherited in a manner of single dominant powdery mildew resistance gene, which was tentatively designated as PmCAHM. By using bulk segregation analysis, we constructed a genetic map encompassing Xgwm273, Xwmc626, Xgwm11, Xgwm18, Xgdm28, Xgpw7812, Xgpw5195, Xwmc694 and PmCAHM. Among these markers, Xgpw7812 and Xgpw5195 were flanking markers tightly linked to PmCAHM at a genetic distance of 2.5 cM and 8.4 cM, respectively. Furthermore, nullisomic-tetrasomic analysis revealed that PmCAHM is located on chromosome 1B. These results indicate that PmCAHM differs from the internationally recognized powdery mildew resistance genes in both location and source. In addition, a new germplasm/line NW1748 with large seeds and powdery mildew resistance was developed from the cross and back cross populations of Fengyou 1718 (FY1718)/CAHM//FY 1718. Therefore, PmCAHM can serve as a novel powdery mildew resistance source for breeding of wheat by using NW1748 as the donor in the future.

Improper design of friction pendulum bearings can lead to poor seismic reduction performance and may result in the failure of local vulnerable components. And the parameter design of friction pendulum bearings mainly relies on experience and verification calculations at present. This paper proposes an adaptive genetic algorithm considering the overall evolution state of the population, adjusting crossover and mutation probabilities adaptively based on individual fitness and population diversity. Compared to traditional algorithms, it exhibits better global search capabilities and convergence efficiency. Combining the improved genetic algorithm with finite element models, a parameter optimization method is proposed. The parameters of friction pendulum bearings are optimized, providing reference for the design of seismic isolation devices.

Centropomus undecimalis (common snook, sergeant fish or robalo) and Centropomus parallelus (fat snook, smallscale fat snook, little snook or chucumite) have tropical and subtropical wide distribution from southern Florida (Gulf of Mexico) to southern Brazil (Rio Grande do Sul) waters, and due the value of their flesh, these species have been explored with predatory fishing, generating a deficiency for their conservation. Adequate conservation of this species depends on population genetic parameters. Thus, in this study, we used microsatellite markers to investigate the genetic variability and the genetic characterization of C. undecimalis and C. parallelus populations, identifying if the citations species are geographically connected, using 10 loci of microsatellites, bringing strategies of management, sustainability, and conservation for species citations, and providing genetic subsidies needed to be used in breeding programs for snooks. Microsatellite molecular genetic markers were efficient in the study of the genetic variability of the samples of C. parallelus and C. undecimalis, resulting in data on the genetic structuring of these populations, thus aiding in their management and conservation. The data presented may provide subsidies for future use in programs aimed at animal breeding of sea bass, establishing tools for use in fish farming. In addition, despite being two species that are not on the endangered list, we can observe that there are genetic differences between the studied populations of the same species.

Grain quality, cooking quality and nutritional quality traits are some of the major attributes that enhance uptake and utilisation of improved cowpea varieties. Therefore, there is need for a better understanding of the genetic variation and inter-relationships among these quality traits in cowpea to integrate them in cowpea breeding programs. The study was conducted to determine genetic variability among 306 cowpea genotypes for grain quality, cooking quality and nutritional quality traits and to understand the interrelationships among these traits for exploitation in breeding programs. The results showed highly significant differences (P

As a result of the spread of SARS-CoV-2, a global pandemic was declared. Indiscriminate COVID-19 vaccination has been extended to include age groups and naturally immune people with minimal danger of suffering serious complications due to COVID-19. Solid immuno-histopathological evidence demonstrates that the COVID-19 genetic vaccines can display an off-target distribution in tissues that are terminally differentiated, triggering autoimmune reactions. These include the heart and brain, which may incur in situ production of spike protein eliciting a strong autoimmunological inflammatory response. Due to the fact that every human cell which synthesizes non-self antigens becomes inevitably the target of the immune system, and since the human body is not a strictly compartmentalized system, accurate pharmacokinetic and pharmacodynamic studies are needed in order to determine precisely which tissues can be harmed. Therefore, our article aims to draw the attention of the scientific and regulatory communities on the critical need of bio-distribution studies for the genetic vaccines against COVID-19, as well as of rational harm-benefit assessments by age group.

The winged bean is an underutilized legume that has the potential to contribute to nutrition and food security globally, especially in sub-Saharan Africa (SSA). However, very little research attention is paid to exploiting its full potential due to a lack of adequate knowledge of the existing genetic diversity in the available winged bean germplasm. To bridge this gap, seed morphometric and selected agro-morphological traits which are crucial for yield and seed quality determination in crop plants were used to assess the genetic diversity of thirty accessions of winged bean sourced from the Gene bank of the International Institute of Tropical Agriculture (IITA), Ibadan. The thirty winged accessions were evaluated under field trials at three agro-ecological zones in Nigeria. The agro-morphological data as well as the seed morphometric dataset obtained from a Multi-Spectral Imaging (MSI) system were subjected to Analysis of Variance (ANOVA), Principal Component (PC) analysis, cluster analysis, and correlation analysis. Significant differences (p &lt; 0.05) were observed among the accessions for all measured traits. The first three PCs accounted for 93% of the variation observed among the accessions for all measured traits. Accessions were grouped into three clusters based on the agro- morphological traits and three clusters under the MSI system. Seed yield per plant had the highest estimates of significant positive correlation with hundred seed weight, pod weight per plant, etc. at both phenotypic and genotypic levels. TPt-31 had the highest seed yield per plant, pod weight per plant, and early maturity while TPt-7 had an extended flowering and maturity period, the highest number of pods per peduncle and pods per plant as well as the lowest seed yield. These accessions could serve as a good resource for future winged bean improvement programs. The result also confirmed that the MSI system is an invaluable tool for discriminating among accessions of the same crop species.

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

Cerebellar ataxia, neuronopathy, and vestibular areflexia syndrome (CANVAS) is caused by biallelic pathogenic expansions, or compound heterozygosity with other pathogenic variants in the RFC1 gene. CANVAS is estimated to be underdiagnosed, both because of the lack of formal diagnostic criteria and molecular challenges that translate to lesser access and high cost of routine testing. Our aim was to address the need for making CANVAS genetic testing routine, by designing a streamlined two-step PCR consisting of a short-allele screening PCR and a confirmatory PCR with fragment capillary electrophoresis detection. Exome sequencing of RFC1 was additionally foreseen to resolve potential compound heterozygosity cases. Specificity of our approach was evaluated using ataxia patients with known non-CANVAS diagnoses, and optimized using Southern blot confirmed CANVAS patients. We evaluated our approach by testing patients consecutively referred for clinically suspected CANVAS using first the two-step PCR, followed by exome sequencing. Our approach was able to accurately identify negative and confirm positive cases in prospectively collected suspected CANVAS patients presenting with at least three typical clinical signs. The proposed testing approach provides an alternative method able to clearly distinguish between CANVAS negative and positive cases and can be easily incorporated into the genetic diagnostic laboratory workflow.

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

Abstract: A large number of collecting expeditions were launched in regions of ‘centers of diversity’ and hundreds of thousands of sample have been collected and stored in gene banks as ‘genetic resources’. So far, only a small number of the samples have been evaluated for their biotic and abiotic stress tolerance. Now, their time to become useful has come. A new global phenomenon has arisen – climate change. The crop genetic resources and their wild progenitors that have survived countless years of changing environment during the last 11,000 years could harbor genes that may be useful under the new growing conditions and environmental factors thrown up by climate change and global warming. With the deployment of modern bio-engineering techniques selected genes or gene fragments can be transferred from genetic resources to modern varieties of crop plants to make them well-prepared to mitigate the effects of global warming and climate change. The latter is the most serious issue facing plant breeders today. New pests and diseases could affect crop production. These review paper discusses various impacts and issues as a result of this phenomenon and suggest ways to safeguard our most important crops through better management of crop plant genetic resources in the near future.

Nowadays industrial dryers are used instead of traditional methods for drying. In designing dryers suitable for controlling the process of drying and reaching a high quality product, it is necessary to predict the instantaneous moisture loss during drying. For this purpose, ten mathematical-experimental models with a neural network model based on the kinetic data of pistachio drying are studied. The data obtained from the cabinet dryer will be evaluated at four temperatures of inlet air and different air velocities. The pistachio seeds will be placed in a thin layer on an aluminum sheet on a drying tray and weighed by a scale attached to the computer at different times. In the neural network, data are divided into three parts: educational (60%), validation (20%) and test (20%). Finally, the best mathematical-experimental model using genetic algorithm and the best neural network structure for predicting instantaneous moisture are selected based on the least squared error and the highest correlation coefficient.

Plant height is significantly correlated with grain traits, which is a component of wheat yield. The purpose of this study is to investigate the main QTLs that control plant height and grain-related traits in multiple environments. In this study, we constructed a high-density genetic linkage map using the Wheat50K SNP Array to map quantitative trait loci (QTLs) for these traits in 198 recombinant inbred lines (RILs). The two ends of the chromosome were identified as re-combination-rich areas in all chromosomes except chromosome 1B. The middle area of the chro-mosomes was identified as the recombination-barren area. Both the genetic map and the physical map showed a significant correlation when p=0.001, with a correlation coefficient between 0.63 and 0.99. However, there was almost no recombination between 1RS and 1BS. In terms of plant height, 1RS contributed to the reduction of plant height by 3.43cm. In terms of grain length, 1RS contributed to the elongation of grain by 0.11mm. A total of 43 QTLs were identified, including 8 QTLs for Plant height(PH), 11 QTLs thousand grain weight(TGW), 15 QTLs for grain length(GL),and 9 QTLs for grain width(GW), which explained 1.36%–33.08% of the phenotypic variation. Seven were environment-stable QTLs, including two loci Qph.nwafu-4B and Qph.nwafu-4D that determined plant height. The explanation rates of phenotypic variation were 7.39%-12.26% and 20.11%-27.08%, respectively. One QTL, Qtgw.nwafu-4B, which influenced TGW, showed an explanation rate of 3.43%-6.85% for phenotypic variation, two co-segregating KASP markers were developed, the physical locations corresponding to KASP_AX-109316968 and KASP_AX-109519968 were 25.888344 MB and 25.847691 MB. Another QTL, Qgw.nwafu-4D, which determined grain width, had an explanation rate of 3.43%-6.85%. Three loci that affected the grain length were Qgl.nwafu-5A, Qgl.nwafu-5D.2 and Qgl.nwafu-6B, illustrating the explana-tion rates of phenotypic variation as 6.72%-9.59%, 5.62%-7.75%, and 6.68%-10.73%, respectively. Two QTL clusters were identified on chromosomes 4B and 4D.

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

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

Fungal endophytes are the most ubiquitous plant symbionts on earth and are phylogenetically diverse. Studies on the fungal endophytes in tobacco have shown that they are widely distributed in the leaves, stems, and roots, and play important roles in the composition of the microbial ecosystem of tobacco. Herein, we analyzed and quantified the endophytic fungi of healthy tobacco leaves at the seedling (SS), resettling growth (RGS), fast-growing (FGS), and maturing (MS) stages at three altitudes [600 (L), 1000 (M), and 1300 m (H)]. We sequenced the ITS region of fungal samples to delimit operational taxonomic units (OTUs) and phylogenetically characterize the communities. The result showed the number of clustering OTUs at SS, RGS, FGS and MS were greater than 170, 245, 140, and 164, respectively. At the phylum level, species in Ascomycota and Basidiomycota had absolute predominance, representing 97.8% and 2.0 % of the total number of species. We also found the number of unique fungi at the RGS and FGS stages were higher than those in the other two stages. Additionally, OTU richness was determined by calculating the Observed Species, Shannon, Simpson, Chao1, ACE, Goods coverage and PD_whole_tree indices based on the total number of species. Our results showed RGS samples had the highest diversity indices. Furthermore, we found that the diversity of fungal communities tended to decrease with increasing tobacco growth altitude. The results from this study indicated that the tobacco harbors an abundant and diverse endophytic fungal population, which provides new opportunities for exploration their potential utilization.

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

The advancement of technology and the availability of specialized digital signal processing chips have made digital filter design and implementation more feasible in a variety of fields, including biomedical engineering. This paper makes two key contributions. First, it uses a genetic algorithm to optimize the coefficients of Finite Impulse Response (FIR) filters. Second, it conducts a case study on using genetic algorithms to optimize FIR filters for electrocardiogram (ECG) biomedical signal noise removal. The goal of the proposed filter design approach is to achieve the desired signal bandwidth while minimizing the side lobe level and eliminating unwanted signals using a genetic algorithm. The results of the comprehensive analysis of the impact of different genetic operators show that the genetic algorithm-based filter outperforms conventional filter designs.

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

Despite advancements in antiretroviral therapy (ART) that reduces the viral load to undetectable levels, viral eradication has not been achieved due to HIV-1 persistence in resting CD4+ T-cells. We, therefore, characterized the tat gene, which is essential for HIV-1 replication, from 20 viro-logically controlled HIV-infected (HIV+) older patients on long-term ART with improved CD4+ T-cell counts. PBMC genomic DNA from HIV+ patients were used to amplify tat gene by PCR fol-lowed by nucleotide sequencing and analysis. Phylogenetic analysis showed that each patient’s tat sequences were confined to their own subtrees and well discriminated from other patients’ sequences. Moreover, there was a low degree of viral heterogeneity and lower estimates of genetic diversity within these patients’ tat sequences. Most patients’ Tat deduced amino acid sequences showed intact open reading frames and maintained the important functional domains for Tat functions, including transactivation, TAR binding and nuclear localization. Furthermore, Tat deduced amino acid sequences showed variation in previously characterized cytotoxic T lym-phocytes (CTL) epitopes, suggesting escape mutants. In conclusion, a low degree of genetic varia-bility and conservation of functional domains and variations in CTL epitopes were the features of tat sequences from 20 HIV+ older patients with undetectable viral load on long-term ART.

The objectives of this study were to evaluate fixed risk factors associated with PWM and to estimate genetic parameters for PWM. A total of 927 birth records from a mixed population of purebred and crossbred Boer and Spanish goats born between 2016 and 2023 at the International Goat Research Center (IGRC) were used for this study. Four binary traits were studied: D0-3 (death within 3 days after birth), D4-60 (death between 4 and 60 days), D61-90 (death between 61 and 90 days), and D0-90 (death within 90 days). Logistic regression models were used to evaluate the risk factors associated with PWM traits. Bayesian threshold models and Gibbs sampling were used to estimate the genetic parameters. Birth weight, season, litter size, sex, dam age, breed, and heterosis were found to be significantly associated with at least one of the PWM traits. Heritability estimates were 0.263, 0.124, 0.080, and 0.207, for D0-3, D4-60, D61-90, and D0-90, respectively. The genetic correlations between the studied traits ranged from 0.892 (D0-3 and D0-90) to 0.999 (D0-3 and D61-90). These results suggest that PWM in goats is influenced by both non-genetic and genetic factors and can be reduced by management, genetic selection, and crossbreeding approaches.

Rice (Oryza sativa L.) is primary dietary source for half of the global population that comprising both essential nutrients and toxic heavy metal elements for human health. A number of nutrients are required within the diet and generally lacking in human diets, and need to biofortify into the rice grains, such as iron (Fe), zinc (Zn), calcium (Ca), potassium (K), sodium (Na), magnesium (Mg), phosphorus (P), copper (Cu), iodine (I), selenium (Se), and Sulphur (S). Meanwhile, some elements are toxic to human, including arsenic (As), cadmium (Cd), chromium (Cr), cobalt (Co), mercury (Hg), manganese (Mn), nickel (Ni), and lead (Pb) which need to be eliminated from the rice grains. This article reviews the aspects of phenotypic variation of grain elemental concentration in the diverse rice genotypes, relationship of environmental conditions and rice grain elemental accumulation, correlation between rice grain elemental content and others agronomic traits, and also genetic basis of grain elemental concentration in rice. All of these aspects are important to develop rice varieties with a balanced elemental nutrients and lower toxic heavy metal elements. Enhancing the concentration of essential mineral elements and reducing the accumulation of toxic elements in the rice grain are important to improve the rice quality for human health in addressing mineral deficiency and toxicity that could be accomplished by using plant breeding, agronomic, and genetic engineering approaches.

The construction industry has a notable negative impact on the environment; thus, the promotion of the use of timber structures is an alternative to mitigate its effects. The research develops an artificial intelligence-based decision approach in the calculation of timber structures focused on the enhancement of the sustainability of roof structures. Based on the optimization carried out through genetic algorithms and the framework established in Eurocode 5, a general set of equations has been proposed for a laminated timber roof structure. The tool, which determines the most suitable roof structure for each strength class of laminated timber, allows for the determination of the dimensions of beams and purlins and their respective separations in order to minimize wood consumption. The ultimate goal is to offer multiple solutions regarding strength classes and structural designs in order to foster informed sustainable choices that promote efficient use of resources in construction.

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

Fat has a high energy density and excessive fatness has been recognized as a problem for egg production and the welfare of chickens. The identification of a genetic polymorphism controlling fat deposition would be helpful to select against excessive fatness in the laying hen. This study aimed to estimate genomic heritability and identify the genetic architecture of abdominal fat deposition in a population of chickens from a Dongxiang blue-shelled local breed crossbred with the White Leghorn. A genome-wide association study wase conducted on abdominal fat percentage, egg production and body weights using a sample of 1534 hens genotyped with a 600K Chicken Genotyping Array. The analysis yielded a heritability estimate of 0.19 ± 0.04 for abdominal fat percentage, 0.56 ± 0.04 for body weight at 72 weeks, 0.11 ± 0.03 for egg production and 0.24 ± 0.04 for body weight gain. The genetic correlation of abdominal fat percentage with egg production between 60 and 72 weeks of age was 0.35 ± 0.18. This implies a potential trade-off between these two traits related to allocation of resource. Strong positive genetic correlations were found between fat deposition and weight traits. A promising locus close to COL12A1 on chromosome 3, associated with abdominal fat percent, was found in the present study. The other region located around HTR2A on chromosome 1, where allele substitution was predicted to be associated with body weight gain, accounted for 2.9% of phenotypic variance. Another region located on chromosome 1, but close to SOX5, was associated with egg production. These results may be used to influence the balanced genetic selection for laying hens.

Throughout history, the pursuit of diagnosing and predicting crop yields has evidenced genetics, environment, and management practices' intertwined roles in achieving food security. However, the sensitivity of crop phenotypes and genetic responses to weather and climate remains unclear, hampering the identification of the underlying abilities of plants to adapt to climate change. We hypothesize that the PAWN global sensitivity analysis (GSA) coupled with a genetic by environment (GxE) model -built of environmental covariance and genetic markers structures- can evidence the contributions of climate on the predictability of maize yields in the U.S. and Ontario, Canada (US-CA). The GSA-GxE modeling framework estimates the relative contribution of climate variables such as solar radiation, temperature, rainfall, and relative humidity on improving maize yield predictions in US-CA. We use an improved version of the Genomes to Fields (G2F) initiative multi-dimensional database to build the environmental covariance matrices for the proposed GSA-GxE framework. The PAWN indices show that the aggregated GxE model’s highest sensitivity levels over US-CA were attributed to solar radiation, temperature, rainfall, and relative humidity. In one-third of the locations, rainfall was the primary climate variable responsible for maize yield predictability. Also, a consistent pattern of top sensitivity indices by location indicates that Relative Humidity, Solar Radiation, and Temperature were distributed as the main or the second most relevant drivers of maize yield predictability.

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

The feasibility of recovering bioactive antioxidants from Phyllantus emblica leaves using ethanol -water mixture (0 – 100%) and heat-assisted extraction technology (HAE-T) was investigated in this present study. To this effect, the process parameter of operating temperature (OT: 30 -50 oC), solid -to-liquid ratio (S:L: 1:20 - 1:60 g/mL) and extraction time (ET: 45 - 180 min) were investigated on the extract total phenolic content (TPC, mg GAE/g d.w), yield (EY, %) and antioxidant activity (μM AAE/g d.w) by employing the Box-Behnken experimental design (BBD) available in response surface methodology (RSM). Multi-objective process optimization using the desirability function algorithm to determine set of process variables that simultaneously optimize process responses of TPC, EY and AA was also investigated. The recovery process was thereafter modeled with BBD-RSM and multi gene genetic programming (MGGP) algorithm and model reliability assessment via Monte Carlo simulation were conducted for the best performing set of models for predicting TPC, EY and AA values. The HPLC characterization of the recovered extract was carried out to ascertain the phenolic compound constituents of the Phyllantus emblica leaf extract. The 50% ethanol solution was found to be the best for the optimal extraction of antioxidants from Phyllantus emblica leaves. The HAE-T was suitable for recovering bioactive extract from Phyllantus emblica leaves and investigated process parameters have impacts on the recovery process. The process parameters that simultaneously gave the optimum EY of 21.6565%, TPC of 67.116 mg GAE/g and AA of 3.68583 µM AAE/g were OT of 41.61 oC, S:L of 1:60 g/mL and ET of 180 min. The HPLC extract profiling revealed that the bioactive constituents present in the recovered extract were betulinic acid, gallic acid, chlorogenic acid, caffeic acid, ellagic acid, and ferulic acid. Both the developed BBD-RSM and evolved MGGP-based models predicted the observed process response of TPC, EY and AA satisfactorily with BBD-RSM models marginally better in their predictions. The reliability results showed that the BBD-RSM models predicted TPC, EY and AA values with high certainty of 99.985%, 97.569% and 98.661%, respectively.

Members of the genus Armillaria are widespread forest pathogens against which effective protection has not yet been developed. Due to their longevity and the creation of large-scale cloning of Armillaria individuals, the use of mycoviruses as biocontrol agents (BCAs) against these pathogens could be an effective alternative. This work describes the screening of double-stranded RNA banding patterns as a first approach for potential virus discovery and the use of stranded total RNA-seq for viral detection and characterization in Armillaria spp. collected in the Czech Republic. A total of five single-stranded RNA viruses were detected in Armillaria ostoyae and A. cepistipes, including the first virus of the family Tymoviridae found infecting a member the genus Armillaria and four viruses belonging to the recently described virus group "ambiviruses" with circular ambisense genome organization. Both hammerhead (HHRz) and hairpin (HpRz) ribozymes were detected in all the ambiviricot sequences. Armillaria viruses were compared by phylogenetic analysis and confirmed their specific host by direct RT-PCR, which showed the occurrence of interspecies transmission.

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

Mercury (Hg) pollution not only poses a threat to the environment but also adversely affects the growth and development of plants, with potential repercussions for animals and humans through bioaccumulation in the food chain. Maize, a crucial source of food, industrial materials, and livestock feed, requires special attention in understanding the genetic factors influencing mercury accumulation. Developing maize varieties with low mercury accumulation is vital for both maize production and human health. In this study, a comprehensive genome-wide association study (GWAS) was conducted using an enlarged SNP panel comprising 1.25 million single nucleotide polymorphisms (SNPs) in 230 maize inbred lines across three environments. The analysis identified 111 significant SNPs within 78 quantitative trait loci (QTL), involving 169 candidate genes under the Q model. Compared to the previous study, the increased marker density and optimized statistical model led to the discovery of 74 additional QTL, demonstrating improved statistical power. Gene Ontology (GO) enrichment analysis revealed that most genes participate in arsenate reduction and stress responses. Notably, GRMZM2G440968, associated with the significant SNP chr6.S_155668107 of axis tissue, encodes a cysteine proteinase inhibitor, suggesting its potential role in alleviating Hg toxicity by inhibiting cysteine. Haplotype analyses provided further insights, indicating that lines carrying hap3 exhibited the lowest mercury content compared to other haplotypes. In summary, our study significantly enhances the statistical power of GWAS, identifying additional genes related to mercury accumulation and metabolism. These findings offer valuable insights into unraveling the genetic basis of mercury content in maize and contribute to the development of maize varieties with low mercury accumulation.

During the 1970s, Tunisian durum wheat landraces were replaced progressively by modern cultivars. These landraces are nowadays maintained by smallholder farmers in some ecological niches and are threatened gradually by extinction resulting on the narrowing of the genetic diversity. This study aims to investigate patterns of phenotypic variability using twelve quantitative traits in a panel of 189 durum wheat landraces and seven checks, based on farmer’s population name attribution and genetic structure. Our results showed high phenotypic variability among and within landraces and checks for the majority of the studied traits. The principal components analysis showed similar grouping using farmers name attribution and genetic structure using K = 6, which confirmed the identification of a new gene pool in the oases of Tunisia, represented by the sub-population Jenah Zarzoura and the robustness and high relationships between phenotypic and genome-wide genetic structure using DArTseq method. These findings will enhance the conservation efforts of these landraces and their use in breeding efforts at national and international levels to adapt to dry conditions.

The aim of this study has been to develop a novel two-level multi-objective genetic algorithm (GA) to optimize time series forecasting data for fans used in road tunnels by the Swedish Transport Administration (Trafikverket). Level 1 is for the process of forecasting time series cost data, while level 2 evaluates the forecasting. Level 1 implements either a multi-objective GA based on the ARIMA model or a multi-objective GA based on the dynamic regression model. Level 2 utilises a multi-objective GA based on different forecasting error rates to identify a proper forecasting. Our method is compared with using the ARIMA model only. The results show the drawbacks of time series forecasting using only the ARIMA model. In addition, the results of the two-level model show the drawbacks of forecasting using a multi-objective GA based on the dynamic regression model. A multi-objective GA based on the ARIMA model produces better forecasting results. In level 2, five forecasting accuracy functions help in selecting the best forecasting. Selecting a proper methodology for forecasting is based on the averages of the forecasted data, the historical data, the actual data and the polynomial trends. The forecasted data can be used for life cycle cost (LCC) analysis.

Marek’s disease (MD) is an important neoplastic disease caused by serotype 1 Marek's disease virus (MDV-1), which results in severe economic losses worldwide. Despite vaccination practices that have controlled the MD epidemic, current increasing MD-suspected cases indicate the persistent viral infections circulating among vaccinated chicken farms in many countries. However, the lack of available information about phylogeny and molecular characterization of circulating MDV-1 field strains in Taiwan reveals a potential risk in MD outbreaks. This study investigated the genetic characteristics of 18 MDV-1 isolates obtained from 17 vaccinated chicken flocks in Taiwan between 2018 and 2020. Based on the sequences of the meq oncogene, phylogenetic analysis demonstrated that the circulating Taiwanese MDV-1 field strains were predominantly in a single cluster that showed high similarity with strains/isolates from countries of East Asian region. Because the isolated strains were obtained from CVI988/Rispens vaccinated chicken flocks and the molecular characteristics of the Meq oncoprotein showed the features like vvMDV and vv+MDV strains, the circulating Taiwanese MDV-1 field strains may have higher virulence compared with vvMDV pathotype. In conclusion, the presented data demonstrate the circulation of hypervirulent MDV-1 strains in Taiwan and highlight the importance of routine surveillance and precaution strategies in response to the emergence of enhanced virulent MDV-1.

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

In order to coordinate the damping performance and energy regenerative performance of energy regenerative suspension, this paper proposes a structure of vehicle semi-active energy regenerative suspension with electro-hydraulic actuator (EHA). In light of the proposed concept, a specific energy regenerative scheme is designed and the mechanical properties test is carried out. Based on the test results, the parameter identification for the system model is conducted using recursive least squares algorithm. On the basis of system principle, the nonlinear model of the semi-active energy regenerative suspension with EHA is built. Meanwhile, LQG control strategy of the system is designed. And then the influence of the main parameters of EHA on the damping performance and energy regenerative performance of suspension is analyzed. Finally, the main parameters of EHA actuator are optimized via genetic algorithm. The test results show that when sinusoidal is input at the frequency of 2Hz and the amplitude of 30mm, the spring mass acceleration RMS value of optimized EHA semi-active energy regenerative suspension is reduced by 22.23% and energy regenerative power RMS value is increased by 40.51%, which means while meeting the requirements of certain vehicle ride comfort and driving safety, energy regenerative performance is improved significantly.

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

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

Artemia franciscana, native to America, has recently colonized non-indigenous populations in Eurasia, Mediterranean regions and Australia. In present we sought to evaluate the potential effects of colonization of A. franciscana on genetic differentiation in the new environments in UAE. We used the COI marker to determine population genetic structure and identify the origins of exotic populations in UAE. Our findings have confirmed the colonization of both localities by A. franciscana. Genetic variation of invasive A. franciscana were exclusively lower than native population in Great Salt Lake and San Francisco Bay. Results have showed the studied population could not possibly have colonized directly from natural American localities, perhaps resulting from secondary introduction events from other non-indigenous populations. Genetic analysis have yielded different demographic patterns for invasive studied populations. Al Wathba Wetland Reserve (AWWR) population have represented demographic expansion. In contrast, Godolphin Lakes (GL) population was at demographic equilibrium. Neutrality tests have documented the excess of both recent and historical mutations in the COI gene pool of invasive AWWR Artemia throughout establishment in the new environment.

To better understand anogenital warts (AGW) natural history and dynamic of HPV6/11 infection in regional hairs, 32 newly diagnosed AGW male patients and 32 age-matched healthy controls were closely followed. During enrollment and six follow-up visits (every 2.6 months) 43 AGW tissue and 1,232 anogenital and eyebrow hair samples were collected. This represents the closest longitudinal monitoring of AGW patients to date. Patients were treated according to standard-of-care. HPV6/11 prevalence was 19.9% in patients’ hair samples (HPV6 B1 in 87.5%) and 0% in controls. The highest HPV6/11 prevalence was found in pubic hairs (29.0%) and lowest in eyebrows (7.1%). Odds for HPV6/11-positive hairs increased with smoking, shaving anogenital region and age. A close association between HPV6/11 presence in hairs and clinically visible AGW was observed. The proportion of patients with visible AGW and HPV6/11-positive hairs declined during follow-up with similar trends. No particular HPV6/11 variant was linked with increased AGW recurrence, but sublineage HPV6 B1 showed significantly higher clearance from hairs. Despite treatment, 78.1% and 62.5% of AGW patients experienced one and two/more post-initial AGW episodes, respectively. Patients having HPV6/11-positive hairs or visible AGW at preceding visit demonstrated substantially higher odds for presenting with visible AGW in subsequent visit.

Densimetric Froude (Fr) is the minimum velocity required to prevent sediment deposition in pipes. Prediction of Fr is of utmost importance in numerous applications in civil engineering. In this paper through using a new hybrid method. Genetic Algorithm (GA) is used for optimum selection of membership functions of Adaptive Neuro-Fuzzy Inference System (ANFIS), and Singular Value Decomposition (SVD) method is used to compute the linear parameters of ANFIS’s results section (ANFIS-GA/SVD). Also, two different target functions are known as training error (TE) and prediction error (PE) by Pareto curve, the trade-off between these functions is selected as the optimal modeling point. First, different models will be presented using the parameters affecting Fr prediction, classifying them in different groups; then the Fr parameter will be predicted for all the different models through utilizing three different sets of data and the ANFIS-GA/SVD technique. The results of the models indicate that the best Fr prediction is obtained when independent parameters such as volumetric sediment concentration (CV), ratio of median diameter of particle size to pipe diameter (d/D), ratio of median diameter of particle size to hydraulic radius (d/R) and overall friction factor of sediment (λs) use as input variables in prediction of Fr. A sensitivity analysis is also conducted for the purpose of examining the effect of each of the dimensionless parameters on Fr prediction accuracy. Comparing the results of the suggested models with the existing regression-based equations shows that ANFIS-GA/SVD (R²=0.986, MAPE=4.397, RMSE=0.206, SI=0.053, ρ=0.026, BIAS=-0.025) is more accurate than the rest of the models.

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

The current work addresses the control of two-input two-output (TITO) Wood and Berry model of a binary distillation column. The controller design problem is formulated in terms of multivariable H_∞ control synthesis. The controller structure takes the form of simplest static output feedback (SOF) control. The controller synthesis is performed using a hybrid approach of blending linear matrix inequalities (LMI) and genetic algorithm (GA). The performance of the static output feedback controller is compared with three other controllers designed for Wood and Berry model available in the literature. The first simulation study is performed for the case of tracking a unit step command in the presence of a step change in output disturbance. A second simulation study is performed for rejecting a change in sinusoidal output disturbance.

The primary considerations while producing rice (Oryza sativa L.) include improving its nutritional quality and production. To tackle widespread hunger globally, better nutritional, high-yielding rice cultivars need to be developed. The conventional ways are to increase the production of rice and add balanced nutrients in the daily diet to fulfill the need of yield and nutrient quality. This article focuses on nutritional strategies for rice and illustrates the availability of omics technologies. Current advancements providing many methodologies and approaches for exploring genetic resources and for understanding the molecular mechanisms involved in trait formation have been highlighted. Studying the genetic influences of various characteristics has been proven to expedite crop breeding processes. In this perspective, genome-wide association research, genome selection (GS), and QTL mapping are all genetic analysis that helps in increasing the nutritional content of rice. Implementation of several omic techniques are effective approaches to enhance and regulate the nutritional quality of rice cultivars. Advancements in different types of omics including genomics and pangenomics, transcriptomics, metabolomics, nutrigenomics, and proteomics are also relevant to rice development initiatives. This review article compiles genes, locus, mutants and all omic approaches for rice enhancement. This knowledge will be very useful for now and for the future regarding rice studies.

There are many theories of carcinogenesis arguing against the orthodox mutation theory, debating on such as “epigenetic alteration” that is inheritable and yet, theoretically, reversible. Our integrated theory describes that any extracellular, intracellular, or intranuclear stressors, mutagenic or not, can initiate a lengthy tumorigenesis to engender a benign or malignant tumor, but the aberrations directly establishing cellular immortality and autonomy may be epigenetic or genetic alterations in the genomic DNA. A neoplasm is considered a semi-new organism with autonomy; it therefore should have genetic mutations to be “new”. We may be able to direct cancer cells towards differentiation as a remedy, because the extracellular milieu may control the phenotype of a cell and the tissue or organ made of the cell’s progenies, and the cytoplasm of a cell may override the nucleus in the phenotypic control. However, the nucleus retains the capacity to manifest itself if allowed by the microenvironment, which then allows the already reversed cells to revert back to tumor cells again. Neoplasms are malignant if they bear epigenetic or genetic anomalies in mutator genes defined as those whose alterations allow or accelerate alterations to occur in other genes, whereas neoplasms are benign if they bear epigenetic or genetic aberrations only in non-mutator genes. It is imperative to identify the immediate tumor-causing cellular alterations defined as those directly responsible for immortality and autonomy, and for treatment purposes to identify the extracellular and intracellular factors that control the phenotype of cancer cells.

The optimal structural design is imperative to minimize material consumption and reduce the environmental impact of construction. Given the complexity in the formulation of structural design problems, the process of optimization is commonly performed using artificial intelligence (AI) global optimization, such as Genetic Algorithm (GA). However, the integration of AI-based optimization together with visual programming (VP) in building information modeling (BIM) projects warrants further investigation. This study proposes a workflow by combining structure analysis, VP, BIM, and GA to optimize trusses. The methodology encompasses several steps including: (i) generation of parametric trusses in Dynamo VP; (ii) performing the finite element modeling (FEM) using Robot Structural Analysis (RSA); (iii) retrieving and evaluating the FEM results interchangeably between Dynamo and RSA; (iv) finding the best solution using GA; and (v) importing the optimized model into Revit, enabling the user to perform simulations and engineering analysis, such as life cycle assessment (LCA) and quantity surveying. The feasibility of the proposed workflow was tested on benchmark problems and compared with open literature. The outcomes of this study offer insight into the opportunities and limitations of combining VP, GA, FEA, and BIM for structural optimization applications, particularly to enhance structural efficiency and sustainability in construction.

Background: Advancements in the next-generation sequencing (NGS) techniques have allowed for efficient genetic variant detection at reduced costs. Methods: We describe an ad hoc NGS-based custom designed resequencing gene panel to identify genetic variants in 8 patients with dementing disorders. Results: We found variants of TREM2 and APP genes in three patients; these have been previously identified as pathogenic or likely pathogenic and, therefore, considered as “Disease Causing”. In the remaining subjects, the pathogenicity was evaluated on the in silico analysis, according to the guidelines of the American College of Medical Genetics. In one patient, the p.R205W variant was causative of the disease, thus considered as “Possibly Disease Causing”. The variants found from the other four subjects in the CSF1R, SERPINI1, GRN, and APP genes revealed discordant in silico results and, therefore, it was not possible to assign a definitive pathogenicity. Conclusions: Notwithstanding the limitations of a customized panel, we detected some rare genetic variants with a probable disease association. The future application of NGS techniques and the further replication of these experimental data will replace the so-called “gene by gene” approach with a “panel of genes” strategy, that offers promising perspectives in the diagnosis and management of neurodegenerative disorders.

The nutraceutical value of pomegranate in the treatment of many neoplastic, cardiovascular, viral, inflammatory, metabolic, microbial, intestinal, reproductive and skin diseases is well-documented and is linked to its richness in phenolic compounds. This study aims to evaluate nutraceutical and genetic diversity of novel pomegranate genotypes (G1-G5) in comparison to leading commercial pomegranate varieties i.e. ‘Wonderful’, ‘Primosole’, ‘Dente di Cavallo’ and ‘Valenciana’. Morphometric measurements were carried out on fruits, accompanied by chemical characterization and the development of four new polymorphic SSR markers involved in the flavonoid pathway. The cultivars displayed a marked variability in the weight and shape of fruits, as well as in the weight of arils and juice yield. The highest level of total phenolic content and antioxidant activity was found in ‘Wonderful’ and G4, while the lowest was in ‘Dente di Cavallo’. Furthermore, the results showed that the pomegranate juice, is an excellent source of minerals, especially potassium, which plays a key role in organ functioning. The new flavonoid-related markers effectively differentiated the cultivars with the same diversity pattern as morpho-chemical characterization, so the SSRs developed in the present study can be used as a rapid tool for the identification of pomegranate cultivars with relevant nutraceutical traits.

Abstract: Congenital hyperinsulinism (CHI) is a rare disorder of glucose metabolism and is the most common cause of severe and persistent hypoglycemia (hyperinsulinemic hypoglycemia, HH) in the neonatal period and childhood. Most cases are caused by mutations in the ABCC8 and KCNJ11 genes that encode the ATP-sensitive potassium channel (KATP). We present the correlation between genetic heterogeneity and the variable phenotype in patients with early-onset hyperinsulinemic hypoglycemia caused by ABCC8 gene mutations. In the first patients, which presented persistent severe hypoglycemia since the first day of life, molecular genetic testing revealed the presence of a homozygous mutation in the ABCC8 gene [deletion in the ABCC8 gene c.(2390+1_2391-1)_(3329+1_3330- 1)del] correlated with a diffuse form of hyperinsulinism (the parents being healthy heterozygous carriers). In the second patient, the onset was on the third day of life with severe hypoglycemia, and genetic testing identified a heterozygous mutation in the ABCC8 gene c.1792C&gt;T (p.Arg598*) inherited on the paternal line, which led to the diagnosis of focal form of hyperinsulinism. To locate the focal lesions, (18)F-DOPA (3,4-dihydroxy-6-[18F]fluoro-L-phenylalanine) PET/CT was recommended (an investigation that cannot be carried out in the country), but the parents refused to carry out the investigation abroad. In this case, early surgical treatment could have been curative. In addition, the second child also presented secondary adrenal insufficiency requiring replacement therapy. At the same time, she developed early recurrent seizures that required antiepileptic treatment. We emphasize the importance and of molecular genetic testing for diagnosis, management and genetic counseling in patients with HH.

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

Blueberries (Vaccinium section Cyanococcus) are perennial shrubs widely cultivated for their edible fruits. In this study, we used admixture and genetic relatedness analysis of northern highbush (NHB, V. corymbosum) and southern highbush (SHB, V. darrowii) blueberry genotypes and F2 progenies of the V. corymbosum × V. darrowii cross. Using genotyping-by-sequencing (GBS), we generated ~3.34 billion reads (75 bp). The GBS reads were aligned to the Vaccinium corymbosum cv. Draper v1.0 reference genome sequence, and ~2.8 million reads were successfully mapped. From the alignments, we identified 2,244,039 single nucleotide polymorphisms (SNPs), which were used for principal component, haplotype, and admixture analysis. PCA formed three main groups: 1) NHB cultivars, 2) SHB cultivars, and 3) BNJ16-5 progenies. The overall fixation index (FST) and nucleotide diversity for NHB and SHB, indicated wide genetic differentiation, and haplotype analysis revealed that SHB cultivars are more genetically diverse than NHB cultivars. The admixture analysis identified a mix of various lineages of parental genomic introgression. This study demonstrated the effectiveness of GBS-derived SNP markers in genetic and admixture analyses to reveal genetic relatedness and to examine parental lineages in blueberry, which may be useful for future breeding plans.