Candidate Regions Research Articles

Leveraging ancient DNA to uncover signals of natural selection in Europe lost due to admixture or drift.

Large ancient DNA (aDNA) studies offer the chance to examine genomic changes over time, providing direct insights into human evolution. While recent studies have used time-stratified aDNA for selection scans, most focus on single-locus methods. We conducted a multi-locus genotype scan on 708 samples spanning 7000 years of European history. We show that the G12 statistic, originally designed for unphased diploid data, can effectively detect selection in aDNA processed to create 'pseudo-haplotypes'. In simulations and at known positive control loci (e.g., lactase persistence), G12 outperforms the allele frequency-based selection statistic, SweepFinder2, previously used on aDNA. Applying our approach, we identified 14 candidate regions of selection across four time periods, with half the signals detectable only in the earliest period. Our findings suggest that selective events in European prehistory, including from the onset of animal domestication, have been obscured by neutral processes like genetic drift and demographic shifts such as admixture.

Unveiling resistance expression profile to powdery mildew in wheat via Bulked Segregant RNA-Seq.

Developing wheat cultivars with durable resistance to powdery mildew, caused by Blumeria graminis f. sp. tritici (Bgt), is crucial for sustainable agriculture. The wheat genotype MYC exhibited high resistance to the Bgt isolate E09 at the seedling stage. Genetic analysis identified a recessive gene, temporarily named PmMYC, responsible for this resistance. Understanding the molecular mechanisms underlying this resistance is essential for advancing breeding programs. Bulked Segregant RNA-Seq revealed numerous alternative splicing events generated following Bgt infection, suggesting powdery mildew may disrupt alternative splicing and affect immune responses. Gene Ontology (GO) analysis indicated significant enrichment of differentially expressed genes in "response to stimuli" and "immune system processes", implying their roles in disease defense. BSR-Seq analysis identified two high-confidence candidate regions for PmMYC on chromosome 2B, spanning 40,451,950 - 102,426,703bp and 421,707,046-449,840,516bp. Within these intervals, 740 genes were identified, with nonsynonymous mutations in 46 genes in the parents and bulks. Real-time PCR showed distinct expression profiles in four genes in resistant MYC compared to susceptible Yannong 21. KEGG and COG analyses of differentially expressed genes in candidate intervals revealed enrichment in immune processes related to plant-pathogen interactions, confirming that PmMYC initiated a broad immune response to prevent Bgt invasion. The study identified key genetic intervals and genes involved in the resistance of wheat genotype MYC to Bgt. The identified genes, particularly those with altered expression profiles, could serve as valuable targets for breeding programs aimed at developing wheat cultivars with durable resistance to powdery mildew. These findings enhanced our understanding of plant-pathogen interactions and provided a foundation for future genetic and functional studies.

Persistent threat avoidance following negative reinforcement is not associated with elevated state anxiety.

Obsessive-Compulsive Disorder (OCD) is a debilitating illness consisting of obsessions and compulsions. OCD severity and treatment response are correlated with avoidant behaviors thought be performed to alleviate obsession-related anxiety. However, little is known about either the role of avoidance in the development of OCD or the interplay between anxiety states and avoidance behaviors. We have developed an instrumental negative reinforcement (i.e. active avoidance) paradigm in which mice must lever-press to avoid upcoming foot shocks. We show that mice (both sexes) can learn this task with high acquisition rates (75%) and that this behavior is largely stable when introducing uncertainty and modifying task structure. Furthermore, mice continue to perform avoidance responses on trials where lever pressing is not reinforced and increase response rates as they are maintained on this paradigm. With this paradigm, we did not find a relationship between negative reinforcement history and anxiety-related behaviors in well-established anxiety assays. Finally, we performed exploratory analyses to identify candidate regions involved in well-trained negative reinforcement using expression of the immediate early gene c-Fos. We detected correlated c-Fos expression in 1) cortico-striatal regions which regulate active avoidance in other paradigms and 2) amygdala circuits known to regulate conditioned defensive behaviors.Significance Statement Studies in patients with OCD suggest that compulsions are performed to avoid perceived threats and modulate anxiety tied to obsessions and/or compulsions. The negative reinforcement of avoidance and alleviated anxiety could therefore be a key driver of compulsive behaviors. However, there are still outstanding questions concerning the relationship between these two behaviors and the neural circuits involved in mediating negative reinforcement. We have developed an operant negative reinforcement paradigm in mice with discrete avoid and escape behaviors that can be learned without prior reward training with high throughput (75% acquisition) with responding that persists during nonreinforced trials. However, no differences were observed between negative reinforcement vs. unshocked and inescapably shocked controls, suggesting that continued negative reinforcement did not impact anxiety.

Oculomotor functional connectivity associated with motor sequence learning.

Acquisition of learned motor sequences involves saccades directed toward the goal to gather visual information prior to reaching. While goal-directed actions involve both eye and hand movements, therole of brain areas controlling saccades during motor sequence learning is still unclear. This study aimed to determine whether resting-state functional connectivity of oculomotor regions is associated with behavioral changes resulting from motor sequence learning. We investigated connectivity between oculomotor control regions and candidate regions involved in oculomotor control and motor sequence learning. Twenty adults had brain scans before 3 days of motor task practice and after a 24-hour retention test, which was used to assess sequence-specific learning. During testing, both saccades and reaches were tracked. Stronger connectivity in multiple oculomotor regions prior to motor task practice correlated with greater sequence-specific learning for both saccades and reaches. A more negative connectivity change involving oculomotor regions from pre- to post-training correlated with greater sequence-specific learning for both saccades and reaches. Overall, oculomotor functional connectivity was associated with themagnitude of behavioral change resulting from motor sequence learning, providing insight into thefunction of the oculomotor system during motor sequence learning.

Identification of candidate genes and genomic prediction for early heifer pregnancy in Nelore beef cattle

This study aimed to assess the influence of differential weighting in genomic regions harboring candidate causal loci on the prediction accuracy and inflation for early heifer pregnancy (heifers that calved up to 30 months of age) in Nellore (Bos indicus) heifers using the single step genomic BLUP model (ssGBLUP). Phenotypic records of 102,294 Nellore heifers born between 2010 and 2017 were used in this study. The pedigree dataset harbored information from 176,107 animals born between 1998 and 2017, including 5,145 sires and 35,705 dams. Seven different models for genomic prediction were defined by combining the SNP weights obtained in the iterations (1st and 2nd) of the weighted single step GWAS (ssw1GBLUP and ssw2GBLUP) or candidate QTLs reported in the literature. Hence, the lambda (λ) values estimated in the WssGWAS were used to weight the SNPs adjacent to the candidate regions or QTL previously reported in the literature. To estimate the genetic parameters and perform the WssGWAS and WssGBLUP for early heifer pregnancy, a single-trait Bayesian analysis considering a threshold animal model was used. Accuracy, bias, and inflation parameters were evaluated in the validation subset based on the linear regression (LR) method. Genomic windows of ten consecutive SNPs that explained more than 0.5% of the additive genetic variance were selected to explore and determine possible candidate genes. Among the identified genes, we can highlight the PGRMC2, TENM3, GRIP1, TMEM45A, and KLF3, given their roles in endocrine fertility, expression of contractile proteins, average daily gain, dry matter intake, and fat deposition. Several genomic regions associated with QTL related to early heifer pregnancy were identified. The identification of such regions and the respective candidate genes associated with sexual precocity and fertility would contribute to improve the genetic knowledge regarding early sexual precocity of Nellore cattle. The prediction accuracy increased roughly 25.6% using the ssGBLUP compared to BLUP models. The prediction accuracy with the WssGBLUP when incorporating weighted SNPs with the λ values obtained in the 1st (ssw1GBLUP) and 2nd iteration (ssw2GBLUP) of the WssGWAS was higher (∼18%) than that described for the ssGBLUP model. The inflation also increased the weighting of the most relevant SNPs obtained with the GWAS, most likely overestimating the GEBV. The models that weighted SNPs close to QTLs reported in the literature yielded to less biased and deflated predictions compared to ssw1GBLUP and ssw2GBLUP models. Genomic selection is a feasible alternative for genomic evaluation of early heifer pregnancy in Nellore beef cattle by increasing the prediction accuracy of young animals. In addition, the use of information obtained from the WssGWAS is an alternative to increase reliability and reduce genomic prediction bias. Therefore, the results obtained herein indicate that it is possible to improve the prediction accuracy and reduce the bias of genomic prediction by using genomic information and differentially weighted genomic regions harboring candidate QTLs previously reported in the literature for early heifer pregnancy.

NH3 (1,1) hyperfine intensity anomalies in infall sources

Identifying infall motions is crucial for our understanding of accretion processes in regions of star formation. The NH3 (1,1) hyperfine intensity anomaly (HIA) has been proposed to be a readily usable tracer for such infall motions in star-forming regions harboring young stellar objects at very early evolutionary stages. In this paper, we seek to study the HIA toward 15 infall candidate regions in order to assess its reliability as an infall tracer. Using deep observations of the NH3 (1, 1) transition with the Effelsberg 100 m telescope, we identified HIAs toward all 15 targets. Of the 15 sources, 14 exhibit anomalous intensities in either the inner or outer satellite lines. All the derived HIAs conform to the framework of the existing two models, namely hyperfine selective trapping (HST) and systematic contraction or expansion motion (CE) models. In our sample of infall candidates, the majority of the HIAs remain consistent with the HST model. Only in three targets are the HIAs consistent with infall motions under the CE model. Thus, the HIA could indeed be used as an infall tracer, but does not appear to be highly sensitive to infall motions in our single-dish data. Nevertheless, the emission could be blended with emission from outflow activities. HIAs consistent with the HST model show stronger anomalies with increasing kinetic temperatures (TK), which is expected based on the HST model. On the other hand, HIAs consistent with infall motions show little dependence on Tk . Therefore, HIAs may preferably trace the infall of cold gas.

Copy Number Variation and Selection Signal: Exploring the Domestication History and Phenotype Differences Between Duroc and the Chinese Native Ningxiang Pigs.

The Ningxiang pig, one of the well-known Chinese native pig breeds, has the advantages of tender meat, high intramuscular fat (IMF) content, and roughage tolerance, compared to the commercial lean pig breeds. The genetic basis for complex traits in Ningxiang pigs has been previously studied through other genetic markers, such as Single Nucleotide Polymorphism (SNP), while the characteristics of copy number variation (CNV) and the selection signal have not been investigated yet. In this study, GGP 50 k genotyping data of 2242 Ningxiang pigs (NX) and 1137 Duroc pigs (Duroc) were involved in CNV atlas construction and selection signals identification. Annotations of genes and quantitative trait locus (QTLs) were performed on the target candidate regions, as follows: (1) 162 CNVs were detected in Ningxiang pigs, while 326 CNVs were detected in Duroc pigs, and there are 21 copy number variation regions (CNVRs) shared between them; (2) The CNVRs of Duroc are more abundant, with 192 CNVRs, accounting for 1.61% of the entire genome, while those of Ningxiang pigs only have 98 CNVRs, accounting for 0.49%; (3) The QTLs annotated on CNVs and selected regions of Ningxiang pigs were mainly associated with meat quality and fertility. In contrast, the Duroc QTLs' notes relate primarily to the carcass and immunity, and explain why they have a higher slaughter rate and immunity; (4) There is a presence of high-frequency acquired CNVs, specifically in Ningxiang pigs, with 24 genes significantly enriched in the sensory receptor-related pathway in this region; (5) Based on the CNVs atlas, candidate genes such as 3 inositol 1,4,5-triphosphate receptor, type 3 (ITPR3), forkhead box protein K2 (FOXK2), G-protein coupled estrogen receptor 1 (GPER1), Glyceraldehyde 3-phosphate dehydrogenase (GAPDH), triosephosphate isomerase 1 (TPI1), and other candidate genes related to fat deposition and differentiation were screened. In general, this study improved our knowledge about copy number variation and selection signal information of Ningxiang pigs, which can not only further explain the genetic differences between Chinese native and Western commercial pig breeds, but also provide new materials for the analysis of the genetic basis of complex traits.

Evaluating potential landing sites for the Artemis III mission using a multi-criteria decision making approach

The selection of a landing site within the Artemis Exploration Zone (AEZ) involves multiple factors and presents a complex problem. This study evaluates potential landing sites for the Artemis III mission using a combination of Geographic Information Systems (GIS) and Multi-Criteria Decision Making (MCDM) methodologies, specifically the TOPSIS algorithm. By integrating topographic, illumination, and mineralogy data of the Moon, we assess 1247 locations that meet the Human Landing System (HLS) requirements within 13 candidate regions and Site 004 near the lunar south pole. Criteria considered include surface visibility, HLS-astronaut line of sight, Permanently Shadowed Regions (PSRs), sunlight exposure, direct communication with Earth, geological units, and mafic mineral abundance. Site DM2 (Nobile Rim 2), particularly the point at latitude 84°12’5.61”S (−84.20156°) and longitude 60°41’59.61”E (60.69989°), is the optimal location for landing. Sensitivity analysis confirms the robustness of our approach, validating the suitability of the best location despite the MCDM method employed and variations in criteria weightings to prioritize illumination and PSRs. This research demonstrates the applicability of GIS-MCDM techniques for lunar exploration and the potential benefits they can bring to the Artemis program.

Photosynthetic characteristics and genetic mapping of a yellow-green leaf mutant jym165 in soybean

BackgroundLeaves are important sites for photosynthesis and can convert inorganic substances into organic matter. Photosynthetic performance is an important factor affecting crop yield. Leaf colour is closely related to photosynthesis, and leaf colour mutants are considered an ideal material for studying photosynthesis.ResultsWe obtained a yellow-green leaf mutant jym165, using ethyl methane sulfonate (EMS) mutagenesis. Physiological and biochemical analyses indicated that the contents of chlorophyll a, chlorophyll b, carotenoids, and total chlorophyll in the jym165 mutant decreased significantly compared with those in Jiyu47 (JY47). The abnormal chloroplast development of jym165 led to a decrease in net photosynthetic rate and starch content compared with that of JY47. However, quality traits analysis showed that the sum of oil and protein contents in jym165 was higher than that in JY47. In addition, the regional yield (seed spacing: 5 cm) of jym165 increased by 2.42% compared with that of JY47 under high planting density. Comparative transcriptome analysis showed that the yellow-green leaf phenotype was closely related to photosynthesis and starch and sugar metabolism pathways. Genetic analysis suggests that the yellow-green leaf phenotype is controlled by a single recessive nuclear gene. Using Mutmap sequencing, the candidate regions related of leaf colour was narrowed to 3.44 Mb on Chr 10.ConclusionsAbnormal chloroplast development in yellow-green mutants leads to a decrease in the photosynthetic pigment content and net photosynthetic rate, which affects the soybean photosynthesis pathway and starch and sugar metabolism pathways. Moreover, it has the potentiality to increase soybean yield under dense planting conditions. This study provides a useful reference for studying the molecular mechanisms underlying photosynthesis in soybean.

Information transfer from spatial to social distance in rats: implications for the role of the posterior parietal cortex in spatial-social integration.

Humans and other social animals can represent and navigate complex networks of social relationships in ways that are suggestive of representation and navigation in space. There is some evidence that cortical regions initially required for processing space have been adapted to include processing of social information. One candidate region for supporting both spatial and social information processing is the posterior parietal cortex (PPC). We examined the hypothesis that rats can transfer or generalize distance information across spatial and social domains and that this phenomenon requires the PPC. In a novel apparatus, rats learned to discriminate two conspecifics positioned at different spatial distances (near vs. far) in a goal-driven paradigm. Following spatial learning, subjects were tested on probe trials in which spatial distance was replaced with social distance (cagemate vs. less familiar conspecific). The PPC was chemogenetically inactivated during a subset of probe sessions. We predicted that, in control probe trials, subjects would select conspecifics whose social distance matched the previously learned spatial distance. That is, if trained on the near distance, the rat would choose the highly familiar cagemate, and if trained on the far distance, the rat would choose the less familiar conspecific. Subjects learned to discriminate conspecifics based on spatial distance in our goal-driven paradigm. Moreover, choice for the appropriate social distance in the first probe session was significantly higher than chance. This result suggests that rats transferred learned spatial information to social contexts. Contrary to our predictions, PPC inactivation did not impair spatial to social information transfer. Possible reasons are discussed. To our knowledge, this is the first study to provide evidence that spatial and social distance are processed by shared cognitive mechanisms in the rat model.

Discovery of key volatile compound and regulatory gene governing the ‘taro-like’ aroma in wax gourd (Benincasa hispida)

The distinctive ‘taro-like’ aroma in wax gourd has become a crucial flavor characteristic, significantly enhancing its market value. Despite its importance, the volatile compounds responsible for this aroma and their genetic regulatory mechanisms have not been sufficiently studied. Utilizing headspace-gas chromatography-quadrupole time of flight-mass spectrometry (HS-GC-QTOF-MS), this research examined the volatile compounds in wax gourd germplasms with and without the ‘taro-like’ aroma, identifying 2-acetyl-1-pyrroline (2-AP) as the primary volatile responsible for this aroma. To uncover the genetic basis of this trait, segregating populations and recombinant inbred lines (RILs) were developed from MY-1 x GX-71 germplasms, which exhibit significant differences in aroma expression. Genetic analysis revealed that a single recessive gene governs the ‘taro-like’ aroma trait in wax gourd. Through bulked segregant analysis sequencing (BSA-seq) and quantitative trait loci (QTL) mapping, the candidate region for this aroma gene was localized to a 285.29 kb interval on Chromosome 2. Further examination of candidate gene expression and sequence variations highlighted the BhBADH gene's critical role in the formation of the ‘taro-like’ aroma. Additionally, RNA-seq analysis revealed significant enrichment of differentially expressed genes (DEGs) associated with metabolic pathways involved in environmental information processing, which contribute to 2-AP synthesis in wax gourd with a ‘taro-like’ aroma. Our research provides a theoretical foundation for the molecular breeding to enhance the aroma quality of wax gourd.

Cy-1, a major QTL for tomato leaf curl New Delhi virus resistance, harbors a gene encoding a DFDGD-Class RNA-dependent RNA polymerase in cucumber (Cucumis sativus)

BackgroundTomato leaf curl New Delhi virus (ToLCNDV) (family Geminiviridae, genus Begomovirus) is a significant threat to cucumber (Cucumis sativus) production in many regions. Previous studies have reported the genetic mapping of loci related to ToLCNDV resistance, but no resistance genes have been identified.ResultsWe conducted map-based cloning of the ToLCNDV resistance gene in cucumber accession No.44. Agroinfiltration and graft-inoculation analyses confirmed the resistance of No.44 to ToLCNDV isolates from the Mediterranean and Asian countries. Initial mapping involving two rounds of phenotyping with two independent F2 populations generated by crossing the begomovirus-susceptible cultivar SHF and No.44 consistently detected major quantitative trait loci (QTLs) on chromosomes 1 and 2 that confer resistance to ToLCNDV. Fine-mapping of Cy-1, the dominant QTL on chromosome 1, using F3 populations narrowed the candidate region to a 209-kb genomic segment harboring 24 predicted genes. Among these genes, DFDGD-class RNA-dependent RNA polymerase (CsRDR3), an ortholog of Ty-1/Ty-3 of tomato and Pepy-2 of capsicum, was found to be a strong candidate conferring ToLCNDV resistance. The CsRDR3 sequence of No.44 contained multiple amino acid substitutions; the promoter region of CsRDR3 in No.44 had a large deletion; and the CsRDR3 transcript levels were greater in No.44 than in SHF. Virus-induced gene silencing (VIGS) of CsRDR3 using two chromosome segment substitution lines harboring chromosome 1 segments derived from No.44 compromised resistance to ToLCNDV.ConclusionsForward and reverse genetic approaches identified CsRDR3, which encodes a DFDGD-class RNA-dependent RNA polymerase, as the gene responsible for ToLCNDV resistance at the major QTL Cy-1 on chromosome 1 in cucumber. Marker-assisted breeding of ToLCNDV resistance in cucumber will be expedited by using No.44 and the DNA markers developed in this study.

Deep Learning Driven Real-Time Airspace Monitoring Using Satellite Imagery

Detecting aircraft in remote sensing images poses a formidable challenge due to the diverse characteristics of aircraft, including type, size, pose, and intricate backgrounds. Traditional algorithms encounter difficulties in manually extracting features from numerous candidate regions. This paper introduces an innovative aircraft detection approach that combines corner clustering with a diverse set of Deep Learning (DL) models. The proposed method involves two main stages: region proposal and classification. In the region proposal stage, initial candidate regions are generated using a mean-shift clustering algorithm applied to corners detected on binary images. Subsequently, a comprehensive set of classifiers, encompassing CNN, DenseNet, MobileNetV2, Inception v3, Random Forest (R.F), ResNet50, ResNeXT, Support Vector Machine (SVM), VGG16, Xception, EfficientNet, and InceptionResNetv2, is employed for feature extraction and classification. The presented approach demonstrates superior accuracy and efficiency compared to conventional methods. By leveraging the autonomous learning capabilities of CNN and DL models on extensive datasets, the methodology generates a reduced yet high-quality set of candidate regions. Inspired by the detection methodology employed by image analysts, the approach adopts a coarse-to-fine strategy using CNN and DL models. The first CNN proposes coarse candidate regions, and the second identifies individual airplanes within these regions in finer detail. This framework results in a decreased number of candidate regions compared to existing literature while extracting distinctive deep features. Experimental evaluations on Google Earth images validate the efficiency of the proposed method, underscoring its potential for practical applications in both civilian and military contexts.

A genome-wide association study reveals candidate genes and regulatory regions associated with birth weight in pigs.

Piglet birth weight is associated with preweaning survival, and its related traits have been included in the breeding program. Thus, understanding its genetic basis is essential. This study identified four birth weight-associated genomic regions on chromosomes 2, 4, 5, and 7 through genome-wide association study analysis in 7286 pigs from three different pure breeds using the FarmCPU model. The genetic and phenotypic variance explained by the four candidate regions is 8.42% and 1.85%, respectively. Twenty-eight candidate genes were detected, of which APPL2, TGFBI, MACROH2A1, and SEC22B have been reported to affect body growth or development. In addition, 21 H3K4me3-enriched peaks overlapped with the birth weight-associated genomic regions were identified by integrating the genome-wide association study results with our previous ChIP-seq and RNA-seq data generated in the pig placenta, a fetal organ relevant to birth weight, and three of the regulatory regions influence TGFBI, MACROH2A1, and SEC22B expression. This study provides new insights into understanding the mechanisms for birth weight. Further investigating the variants in the regulatory regions would help identify the functional variants for birth weight in pigs.

GLCV-NET: An automatic diagnosis system for advanced liver fibrosis using global–local cross view in B-mode ultrasound images

Background and objective: Advanced liver fibrosis is a critical stage in the evaluation of chronic liver disease (CLD), holding clinical significance in the development of treatment strategies and estimating the disease progression. Methods:This paper proposes an innovative Global–Local Cross-View Network (GLCV-Net) for the automatic diagnosis of advanced liver fibrosis from ultrasound (US) B-mode images. The proposed method consists of three main components: 1. A Segmentation-enhanced Global Hybrid Feature Extractor for segmenting the liver parenchyma and extracting global features; 2. A Heatmap-weighted Local Feature Extractor for selecting candidate regions and automatically identifying suspicious areas to construct local features; 3. A Scale-adaptive Fusion Module to balance the contributions of global and local scales in evaluating advanced liver fibrosis. Results:The predictive performance of the model was validated on an internal dataset of 1003 chronic liver disease (CLD) patients and an external dataset of 46 CLD patients, both subjected to liver fibrosis staging through pathological assessment. On the internal dataset, GLCV-Net achieved 86.9% accuracy, 85.0% recall, 85.4% precision, and 85.2% F1-score. Further validation on the external dataset confirmed its robustness, with scores of 86.1% in accuracy, 83.1% in recall, 80.8% in precision, and 81.9% in F1-score. Conclusion:These results underscore the GLCV-Net’s potential as a promising approach for non-invasively and accurately diagnosing advanced liver fibrosis in CLD patients, breaking through the limitations of traditional methods by integrating global and local information of liver fibrosis, significantly enhancing diagnostic accuracy.

Are Methylation Patterns in the KALRN Gene Associated with Cognitive and Depressive Symptoms? Findings from the Moli-sani Cohort.

The KALRN gene (encoding kalirin) has been implicated in several neuropsychiatric and neurodegenerative disorders. However, genetic evidence supporting this implication is limited and targeted epigenetic analyses are lacking. Here, we tested associations between epigenetic variation in KALRN and interindividual variation in depressive symptoms (PHQ9) and cognitive (MoCA) performance, in an Italian population cohort (N = 2409; mean (SD) age: 67 (9) years; 55% women). First, we analyzed the candidate region chr3:124584826-124584886 (hg38), within the KALRN promoter, through pyrosequencing of 1385 samples. Then, we widened the investigated region by analyzing 137 CpGs annotated to the whole gene, rescued from epigenome-wide (Illumina EPIC) data from 1024 independent samples from the same cohort. These were tested through stepwise regression models adjusted for age, sex, circulating leukocytes fractions, education, prevalent health conditions and lifestyles. We observed no statistically significant associations with methylation levels in the three CpGs tested through pyrosequencing, or in the gene-wide association analysis with MoCA score. However, we observed a statistically significant association between PHQ9 and cg13549966 (chr3:124106738; β (Standard Error) = 0.28 (0.08), Bonferroni-corrected p = 0.025), located close to the transcription start site of the gene. This association was driven by a polychoric factor tagging somatic depressive symptoms (β (SE) = 0.127 (0.064), p = 0.048). This evidence underscores the importance of studying epigenetic variation within the KALRN gene and the role that it may play in brain diseases, particularly in atypical depression, which is often characterized by somatic symptoms.

Association mapping of mesocotyl and coleoptile length in rice using various genome‐wide association study models

AbstractThe mechanized direct seeding of rice (Oryza sativa L.) is a major trend nowadays. The elongation of rice mesocotyl and coleoptile can facilitate the rapid emergence of seedlings under deep mechanized sowing. Currently, most of the cultivated rice accessions have short mesocotyls or coleoptiles, with only a few related genes cloned. However, understanding and enhancing the ability of rice seedlings to rapidly emerge from deep sowing depths is crucial. Herein, we assessed 745 core rice germplasm accessions sown under a soil cover depth of 10 cm and found few long mesocotyl and coleoptile germplasms. We conducted genome‐wide association study using six models to obtain three or more multi‐model co‐localization candidate regions and calculated Fst between the phenotypes of extreme samples to determine genetic differences. The candidate regions associated with mesocotyl and coleoptile lengths were identified by integrating Fst and multi‐model localization results. This multi‐model localization method may accelerate the mining of genes related to the mesocotyl and coleoptile, providing valuable targets for functional validation and marker‐assisted selection in rice breeding programs.

Genetic and genomic analysis of reproduction traits in holstein cattle using SNP chip data and imputed sequence level genotypes

BackgroundReproductive performance plays an important role in animal welfare, health and profitability in animal husbandry and breeding. It is well established that there is a negative correlation between performance and reproduction in dairy cattle. This relationship is being increasingly considered in breeding programs. By elucidating the genetic architecture of underlying reproduction traits, it will be possible to make a more detailed contribution to this. Our study followed two approaches to elucidate this area; in a first part, variance components were estimated for 14 different calving and fertility traits, and then genome-wide association studies were performed for 13 reproduction traits on imputed sequence-level genotypes with subsequent enrichment analyses.ResultsVariance components analyses showed a low to moderate heritability (h2) for the traits analysed, ranging from 0.014 for endometritis up to 0.271 for stillbirth, indicating variable degrees of variation within the reproduction traits. For genome-wide association studies, we were able to detect genome-wide significant association signals for nine out of 13 analysed traits after Bonferroni correction on chromosome 6, 18 and the X chromosome. In total, we detected over 2700 associated SNPs encircling more than 90 different genes using the imputed whole-genome sequence data. Functional associations were reviewed so far known and potential candidate regions in the proximity of reproduction events were hypothesised.ConclusionOur results confirm previous findings of other authors in a comprehensive cohort including 13 different traits at the same time. Additionally, we identified new candidate genes involved in dairy cattle reproduction and made initial suggestions regarding their potential impact, with special regard to the X chromosome as a putative information source for further research. This work can make a contribution to reveal the genetic architecture of reproduction traits in context of trait specific interactions.

Application of mask R-CNN for building detection in UAV remote sensing images

This study aims to tackle the challenges of low accuracy in building feature extraction and insufficient details in three-dimensional (3D) modeling faced by traditional methods, particularly in complex backgrounds. To address these issues, a method for building feature extraction based on Mask Region-Convolutional Neural Network (Mask R-CNN) is proposed. This approach combines deep learning techniques with aerial images to ensure precise and efficient automatic detection and feature extraction. Urban building images are captured through aerial photography, and building outlines are annotated to create a comprehensive dataset of building features. The Mask R-CNN-based method efficiently processes and classifies the features of the dataset, generating candidate regions for further analysis. Additionally, this method demonstrates significant advantages in building feature extraction by employing the Mask R-CNN model to generate adaptive features. Comparative analysis with models such as Convolutional Neural Network (CNN), Region-based Convolutional Neural Network (R-CNN), Fast Region-based Convolutional Neural Network (Fast R-CNN), Faster Region-based Convolutional Neural Network (Faster R-CNN), and Generative Adversarial Network (GAN) indicates that Mask R-CNN exhibits superior performance in building feature extraction. The Mask R-CNN-based approach achieved approximately 95 % classification accuracy, while also showcasing strong stability and generalization capabilities. This study provides new methodologies and insights for enhancing feature extraction in aerial building imagery, offering significant reference value for the fields of architectural design and urban planning.

Genomic signatures of positive selection in Awarik dromedary camels from southwestern of Saudi Arabia.

The Awarik camel population in southwestern Saudi Arabia exhibits unique genetic and phenotypic traits compared to other domestic camel populations. This study aims to explore the genomic signatures of positive selection in Awarik camels to understand their evolutionary history and identify genetic adaptations potentially shared with East African camel populations. Whole genome sequencing data from nine Awarik camels were analyzed using two robust intra-population haplotype-based statistical methods: integrated haplotype score (iHS) and number of segregating sites by length (nSL). These analyses were conducted to identify candidate regions under positive selection within the Awarik camel genome. These analyses identified 66 and 53 candidate selection regions, encompassing 185 and 123 genes, respectively. The iHS analysis revealed significant selection signals on chromosomes 15 and 16, including a robust overlap on chromosome 15 (10 regions) involving the TRNAI-AAU gene, suggesting its critical role in adaptive processes. Additionally, chromosome 3 exhibited the highest number of candidate regions totaling 10. The nSL analysis highlighted statistically significant regions on chromosomes 2 and 7, as well as a high concentration of candidate regions on chromosome 14, totaling five regions. Notably, large candidate regions were also identified on chromosome 11 (200 kb: 51.750-51.950 kb) and chromosome 9 (325 kb: 45.825-46.150 kb). Functional annotation of these genes revealed involvement in diverse biological processes including olfactory activity, immune regulation, metabolism, insulin secretion, reproductive performance, kidney function, and cellular signaling, with specific genes like BAG5, septin 7, SLC13A1, PCED1B, BMPR1B, ZAR1, JAKMIP2, and NOTCH2 highlighted. These findings contribute to our understanding of the adaptive mechanisms of Awarik camels and have important implications for breeding and conservation strategies. Further research on these genetic adaptations, particularly those affecting immune response, is crucial to mitigate the impacts of climate change on camel populations.