The mean staple length of wool fibre is associated with variation in the ovine keratin-associated protein 21-2 gene

Wool and hair fibres consist of a variety of proteins, including the keratin-associated proteins (KAPs). In this study, a putative ovine homologue of the human KAP21-2 gene (KRTAP21-2) was identified. It was located on chromosome 1 as a 201-bp open reading frame (ORF) in the ovine genome assembly from a Texel sheep (v.4 NC_019458.2: nt122932727 to 122932927). A polymerase chain reaction-single strand conformation polymorphism (PCR-SSCP) analysis of this ORF, and subsequent DNA sequencing, identified five sequences (named A-E). The putative amino acid sequences that would be produced, shared some identity with each other and with other KAPs, but they were most similar to ovine KAP21-1, and phylogenetically related to human KAP21-2. The location of the ovine KRTAP21-2 sequence was consistent with the location of human KRTAP21-2, and this suggests they represent different variant forms of ovine KRTAP21-2. Variation in this gene was investigated in 389 Merino (sire) × Southdown-cross (ewe) lambs. These were derived from four independent sire-lines. The sequence variation was found to be associated with variation in five wool traits: including mean staple length (MSL), mean fibre diameter (MFD), fibre diameter standard deviation (FDSD), prickle factor (PF), and greasy fleece weight (GFW). The most persistent effect of KRTAP21-2 variation was with variation in MSL; with the MSL of sheep of genotype AC being 12.5% greater than those of genotype CE. A similar effect was observed from individual variant absence/presence models. This suggests that KRTAP21-2 should be further investigated as a possible gene-marker for improving MSL

Mutual learning with memory for semi-supervised pest detection

Effectively monitoring pest-infested areas by computer vision is essential in precision agriculture in order to minimize yield losses and create early scientific preventative solutions. However, the scale variation, complex background, and dense distribution of pests bring challenges to accurate detection when utilizing vision technology. Simultaneously, supervised learning-based object detection heavily depends on abundant labeled data, which poses practical difficulties. To overcome these obstacles, in this paper, we put forward innovative semi-supervised pest detection, PestTeacher. The framework effectively mitigates the issues of confirmation bias and instability among detection results across different iterations. To address the issue of leakage caused by the weak features of pests, we propose the Spatial-aware Multi-Resolution Feature Extraction (SMFE) module. Furthermore, we introduce a Region Proposal Network (RPN) module with a cascading architecture. This module is specifically designed to generate higher-quality anchors, which are crucial for accurate object detection. We evaluated the performance of our method on two datasets: the corn borer dataset and the Pest24 dataset. The corn borer dataset encompasses data from various corn growth cycles, while the Pest24 dataset is a large-scale, multi-pest image dataset consisting of 24 classes and 25k images. Experimental results demonstrate that the enhanced model achieves approximately 80% effectiveness with only 20% of the training set supervised in both the corn borer dataset and Pest24 dataset. Compared to the baseline model SoftTeacher, our model improves [email protected] (mean Average Precision) at 7.3 compared to that of SoftTeacher at 4.6. This method offers theoretical research and technical references for automated pest identification and management

Causal relationship between depression and metabolic dysfunction-associated steatotic liver disease: a bidirectional Mendelian randomized study

BackgroundWith the global rise in obesity, metabolic dysfunction-associated steatotic liver disease (MASLD) has emerged as the most common chronic liver disease. Concurrently, depression is a highly prevalent mental disorder. As the incidence of MASLD and depression continues to increase, a growing body of research indicates a potential association between the two conditions. However, the direction of causality between depression and MASLD remains uncertain. To address this gap, our study utilizes a two-sample Mendelian randomization (MR) approach to explore the bidirectional causal relationship between depression and MASLD.MethodsWe extracted single nucleotide polymorphisms (SNPs) associated with depression and MASLD from pooled data of genome-wide association studies (GWAS). A comprehensive assessment of possible causality was also performed. Possible mediating effects of liver enzymes on MASLD were also assessed.ResultsA total of three GWAS pooled data on depression as well as GWAS data related to MASLD and GWAS data on four liver enzymes were used in this study. Our findings indicated a strong causal relationship between depression and MASLD (OR, 1.557; 95% CI, 1.097–2.211; P = 0.016). And we found a mediating effect of gamma-glutamyl transferase (GGT), alanine aminotransferase (ALT) and aspartate aminotransferase (AST). ALT 10% (95% CI: 7% - 13%, P< 0.0002). AST, 4.14% (95% CI: 2.34% - 5.94%, P < 0.05). GGT 0.19% (95% CI: 0.15% - 0.22%, P< 0.000000002). However, we did not find a mediating effect of alkaline phosphatase (ALP). Our inverse MR analysis did not reveal any causal relationship between MASLD and depression.ConclusionsThe MR analysis revealed a positive causal relationship between depression and MASLD, while no reverse causal relationship was identified. Liver enzymes may mediate the role between depression and MASLD

Molecular differences in brain regional vulnerability to aging between males and females

BackgroundAging-related cognitive decline is associated with brain structural changes and synaptic loss. However, the molecular mechanisms of cognitive decline during normal aging remain elusive.ResultsUsing the GTEx transcriptomic data from 13 brain regions, we identified aging-associated molecular alterations and cell-type compositions in males and females. We further constructed gene co-expression networks and identified aging-associated modules and key regulators shared by both sexes or specific to males or females. A few brain regions such as the hippocampus and the hypothalamus show specific vulnerability in males, while the cerebellar hemisphere and the anterior cingulate cortex regions manifest greater vulnerability in females than in males. Immune response genes are positively correlated with age, whereas those involved in neurogenesis are negatively correlated with age. Aging-associated genes identified in the hippocampus and the frontal cortex are significantly enriched for gene signatures implicated in Alzheimer’s disease (AD) pathogenesis. In the hippocampus, a male-specific co-expression module is driven by key synaptic signaling regulators including VSNL1, INA, CHN1 and KCNH1; while in the cortex, a female-specific module is associated with neuron projection morphogenesis, which is driven by key regulators including SRPK2, REPS2 and FXYD1. In the cerebellar hemisphere, a myelination-associated module shared by males and females is driven by key regulators such as MOG, ENPP2, MYRF, ANLN, MAG and PLP1, which have been implicated in the development of AD and other neurodegenerative diseases.ConclusionsThis integrative network biology study systematically identifies molecular signatures and networks underlying brain regional vulnerability to aging in males and females. The findings pave the way for understanding the molecular mechanisms of gender differences in developing neurodegenerative diseases such as AD

Identification and characterization of circular RNAs in mammary gland tissue from sheep at peak lactation and during the nonlactating period

Circular RNAs are a class of noncoding RNA with a widespread occurrence in eukaryote tissues, and with some having been demonstrated to have clear biological function. In sheep, little is known about the role of circular RNAs in mammary gland tissue, and therefore an RNA sequencing approach was used to compare mammary gland tissue expression of circular RNAs in 9 Small Tail Han sheep at peak lactation, and subsequently when they were not lactating. These 9 sheep had their RNA pooled for analysis into 3 libraries from peak lactation and 3 from the nonlactating period. A total of 3,278 and 1,756 circular RNAs were identified in the peak lactation and nonlactating mammary gland tissues, respectively, and the expression and identity of 9 of them was confirmed using reverse transcriptase-polymerase chain reaction analysis and DNA sequencing. The type, chromosomal location and length of the circular RNAs identified were ascertained. Forty upregulated and one downregulated circular RNAs were characterized in the mammary gland tissue at peak lactation compared with the nonlactating mammary gland tissue. Gene ontology enrichment analysis revealed that the parental genes of these differentially expressed circular RNAs were related to molecular function, binding, protein binding, ATP binding, and ion binding. Five differentially expression circular RNAs were selected for further analysis to predict their target microRNAs, and some microRNAs reportedly associated with the development of the mammary gland were found in the constructed circular RNA–microRNA network. This study reveals the expression profiles and characterization of circular RNAs at 2 key stages of mammary gland activity, thereby providing an improved understanding of the roles of circular RNAs in the mammary gland of sheep

Electromagnetic Wave Theory and Applications

Contains table of contents for Section 3, reports on three research projects and a list of publications.California Institute of Technology/Jet Propulsion Laboratory Contract 959548National Aeronautics and Space Administration Grant NAGW-1617National Aeronautics and Space Administration Grant Contract 958461U.S. Navy - Office of Naval Research Grant N00014-92-J-1616U.S. Navy - Office of Naval Research Grant N00014-92-J-4098Digital Equipment Corporation AGMT DTD 11/16/93Joint Services Electronics Program Contract DAAL03-92-C-0001Joint Services Electronics Program Grant DAAH04-95-1-0038MIT Lincoln Laboratory P.O. No. BX-5424U.S. Navy - Office of Naval Research Grant N00014-90-J-1002U.S. Navy - Office of Naval Research Grant N00014-89-J-1019DEMACO Agreement 11/15/93Federal Aviation Administration Grant 94-G-007U.S. Army Cold Regions Research and Engineering Laboratory Contract DACA89-93-K-000

Efficient Elitist Cooperative Evolutionary Algorithm for Multi-Objective Reinforcement Learning

Sequential decision-making problems with multiple objectives are known as multi-objective reinforcement learning. In these scenarios, decision-makers require a complete Pareto front that consists of Pareto optimal solutions. Such a front enables decision-makers to understand the relationship between objectives and make informed decisions from a broad range of solutions. However, existing methods may be unable to search for solutions in concave regions of the Pareto front or lack global optimization ability, leading to incomplete Pareto fronts. To address this issue, we propose an efficient elitist cooperative evolutionary algorithm that maintains both an evolving population and an elite archive. The elite archive uses cooperative operations with various genetic operators to guide the evolving population, resulting in efficient searches for Pareto optimal solutions. The experimental results on submarine treasure hunting benchmarks demonstrate the effectiveness of the proposed method in solving various multi-objective reinforcement learning problems and providing decision-makers with a set of trade-off solutions between travel time and treasure amount, enabling them to make flexible and informed decisions based on their preferences. Therefore, the proposed method has the potential to be a useful tool for implementing real-world applications