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All extant core-eudicot plants share a common ancestral genome that has experienced cyclic polyploidizations and (re)diploidizations. Reshuffling of the ancestral core-eudicot genome generates abundant genomic diversity, but the role of this diversity in shaping the hierarchical genome architecture, such as chromatin topology and gene expression, remains poorly understood. Here, we assemble chromosome-level genomes of one diploid and three tetraploid Panax species and conduct in-depth comparative genomic and epigenomic analyses. We show that chromosomal interactions within each duplicated ancestral chromosome largely maintain in extant Panax species, albeit experiencing ca. 100–150 million years of evolution from a shared ancestor. Biased genetic fractionation and epigenetic regulation divergence during polyploidization/(re)diploidization processes generate remarkable biochemical diversity of secondary metabolites in the Panax genus. Our study provides a paleopolyploidization perspective of how reshuffling of the ancestral core-eudicot genome leads to a highly dynamic genome and to the metabolic diversification of extant eudicot plants.

Metabolic processes can influence disease risk and provide therapeutic targets. By conducting genome-wide association studies of 1,091 blood metabolites and 309 metabolite ratios, we identified associations with 690 metabolites at 248 loci and associations with 143 metabolite ratios at 69 loci. Integrating metabolite-gene and gene expression information identified 94 effector genes for 109 metabolites and 48 metabolite ratios. Using Mendelian randomization (MR), we identified 22 metabolites and 20 metabolite ratios having estimated causal effect on 12 traits and diseases, including orotate for estimated bone mineral density, α-hydroxyisovalerate for body mass index and ergothioneine for inflammatory bowel disease and asthma. We further measured the orotate level in a separate cohort and demonstrated that, consistent with MR, orotate levels were positively associated with incident hip fractures. This study provides a valuable resource describing the genetic architecture of metabolites and delivers insights into their roles in common diseases, thereby offering opportunities for therapeutic targets. Genome-wide association studies comprising 1,091 metabolites and 309 metabolite ratios in 8,299 individuals from the Canadian Longitudinal Study on Aging provide insights into the genetic architecture of metabolites and their role in human diseases.

With the rapid development of emerging technologies such as self-media, the Internet of Things, and cloud computing, massive data applications are crossing the threshold of the era of real-time analysis and value realization, which makes data streams ubiquitous in all kinds of industries. Therefore, detecting anomalies in such data streams could be very important and full of challenges. For example, in industries such as electricity and finance, data stream anomalies often contain information that can help avoiding risks and support decision making. However, most traditional anomaly detection algorithms rely on acquiring global information about the data, which is hard to apply to stream data scenarios. Currently, the reviews of the algorithm in the field of anomaly detection, both domestically and internationally, tend to focus on the exposition of anomaly detection algorithms in static data environments, while lacking in the induction and analysis of anomaly detection algorithms in the context of streaming data. As a result, unlike the existing literature reviews, this review provides the current mainstream anomaly detection algorithms in data streaming scenarios and categorizes them into three types on the basis of their fundamental principles: (1) based on offline learning; (2) based on semi-online learning; (3) based on online learning. This review discusses the current state of research on data stream anomaly detection and studies the key issues in various algorithms for detecting anomalies in data streams on the basis of concise summarization. Moreover, the review conducts a detailed comparison of the pros and cons of the algorithms. Finally, the future challenges in the field are analyzed, and future research directions are proposed.

Purpose Identifying rare genetic causes of common diseases can improve diagnostic and treatment strategies, but incurs high costs. We tested whether individuals with common disease and low polygenic risk score (PRS) for that disease generated from less expensive genome-wide genotyping data are more likely to carry rare pathogenic variants. Methods We identified patients with one of five common complex diseases among 44,550 individuals who underwent exome sequencing in the UK Biobank. We derived PRS for these five diseases, and identified pathogenic rare variant heterozygotes. We tested whether individuals with disease and low PRS were more likely to carry rare pathogenic variants. Results While rare pathogenic variants conferred, at most, 5.18-fold (95% confidence interval [CI]: 2.32–10.13) increased odds of disease, a standard deviation increase in PRS, at most, increased the odds of disease by 5.25-fold (95% CI: 5.06–5.45). Among diseased patients, a standard deviation decrease in the PRS was associated with, at most, 2.82-fold (95% CI: 1.14–7.46) increased odds of identifying rare variant heterozygotes. Conclusion Rare pathogenic variants were more prevalent among affected patients with a low PRS. Therefore, prioritizing individuals for sequencing who have disease but low PRS may increase the yield of sequencing studies to identify rare variant heterozygotes.

Single-cell analysis across multiple samples and conditions requires quantitative modeling of the interplay between the continuum of cell states and the technical and biological sources of sample-to-sample variability. We introduce GEDI, a generative model that identifies latent space variations in multi-sample, multi-condition single-cell datasets and attributes them to sample-level covariates. GEDI enables cross-sample cell state mapping on par with state-of-the-art integration methods, cluster-free differential gene expression analysis along the continuum of cell states, and machine learning-based prediction of sample characteristics from single-cell data. GEDI can also incorporate gene-level prior knowledge to infer pathway and regulatory network activities in single cells. Finally, GEDI extends all these concepts to previously unexplored modalities that require joint consideration of dual measurements, such as the joint analysis of exon inclusion/exclusion reads to model alternative cassette exon splicing, or spliced/unspliced reads to model the mRNA stability landscapes of single cells. Single-cell analysis of multi-condition cohorts requires modelling the interaction between sample variables and cell states. Here, authors develop GEDI to enable integration, cluster-free differential expression analysis and regulon analysis for both gene expression and alternative splicing modalities.

FeCoNiCuAl high-entropy alloys exhibit remarkable mechanical properties; nevertheless, these materials struggle to withstand harsh environments because of their insufficient resistance to wear and corrosion. The addition of Si can significantly enhance the alloy’s high-temperature performance, hardness, and wear resistance, thereby making it more suitable for applications in high-temperature or corrosive environments. To overcome these drawbacks, this research investigates how varying Si content affects the microstructure and properties of FeCoNiCuAl coatings. Composite coatings of FeCoNiCuAlSix (x = 0, 0.5, 1.0, 1.5, 2.0) were fabricated on 65 Mn substrates using laser cladding. Various testing methods, including metallographic microscopy, Vickers hardness testing, friction and wear testing, and electrochemical analysis, were employed to examine the phase structure, microstructure, and hardness of the coating. It is observed that the FeCoNiCuAl coating begins with a uniform FCC phase structure. However, as the Si content increases, a phase transformation to the BCC structure occurs. The microstructure is primarily composed of isometric crystals and dendrites that become finer and more compact with higher Si content. For the FeCoNiCuAlSi2.0 coating, the microhardness reaches 581.05 HV0.2. Additionally, wear resistance shows a positive correlation with Si content. Electrochemical testing in NS4 solution shows that the corrosion potential of the coating increases from −0.471 V for FeCoNiCuAl to −0.344 V for FeCoNiCuAlSi2.0, while the corrosion current density decreases from 1.566 × 10−6 A/cm2 to 4.073 × 10−6 A/cm2. These results indicate that Si addition plays a crucial role in enhancing the mechanical properties and corrosion resistance of FeCoNiCuAl coatings, making them more suitable for high-performance applications in extreme environments.

Adult stem cells are indispensable for tissue regeneration, but their function declines with age. The niche environment in which the stem cells reside plays a critical role in their function. However, quantification of the niche effect on stem cell function is lacking. Using muscle stem cells (MuSC) as a model, we show that aging leads to a significant transcriptomic shift in their subpopulations accompanied by locus-specific gain and loss of chromatin accessibility and DNA methylation. By combining in vivo MuSC transplantation and computational methods, we show that the expression of approximately half of all age-altered genes in MuSCs from aged male mice can be restored by exposure to a young niche environment. While there is a correlation between gene reversibility and epigenetic alterations, restoration of gene expression occurs primarily at the level of transcription. The stem cell niche environment therefore represents an important therapeutic target to enhance tissue regeneration in aging. Aging leads to significant alteration in the gene expression of muscle stem cells. In vivo exposure of muscle stem cells from aged mice to a young niche environment restores the expression of a significant portion of age-altered genes in mice.

Background Vitamin B12 deficiency can lead to pernicious anemia and has been associated with various neuropsychiatric diseases and cognitive decline. However, it is unclear whether increasing serum vitamin B12 levels can help to prevent the onset of psychiatric disorders and cognitive impairment in the general population. Methods Leveraging large-scale genome-wide association studies (GWASs), we conducted Mendelian randomization (MR) and sensitivity analyses to estimate the potential effects of serum vitamin B12 levels on eight psychiatric disorders, educational attainment and cognitive performance. We conducted additional MR analyses utilizing within-sibship studies to mitigate potential residual confounding effects. Results As a positive control, we confirm that a one standard deviation increase in genetically increased vitamin B12 levels is strongly protective against pernicious anemia (odds ratio, OR = 0.24; 95% CI: 0.15–0.40; p -value = 2.1×10 -8 ). In contrast, MR estimates of vitamin B12 effects on all eight psychiatric disorders, educational attainment and cognitive performance largely overlap with the null. For example, a one standard deviation increase in genetically predicted vitamin B12 levels is associated with an OR of 1.02 for depression (95% CI: 1.00 – 1.04; p -value = 0.11), a 0.0077 standard deviation increase in educational attainment (95% CI: −0.010 – 0.025; p -value = 0.39) and a 0.013 standard deviation increase in cognitive performance (95% CI: −0.0088 – 0.035; p -value = 0.24). No significant associations are identified in sensitivity analyses excluding pleiotropic genetic instruments or MR analyses based on within-sibship studies. Conclusions Our findings suggest that increasing overall vitamin B12 levels may not meaningfully protect against the investigated psychiatric disorders or cognitive impairment in the general population. Plain Language Summary Low vitamin B12 levels are linked to pernicious anemia and have been associated with several psychiatric and cognitive conditions. However, it is unclear whether increasing B12 levels through supplementation can help to prevent these outcomes. Using genetic data from large studies, we found strong evidence that higher B12 levels protect against pernicious anemia, but no evidence that they reduce the risk of psychiatric disorders or cognitive impairment. Our findings suggest that increasing vitamin B12 levels is unlikely to meaningfully improve mental health or cognitive function in the general population. Lu et al. evaluate the potential impact of serum vitamin B12 levels on major psychiatric disorders and cognitive impairment. Findings suggest that higher overall serum vitamin B12 levels are unlikely to offer meaningful protection against these outcomes in the general population.

Background It is a long established fact that sex is an important factor that influences the transcriptional regulatory processes of an organism. However, understanding sex-based differences in gene expression has been limited because existing studies typically sequence and analyze bulk tissue from female or male individuals. Such analyses average cell-specific gene expression levels where cell-to-cell variation can easily be concealed. We therefore sought to utilize data generated by the rapidly developing single cell RNA sequencing (scRNA-seq) technology to explore sex dimorphism and its functional consequences at the single cell level. Methods Our study included scRNA-seq data of ten well-defined cell types from the brain and heart of female and male young adult mice in the publicly available tissue atlas dataset, Tabula Muris. We combined standard differential expression analysis with the identification of differential distributions in single cell transcriptomes to test for sex-based gene expression differences in each cell type. The marker genes that had sex-specific inter-cellular changes in gene expression formed the basis for further characterization of the cellular functions that were differentially regulated between the female and male cells. We also inferred activities of transcription factor-driven gene regulatory networks by leveraging knowledge of multidimensional protein-to-genome and protein-to-protein interactions and analyzed pathways that were potential modulators of sex differentiation and dimorphism. Results For each cell type in this study, we identified marker genes with significantly different mean expression levels or inter-cellular distribution characteristics between female and male cells. These marker genes were enriched in pathways that were closely related to the biological functions of each cell type. We also identified sub-cell types that possibly carry out distinct biological functions that displayed discrepancies between female and male cells. Additionally, we found that while genes under differential transcriptional regulation exhibited strong cell type specificity, six core transcription factor families responsible for most sex-dimorphic transcriptional regulation activities were conserved across the cell types, including ASCL2, EGR, GABPA, KLF/SP, RXRα, and ZF. Conclusions We explored novel gene expression-based biomarkers, functional cell group compositions, and transcriptional regulatory networks associated with sex dimorphism with a novel computational pipeline. Our findings indicated that sex dimorphism might be widespread across the transcriptomes of cell types, cell type-specific, and impactful for regulating cellular activities.

Both genetic and early life risk factors play important roles in the pathogenesis and progression of adult depression. However, the interplay between these risk factors and their added value to risk prediction models have not been fully elucidated. Leveraging a meta-analysis of major depressive disorder genome-wide association studies ( = 45,591 cases and 97,674 controls), we developed and optimized a polygenic risk score for depression using LDpred in a model selection dataset from the UK Biobank ( = 130,092 European ancestry individuals). In a UK Biobank test dataset ( = 278,730 European ancestry individuals), we tested whether the polygenic risk score and early life risk factors were associated with each other and compared their associations with depression phenotypes. Finally, we conducted joint predictive modeling to combine this polygenic risk score with early life risk factors by stepwise regression, and assessed the model performance in identifying individuals at high risk of depression. In the UK Biobank test dataset, the polygenic risk score for depression was moderately associated with multiple early life risk factors. For instance, a one standard deviation increase in the polygenic risk score was associated with 1.16-fold increased odds of frequent domestic violence (95% CI: 1.14-1.19) and 1.09-fold increased odds of not having access to medical care as a child (95% CI: 1.05-1.14). However, the polygenic risk score was more strongly associated with depression phenotypes than most early life risk factors. A joint predictive model integrating the polygenic risk score, early life risk factors, age and sex achieved an AUROC of 0.6766 for predicting strictly defined major depressive disorder, while a model without the polygenic risk score and a model without any early life risk factors had an AUROC of 0.6593 and 0.6318, respectively. We have developed a polygenic risk score to partly capture the genetic liability to depression. Although genetic and early life risk factors can be correlated, joint predictive models improved risk stratification despite limited improvement in magnitude, and may be explored as tools to better identify individuals at high risk of depression.