Explore the vast range of titles available.

Previous meta-analyses found abnormal brain activations in schizophrenia patients compared with normal controls when performing working memory tasks. Although most studies focused on dysfunction of the working memory activation network in schizophrenia patients, deactivation abnormalities of the working memory in the default mode network have also been reported in schizophrenia but have received less attention. Our goal was to discover whether deactivation abnormalities can also be consistently found in schizophrenia during working memory tasks and, further, to consider both activation and deactivation abnormalities. Fifty-two English language peer-reviewed studies were included in this meta-analysis. Compared with normal controls, the schizophrenia patients showed activation dysfunction of the bilateral dorsolateral prefrontal cortex and posterior parietal cortex as well as the anterior insula, anterior cingulate cortex, and supplementary motor area, which are core nodes of the central executive and salience network. In addition to dysfunction of the activation networks, the patients showed deactivation abnormalities in the ventral medial prefrontal cortex and posterior cingulate cortex, which are core nodes of the default mode network. These results suggest that both activation and deactivation abnormalities exist in schizophrenia patients and that these abnormalities should both be considered when investigating the pathophysiological mechanism of schizophrenia.

As one of the great survivors of the plant kingdom, barnyard grasses ( Echinochloa spp.) are the most noxious and common weeds in paddy ecosystems. Meanwhile, at least two Echinochloa species have been domesticated and cultivated as millets. In order to better understand the genomic forces driving the evolution of Echinochloa species toward weed and crop characteristics, we assemble genomes of three Echinochloa species (allohexaploid E. crus-galli and E. colona , and allotetraploid E. oryzicola ) and re-sequence 737 accessions of barnyard grasses and millets from 16 rice-producing countries. Phylogenomic and comparative genomic analyses reveal the complex and reticulate evolution in the speciation of Echinochloa polyploids and provide evidence of constrained disease-related gene copy numbers in Echinochloa . A population-level investigation uncovers deep population differentiation for local adaptation, multiple target-site herbicide resistance mutations of barnyard grasses, and limited domestication of barnyard millets. Our results provide genomic insights into the dual roles of Echinochloa species as weeds and crops as well as essential resources for studying plant polyploidization, adaptation, precision weed control and millet improvements. Echinochloa is an important genus in the grass family as many of them are either problematic weeds or domesticated millets. Here, the authors assemble three polyploidy genomes in this genus using the diploid-assisted scaffolding method DipHic and provide genomic insights into the dual roles of some species as weeds and orphan crops.

Background Asian rice is one of the world’s most widely cultivated crops. Large-scale resequencing analyses have been undertaken to explore the domestication and de-domestication genomic history of Asian rice, but the evolution of rice is still under debate. Results Here, we construct a syntelog-based rice pan-genome by integrating and merging 74 high-accuracy genomes based on long-read sequencing, encompassing all ecotypes and taxa of Oryza sativa and Oryza rufipogon . Analyses of syntelog groups illustrate subspecies divergence in gene presence-and-absence and haplotype composition and identify massive genomic regions putatively introgressed from ancient Geng/ japonica to ancient Xian/ indica or its wild ancestor, including almost all well-known domestication genes and a 4.5-Mbp centromere-spanning block, supporting a single domestication event in main rice subspecies. Genomic comparisons between weedy and cultivated rice highlight the contribution from wild introgression to the emergence of de-domestication syndromes in weedy rice. Conclusions This work highlights the significance of inter-taxa introgression in shaping diversification and divergence in rice evolution and provides an exploratory attempt by utilizing the advantages of pan-genomes in evolutionary studies.

Barnyardgrass ( Echinochloa crus-galli ) is a pernicious weed in agricultural fields worldwide. The molecular mechanisms underlying its success in the absence of human intervention are presently unknown. Here we report a draft genome sequence of the hexaploid species E. crus-galli , i.e., a 1.27 Gb assembly representing 90.7% of the predicted genome size. An extremely large repertoire of genes encoding cytochrome P450 monooxygenases and glutathione S-transferases associated with detoxification are found. Two gene clusters involved in the biosynthesis of an allelochemical 2,4-dihydroxy-7-methoxy-1,4-benzoxazin-3-one (DIMBOA) and a phytoalexin momilactone A are found in the E. crus-galli genome, respectively. The allelochemical DIMBOA gene cluster is activated in response to co-cultivation with rice, while the phytoalexin momilactone A gene cluster specifically to infection by pathogenic Pyricularia oryzae . Our results provide a new understanding of the molecular mechanisms underlying the extreme adaptation of the weed. Barnyardgrass is a problematic agricultural weed. Here, via genomic analysis, Guo et al. identify genes potentially underlying its success, including a gene cluster activated in response to co-cultivation with rice that leads to synthesis of the allelochemical DIMBOA.

Background Worldwide feralization of crop species into agricultural weeds threatens global food security. Weedy rice is a feral form of rice that infests paddies worldwide and aggressively outcompetes cultivated varieties. Despite increasing attention in recent years, a comprehensive understanding of the origins of weedy crop relatives and how a universal feralization process acts at the genomic and molecular level to allow the rapid adaptation to weediness are still yet to be explored. Results We use whole-genome sequencing to examine the origin and adaptation of 524 global weedy rice samples representing all major regions of rice cultivation. Weed populations have evolved multiple times from cultivated rice, and a strikingly high proportion of contemporary Asian weed strains can be traced to a few Green Revolution cultivars that were widely grown in the late twentieth century. Latin American weedy rice stands out in having originated through extensive hybridization. Selection scans indicate that most genomic regions underlying weedy adaptations do not overlap with domestication targets of selection, suggesting that feralization occurs largely through changes at loci unrelated to domestication. Conclusions This is the first investigation to provide detailed genomic characterizations of weedy rice on a global scale, and the results reveal diverse genetic mechanisms underlying worldwide convergent rice feralization.

In this paper, the high-dimensional linear regression model is considered, where the covariates are measured with additive noise. Different from most of the other methods, which are based on the assumption that the true covariates are fully obtained, results in this paper only require that the corrupted covariate matrix is observed. Then, by the application of information theory, the minimax rates of convergence for estimation are investigated in terms of the ℓp(1≤p<∞)-losses under the general sparsity assumption on the underlying regression parameter and some regularity conditions on the observed covariate matrix. The established lower and upper bounds on minimax risks agree up to constant factors when p=2, which together provide the information-theoretic limits of estimating a sparse vector in the high-dimensional linear errors-in-variables model. An estimator for the underlying parameter is also proposed and shown to be minimax optimal in the ℓ2-loss.

Different patterns of brain activity are observed in various subjects across a wide functional domain. However, these individual differences, which are often neglected through the group average, are not yet completely understood. Based on the fundamental assumption that human behavior is rooted in the underlying brain function, we speculated that the individual differences in brain activity are reflected in the individual differences in behavior. Adopting 98 behavioral measures and assessing the brain activity induced at seven task functional magnetic resonance imaging states, we demonstrated that the individual differences in brain activity can be used to predict behavioral measures of individual subjects with high accuracy using the partial least square regression model. In addition, we revealed that behavior-relevant individual differences in brain activity transferred between different task states and can be used to reconstruct individual brain activity. Reconstructed individual brain activity retained certain individual differences which were lost in the group average and could serve as an individual functional localizer. Therefore, our results suggest that the individual differences in brain activity contain behavior-relevant information and should be included in group averaging. Moreover, reconstructed individual brain activity shows a potential use in precise and personalized medicine.

Brain connectivity plays an important role in determining the brain region’s function. Previous researchers proposed that the brain region’s function is characterized by that region’s input and output connectivity profiles. Following this proposal, numerous studies have investigated the relationship between connectivity and function. However, this proposal only utilizes direct connectivity profiles and thus is deficient in explaining individual differences in the brain region’s function. To overcome this problem, we proposed that a brain region’s function is characterized by that region’s multi-hops connectivity profile. To test this proposal, we used multi-hops functional connectivity to predict the individual face activation of the right fusiform face area (rFFA) via a multi-layer graph neural network and showed that the prediction performance is essentially improved. Results also indicated that the two-layer graph neural network is the best in characterizing rFFA’s face activation and revealed a hierarchical network for the face processing of rFFA.

As environmentally friendly pile foundations with small diameters and higher slenderness ratios, micropiles are widely used in fields such as transmission line engineering and building reinforcement. However, the available research has primarily focused on their bearing performance under compressive and horizontal loads, and there is insufficient research on predicting the uplift capacity of micropiles. This study investigated the load transfer mechanism and the behavior of the surrounding soil using model tests and finite element simulations. The ultimate uplift capacities and load distributions of micropiles with different slenderness ratios were analyzed. The results show that as the slenderness ratio increases, the ultimate uplift capacity of a pile gradually increases. However, this rate of increase diminishes gradually. Additionally, the restraining effect and range of the surrounding soil at the lower part of the pile are enhanced. The critical embedment depth of the micropiles shifts further away from the pile tip as the slenderness ratio increases. Finally, this study proposed a novel modification to Shanker’s model of incorporating variations in the critical embedment depth based on the slenderness ratios. Subsequently, a modified model for the ultimate uplift capacity of micropiles was proposed and validated using a model test. The proposed model effectively predicts the uplift bearing capacity of micropiles with high slenderness ratios, which is practical for engineering applications.

Background Data from genome-wide association studies (GWASs) have been used to estimate the heritability of human complex traits in recent years. Existing methods are based on the linear mixed model, with the assumption that the genetic effects are random variables, which is opposite to the fixed effect assumption embedded in the framework of quantitative genetics theory. Moreover, heritability estimators provided by existing methods may have large standard errors, which calls for the development of reliable and accurate methods to estimate heritability. Results In this paper, we first investigate the influences of the fixed and random effect assumption on heritability estimation, and prove that these two assumptions are equivalent under mild conditions in the theoretical aspect. Second, we propose a two-stage strategy by first performing sparse regularization via cross-validated elastic net, and then applying variance estimation methods to construct reliable heritability estimations. Results on both simulated data and real data show that our strategy achieves a considerable reduction in the standard error while reserving the accuracy. Conclusions The proposed strategy allows for a reliable and accurate heritability estimation using GWAS data. It shows the promising future that reliable estimations can still be obtained with even a relatively restricted sample size, and should be especially useful for large-scale heritability analyses in the genomics era.