To elaborate how the viral load of HBV affects the gestational diabetes mellitus (GDM).
We enrolled 196 chronic HBV-infected pregnant patients in this hospital between January 2012 and December 2017 ...for delivery in this study. According to the viral load of HBV-DNA, these patients were divided into the HBV-DNA negative group (n = 107, <1 × 103 copies/mL) and HBV-DNA positive group (n = 89, ≥1 × 103 copies/mL). Simultaneously, 100 HBV-free pregnant women who were admitted to the hospital for delivery were included in the control group. Before delivery, fasting venous blood was drawn from the pregnant women to perform the HBV-DNA quantification through qRT-PCR; from the 24th to 28th gestation week, all pregnant women underwent OGTT, with the third-trimester-of-pregnancy as the endpoint. Besides, we also measured the FBG, 2hPG and hemoglobin A1c (HbAIc).
Among 168 pregnant patients carrying chronic HBV, viral load of 107 patients was less than 1 × 103 copies/mL (54.6%), and 89 not less than 1 × 103 copies/mL (45.4%). The incidence rates of GDM in the HBV-DNA negative group and HBV-DNA positive group were 18.7% and 19.1%, respectively, significantly higher than that in the control group (p < 0.05), while the difference of the incidence rates of GDM between two HBV-DNA groups were not significant (p > 0.05). In HBV-DNA negative group and HBV-DNA positive group, FBGs, 2hPGs and HbAIcs were respectively (6.96 ± 0.36) mmol/L and (7.04 ± 0.37) mmol/L, (10.26 ± 1.29) mmol/L and (10.16 ± 1.12) mmol/L, and (8.66 ± 0.97) % and (8.91 ± 0.90) %, significantly higher than (4.57 ± 0.34) mmol/L, (6.16 ± 0.86) mmol/L and (5.13 ± 0.57) % (p < 0.05); however, between two HBV-DNA groups, comparisons of the FBG, 2hPG and HbAIc suggested no significant differences (p > 0.05). In 196 patients carrying chronic HBV, positive correlations were identified between the viral load of HBV-DNA, and FBG, 2hPG and HbAIc (p < 0.01).
HBV infection can increase the incidence rate of GDM, and the viral load of HBV-DNA is correlated with the glucose level of pregnant patients.
Time series classification is an important task in time series data mining, and has attracted great interests and tremendous efforts during last decades. However, it remains a challenging problem due ...to the nature of time series data: high dimensionality,large in data size and updating continuously. The deep learning techniques are explored to improve the performance of traditional feature-based approaches. Specifically, a novel convolutional neural network (CNN) framework is proposed for time series classification.Different from other feature-based classification approaches,CNN can discover and extract the suitable internal structure to generate deep features of the input time series automatically by using convolution and pooling operations. Two groups of experiments are conducted on simulated data sets and eight groups of experiments are conducted on real-world data sets from different application domains. The final experimental results show that the proposed method outperforms state-of-the-art methods for time series classification in terms of the classification accuracy and noise tolerance.
Sparse signal recovery in high-dimensional settings via regularization techniques has been developed in the past two decades and produces fruitful results in various areas. Previous studies mainly ...focus on the idealized assumption where covariates are free of noise. However, in realistic scenarios, covariates are always corrupted by measurement errors, which may induce significant estimation bias when methods for clean data are naively applied. Recent studies begin to deal with the errors-in-variables models. Current method either depends on the distribution of covariate noise or does not depends on the distribution but is inconsistent in parameter estimation. A novel covariate relaxation method that does not depend on the distribution of covariate noise is proposed. Statistical consistency on parameter estimation is established. Numerical experiments are conducted and show that the covariate relaxation method achieves the same or even better estimation accuracy than that of the state of art nonconvex Lasso estimator. The advantage that the covariate relaxation method is independent of the distribution of covariate noise while produces a small estimation error suggests its prospect in practical applications.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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