Understanding soybean (Glycine max) domestication and improvement at a genetic level is important to inform future efforts to further improve a crop that provides the world's main source of oilseed. ...We detect 230 selective sweeps and 162 selected copy number variants by analysis of 302 resequenced wild, landrace and improved soybean accessions at >11× depth. A genome-wide association study using these new sequences reveals associations between 10 selected regions and 9 domestication or improvement traits, and identifies 13 previously uncharacterized loci for agronomic traits including oil content, plant height and pubescence form. Combined with previous quantitative trait loci (QTL) information, we find that, of the 230 selected regions, 96 correlate with reported oil QTLs and 21 contain fatty acid biosynthesis genes. Moreover, we observe that some traits and loci are associated with geographical regions, which shows that soybean populations are structured geographically. This study provides resources for genomics-enabled improvements in soybean breeding.
Recently, ithas been observed that {0,±1}‐ternary codes, which are simply generated from deep features by hard thresholding, tend to outperform {−1,1}‐binary codes in image retrieval. To obtain ...better ternary codes, the authors for the first time propose to jointly learn the features with the codes by appending a smoothed function to the networks. During training, the function could evolve into a non‐smoothed ternary function by a continuation method, and then generate ternary codes. The method circumvents the difficulty of directly training discrete functions and reduces the quantization errors of ternary codes. Experiments show that the proposed joint learning indeed could produce better ternary codes. For the first time, the authors propose to generate ternary hash codes by jointly learning the codes with deep features via a continuation method. Experiments show that the proposed method outperforms existing methods.
Aberrant sperm flagella impair sperm motility and cause male infertility, yet the genes which have been identified in multiple morphological abnormalities of the flagella (MMAF) can only explain the ...pathogenic mechanisms of MMAF in a small number of cases. Here, we identify and functionally characterize homozygous loss-of-function mutations of QRICH2 in two infertile males with MMAF from two consanguineous families. Remarkably, Qrich2 knock-out (KO) male mice constructed by CRISPR-Cas9 technology present MMAF phenotypes and sterility. To elucidate the mechanisms of Qrich2 functioning in sperm flagellar formation, we perform proteomic analysis on the testes of KO and wild-type mice. Furthermore, in vitro experiments indicate that QRICH2 is involved in sperm flagellar development through stabilizing and enhancing the expression of proteins related to flagellar development. Our findings strongly suggest that the genetic mutations of human QRICH2 can lead to male infertility with MMAF and that QRICH2 is essential for sperm flagellar formation.
Male infertility is a major concern affecting human reproductive health. Asthenoteratospermia can cause male infertility through reduced motility and abnormal morphology of spermatozoa. Several ...genes, including DNAH1 and some CFAP family members, are involved in multiple morphological abnormalities of the sperm flagella (MMAF). However, these known genes only account for approximately 60% of human MMAF cases. Here, we conducted further genetic analyses by using whole-exome sequencing in a cohort of 65 Han Chinese men with MMAF. Intriguingly, bi-allelic mutations of TTC21A (tetratricopeptide repeat domain 21A) were identified in three (5%) unrelated, MMAF-affected men, including two with homozygous stop-gain mutations and one with compound heterozygous mutations of TTC21A. Notably, these men consistently presented with MMAF and additional abnormalities of sperm head-tail conjunction. Furthermore, a homozygous TTC21A splicing mutation was identified in two Tunisian cases from an independent MMAF cohort. TTC21A is preferentially expressed in the testis and encodes an intraflagellar transport (IFT)-associated protein that possesses several tetratricopeptide repeat domains that perform functions crucial for ciliary function. To further investigate the potential roles of TTC21A in spermatogenesis, we generated Ttc21a mutant mice by using CRISPR-Cas9 technology and revealed sperm structural defects of the flagella and the connecting piece. Our consistent observations across human populations and in the mouse model strongly support the notion that bi-allelic mutations in TTC21A can induce asthenoteratospermia with defects of the sperm flagella and head-tail conjunction.
Drought, heat and other abiotic stresses during grain filling can result in reductions in grain weight. Conserved water-soluble carbohydrates (WSC) at early grain filling play an important role in ...partial compensation of reduced carbon supply. A diverse population of 262 historical winter wheat accessions was used in the present study. There were significant correlations between 1000-grain weight (TGW) and four types of WSC, viz. (1) total WSC at the mid-grain filling stage (14 days after flowering) produced by leaves and non-leaf organs; (2) WSC contributed by current leaf assimilation during the mid-grain filling; (3) WSC in non-leaf organs at the mid-grain filling, excluding the current leaf assimilation; and (4) WSC used for respiration and remobilization during the mid-grain filling. Association and favorable allele analyses of 209 genome-wide SSR markers and the four types of WSC were conducted using a mixed linear model. Seven novel favorable WSC alleles exhibited positive individual contributions to TGW, which were verified under 16 environments. Dosage effects of pyramided favorable WSC alleles and significantly linear correlations between the number of favorable WSC alleles and TGW were observed. Our results suggested that pyramiding more favorable WSC alleles was effective for improving both WSC and grain weight in future wheat breeding programs.
Abstract
As renewable energy becomes increasingly dominant in the energy mix, the power system is evolving towards high proportions of renewable energy installations and power electronics-based ...equipment. This transition introduces significant challenges to the grid’s safe and stable operation. On the one hand, renewable energy generation equipment inherently provides weak voltage support, necessitating improvements in the voltage support capacity at renewable energy grid points. This situation leads to frequent curtailments and power limitations. On the other hand, the output of renewable energy is characterized by its volatility and randomness, resulting in substantial power curtailment. The joint intelligent control and optimization technology of “renewable energy + energy storage + synchronous condenser” can effectively enhance the deliverable capacity limits of renewable energy, boost its utilization rates, and meet the demands for renewable energy transmission and consumption. Initially, the paper discusses the mechanism by which distributed synchronous condensers improve the short-circuit ratio based on the MRSCR (Multiple Renewable Energy Station Short-Circuits Ratio) index. Subsequently, with the minimum total cost of system operation as the optimization objective, a time-series production simulation optimization model is established. A corresponding optimization method, considering the joint configuration of “renewable energy + energy storage + synchronous condenser,” is proposed. Finally, the effectiveness of the proposed method is verified through common calculations using BPA, SCCP, and the production simulation model, considering a real-world example involving large-scale renewable and thermal energy transmission through an AC/DC system. The study reveals that the joint intelligent control and optimization technology can enhance both the sending and absorbing capacities of renewable energy while yielding favorable economic benefits.
Abstract
We investigated the use of an Artificial Neural Network (ANN) to predict the Local Bond Stress (LBS) between Ultra-High-Performance Concrete (UHPC) and steel bars, in order to evaluate the ...accuracy of our LBS equation, proposed by Multiple Linear Regression (MLR). The experimental and numerical LBS results of specimens, based on RILEM standards and using pullout tests, were assessed by the ANN algorithm using the TensorFlow platform. For each specimen, steel bar diameters (
$$d_{b} )$$
d
b
)
of 12, 14, 16, 18, and 20, concrete compressive strength (
$$f_{c}^{\prime }$$
f
c
′
), bond lengths (
$$L$$
L
), and concrete covers (
$$C$$
C
) of
$$d_{b}$$
d
b
,
$$2d_{b}$$
2
d
b
,
$$3d_{b}$$
3
d
b
and
$$4d_{b}$$
4
d
b
were used as input parameters for our ANN. To obtain an accurate LBS equation, we first modified the existing formula, then used MLR to establish a new LBS equation. Finally, we applied ANN to verify our new proposed equation. The numerical pullout test values from ABAQUS and experimental results from our laboratory were compared with the proposed LBS equation and ANN algorithm results. The results confirmed that our LBS equation is logically accurate and that there is a strong agreement between the experimental, numerical, theoretical, and the predicted LBS values. Moreover, the ANN algorithm proved the precision of our proposed LBS equation.
Heart failure (HF) is not only a common complication in patients with end-stage renal disease (ESRD) but also a major cause of death. Although clinical studies have shown that there is a close ...relationship between them, the mechanism of its occurrence is unclear. The aim of this study is to explore the molecular mechanisms between HF and ESRD through comprehensive bioinformatics analysis, providing a new perspective on the crosstalk between these two diseases.
The HF and ESRD datasets were downloaded from the Gene Expression Omnibus (GEO) database; we identified and analyzed common differentially expressed genes (DEGs). First, Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and gene set variation analyses (GSVA) were applied to explore the potential biological functions and construct protein-protein interaction (PPI) networks. Also, four algorithms, namely, random forest (RF), Boruta algorithm, logical regression of the selection operator (LASSO), and support vector machine-recursive feature elimination (SVM-RFE), were used to identify the candidate genes. Subsequently, the diagnostic efficacy of hub genes for HF and ESRD was evaluated using eXtreme Gradient Boosting (XGBoost) algorithm. CIBERSORT was used to analyze the infiltration of immune cells. Thereafter, we predicted target microRNAs (miRNAs) using databases (miRTarBase, TarBase, and ENOCRI), and transcription factors (TFs) were identified using the ChEA3 database. Cytoscape software was applied to construct mRNA-miRNA-TF regulatory networks. Finally, the Drug Signatures Database (DSigDB) was used to identify potential drug candidates.
A total of 68 common DEGs were identified. The enrichment analysis results suggest that immune response and inflammatory factors may be common features of the pathophysiology of HF and ESRD. A total of four hub genes (BCL6, CCL5, CNN1, and PCNT) were validated using RF, LASSO, Boruta, and SVM-RFE algorithms. Their AUC values were all greater than 0.8. Immune infiltration analysis showed that immune cells such as macrophages, neutrophils, and NK cells were altered in HF myocardial tissue, while neutrophils were significantly correlated with all four hub genes. Finally, 11 target miRNAs and 10 TFs were obtained, and miRNA-mRNA-TF regulatory network construction was performed. In addition, 10 gene-targeted drugs were discovered.
Our study revealed important crosstalk between HF and ESRD. These common pathways and pivotal genes may provide new ideas for further clinical treatment and experimental studies.
With the sustainable development of the construction industry, recycled aggregate (RA) has been widely used in concrete preparation to reduce the environmental impact of construction waste. ...Compressive strength is an essential measure of the performance of recycled aggregate concrete (RAC). In order to understand the correspondence between relevant factors and the compressive strength of recycled concrete and accurately predict the compressive strength of RAC, this paper establishes a model for predicting the compressive strength of RAC using machine learning and hyperparameter optimization techniques. RAC experimental data from published literature as the dataset, extreme gradient boosting (XGBoost), random forest (RF), K-nearest neighbour (KNN), support vector machine regression Support Vector Regression (SVR), and gradient boosted decision tree (GBDT) RAC compressive strength prediction models were developed. The models were validated and compared using correlation coefficients (
R
2
), Root Mean Square Error (RMSE), mean absolute error (MAE), and the gap between the experimental results of the predicted outcomes. In particular, The effects of different hyperparameter optimization techniques (Grid search, Random search, Bayesian optimization-Tree-structured Parzen Estimator, Bayesian optimization- Gaussian Process Regression) on model prediction efficiency and prediction accuracy were investigated. The results show that the optimal combination of hyperparameters can be searched in the shortest time using the Bayesian optimization algorithm based on TPE (Tree-structured Parzen Estimator); the BO-TPE-GBDT RAC compressive strength prediction model has higher prediction accuracy and generalisation ability. This high-performance compressive strength prediction model provides a basis for RAC’s research and practice and a new way to predict the performance of RAC.
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