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Abstract Clubroot disease caused by the infection of Plasmodiophora brassicae is widespread in China, and significantly reduces the yield of Chinese cabbage (Brassica rapa L. ssp. pekinensis). However, the resistance mechanism of Chinese cabbage against clubroot disease is still unclear. It is important to exploit the key genes that response to early infection of P. brassicae. In this study, it was found that zoospores were firstly invaded hair roots on the 8th day after inoculating with 1 × 107 spores/mL P. brassicae. Transcriptome analysis found that the early interaction between Chinese cabbage and P. brassicae caused the significant expression change of some defense genes, such as NBS-LRRs and pathogenesis-related genes, etc. The above results were verified by quantitative reverse-transcription polymerase chain reaction (qRT-PCR). Otherwise, peroxidase (POD) salicylic acid (SA) and jasmonic acid (JA) were also found to be important signal molecules in the resistance to clubroot disease in Chinese cabbage. This study provides important clues for understanding the resistance mechanism of Chinese cabbage against clubroot disease.

Abstract Background Therapeutic approaches that combine conventional photodynamic therapy (PDT) with gas therapy (GT) to sensitize PDT are an attractive strategy, but the molecular structure design of the complex lacks effective guiding strategies. Results Herein, we have developed a nanoplatforms Cy-NMNO@SiO2 based on mesoporous silica materials loaded NIR-activatable small-molecule fluorescent probe Cy-NMNO for the synergistic treatment of photodynamic therapy/gas therapy (PDT/GT) in antibacterial and skin cancer. The theoretical calculation results showed that the low dissociation of N-NO in Cy-NMNO enabled it to dissociate effectively under NIR light irradiation, which is conducive to produce Cy and NO. Cy showed better 1O2 generation performance than Cy-NMNO. The cytotoxicity of Cy-NMNO obtained via the synergistic effect of GT and PDT synergistically enhances the effect of photodynamic therapy, thus achieving more effective tumor treatment and sterilization than conventional PDT. Moreover, the nanoplatforms Cy-NMNO@SiO2 realized efficient drug loading and drug delivery. Conclusions This work not only offers a promising approach for PDT-GT synergistic drug delivery system, but also provides a valuable reference for the design of its drug molecules. Graphical Abstract

Toxoplasma gondii is an opportunistic and pathogenic obligate intracellular parasitic protozoan that is widespread worldwide and can infect most warm-blooded animals, seriously endangering human health and affecting livestock production. Toxoplasmosis caused by T. gondii infection has different clinical manifestations, which are mainly determined by the virulence of T. gondii and host differences. Among the manifestations of this condition, abortion, stillbirth, and fetal malformation can occur if a woman is infected with T. gondii in early pregnancy. Here, we discuss how the T. gondii rhoptry protein affects host pregnancy outcomes and speculate on the related signaling pathways involved. The effects of rhoptry proteins of T. gondii on the placental barrier are complex. Rhoptry proteins not only regulate interferon-regulated genes (IRGs) to ensure the survival of parasites in activated cells but also promote the spread of worms in tissues and the invasive ability of the parasites. The functions of these rhoptry proteins and the associated signaling pathways highlight relevant mechanisms by which Toxoplasma crosses the placental barrier and influences fetal development and will guide future studies to uncover the complexity of the host-pathogen interactions.

Abstract Excitotoxicity from the impairment of glutamate uptake constitutes an important mechanism in neurodegenerative diseases such as Alzheimer’s, multiple sclerosis, and Parkinson's disease. Within the eye, excitotoxicity is thought to play a critical role in retinal ganglion cell death in glaucoma, diabetic retinopathy, retinal ischemia, and optic nerve injury, yet how excitotoxic injury impacts different retinal layers is not well understood. Here, we investigated the longitudinal effects of N-methyl-D-aspartate (NMDA)-induced excitotoxic retinal injury in a rat model using deep learning-assisted retinal layer thickness estimation. Before and after unilateral intravitreal NMDA injection in nine adult Long Evans rats, spectral-domain optical coherence tomography (OCT) was used to acquire volumetric retinal images in both eyes over 4 weeks. Ten retinal layers were automatically segmented from the OCT data using our deep learning-based algorithm. Retinal degeneration was evaluated using layer-specific retinal thickness changes at each time point (before, and at 3, 7, and 28 days after NMDA injection). Within the inner retina, our OCT results showed that retinal thinning occurred first in the inner plexiform layer at 3 days after NMDA injection, followed by the inner nuclear layer at 7 days post-injury. In contrast, the retinal nerve fiber layer exhibited an initial thickening 3 days after NMDA injection, followed by normalization and thinning up to 4 weeks post-injury. Our results demonstrated the pathological cascades of NMDA-induced neurotoxicity across different layers of the retina. The early inner plexiform layer thinning suggests early dendritic shrinkage, whereas the initial retinal nerve fiber layer thickening before subsequent normalization and thinning indicates early inflammation before axonal loss and cell death. These findings implicate the inner plexiform layer as an early imaging biomarker of excitotoxic retinal degeneration, whereas caution is warranted when interpreting the ganglion cell complex combining retinal nerve fiber layer, ganglion cell layer, and inner plexiform layer thicknesses in conventional OCT measures. Deep learning-assisted retinal layer segmentation and longitudinal OCT monitoring can help evaluate the different phases of retinal layer damage upon excitotoxicity.

Abstract Toxoplasma gondii, a widespread obligate intracellular parasite, can infect almost all warm-blooded animals, including humans. The cellular barrier of the central nervous system (CNS) is generally able to protect the brain parenchyma from infectious damage. However, T. gondii typically causes latent brain infections in humans and other vertebrates. Here, we discuss how T. gondii rhoptry proteins (ROPs) affect signaling pathways in host cells and speculate how this might affect the outcome of Toxoplasma encephalitis.

Lithium–sulfur (Li-S) batteries offer a high theoretical energy density but suffer from poor cycling stability and polysulfide shuttling, which limits their practical application. To address these challenges, we developed a PANI-modified MoS2-NG composite, where MoS2 nanoflowers were uniformly grown on graphene oxide (GO) through PANI modification, resulting in an increased interlayer spacing of MoS2. This expanded spacing exposed more active sites, enhancing polysulfide adsorption and catalytic conversion. The composite was used to prepare MoS2-NG/PP separators for Li-S batteries, which achieved a high specific capacity of 714 mAh g−1 at a 3 C rate and maintained a low capacity decay rate of 0.085% per cycle after 500 cycles at 0.5 C. The larger MoS2 interlayer spacing was key to improving redox reaction kinetics and suppressing the shuttle effect, making the MoS2-NG composite a promising material for enhancing the performance and stability of Li-S batteries.

Excessive cadmium (Cd) in rice, caused by Cd pollution of farmlands, poses a serious threat to human health. In this study, a pot experiment was conducted to investigate the effects of two doses of CaCO3 (Ca1: 2 g kg-1, Ca2: 10g kg-1), two types of Fe(II) (EDTA-Fe(II) and FeSO4; 0.14 g Fe kg-1), and their combined application on the uptake and accumulation of Cd in rice plants grown in Cd-contaminated acidic soil. The results revealed that FeSO4 significantly increased rice grain biomass, whereas the other treatments had no significant effects. Further, the addition of EDTA-Fe(II) or FeSO4 significantly enhanced iron plaque formation on the root surface and increased the Fe content in the rice plants and porewater. Compared to the control, CaCO3 addition weakened the formation of iron plaque and reduced the Fe concentration in the porewater and root tissue, stems and leaves, whereas the Fe concentration in brown rice and the husks remained unaffected. Combined application of CaCO3 and Fe(II) significantly promoted the formation of iron plaque and increased the Fe concentration in brown rice. However, the Cd concentration in the iron plaque was reduced by CaCO3 addition but increased by Fe(II) treatment. Notably, all treatments reduced the Cd concentration in all rice plant tissues. The application of Ca1, Ca2, EDTA-Fe(II), FeSO4, Ca1+EDTA-Fe(II), Ca1+FeSO4, Ca2+EDTA-Fe(II) and Ca2+FeSO4 significantly reduced the Cd concentration in brown rice by 69%, 63%, 51%, 60%, 46%, 39%, 38%, and 29%, respectively. These results indicate that the application of CaCO3, EDTA-Fe(II)/FeSO4, or their combination can effectively reduce Cd accumulation and translocation in rice plants.

Abstract Recently, many analysis tools have been devised to offer insights into data generated via cytometry by time-of-flight (CyTOF). However, objective evaluations of these methods remain absent as most evaluations are conducted against real data where the ground truth is generally unknown. In this paper, we develop Cytomulate, a reproducible and accurate simulation algorithm of CyTOF data, which could serve as a foundation for future method development and evaluation. We demonstrate that Cytomulate can capture various characteristics of CyTOF data and is superior in learning overall data distributions than single-cell RNA-seq-oriented methods such as scDesign2, Splatter, and generative models like LAMBDA.

Abstract Random-effects (RE) meta-analysis is a crucial approach for combining results from multiple independent studies that exhibit heterogeneity. Recently, two frequentist goodness-of-fit (GOF) tests were proposed to assess the fit of RE model. However, they tend to perform poorly when assessing rare binary events. Under a general binomial-normal framework, we propose a novel GOF test for the meta-analysis of rare events. Our method is based on pivotal quantities that play an important role in Bayesian model assessment. It further adopts the Cauchy combination idea proposed in a 2019 JASA paper, to combine dependent p-values computed using posterior samples from Markov Chain Monte Carlo. The advantages of our method include clear conception and interpretation, incorporation of all data including double zeros without the need for artificial correction, well-controlled Type I error, and generally improved ability in detecting model misfits compared to previous GOF methods. We illustrate the proposed method via simulation and three real data applications.

Abstract In order to achieve global carbon neutrality in the middle of the 21st century, efficient utilization of fossil fuels is highly desired in diverse energy utilization sectors such as industry, transportation, building as well as life science. In the energy utilization infrastructure, about 75% of the fossil fuel consumption is used to provide and maintain heat, leading to more than 60% waste heat of the input energy discharging to the environment. Types of low-grade waste heat recovery technologies are developed to increase the energy efficiency. However, due to the spatial and temporal mismatch between the need and supply of the thermal energy, much of the waste thermal energy is difficult to be recovered. Thermal energy storage (TES) technologies in the forms of sensible, latent and thermochemical heat storage are developed for relieving the mismatched energy supply and demand. Diverse TES systems are developed in recent years with the superior features of large density, long-term, durable and low-cost. These technologies are vital in efficient utilization of low-grade waste heat and expected for building a low or zero carbon emission society. This paper reviews the thermal storage technologies for low carbon power generation, low carbon transportation, low carbon building as well as low carbon life science, in addition, carbon capture, utilization, and storage are also considered for carbon emission reduction. The conclusion and perspective are raised after discussing the specific technologies. This study is expected to provide a reference for the TES technologies in achieving zero-carbon future.

The mechanisms governing gene regulation in domestic Yuzhong pigeon breast muscle development remain largely elusive. Here, we conducted a comparative analysis using Iso-seq and RNA-seq data from domestic Yuzhong pigeons and European meat pigeons to uncover signaling pathways and genes involved in breast muscle development. The Iso-seq data from domestic Yuzhong pigeons yielded 131,377,075 subreads, resulting in 16,587 non-redundant high-quality full-length transcripts post-correction. Furthermore, utilizing pfam, CPC, PLEK, and CPAT, we predicted 5575, 4973, 2333, and 4336 lncRNAs, respectively. Notably, several genes potentially implicated in breast muscle development were identified, including tropomyosin beta chain, myosin regulatory light chain 2, and myosin binding protein C. KEGG enrichment analysis revealed critical signaling pathways in breast muscle development, spanning carbon metabolism, biosynthesis of amino acids, glycolysis/gluconeogenesis, estrogen signaling, PI3K-AKT signaling, protein processing in the endoplasmic reticulum, oxidative phosphorylation, pentose phosphate pathway, fructose and mannose metabolism, and tight junctions. These findings offer insights into the biological processes driving breast muscle development in domestic Yuzhong pigeon, contributing to our understanding of this complex phenomenon.

Abstract While experimental and informatic techniques around single cell sequencing (scRNA-seq) are advanced, research around mass cytometry (CyTOF) data analysis has severely lagged behind. CyTOF data are notably different from scRNA-seq data in many aspects. This calls for the evaluation and development of computational methods specific for CyTOF data. Dimension reduction (DR) is one of the critical steps of single cell data analysis. Here, we benchmark the performances of 21 DR methods on 110 real and 425 synthetic CyTOF samples. We find that less well-known methods like SAUCIE, SQuaD-MDS, and scvis are the overall best performers. In particular, SAUCIE and scvis are well balanced, SQuaD-MDS excels at structure preservation, whereas UMAP has great downstream analysis performance. We also find that t-SNE (along with SQuad-MDS/t-SNE Hybrid) possesses the best local structure preservation. Nevertheless, there is a high level of complementarity between these tools, so the choice of method should depend on the underlying data structure and the analytical needs.

Qingke (highland barley) Baijiu is a special Chinese Baijiu which is mainly produced from Qinghai-Tibet Plateau. Since the pine board is used as the bottom of the fermentation pit, we deduced that the terpenoids and norisoprenoids in pine board might be introduced into Qingke Baijiu. Thus, the terpenoids and norisoprenoids in Qingke Baijiu were investigated by headspace solid phase microextraction (HS-SPME) combined with comprehensive two-dimensional gas chromatography coupled to time-of-flight mass spectrometry (GC × GC-TOFMS). The results showed that γ-terpinene (0.70–530.72 μg/L), α-phellandrene (0.34–256.66 μg/L), longicyclene (0–38.55 μg/L), α-pinene (1.21–35.54 μg/L) and limonene (0.93–23.69 μg/L) were the top 5 terpenoids/norisoprenoids in Qingke Baijiu. Pulegone was detected and reported for the first time in Baijiu, and the concentrations in Qingke Baijiu were 0.78 μg/L (fresh) and 3.90 μg/L (7 years old). According to the principal component analysis (PCA) plot, young, aged, and retail Qingke Baijiu could be differentiated clearly. Fold change (FC) and t-tests analysis indicated that β-pinene, γ-terpinene, and α-selinene were the most different terpenoids/norisoprenoids between young and aged Qingke Baijiu, and longifolene was the most different terpenoids/norisoprenoids between base and retail Qingke Baijiu. The terpenoids and norisoprenoids, such as (E)-β-ionone, isoborneol and β-cyclocitral, could be potential markers indicating the ageing process of Qingke Baijiu.

Abstract Early detection of cancers has been much explored due to its paramount importance in biomedical fields. Among different types of data used to answer this biological question, studies based on T cell receptors (TCRs) are under recent spotlight due to the growing appreciation of the roles of the host immunity system in tumor biology. However, the one-to-many correspondence between a patient and multiple TCR sequences hinders researchers from simply adopting classical statistical/machine learning methods. There were recent attempts to model this type of data in the context of multiple instance learning (MIL). Despite the novel application of MIL to cancer detection using TCR sequences and the demonstrated adequate performance in several tumor types, there is still room for improvement, especially for certain cancer types. Furthermore, explainable neural network models are not fully investigated for this application. In this article, we propose multiple instance neural networks based on sparse attention (MINN-SA) to enhance the performance in cancer detection and explainability. The sparse attention structure drops out uninformative instances in each bag, achieving both interpretability and better predictive performance in combination with the skip connection. Our experiments show that MINN-SA yields the highest area under the ROC curve scores on average measured across 10 different types of cancers, compared to existing MIL approaches. Moreover, we observe from the estimated attentions that MINN-SA can identify the TCRs that are specific for tumor antigens in the same T cell repertoire.

ABSTRACT: Columbian coloration patterns in plumage are widespread phenomena in several standard breeds of poultry, such as the Columbian Plymouth Rock chicken. H line chicken plumage is generally a pure white except in the hackle, wing, and tail plumage, which coloration is very similar to the Columbian plumage pattern, but with the barring substituting for the black vertical striping. Thus, we refer to this plumage coloration as “sub-Columbian” pattern. However, the genetic basis of this phenotype remains unknown. Here, a F3 cross population between yellow plumage roosters and sub-Columbian plumage hens was constructed, for verifying sub-Columbian plumage was sex-linked dominant inheritance. To identify the candidate regions, F2 generation sub-Columbian plumage hens and yellow plumage hens with their parental lines were used for BSA-seq, and sub-Columbian plumage genes were mapped to a 10.46 Mb interval on chromosome Z. Remarkably, by transcriptome analysis of the neck and wing tip follicle tissues of the 2 plumage colors, we demonstrated that within the interval, only 1 gene, SLC45A2 expressed significant differently (P < 0.05). Through KASP, we identified L347M and A10331272T in solute carrier family 45 member 2 (SLC45A2), and B2 haplotype of cyclin-dependent kinase inhibitor 2A (CDKN2A), showed near complete association with the phenotype. Eventually, we designed a hybridization experiment for verifying the locus of sub-Columbian plumage, which is inherited through Z-linked dominant inheritance and is controlled by SLC45A2 and CDKN2A.

Our study aims to explore the long-term performance patterns for deep learning (DL) models deployed in clinic and to investigate their efficacy in relation to evolving clinical practices. We conducted a retrospective study simulating the clinical implementation of our DL model involving 1328 prostate cancer patients treated between January 2006 and August 2022. We trained and validated a U-Net-based auto-segmentation model on data obtained from 2006 to 2011 and tested on data from 2012 to 2022, simulating the model’s clinical deployment starting in 2012. We visualized the trends of the model performance using exponentially weighted moving average (EMA) curves. Additionally, we performed Wilcoxon Rank Sum Test and multiple linear regression to investigate Dice similarity coefficient (DSC) variations across distinct periods and the impact of clinical factors, respectively. Initially, from 2012 to 2014, the model showed high performance in segmenting the prostate, rectum, and bladder. Post-2015, a notable decline in EMA DSC was observed for the prostate and rectum, while bladder contours remained stable. Key factors impacting the prostate contour quality included physician contouring styles, using various hydrogel spacers, CT scan slice thickness, MRI-guided contouring, and intravenous (IV) contrast ( p < 0.0001, p < 0.0001, p = 0.0085, p = 0.0012, p < 0.0001, respectively). Rectum contour quality was notably influenced by factors such as slice thickness, physician contouring styles, and the use of various hydrogel spacers. The quality of the bladder contour was primarily affected by IV contrast. The deployed DL model exhibited a substantial decline in performance over time, aligning with the evolving clinical settings.

Background Fatty acid-binding protein 4 (FABP4) has been demonstrated to be a predictor of early diabetic nephropathy. However, little is known about the relationship between FABP4 and diabetic retinopathy (DR). This study explored the value of FABP4 as a biomarker of DR in patients with type 2 diabetes mellitus (T2DM). Methods A total of 238 subjects were enrolled, including 20 healthy controls and 218 T2DM patients. Serum FABP4 levels were measured using a sandwich enzyme-linked immunosorbent assay. The grade of DR was determined using fundus fluorescence angiography. Based on the international classification of DR, all T2DM patients were classified into the following three subgroups: non-DR group, non-proliferative diabetic retinopathy (NPDR) group, and proliferative diabetic retinopathy (PDR) group. Multivariate logistic regression analyses were employed to assess the correlation between FABP4 levels and DR severity. Results FABP4 correlated positively with DR severity (r=0.225, P=0.001). Receiver operating characteristic curve analysis was used to assess the diagnostic potential of FABP4 in identifying DR, with an area under the curve of 0.624 (37% sensitivity, 83.6% specificity) and an optimum cut-off value of 76.4 μg/L. Multivariate logistic regression model including FABP4 as a categorized binary variable using the cut-off value of 76.4 μg/L showed that the concentration of FABP4 above the cut-off value increased the risk of NPDR (odds ratio [OR], 3.231; 95% confidence interval [CI], 1.574 to 6.632; P=0.001) and PDR (OR, 3.689; 95% CI, 1.306 to 10.424; P=0.014). Conclusion FABP4 may be used as a serum biomarker for the diagnosis of DR.

An appropriate cell microenvironment is key to tissue engineering and regenerative medicine. Revealing the factors that influence the cell microenvironment is a fundamental research topic in the fields of cell biology, biomaterials, tissue engineering, and regenerative medicine. The cell microenvironment consists of not only its surrounding cells and soluble factors, but also its extracellular matrix (ECM) or nearby external biomaterials in tissue engineering and regeneration. This review focuses on six aspects of biomaterial–related cell microenvironments: ① chemical composition of materials, ② material dimensions and architecture, ③ material–controlled cell geometry, ④ effects of material charges on cells, ⑤ matrix stiffness and biomechanical microenvironment, and ⑥ surface modification of materials. The present challenges in tissue engineering are also mentioned, and eight perspectives are predicted.

IntroductionChinese cabbage is one of the most important vegetable crops in China. However, the clubroot disease caused by the infection of Plasmodiophora brassicae (P. brassicae) has seriously affected the yield and quality of Chinese cabbage. In our previous study, BrUFO gene was found to be significantly up-regulated in diseased roots of Chinese cabbage after inoculation with P. brassicae. UFO (UNUSUAL FLORAL ORGANS) have the properties of substrate recognition during ubiquitin-mediated proteolysis. A variety of plant can activate immunity response through the ubiquitination pathway. Therefore, it is very important to study the function of UFO in response to P. brassicae.MethodsIn this study, The expression pattern of BrUFO Gene was measured by qRT-PCR and In situ Hybridization (ISH). The expression location of BrUFO in cells was determined by subcellular localization. The function of BrUFO was verified by Virus-induced Gene Silencing (VIGS). proteins interacting with BrUFO protein were screened by yeast two-hybrid.ResultsQuantitative real-time polymerase chain reactions (qRT-PCR) and in situ hybridization analysis showed that expression of BrUFO gene in the resistant plants was lower than that in susceptible plants. Subcellular localization analysis showed that BrUFO gene was expressed in the nucleus. Virus-induced gene silencing (VIGS) analysis showed that silencing of BrUFO gene reduced the incidence of clubroot disease. Six proteins interacting with BrUFO protein were screened by Y2H assay. Two of them (Bra038955, a B-cell receptor-associated 31-like protein and Bra021273, a GDSL-motif esterase/acyltransferase/lipase Enzyme) were confirmed to strongly interact with BrUFO protein.DiscussionBrUFO gene should be a key gene of chinese cabbage against the infection of P. brassicae. BrUFO gene silencing improves the resistance of plants to clubroot disease. BrUFO protein may interact with CUS2 to induce ubiquitination in PRR-mediated PTI reaction through GDSL lipases, so as to achieve the effect of Chinese cabbage against the infection of P. brassicae.

The design of well-defined hierarchical free-standing electrodes for robust high-performance energy storage is challenging. We report herein that azo-linkage redox metal–organic frameworks (MOFs) incorporate single-walled carbon nanotubes (CNTs) as flexible electrodes. The in situ-guided growth, crystallinity and morphology of UiO-66-NO2 MOFs were finely controlled in the presence of CNTs. The MOFs’ covalent anchoring to CNTs and solvothermal grafting anthraquinone (AQ) pendants endow the hybrid (denoted as CNT@UiO-66-AQ) with greatly improved conductivity, charge storage pathways and electrochemical dynamics. The flexible CNT@UiO-66-AQ displays a highest areal specific capacitance of 302.3 mF cm−2 (at 1 mA cm−2) in −0.4~0.9 V potential window, together with 100% capacitance retention over 5000 cycles at 5 mA cm−2. Its assembled symmetrical supercapacitor (SSC) achieves a maximum energy density of 0.037 mWh cm−2 and a maximum power density of 10.4 mW cm−2, outperforming many MOFs-hybrids-based SSCs in the literature. Our work may open a new avenue for preparing azo-coupled redox MOFs hybrids with carbaneous substrates for high-performance robust aqueous energy storage.

Fermentation vessels affect the characteristics of food fermentation; however, we lack an approach to identify the biomarkers indicating fermentation. In this study, we applied metabolomics and high-throughput sequencing analysis to reveal the dynamic of metabolites and microbial communities in age-gradient fermentation vessels for baijiu production. Furthermore, we identified 64 metabolites during fermentation, and 19 metabolites significantly varied among the three vessels (p < 0.05). Moreover, the formation of these 19 metabolites were positively correlated with the core microbiota (including Aspergillus, Saccharomyces, Lactobacillus, and Bacillus). In addition, ethyl lactate or ethyl acetate were identified as the biomarkers for indicating the metabolism among age-gradient fermentation vessels by BP-ANN (R2 > 0.40). Therefore, this study combined the biological analysis and predictive model to identify the biomarkers indicating metabolism in different fermentation vessels, and it also provides a potential approach to assess the profiling of food fermentations.

Abstract Background Lung function is highly age-dependent as it decreases in varying degrees with age, even in healthy people. Decreased lung function results in less elastic lung tissue, reduced chest wall compliance, reduced area for gas exchange, and even a variety of chronic diseases. Traditional Chinese health exercise (TCHE) has three components: “breath regulation,” “body regulation,” and “heart regulation,” which play an important role in the improvement of lung function. However, which component has the most significant effect on lung functioning remains unclear. Therefore, depending on the modality of conditioning, TCHEs will be divided into three exercise intervention groups: breath regulation group, body regulation group, and heart regulation group, in order to explore the magnitude of the effect of the different modalities of conditioning on the improvement of lung function. Methods The prospective, parallel, single-blind, randomized controlled trial will evaluate the effects of different conditioning methods of TCHE on lung function in middle-aged and elderly people. The study subjects are healthy middle-aged and elderly adults, who will be randomly divided into the “breath regulation group,” “body regulation group,” “heart regulation group,” and “control group.” The control group will receive health education. Health education and exercise intervention in the three intervention groups will be provided for 6 months, 5 times a week, with each session lasting 60 min. The outcomes of interest include changes in the pulmonary function tests measured at baseline and 3 and 6 months after the beginning of the intervention. The primary outcome is the forced vital capacity (FVC), while the secondary outcomes include forced expiratory volume in 1 s (FEV1), FVC/FEV1 ratio, vital capacity (VC), and maximal voluntary ventilation (MVV). Discussion This study will assess the effects of different conditioning methods of TCHE on lung function in middle-aged and elderly people. The final findings of this study will validate the effectiveness and safety of TCHE on lung function interventions in middle-aged and elderly people. Trial registration China Clinical Trial Registry ChiCTR2100052687 . Registered on November 3, 2021

Protozoan diseases cause great harm in animal husbandry and require human-provided medical treatment. Protozoan infection can induce changes in cyclooxygenase-2 (COX-2) expression. The role played by COX-2 in the response to protozoan infection is complex. COX-2 induces and regulates inflammation by promoting the synthesis of different prostaglandins (PGs), which exhibit a variety of biological activities and participate in pathophysiological processes in the body in a variety of ways. This review explains the roles played by COX-2 in protozoan infection and analyzes the effects of COX-2-related drugs in protozoan diseases.

Endometriosis is a common inflammatory illness in which endometrial tissue grows outside the uterine cavity. Immune dysfunction is now widely acknowledged as the primary cause of endometriosis. The immune cell population represented by neutrophils is thought to play an essential role in the etiology, pathophysiology, and associated clinical outcome. There is growing evidence that neutrophils have a role in chronic and aseptic inflammatory diseases, and endometriosis patients have increased levels of neutrophils in plasma, peritoneal fluid, and ectopic endometrium. Here, we sought to review the function of neutrophils in the pathogenesis of endometriosis, with an emphasis on the role of neutrophils in regulating endometrial angiogenesis and the local inflammatory microenvironment.

IntroductionThe clubroot disease caused by Plasmodiophora brassicae (P. brassicae) poses a serious threat to the economic value of cruciferous crops, which is a serious problem to be solved worldwide. Some resistance genes to clubroot disease in Brassica rapa L. ssp pekinensis cause by P. brassicae have been located on different chromosomes. Among them, Rcr1 and Rcr2 were mapped to the common candidate gene Bra019410, but its resistance mechanism is not clear yet.MethodsIn this experiment, the differences of BrRPP1 between the resistant and susceptible material of Chinese cabbage were analyzed by gene cloning and qRT-PCR. The gene function was verified by Arabidopsis homologous mutants. The expression site of BrRPP1 gene in cells was analyzed by subcellular localization. Finally, the candidate interaction protein of BrRPP1 was screened by yeast two-hybrid library.ResultsThe results showed that the cDNA sequence, upstream promoter sequence and expression level of BrRPP1 were quite different between the resistant and susceptible material. The resistance investigation found that the Arabidopsis mutant rpp1 was more susceptible to clubroot disease than the wild type, which suggested that the deletion of rpp1 reduces resistance of plant to clubroot disease. Subcellular location analysis confirmed that BrRPP1 was located in the nucleus. The interaction proteins of BrRPP1 screened from cDNA Yeast Library by yeast two-hybrid are mainly related to photosynthesis, cell wall modification, jasmonic acid signal transduction and programmed cell death.DiscussionBrRPP1 gene contains TIR-NBS-LRR domain and belongs to R gene. The cDNA and promoter sequence of BrRPP1 in resistant varieties was different from that in susceptible varieties led to the significant difference of the gene expression of BrRPP1 between the resistant varieties and the susceptible varieties. The high expression of BrRPP1 gene in resistant varieties enhanced the resistance of Chinese cabbage to P. brassicae, and the interaction proteins of BrRPP1 are mainly related to photosynthesis, cell wall modification, jasmonic acid signal transduction and programmed cell death. These results provide important clues for understanding the mechanism of BrRPP1 in the resistance of B. rapa to P. brassicae.

Abstract Introduction In recent years, the prevalence of infertility has significantly increased and has become a global reproductive health problem. The female ovarian reserves have been shown to decrease progressively with an increase in age. Besides, the rate of embryo implantation and clinical pregnancy also decreases. Traditional Chinese medicine has been widely applied in assisted reproductive technology. It is reported to have a significant influence on improving the quality of oocytes, improving endometrial receptivity, increasing clinical pregnancy rate, reducing pregnancy-related complications, etc. Therefore, this study will investigate the effect of Guilu Xian, a traditional Chinese medicine formula on IVF-ET outcome in older women with low prognosis. Methods and analysis This trial is a prospective, multicenter, randomized double-blind clinical trial. A total of 120 infertile patients with low prognosis and receiving IVF or ICSI in 3 public hospitals in China will be randomly divided into two parallel groups: Guilu Xian group (n = 60) and placebo group (n = 60). Patients in both groups will be treated with antagonist regimens to promote ovulation, and all the patients will be required to take the medication from the 2nd to 4th day of the menstrual cycle to the day of egg retrieval. A comparison of the total number of oocytes obtained, the fertilization rate, clinical pregnancy rate, embryo quality, embryo implantation rate, and early spontaneous abortion rate between the experimental group and the placebo group will be performed. Trial registration Chinese Clinical Trials Registry ChiCTR1900028255 . Registered on 16 December 2019.

Abstract Coping with the outbreak of Coronavirus disease 2019 (COVID-19), many countries have implemented public-health measures and movement restrictions to prevent the spread of the virus. However, the strict mobility control also brought about production stagnation and market disruption, resulting in a severe worldwide economic crisis. Quantifying the economic stagnation and predicting post-pandemic recovery are imperative issues. Besides, it is significant to examine how the impact of COVID-19 on economic activities varied with industries. As a reflection of enterprises’ production output, high-frequency electricity-consumption data is an intuitive and effective tool for evaluating the economic impact of COVID-19 on different industries. In this paper, we quantify and compare economic impacts on the electricity consumption of different industries in eastern China. In order to address this problem, we conduct causal analysis using a difference-in-difference (DID) estimation model to analyze the effects of multi-phase public-health measures. Our model employs the electricity-consumption data ranging from 2019 to 2020 of 96 counties in the Eastern China region, which covers three main economic sectors and their 53 sub-sectors. The results indicate that electricity demand of all industries (other than information transfer industry) rebounded after the initial shock, and is back to pre-pandemic trends after easing the control measures at the end of May 2020. Emergency response, the combination of all countermeasures to COVID-19 in a certain period, affected all industries, and the higher level of emergency response with stricter movement control resulted in a greater decrease in electricity consumption and production. The pandemic outbreak has a negative-lag effect on industries, and there is greater resilience in industries that are less dependent on human mobility for economic production and activities.

Low-resistivity shales are widely developed in the Sichuan Basin. The production of low-resistivity shale gas reservoirs ranges from high to low to none. The existing methods for gas-content prediction cannot accurately predict the gas content of low-resistivity shale. This increases the risk of shale-gas exploration. To prove that the random forest algorithm has apparent advantages in predicting the gas content of low-resistivity shale and reducing the risks associated with shale-gas exploration and development, three prediction methods were selected in this paper to compare their effects. The first method is known as the grey-correlation multiple linear regression method. Low-resistivity shale-gas content logging series were optimized using the grey-correlation approach, and then the low-resistivity shale-gas-content prediction model was established using the multiple linear regression method. The second method we selected was the resistivity method. The improved water-saturation model was used to predict the water saturation of low-resistivity shale, and then the gas content of low-resistivity shale was predicted based on the free-gas content and the adsorbed-gas-content model. The random forest algorithm was the third method we selected. Fourteen logging series were used as input data and the measured gas content was used as supervised data to train the model and to apply the trained model to the gas-content prediction. The findings demonstrated that the grey-correlation multiple regression method had poor accuracy in predicting gas content in low-resistivity shale; The resistivity method accurately predicted water saturation, and the predicted gas content was higher than the actual gas content. Because the random forest algorithm accurately predicted low-resistivity shale-gas content, its use in the Sichuan Basin was advantageous. The selection of a low-resistivity shale-gas-content prediction model was guided by the research findings.

Alzheimer’s disease (AD) is a progressive, irreversible neurodegenerative disorder that requires early diagnosis for timely treatment. Functional magnetic resonance imaging (fMRI) is a non-invasive neuroimaging technique for detecting brain activity. To improve the accuracy of Alzheimer’s disease diagnosis, we propose a new network architecture called Dynamic Multi-Task Graph Isomorphism Network (DMT-GIN). This approach uses fMRI images transformed into brain network structures to classify Alzheimer’s disease more effectively. In the DMT-GIN architecture, we integrate an attention mechanism with the Graph Isomorphism Network (GIN) to capture node features and topological structure information. To further enhance AD classification performance, we incorporate auxiliary tasks of gender and age classification prediction alongside the primary AD classification task in the network. This is achieved through sharing network parameters and adaptive weight adjustments for simultaneous task optimization. Additionally, we introduce a method called GradNorm for dynamically balancing gradient updates between tasks. Evaluation results demonstrate that the DMT-GIN model outperforms existing baseline methods on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database, leading in various metrics with a prediction accuracy of 90.44%. This indicates that our DMT-GIN model effectively captures brain network features, providing a powerful auxiliary means for the early diagnosis of Alzheimer’s disease.

Abstract Background Single-cell RNA sequencing (scRNA-seq) has led to remarkable progress in our understanding of tissue heterogeneity in health and disease. Recently, the need for scRNA-seq sample fixation has emerged in many scenarios, such as when samples need long-term transportation, or when experiments need to be temporally synchronized. Methanol fixation is a simple and gentle method that has been routinely applied in scRNA-sEq. Yet, concerns remain that fixation may result in biases which may change the RNA-seq outcome. Results We adapted an existing methanol fixation protocol and performed scRNA-seq on both live and methanol fixed cells. Analyses of the results show methanol fixation can faithfully preserve biological related signals, while the discrepancy caused by fixation is subtle and relevant to library construction methods. By grouping transcripts based on their lengths and GC content, we find that transcripts with different features are affected by fixation to different degrees in full-length sequencing data, while the effect is alleviated in Drop-seq result. Conclusions Our deep analysis reveals the effects of methanol fixation on sample RNA integrity and elucidates the potential consequences of using fixation in various scRNA-seq experiment designs.

Protozoan diseases seriously affect the health of human beings, livestock and poultry and lead to high economic and medical costs. Extracellular vesicles (EVs) are membranous structures formed through biological processes that play important roles in immune regulation. Studies have shown that parasites transmit information to hosts through EVs to modulate host immune responses. The major roles played by EVs released from parasites involve facilitating parasitization of the host. In this review, we discuss relevant recently obtained data on EVs secreted by different kinds of protozoa, including their molecular mechanisms, and discuss the roles played by EVs in the occurrence and development of parasitic diseases.

Background Dysimmunity plays a key role in diabetes, especially type 1 diabetes mellitus. Islet-specific autoantibodies (ISAs) have been used as diagnostic markers for different phenotypic classifications of diabetes. This study was aimed to explore the relationships between ISA titers and the clinical characteristics of diabetic patients. Methods A total of 509 diabetic patients admitted to Department of Endocrinology and Metabolism at the Affiliated Hospital of Nantong University were recruited. Anthropometric parameters, serum biochemical index, glycosylated hemoglobin, urinary microalbumin/creatinine ratio, ISAs, fat mass, and islet β-cell function were measured. Multiple linear regression analysis was performed to identify relationships between ISA titers and clinical characteristics. Results Compared with autoantibody negative group, blood pressure, weight, total cholesterol (TC), low density lipoprotein cholesterol (LDL-C), visceral fat mass, fasting C-peptide (FCP), 120 minutes C-peptide (120minCP) and area under C-peptide curve (AUCCP) of patients in either autoantibody positive or glutamate decarboxylase antibody (GADA) positive group were lower. Body mass index (BMI), waist circumference, triglycerides (TGs), body fat mass of patients in either autoantibody positive group were lower than autoantibody negative group. GADA titer negatively correlated with TC, LDL-C, FCP, 120minCP, and AUCCP. The islet cell antibody and insulin autoantibody titers both negatively correlated with body weight, BMI, TC, TG, and LDL-C. After adjusting confounders, multiple linear regression analysis showed that LDL-C and FCP negatively correlated with GADA titer. Conclusion Diabetic patients with a high ISA titer, especially GADA titer, have worse islet β-cell function, but less abdominal obesity and fewer features of the metabolic syndrome.

Abstract Background The influence of β-glucan on the human gut microbiota is closely related to the physicochemical structure of β-glucan. We purified a homogeneous water-soluble polysaccharide from Tibetan hull-less barley 25 and studied its structure and the in vitro fermentation profile. Results Analysis by gas chromatography (GC), Fourier-transformed infrared (FT-IR), nuclear magnetic resonance (NMR), scanning electron microscopy (SEM), and atomic force micrograph (AFM) helped determine the hull-less barley polysaccharide to be a β-glucan (molecular weight: 3.45 × 104 Da), which was further characterized as mixed-linkage (1 → 3)(1 → 4)-linked β-d-glucans. SEM images demonstrated an intricate web structure of the hull-less barley polysaccharide, while the AFM images revealed the presence of small spherical particles in its structure. In addition, the microbiota composition of the hull-less barley polysaccharide group was found to be altered, wherein the abundance of Pantoea, Megamonas, Bifidobacterium, and Prevotella-9 were increased. On the other hand, in vitro fermentation revealed that hull-less barley polysaccharide significantly decreased the pH value and increased the production of acetate, propionate, and butyrate. Conclusions Hull-less barley polysaccharide is a type of dietary fiber, and its analysis suggested that it may serve as a prebiotic food supplement for the regulation of the gut microbiota.

Network traffic classification technology plays an important role in network security management. However, the inherent limitations of traditional methods have become increasingly obvious, and they cannot address existing traffic classification tasks. Very recently, neural architecture search (NAS) has aroused widespread interest as a tool to automate the manual architecture construction process. To this end, this paper proposes NAS based on multiobjective evolutionary algorithms (MOEAs) to classify malicious network traffic. The main purpose is to simplify the search space by reducing the spatial ratio and number of channels of the model. In addition, the search strategy is changed in the effective search space, and the utilized strategies include EAs with the nondominated sorting genetic algorithm with the elite retention strategy (NSGA-II), strength Pareto evolutionary algorithm (SPEA-II) and multiobjective particle swarm optimization (MOPSO) to solve the formulated multiobjective NAS. Through comprehensive comparison of the population convergence times, model accuracies, Pareto optimality sets, model complexities and running speeds of the strategies, it is concluded that the model based on NSGA-II search has the best performance. The experimental results of the current machine learning algorithms and artificial learning methods based on the network are compared, showing that our method achieved better classification performance on two public datasets with a lower computational complexity, as mainly measured by FLOPs. Our approach is able to achieve 99.806% and 99.369% F1-score with 11.501 MB and 4.718 MB FLOPs on both IDS2012 and ISCX VPN dataset respectively.

As a branch of machine learning, multiple instance learning (MIL) learns from a collection of labeled bags, each containing a set of instances. The learning process is weakly supervised due to ambiguous instance labels. Since its emergence, MIL has been applied to solve various problems including content-based image retrieval, object tracking/detection, and computer-aided diagnosis. In biomedical research, the use of MIL has been focused on medical image analysis and molecule activity prediction. We review and apply 16 methods to investigate the applicability of MIL to a novel biomedical application, cancer detection using T-cell receptor (TCR) sequences. This important application can be a viable approach for large-scale cancer screening, as TCRs can be easily profiled from a subject’s peripheral blood. We consider two feasible data-generating mechanisms, and for the purpose of performance evaluation, we simulate data under each mechanism, where we vary potentially important factors to mimic realistic situations. We also apply the methods to sequencing data of ten cancer types from The Cancer Genome Atlas, as an early proof of concept for distinguishing tumor patients from healthy individuals via TCR sequencing of peripheral blood. We find that given an appropriate MIL method is used, satisfactory performance with Area Under the Receiver Operating Characteristic Curve above 80% can be achieved for five in the ten cancers. Based on our numerical results, we make suggestions about selection of a proper method and avoidance of any method with poor performance. We further point out directions of future research as well as identify a pressing need of new MIL methodologies for improved performance (for some cancer types) and more explainable outcomes.

As the area of salinized soils increases, and freshwater becomes more scarcer worldwide, an urgent measure for agricultural production is to use salinized land and conserve freshwater resources. Ornamental flowering plants, such as carnations, roses, chrysanthemums, and gerberas, are found around the world and have high economic, ornamental, ecological, and edible value. It is therefore prudent to improve the salt tolerance of these important horticultural crops. Here, we summarize the salt-adaptive mechanisms, genes, and molecular breeding of ornamental flowering crops. We also review the genome editing technologies that provide us with the means to obtain novel varieties with high salinity tolerance and improved utility value, and discuss future directions of research into ornamental plants like salt exclusion mechanism. We considered that the salt exclusion mechanism in ornamental flowering plants, the acquisition of flowers with high quality and novel color under salinity condition through gene editing techniques should be focused on for the future research.

The aim of this study was to examine the interactive effects of situational variables (competition stage, match location, and match outcome) on teams’ technical performance in the UEFA Champion League. Match data of 19 technical actions and events were collected and classified into three groups (variables related to goal scoring, offense, and defense) during eight seasons (2009/2010–2016/2017). Repeated-measures analysis of variance (RMANOVA) was used to identify the differences in the technical performances among teams. Results showed that the significant differences in the technical performance between the group stage and the knockout stage were only found in dribble and yellow card. However, differences in the variables related to goal scoring and offense were more significant than in the variables related to defense under the comparisons across competition situations, and the differences in the variables related to defense were mainly detected in the yellow card. The number of variables that showed significant differences among match location and match outcome in the group stage were less than in the knockout stage. Therefore, the identified key performance indicators by considering the interactive effects of situational variables may provide detailed and practical insights for coaches to develop useful training interventions and match strategies for upcoming match playing in specific competition situations.

Abstract Background The SH3RF2 gene is a protein-coding gene located in a quantitative trait locus associated with body weight, and its deletion has been shown to be positively associated with body weight in chickens. Results In the present study, CNV in the SH3RF2 gene was detected in 4079 individuals from 17 populations, including the “Gushi ×Anka” F2 resource population and populations of Chinese native chickens, commercial layers, and commercial broilers. The F2 resource population was then used to investigate the genetic effects of the chicken SH3RF2 gene. The results showed that the local chickens and commercial layers were all homozygous for the wild-type allele. Deletion mutation individuals were detected in all of the commercial broiler breeds except Hubbard broiler. A total of, 798 individuals in the F2 resource group were used to analyze the effects of genotype (DD/ID/II) on chicken production traits. The results showed that CNV was associated with 2-, 6-, 10-, and 12-week body weight (P = 0.026, 0.042, 0.021 and 0.039 respectively) and significantly associated with 8-week breast bone length (P = 0.045). The mutation was significantly associated with 8-week body weight (P = 0.007) and 4-week breast bone length (P = 0.010). CNV was significantly associated with evisceration weight, leg muscle weight, carcass weight, breast muscle weight and gizzard weight (P = 0.032, 0.033, 0.045, 0.004 and 0.000, respectively). Conclusions CNV of the SH3RF2 gene contributed to variation in the growth and weight gain of chickens.

Allergic rhinitis (AR) is a common heterogeneous chronic disease with a high prevalence and a complex pathogenesis influenced by numerous factors, involving a combination of genetic and environmental factors. To gain insight into the pathogenesis of AR and to identity diagnostic biomarkers, we combined systems biology approach to analyze microbiome and serum composition. We collected inferior turbinate swabs and serum samples to study the microbiome and serum metabolome of 28 patients with allergic rhinitis and 15 healthy individuals. We sequenced the V3 and V4 regions of the 16S rDNA gene from the upper respiratory samples. Metabolomics was used to examine serum samples. Finally, we combined differential microbiota and differential metabolites to find potential biomarkers. We found no significant differences in diversity between the disease and control groups, but changes in the structure of the microbiota. Compared to the HC group, the AR group showed a significantly higher abundance of 1 phylum (Actinobacteria) and 7 genera (Klebsiella, Prevotella and Staphylococcus, etc.) and a significantly lower abundance of 1 genus (Pelomonas). Serum metabolomics revealed 26 different metabolites (Prostaglandin D2, 20-Hydroxy-leukotriene B4 and Linoleic acid, etc.) and 16 disrupted metabolic pathways (Linoleic acid metabolism, Arachidonic acid metabolism and Tryptophan metabolism, etc.). The combined respiratory microbiome and serum metabolomics datasets showed a degree of correlation reflecting the influence of the microbiome on metabolic activity. Our results show that microbiome and metabolomics analyses provide important candidate biomarkers, and in particular, differential genera in the microbiome have also been validated by random forest prediction models. Differential microbes and differential metabolites have the potential to be used as biomarkers for the diagnosis of allergic rhinitis.

The obvious anisotropy of layered rock mass prevents the built-in constitutive model of commercial simulation software from accurately describing the elastoplastic behavior of the rock mass. In this paper, a transversely isotropic elastoplastic constitutive model (called the AN-MC model) that characterizes the elastoplastic failure behavior of the layered rock mass is developed by introducing the Mohr-Coulomb yield criterion with the tension cutoff in the model based on the transversely isotropic constitutive model and deriving its finite difference scheme. The development and programming of the transversely isotropic elastoplastic constitutive model are achieved on the basis of the secondary development platform of FLAC3D and the VC++ environment. The accuracy and rationality of the proposed constitutive model are verified in terms of consistency of the calculation results of the developed transversely isotropic elastoplastic constitutive model and the built-in constitutive models. In the numerical analysis of the Queerxi tunnel project, the calculation results of the AN-MC model are in good agreement with the field monitoring data. Thus, the deformation characteristics of the tunnel surrounding rock are well characterized. Comparative analysis of the deformation and failure laws of the layered surrounding rock and isotropic surrounding rock indicates that the plastic failure range of the layered surrounding rock tunnel is larger, and the arch foot and waist are prone to plastic penetration failure with butterfly-shaped characteristics. The research results are important for the scientific prediction and precise support of layered rock mass deformation.

In spring, rivers at middle and high latitudes in the Northern Hemisphere are prone to ice jams, which threaten the safety of hydraulic structures in rivers. Heilongjiang Province is located on the highest latitude in China, starting at 43°26′ N and reaching 53°33′ N. Rivers in Heilongjiang Province freeze in winter and break up in spring. Forecasting the break-up date of river ice accurately can provide an important reference for the command, dispatch, and decision-making of ice flood preventing and shipping. Based on the observed break-up date series of river ice from seven representative hydrological stations in Heilongjiang Province, the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) was used to decompose the observed break-up date series of river ice into several subsequences, and the long-short term memory neural network (LSTM) was used to forecast the subsequences decomposed by CEEDMAN. Then, the forecast results of each subsequence were summed to obtain the forecasting value for the break-up date of river ice proceeded by CEEMDAN-LSTM. Compared with the LSTM, the forecast accuracy of CEEMDAN-LSTM for the break-up date of river ice had been significantly improved, with the mean absolute error reduced from 0.80–6.40 to 0.75–3.40, the qualification rate increased from 60–100% to 80–100%, the root-mean-square difference reduced from 1.37–5.97 to 0.95–1.69, the correlation coefficient increased from 0.51–0.97 to 0.97–0.98, and the Taylor skill score increased from 0.87–0.99 to 0.99. CEEMDAN-LSTM performed well in forecasting the break-up date of river ice in the Heilongjiang Province, which can provide important information for command, dispatch, and decision-making of ice flood control.

In the diagnosis of Alzheimer’s Disease (AD), the brain network analysis method is often used. The traditional network can only reflect the pairwise association between two brain regions, but ignore the higher-order relationship between them. Therefore, a brain network construction method based on hypergraph, called hyperbrain network, is adopted. The brain network constructed by the conventional static hyperbrain network cannot reflect the dynamic changes in brain activity. Based on this, the construction of a dynamic hyperbrain network is proposed. In addition, graph convolutional networks also play a huge role in AD diagnosis. Therefore, an evolving hypergraph convolutional network for the dynamic hyperbrain network is proposed, and the attention mechanism is added to further enhance the ability of representation learning, and then it is used for the aided diagnosis of AD. The experimental results show that the proposed method can effectively improve the accuracy of AD diagnosis up to 99.09%, which is a 0.3 percent improvement over the best existing methods.

Accurate and timely crop yield prediction over large spatial regions is critical to national food security and sustainable agricultural development. However, designing a robust model for crop yield prediction over a large spatial region remains challenging due to inadequate surveyed samples and an under-development of deep-learning frameworks. To tackle this issue, we integrated multi-source (remote sensing, weather, and soil properties) data into a dual-stream deep-learning neural network model for winter wheat in China’s major planting regions. The model consists of two branches for robust feature learning: one for sequential data (remote sensing and weather series data) and the other for statical data (soil properties). The extracted features by both branches were aggregated through an adaptive fusion model to forecast the final wheat yield. We trained and tested the model by using official county-level statistics of historical winter wheat yields. The model achieved an average R2 of 0.79 and a root-mean-square error of 650.21 kg/ha, superior to the compared methods and outperforming traditional machine-learning methods. The dual-stream deep-learning neural network model provided decent in-season yield prediction, with an error of about 13% compared to official statistics about two months before harvest. By effectively extracting and aggregating features from multi-source datasets, the new approach provides a practical approach to predicting winter wheat yields at the county scale over large spatial regions.

Acinetobacter baumannii is a Gram-negative pathogen that has emerged as one of the most troublesome pathogens for healthcare institutions globally. Bacterial quorum sensing (QS) is a process of cell-to-cell communication that relies on the production, secretion, and detection of autoinducer (AI) signals to share information about cell density and regulate gene expression accordingly. The molecular and genetic bases of A. baumannii virulence remains poorly understood. Therefore, the contribution of the abaI/abaR QS system to growth characteristics, morphology, biofilm formation, resistance, motility, and virulence of A. baumannii was studied in detail. RNA sequencing (RNA-seq) analysis indicated that genes involved in various aspects of energy production and conversion; valine, leucine, and isoleucine degradation; and lipid transport and metabolism are associated with bacterial pathogenicity. Our work provides a new insight into the abaI/abaR QS system effects on pathogenicity in A. baumannii. We propose that targeting the acyl homoserine lactone (AHL) synthase enzyme abaI could provide an effective strategy for attenuating virulence. On the contrary, interdicting the AI synthase receptor abaR elicits unpredictable consequences, which may lead to enhanced bacterial virulence.

Abstract Background DNA methylation is an important epigenetic factor that maintains and regulates gene expression. The mode and level of DNA methylation depend on the roles of DNA methyltransferase and demethylase, while DNA demethylase plays a key role in the process of DNA demethylation. The results showed that the plant’s DNA demethylase all contained conserved DNA glycosidase domain. This study identified the cotton DNA demethylase gene family and analyzed it using bioinformatics methods to lay the foundation for further study of cotton demethylase gene function. Results This study used genomic information from diploid Gossypium raimondii JGI (D), Gossypium arboreum L. CRI (A), Gossypium hirsutum L. JGI (AD1) and Gossypium barbadebse L. NAU (AD2) to Arabidopsis thaliana. Using DNA demethylase genes sequence of Arabidopsis as reference, 25 DNA demethylase genes were identified in cotton by BLAST analysis. There are 4 genes in the genome D, 5 genes in the genome A, 10 genes in the genome AD1, and 6 genes in the genome AD2. The gene structure and evolution were analyzed by bioinformatics, and the expression patterns of DNA demethylase gene family in Gossypium hirsutum L. were analyzed. From the phylogenetic tree analysis, the DNA demethylase gene family of cotton can be divided into four subfamilies: REPRESSOR of SILENCING 1 (ROS1), DEMETER (DME), DEMETER-LIKE 2 (DML2), and DEMETER-LIKE3 (DML3). The sequence similarity of DNA demethylase genes in the same species was higher, and the genetic relationship was also relatively close. Analysis of the gene structure revealed that the DNA demethylase gene family members of the four subfamilies varied greatly. Among them, the number of introns of ROS1 and DME subfamily was larger, and the gene structure was more complex. For the analysis of the conserved domain, it was known that the DNA demethylase family gene member has an endonuclease III (ENDO3c) domain. Conclusion The genes of the DNA demethylase family are distributed differently in different cotton species, and the gene structure is very different. High expression of ROS1 genes in cotton were under abiotic stress. The expression levels of ROS1 genes were higher during the formation of cotton ovule. The transcription levels of ROS1 family genes were higher during cotton fiber development.

Background: Wild rodents are the intermediate hosts of Toxoplasma gondii. The distribution of genetic diversity of T. gondii in wild rodents is of importance to understand the transmission of this parasite. This study aimed to genetically characterize T. gondii isolates from wild rodents in Sichuan province, southwestern China in 2013. Methods: Genomic DNA was extracted from 10 g wild rodents’ brain samples. Semi-nested PCR and multilocous PCR-RFLP technology were performed to examine genetic diversity of T. gondii isolates as described previously. Results: Overall, 181 brain tissues of different wild rodents, including Eothenomys miletus (n=88), Crocidura attenuate (n=9), Rattus rattus sladeni (n=46), Mus musculus Linnaeus (n=6) and R. niviventer (n=32) were tested for T. gondii DNA, respectively. Six of them were positive for the T. gondii B1 gene by semi-nested PCR amplification, 4 showed complete genotyping results for all 11 polymorphic loci (SAG1, SAG2, alt. SAG2, SAG3, BTUB, GRA6, L358, PK1, C22-8, C29-2 and Apico) by PCR-RFLP, determined to represent a potential new genotype (http://toxodb.org/toxo/). Conclusion: These results documented genetic characterization of T. gondii in wild rodents from Sichuan province, and enriched the genetic diversity of T. gondii in China.

As the brain standard template for medical image registration has only been constructed with an MRI template, there is no three-dimensional fMRI standard template for use, and when the subject’s brain structure is quite different from the standard brain structure, the registration to the standard space will lead to large errors. Registration to an individual space can avoid this problem. However, in the current fMRI registration algorithm based on individual space, the reference image is often selected by researchers or randomly selected fMRI images at a certain time point. This makes the quality of the reference image very dependent on the experience and ability of the researchers and has great contingency. Whether the reference image is appropriate and reasonable affects the rationality and accuracy of the registration results to a great extent. Therefore, a method for constructing a 3D custom fMRI template is proposed. First, the data are preprocessed; second, by taking a group of two-dimensional slices corresponding to the same layer of the brain in three-dimensional fMRI images at multiple time points as image sequences, each group of slice sequences are registered and fused; and finally, a group of fused slices corresponding to different layers of the brain are obtained. In the process of registration, in order to make full use of the correlation information between the sequence data, the feature points of each two slices of adjacent time points in the sequence are matched, and then according to the transformation relationship between the adjacent images, they are recursively forwarded and mapped to the same space. Then, the fused slices are stacked in order to form a three-dimensional customized fMRI template with individual pertinence. Finally, in the classic registration algorithm, the difference in the registration accuracy between using a custom fMRI template and different standard spaces is compared, which proves that using a custom template can improve the registration effect to a certain extent.