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This study aimed to study the effects of three current popular high-temperature treatments (electric roasting, air frying and superheated steam) on the physicochemical properties and taste quality of grass carp meat. Physicochemical properties were analyzed by measuring pH, water holding capacity, color value, thiobarbituric acid reactive substance and protein carbonyl content of fish meat. Taste quality was characterized by measuring the free amino acids content and flavor nucleotides content of fish meat. The results showed that compared with the raw grass carp meat in the control group, the indexes of physicochemical properties of grass carp meat processed by different high-temperature treatments were significantly increased in different degrees (P

Many pathogenic bacteria use type IV secretion systems (T4SSs) to deliver effectors (T4SEs) into the cytoplasm of eukaryotic cells, causing diseases. The identification of effectors is a crucial step in understanding the mechanisms of bacterial pathogenicity, but this remains a major challenge. In this study, we used the full-length embedding features generated by six pre-trained protein language models to train classifiers predicting T4SEs and compared their performance. We integrated three modules into a model called T4SEpp. The first module searched for full-length homologs of known T4SEs, signal sequences, and effector domains; the second module fine-tuned a machine learning model using data for a signal sequence feature; and the third module used the three best-performing pre-trained protein language models. T4SEpp outperformed other state-of-the-art (SOTA) software tools, achieving ∼0.98 accuracy at a high specificity of ∼0.99, based on the assessment of an independent validation dataset. T4SEpp predicted 13 T4SEs from Helicobacter pylori, including the well-known CagA and 12 other potential ones, among which eleven could potentially interact with human proteins. This suggests that these potential T4SEs may be associated with the pathogenicity of H. pylori. Overall, T4SEpp provides a better solution to assist in the identification of bacterial T4SEs and facilitates studies of bacterial pathogenicity. T4SEpp is freely accessible at https://bis.zju.edu.cn/T4SEpp.

Abstract Background The endothelial-to-hematopoietic transition (EHT) process during definitive hematopoiesis is highly conserved in vertebrates. Stage-specific expression of transposable elements (TEs) has been detected during zebrafish EHT and may promote hematopoietic stem cell (HSC) formation by activating inflammatory signaling. However, little is known about how TEs contribute to the EHT process in human and mouse. Results We reconstructed the single-cell EHT trajectories of human and mouse and resolved the dynamic expression patterns of TEs during EHT. Most TEs presented a transient co-upregulation pattern along the conserved EHT trajectories, coinciding with the temporal relaxation of epigenetic silencing systems. TE products can be sensed by multiple pattern recognition receptors, triggering inflammatory signaling to facilitate HSC emergence. Interestingly, we observed that hypoxia-related signals were enriched in cells with higher TE expression. Furthermore, we constructed the hematopoietic cis-regulatory network of accessible TEs and identified potential TE-derived enhancers that may boost the expression of specific EHT marker genes. Conclusions Our study provides a systematic vision of how TEs are dynamically controlled to promote the hematopoietic fate decisions through transcriptional and cis-regulatory networks, and pre-train the immunity of nascent HSCs.

Low temperatures are one of the major abiotic stresses that affect alfalfa's development and yield. Enhancing frost resistance through resistance-related genes is one of the most effective ways to address this issue in alfalfa. Therefore, exploring cold-resistant gene resources and the cultivation of cold-resistant alfalfa cultivars is inevitable in order to achieve high yield and quality. In this study, we conducted transcriptome profiling of roots obtained from two alfalfa genotypes, i.e., Qingda No.1 for freeze tolerance and Gannong No.9 for freeze sensitivity. We observed that Qingda No.1 had more lateral roots and a more developed root system after overwintering, while Gannong No.9 had fewer lateral roots and an underdeveloped root system. After overwintering, Qingda No.1 exhibited higher superoxide dismutase (SOD) activity compared to Gannong No.9, while Gannong No.9 showed higher perosuperoxide dismutasexidase (POD) activity than Qingda No.1. We identified 25,935 differentially expressed genes, with 12 979 and 12 956 differential genes found in the freeze-tolerant variety Qingda No.1 group and the freeze-sensitive Gannong No.9 group, respectively. The enrichment of the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways also differed between the two groups. We also discovered several gene family members, and the most frequent transcription factors were bHLH, B3, NAC, WRKY, and MYB_related. These findings provide comprehensive information to further understand the molecular mechanisms of adaptation to freezing stress in alfalfa and offer potential functional candidate genes for adaptation to abiotic stress.

Aiming at the challenges of low detection accuracy, susceptibility to complex background interference, difficulty in detecting small objects, and multi-scale object issues in aerial images, our proposed an improved YOLOv8-based object detection algorithm, named YOLO-RLDW. Leveraging the advantages of Receptive Field Attention Convolution (RFAConv), we designed a feature extraction module named C2f-RFA to enhance the feature extraction capability for small objects in aerial images. Inspired by the concept of Large Separable Kernel Attention (LSKA), we developed the SPPF-LSKA module, which effectively reduces the interference of aerial backgrounds in object detection. We replaced the YOLOv8 detection head with a Dynamic Head (DyHead), further enhancing the model’s generalization and adaptability. Finally, we employed as boundary box regression loss based on a dynamic focusing mechanism, WIoU, as the loss function, which accelerates model convergence while improving the localization capability for multi-scale objects. Experimental results demonstrate that on the VisDrone2021 dataset, the proposed algorithm achieves improvements of 5.5%, 3.9%, 5.4%, and 3.7% in precision (P), recall (R), mean average precision (mAP50), and mAP95, respectively, compared to the original algorithm. On our self-built remote sensing image dataset RSI, the accuracy, recall, and mean average precision reach 94.2%, 91.0%, and 95.4%, respectively, demonstrating good performance in detecting objects in aerial images. Comparison with other mainstream object detection algorithms validates the effectiveness and superiority of the proposed method.

Abstract Human aging is a natural and inevitable biological process that leads to an increased risk of aging-related diseases. Developing anti-aging therapies for aging-related diseases requires a comprehensive understanding of the mechanisms and effects of aging and longevity from a multi-modal and multi-faceted perspective. However, most of the relevant knowledge is scattered in the biomedical literature, the volume of which reached 36 million in PubMed. Here, we presented HALD, a text mining-based human aging and longevity dataset of the biomedical knowledge graph from all published literature related to human aging and longevity in PubMed. HALD integrated multiple state-of-the-art natural language processing (NLP) techniques to improve the accuracy and coverage of the knowledge graph for precision gerontology and geroscience analyses. Up to September 2023, HALD had contained 12,227 entities in 10 types (gene, RNA, protein, carbohydrate, lipid, peptide, pharmaceutical preparations, toxin, mutation, and disease), 115,522 relations, 1,855 aging biomarkers, and 525 longevity biomarkers from 339,918 biomedical articles in PubMed. HALD is available at https://bis.zju.edu.cn/hald .

Current shale gas well production capacity predictions primarily rely on analytical and numerical simulation methods, which necessitate extensive calculations and manual parameter tuning and produce lowly accurate predictions. Although employing neural networks yields highly accurate predictions, they can easily fall into local optima. This paper suggests a new way to use Cuckoo Search (CS)-optimized neural networks to make shale gas well production capacity predictions more accurate and to solve the problem of local optima. It aims to assist engineers in devising more effective development plans and production strategies, optimizing resource allocation, and reducing risk. The method first analyzes the factors influencing the production capacity of shale gas wells in a block located in western China through correlation coefficients. It identifies the main factors affecting the gas test absolute open flow as organic carbon content, small-layer passage rate, fracture pressure, acid volume, pump-in fluid volume, brittle mineral content in the rock, and rock density. Subsequently, we used the CS algorithm to conduct the global training of the neural network, avoiding the problem of local optima, and established a neural network model for predicting shale gas well production capacity optimized by the CS algorithm. A comparative analysis with other relevant methods demonstrates that the CS-optimized neural network model can accurately predict production capacity, enabling a more rational and effective exploitation of shale gas resources, which lower development costs and increase the economic returns of oil and gas fields. Compared to numerical simulation, SVM, and BP neural network algorithms, the CS-optimized BP neural network (CS-BP) exhibits significantly lower prediction error. Its correlation coefficient between predicted and actual values reaches as high as 0.9924. Verification experiments conducted on another shale gas well also demonstrate that, in comparison to the BP neural network algorithm, CS-BP offers superior prediction performance, with model validation showing a prediction error of only 0.05. This study can facilitate more rational and efficient exploitation of shale gas resources, reduce development costs, and enhance the economic benefits of oil and gas fields.

Abstract Carrying the apolipoprotein E (ApoE) Ɛ4 allele is associated with an increased risk of cerebral amyloidosis and late-onset Alzheimer’s disease, but the degree to which apoE glycosylation affects its development is not clear. In a previous pilot study, we identified distinct total and secondary isoform-specific cerebral spinal fluid (CSF) apoE glycosylation profiles, with the E4 isoform having the lowest glycosylation percentage (E2 > E3 > E4). In this work, we extend the analysis to a larger cohort of individuals (n = 106), utilizing matched plasma and CSF samples with clinical measures of AD biomarkers. The results confirm the isoform-specific glycosylation of apoE in CSF, resulting from secondary CSF apoE glycosylation patterns. CSF apoE glycosylation percentages positively correlated with CSF Aβ42 levels (r = 0.53, p E3 and increased binding affinity to heparin. These results indicate that apoE glycosylation has a new and important role in influencing brain Aβ metabolism and can be a potential target of treatment.

Rapid and accurate agricultural land evaluation provides essential guidance for the supervision and allocation of agricultural land resources; it also helps to ensure food security. Previous work has mainly evaluated the land quality at the county level by using field sampling data and based on a factor approach. However, it is difficult to achieve uniform, large-scale agricultural land evaluation via conventional approaches because of its spatial heterogeneity, as well as the large temporal and economic costs associated with data acquisition. In this study, we integrated publicly available multimodal data (i.e., satellite remote sensing, environmental, and socioeconomic data) into the Google Earth Engine (GEE) platform, selected the best indicators from each modality using the geodetector, on the basis of which different combinations of input models were designed. And then we developed machine learning (random forest, RF) and deep learning (deep neural network, DNN) models to evaluate the land quality in paddy field and dry land systems in 2013 throughout Guangdong Province, China. The results showed that the performance of our combination of variables decreased in the following order: multimodal > bimodal > unimodal. With the best input combination, the RF model (R2 = 0.91, RMSE = 97.56, and CCC = 0.95) outperformed the DNN model (R2 = 0.89, RMSE = 108.72, and CCC = 0.94) in terms of predicting the quality of paddy field. The RF model (R2 = 0.90, RMSE = 104.27, and CCC = 0.95) also outperformed the DNN model (R2 = 0.86, RMSE = 124.38, and CCC = 0.93) in terms of predicting the quality of dry land. The agricultural land quality estimates obtained using the RF and DNN models were more accurate for paddy field than for dry land systems because of greater land quality homogeneity in paddy fields. This research proposed a simple, low-cost for rapid and accurate agricultural land evaluation at the provincial scale using publicly available multimodal data, which can help to achieve control of the agricultural land grade at multiple spatial and temporal scales.

With the development of human society, the cultivated land resources' utilization has become more diversified, and the multifunction of cultivated land is critical to meet the human demand. However, the studies on the multifunction of cultivated land are mostly based on the data of single time node or discontinuous years, which the results may be influenced by abnormal function years. To assess the multifunction of cultivated land and its trade-off/synergy relationships accurately and objectively, this study uses continuous long time series data to analyze multifunctionality of cultivated land. Firstly, the Long short-term memory (LSTM) method was applied to predict the data of 2021–2030 based on the data from 2010 to 2020. Then, the Theil-Sen Median method was used to analyze the spatiotemporal evolution trends of the multifunction of cultivated land from 2010 to 2030. Secondly, based on the trends, the long time series cultivated land function trade-off/synergy degree (LCFTD) was constructed to explore the stable trade-off/synergy relationships among functions. Finally, the cultivated land resources were divided into zones based on the spectral clustering. This study analyzed the multifunction of cultivated land in Guangdong from 2010 to 2030. (1) The multifunction of cultivated land had spatially heterogeneous and temporally fluctuating. (2) The production-ecological, ecological-life, and production-life functions were all dominated by synergy during 2010–2020. Without changing the current utilization state of cultivated land resource, the trade-off among functions will increase in the future (2021–2030). (3) The cultivated land was divided into five type zones: stable development, production core, ecological conservation, comprehensive lifting, and fallow renewal. The LCFTD can better avoid the influence of abnormal function years, and the long time series data can also predict the future development trend reliably. This study idea can help to formulate long time cultivated land management policies and achieve the goal of sustainable development of cultivated land resources.

In order to realize the innovative application of fish protein gelatin, increase the added value of the mango industry, and enrich the nutritional value of fruit cakes, green mango was used as raw material to make mango cakes with fish protein gelatin. On the basis of single factor experiments, taking sensory score as response value, the processing formula of fish protein gelatin mango cake was optimized by response surface method combined with texture analysis, and the physicochemical and microbiological indexes were determined. The results showed that the best quality ratio of fish protein gelatin to pectin was 1:1.5, and the optimal formula was 2.5% of the total amount of gelatine, 40% of sugar and 0.7% of citric acid. The fish protein gelatin mango cake made by this process formula had the best taste and quality, qualified physical and chemical indexes and microbial indexes, and the protein content reached 2.19 g/100 g. It was a new fruit cake with comprehensive nutrition, which would provide new ideas and technical reference for the development and application of mango and fish protein gelatin.

Cropland is a comprehensive system influenced by the natural environment and human activities. This article collects the data of cropland use, soil, and other geographic, social and economic factors in the study area and then uses the methods of system analysis, induction and deduction to propose a new research perspective for establishing a cognitive framework and analyzing cropland resources and their functions. The framework is used to assess the rapidly urbanizing region of Guangzhou and investigate the production, ecological, and living functions provided by cropland resources. Synergistic relationships between functions are analyzed using the hot and cold spot methods. The results indicate that the production function of cropland resources in Guangzhou is good, the ecological function is favorable, and the living function is relatively low. A synergistic relationship between the three functions is observed in 91% of areas of Guangzhou, whereas a balanced relationship occurs in some areas of the southern part of Zengcheng, the northwestern and northeastern parts of Conghua, and the western part of Nansha. This research provides guidance for managing cropland resources and ensuring their sustainable utilization.

This study aimed to diminish the reliance on water-in-oil-in-water (W/O/W) emulsions on the synthetic emulsifier polyglycerol polyricinoleate (PGPR). Considering the potential synergistic effects of proteins and PGPR, various protein types (whey, pea and chickpea protein isolates) were incorporated into the internal aqueous phase to formulate W/O/W emulsions. The effects of the combination of PGPR and protein at different ratios (5:0, 4:1, 3:2, 1:1 and 2:3) on the stability and encapsulation properties of W/O/W emulsions co-encapsulating crocin and quercetin were investigated. The findings indicated that the combination of PGPR and protein resulted in a slight reduction in the encapsulation efficiency of the emulsions, compared to that of PGPR (the control). Nonetheless, this combination significantly enhanced the physical stability of the emulsions. This result was primarily attributed to the smaller droplet sizes and elevated viscosity. These factors contributed to increased retentions of crocin (exceeding 70.04%) and quercetin (exceeding 80.29%) within the emulsions after 28 days of storage, as well as their improved bioavailability (increases of approximately 11.62~20.53% and 3.58~7.98%, respectively) during gastrointestinal digestion. Overall, combining PGPR and protein represented a viable and promising strategy for reducing the amount of PGPR and enhancing the stability of W/O/W emulsions. Notably, two plant proteins exhibited remarkable favorability in this regard. This work enriched the formulations of W/O/W emulsions and their application in the encapsulation of bioactive substances.

The authors wish to make the following corrections in Section 3 [...]

The harmonic drive (HD) is increasingly used due to its outstanding advantages, especially for equipment requiring high precision and a large gear ratio. This research is based on the Finite Element Method (FEM) and mechanical analysis to investigate stress calculation methods for the flexspline (FS), a significant component of HD transmission, providing a basis for assessing the durability and failure of the FS. First, the FS deformation and stress analysis model based on the FEM is proposed. Since the two boundary elements of the model have the form of a rotation and translational joint, which have not been provided, based on the stiffness matrix of the fixed beam to build the stiffness matrix for these elements, which completes the proposed analytical model. Then, the structures of an HD reduced are designed and applying the proposed force analysis model, the deformation and stress distributions of the FS are obtained. Finally, the theoretical safety factor is calculated and analyzed. The results of the simulation completely coincide with the theoretical calculation and show that the most concentrated stress is at the root of the teeth, which is the leading cause of the FS failure. So, the study as reference material for rapidly evaluating the stress state and calculating the safety when designing and optimizing the FS.

Apolipoproteins (apo) C-I and C-II are key regulators of triglyceride and HDL metabolism. Both exist as full-size native and truncated (apoC-I'; apoC-II') posttranslational proteoforms. However, the determinants and the role of these proteoforms in lipid metabolism are unknown. Here, we measured apoC-I and apoC-II proteoforms by mass spectrometry immunoassay in baseline and 10-year follow-up plasma samples from the Multi-Ethnic Study of Atherosclerosis. We found that baseline total apoC-I (mean = 9.2 mg/dl) was lower in African Americans (AA), Chinese Americans (CA), and Hispanics (by 1.8; 1.0; 1.0 mg/dl vs. whites), higher in women (by 1.2 mg/dl), and positively associated with plasma triglycerides and HDL. Furthermore, we observed that the truncated-to-native apoC-I ratio (apoC-I'/C-I) was lower in CA, negatively associated with triglycerides, and positively associated with HDL. We determined that total apoC-II (8.8 mg/dl) was lower in AA (by 0.8 mg/dl) and higher in CA and Hispanics (by 0.5 and 0.4 mg/dl), positively associated with triglycerides, and negatively associated with HDL. In addition, apoC-II'/C-II was higher in AA and women, negatively associated with triglycerides, and positively associated with HDL. We showed that the change in triglycerides was positively associated with changes in total apoC-I and apoC-II and negatively associated with changes in apoC-I'/C-I and apoC-II'/C-II, whereas the change in HDL was positively associated with changes in total apoC-I and apoC-II'/C-II and negatively associated with change in total apoC-II. This study documents racial/ethnic variation in apoC-I and apoC-II plasma levels and highlights apolipoprotein posttranslational modification as a potential regulator of plasma lipids.

Soil exchange cations are a basic indicator of soil quality and environmental clean-up potential. The accurate and efficient acquisition of information on soil cation content is of great importance for the monitoring of soil quality and pollution prevention. At present, few scholars focus on soil exchangeable cations using remote sensing technology. This study proposes a new method for estimating soil cation content using hyperspectral data. In particular, we introduce Boruta and successive projection (SPA) algorithms to screen feature variables, and we use Guangdong Province, China, as the study area. The backpropagation neural network (BPNN), genetic algorithm–based back propagation neural network (GABP) and random forest (RF) algorithms with 10-fold cross-validation are implemented to determine the most accurate model for soil cation (Ca2+, K+, Mg2+, and Na+) content estimations. The model and hyperspectral images are combined to perform the spatial mapping of soil Mg2+ and to obtain the spatial distribution information of images. The results show that Boruta was the optimal algorithm for determining the characteristic bands of soil Ca2+ and Na+, and SPA was the optimal algorithm for determining the characteristic bands of soil K+ and Mg2+. The most accurate estimation models for soil Ca2+, K+, Mg2+, and Na+ contents were Boruta-RF, SPA-GABP, SPA-RF and Boruta-RF, respectively. The estimation effect of soil Mg2+ (R2 = 0.90, ratio of performance to interquartile range (RPIQ) = 3.84) was significantly better than the other three elements (Ca2+: R2 = 0.83, RPIQ = 2.47; K+: R2 = 0.83, RPIQ = 2.58; Na+: R2 = 0.85, RPIQ = 2.63). Moreover, the SPA-RF method combined with HJ-1A HSI images was selected for the spatial mapping of soil Mg2+ content with an R2 of 0.71 and RPIQ of 2.05. This indicates the ability of the SPA-RF method to retrieve soil Mg2+ content at the regional scale.

PurposeFalls are a major public health problem, especially for older people. This research aimed to provide a direct illustration of fall risks among the homebound older people with dementia in China, and to identify the risk factors associated with it.MethodsIn 2020, a questionnaire-based field survey was used to assess 1,042 people aged over 60 years in Ningbo, Eastern China. The Morse Fall Risk Scale's result was employed as the dependent variable, while the basic health problems, living environment difficulties, social support problems, and behavioral awareness issues were utilized as the independent variables; subsequently, chi-squared tests and four multivariate ordinarily ordered logistic regression models were performed.ResultsOverall, nine hundred and thirty-one older people with dementia were included in this study (the effective rate was 89.34%), with the majority of them having severe dementia (27.9%). Furthermore, 16.2% had fallen in the past 3 months, and 16.8% were at a high risk of falling. The risk factors for the older people's cognitive function included 80–90 years old, vascular dementia, marital status, and history of falls (P < 0.05); the kinds of chronic diseases, the activities of daily living, living environment, caregiver burden, caregiver knowledge, the Cohen Mansfield Agitation Inventory results, and the Clinical Dementia Rating were the protective factors for the risk of falls in them (P < 0.05).ConclusionThe risk of falling of the Chinese homebound older people with dementia was high. Their caregivers, such as relatives, need to pay attention to these risk factors and perform appropriate measures to prevent falls.

Pectin, a kind of natural polysaccharide, shows the attractive potential as a natural stabilizer for protein emulsion. The aim of this study is to investigate the effect of pectin on the physical stability, rheology, interface, and interaction properties of the fish gelatin (FG) emulsion, as pectin was utilized to improve the stability of FG, fish oil emulsion. During the study, when pH < 6, the FG-pectin emulsion displayed better storage stability and salinity tolerance. Analyzing the result, pectin could avoid phase separation at the freeze-thaw process and prevent the liquid-gel transition of FG emulsions during storage. On the other hand, when pH ≥ 6, the emulsion displayed high viscosity due to the complex flocculation and stratified during long-term storage. Electrostatic interactions, hydrophobic interactions, and hydrogen bonding of the FG-pectin complexes in the emulsion were all reduced. Overall, pectin improved the stability of FG emulsions through electrostatic repulsion, hydrophobic interactions, and steric hindrance.

The rapid development of industrialization and urbanization has posed serious challenges for coastal farmland ecosystems. Source apportionment of soil heavy metals is an effective way for the detection of non-point source pollution in farmland to help support the high-quality development of coastal agriculture. To this end, 113 surface soil samples were collected in the coastal delta of China, and the contents of As, Cd, Cr, Cu, Ni, Pb, and Zn were determined. A variety of models were integrated to apportion the source of soil heavy metals, including positive matrix factorization (PMF), geographical detector (GD), eXtreme gradient boosting (XGBoost), and structural equation modeling (SEM). The result of PMF models revealed that there was collinearity between various heavy metals, and the same heavy metal may have a mixed source. The XGBoost model analysis indicated that there were significant non-linear relationships between soil heavy metals and source factors. A synergy between air quality and human activity factors was the key source of heavy metal that entered the study area, based on the results of the GD. Furthermore, the input path effect of heavy metals in the soil of the study area was quantified by SEM. The balance of evidence from the above models showed that air quality (SO2 and NO2) and factories in the study area had the greatest impacts on Cd, Cr, and Zn. Natural sources were dominant for Pb, while As, Cu, and Ni were contributed by soil parent material and factories. The above results led to the conclusion that there was a cycle path in the study area that continuously promoted the migration and accumulation of heavy metals in farmland soil; that is, the heavy metals discharged during oil exploitation and smelting entered the atmosphere and then accumulated in the farmland soil through precipitation, atmospheric deposition, and other paths. In this study, it is shown that a variety of models can be used to more comprehensively assess the sources of soil heavy metals. This approach can provide effective support for the rapid prevention and decision-making management of soil heavy metal pollution in coastal areas.

The accurate extraction of cropland distribution is an important issue for precision agriculture and food security worldwide. The complex characteristics in southern China pose great challenges to the extraction. In this study, for the objective of accurate extraction and mapping of cropland parcels in multiple crop growth stages in southern China, we explored a method based on unmanned aerial vehicle (UAV) data and deep learning algorithms. Our method considered cropland size, cultivation patterns, spectral characteristics, and the terrain of the study area. From two aspects—model architecture of deep learning and the data form of UAV—four groups of experiments are performed to explore the optimal method for the extraction of cropland parcels in southern China. The optimal result obtained in October 2021 demonstrated an overall accuracy (OA) of 95.9%, a Kappa coefficient of 89.2%, and an Intersection-over-Union (IoU) of 95.7%. The optimal method also showed remarkable results in the maps of cropland distribution in multiple crop growth stages, with an average OA of 96.9%, an average Kappa coefficient of 89.5%, and an average IoU of 96.7% in August, November, and December of the same year. This study provides a valuable reference for the extraction of cropland parcels in multiple crop growth stages in southern China or regions with similar characteristics.

Gram-negative bacteria harness multiple protein secretion systems and secrete a large proportion of the proteome. Proteins can be exported to periplasmic space, integrated into membrane, transported into extracellular milieu, or translocated into cytoplasm of contacting cells. It is important for accurate, genome-wide annotation of the secreted proteins and their secretion pathways. In this review, we systematically classified the secreted proteins according to the types of secretion systems in Gram-negative bacteria, summarized the known features of these proteins, and reviewed the algorithms and tools for their prediction.

BackgroundThis study aimed to investigate the prevalence of anxiety symptoms among patients with chronic low back pain and explore its related factors.MethodsA cross-sectional study was conducted on patients with chronic low back pain from two general hospitals in China. Anxiety symptoms were assessed by the Generalized Anxiety Disorder-7. Binary logistic regression was used to examine the association between demographic characteristics, pain severity, pain self-efficacy, family functioning and anxiety symptoms.ResultsThis study involved 1,172 chronic low back pain patients, with an effective rate of 94.67%. The prevalence of anxiety symptoms among patients with chronic low back pain in China was 23.89%. In the binary logistic regression, patients with more severe pain (OR = 1.15, 95%CI: 1.11–1.18) and pain duration between 1~5 years (1~3 years: OR = 2.45, 95%CI: 1.38–4.36; 3~5 years: OR = 2.99, 95%CI: 1.49–6.00) had a higher risk to anxiety symptoms. In contrast, patients with higher monthly income (OR = 0.62, 95%CI: 0.39–0.98), better family functioning (highly functional family: OR = 0.22, 95% CI: 0.13–0.37; moderately dysfunctional family: OR = 0.44, 95% CI: 0.27–0.72) and higher pain self-efficacy (OR = 0.95, 95%CI: 0.94–0.96) had a lower risk to anxiety symptoms.ConclusionThe prevalence of anxiety symptoms among chronic low back pain patients was high in China. Targeted intervention measures should be taken to reduce anxiety symptoms levels of chronic low back pain patients.

There have been some limitations in acquiring an accurate representation of remotely sensed data-derived soil moisture. Here, we propose a simplified thermal inertia model (STIM). This model requires only the albedo and surface maximum temperature easily obtained from satellite imagery, such as that of the moderate resolution imaging spectroradiometer (MODIS). In this study, we defined a soil moisture index (SMI) using the MODIS imagery to obtain a simplified thermal inertia-ratio vegetation index spectral feature space. SMI results from STIM were validated at several locations in the study area of western Inner Mongolia and compared with those from the apparent thermal inertia (ATI) model. Our results showed that our SMI model could explain 71% of the variance in the surface soil moisture, approximately 5% higher than that of the ATI model. In a comparison of field-measured soil moisture data with data simulated using two methods, the SMI and ATI, the SMI showed better retrieval accuracy by lessening the effective error due to the vegetation by 4.2%-10.8%, whereas soil moisture data simulated with ATI showed an effective error of 4.5%-17.0%. The SMI model was also used to map soil moisture; a relative root-mean-square error of 7.67% was recorded for the region, implying the ability of the model to map soil moisture over large areas. Here, the proposed SMI model was proven to be more suitable for estimating soil moisture in locations in which the vegetation index values ranged from 0 to 3. Thus, the proposed SMI model provides a new approach using remote sensing thermal inertia methods to quantify soil moisture at the regional scale.

Type 1 secretion systems play important roles in pathogenicity of Gram-negative bacteria. However, the substrate secretion mechanism remains largely unknown. In this research, we observed the sequence features of repeats-in-toxin (RTX) proteins, a major class of type 1 secreted effectors (T1SEs). We found striking non-RTX-motif amino acid composition patterns at the C termini, most typically exemplified by the enriched “[FLI][VAI]” at the most C-terminal two positions. Machine-learning models, including deep-learning ones, were trained using these sequence-based non-RTX-motif features and further combined into a tri-layer stacking model, T1SEstacker, which predicted the RTX proteins accurately, with a fivefold cross-validated sensitivity of ∼0.89 at the specificity of ∼0.94. Besides substrates with RTX motifs, T1SEstacker can also well distinguish non-RTX-motif T1SEs, further suggesting their potential existence of common secretion signals. T1SEstacker was applied to predict T1SEs from the genomes of representative Salmonella strains, and we found that both the number and composition of T1SEs varied among strains. The number of T1SEs is estimated to reach 100 or more in each strain, much larger than what we expected. In summary, we made comprehensive sequence analysis on the type 1 secreted RTX proteins, identified common sequence-based features at the C termini, and developed a stacking model that can predict type 1 secreted proteins accurately.

Background: Few studies have focused on depressive symptoms among patients with chronic low back pain in China. The aim of this cross-sectional study was to assess the prevalence and associated factors of depressive symptoms in patients with chronic low back pain.Methods: From May to August 2021, 1,172 patients with chronic low back pain were recruited in China. Depressive symptoms were assessed through the Patient Health Questionnaire. Associations of demographic characteristics, clinical characters and social-psychological factors with depressive symptoms were investigated among patients with chronic low back pain.Results: The prevalence of depressive symptoms was 25.00%. Logistic regression analysis found that duration of pain in 1-5 years (1-3 years: OR = 2.91, 95%CI: 1.65-5.14, 3-5 years: OR = 3.09, 95%CI: 1.55-6.15) and more severe pain (OR = 1.13, 95%CI: 1.10-1.17) were associated with higher risks of depressive symptoms. Better family function (good family function: OR = 0.25, 95%CI: 0.15-0.41, moderate family dysfunction: OR = 0.47, 95%CI: 0.29-0.77) and higher pain self-efficacy (OR = 0.94, 95%CI: 0.93-0.95) were associated with lower risks of depressive symptoms.Conclusion: Patients with chronic low back pain have a high prevalence of depressive symptoms in China. Duration of pain, pain severity, family function and pain self-efficacy were predictors of depressive symptoms among chronic low back pain patients in China. Early identification of the associated factors may be helpful for the timely management of depressive symptoms.

The ability to rapidly and effectively monitor the alpine grassland aboveground biomass (AG-AGB) is very important to grassland resource management and sustainable use. The present satellite-driven models for estimating AG-AGB still need be improved due to incomplete monitoring indicators and a lack of sufficient in situ measurements. Thus, this study proposes a new AG-AGB estimation model based on MODIS and SRTM data for improving the estimation accuracy of AG-AGB. In this new model, first six estimation indicators were obtained from 33 spectral and environmental indicators using Extreme Gradient Boosting (XGBoost) and correlation analysis. Second, multiple linear regression (MLR), a back propagation neural network (BPNN), a support vector machine (SVM), and the random forest (RF) algorithm were used to build the new model based on the long-term AG-AGB field measurements and the corresponding to six estimating indicators for 6128 samples. Third, the new model was used to map the spatial distribution of the AG-AGB in Qinghai Province, China. And, the trends of the AG-AGB changes from 2005 to 2018 were analyzed using Sen and Mann-Kendall trend analysis and Hurst exponent analysis. The results show that 1) the six estimation indicators (EVI, radiation, altitude, B5/B7, latitude, and precipitation) were confirmed to be important for estimating the AG-AGB. 2) Among the MLR, BPNN, SVM, and RF, the model based on MODIS and SRTM data constructed using the RF algorithm was determined to be the best new model, with an R2 of 0.938 and a relative root mean square error (RRMSE) of 19.88%, for accurately estimating the AG-AGB based on 6128 samples. 3) Moreover, a set of 1200 samples was determined to be the lowest sample size for constructing the new model, with an R2 of nearly 0.923 and an RRMSE of 25%. 4) The AG-AGB can be more accurately mapped using the new model (R2 of 0.800 and RRMSE of 23.91%), indicating that the new model based on MODIS and SRTM data has the potential to map the AG-AGB over a large area. And over the last 14 years, the AG-AGB overall trends were that the areas with increasing AG-AGB (67.65%) were much larger than those with decreasing AG-AGB (32.35%), and the area of the sustainable AG-AGB increase accounts for 39.75% of the total alpine grassland area in northeastern and southwestern parts of Qinghai Province. The proposed estimation model based on MODIS and SRTM data is more suitable, greatly improves the accuracy of AG-AGB modeling, and provides a scientific basis for the reasonable use and management of alpine grassland resources.

The effects of different emulsifiers, such as soy protein isolate–sucrose ester (SPI-SE) and whey protein isolate–sucrose ester (WPI-SE), on the properties of the emulsion during the microencapsulation of cannabis oil were studied. The influence of SE concentration on the emulsion properties of the two emulsifying systems was analyzed. The results of the adsorption kinetics show that SE can decrease the interfacial tension, particle size and zeta potential of the emulsions. The results of the interfacial protein concentration show that SE could competitively replace the protein at the oil-water interface and change the strength of the interfacial film. The results of the viscoelastic properties show that the emulsion structure of the two emulsion systems results in the maximum value when the concentration of SE is 0.75% (w/v), and the elastic modulus (G’) of the emulsion prepared with SPI-SE is high. The viscosity results show that all emulsions show shear-thinning behavior and the curve fits well with the Ostwald–Dewaele model. The addition of SE in the emulsions of the two emulsion systems can effectively stabilize the emulsion and change the composition and strength of the oil–water interface of the emulsion. The cannabis oil microcapsules prepared with protein-SE as an emulsion system exhibit high quality.

Cultivated land quality (CLQ) is associated with national food security, benign economic development, social harmony, and stability. The scientific evaluation of CLQ provides the basis for achieving the “trinity” protection of cultivated land quantity, and quality, as well as ecology. However, the current research on CLQ evaluation has some limitations, mainly the poor consideration of evaluation indicators, time-consuming and labor-intensive data acquisition, and low precision of evaluation at the regional scale. Therefore, this study introduced multisource data to evaluate CLQ and proposed a new method for CLQ evaluation (natural grade evaluation, utilization grade evaluation, and economic grade evaluation), combining multisource data and the recurrent neural network (RNN) algorithm. Initially, optimal indicators were determined by correlation analysis and generalized linear regression coefficient methods based on factors related to CLQ acquired from multisource data. Then, CLQ evaluation models were constructed with the RNN algorithm on the basis of the aforementioned optimal indicators. Finally, the models were adopted to map CLQ. The present study was carried out in Guangzhou City, Guangdong Province, China. According to the results: (1) CLQ showed close relationship to pH, effective soil layer thickness (EST), chemical fertilizer application rate (CHFE), organic matter content (OMC), annual accumulated temperature (TEMA), 5–15 cm soil depth soil cation exchange capacity (CEC515), 0–5 cm soil depth soil cation exchange capacity (CEC05), 5–15 cm soil depth soil organic carbon content (SOC515), 0–5 cm soil depth soil organic carbon content (SOC05), field slope (FS), groundwater level (GWL), and terrain slope (TS). (2) All modeling accuracies (R2) were greater than 0.80 for the CLQ evaluation models constructed based on the RNN algorithm. The area and spatial distribution of each grade of CLQ evaluation were consistent with the actual situation. The best and the worst quality cultivated land occupied a small area, and the area without a gap with the actual CLQ was as high as 76%, indicating that the model results were reliable. The study shows the suitability of the method for evaluating CLQ at the regional scale, offering a scientific foundation for the rational utilization and management of cultivated land resources, as well as a reference for evaluating CLQ in the future.

Cultivated land fragmentation (CLF) is a key obstacle to agricultural development and has a strong relationship with regional food security and global sustainable development. However, few studies have analyzed the spatio-temporal distribution pattern and evolution characteristics of CLF and the complex interactions among their influencing factors in rapidly developing regions. In this study, first, the GlobeLand30 datasets were used to obtain characteristic parameters of cultivated land in counties in Guangdong Province in 2000, 2010, and 2020. Then, the linear weighted comprehensive evaluation model based on the principal component analysis (PCA) was used to measure the extent of CLF. Finally, the exploratory spatial data analysis (ESDA) was used to analyze the spatio-temporal distribution pattern and evolution characteristics of CLF, and geodetector (GD) and random forest (RF) models were used to explore the factors influencing the spatial difference in CLF. The results showed that the spatial differences in the distribution of cultivated land resources in Guangdong Province are relatively large and the extent of agglomeration is generally low. The extent of CLF on the county scale is mainly medium and higher. The overall spatial distribution shows an increasing trend from the south to the north and from the west to the east, and the spatial distribution pattern with agglomeration and randomness remains relatively stable. From 2000 to 2020, the overall CLF continued to intensify and the evolution of CLF on the county scale mainly increased. The spatial difference in CLF is the result of that based on the natural environment and influenced by factors such as social, economic, and agricultural development. The interaction between influencing factors is very strong, dominated by nonlinear enhancement. The results are of great significance for promoting the intensive and efficient utilization of cultivated land resources and sustainable regional development.

Stably Expressed Genes (SEGs) are a set of genes with invariant expression. Identification of SEGs, especially among both healthy and diseased tissues, is of clinical relevance to enable more accurate data integration, gene expression comparison and biomarker detection. However, it remains unclear how many global SEGs there are, whether there are development-, tissue- or cell-specific SEGs, and whether diseases can influence their expression. In this research, we systematically investigate human SEGs at single-cell level and observe their development-, tissue- and cell-specificity, and expression stability under various diseased states. A hierarchical strategy is proposed to identify a list of 408 spatial-temporal SEGs. Development-specific SEGs are also identified, with adult tissue-specific SEGs enriched with the function of immune processes and fetal tissue-specific SEGs enriched in RNA splicing activities. Cells of the same type within different tissues tend to show similar SEG composition profiles. Diseases or stresses do not show influence on the expression stableness of SEGs in various tissues. In addition to serving as markers and internal references for data normalization and integration, we examine another possible application of SEGs, i.e., being applied for cell decomposition. The deconvolution model could accurately predict the fractions of major immune cells in multiple independent testing datasets of peripheral blood samples. The study provides a reliable list of human SEGs at the single-cell level, facilitates the understanding on the property of SEGs, and extends their possible applications.

Abstract Background Lung adenocarcinoma is the most common type of lung cancers. Whole-genome sequencing studies disclosed the genomic landscape of lung adenocarcinomas. however, it remains unclear if the genetic alternations could guide prognosis prediction. Effective genetic markers and their based prediction models are also at a lack for prognosis evaluation. Methods We obtained the somatic mutation data and clinical data for 371 lung adenocarcinoma cases from The Cancer Genome Atlas. The cases were classified into two prognostic groups (3-year survival), and a comparison was performed between the groups for the somatic mutation frequencies of genes, followed by development of computational models to discrete the different prognosis. Results Genes were found with higher mutation rates in good (≥ 3-year survival) than in poor (

In this paper, two image smoothing models are proposed for the visual inspection of high-density flexible IC package substrates with strict requirements on line width and line distance which are applied to the de-noising of high-density flexible IC package substrate images. First of all, the two models proposed in this paper combines the level set curvature feature of the image with gradient threshold, using more abundant second-order differential information as the detection factor to remove the noise in the image. Second, the theoretical analysis shows that the de-noised image obtained by the two models proposed can retain more detailed texture information and edge information of the original image. What is more, the experimental analysis shows that the proposed models have the highest structural similarity and peak signal-to-noise ratio, and have a relatively high edge-preserving index and the lowest mean squared error compared with other models. In particular, the de-noised image through Model 1 has the highest structural similarity and peak signal-to-noise ratio, as well as the lowest mean squared error. The de-noised image through Model 2 has a relatively high edge retention index. The methods proposed in this paper can effectively remove the noise of the image of the high-density flexible IC package substrate and can retain the original details and edges information of the image.

As a typical representative of the recurrent neural network (RNN), Zhang neural network (ZNN) has been proved as a powerful parallel-processing neural solver for time-varying matrix problems. Recent studies have shown that, in the absence of noise, the ZNN model activated by a combined activation function (CAF), which is the linear combination of sign-bi-power (SBP) and linear functions (termed CAF-ZNN), can achieve much better finite-time convergence compared with other ZNN models for time-varying matrix pseudoinversion. This paper investigates for the first time the influence of noises on such a CAF-ZNN model. We not only present the upper bound of steady-state solution error synthesized by the noise-polluted CAF-ZNN model for time-varying matrix pseudoinversion but also derive the finite convergence time for the solution error reaching the upper bound. Both the theoretical analyses and simulation observation reveal that the solution error can be made arbitrarily small by choosing some design parameters of CAF-ZNN. At last, a robotic application is successfully performed to further illustrate the feasibility of the CAF-ZNN model to kinematic control of redundant robot manipulator.

Soil fertility affects crop yield and quality. A quick, accurate evaluation of soil fertility is crucial for agricultural production. Few satellite image-based evaluation studies have quantified soil fertility during the crop growth period. Therefore, this study proposes a new approach to the quantitative evaluation of soil fertility. Firstly, the optimal crop spectral variables were selected using the integration of an extreme gradient boosting (XGBoost) algorithm with variance inflation factor (VIF). Then, based on the optimal crop spectral variables where the red-edge indices were introduced for the first time, the estimation models were developed using the backpropagation neural network (BPNN) algorithm to assess soil fertility. The model was finally adopted to map the soil fertility using Sentinel-2 imagery. This study was performed in the Conghua District of Guangzhou, Guangdong Province, China. The results of our research are as follows: (1) five crop spectral variables (inverted red-edge chlorophyll index (IRECI), chlorophyll vegetation index (CVI), normalized green-red difference index (NGRDI), red-edge position (REP), and triangular greenness index (TGI)) were the optimal variables. (2) The BPNN model established with optimal variables provided reliable estimates of soil fertility, with the determination coefficient (R2) of 0.66 and a root mean square error (RMSE) of 0.17. A nonlinear relation was found between soil fertility and the optimal crop spectral variables. (3) The BPNN model provides the potential for soil fertility mapping using Sentinel-2 images, with an R2 of 0.62 and an RMSE of 0.09 for the measured and estimated results. This study suggests that the proposed method is suitable for the estimation of soil fertility in paddy fields.

Dike-ponds in fisheries often present multiple pond conditions such as pure, suspended sediment, water bloom, semidry conditions, etc. However, the impact of these conditions on the performance of extracting dike-pond from remote sensing images has not been studied. To solve this problem, we explore the existence of such impacts by comparing the performance of four rule-based methods in two groups of test regions. The first group has few multiple pond conditions, while the second has more. The results show that various measure values deteriorate as the proportion of multiple pond conditions in the regions increases. All four methods performed worse in the second group than the first, where the overall accuracy decreased by 8.80%, misclassification error increased by 3.69%, omission error raised by 10.53%, and correct quantity rate dropped by 8.23%, respectively. The extraction method that ingested multiple pond conditions performed indistinguishably from the other methods in the first group. However, it outperformed the other methods in the second group, with a 4.22% improvement in overall accuracy, a 10.25% decrease in misclassification error, and a 19.03% increase in the correct quantity rate. These findings suggest that multiple pond conditions can negatively impact the extraction performance and should be considered in dike-pond applications that require a precise pond size, number, and shape.

Apolipoproteins C-I, C-II, and C-III interact with ApoE to regulate lipoprotein metabolism and contribute to Alzheimer's disease pathophysiology. In plasma, apoC-I and C-II exist as truncated isoforms, while apoC-III exhibits multiple glycoforms. This study aimed to 1) delineate apoC-I, C-II, and C-III isoform profiles in cerebrospinal fluid (CSF) and plasma in a cohort of nondemented older individuals (n = 61), and 2) examine the effect of APOE4 on these isoforms and their correlation with CSF Aβ42, a surrogate of brain amyloid accumulation. The isoforms of the apoCs were immunoaffinity enriched and measured with MALDI-TOF mass spectrometry, revealing a significantly higher percentage of truncated apoC-I and apoC-II in CSF compared with matched plasma, with positive correlation between CSF and plasma. A greater percentage of monosialylated and disialylated apoC-III isoforms was detected in CSF, accompanied by a lower percentage of the two nonsialylated apoC-III isoforms, with significant linear correlations between CSF and plasma. Furthermore, a greater percentage of truncated apoC-I in CSF and apoC-II in plasma and CSF was observed in individuals carrying at least one APOE Ɛ4 allele. Increased apoC-I and apoC-II truncations were associated with lower CSF Aβ42. Finally, monosialylated apoC-III was lower, and disialylated apoC-III greater in the CSF of Ɛ4 carriers. Together, these results reveal distinct patterns of the apoCs isoforms in CSF, implying CSF-specific apoCs processing. These patterns were accentuated in APOE Ɛ4 allele carriers, suggesting an association between APOE4 genotype and Alzheimer's disease pathology with apoCs processing and function in the brain.

Urban resilience to natural disasters (e.g., flooding), in the context of climate change, has been becoming increasingly important for the sustainable development of cities. This paper presents a method to assess the urban resilience to flooding in terms of the recovery rate of different subdistricts in a city using all-weather synthetic aperture radar imagery (i.e., Sentinel-1A imagery). The factors that influence resilience, and their relative importance, are then determined through principal component analysis. Jakarta, a flood-prone city in Indonesia, is selected as a case study. The resilience of 42 subdistricts in Jakarta, with their gross domestic product data super-resolved using nighttime-light satellite images, was assessed. The association between resilience levels and influencing factors, such as topology, mixtures of religion, and points-of-interest density, were subsequently derived. Topographic factors, such as elevation (coefficient = 0.3784) and slope (coefficient = 0.1079), were found to have the strongest positive influence on flood recovery, whereas population density (coefficient = −0.1774) a negative effect. These findings provide evidence for policymakers to make more pertinent strategies to improve flood resilience, especially in subdistricts with lower resilience levels.

An accurate assessment of the stocking rate is crucial for maintaining the stable function and the sustainable use of the alpine grassland ecosystem. A new scenario design method to evaluate the reasonable stocking rate is presented in the current work. First, climate change is quantified by potential net primary productivity (NPPp) and measured by adopting the Zhou Guangsheng model, and the NPP generated by anthropogenic activities (NPPh) is estimated by the distinction between NPPp and actual NPP (NPPa) calculated with the application of the Carnegie–Ames–Stanford Approach (CASA) model. Second, using the NPPh and actual grassland productivity consumed by livestock (NPPac), the reasonable stocking rate is obtained. Finally, the driving factors of NPP change in alpine grassland and the reasonable stocking rate are clarified in Qinghai Province during 2005–2018. The results reveal that the temperature of alpine grassland in Qinghai Province has a slight upward trend from 2005 to 2018, and precipitation displays a downward trend. The overall NPPp of alpine grassland demonstrated a downward trend, and precipitation is regarded as the major influencing factor. In addition, the overall NPPh of alpine grassland exhibited a downward trend. The NPPa demonstrated an overall upward trend, where 58.32% of the regional NPPa is in a state of growth, and 41.68% of the regional NPPa is in a state of degradation. According to contribution analysis, anthropogenic activities provided the primary driving factor to promote the restoration of alpine grassland in Qinghai Province. Moreover, the stocking rate must be reduced in 60.77% of the alpine grasslands in Qinghai Province, mostly situated in the eastern and southwestern parts of Qinghai Province, and the other areas must not increase future stocking rates. The current study can theoretically and technically support the construction of Qinghai as the green organic agricultural and livestock product demonstration province and the creation of an ecological civilization highland.

Plant transcriptomes encompass a large number of functional non-coding RNAs (ncRNAs), only some of which have protein-coding capacity. Since their initial discovery, ncRNAs have been classified into two broad categories based on their biogenesis and mechanisms of action, housekeeping ncRNAs and regulatory ncRNAs. With advances in RNA sequencing technology and computational methods, bioinformatics resources continue to emerge and update rapidly, including workflow for in silico ncRNA analysis, up-to-date platforms, databases, and tools dedicated to ncRNA identification and functional annotation. In this review, we aim to describe the biogenesis, biological functions, and interactions with DNA, RNA, protein, and microorganism of five major regulatory ncRNAs (miRNA, siRNA, tsRNA, circRNA, lncRNA) in plants. Then, we systematically summarize tools for analysis and prediction of plant ncRNAs, as well as databases. Furthermore, we discuss the silico analysis process of these ncRNAs and present a protocol for step-by-step computational analysis of ncRNAs. In general, this review will help researchers better understand the world of ncRNAs at multiple levels.

ABSTRACT Many Gram-negative bacteria infect hosts and cause diseases by translocating a variety of type III secreted effectors (T3SEs) into the host cell cytoplasm. However, despite a dramatic increase in the number of available whole-genome sequences, it remains challenging for accurate prediction of T3SEs. Traditional prediction models have focused on atypical sequence features buried in the N-terminal peptides of T3SEs, but unfortunately, these models have had high false-positive rates. In this research, we integrated promoter information along with characteristic protein features for signal regions, chaperone-binding domains, and effector domains for T3SE prediction. Machine learning algorithms, including deep learning, were adopted to predict the atypical features mainly buried in signal sequences of T3SEs, followed by development of a voting-based ensemble model integrating the individual prediction results. We assembled this into a unified T3SE prediction pipeline, T3SEpp, which integrated the results of individual modules, resulting in high accuracy (i.e., ∼0.94) and >1-fold reduction in the false-positive rate compared to that of state-of-the-art software tools. The T3SEpp pipeline and sequence features observed here will facilitate the accurate identification of new T3SEs, with numerous benefits for future studies on host-pathogen interactions. IMPORTANCE Type III secreted effector (T3SE) prediction remains a big computational challenge. In practical applications, current software tools often suffer problems of high false-positive rates. One of the causal factors could be the relatively unitary type of biological features used for the design and training of the models. In this research, we made a comprehensive survey on the sequence-based features of T3SEs, including signal sequences, chaperone-binding domains, effector domains, and transcription factor binding promoter sites, and assembled a unified prediction pipeline integrating multi-aspect biological features within homology-based and multiple machine learning models. To our knowledge, we have compiled the most comprehensive biological sequence feature analysis for T3SEs in this research. The T3SEpp pipeline integrating the variety of features and assembling different models showed high accuracy, which should facilitate more accurate identification of T3SEs in new and existing bacterial whole-genome sequences.

BackgroundStomach adenocarcinoma (STAD) is one of the most common malignancies worldwide with poor prognosis. It remains unclear whether the prognosis is associated with somatic gene mutations.MethodsIn this research, we collected two independent STAD cohorts with both genetic profiling and clinical follow-up data, systematically investigated the association between the prognosis and somatic mutations, and analyzed the influence of heterogeneity on the prognosis-genetics association.ResultsTypical association was identified between somatic mutations and overall prognosis for individual cohorts. In The Cancer Genome Atlas (TCGA) cohort, a list of 24 genes was also identified that tended to mutate within cases of the poorest prognosis. The association showed apparent heterogeneity between different cohorts, although common signatures could be identified. A machine-learning model was trained with 20 common genes that showed a similar mutation rate difference between prognostic groups in the two cohorts, and it classified the cases in each cohort into two groups with significantly different prognosis. The model outperformed both single-gene models and TNM-based staging system significantly.ConclusionThe study made a systematic analysis on the association between STAD prognosis and somatic mutations, identified signature genes that showed mutation preference in different prognostic groups, and developed an effective multi-gene model that can effectively predict the overall prognosis of STAD in different cohorts.