Duplication and sub-functionalization of flavonoid biosynthesis genes plays important role in Leguminosae root nodule symbiosis evolution.

Gene innovation plays an essential role in trait evolution. Rhizobial symbioses, the most important N2-fixing agent in agricultural systems that exists mainly in Leguminosae, is one of the most attractive evolution events. However, the gene innovations underlying Leguminosae root nodule symbiosis (RNS) remain largely unknown. Here, we investigated the gene gain event in Leguminosae RNS evolution through comprehensive phylogenomic analyses. We revealed that Leguminosae-gain genes were acquired by gene duplication and underwent a strong purifying selection. Kyoto Encyclopedia of Genes and Genomes analyses showed that the innovated genes were enriched in flavonoid biosynthesis pathways, particular downstream of chalcone synthase (CHS). Among them, Leguminosae-gain type Ⅱ chalcone isomerase (CHI) could be further divided into CHI1A and CHI1B clades, which resulted from the products of tandem duplication. Furthermore, the duplicated CHI genes exhibited exon-intron structural divergences evolved through exon/intron gain/loss and insertion/deletion. Knocking down CHI1B significantly reduced nodulation in Glycine max (soybean) and Medicago truncatula; whereas, knocking down its duplication gene CHI1A had no effect on nodulation. Therefore, Leguminosae-gain type Ⅱ CHI participated in RNS and the duplicated CHI1A and CHI1B genes exhibited RNS functional divergence. This study provides functional insights into Leguminosae-gain genetic innovation and sub-functionalization after gene duplication that contribute to the evolution and adaptation of RNS in Leguminosae.

Dietary inflammation influences the prevalence of cardiovascular diseases in prediabetes and diabetes patients: findings from the National Health and Nutrition Examination Survey (NHANES 2001-2018).

Prediabetes is an early phase before diabetes. Diabetes and dietary inflammation are two crucial factors that are strongly associated with cardiovascular diseases (CVDs). Dietary interventions slowed the progression of diabetes and CVD. However, the associations between CVDs and dietary inflammation in different stages of pathoglycaemia have not been investigated. To explore the effect of a proinflammatory diet on CVD incidence at different stages of diabetes, NHANES (2001-2018) data were collected and analysed. A total of 3137 CVD patients with a comparable non-CVD group (n = 3137) were enrolled after propensity score matching (PSM) analysis. These patients were subsequently categorized into three subgroups: those with diabetes (n = 3043), those with prediabetes (n = 1099) and those with normoglycemia (n = 2132). The DII (Dietary inflammatory index) is a risk factor for CVD, both in overall individuals and in each subgroup of population-based information. In diabetic individuals, the odds ratios (ORs) (95% CIs) of CVD incidence for the DII were 1.10 (1.05, 1.15) and 1.08 (1.03, 1.13) according to the crude and adjusted models, respectively. For individuals with prediabetes, the ORs (95% CIs) of CVD risk for DII were 1.05 (0.97, 1.14) and 1.11 (1.01, 1.22) according to the crude and adjusted models, respectively. After adjusting for population-based information and hypertension status, the DII appeared to have the highest OR for individuals with prediabetes, and no significant association was found between the DII score and CVD risk in the normoglycemia group. Moreover, the OR of CVD for DII in the uncontrolled diabetes group was 1.06 (0.98, 1.16)*. These results suggest that the DII is more closely associated with the risk of CVDs in prediabetic and diabetic populations, and we should pay more attention to diet control before a person develops diabetes to prevent CVD progression.

Analyzing the heterogenous effects of factors on high-range speeding likelihood of taxi speeders: Does explainable deep learning provides more insights than random parameter approach?

The random parameters Generalized Linear Model (GLM) is frequently used to model speeding characteristics and capture the heterogenous effects of factors. However, this statistical approach is seldom employed for prediction and generalization due to the challenge of transferring its predefined errors. Recently, the emergence of explainable AI techniques has illuminated a new path for analyzing factors associated with risky driving behaviors. Despite this, there remains a gap that comparing results from machine and deep learning (ML/DL) approaches with those from random parameters GLM. This study aims to apply the random parameter GLM and explainable deep learning to evaluate the heterogenous effects of factors on the taxis' high-range speeding likelihood. Initially, a Beta GLM with random parameters (BGLM-RP) is developed to model the high-range speeding likelihood among taxi drivers. Additionally, XGBoost, a simple convolutional neural network (Simple-CNN), a deeper CNN (DCNN), and a deeper CNN with self-attention (DCNN-SA) are developed. The quantified explanations and illustrations of the factors' heterogenous effects from ML/DL models are derived from pseudo coefficients by decomposing factors' SHapley Additive exPlanations (SHAP) values. All the developed statistical, ML, and DL models are compared in terms of mean absolute errors and mean square errors on testing and full data. Results show that DCNN-SA excels in prediction on testing data, indicating its superior generalization capabilities, while BGLM-RP outperforms other models on full data. The DCNN-SA can reveal the heterogenous effects of factors for both in-sample and out-of-sample data, which is not possible for the random parameter GLM. However, BGLM-RP can reveal larger magnitudes of the factors' heterogenous effects for in-sample data. The signs and significances are identical between the varying coefficients from BGLM-RP and the pseudo coefficients from the ML/DL models, demonstrating the validity and rationale of using the proposed explanation framework to quantify the factors' effects in ML/DL models. The study also discusses the contributions of various factors to the high-range speeding likelihood of taxi drivers.

The gut microbiota-brain axis in neurological disorders.

Previous studies have shown a bidirectional communication between human gut microbiota and the brain, known as the microbiota-gut-brain axis (MGBA). The MGBA influences the host's nervous system development, emotional regulation, and cognitive function through neurotransmitters, immune modulation, and metabolic pathways. Factors like diet, lifestyle, genetics, and environment shape the gut microbiota composition together. Most research have explored how gut microbiota regulates host physiology and its potential in preventing and treating neurological disorders. However, the individual heterogeneity of gut microbiota, strains playing a dominant role in neurological diseases, and the interactions of these microbial metabolites with the central/peripheral nervous systems still need exploration. This review summarizes the potential role of gut microbiota in driving neurodevelopmental disorders (autism spectrum disorder and attention deficit/hyperactivity disorder), neurodegenerative diseases (Alzheimer's and Parkinson's disease), and mood disorders (anxiety and depression) in recent years and discusses the current clinical and preclinical gut microbe-based interventions, including dietary intervention, probiotics, prebiotics, and fecal microbiota transplantation. It also puts forward the current insufficient research on gut microbiota in neurological disorders and provides a framework for further research on neurological disorders.

The nasal microbiota is a potential diagnostic biomarker for sepsis in critical care units.

This study aimed to characterize the composition of intestinal and nasal microbiota in septic patients and identify potential microbial biomarkers for diagnosis. A total of 157 subjects, including 89 with sepsis, were enrolled from the affiliated hospital. Nasal swabs and fecal specimens were collected from septic and non-septic patients in the intensive care unit (ICU) and Department of Respiratory and Critical Care Medicine. DNA was extracted, and the V4 region of the 16S rRNA gene was amplified and sequenced using Illumina technology. Bioinformatics analysis, statistical processing, and machine learning techniques were employed to differentiate between septic and non-septic patients. The nasal microbiota of septic patients exhibited significantly lower community richness (P = 0.002) and distinct compositions (P = 0.001) compared to non-septic patients. Corynebacterium, Staphylococcus, Acinetobacter, and Pseudomonas were identified as enriched genera in the nasal microbiota of septic patients. The constructed machine learning model achieved an area under the curve (AUC) of 89.08, indicating its efficacy in differentiating septic and non-septic patients. Importantly, model validation demonstrated the effectiveness of the nasal microecological diagnosis prediction model with an AUC of 84.79, while the gut microecological diagnosis prediction model had poor predictive performance (AUC = 49.24). The nasal microbiota of ICU patients effectively distinguishes sepsis from non-septic cases and outperforms the gut microbiota. These findings have implications for the development of diagnostic strategies and advancements in critical care medicine.IMPORTANCEThe important clinical significance of this study is that it compared the intestinal and nasal microbiota of sepsis with non-sepsis patients and determined that the nasal microbiota is more effective than the intestinal microbiota in distinguishing patients with sepsis from those without sepsis, based on the difference in the lines of nasal specimens collected.

Comparison of the upper and lower airway microbiome in early postoperative lung transplant recipients.

The upper and lower respiratory tract may share microbiome because they are directly continuous, and the nasal microbiome contributes partially to the composition of the lung microbiome. But little is known about the upper and lower airway microbiome of early postoperative lung transplant recipients (LTRs). Using 16S rRNA gene sequencing, we compared paired nasal swab (NS) and bronchoalveolar lavage fluid (BALF) microbiome from 17 early postoperative LTRs. The microbiome between the two compartments were significantly different in Shannon diversity and beta diversity. Four and eight core NS-associated and BALF-associated microbiome were identified, respectively. NS samples harbored more Corynebacterium, Acinetobacter, and Pseudomonas, while BALF contained more Ralstonia, Stenotrophomonas, Enterococcus, and Pedobacter. The within-subject dissimilarity was higher than the between-subject dissimilarity, indicating a greater impact of sampling sites than sampling individuals on microbial difference. There were both difference and homogeneity between NS and BALF microbiome in early postoperative LTRs. High levels of pathogens were detected in both samples, suggesting that both of them can reflect the diseases characteristics of transplanted lung. The differences between upper and lower airway microbiome mainly come from sampling sites instead of sampling individuals. IMPORTANCE: Lung transplantation is the only therapeutic option for patients with end-stage lung disease, but its outcome is much worse than other solid organ transplants. Little is known about the NS and BALF microbiome of early postoperative LTRs. Here, we compared paired samples of the nasal and lung microbiome from 17 early postoperative LTRs and showed both difference and homogeneity between the two samples. Most of the "core" microbiome in both NS and BALF samples were recognized respiratory pathogens, suggesting that both samples can reflect the diseases characteristics of transplanted lung. We also found that the differences between upper and lower airway microbiome in early postoperative LTRs mainly come from sampling sites instead of sampling individuals.

Research development on gut microbiota and vulnerable atherosclerotic plaque.

The relationship between gut microbiota and its metabolites with cardiovascular disease (CVD) has been proven. In this review, we aim to conclude the potential mechanism of gut microbiota and its metabolites on inducing the formation of vulnerable atherosclerotic plaque, and to discuss the effect of intestinal metabolites, including trimethylamine-N-oxide (TMAO), lipopolysaccharide (LPS), phenylacetylglutamine (PAG), short-chain fatty acids (SCFAs) on plaque stability. Finally, we include the impact of gut microbiota and its metabolites on plaque stability, to propose a new therapeutic direction for coronary heart disease. Gut microbiota regulation intervenes the progress of arteriosclerosis, especially on coronary atherosclerosis, by avoiding or reducing the formation of vulnerable plaque, to lower the morbidity rate of myocardial infarction.

Who might encounter hard-braking while speeding? Analysis for regular speeders using low-frequency taxi trajectories on arterial roads and explainable AI.

Regular speeders are those who commit speeding recidivism during a period. Among their speeding behaviors, some occurring in specific scenarios may cause more hazards to road users. Therefore, there is a need to evaluate the driving risks if the regular speeders have different speeding propensities. This study considers speeding-related hard-braking events (SHEs) as a safety surrogate measure and recognizes the regular speeders who encounter at least one SHEs during the study period as risky individuals. To identify speeding behaviors and hard-braking events from low-frequency GPS trajectories, we compare the average travel speed between pairwise adjacent GPS points to the posted speed limit and examine the speed curve and the corresponding travel distance between these GPS points, respectively. Thereafter, a logistic model, XGBoost, and three 1D Convolutional Neural Networks (CNNs) including AlexNet CNN, Mini-AlexNet CNN, and Simple CNN are respectively developed to recognize the regular speeders who encountered SHEs based on their speeding propensities. The proposed Mini-AlexNet CNN achieves a global F1-score of 91% and recall of 90% on the testing data, which are superior to other models. Further, the study uses the Shapley Additive exPlanation (SHAP) framework to visually interpret the contribution of speeding propensities on SHE likelihood. It is found that speeding by 50% or greater for no more than 285 m is the most dangerous kind among all the speeding behaviors. Speeding on roads without bicycle lanes or on roads with roadside parking and excessive accesses increases the probability of encountering SHEs. Based on the analyses, we put forward tailored recommendations that aim to restrict hazard-related speeding behaviors rather than speeding behaviors of all kinds.

Urinary peptidome analysis in CKD and IgA nephropathy.

Background: Chronic kidney disease (CKD) has emerged as a significant challenge to human health and economic stability in aging societies worldwide. Current clinical practice strategies remain insufficient for the early identification of kidney dysfunction, and the differential diagnosis of immunoglobulin A nephropathy (IgAN) predominantly relies on invasive kidney biopsy procedures. Methods: First, we assessed a case-control cohort to obtain urine samples from healthy controls and biopsy-confirmed CKD patients. Matrix-assisted laser desorption ionization time-of-flight (MALDI-TOF) mass spectrometry (MS) was applied to detect urinary peptide and then these urinary peptide profiles were used to construct diagnostic models to distinguish CKD patients from controls and identify IgAN patients from other nephropathy patients. Furthermore, we assessed the robustness of the diagnostic models and their reproducibility by applying different algorithms. Results: A rapid and accurate working platform for detecting CKD and its IgAN subtype based on urinary peptide pattern detected by MALDI-TOF MS was established. Naturally occurring urinary peptide profiles were used to construct a diagnostic model to distinguish CKD patients from controls and identify IgAN patients from other nephropathy patients. The performance of several algorithms was assessed and demonstrated that the robustness of the diagnostic models as well as their reproducibility were satisfactory. Conclusions: The present findings suggest that the CKD-related and IgAN-specific urinary peptides discovered facilitate precise identification of CKD and its IgAN subtype, offering a dependable framework for screening conditions linked to renal dysfunction. This will aid in comprehending the pathogenesis of nephropathy and identifying potential protein targets for the clinical management of nephropathy.

Cellular and molecular basis of symbiotic nodule development.

Root nodule development plays a vital role in establishing the mutualistic relationship between legumes and nitrogen-fixing rhizobia. Two primary processes are involved in nodule development: formative cell divisions in the root cortex and the subsequent differentiation of nodule cells. The first process involves the mitotic reactivation of differentiated root cortex cells to form nodule primordium after perceiving symbiotic signals. The second process enables the nascent nodule primordium cells to develop into various cell types, leading to the creation of a functional nodule capable of supporting nitrogen fixation. Thus, both division and differentiation of nodule cells are crucial for root nodule development. This review provides an overview of the most recent advancements in comprehending the cellular and molecular mechanisms underlying symbiotic nodule development in legumes.

Evaluation of a two-test strategy for HIV screening in a low-prevalence setting and the indications for optimizing clinical management.

Objectives: To evaluate a two-test strategy for HIV screening in the low-prevalence population and to assess the feasibility of utilizing the optimal signal-to-cutoff (S/CO) threshold on the chemiluminescence immunoassay(CMIA) and an additional rapid test on the gold immune-chromatography assay (GICA) for screening positive patients and optimization of clinical management. Methods: We conducted a retrospective study of samples analyzed by the fourth-generation Architect HIV Ag/Ab combo assay (CMIA) in a large medical center between June 2017 and August 2020. Reactive samples underwent a second screening test using the rapid test GICA, followed by Western blot (WB) as the confirmatory test. Receiver operating characteristic (ROC) curve analysis was used to determine the optimal S/CO. We calculated sensitivity, specificity, and predictive value based on our population. The performance of the single-test strategy (CMIA) was compared with that of the two-test strategy (CMIA and GICA). Logistic regression was used to analyze the factors of clinical characteristics leading to false positive results. Results: A total of 220558 samples were screened by CMIA, and 429 patients met the inclusion criteria. Of these, CMIA produced 199 false-positive results with a median S/CO of 1.93(IQR1.45-3.68) and 230 positive results with a median S/CO of 455.1 (IQR169.3-709.7). The optimal S/CO of the single-test strategy was 8.82, which achieved a sensitivity of 100% and a positive predictive value (PPV) of 90.9%. The two-test strategy (CMIA and GICA) provided a sensitivity of 100% and a PPV of 98.7%, which best correlated with the confirmatory test WB. The combination of S/CO 8.82 on the CMIA assay and additional test results of GICA can be defined as four types used to interpret HIV serostatus. The false positive rate (FPR) was high in the female, the age≤18 group, the pre-operative patients, and the patients from the clinical departments of Pediatrics, Gynecology and Obstetrics, and Oncology, etc. Conclusions: The false positive rate is high in the low-prevalence setting by using CMIA. The two-test strategy (CMIA and GICA) is recommended for HIV screening in hospitals. Hopefully, the clinicians will be able to interpret HIV serostatus and facilitate clinical decision-making while waiting for the confirmatory results.

Detection of Pilot's Mental Workload Using a Wireless EEG Headset in Airfield Traffic Pattern Tasks.

Elevated mental workload (MWL) experienced by pilots can result in increased reaction times or incorrect actions, potentially compromising flight safety. This study aims to develop a functional system to assist administrators in identifying and detecting pilots' real-time MWL and evaluate its effectiveness using designed airfield traffic pattern tasks within a realistic flight simulator. The perceived MWL in various situations was assessed and labeled using NASA Task Load Index (NASA-TLX) scores. Physiological features were then extracted using a fast Fourier transformation with 2-s sliding time windows. Feature selection was conducted by comparing the results of the Kruskal-Wallis (K-W) test and Sequential Forward Floating Selection (SFFS). The results proved that the optimal input was all PSD features. Moreover, the study analyzed the effects of electroencephalography (EEG) features from distinct brain regions and PSD changes across different MWL levels to further assess the proposed system's performance. A 10-fold cross-validation was performed on six classifiers, and the optimal accuracy of 87.57% was attained using a multi-class K-Nearest Neighbor (KNN) classifier for classifying different MWL levels. The findings indicate that the wireless headset-based system is reliable and feasible. Consequently, numerous wireless EEG device-based systems can be developed for application in diverse real-driving scenarios. Additionally, the current system contributes to future research on actual flight conditions.

The airway microbiome mediates the interaction between environmental exposure and respiratory health in humans.

Exposure to environmental pollution influences respiratory health. The role of the airway microbial ecosystem underlying the interaction of exposure and respiratory health remains unclear. Here, through a province-wide chronic obstructive pulmonary disease surveillance program, we conducted a population-based survey of bacterial (n = 1,651) and fungal (n = 719) taxa and metagenomes (n = 1,128) from induced sputum of 1,651 household members in Guangdong, China. We found that cigarette smoking and higher PM2.5 concentration were associated with lung function impairment through the mediation of bacterial and fungal communities, respectively, and that exposure was associated with an enhanced inter-kingdom microbial interaction resembling the pattern seen in chronic obstructive pulmonary disease. Enrichment of Neisseria was associated with a 2.25-fold increased risk of high respiratory symptom burden, coupled with an elevation in Aspergillus, in association with occupational pollution. We developed an individualized microbiome-based health index, which covaried with exposure, respiratory symptoms and diseases, with potential generalizability to global datasets. Our results may inform environmental risk prevention and guide interventions that harness airway microbiome.

Bidirectional effects of oral anticoagulants on gut microbiota in patients with atrial fibrillation.

Background: The imbalance of gut microbiota (GM) is associated with a higher risk of thrombosis in patients with atrial fibrillation (AF). Oral anticoagulants (OACs) have been found to significantly reduce the risk of thromboembolism and increase the risk of bleeding. However, the OAC-induced alterations in gut microbiota in patients with AF remain elusive. Methods: In this study, the microbial composition in 42 AF patients who received long-term OAC treatment (AF-OAC group), 47 AF patients who did not (AF group), and 40 volunteers with the risk of AF (control group) were analyzed by 16S rRNA gene sequencing of fecal bacterial DNA. The metagenomic functional prediction of major bacterial taxa was performed using the Phylogenetic Investigation of Communities by Reconstruction of Unobserved States (PICRUSt) software package. Results: The gut microbiota differed between the AF-OAC and AF groups. The abundance of Bifidobacterium and Lactobacillus decreased in the two disease groups at the genus level, but OACs treatment mitigated the decreasing tendency and increased beneficial bacterial genera, such as Megamonas. In addition, OACs reduced the abundance of pro-inflammatory taxa on the genus Ruminococcus but increased certain potential pathogenic taxa, such as genera Streptococcus, Escherichia-Shigella, and Klebsiella. The Subgroup Linear discriminant analysis effect size (LEfSe) analyses revealed that Bacteroidetes, Brucella, and Ochrobactrum were more abundant in the anticoagulated bleeding AF patients, Akkermansia and Faecalibacterium were more abundant in the non-anticoagulated-bleeding-AF patients. The neutrophil-to-lymphocyte ratio (NLR) was lower in the AF-OAC group compared with the AF group (P < 0.05). Ruminococcus was positively correlated with the NLR and negatively correlated with the CHA2DS2-VASc score (P < 0.05), and the OACs-enriched species (Megamonas and Actinobacteria) was positively correlated with the prothrombin time (PT) (P < 0.05). Ruminococcus and Roseburia were negatively associated with bleeding events (P < 0.05). Conclusions: Our study suggested that OACs might benefit AF patients by reducing the inflammatory response and modulating the composition and abundance of gut microbiota. In particular, OACs increased the abundance of some gut microbiota involved in bleeding and gastrointestinal dysfunction indicating that the exogenous supplementation with Faecalibacterium and Akkermansia might be a prophylactic strategy for AF-OAC patients to lower the risk of bleeding after anticoagulation.

SLAM-Drop-seq reveals mRNA kinetic rates throughout the cell cycle.

RNA abundance is tightly regulated in eukaryotic cells by modulating the kinetic rates of RNA production, processing, and degradation. To date, little is known about time­dependent kinetic rates during dynamic processes. Here, we present SLAM­Drop­seq, a method that combines RNA metabolic labeling and alkylation of modified nucleotides in methanol­fixed cells with droplet­based sequencing to detect newly synthesized and preexisting mRNAs in single cells. As a first application, we sequenced 7280 HEK293 cells and calculated gene­specific kinetic rates during the cell cycle using the novel package Eskrate. Of the 377 robust­cycling genes that we identified, only a minor fraction is regulated solely by either dynamic transcription or degradation (6 and 4%, respectively). By contrast, the vast majority (89%) exhibit dynamically regulated transcription and degradation rates during the cell cycle. Our study thus shows that temporally regulated mRNA degradation is fundamental for the correct expression of a majority of cycling genes. SLAM­Drop­seq, combined with Eskrate, is a powerful approach to understanding the underlying mRNA kinetics of single­cell gene expression dynamics in continuous biological processes.

Multi-omics analyses of airway host-microbe interactions in chronic obstructive pulmonary disease identify potential therapeutic interventions.

The mechanistic role of the airway microbiome in chronic obstructive pulmonary disease (COPD) remains largely unexplored. We present a landscape of airway microbe-host interactions in COPD through an in-depth profiling of the sputum metagenome, metabolome, host transcriptome and proteome from 99 patients with COPD and 36 healthy individuals in China. Multi-omics data were integrated using sequential mediation analysis, to assess in silico associations of the microbiome with two primary COPD inflammatory endotypes, neutrophilic or eosinophilic inflammation, mediated through microbial metabolic interaction with host gene expression. Hypotheses of microbiome-metabolite-host interaction were identified by leveraging microbial genetic information and established metabolite-human gene pairs. A prominent hypothesis for neutrophil-predominant COPD was altered tryptophan metabolism in airway lactobacilli associated with reduced indole-3-acetic acid (IAA), which was in turn linked to perturbed host interleukin-22 signalling and epithelial cell apoptosis pathways. In vivo and in vitro studies showed that airway microbiome-derived IAA mitigates neutrophilic inflammation, apoptosis, emphysema and lung function decline, via macrophage-epithelial cell cross-talk mediated by interleukin-22. Intranasal inoculation of two airway lactobacilli restored IAA and recapitulated its protective effects in mice. These findings provide the rationale for therapeutically targeting microbe-host interaction in COPD.

Exploring the role of uterine fibroids in promotion of cardiovascular diseases by diabetes exposure: Findings from national health and nutrition examination survey 1999-2006.

Objective: The relationship between uterine fibroids (UF) and cardiovascular diseases (CVDs) in the diabetes population seemed to remain undetermined in previous studies. This study aims to explore the association between UF and CVDs by using the database from the National Health and Nutrition Examination Survey (NHANES). To further evaluate the connection between UF and CVDs we also tested the potential differences due to diabetes exposure. Materials and methods: National Health and Nutrition Examination Survey data (1999-2006) were collected and used in this study. A total of 5,509 individuals were included and analyzed. The student's t-test and the chi-squared test were used to explore the demographic characteristic between UF and non-UF groups. Logistic regression analysis was performed to determine the odds ratios of UF and covariates. Results: Female participants were divided into UF (n = 694, 12.60%) and non-UF (n = 4,815, 87.40%) groups. The incidence of CVDs in UF patients (n = 245, 35.30%) were higher than non-UF individuals (n = 776, 16.12%) (p < 0.001). In addition, each subtype of CVDs were also different, which contains hypertension (33.29 vs. 15.31%, p < 0.001), heart failure (1.59 vs. 0.52%, p < 0.01), angina (2.59 vs. 0.62%, p < 0.001), heart attack (1.73 vs. 0.58%, p < 0.01) and coronary heart disease (1.44 vs. 0.54%, p < 0.01). The odds ratios of CVDs according to logistic regression were 2.840 (95% CI: 2.387-3.379) for UF patients (p < 0.001), while the odds ratios (ORs) were 1.438 (95% CI: 1.175-1.760) after taking account for the age, body mass index (BMI), diabetes, race, education, and annual family income (p < 0.001). In addition, secondary analysis indicated more adverse effects in by UF exposure on CVDs risk among non-diabetes individuals (OR = 1.389, 95% CI = 1.124-1.718, p < 0.01) than diabetes patients (p = 0.063). Conclusion: Overall, UFs were positively associated with CVDs, and this effect seems blunted by diabetes exposure.

Constitutive activation of a nuclear-localized calcium channel complex in Medicago truncatula.

Nuclear Ca2+ oscillations allow symbiosis signaling, facilitating plant recognition of beneficial microsymbionts, nitrogen-fixing rhizobia, and nutrient-capturing arbuscular mycorrhizal fungi. Two classes of channels, DMI1 and CNGC15, in a complex on the nuclear membrane, coordinate symbiotic Ca2+ oscillations. However, the mechanism of Ca2+ signature generation is unknown. Here, we demonstrate spontaneous activation of this channel complex, through gain-of-function mutations in DMI1, leading to spontaneous nuclear Ca2+ oscillations and spontaneous nodulation, in a CNGC15-dependent manner. The mutations destabilize a hydrogen-bond or salt-bridge network between two RCK domains, with the resultant structural changes, alongside DMI1 cation permeability, activating the channel complex. This channel complex was reconstituted in human HEK293T cell lines, with the resultant calcium influx enhanced by autoactivated DMI1 and CNGC15s. Our results demonstrate the mode of activation of this nuclear channel complex, show that DMI1 and CNGC15 are sufficient to create oscillatory Ca2+ signals, and provide insights into its native mode of induction.

Bayesian spatial correlation, heterogeneity and spillover effect modeling for speed mean and variance on urban road networks.

Analyzing speed mean and variance is vital to safety management in urban roadway networks. However, modeling speed mean and variance on structured roads could be influenced by the spatial effects, which are rarely addressed in the existing studies. The inadequacy may lead to biased conclusions when considering vehicle speed as a surrogate safety measure. The current study focuses on developing a Bayesian modeling approach with three types of spatial effects, i.e., spatial correlation, spatial heterogeneity, and spillover effect. To capture the spatial correlation, the study employs the intrinsic conditional autoregressive (ICAR) models, spatial lag models (SLM), and spatial error models (SEM). Spatial heterogeneity and spillover effect are considered by the random parameters approach and spatially lagged covariates (SLCs). Speed data are collected from the float cars running on 134 urban arterials in Chengdu, China. The results indicate that the random parameters ICAR model with SLCs (RPICAR-SLC) outperforms others in terms of goodness-of-fit, accuracy, and efficiency for modeling speed mean, while the random parameters ICAR model (RPICAR) is the best for modeling speed variance. Moreover, RPICAR-SLC and RPICAR models are beneficial to address spatial correlation of residuals, explaining the unobserved influence among the observations, and are less likely to cause biased or overestimated parameters. The study also discusses how traffic conditions, road characteristics, traffic management strategies, and facilities on roadway networks influence speed mean and variance. The findings highlight the importance of multi-type spatial effects on modeling speed mean and variance along the structured roadways.

TB-Net: A Tailored, Self-Attention Deep Convolutional Neural Network Design for Detection of Tuberculosis Cases From Chest X-Ray Images.

Tuberculosis (TB) remains a global health problem, and is the leading cause of death from an infectious disease. A crucial step in the treatment of tuberculosis is screening high risk populations and the early detection of the disease, with chest x-ray (CXR) imaging being the most widely-used imaging modality. As such, there has been significant recent interest in artificial intelligence-based TB screening solutions for use in resource-limited scenarios where there is a lack of trained healthcare workers with expertise in CXR interpretation. Motivated by this pressing need and the recent recommendation by the World Health Organization (WHO) for the use of computer-aided diagnosis of TB in place of a human reader, we introduce TB-Net, a self-attention deep convolutional neural network tailored for TB case screening. We used CXR data from a multi-national patient cohort to train and test our models. A machine-driven design exploration approach leveraging generative synthesis was used to build a highly customized deep neural network architecture with attention condensers. We conducted an explainability-driven performance validation process to validate TB-Net's decision-making behavior. Experiments on CXR data from a multi-national patient cohort showed that the proposed TB-Net is able to achieve accuracy/sensitivity/specificity of 99.86/100.0/99.71%. Radiologist validation was conducted on select cases by two board-certified radiologists with over 10 and 19 years of experience, respectively, and showed consistency between radiologist interpretation and critical factors leveraged by TB-Net for TB case detection for the case where radiologists identified anomalies. The proposed TB-Net not only achieves high tuberculosis case detection performance in terms of sensitivity and specificity, but also leverages clinically relevant critical factors in its decision making process. While not a production-ready solution, we hope that the open-source release of TB-Net as part of the COVID-Net initiative will support researchers, clinicians, and citizen data scientists in advancing this field in the fight against this global public health crisis.