Prevalence and factors associated with burnout among primary health-care workers in China during COVID-19: a national survey.

BACKGROUND: Primary health-care workers (PHWs) managed increased workloads and pressure during the COVID-19 pandemic. This study conducted a national survey examining burnout among PHWs at the end of the COVID-19 pandemic, and identifies related factors. By doing so, it addresses the gap in understanding the burnout situation among PHWs at a national level, taking into account urban-rural disparities. METHODS: We conducted a nationwide cross-sectional survey of PHWs in China from May to October 2022, covering 31 provinces. The MBI-HSS was used to measure overall burnout and emotional exhaustion (EE), depersonalization (DP), and reduced personal accomplishment (PA). We used multivariable logistic regression to identify risk factors, and subgroup analyses to identify differences between rural and urban areas. RESULTS: 3769 PHWs from 44 primary health-care institutions completed the survey. Overall, 16.6% reported overall burnout, and the prevalence of EE, DP, and reduced PA was 29.7%, 28.0%, and 62.9%, respectively. The prevalence of overall burnout (17.6% vs. 13.7%, P = 0.004) and EE (31.5% vs. 24.8%, P < 0.001) was higher in urban than rural areas (AOR = 1.285; 95%CI, 1.021-1.617). Job satisfaction was a protective factor against burnout in both settings. The protective factors of overall burnout, EE and DP vary between urban and rural areas. CONCLUSIONS: The Mental Health Status Questionnaire-Short Form (MSQ-SF) score functioned as a protective factor against burnout across both rural and urban locales, highlighting the intrinsic link between job satisfaction and burnout. Other influencing factors differed between urban and rural areas, so interventions should be tailored to local conditions. Rural married PHWs experienced the lower prevalence of burnout indicates the support structure may play a significant role. In urban settings, it is recommended to strategically pre-emptively stock essential supplies like PPE.

Hairpin self-assembly powered by exonuclease III for highly sensitive and cross-validated miRNA-155 detection.

Cancer is one of the most important causes of human death and poses a serious threat to human health. As a cancer biomarker, microRNA-155 (miRNA-155) is highly expressed in various types of cancer tissues and is involved in the proliferation of tumor cells. Therefore, developing a miRNA-155 detection technology with high specificity and sensitivity is of great significance for the early detection, accurate treatment and prognostic evaluation of tumors. Here, we developed a fluorescence detection method using exonuclease III-assisted target cycling and catalytic hairpin assembly (CHA) as a signal amplification technique. This study developed a biosensor for the detection of miRNA-155, utilizing a DNA hairpin (Hp) for target recognition and generating double-stranded DNA (dual-Hp-T). The 3' flat end of the double-stranded DNA can be cleaved by exonuclease III to achieve the target cycle, and a large amount of single-stranded DNA (fuel) can trigger CHA to achieve signal amplification. Simultaneously, the fluorescence resonance energy transfer (FRET) of signal probes with different fluorescence labels on H1 and H2 ends occurs with the CHA reaction. The two fluorescence signals obtained can be used to cross-validate the experimental results. The biosensor exhibits excellent performance of high recovery, high sensitivity and high operability, which can achieve the specific detection of miRNA-155 with a detection limit as low as 8.3 pM. Additionally, the detection efficacy in a human serum environment is also highly satisfactory. This technology provides strong technical support for the development of nucleic acid probes and the diagnosis and treatment of cancer, demonstrating significant practical application value.

Integration of biological and information technologies to enhance plant autoluminescence.

Autoluminescent plants have been genetically modified to express the fungal bioluminescence pathway (FBP). However, a bottleneck in precursor production has limited the brightness of these luminescent plants. Here, we demonstrate the effectiveness of utilizing a computational model to guide a multiplex five-gene-silencing strategy by an artificial microRNA array to enhance caffeic acid and hispidin levels in plants. By combining loss-of-function-directed metabolic flux with a tyrosine-derived caffeic acid pathway, we achieved substantially enhanced bioluminescence levels. We successfully generated eFBP2 plants that emit considerably brighter bioluminescence for naked-eye reading by integrating all validated DNA modules. Our analysis revealed that the luminous energy conversion efficiency of the eFBP2 plants is currently very low, suggesting that luminescence intensity can be improved in future iterations. These findings highlight the potential to enhance plant luminescence through the integration of biological and information technologies.

Hierarchical Heuristic Species Delimitation under the Multispecies Coalescent Model with Migration.

The multispecies coalescent (MSC) model accommodates genealogical fluctuations across the genome and provides a natural framework for comparative analysis of genomic sequence data from closely related species to infer the history of species divergence and gene flow. Given a set of populations, hypotheses of species delimitation (and species phylogeny) may be formulated as instances of MSC models (e.g., MSC for one species versus MSC for two species) and compared using Bayesian model selection. This approach, implemented in the program bpp, has been found to be prone to over-splitting. Alternatively heuristic criteria based on population parameters (such as popula- tion split times, population sizes, and migration rates) estimated from genomic data may be used to delimit species. Here we develop hierarchical merge and split algorithms for heuristic species delimitation based on the genealogical divergence index (𝑔𝑑𝑖) and implement them in a python pipeline called hhsd. We characterize the behavior of the 𝑔𝑑𝑖 under a few simple scenarios of gene flow. We apply the new approaches to a dataset simulated under a model of isolation by distance as well as three empirical datasets. Our tests suggest that the new approaches produced sensible results and were less prone to over-splitting. We discuss possible strategies for accommodating paraphyletic species in the hierarchical algorithm, as well as the challenges of species delimitation based on heuristic criteria.

Image Captioning via Dynamic Path Customization.

This article explores a novel dynamic network for vision and language (V&L) tasks, where the inferring structure is customized on the fly for different inputs. Most previous state-of-the-art (SOTA) approaches are static and handcrafted networks, which not only heavily rely on expert knowledge but also ignore the semantic diversity of input samples, therefore resulting in suboptimal performance. To address these issues, we propose a novel Dynamic Transformer Network (DTNet) for image captioning, which dynamically assigns customized paths to different samples, leading to discriminative yet accurate captions. Specifically, to build a rich routing space and improve routing efficiency, we introduce five types of basic cells and group them into two separate routing spaces according to their operating domains, i.e., spatial and channel. Then, we design a Spatial-Channel Joint Router (SCJR), which endows the model with the capability of path customization based on both spatial and channel information of the input sample. To validate the effectiveness of our proposed DTNet, we conduct extensive experiments on the MS-COCO dataset and achieve new SOTA performance on both the Karpathy split and the online test server. The source code is publicly available at https://github.com/xmu-xiaoma666/DTNet.

Digital pathology assessment of kidney glomerular filtration barrier ultrastructure in an animal model of podocytopathy.

Background: Transmission electron microscopy (TEM) images can visualize kidney glomerular filtration barrier ultrastructure, including the glomerular basement membrane (GBM) and podocyte foot processes (PFP). Podocytopathy is associated with glomerular filtration barrier morphological changes observed experimentally and clinically by measuring GBM or PFP width. However, these measurements are currently performed manually. This limits research on podocytopathy disease mechanisms and therapeutics due to labor intensiveness and inter-operator variability. Methods: We developed a deep learning-based digital pathology computational method to measure GBM and PFP width in TEM images from the kidneys of Integrin-Linked Kinase (ILK) podocyte-specific conditional knockout (cKO) mouse, an animal model of podocytopathy, compared to wild-type (WT) control mouse. We obtained TEM images from WT and ILK cKO littermate mice at 4 weeks old. Our automated method was composed of two stages: a U-Net model for GBM segmentation, followed by an image processing algorithm for GBM and PFP width measurement. We evaluated its performance with a 4-fold cross-validation study on WT and ILK cKO mouse kidney pairs. Results: Mean (95% confidence interval) GBM segmentation accuracy, calculated as Jaccard index, was 0.73 (0.70-0.76) for WT and 0.85 (0.83-0.87) for ILK cKO TEM images. Automated and manual GBM width measurements were similar for both WT (p=0.49) and ILK cKO (p=0.06) specimens. While automated and manual PFP width measurements were similar for WT (p=0.89), they differed for ILK cKO (p<0.05) specimens. WT and ILK cKO specimens were morphologically distinguishable by manual GBM (p<0.05) and PFP (p<0.05) width measurements. This phenotypic difference was reflected in the automated GBM (p<0.05) more than PFP (p=0.06) widths. Conclusions: These results suggest that certain automated measurements enabled via deep learning-based digital pathology tools could distinguish healthy kidneys from those with podocytopathy. Our proposed method provides high-throughput, objective morphological analysis and could facilitate podocytopathy research and translate into clinical diagnosis.

Proteogenomic data and resources for pan-cancer analysis.

The National Cancer Institute's Clinical Proteomic Tumor Analysis Consortium (CPTAC) investigates tumors from a proteogenomic perspective, creating rich multi-omics datasets connecting genomic aberrations to cancer phenotypes. To facilitate pan-cancer investigations, we have generated harmonized genomic, transcriptomic, proteomic, and clinical data for >1000 tumors in 10 cohorts to create a cohesive and powerful dataset for scientific discovery. We outline efforts by the CPTAC pan-cancer working group in data harmonization, data dissemination, and computational resources for aiding biological discoveries. We also discuss challenges for multi-omics data integration and analysis, specifically the unique challenges of working with both nucleotide sequencing and mass spectrometry proteomics data.

Proteogenomic analysis of chemo-refractory high-grade serous ovarian cancer.

To improve the understanding of chemo-refractory high-grade serous ovarian cancers (HGSOCs), we characterized the proteogenomic landscape of 242 (refractory and sensitive) HGSOCs, representing one discovery and two validation cohorts across two biospecimen types (formalin-fixed paraffin-embedded and frozen). We identified a 64-protein signature that predicts with high specificity a subset of HGSOCs refractory to initial platinum-based therapy and is validated in two independent patient cohorts. We detected significant association between lack of Ch17 loss of heterozygosity (LOH) and chemo-refractoriness. Based on pathway protein expression, we identified 5 clusters of HGSOC, which validated across two independent patient cohorts and patient-derived xenograft (PDX) models. These clusters may represent different mechanisms of refractoriness and implicate putative therapeutic vulnerabilities.

iProMix: A mixture model for studying the function of ACE2 based on bulk proteogenomic data.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused over six million deaths in the ongoing COVID-19 pandemic. SARS-CoV-2 uses ACE2 protein to enter human cells, raising a pressing need to characterize proteins/pathways interacted with ACE2. Large-scale proteomic profiling technology is not mature at single-cell resolution to examine the protein activities in disease-relevant cell types. We propose iProMix, a novel statistical framework to identify epithelial-cell specific associations between ACE2 and other proteins/pathways with bulk proteomic data. iProMix decomposes the data and models cell-type-specific conditional joint distribution of proteins through a mixture model. It improves cell-type composition estimation from prior input, and utilizes a non-parametric inference framework to account for uncertainty of cell-type proportion estimates in hypothesis test. Simulations demonstrate iProMix has well-controlled false discovery rates and favorable powers in non-asymptotic settings. We apply iProMix to the proteomic data of 110 (tumor adjacent) normal lung tissue samples from the Clinical Proteomic Tumor Analysis Consortium lung adenocarcinoma study, and identify interferon α/γ response pathways as the most significant pathways associated with ACE2 protein abundances in epithelial cells. Strikingly, the association direction is sex-specific. This result casts light on the sex difference of COVID-19 incidences and outcomes, and motivates sex-specific evaluation for interferon therapies.

Metabolic engineering and mechanical investigation of enhanced plant autoluminescence.

The fungal bioluminescence pathway (FBP) was identified from glowing fungi, which releases self-sustained visible green luminescence. However, weak bioluminescence limits the potential application of the bioluminescence system. Here, we screened and characterized a C3'H1 (4-coumaroyl shikimate/quinate 3'-hydroxylase) gene from Brassica napus, which efficiently converts p-coumaroyl shikimate to caffeic acid and hispidin. Simultaneous expression of BnC3'H1 and NPGA (null-pigment mutant in A. nidulans) produces more caffeic acid and hispidin as the natural precursor of luciferin and significantly intensifies the original fungal bioluminescence pathway (oFBP). Thus, we successfully created enhanced FBP (eFBP) plants emitting 3 × 1011 photons/min/cm2 , sufficient to illuminate its surroundings and visualize words clearly in the dark. The glowing plants provide sustainable and bio-renewable illumination for the naked eyes, and manifest distinct responses to diverse environmental conditions via caffeic acid biosynthesis pathway. Importantly, we revealed that the biosynthesis of caffeic acid and hispidin in eFBP plants derived from the sugar pathway, and the inhibitors of the energy production system significantly reduced the luminescence signal rapidly from eFBP plants, suggesting that the FBP system coupled with the luciferin metabolic flux functions in an energy-driven way. These findings lay the groundwork for genetically creating stronger eFBP plants and developing more powerful biological tools with the FBP system.

MiXcan: a framework for cell-type-aware transcriptome-wide association studies with an application to breast cancer.

Human bulk tissue samples comprise multiple cell types with diverse roles in disease etiology. Conventional transcriptome-wide association study approaches predict genetically regulated gene expression at the tissue level, without considering cell-type heterogeneity, and test associations of predicted tissue-level expression with disease. Here we develop MiXcan, a cell-type-aware transcriptome-wide association study approach that predicts cell-type-level expression, identifies disease-associated genes via combination of cell-type-level association signals for multiple cell types, and provides insight into the disease-critical cell type. As a proof of concept, we conducted cell-type-aware analyses of breast cancer in 58,648 women and identified 12 transcriptome-wide significant genes using MiXcan compared with only eight genes using conventional approaches. Importantly, MiXcan identified genes with distinct associations in mammary epithelial versus stromal cells, including three new breast cancer susceptibility genes. These findings demonstrate that cell-type-aware transcriptome-wide analyses can reveal new insights into the genetic and cellular etiology of breast cancer and other diseases.

Estimating the causal effects of multiple intermittent treatments with application to COVID-19.

To draw real-world evidence about the comparative effectiveness of multiple time-varying treatments on patient survival, we develop a joint marginal structural survival model and a novel weighting strategy to account for time-varying confounding and censoring. Our methods formulate complex longitudinal treatments with multiple start/stop switches as the recurrent events with discontinuous intervals of treatment eligibility. We derive the weights in continuous time to handle a complex longitudinal dataset without the need to discretize or artificially align the measurement times. We further use machine learning models designed for censored survival data with time-varying covariates and the kernel function estimator of the baseline intensity to efficiently estimate the continuous-time weights. Our simulations demonstrate that the proposed methods provide better bias reduction and nominal coverage probability when analyzing observational longitudinal survival data with irregularly spaced time intervals, compared to conventional methods that require aligned measurement time points. We apply the proposed methods to a large-scale COVID-19 dataset to estimate the causal effects of several COVID-19 treatments on the composite of in-hospital mortality and ICU admission.

The miR408a-BBP-LAC3/CSD1 module regulates anthocyanin biosynthesis mediated by crosstalk between copper homeostasis and ROS homeostasis during light induction in Malus plants.

INTRODUCTION: The expression of miR408 is affected by copper (Cu) conditions and positively regulates anthocyanin biosynthesis in Arabidopsis. However, the underlying mechanisms by which miR408 regulates anthocyanin biosynthesis mediated by Cu homeostasis and reactive oxygen species (ROS) homeostasis remain unclear in Malus plants. OBJECTIVES: Our study aims to elucidate how miR408a and its target, basic blue protein (BBP) regulate Cu homeostasis and ROS homeostasis, and anthocyanin biosynthesis in Malus plants. METHODS: The roles of miR408a and its target BBP in regulating anthocyanin biosynthesis, Cu homeostasis, and ROS homeostasis were mainly identified in Malus plants. RESULTS: We found that the BBP protein interacted with the copper-binding proteins LAC3 (laccase) and CSD1 (Cu/Zn SOD superoxide dismutase), indicating a potential crosstalk between Cu homeostasis and ROS homeostasis might be mediated by miR408 to regulate the anthocyanin accumulation. Further studies showed that overexpressing miR408a or suppressing BBP transiently significantly increased the expression of genes related to Cu binding and Cu transport, leading to anthocyanin accumulation under light induction in apple fruit and Malus plantlets. Consistently, opposite results were obtained when repressing miR408a or overexpressing BBP. Moreover, light induction significantly increased the expression of miR408a, CSD1, and LAC3, but significantly reduced the BBP expression, resulting in increased Cu content and anthocyanin accumulation. Furthermore, excessive Cu significantly increased the anthocyanin accumulation, accompanied by reduced expression of miR408a and Cu transport genes, and upregulated expression of Cu binding proteins including BBP, LAC3, and CSD1 to maintain the Cu homeostasis and ROS homeostasis in Malus plantlets. CONCLUSION: Our findings provide new insights into the mechanism by which the miR408a-BBP-LAC3/CSD1 module perceives light and Cu signals regulating Cu and ROS homeostasis, ultimately affecting anthocyanin biosynthesis in Malus plants.

Power of Bayesian and Heuristic Tests to Detect Cross-Species Introgression with Reference to Gene Flow in the Tamias quadrivittatus Group of North American Chipmunks.

In the past two decades, genomic data have been widely used to detect historical gene flow between species in a variety of plants and animals. The Tamias quadrivittatus group of North America chipmunks, which originated through a series of rapid speciation events, are known to undergo massive amounts of mitochondrial introgression. Yet in a recent analysis of targeted nuclear loci from the group, no evidence for cross-species introgression was detected, indicating widespread cytonuclear discordance. The study used the heuristic method HYDE to detect gene flow, which may suffer from low power. Here we use the Bayesian method implemented in the program BPP to re-analyze these data. We develop a Bayesian test of introgression, calculating the Bayes factor via the Savage-Dickey density ratio using the Markov chain Monte Carlo (MCMC) sample under the model of introgression. We take a stepwise approach to constructing an introgression model by adding introgression events onto a well-supported binary species tree. The analysis detected robust evidence for multiple ancient introgression events affecting the nuclear genome, with introgression probabilities reaching 63%. We estimate population parameters and highlight the fact that species divergence times may be seriously underestimated if ancient cross-species gene flow is ignored in the analysis. We examine the assumptions and performance of HYDE and demonstrate that it lacks power if gene flow occurs between sister lineages or if the mode of gene flow does not match the assumed hybrid-speciation model with symmetrical population sizes. Our analyses highlight the power of likelihood-based inference of cross-species gene flow using genomic sequence data. [Bayesian test; BPP; chipmunks; introgression; MSci; multispecies coalescent; Savage-Dickey density ratio.].

A Flexible Approach for Assessing Heterogeneity of Causal Treatment Effects on Patient Survival Using Large Datasets with Clustered Observations.

Personalized medicine requires an understanding of treatment effect heterogeneity. Evolving toward causal evidence for scenarios not studied in randomized trials necessitates a methodology using real-world evidence. Herein, we demonstrate a methodology that generates causal effects, assesses the heterogeneity of the effects and adjusts for the clustered nature of the data. This study uses a state-of-the-art machine learning survival model, riAFT-BART, to draw causal inferences about individual survival treatment effects, while accounting for the variability in institutional effects; further, it proposes a data-driven approach to agnostically (as opposed to a priori hypotheses) ascertain which subgroups exhibit an enhanced treatment effect from which intervention, relative to global evidence-average treatment effects measured at the population level. Comprehensive simulations show the advantages of the proposed method in terms of bias, efficiency and precision in estimating heterogeneous causal effects. The empirically validated method was then used to analyze the National Cancer Database.

Homogeneous Lateral Lithium Intercalation into Transition Metal Dichalcogenides via Ion Backgating.

Lithium intercalation has become a versatile tool for realizing emergent quantum phenomena in two-dimensional (2D) materials. However, the insertion of lithium ions may be accompanied by the creation of wrinkles and cracks, which prevents the material from manifesting its intrinsic properties under substantial charge injection. By using the recently developed ion backgating technique, we successfully realize lateral intercalation in 1T-TiSe2 and 2H-NbSe2, which shows substantially improved sample homogeneity. The homogeneity at high lithium doping is not only demonstrated via low-temperature transport measurements but also directly visualized by topographical imaging through in situ atomic force microscopy (AFM). The application of lateral intercalation to a broad spectrum of 2D materials can greatly facilitate the search for exotic quantum phenomena.

A flexible approach for causal inference with multiple treatments and clustered survival outcomes.

When drawing causal inferences about the effects of multiple treatments on clustered survival outcomes using observational data, we need to address implications of the multilevel data structure, multiple treatments, censoring, and unmeasured confounding for causal analyses. Few off-the-shelf causal inference tools are available to simultaneously tackle these issues. We develop a flexible random-intercept accelerated failure time model, in which we use Bayesian additive regression trees to capture arbitrarily complex relationships between censored survival times and pre-treatment covariates and use the random intercepts to capture cluster-specific main effects. We develop an efficient Markov chain Monte Carlo algorithm to draw posterior inferences about the population survival effects of multiple treatments and examine the variability in cluster-level effects. We further propose an interpretable sensitivity analysis approach to evaluate the sensitivity of drawn causal inferences about treatment effect to the potential magnitude of departure from the causal assumption of no unmeasured confounding. Expansive simulations empirically validate and demonstrate good practical operating characteristics of our proposed methods. Applying the proposed methods to a dataset on older high-risk localized prostate cancer patients drawn from the National Cancer Database, we evaluate the comparative effects of three treatment approaches on patient survival, and assess the ramifications of potential unmeasured confounding. The methods developed in this work are readily available in the R $$ \mathsf{R}\kern.15em $$ package riAFTBART $$ \mathsf{riAFTBART} $$ .

Gestational Diabetes, Subsequent Type 2 Diabetes, and Food Security Status: National Health and Nutrition Examination Survey, 2007-2018.

INTRODUCTION: Despite many studies linking various risk factors to the association between gestational diabetes and subsequent type 2 diabetes, little is known about how food insecurity affects their association. We aimed to assess how the association between gestational diabetes and subsequent type 2 diabetes varies by food security status among women in the US. METHODS: This study is a secondary data analysis of 9,505 US women aged 20 years or older who had at least 1 live birth; we used cross-sectional data from the National Health and Nutrition Examination Survey (NHANES) from 2007 through 2018. The main outcome was a diagnosis of type 2 diabetes in the subsequent years after the first live birth. We used multivariable survey-weighted negative binomial regressions to examine whether the association between gestational diabetes and subsequent type 2 diabetes differed by food security status, with and without adjusting for health behavior factors. RESULTS: Gestational diabetes was significantly associated with subsequent type 2 diabetes (incidence rate ratio [IRR], 2.57; 95% CI, 2.45-2.69). The association between gestational diabetes and subsequent type 2 diabetes was significantly different by food security status (IRR, 2.34; 95% CI, 2.23-2.45 among food-secure women; IRR, 2.99; 95% CI, 2.70-3.28 among food-insecure women). CONCLUSION: The association between gestational diabetes and subsequent type 2 diabetes differs significantly by food security status. Public health and health care practitioners should consider food security status when designing and implementing diabetes prevention interventions for women with a history of gestational diabetes.

Knowing What to Learn: A Metric-Oriented Focal Mechanism for Image Captioning.

Despite considerable progress, image captioning still suffers from the huge difference in quality between easy and hard examples, which is left unexploited in existing methods. To address this issue, we explore the hard example mining in image captioning, and propose a simple yet effective mechanism to instruct the model to pay more attention to hard examples, thereby improving the performance in both general and complex scenarios. We first propose a novel learning strategy, termed Metric-oriented Focal Mechanism (MFM), for hard example mining in image captioning. Differing from the existing strategies for classification tasks, MFM can adopt the generative metrics of image captioning to measure the difficulties of examples, and then up-weight the rewards of hard examples during training. To make MFM applicable to different datasets without tedious parameter tuning, we further introduce an adaptive reward metric called Effective CIDEr (ECIDEr), which considers the data distribution of easy and hard examples during reward estimation. Extensive experiments are conducted on the MS COCO benchmark, and the results show that while maintaining the performance on simple examples, MFM can significantly improve the quality of captions for hard examples. The ECIDEr-based MFM is equipped on the current SOTA method, e.g., DLCT (Luo et al., 2021), which outperforms all existing methods and achieves new state-of-the-art performance on both the off-line and on- line testing, i.e., 134.3 CIDEr for the off-line testing and 136.1 for the on- line testing of MSCOCO. To validate the generalization ability of ECIDEr-based MFM, we also apply it to another dataset, namely Flickr30k, and superior performance gains can also be obtained.