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Hospital-acquired infections (HAIs) significantly affect patient morbidity, mortality, and healthcare costs. Sterilization and supply centers (SSCs) are crucial in preventing these infections. This study evaluates the impact of implementing cleaning quality traceability systems in SSCs on infection control. A retrospective analysis was conducted in five hospitals, examining compliance with cleaning protocols, HAI incidence rates, and operational efficiency before and after implementing traceability systems. Key outcomes included changes in compliance rates and the incidence of HAIs, including surgical site infections (SSIs) and catheter-associated urinary tract infections (CAUTIs). Compliance with cleaning protocols improved from 78.5 to 96.4% after implementing the traceability systems. This enhancement correlated with a 42.6% reduction in HAI rates, decreasing from 5.4 to 3.1 per 1,000 patient days (  < 0.01). Specifically, SSIs decreased from 1.8 to 1.0, and CAUTIs from 1.5 to 0.9 per 1,000 patient days. Operational data showed minimal downtime for the systems, with issues resolved promptly. The introduction of cleaning quality traceability systems in SSCs is significantly associated with increased protocol compliance and a reduction in HAI rates. These systems are associated with enhanced infection control measures, which are in turn associated with improved patient outcomes and greater efficiency in hospital operations.

HighlightsHistory and benefits of zinc ion hybrid capacitors are introduced.Carbon materials with different dimensions are developed for the cathodes.Relationship between carbon structures and capacitive performances are discussed.Current collectors and separators are firstly showcased and summarized.Zinc ion hybrid capacitors (ZIHCs), which integrate the features of the high power of supercapacitors and the high energy of zinc ion batteries, are promising competitors in future electrochemical energy storage applications. Carbon-based materials are deemed the competitive candidates for cathodes of ZIHC due to their cost-effectiveness, high electronic conductivity, chemical inertness, controllable surface states, and tunable pore architectures. In recent years, great research efforts have been devoted to further improving the energy density and cycling stability of ZIHCs. Reasonable modification and optimization of carbon-based materials offer a remedy for these challenges. In this review, the structural design, and electrochemical properties of carbon-based cathode materials with different dimensions, as well as the selection of compatible, robust current collectors and separators for ZIHCs are discussed. The challenges and prospects of ZIHCs are showcased to guide the innovative development of carbon-based cathode materials and the development of novel ZIHCs.

Digital health technologies (ie, the integration of digital technology and health information) aim to increase the efficiency of health care delivery; they are rapidly adapting to health care contexts to provide improved medical services for citizens. However, contrary to expectations, their rapid adoption appears to have led to health inequities, with differences in health conditions or inequality in the distribution of health care resources among different populations. This scoping review aims to identify and describe the inequities of health care services brought about by the adoption of digital health technologies. The factors influencing such inequities, as well as the corresponding countermeasures to ensure health equity among different groups of citizens, were also studied. Primary studies and literature, including articles and reviews, published in English between 1990 and 2020 were retrieved using appropriate search strategies across the following three electronic databases: Clarivate Analytics' Web of Science, PubMed, and Scopus. Data management was performed by two authors (RY and WZ) using Thomson Endnote (Clarivate Analytics, Inc), by systematically screening and identifying eligible articles for this study. Any conflicts of opinion were resolved through discussions with the corresponding author. A qualitative descriptive synthesis was performed to determine the outcomes of this scoping review. A total of 2325 studies were collected during the search process, of which 41 (1.76%) papers were identified for further analysis. The quantity of literature increased until 2016, with a peak in 2020. The United States, the United Kingdom, and Norway ranked among the top 3 countries for publication output. Health inequities caused by the adoption of digital health technologies in health care services can be reflected in the following two dimensions: the inability of citizens to obtain and adopt technology and the different disease outcomes found among citizens under technical intervention measures. The factors that influenced inequities included age, race, region, economy, and education level, together with health conditions and eHealth literacy. Finally, action can be taken to alleviate inequities in the future by government agencies and medical institutions (eg, establishing national health insurance), digital health technology providers (eg, designing high-quality tools), and health care service recipients (eg, developing skills to access digital technologies). The application of digital health technologies in health care services has caused inequities to some extent. However, existing research has certain limitations. The findings provide a comprehensive starting point for future research, allowing for further investigation into how digital health technologies may influence the unequal distribution of health care services. The interaction between individual subjective factors as well as social support and influencing factors should be included in future studies. Specifically, access to and availability of digital health technologies for socially disadvantaged groups should be of paramount importance.

The objective of this study is to verify the feasibility of using biochar made from crop straw as a bitumen additive to improve some properties of bitumen. The differences between crop straw biochar prepared in a laboratory and commercial charcoal were investigated through scanning electron microscopy and laser particle size analyses. Furthermore, biochar-modified asphalt was prepared using the high-speed shear method, and the penetration, softening point, ductility at 15°C, and apparent viscosity of the asphalt binder with 6% biochar were measured at 120, 135, 150, 160, and 175°C. It was found that both the crop straw biochar and the commercial charcoal consist mainly of C, O, Si, and K, but the C content of crop straw biochar is slightly higher than that of commercial charcoal. The particle size of biochar is smaller than that of commercial charcoal, while the specific surface area is larger. It was determined that the addition of crop straw biochar significantly improved the high-temperature performance of asphalt, and that biochar and commercial charcoal have a similar influence on the high temperature performance of asphalt.

Enhancers are important regulators of gene expression in eukaryotes. Enhancers function independently of their distance and orientation to the promoters of target genes. Thus, enhancers have been difficult to identify. Only a few enhancers, especially distant intergenic enhancers, have been identified in plants. We developed an enhancer prediction system based exclusively on the DNase I hypersensitive sites (DHSs) in the Arabidopsis thaliana genome. A set of 10,044 DHSs located in intergenic regions, which are away from any gene promoters, were predicted to be putative enhancers. We examined the functions of 14 predicted enhancers using the β-glucuronidase gene reporter. Ten of the 14 (71%) candidates were validated by the reporter assay. We also designed 10 constructs using intergenic sequences that are not associated with DHSs, and none of these constructs showed enhancer activities in reporter assays. In addition, the tissue specificity of the putative enhancers can be precisely predicted based on DNase I hypersensitivity data sets developed from different plant tissues. These results suggest that the open chromatin signature-based enhancer prediction system developed in Arabidopsis may serve as a universal system for enhancer identification in plants.

Gene expression and regulation in eukaryotes is controlled by orchestrated binding of regulatory proteins, including both activators and repressors, to promoters and other cis-regulatory DNA elements. An increasing number of plant genomes have been sequenced; however, a similar effort to the ENCODE project, which aimed to identify all functional elements in the human genome, has yet to be initiated in plants. Here we report genome-wide high-resolution mapping of DNase I hypersensitive (DH) sites in the model plant Arabidopsis thaliana. We identified 38,290 and 41,193 DH sites in leaf and flower tissues, respectively. The DH sites were depleted of bulk nucleosomes and were tightly associated with RNA polymerase II binding sites. Approximately 90% of the binding sites of two well-characterized MADS domain transcription factors, APETALA1 and SEPALLATA3, were covered by the DH sites. We demonstrate that protein binding footprints within a specific genomic region can be revealed using the DH site data sets in combination with known or putative protein binding motifs and gene expression data sets. Thus, genome-wide DH site mapping will be an important tool for systematic identification of all cis-regulatory DNA elements in plants.

1-Deoxynojirimycin (DNJ), a representative iminopyranose, is widely used in anti-diabetic, antioxidant, anti-inflammatory, and anti-obesity applications. It naturally exists in plants, insects, and the culture broth of some microbes as a secondary metabolite product. However, the content of DNJ in plants and insects is relatively low, which is an obstacle to investigating DNJ in terms of structure–function relationships and restricts large-scale industrial production. With the development of micro-biotechnology, the production of DNJ during microbial fermentation has potential for industrial applications. In this review, we primarily focus on the microbial production of DNJ. The review covers sources of DNJ, determination methods, and physiological functions and applications. In a discussion of the efficient production of DNJ microorganisms, we summarize the current methods for DNJ screening and how to guide industrialized large-scale production of DNJ through fermentation regulation strategies. In addition, differences in the biosynthetic pathways of DNJ in different sources are also summarized. Finally, a comparison of DNJ content derived from different sources is discussed.

Background Cold stress can greatly affect plant growth and development. Plants have developed special systems to respond to and tolerate cold stress. While plant scientists have discovered numerous genes involved in responses to cold stress, few studies have been dedicated to investigation of genome-wide chromatin dynamics induced by cold or other abiotic stresses. Results Genomic regions containing active cis -regulatory DNA elements can be identified as DNase I hypersensitive sites (DHSs). We develop high-resolution DHS maps in potato ( Solanum tuberosum ) using chromatin isolated from tubers stored under room (22 °C) and cold (4 °C) conditions. We find that cold stress induces a large number of DHSs enriched in genic regions which are frequently associated with differential gene expression in response to temperature variation. Surprisingly, active genes show enhanced chromatin accessibility upon cold stress. A large number of active genes in cold-stored tubers are associated with the bivalent H3K4me3-H3K27me3 mark in gene body regions. Interestingly, upregulated genes associated with the bivalent mark are involved in stress response, whereas downregulated genes with the bivalent mark are involved in developmental processes. In addition, we observe that the bivalent mark-associated genes are more accessible than others upon cold stress. Conclusions Collectively, our results suggest that cold stress induces enhanced chromatin accessibility and bivalent histone modifications of active genes. We hypothesize that in cold-stored tubers, the bivalent H3K4me3-H3K27me3 mark represents a distinct chromatin environment with greater accessibility, which may facilitate the access of regulatory proteins required for gene upregulation or downregulation in response to cold stress.

This study explores the application of machine learning algorithms in predicting high-risk pregnancy among expectant mothers, aiming to construct an efficient predictive model to improve maternal health management. The study is based on the maternal health risk dataset (MHRD) from Bangladesh, covering multiple hospitals, community clinics, and maternal healthcare centers, and encompassing health data from 1014 pregnant women. Six machine learning algorithms—multilayer perceptron (MLP), logistic regression (LR), decision tree (DT), random forest (RF), eXtreme Gradient Boosting (XGBoost), and support vector machine (SVM)—are employed to construct predictive models. It is worth noting that MLP demonstrates superior performance compared with the other five algorithms. By applying the MLP method, the study successfully established an efficient pregnancy risk prediction model. The model evaluation results indicate that it has high accuracy in predicting pregnancy risks, with an overall accuracy rate of 82%, and particularly high accuracy in high-risk predictions, reaching 91%. With the computational support of an NVIDIA GPU RTX3050Ti, the model demonstrated excellent data processing capabilities, capable of predicting and processing 500 sets of data items per second. This study not only showcases the enormous potential of machine learning technology in the healthcare field, especially in the rapid and accurate identification of high-risk pregnancies, providing a powerful decision-support tool for medical professionals, but also offers significant reference value for future research in this area.

We address the challenge that occlusions in on-branch soybean images impede accurate pod-level phenotyping. We propose a lab on-branch pipeline that couples a prior-guided synthetic data generator (producing synchronized visible and amodal labels) with an amodal instance segmentation framework based on an improved Swin Transformer backbone with a Simple Attention Module (SimAM) and dual heads, trained via three-stage transfer (synthetic excised → synthetic on-branch → few-shot real). Guided by complete (amodal) masks, a morphology-driven module performs pose normalization, axial geometric modeling, multi-scale fused density mapping, marker-controlled watershed, and topological consistency refinement to extract seed per pod (SPP) and geometric traits. On real on-branch data, the model attains Visible Average Precision (AP) 50/75 of 91.6/77.6 and amodal AP50/75 of 90.1/74.7, and incorporating synthetic data yields consistent gains across models, indicating effective occlusion reasoning. On excised pod tests, SPP achieves a mean absolute error (MAE) of 0.07 and a root mean square error (RMSE) of 0.26; pod length/width achieves an MAE of 2.87/3.18 px with high agreement (R2 up to 0.94). Overall, the co-designed data–model–task pipeline recovers complete pod geometry under heavy occlusion and enables non-destructive, high-precision, and low-annotation-cost extraction of key traits, providing a practical basis for standardized laboratory phenotyping and downstream breeding applications.