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Enabling surfaces with passive anti-icing properties is an emerging, facile, economical, and energy-saving strategy to mitigate the harm caused by ice accretion. However, the combination of icephobicity and robustness remains a daunting challenge. Herein, we present an ultra-robust transparent icephobic coating with high toughness, strong substrate adhesion, and self-healing capability. Hydrophobicity, smoothness, and softness of the coating guarantee low ice adhesion strength. By incorporating a spongy structure, the ice adhesion strength of the coating is lowered further down to 26.7 ± 1.1 kPa. Importantly, the coating exhibits high toughness, strong adhesion to the substrate, and self-healing capability due to the presence of multiple hydrogen bonding. Consequently, the coating maintains its icephobicity after 35 icing/deicing cycles and 600 abrasion cycles, is resilient to delamination, and is able to heal and recover its icephobicity from the mechanical damage introduced by both cuts and abrasions. Moreover, the coating sustains its icephobicity after eight months of immersion in saltwater, as well as exposure to the near-arctic weather in Trondheim (Norway). This work presents new insights into the design of robust icephobic coatings that can sustain severe mechanical loading for use in real complex environments.

Excessive cardiac fibrosis is central to adverse cardiac remodeling and dysfunction leading to heart failure in many cardiac diseases. Histone methylation plays a crucial role in various pathophysiological events. However, the role of histone methylation modification enzymes in pathological cardiac fibrosis needs to be fully elucidated. Here, we identified lysine demethylase 5B (KDM5B), a histone H3K4me2/me3 demethylase, as a key epigenetic mediator of pathological cardiac fibrosis. KDM5B expression was upregulated in cardiac fibroblasts and myocardial tissues in response to pathological stress. KDM5B deficiency markedly ameliorated cardiac fibrosis, improved cardiac function, and prevented adverse cardiac remodeling following myocardial infarction (MI) or pressure overload. KDM5B knockout or inhibitor treatment constrained the transition of cardiac fibroblasts to profibrogenic myofibroblasts and suppressed fibrotic responses. KDM5B deficiency also facilitated the transformation of cardiac fibroblasts to endothelial-like cells and promoted angiogenesis in response to myocardial injury. Mechanistically, KDM5B bound to the promoter of activating transcription factor 3 (Atf3), an antifibrotic regulator of cardiac fibrosis, and inhibited ATF3 expression by demethylating the activated H3K4me2/3 modification, leading to the enhanced activation of TGF-β signaling and excessive expression of profibrotic genes. Our study indicates that KDM5B drives pathological cardiac fibrosis and represents a candidate target for intervention in cardiac dysfunction and heart failure.

Genome sequencing of cancers often reveals mosaics of different subclones present in the same tumour1–3. Although these are believed to arise according to the principles of somatic evolution, the exact spatial growth patterns and underlying mechanisms remain elusive4,5. Here, to address this need, we developed a workflow that generates detailed quantitative maps of genetic subclone composition across whole-tumour sections. These provide the basis for studying clonal growth patterns, and the histological characteristics, microanatomy and microenvironmental composition of each clone. The approach rests on whole-genome sequencing, followed by highly multiplexed base-specific in situ sequencing, single-cell resolved transcriptomics and dedicated algorithms to link these layers. Applying the base-specific in situ sequencing workflow to eight tissue sections from two multifocal primary breast cancers revealed intricate subclonal growth patterns that were validated by microdissection. In a case of ductal carcinoma in situ, polyclonal neoplastic expansions occurred at the macroscopic scale but segregated within microanatomical structures. Across the stages of ductal carcinoma in situ, invasive cancer and lymph node metastasis, subclone territories are shown to exhibit distinct transcriptional and histological features and cellular microenvironments. These results provide examples of the benefits afforded by spatial genomics for deciphering the mechanisms underlying cancer evolution and microenvironmental ecology.

Many factors were reported to be associated with diabetic retinopathy (DR); however, their contributions remained unclear. We aimed to evaluate the prognostic and diagnostic accuracy of logistic regression and three machine learning models based on various medical records.This was a cross-sectional study. We investigated the prevalence and associations of DR among 757 participants aged 40 years or older in the 2005-2006 National Health and Nutrition Examination Survey (NHANES). We trained the models to predict if the participants had DR with 15 predictor variables. Area under the receiver operating characteristic (AUROC) and mean squared error (MSE) of each algorithm were compared in the external validation dataset using a replicate cohort from NHANES 2007-2008.Among the 757 participants, 53 (7.00%) subjects had DR, the mean (standard deviation, SD) age was 57.7 (13.04), and 78.0% were male (n = 42). Logistic regression revealed that female gender (OR = 4.130, 95% CI: 1.820-9.380; P 0.05), HbA1c (OR = 1.665, 95% CI: 1.197-2.317; P 0.05), serum creatine level (OR = 2.952, 95% CI: 1.274-6.851; P 0.05), and eGFR level (OR = 1.009, 95% CI: 1.000-1.014, P 0.05) increased the risk of DR. The average performance obtained from internal validation was similar in all models (AUROC ≥ 0.945), and k-nearest neighbors (KNN) had the highest value with an AUROC of 0.984. In external validation, they remained robust or with modest reductions in discrimination with AUROC still ≥ 0.902, and KNN also performed the best with an AUROC of 0.982. Both logistic regression and machine learning models had good performance in the clinical diagnosis of DR.This study highlights the utility of comparing traditional logistic regression to machine learning models. We found that logistic regression performed as well as optimized machine learning methods when classifying DR patients.

Background Cataract is the primary cause of blindness globally, and surgery offers the only method by which to remove cataracts. We aimed to examine whether previous cataract surgery is associated with cognitive function. Methods Our study included 13,824 participants. Data from the baseline of the China Health and Retirement Longitudinal Study (CHARLS) were used. The participants were categorized into two groups: with and without previous cataract surgery. Weighted multiple linear regression was used to obtain the β and 95% confidence intervals (CI). Results The participants who had previous cataract surgery (n = 261) scored lower in cognition, including both memory and mental state, than those without previous cataract surgery. After adjusting for socioeconomic factors and metabolic measures, a negative association was evident between previous cataract surgery and cognition (β = −0.647, 95% CI: −1.244, − 0.049). Furthermore, the participants who were older and female demonstrated a decline in cognition, while living in cities and having higher levels education were associated with higher cognition. Conclusions Better cognitive function was associated with less previous cataract surgery or cataract occurrence. This suggests that a period of vision loss due to cataract leads to cognitive decline, however further studies are need to dissect the impact of vision loss and cataract surgery on cognitive decline.

High-performance refractory alloys with ultrahigh strength and ductility are in demand for a wide range of critical applications, such as plasma-facing components. However, it remains challenging to increase the strength of these alloys without seriously compromising their tensile ductility. Here, we put forward a strategy to “defeat” this trade-off in tungsten refractory high-entropy alloys by stepwise controllable coherent nanoprecipitations (SCCPs). The coherent interfaces of SCCPs facilitate the dislocation transmission and relieve the stress concentrations that can lead to premature crack initiation. As a consequence, our alloy displays an ultrahigh strength of 2.15 GPa with a tensile ductility of 15% at ambient temperature, with a high yield strength of 1.05 GPa at 800 °C. The SCCPs design concept may afford a means to develop a wide range of ultrahigh-strength metallic materials by providing a pathway for alloy design.

Introduction The relationship between relative hyperglycemia and ventricular arrhythmia (VA) in critically ill patients admitted to intensive care units (ICU) remains unclear. This study aims to investigate the association between stress hyperglycemia ratio (SHR) and VA in this population. Methods This retrospective and observational study analyzed data from 4324 critically ill patients admitted to the ICU, obtained from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database. The SHR was calculated as the highest blood glucose level during the first 24 h of ICU admission divided by the admission blood glucose level. Based on the optimal cut-off values under the receiver operating characteristic curve, patients were stratified into high SHR (≥ 1.31) and low SHR ( Results A total of 4,324 critically ill patients were included in this retrospective and observational study. The incidence of VA was higher in the high SHR group. Multiple-adjusted RCS revealed a “J-shaped” correlation between SHR and VA morbidity. The logistic regression model demonstrated that high SHR was associated with VA. The high SHR/non-DM group had a higher risk of VA than other groups stratified based on SHR and DM. Subgroup analysis showed that high SHR was associated with an increased risk of VA in patients with coronary artery disease. Conclusion High SHR is an independent risk factor and has potential as a biomarker of higher VT/VF risk in ICU-admitted patients.

Background Previous studies discovered the presence of abnormal structures and functions in the brain regions of patients with obsessive-compulsive disorder (OCD). Nevertheless, whether structural changes in brain regions are coupled with alterations in dynamic functional connectivity (dFC) at rest in medicine-free patients with OCD remains vague. Methods Three-dimensional T1-weighed magnetic resonance imaging (MRI) and resting-state functional MRI were performed on 50 medicine-free OCD and 50 healthy controls (HCs). Firstly, the differences in gray matter volume (GMV) between OCD and HCs were compared. Then, brain regions with aberrant GMV were used as seeds for dFC analysis. The relationship of altered GMV and dFC with clinical parameters in OCD was explored using partial correlation analysis. Finally, support vector machine was applied to examine whether altered multimodal imaging data might be adopted to distinguish OCD from HCs. Results Our findings indicated that GMV in the left superior temporal gyrus (STG) and right supplementary motor area (SMA) was reduced in OCD, and the dFC between the left STG and the left cerebellum Crus I and left thalamus, and between the right SMA and right dorsolateral prefrontal cortex (DLPFC) and left precuneus was decreased at rest in OCD. The brain regions both with altered GMV and dFC values could discriminate OCD from HCs with the accuracy of 0.85, sensitivity of 0.90 and specificity of 0.80. Conclusion The decreased gray matter structure coupling with dynamic function in the left STG and right SMA at rest may be crucial in the pathophysiology of OCD. Trial registration Study on the mechanism of brain network in obsessive-compulsive disorder with multi-model magnetic resonance imaging (registration date: 08/11/2017; registration number: ChiCTR-COC-17,013,301).

Recent studies have shown a correlation between high-density lipoprotein cholesterol (HDL-C) and bone mineral density (BMD) in adults, but their relationship is unclear in adolescents. This study aimed to explore whether a correlation existed between them among adolescents aged 12–19. Data analyzed in our study was fetched from the National Health and Nutrition Examination Survey (NHANES) database 2011–2018. The relationship between HDL-C level and total BMD value was analyzed by multivariate logistic regression models, fitted smoothing curves, and generalized additive models. 3770 participants participated in this analysis. After adjusting for all relevant covariates involved in this study, we found a negative correlation between HDL-C levels and total bone density in male adolescents.Furthermore, the stratified analysis showed that all covariables-adjusted models retained the negative correlation excepting female, black, or Mexican American subgroups. An inverted U-shaped curve represented the correlation of HDL-C and total BMD among adolescents aged 16 to 19, and the turning point was 1.06 mmol/L. After adjusting for all relevant covariates involved in this study, the study found a negative correlation between HDL-C levels and total BMD in male adolescents aged 12 to 19, particularly among those of races other than Black and Mexican. There was a saturation effect between HDL-C level and total BMD in 16–19-year-old adolescents. The turning point was 1.06 mmol/L. Therefore, HDL-C might be a biomarker to detect bone health and further perform a more detailed examination.

Software knowledge community contains a large scale of software knowledge entity information, complex structure and rich semantic correlations. It is significant to recognize and extract software knowledge entity from software knowledge community, as it has great impact on entity-centric tasks such as software knowledge graph construction, software document generation and expert recommendation. Since the texts of the software knowledge community are unstructured by user-generated texts, it is difficult to apply the traditional entity extraction method in the domain of the software knowledge community due to the problems of entity variation, entity sparsity, entity ambiguity, out-of-vocabulary (OOV) words and the lack of annotated data sets. This paper proposes a novel software knowledge entity extraction model, named AttenSy-SNER, which integrates syntactic features and semantic augmentation information, to extract fine-grained software knowledge entities from unstructured user-generated content. The input representation layer utilizes Bidirectional Encoder Representations from Transformers (BERT) model to extract the feature representation of the input sequence. The contextual coding layer leverages the Bidirectional Long Short-Term Memory (BiLSTM) network and Graph Convolutional Network (GCN) for contextual information and syntactic dependency information, and a semantic augmentation strategy based on attention mechanism is introduced to enrich the semantic feature representation of sequences as well. The tag decoding layer leverages Conditional Random Fields (CRF) to solve the dependency between the output tags and obtain the global optimal label sequence. The results of model comparison experiments show that the proposed model has better performance than the benchmark model in software engineering domain.

Purpose Wood decay fungi are the only forms of life capable of degrading wood to its initial constituents, greatly contributing to the soil ecosystem. This study summarizes the current research status and development characteristics of global wood decay fungi research, in order to better understand their role in soils. Methods A bibliometric analysis was applied to the literature from 1913 to 2020, based on data from the Web of Science (WOS) Core Collection. For this, various bibliometric analysis methods, R (Biblioshiny package), and VOSviewer were applied. Results A total of 8089 documents in this field were identified in the WOS Core Collection. The annual number of publications tended to increase, with exponential growth after 2008. Researchers in this field were mainly concentrated in North Europe, the USA, and China. Biotechnology, applied microbiology, environmental sciences, and microbiology were the most popular WOS categories. Bioresource Technology and Applied Environmental Microbiology were the top two journals with the most citations. The top three authors with the most published papers were Dai YC, Martinez AT, and Cui BK. Co-occurrence analysis of author keywords identified six clusters, mainly divided into three categories: the classification and diversity, the degradation mechanisms, and the ecological functions of wood decay fungi. Clustering results further showed that the lignin degradation process and the application of wood decay fungi in industrial production and soil contamination remediation are current research hotspots. Conclusions We present a comprehensive and systematic overview of research related to wood decay fungi and provide a deep perspective to understand the associated research progress. This is important for facilitating the development of a profound understanding of the contribution of wood decay fungi to soil systems and the degradation of soil contaminants.