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Aim: Climate is widely understood to determine elevational patterns of soil microbial communities, whereas the effects of parental material are uncertain. Changes in the composition of parental materials along elevational transects could also affect soil microbial communities by influencing soil pH and nutrient availability. Here, we aim to illustrate the combined effects of climate and parental material on the biomass and composition of soil microbial communities along an elevational transect. Location: A subtropical forest on a karst mountain (Mt. Jinfo), China. Taxon: Bacteria and Fungi. Methods: We use phospholipid fatty acid analysis (PLFA) and DNA amplicon high‐throughput sequencing to determine biomass and diversity patterns of soil microbial communities along a subtropical elevational gradient with contrasting parental materials (limestone and clasolite). Results: We observed that the microbial communities were more diverse (α‐diversity) and productive (biomass) on limestone than on clasolite. Additionally, we found that parental material played a role in shaping the composition (β‐diversity) of soil microbial communities along the elevational gradient. The impact of climate on soil microbial communities was found to be significant, albeit relatively weak. Structural equation models provided evidence for both direct and indirect effects of climate and parental material on microbial biomass and α‐diversity along the elevational gradient. Notably, the changes in soil pH, influenced by both parental material and climate, were identified as a key factor driving these effects. Main Conclusions: Our results underline the importance of both climate and parental material variations in space‐for‐time studies investigating soil microbial communities along elevational gradients. [ABSTRACT FROM AUTHOR]

FeCoNiCrAl0.8Cu0.5Si0.5 high-entropy alloys were fabricated using vacuum induction melting and laser deposition processes, followed by a comparison of the structural and mechanical properties of two distinct sample types. The as-cast FeCoNiCrAl0.8Cu0.5Si0.5 alloy is comprised of BCC1, BCC2, and Cr3Si phases, while the laser-deposited alloy primarily features BCC1 and BCC2 phases. Microstructural analysis revealed that the as-cast alloy exhibits a dendritic morphology with secondary dendritic arms and densely packed grains, and the laser-deposited alloy displays a dendritic structure without the formation of granular interdendritic regions. For mechanical properties, the as-cast FeCoNiCrAl0.8Cu0.5Si0.5 alloy demonstrated higher hardness than the as-deposited alloy, with values of 586 HV0.2 and 557 HV0.2, respectively. The wear rate for the as-cast alloy was observed at 3.5 × 10−7 mm3/Nm, with abrasive wear being the primary wear mechanism. Conversely, the as-deposited alloy had a wear rate of 9.0 × 10−7 mm3/Nm, characterized by adhesive wear. The cast alloy exhibited an icorr of 4.062 μA·cm−2, with pitting as the form of corrosion. The laser-deposited alloy showed an icorr of 3.621 μA·cm−2, with both pitting and intergranular corrosion observed. The laser-deposited alloy demonstrated improved corrosion resistance. The investigation of their microstructure and mechanical properties demonstrates the application potential of FeCoNiCrAl0.8Cu0.5Si0.5 alloys in scenarios requiring high hardness and enhanced wear resistance. [ABSTRACT FROM AUTHOR]

The rapid development of miniaturized, high-frequency, and highly integrated microelectronic devices has brought about critical issues in electromagnetic compatibility and thermal management. In recent years, there has been significant interest in lightweight polymer-based composites that offer both electromagnetic interference (EMI) shielding and thermal conductivity. One promising approach involves constructing three-dimensional (3D) interconnection networks using functional fillers in the polymer matrix. These networks have been proven effective in enhancing the thermal and electrical conductivity of the composites. This mini-review focuses on the preparation and properties of 3D network-reinforced polymer composites, specifically those incorporating metal, carbon, ceramic, and hybrid networks. By comparing the effects of different filler types and distribution on the composite materials, the advantages of 3D interconnected conductive networks in polymer composites are highlighted. Additionally, this review addresses the challenges faced in the field of multifunctional thermal management and electromagnetic protection materials and provides insights into future development trends and application prospects of 3D structured composites. [ABSTRACT FROM AUTHOR]

Background: The present study aimed to investigate the potential role of perceived stress, impulsivity trait, executive dysfunction in non-suicidal self-injury (NSSI) thoughts among college students, as well as the gender differences. Methods: A sample of 890 university students completed self-report measures of NSSI thoughts in the past month, the level of perceived stress, impulsivity traits, and executive dysfunction. Results: Compared to those with low level of perceived stress, participants with high level of perceived stress reported significant higher levels of impulsivity trait and executive dysfunction, and higher frequency of NSSI thoughts, and there were no gender differences. Male participants with NSSI thoughts, compared to males without NSSI thoughts, reported significant higher levels of perceived stress and executive dysfunction. Female participants with NSSI thoughts, compared to females without NSSI thoughts, reported significant higher levels of perceived stress, impulsivity trait, and executive dysfunction. Hierarchical regression analysis revealed only executive dysfunction was associated with NSSI thoughts in males, while only perceived stress was associated with NSSI thoughts in females. Conclusion: This study revealed different influence factors for NSSI thoughts in male and female college students. NSSI thoughts in males were more likely associated with executive dysfunction while in females were due to recently perceived stress. [ABSTRACT FROM AUTHOR]

Source-free domain adaptation (SFDA) mainly aims to the problem of not being able to access the source domain data during the model migration process. Although significant breakthroughs have been achieved, the current works meet performance ceiling. The key problem is that the source bias of the adapted model is difficult to be eliminated. In this work, we propose a novel SFDA method named rectiFication Upon SEmantic information (FUSE). The key idea is to reduce source bias of the adapted model with the help of pre-trained vision–language model (e.g., CLIP). Two strategies are adapted. The first is named source bias reduction, which is to restrict the impact of the samples with inconsistent predictions between the source and pre-trained models. The samples with high confidence classification based on pre-trained model automatically assume the task of supervision. Another one adjusts the pre-trained model to fit the distribution of the target domain. The features that better represent class centers are extracted. Except these two strategies, we also adapt pseudo-labeling method to further improve the performance of the adapted model. Experiments on three benchmark datasets show that our method achieves the state-of-the-art results. [ABSTRACT FROM AUTHOR]

Convolutional neural networks, recurrent neural networks, and transformers have excelled in representation learning for large-scale multi-label text classification. However, there have been very few works that have incorporated topic information in the process of encoding textual sequential semantics, partly because the text's topic needs to be modeled separately. To address this, we introduce the latent topic-aware encoder (TAE), designed for large-scale multi-label text classification. The TAE features two key components: a latent topic attention module that correlates latent topic vectors with word hidden vectors and a topic-fused channel attention module that processes topic-specific text representations to produce a refined final text representation. Our experiments demonstrate that TAE seamlessly integrates with existing deep models, significantly enhancing their classification accuracy and convergence speed across various datasets. [ABSTRACT FROM AUTHOR]

Taxol (paclitaxel) is the first approved microtubule‐stabilizing agent (MSA) by binding stoichiometrically to tubulin, which is considered to be one of the most significant advances in first‐line chemotherapy against diverse tumors. However, a large number of residue missence mutations harboring in the tubulin have been observed to cause acquired drug resistance, largely limiting the clinical application of Taxol and its analogs in chemotherapy. A systematic investigation of the intermolecular interactions between the Taxol and various tubulin mutants would help to establish a comprehensive picture of drug response to tubulin mutations in clinical treatment of cancer, and to design new MSA agents with high potency and selectivity to overcome drug resistance. In this study, we described an integration of in silico analysis and in vitro assay (iSiV) to profile Taxol against a panel of 149 clinically observed, cancer‐associated missence mutations in β‐tubulin at molecular and cellular levels, aiming to a systematic understanding of molecular mechanism and biological implication underlying drug resistance and sensitivity conferring from tubulin mutations. It is revealed that the Taxol‐resistant mutations can be classified into three types: (I) nonbonded interaction broken due to mutation, (II) steric hindrance caused by mutation, and (III) conformational change upon mutation. In addition, we identified three new Taxol‐resistant mutations (C239Y, T274I, and R320P) that can largely reduce the binding affinity of Taxol to tubulin at molecular level, in which the T274I and R320P were observed to considerably impair the antitumor activity of Taxol at cellular level. Moreover, a novel drug‐susceptible mutation (M363T) was also identified, which improves Taxol affinity by 2.6‐fold and decreases Taxol antitumor EC50 values from 29.4 to 18.7 μM. [ABSTRACT FROM AUTHOR]

Purpose: In the context of fixed-wing aircraft wing assembly, there is a need for a rapid and precise measurement technique to determine the center distance between two double-hole components. This paper aims to propose an optical-based spatial point distance measurement technique using the spatial triangulation method. The purpose of this paper is to design a specialized measurement system, specifically a spherically mounted retroreflector nest (SMR nest), equipped with two laser displacement sensors and a rotary encoder as the core to achieve accurate distance measurements between the double holes. Design/methodology/approach: To develop an efficient and accurate measurement system, the paper uses a combination of laser displacement sensors and a rotary encoder within the SMR nest. The system is designed, implemented and tested to meet the requirements of precise distance measurement. Software and hardware components have been developed and integrated for validation. Findings: The optical-based distance measurement system achieves high precision at 0.04 mm and repeatability at 0.02 mm within a range of 412.084 mm to 1,590.591 mm. These results validate its suitability for efficient assembly processes, eliminating repetitive errors in aircraft wing assembly. Originality/value: This paper proposes an optical-based spatial point distance measurement technique, as well as a unique design of a SMR nest and the introduction of two novel calibration techniques, all of which are validated by the developed software and hardware platform. [ABSTRACT FROM AUTHOR]

Objective To explore the effects of the multiple shared decision-making mode using a decision aid manual in conjunction with online labor and delivery decision support on the delivery mode for pregnant women with a scarred uterus. Methods A total of 94 women with scarred uterus who received prenatal care at a tertiary hospital from September 2019 to October 2022 were enrolled and assigned to experimental and control groups using the random number table method. The control group received standard prenatal education, and the experimental group received multiple shared decision-making interventions in addition to standard prenatal education. The degree of conflict in decision-making for delivery, preference for delivery mode, postpartum decision regret, and the final delivery mode between the two groups were compared, respectively. Results Following the multiple shared decision intervention, decision conflict scores in the experimental group were significantly reduced (P < 0.001). In the survey on delivery mode preferences, there was a reduction in the number of individuals in the experimental group expressing "uncertainty", and an increase in those choosing vaginal delivery. Ultimately, in the experimental group, 30 women (68.2%) underwent cesarean sections, and 14 (31.8%) had vaginal deliveries. The level of post-decision regret in the experimental group was lower than that in the control group (P < 0.001). Conclusions Multiple shared decision-making for women pregnant with a scarred uterus could reduce the level of decision-making conflict, increase the willingness for vaginal delivery, and assist them in making rational and scientifically informed decisions regarding childbirth. [ABSTRACT FROM AUTHOR]

Fluorescent imaging probes are crucial for exploring nucleus-related cellular events in live cells. Ideal probes should be photostable, small-sized, highly contrasted, and low in background. Here, we discovered that malachite green is a water-soluble near-infrared luminogen with aggregation-induced emission properties. Importantly, it can be used for living cell nucleus staining in a wash-free manner. [ABSTRACT FROM AUTHOR]

Leonurus japonicus Houtt. (Lamiaceae) is a perennial herb, which is commonly used in the treatment of cardiovascular, uterine, and gynecological diseases. In the present study, we constructed a phylogenetic tree based on the ITS + psbA-trnH + rbcL + rpoB concatenation sequence and performed partial least squares-discriminant analysis was used high-performance liquid chromatography. The results indicated that the concatenation sequence could distinguish among L. japonicus from different origins. Additionally, chemical analysis revealed intergroup differences, albeit of lower quality than the molecular phylogeny. By combining both methods, we were able to correlate the genetic and chemical differences among L. japonicus derived from different origins. Furthermore, our combined chemical and phylogenetic analyses suggested that differences in metabolites are not solely influenced by genetic differences but also environmental factors. These findings can be valuable for the artificial cultivation of L. japonicus and provide new insights for improving its quality. [ABSTRACT FROM AUTHOR]

MXenes, as a new type of two-dimensional nanomaterial, have great potential for biomedical applications due to their unique structural features and excellent physicochemical properties. In recent years, numerous studies have demonstrated that MXenes materials also possess good antibacterial, anti-inflammatory, and antioxidant activities, which has greatly promoted the development of MXenes in the field of tissue engineering and skin regeneration. A series of emerging studies on the introduction of MXenes into wound dressings has significantly advanced the research progress of novel wound dressings. This review provides a brief overview of the application of MXenes in wound healing, focusing on the properties of MXenes in wound healing and the specific applications of MXenes as wound dressings, as well as some suggestions and opinions on their future development. This review pioneers the progress and prospects of MXenes in the field of wound healing, which is of great significance for the development of MXene-based wound dressings. [ABSTRACT FROM AUTHOR]

Aims/Introduction: Serum amyloid A (SAA) is an acute phase reactive protein that plays a vital role in the early diagnosis, risk prediction, efficacy observation and prognosis evaluation of infectious diseases. This study aimed to assess the association between SAA levels and the prognosis of patients with coronavirus disease 2019 (COVID‐19) and diabetes. Materials and Methods: We carried out this retrospective cohort study from March 2022 to May 2022. The population was stratified by tertiles of SAA levels: low (<8.5 mg/L), medium (8.5–36 mg/L) and high (>36 mg/L). The primary outcome was whether the patient developed severe COVID‐19, and secondary outcomes included the need for invasive mechanical ventilation and length of hospital stay. Logistic regression analyses were carried out to identify risk factors affecting the prognosis of patients with COVID‐19 and diabetes. Results: We analyzed 910 diabetes patients with COVID‐19. The median age of the patients was 69 years, and 52.3% were men. As SAA levels increased, the proportion of severe COVID‐19 (6.3% vs 7.3% vs 22.8%, P < 0.001) and the proportion of invasive mechanical ventilation also increased among the three groups. Patients with high SAA levels had a longer length of hospital stay compared with patients with medium SAA and low SAA levels. Univariate logistic regression analysis showed that SAA >36 mg/L further increased the odds ratio to 4.423 (P < 0.001) for the development of severe COVID‐19 compared with low SAA. Multivariate logistic regression analysis, adjusted for age and sex, confirmed that SAA >36 mg/L remained an independent risk factor for the development of severe COVID‐19 (adjusted odds ratio 3.038, P < 0.001). Conclusions: SAA levels are strongly associated with poor prognosis in patients with COVID‐19 and diabetes. [ABSTRACT FROM AUTHOR]

Soil extracellular electron transfer (EET) is a pivotal biological process within the realm of soil. Unfortunately, EET suffers from a lack of predictive models. Herein, an intricately crafted machine learning model has been developed for the purpose of predicting soil EET by using the physicochemical properties of soil as independent input variables and the EET capabilities in terms of current density (jmax) and Coulombic charge (Cout) as dependent output variables. An autoencoder ensemble stacking (AES) model was developed to address the aforementioned issue by integrating support vector machine, multilayer perceptron, extreme gradient boosting, and light gradient boosting machine algorithms as the stacking algorithms. With 10-fold cross-validation, the AES model exhibited notable improvements in predicting jmax and Cout, with average test R2 values of 0.83 and 0.84, respectively, surpassing those of single machine learning (ML) models and the basic ensemble model. By utilizing partial correlation plots (PDPs), Shapley Additive explanations (SHAP) values, and SHAP decision plots, we quantitatively explained the impact and contribution of the input molecules on the AES model's predictions of jmax and Cout. In the context of the SHAP method for the AES model, total carbon (TC) was identified as the most correlated descriptor for jmax, while total organic carbon (TOC) stood out as the most relevant descriptor for Cout. In the prediction tasks of jmax and Cout within the AES model, employing a multitask ML approach allowed the model to benefit from the shared information of input variables, thereby enhancing its overall generalizability. This study provides a feasible tool for the prediction of soil EET from soil physiochemical properties and an advanced understanding of the relationship between soil physiochemical properties and EET capability. [ABSTRACT FROM AUTHOR]

In order to prepare a flexible strain sensor with a large monitoring range, high sensitivity and excellent stability, a polyolefin elastomer (POE)/carbon nanotubes (CNTs) conductive polymer composite with a bilayer structure was prepared by biaxially stretching in this work. The results showed that the bilayer structured nanocomposites had better processability and better stability than monolayer CNT/POE nanocomposites during biaxial stretching. The biaxial stretching process could promote the dispersion and planar orientation of CNTs within the polymer, improving the crystallinity of nanocomposites and the sensing performance of strain sensors. The optimal stretching ratios (SRs) of bilayer structured nanocomposites were studied. The 8 wt% CNT/POE‐POE strain sensor with an SR of 2.5 exhibited a high sensitivity (gauge factor/GF = 72604.7 at 900% strain), wide strain range (0%–900%), and stable cycling performance (1000 cycles at 30% strain), showing a broad application prospect in wearable electronic devices. This study provides a valuable method for efficiently manufacturing high performance and low‐cost flexible strain sensors. [ABSTRACT FROM AUTHOR]

Discovering spatial co-location patterns is a process of finding groups of distinct spatial features whose instances are frequently located together in spatial proximity. A co-location pattern is prevalent if its participation index is no less than a minimum prevalence threshold given by users. Most of the existing algorithms are very sensitive to the prevalence threshold, when users change the prevalence threshold, these algorithms have to re-collect table instances and re-calculate participation indexes of all patterns for mining the prevalent patterns that users expect to acquire. To tackle this issue, we propose an overlapping clique-based spatial co-location pattern mining framework (OCSCP). In our framework, we design a two-level filter mechanism with the first level is a feature type filter and the second level is a neighboring instance filter. By employing the mechanism, under a certain neighbor relationship, spatial instances are divided into a set of overlapping cliques and each clique is also a co-location instance of a pattern. And then, a co-location pattern hash map structure is designed to store table instances of patterns based on these overlapping cliques. The participation index of each pattern can be fast and directly calculated from the hash map structure. Thus, when the prevalence threshold is changed, the proposed framework does not need to re-gather table instances, and the mining result can be adaptively and quickly given to users. The proposed algorithms are performed on both synthetic and real-world data sets to demonstrate that our algorithms can rapidly respond to user requirements compared to the previous algorithms. [ABSTRACT FROM AUTHOR]

Carbon nanotube (CNT)/metal composites have attracted much attention due to their enhanced electrical and thermal performance. How to achieve the scalable fabrication of composites with efficient dispersion of CNTs to boost their performance remains a challenge for their wide realistic applications. Herein, the nanoscale dispersion of CNTs in the Stannum (Sn) matrix to boost thermal and electrical conductivity via facile ball milling techniques was demonstrated. The results revealed that CNTs were tightly attached to metal Sn, resulting in a much lower resistivity than that of bare Sn. The resistivity of Sn with 1 wt.% and 2 wt.% CNTs was 0.087 mΩ·cm and 0.056 mΩ·cm, respectively. The theoretical calculation showed that there was an electronic state near the Fermi level, suggesting its electrical conductivity had been improved to a certain extent. In addition, the thermal conductivity of Sn with 2 wt.% CNTs was 1.255 W·m−1·K−1. Moreover, Young's modulus of the composites with CNTs mass fraction of 10 wt.% had low values (0.933 MPa) under low strain conditions, indicating the composite shows good potential for various applications with different flexible requirements. The good electrical and thermal conductive CNT networks were formed in the metal matrix via facile ball milling techniques. This strategy can provide guidance for designing high-performance metal samples and holds a broad application potential in electronic packaging and other fields. [ABSTRACT FROM AUTHOR]

Various studies have shown that visfatin may be connected to metabolic syndrome (MS). However, epidemiological studies yielded conflicting outcomes. The purpose of this article was to highlight the relationship between the plasma visfatin level and MS risk by conducting a meta-analysis of available literature. A comprehensive literature search of eligible studies was done up to January 2023. Data were presented as standard mean difference (SMD). Observational methodological meta-analysis was conducted to assess the relationships between visfatin levels and MS. The visfatin levels between patients with MS or not were calculated by SMD and 95% confidence interval (CI) using the random-effects model. Funnel plot (visually inspect publication bias), Egger's linear regression test and Begger's linear regression test were applied to describe the risk of publication bias. A sensitivity analysis was performed via sequentially omitting each of the study one by one. In total, 16 eligible studies comprising 1016 cases and 1414 healthy controls finally enrolled in the current meta-analysis for pooling meta-analysis. Overall, the meta-analysis results revealed that visfatin levels in MS patients were significantly greater than that of controls group (SMD: 0.60, 95% CI=0.18–1.03, I2=95%, p<0.001). The results of the subgroup analysis showed that gender did not affect the results of meta-analysis. This meta-analysis shed light on the fact that circulating visfatin levels were significantly higher in patients with MS than in the controls group. Visfatin may a chance to predict the occurrence of MS. [ABSTRACT FROM AUTHOR]

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Purpose: To investigate the synergistic effect of vitamin D and neferine on the growth and metastasis of colorectal cancer (CRC). Methods: The synergistic effect of biologically active form of vitamin D, VD3 and neferine on the treatment of CRC was investigated by bliss analysis. Colony formation and wound healing ability, migration and invasion ability, and epithelial mesenchymal transition of HCT-116 cells, as a response to the combination treatment with VD3 and neferine were evaluated. Results: VD3 and neferine showed a synergistic effect on CRC cell growth at a relatively low dose. The wound healing and colony formation capacity, cell migration and invasion abilities were all decreased by combination use of VD3 and neferine, compared to the VD3 or neferine treated single group. Furthermore, VD3 and neferine significantly decreased the expressions of N-cadherin, vimentin, snail, and slug in HCT-116 cells. Conclusion: These data suggest that neferine enhances the anticancer capability of VD3 and reduces the dose dependency of VD3. The combination of vitamin D with neferine appears to be a potential therapeutic strategy for CRC. [ABSTRACT FROM AUTHOR]

Atmospheric pollution by fine particulate matter (PM2.5) and ozone (O3) is a serious concern in China for its danger to human health and to the environment. As such, it has become, since 2013, the target of governmental emission reduction policies. Subsequently, PM2.5 concentrations in China have decreased rapidly, but surface O3 concentration is still measurably increasing in most regions of China. Indeed, although emission reduction policies influence O3 chemical production and loss processes by their impact on O3 precursor concentrations, O3 pollution is also affected by meteorological factors. In this study, we analyzed the spatial distribution and temporal variations of surface solar radiation and aerosol extinction to explain the recent increase in surface O3 concentration. Our results confirmed a marked PM2.5 concentration decrease between 2015 and 2019, especially in northern China, and a simultaneous O3 concentration increase. Surface solar radiation showed geographically consistent increases, likely caused by the decreasing PM2.5 concentrations and the resulting lower aerosol optical thickness. The surface solar radiation increasing enhanced photochemical O3 production. We also investigated cloud cover distribution and trends. It demonstrated that the surface solar radiation intensity increase in northern China was caused by decreasing aerosol concentrations, not by cloud cover differences. Moreover, most emission reduction policies focus on sulfur and nitrogen oxides, less on other important O3 precursors, such as the non-methane volatile organic compounds (NMVOCs). The emission of O3 precursors has not reached the level of suppressing O3 pollution. Stricter emission reduction policies are, therefore, required to mitigate O3 pollution. [ABSTRACT FROM AUTHOR]

Soil aggregate stability and soil erodibility (k) are crucial indicators of soil quality that exhibit high sensitivity to changes in soil function. Therefore, it is of great significance to explore the quantitative relationship between these indicators and soil quality for effective ecosystem monitoring and assessment. In this study, soil samples were collected from eight altitude gradients in a karst mountainous area; we analyzed 11 soil physical, chemical, and biological properties, and assessed soil quality using the minimum data set (MDS) method. The results revealed that soil aggregate stability, bulk density (BD), pH, and fungal community diversity exhibited a unimodal altitudinal pattern, whereas the soil organic carbon (SOC), total nitrogen (TN), and C:N ratio showed an increasing trend. Among the factors considered, SOC, BD, soil pH, mechanical composition, and fungal community diversity were found to explain the most variation in soil aggregate stability and soil erodibility (k). Principal component analysis (PCA) identified soil fungal community diversity, C:N ratio, coarse sand, and macro-aggregate (MA) content as highly weighted indicators for MDS. The integrated soil quality index (SQI) values, ranging from 0.30 to 0.62 across the eight altitude gradients, also exhibited a unimodal altitudinal pattern. The analysis indicated a significant linear relationship between the fractal dimension (D) and soil erodibility of the EPIC model (Kepic) with SQI, suggesting that D and Kepic can serve as alternative indicators for soil quality. These findings further enhance our understanding of the response of soil properties to altitude changes, and provide a novel method for assessing and monitoring soil quality in karst mountainous areas. [ABSTRACT FROM AUTHOR]

Carbon-based catalysts for activating persulfate to drive advanced oxidation processes (AOPs) are widely used in wastewater treatment. In this study, Shewanella oneidensis MR-1, a typical ferric reducing electroactive microorganism, was utilized as the raw material of biochar (BC) to prepare a novel green catalyst (MBC). The effect of MBC on activating persulfate (PS) to degrade rhodamine B (RhB) was evaluated. Experimental results showed that MBC could effectively activate PS to degrade RhB to reach 91.70% within 270 min, which was 47.4% higher than that of pure strain MR-1. The increasing dosage of PS and MBC could improve the removal of RhB. Meanwhile, MBC/PS can well perform in a wide pH range, and MBC showed good stability, achieving 72.07% removal of RhB with MBC/PS after 5 cycles. Furthermore, the free radical quenching test and EPR experiments confirmed the presence of both free radical and non-free radical mechanisms in the MBC/PS system, with •OH, SO4•− and 1O2 contributing to the effective degradation of RhB. This study successfully provided a new application for bacteria to be used in the biochar field. [ABSTRACT FROM AUTHOR]

Source-free domain adaptation (SFDA) is from unsupervised domain adaptation (UDA) and do apply to the special situation in reality that the source domain data is not accessible. In this subject, self-supervised learning is widely used in previous works. However, inaccurate pseudo-labels are hardly avoidable and that degenerates the adapted target model. In this work, we propose an effective method, named RS2L (robust self-supervised learning), to reduce the negative impact due to inaccurate pseudo-labels. Two strategies are adapted. The first is called structure-preserved pseudo-labeling strategy which generates much better pseudo-labels by stored predictions of k-closest neighbors. Another is self-supervised learning with mask. We use threshold masks to select samples for different operations, i.e., self-supervised learning and structure-preserved learning. For different masks, the threshold values are different. So it is not excluded that some samples participate in both two operations. Experiments on three benchmark datasets show that our method achieves the state-of-the-art results. [ABSTRACT FROM AUTHOR]

A cerium (Ce) metal is an important rare earth element that is extremely reactive in air. The Ce surface chemistry can provide insight into actinides and is widely used in nuclear science. Nickel (Ni) is an inert metal with the potential to passivate the Ce surface. The surface and interface properties of a Ce metal with an ultrathin Ni film before and after air exposure have been studied. To prevent air exposure, in situ magnetron sputtering of Ce and Ni metal and surface analysis were carried out. Compared to the ex situ results, the in situ deposition of an Ni film improves the stability of both Ce and Ni. This work provides insight into the impact of the history of the oxidation process to Ce metal properties. [ABSTRACT FROM AUTHOR]

Representation learning based on graph data has shown significant value for graph downstream tasks such as recommendation system and link prediction. However, current methods have some drawbacks: the fixed propagation of graph neural network limits the semantic expression of node representations, and encoder-decoder architecture with regularized reconstruction is prevented from learning differentiated features between nodes, which may lead to node representations not well suited to some graph downstream tasks. Therefore, a multi-level feature augmented graph representation learning model has been proposed via mutual information maximization, which is capable of learning high quality node representations in an unsupervised manner. This model first uses an extractor to preserve distinguishable features contained in original attributes, which are then fed to an aggregator to maintain the local relevance and global difference of nodes in encoding space. Finally, the strategy of deep graph infomax is applied to unify the global encoding rules. Experimental results demonstrate that the encoding performance of the model completely outperforms all mainstream comparative baselines on several classification benchmark datasets for transductive and inductive learning. [ABSTRACT FROM AUTHOR]

Deep neural network-based methods have achieved outstanding results in the task of text classification. However, the relationship of text–label and label–label has not been thoroughly investigated for most existing methods. Furthermore, these methods have excessive computational and memory overhead for large-scale classification. To address these challenges, we propose a novel framework with metric learning and knowledge distillation. We first project the texts and labels into the same embedding space by utilizing the symmetry metric learning on both text–centric and label–centric relationships. Then the distillation component is introduced to learn the text representation features with a deep module. Finally, we use this distilled module to encode new text and make predictions with label embeddings in the metric space. Experimental results on four real datasets show that our model achieves very competitive prediction accuracy while improving training and prediction efficiency. [ABSTRACT FROM AUTHOR]

Mitochondria play a vital role in maintaining cellular homeostasis. In recent years, studies have found that mitochondria have an important role in the occurrence and development of tumors, and targeting mitochondria has become a new strategy for tumor treatment. Lonidamine (LND), as a hexokinase inhibitor, can block the energy supply and destroy mitochondria. However, poor water solubility and low mitochondrial selectivity limit its clinical application. To overcome these obstacles, we report redox-activated self-assembled carrier-free nanoparticles (Cy-TK-LND NPs) based on a small molecule prodrug, in which photosensitizer IR780 (Cy) which targets mitochondria is conjugated to LND via a sensitive thioketal (TK) linker. Intracellular oxidative stress induced by laser radiation leads to the responsive cleavage of Cy-TK-LND NPs, facilitating the release of free LND into mitochondria. Subsequently, LND damages mitochondria, triggering the apoptosis pathway. The results show the effective killing effect of Cy-TK-LND NPs on cancer cells in vitro and in vivo. The IC50 value of irradiated Cy-TK-LND NPs is 5-fold lower than that of free LND. Moreover, tumor tissue section staining results demonstrate that irradiated Cy-TK-LND NPs induce necrosis and apoptosis of tumor cells, upregulate cytochrome C and pro-apoptotic Bax, and downregulate anti-apoptotic Bcl-2. Generally, Cy-TK-LND NPs exhibit efficient mitochondria-targeted delivery to improve the medicinal availability of LND. Accordingly, such a carrier-free prodrug-based nanomedicine holds promise as an effective cancer chemotherapy strategy. [ABSTRACT FROM AUTHOR]

The East Asian summer monsoon (EASM) was extremely strong in 2018, which substantially affected surface ozone (O3) in China. Taking 2018 and the average synthesis of 2003 and 2010 to represent the strong and weak EASM cases, respectively, GEOS-Chem with constant anthropogenic emission was employed to investigate the impact of the EASM on surface O3 in the east of China. Simulations show that surface O3 decreased in the northeast and the eastern coast of China and increased in most of the remaining regions during strong EASM. The difference in surface O3 between strong and weak EASM was around −15~7 ppbv. After analyzing relevant meteorological fields, it is found that the decrease in northeast China was mainly attributed to the large increase in vertical upward transport. The considerable decrease in the Huang-Huai-Hai region depended on the dilution and diffusion of eastward anomalous horizontal circulation. The increase in Hunan-Hubei-Guangdong Province was largely due to input from the north. In addition, the vast areas between the Yangtze River and the Yellow River were supported by higher temperatures and stronger shortwave solar radiation that promoted photochemical reactions. The reasons for changes in Shanxi-Sichuan-Yunnan Province were relatively more complex and thus require more in-depth exploration. [ABSTRACT FROM AUTHOR]

Flexible strain sensors from biaxially stretched carbon nanotubes (CNTs)/polydimethylsiloxane (PDMS) nanocomposites are fabricated in this study. It is shown that biaxial stretching promotes the homogeneous distribution and alignment of CNTs in the stretching plane, improving the sensing performance of the strain sensors. The optimized stretching ratios (SRs) of CNT/PDMS nanocomposites are determined. Compared to an unstretched CNT/PDMS‐1.0 sensor (gauge factor [GF] value = 0.73, detectable range from 0 to 60%), the 1.5 wt% CNT/PDMS‐1.5 sensor (SR = 1.5) exhibits enhanced strain sensitivity (GF = 2.8), a wider detectable range (0–370%) and better performance stability. The GF values of CNT/PDMS‐2.0 and CNT/PDMS‐2.5 sensors with SRs of 2.0 and 2.5, respectively, were 1.18 and 1.06, respectively, due to more significant conductive network reconstruction in the process of applying strain, leading to a decreasing GF. The possibility of sensors in the application of wearable electronic components is also demonstrated. The sensor shows a clear and stable signal output when different strain modes are applied, such as tensile, compressive, bending, and twisting. [ABSTRACT FROM AUTHOR]

Microtubules are highly dynamic polymers of α/β‐tubulin that represent major components of the cytoskeleton and have been established as an attractive druggable target of tumors. Taxol, also known as Paclitaxel, is a microtubule‐stabilizing agent that binds stoichiometrically to a specific site in β‐tubulin, where is out of but nearby the interacting interface of α/β‐tubulin complex, to improve the complex stability through an allosterically regulatory mechanism. In this study, the systematic binding profile of taxol and its 22 structurally diverse, medicinally relevant analogues (termed as taxalogues) to the specific site of β‐tubulin as well as their effects on the α/β‐tubulin complex stability were created and investigated by using structural modeling, dynamics simulation, and energetics analysis. Two helices H1 and H2 of β‐tubulin were identified as key hotspots at the α/β‐tubulin interface to mediate the binding event of β‐tubulin to α‐tubulin. Taxalogue binding can trigger a conformational displacement in the H1 and H2 to elicit the stabilization (or destabilization) of α/β‐tubulin. However, strong taxalogue binding potency to β‐tubulin does not mean effective α/β‐tubulin stabilization; there is only an indirect, moderate correlation between them. Cell viability assay revealed that the taxalogue‐induced α/β‐tubulin stabilization, but not the taxalogue binding, directly influences the antitumor activity of taxalogues. The activity increases in the order: SB‐T‐1214 < docetaxel < taxol < larotaxel < cisplatin < cabazitaxel. [ABSTRACT FROM AUTHOR]

Polymethylsilsesquioxane microspheres were synthesized by hydrolysis condensation with methyltrimethoxysilane as raw material. The effects of raw material ratio, hydrolysis time, hydrolysis pH, polycondensation time and polycondensation pH on particle size were discussed. The optimum synthesis conditions are as follows: the mass ratio of methyltrimethoxysilane to ultrapure water is 1: (7-8), hydrolysis time is 1 h, hydrolysis pH value is 5-6 polycondensation time is 2 h, and polycondensation pH value is 9. At the same time, the filtrate generated in the reaction process shall be reused as solvent to prepare polymethylsilsesquioxane microspheres, which achieves the goal of environmental protection. When polymethylsilsesquioxane microspheres are used as light diffusion agent of polycarbonate, the light diffusion plate has higher light transmittance and haze compared with the similar products in the market. [ABSTRACT FROM AUTHOR]

Diagnosis prediction exploits electronic health records (EHRs) to predict the future diagnoses of patients, further supporting clinical decision making and personalized treatments. However, a patient's EHR is an irregular sequence of visits that contains a large number of medical concepts. The disease progression patterns are closely related to the visits, as well as the contextual knowledge of each visit. The existing diagnosis prediction methods ignore the complex relationships between the visits and the contextual knowledge, and thus cannot achieve satisfactory performance. Therefore, we develop a knowledge‐aware representation learning method to comprehensively model these complex relationships. Specifically, we first construct a medical knowledge graph to model the correlations between medical concepts in EHRs, and project the contextual knowledge into the pre‐learned vectors. We then devise an enhanced gated recurrent unit (GRU) neural network to extract the long‐term intra‐relationships between visits, and design a novel knowledge attention module to capture the complex inter‐relationships between the visits and the contextual knowledge. Armed with these, we provide a powerful and flexible framework to capture the long‐term discriminative disease progression patterns for diagnosis prediction. Intensive experiments are conducted on two real‐world EHR datasets. And the model achieves a competitive performance with Code‐level Accuracy@20 of 0.7465 and Visit‐level Precision@20 of 0.7547 on MIMIC‐III, and Code‐level Accuracy@20 of 0.9337 and Visit‐level Precision@20 of 0.9427 on MIMIC‐IV. [ABSTRACT FROM AUTHOR]

Background: Postmenopausal osteoporosis (POP) is a prevalent skeletal disease among elderly women. Previous study indicated that suppressor of cytokine signaling 3 (SOCS3) participates in the regulation of bone marrow stromal cell (BMSC) osteogenesis. Here, we further investigated the exact function and mechanism of SOCS3 in POP progression. Methods: BMSCs were isolated from Sprague–Dawley rats and treated with Dexamethasone (Dex). Alizarin Red staining and ALP activity assays were applied to assess the osteogenic differentiation of rat BMSCs under the indicated conditions. Osteogenic genes (ALP, OPN, OCN, COL1) mRNA levels were determined using quantitative RT-PCR. Luciferase reporter assay verified the interaction between SOCS3 and miR-218-5p. Rat models of POP were established in ovariectomized (OVX) rats to detect the in vivo effects of SOCS3 and miR-218-5p. Results: We found that silencing SOCS3 antagonized the suppressive effects of Dex on the osteogenic differentiation of BMSCs. SOCS3 was found to be targeted by miR-218-5p in BMSCs. The SOCS3 levels were negatively modulated by miR-218-5p in femurs of POP rats. MiR-218-5p upregulation promoted the BMSC osteogenic differentiation, while SOCS3 overexpression reversed the effects of miR-218-5p. Moreover, SOCS3 was highly expressed and miR-218-5p was downregulated in the OVX rat models, and silencing SOCS3 or overexpressing miR-218-5p alleviated POP in OVX rats by promoting osteogenesis. Conclusion: SOCS3 downregulation mediated by miR-218-5p increases osteoblast differentiation to alleviate POP. [ABSTRACT FROM AUTHOR]

Aim: The aim of this study was to explore the factors that are associated with posttraumatic growth among spouses of women diagnosed with gynaecological cancer. Design: A cross‐sectional descriptive study. Methods: A convenience sample of 312 spouses of women diagnosed with gynaecological cancer was recruited from two comprehensive hospitals in China, from March 2018 to March 2020. Demographic characteristics, cancer‐related characteristics, posttraumatic growth, perceived social support and coping were assessed using self‐reported questionnaires. Descriptive statistics and multiple linear regression analysis were performed. The methods were guided by the STROBE checklist. Results: The mean score of posttraumatic growth was 46.7 (standard deviation = 16.7). The associated factors of posttraumatic growth were spouses' age, perceived social support, problem‐focused coping, dysfunctional coping (e.g. denial) and cancer treatment received by partners, which accounted for 34% of total posttraumatic growth score. Patient or public contribution: All participants contributed to the conducting of this study by completing self‐reported questionnaires. [ABSTRACT FROM AUTHOR]

Considering the problem of low classification performance caused by imbalanced dataset in the classification task, an EM (expectation- maximization) clustering oversampling algorithm for imbalanced data is proposed, which can solve the problem of imbalanced data fundamentally by increasing the number of samples of a few classes through oversampling. Firstly, the clustering technology is adopted to measure the similarity between samples by Euclidean distance, and the center point of each cluster is selected as the oversampling point, which solves the problem of insufficient importance of samples to some extent. Secondly, the problem that SMOTE, Cluster- SMOTE and other methods have no pertinence in clustering space can be solved by sampling in a few sample spaces directly. At the same time, through over-sampling 30% of the number of samples of a few categories, the problems that undersampling based on Cluster clustering blindly pursues the balance of the number of samples of two categories and SMOTE and other algorithms do not have clear sampling rate are effectively solved. Experiments on 24 public datasets with class imbalance are carried out to verify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Spatial co-location pattern mining (SCPM) aims to discover subsets of spatial features frequently located together in proximate areas. Previous studies of SCPM solely concern the inter-features association of a pattern, but neglect the interesting intra-feature behavior. In this paper, we propose the task of spatial co-location pattern mining with coupling relation consideration (SCPM-CR) to capture complex relations embedded in a co-location. Specifically, InterPCI measure is designed to capture the inter-features coupling by considering the comprehensive interaction of objects for the features in a pattern, and luckily it possesses the anti-monotone property. Another measure, IntraCAI, is proposed to capture the congregating behavior of intra-feature objects under the restriction of a co-location. A general framework is designed for SCPM-CR task and experiments show that a large fraction of computation time is devoted to identifying the participating objects of a candidate pattern. To tackle this calculation bottleneck, a novel candidate-and-search algorithm is suggested, CS-HBS, equipped with heuristic backtracking search. Extensive experiments are conducted on real and synthetic datasets to demonstrate the superiority of SCPM-CR compared with traditional SCPM methods, and also to validate the efficiency and scalability of CS-HBS. Experimental results show that CS-HBS outperforms the baselines by several times or even orders of magnitude. [ABSTRACT FROM AUTHOR]

Unsupervised domain adaptation (UDA), which can transfer knowledge from labeled source domain to unlabeled target domain, needs to access a large number of labeled source data in the process of generalization. However, the data of two domains may not be accessed at the same time due to data privacy protection. To solve this problem, source-data free domain adaptation (SFDA) began to receive attention. However, too little source information will lead to some performance gaps. To balance the issues between UDA and SFDA, a new setting called Prototype-based domain adaptation (Prototype-DA) is proposed, which further improves the practicability of UDA by using source category prototype instead of source data. At the same time, it can also ensure the privacy of source data like SFDA. Specifically, our training process can be divided into two steps. First, the source data is used to pre-train a source model, and the source category prototypes are obtained after the training of source model. Then, to generalize the source model to the target domain, category maximum mean discrepancy (Category-MMD) is defined so that the target data can be aligned with the source category prototypes. In this way, source category prototypes will transfer knowledge to the target domain together with the source model. Through source category prototypes, Prototype-DA can not only achieve the comparable results than the method using source data, but also protect the privacy of source data to some extent. Furthermore, the target category prototypes are constructed and the consistency between the labels of target category prototypes and the classification results is required. This prototype-label consistency regularization, proposed by us for the first time, helps to extract discriminative features in the target domain. Compared with the previous UDA methods and SFDA methods, extensive experiments on multiple public domain adaptation datasets show that Prototype-DA achieves the state-of-the-art results. At the same time, the traditional UDA theory is expanded to our method setting and makes a theoretical analysis to ensure the effectiveness of our method. [ABSTRACT FROM AUTHOR]

Childhood witnessing domestic violence (CWDV) exerts short- and long-term negative impacts on emotional and behavioral health. The present study investigated the epidemiological features of CWDV, and its negative impact on psychological outcomes in a sample of Chinese college students. The mediating role of emotion regulation strategies on CWDV associations with psychological outcomes and gender differences in these relationships were examined. A total of 3,126 respondents (1,034 males; 2,086 females; 6 missing data in gender) completed the study questionnaire, which included demographic characteristics, CWDV, and suicide attempt history items as well as the Emotion Regulation Questionnaire (ERQ) and Affect Lability Scale-18 (ALS-18). Overall, 43.03% of the respondents reported CWDV, including 44.87% of males and 42.09% of females. Higher frequencies of CWDV were found to be related to the following factors: unstable marital status of parents; not being an only child; being a left-behind child, family financial difficulties, consumption of alcohol in the past year, and being in relatively poor physical condition. Among males, ERQ suppression scores were significantly higher for those men who experienced "often or every day" CWDV than for men who indicated that they did not have any history of CWDV (Bonferroni-corrected p = 0.047). More frequent CWDV was associated with higher ALS-18 scores and increased risk of suicide attempts in males and females (p < 0.05), and emotion regulation (suppression) was found to mediate the association between CWDV and affective lability among males. This study revealed high rates of CWDV, and serious impacts of CWDV on mental health in male and female Chinese college students. In males, but not females, emotion regulation strategy use, use of suppression, was found to act as a mediator in the association of CWDV with affective lability. Our findings suggest that interventions for individuals with CWDV should focus on the emotional regulation, which may help them improve mental health, especially in males. [ABSTRACT FROM AUTHOR]

Brachial plexus root avulsion (BPRA) is frequently caused by high-energy trauma including traffic accident and birth trauma, which will induces massive motoneurons (MNs) death as well as loss of motor and sensory function in the upper limb. The death of MNs is attributed to energy deficiency, neuroinflammation and oxidative stress at the injured ventral horn of spinal cord triggered by BPRA injury. It has been reported which aldose reductase (AR), an endogenous enzyme that catalyzes fructose synthesis, positively correlates with the poor prognosis following cerebral ischemic injury, diabetic retinopathy and diabetic peripheral neuropathy. However, the role of AR in BPRA remains unknown. Herein, we used a mouse model and found that in the spinal cord of BPRA mice, the upregulation of AR correlated significantly with (1) an inactivated SIRT1-AMPK-mTOR pathway and disrupted autophagy; (2) increased byproducts accumulation of lipid peroxidation metabolism and neuroinflammation; and (3) increased MNs death. Furthermore, our results demonstrated the role of AR in BPRA injury whereby the absence of AR (AR knockout mice, AR-/-) prevented the hyper-neuroinflammation and disrupted autophagy as well as motor neuron death caused by BPRA injury. Finally, we further demonstrate that AR inhibitor epalrestat is neuroprotective against BPRA injury by increasing autophagy level, alleviating neuroinflammation and rescuing MNs death in mice. Collectively, our data demonstrate that the AR upregulation in the spinal cord is an important factor contributing to autophagy disruption, neuroinflammation and MNs death following brachial plexus roots avulsion in mice. Our study also provides a promising therapy drug to assist re-implantation surgery for the treatment of BPRA. [ABSTRACT FROM AUTHOR]