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Abstract Streptococcus suis infection represents a major challenge in pig farming and public health due to its zoonotic potential and diverse serotypes, while existing vaccines lack effective cross-protection. This study employed reverse vaccinology and immunoinformatics to identify 8 conserved proteins across 11 prevalent serotypes of S. suis. 16 candidate epitopes were selected to design three multi-epitope antigens against S. suis (designated as MEASs), which fused with a dendritic cell-targeting peptide to improve antigen presentation in host. Purified MEASs displayed favorable cross-reactogenicity against 29 serotype-specific antiserums. Robust humoral and cellular immune responses can be induced by MEAS 1 and MEAS 3 in a mouse model, which provided substantial protection against virulent strains from two different serotypes. In particular, their immune serums exhibited positive opsonization effects within bloodstream and macrophage phagocytosis. Taken together, we identified two promising MEASs with excellent cross-protection, offering potential in preventing S. suis infections in a mouse model.

Abstract A high‐salt diet can induce hypertension, so salt reduction can prevent hypertension. γ‐Glutamyl peptides (GGPs) have obvious taste‐enhancing effects, while their contents in natural foods are relatively low. Yeast protein rich in Glu/Gln is a good precursor for the preparation of GGPs. In this study, yeast protein‐derived GGPs were prepared through hydrolysis and transpeptidation reactions, followed by sensory evaluation and E‐tongue analysis. Peptide sequences were identified by LC−MS/MS and screened for molecular docking. The optimal reaction conditions were hydrolysis for 4 h, enzyme concentration of 16 U/g, and transpeptidation for 4 h. GGPs could increase salt and umami intensity by 60.78% and 40.93% based on sensory evaluation, 22.52%, and 16.40% according to E‐tongue analysis. Fifteen γ‐glutamyl peptides with different peptide lengths were selected for molecular docking. Molecular docking confirmed their binding to calcium‐sensing receptors (CaSr) through hydrogen bonds and hydrophobic interaction, while interaction between CaSR receptor and γ‐glutamyl di‐, tri‐, and oligo‐peptides varied in binding energy. The stimulation received by CaSR lasted a longer time and varied in intensity. It was further proved that the flavor of mixed peptides has a layered sense and can give people a rich taste experience. Overall, yeast protein‐derived GGPs can enhance salt and umami taste, which can reduce salt usage without compromising taste.

This study examined the influence of lifestyle habits on health self-assessments among elderly rural men and the effect of age on the strength of the impact to provide a basis for targeted health improvement in this population. Data from the 2018 Chinese longitudinal healthy longevity survey were analyzed using one-way analysis of variance, regression analysis, and Shapley value decomposition tests. The model analysis results showed that elderly rural men who did not drink alcohol or were moderate drinkers, maintained regular sleep patterns, exercised regularly, and had active social interactions had better health self-assessment results (p < 0.01). Notably, the impact of lifestyle habits on health perceptions varied with age. Specifically, the influence of alcohol consumption and sleep duration decreased with age, while the benefits of physical activity and social engagement became more pronounced. These results suggest that lifestyle habits significantly impact the health self-assessments of elderly rural men, with notable variations observed in the strength of these effects across different ages. To improve the overall health of elderly rural men, alcohol cessation interventions should be implemented as early as possible, attention should be paid to the sleep habits of young male seniors, and low-intensity group exercise and social activities should be actively organized for older male seniors.

Abstract Long-term mortality effects of particulate air pollution have been investigated in a causal analytic frame, while causal evidence for associations with gaseous air pollutants remains extensively lacking, especially for carbon monoxide (CO) and sulfur dioxide (SO2). In this study, we estimated the causal relationship of long-term exposure to nitrogen dioxide (NO2), CO, SO2, and ozone (O3) with mortality. Utilizing the data from National Morbidity, Mortality, and Air Pollution Study, we applied a variant of difference-in-differences (DID) method with conditional Poisson regression and generalized weighted quantile sum regression (gWQS) to investigate the independent and joint effects. Independent exposures to NO2, CO, and SO2 were causally associated with increased risks of total, nonaccidental, and cardiovascular mortality, while no evident associations with O3 were identified in the entire population. In gWQS analyses, an interquartile range-equivalent increase in mixture exposure was associated with a relative risk of 1.067 (95% confidence interval: 1.010–1.126) for total mortality, 1.067 (1.009–1.128) for nonaccidental mortality, and 1.125 (1.060–1.193) for cardiovascular mortality, where NO2 was identified as the most significant contributor to the overall effect. This nationwide DID analysis provided causal evidence for independent and combined effects of NO2, CO, SO2, and O3 on increased mortality risks among the US general population.

Abstract Background Minimally invasive treatments for spinal cord tumours are common. The aim of this study was to compare the perioperative outcomes of patients with thoracic extramedullary spinal tumours (TEST) treated by microendoscopic minimally invasive surgery—hemilaminectomy through a homemade tubular retractor (MIS-TR) and microscopic full laminectomy (open surgery). Methods Between February 2016 and February 2021, 51 patients with TEST were included. According to their clinical data, patients were classified into the MIS-TR group (n = 30) and the open surgery group (n = 21) and assessed. Results In both groups, the mean operation time, change in perioperative ASIA score, and modified Macnab score were comparable. The average postoperative hospital stay in the MIS-TR group was substantially shorter than that in the open surgery group (p

Abstract Autism Spectrum Disorders (ASD) are neurodevelopmental disorders that cause people difficulties in social interaction and communication. Identifying ASD patients based on resting-state functional magnetic resonance imaging (rs-fMRI) data is a promising diagnostic tool, but challenging due to the complex and unclear etiology of autism. And it is difficult to effectively identify ASD patients with a single data source (single task). Therefore, to address this challenge, we propose a novel multi-task learning framework for ASD identification based on rs-fMRI data, which can leverage useful information from multiple related tasks to improve the generalization performance of the model. Meanwhile, we adopt an attention mechanism to extract ASD-related features from each rs-fMRI dataset, which can enhance the feature representation and interpretability of the model. The results show that our method outperforms state-of-the-art methods in terms of accuracy, sensitivity and specificity. This work provides a new perspective and solution for ASD identification based on rs-fMRI data using multi-task learning. It also demonstrates the potential and value of machine learning for advancing neuroscience research and clinical practice.

Recycled aggregate concrete (RAC) is widely recognized as a promising approach for recycling construction and demolition waste. However, its practical application remains limited. A contributing factor to this limitation is the incomplete understanding of RAC’s durability characteristics, particularly its permeability, which is closely tied to the transport of harmful ions within the concrete matrix. Despite the critical importance of this issue, research in this area is still relatively sparse. This knowledge gap has motivated the current study, which seeks to thoroughly investigate the water permeability properties of RAC. In this comprehensive study, 84 specimens were fabricated for permeability testing. This study explores the influence of various factors, including the sources of coarse and fine recycled aggregates (RAs), the RA replacement ratios, and the water-to-binder ratio. The results indicate that the inclusion of both coarse and fine RAs reduces the impermeability of concrete. However, enhancing the quality of these RAs—specifically by increasing the compressive strength of the source concrete—can mitigate the reduction in impermeability. A statistical relationship is established between the average and maximum water permeation depths measured during permeability testing, leading to the formulation of a correlation between the permeability coefficient and the impermeability grade of RAC. Furthermore, mercury intrusion porosimetry testing was conducted to quantitatively analyze the pore structure of the mortar in RAC, providing a microscopic perspective that explains the macroscopic permeability behavior observed. Finally, a predictive model for estimating the permeability coefficient of RAC is proposed, demonstrating a high level of accuracy.

Lipid droplets (LDs) are organelles associated with lipid storage and energy metabolism, thus, their morphology and quantity are of significant research interest. While commercially available BODIPY dye effectively labels LDs in various cell types, it also labels lysosome-related organelles (LROs) in C. elegans, leading to non-specific LD quantification. Here, we report that the fluorescent signals of BODIPY exhibit distinct fluorescence lifetime patterns for LROs and LDs, which can be captured, visualized, and filtered by fluorescence lifetime imaging microscopy. Furthermore, we proposed and validated a method based on fluorescence lifetime that can improve the accuracy of fat storage quantification in BODIPY vital-staining worms, which holds broad applications, including rapid and accurate LD quantification in forward genetic screening. Additionally, our method enables observing dynamic LD-LRO interactions in living worms, a unique capability of BODIPY vital staining. Our findings highlight distinct BODIPY fluorescence lifetime characteristics of LDs and LROs, providing a valuable tool for future research on LDs, LROs, or their interactions.

Construction and Demolition Wastes (CDW) have a significant impact on global waste streams. Brick waste stands out as a prominent type of CDW, and numerous studies have explored its recycling for the creation of environmentally-friendly concrete. Reverse design of recycled brick aggregate concrete (RBAC) mixture proportion is presented in this paper with a focus on four key objectives, that is: compressive strength, cost, and environmental elements (i.e., energy consumption and carbon emission). Based on compiled experimental datasets of 374 samples, the back propagation neural network (BP), random forest (RF), and four meta-heuristic algorithm optimization models were constructed to achieve the desired compressive strength objective. In all machine learning (ML) methods, the compressive strength of RBAC can be predicted with high accuracy, with the SSA-BP (optimized back propagation neural network model using the sparrow search algorithm) model achieving superior results (i.e., NSE=0.91, RPD=3.2). The SSA-BP is therefore used as the objective function for compressive strength. The economic objective is primarily influenced by material costs, and the objective functions of energy consumption and carbon emission are determined by various aspects of production, transportation, and their mixing processes. In order to obtain the optimal RBAC design, the Non-Dominated Sorting Genetic Algorithm (NSGA-III) was implemented considering imperative constraints. Results indicate that cement amount and recycled brick aggregate (RBA)-to-natural aggregate proportion have a positive impact on the compressive strength. The suggested design framework allows for the creation of RBAC composite designs with varying levels of RBA substitution rates and strength targets, providing valuable guidance for tackling the CDW challenge and optimizing RBA usage.

The microbial communities inhabiting polar ecosystems, particularly in Maxwell Bay, Antarctica, play a pivotal role in nutrient cycling and ecosystem dynamics. However, the diversity of these microbial communities remains underexplored. In this study, we aim to address this gap by investigating the distribution, environmental drivers, and metabolic potential of microorganisms in Maxwell Bay. We analyzed the prokaryotic and eukaryotic microbiota at 11 stations, revealing distinctive community structures and diverse phylum dominance by using high-throughput sequencing. Spatial analysis revealed a significant impact of longitude on microbial communities, with microeukaryotes exhibiting greater sensitivity to spatial factors than microprokaryotes. We constructed co-occurrence networks to explore the stability of microbial communities, indicating the complexity and stability of microprokaryotic communities compared with those of microeukaryotes. Our findings suggest that the microeukaryotic communities in Maxwell Bay are more susceptible to disturbances. Additionally, this study revealed the spatial correlations between microbial communities, diversity, and environmental variables. Redundancy analysis highlighted the significance of pH and dissolved oxygen in shaping microprokaryotic and microeukaryotic communities, indicating the anthropogenic influence near the scientific research stations. Functional predictions using Tax4Fun2 and FUNGuild revealed the metabolic potential and trophic modes of the microprokaryotic and microeukaryotic communities, respectively. Finally, this study provides novel insights into the microbial ecology of Maxwell Bay, expanding the understanding of polar microbiomes and their responses to environmental factors.

Abstract Background Artificial intelligence (AI) that utilizes deep learning (DL) has potential for systemic disease prediction using retinal imaging. The retina’s unique features enable non-invasive visualization of the central nervous system and microvascular circulation, aiding early detection and personalized treatment plans for personalized care. This review explores the value of retinal assessment, AI-based retinal biomarkers, and the importance of longitudinal prediction models in personalized care. Main text This narrative review extensively surveys the literature for relevant studies in PubMed and Google Scholar, investigating the application of AI-based retina biomarkers in predicting systemic diseases using retinal fundus photography. The study settings, sample sizes, utilized AI models and corresponding results were extracted and analysed. This review highlights the substantial potential of AI-based retinal biomarkers in predicting neurodegenerative, cardiovascular, and chronic kidney diseases. Notably, DL algorithms have demonstrated effectiveness in identifying retinal image features associated with cognitive decline, dementia, Parkinson’s disease, and cardiovascular risk factors. Furthermore, longitudinal prediction models leveraging retinal images have shown potential in continuous disease risk assessment and early detection. AI-based retinal biomarkers are non-invasive, accurate, and efficient for disease forecasting and personalized care. Conclusion AI-based retinal imaging hold promise in transforming primary care and systemic disease management. Together, the retina’s unique features and the power of AI enable early detection, risk stratification, and help revolutionizing disease management plans. However, to fully realize the potential of AI in this domain, further research and validation in real-world settings are essential.

Identity-based threshold signature (IDTHS) allows a threshold number of signers to generate signatures to improve the deterministic wallet in the blockchain. However, the IDTHS scheme cannot determine the identity of malicious signers in case of misinformation. To solve this challenge, we propose an identity-based threshold (multi) signature with private accountability (for short AIDTHS) for privacy-preserving blockchain. From the public perspective, AIDTHS is completely private and no user knows who participated in generating the signature. At the same time, when there is a problem with the transaction, a trace entity can trace and be accountable to the signers. We formally define the syntax and security model of AIDTHS. To address the issue of identifying malicious signers, we improve upon traditional identity-based threshold signatures by incorporating zero-knowledge proofs as part of the signature and leveraging a tracer holding tracing keys to identify all signers. Additionally, to protect the privacy of signers, the signature is no longer achievable by anyone, which requires a combiner holding the keys to produce a valid signature. We give a concrete construction of AIDTHS and prove its security. Finally, we implement the AIDTHS scheme and compare it with existing schemes. The key distribution algorithm of AIDTHS takes 34.60 μsand the signature algorithm takes 13.04 ms. The verification algorithm takes 1 s, which is one-third of the time the TAPS scheme uses.

Objective: Triglycerides (TGs) associate with apolipoprotein B100 (apoB100) to form very low density lipoproteins (VLDLs) in the liver. The repertoire of factors that facilitate this association is incompletely understood. FITM2, an integral endoplasmic reticulum (ER) protein, was originally discovered as a factor participating in cytosolic lipid droplet (LD) biogenesis in tissues that do not form VLDL. We hypothesized that in the liver, in addition to promoting cytosolic LD formation, FITM2 would also transfer TG from its site of synthesis in the ER membrane to nascent VLDL particles within the ER lumen. Methods: Experiments were conducted using a rat hepatic cell line (McArdle-RH7777, or McA cells), an established model of mammalian lipoprotein metabolism, and mice. FITM2 expression was reduced using siRNA in cells and by liver specific cre-recombinase mediated deletion of the Fitm2 gene in mice. Effects of FITM2 deficiency on VLDL assembly and secretion in vitro and in vivo were measured by multiple methods, including density gradient ultracentrifugation, chromatography, mass spectrometry, stimulated Raman scattering (SRS) microscopy, sub-cellular fractionation, immunoprecipitation, immunofluorescence, and electron microscopy. Main findings: 1) FITM2-deficient hepatic cells in vitro and in vivo secrete TG-depleted VLDL particles, but the number of particles is unchanged compared to controls; 2) FITM2 deficiency in mice on a high fat diet (HFD) results in decreased plasma TG levels. The number of apoB100-containing lipoproteins remains similar, but shift from VLDL to low density lipoprotein (LDL) density; 3) Both in vitro and in vivo, when TG synthesis is stimulated and FITM2 is deficient, TG accumulates in the ER, and despite its availability this pool is unable to fully lipidate apoB100 particles; 4) FITM2 deficiency disrupts ER morphology and results in ER stress. Conclusion: The results suggest that FITM2 contributes to VLDL lipidation, especially when newly synthesized hepatic TG is in abundance. In addition to its fundamental importance in VLDL assembly, the results also suggest that under dysmetabolic conditions, FITM2 may be an important factor in the partitioning of TG between cytosolic LDs and VLDL particles.

BackgroundPatient-reported outcomes (PROs) can be defined as any report of a patient’s health taken directly from the patient. Routine collection of PRO data has been shown to offer potential benefits to patient-doctor communication. Electronic forms of PRO measures (PROMs) could be more beneficial in comparison to traditional PROMs in obtaining PROs from patients. However, it is currently unclear whether the routine collection of electronic PRO data could result in better outcomes for patients undergoing laparoscopic cholecystectomy (LC). ObjectiveThis study aims to explore the perspectives of patients and surgeons on the use of electronic PROMs. Based on prior research, technical skill and experience level of the surgeon, long-term quality of life, patient involvement in decision-making, communication skills of the surgeon, cleanliness of the ward environment, and standards of nursing care are identified to be the most important factors for the patients. MethodsThis is a mixed methods prospective study that will collect both quantitative (survey) and qualitative (interview) data. The study has two components. The first involves the distribution of an electronic presurvey to patients who received elective LC within 48 hours of their surgery (n=80). This survey will explore the perspective of patients regarding the procedure, hospital experience, long-term outcomes, and the perceived value of using PROMs. These patients will then be followed up after 1 year and given another survey. The second component involves the distribution of the same survey and the completion of structured interviews with general surgeons (n=10). The survey will ascertain what PROs from the participants are most useful for the surgeons and the interviews will focus on how the surgeons view routine PRO collection. A convenience sampling approach will be used. Surveys will be distributed through Qualtrics and interviews will be completed on Microsoft Teams. ResultsData collection began on February 14, 2023. As of February 12, 2024, 71 of 80 recruited patients have been given the presurvey. The follow-up with the patients and the general surgeon components of the study have not begun. The expected completion date of this study is in April 2025. ConclusionsOverall, this study will investigate the potential of electronic PRO collection to offer value for patients and general surgeons. This approach will ensure that patient care is investigated in a multifaceted way, offering patient-centric guidance to surgeons in their approach to care. International Registered Report Identifier (IRRID)DERR1-10.2196/57344

Basalt fiber reinforced recycled aggregate concrete (BFRAC) is a high-performance, environmentally friendly material that combines lightweight, high-strength fibers with low-carbon recycled aggregates (RAs), positioned for extensive use in building structures. However, research on its constitutive relationships is currently scarce, which partly restricts component design and analysis. In this context, the current study thoroughly explores the stress–strain relationship and variability of BFRAC under compression, using 240 cylinders for testing to investigate the influence of factors like coarse/fine RA sources, RA replacement rates, and fiber dosage. The study found that the addition of RAs and fibers reduced the workability of the mixture, particularly with the inclusion of fine RAs and short-cut fibers. Using coarse and fine RAs generally reduces the material’s elastic modulus, compressive strength, and post-peak ductility. Adding fibers can slightly improve compressive strength and peak strain, significantly reduce material brittleness, and have a minimal impact on elastic modulus. Importantly, the study noted that the pre-peak segment of the stress–strain curve of BFRAC is most sensitive to the addition of fine RAs, while the post-peak segment is most sensitive to fiber content. Despite this, using high-quality RAs up to 50% replacement and adding 0.4% by volume of fiber can make BFRAC with mechanical properties comparable to natural aggregate concrete. Based on the observed tests, this paper proposes constitutive relationships that incorporate skeleton curves and variability at different points for the compressive stress–strain behavior of BFRAC.

Existing semi-empirical formulas for predicting punching shear capacity in FRP bar reinforced concrete flat slabs without shear reinforcement often prove inaccurate and unstable. This is primarily due to limited modeling data, inadequate consideration of key variables and neglect of complex nonlinear relationships. To address these challenges, this study delves into the utilization of advanced machine learning (ML) algorithms to offer precise and dependable estimates of punching shear capacity in such structural components. The study initially compiled a comprehensive database comprising 165 sets of test data, integrating eight crucial variables for model development. Subsequently, four data-driven models including back propagation artificial neural network (BPANN), support vector regression (SVR), random forest (RF) and gradient boosting regression tree (GBRT) were formulated to estimate the shear capacity. The efficacy of these models was assessed in comparison to existing prediction formulas. To interpret the models, this study also introduced shapley additive explanation (SHAP) and partial dependence plot (PDP) to quantitatively evaluate the influence of variables on predicted results. Research findings suggest that: (a) Among 25 existing formulas, Ju et al.’s approach performs notably well, with R2, Pre/Exp, MAPE and RMSE values at 0.76, 1.02, 22.2 % and 142.8 kN, respectively. (b) ML models surpass traditional formulas in predictive accuracy, with R2, Pre/Exp, MAPE and RMSE values ranging from 0.89 to 0.93, 1.03–1.09, 4.8–9.5 % and 55.4–69.0 kN, respectively. The GBRT model demonstrates the highest precision. (c) SHAP analysis of the GBRT model reveals that effective slab height and column section aspect ratio are pivotal variables influencing punching shear capacity. (d) PDP analysis quantitatively illustrates how punching shear capacity varies with each key variable.

Recent advances highlight the potential of low-carbon concrete using recycled aggregates from precast rejects. This material, called precast recycled aggregate concrete (PRAC), shows superior properties compared to traditional recycled aggregate concrete. However, accurately predicting PRAC’s mechanical properties remains challenging due to numerous influencing variables. This challenge is further compounded when PRAC’s design must align with mechanical, economic and ecological objectives. In light of these challenges, this study presents a framework that leverages Bayesian model updating and metaheuristic techniques to optimize PRAC’s mix proportions. Initially, a comprehensive database was compiled from existing literature. Subsequently, employing Bayesian model updating, two definitive expressions were established linking PRAC’s mechanical strengths to key variables. Finally, a multi-objective optimization approach, integrating the posterior model and the Cuckoo search algorithm, was devised to identify optimal mix designs for PRAC. The proposed models adeptly capture PRAC’s nuanced property trends, while the Bayesian-Cuckoo search hybrid significantly expedites the mix optimization process. Results advocate for a minimum of 60 % recycled aggregate substitution to strike a balance between PRAC’s strength and environmental impact. In essence, this framework furnishes dependable solutions for PRAC formulations and demonstrates adaptability for potential use in broader concrete mix design scenarios.

In order to study the frost resistance of alkali activated concrete (AAC), 15 AAC cube specimens and 15 cylindrical specimens were designed and manufactured. The frost thawing cycle test and mechanical performance test were used to study the frost resistance of AAC. The results showed that after freeze–thaw cycles, small pores appeared on the surface of the specimen, and the surface roughness increased, resulting in a darker appearance. As the number of freeze–thaw cycles increases, the highest mass loss rate of AAC reaches 1.88%. The cube compressive strength and axial compressive strength of AAC first increase and then decrease, while the elastic modulus gradually decreases. After 50 freeze–thaw cycles, the compressive strength of the specimen was greater than that of the normal temperature specimen. When the freeze–thaw cycles reached 200, the elastic modulus of the specimen degraded by 9.7%, the compressive strength decreased by 18.6%, and the axial compressive strength decreased by 31.2%. Based on the experimental results, a method for calculating the residual strength of AAC after freeze–thaw cycles and key parameters of the freeze–thaw damage constitutive equation were proposed.

Objective To analyze the high risk factors of obstetric brachial plexus palsy （OBPP）, and to explore how to evaluate the relationship between fault medical behavior and OBPP in the process of medical damage forensic identification. Methods A retrospective analysis was carried out on 25 cases of medical damage liability disputes related to OBPP from 2017 to 2021 in Beijing Fayuan Judicial Science Evidence Appraisal Center. The shortcomings of hospitals in birth weight assessment, delivery mode selection, labor process observation and shoulder dystocia management, and the causal relationship between them and the damage consequences of the children were summarized. Results Fault medical behavior was assessed as the primary cause in 2 cases, equal cause in 10 cases， secondary cause in 8 cases, minor cause in 1 case, no causal relationship in 1 case, and unclear causal force in 3 cases. Conclusion In the process of forensic identification of OBPP, whether medical behaviors fulfill diagnosis and treatment obligations should be objectively analyzed from the aspects of prenatal evaluation, delivery mode notification, standardized use of oxytocin, standard operation of shoulder dystocia, etc. Meanwhile, it is necessary to fully consider the objective risk of different risk factors and the difficulty of injury prevention, and comprehensively evaluate the causal force of fault medical behavior in the damage consequences.

Abstract Aims Circulating biomarkers can provide important information for the diagnosis and prognosis of dilated cardiomyopathy (DCM). We explored novel biomarkers for the diagnosis and prognosis of DCM to improve clinical decision‐making. Methods and results A total of 238 DCM patients and 65 control were consecutively enrolled at Zhongshan Hospital between January 2017 and January 2019. In the screening set, four DCM patients and four controls underwent measurements of serum proteomic analysis. Seventy‐six differentially expressed circulating proteins were screened by data‐independent acquisition proteomics, and three of these proteins (S100A4, S100A8/A9, and S100A12) were validated by multiple‐reaction monitoring‐mass spectrometry. In the validation set, subsequently, a total of 234 DCM patients and 61 control subjects were evaluated by enzyme‐linked immunosorbent assay. Circulating S100A4, S100A8/A9, and S100A12 were significantly increased in DCM patients (P 0.05). Cox proportional hazards regression analysis revealed that S100A4 [≥75th percentile vs.

The generation of complex tailings from iron mining is continuously increasing, reaching a reserve of 500 million tons. The iron tailings mainly contain 0.10–0.50 wt% ultrafine SnO2 (−9.6 μm, >50 %), acid-solule Sn (MgSn(OH)6) and 0.20–1.50 wt% Zn and are recognized as an important secondary Sn resource. Unfortunately, traditional mineral processing techniques have a low Sn recovery ratio. In this study, a novel method is proposed for effectively recovering Sn and Zn from complex iron tailings through reduction-sulfurization roasting, and their transformation behavior is determined. The results reveal that MgSn(OH)6 is first transformed into MgSnO3 at 1073 K, and SnO2 is reduced to SnO(g) and then sulfurized to SnS(g). COS acts as the main curing agent during the roasting process, which originates from the oxidation of FeS. As the temperature increases from 1173 to 1273 K, part of SnO2 can combine with CaO to form Ca2SnO4, and the generated CaSnO3 and MgSnO3 are further reduced to Sn(l) and Fe–Sn alloy. Meanwhile, ZnO and ZnS can be efficiently converted to Zn(g). Interestingly, the Fe–Sn alloy cannot be removed due to low Sn activity (a[Sn]). However, the Sn recovery ratio increases to 90 % due to low SnS partial pressure (PSnS) at 1373 K. Under optimal conditions, 90.10 wt% Sn and 94.90 wt% Zn can be extracted in the dust, which can be further processed into higher value-added Sn concentrate. Compared with current beneficiation process, the recovery ratio of SnO2 increased by 20 %. More importantly, MgSn(OH)6 can be efficiently recovered. This is of great significance for sustainable metallurgy of Sn.

Abstract Background Intimate Partner Violence (IPV) is prevalent among HIV-infected men who have sex with men (MSM), with well-established risk factors and adverse outcomes. However, there is a lack of comprehensive investigation of both upstream risk factors and downstream adverse outcomes among HIV-infected MSM in a Chinese context. This study aimed to examine IPV and its associations among a Chinese sample of HIV-infected MSM. Methods A cross-sectional study was conducted among 294 HIV-infected MSM in Jinan City from June to December 2020. All data were collected through an online questionnaire, which included IPV, sexual risk behavior, antiretroviral therapy (ART) adherence, depression, anxiety, and suicidal ideation. Chi-square tests and multivariate logistic regressions were performed to examine risk factors and adverse outcomes of IPV. Results Of the 294 HIV-infected MSM, 71.1% experienced any IPV, including control (37.1%), threat of public identity (30.6%), emotional violence (25.2%), security threat (18.4%), and physical violence (13.9%). The prevalence of sexual risk behavior, good ART adherence, depression, anxiety, and suicidal ideation was 55.1%, 53.4%, 48.3%, 32.3%, and 65.0%, respectively. Abuse of methamphetamine (METH) (aOR:2.79; 95%CI:1.43 ~ 5.45), capsule 0 or stimulating liquid (aOR:2.68; 95%CI:1.31 ~ 5.47), Magu (aOR:3.16; 95%CI:1.51 ~ 6.60), and other new drugs (aOR:2.87; 95%CI:1.52 ~ 5.43), disclosing HIV infection to partners (aOR:2.03; 95%CI:1.10 ~ 3.78), and gay sexual orientation (aOR = 3.32; 95%CI: 1.82 ~ 6.05) were significantly correlated with the experience of IPV. In addition, IPV was significantly associated with sexual risk behavior (aOR = 2.02; 95%CI:1.16 ~ 3.53), ART adherence (aOR = 2.63; 95%CI:1.46 ~ 4.74), depression (aOR = 3.83; 95%CI:2.09 ~ 7.02), anxiety (aOR = 2.27; 95%CI:1.19 ~ 4.35), and suicidal ideation (aOR = 3.78; 95%CI:2.11 ~ 6.80). Conclusions IPV is prevalent among HIV-infected MSM and is associated with poor behavioral and mental health, highlighting more efforts are needed to address this issue. The finding that new drug abuse, HIV disclosure, and gay sexual orientation are associated with increased risk of IPV provides essential insights for the development of comprehensive and targeted IPV prevention and intervention programs in the future.

The objective of this study was to evaluate the accuracy of a CT-based, 3D-printed, patient-specific brain biopsy guide (3D-psBBG) through the application of a transfrontal approach in canine cadavers. A total of ten canine cadavers, with weights ranging from 4.36 to 14.4 kg, were subjected to preoperative CT scans to generate 3D skull models. Customized biopsy guides were created based on these models and manufactured using 3D printing technology. Twenty spinal needle insertions were performed, and the accuracy of needle placement was evaluated through both CT and 3D volume-rendering techniques. The mean needle placement error was 2.1 mm, with no significant differences observed between insertions targeting the fronto-olfactory and piriform lobes. The 3D volume-rendering method demonstrated superior accuracy compared to the CT method, with statistically significant differences in placement errors for both targets. The average time required for the design and manufacture of the guides was 249 min. These findings indicate the high accuracy and potential clinical application of CT-based 3D-psBBG for improving diagnostic outcomes in veterinary neurology.

In residential buildings that are private, autonomous, and occupied spaces for most of the time, it is necessary to maintain good indoor air quality (IAQ), especially when there are children, elderly, or other vulnerable users. Within the development of sensors, their low-cost features with adequate accuracy and reliability, as well as Internet of Things applications, make them affordable, flexible, and feasible even for ordinary occupants to guarantee IAQ monitoring in their homes. This systematic review searched papers based on Scopus and Web of Science databases about the Low-Cost Sensors (LCS) and IoT applications in residential IAQ research, and 23 studies were included with targeted research contents. The review highlights several aspects of the active monitoring strategies in residential buildings, including the following: (1) Applying existing appropriate sensors and their target pollutants; (2) Applying micro-controller unit selection; (3) Sensors and devices’ costs and their monitoring applications; (4) Data collection and storage methods; (5) LCS calibration methods in applications. In addition, the review also discussed some possible solutions and limitations of LCS applications in residential buildings based on the applications from the included works and past device development experiences.

The accumulation of cadmium (Cd) in agricultural soils presents a significant threat to crop safety, emphasizing the critical necessity for effective monitoring and management of soil Cd levels. Despite technological advancements, accurately monitoring soil Cd concentrations using satellite hyperspectral technology remains challenging, particularly in efficiently extracting spectral information. In this study, a total of 304 soil samples were collected from agricultural soils surrounding a tungsten mine located in the Xiancha River basin, Jiangxi Province, Southern China. Leveraging hyperspectral data from the ZY1-02D satellite, this research developed a comprehensive framework that evaluates the predictive accuracy of nine spectral transformations across four modeling approaches to estimate soil Cd concentrations. The spectral transformation methods included four logarithmic and reciprocal transformations, two derivative transformations, and three baseline correction and normalization transformations. The four models utilized for predicting soil Cd were partial least squares regression (PLSR), support vector machine (SVM), bidirectional recurrent neural networks (BRNN), and random forest (RF). The results indicated that these spectral transformations markedly enhanced the absorption and reflection features of the spectral curves, accentuating key peaks and troughs. Compared to the original spectral curves, the correlation analysis between the transformed spectra and soil Cd content showed a notable improvement, particularly with derivative transformations. The combination of the first derivative (FD) transformation with the RF model yielded the highest accuracy (R2 = 0.61, RMSE = 0.37 mg/kg, MAE = 0.21 mg/kg). Furthermore, the RF model in multiple spectral transformations exhibited higher suitability for modeling soil Cd content compared to other models. Overall, this research highlights the substantial applicative potential of the ZY1-02D satellite hyperspectral data for detecting soil heavy metals and provides a framework that integrates optimal spectral transformations and modeling techniques to estimate soil Cd contents.

Rosacea, a common and chronic skin disorder, presents with various cutaneous manifestations. The phenotype of rosacea determines the approach to treatment. Refractory rosacea with persistent erythema invariably necessitates pharmacologic agents. Herein, we report a case series on three female patients with persistent erythema of rosacea treated by abrocitinib, a JAK-1 inhibitor.

We present a high-efficiency continuous-wave Ho: LSO laser wing-pumped by a 1.91 μm laser diode. The impact of different output transmittances on the laser output power is compared and analyzed. The optimal result was achieved with a −500-mm radius curvature and a 6% transmission output coupler. The maximum output power of the Ho: LSO laser is 7.81 W, with a slope efficiency of 44.7%, a center wavelength of 2,106.6 nm, and beam quality factors of 1.4 in the x-direction and 1.3 in the y-direction.

Integrative and conjugative elements (ICEs) play a vital role in bacterial evolution by carrying essential genes that confer adaptive functions to the host. Despite their importance, the mechanism underlying the stable inheritance of ICEs, which is necessary for the acquisition of new traits in bacteria, remains poorly understood. Here, we identified SezAT, a type II toxin-antitoxin (TA) system, and AbiE, a type IV TA system encoded within the ICESsuHN105, coordinately promote ICE stabilization and mediate multidrug resistance in Streptococcus suis. Deletion of SezAT or AbiE did not affect the strain's antibiotic susceptibility, but their duple deletion increased susceptibility, mainly mediated by the antitoxins SezA and AbiEi. Further studies have revealed that SezA and AbiEi affect the genetic stability of ICESsuHN105 by moderating the excision and extrachromosomal copy number, consequently affecting the antibiotic resistance conferred by ICE. The DNA-binding proteins AbiEi and SezA, which bind palindromic sequences in the promoter, coordinately modulate ICE excision and extracellular copy number by binding to sequences in the origin-of-transfer (oriT) and the attL sites, respectively. Furthermore, AbiEi negatively regulates the transcription of SezAT by binding directly to its promoter, optimizing the coordinate network of SezAT and AbiE in maintaining ICESsuHN105 stability. Importantly, SezAT and AbiE are widespread and conserved in ICEs harbouring diverse drug-resistance genes, and their coordinated effects in promoting ICE stability and mediating drug resistance may be broadly applicable to other ICEs. Altogether, our study uncovers the TA system's role in maintaining the genetic stability of ICE and offers potential targets for overcoming the dissemination and evolution of drug resistance.

The study investigated the load capacity of cross-shaped concrete-filled steel tubular (CFST) columns under axial and eccentric compression using finite element software ABAQUS. It analyzed six specimens with measured data and an additional 26 specimens with varied parameters, including eccentricity, slenderness ratio, section steel ratio and material properties such as concrete strength and steel yield strength.The objective was to understand how these parameters affect the load capacity of cross-shaped CFST columns. The research findings suggest that as eccentricity and slenderness ratio increase, the ultimate capacity decreases. Conversely, it increases with higher steel content, concrete strength and steel yield strength. Moreover, the bearing capacity deteriorates more rapidly with reduced eccentricity and concrete strength, while it demonstrates a nearly linear increase with greater steel content. Additionally, the study found that enhancing the resilience of the channel steel significantly boosts the load-bearing capacity of the column. Based on these findings, practical design equations were developed to determine the maximum bearing capacity of cross-shaped CFST columns under axial and eccentric compression. These equations are grounded in confined concrete theory and demonstrate robust applicability for practical design purposes.

PurposeThis study aims to investigate the relationship between the total muscle-to-fat ratio (tMFR) and female urinary incontinence (UI), determine whether tMFR can serve as a useful index for predicting UI, and identify factors that may influence this relationship.MethodsWe retrospectively analyzed data from 4391 adult women participating in the National Health and Nutrition Examination Survey (NHANES) conducted between 2011 and 2018. The correlation between tMFR and UI was examined using a dose-response curve generated through a restricted cubic spline (RCS) function, LASSO and multivariate logistic regression. Furthermore, predictive models were constructed incorporating factors such as age, race, hypertension, diabetes, cotinine levels, and tMFR. The performance of these predictive models was evaluated using training and test datasets, employing calibration curves, receiver operating characteristic curves, and clinical decision curves. Mediation effects were also analyzed to explore potential relationships between tMFR and female UI.ResultsIn a sample of 4391 adult women, 1073 (24.4%) self-reported experiencing UI, while 3318 (75.6%) reported not having UI. Based on the analyses involving LASSO regression and multivariate logistic regression, it was found that tMFR exhibited a negative association with UI (OR = 0.599, 95% CI: 0.497-0.719, P < 0.001). The results from the restricted cubic spline chart indicated a decreasing risk of UI in women as tMFR increased. Furthermore, the model constructed based on logistic regression analysis demonstrated a certain level of accuracy (in the training dataset: area under the curve (AUC) = 0.663; in the test dataset: AUC = 0.662) and clinical applicability. The mediation analysis revealed that the influence of tMFR on the occurrence of UI in women might potentially occur through the blood index lymphocyte count (P = 0.040).ConclusionA high tMFR serves as a protective factor against UI in women. Furthermore, lymphocyte might be involved in the relationship between tMFR and female UI.

Self-oscillating systems can directly convert ambient energy to mechanical work, and new type self-oscillating systems are worth designing for applications in energy harvesters, engines, and actuators. Taking inspiration from the hand drill, we have developed a novel self-rotating drill system, which is consist of a turnplate and a liquid crystal elastomer (LCE) fiber under steady illumination. To investigate the self-rotating behaviors of the LCE drill, we have proposed a nonlinear theoretical model of the LCE drill under steady illumination based on the well-established dynamic LCE model. Numerical calculation reveals that the LCE drill can undergo a supercritical Hopf bifurcation between the static regime and the self-rotation regime. The self-rotation of drill originates from the contraction of winding portion of LCE fiber in illumination at winding state, and its continuous periodic motion is sustained by the interrelation between light energy and damping dissipation. The Hopf bifurcation conditions are also investigated in detail, as well as the vital system parameters affecting its frequency and amplitude. In contrast to the abundant existing self-oscillating systems, this self-rotating drill stands out due to its simple and lightweight structure, customizable dimensions, and high speed, and thus facilitates the design of compact and integrated systems, enhancing their applicability in microdevices and systems. This bears great significance in fields like micro-robotics, micro-sensors, and medical instruments, enabling the realization of smaller and higher-performance devices.

Abstract Methyltransferase-like 3 (METTL3) is the key subunit of methyltransferase complex responsible for catalyzing N6-methyladenosine (m6A) modification on mRNA, which is the most prevalent post-transcriptional modification in eukaryotes. In this study, we utilized online databases to analyze the association between METTL3 expression and various aspects of tumorigenesis, including gene methylation, immunity, and prognosis. Our investigation revealed that METTL3 serves as a prognostic marker and therapeutic target for liver hepatocellular carcinoma (LIHC). Through experimental studies, we observed frequent upregulation of METTL3 in LIHC tumor tissue and cells. Subsequent inhibition of METTL3 using a novel small molecule inhibitor, STM2457, significantly impeded tumor growth in LIHC cell lines, spheroids, and xenograft tumor model. Further, transcriptome and m6A sequencing of xenograft bodies unveiled that inhibition of METTL3-m6A altered genes enriched in SMAD and MAPK signaling pathways that are critical for tumorigenesis. These findings suggest that targeting METTL3 represents a promising therapeutic strategy for LIHC.

Abstract Background Systemic sclerosis (SSc), with unclear pathophysiology, is a paradigmatic rheumatic disease of immunity dysfunction-driven multi-organ inflammation and ultimate fibrosis. Pathogenesis breakthroughs are urgently needed for available treatments halting its unremitting stiffness. This study aims to investigate whether ferroptosis can regulate the progressive SSc fibrosis. Methods In vivo, bleomycin (BLM)-induced mice model was subjected to ferroptosis detection using western blotting, malondialdehyde (MDA), and glutathione (GSH) assays. Pharmacological inhibitor of the acyl-CoA synthetase long-chain family member 4 (ACSL4) was utilized to explore its potential therapeutic effects for fibrosis, from histological, biochemical, and molecular analyses. In vitro, bone marrow-derived macrophages (BMDM) were activated into inflammatory phenotype and then the relationship was evaluated between activation level and ferroptosis sensitivity in lipopolysaccharide (LPS) incubation with gradient concentrations. The potential calpain/ACSL4 axis was analyzed after calpain knockdown or over-expression in Raw264.7. Results Both skin and lung tissue ferroptosis were present in SSc mice with enhanced ACSL4 expression, while ACSL4 inhibition effectively halted fibrosis progressing and provides protection from inflammatory milieu. Meanwhile, a positive regulation relationship between LPS-induced macrophage activity and ferroptosis sensitivity can be observed. After calpain knockdown, both inflammatory macrophage ferroptosis sensitivity and ACSL4 expression decreased, while its over-expression renders ACSL4-envoking condition. Also, calpain pharmacological inhibition reduced both ferroptosis and fibrosis aptitude in mice. Conclusions ACSL4 induces inflammatory macrophage ferroptosis to aggravate fibrosis progressing. ACSL4 and its upregulators of calpains may be potential therapeutic targets for BLM model of SSc.

Abstract Background Group 2 innate lymphoid cells (ILC2s) are critical sources of type 2 cytokines and represent one of the major tissue-resident lymphoid cells in the mouse lung. However, the molecular mechanisms underlying ILC2 activation under challenges are not fully understood. Results Here, using single-cell transcriptomics, genetic reporters, and gene knockouts, we identify four ILC2 subsets, including two non-activation subsets and two activation subsets, in the mouse acute inflammatory lung. Of note, a distinct activation subset, marked by the transcription factor Nr4a1, paradoxically expresses both tissue-resident memory T cell (Trm), and effector/central memory T cell (Tem/Tcm) signature genes, as well as higher scores of proliferation, activation, and wound healing, all driven by its particular regulons. Furthermore, we demonstrate that the Nr4a1+ILC2s are restrained from activating by the programmed cell death protein-1 (PD-1), which negatively modulates their activation-related regulons. PD-1 deficiency places the non-activation ILC2s in a state that is prone to activation, resulting in Nr4a1+ILC2 differentiation through different activation trajectories. Loss of PD-1 also leads to the expansion of Nr4a1+ILC2s by the increase of their proliferation ability. Conclusions The findings show that activated ILC2s are a heterogenous population encompassing distinct subsets that have different propensities, and therefore provide an opportunity to explore PD-1's role in modulating the activity of ILC2s for disease prevention and therapy.

The journal retracts the article, “A Publicly Verifiable E-Voting System Based on Biometrics” [...]

Rapid and accurate mapping of soil properties in farmlands is crucial for guiding agricultural production and maintaining food security. Traditional methods using spectral features from remote sensing prove valuable for estimating soil properties, but are restricted to short periods of bare soil occurrence within agricultural settings. Addressing the challenge of predicting soil properties under crop cover, this study proposed an improved soil modeling framework that integrates dynamic crop growth information with machine learning techniques. The methodology’s robustness was tested on six key soil properties in an agricultural region of China, including soil organic carbon (SOC), total nitrogen (TN), total phosphorus (TP), dissolved organic carbon (DOC), dissolved organic nitrogen (DON), and pH. Four experimental scenarios were established to assess the impact of crop growth information, represented by the normalized difference vegetation index (NDVI) and phenological parameters. Specifically, Scenario I utilized only natural factors (terrain and climate data); Scenario II added phenological parameters based on Scenario I; Scenario III incorporated time-series NDVI based on Scenario I; and Scenario IV combined all variables (traditional natural factors and crop growth information). These were evaluated using three advanced machine learning models: random forest (RF), Cubist, and Extreme Gradient Boosting (XGBoost). Results demonstrated that incorporating phenological parameters and time-series NDVI significantly improved model accuracy, enhancing predictions by up to 36% over models using only natural factors. Moreover, although both are crop growth factors, the contribution of the time-series NDVI variable to model accuracy surpassed that of the phenological variable for most soil properties. Relative importance analysis suggested that the crop growth information, derived from time-series NDVI and phenology data, collectively explained 14–45% of the spatial variation in soil properties. This study highlights the significant benefits of integrating remote sensing-based crop growth factors into soil property inversion under crop-covered conditions, providing valuable insights for digital soil mapping.

The use of slag-based alkali-activated concrete (AAC) could effectively address the issues of resource extraction and carbon emissions caused by traditional Portland cement. In this study, the effects of the fly ash content, water–binder ratio, and alkali activation modulus on the early mechanical properties of AAC were investigated. A total of 105 cube specimens (35 sets) were designed and subjected to compressive strength tests, scanning electron microscope tests, and x-ray diffraction composition analysis. The results indicated that excellent early strength was observed in AAC, with a compressive strength reaching ∼90% of the 28th day strength before 14 days. An increase in the fly ash content and a decrease in the water–binder ratio significantly improved the compressive strength within the same age period. The impact of a lower alkaline activation modulus on strength was found to be insignificant. Based on the experimental results, a calculation method and a constitutive model for early strength were proposed, and the theoretical values exhibited a high level of agreement with the measured values.

Alzheimer's disease(AD) poses a significant challenge due to its widespread prevalence and the lack of effective treatments, highlighting the urgent need for early detection. This research introduces an enhanced neural network, named ADnet, which is based on the VGG16 model, to detect Alzheimer's disease using two-dimensional MRI slices. ADNet incorporates several key improvements: it replaces traditional convolution with depthwise separable convolution to reduce model parameters, replaces the ReLU activation function with ELU to address potential issues with exploding gradients, and integrates the SE(Squeeze-and-Excitation) module to enhance feature extraction efficiency. In addition to the primary task of MRI feature extraction, ADnet is simultaneously trained on two auxiliary tasks: clinical dementia score regression and mental state score regression. Experimental results demonstrate that compared to the baseline VGG16, ADNet achieves a 4.18% accuracy improvement for AD vs. CN classification and a 6% improvement for MCI vs. CN classification. These findings highlight the effectiveness of ADnet in classifying Alzheimer's disease, providing crucial support for early diagnosis and intervention by medical professionals. The proposed enhancements represent advancements in neural network architecture and training strategies for improved AD classification.

Introduction: The Multi-Channel Imager (MCI), one of the back-end modules of the future China Space Station Telescope (CSST), is designed for high-precision spacebased astronomical observations. This paper evaluates the astrometric capability of the MCI based on simulated observational images and Gaia data: the M31 galaxy is selected as a representative case to validate the astrometric capability by calculating the proper motions (PMs) of the M31 member stars.Method: We analyze the stellar centroids of the simulated images in the R, I and G bands, positional uncertainty of 2.5 mas for brighter foreground reference stars from the Gaia DR3 catalog and of 7.5 mas for the fainter M31 member stars, are adopted respectively. The theoretical PMs are generated from the adopted velocity field model, rotation curve, and stellar surface density profile. And the simulated observed PMs are generated from the aforementioned position uncertainties and theoretical PMs.Result: We conclude that the precision of the MCI derived PMs strongly depends on the number of astrometric epochs per year. Specifically, uncertainty of 10 μas/yr is achievable with 10 epochs per year, and of 5 μas/yr with 50 epochs ignoring possible systematic effects. And symmetrically distributed observed fields yield better M31 kinematic parameters.Discussion: Unknown systematic errors, space environment effects on detectors, dithering strategies, and observation schedules can affect the PMs of M31, the above issues need further analysis and validation in future work.

Current perovskite oxide electrolytes, i.e., acceptor-doped Ba(Ce,Zr)O3-δ, exhibit proton conductivity ranging from 10−3 to 10−2 S cm−1 at 600 °C for protonic ceramic fuel cells (PCFCs), which rely on the structural defects. However, bulk doping and sintering restrict these oxides to possess higher ionic conductivity. New-generation PCFCs with alternative ion conduction mechanism need to be developed. This study presents a novel approach to realize high proton conduction along a fluorite oxide-ion conductor gadolinium-doped ceria (GDC: Gd0.1Ce0.9O2-δ) by electrochemical proton injection via a fuel cell process. A high protonic conductivity of 0.158 S cm−1 has been achieved. This fuel cell employing a 400-μm-thick GDC electrolyte delivered a peak power output close to 1,000 mW cm−2 at 500 °C. Proton conduction is verified by electrochemical impedance spectroscopy, proton filtering cell and isotopic effect, and so on. Proton injection into GDC after fuel cell testing is clarified by x-ray photoelectron spectroscopy, Raman spectra, 1H solid-state nuclear magnetic resonance spectra, and so on. Furthermore, a synergistic mechanism involving both surface proton conduction and bulk oxygen-ion migration is proposed by comparing electrochemical impedance spectroscopy with distribution of relaxation time results of GDC and pure ceria. This finding may provide new insights into the ion transport mechanism on fluorite oxides and open new avenues for advanced low-temperature PCFCs.

Lysosomes are active sites to integrate cellular metabolism and signal transduction. A collection of proteins associated with the lysosome mediate these metabolic and signaling functions. Both lysosomal metabolism and lysosomal signaling have been linked to longevity regulation; however, how lysosomes adjust their protein composition to accommodate this regulation remains unclear. Using deep proteomic profiling, we systemically profiled lysosome-associated proteins linked with four different longevity mechanisms. We discovered the lysosomal recruitment of AMP-activated protein kinase and nucleoporin proteins and their requirements for longevity in response to increased lysosomal lipolysis. Through comparative proteomic analyses of lysosomes from different tissues and labeled with different markers, we further elucidated lysosomal heterogeneity across tissues as well as the increased enrichment of the Ragulator complex on Cystinosin-positive lysosomes. Together, this work uncovers lysosomal proteome heterogeneity across multiple scales and provides resources for understanding the contribution of lysosomal protein dynamics to signal transduction, organelle crosstalk, and organism longevity.

Abstract Background The HIV/AIDS (human immunodeficiency virus/acquired immune deficiency syndrome) remains a global threat to health. Suicidal ideation has been a serious public health problem among people living with HIV (PLWH). However, the suicide prevention mechanism among PLWH still unclear. This study aims to analyze the suicidal ideation and its related factors in PLWH, and further explore the relationships between suicidal ideation and depression, anxiety and perceived social support. Methods This is a cross-sectional study. A total of 1146 PLWH were investigated by the general information questionnaire, the perceived social support scale (PSSS), the Beck scale for suicide ideation of Chinese version (BSI-CV), the generalized anxiety disorder scale-2 (GAD-2) and the patient health questionnaire-2 (PHQ-2) though the WeChat in China in 2018. By using statistical description and the binary unconditional logistic regression, we assessed the incidence of suicidal ideation and its related factors in PLWH. Besides, the intermediary effect of social support between anxiety, depression and suicidal ideation were explored by the stepwise test and Bootstrap method. Results The incidence of suicide ideation was 54.0% (619/1146) among the PLWH in the last week or during the most serious depression. Binary logistic regression analysis results showed that the PLWH who with short time for HIV positive diagnosis (aOR (adjusted odd ratio) = 1.754, 95% CI (confidence interval):1.338–2.299), low monthly income (aOR = 1.515, 95%CI:1.098–2.092), other chronic diseases except HIV (aOR = 1.555, 95%CI:1.134–2.132), irregular lovers (aOR = 1.369, 95%CI:1.021–1.837), anxiety (aOR = 2.711, 95%CI:1.767–4.161), depression (aOR = 1.614, 95%CI:1.078–2.417), low PSSS (aOR = 2.139, 95%CI:1.345–3.399) had high risk of suicide ideation.The social support played a mediating role between the anxiety (the mediating effect accounted for 30.43% of the total effect), depression (the mediating effect accounted for 23.76% of the total effect) and the suicide ideation among PLWH. Conclusion The incidence of suicide ideation of PLWH was high. Anxiety, depression, and social support are the key factors of suicide ideation of PLWH. Social support plays a partial mediating role between anxiety, depression and suicidal ideation, which provides a new approach for prevention of suicidal ideation in PLWH and should be known widely for people to prevent suicide.

Abstract Despite the advent of precision medicine and immunotherapy, mortality due to lung cancer remains high. The sonic hedgehog (SHH) cascade and its key terminal factor, glioma-associated oncogene homolog 1 (GLI1), play a pivotal role in the stemness and drug resistance of lung cancer. Here, we investigated the molecular mechanism of non-canonical aberrant GLI1 upregulation. The SHH cascade was upregulated in stem spheres and chemo-resistant lung cancer cells and was accountable for drug resistance against multiple chemotherapy regimens. GLI1 and the long non-coding RNA SOX2OT were positively regulated, and the GLI1-SOX2OT loop mediated the proliferation of parental and stem-like lung cancer cells. Further mechanistic investigation revealed that SOX2OT facilitated METTL3/14/IGF2BP2-mediated m6A modification and stabilization of the GLI1 mRNA. Additionally, SOX2OT upregulated METTL3/14/IGF2BP2 by sponging miR-186-5p. Functional analysis corroborated that GLI1 acted as a downstream target of METTL3/14/IGF2BP2, and GLI1 silencing could block the oncogenicity of lung cancer stem-like cells. Pharmacological inhibition of the loop remarkably inhibited the oncogenesis of lung cancer cells in vivo. Compared with paired adjacent normal tissues, lung cancer specimens exhibited consistently upregulated GLI1/SOX2OT/METTL3/14/IGF2BP2. The m6A-modified GLI1-SOX2OT loop may serve as a potential therapeutic target and prognostic predictor for lung cancer therapy and diagnosis in the clinic.

Abstract Background Measurement of the Chinese burden of disease with disability-adjusted life-years (DALYs) requires disability weight (DW) that quantify health losses for all non-fatal consequences of disease and injury. The Global Burden of Disease (GBD) 2013 DW study indicates that it is limited by lack of geographic variation in DW data and by the current measurement methodology. We aim to estimate DW for a set of health states from major diseases in the Wuhan population. Methods We conducted the DW measurement study for 206 health states through a household survey with computer-assisted face-to-face interviews and a web-based survey. Based on GBD 2013 DW study, paired comparison (PC) and Population health equivalence (PHE) method was used and different PC/PHE questions were randomly assigned to each respondent. In statistical analysis, the PC data was analyzed by probit regression. The probit regression results will be anchored by results from the PHE data analyzed by interval regression on the DW scale units between 0 (no loss of health) and 1 (loss equivalent to death). Results A total of 2610 and 3140 individuals were included in the household and web-based survey, respectively. The results from the total pooled data showed health state “mild anemia” (DW = 0.005, 95% UI 0.000–0.027) or “allergic rhinitis (hay fever)” (0.005, 95% UI 0.000–0.029) had the lowest DW and “heroin and other opioid dependence, severe” had the highest DW (0.699, 95% UI 0.579–0.827). A high correlation coefficient (Pearson’s r = 0.876; P

Abstract Background To evaluate the value of a deep learning-based computer-aided diagnostic system (DL-CAD) in improving the diagnostic performance of acute rib fractures in patients with chest trauma. Materials and methods CT images of 214 patients with acute blunt chest trauma were retrospectively analyzed by two interns and two attending radiologists independently firstly and then with the assistance of a DL-CAD one month later, in a blinded and randomized manner. The consensusdiagnosis of fib fracture by another two senior thoracic radiologists was regarded as reference standard. The rib fracture diagnostic sensitivity, specificity, positive predictive value, diagnostic confidence and mean reading time with and without DL-CAD were calculated and compared. Results There were 680 rib fracture lesions confirmed as reference standard among all patients. The diagnostic sensitivity and positive predictive value of interns weresignificantly improved from (68.82%, 84.50%) to (91.76%, 93.17%) with the assistance of DL-CAD, respectively. Diagnostic sensitivity and positive predictive value of attendings aided by DL-CAD (94.56%, 95.67%) or not aided (86.47%, 93.83%), respectively. In addition, when radiologists were assisted by DL-CAD, the mean reading time was significantly reduced, and diagnostic confidence was significantly enhanced. Conclusions DL-CAD improves the diagnostic performance of acute rib fracture in chest trauma patients, which increases the diagnostic confidence, sensitivity, and positive predictive value for radiologists. DL-CAD can advance the diagnostic consistency of radiologists with different experiences.

This study focuses on the prediction of concrete cover separation (CCS) in reinforced concrete beams strengthened by fiber-reinforced polymer (FRP) in flexure. First, machine learning models were constructed based on linear regression, support vector regression, BP neural networks, decision trees, random forests, and XGBoost algorithms. Secondly, the most suitable model for predicting CCS was identified based on the evaluation metrics and compared with the codes and the researcher’s model. Finally, a parametric study based on SHapley Additive exPlanations (SHAP) was carried out, and the following conclusions were obtained: XGBoost is best-suited for the prediction of CCS and codes, and researchers’ model accuracy needs to be improved and suffers from over or conservative estimation. The contributions of the concrete to the shear force and the yield strength of the reinforcement are the most important parameters for the CCS, where the shear force at the onset of CCS is approximately proportional to the contribution of the concrete to the shear force and approximately inversely proportional to the yield strength of the reinforcement.

The achievement and control of desired motions in active machines often involves precise manipulation of artificial muscles in a distributed and sequential manner, which poses significant challenges. A novel motion control strategy based on self-oscillation in active machines offers distinctive benefits, such as direct energy harvesting from the ambient environment and the elimination of complex controllers. Drawing inspiration from automobiles, a self-moving automobile designed for operation under steady illumination is developed, comprising two wheels and a liquid crystal elastomer fiber. To explore the dynamic behavior of this self-moving automobile under steady illumination, a nonlinear theoretical model is proposed, integrating with the established dynamic liquid crystal elastomer model. Numerical simulations are conducted using the Runge-Kutta method based on MATLAB software, and it is observed that the automobile undergoes a supercritical Hopf bifurcation, transitioning from a static state to a self-moving state. The sustained periodic self-moving is facilitated by the interplay between light energy and damping dissipation. Furthermore, the conditions under which the Hopf bifurcation occurs are analyzed in detail. It is worth noting that increasing the light intensity or decreasing rolling resistance coefficient can improve the self-moving average velocity. The innovative design of the self-moving automobile offers advantages such as not requiring an independent power source, possessing a simple structure, and being sustainable. These characteristics make it highly promising for a range of applications including actuators, soft robotics, energy harvesting, and more.