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Abstract Background Osteoporosis (OP) is a complex skeletal disorder characterized by reduced bone mass, microarchitectural deterioration of bone tissue, and increased susceptibility to fractures. Bone mineral density (BMD), as the best indicator of bone mineral content per unit area of bone, is one of the key diagnostic factors for OP. Platelets (PLT), serving as important immune cells and components of the coagulation system, have been demonstrated to be associated with bone formation, resorption, and remodeling processes. However, no research has established the relationship between BMD and platelet count (PC) in the American population thus far. This study aims to investigate the correlation between BMD and PC among the American population, and to appraise the effects of additional risk factors on this association. Methods This investigation examined the relationship between BMD and PC by analyzing data from the National Health and Nutrition Examination Survey (NHANES) between 2011 and 2018. A weighted multivariate logistic regression analysis was employed to assess this correlation. Additionally, subgroup and smooth curve analyses were conducted to delve deeper into the BMD-PC relationship and to identify other potential determinants of PC. Results This study reveals a significant negative correlation between BMD and PC in the American adult population (β=-15.05, 95% CI: -22.07 to -8.03, p

Abstract Microbial fuel cells utilize exoelectrogenic microorganisms to directly convert organic matter into electricity, offering a compelling approach for simultaneous power generation and wastewater treatment. However, conventional microbial fuel cells typically require thick biofilms for sufficient metabolic electron production rate, which inevitably compromises mass and charge transport, posing a fundamental tradeoff that limits the achievable power density (

Abstract Broad‐spectrum antibacterial drugs often lack specificity, leading to indiscriminate bactericidal activity, which can disrupt the normal microbial balance of the host flora and cause unnecessary cytotoxicity during systemic administration. In this study, we constructed a specifically targeted antimicrobial peptide against Staphylococcus aureus by introducing a phage‐displayed peptide onto a broad‐spectrum antimicrobial peptide and explored its structure–function relationship through one‐factor modification. SFK2 obtained by screening based on the selectivity index and the targeting index showed specific killing ability against S. aureus. Moreover, SFK2 showed excellent biocompatibility in mice and piglet, and demonstrated significant therapeutic efficacy against S. aureus infection. In conclusion, our screening of phage‐derived heptapeptides effectively enhances the specific bactericidal ability of the antimicrobial peptides against S. aureus, providing a theoretical basis for developing targeted antimicrobial peptides.

Abstract Despite the importance of measuring racial-ethnic segregation and diversity in the United States, current measurements are largely based on the Census and, thus, only reflect segregation and diversity as understood through residential location. This leaves out the social contexts experienced throughout the course of the day during work, leisure, errands, and other activities. The National Experienced Racial-ethnic Diversity (NERD) dataset provides estimates of diversity for the entire United States at the census tract level based on the range of place and times when people have the opportunity to come into contact with one another. Using anonymized and opted-in mobile phone location data to determine co-locations of people and their demographic backgrounds, these measurements of diversity in potential social interactions are estimated at 38.2 m × 19.1 m scale and 15-minute timeframe for a representative year and aggregated to the Census tract level for purposes of data privacy. As well, we detail some of the characteristics and limitations of the data for potential use in national, comparative studies.

Abstract The success of immunotherapy for cancer treatment is limited by the presence of an immunosuppressive tumor microenvironment (TME); Therefore, identifying novel targets to that can reverse this immunosuppressive TME and enhance immunotherapy efficacy is essential. In this study, enrichment analysis based on publicly available single‐cell and bulk RNA sequencing data from gastric cancer patients are conducted, and found that tumor‐intrinsic interferon (IFN) plays a central role in TME regulation. The results shows that KDM3A over‐expression suppresses the tumor‐intrinsic IFN response and inhibits KDM3A, either genomically or pharmacologically, which effectively promotes IFN responses by activating endogenous retroviruses (ERVs). KDM3A ablation reconfigures the dsRNA‐MAVS‐IFN axis by modulating H3K4me2, enhancing the infiltration and function of CD8 T cells, and simultaneously reducing the presence of regulatory T cells, resulting in a reshaped TME in vivo. In addition, combining anti‐PD1 therapy with KDM3A inhibition effectively inhibited tumor growth. In conclusions, this study highlights KDM3A as a potential target for TME remodeling and the enhancement of antitumor immunity in gastric cancer through the regulation of the ERV‐MAVS‐IFN axis.

Abstract Nonlinear optical processing of ambient natural light is highly desired for computational imaging and sensing. Strong optical nonlinear response under weak broadband incoherent light is essential for this purpose. By merging 2D transparent phototransistors (TPTs) with liquid crystal (LC) modulators, we create an optoelectronic neuron array that allows self-amplitude modulation of spatially incoherent light, achieving a large nonlinear contrast over a broad spectrum at orders-of-magnitude lower intensity than achievable in most optical nonlinear materials. We fabricated a 10,000-pixel array of optoelectronic neurons, and experimentally demonstrated an intelligent imaging system that instantly attenuates intense glares while retaining the weaker-intensity objects captured by a cellphone camera. This intelligent glare-reduction is important for various imaging applications, including autonomous driving, machine vision, and security cameras. The rapid nonlinear processing of incoherent broadband light might also find applications in optical computing, where nonlinear activation functions for ambient light conditions are highly sought.

Abstract Staphylococcus aureus (S. aureus) is a common pathogenic bacterium in animal husbandry that can cause diseases such as mastitis, skin infections, arthritis, and other ailments. The formation of biofilms threatens and exacerbates S. aureus infection by allowing the bacteria to adhere to pathological areas and livestock product surfaces, thus triggering animal health crises and safety issues with livestock products. To solve this problem, in this review, we provide a brief overview of the harm caused by S. aureus and its biofilms on livestock and animal byproducts (meat and dairy products). We also describe the ways in which S. aureus spreads in animals and the threats it poses to the livestock industry. The processes and molecular mechanisms involved in biofilm formation are then explained. Finally, we discuss strategies for the removal and eradication of S. aureus and biofilms in animal husbandry, including the use of antimicrobial peptides, plant extracts, nanoparticles, phages, and antibodies. These strategies to reduce the spread of S. aureus in animal husbandry help maintain livestock health and improve productivity to ensure the ecologically sustainable development of animal husbandry and the safety of livestock products.

Immune thrombocytopenia (ITP) is an autoimmune disease characterized by increased platelet destruction and impaired production, leading to an elevated bleeding tendency. Recent studies have demonstrated an important link between the gut microbiota and the onset and progression of several immune diseases in humans, emphasizing that gut microbiota-derived metabolites play a non-negligible role in autoimmune diseases. The gut microbiota and its metabolites, such as short-chain fatty acids, oxidized trimethylamine, tryptophan metabolites, secondary bile acids and lipopolysaccharides, can alter intestinal barrier permeability by modulating immune cell differentiation and cytokine secretion, which in turn affects the systemic immune function of the host. It is therefore reasonable to hypothesize that ecological dysregulation of the gut microbiota may be an entirely new factor in the triggering of ITP. This article reviews the potential immune-related mechanisms of the gut microbiota and representative metabolites in ITP, as well as the important influence of leaky gut on the development of ITP, with a view to enriching the theoretical system of ITP-related gut microecology and providing new ideas for the study of ITP.

Abstract An apparent sexual dimorphism, such as growth rate and immunocompetence, is observed in the Chinese soft‐shelled turtle (Pelodiscus sinensis). The identification of sex‐related markers and candidate genes holds significance for the sex‐controlled breeding of P. sinensis. In this study, through an integration of whole‐genome resequencing and Sanger sequencing, 21 female‐specific single nucleotide polymorphisms (SNPs) were identified. Based on these SNPs, the rapid and cost‐effective molecular methodologies for genetic sex identification in P. sinensis were established. Of note, 19 validated SNPs are located within the zinc and ring finger 3 (ZNRF3) gene, which has been reported to be testis‐determining in mammals. Cloning of the ZNRF3 gene from P. sinensis (PsZNRF3) and analysis of its expression profile in tissues and in response to sex steroid hormone treatment demonstrated exhibiting a female‐biased expression pattern. These findings not only provide molecular markers for sex‐controlled breeding in P. sinensis but also open up a solid basis for revealing the sex‐determination mechanism of this species.

BackgroundPatients with chronic obstructive pulmonary disease (COPD) after acute coronary artery syndrome (ACS) are at an increased risk of heart failure and death. However, β-blockers have been underused in this population group due to concerns of adverse reactions.ObjectiveThis study aims to investigate the β-blocker prescription at admission and its impact on the in-hospital outcomes in patients with COPD after ACS in a Chinese national cohort.MethodsAmong 113,650 patients with ACS enrolled in the national registry of the Improving Care for Cardiovascular Disease in China between November 2014 and July 2019, a total of 1,084 ACS patients with COPD were included in this study. The primary endpoint was in-hospital mortality, and the secondary endpoint was the composite of in-hospital all-cause death and heart failure.ResultsEarly oral β-blocker therapy was administered to 49.8% of patients. The Kaplan–Meier analysis showed that the early β-blocker treatment group had lower all-cause mortality (0.9% vs. 2.9%; P

The deployment of multi-agent systems (MASs) is widely used in the fields of unmanned agricultural machineries, unmanned aerial vehicles, intelligent transportation, etc. To make up for the defect that the existing PDE-based results are overly idealistic in terms of system models and control strategies, we study the PDE-based deployment of clustered nonlinear first-order and second-order MASs over a finite-time interval (FTI). By designing special communication protocols, the collective dynamics of numerous agents are modeled by simple fist-order and second-order PDEs. Two practical factors, external disturbance and Markov switching topology, are considered in this paper to better match actual situations. Besides, T–S fuzzy technology is used to approximate the unknown nonlinearity of MASs. Then, by using boundary control scheme with collocated measurements, two theorems are obtained to ensure the finite-time $ H_\infty $ deployment of first-order and second-order agents, respectively. Finally, numerical examples are provided to illustrate the effectiveness of the proposed approaches.

Identifying the green efficiency of water resources and its driving factors is paramount for promoting sustainable development in China. The existing research has primarily focused on the spatial heterogeneity of individual factors that impact green efficiency of water resources. However, it has often overlooked the heterogeneity in the interactions between these factors. In this study, we utilized a multiscale geographically weighted regression (MGWR) model to discern the spatial heterogeneity of the individual factors influencing the green efficiency of water resources in China between 2002 and 2016. Subsequently, we demarcated several subregions based on the coefficients derived from the MGWR model. Employing a geographical detector (GD), we quantified the interactive impacts of different factors within these subregions. Our findings unveiled, for the first time, the diverse patterns in the temporal and spatial fluctuations in the factors impacting the eco-friendliness of water resources. The findings underscored that disregarding the spatial heterogeneity of these interactive effects may result in an underestimation of the interactions among factors. Significantly, in 2016, the impact of tertiary industry proportion and completed investment in pollution treatment displayed an enhanced non-linear effect across the entire sample and concurrently demonstrated a bivariate enhanced effect within subregions. These discoveries contribute to a deeper comprehension of the mechanisms influencing these factors, providing valuable insights for policymakers in crafting region-specific water resource policies tailored to the unique developmental requirements of different areas.

Black soil is a precious soil resource, yet it is severely affected by gully erosion, which is one of the most serious manifestations of land degradation. The determination of the location and shape of gullies is crucial for the work of gully erosion control. Traditional field measurement methods consume a large amount of human resources, so it is of great significance to use artificial intelligence techniques to automatically extract gullies from satellite remote sensing images. This study obtained the gully distribution map of the southwestern region of the Dahe Bay Farm in Inner Mongolia through field investigation and measurement and created a gully remote sensing dataset. We designed a multi-scale content structure feature extraction network to analyze remote sensing images and achieve automatic gully extraction. The multi-layer information obtained through the resnet34 network is input into the multi-scale structure extraction module and the multi-scale content extraction module designed by us, respectively, obtained richer intrinsic information about the image. We designed a structure content fusion network to further fuse structural features and content features and improve the depth of the model’s understanding of the image. Finally, we designed a muti-scale feature fusion module to further fuse low-level and high-level information, enhance the comprehensive understanding of the model, and improve the ability to extract gullies. The experimental results show that the multi-scale content structure feature extraction network can effectively avoid the interference of complex backgrounds in satellite remote sensing images. Compared with the classic semantic segmentation models, DeepLabV3+, PSPNet, and UNet, our model achieved the best results in several evaluation metrics, the F1 score, recall rate, and intersection over union (IoU), with an F1 score of 0.745, a recall of 0.777, and an IoU of 0.586. These results proved that our method is a highly automated and reliable method for extracting gullies from satellite remote sensing images, which simplifies the process of gully extraction and provides us with an accurate guide to locate the location of gullies, analyze the shape of gullies, and then provide accurate guidance for gully management.

Stroke is a cerebrovascular illness that brings about the demise of brain tissue. It is the third most prevalent cause of mortality worldwide and a significant contributor to physical impairment. Generally, stroke is triggered by blood clots obstructing the brain’s blood vessels, or when these vessels rupture. And, the cognitive impairment’s evaluation and detection after stroke is crucial research issue and significant project. Thus, the objective of this work is to explore an potential neuroimage tool and find their EEG biomarkers to evaluate and detect four cognitive impairment levels after stroke. In this study, power density spectrum (PSD), functional connectivity map, and one-way ANOVA methods were proposed to analyze the EEG biomarker differences, and the number of patient participants were thirty-two human including eight healthy control, mild, moderate, severe cognitive impairment levels, respectively. Finally, healthy control has significant PSD differences compared to mid, moderate and server cognitive impairment groups. And, the theta and alpha bands of severe cognitive impairment groups have presented consistent superior PSD power at the right frontal cortex, and the theta and beta bands of mild, moderated cognitive impairment (MoCI) groups have shown significant similar superior PSD power tendency at the parietal cortex. The significant gamma PSD power difference has presented at the left-frontal cortex in the mild cognitive impairment (MCI) groups, and severe cognitive impairment (SeCI) group has shown the significant PSD power at the gamma band of parietal cortex. At the point of functional connectivity map, the SeCI group appears to have stronger functional connectivity compared to the other groups. In conclusion, EEG biomarkers can be applied to classify different cognitive impairment groups after stroke. These findings provide a new approach for early detection and diagnosis of cognitive impairment after stroke and also for the development of new treatment options.

Nucleotide-binding and oligomerization domain (NOD)-like receptors (NLRs), classified as pattern recognition receptors (PRRs), play crucial roles in innate immune responses. However, research on this topic in crustaceans remains limited. In the present study, 27 NLR family members were identified and characterized based on the redclaw crayfish genome. Gene structure, conserved domain, and motif analyses revealed that these NLR genes were classified into four subfamilies with three major domains, and phylogenetic and structural analyses further confirmed their evolutionary conservation. qRT-PCR analysis showed that the relative expression levels of several NLR genes changed significantly under the DIV1 challenge. More importantly, in vivo silencing of NAWD1 gene in Cherax quadricarinatus promoted DIV1 replication . These findings, together with those of previous studies, suggest that NLR genes in crustaceans may play a key role in immune responses to viral infections.

Abstract Background Cardiovascular complications significantly augment the overall COVID-19 mortality, largely due to the susceptibility of human cardiomyocytes (CMs) to SARS-CoV-2 virus. SARS-CoV-2 virus encodes 27 genes, whose specific impacts on CM health are not fully understood. This study elucidates the deleterious effects of SARS-CoV-2 genes Nsp6, M, and Nsp8 on human CMs. Methods CMs were derived from human pluripotent stem cells (hPSCs), including human embryonic stem cells and induced pluripotent stem cells, using 2D and 3D differentiation methods. We overexpressed Nsp6, M, or Nsp8 in hPSCs and then applied whole mRNA-seq and mass spectrometry for multi-omics analysis. Co-immunoprecipitation mass spectrometry was utilized to map the protein interaction networks of Nsp6, M, and Nsp8 within host hiPSC-CMs. Results Nsp6, Nsp8, and M globally perturb the transcriptome and proteome of hPSC-CMs. SARS-CoV-2 infection and the overexpression of Nsp6, Nsp8, or M coherently upregulated genes associated with apoptosis and immune/inflammation pathways, whereas downregulated genes linked to heart contraction and functions. Global interactome analysis revealed interactions between Nsp6, Nsp8, and M with ATPase subunits. Overexpression of Nsp6, Nsp8, or M significantly reduced cellular ATP levels, markedly increased apoptosis, and compromised Ca2+ handling in hPSC-CMs. Importantly, administration of FDA-approved drugs, ivermectin and meclizine, could restore ATP levels, thereby mitigating apoptosis and dysfunction in hPSC-CMs overexpressing Nsp6, Nsp8, or M. Conclusion Overall, our findings uncover the extensive damaging effects of Nsp6, Nsp8, and M on hPSC-CMs, underlining the crucial role of ATP homeostasis in CM death and functional abnormalities induced by these SARS-CoV-2 genes, and reveal the potential therapeutic strategies to alleviate these detrimental effects with FDA-approved drugs.

Abstract Mitochondrial (MT) dysfunction has been associated with several neurodegenerative diseases including Alzheimer’s disease (AD). While MT-copy number differences have been implicated in AD, the effect of MT heteroplasmy on AD has not been well characterized. Here, we analyzed over 1800 whole genome sequencing data from four AD cohorts in seven different tissue types to determine the extent of MT heteroplasmy present. While MT heteroplasmy was present throughout the entire MT genome for blood samples, we detected MT heteroplasmy only within the MT control region for brain samples. We observed that an MT variant 10398A>G (rs2853826) was significantly associated with overall MT heteroplasmy in brain tissue while also being linked with the largest number of distinct disease phenotypes of all annotated MT variants in MitoMap. Using gene-expression data from our brain samples, our modeling discovered several gene networks involved in mitochondrial respiratory chain and Complex I function associated with 10398A>G. The variant was also found to be an expression quantitative trait loci (eQTL) for the gene MT-ND3. We further characterized the effect of 10398A>G by phenotyping a population of lymphoblastoid cell-lines (LCLs) with and without the variant allele. Examination of RNA sequence data from these LCLs reveal that 10398A>G was an eQTL for MT-ND4. We also observed in LCLs that 10398A>G was significantly associated with overall MT heteroplasmy within the MT control region, confirming the initial findings observed in post-mortem brain tissue. These results provide novel evidence linking MT SNPs with MT heteroplasmy and open novel avenues for the investigation of pathomechanisms that are driven by this pleiotropic disease associated loci.

Science popularization is not only a prerequisite for national development, but also an effective means of enhancing citizens' personal quality. All sectors of society, represented by colleges and universities, bear the responsibility of promoting popular science. The integration of popular science and tourism in popular science tourism serves to advance both the field of popular science and the tourism industry simultaneously. The Guangzhou Higher Education Mega Center (HEMC) possesses abundant resources for science popularization and has the potential to develop popular science tourism, yet its current development in this area remains insufficient. This study utilizes Guangzhou HEMC as a case study and modifies the American Customer Satisfaction Index model by incorporating relevant questions pertaining to popular science tourism. A total of 280 valid questionnaires were collected through surveying, which were then analyzed to measure tourist satisfaction using the Tourist Satisfaction Index. The partial least squares structural equation model was employed for analysis, and on the basis of calculation results, the IPA map was constructed. The research revealed that tourists' satisfaction with popular science tourism at Guangzhou HEMC was suboptimal. Among the factors correlating to satisfaction, expectations, quality, and price are all important factors to consider when making a purchase decision; however, prioritizing expectations and quality can lead to greater satisfaction in the long run. Therefore, there is still ample room for improvement in the popular science tourism of HEMC Guangzhou. This can be achieved by intensifying publicity efforts, enhancing infrastructure, improving the quality and safety of catering services, strengthening the introduction and construction of popular science content, as well as appropriately reducing the price of popular science products and services.

Abstract The COVID-19 pandemic has affected all domains of human life, including the economic and social fabric of societies. One of the central strategies for managing public health throughout the pandemic has been through persuasive messaging and collective behaviour change. To help scholars better understand the social and moral psychology behind public health behaviour, we present a dataset comprising of 51,404 individuals from 69 countries. This dataset was collected for the International Collaboration on Social & Moral Psychology of COVID-19 project (ICSMP COVID-19). This social science survey invited participants around the world to complete a series of moral and psychological measures and public health attitudes about COVID-19 during an early phase of the COVID-19 pandemic (between April and June 2020). The survey included seven broad categories of questions: COVID-19 beliefs and compliance behaviours; identity and social attitudes; ideology; health and well-being; moral beliefs and motivation; personality traits; and demographic variables. We report both raw and cleaned data, along with all survey materials, data visualisations, and psychometric evaluations of key variables.

In the context of population aging, the growing problem of Alzheimer’s disease (AD) poses a great challenge to mankind. Although there has been considerable progress in exploring the etiology of AD, i.e., the important role of amyloid plaques and neurofibrillary tangles in the progression of AD has been widely accepted by the scientific community, traditional treatment and monitoring modalities have significant limitations. Therefore novel evaluation and treatment modalities for Alzheimer’s disease are called for emergence. In this research, we sought to review the effectiveness of digital treatment based on monitoring using functional near-infrared spectroscopy (fNIRS) and electroencephalography (EEG). This work searched four electronic databases using a keyword approach and focused on journals focusing on AD and geriatric cognition. Finally, 21 articles were included. The progress of digital therapy and outcome monitoring in AD was reviewed, including digital therapy approaches on different platforms and different neuromonitoring techniques. Because biomarkers such as theta coherence, alpha and beta rhythms, and oxyhemoglobin are effective in monitoring the cognitive level of AD patients, and thus the efficacy of digital therapies, this review particularly focuses on the biomarker validation results of digital therapies. The results show that digital treatment based on biomarker monitoring has good effectiveness. And the effectiveness is reflected in the numerical changes of biomarker indicators monitored by EEG and fNIRS before and after digital treatment. Increases or decreases in the values of these indicators collectively point to improvements in cognitive function (mostly moderate to large effect sizes). The study is the first to examine the state of digital therapy in AD from the perspective of multimodal monitoring, which broadens the research perspective on the effectiveness of AD and gives clinical therapists a “reference list” of treatment options. They can select a specific protocol from this “reference list” in order to tailor digital therapy to the needs of individual patients.

Aiming at the problem that computing and storage resources of edge node in a mobile edge computing (MEC) system were limited, and excessive task offloading would cause the mismatch between the computing capacity and the load capacity of the edge server, resulting in the increase of task processing delay, the joint optimization of task offloading and storage resources under the three-layer network architecture composed of multi-user, edge server and cloud server was studied to reduce the overall system delay.For the problem was a mixed integer nonlinear problem, a joint optimization strategy of service caching and resource allocation was proposed.First, the continuous and discrete variables of the original problem were decoupled into two sub-problems, namely, the service cache decision problem and the joint optimization problem of computing resources and communication resources.Then, the linear reconstruction, relaxation method and convex optimization method were used to alternately optimize the two sub-problems to obtain the near-optimal solution.Simulation results demonstrate that the proposed strategy can obtain a near-optimal performance with low complexity, and can reduce up to 10% of the task duration compared with other strategies.

A detailed examination of the traffic in production data centers (DCs) suggests a new way to build optical network architectures (ONAs). As a result, recent efforts have pursued high-fan-out and cluster-based ONAs. A high-fan-out and flexible-cluster (HFFC) ONA for DCs is proposed in this article. The HFFC architecture is made up of an optical switch and a number of clusters, with the latter consisting of a number of top-of-rack (ToR) switches. Compared to a high-fan-out, scalable, and cluster-based optical interconnect (FSCOI), the HFFC architecture solves a congestion problem by removing the FSCOI's couplers and increasing the available bandwidth between clusters. By removing the couplers, the value of fan-out can be increased. In addition, one or several clusters can constitute a flexible cluster. The flexible cluster can be tailored for diverse intra-flexible-cluster traffic. The performance of the HFFC architecture is evaluated by simulation using synthetic traffic patterns. Compared to the FSCOI architecture, our approach has better performance in various traffic scenarios. In addition, the HFFC architecture has a flexible cluster capacity, making it more suited to large-scale intra-flexible-cluster traffic.

Honeycomb (Nidus vespae) is traditional Chinese medicine and can treat rheumatoid arthritis (RA), and protocatechuic acid (PCA) is a bioactive component of honeycomb. This study aimed to investigate whether PCA could reduce the H2O2-induced migration and oxidative stress of RA fibroblast-like synoviocytes (RA-FLSs). H2O2-induced RA-FLSs were used to simulate the in vitro model of RA. The viability, apoptosis, migration, invasion, and oxidative stress of RA-FLSs were detected by Cell Counting Kit-8 (CCK-8), terminal deoxynucleotidyl transferase dUTP nick-end labeling assay, wound healing, transwell assays, DCFDA staining, and malonaldehyde and superoxide dismutase enzyme-linked immunosorbent assay kits. The expression of migration and invasion-related proteins and Nrf2/Keap1 signaling pathway-related proteins was analyzed by western blotting. As a result, PCA suppressed the viability, migration, invasion, and oxidative and promoted apoptosis of H2O2-induced RA-FLSs by activating the Nrf2/Keap1 signaling pathway. ML-385, an Nrf2 inhibitor, could enhance the viability, migration, invasion, and oxidative and inhibited apoptosis of H2O2-induced RA-FLSs. In conclusion, PCA reduced H2O2-induced migration and oxidative stress of RA-FLSs by activating the Nrf2-Keap1 signaling pathway.

Abstract Background Dog Bone™ button fixation is frequently used to treat acromioclavicular joint (ACJ) dislocation. However, various studies have reported complications after fixation. Objective To investigate the effect of the coracoid bone tunnel location on the treatment of ACJ dislocation through single-tunnel coracoclavicular (CC) ligament fixation with the Dog Bone™ button. Methods Six cadaveric shoulders were used. Each specimen was subjected to five testing conditions in the following order: (1) normal ACJ (Gn); (2) acromioclavicular and CC ligaments were removed (G0); (3) CC ligament reconstruction was performed using the Dog Bone™ technique, and the coracoid bone tunnel was at the center of the coracoid base (G1); (4) reconstruction was performed at 5 mm distal from the G1 site, along the axis of the coracoid (G2); (5) reconstruction was performed at 10 mm distal from the G1 site, along the axis of the coracoid (G3). The angles of pronation and supination of the clavicle under the same load (30 N) were measured. Next, a finite element (FE) model was created using computed tomography (CT) images of the normal shoulder. Model 1 (M1), model 2 (M2), and model 3 (M3) correspond to G1, G2, and G3, respectively. A force of 70 N was applied as a vertical upward load to the distal clavicle. Subsequently, the von Mises stress, the strain LE along the FiberWire, and the displacement nephogram of the three models were obtained. Results After single-tunnel CC ligament fixation using the Dog Bone™ technique, the clavicle in the G2 group (20.50 (19.50, 21.25) °, 20.00 (18.75, 21.25) °) had the best rotational stability. The peak von Mises stress, the strain LE along the FiberWire, and the maximum displacement were smaller in M2 than in M1 and M3. Conclusions When the coracoid bone tunnel was located 5 mm anterior to the center of the coracoid base (along the axis of the coracoid), the clavicle showed greater rotational stability.

Exploration of deep sea areas relies on sonar and other visual and auditory sensors, but gaining information via temperature sensing in deep sea areas is difficult to realize. Here, the authors design an elastic, self-healable and sensitive thermal sensor which can identify a temperature difference as low as 0.01 K with a resolution of 0.001 K

Abstract Aims Idiopathic membranous nephropathy (IMN) is a common cause of adult nephrotic syndrome. Currently, the diagnosis of IMN mainly depends on renal biopsy, which is invasive. What’s more, markers already known for the clinical diagnosis of IMN are not sensitive enough. The present study aims to investigate the profiling of urinary exosomal circular RNAs (circRNAs) of IMN, and to look for a potential biomarker for diagnosis of IMN. Methods Urine exosomes were collected from patients with IMN and idiopathic nephrotic syndrome (INS), as well as healthy controls (HCs) by ultracentrifuge. A pairwise comparison between 5 IMN and 5 HC was performed by high-throughput sequencing. Enrichment analysis were performed to explore the potential functions of differentially expressed circRNAs in IMN. Among three differentially expressed circRNAs which may be involved in signaling pathways of pathogenesis of IMN and matched conserved mouse circRNAs, hsa_circ_0001250 was selected as the target circRNA after quantitative polymerase chain reaction among 23 IMN, 19 INS and 23HC. Sanger sequencing and RNase R digestion assay were performed to validated the ring-structure and sequence of hsa_circ_0001250. ROC (Receiver Operating Characteristic) curve correlation analysis was used to further validate the potential utility of hsa_circ_0001250 as a diagnostic biomarker of IMN. A circRNA-miRNA-mRNA network was constructed to reflect the relationship between hsa_circ_0001250 and its target miRNAs and mRNAs. Results 766 up-regulated and 283 down-regulated circRNAs were identified in IMN patients. Kyoto Encyclopedia of Genes and Genomes pathway analysis revealed signaling pathways of pathogenesis of IMN which the different expressed circRNAs may participate in. The ring-structure and the sequence of hsa_circ_0001250 were confirmed, the expression of hsa_circ_0001250 was validated significantly increased in IMN, relevant with high level of proteinuria. A circRNA-miRNA-mRNA network reflected that hsa_circ_0001250 may play a role in the pathogenesis of IMN by target hsa-miR-639 and hsa-miR-4449. Conclusion We revealed the expression and functional profile of differentially expressed urinary exosomal circRNAs of IMN patients. Urinary exosomal hsa_circ_0001250 was tested as a potential biomarker of IMN and a predicted circRNA-miRNA-mRNA network was constructed.

The star formation history (SFH) is a key issue in the evolution of galaxies. In this work, we developed a model based on a Gaussian and gamma function mixture to fit SFHs with varying numbers of components. Our primary objective was to use this model to reveal the shape of SFHs and the corresponding physical driving factors. Specifically, we applied this model to fit SFHs from the TNG100-1 simulation. Our study led to the following findings. (1) Our model fits with TNG star formation histories well, especially for high-mass and red galaxies; (2) a clear relationship exists between the number and shape of fitted components and the mass and color of galaxies, with notable differences observed between central/isolated and satellite galaxies. (3) Our model allows us to extract different episodes of star formation within star formation histories with ease and analyze the duration and timing of each star formation episode. Our findings indicate a strong relationship between the timing of each star formation episode and galaxy mass and color.

Rapid advances in deep learning have brought not only a myriad of powerful neural networks, but also breakthroughs that benefit established scientific research. In particular, automatic differentiation (AD) tools and computational accelerators like GPUs have facilitated forward modeling of the Universe with differentiable simulations. Based on analytic or automatic backpropagation, current differentiable cosmological simulations are limited by memory, and thus are subject to a trade-off between time and space/mass resolution, usually sacrificing both. We present a new approach free of such constraints, using the adjoint method and reverse time integration. It enables larger and more accurate forward modeling at the field level, and will improve gradient-based optimization and inference. We implement it in an open-source particle-mesh (PM) N -body library pmwd (PM with derivatives). Based on the powerful AD system JAX , pmwd is fully differentiable, and is highly performant on GPUs.

The outbreak of the novel coronavirus disease 2019 (COVID-19) has led to significant mental stress for frontline medical workers treating patients with confirmed COVID-19 in China. Psychological stress has an impact on the immune system. The number and percentage of lymphocyte subsets are standard indicators of cellular immune detection. Here, we reported the differences in CD3, CD4, CD8, CD19, and CD56 lymphocytes between 158 frontline medical workers and 24 controls from medical staffs of the outpatient and emergency departments. We found that frontline medical workers had significantly lower absolute values and percentages of CD19+ B cells, especially in the female and the aged ≥40 years subgroup. Stratification analysis showed that the absolute values of CD4+ T cells were significantly lower in the aged

Aggregation is a key process for determining the environmental behavior and impact of a nanoparticle (NP). Since organophosphate esters (OPEs), which are recognized as emerging contaminants, are distributed widely in the natural aquatic environment, they may contribute to interacting with NPs and ultimately influence their transport and fate. Here, we investigated two typical organophosphate esters OPEs on aggregation the Fe2O3 NP in aquatic environments. The results showed that both tri-ethylhexyl phosphate (TEHP) and tris (chloroisopropyl) phosphate (TCPP) improved the colloidal stability of Fe2O3 NP in artificial water and environmental matrices. TEHP exhibited an obvious effect than TCPP on the Zeta potential and aggregation rates of Fe2O3 NP in artificial water. In the presence of electrolyte, 10 mg/L TCPP and TEHP increased the critical coagulation concentration (CCC) by 3.6 times and 17.4 times, respectively. Compared with pore-water, the aggregation rates of Fe2O3 NP in river water were slightly higher than those in pore-water, which can be attributed to the higher DOC in pore-water. We suggested that the high hydrophobicity and molecular weight of OPEs were considered important factors against the aggregation of Fe2O3 NP in water. Greater surface charge and steric hindrance originating from TCPP and TEHP dominated the colloidal stability of Fe2O3 NP. HIGHLIGHTS TCPP/TEHP improve the colloidal stability of Fe2O3 NP.; Hydrophobicity and molecular weight of OPEs have a distinct effect on Fe2O3 NP agglomeration.; Electrostatic repulsions and steric stabilization control colloidal stability.;

Stiffness, toughness, and fatigue resistance are seemingly incompatible in materials design. Here the authors demonstrate a hierarchical crosslinking strategy using lithium ion oxygen interactions and PMMA nanoaggregates to enable energy dissipation in the network, leading to stiff yet tough polymer materials.

The location identification of dynamic load is an important part of load-identification technology. Traditional methods are mostly aimed at the identification of dynamic load’s amplitude and phase. A new method for dynamic load location identification is proposed in this paper. An amplitude ratio or a phase difference between structural dynamic response signals is only related to the frequency response function (FRF), which is a complex-valued function that implies the location information of the load to be identified. In this method, the amplitude and phase variables of the excitation can be eliminated with symbolic calculations, and the location variables can be extracted from the FRF in a complex-number field. An excitation location can be identified quickly with parameter optimization using a Genetic Algorithm (GA), avoiding the ill-posed problem caused by matrix inversion. Numerical simulations and experiments demonstrate that this method can realize the fast recognition of several excitation positions, and has a high recognition accuracy, a short calculation time and a strong anti-noise ability.

Understanding collective behaviour is an important aspect of managing the pandemic response. Here the authors show in a large global study that participants that reported identifying more strongly with their nation reported greater engagement in public health behaviours and support for public health policies in the context of the pandemic.

Enterotoxigenic Escherichia coli (ETEC) is a major cause of diarrhea in children and travelers in developing countries. ETEC is characterized by the ability to produce major virulence factors including colonization factors (CFs) and enterotoxins, that bind to specific receptors on epithelial cells and induce diarrhea. The gut microbiota is a stable and sophisticated ecosystem that performs a range of beneficial functions for the host, including protection against pathogen colonization. Understanding the pathogenic mechanisms of ETEC and the interaction between the gut microbiota and ETEC represents not only a research need but also an opportunity and challenge to develop precautions for ETEC infection. Herein, this review focuses on recent discoveries about ETEC etiology, pathogenesis and clinical manifestation, and discusses the colonization resistances mediated by gut microbiota, as well as preventative strategies against ETEC with an aim to provide novel insights that can reduce the adverse effect on human health.

Danjiangkou Reservoir is a world-famous large artificial freshwater lake that offers water resources for the middle route of the South-to-North Water Diversion Project in China. In this study, the distribution of carbon, nitrogen, and phosphorus in reservoir sediments and their pollution assessments were elucidated at different water periods. The average TN (total nitrogen), TP (total phosphorus), and TOM (total organic matter) contents were 794.8 mg/kg, 807.2 mg/kg, and 8.7% in the nonflood season, respectively. When the time comes to flood season, with the large amount of nitrogen pollution inputted from peripheral nonpoint sources and phosphorus released by the accelerated exchange of water bodies, the average TN concentration increased to 1061.2 mg/kg. In addition, the average TP and TOM contents were reduced to 559.5 mg/kg and 6.3%. Nutrient pollution fluctuated between the safe and lowest level. Reservoir eutrophication risk was low. There was a certain nitrogen enrichment in the Danjiangkou Reservoir, and the flood season was the main period of nitrogen pollution input. Water exchange during flood season might accelerate organic matter degradation. Near the dam, sediment organic matter content increased significantly, reaching severe pollution levels. The results of the simulated sediment nutrient release test showed that the nitrogen and phosphorus in the reservoir would release slowly. Moreover, their annual release flux was calculated as 470.4 t and 87.9 t, respectively. It illustrated that the internal pollution of Danjiangkou Reservoir was light, and the release amount was small, so it was not the main pollution source of the reservoir at present.

Photodynamic therapy (PDT) and photothermal therapy (PTT) have emerged as promising non-invasive approaches to cancer treatment. However, the development of multifunctional nanomedicines is necessary to enhance these approaches’ effectiveness and safety. In this study, we investigated a polydopamine-based nanoparticle (PDA-ZnPc+ Nps) loaded with the efficient photosensitizer ZnPc(4TAP)12+ (ZnPc+) through in vitro and in vivo experiments to achieve synergistic PDT and PTT. Our results demonstrated that PDA-ZnPc+ Nps exhibited remarkable efficacy due to its ability to generate reactive oxygen species (ROS), induce photothermal effects, and promote apoptosis in cancer cells. Moreover, in both MCF-7 cells and MCF-7 tumor-bearing mice, the combined PDT/PTT treatment with PDA-ZnPc+ Nps led to synergistic effects. Subcellular localization analysis revealed a high accumulation of ZnPc+ in the cytoplasm of cancer cells, resulting in cellular disruption and vacuolation following synergistic PDT/PTT. Furthermore, PDA-ZnPc+ Nps exhibited significant antitumor effects without causing evident systemic damage in vivo, enabling the use of lower doses of photosensitizer and ensuring safer treatment. Our study not only highlights the potential of PDA-ZnPc+ Nps as a dual-functional anticancer agent combining PDA and PTT but also offers a strategy for mitigating the side effects associated with clinical photosensitizers, particularly dark toxicity.

High-temperature vulcanized silicone rubber (HTV-SR) employed for composite insulators is continuously subjected to a complex environment of alternating heat, corona discharge, humidity, etc. These stresses (especially alternating heat) complicate the aging mechanism of HTV-SR, which lacks systematic investigation. In this paper, a multi-factor aging platform considering temperature cycling, moisture, and corona discharge is established. Specifically, four temperature-cycling settings are employed, each of which lasts for 15 cycles. The surface morphology, hydrophobicity, and chemical, mechanical, and electrical properties of aged samples are methodically characterized. Experimental results show that the aging degree is correlated to the range of temperature cycling, which is attributed to diverse crosslink-degradation degrees with different temperature differences. Under a large temperature difference (70 °C), HTV-SR possesses a high crosslinking degree and a low degradation degree, making the material hard but easy to crack with alternating thermal stress. Then, severe defects and water condensation emerge on the HTV-SR surface, which promote the diffusion of corona products and water molecules into the material. The subsequent rise in crosslinking density caused by in-depth oxidation further exacerbates the aging of the material. Consequently, it brings about poor hydrophobicity, high interfacial polarization, and shallow trap energy levels in HTV-SR. This work provides a detailed analysis of the aging mechanism of HTV-SR in a simulated on-site environment.

There are several types of kidney diseases with complex causes. If left untreated, these diseases irreversibly progress to end-stage renal disease. Thus, their early diagnosis and targeted treatment are important. Exosomes—extracellular vesicles released by a variety of cells—are ideal carriers for DNA, RNA, proteins, and other metabolites owing to their bilayer membranes. Studies have shown that almost all renal cells can secrete exosomes. While research on exosomal microRNAs in the context of renal diseases begun only recently, rapid progress has been achieved. This review summarizes the changes in exosomal microRNA expression in different kidney diseases. Thus, it highlights the diagnostic and prognostic value of these exosomal microRNAs. Further, this review analyzes their roles in the development of different kidney diseases, guiding research on molecular mechanisms and therapeutic strategies.

Diabetes is a complex metabolic disorder of carbohydrate metabolism, characterized by high blood glucose levels either due to an absolute deficiency of insulin secretion or an ineffective response of cells to insulin, a hormone synthetized by β-cells in the pancreas. Despite the current substantial progress of new drugs and strategies to prevent and treat diabetes, we do not understand precisely the exact cause of the failure and impairment of β-cells. Therefore, there is an urgent need to find new methods to restore β-cells. In recent years, pluripotent stem cells (PSCs) such as embryonic stem cells (ESCs) and induced pluripotent stem cells (iPSC) can serve as an ideal alternative source for the pancreatic β-cells. In this review, we systematically summarize the current progress and protocols of generating pancreatic β-cells from human PSCs. Meanwhile, we also discuss some challenges and future perspectives of human PSCs treatments for diabetes.

Objective: To remove blood from an incision and find the incision spot is a key task during surgery, or else over discharge of blood will endanger a patient’s life. However, the repetitive manual blood removal involves plenty of workload contributing fatigue of surgeons. Thus, it is valuable to design a robotic system which can automatically remove blood on the incision surface. Methods: In this paper, we design a robotic system to fulfill the surgical task of the blood removal. The system consists of a pair of dual cameras, a 6-DoF robotic arm, an aspirator whose handle is fixed to a robotic arm, and a pump connected to the aspirator. Further, a path-planning algorithm is designed to generate a path, which the aspirator tip should follow to remove blood. Results: In a group of simulating bleeding experiments on ex vivo porcine tissue, the contour of the blood region is detected, and the reconstructed spatial coordinates of the detected blood contour is obtained afterward. The BRR robot cleans thoroughly the blood running out the incision. Conclusions: This study contributes the first result on designing an autonomous blood removal medical robot. The skill of the surgical blood removal operation, which is manually operated by surgeons nowadays, is alternatively grasped by the proposed BRR medical robot.

Abstract As an analytic pipeline for quantitative imaging feature extraction and analysis, radiomics has grown rapidly in the past decade. On the other hand, recent advances in deep learning and transfer learning have shown significant potential in the quantitative medical imaging field, raising the research question of whether deep transfer learning features have predictive information in addition to radiomics features. In this study, using CT images from Pancreatic Ductal Adenocarcinoma (PDAC) patients recruited in two independent hospitals, we discovered most transfer learning features have weak linear relationships with radiomics features, suggesting a potential complementary relationship between these two feature sets. We also tested the prognostic performance for overall survival using four feature fusion and reduction methods for combining radiomics and transfer learning features and compared the results with our proposed risk score-based feature fusion method. It was shown that the risk score-based feature fusion method significantly improves the prognosis performance for predicting overall survival in PDAC patients compared to other traditional feature reduction methods used in previous radiomics studies (40% increase in area under ROC curve (AUC) yielding AUC of 0.84).

Abstract As a novel type of antibiotic alternative, peptide‐based antibacterial drug shows potential application prospects attributable to their unique mechanism for lysing the membrane of pathogenic bacteria. However, peptide‐based antibacterial drugs suffer from a series of problems, most notably their immature stability, which seriously hinders their application. In this study, self‐assembling chimeric peptide nanoparticles (which offer excellent stability in the presence of proteases and salts) are constructed and applied to the treatment of bacterial infections. In vitro studies are used to demonstrate that peptide nanoparticles NPs1 and NPs2 offer broad‐spectrum antibacterial activity and desirable biocompatibility, and they retain their antibacterial ability in physiological salt environments. Peptide nanoparticles NPs1 and NPs2 can resist degradation under high concentrations of proteases. In vivo studies illustrate that the toxicity caused by peptide nanoparticles NPs1 and NPs2 is negligible, and these nanoparticles can alleviate systemic bacterial infections in mice and piglets. The membrane permeation mechanism and interference with the cell cycle differ from that of antibiotics and mean that the nanoparticles are at a lower risk of inducing drug resistance. Collectively, these advances may accelerate the development of peptide‐based antibacterial nanomaterials and can be applied to the construction of supramolecular nanomaterials.

The accuracy of agricultural unmanned ground vehicles’ localization directly affects the accuracy of their navigation. However, due to the changeable environment and fewer features in the agricultural scene, it is challenging for these unmanned ground vehicles to localize precisely in global positioning system-denied areas with a single sensor. In this article, we present an efficient and adaptive sensor-fusion odometry framework based on simultaneous localization and mapping to handle the localization problems of agricultural unmanned ground vehicles without the assistance of a global positioning system. The framework leverages three kinds of sub-odometry (lidar odometry, visual odometry and inertial odometry) and automatically combines them depending on the environment to provide accurate pose estimation in real time. The combination of sub-odometry is implemented by trading off the robustness and the accuracy of pose estimation. The efficiency and adaptability are mainly reflected in the novel surfel-based iterative closest point method for lidar odometry we propose, which utilizes the changeable surfel radius range and the adaptive iterative closest point initialization to improve the accuracy of pose estimation in different environments. We test our system in various agricultural unmanned ground vehicles’ working zones and some other open data sets, and the results prove that the proposed method shows better performance mainly in accuracy, efficiency and robustness, compared with the state-of-art methods.

Abstract Background Laurel wilt caused by Raffaelea lauricola is a lethal vascular disease of North American members of the Lauraceae plant family. This fungus and its primary ambrosia beetle vector Xyleborus glabratus originated from Asia; however, there is no report of laurel wilt causing widespread mortality on native Lauraceae trees in Asia. To gain insight into why R. lauricola is a tree-killing plant pathogen in North America, we generated and compared high quality draft genome assemblies of R. lauricola and its closely related non-pathogenic species R. aguacate. Results Relative to R. aguacate, the R. lauricola genome uniquely encodes several small-secreted proteins that are associated with virulence in other pathogens and is enriched in secondary metabolite biosynthetic clusters, particularly polyketide synthase (PKS), non-ribosomal peptide synthetase (NRPS) and PKS-NRPS anchored gene clusters. The two species also exhibit significant differences in secreted proteins including CAZymes that are associated with polysaccharide binding including the chitin binding CBM50 (LysM) domain. Transcriptomic comparisons of inoculated redbay trees and in vitro-grown fungal cultures further revealed a number of secreted protein genes, secondary metabolite clusters and alternative sulfur uptake and assimilation pathways that are coordinately up-regulated during infection. Conclusions Through these comparative analyses we have identified potential adaptations of R. lauricola that may enable it to colonize and cause disease on susceptible hosts. How these adaptations have interacted with co-evolved hosts in Asia, where little to no disease occurs, and non-co-evolved hosts in North America, where lethal wilt occurs, requires additional functional analysis of genes and pathways.

Abstract Background Cox proportional hazard model (CPH) is commonly used in clinical research for survival analysis. In quantitative medical imaging (radiomics) studies, CPH plays an important role in feature reduction and modeling. However, the underlying linear assumption of CPH model limits the prognostic performance. In this work, using transfer learning, a convolutional neural network (CNN) based survival model was built and tested on preoperative CT images of resectable Pancreatic Ductal Adenocarcinoma (PDAC) patients. Results The proposed CNN-based survival model outperformed the traditional CPH-based radiomics approach in terms of concordance index and index of prediction accuracy, providing a better fit for patients’ survival patterns. Conclusions The proposed CNN-based survival model outperforms CPH-based radiomics pipeline in PDAC prognosis. This approach offers a better fit for survival patterns based on CT images and overcomes the limitations of conventional survival models.

The introduction of the relay in heterogeneous networks (HetNets) can enhance cell-edge signals, but it will also bring coexistence of multiple interference which is composed of co-tier, cross-tier and cross time slot (caused by the half-duplex mode of the relay) interference, resulting in the ineffectiveness of the existing interference alignment (IA) schemes. To solve the above problem, the partial IA scheme, which achieves IA in the case of limited antennas by rationally designing the order and method of eliminating interference, is proposed in this paper. First, we align the interference from other base stations (BSs) into the interference space generated by pico BS to reduce the interference dimension of the relay, and then solve the interference between BSs and users by null space and concatenated precoding schemes. Furthermore, the feasibility condition of antenna configuration is analyzed. The simulation results show that the proposed scheme is superior to the traditional IA scheme in sum rate, and the performance of the proposed scheme is further improved as the relay transmission power increases.

Ship target detection in synthetic aperture radar (SAR) images is an important application field. Due to the existence of sea clutter, especially the SAR imaging in huge wave area, SAR images contain a lot of complex noise, which brings great challenges to the effective detection of ship targets in SAR images. Although the deep semantic segmentation network has been widely used in the detection of ship targets in recent years, the global information of the image cannot be fully utilized. To solve this problem, a new convolutional neural network (CNN) method based on wavelet and attention mechanism was proposed in this paper, called the WA-CNN algorithm. The new method uses the U-Net structure to construct the network, which not only effectively reduces the depth of the network structure, but also significantly improves the complexity of the network. The basic network of WA-CNN algorithm consists of encoder and decoder. Dual tree complex wavelet transform (DTCWT) is introduced into the pooling layer of the encoder to smooth the speckle noise in SAR images, which is beneficial to preserve the contour structure and detail information of the target in the feature image. The attention mechanism theory is added into the decoder to obtain the global information of the ship target. Two public SAR image datasets were used to verify the proposed method, and good experimental results were obtained. This shows that the method proposed in this article is effective and feasible.