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Abstract Idiopathic pulmonary fibrosis (IPF) is a chronic, progressive, fibrotic lung disease with limited treatment options and efficacy. Evidence suggests that IPF arises from genetic, environmental, and aging-related factors. The pathogenic mechanisms of IPF primarily involve dysregulated repeated microinjuries to epithelial cells, abnormal fibroblast/myofibroblast activation, and extracellular matrix (ECM) deposition, but thus far, the exact etiology remains unclear. Noncoding RNAs (ncRNAs) play regulatory roles in various biological processes and have been implicated in the pathophysiology of multiple fibrotic diseases, including IPF. This review summarizes the roles of ncRNAs in the pathogenesis of IPF and their potential as diagnostic and therapeutic targets.

Abstract This research investigates the intricate involvement of nuclear Transcription Factor Y Subunit Alpha (NFYA) in the advancement of castration-resistant prostate cancer (CRPC) and its consequential impact on early Growth Response 4 (EGR4) expression. NFYA demonstrates a significant elevation in CRPC tissues and cell lines, displaying robust upregulation in metastatic prostate cancer (mPCa) samples, closely associated with the Gleason score. Immunohistochemistry validates heightened nuclear staining of NFYA in CRPC patients, highlighting its crucial role in the progression of advanced prostate cancer. Silencing NFYA through siRNA in androgen-independent cell lines markedly impedes cell growth and migration, emphasizing NFYA’s pivotal role in promoting CRPC behavior. RNA-seq analysis identifies EGR4 as a downstream target of NFYA, with both genes consistently upregulated in CRPC. Validating this finding, heightened expression of EGR4 is observed in CRPC samples. In vivo studies utilizing a mouse model demonstrate that NFYA silencing substantially inhibits LNCaP-AI/22RV1shNFYA xenograft tumor growth, accompanied by reduced expression of EGR4 and Ki67. This comprehensive study reveals the multifaceted role of NFYA in CRPC progression, elucidates its downstream impact on EGR4, and underscores the therapeutic potential of targeting NFYA to inhibit CRPC growth in vivo. These findings contribute valuable insights into potential therapeutic strategies for managing CRPC.

Abstract The continuous generation of multi-omics and phenotype data is propelling advancements in precision oncology. UCSCXenaShiny was developed as an interactive tool for exploring thousands of cancer datasets available on UCSC Xena. However, its capacity for comprehensive and personalized pan-cancer data analysis is being challenged by the growing demands. Here, we introduce UCSCXenaShiny v2, a milestone update through a variety of improvements. Firstly, by integrating multidimensional data and implementing adaptable sample settings, we create a suite of robust TPC (TCGA, PCAWG, CCLE) analysis pipelines. These pipelines empower users to conduct in-depth analyses of correlation, comparison, and survival in three modes: Individual, Pan-cancer and Batch screen. Additionally, the tool includes download interfaces that enable users to access diverse data and outcomes, several features also facilitate the joint analysis of drug sensitivity and multi-omics of cancer cell lines. UCSCXenaShiny v2 is an open-source R package and a web application, freely accessible at https://github.com/openbiox/UCSCXenaShiny .

Abstract Background Plants have evolved various defense mechanisms against insect herbivores, including the formation of physical barriers, the synthesis of toxic metabolites, and the activation of phytohormone responses. Although plant-associated microbiota influence plant growth and health, whether they play a role in plant defense against insect pests in natural ecosystems is unknown. Results Here, we show that leaves of beetle-damaged weeping willow (Salix babylonica) trees are more resistant to the leaf beetle Plagiodera versicolora (Coleoptera) than those of undamaged leaves. Bacterial community transplantation experiments demonstrated that plant-associated microbiota from the beetle-damaged willow contribute to the resistance of the beetle-damaged willow to P. versicolora. Analysis of the composition and abundance of the microbiome revealed that Pseudomonas spp. is significantly enriched in the phyllosphere, roots, and rhizosphere soil of beetle-damaged willows relative to undamaged willows. From a total of 49 Pseudomonas strains isolated from willows and rhizosphere soil, we identified seven novel Pseudomonas strains that are toxic to P. versicolora. Moreover, re-inoculation of a synthetic microbial community (SynCom) with these Pseudomonas strains enhances willow resistance to P. versicolora. Conclusions Collectively, our data reveal that willows can exploit specific entomopathogenic bacteria to enhance defense against P. versicolora, suggesting that there is a complex interplay among plants, insects, and plant-associated microbiota in natural ecosystems. Graphical Abstract Video Abstract

Early identification and treatment of stroke can greatly improve patient outcomes and quality of life. Although clinical tests such as the Cincinnati Pre-hospital Stroke Scale (CPSS) and the Face Arm Speech Test (FAST) are commonly used for stroke screening, accurate administration is dependent on specialized training. In this study, we proposed a novel multimodal deep learning approach, based on the FAST, for assessing suspected stroke patients exhibiting symptoms such as limb weakness, facial paresis, and speech disorders in acute settings. We collected a dataset comprising videos and audio recordings of emergency room patients performing designated limb movements, facial expressions, and speech tests based on the FAST. We compared the constructed deep learning model, which was designed to process multi-modal datasets, with six prior models that achieved good action classification performance, including the I3D, SlowFast, X3D, TPN, TimeSformer, and MViT. We found that the findings of our deep learning model had a higher clinical value compared with the other approaches. Moreover, the multi-modal model outperformed its single-module variants, highlighting the benefit of utilizing multiple types of patient data, such as action videos and speech audio. These results indicate that a multi-modal deep learning model combined with the FAST could greatly improve the accuracy and sensitivity of early stroke identification of stroke, thus providing a practical and powerful tool for assessing stroke patients in an emergency clinical setting.

By benchmarking with the iteration of drilling technology, fracturing technology and well placement mode for shale oil and gas development in the United States and considering the geological characteristics and development difficulties of shale oil in the Jiyang continental rift lake basin, East China, the development technology system suitable for the geological characteristics of shale oil in continental rift lake basins has been primarily formed through innovation and iteration of the development, drilling and fracturing technologies. The technology system supports the rapid growth of shale oil production and reduces the development investment cost. By comparing it with the shale oil development technology in the United States, the prospect of the shale oil development technology iteration in continental rift lake basins is proposed. It is suggested to continuously strengthen the overall three-dimensional development, improve the precision level of engineering technology, upgrade the engineering technical indicator system, accelerate the intelligent optimization of engineering equipment, explore the application of complex structure wells, form a whole-process integrated quality management system from design to implementation, and constantly innovate the concept and technology of shale oil development, so as to promote the realization of extensive, beneficial and high-quality development of shale oil in continental rift lake basins.

BackgroundWe aimed to assess temporal trends in type 2 diabetes mellitus (T2DM)-related deaths and disability-adjusted life years (DALYs) at global and cross-social demographic index (SDI) levels, using data from the Global Burden of Disease (GBD) in 2021.MethodsWe used geospatial mapping to visualize the global distribution of T2DM-related mortality and DALYs in 2021. Joinpoint regression assessed annual and average percent changes in DALYs and deaths from 1990 to 2021 across SDI regions. Age-period-cohort modeling examined the effects of age, period, and cohort on trends. Decomposition analysis evaluated the impact of population growth, aging, and epidemiological changes on DALY trends. A stratified projection forecasted future T2DM burden by age and sex from 2020 to 2044.ResultsT2DM-related mortality and DALYs were highest in low-SDI regions. Globally, T2DM-related deaths and DALYs have increased, with the most rapid rise in low and low-middle SDI regions, driven by population growth and epidemiological shifts. High-SDI countries showed a slower increase in DALYs, influenced more by aging. Age-period-cohort analysis indicated higher DALY rates in later birth cohorts and recent periods, especially in high-SDI regions. Future projections show a significant increase in the 70-74 age group and a gradual rise in other age groups.ConclusionThe burden of T2DM is projected to continue increasing, especially in low-SDI and low-middle SDI regions, where population growth and epidemiological shifts are the main contributors. This underscores the need for targeted, region-specific healthcare policies, preventive strategies, and age-specific interventions to address the increasing T2DM burden globally.

This study investigated the enhancing effects of soil treatment with ethylene diamine tetraacetic acid (EDTA) and oxalic acid (OA) on the remediation of Cr(VI) contaminated soil by Datura stramonium L. A greenhouse pot experiment was conducted, where Cr(VI) contaminated soil was treated with 100 mg/kg Cr(VI) and varying concentrations of EDTA (5 and 10 mmol/kg) and OA (5 and 10 mmol/kg). The effects of these soil treatments on biomass, chlorophyll content, antioxidant enzyme activities, and Cr(VI) enrichment and translocation efficiency of D. stramonium were evaluated. The results showed that added OA to soil significantly increased the biomass and chlorophyll content of D. stramonium. The addition of 10 mmol/kg of OA to soil increased the plant biomass by 67.16 % and chlorophyll b content by 40.01 %. In addition, OA soil treatment significantly enhanced the activities of superoxide dismutase (SOD) by 6.36 %, peroxidase (POD) by 163.13 %, catalase (CAT) by 36.92 %, and ascorbate peroxidase (APX) by 32.12 %, which effectively alleviated the oxidative stress induced by Cr(VI). In contrast, soil treatment with a high concentration of EDTA (10 mmol/kg) significantly reduced plant biomass and chlorophyll content, although it increased the biological concentration factor (BCF) of the stem and leaf, as well as the translocation factor (TF). In conclusion, appropriate amounts of EDTA and OA added to soil can enhance the phytoremediation efficiency of D. stramonium grown in Cr(VI) contaminated soil, with OA added to soil being more effective than addition of EDTA. This study revealed the potential mechanisms of chelating agents EDTA and OA in enhancing the phytoremediation of Cr(VI) contaminated soil by D. stramonium, providing a scientific basis for further optimization of phytoremediation techniques.

Studying the diversity of gut microbiota and antibiotic resistance genes (ARGs) in endangered migratory birds, as well as their responses to different environments, can reflect the health status of migratory birds and the ecological risk of ARG transmission. The globally endangered Scaly-sided merganser (Mergus squamatus) is a typical migratory bird, yet the diversity of its intestinal microbiota and ARGs has not been reported. In this study, fecal samples were collected from two major riparian foraging areas utilized by M. squamatus in the Changbai Mountains of China: the Lushui River and the Manjiang River. The gut microbiota and ARG composition were characterized using 16S rRNA amplicon and metagenomic sequencing. The results indicated that the relative abundance of gut microbiota in the two habitats differed significantly, but the core flora was similar. Moreover, the relative abundance of pathogenic bacteria was high, suggesting that M. squamatus faces a high risk of disease. A total of 382 ARGs were identified, which were primarily resistant to multidrug, tetracycline, macrolides-lincosamides-streptogramins, and mupirocin. MsbA, abcA, rpoB2, and tetT were the most abundant shared ARGs between the two sites. The findings demonstrate that habitat influences the diversity of gut microbiota and ARGs in the endangered migratory M. squamatus. This species could serve as a reservoir for pathogenic bacteria and ARGs. Therefore, monitoring the diversity of gut microbiota and ARGs in M. squamatus across different habitats is essential for a comprehensive assessment of its health status and the ecological risk of ARG transmission in the future.

PIK3CA gene encodes the p110α catalytic subunit of PI3K, which regulates the PI3K/AKT/mTOR signaling pathway. PIK3CA gene mutation is one of the most common mutations in colorectal cancer (CRC), affecting about 15%–20% of CRC patients. PIK3CA gene mutation leads to the persistent activation of the PI3K/AKT/mTOR signaling pathway, which promotes the proliferation, invasion, metastasis, and drug resistance of CRC. This article provides a summary of the key detection methods for PIK3CA gene mutation, and provides an introduction to the existing colorectal cancer treatments and their practical applications in the clinic. Besides, this article summarizes the role and mechanism of PIK3CA gene mutation in the occurrence and development of CRC. It also explores the relationship between PIK3CA gene mutation and the clinical features and prognosis of CRC. This article focuses on the influence and mechanism of PIK3CA gene mutation on the targeted therapy and immunotherapy of CRC, and discusses the potential value and future direction of PIK3CA gene mutation in the personalized therapy of CRC. We aim to provide new perspectives and ideas for the precise diagnosis and treatment of CRC.

Abstract Single‐atom catalysts (SACs) have rapidly become a hot topic in photocatalytic research due to their unique physical and chemical properties, high activity, and high selectivity. Among many semiconductor carriers, the special structure of carbon nitride (C3N4) perfectly meets the substrate requirements for stabilizing SACs; they can also compensate for the photocatalytic defects of C3N4 materials by modifying energy bands and electronic structures. Therefore, developing advanced C3N4‐based SACs is of great significance. In this review, we focus on elucidating efficient preparation strategies and the burgeoning photocatalytic applications of C3N4‐based SACs. We also outline prospective strategies for enhancing the performance of SACs and C3N4‐based SACs in the future. A comprehensive array of methodologies is presented for identifying and characterizing C3N4‐based SACs. This includes an exploration of potential atomic catalytic mechanisms through the simulation and regulation of atomic catalytic behaviors and the synergistic effects of single or multiple sites. Subsequently, a forward‐looking perspective is adopted to contemplate the future prospects and challenges associated with C3N4‐based SACs. This encompasses considerations, such as atomic loading, regulatory design, and the integration of machine learning techniques. It is anticipated that this review will stimulate novel insights into the synthesis of high‐load and durable SACs, thereby providing theoretical groundwork for scalable and controllable applications in the field.

Chimeric antigen receptor (CAR) T-cell therapy is a revolutionary immunotherapeutic strategy that has shown efficacy in hematological malignancies. However, its application in solid tumors, particularly gastrointestinal cancers, faces significant challenges. These include the selection of target antigens, the complexity of the tumor microenvironment, and safety and toxicity concerns. This review provides a current overview of CAR-T therapy in various gastrointestinal cancers, such as esophageal, gastric, colorectal, pancreatic, and liver cancers. It discusses the limitations and future directions of CAR-T therapy in this context. This review highlights innovative strategies, including novel target antigens, multispecific CAR-T cells, armored CAR-T cells, and the development of universal CAR-T cells. These insights aim to inform ongoing research and foster advancements in CAR-T therapy for gastrointestinal cancers.

BackgroundIntramuscular hemangioma is a vascular malformation occurring in muscle tissues. It is most common in skeletal muscles of limbs, especially lower limbs in childhood. The intercostal intramuscular hemangioma with sternal invasion is very rare.Case presentationThe 47-year-old female patient was hospitalized due to “suddenly chest pain for 4 days”. Chest contrast-enhanced CT indicated bony changes in the sternum, accompanied by soft tissue shadows in the posterior sternum and diaphragmatic angle. After admission, physical examination showed: peristernal tenderness, percussion pain, other no obvious positive signs. PET-CT examination indicated that there was a high possibility of benign sternal changes. No cancer cells were found by sternal puncture. Surgical biopsy was performed and pathological findings were consistent with intramuscular hemangioma.ConclusionIntramuscular hemangioma with bone invasion is very rare, imaging examination is difficult to indicate obvious evidence, preoperative diagnosis is difficult, complete surgical resection is very important, more personalized treatment should be provided according to the overall situation of the patient.

X-linked hereditary Alport syndrome (XLAS) type 1 (OMIM: 301050) results from a pathogenic variant in the collagen type IV alpha 5 chain (COL4A5) gene.A human induced pluripotent stem cell (iPSC) line was generated from peripheral blood mononuclear cells of a 7-year-old male patient with XLAS using non-integrating episomal vector technique. The male donor had a heterozygous variant in the COL4A5 gene. The resulting iPSC line has a standard karyotype, can express pluripotent biomarkers, and is able to create germ layers in vivo. It can serve as a valuable cellular model for investigating the underlying mechanisms of XLAS.

Heavy oil represents a vital petroleum resource worldwide. As one of the major producers, China is facing great challenges in effective and economic production of heavy oil due to reservoir complexity. Plenty of efforts have been made to promote innovative advances in thermal recovery modes, methods, and processes for heavy oil in the country. The thermal recovery mode has been shifted from simple steam injection to a more comprehensive “thermal+'' strategy, such as a novel N2-steam hybrid process and CO2-enhanced thermal recovery techniques. These advanced techniques break through the challenges of heavy oil extraction from less accessible reservoirs with thinner oil layers and greater burial depths. Regarding thermal recovery methods, China has developed the steam-assisted gravity drainage method integrating flooding and drainage (also referred to as the hybrid flooding-drainage SAGD technology) for highly heterogeneous ultra-heavy oil reservoirs and the fire flooding method for nearly depleted heavy oil reservoirs, substantially improving oil recovery. Furthermore, a range of processes have been developed for heavy oil production, including the open hole completion process using sand control screens for horizontal wells, the process of integrated injection-recovery with horizontal pump for horizontal wells, the steam dryness maintenance, measurement, and control process, efficient and environment-friendly circulating fluidized bed (CFB) boilers with high steam dryness, the recycling process of produced water, and the thermal recovery process for offshore heavy oil. Based on the advances in methodology, technology, and philosophy, a series of supporting technologies for heavy oil production have been developed, leading to the breakthrough of existing technical limit of heavy oil recovery and the expansion into new exploitation targets. For the future heavy oil production in China, it is necessary to embrace a green, low-carbon, and energy-efficient development strategy, and to expand heavy oil extraction in reservoirs with larger burial depth, more viscous oil, thinner oil layers, and lower permeability. Moreover, it is highly recommended to collaboratively maximize oil recovery and oil-to-steam ratio through technological innovations, and boost intelligentization of heavy oil production.

In this review, we explore the effects of food additives on intestinal health. Food additives, such as preservatives, antioxidants and colorants, are widely used to improve food quality and extend shelf life. However, their effects on intestinal microecology May pose health risks. Starting from the basic functions of food additives and the importance of intestinal microecology, we analyze in detail how additives affect the diversity of intestinal flora, oxidative stress and immune responses. Additionally, we examine the association between food additives and intestinal disorders, including inflammatory bowel disease and irritable bowel syndrome, and how the timing, dosage, and individual differences affect the body’s response to additives. We also assess the safety and regulatory policies of food additives and explore the potential of natural additives. Finally, we propose future research directions, emphasizing the refinement of risk assessment methods and the creation of safer, innovative additives.

The changing climate poses a growing challenge to the population health. The objective of this study was to assess the association between ambient temperature and cause-specific mortality in Suzhou. Based on the non-accidental mortality data collected during 2008–2022 in Suzhou, China, this study utilized an individual-level case-crossover design to evaluate the associations of temperature with cause-specific mortality. We applied a distributed lag nonlinear model with a maximum lag of 14 days to account for lag effects. Mortality risk due to extreme cold (97.5th percentile) was analyzed. A total of 634,530 non-accidental deaths were analyzed in this study. An inverse J-shaped exposure-response relationship was observed between ambient temperature and non-accidental mortality, with the minimum mortality temperature (MMT) at 29.1℃. The relative risk (RR) of mortality associated with extreme cold (2.5th percentile) was 1.37 [95 % confidence interval (CI): 1.30, 1.44], higher than estimate of 1.09 (95 %CI: 1.07, 1.11) for extreme heat (97.5th percentile) relative to the MMT. Heat effect lasted for 2–3 days, while cold effect could persist for almost 14 days. Higher mortality risk estimates were observed for cardiorespiratory deaths compared to total deaths, with statistically significant between-group differences. Consequently, this study provides first-hand evidence on the associations between ambient temperatures and mortality risks from various causes, which could help local government and policy-makers in designing targeted strategies and public health measures against the menace of climate change.

The effect of coupling of copper and phosphorus in steel on atmospheric corrosion behavior was investigated using high-throughput experimental methods. Wire beam electrodes and plates with discrete distribution of composition gradients were prepared through a selective laser melting method. By combining array electrode scanning tests and EPMA, the electrochemical properties and the relationship between different copper and phosphorus contents were rapidly determined. Plenty of data obtained were analyzed according to relevant standards and screened to identify compositional ranges corresponding with the relevant compositions of existing grades of steel. To further analyze the effect of copper and phosphorus on the corrosion behavior, dry/wet cyclic corrosion test were performed using plates. A combination of XRD, FTIR, SEM, EDS, and EPMA were used to analyze the corrosion behavior. It was observed that both copper and phosphorus promoted the formation of amorphous substances, supporting the proposed mechanism by previous researchers. Furthermore, in the absence of manganese interference, copper could accumulate at the metal/rust layer interface, rendering the rust layer cation-selective and effectively inhibiting the deep penetration of chloride ions. Moving forward, we expect to develop this high-throughput experiments to investigate the coupling effects of other elements on corrosion behavior.

Abstract In industrial manufacturing, how to accurately classify defective products and locate the location of defects has always been a concern. Previous studies mainly measured similarity based on extracting single‐scale features of samples. However, only using the features of a single scale is hard to represent different sizes and types of anomalies. Therefore, the authors propose a set of memory banks of multi‐scale features (MBMF) to enrich feature representation and detect and locate various anomalies. To extract features of different scales, different aggregation functions are designed to produce the feature maps at different granularity. Based on the multi‐scale features of normal samples, the MBMF are constructed. Meanwhile, to better adapt to the feature distribution of the training samples, the authors proposed a new iterative updating method for the memory banks. Testing on the widely used and challenging dataset of MVTec AD, the proposed MBMF achieves competitive image‐level anomaly detection performance (Image‐level Area Under the Receiver Operator Curve (AUROC)) and pixel‐level anomaly segmentation performance (Pixel‐level AUROC). To further evaluate the generalisation of the proposed method, we also implement anomaly detection on the BeanTech AD dataset, a commonly used dataset in the field of anomaly detection, and the Fashion‐MNIST dataset, a widely used dataset in the field of image classification. The experimental results also verify the effectiveness of the proposed method.

To deal with the spectrum demand of new low earth orbit satellite (LEO) communication component and the aggregate interference generated by incumbent terrestrial component in future integrated satellite-terrestrial system, a novel space-borne spectrum-sensing (SS) and spectrum-database assisted spectrum-sharing scheme with interweave model for LEO cognitive system was proposed.Firstly, considering the adverse impact of the sparse distribution of LEO cognitive node (CN) on SS and interference cartography (IC), the concept of cognitive node position was put forward.Secondly, the spectrum-DB was built based on dual-threshold hybrid SS and IC with LEO cognitive system.Finally, a spectrum-sharing scheme with multi-state virtual network (MSVN) was designed, which could make the coexistence between terrestrial component and uplink of LEO component available.Simulation results indicate that the spectrum-database approach based on the proposed dual-threshold hybrid SS and IC can support the spectrum awareness of high reliability with relatively low-density LEO CN, and that the proposed cooperative access scheme based on spectrum-database and interweave model for LEO component can share spectrum with terrestrial one, but not significantly increase the end-to-end delay of LEO component.

Background There is currently no research on the correlation between novel inflammatory indexes systemic immune-inflammation index (SII), neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), and the risk of anemia in chronic kidney disease (CKD) population, as well as survival analysis in CKD with anemia.Methods This investigation encompassed 4444 adult subjects out of the National Health and Nutrition Examination Survey (NHANES) between 2005 and 2018. The study utilized multi-variable logistic regression to assess the relationship between SII, NLR, PLR, and anemia risk occurrence in CKD population. Survival differences in CKD patients with anemia, based on varying levels of SII, NLR, and PLR were evaluated employing Kaplan–Meier and Cox proportional hazards models.Results The adjusted logistic regression model demonstrates that SII, NLR, and PLR are associated with the risk of anemia occurrence in CKD population. Kaplan–Meier’s analysis reveals significant differences in survival rates among CKD patients with anemia stratified by NLR levels. The adjusted Cox proportional hazards model shows that the higher NLR group has a 30% elevated risk of all-cause mortality contrasted with lower group (hazard ratio, HR: 1.30, confidence interval (CI) [1.01, 1.66], p value

The sub-instability refers to a stress state that exists between the maximum stress the rock can withstand and its final failure. This stress state should be an indicator of impending large earthquakes, as it occurs in the final stage of earthquake nucleation. We apply the Load/Unload Response Ratio (LURR) method to explore sub-instability in the source media before large earthquakes. The earthquake-related observation data such as the groundwater, crustal deformation, electromagnetism and so on were adopted as data input. The load and unload phases at each observation station are discriminated by the change of Coulomb failure stress caused by earth tides along the tectonically optimal sliding direction. Retrospective studies of this approach on the 2020 Mw6.0 Jiashi, Xinjiang, 2022 Mw6.6 Menyuan, Qinghai, and Mw6.6 Luding, Sichuan earthquakes show that the LURR time series derived from the observation stations near the epicenter exceed 2 standard deviations of the mean within several days to years before the mainshocks. Additionally, as the time of mainshock approaches, the distance between the stations detecting LURR anomaly and epicenter decreases. The LURR anomalies indicate the presence of sub-instability prior to a large earthquake, and their spatio-temporal evolution may suggest the weakening processes in the source media.

Abstract BackgroundStudies have reported the adverse effects of cold events on influenza. However, the role of critical factors, such as characteristics of cold spells, and regional variations remain unresolved. ObjectiveWe aimed to systematically evaluate the association between cold spells and influenza incidence in mainland China. MethodsThis time series analysis used surveillance data of daily influenza from 325 sites in China in the 2014‐2019 period. A total of 15 definitions of cold spells were adopted based on combinations of temperature thresholds and days of duration. A distributed lag linear model was used to estimate the short-term effects of cold spells on influenza incidence during the cool seasons (November to March), and we further explored the potential impact of cold spell characteristics (ie, intensity, duration, and timing during the season) on the estimated associations. Meta-regressions were used to evaluate the modification effect of city-level socioeconomic indicators. ResultsThe overall effect of cold spells on influenza incidence increased with the temperature threshold used to define cold spells, whereas the added effects were generally small and not statistically significant. The relative risk of influenza-associated with cold spells was 3.35 (95% CI 2.89‐3.88), and the estimated effects were stronger during the middle period of cool seasons. The health effects of cold spells varied geographically and residents in Jiangnan region were vulnerable groups (relative risk 7.36, 95% CI 5.44‐9.95). The overall effects of cold spells were positively correlated with the urban population density, population size, gross domestic product per capita, and urbanization rate, indicating a sterner response to cold spells in metropolises. ConclusionsCold spells create a substantial health burden on seasonal influenza in China. Findings on regional and socioeconomic differences in the health effects of cold spells on seasonal influenza may be useful in formulating region-specific public health policies to address the hazardous effects of cold spells.

The restoration of cartilage injuries remains a formidable challenge in orthopedics, chiefly attributed to the absence of vascularization and innervation in cartilage. Decellularized extracellular matrix (dECM) derived from cartilage, following antigenic removal through decellularization processes, has exhibited remarkable biocompatibility and bioactivity, rendering it a viable candidate for cartilage repair. Additionally, extracellular vesicles (EVs) generated from cartilage have demonstrated a synergistic effect when combined with dECM, potentially mitigating the inhibitory impact on protein synthesis by phosphorylating 4ebp, thereby promoting the synthesis of cartilage-related proteins such as collagen. In pursuit of this objective, we have innovated a novel bioink and repair scaffold characterized by exceptional biocompatibility, bioactivity, and biodegradability, establishing a tissue-specific microenvironment conducive to chondrogenesis. Within rat osteochondral defects, the biologically active scaffold successfully prompted the formation of transparent cartilage, featuring adequate mechanical strength, favorable elasticity, and dECM deposition indicative of cartilage. In summary, this study has effectively engineered a hydrogel bioink tailored for cartilage repair and devised a bioactive cartilage repair scaffold proficient in instigating cell differentiation and fostering cartilage repair.

Abstract Examining patterns of genetic diversity are crucial for conservation planning on endangered species, while inferring the underlying process of recent anthropogenic habitat modifications in the context potential long‐term demographic changes remains challenging. The globally endangered scaly‐sided merganser (SSME), Mergus squamatus, is endemic to a narrow range in Northeast Asia, and its population has recently been contracted into two main breeding areas. Although low genetic diversity has been suggested in the Russian population, the genetic status and demographic history of these individuals have not been fully elucidated. We therefore examined the genetic diversity and structure of the breeding populations of the SSME and investigated the relative importance of historical and recent demographic changes to the present‐day pattern of genetic diversity. Using 10 nuclear microsatellite (SSR) markers and mitochondrial DNA (mtDNA) control region sequences, we found limited female‐inherited genetic diversity and a high level of nuclear genetic diversity. In addition, analysis of both markers consistently revealed significant but weak divergence between the breeding populations. Inconsistent demographic history parameters calculated from mtDNA and bottleneck analysis results based on SSR suggested a stable historical effective population size. By applying approximate Bayesian computation, it was estimated that populations started to genetically diverge from each other due to recent fragmentation events caused by anthropogenic effects rather than isolation during Last Glacial Maximum (LGM) and post‐LGM recolonization. These results suggest that limited historical population size and shallow evolutionary history may be potential factors contributing to the contemporary genetic diversity pattern of breeding SSME populations. Conservation efforts should focus on protecting the current breeding habitats from further destruction, with priority given to both the Russian and Chinese population, as well as restoring the connected suitable breeding grounds.

Abstract Niche theory predicts that ecologically similar sympatric species should show differentiation in at least one of the main niche dimensions (time, space, and/or food). Here, we combined observations of breeding timing, nest site selection, and diet (the latter determined using DNA metabarcoding) to analyze the niche overlap and differentiation between two sympatric secondary cavity‐nesting birds, the Japanese Tit Parus minor and the Yellow‐rumped Flycatcher Ficedula zanthopygia. The results showed that (1) there were significant differences in the first egg laying date, length of the egg laying period, incubation date, and hatching date between tits and flycatchers, and the breeding time of flycatchers peaked later (about 30 days) than that of tits; (2) the two species had a large overlap in nest site selection, although the canopy coverage and shrub density of flycatchers were significantly higher than those of tits; and (3) the niche overlap in diet was minimal, with both species heavily relying on Lepidoptera (39.6% and 63.7% for tits and flycatchers, respectively), but with flycatchers consuming significantly higher percentages of Lepidoptera, Diptera, and Coleoptera than tits. The results indicate that these two sympatric secondary cavity‐nesting species have significant niche differentiation in breeding time and diet, but little differentiation in nest site selection.

Objective Fast progression (FP) represents a desperate situation for advanced non-small cell lung cancer (NSCLC) patients undergoing immune checkpoint inhibitor therapy. We aimed to develop a predictive framework based on machine learning (ML) methods to identify FP in advanced NSCLC patients using blood test biomarkers.Methods and analysis We extracted data of 1546 atezolizumab-treated patients from four multicentre clinical trials. In this study, patients from the OAK trial were taken for model training, whereas patients from the other trials were used for independent validations. The FP prediction model was developed using 21 pretreatment blood test variables in seven ML approaches. Prediction performance was evaluated by the receiver operating characteristic (ROC) curve.Results The prevalence of FP was 7.6% (118 of 1546) in all atezolizumab-treated patients. The most important variables for the prediction model were: C reactive protein, neutrophil count, lactate dehydrogenase and alanine transaminase. The Support Vector Machine (SVM) algorithm applied to these four blood test parameters demonstrated good performance: the area under the ROC curve obtained from the training cohort (OAK), validation cohort 1 (BIRCH) and cohort 2 (merged POPLAR and FIR) were 0.908, 0.666 and 0.776, respectively. In addition, the absolute difference in median survival between the SVM-predicted FP and non-FP groups was significant in both progression-free survival and overall survival (p

Abstract Emergent plants have been remarkably effective in reducing phosphorus (P) discharge from ecological ditches; however, the treatment and recycling of these residues is a great challenge. In this study, magnetic biochars (MBs, i.e., MB-A, MB-C, and MB-T) were fabricated from three emergent plant residues (Acorus calamus L., Canna indica L., and Thalia dealbata Fraser, respectively) and modified with Fe(II)/Fe(III). Scanning electron microscopy-energy dispersive spectroscopy and X-ray diffraction spectra confirmed the successful loading of Fe3O4 and FeO(OH) onto the surfaces of the MBs. Batch adsorption experiments showed that MBs exhibited a higher P adsorption capacity than that of the raw biochars. Within the range of 0.8–43.0 mg L−1 in solution, the adsorption capacities of P by MB-A, MB-C, and MB-T were 304.6–5658.8, 314.9–6845.6, and 292.8–5590.0 mg kg−1, with adsorption efficiencies of 95.2–32.9%, 98.4–39.8%, and 91.5–32.5%, respectively. The primary mechanisms that caused P to adsorb onto the MBs were inner-sphere complexation and electrostatic attraction. Low pH conditions were more beneficial for the P adsorption of the MBs, while co-existing anions had a negative impact with the following order: HCO3 − > SO4 2− > Cl−≈NO3 −. The P-31 nuclear magnetic resonance results further demonstrated that the main adsorbed P species on the MBs was orthophosphate, followed by orthophosphate monoesters and DNA. Overall, MBs offer a resource utilization strategy for emergent plant residues and P-laden MBs are promising alternative P fertilizers. Graphical Abstract

Pedestrian trajectory prediction attracts a great deal of attention as a fundamental research in the automatic drive system, human-robot interaction, and intelligent surveillance. This paper proposes a spatiotemporal framework based on Transformer and conditional variational autoencoder (CVAE) models to predict pedestrian trajectories while accounting for uncertainty. This paper proposes a spatiotemporal framework based on Transformer and conditional variational autoencoder (CVAE) models to predict pedestrian trajectories while accounting for uncertainty. The Transformer encoder-decoder modules capture spatial and temporal features. The CVAE compares predictions to ground truth across frames to learn motion uncertainty distributions. Experiments on two benchmark datasets demonstrate the approach reduces average displacement error by 0.04 - 0.1 m and final displacement error by 0.05 - 0.15 m compared to prior methods. The results highlight the importance of modeling uncertainty for accurate pedestrian trajectory forecasting.

In practical engineering scenarios, machines are seldom in a faulty operating state, so it is difficult to get enough available sample data to train the fault diagnosis model, leading to the problem of the small and unbalanced number of rotating machinery fault samples and low fault diagnosis accuracy. To solve this problem, this paper introduces a novel approach to machinery fault diagnosis. This approach involves the integration of a Convolutional Attention Residual Network (CBAM-ResNet) with a Graph Convolutional Neural Network (GCN). Firstly, to comprehensively exploit time-domain information from one-dimensional vibration signals, this study utilize Gram Angular Field (GAF) coding to transform traits of vibration signals into two-dimensional image characteristics. The resultant two-dimensional image is then expanded by applying the Wasserstein Distance Gradient Penalty Generation Adversarial Network (WGAN-GP) to produce a representative sample image. Secondly, the image is input to CBAM-ResNet to perform focused feature extraction and construct the feature matrix. Lastly, the adjacency matrix is derived through Graph Generation Layer (GGL); subsequently, the feature matrix and adjacency matrix are utilized as inputs for the GCN. After deep feature extraction, fault feature classification is executed via Softmax. Performance tests were conducted using the Case Western Reserve University bearing dataset and the planetary gearbox dataset. The method demonstrated remarkable results, achieving an accuracy of over 99% on the unbalanced dataset and surpassing 98% in 0dB noise compared to various other models. This illustrates the effectiveness and feasibility of the proposed method.

To address the problem that uneven sample distribution can affect the accuracy and stability of fault diagnosis outcomes, we propose a deep transfer learning-Res2Net-convolutional block attention mechanism model. Firstly, the deep migration technique is used to transfer weights of the imbalanced source domain samples to the balanced target domain, expanding the data samples. Secondly, in the feature extraction and detection phase, All eight residual blocks are embedded in convolution block attention to ensure that interference signals are suppressed and that the key fault features are retained. Multilayer feature fusion extracts faulty sample features from multiple residual blocks of the network, which are combined into the feature fusion layer by parallel splicing. Finally, this model is experimentally validated using two different bearing datasets, and the combined evaluation indexes of the new model under the severe imbalance condition are 95.80% and 95.85%, respectively, demonstrating the feasibility and excellent performance of the model.

In the process of the vigorous development of satellite internet of things, random access protocol is regarded as the most suitable communication protocol due to its characteristics of low signaling overhead, high transmission efficiency, high flexibility and easy implementation.However, due to drastic load changes, conventional random access mode only has advantages in the specific load range.Dynamic allocation of random access resources requires the accurate number of time slot access applications.In view of the above problems, a random access adaptive scheme was proposed, which could adaptively selected the appropriate random access mode according to the future load size, so as to improved the system throughput.The load size could be obtained through the proposed load estimation and prediction scheme.When narrowband interference exists in the transmission environment, the spread-spectrum communication means was introduced to made the adaptive scheme of random access run successfully.Moreover, the proposed adaptive scheme also improved the system throughput.

Abstract To improve the accuracy of remote-sensing image semantic segmentation in complex scenario, an improved DeepLabv3+ lightweight neural network is proposed. Specifically, the lightweight network MobileNetv2 is used as the backbone network. In atrous spatial pyramid pooling (ASPP), to alleviate the gridding effect, the Dilated Convolution in original DeepLabv3+ network is replaced with the Hybrid Dilated Convolution (HDC) module. In addition, the traditional spatial mean pooling is replaced by the strip pooling module (SPN) to improve the local segmentation effect. In the decoder, to obtain the rich low-level target edge information, the ResNet50 residual network is added after the low-level feature fusion. To enhance the shallow semantic information, the efficient and lightweight Normalization-based Attention Module (NAM) is added to capture the feature information of small target objects. The results show that, under the INRIA Aerial Image Dataset and same parameter setting, the Mean Pixel Accuracy (MPA) and Mean Intersection over Union (MIoU) are generally best than DeepLabv3+ , U-Net, and PSP-Net, which are respectively improved by 1.22%, − 0.22%, and 2.22% and 2.17%, 1.35%, and 3.42%. Our proposed method has also a good performance on the small object segmentation and multi-object segmentation. What’s more, it significantly converges faster with fewer model parameters and stronger computing power while ensuring the segmentation effect. It is proved to be robust and can provide a methodological reference for high-precision remote-sensing image semantic segmentation.

Abstract Nanoparticles with small sizes formed in the process of food thermal treatment have gained substantial attention due to their safety uncertainty and latent risks to human health. Herein, the physicochemical features of fluorescent carbon dots (CDs) from roasted Spanish mackerel were studied, it was found that the size distribution, fluorescence properties, surface groups, and formation process of CDs were highly dependent on the roasting time of Spanish mackerel. The interaction of CDs with digestive proteases resulted in the structural change and activity inhibition of proteases. When the concentration of CDs was 120 μg mL−1, the enzyme activity of pepsin and trypsin was reduced from 100% to 75.95% and 78.5%, respectively. The CDs caused static fluorescence quenching of pepsin and trypsin as confirmed by the fluorescence analysis. After oral administration, CDs can be transported to the main organs through the blood. The cytotoxicity assessment revealed that the cell viability decreased with the prolonging of roasting time. The CDs roasted at 230°C for 40 min showed a significant hemolytic effect even at a low concentration of 0.5 mg mL−1. Moreover, the cellular uptake level and pathway of CDs were significantly affected by blood protein adsorption. This study helps to evaluate the in vivo biodistribution, the interaction with proteases during gastrointestinal digestion, and the cellular uptake of CDs after transport through the blood, providing a theoretical basis for revealing the potential effects of foodborne nanoparticles.

Abstract Telomerase allows eukaryotic cells to proliferate indefinitely, an important characteristic of tumor cells. Telomerase-related long no coding RNAs (TERLs) are involved in prognosis and drug sensitivity prediction; however, their association with bladder cancer (BLCA) is still unreported. The objective of this research is to determine a predictive prognostic TERL signature for OS and to provide an efficient treatment option for BLCA. The RNA sequence, clinical information, and mutational data of BLCA patients were acquired from The Cancer Genome Atlas (TCGA) database. With the help of the data from least absolute shrinkage and selection operator (LASSO) regression and Cox regression, a prognostic signature was established including 14 TERLs, which could divide BLCA patients into low-risk (L-R) and high-risk (H-R) cohorts. The time-dependent receiver operating characteristic (ROC) curve demonstrated the greater predictive power of the model. By combing the TERLs-based signature and clinical risk factors (age, sex, grade, and stage), a prognostic nomogram was constructed to forecast the survival rates of patients with BLCA at 1-, 3-, and 5-years, which was well matched by calibration plots C-index and Decision curve analysis (DCA). Furthermore, the L-R cohort showed higher tumor mutation burden (TMB) and lower tumor immune dysfunction and exclusion (TIDE) than the H-R cohort, as well as substantial variability in immune cell infiltration and immune function between the two cohorts was elucidated. As for external validation, LINC01711 and RAP2C-AS1 were identified as poor prognostic factors by survival analysis from the Kaplan–Meier Plotter database, which were validated in BLCA cell lines (EJ, 253J, T24, and 5637) and SV-HUC-1 cells as the control group using qRT-PCR. In addition, interference with the expression of RAP2C-AS1 suppresses the proliferation and migration of BLCA cells, and RAP2C-AS1 could affect the expression of CD274 and CTLA4, which could serve as prognostic markers and characterize the tumor microenvironment in BLCA. Overall, the model based on the 14-TERLs signature can efficiently predict the prognosis and drug treatment response in individuals with bladder cancer.

The spatial variability of land surface temperature (LST) is considerably affected by urban morphology. Previous research has focused separately on the thermal effects of urban morphology and the cooling effects of water bodies and urban parks. However, the combined influence of intra- and extra-block factors on LST has not been thoroughly examined. To bridge this research gap, we conducted an extensive analysis of 17 urban morphology factors in Hangzhou by employing a novel stacked ensemble approach. Results showed that the stacked ensemble models outperformed commonly used techniques, such as random forest and boosted regression trees. Extra-block factors, alongside building density, average building height, and vegetation coverage within blocks, predominantly influenced the LST distribution across all seasons. Building density was positively correlated with LST, with a maximum influence of 1.5 °C in spring, whereas building height was negatively correlated with it, with a maximum influence of 1.8 °C in winter. The cooling distance of the Qiantang River extends up to 2500 m into the urban blocks and has a maximum effect of 2 °C in summer. These insights deepen our comprehension of the interplay between LST and intra- and extra-block urban morphologies, thus offering valuable guidance for urban planners and policymakers.

Pesticide residues pose significant risks to human health and the environment, emphasizing the need for sensitive detection and analysis methods. Fluorescence-based sensors, particularly those utilizing aggregation-induced emission (AIE) fluorophores (AIEgens), have demonstrated exceptional performance in this area. This review summarizes key advancements in pesticide detection sensors based on AIEgens, detailing their luminescence mechanisms and fluorescence sensing principles. It explores various applications of AIEgens in fluorescence sensors, including organic small-molecule sensors, nanocomposite sensors, metal-organic framework sensors, supramolecular sensors, fluorescent porous organic polymer sensors, and lateral flow immunoassay sensors, with specific examples illustrating their detection mechanisms and performance. This review also discusses current challenges and future perspectives for the development of these sensors. We anticipate that this review will serve as a valuable and timely resource for researchers working to advance the development and application of AIEgens-based sensors in pesticide detection.

Sequential recommendations aim to predict users’ next interactions by modeling their interaction sequences. Most existing work concentrates on user preferences within these sequences, overlooking the complex item relationships across sequences. Additionally, these studies often fail to address the diversity of user interests, thus not capturing their varied latent preferences effectively. To tackle these problems, this paper develops a novel recommendation algorithm based on hypergraphs and contrastive learning named HyperCLR. It dynamically incorporates the time and location embeddings of items to model high-order relationships in user preferences. Moreover, we developed a graph construction approach named IFDG, which utilizes global item visit frequencies and spatial distances to discern item relevancy. By sampling subgraphs from IFDG, HyperCLR can align the representations of identical interaction sequences closely while distinguishing them from the broader global context on IFDG. This approach enhances the accuracy of sequential recommendations. Furthermore, a gating mechanism is designed to tailor the global context information to individual user preferences. Extensive experiments on Taobao, Books and Games datasets have shown that HyperCLR consistently surpasses baselines, demonstrating the effectiveness of the method. In particular, in comparison to the best baseline methods, HyperCLR demonstrated a 29.1% improvement in performance on the Taobao dataset.

The global shortage of clean water is a major problem, even in water-rich regions. To solve this problem, low-cost and energy-efficient water treatment methods are needed. Membrane separation technology (MST), as a separation method with low energy consumption, low cost, and good separation effect, has been widely used to deal with seawater desalination, resource recovery, industrial wastewater treatment, and other fields. With the continuous progress of scientific and technological innovation and the increasing demand for use, NF/RO membranes based on the TFC structure are constantly being upgraded. This paper presents the recent research progress of NF and RO membranes based on TFC structures and their applications in different fields, especially the formation mechanism and regulation of selective layer structures and the modification methods of selective layers. Our summary provides fundamental insights into the understanding of NF and RO membrane processes and hopefully triggers further thinking on the development of membrane filtration process optimization.

This study introduces a novel direct expansion air conditioning (DX AC) system with three evaporators (DX-TE) to enhance indoor temperature and humidity control. Operating in two modes, the DX-TE system provides variable cooling output, adapting to fluctuating indoor cooling loads while maintaining uniform air supply. Experimental and simulation studies were conducted to investigate the system’s operational characteristics. An experimental setup was established to obtain preliminary steady-state data, followed by the development and validation of a steady-state mathematical model. Simulation studies were then performed to optimize the evaporator sizes. The results indicate that the DX-TE system delivers variable cooling capacities at a constant compressor speed and airflow rate, outperforming conventional variable frequency DX AC systems in cooling and dehumidification. The evaporator area ratio significantly impacts the system’s performance, with smaller ratios yielding a larger output range. As the area ratio increases from 1:1 to 1:3, the cooling capacity range in Modes 1 and 2 increases by 33.6% and 14.3%, respectively, while the dehumidification range expands by 58.6% and 51.69%.

IntroductionIn recent years, the perceptual capabilities of robots have been significantly enhanced. However, the task execution of the robots still lacks adaptive capabilities in unstructured and dynamic environments.MethodsIn this paper, we propose an ontology based autonomous robot task processing framework (ARTProF), to improve the robot's adaptability within unstructured and dynamic environments. ARTProF unifies ontological knowledge representation, reasoning, and autonomous task planning and execution into a single framework. The interface between the knowledge base and neural network-based object detection is first introduced in ARTProF to improve the robot's perception capabilities. A knowledge-driven manipulation operator based on Robot Operating System (ROS) is then designed to facilitate the interaction between the knowledge base and the robot's primitive actions. Additionally, an operation similarity model is proposed to endow the robot with the ability to generalize to novel objects. Finally, a dynamic task planning algorithm, leveraging ontological knowledge, equips the robot with adaptability to execute tasks in unstructured and dynamic environments.ResultsExperimental results on real-world scenarios and simulations demonstrate the effectiveness and efficiency of the proposed ARTProF framework.DiscussionIn future work, we will focus on refining the ARTProF framework by integrating neurosymbolic inference.

Short-form video apps, such as TikTok, have become popular worldwide. Compared to traditional social media, they have powerful push algorithms and are more entertaining, which might lead to some negative effects. Research has attempted to identify the relationship between short-form video use and depression, but the processes mechanism underly the relationship was few in number. The present study explored the association between short-form video use and depression among Chinese adolescents and analyzed the mediating roles of need gratification and short-form video addiction. The participants included 1302 senior high school students (Mage = 16.03, SD = 0.76, 42.6 % boys). And a structure equation model with chain mediating was established using Mplus. The results showed (1) a direct association between short-form video use and adolescent depression, (2) that entertainment need gratification, social-related need gratification, and short-form video addiction acted as chain mediating factors, and (3) no gender difference in the model. The present study's findings revealed the important mediating role of addictive behavior between normal use behavior and depression and suggested that preventative and interventional plans based on need gratification should be developed to reduce short-form video addiction and improve mental health.

Biogas slurry (BS), waste water of energy production, holds potential as both an irrigation water resource and a liquid fertilizer source. Typically combined with water and mineral fertilizer at specific ratios, BS is applied in fields with drip irrigation systems to enhance crop growth. However, the soil water infiltration process with BS drip irrigation remains poorly understood, mainly owing to the BS's differing characteristics from conventional water sources. This study investigated the morphological characteristics, transport and distribution of water in three ratios of BS-water using a soil column experiment, with the post-irrigation surface soil pores and elements analyzed using electron microscopy and energy spectrum scanning techniques. The findings reveal that BS drip irrigation significantly alters the water morphological characteristics, transport process and distribution compared to conventional water sources. The morphology of the wetting-front changed from nearly ''hemispherical'' to a ''half-pear'' shape with time in BS drip irrigation. The soil-wetting front's vertical distance was notably smaller, approximately 50% of the vertical depth seen with traditional water source drip irrigation, even after redistribution of soil moisture, it was still difficult to reach the depth of the main root zone of most crops. Moreover, The carbon content on the soil surface was increased, ranging between 19.05–47.62% in the BS irrigation scenario, which led to soil pore blockage and a decrease in porosity ranging between 11.99–40.5%. The dynamic viscosity of BS is approximately 50% higher than that of CF.Theses indicate that the combined effect of soil porosity and dynamic viscosity affects the BS infiltration.In conclusion, this paper proposes a BS drip irrigation model with integrated agronomic measures to mitigate the potential adverse effects of BS drip irrigation caused by changes in soil water transportation and distribution.

Limited treatment options have been shown to alter the natural course of constipation-predominant irritable bowel syndrome (IBS-C). Therefore, safer and more effective approaches are urgently needed. We investigated the effects of transcutaneous auricular vagus nerve stimulation (taVNS) in a mouse model of IBS-C. In the current study, C57BL/6 mice were randomly divided into normal control, IBS-C model control, sham-electrostimulation (sham-ES), taVNS, and drug treatment groups. The effects of taVNS on fecal pellet number, fecal water content, and gastrointestinal transit were evaluated in IBS-C model mice. We assessed the effect of taVNS on visceral hypersensitivity using the colorectal distention test. 16S rRNA sequencing was used to analyze the fecal microbiota of the experimental groups. First, we found that taVNS increased fecal pellet number, fecal water content, and gastrointestinal transit in IBS-C model mice compared with the sham-ES group. Second, taVNS significantly decreased the abdominal withdrawal reflex (AWR) score compared with the sham-ES group, thus relieving visceral hyperalgesia. Third, the gut microbiota outcomes showed that taVNS restored Lactobacillus abundance while increasing Bifidobacterium probiotic abundance at the genus level. Notably, taVNS increased the number of c-kit-positive interstitial cells of Cajal (ICC) in the myenteric plexus region in IBS-C mice compared with the sham-ES group. Therefore, our study indicated that taVNS effectively ameliorated IBS-C in the gut microbiota and ICC.

High fiber strength (FS) premium cotton has significant market demand. Consequently, enhancing FS is a major objective in breeding quality cotton. However, there is a notable lack of known functionally applicable genes that can be targeted for breeding. To address this issue, our study used specific length–amplified fragment sequencing combined with bulk segregant analysis to study FS trait in an F2 population. Subsequently, we integrated these results with previous quantitative trait locus mapping results regarding fiber quality, which used simple sequence repeat markers in F2, F2:3, and recombinant inbred line populations. We identified a stable quantitative trait locus qFSA06 associated with FS located on chromosome A06 (90.74–90.83 Mb). Within this interval, we cloned a gene, GhALDH7B4_A06, which harbored a critical mutation site in coding sequences that is distinct in the two parents of the tested cotton line. In the paternal parent Ji228, the gene is normal and referred to as GhALDH7B4_A06O; however, there is a nonsense mutation in the maternal parent Ji567 that results in premature termination of protein translation, and this gene is designated as truncated GhALDH7B4_A06S. Validation using recombinant inbred lines and gene expression analysis revealed that this mutation site is correlated with cotton FS. Virus-induced gene silencing of GhALDH7B4 in cotton caused significant decreases in FS and fiber micronaire. Conversely, GhALDH7B4_A06O overexpression in Arabidopsis boosted cell wall component contents in the stem. The findings of our study provide a candidate gene for improving cotton fiber quality through molecular breeding.

IntroductionSubjective tinnitus, the perception of sound without an external acoustic source, is often subsequent to noise-induced hearing loss or ototoxic medications. The condition is believed to result from neuroplastic alterations in the auditory centers, characterized by heightened spontaneous neural activities and increased synchrony due to an imbalance between excitation and inhibition. However, the role of the thalamic reticular nucleus (TRN), a structure composed exclusively of GABAergic neurons involved in thalamocortical oscillations, in the pathogenesis of tinnitus remains largely unexplored.MethodsWe induced tinnitus in mice using sodium salicylate and assessed tinnitus-like behaviors using the Gap Pre-Pulse Inhibition of the Acoustic Startle (GPIAS) paradigm. We utilized combined viral tracing techniques to identify the neural circuitry involved and employed immunofluorescence and confocal imaging to determine cell types and activated neurons.ResultsSalicylate-treated mice exhibited tinnitus-like behaviors. Our tracing clearly delineated the inputs and outputs of the auditory-specific TRN. We discovered that chemogenetic activation of the auditory TRN significantly reduced the salicylate-evoked rise in c-Fos expression in the auditory cortex.DiscussionThis finding posits the TRN as a potential modulatory target for tinnitus treatment. Furthermore, the mapped sensory inputs to the auditory TRN suggest possibilities for employing optogenetic or sensory stimulations to manipulate thalamocortical activities. The precise mapping of the auditory TRN-mediated neural pathways offers a promising avenue for designing targeted interventions to alleviate tinnitus symptoms.

Aim: To evaluate the subclinical cardiac involvement in COVID-19 patients without clinical cardiac evidence using cardiac MR imaging. Material and methods: Participants recovered from COVID-19 without cardiac symptoms and no cardiovascular medical history were enrolled in a prospective cohort study. They underwent baseline cardiac MR and follow-up cardiac MR > 300 days after discharge (n = 20). The study also included healthy controls (n = 20). Extracellular volume fraction (ECV), native T1, and 2D strain data were assessed and compared. Results: The ECV values of participants at baseline [30.0% (28.3%–32.5%)] and at follow-up [31.0% (28.0%–32.8%)] were increased compared to the healthy control group [27.0% (25.3%–28.0%)] (both p

Abstract Background Diffuse pleural mesothelioma (DPM) is an aggressive therapy-resistant cancer with unique molecular features. Numerous agents have been tested, but clinically effective ones remain elusive. Herein, we propose to use a small molecule CBL0137 (curaxin) that simultaneously suppresses nuclear factor-κB (NF-κB) and activates tumor suppressor p53 via targeting FAcilitates Chromatin Transcription (FACT) complex, a histone chaperone critical for DNA repair. Methods We used DPM cell lines, murine models (xeno- and allo-grafts), plus DPM patient samples to characterize anti-tumor effects of CBL0137 and to delineate specific molecular mechanisms. Results We verified that CBL0137 induced cell cycle arrest and apoptosis. We also discovered that DPM is a FACT-dependent cancer with overexpression of both subunits structure-specific recognition protein 1 (SSRP1), a poor prognosis indicator, and suppressor of Ty 16 (SUPT16H). We defined several novel uses of CBL0137 in DPM therapy. In combination with cisplatin, CBL0137 exhibited additive anti-tumor activity compared to monotherapy. Similarly, CBL0137 (systemic) could be combined with other novel agents like microRNA-215 (intrapleural) as a more effective regimen. Importantly, we established that CBL0137 induces immunogenic cell death that contributes to activating immune response pathways in DPM. Therefore, when CBL0137 is combined with dual immune checkpoint inhibitors DPM tumor growth is significantly suppressed. Conclusions We identified an unrecognized molecular vulnerability of DPM based on FACT dependency. CBL0137 alone and in several combinations with different therapeutics showed promising efficacy, including that of improved anti-tumor immunity. Overall, these preclinical findings suggest that CBL0137 could be ideally suited for use in DPM clinical trials.

Inflammation plays an important role in the occurrence and development of many inflammatory diseases. The purpose of this study was to evaluate the anti-inflammatory effect and metabolic behavior of the dual targeting procyanidins (PC) nanoparticles on lipopolysaccharide (LPS)-stimulated inflammatory macrophages by metabolomics method. The double-targeting PC nanoparticles could specifically target both the CD44 receptor and mitochondria, while the single targeting PC-loaded nanoparticles that could target the CD44 receptor on the surface of macrophages. The double-targeting PC nanoparticles had better inhibitory effect than single-targeting PC nanoparticles on the leakage of lactate dehydrogenase and reactive oxygen species overexpression induced by LPS. Amino acid metabolism, energy metabolism and purine metabolism were disordered in LPS-treated group, and metabolic pathway analysis indicated that the double-targeting PC nanoparticles reversed some of LPS impacts. The changes of these potential biomarkers and their corresponding pathways are helpful to further understand the mechanism of PC nanoparticles in alleviating inflammation, and promote their application in nutrition intervention.