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Abstract Background Previous observational studies have highlighted potential relationships between the telomerase reverse transcriptase (TERT) gene, short leukocyte telomere length (LTL), and cerebrovascular disease. However, it remains to be established as to whether TERT gene variants are associated with an elevated risk of cerebral small vessel disease (CSVD), and whether there is a causal relationship between LTL and CSVD. Methods Five TERT single nucleotide polymorphisms (SNPs) were analyzed in 307 CSVD patients and 320 healthy controls in whom LTL values were quantified. Allele models and four genetic models were used to explore the relationship between these SNP genotypes and CSVD risk. A Mendelian randomization analysis of CSVD risk was then performed using LTL-related SNPs and the polygenic risk score (PRS) constructed from these SNPs as genetic instrumental variables to predict the causal relationship between LTL and CSVD risk. Results Model association analyses identified two SNPs that were significantly associated with CSVD risk. LTL was significantly correlated with age (P

Abstract Multicellular organisms are composed of diverse cell types that must coordinate their behaviors through communication. Cell–cell communication (CCC) is essential for growth, development, differentiation, tissue and organ formation, maintenance, and physiological regulation. Cells communicate through direct contact or at a distance using ligand–receptor interactions. So cellular communication encompasses two essential processes: cell signal conduction for generation and intercellular transmission of signals, and cell signal transduction for reception and procession of signals. Deciphering intercellular communication networks is critical for understanding cell differentiation, development, and metabolism. First, we comprehensively review the historical milestones in CCC studies, followed by a detailed description of the mechanisms of signal molecule transmission and the importance of the main signaling pathways they mediate in maintaining biological functions. Then we systematically introduce a series of human diseases caused by abnormalities in cell communication and their progress in clinical applications. Finally, we summarize various methods for monitoring cell interactions, including cell imaging, proximity-based chemical labeling, mechanical force analysis, downstream analysis strategies, and single-cell technologies. These methods aim to illustrate how biological functions depend on these interactions and the complexity of their regulatory signaling pathways to regulate crucial physiological processes, including tissue homeostasis, cell development, and immune responses in diseases. In addition, this review enhances our understanding of the biological processes that occur after cell–cell binding, highlighting its application in discovering new therapeutic targets and biomarkers related to precision medicine. This collective understanding provides a foundation for developing new targeted drugs and personalized treatments.

Abstract Background Serum neurofilament light chain (sNfL) has been identified as a biomarker for neurologic diseases. However, sNfL remains unknown to be responsible for depression. Aims The aim of this research was to explore the relationship between sNfL levels and depression in US adults. Methods In this cross-sectional survey of the general population, we investigated representative data involving 10,175 participants from the 2013–2014 cycle of the National Health and Nutrition Examination Survey (NHANES). Depression was diagnosed using the Patient Health Questionnaire-9 (PHQ-9). The effect of related factors on depression was analyzed by conducting a univariate analysis. Stratified analysis was utilized to detect the stability and sensitivity of the relationship. After adjusting for race, education, marital status, smoking status, body mass index (BMI), sleep duration, income, and a history of hypertension, sedentary behavior and stroke, multivariable linear regression was performed to demonstrate the correlation between sNfL and depression. Results A total of 1301 individuals between the ages of 20 and 75 were involved in this investigation, of which 108 (8.3%) were diagnosed with depression. A significant positive correlation between sNfL and depression among adults in the US was observed by conducting univariable analyses. After adjusting for confounding factors, the multivariate analyses indicated that elevated sNfL levels might play a pivotal role in the development of depression (odds ratio (OR) = 3.0; 95% confidence interval (CI): (1.5, 6.1), P = 0.002). Conclusion These results indicated that sNfL is closely linked to depression in a nationally representative individual. However, further studies are needed to confirm the biological mechanism as well as the clinical implications of sNfL and depression.

Abstract The design and implementation of virtual museums have evolved over decades, broadening the scope of traditional museum exhibitions and serving as an effective complement to physical museums. However, virtual visual effects have consistently fallen short of replicating the authentic exhibition experience and cannot substitute for physical museums. In this paper, we report a dual-mode mixed visualization system combining computational photography and binocular stereo vision techniques, and introduce an innovative virtual museum for Sanxingdui bronzes. This approach enables visitors to engage with the artifacts in a multifaceted manner-allowing for navigation, interaction, auditory guidance, and the examination of textural details as if handling the artifacts directly. Further analysis of participant responses in terms of visual cognition and aesthetic appreciation across different settings demonstrates that this dual-mode mixed visualization delivers not only a spatiotemporal experience consistent with the real world but also surreal visual effects. This immersive experience allows audiences to experience Sanxingdui art intimately, offering both high-resolution visuals and cost-effectiveness.

Abstract In mines, tunnel ventilation is monitored using wind speed sensors to measure the stability of the mine ventilation system. However, opening and closing the air door will cause violent fluctuations in the monitoring data of the wind speed sensors. When false alarms are triggered, the staff can diagnose only the mine ventilation system based on their experience. A numerical simulation method is adopted to explore the changes in the flow field during the opening and closing of the air door to address this issue. In addition, a method that is based on the wind speed data of a single sensor is proposed to identify the time and divide the stages of air door opening and closing. The experimental results showed that the proposed method can successfully identify the air door opening and closing time and apply stage division when needed.

Abstract Background Glioblastoma multiforme (GBM) is the most common primary CNS tumor, characterized by high mortality and heterogeneity. However, the related lncRNA signatures and their target microRNA (miRNA) for GBM are still mostly unknown. Therefore, it is critical that we discover lncRNA markers in GBM and their biological activities. Materials and methods GBM-related RNA-seq data were obtained from the Cancer Genome Atlas (TCGA) database. The “edger” R package was used for differently expressed lncRNAs (DELs) identification. Then, we forecasted prospective miRNAs that might bind to lncRNAs by Cytoscape software. Survival analysis of those miRNAs was examined by the starBase database, and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis of the miRNAs’ target genes was conducted by the Gene Set Enrichment Analysis (GSEA) database and R software. Moreover, the proliferative ability of unc-5 netrin receptor B antisense RNA 1 (UNC5B-AS1) cells was evaluated by Cell Counting Kit-8 (CCK-8) analysis. Mechanistically, the regulatory interaction between UNC5B-AS1 and miRNA in GBM biological processes was studied using CCK-8 analysis. Results Our results indicated that overexpression of UNC5B-AS1 has been shown to suppress GBM cell growth. Mechanistically, miR-24-3p in GBM was able to alleviate the anti-oncogenic effects of UNC5B-AS1 on cell proliferation. Conclusion The discovery of the novel UNC5B-AS1-miR-24-3p network suggests possible lncRNA and miRNA roles in the development of GBM, which may have significant ramifications for the analysis of clinical prognosis and the development of GBM medications.

Increases in litter size, which are influenced by ovulation, are responsible for between 74% and 96% of the economic value of genetic progress, which influences selection. For the selection and breeding of highly prolific goats, genetic mechanisms underlying variations in litter size should be elucidated. Here, we used single-nucleus RNA sequencing to analyze 44,605 single nuclei from the ovaries of polytocous and monotocous goats during the follicular phase. Utilizing known reference marker genes, we identified 10 ovarian cell types characterized by distinct gene expression profiles, transcription factor networks, and reciprocal interaction signatures. An in-depth analysis of the granulosa cells revealed three subtypes exhibiting distinct gene expression patterns and dynamic regulatory mechanisms. Further investigation of cell-type-specific prolificacy-associated transcriptional changes elucidated that “downregulation of apoptosis”, “increased anabolism”, and “upstream responsiveness to hormonal stimulation” are associated with prolificacy. This study provides a comprehensive understanding of the cell-type-specific mechanisms and regulatory networks in the goat ovary, providing insights into the molecular mechanisms underlying goat prolificacy. These findings establish a vital foundation for furthering understanding of the molecular mechanisms governing folliculogenesis and for improving the litter size in goats via molecular design breeding.

Abstract Background Forests are essential for maintaining species diversity, stabilizing local and global climate, and providing ecosystem services. Exploring the impact of paleogeographic events and climate change on the genetic structure and distribution dynamics of forest keystone species could help predict responses to future climate change. In this study, we combined an ensemble species distribution model (eSDM) and multilocus phylogeography to investigate the spatial genetic patterns and distribution change of Quercus glauca Thunb, a keystone of East Asian subtropical evergreen broad-leaved forest. Results A total of 781 samples were collected from 77 populations, largely covering the natural distribution of Q. glauca. The eSDM showed that the suitable habitat experienced a significant expansion after the last glacial maximum (LGM) but will recede in the future under a general climate warming scenario. The distribution centroid will migrate toward the northeast as the climate warms. Using nuclear SSR data, two distinct lineages split between east and west were detected. Within-group genetic differentiation was higher in the West than in the East. Based on the identified 58 haplotypes, no clear phylogeographic structure was found. Populations in the Nanling Mountains, Wuyi Mountains, and the southwest region were found to have high genetic diversity. Conclusions A significant negative correlation between habitat stability and heterozygosity might be explained by the mixing of different lineages in the expansion region after LGM and/or hybridization between Q. glauca and closely related species. The Nanling Mountains may be important for organisms as a dispersal corridor in the west-east direction and as a refugium during the glacial period. This study provided new insights into spatial genetic patterns and distribution dynamics of Q. glauca.

Abstract Background The triglyceride–glucose (TyG) index and high‐sensitivity C‐reactive protein (hsCRP) are the commonly used biomarkers for insulin resistance and systemic inflammation, respectively. We aimed to investigate the combined association of TyG and hsCRP with the major adverse cardiovascular events (MACE) in patients with chronic coronary syndrome (CCS). Methods A total of 9421 patients with CCS were included in this study. The primary endpoint was defined as a composite of MACE covering all‐cause death, nonfatal myocardial infarction, and revascularization. Results During the 2‐year follow‐up period, 660 (7.0%) cases of MACE were recorded. Participants were divided equally into three groups according to TyG levels. Compared with the TyG T1 group, the risk of MACE was significantly higher in the TyG T3 group. It is noteworthy that among patients in the highest tertile of TyG, hsCRP >3 mg/L was significantly associated with an increased risk of MACE, whereas the results were not significant in the medium to low TyG groups. When patients were divided into six groups according to hsCRP and TyG, the Cox regression analysis showed that patients in the TyG T3 and hsCRP >3 mg/L group had a significantly higher risk of MACE than those in the TyG T1 and hsCRP ≤3 mg/L group. However, no significant interaction was found between TyG and hsCRP on the risk of MACE. Conclusion Our study suggests that the concurrent assessment of TyG and hsCRP may be valuable in identifying high‐risk populations and guiding management strategies among CCS patients.

Soil salinization hinders sustainable agricultural development in coastal regions. Developing a multi-layer soil salinity inversion model and accurately predicting water demand for salt regulation are essential for improving soil salinity management. Wudi County in Shandong Province was selected as the research area, with 79 sampling sites chosen. Soil salinity was measured at the surface (0–20 cm), middle (20–40 cm), and bottom (40–60 cm) layers. Vegetation and salinity indices were extracted from Landsat 8 remote sensing imagery to estimate surface soil salinity. A correlation-based inversion method was developed to obtain multi-layer soil salinity data by leveraging the strong correlation between adjacent soil layers. The water requirement for salt regulation was optimized and predicted by integrating the results from multi-layer soil salinity estimation with Groundwater Management System (GMS) software. The results indicated that the surface layer soil salinity inversion model performed well, with an R2 > 0.75 and an RMSE < 0.43 g/kg for both the training and validation sets. Additionally, the prediction accuracy of the correlation-based inversion method exceeded that of the direct modeling approach, with the middle and bottom layer soil salinity models achieving an R2 > 0.6 and an RMSE < 1 g/kg. Soil salinization in the study area was more severe in the northeast than in the southwest, with both measured and estimated data showing similar spatial distributions. Over the past decade, the overall trend of soil salinization has shown a general decline with localized intensification. The salt distribution patterns in saline soil profiles were predominantly homogeneous and bottom-accumulated. The projected water demand for salt regulation calculated from the estimated data was slightly lower than the actual measurements, yet their spatial distribution was nearly identical. This study provides a scientific foundation for the dynamic monitoring and precise management of soil salinity in coastal regions.

Summary: This paper presents the design of an ultra-wide-angle multispectral narrow-band absorber for reconstructing infrared spectra. The absorber offers several advantages, including polarization sensitivity, robustness against structural wear, wide azimuthal angle coverage, high narrow-band absorption, and adjustable working wavelength. To accomplish infrared spectrum reconstruction, an absorber is employed as a spectral sampling channel, eliminating the influence of slits or complex optical splitting elements in spectral imaging technology. Additionally, we propose using a truncation regularization algorithm based on the design matrix singular value ratio, namely IReg, which can enable high-precision spectral reconstruction under largely disturbed environments. The results demonstrate that, even when the number of absorption spectrum curve is reduced to a range of 1/2 to 1/3, high-precision spectral reconstruction is achievable for both flat and high-energy steep mid- and long-infrared spectral targets, while effectively accomplishing data dimension reduction.

ObjectivesTo explore the utility of gray-scale ultrasound (GSUS) and mammography (MG) for radiomic analysis in distinguishing between breast adenosis and invasive ductal carcinoma (IDC).MethodsData from 147 female patients with pathologically confirmed breast lesions (breast adenosis: 61 patients; IDC: 86 patients) between January 2018 and December 2022 were retrospectively collected. A training cohort of 113 patients (breast adenosis: 50 patients; IDC: 63 patients) diagnosed from January 2018 to December 2021 and a time-independent test cohort of 34 patients (breast adenosis: 11 patients; IDC: 23 patients) diagnosed from January 2022 to December 2022 were included. Radiomic features of lesions were extracted from MG and GSUS images. The least absolute shrinkage and selection operator (LASSO) regression was applied to select the most discriminant features, followed by logistic regression (LR) to construct clinical and radiomic models, as well as a combined model merging radiomic and clinical features. Model performance was assessed using receiver operating characteristic (ROC) analysis.ResultsIn the training cohort, the area under the curve (AUC) for radiomic models based on MG features, GSUS features, and their combination were 0.974, 0.936, and 0.991, respectively. In the test cohort, the AUCs were 0.885, 0.876, and 0.949, respectively. The combined model, incorporating clinical and all radiomic features, and the MG plus GSUS radiomics model were found to exhibit significantly higher AUCs than the clinical model in both the training cohort and test cohort (p0.05).ConclusionThe effectiveness of radiomic features derived from GSUS and MG in distinguishing between breast adenosis and IDC is demonstrated. Superior discriminatory efficacy is shown by the combined model, integrating both modalities.

BackgroundCardiac stereotactic body radiotherapy (CSBRT) with photons efficaciously and safely treats cardiovascular arrhythmias. Proton therapy, with its unique physical and radiobiological properties, can offer advantages over traditional photon-based therapies in certain clinical scenarios, particularly pediatric tumors and those in anatomically challenging areas. However, dose uncertainties induced by cardiorespiratory motion are unknown.ObjectiveThis study investigated the effect of cardiorespiratory motion on intensity-modulated proton therapy (IMPT) and the effectiveness of motion-encompassing methods.MethodsWe retrospectively included 12 patients with refractory arrhythmia who underwent CSBRT with four-dimensional computed tomography (4DCT) and 4D cardiac CT (4DcCT). Proton plans were simulated using an IBA accelerator based on the 4D average CT. The prescription was 25 Gy in a single fraction, with all plans normalized to ensure that 95% of the target volume received the prescribed dose. 4D dose reconstruction was performed to generate 4D accumulated and dynamic doses. Furthermore, dose uncertainties due to the interplay effect of the substrate target and organs at risk (OARs) were assessed. The differences between internal organs at risk volume (IRV) and OARreal (manually contoured on average CT) were compared. In 4D dynamic dose, meeting prescription requirements entails V25 and D95 reaching 95% and 25 Gy, respectively.ResultsThe 4D dynamic dose significantly differed from the 3D static dose. The mean V25 and D95 were 89.23% and 24.69 Gy, respectively, in 4DCT and 94.35% and 24.99 Gy, respectively, in 4DcCT. Eleven patients in 4DCT and six in 4DcCT failed to meet the prescription requirements. Critical organs showed varying dose increases. All metrics, except for Dmean and D50, significantly changed in 4DCT; in 4DcCT, only D50 remained unchanged with regards to the target dose uncertainties induced by the interplay effect. The interplay effect was only significant for the Dmax values of several OARs. Generally, respiratory motion caused a more pronounced interplay effect than cardiac pulsation. Neither IRV nor OARreal effectively evaluated the dose discrepancies of the OARs.ConclusionsComplex cardiorespiratory motion can introduce dose uncertainties during IMPT. Motion-encompassing techniques may mitigate but cannot entirely compensate for the dose discrepancies. Individualized 4D dose assessments are recommended to verify the effectiveness and safety of CSBRT.

ObjectiveIn the double-blind, phase III, placebo-controlled RUBY randomized clinical trial, dostarlimab plus carboplatin-paclitaxel significantly increased survival among patients with primary advanced or recurrent endometrial cancer (EC). We conducted a cost-effectiveness analysis of dostarlimab in combination with chemotherapy in these patients stratified by mismatch repair-deficient (dMMR) and mismatch repair-proficient (pMMR) subgroups from the perspective of a United States payer.Materials and methodsA Markov model with three states was employed to simulate patients who were administered either dostarlimab in combination with chemotherapy or chemotherapy based on the RUBY trial. Quality-adjusted life-years (QALYs), lifetime costs, and incremental cost-effectiveness ratio (ICER) were calculated with a willingness-to-pay (WTP) threshold of $150,000 per QALY. Both univariate and probabilistic sensitivity analyses were carried out to explore the robustness of the model.ResultsIn dMMR EC, the combination of dostarlimab and chemotherapy achieved an additional 5.48 QALYs at an incremental cost of $330,747 compared to chemotherapy alone, resulting in an ICER of $60,349.30 per QALY. In pMMR EC, there were 1.51 additional QALYs gained at an extra cost of $265,148, yielding an ICER of $175,788.47 per QALY. With a 15.2% discount on dostarlimab, the ICER decreased to $150,000 per QALY in the pMMR EC. The univariate sensitivity analysis revealed that the cost of dostarlimab, utility of progression-free survival (PFS), and progressive disease (PD) had the most significant impacts on the outcomes. Probabilistic sensitivity analysis revealed that dostarlimab had a 100% likelihood of being considered cost-effective for patients at a WTP threshold of $150,000 per QALY for dMMR EC, whereas this likelihood was only 0.5% for pMMR EC.ConclusionDostarlimab in combination with chemotherapy was cost-effective for primary advanced or recurrent dMMR EC from the perspective of a United States payer at a WTP threshold of $150,000 per QALY, but not for pMMR EC. Lowering the prices of dostarlimab could potentially enhance the cost-effectiveness of treatment for pMMR EC.

The structure of the guide vane centrifugal pump as turbine is complex, and the internal flow characteristics directly affect the energy loss distribution. The numerical method and entropy generation theory are used to analyze the energy loss distribution of each flow component of the guide vane centrifugal pump as turbine at different flow conditions. And the transient fluctuation law of hydraulic loss of passage component is also studied. The results show that the loss in the impeller accounts for 54%–58% under small flow conditions, and the loss in the guide vane accounts for 50%–61% under large flow conditions. The hydraulic loss of the impeller and guide vane is affected by the interaction between the impeller and guide vane, volute. And there are five obvious peaks and troughs in the 1/6 period. The primary and secondary frequencies of impeller hydraulic loss change with the flow rate. At the 0.57QBEP condition, the primary and secondary frequency of the impeller loss is blade frequency and 5 times blade frequency respectively, while the frequencies under the condition of 1.43QBEP are opposite. The primary and secondary frequencies of guide vane hydraulic loss are different under different flow conditions. The hydraulic loss of volute is mainly affected by the interference between the impeller and volute under various flow conditions, and the primary frequency is blade frequency.

Partial coalescence is a key factor contributing to the instability of crystalline oil-in-water emulsions in products like dressings and sauces, reducing shelf life. The intrinsic characteristics of semi-crystalline droplets, including solid fat content, fat crystal arrangement, and polymorphism, play a pivotal role in influencing partial coalescence, challenging prevention efforts even with emulsifiers like amphiphilic proteins. High-intensity ultrasound (HIU) has emerged as an efficient and cost-effective technology for manipulating bulk fat crystallization, thereby enhancing physical properties. This study specifically investigates the impact of HIU treatment on fat crystallization on protein-stabilized crystalline emulsions, utilizing palm olein stearin (POSt) as the lipid phase and sodium caseinate (NaCas) as the surfactant under various HIU powers (100, 150, 200, 300, and 400 W). Results show that increasing HIU power maintained the interfacial potential (−20 mV) provided by NaCas in the emulsions without significant differences. Higher HIU power induced the most stable polymorphic form (β) in the emulsions. Engagingly, the emulsions at 200 W exhibited better storage stability and slower partial coalescence kinetics. Semi-crystalline globules had more uniform and integral crystal clusters that were distributed tangentially near the droplet boundary, perhaps attributed to intermediate subcooling (40.4 °C) at 200 W. The acoustic energy of HIU significantly translates into thermal effects, influencing subcooling degrees as a dominant factor affecting crystallisation in the emulsions. This study establishes ultrasonic crystallization as a novel strategy for modifying the stability of emulsions containing fat crystals.

In recent years, deep learning algorithms represented by the Transformer model have gained widespread recognition in the application of time series anomaly detection. Based on the Boosting algorithm, this paper proposes a novel data-driven improved Saxformer framework for multivariate time series anomaly detection. This framework enhances the Transformer model’s ability to capture local feature information and improves both the robustness to noise and the flexibility in practical applications. A method using Boosting classifiers for fine control is designed to improve the Transformer output layer. By outputting text letter by letter, the method aims to balance the model’s creativity and conservatism, suppressing significant fluctuations in predictions and thereby enhancing detection accuracy. Experimental results show that the proposed model outperforms existing mainstream algorithms on multiple evaluation metrics. The F1 score, Precision, and Recall reach 0.882, 0.946, and 0.825, respectively. Compared with the best-performing baseline model, the F1 score is enhanced by 1.4%, demonstrating high accuracy in time series anomaly detection tasks.

Power transformers are the vital and expensive components of the power system. Timely identifying and diagnosing the transformer faults is critical to maintaining the stability of the power grid. As a sensitive and economical tool, the frequency response analysis (FRA) method has been widely employed to detect winding faults. However, it is still a challenge to accurately identify the fault types and degrees only by the FRA method. In this article, a new diagnosis method that combines the FRA method with a kernel-based extreme learning machine (KELM) optimized by a seagull optimization algorithm (SOA), is proposed to diagnose the fault types and degrees of the winding. First, a series of FRA tests are performed on a laboratory winding model under three different faults to obtain the FRA dataset. Furthermore, various numerical indices are applied to extract the characteristics of FRA signatures to train the SOA-KELM model. Then, the trained SOA-KELM model is utilized to classify fault types and degrees of the winding. Finally, the feasibility and superiority of SOA-KELM are verified by comparing with SOA optimized support vector machine (SOA-SVM) and random forest (SOA-RF), particle swarm optimization (PSO) algorithm optimized KELM (PSO-KELM), PSO-SVM, PSO-RF, SVM, RF, and KELM from the aspects of diagnosis accuracy and running time. The comprehensive comparison results show that SOA-KELM has the best diagnosis performance.

In distributed systems, the method for data storage is crucial. Previous data storage work use the replication or Erasure Coding method to store data. Such single storage method leads to the excessive storage overheads for cold data with low access frequency or the low reading performance for hot data with high access frequency. Nowadays, the research on the hybrid storage has become a hot topic of concern for many scholars. Existing hybrid storage works take into account data reading performance and the storage overheads, and use the replication and Erasure Coding methods to store the hot data and cold data respectively. However, in the scenarios of sufficient disk space or low disk space, these fixed data storage methods will lead to the relatively low system data reading performance or the excessively low disk space of the system. In this paper, we propose HSM, a hybrid storage method based on the heat of data and global disk space utilization. HSM fully considers the system’s requirements for the data reading performance and storage overheads under different global disk space utilization scenarios, and adaptively selects appropriate storage methods for data whose heat is different through data deletion, data reconstruction, and data archiving. The experiment results show that when system disk space is sufficient, HSM reduces data reading time by up to 18%; when system disk space is low, although increasing storage overhead by up to 7%, HSM reduces cross-rack data transfer traffic by up to 20% and cross-rack data transfer time by up to 15% compared with ERP in the process of changing the storage methods.

Erasure codes are widely used in large-scale distributed storage systems due to their high efficiency and reliability, but they also face extremely high repair penalties when data corruption occurs. At present, machine learning methods can accurately predict the next failure time and type of machine nodes. Based on this, in order to solve the problem of unnecessary repair traffic caused by temporary failures, as well as the more degraded reads of high-frequency accessed data due to longer failure time of such data in existing repair methods, we propose an Adaptive Classification Predictive Repair method (ACPR) for different fault scenarios. By categorizing the failed blocks into high-risk and low-risk based on the failure type of the soon-to-fail (STF) node and the access heat of STF blocks, ACPR can perform adaptive predictive repair. By quickly repair high-risk blocks to ensure data availability while delaying the repair of low-risk blocks, a large amount of unnecessary repair traffic caused by temporary node failures in the cluster is avoided. Alibaba Cloud Elastic Compute Service (ECS) experiments results show that compared with FastPR and ECPipe, ACPR can shorten the repair time per data block by up to 15.2% and 33.5%, respectively. Moreover, ACPR can reduce repair traffic by up to 74.1% and 84.4%, respectively.

In this study, a broadband mid-infrared photonic filter is designed. It has the advantages of polarization insensitivity, ultrawide angle, high manufacturability, and easy integration. For infrared spectrum reconstruction, the use of filters as spectral sampling channels can eliminate the influence of slits or complex optical splitting elements in spectral imaging technology. Further, we propose a low-rank matrix logarithm regularization algorithm based on a three-segment filter function (IReg algorithm) combined with the designed filter for a variety of mid-infrared detector front ends, which can significantly suppress noise interference. Here, we demonstrate that the stability of the spectral reconstruction accuracy under the large disturbance environment obtained by the IReg algorithm is much higher than those of the L2 and L1 algorithms. In the low-dimensional matrix state, the accuracy and stability of the ill-conditioned linear equations are guaranteed, the anti-noise ability is enhanced, the effect of data dimensionality reduction is realized, and the accuracy of mid-infrared spectral reconstruction is improved.

Background: In the double-blind phase III ADAURA randomized clinical trial, adjuvant osimertinib showed a substantial overall survival benefit in patients with stage IB to IIIA, EGFR-mutated, completely resected non-small cell lung cancer (NSCLC). We conduct a cost-effectiveness analysis comparing the use of adjuvant osimertinib to placebo in patients with stage IB to IIIA, EGFR-mutated, resected NSCLC.Methods: Based on the results obtained from the ADAURA trial, a Markov model with three-state was employed to simulate patients who were administered either osimertinib or placebo until disease recurrence or completion of the study period (3 years). Quality-adjusted life-years (QALYs), lifetime costs, and incremental cost-effectiveness ratio (ICER) were calculated with a willingness-to-pay (WTP) threshold of $150,000 per QALY. Both univariate and probabilistic sensitivity analyses were carried out to explore the robustness of the model.Results: Osimertinib produced additional 1.59 QALYs with additional costs of $492,710 compared to placebo, giving rise to ICERs of $309,962.66/QALY. The results of the univariate sensitivity analysis indicated that the utility of disease-free survival (DFS), cost of osimertinib, and discount rate had the greatest impact on the outcomes. Probabilistic sensitivity analysis showed that osimertinib exhibited a 0% chance of being considered cost-effective for patients using a WTP threshold $150,000/QALY.Conclusion: In our model, osimertinib was unlikely to be cost-effective compared to placebo for stage IB to IIIA, EGFR-mutated, completely resected NSCLC patients from the perspective of a U.S. payer at a WTP threshold of $150,000 per QALY.

Abstract Gastric cancer (GC) has emerged as a significant issue in public health all worldwide as a result of its high mortality rate and dismal prognosis. AT-rich interactive domain 1 A (ARID1A) is a vital component of the switch/sucrose-non-fermentable (SWI/SNF) chromatin remodeling complex, and ARID1A mutations occur in various tumors, leading to protein loss and decreased expression; it then affects the tumor biological behavior or prognosis. More significantly, ARID1A mutations will likely be biological markers for immune checkpoint blockade (ICB) treatment and selective targeted therapy. To provide theoretical support for future research on the stratification of individuals with gastric cancer with ARID1A as a biomarker to achieve precision therapy, we have focused on the clinical significance, predictive value, underlying mechanisms, and possible treatment strategies for ARID1A mutations in gastric cancer in this review.

Abstract Background The Mitogen-activated protein kinase 1 (MAPK1) has both independent functions of phosphorylating histones as a kinase and directly binding the promoter regions of genes to regulate gene expression as a transcription factor. Previous studies have identified elevated expression of MAPK1 in human gastric cancer, which is associated with its role as a kinase, facilitating the migration and invasion of gastric cancer cells. However, how MAPK1 binds to its target genes as a transcription factor and whether it modulates related gene expressions in gastric cancer remains unclear. Results Here, we integrated biochemical assays (protein interactions and chromatin immunoprecipitation (ChIP)), cellular analysis assays (cell proliferation and migration), RNA sequencing, ChIP sequencing, and clinical analysis to investigate the potential genomic recognition patterns of MAPK1 in a human gastric adenocarcinoma cell-line (AGS) and to uncover its regulatory effect on gastric cancer progression. We confirmed that MAPK1 promotes AGS cells invasion and migration by regulating the target genes in different directions, up-regulating seven target genes (KRT13, KRT6A, KRT81, MYH15, STARD4, SYTL4, and TMEM267) and down-regulating one gene (FGG). Among them, five genes (FGG, MYH15, STARD4, SYTL4, and TMEM267) were first associated with cancer procession, while the other three (KRT81, KRT6A, and KRT13) have previously been confirmed to be related to cancer metastasis and migration. Conclusion Our data showed that MAPK1 can bind to the promoter regions of these target genes to control their transcription as a bidirectional transcription factor, promoting AGS cell motility and invasion. Our research has expanded the understanding of the regulatory roles of MAPK1, enriched our knowledge of transcription factors, and provided novel candidates for cancer therapeutics.

Background: There is currently no specific requirement for operator’s attires performing amniocentesis, especially for repetitive amniocenteses. We investigated the contamination status on the surface of operators with or without long-sleeved surgical gowns during repetitive amniocenteses to examine whether they are different. Methods: In a prospective cohort study, consecutive samples were collected from the surface of amniocentesis operators with short-sleeved scrubs (bare arms) or with long-sleeved surgical gowns on 4 sites (including the left and right proximal medial forearm, and the left and right ventral wrist). There were 7 time spots during each sampling period. Results: The highest colony count among all plates was 65 colony forming units (cfu)/plate. We observed 85.3% (382/448) and 65.1% (255/392) plates with 0 colony count from operators with bare arms and in surgical gowns, respectively. Moreover, the bacterial load of the group with surgical gowns had an increasing trend with time while the other group showed a contrary trend. Conclusions: All colony counts observed were within the hygiene specification of the National Health Commission of China. Instead of attires, other measures with high-quality evidence including surveillance of hand hygiene and control of indoor air bacterial load should be given more attention.

High-speed centrifugal pumps are widely used in several industries due to their high efficiency and small footprint. In actual applications, there are issues such as low operational efficiency and a small high-efficiency flow interval; particularly, the leakage occurring in the impeller channel gap presents a significant barrier to the pump’s performance and stability. This study takes the fully open impeller miniature high-speed centrifugal pump as the object and uses a numerical simulation calculation method. The objective of this research endeavor is to analyze the effects of different flow conditions on a high-speed centrifugal pump’s external characteristics, flow field characteristics, and energy loss. The findings indicate that lobe top clearance exerts a substantial impact on the efficiency of high-speed centrifugal pumps. Increasing the lobe top clearance will result in a reduction in pump head and efficiency, particularly under high flow conditions. The lobe top clearance has a significant impact on the complexity of the flow in the impeller, particularly the flow close to the suction surface of the impeller, according to an analysis of the flow field characteristics. The energy loss analysis further confirms the importance of reducing lobe top clearance for improving pump performance and reducing energy loss. These results provide valuable guidance for optimizing centrifugal pump designs with lobe top clearance.

ObjectiveThis study aims to evaluate the efficacy and safety of JAK inhibitors in the treatment of patients with RA.MethodsThe databases CNKI, VIP, Wanfang, CBM, and PubMed, Embase, Cochrane Library and Web of Science were searched to identify relevant randomized controlled trials (RCTs), all from the time of database creation to April 2024. Screening, data extraction, and risk of bias assessment (using Review Manager-5.3 software) were independently performed by at least two authors. The network meta-analysis was conducted using R 4.1.3 software. PROSPERO registration number: CRD42022370444.ResultsThirty-three RCTs included 15,961 patients The experimental groups involved six JAK inhibitors (filgotinib, tofacitinib, decernotinib, baricitinib, upadacitinib and peficitinib) and 12 interventions (different doses of the six JAK inhibitors), and the control group involved adalimumab (ADA) and placebo. Compared with placebo, all JAK inhibitors showed a significant increase in efficacy measures (ACR20/50/70). Compared with ADA, only tofacitinib, low-dose decernotinib, and high-dose peficitinib showed a significant increase in ACR20/50/70. Decernotinib ranked first in the SUCRA ranking of ACR20/50/70. In terms of safety indicators, only those differences between low-dose filgotinib and high-dose upadacitinib, low-dose tofacitinib and high-dose upadacitinib were statistically significant. Low-dose filgotinib ranked first in the SUCRA ranking with adverse events as safety indicators. Only the efficacy and safety of tofacitinib ranked higher among different SUCRA rankings.ConclusionSix JAK inhibitors have better efficacy than placebo. The superior efficacy of decernotinib and safety of low-dose filgotinib can be found in the SUCRA. However, there are no significant differences in safety between the different JAK inhibitors. Head-to-head trials, directly comparing one against each other, are required to provide more certain evidence.

Zero-valent iron (ZVI) has been extensively studied for its efficacy in removing heavy metals, nitrate, and chlorinated organic compounds from contaminated water. However, its limited effectiveness due to rapid passivation and poor selectivity is prompting for alternative solutions, such as the use of aluminium alloys. In this study, the efficacy of five distinct aluminium alloys, namely Al–Mg, Al–Fe, Al–Cu, and Al–Ni, each comprising 50 % Al by mass at a concentration of 10 g/L, was assessed using copper, nitrate and trichloromethane (TCM) as model contaminants. Results show that chemical pollutants reacted immediately with Al–Mg. On the contrary, the remaining three alloys exhibited a delay of 24 h before demonstrating significant reactivity. Remarkably, Al–Mg alloy reduced nitrate exclusively to ammonium, indicating minimal preference for nitrate reduction to N2. In contrast, the Al–Cu, Al–Ni, and Al–Fe alloys exhibited N2 selectivity of 3 %, 5 %, and 19 %, respectively. The removal efficiency of copper, nitrate and TCM reached 99 % within 24 h, 95 % within 48h and 48 % within 48h, respectively. Noteworthy findings included the correlation between Fe concentration within the Al–Fe alloy and an increased N2 selectivity from 9.3 % to 24.1 %. This resulted in an increase of Fe concentration from 10 % to 58 % albeit with a concurrent reduction in reactivity. Cu2+ removal by Al–Fe alloy occurred via direct electron transfer, while the removal of nitrate and TCM was facilitated by atomic hydrogen generated by the alloy's hydrolysis. Intriguingly, nitrate and TCM suppressed Cu2+ reduction, whereas Cu2+ improved nitrate reduction and TCM degradation. These findings demonstrate the great potential of Al–Mg and Al–Fe alloys as highly efficient agents for water remediation.

BackgroundArtificial intelligence-assisted gastroscopy (AIAG) based on deep learning has been validated in various scenarios, but there is a lack of studies regarding diagnosing neoplasms under white light endoscopy. This study explored the potential role of AIAG systems in enhancing the ability of endoscopists to diagnose gastric tumor lesions under white light.MethodsA total of 251 patients with complete pathological information regarding electronic gastroscopy, biopsy, or ESD surgery in Xi’an Gaoxin Hospital were retrospectively collected and comprised 64 patients with neoplasm lesions (excluding advanced cancer) and 187 patients with non-neoplasm lesions. The diagnosis competence of endoscopists with intermediate experience and experts was compared for gastric neoplasms with or without the assistance of AIAG, which was developed based on ResNet-50.ResultsFor the 251 patients with difficult clinical diagnoses included in the study, compared with endoscopists with intermediate experience, AIAG’s diagnostic competence was much higher, with a sensitivity of 79.69% (79.69% vs. 72.50%, p = 0.012) and a specificity of 73.26% (73.26% vs. 52.62%, p

Short-chain fatty acids (SCFAs), the main metabolites of gut microbiota, have been associated with lower blood glucose and lipid levels in diabetic mice. However, a comprehensive summary and comparison of the effects of different SCFA interventions on blood glucose and lipid levels in diabetic mice is currently unavailable. This study aims to compare and rank the effects of different types of SCFAs on blood glucose and lipid levels by collecting relevant animal research. A systematic search through PubMed, Embase, Cochrane Library, and Web of Science database was conducted to identify relevant studies from inception to March 17, 2023. Both pairwise meta-analysis and Bayesian network meta-analysis were used for statistical analyses. In total, 18 relevant studies involving 5 interventions were included after screening 3793 citations and 53 full-text articles. Notably, butyrate therapy (mean difference [MD] = −4.52, 95% confidence interval [−6.29, −2.75]), acetate therapy (MD = −3.12, 95% confidence interval [−5.79, −0.46]), and propionate therapy (MD = −2.96, 95% confidence interval [−5.66, −0.26]) significantly reduced the fasting blood glucose levels compared to the control group; butyrate therapy was probably the most effective intervention, with a surface under the cumulative ranking curve (SUCRA) value of 85.5%. Additionally, acetate plus propionate therapy was probably the most effective intervention for reducing total cholesterol (SUCRA = 85.8%) or triglyceride levels (SUCRA = 88.1%). These findings underscore the potential therapeutic implications of SCFAs for addressing metabolic disorders, particularly in type 2 diabetes mellitus.

ABSTRACTAccumulating evidence shows that the gastric bacterial community may contribute to the development of gastric cancer (GC). However, the reported alterations of gastric microbiota were not always consistent among the literature. To assess reproducible signals in gastric microbiota during the progression of GC across studies, we performed a meta-analysis of nine publicly available 16S datasets with standard tools of the state-of-the-art. Despite study-specific batch effect, significant changes in the composition of the gastric microbiome were found during the progression of gastric carcinogenesis, especially when the Helicobacter pylori (HP) reads were removed from analyses to mitigate its compositional effect as they accounted for extremely large proportions of sequencing depths in many gastric samples. Differential microbes, including Fusobacterium, Leptotrichia, and several lactic acid bacteria such as Bifidobacterium, Lactobacillus, and Streptococcus anginosus, which were frequently and significantly enriched in GC patients compared with gastritis across studies, had good discriminatory capacity to distinguish GC samples from gastritis. Oral microbes were significantly enriched in GC compared to precancerous stages. Intriguingly, we observed mutual exclusivity of different HP species across studies. In addition, the comparison between gastric fluid and mucosal microbiome suggested their convergent dysbiosis during gastric disease progression. Taken together, our systematic analysis identified novel and consistent microbial patterns in gastric carcinogenesis.

Abstract Background Stress hyperglycemia ratio (SHR), a novel biomarker of stress hyperglycemia, was proved to be a reliable predictor of short‐term adverse outcomes in patients with acute coronary syndromes (ACS). However, its impact on long‐term prognosis remained controversial. Methods A total of 7662 patients with ACS from a large nationwide prospective cohort between January 2015 and May 2019 were included. SHR was calculated by the following formula: SHR = admission glucose (mmol/L)/(1.59 × HbA1c [%]−2.59). The primary end point was a major adverse cardiovascular event (MACE) during follow‐up, a composite of all‐cause death, myocardial infarction, and unplanned revascularization. The second end point was the separate components of the primary end points. Results During a median follow‐up of 2.1 years, 779 MACE events occurred. After multivariable adjustment, ACS patients with the highest SHR tertile were significantly associated with increased long‐term risks of MACE (hazard ratio [HR] 1.53, 95% confidence interval [CI] 1.24–1.88), all‐cause death (HR 1.80, 95% CI 1.29–2.51) and unplanned revascularization (HR 1.44, 95% CI 1.09–1.91). Although significant associations between the highest SHR tertile and risks of MACE and all‐cause death were assessed in both diabetic and nondiabetic patients, the patterns of risk were different in these two groups. Conclusion Elevated SHR was independently associated with a higher risk of long‐term outcomes irrespective of diabetic status, suggesting that SHR was a potential biomarker for risk stratification after ACS.

AIM:To clarify the effect of miR-519d-3p on high glucose-induced human retinal microvascular endothelial cells(HRMEC)dysfunction and angiogenesis, and to elucidate the regulatory mechanism of miR-519d-3p on hypoxia inducible factor 1 subunit alpha(HIF-1α).METHODS: The normal glucose(NG)and high glucose(HG)cell models were established by inducing HRMEC with 5 and 30 mmol/L glucose, respectively. Control group: HG cell model was transfected with negative control mimics; mannitol group: the control group was added with 25 mmol/L mannitol; miR-519d-3p overexpression group: HG cell model was transfected with miR-519d-3p mimics; miR-519d-3p combined with HIF-1α overexpression group: HG cell model was co-transfected with miR-519d-3p mimics and HIF-1α overexpression vector. The expression of miR-519d-3p in each group was tested by real-time fluorescence quantitative PCR. The expression of HIF-1α protein in each group was tested by Western blotting. The binding sites between miR-519d-3p and HIF-1α were detected by luciferase reporter gene assay. The cell proliferation of each group was detected by CCK-8. The cell apoptosis of each group was tested by Hoechst 33342 staining. The protein expression of extracellular fluid inflammatory factors tumor necrosis factor-α(TNF-α), interleukin(IL)-1β and IL-6 in each group was tested by ELISA. The formation of new capillary lumen-like structures was detected by tubule formation assay.RESULTS: Compared with the NG, miR-519d-3p expression was significantly reduced in the HG cell model, while HIF-1α protein expression was significantly increased in the HG(all P0.05).CONCLUSION: miR-519d-3p expression is down-regulated while HIF-1α protein expression is up-regulated in high glucose induced HRMEC model. HIF-1α is a target gene of miR-519d-3p. The miR-519d-3p targets HIF-1α to increase cell proliferation and reduce cell apoptosis and inflammation, thereby alleviating high glucose-induced HRMEC dysfunction and inhibiting angiogenesis.

Photovoltaic–thermal (PVT) technology is gaining popularity due to the diminishing availability of traditional fossil fuels and escalating environmental concerns. Enhancing the heat dissipation of PVT to improve its electrical and thermal performance remains a significant task. This study simulates the thermodynamic and heat transfer characteristics in multiple novel PVT structures by examining the impact of various factors such as collector materials, radiation intensity, mass flow rate, and inlet temperature. This work also identifies the optimal mass flow rate for locations with different solar radiation. The numerical results indicate that the electrical efficiency of a designed cylindrical structure has increased by 1.73% while the thermal efficiency has increased by 8.29%. Aluminum is identified as the most cost-effective material for the collector. The optimal mass flow rates in selected locations of Xining, Taiyuan, and Turpan are 0.36 kg/s, 0.35 kg/s, and 0.30 kg/s, respectively. The numerical results provide valuable insight into optimizing the design and operating conditions of PVT systems.

Aircraft targets, as high-value subjects, are a focal point in Synthetic Aperture Radar (SAR) image interpretation. To tackle challenges like limited SAR aircraft datasets and shortcomings in existing detection algorithms (complexity, poor performance, weak generalization), we present the Feature Enhancement and Multi-Scales Fusion Network (FEMSFNet) for SAR aircraft detection. FEMSFNet employs diverse image augmentation and integrates optimized Squeeze-and-Excitation Networks (SE) with residual network (ResNet) in a SdE-Resblock structure for a lightweight yet accurate model. It introduces ssppf-CSP module, an improved pyramid pooling model, to prevent receptive field deviation in deep network training. Tailored for SAR aircraft detection, FEMSFNet optimizes loss functions, emphasizing both speed and accuracy. Evaluation on the SAR Aircraft Detection Dataset (SADD) demonstrates significant improvements compared to the contrasted algorithms: precision rate (92%), recall rate (96%), and F1 score (94%), with a maximum increase of 12.2% in precision, 12.9% in recall, and 13.3% in F1 score.

Oxeiptosis is a reactive oxygen species (ROS)-induced pathway of cell death. The involvement of circular RNAs (circRNAs) has been confirmed in the incidence and progression of intervertebral disc degeneration (IVDD). However, whether oxeiptosis occurs in IVDD and how circRNAs regulate oxeiptosis is still unclear. In this study, we discovered that oxeiptosis could be induced in nucleus pulposus cells (NPCs), and circFOXO3 was significantly upregulated after oxeiptosis induction. Transfection using circFOXO3 small interfering RNA (siRNA) significantly inhibited oxeiptosis in NPCs. Mechanistically, circFOXO3 upregulated acid-sensing ion channel subunit 1 (ASIC1) expression by functioning as a molecular sponge for miR-185-3p and miR-939-5p. Subsequent rescue experiments validated that circFOXO3 could regulate oxeiptosis in NPCs via the miR-185-3p/miR-939-5p-ASIC1 axis. Further research on ASIC1 functions indicated that this regulation was achieved by affecting the Calcium ion (Ca2+) influx mediated by ASIC1. A mouse IVDD model was established, and silencing circFOXO3 in vivo was found to inhibit IVDD development and the activation of the oxeiptosis-related pathway. Overall, circFOXO3 is one of the factors contributing to the progression of IVDD by mediating oxeiptosis.

Abstract Gastric cancer (GC) is a frequent malignant disease and the main cause of cancer‐related death in the world. Podoplanin (PDPN) has been proved to be involved in the progression of various cancers. However, the role and biological mechanism of PDPN in GC are still vague. In our study, we detected the expression of PDPN in GC tissues and cell lines using RT‐qPCR, western blot and datasets. The overall survival of GC patients was analysed with a Kaplan–Meier plot. The effects of PDPN overexpression and silencing on GC cell progression were assessed by Cell Counting Kit‐8, flow cytometry and a wound healing assay. Besides, the modulation of PDPN on ezrin activation was investigated. We further explored the role of PDPN in the crosstalk between GC cells and cancer associated fibroblasts (CAFs). Results showed that PDPN was upregulated in GC tissues and cell lines. High expression of PDPN was correlated with poor prognosis of GC patients. PDPN positively regulated the viability, migration and invasion, but inhibited apoptosis, of GC cells by mediating the activation of ezrin. Meanwhile, the change in PDPN in GC cells activated CAFs and promoted the production of cytokines secreted by CAFs, which induced the progression of GC cells. These findings may provide a novel target for GC therapy.

Abstract Background Empty nesters are older people who live alone or an older couple without children to care for them. In China, empty nesters make up a significant community and are more likely to experience emotional issues, particularly depression. This study investigated the prevalence of depression and the factors influencing depression among Chinese home-bound empty nesters using meta-analysis. Methods Based on previous studies, we used search terms relating to empty nesters and depression in English and Chinese. Databases, including China Journal Full Text Database (CNKI), Wanfang, Wipu, China Biomedical Literature Database (CBM), PubMed, Web of Science, Embase, The Cochrane Library, and UptoDate, were searched in April 2022, for relevant articles. Details including names of authors, year of publication, region of investigation, study type, sample size, depression detection scale, depression detection rate, and influencing factors were captured. The heterogeneity of the studies was assessed based on the I2 index, and data analysis was performed using Stata 16.0 software. Results A total of ten research articles involving 5337 Chinese empty nesters were evaluated in the present meta-analysis. The overall prevalence of depression among empty nesters in China was 43%. The prevalence of depression among urban empty nesters was 38% (95% CI: 0.24,0.52), and 36% (95% CI: 0.18,0.55) among rural empty nesters. Many factors, including female, income, marital status, chronic illness, relationship with children, and social support were linked to depression among urban empty nesters. Conclusion The prevalence of depression among empty nesters was 43%. Therefore, based on the factors influencing depression, government departments can intervene early to improve the mental health of empty nesters. Limitations The meta-analysis only included cross-sectional studies. Therefore, there is a need for more future original studies investigating depression among empty nesters in China.

Abstract Background The evergreen broadleaved forest (EBLF) is an iconic vegetation type of East Asia, and it contributes fundamentally to biodiversity-based ecosystem functioning and services. However, the native habitat of EBLFs keeps on decreasing due to anthropogenic activities. Ormosia henryi is a valuable rare woody species in EBLFs that is particularly sensitive to habitat loss. In this study, ten natural populations of O. henryi in southern China were sampled, and then genotyping by sequencing (GBS) was applied to elucidate the standing genetic variation and population structure of this endangered species. Results In ten O. henryi populations, 64,158 high-quality SNPs were generated by GBS. Based on these markers, a relatively low level of genetic diversity was found with the expected heterozygosity (He) ranging from 0.2371 to 0.2901. Pairwise F ST between populations varied from 0.0213 to 0.1652, indicating a moderate level of genetic differentiation. However, contemporary gene flow between populations were rare. Assignment test and principal component analysis (PCA) both supported that O. henryi populations in southern China could be divided into four genetic groups, and prominent genetic admixture was found in those populations located in southern Jiangxi Province. Mantel tests and multiple matrix regression with randomization (MMRR) analyses suggested that isolation by distance (IBD) could be the possible reason for describing the current population genetic structure. In addition, the effective population size (Ne) of O. henryi was extremely small, and showed a continuous declining trend since the Last Glacial Period. Conclusions Our results indicate that the endangered status of O. henryi is seriously underestimated. Artificial conservation measures should be applied as soon as possible to prevent O. henryi from the fate of extinction. Further studies are needed to elucidate the mechanism that leading to the continuous loss of genetic diversity in O. henryi and help to develop a better conservation strategy.

Abstract Learning-based approaches have made substantial progress in capturing spatially-varying bidirectional reflectance distribution functions (SVBRDFs) from a single image with unknown lighting and geometry. However, most existing networks only consider per-pixel losses which limit their capability to recover local features such as smooth glossy regions. A few generative adversarial networks use multiple discriminators for different parameter maps, increasing network complexity. We present a novel end-to-end generative adversarial network (GAN) to recover appearance from a single picture of a nearly-flat surface lit by flash. We use a single unified adversarial framework for each parameter map. An attention module guides the network to focus on details of the maps. Furthermore, the SVBRDF map loss is combined to prevent paying excess attention to specular highlights. We demonstrate and evaluate our method on both public datasets and real data. Quantitative analysis and visual comparisons indicate that our method achieves better results than the state-of-the-art in most cases.

Abstract Background Malnutrition and inflammation are associated with adverse clinical outcomes in patients with diabetes or coronary artery disease (CAD). Prognostic nutritional index (PNI) is a comprehensive and simple indicator reflecting nutritional condition and immunological status. Whether there is a crosstalk between nutritional-immunological status and diabetes status for the impact on the prognosis of coronary artery disease (CAD) is unclear. Methods A total of 9429 consecutive CAD patients undergoing percutaneous coronary intervention were grouped by diabetes status [diabetes (DM) and non-diabetes (non-DM)] and preprocedural PNI level [high PNI (H-PNI) and low PNI (L-PNI)] categorized by the statistically optimal cut-off value of 48.49. The primary endpoint was all-cause death. Results During a median follow-up of 5.1 years (interquartile range: 5.0–5.1 years), 366 patients died. Compared with the non-DM/H-PNI group, the DM/L-PNI group yielded the highest risk of all-cause death (adjusted hazard ratio: 2.65, 95% confidence interval: 1.97–3.56, p

Abstract Commercial microwave link (CML) has become one of the most widely used opportunistic sensors for rainfall monitoring. The high density and coverage of CMLs enable us to monitor near‐ground rainfall in high spatial resolution. The empirical power‐law (PL) model proposed in 1978 is the leading approach in relating CML attenuation with rain rate. However, while giving good rain rate estimates for CMLs longer than 1 km, in short CMLs, large errors are observed when the PL approach is applied. In this paper, we studied a statistical approach to convert CML attenuation measurement to rainfall intensity. The proposed approach calculates the exceedance probability, p, of rain‐induced attenuation and derives the corresponding rainfall intensity based on the rain rate cumulative distribution curve. Data from two cities in two countries were used to validate the approach. The results of the proposed approach were compared with that of the PL model. Results show that the models derived using our approach outperform PL for short CMLs, while showing similar performance for long ones. Since in the next generation mobile networks short CMLs will become more and more dominant, our work provides a way to derive retrieval models for the future generation CML networks.

Summary: Patients with coronary artery disease (CAD) at low residual inflammatory risk are often overlooked in research and practice. This study examined the associations between fourteen inflammatory indicators and all-cause mortality in 5,339 CAD patients with baseline high-sensitivity C-reactive protein (hsCRP)

PurposeImage segmentation can be time-consuming and lacks consistency between different oncologists, which is essential in conformal radiotherapy techniques. We aimed to evaluate automatic delineation results generated by convolutional neural networks (CNNs) from geometry and dosimetry perspectives and explore the reliability of these segmentation tools in rectal cancer.MethodsForty-seven rectal cancer cases treated from February 2018 to April 2019 were randomly collected retrospectively in our cancer center. The oncologists delineated regions of interest (ROIs) on planning CT images as the ground truth, including clinical target volume (CTV), bladder, small intestine, and femoral heads. The corresponding automatic segmentation results were generated by DeepLabv3+ and ResUNet, and we also used Atlas-Based Autosegmentation (ABAS) software for comparison. The geometry evaluation was carried out using the volumetric Dice similarity coefficient (DSC) and surface DSC, and critical dose parameters were assessed based on replanning optimized by clinically approved or automatically generated CTVs and organs at risk (OARs), i.e., the Planref and Plantest. Pearson test was used to explore the correlation between geometric metrics and dose parameters.ResultsIn geometric evaluation, DeepLabv3+ performed better in DCS metrics for the CTV (volumetric DSC, mean = 0.96, P< 0.01; surface DSC, mean = 0.78, P< 0.01) and small intestine (volumetric DSC, mean = 0.91, P< 0.01; surface DSC, mean = 0.62, P< 0.01), ResUNet had advantages in volumetric DSC of the bladder (mean = 0.97, P< 0.05). For critical dose parameters analysis between Planref and Plantest, there was a significant difference for target volumes (P< 0.01), and no significant difference was found for the ResUNet-generated small intestine (P > 0.05). For the correlation test, a negative correlation was found between DSC metrics (volumetric, surface DSC) and dosimetric parameters (δD95, δD95, HI, CI) for target volumes (P< 0.05), and no significant correlation was found for most tests of OARs (P > 0.05).ConclusionsCNNs show remarkable repeatability and time-saving in automatic segmentation, and their accuracy also has a certain potential in clinical practice. Meanwhile, clinical aspects, such as dose distribution, may need to be considered when comparing the performance of auto-segmentation methods.

Recently, micro-spectrometer based on filter array has received extensive attention in terms of cost and size. Yet, the spectrometer will produce large noise in the work, which has a great impact on the spectral reconstruction. In this paper, a low-dimensional filter array is selected based on the K-means-PSO（Particle Swarm Optimization） method to achieve the purpose of data dimensionality reduction, which further reduces the cost and processing difficulty of the micro-spectrometer. To address the redundancy and poor accuracy of spectral reconstruction data obtained by micro-spectrometers, a convex optimization algorithm constrained by three-segment regularization of a low-rank-matrix (IReg-Cvx algorithm) was proposed for spectral reconstruction in this study. In order to test algorithm universality and stability better, we selected 120 kinds of ground spectral curves, and the low-dimensional filter array is fused with the IReg-Cvx algorithm. Apply the corresponding constraints according to the different slopes of the curve, and the high-quality spectral reconstruction of the ground object target spectrum can be stably realized under the noise environment of 30, 25, and 20 dB.

Abstract. Background:. There are few data comparing clinical outcomes of complex percutaneous coronary intervention (CPCI) when using biodegradable polymer drug-eluting stents (BP-DES) or second-generation durable polymer drug-eluting stents (DP-DES). The purpose of this study was to investigate the safety and efficacy of BP-DES and compare that with DP-DES in patients with and without CPCI during a 5-year follow-up. Methods:. Patients who exclusively underwent BP-DES or DP-DES implantation in 2013 at Fuwai Hospital were consecutively enrolled and stratified into two categories based on CPCI presence or absence. CPCI included at least one of the following features: unprotected left main lesion, ≥2 lesions treated, ≥2 stents implanted, total stent length >40 mm, moderate-to-severe calcified lesion, chronic total occlusion, or bifurcated target lesion. The primary endpoint was major adverse cardiac events (MACE) including all-cause death, recurrent myocardial infarction, and total coronary revascularization (target lesion revascularization, target vessel revascularization [TVR], and non-TVR) during the 5-year follow-up. The secondary endpoint was total coronary revascularization. Results:. Among the 7712 patients included, 4882 (63.3%) underwent CPCI. Compared with non-CPCI patients, CPCI patients had higher 2- and 5-year incidences of MACE and total coronary revascularization. Following multivariable adjustment including stent type, CPCI was an independent predictor of MACE (adjusted hazard ratio [aHR]: 1.151; 95% confidence interval [CI]: 1.017–1.303, P = 0.026) and total coronary revascularization (aHR: 1.199; 95% CI: 1.037–1.388, P = 0.014) at 5 years. The results were consistent at the 2-year endpoints. In patients with CPCI, BP-DES use was associated with significantly higher MACE rates at 5 years (aHR: 1.256; 95% CI: 1.078–1.462, P = 0.003) and total coronary revascularization (aHR: 1.257; 95% CI: 1.052–1.502, P = 0.012) compared with that of DP-DES, but there was a similar risk at 2 years. However, BP-DES had comparable safety and efficacy profiles including MACE and total coronary revascularization compared with DP-DES in patients with non-CPCI at 2 and 5 years. Conclusions:. Patients underwent CPCI remained at a higher risk of mid- to long-term adverse events regardless of the stent type. The effect of BP-DES compared with DP-DES on outcomes was similar in CPCI and non-CPCI patients at 2 years but had inconsistent effects at the 5-year clinical endpoints.

We conducted environmental surveillance to detect avian influenza viruses circulating at live poultry markets (LPMs) and poultry farms in Guangxi Autonomous Region, China, where near the China-Vietnam border. From November through April 2017–2018 and 2018–2019, we collected environmental samples from 14 LPMs, 4 poultry farms, and 5 households with backyard poultry in two counties of Guangxi and tested for avian influenza A, H5, H7, and H9 by real-time reverse transcription-polymerase chain reaction (rRT-PCR). In addition, we conducted four cross-sectional questionnaire surveys among stall owners on biosecurity practices in LPMs of two study sites. Among 16,713 environmental specimens collected and tested, the median weekly positive rate for avian influenza A was 53.6% (range = 33.5% − 66.0%), including 25.2% for H9, 4.9% for H5, and 21.2% for other avian influenza viruses A subtypes, whereas a total of two H7 positive samples were detected. Among the 189 LPM stalls investigated, most stall owners (73.0%) sold chickens and ducks. Therefore, continued surveillance of the avian influenza virus is necessary for detecting and responding to emerging trends in avian influenza virus epidemiology.

The financial technology (FinTech) has promoted the wide application of FinTech with the help of artificial intelligence, blockchain, cloud computing, data science and other new technologies. Trust evaluation has become a forward-looking issue for the rapid development of FinTech. The existing trust evaluation methods of FinTech do not consider the impact of the timeliness, reliability and non-invasive factors of trust on trust evaluation, which results in low accuracy of trust evaluation and incapability of effectively identifying malicious behaviors of users. Firstly, this paper introduces the blockchain technology to construct a four-layer architecture structure and multiple trust evaluation indicators based on blockchain service data. Secondly, the paper proposes a blockchain based FinTech trust evaluation mechanism (BFTEM), which utilizes blockchain to record relevant data and multiple trust parameters during the transmission process of each block, and verify the trust degree issued by the trust holder through the comprehensive trust value of the user. Finally, the block generation time, throughput, delayed response time and comprehensive trust value are experimentally studied through simulation experiments. Simulation experiments show that BFTEM mechanism can better improve the security and reliability of FinTech data, and has advantages in improving the accuracy of trust evaluation and expanding potential applications.

In the real world, large-scale image data sets usually present long-tailed distribution. When traditional visual recognition methods are applied to long-tail image data sets, problems such as model failure and sudden decline in recognition accuracy occur. While, when deep learning models encounter long-tailed datasets, they tend to perform poorly. In order to mitigate the impact of these problems, we propose CWTA (Long-tailed Visual Recognition via improved Cross-Window Self-Attention and TrivialAugment). CWTA uses CNN to better capture the local features of the image, uses the Cross-Window Self-Attention mechanism to dynamically adjust the perception domain to better deal with image noise, and uses TrivialAugment to enhance the diversity of a few types of data samples, thus improving the recognition accuracy of long-tailed distributed images. The experimental results show that the proposed CWTA performs best in the classification accuracy of different categories on different long-tailed datasets. We also compared CWTA with other long-tailed recognition algorithms (such as OLTR, LWS, ResLT, PaCo, and BALLAD), and the CWTA is the best when ResNet-50 as the Backbone. On the CIFAR100-LT, ImageNet-LT, and Places-LT datasets, the acc of all categories of CWTA is 12.9%, 0.4%, and 1.3% higher than that of BALLAD, respectively. For F1-Score on CIFAR100-LT, ImageNet-LT, and Places-LT datasets, CWTA is 6.6%, 2.2%, and 1.5% higher than BALLAD, respectively.