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Abstract Background Stigma and discrimination towards people with mental health conditions by their communities are common worldwide. This can result in a range of negative outcomes for affected persons, including poor access to health care. However, evidence is still patchy from low- and middle-income countries (LMICs) on affordable, community-based interventions to reduce mental health-related stigma and to improve access to mental health care. Methods This study aims to conduct a feasibility (proof-of-principle) pilot study that involves developing, implementing and evaluating a community-based, multi-component, awareness-raising intervention (titled Indigo-Local), designed to reduce stigma and discrimination and to increase referrals of people with mental health conditions for assessment and treatment. It is being piloted in seven sites in five LMICs—China, Ethiopia, India, Nepal and Tunisia—and includes several key components: a stakeholder group workshop; a stepped training programme (using a ‘Training of Trainers’ approach) of community health workers (or similar cadres of workers) and service users that includes repeated supervision and booster sessions; community engagement activities; and a media campaign. Social contact and service user involvement are instrumental to all components. The intervention is being evaluated through a mixed-methods pre-post study design that involves quantitative assessment of stigma outcomes measuring knowledge, attitudes and (discriminatory) behaviour; quantitative evaluation of mental health service utilization rates (optional, where feasible in sites); qualitative exploration of the potential effectiveness and impact of the Indigo-Local intervention; a process evaluation; implementation evaluation; and an evaluation of implementation costs. Discussion The output of this study will be a contextually adapted, evidence-based intervention to reduce mental health-related stigma in local communities in five LMICs to achieve improved access to healthcare. We will have replicable models of how to involve people with lived experience as an integral part of the intervention and will produce knowledge of how intervention content and implementation strategies vary across settings. The intervention and its delivery will be refined to be feasible and ready for larger-scale implementation and evaluation. This study thereby has the potential to make an important contribution to the evidence base on what works to reduce mental health-related stigma and discrimination and improve access to health care.

Abstract Background Previous studies have found that the temporal duration required for males to perceive visual motion direction is significantly shorter than that for females. However, the neural correlates of such shortened duration perception remain yet unclear. Given that motion perception is primarily associated with the neural activity of the middle temporal visual complex (MT+), we here test the novel hypothesis that the neural mechanism of these behavioral sex differences is mainly related to the MT+ region. Methods We utilized ultra-high field (UHF) MRI to investigate sex differences in the MT+ brain region. A total of 95 subjects (48 females) participated in two separate studies. Cohort 1, consisting of 33 subjects (16 females), completed task-fMRI (drafting grating stimuli) experiment. Cohort 2, comprising 62 subjects (32 females), engaged in a psychophysical experiment measuring motion perception along different temporal thresholds as well as conducting structural and functional MRI scanning of MT+. Results Our findings show pronounced sex differences in major brain parameters within the left MT+ (but not the right MT+, i.e., laterality). In particular, males demonstrate (i) larger gray matter volume (GMV) and higher brain’s spontaneous activity at the fastest infra-slow frequency band in the left MT+; and (ii) stronger functional connectivity between the left MT+ and the left centromedial amygdala (CM). Meanwhile, both female and male participants exhibited comparable correlations between motion perception ability and the multimodal imaging indexes of the MT+ region, i.e., larger GMV, higher brain’s spontaneous activity, and faster motion discrimination. Conclusions Our findings reveal sex differences of imaging indicators of structure and function in the MT+ region, which also relate to the temporal threshold of motion discrimination. Overall, these results show how behavioral sex differences in visual motion perception are generated, and advocate considering sex as a crucial biological variable in both human brain and behavioral research.

Abstract In response to the challenges of target misidentification, missed detection, and other issues arising from severe light attenuation, low visibility, and complex environments in current underwater target detection, we propose a lightweight low-light underwater target detection network, named PDSC-YOLOv8n. Firstly, we enhance the input images using the improved Pro MSRCR algorithm for data augmentation. Secondly, we replace the traditional convolutions in the backbone and neck networks of YOLOv8n with Ghost and GSConv modules respectively to achieve lightweight network modeling. Additionally, we integrate the improved DCNv3 module into the C2f module of the backbone network to enhance the capability of target feature extraction. Furthermore, we introduce the GAM into the SPPF and incorporate the CBAM attention mechanism into the last layer of the backbone network to enhance the model’s perceptual and generalization capabilities. Finally, we optimize the training process of the model using WIoUv3 as the loss function. The model is successfully deployed on an embedded platform, achieving real-time detection of underwater targets on the embedded platform. We first conduct experiments on the RUOD underwater dataset. Meanwhile, we also utilized the Pascal VOC2012 dataset to evaluate our approach. The mAP@0.5 and mAP@0.5:0.95 of the original YOLOv8n algorithm on RUOD dataset were 79.6% and 58.2%, respectively, and the PDSC -YOLOv8n algorithm mAP@0.5 and mAP@0.5:0.95 can reach 86.1% and 60.8%. The number of parameters of the model is reduced by about 15.5%, the detection accuracy is improved by 6.5%. The original YOLOv8n algorithm was 73% and 53.2% mAP@0.5 and mAP@0.5:0.95 on the Pascal VOC dataset, respectively. The PDSC-YOLOv8n algorithm mAP@0.5 and mAP@0.5:0.95 were 78.5% and 57%, respectively. The superior performance of PDSC-YOLOv8n indicates its effectiveness in the field of underwater target detection.

Abstract The rapid advancement of Industry 4.0 necessitates close collaboration among material research institutions to accelerate the development of novel materials. However, multi-institutional cooperation faces significant challenges in protecting sensitive data, leading to data silos. Additionally, the heterogeneous and non-independent and identically distributed (non-i.i.d.) nature of material data hinders model accuracy and generalization in collaborative computing. In this paper, we introduce the MatSwarm framework, built on swarm learning, which integrates federated learning with blockchain technology. MatSwarm features two key innovations: a swarm transfer learning method with a regularization term to enhance the alignment of local model parameters, and the use of Trusted Execution Environments (TEE) with Intel SGX for heightened security. These advancements significantly enhance accuracy, generalization, and ensure data confidentiality throughout the model training and aggregation processes. Implemented within the National Material Data Management and Services (NMDMS) platform, MatSwarm has successfully aggregated over 14 million material data entries from more than thirty research institutions across China. The framework has demonstrated superior accuracy and generalization compared to models trained independently by individual institutions.

In community planning, due to the lack of evidence regarding the selection of media tools, this study examines how a common but differentiated ideal speech situation can be created as well as how more appropriate media tools can be defined and selected in the community planning process. First, this study describes the concept and theoretical basis of media used in community planning from the perspectives of the multiple effects of media evolution on communicative planning. Second, the classification criteria and typical characteristics of media tools used to support community planning are clarified from three dimensions: acceptability, cost-effectiveness, and applicability. Third, strategies for applying media tools in the four phases of communicative planning—namely, state analysis, problem identification, contradictory solution and optimization—are described. Finally, trends in the development of media tools for community planning are explored in terms of multistakeholder engagement, supporting scientific decision-making and multiple-type media integration. The results provide a reference for developing more inclusive, effective, and appropriate media tools for enhancing decision-making capacity and modernizing governance in community planning and policy-making processes.

Abstract Schizophrenia (SZ) is a severe psychiatric disorder characterized by perceptual, emotional, and behavioral abnormalities, with cognitive impairment being a prominent feature of the disorder. Recent studies demonstrate irregularity in SZ with increased variability on the neural level. Is there also irregularity on the psychophysics level like in visual perception? Here, we introduce a methodology to analyze the irregularity in a trial-by-trial way to compare the SZ and healthy control (HC) subjects. In addition, we use an unsupervised clustering algorithm K-means + + to identify SZ subgroups in the sample, followed by validation of the subgroups based on intraindividual visual perception variability and clinical symptomatology. The K-means + + method divided SZ patients into two subgroups by measuring durations across trials in the motion discrimination task, i.e., high, and low irregularity of SZ patients (HSZ, LSZ). We found that HSZ and LSZ subgroups are associated with more negative and positive symptoms respectively. Applying a mediation model in the HSZ subgroup, the enhanced irregularity mediates the relationship between visual perception and negative symptoms. Together, we demonstrate increased irregularity in visual perception of a HSZ subgroup, including its association with negative symptoms. This may serve as a promising marker for identifying and distinguishing SZ subgroups.

For many years, multi-scale models of chromatin domains, such as A/B compartments, sub-compartments, topologically associated domains (TADs), sub-TADs, and loops have been popular. However, existing methods can only identify structures at a single scale and cannot partition multi-scale structures. In this paper, we proposed a method (TORNADOES) for chromatin domain partitioning based on hypergraph clustering. First, we use a density clustering algorithm to identify TADs at different scales based on Hi-C data with different resolutions. Then, by combining ChIP-seq data features and TAD results at different scales, we generate a hypergraph based on these TADs. Finally, we partition the chromatin domain structure at different scales, including A/B, A1, A2, B1, B2, and B3 based on the Laplacian matrix feature of the hypergraph. Similarity comparison experiments and ChIP-seq signal enrichment analysis are performed on the A/B region and sub-TAD levels, respectively, demonstrating that our method can identify chromatin domains with distinct features and provide a deeper understanding of the organizational patterns and functional differences in TADs at the genomic hierarchical structure. Comparative analysis of multiple cell line data shows that TORNADOES can better classify different numbers and types of compartments by changing the factors ChIP-seq data and clustering number used to characterize TAD compared to other methods. Source code for the TORNADOES method can be found at https://github.com/ghaiyan/TORNADOES.

Objective To obtain the monitoring measurement range of quality indicators of red blood cells, plasma and derivatives and leukocyte-reduced apheresis platelets provided by blood stations in Hebei province, explore the distribution of monitoring values and the change of monitoring level, so as to further strengthen the homogenization construction of quality control laboratories in blood stations in Hebei. Methods In 2023, the sampling data of 12 blood stations in Hebei from 2015 to 2022 were collected, scatter plots were made and the range markers were set, and the "mean±SD" line was taken as the upper limit and lower limit of the measurement range. In 2024, the monitoring values in 2023 were added, and the changes of two measurement ranges were compared to analyze the stability and overall level. Results Comparison of the measurement range from 2015 to 2022 and the measurement range from 2015 to 2023 showed that the standard deviation of the content of deleukocyte suspension of red blood cells-hemoglobin, washed erythrocyte-hemoglobin, washed erythrocyte-supernatant protein, cryoprecipitate coagulation factor-FⅧ, fresh frozen plasma-FⅧ, leukocyte-reduced apheresis platelets-leukocyte residue and leukocyte-reduced apheresis platelet-red blood cell concentration decreased from 8.132 to 7.993, 6.252 to 6.104, 0.273 to 0.267, 57.506 to 56.276, 0.920 to 0.892, 0.653 to 0.644 and 2.653 to 2.603, respectively.The narrowing of the standard deviation range of the above items led to more concentrated monitoring values and reduced dispersion. Comparison of the measurement range from 2015 to 2022 and the measurement range from 2015 to 2023 showed that the mean value of leukocyte residue of the deleukocyte suspension of red blood cells, hemoglobin content of the wash erythrocyte, protein content of supernatant of the wash erythrocyte, hemolysis rate of the wash erythrocyte, FⅧ content of the cryoprecipitate coagulation factor, plasma protein content of the fresh frozen plasma, FⅧ content of the fresh frozen plasma, platelet content of the leukocyte-reduced apheresis platelets changed from 0.362 to 0.476, 44.915 to 44.861, 0.280 to 0.283, 0.137 to 0.142, 133.989 to 133.271, 60.262 to 60.208, 1.301 to 1.277 and 3.036 to 3.033, respectively, and was closer to the national standard line, which reflects an increase in the number of unqualified monitoring values or values close to the national standard line in 2023. The long-term qualified rate of coagulation items was low, and no improvement has been observed. The stability of biochemical items has been enhanced but overall deviation has occurred, with the average value close to the national standard line. The possibility of subsequent testing failure has increased. The counting items showed no obvious common characteristics. Conclusion The use of "mean±SD" in the analysis can visually display the distribution of monitoring values of different items in Hebei, forming an indicator measurement range covering the past nine years. It shows the characteristics of each item, and provides reference for subsequent quality control laboratory data analysis of each blood stations to takes active measures to improve the monitoring level.

Many studies now indicate that brain disorders are associated with functional connectivity between brain regions, but the impact of interactions among functional connections on diseases remains to be explored. Moreover, existing models for analyzing the temporal and spatial attributes of fMRI data exhibit limitations in their efficacy. Currently, there is an absence of a comprehensive model capable of simultaneously capturing disease-related interactions within functional connectivity, fully leveraging the temporal and spatial characteristics of fMRI, and achieving superior diagnostic accuracy. To address these gaps, we propose a model to classify brain disorders that employs a novel algorithm and multimodal graph convolutional networks to process the temporal and spatial features of fMRI data separately, along with the demographic information. In the proposed model, a deep feature selection algorithm is employed to discover disease-related biomarkers and evaluate their impact on brain disorders. Remarkably, this approach has led to a classification accuracy of 80.66%, which surpasses the performance of graph neural networks by approximately 10%. Notably, our investigations have uncovered novel biomarkers linked to autism, with our findings corroborated by relevant literature. These achievements underscore the model's potential in revealing critical insights pertinent to autism, thereby facilitating groundbreaking experimental research in the field.

Abstract A scalable, reusable, and broad-coverage unified material knowledge representation shows its importance and will bring great benefits to data sharing among materials communities. A knowledge graph (KG) for materials terminology, which is a formal collection of term entities and relationships, is conceptually important to achieve this goal. In this work, we propose a KG for materials terminology, named Materials Genome Engineering Database Knowledge Graph (MGED-KG), which is automatically constructed from text corpus via natural language processing. MGED-KG is the most comprehensive KG for materials terminology in both Chinese and English languages, consisting of 8,660 terms and their explanations. It encompasses 11 principal categories, such as Metals, Composites, Nanomaterials, each with two or three levels of subcategories, resulting in a total of 235 distinct category labels. For further application, a knowledge web system based on MGED-KG is developed and shows its great power in improving data sharing efficiency from the aspects of query expansion, term, and data recommendation.

Sepsis, characterized by a widespread and dysregulated immune response to infection leading to organ dysfunction, presents significant challenges in diagnosis and treatment. In this study, we investigated 203 coagulation-related genes in sepsis patients to explore their roles in the disease. Through differential gene expression analysis, we identified 20 genes with altered expression patterns. Subsequent correlation analysis, visualized through circos plots and heatmaps, revealed significant relationships among these genes. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses indicated that these genes are involved in immune response activation, coagulation, and immune receptor activity. Disease Ontology (DO) enrichment analysis further linked these genes to autoimmune hemolytic anemia and tumor-related signaling pathways. Additionally, the CIBERSORT analysis highlighted differences in immune cell composition in sepsis patients, revealing an increase in neutrophils and monocytes and a decrease in inactive NK cells, CD8 T cells, and B cells. We employed machine learning techniques, including random forest and SVM, to construct a diagnostic model, identifying FCER1G and FYN as key biomarkers. These biomarkers were validated through their expression levels and ROC curve analysis in an independent validation cohort, demonstrating strong diagnostic potential. Single-cell analysis from the GSE167363 dataset further confirmed the distinct expression profiles of these genes across various cell types, with FCER1G predominantly expressed in monocytes, NK cells, and platelets, and FYN in CD4+ T cells and NK cells. Enrichment analysis via GSEA and ssGSEA revealed that these genes are involved in critical pathways, including intestinal immune networks, fatty acid synthesis, and antigen processing. In conclusion, our comprehensive analysis identifies FCER1G and FYN as promising biomarkers for sepsis, providing valuable insights into the molecular mechanisms of this complex condition. These findings offer new avenues for the development of targeted diagnostic and therapeutic strategies in sepsis management.

The development and improvement of electromagnetic underground metal target detection methods can be implemented by a framework that is experimental supporting, modular, and extensible. In this paper, we organize the components of electromagnetic underground metal target detection in a comprehensive, modular, and extensible framework. Furthermore, we present an open-source toolbox in Python called MicEMD (Modeling, Inversion, and Classification in ElectroMagnetic Detection, https://github.com/UndergroundDetection/MICEMD). The graphical user interface (GUI) and the library with a Python application programming interface (API) are contained in MicEMD. Included in MicEMD are staggered frequency-domain and time-domain electromagnetic forward modeling, least-squares inversion, and data-based classification methods at present. MicEMD’s capabilities are presented by two synthetic case studies. The first example shows the application of frequency-domain inversion. The second example shows the application of time-domain classification. It is anticipated that MicEMD offers a flexible tool in electromagnetic underground metal target detection.

Abstract Harmonic resonance is caused by harmonic source matching resonance frequency and presents the characteristics of decentralization and whole network in modern grids. The current resonance suppression methods mainly focus on reducing the harmonic source, and are suitable for single‐site and small‐scale grids. This paper presents a new method for suppressing parallel resonance at system level. The holistic harmonic resonance is suppressed by reducing the harmonic impedance. In addition, new resonances are prevented by weakening the coupling between resonance and non‐resonance frequencies. The resonance modal analysis and resonance frequency shift (RFS) are employed to optimize harmonic impedance. Simulation results indicate that the capacitance parameter has higher sensitivity and is theoretically more suitable for RFS. Considering the accuracy of modal frequency sensitivity, a variable‐limit genetic algorithm for iteration is proposed. The validity of the proposed method was verified using an IEEE 14‐bus test network.

Abstract Obstructive sleep apnea (OSA) is a multifactorial sleep disorder with a high prevalence in the general population. OSA is associated with an increased risk of developing cardiovascular diseases (CVDs), particularly hypertension, and is linked to worse outcomes. Although the correlation between OSA and CVDs is firmly established, the mechanisms are poorly understood. Continuous positive airway pressure is primary treatment for OSA reducing cardiovascular risk effectively, while is limited by inadequate compliance. Moreover, alternative treatments for cardiovascular complications in OSA are currently not available. Recently, there has been considerable attention on the significant correlation between gut microbiome and pathophysiological changes in OSA. Furthermore, gut microbiome has a significant impact on the cardiovascular complications that arise from OSA. Nevertheless, a detailed understanding of this association is lacking. This review examines recent advancements to clarify the link between the gut microbiome, OSA, and OSA‐related CVDs, with a specific focus on hypertension, and also explores potential health advantages of adjuvant therapy that targets the gut microbiome in OSA.

Abstract Magnetic resonance imaging (MRI) has diverse applications in physics, biology, and medicine. Uniform excitation of nuclei spins through circular-polarized transverse magnetic component of electromagnetic field is vital for obtaining unbiased tissue contrasts. However, achieving this in the electrically large human body poses a significant challenge, especially at ultra-high fields (UHF) with increased working frequencies (≥297 MHz). Canonical volume resonators struggle to meet this challenge, while radiative excitation methods like travelling-wave (TW) show promise but often suffer from inadequate excitation efficiency. Here, we introduce a new technique using a subwavelength dielectric waveguide insert that enhances both efficiency and homogeneity at 7 T. Through TE11-to-TM11 mode conversion, power focusing, wave impedance matching, and phase velocity matching, we achieved a 114% improvement in TW efficiency and mitigated the center-brightening effect. This fundamental advancement in TW MRI through effective wave manipulation could promote the electromagnetic design of UHF MRI systems.

ABSTRACTDomain adaptation can effectively enhance a model’s performance on target domain data with limited data. However, when some target domain labels are obtainable, training the model with both source and target domain data simultaneously can lead to worse performance due to the lower density of target domain data. If a large amount of target domain data is labeled without discrimination, it will necessitate a considerable expenditure of human resources. To address this issue, this paper proposes a human-in-one-loop active domain adaptation framework based on Target Domain Feature Generation to solve the problems. The oracle participates in only one iteration of data labeling, and a target domain classifier will take over the subsequent rest iterations. An image generator based on multiple CycleGANs forms an iterative co-training mechanism, which can continuously generate more high-quality labeled fake target domain data in iterations to improve the performance of the target domain classifier. The Top-N labeled data selection method with high confidence is devised to select the most accurately predicted data for labeling, reducing manual labeling workload. This framework can achieve an average accuracy of 0.8869 on six domain pairs, doubling the classical domain adaptation method DSN, requiring only a small amount of manual labeling.

Due to the fatigue and continuous energy input during high-speed dynamic friction polishing (DFP), the diamond crystal beneath the polished surface (roughness 50 nm) and even preferential crystal cleavage with the non-diamond phase (distributing at the position in micrometers range).

Biomedical materials scientific research is increasingly data-driven, thanks to advancements in machine learning technology. The application of biological sequencing technology for assessing the biological functions of biomedical materials demands further optimization. To facilitate comprehensive analysis, it is essential to establish an open, shared infrastructure for storing diverse scientific data from various research fields. This paper presents BioMGE, a case study in database construction, utilizing the flexible and user-defined NMDMS platform (National Materials Data Management and Service Platform). BioMGE is designed for the collection of biomedical materials and multiomics sequencing data. Leveraging NMDMS’s dynamic container framework, users can tailor data submission schemas to their preferences and store data from the domains of biomedical materials and multiomics research. To ensure data interoperability, the data schema creation module is combined with data standards. We also propose a standard specification for biomedical materials data. Employing the dynamic container framework and standard specifications, data submission schemas were established for biomedical material and multiomics data, covering aspects such as material names, experimental design, grouping information for experimental materials, and high-throughput omics sequencing. Since 2019, BioMGE has amassed 1547100 datasets of biomedical material and multiomics data based on these schemas. In order to enable users to analyze this data, BioMGE provides a data export interface. For instance, the BioMGE-viewer module offers one-dimensional, two-dimensional, and three-dimensional visualizations for omics data. The one-dimensional visualization displays gene information in tabular form. The two-dimensional visualization exhibits the topologically associating domains of chromatin using a heatmap. The three-dimensional visualization offers a three-dimensional representation of chromatin structure, aiding users in exploring the relationship between gene function and gene structure. What sets BioMGE apart is that it was constructed directly by researchers, not database designers. This means that researchers without programming expertise in various fields can design personalized data schemas that align with their research characteristics. This approach maximizes the interoperability and usability of NMDMS data. BioMGE has the potential to foster collaborative research across different domains and the joint analysis of biomedical materials and biological sequencing data. It offers fresh insights for the advancement of cell therapy and, concurrently, introduces a novel idea and platform for data sharing in various cross-field research endeavors.

Abstract Modification serves as an excellent approach to enhancing the adsorption performance of biochar for tetracycline. Selective modification further allows the attainment of biochar materials that are not only more efficient but also cost-effective. However, the key structural factors influencing the adsorption of tetracycline by biochar remain unclear at present, hindering the effective guidance for modification strategies. This study established the relationship between carbonization degree and adsorption capacity, constructed a standardized microscopic model for biochar adsorption of tetracycline, and explored potential reaction mechanisms. The results indicated that with increases in the degree of carbonization, the tetracycline adsorption capacity of biochar increased from 16.08 mg L−1 to 98.35 mg L−1. The adsorption energy exhibited a strong correlation with the aromatic condensation of biochar at p ≤ 0.01, with a linear relationship (r2 ≥ 0.94). For low carbonization degrees, the adsorption of tetracycline by biochar was primarily driven by chemical bonds (69.21%) and complemented with electrostatic interactions, weak van der Waals forces or π-π interactions. For high carbonization degrees, the synergistic effects of hydrogen bonding, van der Waals forces, and π-π interactions determined the adsorption of tetracycline on biochar (91.1%). Additionally, larger carbon clusters resulted in stronger and more stable adsorption interactions. Furthermore, carboxyl-functionalized highly carbonized biochar displayed the highest reaction energy of − 1.8370 eV for adsorption of tetracycline through electrostatic interactions. This study suggests that a high degree of aromatic condensation in the carbon structure of biochar is crucial for the efficient adsorption of tetracycline. Graphical Abstract

China has made remarkable progress in controlling particulate matter, while O3 pollution over China has become increasingly severe in recent years according to ground observations. Continuous monitoring of dynamic changes in O3 concentrations on regional and national scales can provide valuable insights for pollution control policies. Therefore, an improved similarity distance-based space-time random forest (SDSTRF) model was developed to estimate the near-surface O3 concentration using the surface measurements, satellite O3 precursors, meteorological variables, and other auxiliary information. The O3 concentration data over China were generated based on the developed model with a spatial resolution of 10 km and a temporal resolution of 1 day from 2016 to 2022. The validation results against the ground measurements indicate that the developed SDSTRF model effectively captures O3 variations, achieving a coefficient of determination of 0.83 and a root mean square error of 20.37 μg/m3. The spatiotemporal variations of O3 concentrations were investigated using the generated dataset. A significant increasing trend of 1.243 μg/m3/yr in O3 concentrations was observed in eastern China during the COVID-19 pandemic, which was attributed to changes in NOx concentrations. In this study, the possible reasons for the increase in O3 concentrations are also discussed. Overall, the improved SDSTRF model and the comprehensive analysis of the spatiotemporal variations of near-surface O3 will significantly contribute to achieving clean air in China.

Abstract The direct bonding copper (DBC) substrates of insulated gate bipolar transistor (IGBT) modules degrade inevitably under cycling thermo‐mechanical stress, causing potential threat to the reliability of IGBT modules. However, little attention has been paid to monitoring their degradation. This paper proposes a DBC degradation monitoring method for IGBT modules, which combines boundary‐dependent thermal network and feedback control. A thermal network is employed to describe the internal material degradation of IGBT modules and can be extracted from a finite‐element method model. The boundary conditions including power losses and DBC degradation are considered, enabling the thermal network suitable for various working conditions and different DBC degradation conditions of IGBT modules. The DBC degradation is characterised by its equivalent thermal conductivities measured in the thermal cycling ageing experiments. On the basis of the boundary‐dependent thermal network, feedback control is applied to monitor DBC degradation by regulating boundary‐dependent thermal impedances. Finally, the proposed model is verified from the effectiveness and accuracy of DBC degradation monitoring and junction temperature calculation. This method casts new light on thermal network modelling and could provide a feasible method for the monitoring of DBC degradation.

Abstract As the core method of cooperative navigation, relative positioning plays a key role in realizing intelligent vehicle driving and vehicle self-assembling network collaboration algorithms. However, when the contamination rate of measurement noise is high, the performance of filtering will be seriously affected. To better address the filtering performance degradation problem due to noise contamination, this paper proposes a vehicular cooperative localization method based on the Maximum Correentropy Robust Square-root Cubature Kalman Filter (MCSCKF). The algorithm not only retains the advantages of Square-root Cubature Kalman Filter (SCKF) but also has strong robustness to non-Gaussian noise. The experimental results of tightly integrated vehicular cooperative navigation show that compared with the Extended Kalman Filter (EKF) and Cubature Kalman Filter (CKF), the localization accuracy of MCSCKF is improved by 35.08% and 31.83%, respectively, which verified the effectiveness in improving the accuracy and robustness of the relative position estimation.

ABSTRACTAlthough the surface energy budget is essential to determine Earth’s climate, site measurements of various radiative components are still too scarce to properly characterize their spatial and temporal variations. This has led to the development of a growing number of surface radiation products, mainly including remotely sensed data, model reanalysis data, and simulations using General Circulation Models (GCMs). This collection of papers introduces new techniques, including the use of machine learning methods for radiation estimation, and evaluates and compares various radiation products, as well as their spatio-temporal variations. These studies show large discrepancies among various products across nearly all radiative parameters in either accuracy or spatio-temporal variations. However, remotely sensed radiation products perform relatively better than others. Despite this, there is an urgent need for further efforts to address these discrepancies and improve the accuracy of these estimates. Even though the major radiative parameters including downward shortwave radiation, net longwave radiation, and albedo, from most products show insignificant long-term variation trends on a global scale, only specific regions, such as the Yunnan-Kweichow Plateau (YKP) and regions with permafrost (i.e. Qinghai-Tibet Plateau and Arctic) and glaciers (i.e. Altai Mountains) exhibit remarkable trends.

Land surface all-wave net radiation (Rn) is crucial in determining Earth's climate by contributing to the surface radiation budget. This study evaluated seven satellite and three reanalysis long-term land surface Rn products under different spatial scales, spatial and temporal variations, and different conditions. The results showed that during 2000-2018, Global Land Surface Satellite Product (GLASS)-Moderate Resolution Imaging Spectroradiometer (MODIS) performed the best (RMSE=25.54 Wm-2, bias=−1.26 Wm-2), followed by ERA5 (the fifth-generation of European Centre for Medium-Range Weather Forecast Reanalysis) (RMSE=32.17 Wm-2, bias=−4.88 Wm-2) and GLASS-AVHRR (Advanced Very-High-Resolution Radiometer) (RMSE=33.10 Wm-2, bias=4.03 Wm-2). During 1983-2018, GLASS-AVHRR and ERA5 ranked top and performed similarly, with RMSE values of 31.70 and 33.08 Wm-2 and biases of −4.56 and 3.48 Wm-2, respectively. The averaged multi-annual mean Rn over the global land surface of satellite products was higher than that of reanalysis products by about 10∼30 Wm-2. These products differed remarkably in long-term trends variations, particularly pre-2000, but no significant trends were observed. Discrepancies were more frequent in satellite data, while reanalysis products showed smoother variations. Large discrepancies were found in regions with high latitudes, reflectance, and elevation which could be attributed to input radiative components, meteorological variables (e.g., cloud properties, aerosol optical thickness), and applicability of the algorithms used. While further research is needed for detailed insights.

Surface incident shortwave radiation (Rs) can promote the circulation of substance and energy, and the accuracy of its estimation is of great significance for climate studies. The Rs can be acquired from satellite retrievals, reanalysis predictions and general circulation model (GCM) simulations. Although Rs estimates have been evaluated and compared in previous studies, most of them focus on evaluating the Rs estimates over specific regions using ground measurements from limited stations. Therefore, it is essential to comprehensively validate Rs estimates from multiple data sources. In this study, ground measurements of 690 stations from BSRN, GEBA, CMA, GC-NET and buoys were employed to validate the Rs estimates from seven representative products (GLASS, GEWEX-SRB, CERES-EBAF, ERA5, MERRA2, CFSR and CMIP6). The validation results indicated that the selected products overestimated Rs globally, with biases ranged from 0.48 to 21.27 W/m2. The satellite retrievals showed relatively better accuracy among seven datasets compared to ground measurements at the selected stations. Moreover, the selected seven products were all in poor accuracy at high-latitude regions with RMSEs greater than 50 W/m2. The long-term variation trends were also analyzed in this study.

Downward shortwave radiation (DSR) is a critical variable in energy balance driving Earth’s surface processes. Satellite-derived and reanalysis DSR products have been developed and continuously improved during the last decades. However, as those products have different temporal resolutions, their performances in different time scales have not been well-documented, particularly in China. This study intended to evaluate several DSR products across multiple time scales (i.e. instantaneous, 1-hourly, daily, and monthly average) and ecosystems in China. Six DSR products, including GLASS, BESS, CLARA-A2, MCD18A1, ERA5 and MERRA-2, were evaluated against ground measurements at Chinese Ecosystem Research Network (CERN) and integrated land-atmosphere interaction observation (TPDC) sites from 2009 to 2012. The instantaneous DSR of MCD18 showed a root mean square error (RMSE) of 146.02 W/m2. The hourly RMSE of ERA5 (155.52 W/m2) was largely smaller than MERRA-2 (188.53 W/m2). On the daily and monthly scale, BESS had the most optimized accuracy among the six products (RMSE of 36.82 W/m2). For the satellite-derived DSR products, the monthly accuracy at CERN can meet the threshold accuracy requirement set by World Meteorological Organization (WMO) for Global Numerical Weather Prediction (20 W/m2).

Hypertension has always posed a severe threat to people’s health. Food-derived angiotensin-converting enzyme (ACE)-inhibitory peptides have the potential to both prevent and treat hypertension. In the current investigation, two ACE-inhibitory peptides (SLPQ and PYVRYL) from goat milk were studied for their endothelial effects using EA.hy926 cells. PYVRYL outperformed SLPQ, yet neither impacted cell survival below 200 μg/mL. Investigation of SLPQ’s impact on EA.hy926 cell expression revealed 114 differentially expressed genes, with 65 downregulated and 49 upregulated. The genes were enriched in cytokine interactions, coagulation cascades, Hippo signaling, and ECM–receptor interaction. Decreased c-x-c motif chemokine ligand 2 (CXCL2), integrin subunit beta 2 (ITGB2), and fbj murine osteosarcoma viral oncogene homologue (FOS) expression and increased secreted phosphoprotein 1 (SPP1) expression may protect endothelial cells from inflammation. Our findings suggest that beyond ACE inhibition, SLPQ aids blood pressure control by influencing endothelial function, paving the way for its use as an antihypertensive food ingredient.

This study aimed to create Pickering emulsions with varying oil fractions and assess the impact of ultrasonic treatment on the properties of Whey Protein Isolates (WPIs). At 640 W for 30 min, ultrasound reduced WPI aggregate size, raised zeta potential, and improved foaming, emulsifying, and water-holding capacities. FTIR analysis showed structural changes, while fluorescence and hydrophobicity increased, indicating tertiary structure alterations. This suggests that sonication efficiently modifies WPI functionality. Under ideal conditions, φ = 80 emulsions were most stable, with no foaming or phase separation. Laser scanning revealed well-organized emulsions at φ = 80. This study provides a reference for modifying and utilizing WPI.

As a major agricultural hazard, drought frequently occurs due to a reduction in precipitation resulting in a continuously propagating soil moisture (SM) deficit. Assessment of the high spatial-resolution SM-derived drought index is crucial for monitoring agricultural drought. In this study, we generated a downscaled random forest SM dataset (RF-SM) and calculated the soil water deficit index (RF-SM-SWDI) at 30 m for agricultural drought monitoring. The results showed that the RF-SM dataset exhibited better consistency with in situ SM observations in the detection of extremes than did the SM products, including SMAP, SMOS, NCA-LDAS, and ESA CCI, for different land cover types in the U.S. and yielded a satisfactory performance, with the lowest root mean square error (RMSE, below 0.055 m3/m3) and the highest coefficient of determination (R2, above 0.8) for most observation networks, based on the number of sites. A vegetation health index (VHI), derived from a Landsat 8 optical remote sensing dataset, was also generated for comparison. The results illustrated that the RF-SM-SWDI and VHI exhibited high correlations (R ≥ 0.5) at approximately 70% of the stations. Furthermore, we mapped spatiotemporal drought monitoring indices in California. The RF-SM-SWDI provided drought conditions with more detailed spatial information than did the short-term drought blend (STDB) released by the U.S. Drought Monitor, which demonstrated the expected response of seasonal drought trends, while differences from the VHI were observed mainly in forest areas. Therefore, downscaled SM and SWDI, with a spatial resolution of 30 m, are promising for monitoring agricultural field drought within different contexts, and additional reliable factors could be incorporated to better guide agricultural management practices.

ABSTRACTSolid organ transplantation is a crucial treatment for patients who have reached the end stage of heart, lung, kidney, or liver failure. However, the likelihood of developing cancer post-transplantation increases. Additionally, primary malignant tumors remain a major obstacle to the long-term survival of transplanted organs. Therefore, it is essential to investigate effective therapies that can boost the immune system’s ability to combat cancer and prevent allograft rejection. We established a mouse orthotopic liver tumor model and conducted allogeneic heterotopic heart transplantation. Various treatments were administered, and survival curves were generated using the Kaplan–Meier method. We also collected graft samples and measured inflammatory cytokine levels in the serum using an inflammatory array. The specificity of the histochemical techniques was tested by staining sections. We administered a combination therapy of phosphoinositide 3-kinase/mammalian target of rapamycin (PI3K/mTOR) dual inhibitor BEZ235 and Lactobacillus rhamnosus HN001 to primary liver cancer model mice with cardiac allografts. Consistent with our prior findings, L. rhamnosus HN001 alleviated the intestinal flora imbalance caused by BEZ235. Our previous research confirmed that the combination of BEZ235 and L. rhamnosus HN001 significantly prolonged cardiac transplant survival.IMPORTANCEWe observed that the combination of phosphoinositide 3-kinase/mammalian target of rapamycin (PI3K/mTOR) dual inhibitor BEZ235 and Lactobacillus rhamnosus HN001 notably prolonged cardiac transplant survival while also inhibiting the progression of primary liver cancer. The combination therapy was efficacious in treating antitumor immunity and allograft rejection, as demonstrated by the efficacy results. We also found that this phenomenon was accompanied by the regulation of inflammatory IL-6 expression. Our study presents a novel and effective therapeutic approach to address antitumor immunity and prevent allograft rejection.

Dithienylethenes (DTEs) are a promising class of organic photoswitches that can be used to create crystalline solids with properties controlled by light. However, the ability of DTEs to adopt multiple conformations, only one of which is photoactive, complicates the rational design of these materials. Herein, the synthesis and structural characterization of 19 crystalline solids containing a single DTE molecule are described. A novel D–D analysis of the molecular geometries obtained from rotational potential energy surface calculations and the ensemble of experimental structures were used to construct a crystal landscape for DTE. Of the 19 crystal structures, 17 contained photoinactive DTE rotamers and only 2 were photoactive. These results highlight the challenges associated with the design of these materials. Overall, the D–D analysis described herein provides rapid, effective and intuitive means of linking the molecular structure to photoactivity that could be applied more broadly to afford a general strategy for producing photoactive diarylethene-based crystalline solids.

Objective To conduct the laboratory quality assessment between 12 blood stations in Hebei province, analyze the results and explore the accuracy and comparability of testing, so as to improve the level of testing ability and quality management. Methods With reference to the external quality assessment rules of National Center for Clinical Laboratories and combined with the instructions of quality assessment samples, daily testing process of the laboratories were assessed. The quality indicators include blood cell count (WBC, RBC, Hb, HCT, MCV, MCH, MCHC and PLT), biochemical items (TP) and coagulation parameters (FIB and FⅧ). Results There are still problems in laboratories in terms of personnel operation, instrument maintenance and the impact of different reagent batches, especially in biochemical items and coagulation parameters. The pass rate of biochemical items was the lowest, only 72.75%, and that of blood cell count was the highest, reaching 98.75%. Conclusion With the progress of the project, the quality monitoring level of daily blood sampling tests in the quality control laboratory of each blood station has been improved. However, it is still necessary for each laboratory to improve the testing ability and quality management to a higher level in Hebei.

Thermal interface materials (TIMs), as important materials for heat dissipation of insulated gate bipolar transistor (IGBT) modules, degrade under long-term thermal cycling, resulting in elevated junction temperatures and threatening the safe operation of IGBT modules. In consideration of the thermal coupling effect in IGBT modules, this paper proposes a three-dimensional thermal network model characterizing the TIM degradation. An IGBT module with the TIM is adopted to establish the finite element method (FEM) model, in which the TIM layer is divided into regular areas. The thermal network is established by simulating the heat transfer paths. The thermal impedance in each path is extracted according to the heat flow through its area considering the TIM degradation. Thereby, the impact of thermal coupling and TIM degradation on junction temperature of IGBT modules could be characterized. Furthermore, the proposed thermal network is verified by simulation from calculation accuracy of junction temperature and TIM temperature.

The Priestley–Taylor model of the Jet Propulsion Laboratory (PT-JPL) evapotranspiration (ET) model is relatively simple and has been widely used based on meteorological and satellite data. However, soil moisture (SM) constraints include a vapor pressure deficit (VPD) that causes large uncertainty. In this study, we proposed a PT-SinRH model by introducing a sine function of air relative humidity (RH) to replace RHVPD to characterize SM constraints, which can improve the accuracy of ET estimations. The PT-SinRH model is validated by eddy covariance (EC) data from 2000–2020. These data were collected by AmeriFlux at 28 sites on the conterminous United States (CONUS), and the land cover types of the sites vary from croplands to wetlands, grasslands, shrub lands and forests. The validation results from daily scale-based on-site and satellite data inputs showed that the PT-SinRH model estimates fit the observations with a coefficient of determination (R2) of 0.55, root-mean-square error (RMSE) of 17.5 W/m2, bias of −1.2 W/m2 and Kling–Gupta efficiency (KGE) of 0.70. Additionally, the PT-SinRH model based on reanalysis and satellite data inputs has an R2 of 0.49, an RMSE of 20.3 W/m2, a bias of −8.6 W/m2 and a KGE of 0.55. The PT-SinRH model showed better accuracy when using the site-measured meteorological data than when using reanalysis meteorological data as inputs. Additionally, compared with the PT-JPL model, the results demonstrate that our approach, i.e., PT-SinRH, improved ET estimates, increasing the R2 and KGE by 0.02 and decreasing the RMSE by about 0.6 W/m2. This simple but accurate method permits us to investigate the decadal variation in regional ET over the land.

With the increase in the demand for large-capacity optical communication capacity, multi-core optical fiber (MCF) communication technology has developed, and both the types of MCFs and related devices have become increasingly mature. The application of MCFs in the field of sensing has also received more and more attention, among which MCF fiber Bragg grating (FBG) devices have received more and more attention and have been widely used in various fields. In this paper, the main writing methods of MCF FBGs and their sensing applications are reviewed. The future development of the MCF FBG is also prospected.

Over recent decades, Landsat satellite data has evolved into a highly valuable resource across diverse fields. Long-term satellite data records with integrity and consistency, such as the Landsat series, provide indispensable data for many applications. However, the malfunction of the Scan Line Corrector (SLC) on the Landsat 7 satellite in 2003 resulted in stripping in subsequent images, compromising the temporal consistency and data quality of Landsat time-series data. While various methods have been proposed to improve the quality of Landsat 7 SLC-off data, existing gap-filling methods fail to enhance the temporal resolution of reconstructed images, and spatiotemporal fusion methods encounter challenges in managing large-scale datasets. Therefore, we propose a method for reconstructing dense time series from SLC-off data. This method utilizes the Neighborhood Similar Pixel Interpolator to fill in missing values and leverages the time-series information to reconstruct high-resolution images. Taking the blue band as an example, the surface reflectance verification results show that the Mean Absolute Error (MAE) and BIAS reach minimum values of 0.0069 and 0.0014, respectively, with the Correlation Coefficient (CC) and Structural Similarity Index Metric (SSIM) reaching 0.93 and 0.94. The proposed method exhibits advantages in repairing SLC-off data and reconstructing dense time-series data, enabling enhanced remote sensing applications and reliable Earth’s surface reflectance data reconstruction.

To reduce the significant losses caused by slope failures and landslides, it is of great significance to detect and predict these disasters scientifically. This study focused on Huangdao District of Qingdao City in Shandong Province, using the improved Faster R-CNN network to detect slope failures and landslides. This study introduced a multi-scale feature enhancement module into the Faster R-CNN model. The module enhances the network’s perception of different scales of slope failures and landslides by deeply fusing high-resolution weak semantic features with low-resolution strong semantic features. Our experiments show that the improved Faster R-CNN model outperformed the traditional version, and that ResNet50 performed better than VGG16 with an AP value of 90.68%, F1 value of 0.94, recall value of 90.68%, and precision value of 98.17%. While the targets predicted by VGG16 were more dispersed and the false detection rate was higher than that of ResNet50, VGG16 was shown to have an advantage in predicting small-scale slope failures and landslides. The trained Faster R-CNN network model detected geological hazards of slope failure and landslide in Huangdao District, missing only two landslides, thereby demonstrating high detection accuracy. This method can provide an effective technical means for slope failures and landslides target detection and has practical implications.

Abstract Traditional fingerprint localization algorithms need help with low localization accuracy, large data volumes, and device dependence. This paper proposes a LoRa-based improved fingerprint localization algorithm-particle swarm optimization-random forest-fingerprint localization for indoor localization. The first improvement step involves creating a new exceptional fingerprint value (referred to as RSSI-RANGE) by adding the Time of Flight ranging value (referred to as RANGE) to the Received Signal Strength Indication (RSSI) value and weighting them together. The second improvement step involves preprocessing the fingerprint data to eliminate gross errors using Gaussian and median filtering. After noise reduction, the particle swarm optimization algorithm is used to optimize the hyper parameters of the random forest algorithm, and the best RSSI-RANGE value is obtained using the random forest algorithm. The Kriging method is then used for interpolation to establish an offline fingerprint database, and the final online recognition and localization are performed. Experimental results demonstrate that the first improvement step improves localization accuracy by 53–57% in different experimental scenarios, while the second improves localization accuracy by 25–31%. When both steps are combined, the localization accuracy is improved by 58–63%. The effectiveness of this method is demonstrated through experiments.

Abstract Background The discovery of immune checkpoint inhibitors (ICIs) is a breakthrough in the field of cancer therapy. However, ICIs may cause immune‐related adverse reactions, including checkpoint inhibitor‐related pneumonitis (CIP). The aim of this study was to investigate cytokines in bronchoalveolar lavage fluid (BALF) of patients with CIP compared with patients with pulmonary infection and patients with cancer. Methods We retrospectively analyzed 34 cytokine levels and T cell subsets in BALF supernatant samples from ICI‐treated patients with CIP (n = 13), pulmonary infection (n = 10), and progressive cancer (n = 12). Cytokine levels and T cell subsets were compared among the three groups of patients. Results We observed significantly higher levels of IFN‐γ‐induced protein 10(IP‐10) (p = 0.002), and percentage of CD3 + CD8 + T cells (p = 0.020) in BALF of patients with CIP compared with the other two groups. However, we found significantly lower levels of interleukin‐21 (p = 0.008) in BALF of patients with progressive disease compared with the other two groups. Conclusions Cytokine profile and character of cell subsets in BALF was helpful for the differential diagnosis of CIP. IP‐10 may play an important role in pathophysiology for CIP and also be a potential therapeutic target.

Three A10 absolute gravimeters (AGs) were first acquired in China by the Hubei Earthquake Agency under the Belt and Road Seismic Monitoring Network Project. Although AG measuring and testing technique is not new, the purchase and simultaneous testing of 3 A10 absolute gravimeters is unprecedented in China. This study conducted the first acceptance testing of the AGs at 3 locations (the Jiufengshan Gravity Observation Station, the Global Navigation Satellite System Observation Station in Wuhan, and the Jiugongshan Observation Station in Xianning). The results were compared using a method based on expert validation, and the acceptance testing scheme was formulated by referring to the Technical Regulations for Tectonic Environment Monitoring Networks in China and Specifications for Gravimetry Control. Based on the repeatability, precision, and consistency of the measured g values, the results from each instrument were evaluated using the air pressure precision test. Comparing the instrument reference values, the final test results can identify the indicator parameters for 3 A10 AGs, the effects of the surrounding environment, and the related parameters on measurement precision. The precision of A10-059, A10-058, and A10-057 exceeded 0.78 μGal, 0.79 μGal, and 0.42 μGal, respectively. This testing scheme can be used as a reference for conducting acceptance testing of AGs in the future and obtaining absolute gravimetric measurements.

Objective To form the sampling data interval by retrospectively analyzing the sampling data of quality monitoring of fresh frozen plasma, cryoprecipitates and leukocyte-free platelets in all blood stations in Hebei Province during the past 7 years. Methods The data of blood component sampling from 12 blood station quality control laboratories in Hebei from 2015 to 2021 were collected. The FⅧ content and plasma protein content of fresh frozen plasma, the FⅧ content and fibrinogen content of cryoprecipitates, and the leukocyte residuals, red blood cell mixed and platelet content of leukocyte-free platelets were taken as the objects for discrete point and fitted curve analysis. Results The FⅧ level of fresh frozen plasma: (1.36±1.1) IU/mL, 5 blood stations showed a representative overall high or low or fluctuated characteristics; Fresh frozen plasm-plasma protein items: overall mean ±SD: (61.13±16.7) g/L, four blood stations showed scattered distribution or continuous high value scattered points; Cryoprecipitates FⅧ: the overall mean ±SD: (134.25±58.7) IU/mL, four blood stations showed the differentiation characteristics of continuous high, low or stable in the middle; Cryoprecipitates-fibrinogen items: the overall mean ±SD: (215.27±83.5) mg, five blood stations showed the overall high or low and fluctuated. Leukocyte-free apheresis platelet-to-leukocyte residual items: overall mean ±SD: 0.37±0.96 (×106/bag), two blood stations showed a relatively high representative overall characteristics, and the rest were concentrated between 0 and 1; The total mean ±SD of platelet-to-red blood cell mixture without leukocyte was 2.45±2.82 (×109/bag), with obvious segmented concentrated distribution, and scattered distribution in 3 blood centers. Platelet content: the overall mean ±SD was 3.14±1.55 (×1011/bag), many deviations were noticed in 3 blood stations, and 1 blood station showed representative overall high characteristics. Conclusion This analysis shows that the distribution status of each blood station in different items is similar. The distribution status of discrete point groups and the change trend of the concentrated part of the fitting curve show that there are some differences in the monitoring level between the quality control laboratories of each blood station, and the update of detection instruments and reagents and the selection of detection methods greatly affect the test results. The summary data presented the index interval framework formed in the past 7 years, which helped to understand the difference between the results of each laboratory, correct the accuracy of the test results, better play the guiding role of quality monitoring in the blood preparation process, and continue to enhance the standardization of the whole process of blood collection and supply in the province.

Objects: This research was performed based on taking one Bacillus amyloliquefaciens BA-DES4 producing the cellulose as the material, and the enzymatic property of its cellulose was researched upon the purification. Methods: This research applied the ammonium sulfate fractional precipitation and SephadexG-75 gel filtration chromatography method to carry out the separation and purification to the cellulose it produced. Determined its molecular weight by means of polyacrylamide gel electrophoresis (SDS-PAGE), and carried out the research of enzymatic property to enzyme solution of cellulase upon purification. Results: The cellulase system components could be obtained in zymotic fluid upon separation and purification (endoglucanase), and its specific activity was 51.08 U/mg through carrying out the measurement of enzyme activity to purified electrophoresis endoglucanase. It was identified that its molecular weight was 22.4 kDa. The primary research of enzymatic property indicated that the optimal reaction temperature and optimal pH for this enzyme were 65 ℃ and 6.0 respectively with a high stability when pH was 5.0～7.0 and temperature was 55～65 ℃. The activation of Mn2+ to cellulase activities was significant with the highest inhibiting effect of Cu2+. Conclusion: This bacterial strain can be used as the potential strain for producing the endoglucanase with the enzymatic activities of endoglucanase can be promoted under the effects of Mn2+, Fe2+. Meanwhile, it can work under the high temperature and acid environment along with participating in the cellulose of high effective degradation and high-temperature acid environment so as to improve the production efficiency. The glucose to be decomposed can be used for fermentation with a potential of producing the fermented wine by applying the high-temperature Daqu, which has laid a foundation for further studying this cellulase.

Abstract Background The c‐MET protein, encoded by the mesenchymal‐epithelial transition factor (MET) gene, can regulate cell proliferation, migration and invasion. Studies have shown that it is one of the essential driver genes for non‐small cell lung cancer (NSCLC). Currently, several clinical studies have carried out objective assessments on the efficacy and safety of different types of MET tyrosine kinase inhibitors (TKIs). However, direct cross‐sectional comparisons between different agents are still not available. Methods Our study was a single‐center retrospective clinical study, which collected the data from MET positive NSCLC patients treated with MET TKIs at the Lung Cancer Center of Peking Union Medical College Hospital. We explored the efficacy and safety of crizotinib versus savolitinib in patients with METex14 skipping and MET amplification, separately. Results Patients with METex14 skipping (median PFS = 10.7 months) had a better clinical response to MET TKIs than MET amplification patients (median PFS = 4.1 months). In the METex14 skipping subgroup, savolitinib did not show better survival benefit with significance than crizotinib (p > 0.05). In the MET amplification subgroup, savolitinib (median PFS = 7.1 months) demonstrated a better progression‐free survival benefit than crizotinib (median PFS = 1.4 months), p = 0.05. The most common adverse effects of both MET TKIs were peripheral edema (41.2%), gastrointestinal reactions (23.5%), and liver injury (14.7%). The incidence rate of peripheral edema was higher in savolitinib than crizotinib. Conclusion In METex14 skipping NSCLC patients, the efficacy of savolitinib and crizotinib did not show significant difference. In MET amplification patients, savolitinib showed better efficacy than crizotinib.

Abstract A vector bending fiber sensor based on core-by-core inscribed fiber Bragg gratings in a twin-core fiber has been proposed and experimentally demonstrated. An in-fiber integrated vector bending sensor is realized by using the thermal diffusion technique to fabricate the coupler. The characteristics of the coupler fabricated by thermal diffusion are simulated and experimented. By inscribing fiber Bragg gratings with different reflection wavelengths in the two cores of a symmetrical twin-core fiber, the curvature sensitivity can be enhanced by tracking the wavelength difference between the fiber Bragg gratings of the two cores. The measured bending sensitivity of the fiber Bragg grating ranges from −161.6 pm/m−1 to +165.5 pm/m−1. The differential sensitivity of the two cores is twice that of a conventional single grating, and the temperature-induced crosstalk is also reduced. The bending sensor proposed in this paper has the advantages of high integration, enhancing the sensitivity and two-dimensional orientation recognizability, and reducing temperature crosstalk, which can be a promising candidate for structural health monitoring or wearable artificial electronics applications.

Involving the effect of atmospheric CO2 fertilization is effective for improving the accuracy of estimating gross primary production (GPP) using light use efficiency (LUE) models. However, the widely used LUE model, the remote sensing-driven Carnegie–Ames–Stanford Approach (CASA) model, scarcely considers the effects of atmospheric CO2 fertilization, which causes GPP estimation uncertainties. Therefore, this study proposed an improved method for estimating GPP by integrating the atmospheric CO2 concentration into the CASA model and generated a long time series GPP dataset with high precision for the Qinghai–Tibet Plateau. The CASA model was improved by considering the impact of atmospheric CO2 on vegetation productivity and discerning variations in CO2 gradients within the canopy and leaves. A 500 m monthly GPP dataset for the Qinghai–Tibet Plateau from 2003 to 2020 was generated. The results showed that the improved GPP estimation model achieved better performances on estimating GPP (R2 = 0.68, RMSE = 406 g C/m2/year) than the original model (R2 = 0.67, RMSE = 499.32 g C/m2/year) and MODIS GPP products (R2 = 0.49, RMSE = 522.56 g C/m2/year). The GPP on the Qinghai–Tibet Plateau increased significantly with the increase in atmospheric CO2 concentration and the gradual accumulation of dry matter. The improved method can also be used for other regions and the generated GPP dataset is valuable for further understanding the ecosystem carbon cycles on the Qinghai–Tibet Plateau.

The detection of Martian polar ice cap change patterns is important for understanding their effects on driving Mars’s global water cycle and for regulating atmospheric circulation. However, current Martian ice cap identification using optical remote sensing data mainly relies on visual interpretation, which makes it difficult to quickly extract ice caps from multiple images and analyze their fine-scale spatiotemporal variation characteristics. Therefore, this study proposes an automatic Martian polar ice cap extraction algorithm for remote sensing data and analyzes the dynamic change characteristics of the Martian North Pole ice cap using time-series data. First, the automatic Martian ice cap segmentation algorithm was developed based on the ice cap features of high reflectance in the blue band and low saturation in the RGB band. Second, the Martian North Pole ice cap was extracted for the three Martian years MY25, 26, and 28 using Mars Orbiter Camera (MOC) Mars Daily Global Maps (MDGMs) data, which had better spatiotemporal continuity to analyze its variation characteristics. Lastly, the spatiotemporal variation characteristics of the ice cap and the driving factors of ice cap ablation were explored for the three aforementioned Martian years. The results indicated that the proposed automatic ice cap extraction algorithm had good performance, and the classification accuracy exceeded 93%. The ice cap ablation boundary retreat rates and spatiotemporal distributions were similar for the three years, with approximately 105 km2 of ice cap ablation for every one degree of areocentric longitude of the Sun (Ls). The main driving factor of ice cap ablation was solar radiation, which was mainly related to Ls. In addition, elevation had a different effect on ice cap ablation at different Ls in the same latitude area near the ablation boundary of the ice cap.

ObjectivesWhile Bayesian networks (BNs) represents a good approach to discussing factors related to many diseases, little attention has been poured into heart attack combined with hypertension (HAH) using BNs. This study aimed to explore the complex network relationships between HAH and its related factors, and to achieve the Bayesian reasoning for HAH, thereby, offering a scientific reference for the prevention and treatment of HAH.MethodsThe data was downloaded from the Online Open Database of CHARLS 2018, a population-based longitudinal survey. In this study, we included 16 variables from data on demographic background, health status and functioning, and lifestyle. First, Elastic Net was first used to make a feature selection for highly-related variables for HAH, which were then included into BN model construction. The structural learning of BNs was achieved using Tabu algorithm and the parameter learning was conducted using maximum likelihood estimation.ResultsAmong 19,752 individuals (9,313 men and 10,439 women) aged 64.73 ± 10.32 years, Among 19,752 individuals (9,313 men and 10,439 women), there are 8,370 ones without HAH (42.4%) and 11,382 ones with HAH (57.6%). What’s more, after feature selection using Elastic Net, Physical activity, Residence, Internet access, Asset, Marital status, Sleep duration, Social activity, Educational levels, Alcohol consumption, Nap, BADL, IADL, Self report on health, and age were included into BN model establishment. BNs were constructed with 15 nodes and 25 directed edges. The results showed that age, sleep duration, physical activity and self-report on health are directly associated with HAH. Besides, educational levels and IADL could indirectly connect to HAH through physical activity; IADL and BADL could indirectly connect to HAH through Self report on health.ConclusionBNs could graphically reveal the complex network relationship between HAH and its related factors. Besides, BNs allows for risk reasoning for HAH through Bayesian reasoning, which is more consistent with clinical practice and thus holds some application prospects.

Abstract Background Immune checkpoint inhibitors (ICIs) have become one important therapeutic strategy for advanced non‐small‐cell lung cancer (NSCLC). It remains imperative to identify reliable and convenient biomarkers to predict both the efficacy and toxicity of immunotherapy, and tumor‐associated autoantibodies (TAAbs) are recognized as one of the promising candidates for this. Patients and Methods This study enrolled 97 advanced NSCLC patients with ICI‐based immunotherapy treatment, who were divided into a training cohort (n = 48) and a validation cohort (n = 49), and measured for the serum level of 35 TAAbs. According to the statistical association between the serum positivity and clinical outcome of each TAAb in the training cohort, a TAAb panel was developed to predict the progression‐free survival (PFS), and further examined in the validation cohort and in different subgroups. Similarly, another TAAb panel was derived to predict the occurrence of immune‐related adverse events (irAEs). Results In the training cohort, a 7‐TAAb panel composed of p53, CAGE, MAGEA4, GAGE7, UTP14A, IMP2, and PSMC1 TAAbs was derived to predict PFS (median PFS [mPFS] 9.9 vs. 4.3 months, p = 0.043). The statistical association between the panel positivity and longer PFS was confirmed in the validation cohort (mPFS 11.1 vs. 4.8 months, p = 0.015) and in different subgroups of patients. Moreover, another 4‐TAAb panel of BRCA2, MAGEA4, ZNF768, and PARP TAAbs was developed to predict the occurrence of irAEs, showing higher risk in panel‐positive patients (71.43% vs. 28.91%, p = 0.0046). Conclusions Collectively, our study developed and validated two TAAb panels as valuable prognostic biomarkers for immunotherapy.

Topologically associated domains (TADs) play a pivotal role in disease detection. This study introduces a novel TADs recognition approach named TOAST, leveraging graph auto-encoders and clustering techniques. TOAST conceptualizes each genomic bin as a node of a graph and employs the Hi-C contact matrix as the graph's adjacency matrix. By employing graph auto-encoders, TOAST generates informative embeddings as features. Subsequently, the unsupervised clustering algorithm HDBSCAN is utilized to assign labels to each genomic bin, facilitating the identification of contiguous regions with the same label as TADs. Our experimental analysis of several simulated Hi-C data sets shows that TOAST can quickly and accurately identify TADs from different types of simulated Hi-C contact matrices, outperforming existing algorithms. We also determined the anchoring ratio of TAD boundaries by analyzing different TAD recognition algorithms, and obtained an average ratio of anchoring CTCF, SMC3, RAD21, POLR2A, H3K36me3, H3K9me3, H3K4me3, H3K4me1, Enhancer, and Promoters of 0.66, 0.47, 0.54, 0.27, 0.24, 0.12, 0.32, 0.41, 0.26, and 0.13, respectively. In conclusion, TOAST is a method that can quickly identify TAD boundary parameters that are easy to understand and have important biological significance. The TOAST web server can be accessed via http://223.223.185.189:4005/. The code of TOAST is available online at https://github.com/ghaiyan/TOAST.