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The release source term of radioactivity becomes a critical foundation for emergency response and accident consequence assessment after a nuclear accident Rapidly and accurately inverting the source term remains an urgent scientific challenge. Today source term inversion based on meteorological data and gamma dose rate measurements is a common method. But gamma dose rate actually includes all nuclides information, and the composition of radioactive nuclides is generally uncertain. This paper introduces a novel nuclear accident source term inversion model, which is Improve Snow Ablation Optimizer-Sensitivity Analysis Pruning Extreme Learning Machine (ISAO-SAELM) model. The model inverts the release rates of 11 radioactive nuclides (I-131, Xe-133, Cs-137, Kr-88, Sr-91, Te-132, Mo-99, Ba-140, La-140, Ce-144, Sb-129). It does not require the use of the physical field of the reactor to obtain prior information and establish a dispersion model. And the robustness is validated through noise analysis test. The mean absolute errors of the release rates of 11 nuclides are 15.52 %, 15.28 %, 15.70 %, 14.99 %, 14.85 %, 15.61 %, 15.96 %, 15.42 %, 15.84 %, 15.13 %, 17.72 %, which show the significant superiority of ISAO-SAELM. ISAO-SAELM model not only achieves notable advancements in accuracy but also receives validation in terms of practicality and feasibility.

Recent advances in virtual reality technologies accelerate the immersive interaction between human and augmented 3D virtual worlds. Here, the authors discuss olfactory feedback technologies that facilitate interaction with real and virtual objects and the evolution of wearable devices for immersive VR/AR applications.

Abstract Rice false smut caused by Ustilaginoidea virens has become one of the most important diseases of rice. Mycoviruses are viruses that can infect fungi with the potential to control fungal diseases. However, little is known about the biocontrol role of hypoviruses in U. virens. In this study, we revealed that the hypovirulence-associated U. virens strain Uv325 was co-infected by four novel mycoviruses from three lineages, designated Ustilaginoidea virens RNA virus 16 (UvRV16), Ustilaginoidea virens botourmiavirus virus 8 (UvBV8), Ustilaginoidea virens botourmiavirus virus 9 (UvBV9), and Ustilaginoidea virens narnavirus virus 13 (UvNV13), respectively. The U. virens strain co-infected by four mycoviruses showed slower growth rates, reduced conidial yield, and attenuated pigmentation. We demonstrated that UvRV16 was not only the major factor responsible for the hypovirulent phenotype in U. vriens, but also able to prevent U. virens to accumulate more mycotoxin, thereby weakening the inhibitory effects on rice seed germination and seedling growth. Additionally, we indicated that UvRV16 can disrupt the antiviral response of U. virens by suppressing the transcriptional expression of multiple genes involved in autophagy and RNA silencing. In conclusion, our study provided new insights into the biological control of rice false smut.

Abstract Background CFAP65 (cilia and flagella associated protein 65) is a fundamental protein in the development and formation of ciliated flagella, but few studies have focused on its role in cancer. This study aimed to investigate the prognostic significance of CFAP65 in colon cancer. Methods The functionally enriched genes related to CFAP65 were analyzed through the Gene Ontology (GO) database. Subsequently, CFAP65 expression levels in colon cancer were evaluated by reverse transcription and quantitative polymerase chain reaction (RT-qPCR) and immunoblotting in 20 pairs of frozen samples, including tumors and their matched paratumor tissue. Furthermore, protein expression of CFAP65 in 189 colon cancer patients were assessed via immunohistochemical staining. The correlations between CFAP65 expression and clinical features as well as long-term survival were statistically analyzed. Results CFAP65-related genes are significantly enriched on cellular processes of cell motility, ion channels, and GTPase-associated signaling. The expression of CFAP65 was significantly higher in colon cancer tissue compared to paratumor tissue. The proportion of high expression and low expression of CFAP65 in the clinical samples of colon cancer were 61.9% and 38.1%, respectively, and its expression level was not associated with the clinical parameters including gender, age, tumor location, histological differentiation, tumor stage, vascular invasion and mismatch repair deficiency. The five-year disease-free survival rate of the patients with CFAP65 low expression tumors was significantly lower than that those with high expression tumors (56.9% vs. 72.6%, P = 0.03), but the overall survival rate has no significant difference (69% vs. 78.6%, P = 0.171). The cox hazard regression analysis model showed that CFAP65 expression, tumor stage and tumor location were independent prognostic factors. Conclusions In conclusion, we demonstrate CFAP65 is a potential predictive marker for tumor progression in colon cancer.

The forming, microstructure, mechanical properties and corrosion resistance of three kinds of vacuum electron beam welded joints with different shapes were studied. The results of X-ray inspection of the joints showed that the three shapes of welded joints could meet the requirements of the quality inspection. The results of microstructure characterization show that the worm or bone-like δ-ferrite appears in the upper and lower regions of the funnel-shaped and bell-shaped joints, while only a small amount of δ-ferrite appears in the local regions of the nail-shaped joints. The test results of mechanical properties show that the micro-hardness of the weld center in the three kinds of shaped joints is the lowest. All the specimens fracture in the weld, and the bell-shaped weld presents the highest impact energy and bending load. Funnel-shaped weld shows the worst corrosion resistance due to the element segregation of the weld. Thus, the bell-shaped joint exhibits the best comprehensive performance.

Abstract With the increasing trend of global aging, sarcopenia has become a significant public health issue. Goji berry, also known as “Gou qi zi” in China, is a traditional Chinese herb that can enhance the structure and function of muscles and bones. Otherwise, previous excellent publications illustrated that plant-derived exosome-like nanoparticles can exert good bioactive functions in different aging or disease models. Thus, we issued the hypothesis that Gouqi-derived nanovesicles (GqDNVs) may also have the ability to improve skeletal muscle health, though the effect and its mechanism need to be explored. Hence, we have extracted GqDNVs from fresh berries of Lycium barbarum L. (goji) and found that the contents of GqDNVs are rich in saccharides and lipids. Based on the pathway annotations and predictions in non-targeted metabolome analysis, GqDNVs are tightly associated with the pathways in metabolism. In muscle atrophy model mice, intramuscular injection of GqDNVs improves the cross-sectional area of the quadriceps muscle, grip strength and the AMPK/SIRT1/PGC1α pathway expression. After separately inhibiting AMPK or PGC1α in C2C12 cells with dexamethasone administration, we have found that the activated AMPK plays the chief role in improving cell proliferation induced by GqDNVs. Furthermore, the energy-targeted metabolome analysis in the quadriceps muscle demonstrates that the GqDNVs up-regulate the metabolism of amino sugar and nucleotide sugar, autophagy and oxidative phosphorylation process, which indicates the activation of muscle regeneration. Besides, the Spearman rank analysis shows close associations between the quality and function of skeletal muscle, metabolites and expression levels of AMPK and SIRT1. In this study, we provide a new founding that GqDNVs can improve the quality and function of skeletal muscle accompanying the activated AMPK/SIRT1/PGC1α signaling pathway. Therefore, GqDNVs have the effect of anti-aging skeletal muscle as a potential adjuvant or complementary method or idea in future therapy and research. Graphical Abstract

Abstract Providing additional degrees of freedom to manipulate light, spatiotemporal optical vortex (STOV) beams carrying transverse orbital angular momentum are of fundamental importance for spatiotemporal control of light-matter interactions. Unfortunately, existing methods to generate STOV are plagued by various limitations such as inefficiency, bulkiness, and complexity. Here, we theoretically propose and experimentally demonstrate a microscale singlet platform composed of a slanted nanograting to generate STOV. Leveraging the intrinsic topological singularity induced by C 2 symmetry and z-mirror symmetry breaking of the slanted nanograting, STOV is generated through the Fourier transform of the spiral phase in the momentum-frequency space to the spatiotemporal domain. In experiments, we observe the space-time evolution of STOV carried by femtosecond pulses using a time-resolved interferometry technique and achieve a generation efficiency exceeding 40%. Our work sheds light on a compact and versatile platform for light pulse shaping, and paves the way towards a fully integrated system for spatiotemporal light manipulation.

Abstract Type 1 diabetes (T1D) is a chronic disease characterized by self‐destruction of insulin‐producing pancreatic β cells by cytotoxic T cell activity. However, the pathogenic mechanism of T cell infiltration remains obscure. Recently, tissue‐resident memory T (TRM) cells have been shown to contribute to cytotoxic T cell recruitment. TRM cells are found present in human pancreas and are suggested to modulate immune homeostasis. Here, the role of TRM cells in the development of T1D is investigated. The presence of TRM cells in pancreatic islets is observed in non‐obese diabetic (NOD) mice before T1D onset. Mechanistically, elevated fatty acid‐binding protein 4 (FABP4) potentiates the survival and alarming function of TRM cells by promoting fatty acid utilization and C‐X‐C motif chemokine 10 (CXCL10) secretion, respectively. In NOD mice, genetic deletion of FABP4 or depletion of TRM cells using CD69 neutralizing antibodies resulted in a similar reduction of pancreatic cytotoxic T cell recruitment, a delay in diabetic incidence, and a suppression of CXCL10 production. Thus, targeting FABP4 may represent a promising therapeutic strategy for T1D.

As the COVID-19 pandemic has progressed, increasing evidences suggest that the gut microbiota may play a crucial role in the effectiveness of SARS-CoV-2 vaccine. Thus, this study was aimed at investigating the influence of SARS-CoV-2 vaccine on the gut microbiota and short-chain fatty acids (SCFAs) of organisms exposed to environmental contaminants, i.e., plasticizers: phthalate esters. We found that in mice, exposure to dioctyl terephthalate (DOTP) and bis −2-ethylhexyl phthalate (DEHP) decreased the blood glucose level and white fat weight, induced inflammatory responses, caused damage to liver and intestinal tissues, and disrupted the gut microbiota composition and SCFAs metabolism. Specifically, the Bacteroidetes phylum was positively correlated with BBIBP-CorV vaccine, while acetic acid was negatively associated with the vaccine. Interestingly, the BBIBP-CorV vaccine somewhat alleviated tissue inflammation and reduced the contents of acetic acid and propionic acid in mice exposed to DEHP and DOTP. These findings were confirmed by a fecal microbiota transplantation assay. Overall, this study revealed that exposure to DEHP and DOTP adversely affects the gut microbiota and SCFAs, while the BBIBP-CorV vaccine can protect mice against these effects. This work highlighted the relationship between BBIBP-CorV vaccination, gut microbiome composition, and responses to plasticizers, which may facilitate the development and risk assessment of SARS-CoV-2 vaccines and environmental contaminants on microbiota health.

We propose and demonstrate a high-quality ($Q$) bound state in the continuum (BIC) cavity based on a hexagonal lattice photonic crystal (PhC) slab with $C_{6}$ symmetry, which carries high-order topological charges of $q=-\text{2}$. By combining the lateral photonic bandgap and high-order topological charge, we realize a $Q$ of $\text{7}\times \text{10}^{4}$ in the experiment which is several times higher than traditional BIC cavities with topological charge of $q=\pm\text{1}$. Specifically, we found that the boundary orientation would significantly influence the performance of lateral confinement. In contrast to an X direction boundary, a gradient J direction boundary can best alleviate the shortcoming of lateral leakage due to incomplete bandgap and the scattering because of momentum mismatching, and relax the strict constraints on wafer thickness. Our findings could broaden the application of BIC cavities from bio-sensing to on-chip telecommunication.

With the expanding scope of human activities in marine environments, the efficient detection and tracking of mobile targets on the ocean's surface have become increasingly crucial. Synthetic aperture radar (SAR) constellation can obtain ground observation data based on user requests and subject to visibility conditions. Now it is an indispensable tool in sea surface moving target search tasks. Satellite constellation resources are scarce and limited, and user demands are diverse. How to rationally dispatch satellite constellation resources to meet user needs to the maximum extent and improve the application efficiency of satellite resources is an urgent scientific problem that needs to be solved. This article mainly expounds two respects of work. First, modeling SAR constellation scheduling problem for sea surface moving target search tasks to establish the objective function. Second, a novel multistrategy discrete constrained differential evolution algorithm denoted as MSDCDE is proposed in the article. The proposed MSDCDE algorithm integrates cross strategy based on discrete variables, constraint handling techniques, population restart strategy, and left-shift local strategy, which can effectively avoid falling into local optimality, thereby achieving global optimality and improving search and rescue performances. Six sets of experiments, totaling 215 runs, have been conducted to validate the effectiveness of the proposed resolution process framework and the MSDCDE algorithm. The proposed method demonstrated an over 48.98% performance improvement compared with some state-of-the-art algorithms and significantly reduced task completion time.

The fully supervised deep convolutional neural network (CNN) cannot detect the discriminant local information that is responsible for spatial transformations in high-resolution remote sensing images. To address the various types and missing labels of natural disasters, a new deep multi-instance convolutional neural network (DMCNN) model for disaster classification in high-resolution remote sensing image is presented in this article. Specifically, based on sample enhancement and atrous spatial pyramid pooling, we first extract and integrate the features via the CNN structure to obtain the instance feature of bags in the image. Besides, introducing a prototype learning layer with distance measure, the instance features extracted from pretrained CNN are mapped into a series of prototype instance features with bag-level. Subsequently, all instance features from prototype and bag take part in disaster detection and image classification. Finally, we conduct extensive experiments on xBD dataset and discussions from qualitative and quantitative aspects. Experimental results show that the proposed DMCNN model achieves better classification accuracy of natural disaster from high-resolution remote sensing images compared to traditional CNNs, and effectively improves the disaster classification performance with weakly supervised from high-resolution remote sensing images.

Remote sensing image semantic segmentation methods have become the main approach for extracting cropland information. However, in the mountainous regions of southwestern China, croplands exhibit narrow and fragmented shapes, as well as complex planting patterns, making it difficult for traditional semantic segmentation methods to accurately delineate fine-grained cropland boundaries. To address these challenges, a multiattention Transformer network named MATNet is proposed in this paper, for fine-grained extraction of cropland at the parcel level in complex scenes. MATNet built upon the fusion of CNN encoder and Transformer decoder. In the encoder, spatial and channel reconstruction units are introduced, reducing information redundancy in the convolutional layers. The Transformer decoder incorporates multiple attention mechanisms, this design feature enhances the attention window's perception of local content and improves the model's ability to extract features from fine-grained cropland parcels through optimized computationnal al location. Taking the experimental results of the Dali cropland dataset as an illustration, MATNet achieved the highest values across five evaluation metrics, including mIoU. Specifically, the Recall, F1, and mIoU scores were 94.68%, 94.69%, and 89.92%, respectively. Compared with six other advanced models, MATNet consistently performed best in terms of extracting fine-grained cropland parcels.

BackgroundAs an important platform for researchers to present their academic findings, medical journals have a close relationship between their evaluation orientation and the value orientation of their published research results. However, the differences between the academic impact and level of disruptive innovation of medical journals have not been examined by any study yet. ObjectiveThis study aims to compare the relationships and differences between the academic impact, disruptive innovation levels, and peer review results of medical journals and published research papers. We also analyzed the similarities and differences in the impact evaluations, disruptive innovations, and peer reviews for different types of medical research papers and the underlying reasons. MethodsThe general and internal medicine Science Citation Index Expanded (SCIE) journals in 2018 were chosen as the study object to explore the differences in the academic impact and level of disruptive innovation of medical journals based on the OpenCitations Index of PubMed open PMID-to-PMID citations (POCI) and H1Connect databases, respectively, and we compared them with the results of peer review. ResultsFirst, the correlation coefficients of the Journal Disruption Index (JDI) with the Journal Cumulative Citation for 5 years (JCC5), Journal Impact Factor (JIF), and Journal Citation Indicator (JCI) were 0.677, 0.585, and 0.621, respectively. The correlation coefficient of the absolute disruption index (Dz) with the Cumulative Citation for 5 years (CC5) was 0.635. However, the average difference in the disruptive innovation and academic influence rankings of journals reached 20 places (about 17.5%). The average difference in the disruptive innovation and influence rankings of research papers reached about 2700 places (about 17.7%). The differences reflect the essential difference between the two evaluation systems. Second, the top 7 journals selected based on JDI, JCC5, JIF, and JCI were the same, and all of them were H-journals. Although 8 (8/15, 53%), 96 (96/150, 64%), and 880 (880/1500, 58.67%) of the top 0.1%, top 1%, and top 10% papers selected based on Dz and CC5, respectively, were the same. Third, research papers with the “changes clinical practice” tag showed only moderate innovation (4.96) and impact (241.67) levels but had high levels of peer-reviewed recognition (6.00) and attention (2.83). ConclusionsThe results of the study show that research evaluation based on innovative indicators is detached from the traditional impact evaluation system. The 3 evaluation systems (impact evaluation, disruptive innovation evaluation, and peer review) only have high consistency for authoritative journals and top papers. Neither a single impact indicator nor an innovative indicator can directly reflect the impact of medical research for clinical practice. How to establish an integrated, comprehensive, scientific, and reasonable journal evaluation system to improve the existing evaluation system of medical journals still needs further research.

Abstract Sedentary, inadequate sleep and exercise can affect human health. Artificial intelligence (AI) and Internet of Things (IoT) create the Artificial Intelligence of Things (AIoT), providing the possibility to solve these problems. This paper presents a novel approach to monitor various human behaviors for AIoT‐based health management using triboelectric nanogenerator (TENG) sensors. The insole with solely one TENG sensor, creating a most simplified system that utilizes machine learning (ML) for personalized motion monitoring, encompassing identity recognition and gait classification. A cushion with 12 TENG sensors achieves real‐time identity and sitting posture recognition with accuracy rates of 98.86% and 98.40%, respectively, effectively correcting sedentary behavior. Similarly, a smart pillow, equipped with 15 sensory channels, detects head movements during sleep, identifying 8 sleep patterns with 96.25% accuracy. Ultimately, constructing an AIoT‐based health management system to analyze these data, displaying health status through human‐machine interfaces, offers the potential to help individuals maintain good health.

NaV1.4 is a voltage-gated sodium channel subtype that is predominantly expressed in skeletal muscle cells. It is essential for producing action potentials and stimulating muscle contraction, and mutations in NaV1.4 can cause various muscle disorders. The discovery of the cryo-EM structure of NaV1.4 in complex with β1 has opened new possibilities for designing drugs and toxins that target NaV1.4. In this review, we summarize the current understanding of channelopathies, the binding sites and functions of chemicals including medicine and toxins that interact with NaV1.4. These substances could be considered novel candidate compounds or tools to develop more potent and selective drugs targeting NaV1.4. Therefore, studying NaV1.4 pharmacology is both theoretically and practically meaningful.

Machine‐learning‐enhanced nanosensors are rapidly emerging as a promising solution in the field of sensor technology, as traditional sensors encounter limitations of data analysis in their development. Since the inception of machine‐learning algorithms being applied to enhance nanosensors, they have gained significant attention due to their adaptive and predictive capabilities, which promise to dramatically improve efficiency in data collection and processing applications. Herein, a comprehensive overview of technological innovation is provided by reviewing the latest developments in cloud computing, edge computing, and the burgeoning realm of neuromorphic computing. Cloud computing has emerged as a powerhouse, harnessing formidable computational capabilities to process vast volumes of high‐dimensional data. Then, the research directions for various applications of these cloud artificial intelligence (AI)‐enhanced nanosensors are outlined. Moreover, the integration of AI and nanosensor technology into chip‐level edge computing, although promising, still faces challenges such as energy‐efficient hardware development, algorithm optimization, and scalability for mass production. Finally, a forward‐looking perspective on the future of machine‐learning‐enhanced nanosensors is provided, delineating the challenges and opportunities for further research and innovation in this exciting field.

Abstract Recently developed enzymes for the depolymerization of polyethylene terephthalate (PET) such as FAST-PETase and LCC-ICCG are inhibited by the intermediate PET product mono(2-hydroxyethyl) terephthalate (MHET). Consequently, the conversion of PET enzymatically into its constituent monomers terephthalic acid (TPA) and ethylene glycol (EG) is inefficient. In this study, a protein scaffold (1TQH) corresponding to a thermophilic carboxylesterase (Est30) was selected from the structural database and redesigned in silico. Among designs, a double variant KL-MHETase (I171K/G130L) with a similar protein melting temperature (67.58 °C) to that of the PET hydrolase FAST-PETase (67.80 °C) exhibited a 67-fold higher activity for MHET hydrolysis than FAST-PETase. A fused dual enzyme system comprising KL-MHETase and FAST-PETase exhibited a 2.6-fold faster PET depolymerization rate than FAST-PETase alone. Synergy increased the yield of TPA by 1.64 fold, and its purity in the released aromatic products reached 99.5%. In large reaction systems with 100 g/L substrate concentrations, the dual enzyme system KL36F achieved over 90% PET depolymerization into monomers, demonstrating its potential applicability in the industrial recycling of PET plastics. Therefore, a dual enzyme system can greatly reduce the reaction and separation cost for sustainable enzymatic PET recycling.

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.

Near-space high-altitude balloon-based platforms have a series of advantages and provide superior conditions for optical observation. In order to ensure the stability of the optical axis of the optical detection load and stable tracking of the target, a near-space high-altitude balloon-based high-precision pointing and tracking system was designed, which can compensate for changes in the pitch angle and azimuth angle of the platform during flight. The system includes a primary platform stable pointing system and a secondary precise tracking system. In the finished flight experiment, the primary platform pointing system and secondary precise tracking system on the balloon-based observation platform worked normally, providing a guarantee for the coronagraph’s stable tracking and detection of the sun. The primary platform pointing system can realize ±1° pointing accuracy, and the simulated accuracy of the secondary precise tracking system is 4″, which guaranteed that the coronagraph obtained more than 20,000 images. In subsequent works, we will upgrade and optimize the whole system and conduct our next flight experiment in the future.

Introduction Haemodialysis is the most common treatment option for patients with life-sustaining end-stage kidney disease (ESKD). In recent years, haemodiafiltration or haemofiltration has been widely used in patients with ESKD, and there are still conflicting findings as to whether both are superior to traditional haemodialysis. This systematic review and meta-analysis were designed to determine whether haemodiafiltration or haemofiltration is more effective than haemodialysis in reducing all-cause mortality risk in patients with ESKD.Methods and analysis We will perform a systematic PubMed, Embase, Cochrane Central Register of Controlled Trials (CENTRAL) in The Cochrane Library and Scopus search, including studies published before September 2023. Randomised controlled trials will be included exploring the effects of haemodiafiltration or haemofiltration compared with haemodialysis on prognosis in patients with ESKD. Outcomes of interest include all-cause mortality, cardiovascular events, dialysis adequacy and adverse effects. The Cochrane Collaboration tools (ROB-2) will assess the bias risk. Available data will be used to calculate effect sizes. Heterogeneity between studies will be evaluated with I2. The trial sequential analysis will be used to eliminate false-positive results. The certainty of the evidence will be assessed using Grading of Recommendations, Assessment, Development and Evaluation criteria.Ethics and dissemination This systematic review and meta-analysis was deemed exempt from ethics review. Results will be disseminated through publication in peer-reviewed journals and research conferences.PROSPERO registration number CRD42023464509.

Cropland is the foundation of agriculture and the cornerstone of ensuring national food security. Ongoing advancement of China’s urbanization and social economy results in a tendency toward ‘non-agriculturalization’ of cropland in pursuit of economic benefits, which greatly threatens food security. Therefore, long-term monitoring of cropland is urgently needed. Based on the Google Earth Engine (GEE) platform, this paper uses the Landsat series and Sentinel-2 multisource images from 1990–2020 to dynamically monitor cropland in Yunnan Province. First, a multiresolution collaborative sample selection method was proposed and sample labels with resolutions better than 30 m from 1990 to 2015 were obtained. An ‘impervious surface-spectrum-texture-geometry’ (ISTG) feature system was constructed and the Jeffries-Matusita (JM) distance method was used for feature optimization. Finally, the proposed collaborative classification system method of land classification and binary classification was used to extract cropland in Yunnan Province. The results show that: (1) The accuracy of cropland extraction was highly reliable, with an overall accuracy (OA) wall-to-wall ranging from 93.15–98.14% and a kappa coefficient (Kappa) ranging from 86.29–96.26%. (2) The change trend of the cropland area in Yunnan Province from 1990 to 2020 exhibited certain fluctuations, but overall decreased annually. (3) The province’s average annual cropland area is 567.71 × 103 hectares. The proposed method has great potential in large-scale cropland extraction.

Abstract Purpose Several studies have demonstrated the effectiveness of anti‐angiogenic drugs in combination with immune checkpoint inhibitors (ICIs) in patients with microsatellite stable (MSS) or mismatch repair proficient (pMMR) metastatic colorectal cancer (mCRC). However, whether combination radiotherapy (RT) can further improve the prognosis of mCRC patients after second‐line treatment remains to be explored. Methods Retrospective analysis of data from mCRC patients who received anti‐angiogenic targeted therapy (TT) and immunotherapy (IT) with or without RT after the failure of standard therapy. Progression‐free survival (PFS), overall survival (OS), objective response rate (ORR), disease control rate (DCR), and safety were evaluated. Results A total of 82 patients who received TT + IT were analyzed. For RT group (n = 42) versus NRT group (n = 40), ORR was 21.4% (9/42) versus 5.0% (2/40); DCR was 83.8% (35/42) versus 65.0% (26/40). Compared with NRT group, RT improved PFS (median: 5.0 vs. 3.6 months; p = 0.04) and OS (median: 15.2 vs. 7.2 months; p = 0.01). In addition, in the population receiving RT, the PFS of RT sequential/simultaneous TT + IT was superior to TT + IT sequential RT (median: 7.1 vs. 6.2 vs. 3.5 months, p = 0.004). Multivariate analysis suggested RT was an independent prognostic factor for PFS and OS. No treatment‐related deaths were reported. Conclusions Compared with TT + IT, RT combined with TT + IT improved survival outcomes in MSS/pMMR mCRC patients, with manageable toxicity. RT sequential/simultaneous TT + IT treatment is expected to be the optimal strategy for MSS/PMMR mCRC.

BackgroundFreezing of gait (FOG) is a common and disabling phenomenon in patients with Parkinson’s disease (PD), but effective treatment approach remains inconclusive. Dysfunctional emotional factors play a key role in FOG. Since primary motor cortex (M1) connects with prefrontal areas via the frontal longitudinal system, where are responsible for emotional regulation, we hypothesized M1 may be a potential neuromodulation target for FOG therapy. The purpose of this study is to explore whether high-frequency rTMS over bilateral M1 could relieve FOG and emotional dysregulation in patients with PD.MethodsThis study is a single-center, randomized double-blind clinical trial. Forty-eight patients with PD and FOG from the Affiliated Hospital of Xuzhou Medical University were randomly assigned to receive 10 sessions of either active (N = 24) or sham (N = 24) 10 Hz rTMS over the bilateral M1. Patients were evaluated at baseline (T0), after the last session of treatment (T1) and 30 days after the last session (T2). The primary outcomes were Freezing of Gait Questionnaire (FOGQ) scores, with Timed Up and Go Test (TUG) time, Standing-Start 180° Turn (SS-180) time, SS-180 steps, United Parkinson Disease Rating Scales (UPDRS) III, Hamilton Depression scale (HAMD)-24 and Hamilton Anxiety scale (HAMA)-14 as secondary outcomes.ResultsTwo patients in each group dropped out at T2 and no serious adverse events were reported by any subject. Two-way repeated ANOVAs revealed significant group × time interactions in FOGQ, TUG, SS-180 turn time, SS-180 turning steps, UPDRS III, HAMD-24 and HAMA-14. Post-hoc analyses showed that compared to T0, the active group exhibited remarkable improvements in FOGQ, TUG, SS-180 turn time, SS-180 turning steps, UPDRS III, HAMD-24 and HAMA-14 at T1 and T2. No significant improvement was found in the sham group. The Spearman correlation analysis revealed a significantly positive association between the changes in HAMD-24 and HAMA-14 scores and FOGQ scores at T1.ConclusionHigh-frequency rTMS over bilateral M1 can improve FOG and reduce depression and anxiety in patients with PD.

In recent years, the problem of food adulteration has become increasingly rampant, seriously hindering the development of food production, consumption, and management. The common analytical methods used to determine food authenticity present challenges, such as complicated analysis processes and time-consuming procedures, necessitating the development of rapid, efficient analysis technology for food authentication. Spectroscopic techniques, ambient ionization mass spectrometry (AIMS), electronic sensors, and DNA-based technology have gradually been applied for food authentication due to advantages such as rapid analysis and simple operation. This paper summarizes the current research on rapid food authenticity analysis technology from three perspectives, including breeds or species determination, quality fraud detection, and geographical origin identification, and introduces chemometrics method adapted to rapid analysis techniques. It aims to promote the development of rapid analysis technology in the food authenticity field.

Abstract The fourth industrial revolution has led to the development and application of health monitoring sensors that are characterized by digitalization and intelligence. These sensors have extensive applications in medical care, personal health management, elderly care, sports, and other fields, providing people with more convenient and real-time health services. However, these sensors face limitations such as noise and drift, difficulty in extracting useful information from large amounts of data, and lack of feedback or control signals. The development of artificial intelligence has provided powerful tools and algorithms for data processing and analysis, enabling intelligent health monitoring, and achieving high-precision predictions and decisions. By integrating the Internet of Things, artificial intelligence, and health monitoring sensors, it becomes possible to realize a closed-loop system with the functions of real-time monitoring, data collection, online analysis, diagnosis, and treatment recommendations. This review focuses on the development of healthcare artificial sensors enhanced by intelligent technologies from the aspects of materials, device structure, system integration, and application scenarios. Specifically, this review first introduces the great advances in wearable sensors for monitoring respiration rate, heart rate, pulse, sweat, and tears; implantable sensors for cardiovascular care, nerve signal acquisition, and neurotransmitter monitoring; soft wearable electronics for precise therapy. Then, the recent advances in volatile organic compound detection are highlighted. Next, the current developments of human-machine interfaces, AI-enhanced multimode sensors, and AI-enhanced self-sustainable systems are reviewed. Last, a perspective on future directions for further research development is also provided. In summary, the fusion of artificial intelligence and artificial sensors will provide more intelligent, convenient, and secure services for next-generation healthcare and biomedical applications.

Abstract The prevalence of obesity and atrial fibrillation (AF), which are inextricably linked, is rapidly increasing worldwide. Obesity rates are higher among patients with AF than healthy individuals. Some epidemiological data indicated that obese patients were more likely to develop AF, but others reported no significant correlation. Obesity-related hypertension, diabetes, and obstructive sleep apnea are all associated with AF. Additionally, increased epicardial fat, systemic inflammation, and oxidative stress caused by obesity can induce atrial enlargement, inflammatory activation, local myocardial fibrosis, and electrical conduction abnormalities, all of which led to AF and promoted its persistence. Weight loss reduced the risk and reversed natural progression of AF, which may be due to its anti-fibrosis and inflammation effect. However, fluctuations in weight offset the benefits of weight loss. Therefore, the importance of steady weight loss urges clinicians to incorporate weight management interventions in the treatment of patients with AF. In this review, we discuss the epidemiology of obesity and AF, summarize the mechanisms by which obesity triggers AF, and explain how weight loss improves the prognosis of AF.

This study explored the dynamics of bacterial community composition, response to environmental factors, and co-occurrence network models across different habitats of Wuliangsuhai Lake during the glacial period. Water quality analysis and high-throughput sequencing were performed at 14 monitoring sites within the lake. Additionally, a co-occurrence network between the two bacterial operational taxonomic unit (OTU)-OTUs was established. The results indicated significant differences in water quality indices, namely total nitrogen (TN), chemical oxygen demand (COD), total dissolved solids (TDS), salinity (SAL), chlorophyll-a (Chl.a), and electrical conductivity (EC), between the ice bodies of Wuliangsuhai Lake and subglacial water. Although there were no significant differences in α diversity across various media, substantial differences were observed in β diversity. The VIF and RDA analyses revealed that lake water quality factors significantly affected the microbial community structure and COD and TDS had the highest explanation for the community composition change. Network analysis demonstrated that competition dominated the bacterial community in water bodies with higher complexity and stability and ice body bacteria exhibited more reciprocal relationships and weaker resistance to external environmental disturbances. The co-occurrence network demonstrated a modular structure in the external environment, with g_Flavobacterium, f_Arcobacteraceae, and g_Sphingobacteriaceae being the main keystone species. Investigating the habitat heterogeneity of lake bacterial communities and identifying major groups and key species using molecular ecological network models and their topological effects can provide a theoretical foundation for monitoring and assessing the structural stability of lake ecosystems in cold regions.

An immune checkpoint is a signaling pathway that regulates the recognition of antigens by T-cell receptors (TCRs) during an immune response. These checkpoints play a pivotal role in suppressing excessive immune responses and maintaining immune homeostasis against viral or microbial infections. There are several FDA-approved immune checkpoint inhibitors (ICIs), including ipilimumab, pembrolizumab, and avelumab. These ICIs target cytotoxic T-lymphocyte-associated protein 4 (CTLA-4), programmed cell death protein 1 (PD-1), and programmed death ligand 1 (PD-L1). Furthermore, ongoing efforts are focused on developing new ICIs with emerging potential. In comparison to conventional treatments, ICIs offer the advantages of reduced side effects and durable responses. There is growing interest in the potential of combining different ICIs with chemotherapy, radiation therapy, or targeted therapies. This article comprehensively reviews the classification, mechanism of action, application, and combination strategies of ICIs in various cancers and discusses their current limitations. Our objective is to contribute to the future development of more effective anticancer drugs targeting immune checkpoints.

A phylogenetic tree can reflect the evolutionary relationships between species or gene families, and they play a critical role in modern biological research. In this review, we summarize common methods for constructing phylogenetic trees, including distance methods, maximum parsimony, maximum likelihood, Bayesian inference, and tree-integration methods (supermatrix and supertree). Here we discuss the advantages, shortcomings, and applications of each method and offer relevant codes to construct phylogenetic trees from molecular data using packages and algorithms in R. This review aims to provide comprehensive guidance and reference for researchers seeking to construct phylogenetic trees while also promoting further development and innovation in this field. By offering a clear and concise overview of the different methods available, we hope to enable researchers to select the most appropriate approach for their specific research questions and datasets.

This article proposes a new deadbeat predictive current control (DPCC) method based on a sliding-mode observer (SMO), which is applied in the field of permanent magnet motor control. A novel DPCC control method based on SMO is proposed according to the inherent issues of DPCC, which can effectively suppress internal parameter mismatch disturbances and external disturbances in the current loop. The mathematical model and derivation process of the proposed method are introduced. A simulation model is built and the effectiveness of the proposed method is verified. An experimental platform is built and the superiority of the proposed method is verified based on comparative experiments. Experimental results show that the proposed algorithm has strong robustness to the motor parameter mismatch. Compared with extended state observer (ESO) and adaptive observer (AO), the proposed algorithm has faster response speed and higher steady-state accuracy.

With the development of sustainable cities, densely populated higher education institutions increasingly emphasize the sustainability of campuses and their impact on the environment. However, there is a lack of means to quantify emission reduction measures. This study aims to propose an evaluation framework that can quantify energy conservation and emission reduction measures and incentive policies. To this end, this study adopts a mixed methods approach, using questionnaires to assess the effectiveness of management and communication interventions and the impact of incentives on residents’ willingness to participate in emission reduction efforts. The survey results show that although the support for the intervention measures is slightly higher than the average, specific measures such as adjusting dormitory lights-out time and providing sports equipment show superior emission reduction potential. Universities could reduce carbon emissions by about 560 tons per year without incentives and just using interventions. However, when incentives and interventions are combined, the university’s annual emissions reductions are expected to increase to 800 to 1045 tons. Research also highlights the importance of understanding the relationship between occupant behavior, energy consumption, and building carbon emissions. By quantifying the impact of carbon reduction measures and incentives on the daily behaviors of residents, universities can more effectively implement sustainable campus strategies.

Urban public open spaces are crucial for residents’ well-being, yet accessibility issues persist, affecting activities and social interactions. To this end, we take the main urban area of Jiamusi City, the most northeastern city in China, as an example. We start by examining both spatial and perceptual dimensions, using the Gaussian two-step moving search method to measure spatial accessibility, combining online data with multi-source data from questionnaires. Furthermore, we utilize structural equation modeling to explore the impact of accessibility on place attachment within urban public open spaces. The results show that (1) accessibility has a positive effect on place attachment, while place satisfaction plays a mediating role. (2) There were significant differences in spatial accessibility across modes of travel. Car travel had the best spatial accessibility, followed by bicycle, and walking had the lowest spatial accessibility. (3) Perceived accessibility and spatial accessibility differ, but perceived accessibility it is more persuasive in explaining and illustrating place attachment. Reducing travel costs and meeting the user needs will effectively increase place attachment and place identity. (4) Social factors such as age, education, and residency length also affect accessibility, satisfaction, and attachment. By understanding the impact of accessibility on place attachment, this study helps urban planners to better design urban spatial layout and transportation networks. It enhances people’s attachment to specific places by improving the convenience of traveling, promotes sustainable urban development, and enriches the discourse on the enhancement of psychological motivation in urban public open spaces.

The reconstruction of the porous media model is crucial for researching the mesoscopic seepage characteristics of porous concrete. Based on a self-compiled MATLAB program, a porous concrete model was modeled by controlling four parameters (distribution probability, growth probability, probability density, and porosity) with clear physical meanings using a quartet structure generation set (QSGS) along with the lattice Boltzmann method (LBM) to investigate permeability. The rationality of the numerical model was verified through Poiseuille flow theory. The results showed that the QSGS model exhibited varied pore shapes and disordered distributions, resembling real porous concrete. Seepage velocity distribution showed higher values in larger pores, with flow rates reaching up to 0.012 lattice point velocity. The permeability–porosity relationship demonstrated high linearity (the Pearson correlation coefficient is 0.92), consistent with real porous concrete behavior. The integration of QSGS-LBM represents a novel approach, and the research results can provide new ideas and new means for subsequent research on the permeability of porous concrete or similar porous medium materials.

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.

With the deepening of the concept of green, low-carbon, and sustainable development, the continuous growth of the ownership of new energy vehicles has led to increasing public concerns about the traffic safety issues of these vehicles. In order to conduct research on the traffic safety of new energy vehicles, three sampling methods, namely, Synthetic Minority Over-sampling Technique (SMOTE), Edited Nearest Neighbours (ENN), and SMOTE-ENN hybrid sampling, were employed, along with cost-sensitive learning, to address the problem of imbalanced data in the UK road traffic accident dataset. Three algorithms, eXtreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), and Categorical Boosting (CatBoost), were selected for modeling work. Lastly, the evaluation criteria used for model selection were primarily based on G-mean, with AUC and accuracy as secondary measures. The TreeSHAP method was applied to explain the interaction mechanism between accident severity and its influencing factors in the constructed models. The results showed that LightGBM had a more stable overall performance and higher computational efficiency. XGBoost demonstrated a balanced combination of computational efficiency and model performance. CatBoost, however, was more time-consuming and showed less stability with different datasets. Studies have found that people using fewer protective means of transportation (bicycles, motorcycles) and vulnerable groups such as pedestrians are susceptible to serious injury and death.

Agricultural products may deteriorate due to various reasons during storage and transportation, resulting in serious losses. However, the mechanism of deterioration is complex, and many mechanisms are currently unclear. In recent years, the rapid development of omics technologies, such as genomics, proteomics, transcriptomics, and metabolomics, has led to breakthroughs in exploring the mechanism of product quality changes during the storage and transportation of agricultural products, and also help to understand molecular mechanisms. By using omics technology to gain a deeper understanding of the changes in agricultural products during storage and transportation, more efficient, environmentally friendly, and resource saving storage and transportation methods can be developed, providing a research foundation for the sustainability of China’s agricultural food system. In addition, research on omics technology has provided theoretical support for screening more suitable storage methods and transportation conditions. This review presents the potential application omics technologies in agricultural product storage and transportation. The application examples of omics technology in the storage and transportation process of agricultural products were discussed. Finally, the prospects indicate that omics technology has unlimited potential.

Water pollutant emission permits (WPEPs) allocation is a competing limited consumption resource designed to address the issue of reconciling water pollution with economic development. In the wake of increasing conflicts, a pressing challenge is how to motivate stakeholders associated with the basin to establish a balance between economic development and environmental preservation. Based on the unidirectional flow of basin pollution, this study established a bidirectional ecological compensation (BEC) mechanism, which incorporated the key water pollution indicators (WPEPs allocation volume) into the BEC mechanism. By integrating the BEC mechanism with the WPEPs allocation model, a multi-objective optimization framework for balancing the fairness and economic efficiency of WPEPs allocation under the BEC synergy mechanism has been proposed. The Tuojiang river basin in China was selected for the study, and the allocation results and ecological compensation mechanisms under different allocation preferences were analyzed to verify the validity and practicality of the model. Results revealed that (1) BEC mechanisms can be considered to ensure equitable and efficient allocation of WPEPs in the basin. (2) Under the perspective of watershed cooperation, the bidirectional compensation mechanism proves to be an effective measure to promote economic development and environmental protection of the watershed. (3) Preferences of watershed managers for allocation and ecological compensation mechanisms can have an impact on the outcomes of allocation and ecological compensation mechanisms. This study provides reference for effective management and adoption of optimal allocation strategy of watershed WPEPs and synergistic management of BEC mechanisms.

With the accelerated pace of market-oriented reform, Internet finance has gained a broad and healthy development environment. Existing studies lack consideration of time trends in financial risk, and treating all features equally may lead to inaccurate predictions. To address the above problems, we propose an LSTM model based on sliding window and attention mechanism. The model uses sliding windows to enable the model to effectively exploit the contextual relevance of loan data. And we introduce the attention mechanism into the model, which enables the model to focus on important information. The result on the Lending Club public desensitization dataset shows that our model outperforms ARIMA, SVM, ANN, LSTM, and GRU models.

To completely eliminate the time delays caused by phasor data compressions for real-time synchrophasor applications, a real-time synchrophasor data compression (RSDC) is proposed in this paper. The two-way rotation characteristic and elliptical trajectory of dynamic synchrophasors are introduced first to enhance the compressions along with a fast solving method for elliptical trajectory fitting equations. The RSDC for phasor data compression and reconstruction is then proposed by combining the interpolation and extrapolation compressions. The proposed RSDC is verified by both the actual phasor measurement data recorded in a two-phase short-circuit incident and a subsynchronous oscillation incident, and the synthetic dynamic synchrophasors. It is also compared with two previous real-time phasor data compression techniques, i.e., phasor swing door trending (PSDT) and exception and swing door trending (SDT) data compression (ESDC). The verification results demonstrate that RSDC can achieve significantly higher compression ratios for offline applications with the interpolation and the zero-delay phasor data compression with the extrapolation for real-time applications simultaneously.

Bis (3′,5′)-cyclic diguanylic acid (c-di-GMP) is a ubiquitous second messenger that controls several metabolic pathways in bacteria. In Streptomyces, c-di-GMP is associated with morphological differentiation, which is related to secondary metabolite production. In this study, we identified and characterized a diguanylate cyclase (DGC), CdgB, from Streptomyces diastatochromogenes 1628, which may be involved in c-di-GMP synthesis, through genetic and biochemical analyses. To further investigate the role of CdgB, the cdgB-deleted mutant strain Δ-cdgB and the cdgB-overexpressing mutant strain O-cdgB were constructed by genetic engineering. A phenotypic analysis revealed that the O-cdgB colonies exhibited reduced mycelium formation, whereas the Δ-cdgB colonies displayed wrinkled surfaces and shriveled mycelia. Notably, O-cdgB demonstrated a significant increase in the toyocamycin (TM) yield by 47.3%, from 253 to 374 mg/L, within 10 days. This increase was accompanied by a 6.7% elevation in the intracellular concentration of c-di-GMP and a higher transcriptional level of the toy cluster within four days. Conversely, Δ-cdgB showed a lower c-di-GMP concentration (reduced by 6.2%) in vivo and a reduced toyocamycin production (decreased by 28.9%, from 253 to 180 mg/L) after 10 days. In addition, S. diastatochromogenes 1628 exhibited a slightly higher inhibitory effect against Fusarium oxysporum f. sp. cucumerinum and Rhizoctonia solani compared to Δ-cdgB, but a lower inhibition rate than that of O-cdgB. The results imply that CdgB provides a foundational function for metabolism and the activation of secondary metabolism in S. diastatochromogenes 1628.

Dynamic monitoring of cropland using high spatial resolution remote sensing images is a powerful means to protect cropland resources. However, when a change detection method based on a convolutional neural network employs a large number of convolution and pooling operations to mine the deep features of cropland, the accumulation of irrelevant features and the loss of key features will lead to poor detection results. To effectively solve this problem, a novel cropland change detection network (CroplandCDNet) is proposed in this paper; this network combines an adaptive receptive field and multiscale feature transmission fusion to achieve accurate detection of cropland change information. CroplandCDNet first effectively extracts the multiscale features of cropland from bitemporal remote sensing images through the feature extraction module and subsequently embeds the receptive field adaptive SK attention (SKA) module to emphasize cropland change. Moreover, the SKA module effectively uses spatial context information for the dynamic adjustment of the convolution kernel size of cropland features at different scales. Finally, multiscale features and difference features are transmitted and fused layer by layer to obtain the content of cropland change. In the experiments, the proposed method is compared with six advanced change detection methods using the cropland change detection dataset (CLCD). The experimental results show that CroplandCDNet achieves the best F1 and OA at 76.04% and 94.47%, respectively. Its precision and recall are second best of all models at 76.46% and 75.63%, respectively. Moreover, a generalization experiment was carried out using the Jilin-1 dataset, which effectively verified the reliability of CroplandCDNet in cropland change detection.

α-Conotoxins (α-CTxs) are structurally related peptides that antagonize nicotinic acetylcholine receptors (nAChRs), which may serve as new alternatives to opioid-based treatment for pain-related conditions. The non-natural amino acid analogues of α-CTxs have been demonstrated with improved potency compared to the native peptide. In this study, we chemically synthesized Dab/Dap-substituted analogues of α-CTx PeIA and evaluated their activity at heterologously expressed human α9α10 nAChRs. PeIA[S4Dap, S9Dap] had the most potent half-maximal inhibitory concentration (IC50) of 0.93 nM. Molecular dynamic simulations suggested that the side chain amino group of Dap4 formed additional hydrogen bonds with S168 and D169 of the receptor and Dap9 formed an extra hydrogen bond interaction with Q34, which is distinctive to PeIA. Overall, our findings provide new insights into further development of more potent analogues of α-CTxs, and PeIA[S4Dap, S9Dap] has potential as a drug candidate for the treatment of chronic neuropathic pain.

Legal prediction presents one of the most significant challenges when applying artificial intelligence (AI) to the legal field. The legal system is a complex adaptive system characterized by the ambiguity of legal language and the diversity of value functions. The imprecision and procedural knowledge inherent in law makes judicial issues difficult to be expressed in a computer symbol system. Current semantic processing and machine learning technologies cannot fully capture the complex nature of legal relations, thereby raising doubts about the accuracy of legal predictions and reliability of judicial models. Cognitive computing, designed to emulate human brain functions and aid in enhancing decision-making processes, offers a better understanding of legal data and the processes of legal reasoning. This paper discusses the advancements made in cognitive methods applied to legal concept learning, semantic extraction, judicial data processing, legal reasoning, understanding of judicial bias, and the interpretability of judicial models. The integration of cognitive neuroscience with law has facilitated several constructive attempts, indicating that the evolution of cognitive law could be the next frontier in the intersection of AI and legal practice.

Myocardial fibrosis, which is a common pathological manifestation of many cardiovascular diseases, is characterized by excessive proliferation, collagen deposition and abnormal distribution of extracellular matrix fibroblasts. In clinical practice, modern medicines, such as diuretic and β receptor blockers, and traditional Chinese medicines, such as salvia miltiorrhiza and safflower extract, have certain therapeutic effects on myocardial fibrosis. We reviewed some representative modern medicines and traditional Chinese medicines (TCMs) and their related molecular mechanisms for the treatment of myocardial fibrosis. These drugs alleviate myocardial fibrosis by affecting related signaling pathways and inhibiting myocardial fibrosis-related protein synthesis. This review will provide more references and help for the research and treatment of myocardial fibrosis.

Background The effect of combined nicorandil and beta-adrenergic receptor blockers (BBs) compared with that of BBs alone on long-term clinical outcomes in patients with coronary artery disease (CAD) remains undetermined. Methods A multicenter retrospective cohort study was performed. Adult patients who had been hospitalized for CAD and treated for angina with a combination of nicorandil and BBs or BBs alone were included. The effect of different treatments on the cumulative incidence of major adverse cardiovascular event (MACE) and their components within a follow-up duration of 2.5 years were analyzed using Kaplan–Meier survival curves. An inverse probability of treatment weighting (IPTW) method was used to adjust for the possible effect of confounding factors. Results A total of 137,714 patients were screened, of whom 16,912 individuals (mean age: 61.5 years, men: 67.1%) were successfully enrolled. Among the enrolled participants, 4669 received the combined treatment of nicorandil and BBs, while 12,243 received BBs alone. After IPTW, the results demonstrated that the combined treatment was associated with a significantly reduced incidence of MACE (hazard ratio [HR] 0.79, 95% conidence interval [CI] 0.72-0.87) and stroke (HR 0.48, 95% CI 0.42-0.54) but not of MI (HR 1.03, 95% CI 0.92-1.15) or all-cause mortality (HR 0.93, 95% CI 0.64-1.37). Sensitivity analyses revealed similar results. Conclusions A combined antiangina treatment of nicorandil and BBs may be more effective than treatment of BBs alone in reducing the long-term incidence of MACE in patients with CAD.

To investigate the impact of the launch valve on the performance of the supercritical CO2 pneumatic launch, a numerical model was developed based on the principles of mass and energy conservation. Specifically, the thermos-physical properties of real gas were used to describe CO2 during the launch process. Expansion experiments were conducted to validate the numerical model's accuracy. The study also examined the effects of the valve's opening mode, full opening time, and full opening area on launch performance. The findings indicate that when using the same launch valve, a decrease in the full opening time of the valve leads to an increase in the average temperature of the low-pressure chamber. Additionally, injecting the medium into the low-pressure chamber at a faster rate results in improved working responsiveness and energy conversion efficiency. Once the valve reaches a certain size, further increases offer little benefit. The parabolic valve is the optimal choice for both overload and final velocity. However, if the aircraft can handle it, a quick-opening valve will result in a shorter launch time and higher final velocity. This study offers theoretical guidance to improve the precision of aircraft cold launches.

Abstract Objective To identify new trends and potential hotspots in research on rheumatoid arthritis‐associated interstitial lung disease (RA‐ILD). Materials and Methods The Web of Science (WOS) database was used to search for RA‐ILD‐related literature published between August 31, 2002 and August 31, 2022. CiteSpace 6.1.R3, VOSviewer version 1.6.17, Scimago Graphica, and Pajek V2.0 visualization software were used to conduct a comprehensive analysis and network visualization mapping of the authors, countries, institutions, journals, cited references, and keywords. Results A total of 2412 articles were retrieved, and the number of articles published has grown annually since 2002. Eric L. Matteson was the most prolific author, and the Mayo Clinic and UNITED STATES have the highest publishing volume and influence. Clinical Rheumatology is the journal with the most papers published. Rheumatology was the most cited journal. The citation clusters and keywords concentrated on the mechanism, treatment, and predictive and prognostic factors. Conclusion Pathogenesis, treatment, and predictive and prognostic factors were among the RA‐ILD research directions and hotspots. Antirheumatoid drugs, especially biologics and small molecule inhibitors, were among the most actively researched treatment options. The results of this study provides an in‐depth understanding of the development of RA‐ILD publications, aids researchers in understanding hotspots and trends and provides a new perspective for future RA‐ILD research.

Abstract Significant growth in the development and deployment of artificial intelligence (AI) is being witnessed. Driven by the great versatility of emerging computer science and material science, various AI sensors provide cost‐effective approaches for a wide range of monitoring applications toward the realization of smart homes and personal healthcare. Advanced AI sensors have multiple sensors capable of detecting multidimensional information and human‐brain‐like computation device for data processing. Herein, this review outlines the recent advances in the development of AI sensors. This review first introduces the materials, fabrication methods, and algorithms of current AI sensors and their applications, i.e., complementary metal oxide semiconductor image sensors for computer vision, microelectromechanical systems, microphone sensors for voice recognition, and wearable sensors for gesture recognition. Then, the recent advances in AI wearables sensors and self‐powered sensor systems are highlighted. Next, the current developments of neuromorphic computing systems, multimodality, and digital twins are reviewed. Last, a perspective on future directions for further research development is also provided. In summary, the trend of advanced AI sensors is the complementary between edge computing and cloud computing, which will show great potential in the applications of smart buildings, individual healthcare, the Internet of things, etc.