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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.

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.

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.

Ice, water, and sediment represent three interconnected habitats in lake ecosystems, and bacteria are crucial for maintaining ecosystem equilibrium and elemental cycling across these habitats. However, the differential characteristics and driving mechanisms of bacterial community structures in the ice, water, and sediments of seasonally frozen lakes remain unclear. In this study, high-throughput sequencing technology was used to analyze and compare the structure, function, network characteristics, and assembly mechanisms of bacterial communities in the ice, water, and sediment of Wuliangsuhai, a typical cold region in Inner Mongolia. The results showed that the bacterial communities in the ice and water phases had similar diversity and composition, with Proteobacteria, Bacteroidota, Actinobacteria, Campilobacterota, and Cyanobacteria as dominant phyla. The bacterial communities in sediments displayed significant differences from ice and water, with Chloroflexi, Proteobacteria, Firmicutes, Desulfobacterota, and Acidobacteriota being the dominant phyla. Notably, the bacterial communities in water exhibited higher spatial variability in their distribution than those in ice and sediment. This study also revealed that during the frozen period, the bacterial community species in the ice, water, and sediment media were dominated by cooperative relationships. Community assembly was primarily influenced by stochastic processes, with dispersal limitation and drift identified as the two most significant factors within this process. However, heterogeneous selection also played a significant role in the community composition. Furthermore, functions related to nitrogen, phosphorus, sulfur, carbon, and hydrogen cycling vary among bacterial communities in ice, water, and sediment. These findings elucidate the intrinsic mechanisms driving variability in bacterial community structure and changes in water quality across different media phases (ice, water, and sediment) in cold-zone lakes during the freezing period, offering new insights for water environmental protection and ecological restoration efforts in such environments.

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.

The Leaf Area Index (LAI) is a critical parameter that sheds light on the composition and function of forest ecosystems. Its efficient and rapid measurement is essential for simulating and estimating ecological activities such as vegetation productivity, water cycle, and carbon balance. In this study, we propose to combine high-resolution GF-6 2 m satellite images with the LESS three-dimensional RTM and employ different machine learning algorithms, including Random Forest, BP Neural Network, and XGBoost, to achieve LAI inversion for forest stands. By reconstructing real forest stand scenarios in the LESS model, we simulated reflectance data in blue, green, red, and near-infrared bands, as well as LAI data, and fused some real data as inputs to train the machine learning models. Subsequently, we used the remaining measured LAI data for validation and prediction to achieve LAI inversion. Among the three machine learning algorithms, Random Forest gave the highest performance, with an R2 of 0.6164 and an RMSE of 0.4109, while the BP Neural Network performed inefficiently (R2 = 0.4022, RMSE = 0.5407). Therefore, we ultimately employed the Random Forest algorithm to perform LAI inversion and generated LAI inversion spatial distribution maps, achieving an innovative, efficient, and reliable method for forest stand LAI inversion.

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.

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.

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.

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.

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.

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.

This study explores a methodology for mapping the 3D spatial information of evacuation routes, aiming to acquire lightweight data to enhance the flexibility of evacuation route development. Utilizing 3D-LiDAR (Light Detection and Ranging) technology, we integrated an Inertial Measurement Unit (IMU) with an existing algorithm to compensate for motion distortion, thereby increasing the mapping accuracy of interior spaces characterized by a paucity of structural features. Given the extensive volume of point cloud data generated by LiDAR, which is impractical for direct application in evacuation route mapping, we categorized the data into local and global maps. This paper presents a strategy to minimize the data volume necessary for generating optimized evacuation route maps, employing the Octomap data format for efficient global map storage. This approach not only addresses the challenges of handling large datasets but also contributes to the development of more accurate and user-friendly evacuation planning tools. [ABSTRACT FROM AUTHOR]

Implantable sensors can directly interface with various organs for precise evaluation of health status. However, extracting signals from such sensors mainly requires transcutaneous wires, integrated circuit chips, or cumbersome readout equipment, which increases the risks of infection, reduces biocompatibility, or limits portability. Here, we develop a set of millimeter-scale, chip-less, and battery-less magnetic implants paired with a fully integrated wearable device for measuring biophysical and biochemical signals. The wearable device can induce a large amplitude damped vibration of the magnetic implants and capture their subsequent motions wirelessly. These motions reflect the biophysical conditions surrounding the implants and the concentration of a specific biochemical depending on the surface modification. Experiments in rat models demonstrate the capabilities of measuring cerebrospinal fluid (CSF) viscosity, intracranial pressure, and CSF glucose levels. This miniaturized system opens the possibility for continuous, wireless monitoring of a wide range of biophysical and biochemical conditions within the living organism. [ABSTRACT FROM AUTHOR]

Grating couplers that interconnect photonic chips to off-chip components are crucial for various optoelectronics applications. Despite numerous efforts in past decades, the existing grating couplers are still far from optimal in energy efficiency and thus hinder photonic integration toward a larger scale. Here, we propose a strategy to achieve ultralow-loss grating couplers by using unidirectional guided resonances (UGRs), suppressing the useless downward radiation with no mirror on the bottom. By engineering the dispersion and apodizing the geometry of grating, we experimentally realize a grating coupler with a record-low loss of -0.34 dB and 1-dB bandwidth exceeding 30 nm at the telecom wavelength of 1550 nm and further demonstrate an optic via with a loss of only -0.94 dB. Given that UGRs ubiquitously exist in a variety of grating geometries, our work sheds light on a systematic method to achieve energy-efficient optical interconnect and paves the way to large-scale photonic integration. [ABSTRACT FROM AUTHOR]

Real-time in situ monitoring of plant physiology is essential for establishing a phenotyping platform for precision agriculture. A key enabler for this monitoring is a device that can be noninvasively attached to plants and transduce their physiological status into digital data. Here, we report an all-organic transparent plant e-skin by micropatterning poly(3,4-ethylenedioxythiophene) polystyrene sulfonate (PEDOT:PSS) on polydimethylsiloxane (PDMS) substrate. This plant e-skin is optically and mechanically invisible to plants with no observable adverse effects to plant health. We demonstrate the capabilities of our plant e-skins as strain and temperature sensors, with the application to Brassica rapa leaves for collecting corresponding parameters under normal and abiotic stress conditions. Strains imposed on the leaf surface during growth as well as diurnal fluctuation of surface temperature were captured. We further present a digital-twin interface to visualize real-time plant surface environment, providing an intuitive and vivid platform for plant phenotyping. [ABSTRACT FROM AUTHOR]