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In a multi-source localization system, direction of arrival (DOA) estimation of angles always suffers from errors due to noise interference, sensor position inaccuracies, and other factors. When the distance between target sources is much smaller than the distance between sensors and target sources, the accuracy of traditional localization algorithms based on direction finding and cross-fixing deteriorates. In this paper, we propose a localization algorithm based on K-means clustering. To tackle the problem of unknown initial positions of target sources, we employ a grid density peak clustering(DPC) method for initial localization. In the K-means algorithm, we integrate a quartile range anomaly detection algorithm to address interference signal issues. Finally, we propose an invalid compensation algorithm to filter out invalid signals, thereby compensating for the estimation errors in angles. Through the collection of real-world data, we compare the performance of the traditional direction finding and cross-fixing algorithms with the proposed algorithm in the localization of nearby target points. Experimental results demonstrate that the proposed algorithm significantly improves localization accuracy.

Abstract Background The objective of this research is to statistically assess the risk of cardiovascular mortality (CVM) between patients with small bowel adenocarcinoma (SBA) and the general population. Additionally, it aims to identify CVM-associated risk factors among individuals with SBA. Methods Data obtained between 2000 and 2017 on SBA patients from the Surveillance, Epidemiology, and End Results (SEER) database were examined. Standardized mortality ratios (SMRs) and absolute excess risks (AERs) were obtained to compare CVM between patients and the general US population. To evaluate the cumulative mortality (CM) rate for all causes of death (COD), cumulative hazard curves were constructed. Two multivariate competing risk models were established to determine the independent predictors for CVM. Results In the cohort of 5,175 SBA patients observed for 15,068.24 person-years, a total of 205 deaths were attributed to cardiovascular disease (CVD). The overall SMR of CVM was 1.41 (95% confidence interval (CI): 1.23–1.62, P

Heterogeneous nuclear ribonucleoprotein (hnRNP) and heterogeneous nuclear RNA (hnRNA) form complexes that regulate RNA splicing and transportation. hnRNPH1 is a member of the hnRNP family of proteins and plays an important role in physiological processes, including embryonic development of the nervous and reproductive systems. hnRNPH1 is highly expressed in various tumor tissues and tumor cells, and its overexpression correlates with poor prognosis in patients. hnRNPH1 enhances tumor cell proliferation and metastasis through the regulation of alternative splicing of genes, modulation of gene expression, formation of fusion genes with other genes, and involvement in epigenetic regulation.Recent studies have shown that hnRNPH1 could serve as a potential biomarker for certain tumors, and hnRNPH1-targeting drugs show preliminary efficacy in a mouse model of melanoma. This article reviews the role and research progress of hnRNPH1 in the initiation and progression of malignant tumors.

Bisphenol A (BPA) poses a significant environmental threat due to its widespread use as an industrial chemical and its classification as an environmental endocrine disruptor. The urgent need for effective BPA removal has driven research toward innovative solutions. In this study, we present the synthesis and application of a novel 2D-3D spherically hierarchical Zn5In2S8 (ZIS) photocatalyst for the photocatalytic degradation of BPA under visible light for the first time. Compared to the conventional g-C3N4 photocatalyst, ZIS exhibits enhanced optical and electrical properties, leading to remarkable photocatalytic performance, with an apparent reaction rate constant of 2.36 h⁻1, 6.56 times greater than that of g-C3N4. This efficacy allows for the degradation of 99.9% of BPA in just 2 h. The photocatalytic mechanism of ZIS was elucidated through various material characterizations and photoelectrochemical assessments, demonstrating improved light absorption and efficient charge separation as key factors facilitating BPA degradation. Notably, ZIS maintains high photocatalytic activity and stability over multiple cycles, indicating its potential as a sustainable photocatalyst. These findings not only contribute to the development of efficient photocatalysts for environmental remediation but also underscore the significant role of Zn5In2S8 in photocatalysis and solar energy conversion.

Lithium-ion battery (LIB) is the most widely used secondary battery and has been extensively studied in the past few decades due to its many advantages compared to other secondary batteries, such as higher energy density and better cycling performance. Considering the potential risks of environmental pollution and fire hazard induced by leakage or overheating of conventional electrolytes composed of flammable organic solvent-dissolving Li salts, non-flammable solid electrolytes (SEs), especially ceramic electrolytes with different structural types have been developed to improve the safety performance of LIBs. As the properties of SEs significantly depend on the microstructure, this review systemically summarized the recent progress in inorganic solid electrolytes based on the classification of microstructure and presented the relevant discussion in detail, including the microstructures, the mechanisms of Li ionic migration and performance optimization. Additionally, several challenges for realizing industrial application of SEs, such as lower ionic conductivities compared to liquid electrolytes and unsatisfactory stability in ambient atmosphere were mentioned. To effectively boost the development of SEs, more advanced transmission electron microscopy and atom probe tomography should be considered to deeply investigate the relationship between microstructure and properties in the future.

Objective: The diversity of electrode placement systems brought the problem of channel location harmonization in large-scale electroencephalography (EEG) applications to the forefront. Therefore, our goal was to resolve this problem by introducing and assessing the reference electrode standardization technique (REST) to transform EEGs into a common electrode distribution with computational zero reference at infinity offline. Methods: Simulation and eye-closed resting-state EEG datasets were used to investigate the performance of REST for EEG signals and power configurations. Results: REST produced small errors (the root mean square error (RMSE): 0.2936–0.4583; absolute errors: 0.2343–0.3657) and high correlations (>0.9) between the estimated signals and true ones. The comparison of configuration similarities in power among various electrode distributions revealed that REST induced infinity reference could maintain a perfect performance similar (>0.9) to that of true one. Conclusion: These results demonstrated that REST transformation could be adopted to resolve the channel location harmonization problem in large-scale EEG applications.

The semantic segmentation of land use and land cover (LULC) is a crucial and widely employed remote sensing task. Conventional convolutional neural networks and vision transformers have been extensively utilized for LULC segmentation. However, high-resolution remote sensing images contain a wealth of spatial and color texture information, which is not fully exploited by traditional deep learning approaches. The information bottleneck of CNNs and transformers results in the loss of a significant amount of texture detail information during the feature extraction process, which further limits the performance of LULC segmentation. We present DDPM-SegFormer, a new framework that merges a denoising diffusion probabilistic model (DDPM) and vision transformer for LULC segmentation. The aim is to address the difficulties arising from extraction in complex geographic landscapes and to alleviate information bottlenecks. The framework utilizes the ability of a DDPM to generate refined semantic features and that of vision transformer to model the global image context. Our framework introduces two main innovations. First, we use a DDPM for the first time in LULC segmentation to generate highly refined multiscale semantic features. This approach alleviates the information bottleneck caused by relying solely on a CNN or transformer architecture. Second, we develop an effective feature-level fusion strategy that utilizes multihead cross-attention between the DDPM and Transformer. This approach achieves the harmonious fusion of fine-scale semantic features, generating continuous and highly refined semantic features that enhance the segmentation accuracy. The results indicate that DDPM-SegFormer achieves an MIOU of 83.72% and an F1-score of 90.97% for the large-scale LoveDA dataset and an MIOU of 90.91% and an F1score of 93.30% for the Tarim Basin LULC dataset in a desert scenario. The research demonstrated that the refined and continuous semantic features produced by DDPM-SegFormer can significantly enhance LULC segmentation performance.

The human gastrointestinal tract houses an enormous microbial ecosystem. Recent studies have shown that the gut microbiota plays significant physiological roles and maintains immune homeostasis in the human body. Dysbiosis, an imbalanced gut microbiome, can be associated with various disease states, as observed in infectious diseases, inflammatory diseases, autoimmune diseases, and cancer. Modulation of the gut microbiome has become a therapeutic target in treating these disorders. Fecal microbiota transplantation (FMT) from a healthy donor restores the normal gut microbiota homeostasis in the diseased host. Ample evidence has demonstrated the efficacy of FMT in recurrent Clostridioides difficile infection (rCDI). The application of FMT in other human diseases is gaining attention. This review aims to increase our understanding of the mechanisms of FMT and its efficacies in human diseases. We discuss the application, route of administration, limitations, safety, efficacies, and suggested mechanisms of FMT in rCDI, autoimmune diseases, and cancer. Finally, we address the future perspectives of FMT in human medicine.