The prognostic values and immune characteristics of polo-like kinases (PLKs) family: A pan-cancer multi-omics analysis.

Background: In the realm of tumor-targeted therapeutics, Polo-like kinases (PLKs) are a significant group of protein kinases that were found recently as being related to tumors. However, the significance of PLKs in pan-cancer remains systematically studied. Methods and materials: We integrated multi-omics data to comprehensively investigate the expression patterns of the PLK family across various cancer types. Subsequently, study examined the associations between tumor mutation burden (TMB), microsatellite instability (MSI), immune subtype classification, immune infiltration, tumor microenvironment scores, immune checkpoint gene expression, and the PLKs expression profiles within various tumor types. Furthermore, using our mRNA sequencing data (TRUCE01) and four bladder cancer (BLCA) cohorts (GSE111636, GSE176307, and IMvigor210), We examined the correlation between the expression level of PLK and immunotherapy effectiveness. Next, Gene set enrichment analysis (GSEA) was evaluated to find that potentially enriched PLK signaling pathways. Utilizing TIMER 2.0, we conducted an immune infiltration analysis underlying transcriptome expression, copy number variations (CNV), or somatic mutations of PLKs in BLCA. Finally, mRNA expression validation of PLK1/3/4 by real-time PCR within 10 paired BLCA tissues, protein expression verification through the Human Protein Atlas (HPA), and PLK4 in vitro cytological studies have been employed in BLCA. Results: The expression of most of the PLK family members exhibits variation between cancerous tissues and adjacent normal tissues across various cancer species. Furthermore, the expression of PLKs demonstrates a significant association with immunotyping, infiltration of immune cell, tumor mutational burden (TMB), microsatellite instability (MSI), immunological checkpoint gene activity and therapeutic effectiveness in pan-tumor tissues. Additional investigation into the correlation between the PLK family and BLCA has revealed that the expression of the PLK genes holds substantial significance in the biological processes of BLCA. Furthermore, a notable association has been observed between the copy number variation, variant status, and the degree of certain immunological cell infiltration. Of note, the expression validation and in vitro phenotypic experiments have demonstrated that PLK4 has a significant function in promoting the BLCA cell proliferation, migration, and invasion. Conclusion: Collectively, based on various databases, our results highlight the involvement of PLK gene family in the formation of different types of tumors and identify PLK-related genes that may be used for therapy.

Molecular Regulation of Porcine Skeletal Muscle Development: Insights from Research on CDC23 Expression and Function.

Cell division cycle 23 (CDC23) is a component of the tetratricopeptide repeat (TPR) subunit in the anaphase-promoting complex or cyclosome (APC/C) complex, which participates in the regulation of mitosis in eukaryotes. However, the regulatory model and mechanism by which the CDC23 gene regulates muscle production in pigs are largely unknown. In this study, we investigated the expression of CDC23 in pigs, and the results indicated that CDC23 is widely expressed in various tissues and organs. In vitro cell experiments have demonstrated that CDC23 promotes the proliferation of myoblasts, as well as significantly positively regulating the differentiation of skeletal muscle satellite cells. In addition, Gene Set Enrichment Analysis (GSEA) revealed a significant downregulation of the cell cycle pathway during the differentiation process of skeletal muscle satellite cells. The protein-protein interaction (PPI) network showed a high degree of interaction between genes related to the cell cycle pathway and CDC23. Subsequently, in differentiated myocytes induced after overexpression of CDC23, the level of CDC23 exhibited a significant negative correlation with the expression of key factors in the cell cycle pathway, suggesting that CDC23 may be involved in the inhibition of the cell cycle signaling pathway in order to promote the differentiation process. In summary, we preliminarily determined the function of CDC23 with the aim of providing new insights into molecular regulation during porcine skeletal muscle development.

Generation of a KCNQ1 (c.1032 + 2 T > C) mutant human embryonic stem cell line via CRISPR base editing.

The KCNQ1 gene encodes a voltage-gated potassium channel, which plays an important role in the repolarization of myocardial action potentials. Mutations in this gene often result in type 1 long QT syndrome (LQT1). Here, we generated a KCNQ1 (c.1032 + 2 T > C) mutant human embryonic stem cell line (WAe009-A-1D) based on the transient expression adenine base editing system that converts base A to G. The WAe009-A-1D cell maintains the morphology, pluripotency, and normal karyotype of the stem cells and is capable of differentiating into all three germ layers in vivo.

Recycled PET/PA6 Fibers from Waste Textile with Improved Hydrophilicity by In-Situ Reaction-Induced Capacity Enhancement.

Due to the increasing amounts of textile waste, textile to textile recycling is of prime concern. Polyethylene terephthalate (PET) represents the most extensively used type of chemical fiber. Its spinnability suffers from impurities and degradation in the processing, which limits its recycling to new fibers. Here, recycled polyester is blended with a small amount of recycled nylon, and the regenerated fibers, which demonstrated good mechanical properties, were obtained via a melt spinning machine. The mechanical properties, thermal properties, rheological properties, and chemical structure of the modified recycled fibers were investigated. It was found that when compared with rPET-T fibers, the elongation at break of rPET-Ax fibers increased to 17.48%, and the strength at break decreased to 3.79 cN/dtex. The compatibility of PET and PA6 copolymer were enhanced by copolymers produced by in-situ reaction in the processing. Meanwhile, the existence of PA6 increases the crystallization temperature and improves the hydrophilicity of the fibers. This study realized the high-value recycling of waste PET fabric to new fibers, which opens a door for the large utilization of waste textiles.

Design and Implementation of a Linear Active Disturbance Rejection Control-Based Position Servo Control System of an Electromotive Valve for Exhaust Gas Recirculation.

An exhaust gas recirculation (EGR) valve is used to quickly and dynamically adjust the amount of recirculated exhaust gas, which is critical for improving engine fuel economy and reducing emissions. To address problems relating to the precise positioning of an electromotive (EM) valve under slowly varying plant dynamics and uncertain disturbances, we propose a servo control system design based on linear active disturbance rejection control (LADRC) for the EGR EM valve driven by a limited angle torque motor (LATM). By analyzing the structure of the LATM and the transmission, the dynamic model of the system is derived. In addition, to solve the problems caused by slowly varying plant dynamics and uncertain disturbances, we combine the effects of uncertain model parameters and external disturbances as the total disturbance, which is estimated in real time by an extended state observer (ESO) and then compensated. In addition, accurate angular information is obtained using a non-contact magnetic angle measurement method, and a high-speed digital communication channel is established to help implement a closed-loop position control system with improved responsiveness and accuracy. Simulation and experimental results show that the proposed servo system design can effectively ensure the precision and real-time performance of the EM valve under slowly changing plant dynamics and uncertain disturbances. The proposed servo system design achieves a full-stroke valve control accuracy of better than 0.05 mm and a full-stroke response time of less than 100 ms. The controlled valve also has good robustness under shock-type external disturbances and excellent airflow control capability. The repeatability of the airflow control is generally within 5%, and the standard deviation is less than 0.2 m3/h.

Exploring the causal relationship between airborne particulate matter and ulcerative colitis: A two-sample mendelian randomization study.

BACKGROUND: Existing research has demonstrated links between airborne particulate matter and ulcerative colitis (UC) onset. Through Mendelian randomization, this study aims to further delineate the causal association between specific types of airborne particulates and UC. METHODS: A two-sample Mendelian randomization analysis was undertaken to investigate the causality between airborne particulate matter and UC. Genetic datasets for both airborne particulates and UC were derived from accessible genome-wide association studies (GWAS). We employed a range of MR techniques, such as inverse variance weighted (IVW), weighted median, MR-Egger, and Wald Ratio, to validate the causality. In addition, sensitivity assessments were executed to ensure result reliability. RESULTS: The data indicate a probable positive correlation between PM2.5 exposure and UC risk (OR: 3.6; 95% CI: [1.2-11.3]; P = 0.026). The statistical strength for causal determination via the IVW approach stood at 0.87, with a Type I error rate set at 0.025. Assessments using Cochran's Q test, MR-Egger intercept, MR-PRESSO, and leave-one-out sensitivity analyses did not identify notable heterogeneity, pleiotropy, or biases in the overall relationship between PM2.5 and UC. Furthermore, the MR-Steiger assessment indicated that PM2.5 exposure level determinants predominantly affect UC vulnerability. CONCLUSION: The findings underscore the potential involvement of PM2.5 in UC pathogenesis.

A Crowd Movement Analysis Method Based on Radar Particle Flow.

Crowd movement analysis (CMA) is a key technology in the field of public safety. This technology provides reference for identifying potential hazards in public places by analyzing crowd aggregation and dispersion behavior. Traditional video processing techniques are susceptible to factors such as environmental lighting and depth of field when analyzing crowd movements, so cannot accurately locate the source of events. Radar, on the other hand, offers all-weather distance and angle measurements, effectively compensating for the shortcomings of video surveillance. This paper proposes a crowd motion analysis method based on radar particle flow (RPF). Firstly, radar particle flow is extracted from adjacent frames of millimeter-wave radar point sets by utilizing the optical flow method. Then, a new concept of micro-source is defined to describe whether any two RPF vectors originated from or reach the same location. Finally, in each local area, the internal micro-sources are counted to form a local diffusion potential, which characterizes the movement state of the crowd. The proposed algorithm is validated in real scenarios. By analyzing and processing radar data on aggregation, dispersion, and normal movements, the algorithm is able to effectively identify these movements with an accuracy rate of no less than 88%.

GLFNet: Global-local fusion network for the segmentation in ultrasound images.

Ultrasound imaging, as a portable and radiation-free modality, presents challenges for accurate segmentation due to the variability of lesions and the similar intensity values of surrounding tissues. Current deep learning approaches leverage convolution for extracting local features and self-attention for handling global dependencies. However, traditional CNNs are spatially local, and Vision Transformers lack image specific bias and are computationally demanding. In response, we propose the Global-Local Fusion Network (GLFNet), a hybrid structure addressing the limitations of both CNNs and Vision Transformers. The GLFNet, featuring Global-Local Fusion Blocks (GLFBlocks), integrates global semantic information with local details to improve segmentation. Each GLFBlock comprises Global and Local Branches for feature extraction in parallel. Within the Global and Local Branches, we introduce the Self-Attention Convolution Fusion Block (SACFBlock), which includes a Spatial-Attention Module and Channel-Attention Module. Experimental results show that our proposed GLFNet surpasses its counterparts in the segmentation tasks, achieving the overall best results with an mIoU of 79.58% and Dice coefficient of 74.62% in the DDTI dataset, an mIoU of 76.61% and Dice coefficient of 71.04% in the BUSI dataset, and an mIoU of 86.77% and Dice coefficient of 87.38% in the BUID dataset. The fusion of local and global features contributes to enhanced performance, making GLFNet a promising approach for ultrasound image segmentation.

Thyroid Ultrasound Image Database and Marker Mask Inpainting Method for Research and Development.

OBJECTIVE: The main objective of this study was to build a rich and high-quality thyroid ultrasound image database (TUD) for computer-aided diagnosis (CAD) systems to support accurate diagnosis and prognostic modeling of thyroid disorders. Because most of the raw thyroid ultrasound images contain artificial markers, which seriously affect the robustness of CAD systems because of their strong prior location information, we propose a marker mask inpainting (MMI) method to erase artificial markers and improve image quality. METHODS: First, a set of thyroid ultrasound images were collected from the General Hospital of the Northern Theater Command. Then, two modules were designed in MMI, namely, the marker detection (MD) module and marker erasure (ME) module. The MD module detects all markers in the image and stores them in a binary mask. According to the binary mask, the ME module erases the markers and generates an unmarked image. Finally, a new TUD based on the marked images and unmarked images was built. The TUD is carefully annotated and statistically analyzed by professional physicians to ensure accuracy and consistency. Moreover, several normal thyroid gland images and some ancillary information on benign and malignant nodules are provided. RESULTS: Several typical segmentation models were evaluated on the TUD. The experimental results revealed that our TUD can facilitate the development of more accurate CAD systems for the analysis of thyroid nodule-related lesions in ultrasound images. The effectiveness of our MMI method was determined in quantitative experiments. CONCLUSION: The rich and high-quality resource TUD promotes the development of more effective diagnostic and treatment methods for thyroid diseases. Furthermore, MMI for erasing artificial markers and generating unmarked images is proposed to improve the quality of thyroid ultrasound images. Our TUD database is available at https://github.com/NEU-LX/TUD-Datebase.

A cell therapy approach based on iPSC-derived midbrain organoids for the restoration of motor function in a Parkinson's disease mouse model.

Parkinson's disease (PD) is a neurodegenerative disease characterized by the degeneration of dopaminergic (DA) neurons in the substantia nigra and loss of DA transmission in the striatum, thus making cell transplantation an effective treatment strategy. Here, we develop a cellular therapy based on induced pluripotent stem cell (iPSC)-derived midbrain organoids. By transplanting midbrain organoid cells into the striatum region of a 6-OHDA-lesioned PD mouse model, we found that the transplanted cells survived and highly efficiently differentiated into DA neurons. Further, using a dopamine sensor, we observed that the differentiated human DA neurons could efficiently release dopamine and were integrated into the neural network of the PD mice. Moreover, starting from four weeks after transplantation, the motor function of the transplanted mice could be significantly improved. Therefore, cell therapy based on iPSC-derived midbrain organoids can be a potential strategy for the clinical treatment of PD.

Application Value of Target Management Mode Based on Chronic Illness Trajectory Framework in Elderly Patients with Diabetes Mellitus and Cardiovascular Diseases.

Background: Diabetes and cardiovascular diseases represent significant global health challenges, leading to organ dysfunction and increased mortality rates. Managing these conditions is complex, especially in the elderly population. The study addresses this pressing issue by exploring the application of the Chronic Illness Trajectory Framework (CITF), aiming to improve self-care and quality of life in elderly patients with diabetes and cardiovascular diseases. Methods: A total of 127 patients with diabetes mellitus and cardiovascular diseases admitted to the hospital were enrolled between January 2020 and January 2022. According to the implementation of CITF management mode, they were divided into a control group (62 cases, non-implementation) and an observation group (65 cases, implementation). The control group was given routine intervention, while the observation group was given CITF-based target management mode for 3 months. The changes in blood glucose, blood lipid, negative emotions, self-efficacy, self-management, compliance, and quality of life before and after intervention in both groups were observed. This study was approved by the Ethics Committee of Zhujiang Hospital. Results: After intervention, levels of fasting plasma glucose (FPG), 2h plasma glucose (2hPG), hemoglobin A1c (HbA1c), total cholesterol (TC), triglyceride (TG) and low-density lipoprotein cholesterol (LDL-C), scores of self-rating depression scale (SDS), self-rating anxiety scale (SAS) and Diabetes Specific Quality of Life Scale (DSQL) were decreased (P < .05), while scores of General Self-Efficacy Scale (GSES) and Scale of the Diabetes Self-Care Activities Chinese version (SDSCA), and compliance rate were increased in both groups (P < .05). The levels of FPG, 2hPG, HbA1c, TC, TG, and LDL-C, scores of SDS, SAS, and DSQL in the observation group were lower than those in the control group (P < .001), and scores of GSES and SDSCA, and compliance rate were higher than those in the control group (P < .001). These results highlight the positive role of comprehensive intervention in improving the physical and mental health of patients with diabetes and provide strong support for the application of comprehensive intervention strategies in diabetes management. Conclusion: CITF-based target management mode can alleviate negative emotions in patients with diabetes mellitus and cardiovascular diseases, improve self-management, self-efficacy, and compliance, effectively control blood glucose and lipids, and improve quality of life. The study conclusions highlight the importance of CITF management models in improving the management of patients with diabetes and cardiovascular disease. This comprehensive intervention helps reduce negative emotions, improve self-management and compliance, effectively control blood sugar and blood lipids, and improve quality of life. These results have important clinical implications and provide strong support for better care of patients with chronic diseases.

Characterizing structural variants based on graph-genotyping provides insights into pig domestication and local adaption.

Structural variants (SVs), such as deletions (DELs) and insertions (INSs), contribute substantially to pig genetic diversity and phenotypic variation. Using a library of SVs discovered from long-read primary assemblies and short-read sequenced genomes, we map pig genomic SVs with a graph-based method for re-genotyping SVs in 402 genomes. Our results demonstrate that those SVs harboring specific trait-associated genes may greatly shape pig domestication and local adaptation. Further characterization of SVs reveals that some population-stratified SVs may alter the transcription of genes by affecting regulatory elements. We identify that the genotypes of two DELs (296-bp DEL, chr7: 52,172,101-52,172,397; 278-bp DEL, chr18: 23,840,143-23,840,421) located in muscle-specific enhancers are associated with the expression of target genes related to meat quality (FSD2) and muscle fiber hypertrophy (LMOD2 and WASL) in pigs. Our results highlight the role of SVs in domestic porcine evolution, and the identified candidate functional genes and SVs are valuable resources for future genomic research and breeding programs in pigs.

Fast DOA Estimation Algorithms via Positive Incremental Modified Cholesky Decomposition for Augmented Coprime Array Sensors.

This paper proposes a fast direction of arrival (DOA) estimation method based on positive incremental modified Cholesky decomposition atomic norm minimization (PI-CANM) for augmented coprime array sensors. The approach incorporates coprime sampling on the augmented array to generate a non-uniform, discontinuous virtual array. It then utilizes interpolation to convert this into a uniform, continuous virtual array. Based on this, the problem of DOA estimation is equivalently formulated as a gridless optimization problem, which is solved via atomic norm minimization to reconstruct a Hermitian Toeplitz covariance matrix. Furthermore, by positive incremental modified Cholesky decomposition, the covariance matrix is transformed from positive semi-definite to positive definite, which simplifies the constraint of optimization problem and reduces the complexity of the solution. Finally, the Multiple Signal Classification method is utilized to carry out statistical signal processing on the reconstructed covariance matrix, yielding initial DOA angle estimates. Experimental outcomes highlight that the PI-CANM algorithm surpasses other algorithms in estimation accuracy, demonstrating stability in difficult circumstances such as low signal-to-noise ratios and limited snapshots. Additionally, it boasts an impressive computational speed. This method enhances both the accuracy and computational efficiency of DOA estimation, showing potential for broad applicability.

Exploring the causal relationship between inflammatory cytokines and migraine: a bidirectional, two-sample Mendelian randomization study.

To further evaluate the causal relationships between inflammatory cytokines and migraine, we conducted a bidirectional, two-sample Mendelian randomization (MR) analysis using genetic data from publicly available genome-wide association studies (GWAS). We used several MR methods, including random-effect inverse-variance weighting (IVW), weighted median, MR-Egger, to test the causal relationships. Sensitivity analyses were also conducted to evaluate the robustness of the results. The results showed that hepatocyte growth factor (HGF) was positively associated with the risk of migraine (odds ratio [OR], 1.004; 95% confidence interval [CI], 1.001-1.008; P = 0.022). In addition, Interleukin-2 (IL-2) was considered a downstream consequence of migraine (OR, 0.012; 95% CI, 0.000-0.0929; P = 0.046). These findings suggest that HGF may be a factor associated with the etiology of migraine, while IL-2 is more likely to be involved in the downstream development of migraine.

A novel signature of the ligand and receptor genes associated with disulfidoptosis for prediction of prognosis, immunologic therapy responses in hepatocellular carcinoma.

Backgroud: We aimed to explore the prognostic features of ligand and receptor genes associated with disulfidoptosis in hepatocellular carcinoma (HCC) and establish a risk signature utilizing these genes to predict the prognosis of HCC patients. Methods: We used scRNA-seq data from GSE166635 to differentiate malignant cells from normal cells using "copykat".The study thoroughly examined the disparities in disulfidoptosis scores and the associated gene expressions between malignant and normal cells.We identified key ligand and receptor genes that are specific to HCC cells.Subsequently, Correlation analysis was conducted to ascertain the ligand and receptor genes associated with disulfidoptosis.We performed univariate Cox regression analysis to identify prognostic ligand and receptor genes associated with disulfidoptosis.We employed LASSO to construct a risk signature using prognostic ligand and receptor genes associated with disulfidoptosis.Lastly, we developed a nomogram model that integrates the risk signature with clinicopathological characteristics. Results: Malignant cells displayed a marked increase in disulfidoptosis scores and the expression of associated genes compared to normal cells.We identified 110 receptor and ligand genes significantly associated with disulfidoptosis, and narrowed them down to create a risk signature comprising eight genes.Multivariate analysis confirmed the risk signature as an independent prognostic factor for HCC and validated its predictive value for immunotherapy outcomes.A novel nomogram was developed, incorporating stage information and the risk signature derived from disulfidoptosis-related receptor and ligand genes, demonstrating excellent predictive accuracy and reliability in HCC prognosis prediction. Conclusion: Risk signatures based on disulfidoptosis-associated ligand and receptor genes can effectively predict HCC prognosis and may inform immunotherapy strategies.

Double graph correlation encryption based on hyperchaos.

Preventing unauthorized access to sensitive data has always been one of the main concerns in the field of information security. Accordingly, various solutions have been proposed to meet this requirement, among which encryption can be considered as one of the first and most effective solutions. The continuous increase in the computational power of computers and the rapid development of artificial intelligence techniques have made many previous encryption solutions not secure enough to protect data. Therefore, there is always a need to provide new and more efficient strategies for encrypting information. In this article, a two-way approach for information encryption based on chaos theory is presented. To this end, a new chaos model is first proposed. This model, in addition to having a larger key space and high sensitivity to slight key changes, can demonstrate a higher level of chaotic behavior compared to previous models. In the proposed method, first, the input is converted to a vector of bytes and first diffusion is applied on it. Then, the permutation order of chaotic sequence is used for diffusing bytes of data. In the next step, the chaotic sequence is used for applying second diffusion on confused data. Finally, to further reduce the data correlation, an iterative reversible rule-based model is used to apply final diffusion on data. The performance of the proposed method in encrypting image, text, and audio data was evaluated. The analysis of the test results showed that the proposed encryption strategy can demonstrate a pattern close to a random state by reducing data correlation at least 28.57% compared to previous works. Also, the data encrypted by proposed method, show at least 14.15% and 1.79% increment in terms of MSE and BER, respectively. In addition, key sensitivity of 10-28 and average entropy of 7.9993 in the proposed model, indicate its high resistance to brute-force, statistical, plaintext and differential attacks.

A lightweight network based on dual-stream feature fusion and dual-domain attention for white blood cells segmentation.

Introduction: Accurate white blood cells segmentation from cytopathological images is crucial for evaluating leukemia. However, segmentation is difficult in clinical practice. Given the very large numbers of cytopathological images to be processed, diagnosis becomes cumbersome and time consuming, and diagnostic accuracy is also closely related to experts' experience, fatigue and mood and so on. Besides, fully automatic white blood cells segmentation is challenging for several reasons. There exists cell deformation, blurred cell boundaries, and cell color differences, cells overlapping or adhesion. Methods: The proposed method improves the feature representation capability of the network while reducing parameters and computational redundancy by utilizing the feature reuse of Ghost module to reconstruct a lightweight backbone network. Additionally, a dual-stream feature fusion network (DFFN) based on the feature pyramid network is designed to enhance detailed information acquisition. Furthermore, a dual-domain attention module (DDAM) is developed to extract global features from both frequency and spatial domains simultaneously, resulting in better cell segmentation performance. Results: Experimental results on ALL-IDB and BCCD datasets demonstrate that our method outperforms existing instance segmentation networks such as Mask R-CNN, PointRend, MS R-CNN, SOLOv2, and YOLACT with an average precision (AP) of 87.41%, while significantly reducing parameters and computational cost. Discussion: Our method is significantly better than the current state-of-the-art single-stage methods in terms of both the number of parameters and FLOPs, and our method has the best performance among all compared methods. However, the performance of our method is still lower than the two-stage instance segmentation algorithms. in future work, how to design a more lightweight network model while ensuring a good accuracy will become an important problem.

RGSB-UNet: Hybrid Deep Learning Framework for Tumour Segmentation in Digital Pathology Images.

Colorectal cancer (CRC) is a prevalent gastrointestinal tumour with high incidence and mortality rates. Early screening for CRC can improve cure rates and reduce mortality. Recently, deep convolution neural network (CNN)-based pathological image diagnosis has been intensively studied to meet the challenge of time-consuming and labour-intense manual analysis of high-resolution whole slide images (WSIs). Despite the achievements made, deep CNN-based methods still suffer from some limitations, and the fundamental problem is that they cannot capture global features. To address this issue, we propose a hybrid deep learning framework (RGSB-UNet) for automatic tumour segmentation in WSIs. The framework adopts a UNet architecture that consists of the newly-designed residual ghost block with switchable normalization (RGS) and the bottleneck transformer (BoT) for downsampling to extract refined features, and the transposed convolution and 1 × 1 convolution with ReLU for upsampling to restore the feature map resolution to that of the original image. The proposed framework combines the advantages of the spatial-local correlation of CNNs and the long-distance feature dependencies of BoT, ensuring its capacity of extracting more refined features and robustness to varying batch sizes. Additionally, we consider a class-wise dice loss (CDL) function to train the segmentation network. The proposed network achieves state-of-the-art segmentation performance under small batch sizes. Experimental results on DigestPath2019 and GlaS datasets demonstrate that our proposed model produces superior evaluation scores and state-of-the-art segmentation results.

Withdrawal notice to: "Pan-cancer analyses of clinical prognosis, immune infiltration, and immunotherapy efficacy for TRPV family using multi-omics data" [Heliyon 9, (2023) 16897].

[This corrects the article DOI: 10.1016/j.heliyon.2023.e16897.].

Human Activity Recognition Method Based on FMCW Radar Sensor with Multi-Domain Feature Attention Fusion Network.

This paper proposes a human activity recognition (HAR) method for frequency-modulated continuous wave (FMCW) radar sensors. The method utilizes a multi-domain feature attention fusion network (MFAFN) model that addresses the limitation of relying on a single range or velocity feature to describe human activity. Specifically, the network fuses time-Doppler (TD) and time-range (TR) maps of human activities, resulting in a more comprehensive representation of the activities being performed. In the feature fusion phase, the multi-feature attention fusion module (MAFM) combines features of different depth levels by introducing a channel attention mechanism. Additionally, a multi-classification focus loss (MFL) function is applied to classify confusable samples. The experimental results demonstrate that the proposed method achieves 97.58% recognition accuracy on the dataset provided by the University of Glasgow, UK. Compared to existing HAR methods for the same dataset, the proposed method showed an improvement of about 0.9-5.5%, especially in the classification of confusable activities, showing an improvement of up to 18.33%.