Database Dataset Research Articles

Hospitalization Trends and Healthcare Resource Utilization for Fecal Impactions in Pediatric Patients with Functional Constipation.

Objectives: To analyze the clinical characteristics, trends in hospitalization, and healthcare resource utilization of pediatric patients with fecal impaction. Methods: We utilized the Healthcare Cost and Utilization Project (HCUP) databases, including the National Inpatient Sample (NIS) and the Kids Inpatient Database (KID) datasets from 2011 to 2019, to include all hospitalizations of patients up to 18 years of age with a primary diagnosis of (1) fecal impaction or (2) a primary diagnosis of abdominal pain or constipation with a secondary diagnosis of fecal impaction. The study analyzed various comorbid factors and clinical characteristics of these patients. For healthcare resource utilization, we analyzed the length of hospital stays and total hospital charges, adjusted for inflation. Results: A total of 23,570 admissions due to fecal impactions in children between the years 2011 and 2019 were analyzed, contributing to 0.18% of the total pediatric admissions. Hospitalization rates nearly doubled from 2011 (0.15%) to 2019 (0.29%). The mean hospitalization charges also trended upwards from 15,234 USD in 2011 to 22,487 USD in 2019. The inflation-adjusted annual rate of increase in hospital charges during this period was 5.9% per year. Aggressive fecal disimpaction procedures (either manual or surgical) were performed in approximately 3% of these admissions. Multivariate regression showed that older children (13-18 years of age) were more likely to require aggressive disimpaction. Female children, those with Hispanic ethnicity, and those with obesity were less likely to be associated with the need for disimpaction. Conclusions: Hospitalizations for fecal impaction have increased significantly over the past decade, creating a substantial burden on healthcare resources. Our study highlights the importance of aggressive outpatient management strategies with close follow-up for fecal impactions, which will potentially minimize these hospitalizations.

Exploratory analysis of the key role of immune function changes in BPD

AbstractBronchopulmonary dysplasia (BPD) is one of the most prevalent and severe chronic lung diseases in premature infants. The objective of the current study was to screen for key BPD‐associated genes and pathways by transcriptomic analysis from clinical patients and animal models. In our study, the differentially expressed genes were screened from 58 children with 14‐day BPD and 40 normal children in the GSE32472 dataset of the Gene Expression Omnibus database. Then, we identified four hub genes (Cd3e, Cd3g, Cd247, and Itk) and a signaling pathway (T cell receptor signaling pathway) by Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis and protein‐protein interaction analysis. The differential expression of the relevant pathways and gene sets among the groups was verified via GSEA analysis. Subsequently, a rat model of BPD with hyperoxia‐induced lung injury was established, and the transcriptome sequencing of the whole lung tissue was performed. A similar analysis was done on the sequencing data of the hub genes and associated pathway screening to verify the accuracy. Ultimately, quantitative polymerase chain reaction was performed to validate the transcriptomics data of core gene expression in the rat model. Our study revealed that the downregulation of the expression of the above four key genes in the course of BPD leads to a decrease in the function of T cell receptor signaling pathways, it causes immune dysfunction and increases the severity of lung inflammation as well as susceptibility to other respiratory infectious diseases.

TAC-UNet: transformer-assisted convolutional neural network for medical image segmentation.

Medical image segmentation is crucial for improving healthcare outcomes. Convolutional neural networks (CNNs) have been widely applied in medical image analysis; however, their inherent inductive biases limit their ability to capture global contextual information. Vision transformer (ViT) architectures address this limitation by leveraging attention mechanisms to model global relationships; however, they typically require large-scale datasets for effective training, which is challenging in the field of medical imaging due to limited data availability. This study aimed to integrate the advantages of CNN and ViT architectures to improve segmentation performance on small-scale medical image datasets. In this study, we established a U-shaped network architecture based on a Transformer-assisted convolutional neural network (TAC-UNet). The TAC-UNet is primarily composed of a hybrid structure integrating CNN and Transformer components. Specifically, the hybrid architecture follows a dual-path design in which the Transformer branch continuously conveys global contextual information to the CNN backbone. This allows the CNN backbone to enhance its global perception while building on the local features it extracts, thereby improving its ability to comprehend complex image structures. A channel cross-attention (CCA) module is also incorporated as a bridge between the encoder and decoder to better reconcile the semantic discrepancies between them. Detailed experiments on three public datasets were conducted. Specifically, our model was trained on 30 images from the Multi-organ Nucleus Segmentation (MoNuSeg) training dataset, 85 images from the Gland Segmentation (GlaS) training dataset, and 551 images from the Computer Vision Center Colorectal Cancer-Clinic Database (CVC-ClinicDB) dataset. We evaluated the performance of our model on the corresponding test sets. Our TAC-UNet achieved the best Dice scores (80.36%, 90.70%, and 91.81% on the MoNuSeg, GlaS, and CVC-ClinicDB datasets, respectively) of all the models. Compared to other CNN-based, Transformer-based, and hybrid methods, the TAC-UNet demonstrated significantly superior segmentation performance. Our TAC-UNet model showed advanced segmentation performance on small-scale medical image datasets. The detailed experimental results showed the effectiveness of the method. Our model's code is available at: https://github.com/hejlhello/TAC-UNet.

Classification of Duration in Global Terorism using ResNet

Terrorism is a global threat that affects the political, economic, and social stability of many countries. The number of victims killed is based on the duration of the terrorism incident. This study uses the Residual Network (ResNet) model to classify terrorism incidents based on the duration of the incident (less than 24 hours and more than 24 hours) using the Global Terrorism Database (GTD) dataset. The GTD data used covers terrorism incidents from 1970 to 2017, with a total of 181,691. After preprocessing the data by converting categorical features to numeric and removing missing values, the data was divided into training, validation, and test sets with a composition of 70%, 15%, and 15%. The results show that the ResNet model is able to achieve a validation accuracy of 99.61% and a validation loss value of 0.0183. These findings show that the ResNet model is effective in classifying the duration of terrorism incidents and has the potential to be used in the development of better terrorism prevention systems.

Diagnostic significance of serum hsa_circ_0000745 and hsa_circ_0001459 in ischemic stroke and its role in the prognosis of interventional therapy

ABSTRACT Objective We aimed to identify hsa_circ_0000745 and hsa_circ_0001459 expression, value as biomarkers in ischemic stroke (IS), and functions in BV2 cells. Methods RNA sequencing datasets in the GEO database were retrieved. The expression of circulating hsa_circ_0000745 and hsa_circ_0001459 was validated by RT-qPCR. The predictive values of hsa_circ_0000745 and hsa_circ_0001459 in the diagnosis and outcome of acute IS were evaluated using receiver operator characteristic curve analysis. BV2 cells were treated with lipopolysaccharide, followed by hsa_circ_0000745 or hsa_circ_0001459 downregulation and subsequent migration and apoptosis assay. The downstream miR-1287-5p was detected using the luciferase reporter gene assay. Results Hsa_circ_0000745 or hsa_circ_0001459 were upregulated in acute IS. Hsa_circ_0000745 or/and hsa_circ_0001459 differentiated between healthy control subjects and patients with IS, resulting in areas under curve (AUC) of 0.85 and 0.83, respectively. Hsa_circ_0000745 or hsa_circ_0001459 was positively correlated with serum pro-inflammatory cytokines and the NIHSS (P<0.001). Longitudinal and ROC analyses of hsa_circ_0001459 and hsa_circ_0000745 expression levels revealed the 90-day-outcome-predicting potential after stroke. Hsa_circ_0001459 and hsa_circ_0000745 promoted the apoptosis and inhibited the migration of LPS-induced BV2 cells. Hsa_circ_0001459 and hsa_circ_0000745 commonly sponged miR-1287-5p. Conclusions Hsa_circ_0001459 and hsa_circ_0000745 showed upregulations in IS and might have clinical utility as a diagnostic and outcome-predicting marker.

Brain tissue classification in hyperspectral images using multistage diffusion features and transformer.

Brain surgery is a widely practised and effective treatment for brain tumours, but accurately identifying and classifying tumour boundaries is crucial to maximise resection and avoid neurological complications. This precision in classification is essential for guiding surgical decisions and subsequent treatment planning. Hyperspectral (HS) imaging (HSI) is an emerging multidimensional optical imaging method that captures detailed spectral information across multiple wavelengths, allowing for the identification of nuanced differences in tissue composition, with the potential to enhance intraoperative tissue classification. However, current frameworks often require retraining models for each HSI to extract meaningful features, resulting in long processing times and high computational costs. Additionally, most methods utilise the deep semantic features at the end of the network for classification, ignoring the spatial details contained in the shallow features. To overcome these challenges, we propose a novel approach called MedDiffHSI, which combines diffusion and transformer techniques. Our method involves training an unsupervised learning framework based on the diffusion model to extract high-level and low-level spectral-spatial features from HSI. This approach eliminates the need for retraining of spectral-spatial feature learning model, thereby reducing time complexity. We then extract intermediate multistage features from different timestamps for classification using a pretrained denoising U-Net. To fully explore and exploit the rich contextual semantics and textual information hidden in the extracted diffusion feature, we utilise a spectral-spatial attention module. This module not only learns multistage information about features at different depths, but also extracts and enhances effective information from them. Finally, we employ a supervised transformer-based classifier with weighted majority voting (WMV) to perform the HSI classification. To validate our approach, we conduct comprehensive experiments on in vivo brain database data sets and also extend the analysis to include additional HSI data sets for breast cancer to evaluate the framework performance across different types of tissue. The results demonstrate that our framework outperforms existing approaches by using minimal training samples (5%) while achieving state-of-the-artperformance.

Are Nomograms Useful for Predicting Sentinel Lymph Node Status in Melanoma Patients?

ABSTRACTBackground and ObjectivesClinical nomograms have been developed to predict sentinel lymph node (SLN) status in early‐stage melanoma patients, but the clinical utility of these tools remains debatable. We created and validated a nomogram using data from a randomized clinical trial and assessed its accuracy against the well‐validated Melanoma Institute Australia (MIA) nomogram.MethodsWe developed our model to predict SLN status using logistic regression on clinicopathological patient data from the Multicenter Selective Lymphadenectomy Trial‐I. The model was externally validated using the National Cancer Database (NCDB) data set, and its performance was compared to that of the MIA nomogram.ResultsOur model had good discrimination between positive and negative SLNs, with a training set area under the curve (AUC) of 0.706 (0.661–0.751). Our model achieved an AUC of 0.715 (0.706–0.724) compared to 0.723 (0.715–0.731) with the MIA model, using the NCDB set.ConclusionOur model performed similarly to the MIA model, confirming that despite using different clinical features and data sets, no clinical nomogram is currently accurate enough for clinical use.

A New Breast Cancer Discovery Strategy: A Combined Outlier Rejection Technique and an Ensemble Classification Method.

Annually, many people worldwide lose their lives due to breast cancer, making it one of the most prevalent cancers in the world. Since the disease is becoming more common, early detection of breast cancer is essential to avoiding serious complications and possibly death as well. This research provides a novel Breast Cancer Discovery (BCD) strategy to aid patients by providing prompt and sensitive detection of breast cancer. The two primary steps that form the BCD are the Breast Cancer Discovery Step (BCDS) and the Pre-processing Step (P2S). In the P2S, the needed data are filtered from any non-informative data using three primary operations: data normalization, feature selection, and outlier rejection. Only then does the diagnostic model in the BCDS for precise diagnosis begin to be trained. The primary contribution of this research is the novel outlier rejection technique known as the Combined Outlier Rejection Technique (CORT). CORT is divided into two primary phases: (i) the Quick Rejection Phase (QRP), which is a quick phase utilizing a statistical method, and (ii) the Accurate Rejection Phase (ARP), which is a precise phase using an optimization method. Outliers are rapidly eliminated during the QRP using the standard deviation, and the remaining outliers are thoroughly eliminated during ARP via Binary Harris Hawk Optimization (BHHO). The P2S in the BCD strategy indicates that data normalization is a pre-processing approach used to find numeric values in the datasets that fall into a predetermined range. Information Gain (IG) is then used to choose the optimal subset of features, and CORT is used to reject incorrect training data. Furthermore, based on the filtered data from the P2S, an Ensemble Classification Method (ECM) is utilized in the BCDS to identify breast cancer patients. This method consists of three classifiers: Naïve Bayes (NB), K-Nearest Neighbors (KNN), and Support Vector Machine (SVM). The Wisconsin Breast Cancer Database (WBCD) dataset, which contains digital images of fine-needle aspiration samples collected from patients' breast masses, is used herein to compare the BCD strategy against several contemporary strategies. According to the outcomes of the experiment, the suggested method is very competitive. It achieves 0.987 accuracy, 0.013 error, 0.98 recall, 0.984 precision, and a run time of 3 s, outperforming all other methods from the literature.

Development of New Models Using Empirical Modeling of Global Solar Radiation and Its Application in Usak City, Turkey

With this study, 12 empirical models in the literature, 2 new models developed within the scope of this study, SARAH and CMSAF satellite-based models, COSMO and ERA5 re-analysis solar radiation data sets in the PVGIS database were compared in order to detect the monthly average global solar radiation coming to the horizontal plane of Usak province. New models developed within the scope of the study; it uses the region's temperature, cloudiness coefficient and sunset hour angle. In comparison of the datas within the scope of the study; coefficient of determination (R²), mean percent error (MPE), deviation error (MBE), root mean square error (RMSE), absolute relative error (ARE) parameters were used. As a result of the evaluations, the method that most successfully predicts the global solar radiation values of Usak province was tried to be determined. According to the monthly evaluation of the models; It was determined that 14 models and satellite-based systems have absolute relative error values below 5% in March-April-May-June, September-October and December. The most accurate estimates were realized for May in 16 of 18 different estimation methods used in the study. The coefficient of determination of empirical models and PVGIS data sets was above 0.97. When the success of the models was evaluated according to the RMSE values, It was determined that the logarithmic based Model 14 (0.90058 RMSE, 0.98327 R2, -1.079894 MPE, -0.05033 MBE, 0.185628 t) which was obtained by using the sunset hour angle and the max-min temperature difference developed within the scope of this study, made the most accurate estimations. COSMO data from spatial data (1.053134 RMSE, 0.979036 R2, -1.196348 MPE, -0.25105 MBE, 0.8141 t) made successful estimations, but the accuracy of the COSMO data was lower than the data estimated by Model 14. It was concluded that used the models and satellite-based systems were generally successful. As a result, In the studies to be carried out for the global solar radiation forecast of Usak province. It has been concluded that Model 14 developed within the scope of the study can be used in precise calculations and COSMO data from PVGIS datas can be used in more superficial or pre-feasibility studies.

AI federated learning based improvised random Forest classifier with error reduction mechanism for skewed data sets

PurposeReferred data set produces reliable information about the network flows and common attacks meeting with real-world criteria. Accordingly, this study aims to focus on the use of imbalanced intrusion detection benchmark knowledge discovery in database (KDD) data set. KDD data set is most preferably used by many researchers for experimentation and analysis. The proposed algorithm improvised random forest classification with error tuning factors (IRFCETF) deals with experimentation on KDD data set and evaluates the performance of a complete set of network traffic features through IRFCETF.Design/methodology/approachIn the current era of applications, the attention of researchers is immersed by a diverse number of existing time applications that deals with imbalanced data classification (ImDC). Real-time application areas, artificial intelligence (AI), Industrial Internet of Things (IIoT), etc. are dealing ImDC undergo with diverted classification performance due to skewed data distribution (SkDD). There are numerous application areas that deal with SkDD. Many of the data applications in AI and IIoT face the diverted data classification rate in SkDD. In recent advancements, there is an exponential expansion in the volume of computer network data and related application developments. Intrusion detection is one of the demanding applications of ImDC. The proposed study focusses on imbalanced intrusion benchmark data set, KDD data set and other benchmark data set with the proposed IRFCETF approach. IRFCETF justifies the enriched classification performance on imbalanced data set over the existing approach. The purpose of this work is to review imbalanced data applications in numerous application areas including AI and IIoT and tuning the performance with respect to principal component analysis. This study also focusses on the out-of-bag error performance-tuning factor.FindingsExperimental results on KDD data set shows that proposed algorithm gives enriched performance. For referred intrusion detection data set, IRFCETF classification accuracy is 99.57% and error rate is 0.43%.Research limitations/implicationsThis research work extended for further improvements in classification techniques with multiple correspondence analysis (MCA); hierarchical MCA can be focussed with the use of classification models for wide range of skewed data sets.Practical implicationsThe metrics enhancement is measurable and helpful in dealing with intrusion detection systems–related imbalanced applications in current application domains such as security, AI and IIoT digitization. Analytical results show improvised metrics of the proposed approach than other traditional machine learning algorithms. Thus, error-tuning parameter creates a measurable impact on classification accuracy is justified with the proposed IRFCETF.Social implicationsProposed algorithm is useful in numerous IIoT applications such as health care, machinery automation etc.Originality/valueThis research work addressed classification metric enhancement approach IRFCETF. The proposed method yields a test set categorization for each case with error reduction mechanism.

Abnormal Expression of COX5B Gene and Disorder of Mitochondrial Function in Cryptorchid Rats.

Cryptorchidism is one of the most common congenital malformations in the paediatric genitourinary tract. Data analysis of cryptorchidism-related datasets in the GEO database and gene sequencing results from our institution, along with bioinformatic analysis of the merged mitochondrial gene datasets, revealed that COX5B is differentially expressed in the testes of children with cryptorchidism. Its encoded protein has attracted our attention as a key component of the mitochondrial respiratory chain complex IV. This study aims to explore the COX5B gene expression changes and related mitochondrial issues in cryptorchid rats. For this purpose, we established a cryptorchid rat model by surgery and used molecular biology and biochemistry techniques to detect and analyse the expression level of the COX5B gene and mitochondrial function indexes. The results indicated a significant decrease in COX5B gene expression in the affected testis of cryptorchid rats. The knockdown of COX5B expression in TM3 cells could be observed as the aggravation of cellular senescence, which led to the reduction of proliferation, as well as accompanied by the obvious disorders of mitochondrial function, including the increase of ROS and the decrease of ATP, in which MMP was significantly reduced. This suggests that the COX5B gene may play an important role in cryptorchid testis-induced reproductive system damage and may be a new target for small molecule-targeted therapy.

Molecular docking and MD simulation approach to identify potential phytochemical lead molecule against triple negative breast cancer

Background Triple-negative breast cancers are defined as tumors that lack the expression of the estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2). It exhibits unique clinical and pathological features, is highly aggressive, and has a relatively poor prognosis and poor clinical outcome. Objective To identify a novel drug target protein against triple-negative breast cancer (TNBC) and potential phytochemical lead molecules against novel drug targets. Methods In this study, we retrieved TNBC samples from NGS and microarray datasets in the Gene Expression Omnibus database and employed a combination of differential gene expression studies, protein-protein interaction analysis, and network topology investigation to identify the target protein. Using molecular docking and molecular dynamics simulation studies, followed by Molecular Mechanics with Generalised Born Surface Area solvation, a potential lead molecule was identified. Result The androgen receptor (AR) was found to be the target protein, and 2-hydroxynaringenin was discovered to be a possible phytochemical lead molecule to combat TNBC. Upregulated genes with LogFC &gt; 1.25 and P-value &lt; 0.05 from the TNBC gene expression dataset were given to STRING tool to investigate the network topology, and androgen receptor (AR) was found to be an appropriate hub gene in the protein-protein interaction network. Phytochemicals that inhibit breast cancer were retrieved from the PubChem database and virtual screening was performed using PyRx against the AR protein. Based on Lipinski’s rule and ADMET properties, molecular interaction studies were analyzed using induced fit docking, wherein significant binding interactions were displayed by 2-hydroxynaringenin. Molecular dynamics studies and MM-GBSA of AR and the 2-hydroxynaringenin complex revealed strong and stable interactions. Conclusion AR was identified as a hub protein that is highly expressed in breast cancer and 2-hydroxynaringenin efficacy of counter TNBC needs to be investigated further in vitro and in vivo.

Lightweight skin cancer detection IP hardware implementation using cycle expansion and optimal computation arrays methods

Skin cancer is recognized as one of the most perilous diseases globally. In the field of medical image classification, precise identification of early-stage skin lesions is imperative for accurate diagnosis. However, deploying these algorithms on low-cost devices and attaining high-efficiency operation with minimal energy consumption poses a formidable challenge due to their intricate computational demands. This study proposes a lightweight hardware design based on a convolutional neural network (CNN) for real-time processing of skin disease classifiers on portable devices. Our skin cancer recognition processor utilizes an optimally parallel designed processing engine (PE) for global computation, which greatly reduces hardware resource utilization by multiplexing of computational unit circuits. In addition, a design approach that provides loop unrolling effectively reduces the number of data accesses, thereby reducing computational complexity and logic resource requirements. The hardware circuits in this design perform data inference in convolutional, pooling, and fully connected layers based on 16-bit floating-point numbers. Evaluation of the HAM10000 database dataset shows that the architecture achieves an average classification accuracy of 97.8 %. We are the first to implement an all-hardware FPGA-based skin cancer detection platform that offers a 3.5x speedup in recognition compared to existing skin cancer accelerators at 50 MHz while consuming only 0.48 W of power. The implementation of this hardware architecture meets the major constraints of portable devices, featuring low resource utilization, low power consumption, and cost-effectiveness, while still providing efficient classification and high accuracy results.

Exploring the Potential Molecular Mechanism of the Shugan Jieyu Capsule in the Treatment of Depression through Network Pharmacology, Molecular Docking, and Molecular Dynamics Simulation.

Shugan Jieyu Capsule (SJC) is a pure Chinese medicine compound prepared with Hypericum perforatum and Acanthopanacis senticosi. SJC has been approved for the clinical treatment of depression, but the mechanism of action is still unclear. Network pharmacology, molecular docking, and molecular dynamics simulation (MDS) were applied in the present study to explore the potential mechanism of SJC in the treatment of depression. TCMSP, BATMAN-TCM, and HERB databases were used, and related literature was reviewed to screen the effective active ingredients of Hypericum perforatum and Acanthopanacis senticosi. TCMSP, BATMAN-TCM, HERB, and STITCH databases were used to predict the potential targets of effective active ingredients. GeneCards database, DisGeNET database, and GEO data set were used to obtain depression targets and clarify the intersection targets of SJC and depression. STRING database and Cytoscape software were used to build a protein-protein interaction (PPI) network of intersection targets and screen the core targets. The enrichment analysis on the intersection targets was conducted. Then the receiver operator characteristic (ROC) curve was constructed to verify the core targets. The pharmacokinetic characteristics of core active ingredients were predicted by SwissADME and pkCSM. Molecular docking was performed to verify the docking activity of the core active ingredients and core targets, and molecular dynamics simulations were performed to evaluate the accuracy of the docking complex. We obtained 15 active ingredients and 308 potential drug targets with quercetin, kaempferol, luteolin, and hyperforin as the core active ingredients. We obtained 3598 targets of depression and 193 intersection targets of SJC and depression. A total of 9 core targets (AKT1, TNF, IL6, IL1B, VEGFA, JUN, CASP3, MAPK3, PTGS2) were screened with Cytoscape 3.8.2 software. A total of 442 GO entries and 165 KEGG pathways (p <0.01) were obtained from the enrichment analysis of the intersection targets, mainly enriched in IL-17, TNF, and MAPK signaling pathways. The pharmacokinetic characteristics of the 4 core active ingredients indicated that they could play a role in SJC antidepressants with fewer side effects. Molecular docking showed that the 4 core active components could effectively bind to the 8 core targets (AKT1, TNF, IL6, IL1B, VEGFA, JUN, CASP3, MAPK3, PTGS2), which were related to depression by the ROC curve. MDS showed that the docking complex was stable. SJC may treat depression by using active ingredients such as quercetin, kaempferol, luteolin, and hyperforin to regulate targets such as PTGS2 and CASP3 and signaling pathways such as IL-17, TNF, and MAPK, and participate in immune inflammation, oxidative stress, apoptosis, neurogenesis, etc.

Exploring Shared Genetic Features and Molecular Mechanisms between Non-Alcoholic Steatohepatitis and Hepatocellular Carcinoma through Bioinformatics.

Hepatocellular Carcinoma (HCC) is one of the most common malignant tumors in the world, characterized by high incidence, high malignancy, and low survival rate. Currently, 1/4 of adults in the world suffer from Non-Alcoholic Fatty Liver Disease (NAFLD), with an incidence rate of 27% in Asia. We used TCGA and GEO public database data sets to conduct weighted gene coexpression network analysis to identify relevant gene modules, defined the intersection of tumorigenesis-related modules and NASH development-related modules as shared genes, and then used single-factor Cox, LASSO, and multivariate Cox regression analysis screened out core shared genes and verified their prognostic value. We further investigated the relationship between core shared genes and immune infiltration, tumor mutational load, and drug sensitivity. Finally, RT-qPCR was used to verify its mRNA expression in different cell lines. We identified Karyopherin α 2 (KPNA2) as the core shared gene between NASH and HCC. Patients were divided into low-risk groups and high-risk groups based on the expression of KPNA2. The prognosis of the low-risk group was significantly better than that of the highrisk group. Furthermore, we found significant differences in tumor immune cell infiltration, somatic mutations, microsatellite instability, and drug sensitivity between different expression groups. There are very few studies on the molecular mechanism of the relationship between NAFLD and HCC. Our study demonstrates that KPNA2 is a potential therapeutic target and immune-related biomarker for patients with NAFLD and HCC.

Attribute Value Weighted Averaged One-Dependence Estimators with Kullback–Leibler Divergence

The Averaged One-Dependence Estimators (AODE) algorithm is an improvement of the naive Bayes algorithm, which allows all the attributes dependent on one common attribute, called parent attribute, thus forming One-Dependence Estimators (ODE). The classification probability is estimated by averaging the conditional probability of the ODE. When there is a dependency relationship between attributes, the AODE algorithm can better capture these relationships, thus improving classification performance. The AODE algorithm treats the parent and child attribute values equally in different ODEs. However, the parent attribute value and the child attribute value in different ODEs have different importance for classification. In this paper, two attribute value weighted AODE based on Kullback–Leibler divergence are proposed, one is parent attribute value weighted AODE, and the other is child attribute value weighted AODE. Comparative experiments were carried out with 30 datasets in the UCI database, and the experiments indicate that the performance of the parent attribute value weighted AODE algorithm with Kullback–Leibler divergence is significantly better than the original AODE algorithm, and its performance is also better than the mutual information weighted AODE algorithm.

Enhancing burned area monitoring with VIIRS dataset: A case study in Sub-Saharan Africa

Since 2001, the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor on board the Aqua and Terra platforms has made great strides in providing information on global burned areas (BA). However, the MODIS mission is nearing its end. The Visible Infrared Imaging Radiometer Suite (VIIRS) sensors, presented as the MODIS Aqua heritage, could be an excellent alternative to ensure the temporal continuity of this information at a moderate resolution. This paper describes and evaluates the effectiveness of our developed hybrid algorithm, which utilizes VIIRS reflectance and active fire products on the Google Earth Engine platform, in producing efficient information about BA. The study investigates the algorithm's performance in sub-Saharan Africa as the region of interest in 2019, using biweekly outputs and a spatial resolution of 250 m. The algorithm encompasses several steps, including pre-processing individual scenes, creating cloud-free composites, generating binary reference data for burned and non-burned areas, conducting a supervised classification using random forest, and performing region shaping. The VIIRS-BA final product, which includes three confidence levels (low, moderate, and high) known as the uncertainty layer, is compared to four other burned area products. The validation is conducted against 27 reference sampling units from the Sentinel-2 Burned Area Reference Database dataset, allowing for a comprehensive uncertainty assessment across five various biomes. The VIIRS-BA product identified 5.1 million km2 of BA, which was significantly larger than other global coarse resolution BA products such as FireCCI51, FireCCIS310, and MCD64A1 and close to the fine resolution FireCCISFD20 with a difference of 7.3%. The differences were less significant in biomes such as “Tropical Savannas” and “Temperate Grasslands” which are characterized by persistent biomass burning. Based on a stratified random sampling, the validation results demonstrate varying levels of accuracy for the VIIRS-BA product across different confidence levels. The commission error (CE) ranges from 7.8% to 23.4%, while the omission error (OE) falls between 29.4% and 58.8%. Notably, there is a significant reduction in OE (ranging from 40.7% to 50.5%) compared to global BA products like FireCCI51, FireCCIS310, and MCD64A1. When compared to VIIRS-BA, the FireCCISFD20 regional product has a 37% better OE performance. While VIIRS-BA shows great potential in detecting fires that global products miss, the VIIRS-BA with low confidence level tends to overestimate BA in regions with high fire activity. To address this, future versions of the algorithm will integrate the updated VIIRS reflectance data alongside VIIRS active fire from the National Oceanic and Atmospheric Administration to reduce CE and improve understanding spatial patterns.

RAB13 regulates macrophage polarization in sepsis

To select the core target (RAB13) in sepsis patients’ peripheral blood and investigate its molecular functions and possible mechanisms. The peripheral blood of sepsis patients (n = 21) and healthy individuals (n = 9) within 24 h after admission were collected for RNA-seq, and differential gene screening was performed by iDEP online analysis software (P < 0.01; log2FC ≥ 2) and enrichment analysis, the potential core target RAB13 was screened out. The association between RAB13 expression and sepsis severity was explored using multiple datasets in the GEO database, and survival analysis was conducted. Subsequently, peripheral blood mononuclear cells (PBMCs) from sepsis and control groups were isolated, and 10 × single-cell sequencing was used to identify the main RAB13-expressing cell types. Finally, LPS was used to stimulate THP1 cells to construct a sepsis model to explore the function and possible mechanism of RAB13. We found that RAB13 was a potential core target, and RAB13 expression level was positively associated with sepsis severity and negatively correlated with survival based on multiple public datasets. A single-cell sequencing indicated that RAB13 is predominantly localized in monocytes. Cell experiments validated that RAB13 is highly expressed in sepsis, and the knockdown of RAB13 promotes the polarization of macrophages towards the M2 phenotype. This mechanism may be associated with the ECM-receptor interaction signaling pathway. The upregulation of RAB13 in sepsis patients promotes the polarization of M2-like macrophages and correlates positively with the severity of sepsis.

Indoor fingerprint localization algorithm based on WKNN and LightGBM-GA

Abstract WiFi-based indoor fingerprint localization is widely used in indoor localization owing to its high accuracy and low deployment costs. Changes in the indoor signal environment directly affect localization accuracy. To improve localization accuracy and stability, this paper proposes a novel indoor fingerprint localization algorithm based on Weighted K-Nearest Neighbors (WKNN) and an enhanced Light Gradient Boosting Machine (LightGBM). First, in the offline phase, Gaussian filtering and K-Nearest Neighbors-Random Forest information completion algorithm with fusion of Euclidean and Manhattan distances are used to remove outliers from the fingerprint database dataset and fill in missing fingerprint information, ensuring the integrity of the fingerprint database. During the online phase, the fingerprint database is divided into training and testing sets. The LightGBM algorithm is used for modeling. Additionally, Genetic Algorithm (GA) is use d to optimize the parameters of LightGBM algorithm to find the best parameters by fitness evaluation. Then, the nearest neighbor set found by the WKNN algorithm is introduced into the LightGBM-GA model. Combining the predictions from the standalone LightGBM algorithm and performing weighted fusion yields the final predicted coordinates. The experiments are conducted in 8 m × 10 m laboratory containing 5 access points and 80 reference points to collect the Received Signal Strength Indication values of 5 WiFi hotspots. The experimental results show that the average localization error of the proposed algorithm is 1.11 m, which is reduced by 6.7%–38.3% compared to K-Nearest Neighbors (KNN), Extreme Gradient Boosting (XGBoost), LightGBM, KNN + XGBoost, WKNN + LightGBM, and WKNN + XGBoost-GA localization algorithms. The localization curve is smoother, and the cumulative distribution function converges faster. Moreover, the localization time is reduced by 13.3%–36.7%, effectively enhancing localization accuracy and decreasing localization time.

Multifeature Fusion Method with Metaheuristic Optimization for Automated Voice Pathology Detection

Voice pathologies occur due to various factors, such as malfunction of the vocal cords. Computerized acoustic examination-based vocal pathology detection is crucial for early diagnosis, efficient follow-up, and improving problematic speech. Different acoustic measurements provide it. Executing this process requires expert monitoring and is not preferred by patients because it is time-consuming and costly. This paper is aimed at detecting metaheuristic-based automatic voice pathology. First, feature maps of ten common diseases, including cordectomy, dysphonia, front lateral partial resection, contact pachyderma, laryngitis, lukoplakia, pure breath, recurrent laryngeal paralysis, vocal fold polyp, and vox senilis, were obtained from the Zero-Crossing Rate (ZCR), Root-Mean-Square Energy (RMSE), and Mel-frequency Cepstral Coefficients (MFCCs) using a thousand voice signals from the Saarbruecken Voice Database (SVD) dataset. Hybridizations of different features obtained from the voices of the same diseases using these three methods were used to increase the model's performance. The Grey Wolf Optimizer (MELGWO) algorithm based on local search, evolutionary operator, and concatenated feature maps derived from various approaches was employed to minimize the number of features, implement the models faster, and produce the best result. The fitness values of the metaheuristic algorithms were then determined using supervised machine learning techniques such as Support Vector Machine (SVM) and K-nearest neighbors (KNN). The F1 score, sensitivity, specificity, accuracy, and other assessment criteria were compared with the experimental data. The best accuracy result was achieved with 99.50% from the SVM classifier using the feature maps optimized by the improved MELGWO algorithms.