Online Datasets Research Articles

Noncoding RNA-Mediated High Expression of PFKFB3 Correlates with Poor Prognosis and Tumor Immune Infiltration of Lung Adenocarcinoma.

There is growing evidence showing that 6-phosphofructo-2-kinase (PFKFB3) plays crucial roles in different types of human cancers, including LUAD; however, the specific mechanism by which PFKFB3 plays a role in LUAD remains unclear. We investigated the expression of PFKFB3 and explored the underlying mechanism as well as the correlation with immune markers using several online datasets, such as Tumor Immune Estimate Resource (TIMER), UALCAN, and the Cancer Genome Atlas (TCGA) databases, miRWalk, Targetscan, MiRDB and starBase database. Western blot and immunohistochemistry analysis were performed to verify the corresponding outcomes. It was shown that the mRNA expression of PFKFB3 was lower in LUAD than in the normal tissues, while its protein expression was not consistent with the mRNA level. The expression of PFKFB3 was correlated with clinicopathological parameters and several signaling pathways. The potential long chain (lnc)RNA/microRNA/PFKFB3 axis and the possible mechanism by which tumor progression in LUAD is promoted was predicted. We obtained the LINC01798/LINC02086/AP000845.1/HAGLR-miR-17-5p-PFKFB3 axis after comprehensive analyses of expression, correlation, and survival. Moreover, the expression of PFKFB3 was positively correlated with immune cells and immune checkpoint expression, including PD-1, PD-L1 and CTLA-4. The present study demonstrated that noncoding RNAs mediated the upregulation of PFKFB3 and was associated with a poor prognosis and immune tumor infiltration in LUAD.

Onion supplementation and health metabolic parameters: A systematic review and meta-analysis of randomized controlled trials

Functional foods, such as onions, have been the center of many recent investigations. In this systematic-review and meta-analysis, we aimed to gather up the existing information with regard to the impact of onion supplementation on anthropometric measurements/indices, lipid profile, indices of glycemic control and hepatic health, systolic and diastolic blood pressures (SBP and DBP), and adiponectin and leptin. All major online datasets (PubMed, Web of Science, Cochrane, and Scopus) were thoroughly searched from inception up to October 2022. Relevant randomized controlled trials (RCTs) were obtained using the eligibility criteria. Weighted mean differences (WMDs) were calculated and reported. Statistical significance was set as p-values <0.05. Among all the retrieved data, 14 RCTs were eligible to be included. The results of the crude analysis showed that onion supplementation significantly improved body fat percentage (BFP), low-density lipoprotein-cholesterol (LDL-c), total cholesterol (TC), high-density lipoprotein-cholesterol (HDL-c), systolic blood pressure, adiponectin, and aspartate aminotransferase (AST). Subgroup analysis revealed that interventions lasting more than 12 weeks can significantly alter weight, waist circumference (WC), body mass index (BMI), BFP, LDL-c, TC, SBP, and DBP. Moreover, the intake of dosages of >300mg/day of onion supplementation could significantly improve weight, WC, BMI, BFP, LDL-c, HDL-C, TC, AST, ALT, SBP and DBP. It seems that the intake of onion supplementation can improve health metabolic parameters. We observed that with either longer follow-up periods or higher dosages of onion supplementation, improvements in cardio-metabolic parameters could be expected.

Classification of Pneumonia, Tuberculosis, and COVID-19 on Computed Tomography Images using Deep Learning

The accurate diagnosis of pneumonia, tuberculosis, and COVID-19 using computed tomography (CT) images is critical for radiologists. Artificial intelligence (AI) has been introduced as a tool to aid in rapid diagnosis. In this study, we evaluated 4 deep learning models, including AlexNet, GoogleNet, ResNet, and deep convolutional neural network (DCNN), to classify CT images of tuberculosis, pneumonia, and COVID-19. We collected 2,134 normal images, 943 images of tuberculosis, 2,041 images of pneumonia, and 3,917 images of COVID-19 from online datasets. To assess the efficiency of the models, we measured their image classification performance such as accuracy, F1 score, and area under the curve. Our performance evaluation indicated that ResNet was the highest-performing model, with the best accuracy, F1 score, and area under the curve (0.966, 0.931, 0.954, respectively). The second-best performing model was DCNN, while AlexNet and GoogleNet had the next-best performance, respectively. The deep learning models exhibit a capability that could be viewed as a substitute for predicting lung diseases and could be employed to support radiologists in CT image screening. HIGHLIGHTS The four deep learning models including AlexNet, GoogleNet, ResNet, and DCNN can classified for 4 classes (tuberculosis, pneumonia, COVID-19, and normal) The AUC indicates the model's ability to distinguish between positive and negative images, ResNet had the highest AUC for normal, pneumonia, and COVID-19, while AlexNet was highest for tuberculosis The ResNet model performed the best in terms of accuracy of model prediction, correctly predicting the labels for all 4 classes of input data (tuberculosis, pneumonia, COVID-19, and normal) GRAPHICAL ABSTRACT

Exploring Language Variation in Internet Buzzwords: A Case Study of the Phrase “Officially Diagnosed as XX”

Internet buzzwords, characterized by their interactivity and permeability, serve as indicators of the shifting emotional landscapes and emotional appeals of netizens within distinct temporal contexts. However, these buzzwords remain far from stagnant, continually undergoing linguistic variations influenced by the psychological dispositions of users and the ever-shifting environmental backdrop. In this case study, a comprehensive exploration of linguistic variation within internet buzzwords is undertaken, focusing intently on the dynamic phrase "officially diagnosed as XX" and its profound reflection of prevailing social mentalities and linguistic motivations. Guided by the conceptual framework of eco-linguistics, which offers a lens for comprehending the economic underpinnings of language and rationalizing the phenomena of internet buzzwords, this study delves into an intricate analysis. By meticulously dissecting online datasets and user commentary, the study unveils the multifaceted employment of "officially diagnosed as XX" as an instrument to articulate an array of emotions and cognitive states, ranging from humor to frustration and even self-deprecation. Through the prism of language evolution in the digital era, the study engages in a scholarly discourse on the intricate phenomenon of linguistic variation within the realm of internet discourse. Ultimately, this research not only illuminates the intricate dynamics between language, society, and technology in the contemporary age but also advances our comprehension of the profound interplay between language, emotion, and human communication within the digital realm.

RT-Ensemble Pred: A tool for retention time prediction of metabolites on different LC-MS systems

Liquid chromatography-mass spectrometry (LC-MS) could provide a large amount of information to assist in metabolites identification. Different liquid chromatographic methods (CMs) could produce different retention times to the same metabolite. To predict the retention time of local dataset by online datasets has become a trend, but the datasets downloaded from different databases were differences in quantity levels. And the imbalanced data could produce bad influence in model prediction. Thus, based on quantitative structure-retention relationships (QSRRs), an ensemble model, named RT-Ensemble Pred, has been successfully built to predict retention time of different LC-MS systems in this study. A total of 76, 807 metabolites (76, 909 retention times) have been collected across 9 CMs, and 19 natural products and 1 antifungal drug (20 retention times) have been collected to test the model applicability. An ensemble sampling was applied for the preprocessing procedure to solve the problem of imbalanced data. Based on the ensemble sampling, RT-Ensemble Pred could better utilize online datasets for the prediction of retention time. RT-Ensemble Pred was built based on the online datasets and tested by local dataset. The predictive accuracy of RT-Ensemble Pred was higher than the models without any sampling methods. The results showed that RT-Ensemble Pred could predict the metabolites which was not included in the database and the metabolites which were from new CMs. It could also be used for the prediction of other compounds beside metabolites. Furthermore, a tool of RT-Ensemble Pred was packed and can be freely downloaded at https://gitlab.com/mikic93/rt-ensemble-pred. It provides convenience for the users who need to predict the retention time of metabolites.

Examining urban polarization in five Spanish historic cities through online datasets and onsite perceptions

At present, the planning and management of historic districts are faced with the challenge of striking a balance between the needs of residents and seasonal pressures from visitors. These socially bustling sites could also benefit from the data cross-referencing of cultural and social patterns in order to identify areas for improvement. This research analyses geo-referenced online datasets and data from social media applications, subsequently contrasting these with onsite data from in-person interviews. These specific variables highlight parallels and conflicts between districts designated World Heritage areas in five Spanish cities. The study provides a quantitative analysis of hotspots and coldspots within the built environment. This is followed by an examination of these two types of areas using qualitative data linked to the three most challenging issues: housing and the built environment; basic services; and cultural services. When analysing the future of historic districts three major challenges to management highlighted in the results should be considered. Firstly, even in socially active districts, imbalances and dysfunctional areas are highlighted by both online data and onsite perceptions. Secondly, the study of the dynamics of districts for observing how stakeholders adapt to this social, economic, and mobility-related polarization. Thirdly, while the study acknowledges the changes to the consumption of culture, there is still potential for improvement in hosting alternative or countercultural movements.

Assessing Violence Risk among Far-Right Extremists: A New Role for Natural Language Processing

ABSTRACT A growing body of research suggests that an individual’s willingness to fight and die for groups is rooted in the fusion of personal and group identities, especially when the group is threatened, violence is condoned, and the group’s enemies are dehumanised or demonised. Here we consider whether the language used by extremists can help with early detection of these risk factors associated with violent extremism. We applied a new fusion-based linguistic violence risk assessment framework to a range of far-right extremist online groups from across the violence spectrum. We conducted an R-based NLP analysis to produce a Violence Risk Index, integrating statistically significant linguistic markers of terrorist manifestos as opposed to non-violent communiqués into one weighted risk assessment score for each group. The language-based violence risk scores for the far-right extremist groups were then compared to those of non-extremist control groups. We complemented our quantitative NLP analysis with qualitative insights that contextualise the violence markers detected in each group. Our results show that the fusion markers combined with several other variables identified across the different online datasets are indeed indicative of the real-world violence level associated with the relevant groups, pointing to new ways of detecting and preventing violent terrorism.

Varicolored Marble in the Central Metasedimentary Belt, Grenville Supergroup. I. Circumstantial Evidence of Melting

Abstract Large expanses of granulite-facies white marble occur in the Central Metasedimentary Belt of the Grenville orogen in western Quebec and in contiguous areas of eastern Ontario and New York. Locally, the white rock is transformed into “varicolored” marble, ranging from pink, orange, yellow, green, gray to blue marble, in some cases closely juxtaposed. We have dissected specimens from three target areas in western Quebec. A powerful analytical workflow including high-resolution ZEISS Atlas 5 large-area imaging was employed in combination with detailed energy-dispersive spectroscopy analyses to create digital map-like online datasets of entire samples to display the textures and characteristics in their entirety. We describe in detail the texture and mineralogy of marble specimens collected from exposures created during the construction of Autoroute 5 near Wakefield and of Autoroute 50 near Grenville-sur-la-Rouge and two specimens collected near Bryson, Quebec. At the inferred pressure and temperature and in the presence of H2O, the white marble seems to have melted. A carbonate melt, a powerful flux, can dissolve silicate wallrocks along a mutual contact. In this way, a marble-derived melt has assimilated wallrocks ranging from peridotite to granite. This melting event may have occurred more than once in the 350-million-year lifespan of the Grenville event; the thermal event associated with the Rigolet stage, during gravitational collapse of the orogen approximately one billion years ago, was the most intense. The marble samples contain no vestiges of older deformed assemblages. Whereas the varicolored marble undoubtedly is metasedimentary, a coeval influx of mantle-derived carbonatitic melt and related fluids from a subjacent complex may have caused localized metasomatism of the marble prior to and after melting.

Transcriptome sequencing and miRNA-mRNA network construction in exosome of macrophage M2 in stomach adenocarcinoma

BackgroundStomach adenocarcinoma (STAD) is the most common histological type of gastric cancer (GC). Macrophages are an essential part of the tumor microenvironment. We attempted to search for potential molecular markers associated with macrophages, which might be helpful for STAD diagnosis and treatment.MethodsFirstly, exosome in macrophages was extracted for RNA sequencing to identify differentially expressed microRNAs (miRNAs) (DEmiRNAs). Then, DEmiRNAs and differentially expressed mRNAs (DEmRNAs) were screened in the Cancer Genome Atlas (TCGA) database. The miRNAs related to macrophage M2 polarization were obtained by intersecting the DEmiRNAs obtained from the sequencing data and TCGA data. Using the Pearson correlation coefficient method, the mRNAs significantly related to macrophage M2 were screened out, followed by construction of the macrophage M2-miRNA-mRNA network. Subsequently, real-time-polymerase chain reaction (RT-PCR) and online datasets were applied to validate the expression of DEmiRNAs and DEmRNAs.ResultsA total of 6 DEmiRNAs were identified in RNA sequencing; 59 DEmiRNAs and 1838 DEmRNAs were identified in TCGA database. Among which, a common miRNA (hsa-miR-133a-3p) associated with the M2 polarization of macrophages was identified. Fifteen common mRNAs were obtained between DEmRNAs and mRNAs targeted by DEmiRNAs. Eventually, a core macrophage M2-1 down-regulated miRNA-7 and up-regulated mRNAs network was constructed, including hsa-miR-133a-3p, SLC39A1, TTYH3, HAVCR2, TPM3, XPO1, POU2F1, and MMP14. The expression of miRNA and mRNAs was in line with the validation results of RT-PCR and online datasets.ConclusionIn this study, the screening of biomarkers in exosome of macrophage M2 may contribute to the prognosis of STAD patients.

An Innovative Approach for Fake News Detection using Machine Learning

This research aims to increase people's awareness of fake news on online social networks and help them determine the reliability of information they consume. It investigates methods for detecting fake news sources, authors, and subjects on online social networks. The project uses an open-source online dataset of fake and real news to determine the credibility of news. Various text feature extraction techniques and classification algorithms are reviewed, with the Support Vector Machine (SVM) linear classification algorithm using TF-IDF feature extraction achieving the highest accuracy of 99.36%. Random Forest (RF) and Naive Bayes (NB) had accuracy scores of 98.25% and 94.74%, respectively.

Adaptive Policy Learning for Offline-to-Online Reinforcement Learning

Conventional reinforcement learning (RL) needs an environment to collect fresh data, which is impractical when online interactions are costly. Offline RL provides an alternative solution by directly learning from the previously collected dataset. However, it will yield unsatisfactory performance if the quality of the offline datasets is poor. In this paper, we consider an offline-to-online setting where the agent is first learned from the offline dataset and then trained online, and propose a framework called Adaptive Policy Learning for effectively taking advantage of offline and online data. Specifically, we explicitly consider the difference between the online and offline data and apply an adaptive update scheme accordingly, that is, a pessimistic update strategy for the offline dataset and an optimistic/greedy update scheme for the online dataset. Such a simple and effective method provides a way to mix the offline and online RL and achieve the best of both worlds. We further provide two detailed algorithms for implementing the framework through embedding value or policy-based RL algorithms into it. Finally, we conduct extensive experiments on popular continuous control tasks, and results show that our algorithm can learn the expert policy with high sample efficiency even when the quality of offline dataset is poor, e.g., random dataset.

Illustrating the application of a skills taxonomy, machine learning and online data to inform career and training decisions

PurposeThis paper aims to demonstrate how skills taxonomies can be used in combination with machine learning to integrate diverse online datasets and reveal skills gaps. The purpose of this study is then to show how the skills gaps revealed by the integrated datasets can be used to achieve better labour market alignment, keep educational offerings up to date and assist graduates to communicate the value of their qualifications.Design/methodology/approachUsing the ESCO taxonomy and natural language processing, this study captures skills data from three types of online data (job ads, course descriptions and resumes), allowing us to compare demand for skills and supply of skills for three different occupations.FindingsThis study illustrates three practical applications for the integrated data, showing how they can be used to help workers who are disrupted by technology to identify alternative career pathways, assist educators to identify gaps in their course offerings and support students to communicate the value of their training to employers.Originality/valueThis study builds upon existing applications of machine learning (detecting skills from a single dataset) by using the skills taxonomy to integrate three datasets. This study shows how these complementary, big datasets can be integrated to support greater alignment between the needs and offerings of educators, employers and job seekers.

B7H3 Role in Reshaping Immunosuppressive Landscape in MSI and MSS Colorectal Cancer Tumours.

The study aimed to assess the expression of B7H3 concerning clinicopathological and histological parameters, including MSI/MSS status, CD-8 cells, tumour-infiltrating lymphocytes (TILs), budding, TNM scale and grading. Moreover, we analyzed the B7H3-related pathways using available online datasets and the immunological context of B7H3 expression, through the 48-cytokine screening panel of cancer tissues homogenates, immunogenic features and immune composition. The study included 158 patients diagnosed with CRC. To assess B7H3 levels, we performed an immunohistochemistry method (IHC) and enzyme-linked immunosorbent assay (ELISA). To elucidate the immune composition of colorectal cancer, we performed the Bio-Plex Pro Human 48-cytokine panel. To study biological characteristics of B7H3, we used online databases. Expression of B7H3 was upregulated in CRC tumour tissues in comparison to adjacent noncancerous margin tissues. The concentrations of B7H3 in tumours were positively associated with T parameter of patients and negatively with tumour-infiltrating lymphocytes score. Additionally, Principal Component Analysis showed that B7H3 expression in tumours correlated positively with cytokines associated with M2-macrophages and protumour growth factors. The expression of B7H3 in tumours was independent of MSI/MSS status. These findings will improve our understanding of B7H3 role in colorectal cancer immunity. Our study suggests that B7-H3 is a promising potential target for cancer therapy. Further studies must clarify the mechanisms of B7H3 overexpression and its therapeutic importance in colorectal cancer.

Behavior-Aware English Reading Article Recommendation System Using Online Distilled Deep Q-Learning

Due to the differences of students' English proficiency and the rapid changes in reading interests, online personalized English reading recommendation is a highly challenging problem. Although some works have been proposed to address the dynamic change of recommendation, there are two issues with these methods. First, it only considers whether students have read the recommended articles. Second, these methods often fail to capture the real-time changing interests of users. To address the above challenges, a deep Q-network based recommendation framework was proposed. The authors further use the user's behavior and scores as reward information to get more user's feedback. In addition, a personalized adaptive module was introduced to capture the short-term interests on the fly and utilized the consistent loss of KL divergence to distill the knowledge from the online model. Extensive experiments on the offline and online dataset in the IWiLL website demonstrate the superior performance of the method.

Pretreatment radiomic biomarker for immunotherapy responder prediction in IB-IV stage NSCLC: A multi-center retrospective study.

e21060 Background: The current biomarker of immune checkpoint inhibitors (ICIs) efficacy is not sufficient. This study investigated the correlation between radiomic biomarker and immunotherapy responder in patients with IB-IV stage non-small-cell lung cancer (NSCLC), including its biological explanation for ICIs treatment response prediction. Methods: CT images from 319 pretreatment NSCLC patients receiving immunotherapy between January, 2015 and November, 2021 were retrospectively collected and were distributed into a discovery (n = 214), independent validation (n = 54), and external test cohort (n = 51). 851 features were extracted from both tumoral and each peri-tumoral VOIs. The reference standard is the durable clinical benefit (DCB) and the crucial exclusion criteria are missing pretreatment CT scans. The predictive value of combined radiomic signature (CRS) was assessed in a cohort of 98 resectable NSCLC patients receiving ICIs. The association of radiomic features and tumor immune landscape on the online dataset (n = 60) was also evaluated. A clinical model was constructed and incorporated with radiomic signature to improve performance further. Results: CRS discriminated between DCB and non-DCB patients in the training cohort (area under the curve (AUC): 0.82, 95% CI: 0.75 to 0.88)) and independent cohort (AUC: 0.75, 95%CI: 0.62 to 0.88). In this study, the predictive value of CRS was better than PD-L1 expression (AUC of PD-L1 subset: 0.59, 95%CI: 0.50 to 0.69) or clinical model (AUC of validation cohort: 0.66, 95%CI: 0.51 to 0.81). When predicting pathological response, CRS divided patients into a major pathological response (MPR) and non-MPR group (AUC: 0.76, 95%CI: 0.67 to 0.81). Moreover, CRS showed a promising stratified ability on overall survival (OS; HR: 0.48, 95%CI: 0.27 to 0.89; p = 0.020) and progression-free survival (PFS; HR: 0.43, 95%CI: 0.26 to 0.74; p = 0.002). Biological pathway analysis on online dataset revealed that dysregulation of Hedgehog and Hippo signaling pathways got involved in the prediction of radiomic features. Conclusions: By analyzing both tumoral and peri-tumoral regions of CT images in a radiomic strategy, we developed a non-invasive biomarker distinguishing responder from ICIs therapy, which might inform clinical decisions on the use of ICIs in advanced as well as resectable NSCLC patients.

Automatic Disease Detection in the Rice Leaves Employing a Support Vector Machine

Rice is a major agricultural crop around the world. Crop diseases, on the other hand, have the potential to reduce yield and quality greatly, posing a major danger to global food supplies. As a result, disease control is essential for rice production. Accurate and prompt disease diagnosis is critical to disease control success, which allows pesticide control measures to be implemented. The most common method for diagnosing rice leaf diseases is a manual decision-making based on disease appearance. There aren't enough skilled workers in the area, for such tasks to be completed on time. As a result, a more effective and convenient way of identifying rice leaf diseases is required. Therefore, this research creates an automatic diagnosis approach for rice leaf disease detection using deep learning. The proposed solution is built with deep learning techniques and a huge dataset containing 2,000 images of three types of rice diseases such as leaf blast, sheath blight, and brown spot, and healthy leaf. The proposed model's robustness is improved by using its real-world rice leaf datasets as well as publicly available online datasets. With an accuracy of 96.0%, the proposed deep-learning-based strategy proved successful in automatically diagnosing the three discriminative diseases of rice leaves. Furthermore, 99.25% of the time, the algorithm accurately detected a healthy rice leaf. The results demonstrate that the suggested deep learning model gives a highly effective technique for identifying rice leaf infections, and is capable of quickly and reliably identifying the most common rice diseases.

FMRI-Based Alzheimer's Disease Detection Using the SAS Method with Multi-Layer Perceptron Network.

In the present scenario, Alzheimer's Disease (AD) is one of the incurable neuro-degenerative disorders, which accounts for nearly 60% to 70% of dementia cases. Currently, several machine-learning approaches and neuroimaging modalities are utilized for diagnosing AD. Among the available neuroimaging modalities, functional Magnetic Resonance Imaging (fMRI) is extensively utilized for studying brain activities related to AD. However, analyzing complex brain structures in fMRI is a time-consuming and complex task; so, a novel automated model was proposed in this manuscript for early diagnosis of AD using fMRI images. Initially, the fMRI images are acquired from an online dataset: Alzheimer's Disease Neuroimaging Initiative (ADNI). Further, the quality of the acquired fMRI images was improved by implementing a normalization technique. Then, the Segmentation by Aggregating Superpixels (SAS) method was implemented for segmenting the brain regions (AD, Normal Controls (NC), Mild Cognitive Impairment (MCI), Early Mild Cognitive Impairment (EMCI), Late Mild Cognitive Impairment (LMCI), and Significant Memory Concern (SMC)) from the denoised fMRI images. From the segmented brain regions, feature vectors were extracted by employing Gabor and Gray Level Co-Occurrence Matrix (GLCM) techniques. The obtained feature vectors were dimensionally reduced by implementing Honey Badger Optimization Algorithm (HBOA) and fed to the Multi-Layer Perceptron (MLP) model for classifying the fMRI images as AD, NC, MCI, EMCI, LMCI, and SMC. The extensive investigation indicated that the presented model attained 99.44% of classification accuracy, 88.90% of Dice Similarity Coefficient (DSC), 90.82% of Jaccard Coefficient (JC), and 88.43% of Hausdorff Distance (HD). The attained results are better compared with the conventional segmentation and classification models.

OptEF-BCI: An Optimization-Based Hybrid EEG and fNIRS-Brain Computer Interface.

Multimodal data fusion (electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS)) has been developed as an important neuroimaging research field in order to circumvent the inherent limitations of individual modalities by combining complementary information from other modalities. This study employed an optimization-based feature selection algorithm to systematically investigate the complementary nature of multimodal fused features. After preprocessing the acquired data of both modalities (i.e., EEG and fNIRS), the temporal statistical features were computed separately with a 10 s interval for each modality. The computed features were fused to create a training vector. A wrapper-based binary enhanced whale optimization algorithm (E-WOA) was used to select the optimal/efficient fused feature subset using the support-vector-machine-based cost function. An online dataset of 29 healthy individuals was used to evaluate the performance of the proposed methodology. The findings suggest that the proposed approach enhances the classification performance by evaluating the degree of complementarity between characteristics and selecting the most efficient fused subset. The binary E-WOA feature selection approach showed a high classification rate (94.22 ± 5.39%). The classification performance exhibited a 3.85% increase compared with the conventional whale optimization algorithm. The proposed hybrid classification framework outperformed both the individual modalities and traditional feature selection classification (p < 0.01). These findings indicate the potential efficacy of the proposed framework for several neuroclinical applications.

Discrepancies between preliminary and final COVID-19 mortality data—the case of Serbia

PurposeSince the start of the COVID-19 pandemic, countries have scrambled to set up data collection and dissemination pipelines for various online datasets. This study aims to evaluate the reliability of the preliminary COVID-19 mortality data from Serbia, which has been included in major COVID-19 databases and utilized for research purposes worldwide. MethodsDiscrepancies between the preliminary mortality data and the final mortality data in Serbia were analyzed. The preliminary data were reported through an emergency-necessitated system, while the final data were generated by the regular vital statistics pipeline. We identified databases that include these data and conducted a literature review of articles that utilized them. ResultsThe number of deaths due to COVID-19 in Serbia, as reported preliminarily, does not align with the final death toll, which is more than three times higher. Our literature review identified at least 86 studies that were impacted by these problematic data. ConclusionsWe strongly advise researchers to disregard the preliminary COVID-19 mortality data from Serbia due to the significant discrepancies with the final data. We recommend validating any preliminary data using excess mortality if all-cause mortality data are available.

An extreme convolutional network model for brain disease prediction using smote and learning approaches

Brain disease is considered a major cause of increased mortality worldwide. Clinical decision support system (CDSS) is utilized for predicting individuals with brain disease in its earlier state. This work proposes a novel disease prediction approach for earlier prediction by handling the dataset issues, where an improved SMOTE sampling approach is used for balancing the target data distribution. Then, Extreme Convolutional Network Model ([Formula: see text] is used for predicting the disease with better accuracy. For the validation purpose, two publicly available ADNI-1 and ADNI-2 online datasets are used for the model construction, and the outcomes are compared with other techniques like Support Vector Machine (SVM), Artificial Neural Network (ANN), Voxel-based SVM (VW-SVM), standard Convolutional Neural Network (CNN), Deep Neural Network (DNN) and Weighted-Score Multimodal DNN (WS-MTDNN). The outcomes show that the proposed [Formula: see text] model outperforms various existing approaches with 94% and 95% accuracies on the input ADNI-1 and ADNI-2 datasets. Also, the CDSS-based framework is designed to assist the doctors in critical cases and help reduce the mortality rate.