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Objectives: This study investigates disruptions in functional brain networks in Parkinson's Disease (PD), using advanced modeling and machine learning. Functional networks were constructed using the Nonlinear Autoregressive Distributed Lag (NARDL) model, which captures nonlinear and asymmetric dependencies between regions of interest (ROIs). Key network metrics and information-theoretic measures were extracted to classify PD patients and healthy controls (HC), using deep learning models, with explainability methods employed to identify influential features. Methods: Resting-state fMRI data from the Parkinson's Progression Markers Initiative (PPMI) dataset were used to construct NARDL-based networks. Metrics, such as Degree, Closeness, Betweenness, and Eigenvector Centrality, along with Network Entropy and Complexity, were analyzed. Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Long Short-Term Memory (LSTM) models, classified PD and HC groups. Explainability techniques, including SHAP and LIME, identified significant features driving the classifications. Results: PD patients showed reduced Closeness (22%) and Betweenness Centrality (18%). CNN achieved 91% accuracy, with Network Entropy and Eigenvector Centrality identified as key features. Increased Network Entropy indicated heightened randomness in PD brain networks. Conclusions: NARDL-based analysis with interpretable deep learning effectively distinguishes PD from HC, offering insights into neural disruptions and potential personalized treatments for PD. [ABSTRACT FROM AUTHOR]

Bumblebees are crucial pollinators, providing essential ecosystem services and global food production. The success of pollination services relies on the interaction between sensory organs and the environment. The antenna functions as a versatile multi‐sensory organ, pivotal in mediating chemosensory/olfactory information, and governs adaptive responses to environmental changes. Despite an increasing number of RNA‐sequencing studies on insect antenna, comprehensive antennal transcriptome studies at the different life stages were not elucidated systematically. Here, we quantified the expression profile and dynamics of coding/microRNA genes of larval head and antennal tissues from early‐ and late‐stage pupa to the adult of Bombus terrestris as suitable model organism among pollinators. We further performed Pearson correlation analyses on the gene expression profiles of the antennal transcriptome from larval head tissue to adult stages, exploring both positive and negative expression trends. The positively correlated coding genes were primarily enriched in sensory perception of chemical stimuli, ion transport, transmembrane transport processes and olfactory receptor activity. Negatively correlated genes were mainly enriched in organic substance biosynthesis and regulatory mechanisms underlying larval body patterning and the formation of juvenile antennal structures. As post‐transcriptional regulators, miR‐1000‐5p, miR‐13b‐3p, miR‐263‐5p and miR‐252‐5p showed positive correlations, whereas miR‐315‐5p, miR‐92b‐3p, miR‐137‐3p, miR‐11‐3p and miR‐10‐3p exhibited negative correlations in antennal tissue. Notably, based on the inverse expression relationship, positively and negatively correlated microRNA (miRNA)–mRNA target pairs revealed that differentially expressed miRNAs predictively targeted genes involved in antennal development, shaping antennal structures and regulating antenna‐specific functions. Our data serve as a foundation for understanding stage‐specific antennal transcriptomes and large‐scale comparative analysis of transcriptomes in different insects. [ABSTRACT FROM AUTHOR]

This study examines the effectiveness of Convolutional Autoencoder (CAE) and Variational Autoencoder (VAE) models in detecting anomalies within occupational accident data from the Mining of Coal and Lignite (NACE05), Manufacture of Other Transport Equipment (NACE30), and Manufacture of Basic Metals (NACE24) sectors. By applying fractional dimension methods—Box Counting, Hall–Wood, Genton, and Wavelet—we aim to uncover hidden risks and complex patterns that traditional time series analyses often overlook. The results demonstrate that the VAE model consistently detects a broader range of anomalies, particularly in sectors with complex operational processes like NACE05 and NACE30. In contrast, the CAE model tends to focus on more specific, moderate anomalies. Among the fractional dimension methods, Genton and Hall–Wood reveal the most significant differences in anomaly detection performance between the models, while Box Counting and Wavelet yield more consistent outcomes across sectors. These findings suggest that integrating VAE models with appropriate fractional dimension methods can significantly enhance proactive risk management in high-risk industries by identifying a wider spectrum of safety-related anomalies. This approach offers practical insights for improving safety monitoring systems and contributes to the advancement of data-driven occupational safety practices. By enabling earlier detection of potential hazards, the study supports the development of more effective safety policies, and could lead to substantial improvements in workplace safety outcomes. [ABSTRACT FROM AUTHOR]

This study conducts an in-depth analysis of clustering small molecules using spectral geometry and deep learning techniques. We applied a spectral geometric approach to convert molecular structures into triangulated meshes and used the Laplace–Beltrami operator to derive significant geometric features. By examining the eigenvectors of these operators, we captured the intrinsic geometric properties of the molecules, aiding their classification and clustering. The research utilized four deep learning methods: Deep Belief Network, Convolutional Autoencoder, Variational Autoencoder, and Adversarial Autoencoder, each paired with k-means clustering at different cluster sizes. Clustering quality was evaluated using the Calinski–Harabasz and Davies–Bouldin indices, Silhouette Score, and standard deviation. Nonparametric tests were used to assess the impact of topological descriptors on clustering outcomes. Our results show that the DBN + k-means combination is the most effective, particularly at lower cluster counts, demonstrating significant sensitivity to structural variations. This study highlights the potential of integrating spectral geometry with deep learning for precise and efficient molecular clustering. [ABSTRACT FROM AUTHOR]

In this study, we present a model which represents the interaction of financial companies in their network. Since the long time series have a global memory effect, we present our model in the terms of fractional integro-differential equations. This model characterize the behavior of the complex network where vertices are the financial companies operating in XU100 and edges are formed by distance based on Pearson correlation coefficient. This behavior can be seen as the financial interactions of the agents. Hence, we first cluster the complex network in the terms of high modularity of the edges. Then, we give a system of fractional integro-differential equation model with two parameters. First parameter defines the strength of the connection of agents to their cluster. Hence, to estimate this parameter we use vibrational potential of each agent in their cluster. The second parameter in our model defines how much agents in a cluster affect each other. Therefore, we use the disparity measure of PMFGs of each cluster to estimate second parameter. To solve model numerically we use an efficient algorithmic decomposition method and concluded that those solutions are consistent with real world data. The model and the solutions we present with fractional derivative show that the real data of Borsa Istanbul Stock Exchange Market always seek for an equilibrium state.

Objectives Paraneoplastic arthritis (PA) is one of the paraneoplastic syndromes. Both laboratory and clinical findings similar to rheumatological diseases can be seen. In this study we aimed to present the clinical and laboratory findings, malignancy types and pathological diagnoses of patients with paraneoplastic arthritis. Methods In a multicentre retrospective study, 92 patients with PA from the last 10 years were included. Results Patients with PA and haematological malignancies exhibited the highest ratio of lymphomas (25.6%). The most common cancer detected in patients with solid malignancy and PA was lung cancer (41.5%). All malignant patients with PA had significant anti-CCP positivity compared with the healthy control group (P  = 0.014). Conclusion Although PA is a rare condition, it can be confused with many rheumatological diseases. The most commonly involved joint is the knee joint, followed by the ankle and hand/wrist. Autoantibody negativity, high lactate dehydrogenase level and arthritis unresponsive to treatment constitute important clues for diagnosis. [ABSTRACT FROM AUTHOR]

This study explores the connection between the fractal dimensions of time series representing sentiments regarding economic news and the fractal dimensions of correlation networks among companies listed in the Borsa Istanbul star section. While there have been many studies on the correlation between different time series, the investigation into the impact of fractal dimensions on correlation networks' dynamics has been somewhat restricted. This study investigates the correlation networks among companies listed in the Borsa Istanbul Stars segment, employing distance and topological filters. The network fractional dimensions are evaluated using the box counting and information dimension techniques. A convolutional neural network is employed to perform analysis of sentiments regarding on 2020 Turkish economic news. The network is trained on user comments and specifically built to identify fluctuations in news editorials. The Zemberek natural language processing framework is beneficial for data preprocessing. Identical analytical methods are employed to quantify the fractal dimensions of each sentiment time series. Experiments are performed on these measurements using various sliding window widths to ascertain both independence and causality. The findings indicate a substantial correlation between market behavior and the feelings expressed in economic news. [ABSTRACT FROM AUTHOR]

In this study, we present a mathematical model of socio-economic process of labor force migration. We present our model in the terms of distributed order fractional partial differential equation. The proposed model involves time varying diffusion coefficient. To solve this model we propose an implicit discrete scheme by using graded meshes, and study numerically labor migration flow data from Turkey along with other countries throughout the period of 1966–2014. [ABSTRACT FROM AUTHOR]

Objective: To retrospectively evaluate the effect of anti-tumor necrosis factor-alpha (TNF-α) drugs on hepatic and renal functions in patients with ankylosing spondylitis (AS). Methods: A total of 148 patients (89 male, 59 female) who were followed up for a minimum duration of 1 year on newly started anti TNF-α therapy were included. Patients were divided into 5 groups based on the TNF-α treatment received. Initially, pre-treatment BASDAI (Bath Ankylosing Spondylitis Disease Activity) scores and laboratory results were compared between the groups before the treatment. Also, ESR (erythrocyte sedimentation rate), C-reactive protein (CRP), aspartate aminotransferase (AST), alanine aminotransferase (ALT), urea, and creatinine values were compared before treatment and at 3, 6, and 12 months after treatment. Also presence of hematuria and proteinuria was examined. Results: Of the overall group, 68 (45%), 33 (22%), 23 (15%), 18 (12%), and 6 (4%) received golimumab, certolizumab, etanercept, adalimumab, and infliximab. Baseline demographic characteristics, disease activity scores, and laboratory parameters were comparable between the groups (P > .05). There was a significant decline in BASDAI scores from baseline at 12 months (pre-treatment 5.24 ± 0.5, 3.01 ± 0.48 post-treatment at 12 months, P < .001). Although there was an increase in AST and ALT from baseline to 3, 6, and 12 months of treatment, the values remained within normal range (P > .05). Also, there were no significant changes in mean creatinine levels (P > .05). There were no correlations between disease activity parameters (ESR, CRP, and BASDAI) and hepatic and renal functions (P > .05). Conclusion: No hepatotoxicity or nephrotoxicity were found in association with the use of anti-TNF-α agents over a 1 year period. However, hepatotoxicity and nephrotoxicity are among known adverse effects of these agents. Based on the existing literature data, routine monitoring of patients in terms of potential hepatic and renal toxicity before and after treatment remains a valid recommendation in clinical practice. [ABSTRACT FROM AUTHOR]

Nowadays, increase of analyzing stock markets as complex systems lead graph theory to play a key role. For instance, detecting graph communities is an important task in the analysis of stocks, and as planar maximally filtered graphs let us to get important information for the topology of the market. In this study, we first obtain correlation network representation of UK's leading stock market network by using a novel threshold method. Then, we determine vertex clusters by using modularity and analyze clusters in planar maximally filtered graph substructures. Our analyze include a new measure called weighted Gini index for measuring the sparsity. The main goal of this paper is to study the hierarchical evolution of the market communities throughout the Brexit referendum, which is known as the stress period for the stock market. Hence, the overall sample is divided into two sub-periods of pre-referendum, and post-referendum to obtain communities and hierarchical structures. Our results indicate that financial companies are leading elements of the clusters. Moreover, the significant changes within the network topologies are observed for insurance, consumer goods, consumer services, mining, and technology sectors whereas oil and gas and health care sectors have not been affected by Brexit stress. [ABSTRACT FROM AUTHOR]