Individual Variability in Brain Connectivity Patterns and Driving-Fatigue Dynamics.

Mental fatigue during driving poses significant risks to road safety, necessitating accurate assessment methods to mitigate potential hazards. This study explores the impact of individual variability in brain networks on driving fatigue assessment, hypothesizing that subject-specific connectivity patterns play a pivotal role in understanding fatigue dynamics. By conducting a linear regression analysis of subject-specific brain networks in different frequency bands, this research aims to elucidate the relationships between frequency-specific connectivity patterns and driving fatigue. As such, an EEG sustained driving simulation experiment was carried out, estimating individuals' brain networks using the Phase Lag Index (PLI) to capture shared connectivity patterns. The results unveiled notable variability in connectivity patterns across frequency bands, with the alpha band exhibiting heightened sensitivity to driving fatigue. Individualized connectivity analysis underscored the complexity of fatigue assessment and the potential for personalized approaches. These findings emphasize the importance of subject-specific brain networks in comprehending fatigue dynamics, while providing sensor space minimization, advocating for the development of efficient mobile sensor applications for real-time fatigue detection in driving scenarios.

Improvement of oxygen reduction activity and stability on a perovskite oxide surface by electrochemical potential.

The instability of the surface chemistry in transition metal oxide perovskites is the main factor hindering the long-term durability of oxygen electrodes in solid oxide electrochemical cells. The instability of surface chemistry is mainly due to the segregation of A-site dopants from the lattice to the surface. Here we report that cathodic potential can remarkably improve the stability in oxygen reduction reaction and electrochemical activity, by decomposing the near-surface region of the perovskite phase in a porous electrode made of La1-xSrxCo1-xFexO3 mixed with Sm0.2Ce0.8O1.9. Our approach combines X-ray photoelectron spectroscopy and secondary ion mass spectrometry for surface and sub-surface analysis. Formation of Ruddlesden-Popper phase is accompanied by suppression of the A-site dopant segregation, and exsolution of catalytically active Co particles onto the surface. These findings reveal the chemical and structural elements that maintain an active surface for oxygen reduction, and the cathodic potential is one way to generate these desirable chemistries.

Integrating Wearable Sensors and Machine Learning for the Detection of Critical Events in Industry Workers.

The event where an industry worker experiences some sort of critical health problems on site, due to factors not strictly related to the job, poses a serious concern and is an issue of research. These events can be mitigated almost entirely if the workers' health is being monitored in real time by an occupational physician along with an artificial intelligence system that can foresee a health incident and act fast and efficiently. For this reason, we developed a framework of devices, systems, and algorithms which help the industry workers along with the industries to monitor such events and, if possible, minimize them. The aforementioned framework performs seamlessly and autonomously and creates a system where the health of the industry workers is being monitored in real time. In the proposed solution, the worker would wear a wrist sensor in the form of a smartwatch as well as a blood pressure device on the ear. These sensors can communicate directly with a cloud storage system to store sensor data, and then real-time data analysis can be performed. Subsequently, all results can be displayed in an interface operated by an occupational physician, and in case of a health issue event, the doctor and the worker will be notified.

Identifying Network Biomarkers for Alzheimer's Disease Using Single-Cell RNA Sequencing Data.

System-level network-based approaches are an emerging field in the biomedical domain since biological networks can be used to analyze complicated biological processes and complex human disorders more efficiently. Network biomarkers are groups of interconnected molecular components causing perturbations in the entire network topology that can be used as indicators of pathogenic biological processes when studying a given disease. Although in the last years computational systems-based approaches have gained ground on the path to discovering new network biomarkers, in complex diseases like Alzheimer's disease (AD), this approach has still much to offer. Especially the adoption of single-cell RNA sequencing (scRNA-seq) has now become the dominant technology for the study of stochastic gene expression. Toward this orientation, we propose an R workflow that extracts disease-perturbed subpathways within a pathway network. We construct a gene-gene interaction network integrated with scRNA-seq expression profiles, and after network processing and pruning, the most active subnetworks are isolated from the entire network topology. The proposed methodology was applied on a real AD-based scRNA-seq data, providing already existing and new potential AD biomarkers in gene network context.

A Consensus Gene Regulatory Network for Neurodegenerative Diseases Using Single-Cell RNA-Seq Data.

Gene regulatory network (GRN) inference from gene expression data is a highly complex and challenging task in systems biology. Despite the challenges, GRNs have emerged, and for complex diseases such as neurodegenerative diseases, they have the potential to provide vital information and identify key regulators. However, every GRN method produced predicts results based on its assumptions, providing limited biological insights. For that reason, the current work focused on the development of an ensemble method from individual GRN methods to address this issue. Four state-of-the-art GRN algorithms were selected to form a consensus GRN from their common gene interactions. Each algorithm uses a different construction method, and for a more robust behavior, both static and dynamic methods were selected as well. The algorithms were applied to a scRNA-seq dataset from the CK-p25 mus musculus model during neurodegeneration. The top subnetworks were constructed from the consensus network, and potential key regulators were identified. The results also demonstrated the overlap between the algorithms for the current dataset and the necessity for an ensemble approach. This work aims to demonstrate the creation of an ensemble network and provide insights into whether a combination of different GRN methods can produce valuable results.

Controlling the Size of Au Nanoparticles on Reducible Oxides with the Electrochemical Potential.

Controlling the size of Au nanoparticles (NPs) and their interaction with the oxide support is important for their catalytic performance in chemical reactions, such as CO oxidation and water-gas shift. It is known that the oxygen vacancies at the surface of support oxides form strong chemical bonding with the Au NPs and inhibit their coarsening and deactivation. The resulting Au/oxygen vacancy interface also acts as an active site for oxidation reactions. Hence, small Au NPs are needed to increase the density of the Au/oxide interface. A dynamic way to control the size of the Au NPs on an oxide support is desirable but has been missing in the field. Here, we demonstrate an electrochemical method to control the size of the Au NPs by controlling the surface oxygen vacancy concentration of the support oxide. Oxides with different reducibilities, La0.8Ca0.2MnO3±Î´ and Pr0.1Ce0.9O2-δ, are used as model support oxides. By applying the electrochemical potential, we achieve a wide range of effective oxygen pressures, pO2 (10-37-1014 atm), in the support oxides. Applying the cathodic potential creates a high concentration of oxygen vacancies and forms finely distributed Au NPs with sizes of 7-13 nm at 700-770 °C in 10 min, while the anodic potential oxidizes the surface and increases the size of the Au NPs. The onset cathodic potential required to create small Au NPs depends strongly on the reducibility of the support oxide. The Au NPs did not undergo sintering even at 700-770 °C under the cathodic potential and also were stable in catalytically relevant conditions without potential.

Anodic Shock-Triggered Exsolution of Metal Nanoparticles from Perovskite Oxide.

Nanoparticles decorated electrodes (NDEs) are useful in fuel cells, electrolyzers, water treatment, and chemical synthesis. Here, we show that by rapidly bringing a mixed ionic-electronic conductor outside its electrochemical stability window, one can achieve uniform dispersion of metallic nanoparticles inside its bulk and at the surface and improve its electrocatalytic performance when back under normal functional conditions. Surprisingly, this can happen under anodic as well as cathodic current/voltage shocks in an ABO3 perovskite oxide, La0.4Ca0.4Ti0.88Fe0.06Ni0.06O3-δ (LCTFN), across a wide range of H2/O2 gas environments at 800 °C. One possible mechanism for bulk Fe0/Ni0 precipitation under anodic shock condition is the incomplete oxygen oxidation (O2- → Oα-, 0 < α < 2), migration and escape of oxygen to interfaces, and "whiplash" transition-metal reduction due to low electronic conductivity. We show that both cathodic and anodic shocks can produce NDEs to enhance electrocatalytic performance, potentially improving the flexibility of this approach in practical devices.

How Far Are We from the Completion of the Human Protein Interactome Reconstruction?

After more than fifteen years from the first high-throughput experiments for human protein-protein interaction (PPI) detection, we are still wondering how close the completion of the genome-scale human PPI network reconstruction is, what needs to be further explored and whether the biological insights gained from the holistic investigation of the current network are valid and useful. The unique structure of PICKLE, a meta-database of the human experimentally determined direct PPI network developed by our group, presently covering ~80% of the UniProtKB/Swiss-Prot reviewed human complete proteome, enables the evaluation of the interactome expansion by comparing the successive PICKLE releases since 2013. We observe a gradual overall increase of 39%, 182%, and 67% in protein nodes, PPIs, and supporting references, respectively. Our results indicate that, in recent years, (a) the PPI addition rate has decreased, (b) the new PPIs are largely determined by high-throughput experiments and mainly concern existing protein nodes and (c), as we had predicted earlier, most of the newly added protein nodes have a low degree. These observations, combined with a largely overlapping k-core between PICKLE releases and a network density increase, imply that an almost complete picture of a structurally defined network has been reached. The comparative unsupervised application of two clustering algorithms indicated that exploring the full interactome topology can reveal the protein neighborhoods involved in closely related biological processes as transcriptional regulation, cell signaling and multiprotein complexes such as the connexon complex associated with cancers. A well-reconstructed human protein interactome is a powerful tool in network biology and medicine research forming the basis for multi-omic and dynamic analyses.

A Sensor-Based Perspective in Early-Stage Parkinson's Disease: Current State and the Need for Machine Learning Processes.

Parkinson's disease (PD) is a progressive neurodegenerative disorder associated with dysfunction of dopaminergic neurons in the brain, lack of dopamine and the formation of abnormal Lewy body protein particles. PD is an idiopathic disease of the nervous system, characterized by motor and nonmotor manifestations without a discrete onset of symptoms until a substantial loss of neurons has already occurred, enabling early diagnosis very challenging. Sensor-based platforms have gained much attention in clinical practice screening various biological signals simultaneously and allowing researchers to quickly receive a huge number of biomarkers for diagnostic and prognostic purposes. The integration of machine learning into medical systems provides the potential for optimization of data collection, disease prediction through classification of symptoms and can strongly support data-driven clinical decisions. This work attempts to examine some of the facts and current situation of sensor-based approaches in PD diagnosis and discusses ensemble techniques using sensor-based data for developing machine learning models for personalized risk prediction. Additionally, a biosensing platform combined with clinical data processing and appropriate software is proposed in order to implement a complete diagnostic system for PD monitoring.

EEG Fingerprints of Task-Independent Mental Workload Discrimination.

In the nascent field of neuroergonomics, mental workload assessment is one of the most important issues and has an apparent significance in real-world applications. Although prior research has achieved efficient single-task classification, scatted studies on cross-task mental workload assessment usually result in unsatisfactory performance. Here, we introduce a data-driven analysis framework to overcome the challenges regarding task-independent workload assessment using a fusion of EEG spectral characteristics and unveil the common neural mechanisms underlying mental workload. Specifically, multi-frequency power spectrum and functional connectivity (FC) were estimated for two workload levels in two working-memory tasks performed by 40 healthy participants, subsequently being fed into a machine learning approach to obtain the importance of each feature vector and evaluate classification performance in a cross-task fashion. Our framework achieved a classification accuracy of 0.94 for task-independent mental workload discrimination. Further investigation of the designated features in terms of their spectral and localization properties revealed task-independent common patterns in the neural mechanisms governing workload. In particular, increased workload was associated with elevated frontal delta and theta power but reduced parietal alpha power, whereas FC exhibited complex frequency- and region-dependent alterations. By implication, the employment of the EEG feature fusion emphasized their utility in serving as promising indicators for different workload conditions applications.

PICKLE 3.0: enriching the human meta-database with the mouse protein interactome extended via mouse-human orthology.

SUMMARY: The PICKLE 3.0 upgrade refers to the enrichment of this human protein-protein interaction (PPI) meta-database with the mouse protein interactome. Experimental PPI data between mouse genetic entities are rather limited; however, they are substantially complemented by PPIs between mouse and human genetic entities. The relational scheme of PICKLE 3.0 has been amended to exploit the Mouse Genome Informatics mouse-human ortholog gene pair collection, enabling (i) the extension through orthology of the mouse interactome with potentially valid PPIs between mouse entities based on the experimental PPIs between mouse and human entities and (ii) the comparison between mouse and human PPI networks. Interestingly, 43.5% of the experimental mouse PPIs lacks a corresponding by orthology PPI in human, an inconsistency in need of further investigation. Overall, as primary mouse PPI datasets show a considerably limited overlap, PICKLE 3.0 provides a unique comprehensive representation of the mouse protein interactome. AVAILABILITY AND IMPLEMENTATION: PICKLE can be queried and downloaded at http://www.pickle.gr. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Mental Workload Drives Different Reorganizations of Functional Cortical Connectivity Between 2D and 3D Simulated Flight Experiments.

Despite the apparent usefulness of efficient mental workload assessment in various real-world situations, the underlying neural mechanism remains largely unknown, and studies of the mental workload are limited to well-controlled cognitive tasks using a 2D computer screen. In this paper, we investigated functional brain network alterations in a simulated flight experiment with three mental workload levels and compared the reorganization pattern between computer screen (2D) and virtual reality (3D) interfaces. We constructed multiband functional networks in electroencephalogram (EEG) source space, which were further assessed in terms of network efficiency and workload classification performances. We found that increased alpha band efficiencies and beta band local efficiency were associated with elevated mental workload levels, while beta band global efficiency exhibited distinct development trends between 2D and 3D interfaces. Furthermore, using a small subset of connectivity features, we achieved a satisfactory multi-level workload classification accuracy in both interfaces (82% for both 2D and 3D). Further inspection of these discriminative connectivity subsets, we found predominant alpha band connectivity features followed by beta and theta band features with different topological patterns between 2D and 3D interfaces. These findings allow for a more comprehensive interpretation of the neural mechanisms of mental workload in relation to real-world assessment.

Functional Connectivity Analysis of Mental Fatigue Reveals Different Network Topological Alterations Between Driving and Vigilance Tasks.

Despite the apparent importance of mental fatigue detection, a reliable application is hindered due to the incomprehensive understanding of the neural mechanisms of mental fatigue. In this paper, we investigated the topological alterations of functional brain networks in the theta band (4 - 7 Hz) of electroencephalography (EEG) data from 40 male subjects undergoing two distinct fatigue-inducing tasks: a low-intensity one-hour simulated driving and a high-demanding half-hour sustained attention task [psychomotor vigilance task (PVT)]. Behaviorally, subjects demonstrated a robust mental fatigue effect, as reflected by significantly declined performances in cognitive tasks prior and post these two tasks. Furthermore, characteristic path length presented a positive correlation with task duration, which led to a significant increase between the first and the last five minutes of both tasks, indicating a fatigue-related disruption in information processing efficiency. However, significantly increased clustering coefficient was revealed only in the driving task, suggesting distinct network reorganizations between the two fatigue-inducing tasks. Moreover, high accuracy (92% for driving; 97% for PVT) was achieved for fatigue classification with apparently different discriminative functional connectivity features. These findings augment our understanding of the complex nature of fatigue-related neural mechanisms and demonstrate the feasibility of using functional connectivity as neural biomarkers for applicable fatigue monitoring.

A mental fatigue index based on regression using mulitband EEG features with application in simulated driving.

Development of accurate fatigue level prediction models is of great importance for driving safety. In parallel, a limited number of sensors is a prerequisite for development of applicable wearable devices. Several EEG-based studies so far have performed classification in two or few levels, while others have proposed indices based on power ratios. Here, we utilized a regression Random Forest model in order to provide more accurate continuous fatigue level prediction. In detail, multiband power features were extracted from EEG data recorded from one hour simulated driving task. Next, cross-subject regression was performed to obtain common fatigue-related discriminative features. We achieved satisfactory prediction accuracy and simultaneously we minimized required electrodes, proposing to use a set of 3 electrodes.

Chromophobe Renal Cell Carcinoma and the Synchronous Presence of Primary Colon Malignancies. Is There a Relation?

While the presence of multiple primary malignancies in the same patient is a well described phenomenon, there is no clear association between various histological subtypes of renal cell carcinoma (RCC) and the synchronous presence of colon malignancies. We present the rare case of an 81-year-old female patient suffering from chromophobe renal cell carcinoma (chRCC) and an angiomyolipoma of her left kidney, synchronous with an adenocarcinoma of the caecum. While there is an established connection between RCC and colon cancer, a literature review is performed to specify this association in regard to chRCC and the synchronous presence of colon malignancies.

Neuroendocrine carcinoma as a rare cause of jejunal intussusception in an adult. Management and literature review.

Intussusception of the small bowel is an uncommon condition, with the majority of cases being observed during infancy. A number of points are responsible, with benign and malignant lesions of the small intestine being the most common. Herein, we present the case of a 75-year-old male patient with vague abdominal pain and black stool during defecation, who underwent surgery due to jejunal intussusception. Pathology report demonstrated a neuroendocrine carcinoma as the underlying cause for his condition, with no additional metastases during the initial diagnosis. Although a conservative approach for management of intussusception is viable, the possibility of gastric outlet obstruction and the presence of malignancy as the primary point usually lead to urgent surgery. In the case of malignancy, adjuvant chemotherapy or additional symptomatic therapy with close follow-up may be required depending on tumor's grade and aggressiveness.

Task-Independent Mental Workload Classification Based Upon Common Multiband EEG Cortical Connectivity.

Efficient classification of mental workload, an important issue in neuroscience, is limited, so far to single task, while cross-task classification remains a challenge. Furthermore, network approaches have emerged as a promising direction for studying the complex organization of the brain, enabling easier interpretation of various mental states. In this paper, using two mental tasks (N-back and mental arithmetic), we present a framework for cross- as well as within-task workload discrimination by utilizing multiband electroencephalography (EEG) cortical brain connectivity. In detail, we constructed functional networks in EEG source space in different frequency bands and considering the individual functional connections as classification features, we identified salient feature subsets based on a sequential feature selection algorithm. These connectivity subsets were able to provide accuracy of 87% for cross-task, 88% for N-back task, and 86% for mental arithmetic task. In conclusion, our method achieved to detect a small number of discriminative interactions among brain areas, leading to high accuracy in both within-task and cross-task classifications. In addition, the identified functional connectivity features, the majority of which were detected in frontal areas in theta and beta frequency bands, helped delineate the shared as well as the distinct neural mechanisms of the two mental tasks.

Strangulated Richter's incisional hernia presenting as an abdominal mass with necrosis of the overlapping skin. A case report and review of the literature.

Although rare, clinicians must be alerted of Richter hernias in patients presenting with innocuous gastrointestinal obstruction symptoms, synchronous with an erythematous hernia. An incarcerated incisional hernia located in a previous laparoscopic insertion site further raises suspicion, while timely surgical management is essential to avoid bowel perforation or resection.