Specific Network Research Articles

Anterior-temporal network hyperconnectivity is key to Alzheimer's disease: from ageing to dementia

Abstract Curing Alzheimer’s disease remains hampered by an incomplete understanding of its pathophysiology and progression. Exploring dysfunction in medial temporal lobe networks, particularly the anterior-temporal (AT) and posterior-medial (PM) systems, may provide key insights, as these networks exhibit functional connectivity alterations along the entire Alzheimer’s continuum, potentially influencing disease propagation. However, the specific changes in each network and their clinical relevance across stages are not yet fully understood. This requires considering commonly used biomarkers, clinical progression, individual variability, and age confounds. Here, we leveraged monocentric longitudinal data from 261 participants spanning the adult lifespan and the Alzheimer’s continuum. The sample included cognitively unimpaired adults aged 19 to 85 years (n = 209; eight out of 64 older adults over 60 were Aβ-positive) and Aβ-positive patients fulfilling diagnostic criteria for mild cognitive impairment (MCI, n = 26; 18 progressed to Alzheimer-dementia within seven years) or Alzheimer’s type dementia (n = 26). Participants underwent structural and resting-state functional (f) MRI, florbetapir and FDG-PET, and global cognitive assessments, with up to three visits over a maximum period of 47 months. Network connectivity was assessed using seed-based analyses with the perirhinal and parahippocampal cortices as seeds, within data-driven masks reflecting the AT and PM networks. Generalized additive and linear mixed models were run to assess age-specific effects and Alzheimer’s-related alterations. In this context, we explored various markers of pathological and clinical severity, including cerebral amyloid uptake, glucose metabolism, hippocampal volume, global cognition, diagnostic staging, and time to dementia onset. Our findings revealed distinct patterns of connectivity linked to normal aging or Alzheimer’s disease. Advancing age throughout adulthood was associated with lower PM connectivity and more subtle changes in AT connectivity, while Alzheimer’s disease was characterised by AT hyperconnectivity without global changes in PM connectivity. Specifically, AT connectivity was higher in MCI and Alzheimer-dementia patients compared to older controls and was positively associated with amyloid burden, glucose hypometabolism, hippocampal atrophy, and global cognitive deficits in older adults, ranging from unimpaired to demented. Additionally, higher AT connectivity correlated with faster progression to Alzheimer-dementia in MCI patients. This comprehensive approach allowed to reveal that excessive connectivity within the AT network is intrinsically linked to the pathological and clinical progression of Alzheimer’s disease. These insights may guide future research to better understand cascading events leading to the disease and hold promise for developing prognostic tools and therapeutic interventions targeting these specific network alterations.

Cell-Type Specific miRNA Regulatory Network Responses to ABA Stress Revealed by Time Series Transcriptional Atlases in Arabidopsis.

In plants, microRNAs (miRNAs) participate in complex gene regulatory networks together with the transcription factors (TFs) in response to biotic and abiotic stresses. To date, analyses of miRNAs-induced transcriptome remodeling are at the whole plant or tissue levels. Here, Arabidopsis's ABA-induced single-cell RNA-seq (scRNA-seq) is performed at different stages of time points-early, middle, and late. Single-cell level primary miRNAs (pri-miRNAs) atlas supported the rapid, dynamic, and cell-type specific miRNA responses under ABA treatment. MiRNAs respond rapidly and prior to target gene expression dynamics, and these rapid response miRNAs are highly cell-type specific, especially in mesophyll and vascular cells. MiRNA-TF-mRNA regulation modules are identified by identifying miRNA-contained feed-forward loops (M-FFLs) in the regulatory network, and regulatory networks with M-FFLs have higher co-expression and clustering coefficient (CC) values than those without M-FFLs, suggesting the hub role of miRNAs in regulatory networks. The cell-type-specific M-FFLs are regulated by these hub miRNAs rather than TFs through sc-RNA-seq network analysis. MiR858a-FBH3-MYB module inhibited the expression of MYB63 and MYB20, which related to the formation of plant secondary wall and the production of lignin, through M-FFL specifically in vascular. These results can provide prominent insights into miRNAs' dynamic and cell-type-specific roles in plant development and stress responses.

Network formation dynamics in asymmetric coordination games

Previous research has examined how endogenous network formation decisions affect consensus or segregation in coordination games with asymmetric payoffs. Agents were assumed to evaluate tie formation or termination based on prospective coordination payoffs and associated cost constraints. The present study extends this line of research by investigating how preferences for specific network configurations influence network formation decisions beyond prospective coordination payoffs. We built an agent-based model with N self-interested agents optimizing a utility function that included: (a) prospective coordination payoffs; (b) agent-specific network formation costs; and (c) network-based preferences, that is, preferences for connecting with high-degree, popular partners, regardless of their choices in the coordination dilemma. We explored the trade-offs and interactions between coordination dynamics, network formation costs, and popularity-based preferences across various simulation scenarios. Our results show that when costs are not prohibitively high, popularity-related network preferences leading to more centralized networks promote more efficient coordination on a consensus outcome. Conversely, segregated and disconnected networks tend to emerge when network formation costs are very low, or the relative importance of popularity-related preferences is lower compared to coordination payoffs.

Establishment of a CRISPR-Based Lentiviral Activation Library for Transcription Factor Screening in Porcine Cells.

Transcription factors play important roles in the growth and development of various tissues in pigs, such as muscle, fat, and bone. A transcription-factor-scale activation library based on the clustered, regularly interspaced, short palindromic repeat (CRISPR)/CRISPR-associated endonuclease Cas9 (Cas9) system could facilitate the discovery and functional characterization of the transcription genes involved in a specific gene network. Here, we have designed and constructed a CRISPR activation (CRISPRa) sgRNA library, containing 5056 sgRNAs targeting the promoter region of 1264 transcription factors in pigs. The sgRNA library, including sgRNA with MS2 loops, is a single-vector system and is packaged with lentivirus for cell screening. Porcine PK15 cells expressing the porcine OCT4 promoter driving EGFP, dCas9 fused with VP64, and MS2-binding protein-p65-HSF1 were constructed, and then, the sgRNA activation library was used to screen the transcription factors regulating OCT4 expression. After the lentiviral transduction and deep sequencing of the CRISPR sgRNAs library, the highest ranking candidate genes were identified, including 31 transcription factors activating OCT4 gene expression and 5 transcription factors inhibiting OCT4 gene expression. The function and gene regulation of the candidate genes were further confirmed by the CRISPR activation system in PK15 cells. The CRISPR activation library targeting pig transcription factors provides a promising platform for the systematic discovery and study of genes that determine cell fate.

Transdiagnostic Network Localization of Social, Language, and Motor Symptoms in Patients With Frontotemporal Lobar Degeneration.

Frontotemporal lobar degeneration (FTLD) includes different clinical syndromes with distinct patterns of symptoms and neuroanatomical locations of neurodegeneration. However, FTLD is clinically heterogeneous (with overlapping symptoms across several domains) and neuroanatomically heterogeneous (with brain atrophy in different locations in different patients). Traditional methods struggle to fully account for this heterogeneity. In this study, we use a relatively new neuroimaging approach, atrophy network mapping, to localize clinical symptoms in patients with FTLD to specific brain networks transdiagnostically. Data were obtained from the Frontotemporal Lobar Degeneration Neuroimaging Initiative and 4-Repeat Tauopathy Neuroimaging Initiative. Inclusion required T1-weighted MRI and a diagnosis of behavioral-variant frontotemporal dementia (bvFTD), semantic-variant primary progressive aphasia (svPPA), nonfluent primary progressive aphasia (nfvPPA), progressive supranuclear palsy Richardson syndrome (PSP-rs), corticobasal syndrome (CBS), or normal cognition. Measures of social cognition (Interpersonal Reactivity Index, Revised Self-Monitoring Scale), language (Boston Naming Test, Animal Fluency), and motor function (Unified Parkinson Disease Rating Scale Part III, PSP Rating Scale) were correlated with neuroimaging measures, including cortical thickness, volume, and atrophy network mapping, a newer method that localizes regions connected to brain atrophy using a functional connectome from cognitively normal persons (n = 1,000). Fifty-seven patients with bvFTD (age 61.2 ± 6.8 years, 35% female), 41 PSP-rs (age 69.7 ± 7.4 years, 54% female), 39 CBS (age 66.2 ± 6.2 years, 51% female), 37 svPPA (age 63.0 ± 6.0 years, 46% female), and 36 nfvPPA (age 68.3 ± 7.3 years, 53% female) and 135 healthy age-matched controls (age 63.3 ± 7.4 years, 58% female) were included. Compared with atrophy alone, atrophy network mapping showed more consistent neuroimaging results across patients with the same clinical syndrome (Dice index mean 0.68 vs 0.11, paired t = 263.1, df = 4,452, p < 0.001), more strongly explained social cognition (F(1, 84) = 10.2, p = 0.002) and motor symptoms (F(1, 185) = 91.3, p < 0.001) across different syndromes, and showed novel neuroanatomical associations, with the temporal parietal junction relating to social cognition; Wernicke area relating to language symptoms; and association sensorimotor cortex, thalamus, and cerebellum relating to motor symptoms. Atrophy network mapping can improve understanding of brain-behavior relationships in clinically and neuroanatomically heterogeneous disorders such as FTLD.

A Comprehensive Multi-Objective Optimization Study on the Thermodynamic Performance of a Supercritical CO2 Brayton Cycle Incorporating Multi-Stage Main Compressor Intermediate Cooling

This study proposes a supercritical carbon dioxide Brayton cycle incorporating multi-stage main compressor intermediate cooling (MMCIC sCO2 Brayton cycle), and conducts an in-depth investigation and discussion on the enhancement of its thermodynamic performance. With the aim of achieving the maximum power cycle thermal efficiency and the maximum specific net work, this study examines the variation of the Pareto frontier with respect to the number of intermediate cooling stages and critical operational parameters. The results indicate that the MMCIC sCO2 Brayton cycle offers significant advantages in improving power cycle thermal efficiency, reducing energy consumption, and mitigating the adverse effects associated with main compressor inlet temperature increasing. Under the investigated operational conditions, the optimal cycle performance is achieved with four intermediate cooling stages, yielding a maximum power cycle thermal efficiency of 67.85% and a maximum specific net work of 0.177 MW·kg−1. Cycles with two or three intermediate cooling stages also deliver competitive cycle performance, and can be regarded as alternative options. Additionally, increasing the turbine inlet temperature proves more effective for enhancing power cycle thermal efficiency, whereas increasing the turbine inlet pressure can substantially improve the specific net work. This study provides a feasible structural layout approach and research framework to improve the thermodynamic performance of the sCO2 Brayton cycle, offering a robust theoretical foundation and technical guidance for its implementation in power engineering.

The Effect of Processing Techniques on the Classification Accuracy of Brain-Computer Interface Systems.

Background/Objectives: Accurately classifying Electroencephalography (EEG) signals is essential for the effective operation of Brain-Computer Interfaces (BCI), which is needed for reliable neurorehabilitation applications. However, many factors in the processing pipeline can influence classification performance. The objective of this study is to assess the effects of different processing steps on classification accuracy in EEG-based BCI systems. Methods: This study explores the impact of various processing techniques and stages, including the FASTER algorithm for artifact rejection (AR), frequency filtering, transfer learning, and cropped training. The Physionet dataset, consisting of four motor imagery classes, was used as input due to its relatively large number of subjects. The raw EEG was tested with EEGNet and Shallow ConvNet. To examine the impact of adding a spatial dimension to the input data, we also used the Multi-branch Conv3D Net and developed two new models, Conv2D Net and Conv3D Net. Results: Our analysis showed that classification accuracy can be affected by many factors at every stage. Applying the AR method, for instance, can either enhance or degrade classification performance, depending on the subject and the specific network architecture. Transfer learning was effective in improving the performance of all networks for both raw and artifact-rejected data. However, the improvement in classification accuracy for artifact-rejected data was less pronounced compared to unfiltered data, resulting in reduced precision. For instance, the best classifier achieved 46.1% accuracy on unfiltered data, which increased to 63.5% with transfer learning. In the filtered case, accuracy rose from 45.5% to only 55.9% when transfer learning was applied. An unexpected outcome regarding frequency filtering was observed: networks demonstrated better classification performance when focusing on lower-frequency components. Higher frequency ranges were more discriminative for EEGNet and Shallow ConvNet, but only when cropped training was applied. Conclusions: The findings of this study highlight the complex interaction between processing techniques and neural network performance, emphasizing the necessity for customized processing approaches tailored to specific subjects and network architectures.

Significant Advances in Application Resiliency: The Data Engineering Perspective on Network Performance Metrics

Purpose: The purpose of the article is to explore how network metrics like latency, packet loss, and throughput, combined with application logs, can help organizations improve the reliability and performance of their applications. It focuses on how these insights support Site Reliability Engineering (SRE) teams in proactively addressing issues to achieve better application resiliency, enhancing user experience and market trust. Methodology: The article uses a combination of case studies and analysis to demonstrate how monitoring specific network metrics and application logs helps identify and resolve performance issues. It examines real-world scenarios where proactive adjustments based on these metrics improved application reliability and aligned with organizational objectives. Findings: The findings show that by analyzing network metrics and application logs, organizations can pinpoint causes of transaction failures, such as high latency, packet loss, or misconfigured firewalls. Proactive resolutions based on these insights result in smoother application performance, reduced downtime, and increased user satisfaction. Unique Contribution to theory, practice and policy: This article makes valuable contributions to theory, practice, and policy. For theory, it expands the understanding of how network metrics and application logs can work together to improve application resiliency, offering a framework for integrating Site Reliability Engineering (SRE) principles with network observability. For practice, it provides clear, actionable steps for SRE teams to identify and resolve performance issues, helping organizations enhance reliability and user satisfaction. For policy, it highlights the importance of proactive network monitoring and metric-driven decision-making, encouraging organizations to adopt policies that prioritize resiliency, ensure consistent performance, and meet service-level agreements (SLAs).

Multi-modality NDE fusion using encoder–decoder networks for identify multiple neurological disorders from EEG signals

Background The complexity and diversity of brain activity patterns make it difficult to accurately diagnose neurological disorders such epilepsy, Parkinson's disease, schizophrenia, stroke, and Alzheimer's disease. Integrated and effective analysis of multiple data sources is often beyond the scope of traditional diagnostic procedures. With the use of multi-modal data, recent developments in neural network approaches present encouraging opportunities for raising diagnostic accuracy. Objectives A novel approach has been proposed toward the integration of different Nondestructive Evaluation data with EEG signals for improving the diagnosis of neurological disorders such as stroke, epilepsy, Parkinson's disease, and schizophrenia, by leveraging advanced neural network techniques in order to improve the identification and correlation of shared latent features across heterogeneous NDE datasets. Methods We determined the 2D scalogram images using a specific encoder-decoder neural network after transforming the EEG signals using wavelet signal processing. Several NDE data types can be easily integrated for thorough analysis due to this network's ability to extract and correlate important aspects from each form of data. Aiming to uncover common patterns indicating of neurological disorders, the technique was evaluated on datasets containing EEG signals and corresponding NDE data. Results Our method demonstrated a significant improvement in diagnostic accuracy and efficiency. The encoder-decoder network effectively identified shared latent features across the heterogeneous NDE datasets, leading to more precise and reliable diagnoses. The fusion of multi-modality NDE data with EEG signals provided a robust framework for the automatic identification of multiple neurological disorders. Conclusions This innovative approach represents a substantial advancement in the field of neurological disorder diagnosis. By integrating diverse NDE data with EEG signals through advanced neural network techniques, we have developed a method that enhances the accuracy and efficiency of diagnosing multiple neurological conditions. This fusion of multi-modality data has the potential to revolutionize current diagnostic practices in neurology, paving the way for more precise and automated identification of neurological disorders.

Theta/Beta Ratio Neurofeedback Effects on Resting and Task-Related Theta Activity in Children with ADHD.

The EEG theta band displays distinct roles in resting and task states. Low resting theta and transient increases in frontal-midline (fm) theta power during tasks are associated with better cognitive control, such as error monitoring. ADHD can disrupt this balance, resulting in high resting theta linked to drowsiness and low fm-theta activity associated with reduced cognitive abilities. Theta/beta ratio (TBR) neurofeedback aims to normalize resting state activity by downregulating theta, which could potentially unfavorably affect task-related fm-theta. This study examines the TBR neurofeedback's impact on both resting and fm-theta activity, hypothesizing that remission depends on these effects. We analyzed data from a multi-center, double-blind randomized controlled trial with 142 children with ADHD and high TBR (ICAN study). Participants were randomized into experimental or sham NF groups. EEG measurements were taken at rest and during an Oddball task before and after neurofeedback, assessing global electrodes for resting theta and fm electrodes during error dynamics. Post-intervention changes were calculated as differences, and ANOVAs were conducted on GROUP, REMISSION, and CONDITION variables. Final analysis included fewer participants for all analyses. Resting state analysis showed no significant effects on global or fm-theta after TBR neurofeedback. Error dynamics analysis was inconclusive for global and fm-theta in both remitters and non-remitters. Results suggest that the current TBR neurofeedback protocol did not reduce aberrant resting state theta, and emphasize the need for refined protocols targeting specific theta-band networks to reduce resting-state theta without affecting fm-theta related to cognitive control.

Brain structural damage networks at different stages of schizophrenia.

Neuroimaging studies have documented brain structural changes in schizophrenia at different stages of the illness, including clinical high-risk (cHR), genetic high-risk (gHR), first-episode schizophrenia (FES), and chronic schizophrenia (ChS). There is growing awareness that neuropathological processes associated with a disease fail to map to a specific brain region but do map to a specific brain network. We sought to investigate brain structural damage networks across different stages of schizophrenia. We initially identified gray matter alterations in 523 cHR, 855 gHR, 2162 FES, and 2640 ChS individuals relative to 6963 healthy controls. By applying novel functional connectivity network mapping to large-scale discovery and validation resting-state functional magnetic resonance imaging datasets, we mapped these affected brain locations to four specific networks. Brain structural damage networks of cHR and gHR had limited and non-overlapping spatial distributions, with the former mainly involving the frontoparietal network and the latter principally implicating the subcortical network, indicative of distinct neuropathological mechanisms underlying cHR and gHR. By contrast, brain structural damage networks of FES and ChS manifested as similar patterns of widespread brain areas predominantly involving the somatomotor, ventral attention, and subcortical networks, suggesting an emergence of more prominent brain structural abnormalities with illness onset that have trait-like stability over time. Our findings may not only provide a refined picture of schizophrenia neuropathology from a network perspective, but also potentially contribute to more targeted and effective intervention strategies for individuals at different schizophrenia stages.

Predicting terrain effects on blast waves: an artificial neural network approach

AbstractLarge yield airbursts generate powerful outdoor blast waves. Over long propagation distances, the blast is significantly altered by the topographical relief. Usually, the terrain effects are quantified by running accurate but expensive hydrodynamics or CFD codes. We present an alternative approach based on artificial neural networks, which is applicable wherever the blast–relief interaction can be approximated by an axisymmetric configuration. A database of overpressures associated with a very large sample of the French topography is constructed by running a high-fidelity hydrodynamics code. The proposed neural networks then learn the relationship between the relief geometry and the ground overpressures. The predictive ability of the networks is assessed extensively over a test database for several error metrics. $${97}{\%}$$ 97 % of the peak overpressure predictions can be considered accurate for most practical purposes, and the pressure impulse predictions are even more accurate. Finally, specific artificial neural networks able to estimate the model uncertainties are presented and their performances are discussed.

Brain tumor-related epilepsy: an overview on neuropsychological, behavioral, and quality of life issues and assessment methodology.

Brain tumor-related epilepsy (BTRE) is a rare disease in which brain tumor (BT) and epilepsy overlap simultaneously and can have a negative impact on a patient's neuropsychological, behavioral, and quality of life (QoL) spheres. In this review we (a) addressed the main neuropsychological, behavioral, and QoL issues that may occur in BTRE patients, (b) described how BT, BTRE, and their respective treatments can impact these domains, and (c) identified tools and standardized evaluation methodologies specific for BTRE patients. Neuropsychological disorders and behavioral issues can be direct consequences of BTRE and all related treatments, such as surgery, anti-cancer and anti-seizure medication, corticosteroids, etc., which can alter the structure of specific brain areas and networks, and by emotional aspects reactive to BTRE diagnosis, including the possible loss of autonomy, poor prognosis, and fear of death. Unfortunately, it seems there is a lack of uniformity in assessment methodologies, such as the administration of different batteries of neuropsychological tests, different times, frames, and purposes. Further research is needed to establish causality and deepen our understanding of the interplay between all these variables and our intervention in terms of diagnosis, treatment, psychosocial assessment, and their timing. We propose that the care of these patients to rely on the concepts of "BTRE-induced disability" and "biopsychosocial model" of BTRE, to prompt healthcare providers to handle and monitor BTRE-related psychological and social aspects, as to maintain the patient's best possible QoL.

Analysis of Expectations in Routine Appointments: A Cross-Sectional Study in Portugal

Preventive medicine is a subject of study due to the increasing evidence that it can cause more harm than good, and the population's interest in routine appointments is widely recognized. The main objective of this study was to understand users' expectations regarding routine appointments in primary health care and compare them to doctors' perceptions regarding these expectations. We carried out a cross-sectional observational study in 2023 through the application of two questionnaires: one for adult patients and another for family physicians. The invitations to answer the questionnaire via Google Forms were shared on specific social networks. For elderly patients, the questionnaire was conducted in-person by one of the researchers. It consisted of 25 questions about preventive attitudes during appointments, analytical tests and their frequency, and recommendations based on sex and age, differing in the language used. A sample of 225 patients and 100 general and family medicine doctors was obtained. The patients selected an average of 7.8 ± 9.7 preventive attitudes during the consultation, and the doctors judged that they would have selected 4.6 ± 2.9 (p = 0.001). Cardiac and pulmonary auscultation (82.6%), physical exercise quantification (74.7%), and smoking habits assessment (72.9%) were the most selected measures, the most selected tests being blood glucose measurement (81.8%), lipid profile (80.4%), and urine analysis (75.1%). For 68.2% of patients, routine check-ups should be annual, with 88.0% of doctors believing that patients would want this frequency. The socioeconomic level of the population did not significantly influence the results. It is important that general practitioners / family doctors are aware of patients' expectations. In this sample, patients revealed a greater appreciation for a higher number of preventive medical measures during consultations and more periodic analytical testing than thought by the doctors. Doctors also judged that users would show greater interest in the frequent implementation of screening programs.

Recent trends in multiple metrics and multimodal analysis for neural activity and pupillometry.

Recent studies focusing on neural activity captured by neuroimaging modalities have provided various metrics for elucidating the functional networks and dynamics of the entire brain. Functional magnetic resonance imaging (fMRI) can depict spatiotemporal functional neural networks and dynamic characteristics due to its excellent spatial resolution. However, its temporal resolution is limited. Neuroimaging modalities such as electroencephalography (EEG) and magnetoencephalography (MEG), which have higher temporal resolutions, are utilized for multi-temporal scale and multi-frequency-band analyzes. With this advantage, numerous EEG/MEG-bases studies have revealed the frequency-band specific functional networks involving dynamic functional connectivity and multiple temporal-scale time-series patterns of neural activity. In addition to analyzing neural data, the examination of behavioral data can unveil additional aspects of brain activity through unimodal and multimodal data analyzes performed using appropriate integration techniques. Among the behavioral data assessments, pupillometry can provide comprehensive spatial-temporal-specific features of neural activity. In this perspective, we summarize the recent progress in the development of metrics for analyzing neural data obtained from neuroimaging modalities such as fMRI, EEG, and MEG, as well as behavioral data, with a special focus on pupillometry data. First, we review the typical metrics of neural activity, emphasizing functional connectivity, complexity, dynamic functional connectivity, and dynamic state transitions of whole-brain activity. Second, we examine the metrics related to the time-series data of pupillary diameters and discuss the possibility of multimodal metrics that combine neural and pupillometry data. Finally, we discuss future perspectives on these multiple and multimodal metrics.

Transcriptome Profiling Implicates Non-Coding RNAs Involved in Flowering and Floral Organ Development in Water Lily

In this study, we performed small RNA and whole-transcriptome sequencing of different tissues of Nymphaea minuta to systematically investigate the roles and regulatory mechanisms of miRNA, lncRNA, and circRNA in the regulation of flowering-related target genes. Fifteen samples were sequenced using the Illumina platform, with strict data quality control to ensure the reliability of the analysis. By applying multiple bioinformatics tools, miRNA, lncRNA, and circRNA were comprehensively identified, annotated, and functionally analyzed, with a focus on screening non-coding RNAs closely related to the flowering process. The results showed significant differential expression of these miRNAs and lncRNAs across different tissues, which influenced the expression of flowering-related genes through specific regulatory networks. The constructed gene co-expression network further revealed the central roles of these non-coding RNAs in flowering regulation. This study provides new insights into the flowering regulatory mechanisms of N. minuta, highlights the potential of this species for studying aquatic plant flowering mechanisms, and provides an important theoretical basis for gene function research in aquatic plants.

TMODINET: A trustworthy multi-omics dynamic learning integration network for cancer diagnostic

Multiple types of omics data contain a wealth of biomedical information which reflect different aspects of clinical samples. Multi-omics integrated analysis is more likely to lead to more accurate clinical decisions. Existing cancer diagnostic methods based on multi-omics data integration mainly focus on the classification accuracy of the model, while neglecting the interpretability of the internal mechanism and the reliability of the results, which are crucial in specific domains such as precision medicine and the life sciences. To overcome this limitation, we propose a trustworthy multi-omics dynamic learning framework (TMODINET) for cancer diagnostic. The framework employs multi-omics adaptive dynamic learning to process each sample to provide patient-centered personality diagnosis by using self-attentional learning of features and modalities. To characterize the correlation between samples well, we introduce a graph dynamic learning method which can adaptively adjust the graph structure according to the specific classification results for specific graph convolutional networks (GCN) learning. Moreover, we utilize an uncertainty mechanism by employing Dirichlet distribution and Dempster-Shafer theory to obtain uncertainty and integrate multi-omics data at the decision level, ensuring trustworthy for cancer diagnosis. Extensive experiments on four real-world multimodal medical datasets are conducted. Compared to state-of-the-art methods, the superior performance and trustworthiness of our proposed algorithm are clearly validated. Our model has great potential for clinical diagnosis.

Interrogating the cognitive‐behavioural impairment in a motor‐dominant neurodegeneration (ALS), based on brain state dynamics of resting‐state EEG

AbstractBackgroundAmyotrophic lateral sclerosis (ALS) shares pathological and genetic underpinnings with frontotemporal dementia (FTD). ALS manifests with diverse symptoms, including progressive neuro‐motor degeneration, muscle weakness, but also cognitive‐behavioural changes in up to half of the cases. Resting‐state EEG measures, particularly spectral power and functional connectivity, have been instrumental for discerning abnormal motor and cognitive network function in ALS [1]–[3]. Based on our recent findings using EEG microstates [4], we hypothesized that dynamic analysis of time‐varying spectral EEG measures at source‐level can further interrogate the altered functional network that underpin the cognition and behaviour in ALS as a motor‐dominant neurodegeneration.MethodFor this purpose, we aimed to identify transient brain states associated with specific functional networks and to characterise the spatio‐temporal and spectral alterations in these brain states using high‐density resting‐state EEG, recorded from 99 individuals with ALS and 78 healthy controls (HC). Using a time‐delay embedded Hidden‐Markov‐Model on source‐reconstructed EEG data [5], we identified transient and recurrent brain states characterised by spectral power and coherence. The model was trained to convert source‐reconstructed time courses into sequences of functional networks (brain states).ResultTwelve brain states were identified with distinct patterns of spectral power and coherence for individuals with ALS and HC. The Brain States 1, 3, 7 and 9 showed significant association between brain states fractional occupancy and behavioural decline (Beaumont Behavioural Inventory [7]; &gt;0.25, q&lt;0.03, &gt;0.65), while state 5 showed association with fluency decline (Edinburgh Cognitive and Behavioural ALS scale [8]; = ‐0.3, q = 0.004, = 0.83). States 1, 7 and 10 were characterised by frontal lobe activation (spectral power higher than the average within the state), while state 3 exhibited activation in the sensorimotor network. Highest spectral power during state 5 was in the supplementary motor area, a region primarily associated with motor planning but also possibly associated with language [9].ConclusionThis study demonstrated the utility of resting‐state EEG and its temporal decomposition based on spatio‐spectral dynamics for investigating and interrogating the less‐dominant cognitive‐behavioural decline in ALS. It shows clear potentials for applications in cognate conditions such as FTD and Alzheimer’s disease with a dominant cognitive‐behavioural decline.

Comparative analysis of two static var compensator models in voltage control of transmission network

The static var compensator (SVC) is a member of the family of flexible alternating current transmission systems controllers used in power system engineering to manage specific transmission network characteristics to enhance the performance of the transmission networks and thus increase the networks’ reliability. Power system engineers typically find it difficult to choose which SVC model to implement for simulations. This research aims to address this issue by conducting a comparative examination of two key SVC models on a transmission network. The two models of SVC variable shunt susceptance and firing angle were mathematically modeled and methodically included into the Newton-Raphson power flow algorithm for the network power flow solution. The IEEE 30-bus network was adopted as the test case, and the method was implemented in the MATLAB/Simulink environment. The network performance metric utilized was the voltage profile of the network. The two SVC models were successful in enhancing the network's performance; however, the variable shunt susceptance model was computationally faster than the firing angle model, as revealed by the simulation results. Therefore, among the two SVC models, the variable shunt susceptance model may be taken into account for simulation to enhance the performance of the transmission networks.

Identifying Associations between Small Nucleolar RNAs and Diseases via Graph Convolutional Network and Attention Mechanism.

Research has shown that small nucleolar RNAs (snoRNAs) play crucial roles in various biological processes, and understanding disease pathogenesis by studying their relationship with diseases is beneficial. Currently, known associations are insufficient, and conventional biological experiments are costly and time-consuming. Therefore, developing efficient computational methods is crucial for identifying potential snoRNA-disease associations. In this paper, a method to identify snoRNA-disease associations based on graph convolutional network and multi-view graph attention mechanism (GCASDA) is proposed. Firstly, the similarity matrices of snoRNAs and diseases are calculated based on biological entity-related information, and the weights of the edges between the snoRNA nodes and the disease nodes are supplemented by random forest. Then two homogeneous graphs and one heterogeneous graph are constructed. Subsequently, different types of embedded features are extracted from the graphs using specific graph convolutional network structure and integrated through a multi-view graph attention mechanism to obtain node embedded feature representations. Finally, for each pair of nodes, in addition to their global features, node interaction features are passed together to a multilayer perceptron neural network (MLP) to identify snoRNA-disease associations. Experimental results show that GCASDA achieves 0.9356 and 0.9294 in AUC and AUPR, respectively, and significantly outperformed other state-of-the-art methods on the basis of different evaluation metrics. Furthermore, the case study could further demonstrate the realistic feasibility of GCASDA.