Network Connectivity Research Articles

Tourist Satisfaction in Kerala Agritourism: Insights from Domestic and International Visitors

Purpose: Tourism has been an essential component of human civilization since the beginning, evolving through various forms, phases, and practices over the centuries. Agritourism is a type of tourism that uses agricultural life as a tourist attraction. It is either agricultural tourism or agricultural marketing. It has the potential to generate additional income and employment for farmers. Agriculture has been a cornerstone of life in Kerala since ancient times, and even in today’s digital era, traditional farming methods remain prevalent among the farming community. It can help to boost local economic development and also helps to preserve biodiversity. Furthermore, rural communities must recognize the value of agricultural biodiversity and the need to protect it. It is important to measure the quality of services offered at the agritourism centre to ensure the operational effectiveness of centres as well as the tourist satisfaction [1]. The study was conducted to assess the level of satisfaction of foreign and domestic tourists in the agritourism destinations. Methodology: Primary survey was conducted among 150 domestic tourists and 150 foreign tourists who visited agritourism centres in Kerala using pre-tested structured questionnaire. Results: The findings indicated low satisfaction with accessibility, disability-friendly environment, telecommunication facilities and medical facilities available at the agritourism center. The low satisfaction level indicates that these elements are not functioning efficiently in agritourism destinations. As mentioned in the earlier discussions, accessibility is vital for the success of agritourism destinations. Most of the tourists were hesitant to revisit the centers because of inadequate accessibility. More than just a practical concern, it is crucial for boosting economic development, enhancing visitor satisfaction, and promoting sustainable agritourism practices. Likewise network connectivity challenges in agritourism centers located in hill areas pose significant obstacles to effective operation and visitor satisfaction. These issues affect communication, emergency response, and financial viability of centres as well as tourists. Medical facilities are essential to agritourism destinations, crucial for safeguarding the well-being of visitors. The agritourism centers are located in remote areas, making it challenging to access hospitals in emergency situations. So having adequate medical services in tourism centers are essential for protecting the health and well-being of tourists. Conclusion: The study assessed the level of satisfaction of foreign and domestic tourists in the agritourism destinations. Visitor satisfaction is critical to the success of agritourism destinations, as it influences visitor loyalty, the destination's reputation, operational improvements, and financial gains. By focusing on providing superior experiences and actively seeking feedback, agritourism operators can enhance their offerings, establish a positive reputation, and foster long-term growth.

Functional network disruptions in youth with concussion using the Adolescent Brain Cognitive Development study

ABSTRACT Objective This study aimed to compare psychosocial outcomes and functional neuroimaging among youth with concussion, youth with anxiety, and age- and sex-matched controls. Methods Using archival data from the Adolescent Brain Cognitive DevelopmentSM Study, we analyzed between-group differences in psychosocial outcomes measured by the Child Behavior Checklist’s internalizing and externalizing problem scales, and assessed brain function using resting-state fMRI network-region connectivity (specifically frontoparietal network (FPN) and default mode network (DMN) connectivity with the amygdala). Results Significant differences in psychosocial outcomes were found across all groups, with the anxiety group reporting the most internalizing problems, followed by the concussion group which significantly differed from controls. Additionally, FPN-amygdala connectivity was significantly reduced in the concussion group only; this reduced connectivity did not predict psychosocial outcomes across groups. Conclusion This study provided preliminary findings that brain connectivity is reduced exclusively in individuals with concussion. Although disruptions were observed in the concussion group, further investigation is warranted to understand how disruptions may be associated with concussion symptoms. Studies that utilize well-defined control and study groups, and comprehensive cognitive and mental health measures will offer a deeper understanding of the relationship between brain function and psychosocial outcomes.

MEGA: Maximum-Entropy Genetic Algorithm for Router Nodes Placement in Wireless Mesh Networks

Over the past decade, wireless mesh networks (WMNs) have seen significant advancements due to their simple deployment, cost-effectiveness, ease of implementation, and reliable service coverage. However, despite these advantages, the placement of nodes in WMNs presents a critical challenge that significantly impacts their performance. This issue is recognized as an NP-hard problem, underscoring the necessity of development optimization algorithms, such as heuristic and metaheuristic approaches. This motivated us to develop the Maximum Entropy Genetic Algorithm (MEGA) to address the issue of mesh router node placement in WMNs. To assess the proposed method, we conducted experiments across various scenarios with different settings, focusing on key metrics such as network connectivity and user coverage. The simulation results showed the comparative performance of MEGA in relation to other prominent algorithms, such as the Coyote Optimization Algorithm (COA), Firefly Algorithm (FA), Genetic Algorithm (GA), and Particle Swarm Optimization (PSO), revealing MEGA’s effectiveness and usability in determining optimal locations for mesh routers.

Cardiorespiratory Fitness and Sleep, but not Physical Activity, are Associated with Functional Connectivity in Older Adults.

Aging results in changes in resting state functional connectivity within key networks associated with cognition. Cardiovascular function, physical activity, sleep, and body composition may influence these age-related changes in the brain. Better understanding these associations may help clarify mechanisms related to brain aging and guide interventional strategies to reduce these changes. In a large (n = 398) sample of healthy community dwelling older adults that were part of a larger interventional trial, we conducted cross sectional analyses of baseline data to examine the relationships between several modifiable behaviors and resting state functional connectivity within networks associated with cognition and emotional regulation. Additionally, maximal aerobic capacity, physical activity, quality of sleep, and body composition were assessed. Associations were explored both through correlation and best vs. worst group comparisons. Greater cardiovascular fitness, but not larger quantity of daily physical activity, was associated with greater functional connectivity within the Default Mode (p = 0.008 r = 0.142) and Salience Networks (p = 0.005, r = 0.152). Better sleep (greater efficiency and fewer nighttime awakenings) was also associated with greater functional connectivity within multiple networks including the Default Mode, Executive Control, and Salience Networks. When the population was split into quartiles, the highest body fat group displayed higher functional connectivity in the Dorsal Attentional Network compared to the lowest body fat percentage (p = 0.011; 95% CI - 0.0172 to - 0.0023). These findings confirm and expand on previous work indicating that, in older adults, higher levels of cardiovascular fitness and better sleep quality, but not greater quantity of physical activity, total sleep time, or lower body fat percentage are associated with increased functional connectivity within key resting state networks.

Analysis of Graph Inverses and Algebraic Connectivity in a Systematic Way

an exhaustive and methodical study of algebraic connectedness and graph inverses. In many contexts, graph theory plays a crucial role, particularly when understanding intricate systems. This paper delves into the fundamental concepts of graph inverses and explains their significance for network analysis and connectivity evaluation. The Laplacian lattice's second-smallest eigenvalue, or algebraic connectivity µN−1, plays a crucial role in some features like network heartiness, synchronisation security, and diffusion processes. In this study, we focus on the algebraic connectedness in the network-of-networks (NoN), which is the general context of linked networks. A key component of science is the graph hypothesis. Algebraic graph hypothesis refers to the use of algebraic techniques to graph problems. This article concludes a focus on algebraic graph hypothesis and presents and examines a different algebraic and mathematical variety idea to regard as the greatest matching of an undirected graph.

FMRI neurofeedback for the modulation of the neural networks associated with depression

ObjectivesFunctional magnetic resonance imaging (fMRI) neurofeedback has emerged as a potential treatment modality for depression, but little is known about its mechanism of action. This study aims to investigate the efficacy of fMRI neurofeedback in modulating neural networks in depression. MethodsFollowing PRISMA guidelines, a systematic review was conducted focusing on fMRI neurofeedback interventions in depression. A comprehensive search across multiple databases yielded 16 eligible studies for review. ResultsThe review demonstrated that fMRI neurofeedback can modulate BOLD activity even in strategy-free protocols and within a single session, with a significant learning effect evident over sessions. Neurofeedback targeting specific regions led to changes in connectivity across broad neural networks, including the default-mode and executive control networks, with effects being region-specific. However, methodological diversity and the absence of standardized protocols in the reviewed studies highlighted the need for more uniform research approaches. ConclusionsfMRI neurofeedback shows promise as a modulatory technique for depression, with the potential to induce significant changes in neural activity and connectivity of networks implicated in depression. SignificanceThe review underscores the necessity for standardized, reproducible neurofeedback protocols with control groups to enhance research comparability and generalizability.

Microbial community composition and co-occurrence network analysis of the rhizosphere soil of the main constructive tree species in Helan Mountain of Northwest China

To understand the microbial diversity and community composition within the main constructive tree species, Picea crassifolia, Betula platyphylla, and Pinus tabuliformis, in Helan Mountain and their response to changes in soil physicochemical factors, a high throughput sequencing technology was used to analyze the bacterial and fungal diversity and community structure. RDA (Redundancy Analysis) and Pearson correlation analysis were used to explore the influence of soil physicochemical factors on microbial community construction, and co-occurrence network analysis was conducted on the microbial communities. The results showed that the fungal and bacterial diversity was highest in B. platyphylla, and lowest in P. crassifolia. Additionally, the fungal/bacterial richness was greatest in the rhizosphere soils of P. tabuliformis and B. platyphylla. RDA and Pearson correlation analysis revealed that NN (nitrate nitrogen) and AP (available phosphorus) were the main determining factors of the bacterial community, while NN and SOC (soil water content) were the main determining factors of the fungal community. Pearson correlation analysis between soil physicochemical factors and the alpha diversity of the microbial communities revealed a significant positive correlation between pH and the bacterial and fungal diversity, while SOC, TN (total nitrogen), AP, and AN (available nitrogen) were significantly negatively correlated with the bacterial and fungal diversity. Co-occurrence network analysis revealed that the soil bacterial communities exhibit richer network nodes, edges, greater diversity, and greater network connectivity. Indicating that bacterial communities exhibit more complex and stable interaction patterns in soil. This study reveals the complex interactive relationship between microbial communities and soil physicochemical factors in forest ecosystems. By analyzing the response of rhizosphere microbial communities of major tree species in Helan Mountain to nutrient dynamics and pH changes, we can deepen our understanding of the role of microorganisms in regulating ecosystem functions and provide theoretical basis for soil improvement and ecological restoration strategies.

Aging-dependent loss of functional connectivity in a mouse model of Alzheimer's disease and reversal by mGluR5 modulator.

Amyloid accumulation in Alzheimer's disease (AD) is associated with synaptic damage and altered connectivity in brain networks. While measures of amyloid accumulation and biochemical changes in mouse models have utility for translational studies of certain therapeutics, preclinical analysis of altered brain connectivity using clinically relevant fMRI measures has not been well developed for agents intended to improve neural networks. Here, we conduct a longitudinal study in a double knock-in mouse model for AD (AppNL-G-F/hMapt), monitoring brain connectivity by means of resting-state fMRI. While the 4-month-old AD mice are indistinguishable from wild-type controls (WT), decreased connectivity in the default-mode network is significant for the AD mice relative to WT mice by 6 months of age and is pronounced by 9 months of age. In a second cohort of 20-month-old mice with persistent functional connectivity deficits for AD relative to WT, we assess the impact of two-months of oral treatment with a silent allosteric modulator of mGluR5 (BMS-984923/ALX001) known to rescue synaptic density. Functional connectivity deficits in the aged AD mice are reversed by the mGluR5-directed treatment. The longitudinal application of fMRI has enabled us to define the preclinical time trajectory of AD-related changes in functional connectivity, and to demonstrate a translatable metric for monitoring disease emergence, progression, and response to synapse-rescuing treatment.

Connectivity and diagnosability of the complete Josephus cube networks under h-extra fault-tolerant model

The h-extra connectivity and the h-extra diagnosability are key parameters for evaluating the reliability and fault-tolerance of the interconnection networks of the multiprocessor systems, and play an important role in designing and maintaining interconnection networks. Recently, various self-diagnostic models have emerged to assess the fault-tolerance in interconnection networks. These interconnection networks are typically expressed by a connected graph G(V,E). For a non-complete graph G and h≥0, the h-extra cut signifies a vertex subset R of G, whose removal results in G−R disconnected, with each remaining component containing at least h+1 vertices. And the h-extra connectivity of G is defined as the minimum cardinality of all h-extra cuts of G. The h-extra diagnosability for a graph G denotes the maximum number of detectable faulty vertices when focusing on these h-extra faulty sets only. The complete Josephus cube CJCn, a variant of Qn, exhibits superior properties compared to hypercube Qn, and also boasts higher connectivity. In this study, with the help of the exact value of the h-extra connectivity of CJCn, the explicit expression of h-extra diagnosability of CJCn under both the PMC model for n≥5 and 1≤h≤⌊n−32⌋ and the MM* model for n≥5 and 2≤h≤⌊n−32⌋ are identified to share the same value (h+1)n−(h−12)+1.

Financial and Market Implications of the CSX-Conrail Acquisition

Conrail, Consolidated Rail Corporation, founded in 1976, was established due to the bankruptcy of multiple railway companies in the Northeast and Midwest regions of the United States. The federal government intervened to establish the company with the aim of restoring the services and profitability of bankrupt railways. As it developed, Conrail's railway network covered major cities and industrial centers in the Northeast and Midwest of the United States, possessing a strong market position and strategic value. Therefore, Conrail had become a target for mergers and acquisitions by large railway companies such as CSX and Norfolk Southern. Through mergers and acquisitions, the acquiring party could expand market coverage, enhance operational network connectivity and competitiveness. This is a complex and multi-layered issue, the CSX acquisition of Conrail involved financial and market implications, legal obstacles, competitive pressures, and strategic objectives. By analyzing these themes in depth, we could understand the key issues and response strategies that enterprises face in the M&A process, providing valuable experience for future M&A transactions. The study applied methods such as comparative analysis and case model analysis to analyze the main challenges, response strategies, and effects comprehensively and systematically in the M&A process, providing reference for similar cases. Overall, CSX's acquisition of Conrail demonstrates how companies can achieve acquisition goals and long-term development through flexible strategies, effective communication, and professional management in the face of complex legal, market, and operational challenges.

Attention Induced Dual Convolutional-Capsule Network (AIDC-CN): A deep learning framework for motor imagery classification

In recent times, Electroencephalography (EEG)-based motor imagery (MI) decoding has garnered significant attention due to its extensive applicability in healthcare, including areas such as assistive robotics and rehabilitation engineering. Nevertheless, the decoding of EEG signals presents considerable challenges owing to their inherent complexity, non-stationary characteristics, and low signal-to-noise ratio. Notably, deep learning-based classifiers have emerged as a prominent focus for addressing the EEG signal decoding process. This study introduces a novel deep learning classifier named the Attention Induced Dual Convolutional-Capsule Network (AIDC-CN) with the specific aim of accurately categorizing various motor imagination class labels. To enhance the classifier’s performance, a dual feature extraction approach leveraging spectrogram and brain connectivity networks has been employed, diversifying the feature set in the classification task. The main highlights of the proposed AIDC-CN classifier includes the introduction of a dual convolution layer to handle the brain connectivity and spectrogram features, addition of a novel self-attention module (SAM) to accentuate the relevant parts of the convolved spectrogram features, introduction of a new cross-attention module (CAM) to refine the outputs obtained from the dual convolution layers and incorporation of a Gaussian Error Linear Unit (GELU) based dynamic routing algorithm to strengthen the coupling among the primary and secondary capsule layers. Performance analysis undertaken on four public data sets depict the superior performance of the proposed model with respect to the state-of-the-art techniques. The code for this model is available at https://github.com/RiteshSurChowdhury/AIDC-CN.

The Propagation of Congestion on Transportation Networks Analyzed by the Percolation Process

Percolation theory has been widely employed in network systems as an effective tool to analyze phase transitions from functional to nonfunctional states. In this paper, we analyze the propagation of congestion on transportation networks and its influence on origin–destination (OD) pairs using the percolation process. This approach allows us to identify the most critical links within the network that, when disrupted due to congestion, significantly impact overall network performance. Understanding the role of these critical links is essential for developing strategies to mitigate congestion effects and enhance network resilience. Building on this analysis, we propose two methods to adjust the capacities of these critical links. First, we introduce a greedy method that incrementally adjusts the capacities based on their individual impact on network connectivity and traffic flow. Second, we employ a Particle Swarm Optimization (PSO) method to strategically increase the capacities of certain critical links, considering the network as a whole. These capacity adjustments are designed to enhance the network’s resilience by ensuring it remains functional even under conditions of high demand and congestion. By preventing the propagation of congestion through strategic capacity enhancements, the transportation network can maintain connectivity between OD pairs, reduce travel times, and improve overall efficiency. Our approach provides a systematic method for improving the robustness of transportation networks against congestion propagation. The results demonstrate that both the greedy method and the PSO method effectively enhance network performance, with the PSO method showing superior results in optimizing capacity allocations. This research is crucial for maintaining efficient and reliable mobility in urban areas, where congestion is a persistent challenge, and offers valuable insights for transportation planners and policymakers aiming to design more resilient transportation infrastructures.

Multi-omics insights into the microbiota-gut-brain axis and cognitive improvement post-bariatric surgery

BackgroundAlthough numerous studies have shown that bariatric surgery results in sustained weight loss and modifications in gut microbiota composition and cognitive function, the exact underlying mechanisms are unclear. This study aimed to investigate the effects of bariatric surgery on cognitive function through the microbiota-gut-brain axis (MGBA).MethodsDemographic data, serum samples, fecal samples, cognitive assessment scales, and resting-state functional connectivity magnetic resonance imaging (rs-fMRI) scans were obtained from 39 obese patients before and after (6 months) laparoscopic sleeve gastrectomy (LSG). PCA analysis, OPLS-DA analysis, and permutation tests were used to conduct fecal 16 S microbiota profiling, serum metabolomics, and neuroimaging analyses, and a bariatric surgery-specific rs-fMRI brain functional connectivity network was constructed. Spearman correlation analysis and Co-inertia analysis were employed to correlate significant alterations in cognitive assessment scales and resting-state functional connectivity difference networks with differential serum metabolites and 16 S microbiota data to identify key gut microbiota and serum metabolic factors.ResultsLSG significantly reduced the weight of obese patients, with reductions of up to 28%. Furthermore, cognitive assessment scale measurements revealed that LSG enhanced cognitive functions, including memory (HVLT, p = 0.000) and executive function (SCWT, p = 0.008). Also, LSG significantly altered gut microbiota composition (p = 0.001), with increased microbial abundance and diversity (p < 0.05). Moreover, serum metabolite levels were significantly altered, revealing intergroup differences in 229 metabolites mapped to 72 metabolic pathways (p < 0.05, VIP > 1). Spearman correlation analysis among cognitive assessment scales, gut microbiota species, and serum metabolites revealed correlations with 68 gut microbiota species and 138 serum metabolites (p < 0.05). Furthermore, pairwise correlations were detected between gut microbiota and serum metabolites (p < 0.05). Functional neuroimaging analysis revealed that LSG increased functional connectivity in cognitive-related frontotemporal networks (FPN, p < 0.01). Additionally, normalization of the default mode network (DMN) and salience network (SN) connectivity was observed after LSG (p < 0.001). Further canonical correlation and correlation analysis suggested that the cognitive-related brain network changes induced by LSG were associated with key gut microbiota species (Akkermansia, Blautia, Collinsella, Phascolarctobacterium, and Ruminococcus, p < 0.05) and neuroactive metabolites (Glycine, L-Serine, DL-Dopa, SM (d18:1/24:1(15Z), p < 0.05).ConclusionThese findings indicate the pathophysiological role of the microbiota-gut-brain axis in enhancing cognitive function after bariatric surgery, and the study provides a basis for clinical dietary adjustments, probiotic supplementation, and guidance for bariatric surgery, but further research is still needed.Trial registrationChinese Clinical Trial Registry, ChiCTR2100049403. Registered 02 August 2021, https://www.chictr.org.cn/.

Privacy-Preserving Handover Optimization Using Federated Learning and LSTM Networks.

The rapid evolution of wireless communication systems necessitates advanced handover mechanisms for seamless connectivity and optimal network performance. Traditional algorithms, like 3GPP Event A3, often struggle with fluctuating signal strengths and dynamic user mobility, leading to frequent handovers and suboptimal resource utilization. This study proposes a novel approach combining Federated Learning (FL) and Long Short-Term Memory (LSTM) networks to predict Reference Signal Received Power (RSRP) and the strongest nearby Reference Signal Received Power (RSRP) signals. Our method leverages FL to ensure data privacy and LSTM to capture temporal dependencies in signal data, enhancing prediction accuracy. We develop a dynamic handover algorithm that adapts to real-time conditions, adjusting thresholds based on predicted signal strengths and historical performance. Extensive experiments with real-world data show our dynamic algorithm significantly outperforms the 3GPP Event A3 algorithm, achieving higher prediction accuracy, reducing unnecessary handovers, and improving overall network performance. In conclusion, this study introduces a data-driven, privacy-preserving approach that leverages advanced machine learning techniques, providing a more efficient and reliable handover mechanism for future wireless networks.

Strengthening network DDOS attack detection in heterogeneous IoT environment with federated XAI learning approach

Due to the rising use of the Internet of Things (IoT), the connectivity of networks increases the risk of Distributed Denial of Service (DDoS) attacks. Decentralized systems commonly used in centralized security systems fail to adequately prevent potential cyber threats in IoT because of the issues of privacy and scaling. The method proposed in this study seeks to remedy these facts by employing Explainable Artificial Intelligence (XAI) together with Federated Deep Neural Networks (FDNNs) to detect and prevent DDoS attacks. Our approach is thus to use federated learning models that are to be trained on distributed and dissimilar sources of data without compromising on the privacy aspect. FDNNs were trained over three rounds with information from three client gadgets incorporating pre-processed datasets of various types of DDoS attacks. Additionally, for feature selection, we integrated XGBoost with SHapley Additive exPlanations (SHAP) to improve model interpretability. The proposed solution can be considered to be quite robust, privacy-preserving, and highly scalable for the detection of DDoS attacks on the IoT network. The results shown on the server side indicate that this approach accurately detects 99.78% of DDoS attacks with a precision rate as high as 99.80%, recall rate (detection rate) going up to 99.74% and F1 score reaching 99.76%. They emphasize that FL-based IDSs are strong enough to cope with cybersecurity challenges in IoT, thus offering hope for securing modern network infrastructures against ever-growing cyber threats.

Shedding Light on the Aftermath: Childhood Maltreatment's Role in Modifying the Association Between Recent Life Stress and Resting-State Network Connectivity.

Childhood maltreatment has been demonstrated to impact brain development. However, whether childhood maltreatment can influence the effects of recent stress on brain networks remains unclear. This study aimed to investigate whether childhood maltreatment moderates the longitudinal relationship between recent life stress and within- and between-network connectivity in key brain networks, including the anterior salience (ASN), central executive (CEN), default mode (DMN), and emotional regulation network (ERN). A cohort of 172 individuals from the Neuroscience in Psychiatry Network (NSPN) underwent MRI scans at two specific time points and undertook evaluations of childhood maltreatment and recent life stress. The results showed that childhood abuse moderated the association of recent life stress with the within-network connectivity of ASN and ERN but not DMN and CEN. Furthermore, recent life stress significantly interacted with childhood abuse to be associated with the between-network connectivity of ASN-DMN, ASN-CEN, ASN-ERN, DMN-ERN and CEN-ERN. Overall, among youth exposed to higher degrees of childhood abuse, greater recent life stress was longitudinally associated with increased network connectivity. Understanding these interactions can provide valuable insights for developing prevention strategies and interventions aimed at mitigating the lasting impact of childhood maltreatment on brain development and overall well-being.

Small world properties of schizophrenia and OCD patients derived from fNIRS based functional brain network connectivity metrics

Individuals suffering from obsessive compulsive disorder (OCD) and schizophrenia (SCZ) frequently exhibit symptoms of cognitive disassociations, which are linked to poor functional integration among brain regions. The loss of functional integration can be assessed using graph metrics computed from functional connectivity matrices (FCMs) derived from neuroimaging data. A healthy brain at rest is known to exhibit small-world features with high clustering coefficients and shorter path lengths in contrast to random networks. The aim of this study was to compare the small-world properties of prefrontal cortical functional networks of healthy subjects with OCD and SCZ patient groups by use of hemodynamic data obtained with functional near infrared spectroscopy (fNIRS). 13 healthy subjects and 47 patients who were clinically diagnosed with either OCD (N = 21) or SCZ (N = 26) completed a Stroop test while their prefrontal cortex (PFC) hemodynamics were monitored with fNIRS. The Stroop test had a block design consisting of neutral, congruent and incongruent stimuli. For each subject and stimuli type, FCMs were derived separately which were then used to compute small world features that included (i) global efficiency (GE), (ii) clustering coefficient (CC), (iii) modularity (Q), and (iv) small-world parameter (σ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\sigma $$\\end{document}). Small-world features of patients exhibited random networks which were indicated by higher GE and lower CC values when compared to healthy controls, implying a higher neuronal operational cost.

Functional connectivity in resting-state networks relates to short-term global cognitive functioning in cardiac arrest survivors.

Long-term cognitive impairment is common in cardiac arrest survivors. Screening to identify patients at risk is recommended. Functional magnetic resonance brain imaging (fMRI) holds potential to contribute to prediction of cognitive outcomes. In this study, we investigated the possible value of early changes in resting-state networks for predicting short and long-term cognitive functioning of cardiac arrest survivors. We performed a prospective multicenter cohort study in cardiac arrest survivors in three Dutch hospitals. Resting-state fMRI scans were acquired within a month after cardiac arrest. We primarily focused on functional connectivity within the default-mode network (DMN) and salience network (SN), and additionally explored functional connectivity in seven other networks. Cognitive outcome was measured using the Montreal Cognitive Assessment (MoCA) during hospital admission and at 3 and 12 months, and by neuropsychological examination (NPE) at 12 months. We tested mixed effects models to evaluate the value of connectivity within the networks for predicting global cognitive outcomes at the three time points, and long-term cognitive outcomes in the memory, attention, and executive functioning domains. We included 80 patients (age 60 ± 11 years, 72 (90%) male). MoCA scores increased significantly between hospital admission and 3 months (ΔMoCAhospital-3M = 2.89, p < 0.01), but not between 3 and 12 months (ΔMoCA3M-12M = 0.38, p = 0.52). Connectivity within the DMN, SN, and dorsal attention network (DAN) was positively related to global cognitive functioning during hospital admission (βDMN = 0.85, p = 0.03; βSN = 1.48, p < 0.01; βDAN = 0.96, p = 0.01), but not at 3 and 12 months. Network connectivity was also unrelated to long-term memory, attention, or executive functioning. Resting-state functional connectivity in the DMN, SN, and DAN measured in the first month after cardiac arrest is related to short-term global, but not long-term global or domain-specific cognitive performance of survivors. These results do not support the value of functional connectivity within these RSNs for prediction of long-term cognitive performance after cardiac arrest.

Investigation of optically transparent metasurfaces for gain improvement of an ultrawideband antenna for wireless body area networks and vehicular communications

Abstract Due to higher frequencies which cause signal deterioration, 5G enabled antennas are prone to substantial attenuation. To circumvent this problem, several access points, signal repeaters, and base stations must be installed closer together on building windows, streetlights, and other infrastructure. Optically transparent antennas preserve the aesthetics of the windows by offering the requirements for access points and base stations for providing network connectivity to the cars and pedestrians passing by those buildings. Here we report an ultrawideband (UWB) high gain optically transparent antenna operating from 4-12 GHz. Indium tin oxide (ITO) coated glass has been used as an optically transparent substrate for designing the antenna and metasurface. Two optically transparent metasurfaces have been proposed for band-stop filtering action across the UWB frequency range 4-12 GHz. In absence of the metasurface, the antenna operates in the frequency band ranging from 3.8 to 15 GHz. Average gain enhancement is 5.2 dB with radiation efficiency higher than 93% across the UWB frequency band from 4-12 GHz. Numerical simulations have been carried out for analysis of the proposed antenna and metasurfaces. These simulations have been validated by experimental measurements of the fabricated prototypes.

TARGETING RESIDUAL CANCER FROM THE INVASIVE MARGIN OF GLIOBLASTOMA USING COMPUTATIONAL TOOLKITS

Abstract AIMS Infiltrative margin GBM, as a proxy for residual disease post-surgery, can be identified by 5-aminolevulininc acid (5ALA) to facilitate the discovery of uniquely expressed or upregulated molecular pathways. Complementary bioinformatics approaches offer reproducible ways to enable interoperability of heterogenous data sources towards identification of therapeutic targets associated with GBM infiltration. METHOD RNA-seq primary tumour data was first categorised as: 5ALA+ (infiltrative margin) vs tumour core and 5ALA+ vs 5ALA- (reactive brain). Two levels of filtration (genes with adjusted p-value &amp;lt;0.05; degree of fold change with log2&amp;gt;1.5) were applied to genes, creating two separate gene lists. Cross-referencing gene lists identified transcripts upregulated in both 5ALA+ vs core and 5ALA+ vs 5ALA-. This candidate gene list was inputted into STRING to generate an uncurated protein-protein interaction (PPI) network, which returned a significant PPI enrichment p-value to confirm network connectivity, and next inputted into Metascape to elucidate biological processes associated with tumour infiltration. RESULTS Of 141 upregulated 5ALA+ genes TCP10L3, HELT, MGAM, ALOX5 and FOXI1 exhibited the greatest fold change. The most prevalent 5ALA+ biological processes were primitive streak formation, disaccharide metabolic process and sodium ion transmembrane transport. LOC157273, LINC00200, LINC00656, LOC285626 and LINC01568 were identified as long non-coding RNAs (lncRNA) most abundant in the infiltrative margin of GBM relative to tumour core and reactive brain. Regulatory associations between candidate 5ALA+ lncRNA and genes, and in silico drug repurposing via molecular docking, are being investigated. CONCLUSION The plasticity of GBM warrants analysis of the GBM infiltrative margin via computational toolkits to enhance understanding of upregulated molecular pathways, functionally associated with an invasive phenotype.