Disruption Of Network Research Articles

Unlocking the Molecular Variations of a Micron-Scale Amyloid Plaque in an Early Stage Alzheimer's Disease by a Cellular-Resolution Mass Spectrometry Imaging Platform.

Uncovering the molecular changes at the site where Aβ is deposited plays a critical role in advancing the diagnosis and treatment of Alzheimer's disease. However, there is currently a lack of a suitable label-free imaging method with a high spatial resolution for brain tissue analysis. In this study, we propose a modified desorption electrospray ionization (DESI) mass spectrometry imaging (MSI) method, called segmented temperature-controlled DESI (STC-DESI), to achieve high-resolution and high-sensitivity spatial metabolomics observation by precisely controlling desorption and ionization temperatures. By concentrating the spray plume and accelerating solvent evaporation at different temperatures, we achieved an impressive spatial resolution of 20 μm that enables direct observation of the heterogeneity around a single cell or an individual Aβ plaque and an exciting sensitivity that allows a variety of low-abundance metabolites and less ionizable neutral lipids to be detected. We applied this STC-DESI method to analyze the brains of transgenic AD mice and identified molecular changes associated with individual Aβ aggregates. More importantly, our study provides the first evidence that carnosine is significantly depleted and 5-caffeoylquinic acid (5-CQA) levels rise sharply around Aβ deposits. These observations highlight the potential of carnosine as a sensitive molecular probe for clinical magnetic resonance imaging diagnosis and the potential of 5-CQA as an efficient therapeutic strategy for Aβ clearance in the early AD stage. Overall, our findings demonstrate the effectiveness of our STC-DESI method and shed light on the potential roles of these molecules in AD pathology, specifically in cellular endocytosis, gray matter network disruption, and paravascular Aβ clearance.

Speech-language within and between network disruptions in primary progressive aphasia variants

Primary progressive aphasia (PPA) variants present with distinct disruptions in speech-language functions with little known about the interplay between affected and spared regions within the speech-language network and their interaction with other functional networks.The Neurodegenerative Research Group, Mayo Clinic, recruited 123 patients with PPA (55 logopenic (lvPPA), 44 non-fluent (nfvPPA) and 24 semantic (svPPA)) who were matched to 60 healthy controls. We investigated functional connectivity disruptions between regions within the left-speech-language network (Broca, Wernicke, anterior middle temporal gyrus (aMTG), supplementary motor area (SMA), planum temporale (PT) and parietal operculum (PO)), and disruptions to other networks (visual association, dorsal-attention, frontoparietal and default mode networks (DMN)).Within the speech-language network, multivariate linear regression models showed reduced aMTG-Broca connectivity in all variants, with lvPPA and nfvPPA findings remaining significant after Bonferroni correction. Additional loss in Wernicke-Broca connectivity in nfvPPA, Wernicke-PT connectivity in lvPPA and greater aMTG-PT connectivity in svPPA were also noted. Between-network connectivity findings in all variants showed reduced aMTG-DMN and increased aMTG-dorsal-attention connectivity, with additional disruptions between aMTG-visual association in both lvPPA and svPPA, aMTG-frontoparietal in lvPPA, and Wernicke-DMN breakdown in svPPA.These findings suggest that aMTG connectivity breakdown is a shared feature in all PPA variants, with lvPPA showing more extensive connectivity disruptions with other networks.

Changes of brain structure and structural covariance networks in Parkinson's disease associated cognitive impairment.

Cognitive impairment (CI) is common in Parkinson's disease (PD). Multiple brain regions and their interactions are involved in PD associated CI. Magnetic resonance imaging (MRI) technology is a non-invasive method in investigating brain structure and inter-regional connections. In this study, by comparing cortical thickness, subcortical volume, and brain network topology properties in PD patients with and without CI, we aimed to understand the changes of brain structure and structural covariance network properties in PD associated CI. A total of 18 PD patients with CI and 33 PD patients without CI were recruited. Movement Disorder Society Unified Parkinson's Disease Rating Scale, Hoehn and Yahr stage, Mini Mental State Examination Scale, Non-motor Symptom Rating Scale, Hamilton Anxiety Scale, and Hamilton Depression Scale were assessed. All participants underwent structural 3T MRI. Cortical thickness, subcortical volume, global and nodal network topology properties were measured. Compared with PD patients without CI, the volumes of white matter, thalamus and hippocampus were lower in PD patients with CI. And decreased whole-brain local efficiency is associated with CI in PD patients. While the cortical thickness and nodal network topology properties were comparable between PD patients with and without CI. Our findings support the alterations of brain structure and disruption of structural covariance network are involved in PD associated CI, providing a new insight into the association between graph properties and PD associated CI.

Post-Pandemic Analysis on the Factors Affecting Academic Performance of Tourism and Hospitality Management Students During COVID-19 Pandemic: Basis of an Enhanced Learning Continuity Plan

World Health Organization (WHO) Director-General and Committee members declared in 2023 that COVID-19 will no longer constitute a public health emergency (PHEIC). During the pandemic surge in global business, the educational system worldwide, including hospitality education, has been greatly affected by the pandemic crisis. The influence of COVID-19 causes worldwide school closures that have far-reaching implications for students’ short- and long-term growth because of the disruption of teacher and student networks, leading to poor performance. Thus, the study aims to analyze the factors that affect the academic performance of Tourism and Hospitality Management students of Far Eastern University Manila, Philippines, enrolled in the academic year 2020-2021 during the COVID-19 surge. The study used a descriptive survey design using a self-administered survey instrument with 342 respondents. The findings of the study showed that different factors in technology and learning management system (LMS), student’s environment, study area, delivery of academic subjects offered by the teachers, and the assessment used by the school during the distance learning have impacted the academic performance of students as evidence in the decline of Quality Point Average (QPA) on both programs from the Institute of Tourism and Hospitality Management despite that distance learning is designed to be convenient and easy. The factors influencing academic performance, such as technology and study environment, primarily play a significant and considerable role in how students learn and cope with the distant learning mode. The result of the study will serve as a guide in enhancing the University’s continuity plan for flexible learning options, given the factors that need reinforcement to the scholarly performance of the students. The study can be a benchmarking tool among Higher Education Institutions (HEIs) in the Philippines as a basis to further strengthen their learning strategies relative to flexible teaching and learning modalities, refining the scholarly learning assessment tools as well as sustaining the schools’ continuity framework and achieving the academic goals and aspiration for the success of students and graduates of the HEIs whether in the new normal or the future global crisis.

Homeostasis to Allostasis: Prefrontal Astrocyte Roles in Cognitive Flexibility and Stress Biology.

In the intricate landscape of neurophysiology, astrocytes have been traditionally cast as homeostatic cells; however, their mechanistic involvement in allostasis-particularly how they modulate the adaptive response to stress and its accumulative impact that disrupts cognitive functions and precipitates psychiatric disorders-is now starting to be unraveled. Here, we address the gap by positing astrocytes as crucial allostatic players whose molecular adaptations underlie cognitive flexibility in stress-related neuropsychiatric conditions. We review how astrocytes, responding to stress mediators such as glucocorticoid and epinephrine/norepinephrine, undergo morphological and functional transformations that parallel the maladaptive changes. Our synthesis of recent findings reveals that these glial changes, especially in the metabolically demanding prefrontal cortex, may underlie some of the neuropsychiatric mechanisms characterized by the disruption of energy metabolism and astrocytic networks, compromised glutamate clearance, and diminished synaptic support. We argue that astrocytes extend beyond their homeostatic role, actively participating in the brain's allostatic response, especially by modulating energy substrates critical for cognitive functions.

Influence-Based Analysis of Disruptions in an Energy Distribution Network Following a Main Channel Outage

Entailing a comprehensive examination of energy distribution systems, this study delves into the repercussions of failures in energy distribution channels, with a specific focus on the impact on substitution channels using the Independent Influence Model (ICM). By transcending conventional fault localization approaches, our research uncovers how these failures influence alternatives, expanding our understanding of outage dynamics in energy networks. The results obtained through the application of the ICM offer valuable insights, emphasizing the crucial importance of understanding how failures in energy distribution channels impact the overall resilience of networks. This approach enables a detailed analysis of interdependencies among substitution channels, providing essential guidance for enhancing the resilience of energy networks in the face of uncertainties and disruptions.

Cognition and motion dysfunction-associated brain functional network disruption in diabetic peripheral neuropathy.

Neuroimaging studies have demonstrated extensive brain functional alterations in cognitive and motor functional areas in Type 2 diabetes mellitus (T2DM) with diabetic peripheral neuropathy (DPN), suggesting potential alterations in large-scale brain networks related to DPN and associated cognition and motor dysfunction. In this study, using resting-state functional connectivity (FC) and graph theory computational approaches, we investigated the topological disruptions of brain functional networks in 28 DPN, 43 T2DM without DPN (NDPN), and 32 healthy controls (HCs) and examined the correlations between altered network topological metrics and cognitive/motor function parameters in T2DM. For global topology, NDPN exhibited a significantly decreased shortest path length compared with HCs, suggesting increased efficient global integration. For regional topology, DPN and NDPN had separated topological reorganization of functional hubs compared with HCs. In addition, DPN showed significantly decreased nodal efficiency (Enodal ), mainly in the bilateral superior occipital gyrus (SOG), right cuneus, middle temporal gyrus (MTG), and left inferior parietal gyrus (IPL), compared with NDPN, whereas NDPN showed significantly increased Enodal compared with HCs. Intriguingly, in T2DM patients, the Enodal of the right SOG was significantly negatively correlated with Toronto Clinical Scoring System scores, while the Enodal of the right postcentral gyrus (PoCG) and MTG were significantly positively correlated with Montreal Cognitive Assessment scores. Conclusively, DPN and NDPN patients had segregated disruptions in the brain functional network, which were related to cognition and motion dysfunctions. Our findings provide a theoretical basis for understanding the neurophysiological mechanism of DPN and its effective prevention and treatment in T2DM.

Spatiotemporal Microstate Dynamics of Spike-free Scalp EEG Offer a Potential Biomarker for Refractory Temporal Lobe Epilepsy.

Refractory temporal lobe epilepsy (TLE) is one of the most frequently observed subtypes of epilepsy and endangers more than 50 million people world-wide. Although electroencephalogram (EEG) had been widely recognized as a classic tool to screen and diagnose epilepsy, for many years it heavily relied on identifying epileptic discharges and epileptogenic zone localization, which however, limits the understanding of refractory epilepsy due to the network nature of this disease. This work hypothesizes that the microstate dynamics based on resting-state scalp EEG can offer an additional network depiction of the disease and provide potential complementary evaluation tool for the TLE even without detectable epileptic discharges on EEG. We propose a novel framework for EEG microstate spatial-temporal dynamics (EEG-MiSTD) analysis based on machine learning to comprehensively model millisecond-changing whole-brain network dynamics. With only 100 seconds of resting-state EEG even without epileptic discharges, this approach successfully distinguishes TLE patients from healthy controls and is related to the lateralization of epileptic focus. Besides, microstate temporal and spatial features are found to be widely related to clinical parameters, which further demonstrate that TLE is a network disease. A preliminary exploration suggests that the spatial topography is sensitive to the following surgical outcomes. From such a new perspective, our results suggest that spatiotemporal microstate dynamics is potentially a biomarker of the disease. The developed EEG-MiSTD framework can probably be considered as a general tool to examine dynamical brain network disruption in a user-friendly way for other types of epilepsy.

Network disruption based on multi-modal EEG-MRI in α-synucleinopathies.

Brain network dysfunction has been characterized by resting-state electroencephalography (EEG) and magnetic resonance imaging (MRI) in the prodromal stage. This study aimed to identify multi-modal electrophysiological and neuroimaging biomarkers for differential diagnosis in synucleinopathies and phenoconversion in isolated rapid eye movement sleep behavior disorder (iRBD). We enrolled 35 patients with multiple system atrophy (MSA), 32 with Parkinson's disease (PD), 30 with iRBD and 30 matched healthy controls (HC). Power spectral density (PSD) was calculated in different frequency bands. EEG functional connectivity (FC) was calculated using the weighted Phase Lag Index (wPLI) after source localization. Significant network disruptions were further confirmed by MRI FC analysis. Quantitative EEG analysis demonstrated that delta and theta power spectral density significantly differed among MSA, PD and HC. The increased PSD was correlated with cognitive decline and olfactory dysfunction in PD. Band-specific FC profiles were observed in theta, alpha, and gamma bands. The hypoconnected alpha network significantly correlated with motor dysfunction, while the gamma FC distinguished PD from MSA. By integrating EEG and MRI network analyses, we found that FC between the olfactory cortex and dorsolateral prefrontal cortex was significantly different between MSA and PD. A multimodal discriminative model for MSA and PD, integrating spectral and FC attributes of EEG and MRI, yielded an area under the receiver operating characteristic curve of 0.900. Simultaneously, we found the FC abnormalities were more prominent than spectral features in iRBD indicating prodromal dysfunction. The decreased FC between the angular gyrus and striatum was identified in α-synucleinopathies. This hypoconnectivity was associated with dopaminergic degeneration in iRBD examined by dopamine transporter imaging. Our study demonstrated EEG spectral and functional profiles in prodromal and clinical-defined synucleinopathies. Multimodal EEG and MRI provided a novel approach to discriminate MSA and PD, and monitor neurodegenerative progression in the preclinical phase.

Resilient by Design: Simulating Street Network Disruptions across Every Urban Area in the World

Resilient by Design: Simulating Street Network Disruptions across Every Urban Area in the World

Multicenter Hierarchical Federated Learning With Fault-Tolerance Mechanisms for Resilient Edge Computing Networks.

In the realm of federated learning (FL), the conventional dual-layered architecture, comprising a central parameter server and peripheral devices, often encounters challenges due to its significant reliance on the central server for communication and security. This dependence becomes particularly problematic in scenarios involving potential malfunctions of devices and servers. While existing device-edge-cloud hierarchical FL (HFL) models alleviate some dependence on central servers and reduce communication overheads, they primarily focus on load balancing within edge computing networks and fall short of achieving complete decentralization and edge-centric model aggregation. Addressing these limitations, we introduce the multicenter HFL (MCHFL) framework. This innovative framework replaces the traditional single central server architecture with a distributed network of robust global aggregation centers located at the edge, inherently enhancing fault tolerance crucial for maintaining operational integrity amidst edge network disruptions. Our comprehensive experiments with the MNIST, FashionMNIST, and CIFAR-10 datasets demonstrate the MCHFL's superior performance. Notably, even under high paralysis ratios of up to 50%, the MCHFL maintains high accuracy levels, with maximum accuracy reductions of only 2.60%, 5.12%, and 16.73% on these datasets, respectively. This performance significantly surpasses the notable accuracy declines observed in traditional single-center models under similar conditions. To the best of our knowledge, the MCHFL is the first edge multicenter FL framework with theoretical underpinnings. Our extensive experimental results across various datasets validate the MCHFL's effectiveness, showcasing its higher accuracy, faster convergence speed, and stronger robustness compared to single-center models, thereby establishing it as a pioneering paradigm in edge multicenter FL.

Cortical Network Disruption Is Minimal in Early Stages of Psychosis.

Schizophrenia is associated with white matter disruption and topological reorganization of cortical connectivity but the trajectory of these changes, from the first psychotic episode to established illness, is poorly understood. Current studies in first-episode psychosis (FEP) patients using diffusion magnetic resonance imaging (dMRI) suggest such disruption may be detectable at the onset of psychosis, but specific results vary widely, and few reports have contextualized their findings with direct comparison to young adults with established illness. Diffusion and T1-weighted 7T MR scans were obtained from N = 112 individuals (58 with untreated FEP, 17 with established schizophrenia, 37 healthy controls) recruited from London, Ontario. Voxel- and network-based analyses were used to detect changes in diffusion microstructural parameters. Graph theory metrics were used to probe changes in the cortical network hierarchy and to assess the vulnerability of hub regions to disruption. The analysis was replicated with N = 111 (57 patients, 54 controls) from the Human Connectome Project-Early Psychosis (HCP-EP) dataset. Widespread microstructural changes were found in people with established illness, but changes in FEP patients were minimal. Unlike the established illness group, no appreciable topological changes in the cortical network were observed in FEP patients. These results were replicated in the early psychosis patients of the HCP-EP datasets, which were indistinguishable from controls in most metrics. The white matter structural changes observed in established schizophrenia are not a prominent feature in the early stages of this illness.

Evaluation of White Matter and Cerebellum Structures of Essential Tremor and Parkinson's Patients by Diffusion Tensor Imaging and Volbrain Method

Essential tremor (ET) and Parkinson's disease (PD) are the two most commonly encountered tremor disorders in movement disorders. Diffusion tensor imaging (DTI) is one of the best in vivo ways of mapping white matter pathways in the human brain. The aim of our study was to investigate diffusion variables and cerebellum volume in ET and PD using parcellation methods. Our study included 20 ET, 20 PD and 20 healthy controls. Fraction Anisotropy (FA) and Mean Diffusivity (MD) values were obtained with DTI, while the volume of each lobe of the cerebellum was obtained with T1 images. One-way ANOVA was used for intergroup analysis and Scheffe test was used for post-hoc analysis. Significant differences were found in the diffusion values of the pedunculus cerebellaris, fornix stria, superior longitudinal fasciculus, sagittal stratum, cerebral pedunculus, tapatum and thalamus of ET and PD. Lobule V, Lobule IX, Lobule X volumes of the cerebellum showed significant differences between the groups. Stria thermialis shows involvement of mesolimbic dopaminergic system in PD and it is thought that disruption of strial networks leads to changes in the activity of cerebellar networks and reveals the role of the cerebellum in tremor. It is obvious that cerebellar thalamocortical pathways are affected in Parkinson's disease. In Parkinson's disease, patients should be evaluated for visual processing, conceptualisation, postural instability and gait disturbance to clarify the diagnosis or to differentiate from essential tremor.

Perinatal Mood and Anxiety Disorder in Times of COVID

Between 15-20% of pregnant and birthing people in the United States experience symptoms of Perinatal Mood and Anxiety Disorders (PMAD). This percentage is higher for individuals who are low income, racially minoritized, and/or marginalized. Social systems of care and support have been shown to reduce symptoms of PMAD and help optimize the health of pregnant and postpartum people. For many people, COVID-19 further exacerbated the negative impacts of PMAD, largely because of disruptions of social support networks and health care resources. This article uses testimonio and autoethnography to discuss the stress, abandonment, and feeling of loss associated with the disruption of access to postpartum mental health care and support. While such feelings of loss and abandonment may feel personal, this article argues that it is critical to recognize the structural dimensions of perinatal mental health and postpartum care. The author concludes that misrecognition of the structural dimensions of PMAD contributes to structural violence.

Edge-based graph neural network for ranking critical road segments in a network.

Transportation networks play a crucial role in society by enabling the smooth movement of people and goods during regular times and acting as arteries for evacuations during catastrophes and natural disasters. Identifying the critical road segments in a large and complex network is essential for planners and emergency managers to enhance the network's efficiency, robustness, and resilience to such stressors. We propose a novel approach to rapidly identify critical and vital network components (road segments in a transportation network) for resilience improvement or post-disaster recovery. We pose the transportation network as a graph with roads as edges and intersections as nodes and deploy a Graph Neural Network (GNN) trained on a broad range of network parameter changes and disruption events to rank the importance of road segments. The trained GNN model can rapidly estimate the criticality rank of individual road segments in the modified network resulting from an interruption. We address two main limitations in the existing literature that can arise in capital planning or during emergencies: ranking a complete network after changes to components and addressing situations in post-disaster recovery sequencing where some critical segments cannot be recovered. Importantly, our approach overcomes the computational overhead associated with the repeated calculation of network performance metrics, which can limit its use in large networks. To highlight scenarios where our method can prove beneficial, we present examples of synthetic graphs and two real-world transportation networks. Through these examples, we show how our method can support planners and emergency managers in undertaking rapid decisions for planning infrastructure hardening measures in large networks or during emergencies, which otherwise would require repeated ranking calculations for the entire network.

Analyzing how government spending, incentives, and supply chains affect financial performance in energy poverty alleviation.

Poor access to modern energy services, or energy poverty, continues to be a significant barrier to socioeconomic progress and well-being. The complicated connections between public investment, incentives, supplier chains, and the financial success of energy poverty alleviation efforts are explored in this paper. This research examines the history of eradicating energy poverty worldwide, particularly in China. It examines how these aspects affect the efficiency and long-term viability of reducing energy poverty initiatives. This research synthesizes the evidence on government expenditure patterns and their impact on initiatives to reduce energy poverty via an extensive literature examination. The development of infrastructure, capacity building, and the deployment of energy technology are all made possible in large part by government spending. Financial and regulatory incentives have also been shown to encourage the energy industry's private sector engagement and innovation. These incentives help expand underprivileged groups' access to electricity by creating an atmosphere conducive to investment. This research also examines supply chains' crucial role in combating energy poverty. For the effective installation and upkeep of energy projects, efficient and resilient supply chains are crucial because they guarantee the availability of vital materials and resources. An integrated supply chain strategy may improve project results, save costs, and eliminate risks related to logistical difficulties. Government funding, incentives, and supply chains all have a connection that affects how well energy poverty alleviation programs function financially. Maintaining projects after their first execution requires sustained financial performance. It explores how supply network interruptions may affect financial performance, highlighting the need for robust supply chain management techniques. As a result, this study adds to a comprehensive knowledge of the complex processes underpinning the reduction of energy poverty. It offers insights into developing efficient policies and strategies by examining how government actions, incentives, supply chains, and financial performance interact. These observations are relevant for practitioners, investors, and academics trying to increase access to sustainable energy sources and reduce poverty in addition to policymakers. This report provides helpful recommendations for boosting the effectiveness and endurance of energy poverty reduction activities as nations work to reach global sustainable development objectives.

Disease avoidance threatens social cohesion in a large-scale social networking experiment

People tend to limit social contacts during times of increased health risks, leading to disruption of social networks thus changing the course of epidemics. To what extent, however, do people show such avoidance reactions? To test the predictions and assumptions of an agent-based model on the feedback loop between avoidance behavior, social networks, and disease spread, we conducted a large-scale (2,879 participants) incentivized experiment. The experiment rewards maintaining social relations and structures, and penalizes acquiring infections. We find that disease avoidance dominates networking decisions, despite relatively low penalties for infections; and that participants use more sophisticated strategies than expected (e.g., avoiding susceptible others with infectious neighbors), while they forget to maintain a beneficial network structure. Consequently, we observe low infection numbers, but also deterioration of network positions. These results imply that the focus on a more obvious signal (i.e., infection) may lead to unwanted side effects (i.e., loss of social cohesion).

Reproducible network changes occur in a mouse model of temporal lobe epilepsy but do not correlate with disease severity

Studying the development of brain network disruptions in epilepsy is challenged by the paucity of data before epilepsy onset. Here, we used the unilateral, kainate mouse model of hippocampal epilepsy to investigate brain network changes before and after epilepsy onset and their stability across time.Using 32 epicranial electrodes distributed over the mouse hemispheres, we analyzed EEG epochs free from epileptic activity in 15 animals before and 28 days after hippocampal injection (group 1) and in 20 animals on two consecutive days (d28 and d29, group 2). Statistical dependencies between electrodes were characterized with the debiased-weighted phase lag index. We analyzed: a) graph metric changes from baseline to chronic stage (d28) in group 1; b) their reliability across d28 and d29, in group 2; c) their correlation with epileptic activity (EA: seizure, spike and fast-ripple rates), averaged over d28 and d29, in group 2.During the chronic stage, intra-hemispheric connections of the non-injected hemisphere strengthened, yielding an asymmetrical network in low (4–8 Hz) and high theta (8–12 Hz) bands. The contralateral hemisphere also became more integrated and segregated within the high theta band. Both network topology and EEG markers of EA were stable over consecutive days but not correlated with each other.Altogether, we show reproducible large-scale network modifications after the development of focal epilepsy. These modifications are mostly specific to the non-injected hemisphere. The absence of correlation with epileptic activity does not allow to specifically ascribe these network changes to mechanisms supporting EA or rather compensatory inhibition but supports the notion that epilepsy extends beyond the sole repetition of EA and impacts network that might not be involved in EA generation.

The Utilization of Information Technology for the Professional Development of Islamic Education Teachers in Indonesia

The professional development of Islamic Religious Education (PAI) teachers is crucial to enhancing the quality of PAI learning in schools. One effective means to support their professional growth is through the utilization of technology as a learning medium and information source. This article aims to explore the use of technology by PAI teachers in their professional development, focusing on three key aspects: (1) The Importance of Integrating Technology in Teacher Professionalism; (2) Types and Functions of Technology Used by PAI Teachers; (3) Challenges Encountered by PAI Teachers in Advancing Professionalism through Technology. The research methodology used the qualitative method with the literature review approach, encompassing data collection and analysis from diverse sources such as journals, books, reports, and websites. The findings reveal that PAI teachers in Indonesia employ various technologies, including computers, laptops, smartphones, tablets, internet connectivity, social media platforms, applications, and software, for multiple purposes like information retrieval, communication, collaboration, content creation, presentation, and assessment of learning. The use of technology among PAI teachers yields several advantages, including the enhancement of knowledge, skills, motivation, creativity, networking opportunities, and learning resources. However, they face challenges related to limited infrastructure, access, costs, and support. Moreover, there's a lack of readiness, competence, and awareness regarding ethics and security among these educators. PAI teachers encounter difficulties, obstacles, and risks such as network disruptions, viruses, malware, spam, phishing, hacking, cyberbullying, and plagiarism. The challenges of utilizing technology for teacher professional development encompass skills, attitudes, infrastructure, and ethical considerations. Therefore, PAI teachers must possess adequate competence and readiness to integrate technology into their professional development.

C5 methylation confers accessibility, stability and selectivity to picrotoxinin

Minor changes to complex structures can exert major influences on synthesis strategy and functional properties. Here we explore two parallel series of picrotoxinin (PXN, 1) analogs and identify leads with selectivity between mammalian and insect ion channels. These are the first SAR studies of PXN despite its >100-year history and are made possible by advances in total synthesis. We observe a remarkable stabilizing effect of a C5 methyl, which completely blocks C15 alcoholysis via destabilization of an intermediate twist-boat conformer; suppression of this secondary hydrolysis pathway increases half-life in plasma. C5 methylation also decreases potency against vertebrate ion channels (γ-Aminobutyric acid type A (GABAA) receptors) but maintains or increases antagonism of homologous invertebrate GABA-gated chloride channels (resistance to dieldrin (RDL) receptors). Optimal 5MePXN analogs appear to change the PXN binding pose within GABAARs by disruption of a hydrogen bond network. These discoveries were made possible by the lower synthetic burden of 5MePXN (2) and were illuminated by the parallel analog series, which allowed characterization of the role of the synthetically simplifying C5 methyl in channel selectivity. These are the first SAR studies to identify changes to PXN that increase the GABAA-RDL selectivity index.