Disruption Of Network Research Articles

Effect of molecular chain mobility on the electrothermal stability of polyethylene/multi-walled carbon nanotube electrothermal composites

In this study, we pay close attention to the structure design of high-temperature electrothermal composites. Two kinds of high density polyethylene-based electrothermal composites filled with multi-walled carbon nanotubes (5960G1/MWCNTs and 5000 S/MWCNTs) prepared by melt compounding is employed as examples. The relationship between molecular weight of matrix and electrothermal properties of composites are studied through the investigation of the changes of microstructure and conductive network in the composites during heating-cooling cycles. Benefiting from the increased high-molecular-weight component and decrease of molecular chain mobility, 5960G1/MWCNTs composites shows excellent electrothermal stability with the surface temperature stabilized at about 120 °C within 10 h (applied voltage of 18 V). In addition, owing to the protection of internal conductive network from low molecular chain mobility of the 5960G1, a reduced disruption of conductive network from the thermal expansion of 5960G1/MWCNTs composite can be ensured by the low deformation of matrix with high molecular weight chains, in turn, 5960G1/MWCNTs composite exhibits good electrothermal repeatability at sequent heating-cool cycles. Thus, high molecular weight polyethylene as a matrix favors high temperature use of electrothermal materials and is expected to be applied in the field of commercial high-temperature electrical heating cables.

Default mode network failure and neurodegeneration across aging and amnestic and dysexecutive Alzheimer's disease.

From a complex systems perspective, clinical syndromes emerging from neurodegenerative diseases are thought to result from multiscale interactions between aggregates of misfolded proteins and the disequilibrium of large-scale networks coordinating functional operations underpinning cognitive phenomena. Across all syndromic presentations of Alzheimer's disease, age-related disruption of the default mode network is accelerated by amyloid deposition. Conversely, syndromic variability may reflect selective neurodegeneration of modular networks supporting specific cognitive abilities. In this study, we leveraged the breadth of the Human Connectome Project-Aging cohort of non-demented individuals (N = 724) as a normative cohort to assess the robustness of a biomarker of default mode network dysfunction in Alzheimer's disease, the network failure quotient, across the aging spectrum. We then examined the capacity of the network failure quotient and focal markers of neurodegeneration to discriminate patients with amnestic (N = 8) or dysexecutive (N = 10) Alzheimer's disease from the normative cohort at the patient level, as well as between Alzheimer's disease phenotypes. Importantly, all participants and patients were scanned using the Human Connectome Project-Aging protocol, allowing for the acquisition of high-resolution structural imaging and longer resting-state connectivity acquisition time. Using a regression framework, we found that the network failure quotient related to age, global and focal cortical thickness, hippocampal volume, and cognition in the normative Human Connectome Project-Aging cohort, replicating previous results from the Mayo Clinic Study of Aging that used a different scanning protocol. Then, we used quantile curves and group-wise comparisons to show that the network failure quotient commonly distinguished both dysexecutive and amnestic Alzheimer's disease patients from the normative cohort. In contrast, focal neurodegeneration markers were more phenotype-specific, where the neurodegeneration of parieto-frontal areas associated with dysexecutive Alzheimer's disease, while the neurodegeneration of hippocampal and temporal areas associated with amnestic Alzheimer's disease. Capitalizing on a large normative cohort and optimized imaging acquisition protocols, we highlight a biomarker of default mode network failure reflecting shared system-level pathophysiological mechanisms across aging and dysexecutive and amnestic Alzheimer's disease and biomarkers of focal neurodegeneration reflecting distinct pathognomonic processes across the amnestic and dysexecutive Alzheimer's disease phenotypes. These findings provide evidence that variability in inter-individual cognitive impairment in Alzheimer's disease may relate to both modular network degeneration and default mode network disruption. These results provide important information to advance complex systems approaches to cognitive aging and degeneration, expand the armamentarium of biomarkers available to aid diagnosis, monitor progression and inform clinical trials.

The influence of Hyssopus cuspidatus Boriss extract on lipid mediators metabolism network in asthmatic mice

Current drugs do not provide an absolute cure or modify the course of asthma. Hyssopus cuspidatus Boriss extract (SXCF) has been used as Uyghur medicine for several years to treat bronchial asthma. However, very limited research has been conducted on the therapeutic mechanisms of SXCF. Disruptions in the metabolic network of lipid mediators (LMs) are closely linked to the development of asthma. Here, we explored the therapeutic mechanism of SXCF in asthma based on the metabolic network of LMs, aiming to contribute to the understanding of SXCF in asthma treatment at the molecular level. The UHPLC-MRM strategy was used for the quantitative detection of LMs in the lung tissue and in the peripheral circulatory system (serum). ELISA was used to detect IgE in serum and cytokines in BALF. The lung tissue sections were stained with H&E to observe the infiltration of inflammatory cells, and behavioural changes in mice were observed and recorded throughout the animal experiment. In contrast to the asthma group, the opposite result was observed in the SXCF groups, where the perturbed LMs metabolic network was partly restored in a dose-dependent manner with a significant elevation of anti-inflammatory metabolites, while pro-inflammatory lipids were decreased. As significant downregulation of IgE and pro-inflammatory cytokines was observed, IgE and cytokines analysis also supported the anti-inflammatory effects of SXCF. It was also noticed that SXCF treatment reduced the number of coughs and decreased the inflammatory cell infiltration around the bronchus in mice. These results suggested that SXCF has a significant ameliorative effect on ovalbumin (OVA)-induced asthma. The modulation of LMs is a possible underlying mechanism of the SXCF effects.

Perspectives : Understanding the Pathophysiology of Intraventricular Hemorrhage in Preterm Infants and Considering of the Future Direction for Treatment.

Remarkable advances in neonatal care have significantly improved the survival of extremely low birth weight infants in recent years. However, intraventricular hemorrhage (IVH) continues to be a major complication in preterm infants, leading to a high incidence of cerebral palsy and cognitive impairment. IVH is primarily caused by disruption of the fragile vascular network of the subependymal germinal matrix, and subsequent ventricular dilatation adversely affects the developing infant brain. Based on recent research, periventricular white matter injury is caused not only by ischemia and morphological distortion due to ventricular dilatation but also by free iron and inflammatory cytokines derived from hematoma and its lysates. The current guidelines for the treatment of posthemorrhagic hydrocephalus (PHH) in preterm infants do not provide strong recommendations, but initiating treatment intervention based on ultrasound measurement values before the appearance of clinical symptoms of PHH has been proposed. Moreover, in the past decade, therapeutic interventions that actively remove hematomas and lysates have been introduced. The era is moving beyond cerebrospinal fluid shunt toward therapeutic goals aimed at improving neurodevelopmental outcomes.

Pathomechanisms of cognitive impairment in progressive supranuclear palsy.

Progressive supranuclear palsy (PSP) is a neurodegenerative disorder characterized by early postural instability and falls, oculomotor dysfunction (vertical supranuclear gaze palsy), parkinsonism with poor response to levodopa, pseudobulbar palsy, and cognitive impairment. This four-repeat tauopathy is morphologically featured by accumulation of tau protein in neurons and glia causing neuronal loss and gliosis in the extrapyramidal system associated with cortical atrophy and white matter lesions. Cognitive impairment being frequent in PSP and more severe than in multiple system atrophy and Parkinson disease, is dominated by executive dysfunction, with milder difficulties in memory, and visuo-spatial and naming dysfunctions. Showing longitudinal decline, it has been related to a variety of pathogenic mechanisms associated with the underlying neurodegenerative process, such as involvement of cholinergic and muscarinergic dysfunctions, and striking tau pathology in frontal and temporal cortical regions associated with reduced synaptic density. Altered striatofrontal, fronto-cerebellar, parahippocampal, and multiple subcortical structures, as well as widespread white matter lesions causing extensive connectivity disruptions in cortico-subcortical and cortico-brainstem connections, support the concept that PSP is a brain network disruption disorder. The pathophysiology and pathogenesis of cognitive impairment in PSP, as in other degenerative movement disorders, are complex and deserve further elucidation as a basis for adequate treatment to improve the quality of life of patients with this fatal disease.

Structural correlates of language processing in primary progressive aphasia.

Understanding the relationships between brain structure and language behaviour in primary progressive aphasia provides crucial information about these diseases' pathomechanisms. However, previous investigations have been limited from providing a statistically reliable view of broad language abilities by sample size, variant focus and task focus. In this study, the authors aimed to determine the relationship between brain structure and language behaviour in primary progressive aphasia, to determine the degree to which task-associated regions were atrophied across disease variants and to determine the degree to which task-related atrophy overlaps across disease variants. Participants were 118 primary progressive aphasia patients and 61 healthy, age-matched controls tested from 2011 to 2018 in the German Consortium for Frontotemporal Lobar Degeneration cohort. Diagnosis of primary progressive aphasia required progressive deterioration of mainly speech and language for ≥ 2 years, and variant was diagnosed by the criteria of Gorno-Tempini et al. (Classification of primary progressive aphasia and its variants. Neurology. 2011;76(11):1006-1014). Twenty-one participants not fulfilling a specific subtype were classified as mixed-variant and excluded. Language tasks of interest included the Boston naming test, a German adaptation of the Repeat and Point task, phonemic and category fluency tasks and the reading/writing subtest of the Aachen Aphasia Test. Brain structure was measured by cortical thickness. We observed networks of language task-associated temporal, frontal and parietal cortex. Overlapping task-associated atrophy was observed in the left lateral, ventral and medial temporal lobes, middle and superior frontal gyri, supramarginal gyrus and insula. Some regions, primarily in the perisylvian region, were associated with language behaviour despite showing no significant atrophy. The results crucially extend less powerful studies associating brain and language measures in primary progressive aphasia. Cross-variant atrophy in task-associated regions suggests partially shared underlying deficits, whereas unique atrophy reinforces variant-specific deficits. Language task-related regions that are not obviously atrophied suggest regions of future network disruption and encourage understanding of task deficits beyond clearly atrophied cortex. These results may pave the way for new treatment approaches.

Exploring the Restoration of Brain Connectivity during Weight Normalization in Severe Anorexia Nervosa

Abstract Anorexia nervosa is a persistent and often difficult to treat eating disorder with significant physical and mental health consequences. While it is known that the disorder is associated with alterations in brain functional connectivity during the phase of acute underweight, the effect of weight normalization on brain connectivity remains unclear. This talk focuses on the recovery of intrinsic brain connectivity during weight normalization in severe anorexia nervosa, presenting data from a longitudinal study. Using resting-state functional magnetic resonance imaging, we assessed brain connectivity at three different stages of inpatient treatment. Our findings indicate that patients with severe anorexia nervosa have weaker intrinsic connectivity and altered network topology, which do not improve during treatment. These persistent disruptions in brain networks suggest that severe anorexia nervosa may have long-term effects on the way the brain processes information, even after weight is restored. Disclosure of Interest None Declared

Scenario-based earthquake damage assessment of highway bridge networks

In earthquake-prone regions, the evaluation of seismic impacts on bridges is crucial to mitigation, emergency, and recovery planning for highway networks. The degree of bridge damage determines the cost and time required for repairs and the level of post-earthquake functionality including disruption of transportation network, increased costs due to reduction of traffic flow and restricted access to emergency routes. The article presents the methodological development and implementation of an interactive web application for rapid geospatial assessment and visualisation of earthquake damage scenarios of municipal highway bridge networks based on open access datasets. The proposed framework consists of the following successive models: hazard, inventory, damage, and impact. The seismic hazard model generates spatial distribution of the shaking intensity for earthquake scenarios in terms of ground motion intensity measure using ground motion prediction equations based on seismic hazard model for Eastern Canada. The shaking intensities are then modified with local site amplification factors based on the Canadian highway bridge design code values. The inventory model provides a database of existing bridges based on open-access data which are then classified according to their seismic vulnerability. The damage model assesses seismic performance of classes of bridges by applying respective fragility functions represented as probabilistic relationships between the intensity measure and the degree of expected damage. The impact model evaluates the post-earthquake traffic-carrying capacity of the highway network based on the predicted damage including repair cost as a percentage of replacement cost of bridges and inspection priority. The web-application is demonstrated with a bridge network in Quebec City including 117 bridges subjected to 180 earthquake scenarios. The proposed methodology is particularly useful to facilitate direct communication of potential impacts to emergency managers and city transport officials.

Innovating the RFP process: Forward-looking logistics sourcing strategies

Since the COVID-19 pandemic, transportation professionals have been challenged with managing intricate supply chains. Market volatility has become a major pain point, with unforeseen costs and capacity constraints creating transportation network disruption. This challenge provides the opportunity to re-evaluate existing procurement processes and carrier partnerships. While the typical time to negotiate contracts is during the request for proposal (RFP), the annual event remains costly and inefficient, often administered via e-mail and Excel spreadsheets. With no sign of a slowdown in the demand for goods, there is increased pressure on shippers and carriers to transform their transportation practices to become more efficient. Breakthrough analysed their ecosystem of over 21 million annual shipments and over US$22bn in annual transportation spend to quantify the value of selecting carriers that match network priorities and growing volume with the optimal carriers. Breakthrough found that selecting carriers that match network priorities can result in 14 per cent cost savings. Growing volume with carriers throughout rate cycles can also add additional cost savings of 2.75 per cent or US$0.08 per mile lower than other carriers. Data-driven logistics sourcing strategies that allow transportation professionals to broaden their carrier networks and secure reliable capacity are discussed in this paper.

A Scoping Review of the Online Interventions by Nurses for Reducing Negative Impact of Bullying on Students.

The incidents of bullying on students have increased every year. The negative impacts of bullying are physical problems, psychological problems such as depression and anxiety, and even risk of suicide. Interventions by online to reduce the negative impact of bullying are more effective and efficient. The aim of this study is to explore method of online-based nursing intervention on students for reducing the negative impact of bullying. This study used a scoping review method. The literature from three databases were PubMed, CINAHL, and Scopus. PRISMA Extension for Scoping Reviews was used for search strategy and we used keywords were "nursing care OR nursing intervention" AND "bullying OR victimization" AND "online OR digital" AND "student". The inclusion criteria for articles were primary research, the design of articles are randomized control trial or quasi-experimental, samples were students, and the setting time of publication is last 10 years (2013-2022). We found 686 articles based on initial research, after elimination based on inclusion and exclusion criteria, and found 10 articles discussing about online-based interventions by nurses on students for reducing the negative effects of bullying. The range of respondents in this study is 31-2771 respondents. The method used for the online-based nursing intervention were improving students' skills, increasing social skills, and counseling. The types of media used are videos, audio, modules, and online discussions. Online interventions were effective and efficient so that they are easily accessible to participants, however, it causes difficulties for participants to access interventions due to internet network disruptions. The potential of online-based nursing interventions is effective to reduce the negative impact of bullying with give full attention in physical, psychological, spiritual, and cultural aspect.

Differential Impact of Brain Network Efficiency on Poststroke Motor and Attentional Deficits.

Most studies on stroke have been designed to examine one deficit in isolation; yet, survivors often have multiple deficits in different domains. While the mechanisms underlying multiple-domain deficits remain poorly understood, network-theoretical methods may open new avenues of understanding. Fifty subacute stroke patients (7±3days poststroke) underwent diffusion-weighted magnetic resonance imaging and a battery of clinical tests of motor and cognitive functions. We defined indices of impairment in strength, dexterity, and attention. We also computed imaging-based probabilistic tractography and whole-brain connectomes. To efficiently integrate inputs from different sources, brain networks rely on a rich-club of a few hub nodes. Lesions harm efficiency, particularly when they target the rich-club. Overlaying individual lesion masks onto the tractograms enabled us to split the connectomes into their affected and unaffected parts and associate them to impairment. We computed efficiency of the unaffected connectome and found it was more strongly correlated to impairment in strength, dexterity, and attention than efficiency of the total connectome. The magnitude of the correlation between efficiency and impairment followed the order attention>dexterity ≈ strength (strength: |r|=.03, P=0.02, dexterity: |r|=.30, P=0.05, attention: |r|=.55, P<0.001). Network weights associated with the rich-club were more strongly correlated to efficiency than non-rich-club weights. Attentional impairment is more sensitive to disruption of coordinated networks between brain regions than motor impairment, which is sensitive to disruption of localized networks. Providing more accurate reflections of actually functioning parts of the network enables the incorporation of information about the impact of brain lesions on connectomics contributing to a better understanding of underlying stroke mechanisms.

Network Metadata (NETTA) : Sistem Monitoring Jaringan Dan Metadata UPT BMKG Dengan Notifikasi Berbasis Telegram

ABSTRACT&#x0D; &#x0D; BMKG has a role to disseminate geo-hydrometeorological information in the fields of Meteorology, Climatology and Geophysics. The dissemination process requires data from BMKG operational equipment spread across 183 Technical Implementation Units (UPT) throughout Indonesia. The recorded data must be collected and disseminated using a communication network. The existing network monitoring system currently at BMKG uses the PRTG monitoring application but can only be accessed by central employees. Thus, a system that can be accessed by regional employees to see the condition of the communication network in each UPT is needed, so that it can support operations. This paper discusses monitoring system and communication network metadata design with notifications using telegrams that can be adapted for operations at BMKG called NetTa (Network Metadata). The system consists of a network monitoring dashboard, provider rental metadata dashboard, network monitoring notifications via Telegram bots. The system operates in real time and can be accessed from anywhere using a smartphone and PC. The results of functional testing show that monitoring system of all existing features on the system can be accessed according to the design. Telegram notifications can provide network device status as well as warnings if there is a communication network interruption based on the monitored sensor code&#x0D; &#x0D; Keywords : Monitoring, Network, UPT BMKG, Telegram, Metadata

Age and Sex-Related Effects on Single-Subject Gray Matter Networks in Healthy Participants.

Recent developments in image analysis have enabled an individual's brain network to be evaluated and brain age to be predicted from gray matter images. Our study aimed to investigate the effects of age and sex on single-subject gray matter networks using a large sample of healthy participants. We recruited 812 healthy individuals (59.3 ± 14.0 years, 407 females, and 405 males) who underwent three-dimensional T1-weighted magnetic resonance imaging. Similarity-based gray matter networks were constructed, and the following network properties were calculated: normalized clustering, normalized path length, and small-world coefficients. The predicted brain age was computed using a support-vector regression model. We evaluated the network alterations related to age and sex. Additionally, we examined the correlations between the network properties and predicted brain age and compared them with the correlations between the network properties and chronological age. The brain network retained efficient small-world properties regardless of age; however, reduced small-world properties were observed with advancing age. Although women exhibited higher network properties than men and similar age-related network declines as men in the subjects aged < 70 years, faster age-related network declines were observed in women, leading to no differences in sex among the participants aged ≥ 70 years. Brain age correlated well with network properties compared to chronological age in participants aged ≥ 70 years. Although the brain network retained small-world properties, it moved towards randomized networks with aging. Faster age-related network disruptions in women were observed than in men among the elderly. Our findings provide new insights into network alterations underlying aging.

Single-cell sequencing of entorhinal cortex reveals widespread disruption of neuropeptide networks in Alzheimer's disease.

Abnormalities of neuropeptides (NPs) that play important roles in modulating neuronal activities are commonly observed in Alzheimer's disease (AD). We hypothesize that NP network disruption is widespread in AD brains. Single-cell transcriptomic data from the entorhinal cortex (EC) were used to investigate the NP network disruption in AD. Bulk RNA-sequencing data generated from the temporal cortex by independent groups and machine learning were employed to identify key NPs involved in AD. The relationship between aging and AD-associated NP (ADNP) expression was studied using GTEx data. The proportion of cells expressing NPs but not their receptors decreased significantly in AD. Neurons expressing higher level and greater diversity of NPs were disproportionately absent in AD. Increased age coincides with decreased ADNP expression in the hippocampus. NP network disruption is widespread in AD EC. Neurons expressing more NPs may be selectively vulnerable to AD. Decreased expression of NPs participates in early AD pathogenesis. We predict that the NP network can be harnessed for treatment and/or early diagnosis of AD.

A deep reinforcement learning model for resilient road network recovery under earthquake or flooding hazards

As the backbone and the ‘blood vessel’ of modern cities, road networks provide critical support for community activities and economic growth, with their roles even more crucial due to the dramatic progress in urbanization. The service of road networks is subjected to the increasing frequency of high-consequence natural hazards such as earthquakes, floods, hurricanes, etc. Identifying resilient restoration sequences is essential to mitigate the disruption of such important infrastructure networks. This paper investigates a novel decision-support model to optimize post-disaster road network repair sequence. The model, named as GCN-DRL model, integrates the advantages of deep reinforced learning (DRL) with graph convolutional neural network (GCN), two emerging artificial intelligence (AI) techniques to achieve efficient recovery of road network service. The model is applied to analyze two cases of community road networks in the US that are subjected to different types of hazards, i.e., earthquakes and flooding. The performance of repair sequence by the GCN-DRL model is compared with two commonly used methods, i.e., repair sequence by the genetic algorithm and by prioritization based on graph importance with betweenness centrality. The results showed the decision sequence by GCN-DRL model consistently achieved superior performance in road network restoration than the conventional methods. The AI-based decision model also features high computational efficiency since the GCN-DRL model can be trained before the hazard. With a pre-trained GCN-DRL model, a close to optimal decision-making process can be made available rapidly for different types of new hazards, which is advantageous in efficiently responding to hazards when they happen. This study demonstrates the promise of a new AI-based decision support model to improve the resilience of road networks by enabling efficient post-hazards recovery.

Construction of a 5G-based, three-dimensional, and efficiently connected emergency medical management system

BackgroundShenzhen is unique in its need for ad hoc responses to emergencies. The need for emergency medicine also demonstrates a trend of sustained growth. ObjectiveA three-dimensional and efficiently connected emergency medical management model using fifth generation mobile communication technology (5G) was established to improve the efficiency and level of management in emergency medicine. MethodA mixed-frequency band private network collaborative emergency treatment mode was built under daily emergency scenarios using 5G. The efficiency of a three-dimensional telemedicine treatment mode was tested using prehospital emergency medicine. Also, the feasibility of quickly establishing a temporary network information system using unmanned aerial vehicle (UAV) and/or high-throughput communication satellites under disaster-caused power outages and network interruptions was examined. A monitoring system was constructed for suspected cases using 5G amid public health emergencies, which raised the Emergency Department’s efficiency and security in responding to the pandemic. ResultsThe three-dimensional rescue system supported by 5G showed that the radius of the emergency medical rescue services expanded from 5 to 60 km, and the cross-district emergency reaction time reduced from 1 h to <20 min. Thus, it was feasible to construct a communication network expeditiously with devices carried by UAV under disastrous scenarios. The system developed based on 5G could be used in managing suspected cases of public emergencies. Among the 134 suspected cases in the early stage of the pandemic, no nosocomial infection was detected. ConclusionA three-dimensional, efficiently connected emergency medical management system based on 5G was constructed, following which the emergency rescue radius quickly expanded and the emergency response time reduced. Thus, with the aid of new technology, an emergency information network system was built expeditiously under specific scenarios, such as a natural disaster, and the level of management under public health emergencies advances. The confidentiality of patient information is a critical issue regarding the application of new technology.

In silico anti-quorum sensing activities of phytocompounds of Psidiumguajava in Salmonellaenterica serovar Typhi

Biofilm contributes hugely to the persistence of typhoid fever in human population and quorum sensing (QS) is an integral mechanism involved in biofilms. Interruption of the QS network has therefore been put forward as one of the important anti-virulence strategies. Methanol extract of Psidium guajava leaves has been confirmed to possess antibacterial and anti-biofilm activities against Salmonella Typhi. This study therefore aimed at investigating the interactions of phytocompounds previously identified in the extract with selected QS proteins of S. Typhi in silico. Appropriate formats of compounds were retrieved and translated using online web servers. Quantitative estimate of drug-likeness, as well as absorption, distribution, metabolism, excretion and toxicity profiles of the compounds, were assessed on ADMETlab 2.0. Three-dimensional structures of two QS proteins of S. Typhi were obtained from Protein Data Bank while others were modelled on SWISS-MODEL. Selected compounds (ligands) were docked with the four proteins via AutoDock 1.5.6 and analyzed on Discovery studio. Eight, out of the seventy-two, phyto-compounds of methanol extract of P. guajava possess desirable drug-likeness (QED > 0.67). Three of them have toxic characteristics and thus, were removed from further consideration. Molecular docking revealed that, of the 5 ligands docked against the proteins, only Benzeneethanamine, 4-methoxy- and Cyclopentadecanone, 2-hydroxy- had affinities for the proteins of interest. The affinity of Cyclopenftadecanone,2-hydroxy- for each of the proteins is higher than that of Benzeneethanamine,4-methoxy- with hydrogen bonds contributing significantly to the interactions. Benzeneethanamine, 4-methoxy- and Cyclopentadecanone,2-hydroxy- from Psidium guajava leaves possess inhibitory properties against QS proteins of S. Typhi.

Bayesian Machine Learning Enables Identification of Transcriptional Network Disruptions Associated with Drug-Resistant Prostate Cancer.

The computational method TraRe built on Bayesian machine learning models for investigating transcriptional network structures shows that disruption of ELK3, MXD1, and MYB signaling cascades impacts abiraterone resistance in prostate cancer.

Macrolinguistic function in temporal lobe epilepsy: a reinterpretation of circumstantiality

ABSTRACT Background While language is frequently explored in stroke and focal lesions, complex language disorders in paroxysmal conditions remain relatively underexplored. Individuals with temporal lobe epilepsy (TLE) often have an impairment of conversational language manifesting as verbosity and attributable to disruption of cognitive-linguistic networks. The micro- and macrolinguistic underpinnings of this disturbance, and the role of epilepsy and cognitive variables, are yet to be explored. Methods and Procedures We examined the elicited language output of 16 individuals with TLE and 14 healthy controls under separate monologic discourse tasks: a structured and constrained context, elicited by description of the ‘Cookie Theft’ picture, and an unstructured, unconstrained context, elicited by description of a ‘Typical Day’. We hypothesised that language output in the unstructured context would be characterised by verbosity to a greater extent than language elicited in a structured context. Outcomes and Results Following transcription and coding, detailed multi-level discourse analysis suggested that a constrained context gives rise to microlinguistic disturbances in individuals with TLE, reducing fluency, with more pauses and fillers. Under an unconstrained context, as anticipated, classical aspects of verbosity emerge in those with TLE, manifesting as longer speaking time, a longer duration of pauses, and a higher proportion of repetitive or redundant statements. Macrolinguistic elements such as coherence and informativeness are widely impacted, particularly disturbing language formulation. Correlations suggest that microlinguistic disturbances are closely linked with the immediate impact of seizures on cognitive-linguistic function, while macrolinguistic disturbances are more broadly impacted by disorder severity and word retrieval deficits. Conclusions These findings suggest that cognitive-linguistic disturbances in TLE produce vulnerability to different psycholinguistic impairments as a function of differing linguistic challenges imposed by constrained and unconstrained discourse contexts. Where constrained, output fluency is primarily affected, while in an unconstrained context disturbed discourse planning and organisation declares itself. We conclude that these patterns reflect a dynamic linguistic system taking shape under specific contextual conditions.

Developing a "Disaster Scenario" to prepare for the possibility of disruptions to maritime transportation serving coastal communities of Vancouver Island

Planning for a crisis scenario that interferes with supply chain operations aims to speed up the recovery process. How to reduce disruptions in marine supply networks, especially in coastal communities, has not received adequate research. Therefore, in this research, a catastrophe scenario is created following consultations with a marine stakeholder and a marine geologist, and its viability is then evaluated by two maritime professionals. The scenario for the Fraser River is developed on the British Columbian island of Vancouver. The river's delta is susceptible to underwater landslides since it is composed of 65 % mud and clay and 35 % sand. Following an earthquake in Cascadia Subduction Zone, an underwater landslide may happen at low tide. This forecast makes sense since, as mentioned in earlier research, there is a strong likelihood that an earthquake of magnitude 9.0 with an epicentre location roughly 300 kilometres from Vancouver may occur. It is also anticipated that an underwater landslide with a retrogression toward the land of 450 m would take place according to the developed scenario of this research. Landslides in the Fraser River are likely to be at a location near to Sand Heads Lighthouse since large-scale earthquakes may trigger them. For example, the landslide developed in 1985 was around 100 m away from the Lighthouse. Thus, one of the main outcomes of the scenario is the destruction of the Sand Heads Lighthouse and the buoys from the outer delta. Additionally, the planned scenario may destroy structures along the coast, such as shipyards, quaysides, and other buildings. The Fraser River is mainly used by ferries and barges to bring foods from the mainland to Vancouver Island; as a result, the islanders place a great deal of reliance on this river to support their way of daily life. 90 % of their foods comes from outside the island, and there is only a three-day supply of fresh food in the shops. This signifies the importance of the Fraser River.