Aberrant Behavior Research Articles

Cytogenetic Study and Pollen Viability of Phalaenopsis Queen Beer 'Mantefon'.

Intergeneric and interspecific hybridization has been employed for the breeding of Phalaenopsis to transfer desirable traits between species, producing novel phenotypes with improved size, color, form, and flower-bearing ability. These characteristics are often enhanced; however, many of these hybrids are triploids and have reduced or complete sterility, for example, Phalaenopsis Queen Beer 'Mantefon', an important novelty-type cultivar in Asia, particularly in China, Japan, and Republic of Korea. Despite the increasing demand for the crop for ornamental purposes, little is known about its cytogenetics, which is essential for breeding and, consequently, crop improvement. In this study, karyotyping using fluorescence in situ hybridization, meiotic chromosome behavior analysis, pollen staining, and in vitro viability germination tests were performed to understand the cause of hybrid sterility and pollen abnormality in Phalaenopsis Queen Beer 'Mantefon' from a cytogenetic perspective. Viability tests revealed pollen infertility at all flower developmental stages, confirmed by the absence of pollen tube growth. Aberrant chromosomal behavior was observed in pollen mother cells (PMCs), frequently forming univalents, chromosomal bridges, and laggards during the entire meiotic process. PMCs were also divided irregularly into sporads with varying numbers of micronuclei, which may be responsible for pollen sterility in this cultivar. Altogether, the cytogenetic analyses provided insights into the pollen development of Phalaenopsis Queen Beer 'Mantefon' and the conceivable causes of its infertility.

Bayesian Change-Point Analysis Approach to Detecting Aberrant Test-Taking Behavior Using Response Times

Change-point analysis (CPA) is a method for detecting abrupt changes in parameter(s) underlying a sequence of random variables. It has been applied to detect examinees’ aberrant test-taking behavior by identifying abrupt test performance change. Previous studies utilized maximum likelihood estimations of ability parameters, focusing on detecting one change point for each examinee. This article proposes a Bayesian CPA procedure using response times (RTs) to detect abrupt changes in examinee speed, which may be related to aberrant responding behaviors. The lognormal RT model is used to derive a procedure for detecting aberrant RT patterns. The method takes the numbers and locations of the change points as parameters in the model to detect multiple change points or multiple aberrant behaviors. Given the change points, the corresponding speed of each segment in the test can be estimated, which enables more accurate inferences about aberrant behaviors. Simulation study results indicate that the proposed procedure can effectively detect simulated aberrant behaviors and estimate change points accurately. The method is applied to data from a high-stakes computerized adaptive test, where its applicability is demonstrated.

Sleep disturbances are associated with irritability in ASD children with sensory sensitivities

BackgroundParent reports suggest that 44–84% of children with ASD exhibit sleep disturbances that are of clinical concern. Previous studies have reported that, in children with ASD, the severity of sleep disturbances is associated with the severity of either sensory problems or aberrant behaviors, but none have performed combined analyses with measures of both sensory and aberrant behaviors symptom domains from the same children.MethodsWe examined parent reports of 237 children with ASD, 1.4–8.7 years old, using the child sleep habits questionnaire (CSHQ), sensory profile (SP), and aberrant behaviors checklist (ABC).ResultsThe analyses revealed that sleep disturbances were most strongly associated with SP sensory sensitivity and ABC irritability scores. Together these scores explained 35% of the variance in total CSHQ scores. Moreover, sensory sensitivity scores moderated the association between irritability and sleep disturbances, indicating that sleep disturbances were significantly associated with irritability only in children with moderate to severe sensory sensitivities.ConclusionWe suggest that the three symptom domains may interact and exacerbate each other such that successful intervention in one symptom domain may have positive impact on the others. Further intervention studies testing this hypothesis are highly warranted.

Neuropsychiatric symptoms and imbalance of atrophy in behavioral variant frontotemporal dementia.

Behavioral variant frontotemporal dementia is characterized by heterogeneous frontal, insular, and anterior temporal atrophy patterns that vary along left-right and dorso-ventral axes. Little is known about how these structural imbalances impact clinical symptomatology. The goal of this study was to assess the frequency of frontotemporal asymmetry (right- or left-lateralization) and dorsality (ventral or dorsal predominance of atrophy) and to investigate their clinical correlates. Neuropsychiatric symptoms and structural images were analyzed for 250 patients with behavioral variant frontotemporal dementia. Frontotemporal atrophy was most often symmetric while left-lateralized (9%) and right-lateralized (17%) atrophy were present in a minority of patients. Atrophy was more often ventral (32%) than dorsal (3%) predominant. Patients with right-lateralized atrophy were characterized by higher severity of abnormal eating behavior and hallucinations compared to those with left-lateralized atrophy. Subsequent analyses clarified that eating behavior was associated with right atrophy to a greater extent than a lack of left atrophy, and hallucinations were driven mainly by right atrophy. Dorsality analyses showed that anxiety, euphoria, and disinhibition correlated with ventral-predominant atrophy. Agitation, irritability, and depression showed greater severity with a lack of regional atrophy, including in dorsal regions. Aberrant motor behavior and apathy were not explained by asymmetry or dorsality. This study provides additional insight into how anatomical heterogeneity influences the clinical presentation of patients with behavioral variant frontotemporal dementia. Behavioral symptoms can be associated not only with the presence or absence of focal atrophy, but also with right/left or dorsal/ventral imbalance of gray matter volume.

Integrative Multi-Omics Analysis of Oncogenic EZH2 Mutants: From Epigenetic Reprogramming to Molecular Signatures.

Somatic heterozygous mutations in the active site of the enhancer of zeste homolog 2 (EZH2) are prevalent in diffuse large B-cell lymphoma (DLBCL) and acute myeloid leukemia (AML). The methyltransferase activity of EZH2 towards lysine 27 on histone H3 (H3K27) and non-histone proteins is dysregulated by the presence of gain-of-function (GOF) and loss-of-function (LOF) mutations altering chromatin compaction, protein complex recruitment, and transcriptional regulation. In this study, a comprehensive multi-omics approach was carried out to characterize the effects of differential H3K27me3 deposition driven by EZH2 mutations. Three stable isogenic mutants (EZH2Y641F, EZH2A677G, and EZH2H689A/F667I) were examined using EpiProfile, H3K27me3 CUT&Tag, ATAC-Seq, transcriptomics, label-free proteomics, and untargeted metabolomics. A discrete set of genes and downstream targets were identified for the EZH2 GOF and LOF mutants that impacted pathways involved in cellular proliferation, differentiation, and migration. Disruption of protein networks and metabolic signatures able to sustain aberrant cell behavior was observed in response to EZH2 mutations. This systems biology-based analysis sheds light on EZH2-mediated cell transformative processes, from the epigenetic to the phenotypic level. These studies provide novel insights into aberrant EZH2 function along with targets that can be explored for improved diagnostics/treatment in hematologic malignancies with mutated EZH2.

Cariprazine in Pediatric Patients with Autism Spectrum Disorder: Results of a Pharmacokinetic, Safety and Tolerability Study.

Objective: Cariprazine is a dopamine D3-preferring D3/D2 and serotonin 5-HT1A receptor partial agonist approved to treat adults with schizophrenia and manic/mixed or depressive episodes associated with bipolar I disorder. This study, which is the first to evaluate cariprazine in pediatric patients with autism spectrum disorder (ASD) (including children 5-9 years of age) using an oral solution formulation, evaluated the safety, tolerability, pharmacokinetics (PK), and exploratory efficacy of cariprazine and its two major active metabolites, desmethyl cariprazine (DCAR) and didesmethyl cariprazine (DDCAR). Methods: This clinical pharmacology, open-label, multiple-dose study enrolled 25 pediatric patients from 5 to 17 years of age, who met the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition criteria for ASD. All patients began treatment with cariprazine 0.5 mg once daily (QD) and underwent a titration over 7 days to maintenance doses of 1.5 or 3 mg QD for patients 13-17 years of age at Screening, 0.75 or 1.5 mg QD for patients 10-12 years of age at Screening, and 0.5 or 1.5 mg QD for patients 5-9 years of age at Screening. After 6 weeks total of dosing, there was a 6-week follow-up period. Study assessments included adverse events (AEs), safety parameters, noncompartmental PK parameters, and exploratory efficacy assessments, including the Aberrant Behavior Checklist-Irritability Subscale (ABC-I), Clinical Global Impressions (CGI-S), Caregiver Global Impressions (CgGI-S), Children's Yale-Brown Obsessive Compulsiveness Scale Modified for ASD (CYBOCS-ASD), Social Responsiveness Scale (SRS), and Vineland Adaptive Behavior Scale (VABS-III). Results: All AEs were mild or moderate in severity. Most frequent treatment-emergent adverse events (TEAEs) were increased weight, increased alanine aminotransferase, increased appetite, dizziness, agitation, and nasal congestion. Increases in weight were not considered clinically meaningful. Two subjects reported extrapyramidal symptom-related TEAEs that resolved without leading to discontinuation. Dose-normalized exposures of all analytes were modestly higher in pediatric patients from 5 to 9 years of age when compared to older patients. Consistent with previous studies, at steady state, the rank of exposure in plasma was DDCAR > cariprazine > DCAR. There was numerical improvement on all exploratory endpoints (ABC-I, CGI-S, CgGI-S, CYBOCS-ASD, SRS, and VABS-III). Conclusions: PK of cariprazine and its metabolites were characterized in pediatric patients with ASD at doses up to 3 mg QD (13-17 years) and 1.5 mg QD (5-12 years). Caripazine treatment was generally well tolerated and results from this study will inform the selection of appropriate pediatric doses for subsequent studies.

PEOPLE'S PERCEPTION OF THE SPREADING NEWS HOAX ON RESIDENTS HAS BEEN AFFECTED BY THE COVID-19 OUTBREAK IN THE VILLAGE PERCUT SEI VILLAGE

ABSTRACT The spread of the corona virus, otherwise known as the covid-19, is growing, as news of its origin (hoax) spreads into the citizen timeline +62. Her comments ranged from netizens' upload that makassar's citizens were positive about corona so that it could be spread by air that is disrupting the media sanity. Not only physical and mental health but also mental health. Our daily activities must also be wholesome and aberrant behavior to be avoided. As with the media, media mental health is something to be respected. Failure to receive and process information from many different time lines can easily lead to disaligned channels, leading to user psychology that may be affected. The long-term objective of this study is to produce and provide information to the public, especially so that news may not be easily swayed. Many people, after hearing false reports, quickly conclude that the news is true. Therefore, the purpose of this study is not to analyze people's perceptions as well as to know how their perceptions in response to and receiving news hoaxes in society. The study also detracts from public perception, especially from people's negative assessments of lies that they often hear and receive. The public will learn to digest the news well by seeking information from other media. As more and more hoaxes, more and more information is revealed. The study will be done in the village office of ponds. The method used was qualitative method, a random selection of sample data sources. Sample data source identification, the proposal is still preliminary and will be developed further by field researchers. The main data-gathering techniques are observation, in-depth interviews, library studies, and a combination of all three or cross-examination. Thus, overcoming the completeness of data while doing research will require planning to examine the validity of the data.Keyword: spread, news, hoax, covid-19

The brain serotonin system in autism.

Autism spectrum disorders (ASDs) are among themost common neurodevelopmental diseases. These disorders are characterized by lack of social interaction, byrepetitive behavior, and often anxiety and learning disabilities. The brain serotonin (5-HT) system is known to becrucially implicated in a wide range of physiological functions and in the control of different kinds of normal and pathological behavior. A growing number of studies indicate the involvement of the brain 5-HT system in the mechanisms underlying both ASD development and ASD-related behavioral disorders. There are some review papers describing the role of separate key players of the 5-HT system in an ASD and/or autistic-like behavior. In this review, we summarize existing data on the participation of all members of the brain 5-HT system, namely, 5-HT transporter, tryptophan hydroxylase 2, MAOA, and 5-HT receptors, in autism in human and various animal models. Additionally, we describe the most recent studies involving modern techniques for invivo regulation of gene expression that are aimed at identifying exact roles of 5-HT receptors, MAOA, and 5-HT transporter inthe mechanisms underlying autistic-like behavior. Altogether, results of multiple research articles show that the brain 5-HT system intimately partakes in the control of some types of ASD-related behavior, and that specific changes in a function of a certain 5-HT receptor, transporter, and/or enzyme may normalize this aberrant behavior. These data give hope that some of clinically used 5-HT-related drugs have potential for ASD treatment.

Predicting cognitive decline using neuropsychiatric symptoms in prodromal Lewy body dementia: A longitudinal study

IntroductionNeuropsychiatric symptoms (NPS) in Lewy body dementias (LBD) occur frequently and early in disease progression. Such symptoms are associated with worse quality of life, caregiver burden and functional limitations. Limited evidence exists, however, outlining the longitudinal relationship between NPS and cognitive decline in prodromal LBD. Methods123 participants were derived from three cohort studies. Patients with mild cognitive impairment (MCI) relating to probable dementia with Lewy bodies (MCI-LB, n = 67) and Parkinson's disease (PD-MCI, n = 56) completed comprehensive cognitive and neuropsychiatric assessment and were followed up longitudinally. Linear regression and mixed effects models assessed the relationship between baseline NPS and cognition at baseline and over time. ResultsIn MCI-LB, overall NPS burden was associated with declines over time in executive function (p = 0.026) and processing speed (p = 0.028) and baseline aberrant motor behaviour was associated with declines in attention (p < 0.025). Anxiety was significantly associated with poorer visuospatial functioning (p = 0.016) at baseline and poorer attention both at baseline (p = 0.017) and across time points (p = 0.024). In PD-MCI, psychosis was associated with poorer executive functioning at baseline (p = 0.008) and across time points (p = 0.002) but had no association with changes longitudinally. ConclusionsCore neuropsychiatric components of LBD are not strongly associated with cognition in prodromal disease. This may suggest that neuropathological mechanisms underlying NPS may not be the same as those underlying cognitive impairment. Non-core NPS, however, may be more directly associated with cognitive change. Future studies utilising neuroimaging techniques are needed to explore the neuropathological basis of NPS in prodromal LBD.

Migrating eagles stopping in the Sahara Desert: Aberrant behaviour or foraging strategy?

AbstractThe Sahara Desert is one of the major ecological barriers of the Palearctic‐Africa bird migration system. We analysed the behaviour of GPS‐tagged booted eagles (Aquila pennata) breeding in Spain and wintering in Tropical Africa, focusing on the Sahara crossing. We searched for anomalous track segments recorded during the desert crossing, finding that non‐directional and/or slow movements occur in 35% of the migratory journeys and last up to 31 days (average: 5.9). The analyses show that this behaviour is not triggered by adverse weather conditions, and is shown especially by females during spring. The length of the stopping periods affects the arrival date to the breeding grounds only in males, which means that females buffer the potential delay with an earlier migration start, suggesting that this behaviour is followed deliberately. These wandering movements occur in hyper‐arid areas without vegetation cover. A similar behaviour has not been previously recorded in any other trans‐Saharan raptor and is probably related to the ability of booted eagles to take advantage of food resources unexploited by other predators, such as migratory songbirds stopping in the desert during the day, and migratory locusts. If confirmed by further research involving higher resolution tracking and field observations, our findings may show a new way of combining foraging and migration, without a well‐defined directed trajectory, nor stopping in a restricted spot, but instead wandering over wide areas.

SCZ-SCAN: An automated Schizophrenia detection system from electroencephalogram signals

Schizophrenia (SCZ) is a severe neurological and physiological syndrome that perverts a patient’s perception of reality. SCZ exhibits several symptoms, including hallucinations, delusions, aberrant behavior, and thinking. It affects their professional, academic, personal, and social lives. Neurologists use a variety of verbal and visual tests to determine SCZ. However, these methods are laborious, time-consuming, superficial, and vulnerable to mistakes. Therefore, it is necessary to create an automated model for SCZ detection. Convolutional neural networks have swiftly established themselves in the field of mental health care due to the growth of deep learning in recent decades. Electroencephalogram (EEG) data records the variations in the neural dynamics of human memory. Using EEG data, this study proposes an automatic SCZ detection method using separable convolution attention network (SCZ-SCAN). The proposed network employs depth-wise separable convolution and attention networks on high-level and low-level to aggregate characteristics of 2-D scalogram images acquired from the continuous wavelet transform. The depth-wise separable convolutions help to create a lightweight framework, while attention techniques concentrate on significant features and reduce futile computations by removing the transmission of irrelevant features. The proposed approach has an average classification accuracy of 99% and 95% on the IBIB-PAN and EEG data from the basic sensory task in SZ dataset. Moreover, statistical hypothesis testing is performed using Wilcoxon’s Rank-Sum test to signify the model performance and it proves that SCZ-SCAN is statistically efficient to nine cutting-edge methods.Experimental results show that the PSFAN statistically defeats 11 contemporary methods, proving its effectiveness for medical industrial applications.

Seeger Lecture, Society for Ethnomusicology Virtual Meeting 2021

Seeger Lecture, Society for Ethnomusicology Virtual Meeting 2021

Predicting Fatigue-Associated Aberrant Driving Behaviors Using a Dynamic Weighted Moving Average Model With a Long Short-Term Memory Network Based on Heart Rate Variability.

This study proposed a moving average (MA) approach to dynamically process heart rate variability (HRV) and developed aberrant driving behavior (ADB) prediction models by using long short-term memory (LSTM) networks. Fatigue-associated ADBs have traffic safety implications. Numerous models to predict such acts based on physiological responses have been developed but are still in embryonic stages. This study recorded the data of 20 commercial bus drivers during their routine tasks on four consecutive days and subsequently asked them to complete questionnaires, including subjective sleep quality, driver behavior questionnaire and the Karolinska Sleepiness Scale. Driving behaviors and corresponding HRV were determined using a navigational mobile application and a wristwatch. The dynamic-weighted MA (DWMA) and exponential-weighted MA were used to process HRV in 5-min intervals. The data were independently separated for training and testing. Models were trained with 10-fold cross-validation strategy, their accuracies were evaluated, and Shapley additive explanation (SHAP) values were used to determine feature importance. Significant increases in the standard deviation of NN intervals (SDNN), root mean square of successive heartbeat interval differences (RMSSD), and normalized spectrum of high frequency (nHF) were observed in the pre-event stage. The DWMA-based model exhibited the highest accuracy for both driver types (urban: 84.41%; highway: 80.56%). The SDNN, RMSSD, and nHF demonstrated relatively high SHAP values. HRV metrics can serve as indicators of mental fatigue. DWMA-based LSTM could predict the occurrence of the level of fatigue associated with ADBs. The established models can be used in realistic driving scenarios.

Abnormal Activity Recognition in Private Places Using Deep Learning: A Survey

Abstract: The method of analysing human motion using computer and machine vision technologies is known as "human activity recognition," or HAR. One of the applications of human activity recognition in security systems is anomaly detection. Surveillance cameras have been widely placed as the foundation for video analysis as the demand for security has grown. Identifying aberrant behaviour necessitates considerable human effort, which is one of the major challenges in surveillance video analysis. It is important to set up video recording in order to detect unusual activity automatically. Our intelligent video surveillance system can detect an abnormality in a video using deep learning technologies. Real-time detection of activities is also conceivable, and these video frames will be saved in the system as images for the user to study. The proposed Abnormal Activity Recognition system was designed with the purpose of finding and detecting anomalies in the financial industry, especially in an ATM context, using a live stream. The first part of the research focuses on the use of image deep learning algorithms to recognise different products and detect anomalous behaviour utilizing ATM monitoring systems

Adaptive time series representation for out-of-round railway wheels fault diagnosis in wayside monitoring

Through the integration of advanced sensor technologies and machine learning algorithms, artificial intelligence has revolutionized wayside monitoring in the railway sector. Although several algorithms have been proposed for detecting out-of-roundness, i.e., flats, wear treads and polygonization, they generally fall short in isolating the root cause of the wheel's issue in a train passage. In this context, the paper presents a novel approach for wheel out-of-roundness diagnosis with (1) detection of aberrant train behavior; (2) isolation of specific defective wheels; (3) identification of the severity. For this, the methodology automatically segments a strain gauge signal, capturing the complex nature and temporal dependence of vibration patterns. This segmentation allows the extraction of localized accelerometer features in both the time and frequency domain, as well as implicit axle count and labelling of each wheel passage. Moreover, a single-value damage indicator based on anomaly detection algorithms was proposed. To validate the effectiveness of the proposed methodology, experiments on a set 3D numerical train-track dynamic interaction simulations are performed for different wheel profiles, track irregularities, train speeds, sensor placement and noise, associated to other environmental and operational variations. This demonstrates the potential of artificial intelligence for real-time assessment of wheels without interfering with normal service conditions, suggesting the possibility of automated fault diagnosis.© 2023 Elsevier Inc. All rights reserved.

Clinical and cost evaluation of two models of specialist intensive support teams for adults with intellectual disabilities who display behaviours that challenge: the IST-ID mixed-methods study.

Intensive support teams (ISTs) are recommended for individuals with intellectual disabilities who display behaviours that challenge. However, there is currently little evidence about the clinical and cost-effectiveness of IST models operating in England. To investigate the clinical and cost-effectiveness of IST models. We carried out a cohort study to evaluate the clinical and cost-effectiveness of two previously identified IST models (independent and enhanced) in England. Adult participants (n = 226) from 21 ISTs (ten independent and 11 enhanced) were enrolled. The primary outcome was change in challenging behaviour between baseline and 9 months as measured by the Aberrant Behaviour Checklist-Community version 2. We found no statistically significant differences between models for the primary outcome (adjusted β = 4.27; 95% CI -6.34 to 14.87; P = 0.430) or any secondary outcomes. Quality-adjusted life-years (0.0158; 95% CI: -0.0088 to 0.0508) and costs (£3409.95; 95% CI -£9957.92 to £4039.89) of the two models were comparable. The study provides evidence that both models were associated with clinical improvement for similar costs at follow-up. We recommend that the choice of service model should rest with local services. Further research should investigate the critical components of IST care to inform the development of fidelity criteria, and policy makers should consider whether roll out of such teams should be mandated.

Neural activation of regions involved in food reward and cognitive control in young females with anorexia nervosa and atypical anorexia nervosa versus healthy controls

Anorexia nervosa (AN) and atypical AN (AtypAN) are complex neurobiological illnesses that typically onset in adolescence with an often treatment-refractory and chronic illness trajectory. Aberrant eating behaviors in this population have been linked to abnormalities in food reward and cognitive control, but prior studies have not examined respective contributions of clinical characteristics and metabolic state. Research is needed to identify specific disruptions and inform novel intervention targets to improve outcomes. Fifty-nine females with AN (n = 34) or AtypAN (n = 25), ages 10–22 years, all ≤90% expected body weight, and 34 age-matched healthy controls (HC) completed a well-established neuroimaging food cue paradigm fasting and after a standardized meal, and we used ANCOVA models to investigate main and interaction effects of Group and Appetitive State on blood oxygenation level-dependent (BOLD) activation for the contrast of exposure to high-calorie food images minus objects. We found main effects of Group with greater BOLD activation in the dorsal anterior cingulate cortex (dACC), dorsolateral prefrontal cortex (DLPFC), hippocampus, caudate, and putamen for AN/AtypAN versus HC groups, and in the three-group model including AN, AtypAN, and HC (sub-)groups, where differences were primarily driven by greater activation in the AtypAN subgroup versus HC group. We found a main effect of Appetitive State with increased premeal BOLD activation in the hypothalamus, amygdala, nucleus accumbens, and caudate for models that included AN/AtypAN and HC groups, and in BOLD activation in the nucleus accumbens for the model that included AN, AtypAN, and HC (sub-)groups. There were no interaction effects of Group with Appetitive State for any of the models. Our findings demonstrate robust feeding-state independent group effects reflecting greater neural activation of specific regions typically associated with reward and cognitive control processing across AN and AtypAN relative to healthy individuals in this food cue paradigm. Differential activation of specific brain regions in response to the passive viewing of high-calorie food images may underlie restrictive eating behavior in this clinical population.

Alcohol-induced damage to the fimbria/fornix reduces hippocampal-prefrontal cortex connection during early abstinence

IntroductionAlcohol dependence is characterized by a gradual reduction in cognitive control and inflexibility to contingency changes. The neuroadaptations underlying this aberrant behavior are poorly understood. Using an animal model of alcohol use disorders (AUD) and complementing diffusion-weighted (dw)-MRI with quantitative immunohistochemistry and electrophysiological recordings, we provide causal evidence that chronic intermittent alcohol exposure affects the microstructural integrity of the fimbria/fornix, decreasing myelin basic protein content, and reducing the effective communication from the hippocampus (HC) to the prefrontal cortex (PFC). Using a simple quantitative neural network model, we show how disturbed HC-PFC communication may impede the extinction of maladaptive memories, decreasing flexibility. Finally, combining dw-MRI and psychometric data in AUD patients, we discovered an association between the magnitude of microstructural alteration in the fimbria/fornix and the reduction in cognitive flexibility. Overall, these findings highlight the vulnerability of the fimbria/fornix microstructure in AUD and its potential contribution to alcohol pathophysiology.SummaryFimbria vulnerability to alcohol underlies hippocampal-prefrontal cortex dysfunction and correlates with cognitive impairment.

Innate immune activation and aberrant function in the R6/2 mouse model and Huntington's disease iPSC-derived microglia.

Huntington's disease (HD) is an inherited autosomal dominant neurodegenerative disease caused by CAG repeats in exon 1 of the HTT gene. A hallmark of HD along with other psychiatric and neurodegenerative diseases is alteration in the neuronal circuitry and synaptic loss. Microglia and peripheral innate immune activation have been reported in pre-symptomatic HD patients; however, what "activation" signifies for microglial and immune function in HD and how it impacts synaptic health remains unclear. In this study we sought to fill these gaps by capturing immune phenotypes and functional activation states of microglia and peripheral immunity in the R6/2 model of HD at pre-symptomatic, symptomatic and end stages of disease. These included characterizations of microglial phenotypes at single cell resolution, morphology, aberrant functions such as surveillance and phagocytosis and their impact on synaptic loss in vitro and ex vivo in R6/2 mouse brain tissue slices. To further understand how relevant the observed aberrant microglial behaviors are to human disease, transcriptomic analysis was performed using HD patient nuclear sequencing data and functional assessments were conducted using induced pluripotent stem cell (iPSC)-derived microglia. Our results show temporal changes in brain infiltration of peripheral lymphoid and myeloid cells, increases in microglial activation markers and phagocytic functions at the pre-symptomatic stages of disease. Increases in microglial surveillance and synaptic uptake parallel significant reduction of spine density in R6/2 mice. These findings were mirrored by an upregulation of gene signatures in the endocytic and migratory pathways in disease-associated microglial subsets in human HD brains, as well as increased phagocytic and migratory functions of iPSC-derived HD microglia. These results collectively suggest that targeting key and specific microglial functions related to synaptic surveillance and pruning may be therapeutically beneficial in attenuating cognitive decline and psychiatric aspects of HD.

A multi-scale convolutional neural network-based model for clustering economic risk detection.

After the public health event of COVID-19, more academics are looking into how to predict combined economic hazards associated with public health incidents. There are currently just a few approaches for detecting aberrant behaviour in aggregated financial risk, and most only work after the economic risk has already been inappropriately aggregated. As a result, we provide a multi-scale convolutional neural network-based model for clustering financial risk anomaly detection (MCNN). First, we use MCNN to train a model for counting economic risks that are used to evaluate aberrant risk aggregating data. Second, we can use the test results to extract the financial risk statistics and economic risk precursor coordinate points. Then, we calculate the economic risk distribution entropy, distance, potential energy and density. To train the three elements of the development state and create the prediction model, we finally use the particle swarm optimization-based extreme learning machine (PSO-ELM). The results of the experiments demonstrate that, in comparison to existing algorithms, our model can efficiently realize early warning and detect abnormal behaviours of aggregated economic risks with high timeliness. Additionally, our method achieves a forecast accuracy of 97.68% and can give additional time to take emergency action.