Abnormal Behavior Research Articles

Downregulation of hsa-miR-132 and hsa-miR-129: non-coding RNA molecular signatures of Alzheimer's disease.

Alzheimer's disease (AD) affects the elderly population by causing memory impairments, cognitive and behavioral abnormalities. Currently, no curative treatments exist, emphasizing the need to explore therapeutic options that modify the progression of the disease. MicroRNAs (miRNAs), as non-coding RNAs, demonstrate multifaceted targeting potential and are known to be dysregulated in AD pathology. This mini review focuses on two promising miRNAs, hsa-miR-132 and hsa-miR-129, which consistently exhibit differential regulation in AD. By employing computational predictions and referencing published RNA sequencing dataset, we elucidate the intricate miRNA-mRNA target relationships associated with hsa-miR-132 and hsa-miR-129. Our review consistently identifies the downregulation of hsa-miR-132 and hsa-miR-129 in AD brains as a non-coding RNA molecular signature across studies conducted over the past 15 years in AD research.

First detection of Ichthyophonus sp. in invasive wild pink salmon (Oncorhynchus gorbuscha) from the North Atlantic Ocean.

Pacific pink salmon (Oncorhynchus gorbuscha) were deliberately introduced to rivers surrounding the White Sea and has spread to Norway and several other countries surrounding the North Atlantic Ocean. In August 2021, a female pink salmon displaying pale gills and abnormal behaviour was captured in River Lakselva in Northern Norway and later submitted to the Norwegian Veterinary Institute (NVI) for post-mortem examination. Histological examination of organ samples revealed structures indicative of systemic ichthyophoniasis, caused by Ichthyophonus sp. The parasites appeared to be especially abundant in the heart and skeletal musculature, and local tissue responses were assessed to be absent or very mild. Sequences of the ribosomal 18S rRNA and the mitochondrial cytochrome oxidase 1 (CO1) genes confirmed the diagnosis and identified the pathogen as Ichthyophonus sp. The CO1 sequence further established that the isolate from pink salmon was most similar to sequences of Ichthyophonus sp. from Atlantic salmon, Salmo salar, from the Atlantic Ocean on the east coast of the US and from Atlantic herring, Clupea harengus, from Iceland. We here report the first detection of Ichthyophonus sp. in pink salmon in the North Atlantic Ocean.

Mining Abnormal Patterns in Moving Target Trajectories Based on Multi-Attribute Classification

As a type of time series data, trajectory data objectively record the location information and corresponding time information of an object’s activities. It not only describes the spatial activity trajectory of a moving object but also contains the unique attributes, states, and behavioral characteristics of the moving object itself. It can also reflect the interaction relationship between the object’s activities and various elements in the environment to a certain extent. Therefore, mining from moving target trajectory data to discover implicit, effective, and potentially useful spatiotemporal behavior patterns of moving targets, such as anomaly detection, will have significant research significance. This paper proposes a method for mining abnormal patterns in the trajectory of moving targets based on multi-attribute classification. Firstly, to explore the activity location patterns of single moving targets, a frequent sequence discovery method for moving targets based on sequence patterns is proposed. Furthermore, for moving target trajectory data sets containing multiple attributes, numerical attributes are extracted, and the data are clustered according to attribute classification to extract a set of normal behavior patterns of moving targets. Then, combining the activity location patterns and normal behavior patterns of the moving target, the original trajectory data are compared with them to achieve the goal of detecting abnormal behavior of the moving target. Finally, an incremental anomaly detection scheme is proposed to address the characteristics of fast updates and large numbers of data in trajectory data sets. This involves synchronously updating the frequency of moving target activity patterns and the range of values for normal behavior patterns while updating the trajectory data set, in order to meet the needs of database updates and improve the accuracy and credibility of results.

DDEYOLOv9: Network for Detecting and Counting Abnormal Fish Behaviors in Complex Water Environments

DDEYOLOv9: Network for Detecting and Counting Abnormal Fish Behaviors in Complex Water Environments

Systemic immune challenge exacerbates neurodegeneration in a model of neurological lysosomal disease

Mucopolysaccharidosis type IIIA (MPS IIIA) is a rare paediatric lysosomal storage disorder, caused by the progressive accumulation of heparan sulphate, resulting in neurocognitive decline and behavioural abnormalities. Anecdotal reports from paediatricians indicate a more severe neurodegeneration in MPS IIIA patients, following infection, suggesting inflammation as a potential driver of neuropathology. To test this hypothesis, we performed acute studies in which WT and MPS IIIA mice were challenged with the TLR3-dependent viral mimetic poly(I:C). The challenge with an acute high poly(I:C) dose exacerbated systemic and brain cytokine expression, especially IL-1β in the hippocampus. This was accompanied by an increase in caspase-1 activity within the brain of MPS IIIA mice with concomitant loss of hippocampal GFAP and NeuN expression. Similar levels of cell damage, together with exacerbation of gliosis, were also observed in MPS IIIA mice following low chronic poly(I:C) dosing. While further investigation is warranted to fully understand the extent of IL-1β involvement in MPS IIIA exacerbated neurodegeneration, our data robustly reinforces our previous findings, indicating IL-1β as a pivotal catalyst for neuropathological processes in MPS IIIA.

Fraud Detection of the Electricity Consumption by combining Deep Learning and Statistical Methods

An important issue for the electricity distribution companies is the non-technical loss (NTL), also known as electricity fraud. This issue has a significant impact on the economies of all countries in the world. In this context, we studied the problem of the imbalance between the electrical energy invoiced and the electrical energy supplied within the Algerian economic sector. This article presents an approach to detecting electrical fraud using a combination of Long Short-Term Memory (LSTM) and robust Exponential and Holt-Winters Smoothing (EHWS) methods in order to enhance the accuracy and efficacy of fraud detection mechanisms. The proposed approach investigates the fraudulent behaviour of electricity consumers and unfolds in several key phases. In the first step, monthly consumption forecasts are made and the model with the most accurate results is selected. Then, the phase of detection of anomalies in economic meters and detection of cases of fraud by economic customers begins. This phase relies on the robust exponential and Holt-Winters Smoothing methods for uncovering irregular patterns indicative of potential fraud instances. The proposed model was trained and evaluated, and several experiments were carried out using a large dataset of real users from the economic sector. The dataset comprised approximately 2,000 customers and encompassed 14 years of monthly electricity usage in Bejaia, Algeria. The results of the experiments demonstrate promising performance, underscoring the efficacy of our proposed solution in effectively detecting instances of fraud, leading us to conclude that this proposition is robust and can help improve the accuracy of locating abnormal consumer behaviour and increase the company's profits.

Enhanced hippocampal LTP but normal NMDA receptor and AMPA receptor function in a rat model of CDKL5 deficiency disorder

BackgroundMutations in the X-linked gene cyclin-dependent kinase-like 5 (CDKL5) cause a severe neurological disorder characterised by early-onset epileptic seizures, autism and intellectual disability (ID). Impaired hippocampal function has been implicated in other models of monogenic forms of autism spectrum disorders and ID and is often linked to epilepsy and behavioural abnormalities. Many individuals with CDKL5 deficiency disorder (CDD) have null mutations and complete loss of CDKL5 protein, therefore in the current study we used a Cdkl5−/y rat model to elucidate the impact of CDKL5 loss on cellular excitability and synaptic function of CA1 pyramidal cells (PCs). We hypothesised abnormal pre and/or post synaptic function and plasticity would be observed in the hippocampus of Cdkl5−/y rats.MethodsTo allow cross-species comparisons of phenotypes associated with the loss of CDKL5, we generated a loss of function mutation in exon 8 of the rat Cdkl5 gene and assessed the impact of the loss of CDLK5 using a combination of extracellular and whole-cell electrophysiological recordings, biochemistry, and histology.ResultsOur results indicate that CA1 hippocampal long-term potentiation (LTP) is enhanced in slices prepared from juvenile, but not adult, Cdkl5−/y rats. Enhanced LTP does not result from changes in NMDA receptor function or subunit expression as these remain unaltered throughout development. Furthermore, Ca2+ permeable AMPA receptor mediated currents are unchanged in Cdkl5−/y rats. We observe reduced mEPSC frequency accompanied by increased spine density in basal dendrites of CA1 PCs, however we find no evidence supporting an increase in silent synapses when assessed using a minimal stimulation protocol in slices. Additionally, we found no change in paired-pulse ratio, consistent with normal release probability at Schaffer collateral to CA1 PC synapses.ConclusionsOur data indicate a role for CDKL5 in hippocampal synaptic function and raise the possibility that altered intracellular signalling rather than synaptic deficits contribute to the altered plasticity.LimitationsThis study has focussed on the electrophysiological and anatomical properties of hippocampal CA1 PCs across early postnatal development. Studies involving other brain regions, older animals and behavioural phenotypes associated with the loss of CDKL5 are needed to understand the pathophysiology of CDD.

Individualized high-resolution analysis to categorize diverse learning and memory deficits in tau rTg4510 mice exposed to low-intensity blast

Individualized high-resolution analysis to categorize diverse learning and memory deficits in tau rTg4510 mice exposed to low-intensity blast

Effect of rehabilitation exercise on locomotion performance of captive Bengal slow loris

The Bengal slow loris (Nycticebus bengalensis) is one of the most confiscated and rescued primate species in China. These animals are often subject to physical issues due to poor husbandry practices in captivity. Many individuals rescued from the illegal wildlife trade are confined to captivity, leading to decreased locomotion ability and increased abnormal behavior. Recognizing the potential benefits of rehabilitation exercises, we conducted outdoor rehabilitation activities in forest environments to improve the locomotion performance of rescued Bengal slow lorises, and evaluated how individual characteristics and conditions in captivity influenced their locomotion recovery. The results of the rehabilitation exercises were promising, showing that the animals exhibited more elaborate route selection, improved action scores, longer distances traveled, and reduced periods of inactivity. Bengal slow lorises housed in larger cages and those in short-term captivity demonstrated better overall performance. Additionally, animals displaying stereotypical behavior achieved higher action scores, while male lorises had longer periods of motionlessness compared to females. These findings highlight the positive impact of rehabilitation exercises on the locomotion abilities of captive Bengal slow lorises, emphasizing the importance of enhancing animal welfare and implementing scientifically informed strategies for the successful reintroduction of pre-released animals.

Progressive prediction: Video anomaly detection via multi‐grained prediction

AbstractVideo Anomaly Detection (VAD) has been an active research field for several decades. However, most existing approaches merely extract a single type of feature from videos and define a single paradigm to indicate the extent of abnormalities. A coarse‐to‐fine three‐level prediction is built by integrating different levels of spatio‐temporal representations, better highlighting the difference between normal and abnormal behaviors. First, an object‐level trajectory prediction is proposed to model human historical position using a graph transformer network. Subsequently, skeleton‐level prediction is achieved by incorporating the positional information from the trajectory prediction. More importantly, based on the predicted skeleton, a skeleton‐guided pixel‐level region prediction is performed. A novel Skeleton Conditioned Generative Adversarial Network (SCGAN) is designed to explore the correlation between skeleton‐level and pixel‐level motion prediction. Benefiting from SCGAN, the prediction of human regions is contributed by both coarse‐grained and fine‐grained motion features. This three‐level prediction, namely Progressive Prediction Video Anomaly Detection (P3VAD), enlarges the prediction error on irregular motion patterns. Besides, a pixel‐level analysis method is proposed to achieve Background‐bias Elimination (BE) and denoise the predicted region. Experimental results validate the effectiveness of P3VAD on the four benchmark datasets (ShanghaiTech, CUHK Avenue, IITB‐Corridor, and ADOC).

An R–A dual network detection model for abnormal behavior of running vehicles

An R–A dual network detection model for abnormal behavior of running vehicles

Ramelteon protects against social defeat stress-associated abnormal behaviors

Psychological stress affects the neuroendocrine regulation, which modulates mental status and behaviors. Melatonin, a hormone synthesized primarily by the pineal gland, regulates many brain functions, including circadian rhythms, pain, sleep, and mood. Selective pharmacological melatonin agonist ramelteon has been clinically used to treat mood and sleep disorders. Posttraumatic stress disorder (PTSD) is a psychiatric condition associated with severe trauma; it is generally triggered by traumatic events, which lead to severe anxiety and uncontrollable trauma recall. We recently reported that repeated social defeat stress (RSDS) may induce robust anxiety-like behaviors and social avoidance in mice. In the present study, we investigated whether melatonin receptor activation by melatonin and ramelteon regulates RSDS-induced behavioral changes. Melatonin treatment improved social avoidance and anxiety-like behaviors in RSDS mice. Moreover, treatment of the non-selective MT1/MT2 receptor agonist, ramelteon, markedly ameliorated RSDS-induced social avoidance and anxiety-like behaviors. Moreover, activating melatonin receptors also balanced the expression of monoamine oxidases, glucocorticoid receptors, and endogenous antioxidants in the hippocampus. Taken together, our findings indicate that the activation of both melatonin and ramelteon regulates RSDS-induced anxiety-like behaviors and PTSD symptoms. The current study also showed that the regulatory effects of neuroendocrine mechanisms and cognitive behaviors on melatonin receptor activation in repeated social defeat stress.

Identification of RNA‐dependent liquid‐liquid phase separation proteins using an artificial intelligence strategy

RNA-dependent liquid-liquid phase separation (LLPS) proteins play critical roles in cellular processes such as stress granule formation, DNA repair, RNA metabolism, germ cell development, and protein translation regulation. The abnormal behavior of these proteins is associated with various diseases, particularly neurodegenerative disorders like amyotrophic lateral sclerosis and frontotemporal dementia, making their identification crucial. However, conventional biochemistry-based methods for identifying these proteins are time-consuming and costly. Addressing this challenge, our study developed a robust computational model for their identification. We constructed a comprehensive dataset containing 137 RNA-dependent and 606 non-RNA-dependent LLPS protein sequences, which were then encoded using amino acid composition, composition of K-spaced amino acid pairs, Geary autocorrelation, and conjoined triad methods. Through a combination of correlation analysis, mutual information scoring, and incremental feature selection, we identified an optimal feature subset. This subset was used to train a random forest model, which achieved an accuracy of 90% when tested against an independent dataset. This study demonstrates the potential of computational methods as efficient alternatives for the identification of RNA-dependent LLPS proteins. To enhance the accessibility of the model, a user-centric web server has been established and can be accessed via the link: http://rpp.lin-group.cn.

Phenytoin causes behavioral abnormalities and suppresses kisspeptin expression, reducing reproductive performance in Japanese medaka

Phenytoin, an antiepileptic drug, induces neurotoxicity and abnormal embryonic development and reduces spontaneous locomotor activity in fish. However, its effects on other endpoints remain unclear. Therefore, we investigated the effects of phenytoin on the swimming behavior and reproductive ability of Japanese medaka. Abnormalities in swimming behavior, such as imbalance, rotation, rollover, and vertical swimming, were observed. However, when phenytoin exposure was discontinued, the behavioral abnormality rates decreased. Phenytoin exposure also significantly reduced reproductive ability. By investigating reproduction-related gene expression of gnrh1, gnrh2, fshb, and lhb remained unchanged in males and females. In contrast, kiss1 expression was significantly suppressed due to phenytoin exposure in males and females. kiss2 expression was also significantly suppressed in females but not in males. We filmed videos to examine phenytoin exposure effects on sexual behavior. Females showed no interest in the male's courtship. As the kisspeptin 1 system controls sexual behavior in Japanese medaka, phenytoin exposure may have decreased kiss1 expression, which decreased female reproductive motivation; hence, they did not spawn eggs. This is the first study to show that phenytoin exposure induces behavioral abnormalities, and suppresses kiss1 expression and reproductive performance in Japanese medaka.

Electro-spinnability study of polymer slurry system for proton exchange membrane fuel cell

The electro-spinnability of catalyst slurry determines fabrication availability and the catalyst layer's quality. It was affected by the ink composition, ambient condition, and electrospinning parameters. Abnormal electrospinning behaviors, including blocking, dripping, dripping with spinning, semi-floating fibers, and standing fibers, were observed under the unfavorable combination of the factors. The high blocking frequency can be avoided by using a slurry with higher ionomer content, lower solid content and smaller average particle size. Dripping and dripping with electrospinning phenomena can be solved by restricting the applied voltage above 10 kV. Semi-floating fibers can be avoided by limiting the ambient temperature between 20-30°C and relative humidity between 15-25%. Standing fibers can be prevented by restricting the total applied voltage (the difference between the positive voltage on the needle and the negative voltage on the drum) to a level lower than 15 kV.

Promoting Growth in Behavioral Neurology: A Path Forward.

Behavioral neurology & neuropsychiatry (BNNP) is a field that seeks to understand brain-behavior relationships, including fundamental brain organization principles and the many ways that brain structures and connectivity can be disrupted, leading to abnormalities of behavior, cognition, emotion, perception, and social cognition. In North America, BNNP has existed as an integrated subspecialty through the United Council for Neurologic Subspecialties since 2006. Nonetheless, the number of behavioral neurologists across academic medical centers and community settings is not keeping pace with increasing clinical and research demand. In this commentary, we provide a brief history of BNNP followed by an outline of the current challenges and opportunities for BNNP from the behavioral neurologist's perspective across clinical, research, and educational spheres. We provide a practical guide for promoting BNNP and addressing the shortage of behavioral neurologists to facilitate the continued growth and development of the subspecialty. We also urge a greater commitment to recruit trainees from diverse backgrounds so as to dismantle persistent obstacles that hinder inclusivity in BNNP-efforts that will further enhance the growth and impact of the subspecialty. With rapidly expanding diagnostic and therapeutic approaches across a range of conditions at the intersection of neurology and psychiatry, BNNP is well positioned to attract new trainees and expand its reach across clinical, research, and educational activities.

Fault detection and isolation for multi-type sensors in nuclear power plants via a knowledge-guided spatial–temporal model

Sensor faults in nuclear power plants (NPPs) have the potential to propagate negative impacts on system stability, leading to false alarms and accident misdiagnosis. Existing methods seldom concurrently consider complex spatial–temporal correlations among multi-type sensors in the primary circuit. This study presents a novel sensor fault detection and isolation scheme named the knowledge-guided spatial–temporal model (KGSTM), using the knowledge-guided recurrent unit (KGRU) and the concurrent detection strategy. To organically express part and whole interdependencies from inherent sensor layout, several graphs are specifically designed with pertinent domain knowledge. KGRU consists of the multi-graph convolutional network (MGCN) for fusing various spatial information and the gate recurrent unit (GRU) for extracting dynamic temporal features, further obtaining precise reconstructed signals and residuals. The concurrent detection strategy can explicitly quantify abnormal behaviors to detect and isolate faulty sensors by characterizing spatial–temporal signal variation. Numerical results on two real-world datasets from a pressurized water reactor (PWR) with simulated faults illustrate that the KGSTM has superior performance over various state-of-the-art methods in terms of signal reconstruction and fault detection.

Intrusion Detection System Using Probabilistic Adaptive Learning

Abstract: Machine learning and deep learning techniques are widely used to evaluate intrusion detection systems (IDS) capable of rapidly and automatically recognizing and classifying cyber-attacks on networks and hosts. However, when destructive attacks are becoming more extensive, more challenges develop, needing a comprehensive response. Numerous intrusion detection datasets are publicly accessible for further analysis by the cybersecurity research community. The Intrusion Detection System (IDS) is an effective tool utilized in cybersecurity systems to detect and identify intrusion attacks. With the increasing volume of data generation, the possibility of various forms of intrusion attacks also increases. Feature selection is crucial and often necessary enhance performance. The structure of the dataset can impact the efficiency of the machine learning model. Furthermore, data imbalance can pose a problem, but sampling approaches can help mitigate it. With technological advancements, machine learning-based methods have emerged as the cornerstone of modern intrusion detection, enabling more precise identification of abnormal behaviors and potential intrusions by learning the patterns of normal network traffic. In response to these challenges, this paper introduces an innovative intrusion detection model that amalgamates the Probabilistic Adaptiv eLearning Network architecture.

Alzheimer Disease

Alzheimer's disease (AD) is typified by memory deficiencies that worsen with time, along with cognitive and behavioral abnormalities that eventually result in dementia. AD is one of the most difficult diseases to treat because of its prevalence, high cost of care, effects on patients and caregivers, and lack of mechanism-based therapies. The malfunction and loss of neurons in particular circuits and locations, especially in the populations of nerve cells supporting memory and cognition, causes the AD condition.The neuropathology of AD is characterized by intracellular and extracellular protein aggregation accumulations. In the neuronal perikarya and dystrophic neurites, abnormally phosphorylated tau forms paired helical filaments (PHFs) that combine to form neurofibrillary tangles (NFTs). The extracellular deposition of β-pleated assemblies of Aβ peptide, which result in widespread and neuritic senile plaques, is the second pathogenic hallmark.

An 8-year-old child with Encephalitis Rabies due to delayed case finding, proper Post Exposure Prophylaxis for Rabies infection a case report

Background and Purpose: Until April 2023, it has been documented about 31.113 case of animal rabies-borne bite, which 23.211 cases are given Post Exposure Prophylaxis Anti Rabies Vaccine and 11 death cases in Indonesia and 95% of them are caused by dog bite. This case report was conducted to describe a rare case of an 8 years old child presenting with late complication of encephalitis in Borong General Hospital due to the delayed case finding, management and proper prophylaxis for Rabies infection. Case: One day prior to admission, an 8-year-old girl with irritability, aggressive behavior, and severe speech abnormalities reported to the emergency room. These symptoms quickly escalated to confusion and disturbed mental status. One month before to presentation, the patient's parents stated that his right arm had been bitten by a stray dog. Because there were no obvious bite marks and the family was not aware of the dog bite, PEP was not requested. In the previous five days, her parents described experiences of hydrophobia, photophobia, and aerophobia. The patient's health deteriorated two hours after admission, and palpating the carotid artery revealed that there was no pulse. Despite performing cardiopulmonary resuscitation, ROSC was not reached. Conclusion: Animal rabies control and surveillance are essential, particularly in locations where the disease is endemic. PEP is extremely effective and ought to be given as soon as possible following an exposure to reduce the high fatality rate. Keywords: aware, PEP, mortality, irritability