Behavioral Anomalies Research Articles

Diagnóstico tardio de síndrome de deleção 22q11.2 em criança com hipocalcemia sintomática: relato de caso

22q11.2 Deletion Syndrome is the commonest microdeletion syndrome in humans and has a wide spec-trum of clinical manifestations, such as craniofacial dysmorphism, airway malformations, heart disease, renal malformations, hypoparathyroidism, neurological and behavioral disorders, immunodeficiencies and anomalies of the thymus. The broad phenotype spectrum makes the diagnosis more challenging, and an early identification allows not only the evaluation of comorbidities and interventions, but also adequate genetic counseling. Hypoparathyroidism manifested by symptomatic hypocalcemia is one of the clinical presentations of 22q11.2 Deletion Syndrome. This clinical case shows the diagnosis of 22q11.2 Deletion Syndrome in an 11-year-old child with symptomatic hypocalcemia and emphasizes the possibility of a late diagnosis of 22q11.2 Deletion Syndrome.

Drosophila melanogaster as an indispensable model to decipher the mode of action of neurotoxic compounds

Exposure to some toxic compounds causes structural and behavioral anomalies associated with the neurons in the later stage of life. Those toxic compounds are termed as a neurotoxicant, which can be a physical factor, a toxin, an infection, radiation, or maybe a drug. The incongruities caused due to a neurotoxicant further depend on the toxicity of the compound. More importantly, the neurotoxicity of the compound is associated with the concentration and the time point of exposure. The neurodevelopmental defect appears depending on the toxicity of the compound. A neurodevelopmental defect may be associated with a delay in developmental time, defective growth, structural abnormality of many organs, including sensory organs, behavioral abnormalities, or death in the fetus stage. Numerous model organisms are employed to assess the effect of neurotoxicants. The current review summarizes several methods used to check the effect of neurotoxicant and their effect using the model organism Drosophila melanogaster.

AN ANALYSIS OF BEHAVIORAL BIASES AGAINST VARIOUS PARAMETERS

Behavioral finance attempts to question the efficiency of the financial market. Several anomalies of human behavior are considered as interfering factors while investors make a financial decision, which leads to the criticism of the rationality of investors. Considerable evidence has been provided by psychologists and behavioral economists which demonstrates that investors are exposed to psychological and behavioral biases. Additionally, investors resort to shortcuts during the process of decision-making, and are affected by their past investment profits or losses. They make decisions within a framework of preference, and are influenced by the investment patterns of other investors. This study examines the relationship between behavioral heuristics and biases such as overconfidence, self-attribution, and availability biases and parameters such as age, gender, profession, and income of investors.

Evidence of Adaptive Market Hypothesis in International Financial Markets

Objective: Traditional finance emphasises market efficiency and inherent behavioural anomalies in investors. However, the emergence of the adaptive market hypothesis tends to suggest otherwise. The adaptive market hypothesis challenges market efficiency and behavioural finance by contesting that investors and market participants adapt to changing market environment. In essence, investors learn from their mistakes. The purpose of this study was to explore the concept of an adaptive market hypothesis in five international markets, namely, the JSE, CAC 40, NASDAQ, JPX-NIKKEI and DAX.
 Method: This study used a variance ratio test to explore the adaptive market hypothesis from January 2017 to April 2022.
 Results: the findings revealed the existence of adaptive markets in the CAC 40 and NASDAQ during the period under review. Conversely, there was no statistical evidence to support the adaptive concept in the JSE, JPX-NIKKEI, and the DAX.
 Originality/relevance: The implications of these findings is that investors in the CAC 40 and NASDAQ should consider active volatility scaling because of multiple betas, and hence fundamental analysis is worth the time. This study adds to the literature on adaptive markets hypothesis as well as market efficiency and behavioural finance.
 Keywords: Adaptive markets; market efficiency; behavioural finance; financial markets; variance ratio

IDEMO ZARADITI NA SEZONSKOJ DEPRESIJI NA TRŽIŠTU DIONICA

This paper observes the possibilities of exploiting a behavioural anomaly on the stock market. Previous literature confirms the existence of Seasonal Affective Disorder (SAD) in return series on the Zagreb Stock Exchange. However, a comprehensive set of investment guidance based on such findings is lacking in the related literature. That is why after the confirmation of the existence of SAD effects in this research, the focus is on simulation of trading strategies that take such information into account. The results indicate that there exist SAD and fall effects on the Croatian stock market, alongside precipitation and temperature having significant effects on stock returns as well. Based on daily data ranging from January 2010 until December 2020 for the Croatian stock market index CROBEX, several strategies are observed and compared via performance measures aimed at beating the market. Even with the inclusion of transaction costs, it is shown and commented on possibilities for speculators aiming to obtain extra profits in certain situations. Simpler strategies are considered in the study. However, they provide a starting point for future strategies that combine different (mostly calendar) anomalies with the SAD anomaly. This is due to showing that SAD-driven investors who aim to apply the contrarian strategies against the herd can obtain profits due to the changing risk-aversion of others over the year.

Rasch Analysis On Investment Decision Anomalies in Indonesian Islamic capital market

AbstractPurpose – This Research aims to understand what behavioural anomalies are in making investment decisions in the Indonesian Islamic capital market and what factors are behavioural anomalies in making investment decisions in the Indonesian Islamic capital market.Methodology - This Research uses a quantitative approach by applying Rasch analysis to measure five types of investment behaviour anomalies. Data was collected using a questionnaire. Findings - The endowment and its items are considered the primary considerations for investors. On the other hand, Anchoring and Adjustment of all the items are not considered the primary consideration. Meanwhile, Conservatism, Optimism and Hindsight spread to both categories of items. The difference in responses by gender was found that only on the item, Feeling greater comfort and optimism with their employer's stock, felt that investing there was less risky than investing elsewhere. Meanwhile, the difference in responses based on age was found that the age of thirties most often gave a different response from other respondents, especially in the indications of Endowment and Hindsight. In comparison, the difference in responses at the age of forties occurred in the items indicating optimism. Financial service providers are advised to offer the most suitable financial products based on gender differences. The most important thing is that in their thirties, an age group that tends to maintain their investment for a longer time and that investment decisions and risks in share ownership where they work are responded differently by gender. Keywords: Behavior Finance, Investment Decision, Rasch Analysis.

SMART HOME DATA VISUALIZATION FOR PROACTIVE HEALTH MONITORING OF COMMUNITY DWELLING OLDER ADULTS

Abstract The role of Ambient Assistive Living and smart home technologies, which utilize unobtrusive sensors to detect changes in health, is becoming increasingly important in the delivery of healthcare services to older adults. However, these technologies must be designed to meaningfully incorporate into clinicians’ decision making. Research has shown when clinicians are engaged in the design process of smart home systems, the accuracy and efficacy of the systems are improved. We present the process undertaken by a team of nurse researchers and computer science engineers to design clinically meaningful behavior markers derived from smart home sensor data that can be used by nurses to proactively identify changes in patient status. During the first phase of design, nurse researchers qualitatively analyzed time series from smart home sensors installed in the homes of community dwelling older adults and identified patterns in these data related to significant health changes. From this analysis, we assembled a candidate list of 15 sensor-based behavior metrics, such as percent time spent in each room or frequency of bathroom use. During the second phase of design, we will build on lessons we learned from participatory design to create behavior markers and visualizations that are inspired by clinical experience. These include visualizing behavior change over time, highlighting behavioral anomalies at multiple time scales, and calculating markers that are not directly observable such as time spent out of home. Lessons learned from clinicians using the data visualizations to proactively screen for health changes in near real time will also be discussed.

Azadirachtin in the aquatic environment: Fate and effects on non-target fauna

Abstract The present-day inclination towards increased application of pesticides derived from natural sources is not without its own hazards. Such pesticides are indubitably less harmful compared to the newer classes of low persistence organophosphates; however, these pesticides may be hydrolyzed to yield secondary products which often exhibit significantly different toxicity. Additionally, studies show that the inert materials used in formulation can exhibit significant toxicity on their own. The toxicity of secondary products and inert materials are largely unknown since these molecules are largely uncharacterized. Azadirachtin is such a candidate pesticide. It is derived from the neem tree Azadirachta indica, and it ticks all the above boxes. In its pure form, formulation and crude extracts, it can generate ecotoxicological effects ranging from behavioural anomalies and physiological imbalances to growth suppression. Authors at various times have pointed out the ability of azadirachtin (or that of its metabolites’/break-down products’) to cause specific, documentable effects on growth and reproduction of non-target fauna. Since extensive reports are available on the toxicology of azadirachtin to terrestrial beneficial fauna like bees and earthworms, this review aims to present a coherent picture of the effects of azadirachtin on non-target aquatic organisms through a documentation of relevant reports published during the last three decades. It is hoped that this compilation will help sensitize public opinion about the erroneous supposition that pesticides derived from natural sources are not innately harmful to non-target biota.

ВЛИЯНИЕ НЕФТЯНЫХ УГЛЕВОДОРОДОВ НА БИОХИМИЧЕСКИЕ ПОКАЗАТЕЛИ ЧЕРНОМОРСКОЙ МИДИИ MYTILUS GALLOPROVINCIALIS

Almost all oil components are more or less toxic and have a toxic effect on aquatic organisms, and some groups of petroleum hydrocarbons (PH) can accumulate in organs and tissues and transmitted through food chains. Depending on the duration and scale of PH pollution, a wide range of responses can be observed – from physiological and biochemical, morphological, behavioral anomalies at organism level to structural and functional rearrangements in populations and communities. The study of the biochemical response in tissues of bivalve mollusks living in conditions of elevated PH concentrations in the experiment is necessary to understand the mechanisms of metabolism reorganization and adaptive reactions that occur in the body of hydrobionts when exposed to toxicants. The paper presents the results of a study of the PH effect on the parameters of the prooxidant-antioxidant system parameters (superoxide dismutase and catalase activities, level of lipid peroxidation and oxidized proteins) and amnotransferase activities (alanine aminotransferase and aspartate aminotransferase) in hepatopancreas of the Black Sea mussel Mytilus galloprovincialis in the acute toxicological experiment. The results of the experiment allowed to establish that PH at concentrations of 0.5 mg/l (10 MPC) and 1 mg/l (20 MPC) do not affect the activity of aminotransferases, but stimulate the development of oxidative stress by shifting the prooxidant-antioxidant balance towards the intensification of lipid peroxidation and protein oxidization; at the 1 mg/l concentration PH cause activation of superoxide dismutase and inhibition of catalase activity. Prooxidant-antioxidant system parameters demonstrate high sensitivity to PH and can be used along with other recommended parameters to assess the functional state of mollusks in conditions of oil environmental pollution.

Cybersecurity in Smart Cities: Detection of Opposing Decisions on Anomalies in the Computer Network Behavior

The increased use of urban technologies in smart cities brings new challenges and issues. Cyber security has become increasingly important as many critical components of information and communication systems depend on it, including various applications and civic infrastructures that use data-driven technologies and computer networks. Intrusion detection systems monitor computer networks for malicious activity. Signature-based intrusion detection systems compare the network traffic pattern to a set of known attack signatures and cannot identify unknown attacks. Anomaly-based intrusion detection systems monitor network traffic to detect changes in network behavior and identify unknown attacks. The biggest obstacle to anomaly detection is building a statistical normality model, which is difficult because a large amount of data is required to estimate the model. Supervised machine learning-based binary classifiers are excellent tools for classifying data as normal or abnormal. Feature selection and feature scaling are performed to eliminate redundant and irrelevant data. Of the 24 features of the Kyoto 2006+ dataset, nine numerical features are considered essential for model training. Min-Max normalization in the range [0,1] and [−1,1], Z-score standardization, and new hyperbolic tangent normalization are used for scaling. A hyperbolic tangent normalization is based on the Levenberg-Marquardt damping strategy and linearization of the hyperbolic tangent function with a narrow slope gradient around zero. Due to proven classification ability, in this study we used a feedforward neural network, decision tree, support vector machine, k-nearest neighbor, and weighted k-nearest neighbor models Overall accuracy decreased by less than 0.1 per cent, while processing time was reduced by more than a two-fold reduction. The results show a clear benefit of the TH scaling regarding processing time. Regardless of how accurate the classifiers are, their decisions can sometimes differ. Our study describes a conflicting decision detector based on an XOR operation performed on the outputs of two classifiers, the fastest feedforward neural network, and the more accurate but slower weighted k-nearest neighbor model. The results show that up to 6% of different decisions are detected.

Paternalism and pseudo-rationality: An illustration based on retirement savings

Resource allocations are jointly determined by the actions of social planners and households. We study economies in which households have private information about their tastes and have a distribution of behavioral propensities: optimal, myopic, or passive. In such economies, we show that utilitarian planners enact policies such as Social Security and default savings that cause equilibrium consumption smoothing (on average in the cross-section of households). Our framework resolves tensions in the household savings literature by simultaneously explaining evidence of consumption smoothing and optimal savings on average across households while also predicting behavioral anomalies at the household level. In general, common tests of consumption smoothing are not sufficient to show that households are optimizers, but observational data or natural experiments can be used to identify the fractions of optimizing, myopic, and passive households.

A Learning Analytics Approach Using Social Network Analysis and Binary Classifiers on Virtual Resource Interactions for Learner Performance Prediction

The COVID-19 pandemic induced a digital transformation of education and inspired both instructors and learners to adopt and leverage technology for learning. This led to online learning becoming an important component of the new normal, with home-based virtual learning an essential aspect for learners on various levels. This, in turn, has caused learners of varying levels to interact more frequently with virtual resources to supplement their learning. Even though virtual learning environments provide basic resources to help monitor the learners’ online behaviour, there is room for more insights to be derived concerning individual learner performance. In this study, we propose a framework for visualising learners’ online behaviour and use the data obtained to predict whether the learners would clear a course. We explored a variety of binary classifiers from which we achieved an overall accuracy of 80%–85%, thereby indicating the effectiveness of our approach and that learners’ online behaviour had a significant effect on their academic performance. Further analysis showed that common patterns of behaviour among learners and/or anomalies in online behaviour could cause incorrect interpretations of a learner’s performance, which gave us a better understanding of how our approach could be modified in the future.

Susceptibility and resilience to maternal immune activation are associated with differential expression of endogenous retroviral elements

Endogenous retroviruses (ERVs) are ancestorial retroviral elements that were integrated into the mammalian genome through germline infections and insertions during evolution. While increased ERV expression has been repeatedly implicated in psychiatric and neurodevelopmental disorders, recent evidence suggests that aberrant endogenous retroviral activity may contribute to biologically defined subgroups of psychotic disorders with persisting immunological dysfunctions. Here, we explored whether ERV expression is altered in a mouse model of maternal immune activation (MIA), a transdiagnostic environmental risk factor of psychiatric and neurodevelopmental disorders. MIA was induced by maternal administration of poly(I:C) on gestation day 12 in C57BL/6N mice. Murine ERV transcripts were quantified in the placentae and fetal brains shortly after poly(I:C)-induced MIA, as well as in adult offspring that were stratified according to their behavioral profiles. We found that MIA increased and reduced levels of class II ERVs and syncytins, respectively, in placentae and fetal brain tissue. We also revealed abnormal ERV expression in MIA-exposed offspring depending on whether they displayed overt behavioral anomalies or not. Taken together, our findings provide a proof of concept that an inflammatory stimulus, even when initiated in prenatal life, has the potential of altering ERV expression across fetal to adult stages of development. Moreover, our data highlight that susceptibility and resilience to MIA are associated with differential ERV expression, suggesting that early-life exposure to inflammatory factors may play a role in determining disease susceptibility by inducing persistent alterations in the expression of endogenous retroviral elements.

Detection of Anomalies in the Computer Network Behaviour

Abstract The goal of anomaly-based intrusion detection is to build a system which monitors computer network behaviour and generates alerts if either a known attack or an anomaly is detected. Anomaly-based intrusion detection system detects intrusions based on a reference model which identifies normal behaviour of the computer network and flags an anomaly. Basic challenges in anomaly-based detection are difficulties to identify a ‘normal’ network behaviour and complexity of the dataset needed to train the intrusion detection system. Supervised machine learning can be used to train the binary classifiers in order to recognize the notion of normality. In this paper we present an algorithm for feature selection and instances normalization which reduces the Kyoto 2006+ dataset in order to increase accuracy and decrease time for training, testing and validating intrusion detection systems based on five models: k-Nearest Neighbour (k-NN), weighted k-NN (wk-NN), Support Vector Machine (SVM), Decision Tree, and Feedforward Neural Network (FNN).

Crystal structure, dielectric properties, and optical bandgap control in KNbO3–BiMnO3 ceramics

Abstract(1 − x)KNbO3–xBiMnO3 (0 ≤ x ≤ 0.25) ceramics were prepared by the solid‐state reaction method. An X‐ray diffraction analysis combined with Raman spectroscopy showed the co‐solubility of Bi and Mn in the orthorhombic structure to be less than 5% BiMnO3. Orthorhombic and pseudocubic symmetries coexist in the 0.05 ≤ x ≤ 0.15 region, coinciding with a bimodal grain size distribution. This coexistence of crystal symmetries is further corroborated by several anomalies in the dielectric behavior, which can be ascribed to structural phase transitions. For x ≥ 0.20, only one dielectric anomaly is detected around 100°C, which is commensurate with in situ Raman spectroscopy analysis. This work also shows that Bi/Mn co‐doping can be employed to tailor the bandgap of KNbO3, which narrows continuously with increasing x, resulting in ∼1‐eV narrowing for single‐phase x = 0.25. This may offer the possibility to employ this ferroic material in photoresponsive technologies.

Fluoxetine treatment supports predictive validity of the three hit model of depression in male PACAP heterozygous mice and underpins the impact of early life adversity on therapeutic efficacy.

According to the three hit concept of depression, interaction of genetic predisposition altered epigenetic programming and environmental stress factors contribute to the disease. Earlier we demonstrated the construct and face validity of our three hit concept-based mouse model. In the present work, we aimed to examine the predictive validity of our model, the third willnerian criterion. Fluoxetine treatment was applied in chronic variable mild stress (CVMS)-exposed (environmental hit) CD1 mice carrying one mutated allele of pituitary adenylate cyclase-activating polypeptide gene (genetic hit) that were previously exposed to maternal deprivation (epigenetic hit) vs. controls. Fluoxetine reduced the anxiety level in CVMS-exposed mice in marble burying test, and decreased the depression level in tail suspension test if mice were not deprived maternally. History of maternal deprivation caused fundamental functional-morphological changes in response to CVMS and fluoxetine treatment in the corticotropin-releasing hormone-producing cells of the bed nucleus of the stria terminalis and central amygdala, in tyrosine-hydroxylase content of ventral tegmental area, in urocortin 1-expressing cells of the centrally projecting Edinger-Westphal nucleus, and serotonergic cells of the dorsal raphe nucleus. The epigenetic background of alterations was approved by altered acetylation of histone H3. Our findings further support the validity of both the three hit concept and that of our animal model. Reversal of behavioral and functional-morphological anomalies by fluoxetine treatment supports the predictive validity of the model. This study highlights that early life stress does not only interact with the genetic and environmental factors, but has strong influence also on therapeutic efficacy.

A collaborative network of digital twins for anomaly detection applications of complex systems. Snitch Digital Twin concept

This paper proposes a novel anomaly detection methodology for industrial systems based on Digital Twin (DT) ecosystems. In addition to DTs, conceived as a digital representation of a physical entity, this paper proposes a new concept of DT focused on modeling connections between physical behaviors. This new DT concept is called Snitch Digital Twin (SDT). The scope of the SDT is the study of variations between behaviors and support the detection of anomalies between them. The behavior of each physical entity is characterized by three spatiotemporal features computed from each collected measurement. Behavioral anomalies are identified and quantified through modular patterns based on quantile regression and behavioral indexes. Finally, the robustness of the proposed methodology is assessed by comparing it with the other two commonly used algorithms based on Kernel Principal Component Analysis (KPCA) and One-Class Support Vector Machines (OCSVM) in a case study application. The case study is based on the diagnosis of the cooling system of a power-generator diesel engine. The results obtained prove the advantages and goodness of this novel methodology compared to the two traditional algorithms.

Neuroprotective Potential of HC070, a Potent TRPC5 Channel Inhibitor in Parkinson's Disease Models: A Behavioral and Mechanistic Study.

Transient receptor potential canonical 5 (TRPC5) channels are predominantly expressed in the striatum and substantia nigra of the brain. These channels are permeable to calcium ions and are activated by oxidative stress. The physiological involvement of TRPC5 channels in temperature and mechanical sensation is well documented; however, evidence for their involvement in the pathophysiology of neurodegenerative disorders like Parkinson's disease (PD) is sparse. Thus, in the present study, the role of TRPC5 channels and their associated downstream signaling was elucidated in the 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine/1-methyl-4-phenylpyridinium (MPTP/MPP+) model of PD. Bilateral intranigral administration of MPTP and 24 h MPP+ exposure were performed to induce PD in the Sprague-Dawley rats and SH-SY5Y cells, respectively. MPTP led to behavioral anomalies and TRPC5 overexpression accompanied by increased calcium influx, apoptosis, oxidative stress, and mitochondrial dysfunctions. In addition, tyrosine hydroxylase (TH) expression was significantly lower in the midbrain and substantia nigra compared to sham animals. Intraperitoneal administration of potent and selective TRPC5 inhibitor, HC070 (0.1 and 0.3 mg/kg) reversed the cognitive and motor deficits seen in MPTP-lesioned rats. It also restored the TH and TRPC5 expression both in the striatum and midbrain. Furthermore, in vitro and in vivo studies suggested improvements in mitochondrial health along with reduced oxidative stress, apoptosis, and calcium-mediated excitotoxicity. Together, these results showed that inhibition of TRPC5 channels plays a crucial part in the reversal of pathology in the MPTP/MPP+ model of Parkinson's disease.

Real-time determination of water status upon simultaneous zebrafish exposure to sublethal concentrations of CuSO4

Water pollution from commonly occurring contaminants (metals, xenobiotics, etc.) is a serious global problem. Copper is a commonly occurring water contaminant. A variety of physiological and biological methods have been developed to monitor water quality. The assessment of biological responses is an effective method for identifying the harmful effects of contaminants on ecosystems. Fish is a highly recommended animal model in water quality monitoring. Swimming consistency (firmness) and respiratory metabolism (oxygen consumption rate, carbon dioxide excretion rate and respiratory quotient) are essential for fish to maintain body homeostasis toward coping with environmental stress. We exposed zebrafish to different concentrations (Treatment I-0.1 mg/L and Treatment II-1.58 mg/L) of CuSO4. We have continuously quantified the strength of behavior (swimming consistency) and physiological (respiratory rates) biomarkers for ten days using an online monitoring system of swimming behavior and external respiration. Swimming consistency and respiratory rates of zebrafish (p<0.05) decreased in the CuSO4-treated groups compared to the control group. Avoidance behavior has led to an endpoint behavior at copperiedus. The time-delayed toxic effect has resulted in CuSO4 treatment groups. We checked for swimming consistency aberration on the artificial neural array, Self-organizing map (SOM). Circadian rhythms were influenced by prolonged exposure to CuSO4 toxicity. A concentration- and duration-dependent behavior anomaly was noted in this study. Swimming behavior and respiratory metabolism patterns are sensitive non-invasive stress biomarkers for water quality monitoring studies.

Deformation behaviour and strain rate sensitivity of 304L SS at elevated temperatures

ABSTRACT Tensile deformation behaviour of nuclear grade 304 L SS (PHWR calandria vessel material) was presented at temperatures 300 K to 1123 K and at three different strain rates. The deformation behaviour is influenced by the strain-induced martensitic transformation at 300 K, the occurrence of dynamic strain ageing (DSA) at intermediate temperatures (573–873 K) and the dominance of strain softening at high temperatures (973–1123 K). The occurrence of DSA caused anomalies in flow behaviour and ductility for the steel. At high temperatures, strain softening manifested in a rapid decrease in flow strength with increasing temperature and decreasing strain rate. Further, instantaneous (mi) and quasi-steady state strain rate sensitivity (ms) with strain were evaluated using strain rate jump tests. At low and DSA temperatures, the initial and quasi-steady transient response differs. Contrary to this, the value of mi is equal to ms at high temperatures.