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Based on big data technology, this paper designs a digital management system for the whole process of financial reimbursement and gives the automatic transformation process of digitalization of financial reimbursement claims and accounting documents. In the K-Means algorithm, the AHC algorithm idea is introduced to construct the DH-K-Means clustering algorithm, which in turn enables the financial reimbursement behavioral portrait of enterprise employees to be constructed. To confirm the effectiveness of the system application in this paper, performance testing and portrait analysis were carried out. The results show that the maximum memory utilization of the database server is 50.42% when the number of concurrent users is 100, and the average response time of the system reaches a maximum of 825.63ms when the number of concurrent users is 200. Based on big data, the whole process of financial reporting digital management system has good stability, which can make enterprises more aware of their financial situation and then develop appropriate financial reimbursement strategies.

Vincristine is the drug of choice for Hodgkin's lymphoma, acute lymphoblastic leukemia, and non-Hodgkin lymphoma. Despite its significant anticancer effects, it causes dose-dependent neuropathy, leading to compulsive dose reduction. The available drugs used for vincristine-induced neuropathic pain (VINP) have a range of safety, efficacy, and tolerability issues prompting a search for new therapies. 5,7-Dimethoxycoumarin (5,7-DMC) also known as citropten, is a natural coumarin found in the essential oils of citrus plants such as lime, lemons, and bergamots, and it possesses both antidepressant and anti-inflammatory effects. This study was designed to investigate the possible analgesic and antiallodynic effects of 5,7-DMC in a murine model of VINP. Vincristine was administered to groups of BALB/c male mice (0.1 mg/kg intraperitoneally) once daily for 14 days to induce VINP. Thermal hyperalgesia and mechanical allodynia were quantified using the tail immersion test and von Frey filament application method. The levels of monoamine neurotransmitters and vitamin C in frontal cortical, striatal and hippocampal tissues, as well as the TNF-α level in plasma, were quantified using high performance liquid chromatography and ELISA respectively. On day 15 of the protocol, acute treatment with 5,7-DMC clearly reversed VINP thermal hyperalgesia, mechanical static allodynia, mechanical dynamic allodynia, and cold allodynia. The activity of 5,7-DMC against hyperalgesia and allodynia was inhibited by pretreatment with ondansetron but not naloxone, implicating a 5-HT3 receptor involvement. VINP vitamin C levels were restored by 5,7-DMC in the frontal cortex, and changes in serotonin, dopamine, adenosine, inosine and hypoxanthine levels caused by vincristine were reversed either fully or partially. Additionally, the vincristine-induced rise in hippocampal serotonin, dopamine, inosine and striatal serotonin was appreciably reversed by 5,7-DMC. 5,7-DMC also reversed the vincristine-induced increase in the plasma level of TNF-α. In negating the changes in the levels of some neurotransmitters in the brain caused by vincristine, 5,7-DMC showed stronger effects than gabapentin. It was concluded that, there is a potential role of 5-HT3 receptors and monoamines in the amelioration of VINP induced by 5,7-DMC, and the use of this compound warrants further investigation. [ABSTRACT FROM AUTHOR]

Vincristine is the drug of choice for Hodgkin’s lymphoma, acute lymphoblastic leukemia, and non-Hodgkin lymphoma. Despite its significant anticancer effects, it causes dose-dependent neuropathy, leading to compulsive dose reduction. The available drugs used for vincristine-induced neuropathic pain (VINP) have a range of safety, efficacy, and tolerability issues prompting a search for new therapies. 5,7-Dimethoxycoumarin (5,7-DMC) also known as citropten, is a natural coumarin found in the essential oils of citrus plants such as lime, lemons, and bergamots, and it possesses both antidepressant and anti-inflammatory effects. This study was designed to investigate the possible analgesic and antiallodynic effects of 5,7-DMC in a murine model of VINP. Vincristine was administered to groups of BALB/c male mice (0.1 mg/kg intraperitoneally) once daily for 14 days to induce VINP. Thermal hyperalgesia and mechanical allodynia were quantified using the tail immersion test and von Frey filament application method. The levels of monoamine neurotransmitters and vitamin C in frontal cortical, striatal and hippocampal tissues, as well as the TNF-α level in plasma, were quantified using high performance liquid chromatography and ELISA respectively. On day 15 of the protocol, acute treatment with 5,7-DMC clearly reversed VINP thermal hyperalgesia, mechanical static allodynia, mechanical dynamic allodynia, and cold allodynia. The activity of 5,7-DMC against hyperalgesia and allodynia was inhibited by pretreatment with ondansetron but not naloxone, implicating a 5-HT3 receptor involvement. VINP vitamin C levels were restored by 5,7-DMC in the frontal cortex, and changes in serotonin, dopamine, adenosine, inosine and hypoxanthine levels caused by vincristine were reversed either fully or partially. Additionally, the vincristine-induced rise in hippocampal serotonin, dopamine, inosine and striatal serotonin was appreciably reversed by 5,7-DMC. 5,7-DMC also reversed the vincristine-induced increase in the plasma level of TNF-α. In negating the changes in the levels of some neurotransmitters in the brain caused by vincristine, 5,7-DMC showed stronger effects than gabapentin. It was concluded that, there is a potential role of 5-HT3 receptors and monoamines in the amelioration of VINP induced by 5,7-DMC, and the use of this compound warrants further investigation.

The Domain Name System (DNS) is a prime target for cyber attacks, necessitating the monitoring and analysis of DNS activities to detect malicious behaviors. This paper presents an innovative DNS behavioral profiling approach that addresses challenges posed by the dynamic landscape of cyber threats, encompassing issues like evasion tactics, content variability, discerning malicious intent, navigating URL obfuscation, low and slow tactics, and maintaining accuracy in the face of diverse normal behaviors, contributing to the advancement of robust threat detection. The framework leverages unique feature behaviors and correlations, incorporating a novel feature selection algorithm, pattern extraction methodology, and a robust neural network architecture for accurate profile construction. The research also includes the development of ALFlowLyzer, a custom application layer network flow analyzer, and introduces the BCCC-CIC-Bell-DNS-2024 dataset, addressing limitations in widely used public DNS datasets. Experimental results demonstrate the effectiveness of the proposed model in profiling various DNS activities. [Display omitted] • Introduction of a new graph-based feature selection algorithm. • Introduction of a behavior similarity calculation algorithm. • Introduction of DNS Flow as a vital component in DNS analysis. • Creation of a new and sophisticated behavioral profiling system through pattern extraction and neural network architecture. • Implementation of ALFlowLyzer, an application layer network flow analyzer with more than 120 DNS features. • Introduction of a new malicious DNS dataset, BCCC-CIC-Bell-DNS-2024, with a DNS flow-based approach. [ABSTRACT FROM AUTHOR]

Water pollution poses a significant risk to the environment and human health, necessitating the development of innovative detection methods. In this study, a series of representative psychoactive compounds were selected as model pollutants, and a new approach combining zebrafish embryo behavioral phenotyping with deep learning was used to identify and classify water pollutants. Zebrafish embryos were exposed to 17 psychoactive compounds at environmentally relevant concentrations (1 and 10 μg/L), and their locomotor behavior was recorded at 5 and 6 days post-fertilization (dpf). We constructed six distinct zebrafish locomotor track datasets encompassing various observation times and developmental stages and evaluated multiple deep learning models on these datasets. The results demonstrated that the ResNet101 model performed optimally on the 1-min track dataset at 6 dpf, achieving an accuracy of 65.35 %. Interpretability analyses revealed that the model effectively focused on the relevant locomotor track features for classification. These findings suggest that the integration of zebrafish embryo behavioral analysis with deep learning can serve as an environmentally friendly and economical method for detecting water pollutants. This approach offers a new perspective for water quality monitoring and has the potential to assist existing chemical analytical techniques in detection, thereby advancing environmental toxicology research and water pollution control efforts., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Recommendations are meant to increase sales or ad revenue, as these are the first priority of those who pay for them. As recommender systems match their recommendations with inferred preferences, we should not be surprised if the algorithm optimizes for lucrative preferences and thus co-produces the preferences they mine. This relates to the well-known problems of feedback loops, filter bubbles, and echo chambers. In this article, I discuss the implications of the fact that computing systems necessarily work with proxies when inferring recommendations and raise a number of questions about whether recommender systems actually do what they are claimed to do, while also analysing the often-perverse economic incentive structures that have a major impact on relevant design decisions. Finally, I will explain how the choice architectures for data controllers and providers of AI systems as foreseen in the EU's General Data Protection Regulation (GDPR), the proposed EU Digital Services Act (DSA) and the proposed EU AI Act will help to break through various vicious circles, by constraining how people may be targeted (GDPR, DSA) and by requiring documented evidence of the robustness, resilience, reliability, and the responsible design and deployment of high-risk recommender systems (AI Act).

With the advent of mobile crowd sourcing (MCS) systems and its applications, the selection of the right crowd is gaining utmost importance. The increasing variability in the context of MCS tasks makes the selection of not only the capable but also the willing workers crucial for a high task completion rate. Most of the existing MCS selection frameworks rely primarily on reputation-based feedback mechanisms to assess the level of commitment of potential workers. Such frameworks select workers having high reputation scores but without any contextual awareness of the workers, at the time of selection, or the task. This may lead to an unfair selection of workers who will not perform the task. Hence, reputation on its own only gives an approximation of workers’ behaviors since it assumes that workers always behave consistently regardless of the situational context. However, following the concept of cross-situational consistency, where people tend to show similar behavior in similar situations and behave differently in disparate ones, this work proposes a novel recruitment system in MCS based on behavioral profiling. The proposed approach uses machine learning to predict the probability of the workers performing a given task, based on their learned behavioral models. Subsequently, a group-based selection mechanism, based on the genetic algorithm, uses these behavioral models in complementation with a reputation-based model to recruit a group of workers that maximizes the quality of recruitment of the tasks. Simulations based on a real-life dataset show that considering human behavior in varying situations improves the quality of recruitment achieved by the tasks and their completion confidence when compared with a benchmark that relies solely on reputation. [ABSTRACT FROM AUTHOR]

Pedophilia is a disorder of public concern because of its association with child sexual offense and recidivism. Previous neuroimaging studies of potential brain abnormalities underlying pedophilic behavior, either in idiopathic or acquired (i.e., emerging following brain damages) pedophilia, led to inconsistent results. This study sought to explore the neural underpinnings of pedophilic behavior and to determine the extent to which brain alterations may be related to distinct psychopathological features in pedophilia. To this aim, we run a coordinate based meta-analysis on previously published papers reporting whole brain analysis and a lesion network analysis, using brain lesions as seeds in a resting state connectivity analysis. The behavioral profiling approach was applied to link identified regions with the corresponding psychological processes. While no consistent neuroanatomical alterations were identified in idiopathic pedophilia, the current results support that all the lesions causing acquired pedophilia are localized within a shared resting state network that included posterior midlines structures, right inferior temporal gyrus and bilateral orbitofrontal cortex. These regions are associated with action inhibition and social cognition, abilities that are consistently and severely impaired in acquired pedophiles. This study suggests that idiopathic and acquired pedophilia may be two distinct disorders, in line with their distinctive clinical features, including age of onset, reversibility and modus operandi. Understanding the neurobiological underpinnings of pedophilic behavior may contribute to a more comprehensive characterization of these individuals on a clinical ground, a pivotal step forward for the development of more efficient therapeutic rehabilitation strategies. [ABSTRACT FROM AUTHOR]

This study explores whether social roles mentioned in jihadist literature can be used linguistically to profile the behavior of its consumers. It seeks to empirically understand extremist forums, where such ideological communication is widely disseminated, and to identify different types of user behavior through their discussions in these communities. An Arabic-language corpus was constructed with 1050 users categorized into seven behavioral profiles derived from distinct patterns of communication observed among users in forum networks. Statistical key term extraction was used to find significant social roles referenced in users' posts as these were proposed to differentiate their behavioral profiles. Multinomial logistic regression and post hoc tests were used to find the frequency of identified roles as the highest-ranking terms capable of prediction. Role terms produced high accuracy scores across classification experiments in identifying behavioral profiles (95% CI, 0.92–0.98), with varying intra- and inter-behavioral differentiation abilities emerging from the authority, religion, closeness, and conflict themes of social roles. This suggests simple and complex predictive potential exist in these constructs for separating profiles based on nuanced expressions of social roles by different types of users in extremist contexts, as was the case for distinguishing extremist organizations through their ideological communication in earlier work. [ABSTRACT FROM AUTHOR]

As fraudsters understand the time window and act fast, real-time fraud management systems becomes necessary in Telecommunication Industry. In this work, by analyzing traces collected from a nationwide cellular network over a period of a month, an online behavior-based anomaly detection system is provided. Over time, users' interactions with the network provides a vast amount of usage data. These usage data are modeled to profiles by which users can be identified. A statistical model is proposed that allocate a risk number to each upcoming record which reveals deviation from the normal behavior stored in profiles. Based on the amount of this deviation a decision is made to flag the record as normal or anomaly. If the activity is normal the associated profile is updated; otherwise the record is flagged as anomaly and it will be considered for further investigation. For handling the big data set and implementing the methodology we have used the Apache Spark engine which is an open source, fast and general-purpose cluster computing system for big data handling and analyzes. Experimental results show that the proposed approach can perfectly detect deviations from the normal behavior and can be exploited for detecting anomaly patterns.

Strategy implementation and performance in organizations are influenced by its market orientation. Market orientation is the collective of employee behaviors that affects strategy implementation, how an organization interacts with its environment and adjusts to change within that context. The relationship between market orientation, strategy implementation, and performance is robust across environmental contexts. This study identifies ideal behavioral profiles for organizations seeking to maximize performance by considering the scope and impact of market orientation on strategy implementation. This is accomplished by considering market orientation and strategy profiles in relation to performance in a holistic coalignment perspective, corresponding to competitive contexts characterized by varying degrees of market turbulence and competitive intensity. Important strategic and performance implications of this dynamic are revealed that should be considered by academics and practitioners alike. [ABSTRACT FROM AUTHOR]

Diagnosis of psychiatric disorders in humans is based on comparing individuals to the normal population. However, many animal models analyze averaged group effects, thus compromising their translational power. This discrepancy is particularly relevant in posttraumatic stress disorder (PTSD), where only a minority develop the disorder following a traumatic experience.In our PTSD rat model, we utilize a novel behavioral profiling approach that allows the classification of affected and unaffected individuals in a trauma-exposed population.Rats were exposed to underwater trauma (UWT) and four weeks later their individual performances in the open field and elevated plus maze were compared to those of the control group, allowing the identification of affected and resilient UWT-exposed rats.Behavioral profiling revealed that only a subset of the UWT-exposed rats developed long-lasting behavioral symptoms. The proportion of affected rats was further enhanced by pre-exposure to juvenile stress, a well-described risk factor of PTSD.For a biochemical proof of concept we analyzed the expression levels of the GABAA receptor subunits α1 and α2 in the ventral, dorsal hippocampus and basolateral amygdala. Increased expression, mainly of α1, was observed in ventral but not dorsal hippocampus of exposed animals, which would traditionally be interpreted as being associated with the exposure-resultant psychopathology. However, behavioral profiling revealed that this increased expression was confined to exposed-unaffected individuals, suggesting a resilience-associated expression regulation. The results provide evidence for the importance of employing behavioral profiling in animal models of PTSD, in order to better understand the neural basis of stress vulnerability and resilience.

Vincristine is the drug of choice for Hodgkin's lymphoma, acute lymphoblastic leukemia, and non-Hodgkin lymphoma. Despite its significant anticancer effects, it causes dose-dependent neuropathy, leading to compulsive dose reduction. The available drugs used for vincristine-induced neuropathic pain (VINP) have a range of safety, efficacy, and tolerability issues prompting a search for new therapies. 5,7-Dimethoxycoumarin (5,7-DMC) also known as citropten, is a natural coumarin found in the essential oils of citrus plants such as lime, lemons, and bergamots, and it possesses both antidepressant and anti-inflammatory effects. This study was designed to investigate the possible analgesic and antiallodynic effects of 5,7-DMC in a murine model of VINP. Vincristine was administered to groups of BALB/c male mice (0.1 mg/kg intraperitoneally) once daily for 14 days to induce VINP. Thermal hyperalgesia and mechanical allodynia were quantified using the tail immersion test and von Frey filament application method. The levels of monoamine neurotransmitters and vitamin C in frontal cortical, striatal and hippocampal tissues, as well as the TNF-α level in plasma, were quantified using high performance liquid chromatography and ELISA respectively. On day 15 of the protocol, acute treatment with 5,7-DMC clearly reversed VINP thermal hyperalgesia, mechanical static allodynia, mechanical dynamic allodynia, and cold allodynia. The activity of 5,7-DMC against hyperalgesia and allodynia was inhibited by pretreatment with ondansetron but not naloxone, implicating a 5-HT 3 receptor involvement. VINP vitamin C levels were restored by 5,7-DMC in the frontal cortex, and changes in serotonin, dopamine, adenosine, inosine and hypoxanthine levels caused by vincristine were reversed either fully or partially. Additionally, the vincristine-induced rise in hippocampal serotonin, dopamine, inosine and striatal serotonin was appreciably reversed by 5,7-DMC. 5,7-DMC also reversed the vincristine-induced increase in the plasma level of TNF-α. In negating the changes in the levels of some neurotransmitters in the brain caused by vincristine, 5,7-DMC showed stronger effects than gabapentin. It was concluded that, there is a potential role of 5-HT3 receptors and monoamines in the amelioration of VINP induced by 5,7-DMC, and the use of this compound warrants further investigation., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Usman, Malik, Tokhi, Arif, Huma, Rauf and Sewell.)

• Discussing issues, strengths and limitations that associated with the biometric trait. • Identifying challenges and open research problems. Recent research has shown the possibility of using smartphones' sensors and accessories to extract some behavioral attributes such as touch dynamics, keystroke dynamics and gait recognition. These attributes are known as behavioral biometrics and could be used to verify or identify users implicitly and continuously on smartphones. The authentication systems that have been built based on these behavioral biometric traits are known as active or continuous authentication systems. This paper provides a review of the active authentication systems. We present the components and the operating process of the active authentication systems in general, followed by an overview of the state-of-the-art behavioral biometric traits that used to develop an active authentication systems and their evaluation on smartphones. We discuss the issues, strengths and limitations that associated with each behavioral biometric trait. Also, we introduce a comparative summary between them. Finally, challenges and open research problems are presented in this research field. [ABSTRACT FROM AUTHOR]

ALBRECHT, A., MÜLLER, I., ARDI, Z., ÇALIŞKAN, G., GRUBER, D., IVENS, S., SEGAL, M., BEHR, J., HEINEMANN, U., STORK, O., and RICHTER-LEVIN, G. Neurobiological consequences of juvenile stress: A GABAergic perspective on risk and resilience. NEUROSCI BIOBEHAV REV XXX-XXX, 2016. – Childhood adversity is among the most potent risk factors for developing mood and anxiety disorders later in life. Therefore, understanding how stress during childhood shapes and rewires the brain may optimize preventive and therapeutic strategies for these disorders. To this end, animal models of stress exposure in rodents during their post-weaning and pre-pubertal life phase have been developed. Such ‘juvenile stress’ has a long-lasting impact on mood and anxiety-like behavior and on stress coping in adulthood, accompanied by alterations of the GABAergic system within core regions for the stress processing such as the amygdala, prefrontal cortex and hippocampus. While many regionally diverse molecular and electrophysiological changes are observed, not all of them correlate with juvenile stress-induced behavioral disturbances. It rather seems that certain juvenile stress-induced alterations reflect the system’s attempts to maintain homeostasis and thus promote stress resilience. Analysis tools such as individual behavioral profiling may allow the association of behavioral and neurobiological alterations more clearly and the dissection of alterations related to the pathology from those related to resilience. [ABSTRACT FROM AUTHOR]

Modeling depression in animals is based on the observation of behaviors interpreted as analogue to human symptoms. Typical tests used in experimental depression research are designed to evoke an either-or outcome. It is known that explorative and coping strategies are relevant for depression, however these aspects are generally not considered in animal behavioral testing. Here we investigate the Flinders Sensitive Line (FSL), a rat model of depression, compared to the Sprague-Dawley (SD) rat in three independent tests where the animals are allowed to express a more extensive behavioral repertoire. The multivariate concentric square field™ (MCSF) and the novel cage tests evoke exploratory behaviors in a novel environment and the home cage change test evokes social behaviors in the re-establishment of a social hierarchy. In the MCSF test, FSL rats exhibited less exploratory drive and more risk-assessment behavior compared to SD rats. When re-exposed to the arena, FSL, but not SD rats, increased their exploratory behavior compared to the first trial and displayed risk-assessment behavior to the same extent as SD rats. Thus, the behavior of FSL rats was more similar to that of SDs when the rats were familiar with the arena. In the novel cage test FSL rats exhibited a reactive coping style, consistent with the reduced exploration observed in the MCSF. Reactive coping is associated with less aggressive behavior. Accordingly, FSL rats displayed less aggressive behavior in the home cage change test. Taken together, our data show that FSL rats express altered explorative behavior and reactive coping style. Reduced interest is a core symptom of depression, and individuals with a reactive coping style are more vulnerable to the disease. Our results support the use of FSL rats as an animal model of depression and increase our understanding of the FSL rat beyond the behavioral dimensions targeted by the traditional depression-related tests.

Diagnosis of psychiatric disorders in humans is based on comparing individuals to the normal population. However, many animal models analyze averaged group effects, thus compromising their translational power. This discrepancy is particularly relevant in posttraumatic stress disorder (PTSD), where only a minority develop the disorder following a traumatic experience. In our PTSD rat model, we utilize a novel behavioral profiling approach that allows the classification of affected and unaffected individuals in a trauma-exposed population. Rats were exposed to underwater trauma (UWT) and four weeks later their individual performances in the open field and elevated plus maze were compared to those of the control group, allowing the identification of affected and resilient UWT-exposed rats. Behavioral profiling revealed that only a subset of the UWT-exposed rats developed long-lasting behavioral symptoms. The proportion of affected rats was further enhanced by pre-exposure to juvenile stress, a well-described risk factor of PTSD. For a biochemical proof of concept we analyzed the expression levels of the GABAA receptor subunits α1 and α2 in the ventral, dorsal hippocampus and basolateral amygdala. Increased expression, mainly of α1, was observed in ventral but not dorsal hippocampus of exposed animals, which would traditionally be interpreted as being associated with the exposure-resultant psychopathology. However, behavioral profiling revealed that this increased expression was confined to exposed-unaffected individuals, suggesting a resilience-associated expression regulation. The results provide evidence for the importance of employing behavioral profiling in animal models of PTSD, in order to better understand the neural basis of stress vulnerability and resilience. [ABSTRACT FROM AUTHOR]

It has been a major challenge to systematically evaluate and compare how pharmacological perturbations influence social behavioral outcomes. Although some pharmacological agents are known to alter social behavior, precise description and quantification of such effects have proven difficult. We developed a scalable social behavioral assay for zebrafish named ZeChat based on unsupervised deep learning to characterize sociality at high resolution. High-dimensional and dynamic social behavioral phenotypes are automatically classified using this method. By screening a neuroactive compound library, we found that different classes of chemicals evoke distinct patterns of social behavioral fingerprints. By examining these patterns, we discovered that dopamine D3 agonists possess a social stimulative effect on zebrafish. The D3 agonists pramipexole, piribedil, and 7-hydroxy-DPAT-HBr rescued social deficits in a valproic-acid-induced zebrafish autism model. The ZeChat platform provides a promising approach for dissecting the pharmacology of social behavior and discovering novel social-modulatory compounds., Competing Interests: The authors declare no competing interests., (© 2022 The Author(s).)

The network traffic dataset is a crucial part of anomaly based intrusion detection systems (IDSs). These IDSs train themselves to learn normal and anomalous activities. Properly labeled dataset is used for the training purpose. For the activities based IDSs, proper network traffic activity labeled dataset is the first requirement, however non-availability of such datasets is bottlenecked in the field of IDS research. In this experiment, a synthetic dataset "Panjab University - Intrusion Dataset (PU-IDS)" is created. The purpose of this study is to provide the researchers a reference dataset for the performance evaluation of network traffic activity based IDSs. University of New Brunswick Network Security Laboratory - Knowledge Disscovery in Databases (NSL-KDD) is a benchmark dataset for anomaly detection but it does not contain activity based labeling. So basic characteristics of this dataset are taken for the generation of the new synthetic dataset with various activities based labels. The dataset is first categorized as per protocol and service. Thereafter, as per minimum & maximum values of attributes, activity profiles are synthetically generated. This paper also discusses various statistical characteristics of PU-IDS. The total number of 198533 instances along with 273 of activity profiles are created. This dataset also contain different 98 protocol_service profiles. [ABSTRACT FROM AUTHOR]

Modeling depression in animals is based on the observation of behaviors interpreted as analog to human symptoms. Typical tests used in experimental depression research are designed to evoke an either-or outcome. It is known that explorative and coping strategies are relevant for depression, however these aspects are generally not considered in animal behavioral testing. Here we investigate the Flinders Sensitive Line (FSL), a rat model of depression, compared to the Sprague-Dawley (SD) rat in three independent tests where the animals are allowed to express a more extensive behavioral repertoire. The multivariate concentric square field™ (MCSF) and the novel cage tests evoke exploratory behaviors in a novel environment and the home cage change test evokes social behaviors in the re-establishment of a social hierarchy. In the MCSF test, FSL rats exhibited less exploratory drive and more risk-assessment behavior compared to SD rats. When re-exposed to the arena, FSL, but not SD rats, increased their exploratory behavior compared to the first trial and displayed risk-assessment behavior to the same extent as SD rats. Thus, the behavior of FSL rats was more similar to that of SDs when the rats were familiar with the arena. In the novel cage test FSL rats exhibited a reactive coping style, consistent with the reduced exploration observed in the MCSF. Reactive coping is associated with less aggressive behavior. Accordingly, FSL rats displayed less aggressive behavior in the home cage change test. Taken together, our data show that FSL rats express altered exploratory behavior and reactive coping style. Reduced interest is a core symptom of depression, and individuals with a reactive coping style are more vulnerable to the disease. Our results support the use of FSL rats as an animal model of depression and increase our understanding of the FSL rat beyond the behavioral dimensions targeted by the traditional depression-related tests. [ABSTRACT FROM AUTHOR]

Abstract: Preclinical testing requires rapid and reliable evaluation of the main in vivo effects of novel test substances usually in rodents. Nevertheless, the techniques primarily used up to now involve either automated measurement of motor activity or direct observation of behavioral effects by extensively trained investigators. The advantages of these approaches are respectively high-throughput and comprehensive behavioral assessment. Nevertheless, motor activity is only one aspect of animal behavior and it cannot predict the full neurobehavioral profile of a substance, whereas direct observation is time-consuming. There is thus a need for novel approaches that combine the advantages of both automatic detection and comprehensive behavioral analysis. In the present study, we used the LABORAS™ system to analyze motor and non-motor behavior in rats administered various stimulant substances with or without known psychotomimetic properties or abuse liability (amphetamine, cocaine dizocilpine (MK-801), ketamine, modafinil and nicotine). The data show that LABORAS™ clearly detects the stimulating effects on motor behaviors of amphetamine, cocaine, dizocilpine and ketamine in a dose- and time-dependent manner. Differential effects of these test substances on non-motor behaviors, such as grooming, eating and drinking could also be detected. Nicotine displayed only slight stimulating effects on locomotion, whereas modafinil was virtually without effect on the behaviors evaluated by the system. These data with different stimulant substances suggest that LABORAS™ presents an advantage over classical methods performing automated measurements restricted to locomotion. Furthermore, the procedure is considerably more rapid than behavioral observation procedures. Characterization of the behavioral profile of test substances using LABORAS™ should therefore accelerate preclinical studies. In addition, the multi-faceted parameters measured by LABORAS™ permit a more detailed comparison of the behavioral profiles of novel substances with standard reference substances, thereby providing important indicators for orienting further substance evaluation and supporting drug development. [Copyright &y& Elsevier]

There is growing interest in the ways in which the location of a person can be utilized by new applications and services. Recent advances in mobile technologies have meant that the technical capability to record and transmit location data for processing is appearing in off-the-shelf handsets. This opens possibilities to profile people based on the places they visit, people they associate with, or other aspects of their complex routines determined through persistent tracking. It is possible that services offering customized information based on the results of such behavioral profiling could become commonplace. However, it may not be immediately apparent to the user that a wealth of information about them, potentially unrelated to the service, can be revealed. Further issues occur if the user agreed, while subscribing to the service, for data to be passed to third parties where it may be used to their detriment. Here, we report in detail on a short case study tracking four people, in three European member states, persistently for six weeks using mobile handsets. The GPS locations of these people have been mined to reveal places of interest and to create simple profiles. The information drawn from the profiling activity ranges from intuitive through special cases to insightful. In this paper, these results and further extensions to the technology are considered in light of European legislation to assess the privacy implications of this emerging technology. [ABSTRACT FROM PUBLISHER]

Recommendations are meant to increase sales or ad revenue, as these are the first priority of those who pay for them. As recommender systems match their recommendations with inferred preferences, we should not be surprised if the algorithm optimizes for lucrative preferences and thus co-produces the preferences they mine. This relates to the well-known problems of feedback loops, filter bubbles, and echo chambers. In this article, I discuss the implications of the fact that computing systems necessarily work with proxies when inferring recommendations and raise a number of questions about whether recommender systems actually do what they are claimed to do, while also analysing the often-perverse economic incentive structures that have a major impact on relevant design decisions. Finally, I will explain how the choice architectures for data controllers and providers of AI systems as foreseen in the EU's General Data Protection Regulation (GDPR), the proposed EU Digital Services Act (DSA) and the proposed EU AI Act will help to break through various vicious circles, by constraining how people may be targeted (GDPR, DSA) and by requiring documented evidence of the robustness, resilience, reliability, and the responsible design and deployment of high-risk recommender systems (AI Act)., Competing Interests: The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Hildebrandt.)

Only a minority of trauma-exposed individuals develops Posttraumatic stress disorder (PTSD) and active processes may support trauma resilience. Individual behavioral profiling allows investigating neurobiological alterations related to resilience or pathology in animal models of PTSD and is utilized here to examine the activation of different interneuron subpopulations of the dentate gyrus-amygdala system associated with trauma resilience or pathology. To model PTSD, rats were exposed to juvenile stress combined with underwater trauma (UWT) in adulthood. Four weeks later, individual anxiety levels were assessed in the elevated plus maze test for classifying rats as highly anxious 'affected' vs. 'non-affected', i.e. behaving as control animals. Analyzing the activation of specific interneuron subpopulations in the dorsal and ventral dentate gyrus (DG), the basolateral (BLA) and central amygdala by immunohistochemical double-labeling for cFos and different interneuron markers, revealed an increased activation of cholecystokinin (CCK)-positive interneurons in the ventral DG, together with increased activation of parvalbumin- and CCK-positive interneurons in the BLA of affected trauma-exposed rats. By contrast, increased activation of neuropeptide Y (NPY)-positive interneurons was observed in the dorsal DG of trauma-exposed, but non-affected rats. To test for a direct contribution of NPY in the dorsal DG to trauma resilience, a local shRNA-mediated knock down was performed after UWT. Such a treatment significantly reduced the prevalence of resilient animals. Our results suggest that distinct interneuron populations are associated with resilience or pathology in PTSD with high regional specificity. NPY within the dorsal DG was found to significantly contribute to trauma resilience. • Behavioral Profiling allows identifying resilient vs. vulnerable individuals. • Activation of distinct interneurons relates to trauma resilience or vulnerability. • Activation of CCK(+) interneurons in the ventral DG is associated with vulnerability. • Activation of NPY(+) interneurons in the dorsal DG is associated with resilience. • NPY in the dorsal DG promotes trauma resilience. [ABSTRACT FROM AUTHOR]

Online search engines, social media platforms, and targeted advertising services often employ a "data-driven" business model based on the large-scale collection, analysis, and monetization of personal data. When providing such services significant information asymmetries arise: data-driven companies collect much more personal data than the consumer knows or can reasonably oversee, and data-driven companies have much more (technical) information about how this data is processed than consumers would be able to understand. This article demonstrates the vulnerable position consumers continue to find themselves in as a result of information asymmetries between them and data-driven companies. The GDPR, by itself, is in practice unable to mitigate these information asymmetries, nor would it be able to provide for effective transparency, since it does not account for the unique characteristics of the data-driven business model. Consumers are thus faced with an insurmountable lack of transparency which is inherent in, as well as the inevitable consequence of, the magnitude of the information asymmetries present on the data-driven market. [ABSTRACT FROM AUTHOR]

Opioids are the gold standard drugs for the treatment of acute and chronic severe pain, although their serious side effects constitute a big limitation. In the search for new and safer drugs, 5-HT 1A R agonists are emerging as potential candidates in pain relief therapy. In this work, we evaluated the affinity and activity of enantiomers of the two newly synthesized, potent 5-HT 1A R agonists N -[(2,2-diphenyl-1,3-dioxolan-4-yl)methyl]-2-[2-(pyridin-4-yl)phenoxy]ethan-1-ammonium hydrogenoxalate ( rac -1 ) and N -((2,2-diphenyl-1,3-dioxolan-4-yl)methyl)-2-(2-(1-methyl-1 H -imidazol-5-yl)phenoxy)ethan-1-ammonium hydrogenoxalate ( rac -2 ) in vitro and in vivo . The role of chirality in the interaction with 5-HT 1A R was evaluated by molecular docking. The activity of the rac -1 was tested in mouse models of acute pain (hot plate) and severe tonic nociceptive stimulation (intraplantar formalin test). Rac -1 was active in the formalin test with a reduction in paw licking in both phases at 10 mg/kg, and its effect was abolished by the selective 5-HT 1A R antagonist, WAY-100635. The eutomer ( S )- 1 , but not the racemate, was active during the hot plate test at 10 and 20 mg/kg, and this effect was abolished by 30 min treatment with WAY-100635 at 30 min. Similarly to 8-OH-DPAT, ( S )- 1 evoked a slow outward current and depressed spontaneous glutamatergic transmission in superficial dorsal horn neurons, more effectively than rac - 1 . The eutomer ( S )- 1 showed promising developability properties, such as high selectivity over 5-HT subtypes, no interaction with the μ receptors, and low hepato- and cardiotoxicity. Therefore, ( S )- 1 may represent a potential candidate for the treatment of acute and chronic pain without having the adverse effects that are commonly associated with the classic opioid drugs.

Current antipsychotic drugs are notably ineffective at addressing the cognitive deficits associated with schizophrenia. N -Desmethylclozapine (NDMC), the major metabolite of clozapine, displays muscarinic M 1 receptor (M 1 ) agonism, an activity associated with improvement in cognitive functioning. Preclinical and clinical data support that M 1 agonism may be a desired activity in antipsychotic drugs. However, NDMC failed clinical phase II studies in acute psychotic patients. NDMC analogues were synthesized to establish a structure-activity relationship (SAR) at the M 1 receptor as an indication of potential procognitive properties. In vitro evaluation revealed a narrow SAR in which M 1 agonist activity was established by functionalization in the 4- and 8-positions in the tricyclic core. In vivo behavioral response profiles were used to evaluate antipsychotic efficacy and exposure in zebrafish larvae and peripheral side effect related M 1 activity in adult zebrafish. The NDMC analogue 13f demonstrated antipsychotic activity similar to clozapine including M 1 agonist activity. Cotreatment with trospium chloride, an M1 peripheral acting antagonist, counteracted peripheral side effects. Thus, the NDMC analogue 13f , in combination with a peripherally acting anticholinergic compound, could be suitable for further development as an antipsychotic compound with potential procognitive activity.

New psychopharmacological treatments are needed for affective and nonaffective psychoses, especially for the associated negative and cognitive symptoms. Earlier developments mostly failed, probably partly because of limitations in behavioral models used for validation. Now, deeper understanding of the genetics underlying disease pathogenesis and progress in genetic engineering will generate many rodent models with increased construct validity. To improve these models' translational value, we need complementary data from nonhuman primates. We also have to improve and streamline behavioral test systems to cope with increased demand. Here, we propose a comprehensive neurocognitive test battery that should overcome the disadvantages of single tests and yield cognitive/behavioral profiles for modeling subsets of patient symptoms. Further, we delineate a concept for classifying disease-relevant cognitive endophenotypes to balance between face and construct validity and clinical diagnostics. In summary, this review discusses new concepts and the limitations and future potential of translational research on cognition in psychiatry.
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