Behavioral Profile Research Articles

Is “Rough Sex” a Thing? A Survey of Meaning

ABSTRACT The term “rough sex” is widely used, but it is not always clear what it means. Through exploring people’s working definitions of “rough sex,” we asked what they revealed about the underlying phenomenon it is applied to, and whether it is actually a thing. Our online survey of 567 New Zealand respondents (73.7% women, 21% men, 5.3% gender diverse; mean age, 35.6 years, SD = 10.8) identified various behaviors that many considered to be part of “rough sex” – including hair pulling, holding down someone forcefully, slapping and “choking.” While this behavioral profile was similar to previous U.S. studies, our survey was designed with novel features that allowed us to look more closely at the variation in how people make sense of “rough sex.” Embedded within a critical psychology approach, our descriptive analysis highlighted areas of ambiguity and difference, including points of contradiction across people’s working definitions. Critical qualitative analysis of open-ended textual data further examined categorical misalignments and the difficulties in interpreting behavioral indicators as defining of “rough sex” – especially without more contextual information. Overall, we found considerable variability and some contradictions in people’s definitions of “rough sex” and the meanings they ascribed to it. Our findings led us to argue that the term “rough sex” does not map onto a coherent phenomenon, and that use of the term can be misleading, with potentially problematic implications.

BERT-assisted behavioral profiling of polysemy: contrastive analysis of HONG in Chinese and RED in English

Abstract This study conducts a corpus-based contrastive analysis of the basic color term “red” in Chinese (HONG) and English (RED), innovatively employing the AI model BERT to triangulate, validate, and enrich the findings of the behavioral profile (BP) analysis. Results illustrate HONG’s more variational semasiological structure than RED, with both exhibiting unique usage patterns among similarities. The triangulation with BERT embeddings visualizes the performance of each subjectively annotated BP variable, and further demonstrates the semantic extending process in both HONG and RED. The underlying cognitive and socio-cultural factors are lastly discussed. Theoretically, this study bridges frequency-based AI models and usage-based Cognitive Linguistics by simulating human cognitive processes to abstract linguistic knowledge from extensive data, advancing our understanding of language through AI technology. Methodologically, this study is the first to integrate AI models in assisting BP analysis, with BERT embeddings offering objective corroborations and deeper insights for conclusions drawn from subjectively labeled BP data.

Distinct Behavioral Profiles and Neuronal Correlates of Heroin Vulnerability Versus Resiliency in a Multi-Symptomatic Model of Heroin Use Disorder in Rats.

The behavioral and diagnostic heterogeneity within the opioid use disorder (OUD) diagnosis is not readily captured in current animal models, limiting the translational relevance of the mechanistic research that is conducted in experimental animals. The authors hypothesized that a nonlinear clustering of OUD-like behavioral traits would capture population heterogeneity and yield subpopulations of OUD vulnerable rats with distinct behavioral and neurocircuit profiles. Over 900 male and female heterogeneous stock rats, a line capturing genetic and behavioral heterogeneity present in humans, were assessed for several measures of heroin use and rewarded and non-rewarded seeking behaviors. A nonlinear stochastic block model clustering analysis was used to assign rats to OUD vulnerable, intermediate, and resilient clusters. Additional behavioral tests and circuit analyses using c-fos protein activation were conducted on the vulnerable and resilient subpopulations. OUD vulnerable rats exhibited greater heroin taking and seeking behaviors relative to those in the intermediate and resilient clusters. Akin to human OUD diagnosis, further vulnerable rat subclustering revealed subpopulations with different combinations of behavioral traits, including sex differences. Lastly, heroin cue-induced neuronal patterns of circuit activation differed between resilient and vulnerable phenotypes. Behavioral sex differences were recapitulated in patterns of circuitry activation, including preferential engagement of extended amygdala stress circuitry in males and cortico-striatal drug cue-seeking circuitry in females. Using a nonlinear clustering approach in rats, the analysis captured behavioral diagnostic heterogeneity reflective of human OUD diagnosis. OUD vulnerability and resiliency were associated with distinct neuronal activation patterns, posing this approach as a translational tool in assessing neurobiological mechanisms underpinning OUD.

Cats with inflammatory gastrointestinal or dermatological disorders exhibit increased care-soliciting and anxious behaviors.

This study investigated whether the behavior profiles of cats with inflammatory gastrointestinal or skin diseases differ from those of healthy cats. We identified adult cats within the patient database at the University of Pennsylvania's veterinary hospital that had been diagnosed with inflammatory gastrointestinal (n = 22) or skin disorders (17) and a control group of healthy cats (58). We collected data via owner completion of the Feline Behavioral Assessment & Research Questionnaire from March to May 2023 and conducted an observational study comparing the Feline Behavioral Assessment & Research Questionnaire scores between groups. Cats with inflammatory gastrointestinal or skin disease exhibited more etepimeletic (care-soliciting) behaviors than healthy cats, including purring (U = 1,396.50, Z = 2.03, r = 0.21, 95% CI, 0.00 to 0.50), trainability (U = 1,303.50, Z = 2.33, r = 0.24, 95% CI, 0.00 to 1.00), and sociability to people (U = 367.50, Z = 2.21, r = 0.26, 95% CI, 0.00 to 1.57). The inflammatory group also exhibited more anxious behaviors than the healthy group, including compulsive grooming (U = 1,736.00, Z = 4.91, r = 0.50, 95% CI, 0.33 to 1.00) and fear of novelty (U = 603.00, Z = 2.14, r = 0.25, 95% CI, 0.00 to 1.00). Additionally, cats treated with corticosteroids exhibited more anxious behaviors than healthy and nonsteroid treatment groups, including separation behaviors (χ2[2] = 8.22, η2 = 0.08) and compulsive grooming (χ2[2] = 25.35, η2 = 0.25). Chronic inflammatory response is associated with increased etepimeletic and anxious behaviors in cats, and corticosteroid treatment is associated with anxious behaviors. These findings suggest that monitoring anxious and etepimeletic behaviors may be relevant when assessing inflammatory disease in cats.

Policy complexity suppresses dopamine responses.

Limits on information processing capacity impose limits on task performance. We show that male and female mice achieve performance on a perceptual decision task that is near-optimal given their capacity limits, as measured by policy complexity (the mutual information between states and actions). This behavioral profile could be achieved by reinforcement learning with a penalty on high complexity policies, realized through modulation of dopaminergic learning signals. In support of this hypothesis, we find that policy complexity suppresses midbrain dopamine responses to reward outcomes. Furthermore, neural and behavioral reward sensitivity were positively correlated across sessions. Our results suggest that policy compression shapes basic mechanisms of reinforcement learning in the brain.Significance statement Decision making relies on memory to store information about which actions to produce in which situations. This memory has limited capacity, which means that some information will be lost. The signatures of this information loss can be found in patterns of behavioral bias and randomness. However, relatively little is known about the neural mechanisms which ensure that actions achieve the highest possible reward given the limited capacity of decision memory. In this paper, we show that the neuromodulator dopamine is sensitive to the costs of memory, as predicted by a computational model of capacity-limited learning.

Analysis of operant self-administration behaviors with supervised machine learning: Protocol for video acquisition and pose estimation analysis using DeepLabCut and Simple Behavioral Analysis (SimBA).

The use of supervised machine learning to approximate poses in video recordings allows for rapid and efficient analysis of complex behavioral profiles. Currently, there are limited protocols for automated analysis of operant self-administration behavior. We provide methodology to 1) obtain videos of training sessions via Raspberry Pi microcomputers or GoPros 2) obtain pose estimation data using the supervised machine learning software packages DeepLabCut (DLC) and Simple Behavioral Analysis (SimBA) with local high performance computer cluster, 3) comparison of standard MedPC lever response vs quadrant time data generated from pose estimation regions of interest and 4) generation of predictive behavioral classifiers. Overall, we demonstrate proof-of-concept to use pose estimation outputs from DLC to both generate quadrant time results and obtain behavioral classifiers from SimBA during operant training phases.Significance Statement Substance use disorders are comprised of complex behaviors that promote chronic relapse to drug-seeking and -taking. Rodent operant self-administration is commonly used as a preclinical tool to examine drug-taking, -seeking and craving behavior. We provide protocols to acquire videos of self-administration behavior and obtain pose estimation outputs and unique behavioral classifiers using the supervised learning softwares DeepLabCut and Simple Behavioral Analysis (SimBA).

Effects of cage size on the natural behavior, serum parameters, production performance and hypothalamic mRNA expression profiles of laying hens

The objective of this study was to evaluate the effects of cage size on the natural behavior, serum biochemistry, production performance and hypothalamic transcriptome profiles of laying hens. A total of 360 79-week-old hens were selected and randomly assigned to three groups (with five replicates each) with different cage sizes: large cages (LCs), medium cages (MCs), and small cages (SCs). The stocking density remained consistent across all groups throughout the experimental period. Behavioral expression was evaluated through observation of only six birds per group on a weekly basis. Compared to the SC group, the average frequencies of walking, wing-flapping, feather-pecking and drinking significantly increased in the LC and MC groups (P <0.05), whereas the average frequency of stereotypic behavior significantly decreased (P <0.05), indicating that cage size has positive effects on natural behavior. The average daily step count (DSC) was in the order LC > MC > SC (P <0.05), which suggested an improvement in exercise ability in larger cages. The serum levels of MDA, CORT, AST and T-CH were significantly lower in the LC and MC groups than in the SC group (P <0.05), whereas the serum activities of SOD and GSH-Px were significantly greater (P <0.05). Additionally, compared to the SC group, the laying rates were increased by 1.61% and 2.20% (P <0.05), and the feed conversion ratio (FCR) were improved by 1.35% and 2.25%, respectively (P <0.05) in the LC and MC groups, respectively. In total, 437 and 81 differentially expressed genes (DEGs) were identified in the hypothalamus of birds between the LC and SC groups and the MC and SC groups, respectively. A majority of these DEGs were involved primarily in neuroactive ligand‒receptor interactions, focal adhesion and calcium signaling pathways, which are associated with the regulation of behavioral patterns, the stress response and follicular development. Our results suggest that an appropriate increase in cage size is beneficial to the natural behavior expression, health status and production performance of laying hens.

Integration of cross-organizational business processes with blockchain: identifying key activities for on-chain execution based on behavioral profiles

Integration of cross-organizational business processes with blockchain: identifying key activities for on-chain execution based on behavioral profiles

Automated Credit Card Risk Assessment using Fuzzy Parameterized Neutrosophic Hypersoft Expert Set

In the financial industry, financial fraud is an ever-evolving risk with extreme consequences. Data mining has been instrumental in the recognition of credit card fraud (CCF) during online transactions. CCF recognition, which is a data mining problem, become a challenge owing to its two main reasons - firstly, the profiles of fraudulent and normal behaviors modify continually and then, CCF dataset is extremely lopsided. The implementation of fraud recognition in credit card transactions is tremendously influenced by the sampling methodology on data, detection approach and variable selection utilized. The conception of the neutrosophic hypersoft set (NHSS) is a parameterized family that handles the sub-attributes of the parameter and is an appropriate extension of the NHSS to correctly evaluate the uncertainty, deficiencies, and anxiety in decision-making. In comparison to previous research, NHSS can accommodate additional uncertainty, which is the crucial approach to describe fuzzy datasets in the decision-making algorithm. This study introduces an Automated Credit Card Risk Assessment using Fuzzy Parameterized Neutrosophic Hypersoft Expert Set (ACCRA-FPNHES) technique. In the ACCRA-FPNHES technique, a three-step process is involved. As a primary step, the ACCRA-FPNHES technique designs sparrow search algorithm (SSA) for choosing features. In the second step, the detection of CCF takes place using FPNHES technique. Finally, in the third step, the parameters related to the FPNHES technique can be adjusted by arithmetic optimization algorithm (AOA). The simulation validation of the ACCRA-FPNHES technique can be studied on credit card dataset. The obtained values indicate that the ACCRA-FPNHES technique showcases better performance

The 5-dimensional behavioural coopetitor profile: How to measure it?

The 5-dimensional behavioural coopetitor profile: How to measure it?

Mechanism of starch multi-scale structural in determining the textural properties and formability of starch pearls

Starch pearls are widely used in bubble tea and desserts, yet the mechanistic understanding of the formation process of their textural properties remain unclear. To investigate the relationship between the multi-scale structure of starch and the textural properties of starch pearls, analyses of fine structure, crystal structure, rheological behavior, and textural profiling were conducted. The results showed that starch gels with a higher content of short-chain amylose (100 < X ≤ 1000) exhibited weaker formability during starch pearl preparation, leading to a lower flow behavior index (n*). This, in turn, positively influenced the hardness and resilience of the starch pearls. Cassava, potato, and yam starch pearls contained a large amount of long-chain amylopectin (24 < X ≤ 100) and long-chain amylose (5000 < X ≤ 20,000). The high proportion of long chains resulted in a weaker ordered structure, leading to increased peak viscosities and final viscosities and a higher consistency coefficient (k*). This structural feature enhanced the formability stability, viscosity, and chewability of starch pearls. Our findings reveal that variations in starch multi-scale structure significantly influence the textural quality and formability of starch pearls, providing valuable insights for optimizing starch selection and processing techniques in the food industry.

Chrysanthemum morifolium Ramat. as a traditional tea material: Unraveling the influence of kill-green process on drying characteristics, phytochemical compounds, and volatile profile

The dried capitulum of chrysanthemums is a traditional material in scented tea, and the kill-green process is a critical step in determining their quality. However, the changes in the physicochemical properties during kill-green and the mechanisms by which these changes affect drying characteristics, metabolic components, and aroma profiles remain unclear. Therefore, this study investigated the changes in water status, polyphenol oxidase and peroxidase activities, and microstructure during high-humidity air impingement kill-green (HHAIK) and steam kill-green (SK), and their effects on drying behavior, color, phytochemicals, and volatile profile of dried chrysanthemums. Results showed that the kill-green process increased the freedom degree of immobile water, reduced the relative content of free water, and induced microstructure alterations, thus enhancing the water diffusion and shortening the subsequent drying time by up to 46.15 %. Compared to SK, HHAIK more rapidly inactivated PPO and POD, causing an improved color profile of dried samples. Dried samples treated with HHAIK for 60 s exhibited higher retention of 9 individual phenolics, total sugar, amino acids, and volatile compounds, thus resulting in higher sensorial acceptance than those treated with SK for 60 s. This study offers theoretical insights and technical support for the future development of high-quality chrysanthemum products.

Influence of selected native yeast strains on fermentation behavior and chemical profile in Agave tequilana (blue Weber agave) juice fermentation for tequila production

Influence of selected native yeast strains on fermentation behavior and chemical profile in Agave tequilana (blue Weber agave) juice fermentation for tequila production

Exploring Self-Management Behavior Profiles in Patients with Multimorbidity: A Sequential, Explanatory Mixed-Methods Study.

This study aims to identify self-management behavior profiles in multimorbid patients, and explore how workload, capacity, and their interactions influence these profiles. A sequential explanatory mixed-methods design was employed. In the quantitative phase (August 2022 to May 2023), data were collected from 1,920 multimorbid patients across nine healthcare facilities in Zhejiang Province. Latent Profile Analysis (LPA) was used to identify distinct self-management behavior profiles. Multinomial logistic regression was then used to assess the influence of workload and capacity dimensions (independent variables in Model 1), as well as their interaction (independent variables in Model 2), on these profiles (dependent variables in two models). The qualitative phase (May to August 2023) included semi-structured interviews with 16 participants, and the Giorgi analysis method was used for data categorization and coding. Quantitative analysis revealed three self-management behavior profiles: Symptom-driven Profile (8.0%), Passive-engagement Profile (29.5%), and Active-cooperation Profile (62.5%). Compared to the Active-cooperation Profile, both the Symptom-driven and Passive-engagement Profiles were associated with a higher workload (OR > 1, P < 0.05) and lower capacity (OR < 1, P < 0.05). An interaction of the overall workload and capacity showed a synergistic effect in the Passive-engagement Profile (OR = 1.08, 95% CI = 1.03-1.13, P < 0.05). Qualitative analysis identified six workload themes, and related coping strategies of three self-management behavior profiles. The integrated results highlighted distinct characteristics: Symptom-driven Profile patients exhibited reactive behaviors with limited health awareness, Passive-engagement Profile patients reduced engagement once symptoms stabilized, while Active-cooperation Profile patients proactively managed their conditions. Identifying three distinct self-management behavior profiles and their relationship with workload and capacity provides valuable insights into multimorbid patients' experiences, emphasizing the need for tailored interventions targeting workload and capacity to improve health outcomes.

Behavioural Profile Differences Between Cats in Animal-Assisted Services (AAS) and Non-AAS Cats Using the Fe-BARQ in Flanders

The engagement of cats in animal-assisted services (AAS) is increasing. This is surprising given that feline behavioural needs have been perceived as contradictory to conditions associated with AAS engagement, leading to the assumption that cats as a species are not suitable for AAS. However, important within-species variability in behavioural traits nuancing this assumption has not been addressed. Therefore, this study aimed to map the behavioural profiles of cats engaged in AAS and non-AAS cats to detect behavioural profile differences between these groups. Such differences could point to AAS cats being selected to engage in AAS based upon particular behavioural traits or, relatedly, to AAS cats coping differently with AAS features. Using a Flemish translation of the Feline Behavioural Assessment and Research Questionnaire (Fe-BARQ), cross-sectional survey data on various cats’ (N = 474) behavioural indices were collected. Using Mann–Whitney U-tests, results indicate that AAS cats significantly differed from non-AAS cats on five out of 19 behavioural subscales. Specifically, AAS cats scored higher on sociability with people, attention seeking, sociability with cats and predatory behaviour, and they scored lower on resistance to restraint. While this study did not allow for assessing explanations for these differences, findings suggest that cats in AAS may be intentionally or unintentionally selected for behavioural traits perceived as desirable for engagement in AAS. While we refrain from drawing conclusions regarding the “suitability” of cats based solely on our findings, our study contributes to the ongoing discourse surrounding critical themes around AAS-engaging cats. Furthermore, our findings provide a first indication of the reliability of a translation of a well-validated measurement tool suitable to map behavioural traits of cats in Flanders engaged in AAS.

Adaptive and Behavioral Profiles in Down Syndrome and Co-Occurring Autism Spectrum Disorder: A Case-Control Study.

Down syndrome (DS) is the most frequent genetic cause of intellectual disability (ID). Individuals with DS exhibit an elevated risk of other neurodevelopmental disorders, including autism spectrum disorder (ASD). The primary objective of this study was to explore the clinical characteristics of co-occurring ASD in children and adolescents with DS using a case-control approach. We compared the adaptive and behavioral profiles of a group of participants with both DS and ASD (ASD group) with a group of participants with DS-only matched by age, sex, and IQ (CON group). Participants in the ASD group exhibited significantly lower adaptive skills than the CON group, despite the IQ-matching in the moderate/severe ID range. No group differences emerged on internalizing or externalizing behavioral symptoms, but participants in the ASD group exhibited significantly higher social withdrawal, stereotyped behavior, and restricted interests. These findings indicate that, although children with DS-with or without ASD-who have similar low cognitive functioning often exhibit common behavioral traits, paying close attention to the elevation of stereotyped behaviors or restricted interests can improve the detection of co-occurring ASD in this population, enabling more personalized interventions. Conversely, the presence of ritualistic behaviors or behaviors related to insistence on sameness may not be a strong indicator of underlying ASD in children with DS. Additionally, acknowledging that the presence of ASD contributes to adaptive behavior deficits beyond the impact of moderate-to-severe ID alone highlights the critical need for early interventions to enhance daily living skills in this population.

Longitudinal relationship between 24-Hour Movement behavior patterns and physical function and quality of life after stroke: a latent transition analysis

Background24-hour movement behavior, including Physical activity (PA), Sedentary behavior (SB), and sleep, is independently associated with health after stroke. Few studies have explored 24-hour movement behavior patterns in stroke survivors and their transitions, as well as the health implications of the transitions. This study aimed to explore the different subgroups and stability of 24-hour movement behavior patterns in people after stroke and the relationship of profile transitions with physical function and health-related quality of life (HRQoL).MethodsIn this study, 131 people with first-ever stroke were investigated at one week (T1), one month (T2), three months (T3), and six months (T4) after discharge. The participants were asked to wear a wristband smartwatch for 7 consecutive days during each pe riod to collect 24-hour exercise data. After each period, their physical function and HRQoL were assessed. Latent profile analysis (LPA) identified typologies of 24-hour movement behaviors, and latent transition analysis (LTA) examined the stability and change in these profiles over time. The relationship of transition types with physical function and HRQoL was analyzed using a generalized linear regression model.Results108 participants were categorized into 3 latent profiles of 24-hour movement behavior: “Active, Non-sedentary, and Short sleep,” “Active and Sedentary,” and “Inactive and Sedentary.” The LTA results indicated that the proportion of participants with the “Active, Non-sedentary, and Short Sleep” profile and “Active and Sedentary” profile staying in the original latent profile was high. However, participants in the “Inactive and Sedentary” profile showed a high probability of transitioning to “Active and Sedentary” profile (T1→T2: 65.2%; T2→T3: 76.3%; T3→T4: 51.7%;T1→T4: 54.2%). Transition types are associated with physical function and HRQoL.ConclusionsThe results demonstrated substantial transitions in 24-hour movement behaviors within 6 months of rehabilitation after discharge, associated with later physical function and HRQoL. Furthermore, the participants’ sedentary behavior was highly stable within 24-hour movement behaviors, necessitating prompt diagnosis and intervention.

Enhancing Buccal Drug Delivery: The Impact of Glycerol in Slot-Die-Coated Pectin Films.

Buccal delivery offers a promising alternative to e.g., oral or parenteral drug administrations by leveraging the mucosal membranes of the mouth to enhance drug absorption and enhance patient compliance. Buccal films offer a promising approach for enhancing drug delivery by utilizing the mucoadhesive properties of the biopolymer pectin and glycerol's plasticizing effects. Designed to provide fast drug release, these films address the challenges of patient compliance, particularly among the elderly, children, and individuals with dysphagia. This study characterized the physicochemical properties of slot-die-coated films with pectin containing varying amounts of glycerol, including swelling behavior, disintegration rate, mechanical properties, mucoadhesion, and drug release profiles, using paracetamol as a model drug. Different methods such as quartz crystal microbalance with dissipation and open-source force analyzer were employed for the characterization. The results demonstrated that a high glycerol content in the films led to slower drug release with 95% paracetamol released for film without glycerol (GLY0) compared to only 74% released for film with 20% w/v glycerol (GLY20) after 60 min Additionally, higher glycerol levels resulted in enhanced mucoadhesive properties. Films containing 20% glycerol also showed superior permeability of paracetamol through ex vivo porcine buccal mucosa, with double the amount of paracetamol permeating in the first 120 min from GLY20 films compared to GLY0 films. These findings suggest that the pectin-glycerol buccal films, fabricated with slot-die coating as a novel technique, are user-friendly, exhibit interaction with the mucosa, and can be adjusted for specific disintegration and drug release rates, presenting a promising option for efficient, targeted drug delivery.

Typologies of activity-related behaviours during adolescence and their transitions: a longitudinal analysis of the ELSPAC cohort

ObjectivesThe objective of this study was to identify typologies of activity-related behaviours during adolescence and to explore transitions between the identified typologies. Additionally, we aimed to identify demographic indicators associated...

Identifying food behavior profiles among adult US cancer survivors: a latent class analysis.

This study sought to identify subgroups of adult cancer survivors with distinct food behavior patterns and to examine group characteristics. Data from adult cancer survivors, ages 20-64, in the 2007-2016 National Health and Nutrition Examination Survey was used to conduct latent class analyses to identify food behavior subgroups, based on five indicator variables. Associations between latent food behavior class membership and key sociodemographic and cancer-related characteristics were examined using adjusted multinomial regression models. Four unique food behavior classes were identified and named, characterized by the degree to which they purchased and consumed convenience foods: Home Cooks (48.4% of the sample), Non-Cooks/Eats Out (29.8%), Need Convenience (11.3%), and Quick Food at Home (10.5%). Shorter time since diagnosis was associated with higher adjusted relative risk ratios (ARRR) of belonging to the Non-Cooks/Eats Out class (ARRR 12.4, 95% CI 2.3, 68.0) and the Quick Food at Home class (ARRR 18.6, 95% CI 3.5, 98.4). Older age, lower educational attainment, and larger household size were also associated with a higher likelihood of belonging to the Quick Food at Home class. There are certain patterns of food behaviors among cancer survivors, and these patterns are related to both cancer-related and sociodemographic factors. As food purchasing and consumption behaviors are precursors to dietary outcomes, identifying the food behavior profiles of cancer survivors may help providers recognize those who could benefit from dietary interventions or supports and receive the necessary resources that would support patients in improving their diets.