Establish normative value of Alberta infant motor scale in Pune population

Background: The Alberta infant motor scale (AIMS) is a norm-reference test that assessed the spontaneous motor performance of infant 0-18 month. AIMS is development, motor assessment tools in the evaluation of motor risk in infants, but this scale was formulated by using western samples. In every country various differences are observed in the culture and ethnicity. Therefore, there is a need to establish normative value of AIMS in Pune population. Aim of the study was to establish normative value of AIMS in Pune population.Methods: A descriptive one time study of 420 healthy infants aged between 0 to 18 months was included in the study. Infants were observed in prone, supine, sitting, and standing positions. Infants were measured using the AIMS test and represent normative value in Pune population.Results: Medcalc software was used for the statistical analysis. For each month we calculated the mean AIMS score, and standard deviation, as well as percentiles. Results showed increases in raw scores across age groups from 0 to 15 months of age. The stability of raw scores was observed after 16 months of age. Pune infants demonstrated lower scores in specific ages compared to the Canadian sample.Conclusions: Although the AIMS is used in both research and clinical practice, it has certain limitations in terms of behavioral differentiation before 2 months and after 15 months. This reduced sensitivity at the extremes of the age range may be related to the number of motor items assessed at these ages’ months.

Using machine learning-based analysis for behavioral differentiation between anxiety and depression

Anxiety and depression are distinct—albeit overlapping—psychiatric diseases, currently diagnosed by self-reported-symptoms. This research presents a new diagnostic methodology, which tests rigorously for differences in cognitive biases among subclinical anxious and depressed individuals. 125 participants were divided into four groups based on the levels of their anxiety and depression symptoms. A comprehensive behavioral test battery detected and quantified various cognitive–emotional biases. Advanced machine-learning tools, developed for this study, analyzed these results. These tools detect unique patterns that characterize anxiety versus depression to predict group membership. The prediction model for differentiating between symptomatic participants (i.e., high symptoms of depression, anxiety, or both) compared to the non-symptomatic control group revealed a 71.44% prediction accuracy for the former (sensitivity) and 70.78% for the latter (specificity). 68.07% and 74.18% prediction accuracy was obtained for a two-group model with high depression/anxiety, respectively. The analysis also disclosed which specific behavioral measures contributed to the prediction, pointing to key cognitive mechanisms in anxiety versus depression. These results lay the ground for improved diagnostic instruments and more effective and focused individually-based treatment.

The diversity of Leadership roles in the context of behavioral complexity theory and its impact on leadership effectiveness

This research aims to examine the nature of the relationship between the various leadership roles and the effectiveness of leadership according to the perspective of the theory of behavioral complexity. The faculty at the University of Qadisiyahwas selected to test the hypotheses of research and to verify the objectives. The authors distributed the questionnaire to a sample of (165)individualsworking at different faculties of the university. The behavioral complexity variable consists of two dimensions, behavioral repertoire and behavioral differentiation. The variable of leadership effectiveness is treated asa single dimension variable. The study hypothesized that there is a positive relationship between the behavioral complexity and leadership effectiveness ,In other words , the educational leaders who use a variety of different roles will achieve the highest level of effectiveness. A variety of statistical tools were used to present the statistical description and hypothesis test, namely mean, standard deviation, simple correlation coefficient,andconfirmatory factor analysis , and structural equation modeling. The results reflected the validity of the research hypotheses , and in light of that a set of conclusions and recommendations were formulated

Genomic divergence in allopatric Northern Cardinals of the North American warm deserts is associated with behavioral differentiation

ABSTRACTBiogeographic barriers are thought to be important in initiating speciation through geographic isolation, but they rarely indiscriminately and completely reduce gene flow across the entire community. Understanding which species’ attributes regulate a barrier could help elucidate how speciation is initiated. Here, we investigated the association of behavioral isolation on population differentiation in Northern Cardinals (Cardinalis cardinalis) distributed across the Cochise Filter Barrier, a region of transitional habitat which separates the Sonoran and Chihuahuan deserts. Using genome-wide markers, we modeled demographic history by fitting the data to isolation and isolation-with-migration models. The best-fit model indicated that desert populations diverged in the mid-Pleistocene and there has been historically low, unidirectional gene flow into the Sonoran Desert. We then tested song recognition using reciprocal call-broadcast experiments to compare song recognition between deserts, controlling for song dialect changes within deserts. We found that male Northern Cardinals in both deserts were most aggressive to local songs and failed to recognize across-barrier songs. A correlation of genomic differentiation despite historic introgression and strong song discrimination is consistent with a model where speciation is initiated across a barrier and maintained by behavioral isolation.

Analysis of behavioral differentiation in smart cities based on mobile users’ usage detail record data

The expansion of big data has played an important role in the feasibility of the smart city initiative. The massive amounts of data offer the potential for cities to obtain valuable insights from a large amount of data collected through various sources. Usage detail records not only include plentiful spatial–temporal information, but also describe users’ activities in content space and time. They have three dimensions of information, which makes them favorable for the research of human behavior dynamics. To support smart cities, we collected usage detail records containing three dimensions of information from individuals and analyzed the relationship between them to get modes of users’ behavior. In this article, we propose a method to discover the needed content for users and a way to provide these data to them. The result shows that two of these three dimensions have an invisible association. New behavioral patterns that we discovered from usage detail records can be derived for configuring resources reasonably and supporting creation of smart cities.