Theory of Mind: Development, Neurobiology, Related Fields and Neurodevelopmental Disorders

Theory of mind is a social cognition skills demonstrated its importance in the last forty years with psychiatric clinical trials. Theory of mind is seen as an effective and necessary skill in the social function-ing of human who is a social creature as the ability to recognize the mental states and emotions of others. In the first six years of life, theory of mind has been associated with many fields. Findings related to many neurobiological bases, such as limbic-paralimbic structures, prefrontal cortex, which start with mirror neurons, help this sense of meaning. Areas associated with theory of mind development provide better understanding of theory of mind skills and deficits, the first psychopathology studies have been carried out in children with autism, and the studies about theory of mind skills in the diagnosis of neurodevelopmental disorders are becoming more and more interesting. In this review, theory of mind development, neurobiological basis and related areas will be explained and the relation of theory of mind with psychopathology will be examined

Smart Mobility Blueprint for Illinois

R27-228Connected, automated, shared, and electric (CASE) technologies have invoked Mobility 4.0\u2014a connected, digitized, multimodal, and autonomous system of systems. This project established a flexible and adaptable blueprint that would streamline multidisciplinary and multistakeholder efforts as well as leverage available resources to prepare the Illinois Department of Transportation and other transportation agencies. Illinois has several strengths that make it an attractive location for CASE technology companies, including a talent pool from top-ranked universities, well-developed transportation infrastructure, government support, and a robust ecosystem of collaboration and innovation. Illinois also faces potential challenges (e.g., competition from other states and countries, limited access to funding, regulatory hurdles, and infrastructure readiness for new mobility technologies). Seven smart mobility pillars were identified in this study for Illinois\u2014namely, connected and automated (CA) freight, scaling intelligent transportation systems, farm automation, insurance, urban mobility, CA logistics, and alternative fuels. The balanced scorecard ranked the pillars as follows (from highest): alternative fuels, scaling intelligent transportation systems, CA freight, farm automation, CA logistics, insurance, and urban mobility. Tactical focus areas were also identified per pillar and were prioritized with suggested leads and stakeholders to champion the CASE directives and opportunities. Near-term actions for Illinois were also suggested that included establishing a central structure for Illinois\u2019 CASE program, enriching the knowledge base and experience, preparing transportation infrastructure, partnerships with external stakeholders, and expansion of laws, regulations, and policies that will help administer and grow CASE technology deployment and integration

Examination of neutrophil, platelet, and monocyte-lymphocyte ratios in adolescents with bipolar disorder-manic episode and depression

WOS: 000504845000007Objective: Neutrophil/lymphocyte ratio (NLR), platelet/lymphocyte ratio (PLR), and monocyte/lymphocyte ratio (MLR) are practical inflammation parameters. In bipolar disorder (BD) and major depressive disorder (MDD), these parameters were reported higher than in healthy controls (HC). We aim to compare NLR, PLR, MLR in HC and patients with MDD and BB-manic episode. Method: Forty-six patients with MDD and 43 patients with BD hospitalized between 2013 and 2017 and 40 HC were included in the study. White blood cell, neutrophil, lymphocyte, platelet, and monocyte numbers were entered retrospectively from complete blood counts made at the time of admission, and NLR, PLR, and MLR were calculated from these. Results: NLR and PLR were revealed higher in MDD than HC. NLR and neutrophil values were higher in BD than HC, and there was a positive correlation between NLR and hospitalization period of patients with BD. Conclusion: Findings of our study supported the inflammation hypothesis for MDD and BD in adolescents. Larger-scale studies are necessary to confirm these findings

Use of machine learning methods in prediction of short-term outcome in autism spectrum disorders

OBJECTIVE Studies show partial improvements in some core symptoms of Autism Spectrum Disorders (ASD) in time. However, the predictive factors (e.g. pretreatment IQ, comorbid psychiatric disorders, adaptive, and language skills, etc.) for a better the outcome was not studied with machine learning methods. We aimed to examine the predictors of outcome with machine learning methods, which are novel computational methods including statistical estimation, information theories and mathematical learning automatically discovering useful patterns in large amounts of data. METHOD The study the group comprised 433 children (mean age: 72.3 ± 45.9 months) with ASD diagnosis. The ASD symptoms were assessed by the Autism Behavior Checklist, Aberrant Behavior Checklist, Clinical Global Impression scales at baseline (T0) and 12th (T1), 24th (T2), and 36th (T3) months. We tested the performance of for machine learning algorithms (Naive Bayes, Generalized Linear Model, Logistic Regression, Decision Tree) on our data, including the 254 items in the baseline forms. Patients with ≤2 CGI points in ASD symptoms at in 36 months were accepted as the group who has “better outcome” as the prediction class. RESULTS The significant proportion of the cases showed significant improvement in ASD symptoms (39.7% in T1, 60.7% in T2; 77.8% in T3). Our machine learning model in T3 showed that diagnosis group affected the prognosis. In the autism group, older father and mother age; in PDD-NOS group, MR comorbidity, less birth weight and older age at diagnosis have a worse outcome. In Asperger’s Disorder age at diagnosis, age at first evaluation and developmental cornerstones has affected prognosis. CONCLUSION In accordance with other studies we found early age diagnosis, early start rehabilitation, the severity of ASD symptoms at baseline assessment predicted outcome. Also, we found comorbid psychiatric diagnoses are affecting the outcome of ASD symptoms in clinical observation. The machine learning models reveal several others are more significant (e.g. parental age, birth weight, sociodemographic variables, etc.) in terms of prognostic information and also planning treatment of children with ASD