Gradual Loss of Social Group Support during Competition Activates Anterior TPJ and Insula but Deactivates Default Mode Network.

Group forming behaviors are common in many species to overcome environmental challenges. In humans, bonding, trust, group norms, and a shared past increase consolidation of social groups. Being a part of a social group increases resilience to mental stress; conversely, its loss increases vulnerability to depression. However, our knowledge on how social group support affects brain functions is limited. This study observed that default mode network (DMN) activity reduced with the loss of social group support from real-life friends in a challenging social competition. The loss of support induced anterior temporoparietal activity followed by anterior insula and the dorsal attentional network activity. Being a part of a social group and having support provides an environment for high cognitive functioning of the DMN, while the loss of group support acts as a threat signal and activates the anterior temporoparietal junction (TPJ) and insula regions of salience and attentional networks for individual survival.

The effect of well-known burn-related features on machine learning algorithms in burn patients' mortality prediction.

BACKGROUND: Burns is one of the most common traumas worldwide. Severely injured burn patients have an increased risk for mortality and morbidity. This study aimed to evaluate well-known risk factors for burn mortality and comparison of six machine learn-ing (ML) Algorithms' predictive performances. METHODS: The medical records of patients who had burn injuries treated at Izmir Bozyaka Training and Research Hospital's Burn Treatment Center were examined retrospectively. Patients' demographics such as age and gender, total burned surface area (TBSA), Inhalation injury (II), full-thickness burns (FTBSA), and burn types (BT) were recorded and used as input features in ML models. Pa-tients were analyzed under two groups: Survivors and Non-Survivors. Six ML algorithms, including k-Nearest Neighbor, Decision Tree, Random Forest, Support Vector Machine, Multi-Layer Perceptron, and AdaBoost (AB), were used for predicting mortality. Several different input feature combinations were evaluated for each algorithm. RESULTS: The number of eligible patients was 363. All six parameters (TBSA, Gender, FTBSA, II, Age, BT) that were included in ML algorithms showed a significant difference (p<0.001). The results show that AB algorithm using all input features had the best predic-tion performance with an accuracy of 90% and an area under the curve of 92%. CONCLUSION: ML algorithms showed strong predictive performance in burn mortality. The development of an ML algorithm with the right input features could be useful in the clinical practice. Further investigations are needed on this topic.

Minimization of the threshold voltage parameter of the co-doped ZnO doped liquid crystals by machine learning algorithms.

This study aims to examine the influence of the co-doped semiconductor nanostructure (Al-Cu):ZnO on the electro-optical properties of the E7 coded pure nematic liquid crystal structures and minimize the threshold voltage of pure E7 liquid crystal. To determine the ideal concentration ratios of the materials for the minimum threshold voltage, we employed different machine learning algorithms. In this context, we first produced twelve composite structures through lab experimentation with different concentrations and created an experimental dataset for the machine learning algorithms. Next, the ideal concentration ratios were estimated using the AdaBoost algorithm, which has an [Formula: see text] of 96% on the experimental dataset. Finally, additional composite structures having the estimated concentration ratios were produced. The results show that, with the help of the employed machine learning algorithms, the threshold voltage of pure E7 liquid crystal was reduced by 19% via the (Al-Cu):ZnO doping.

Adsorption of thorium (IV) ions using a novel borate-based nano material Ca3Y2B4O12: Application of response surface methodology and Artificial Neural Network.

Since nuclear wastes are the most important wastes in terms of health and the environment, they are evaluated differently within nuclear reactors as well as in terms of their use in medical and industrial applications. In some cases, emergency intervention is necessary due to the amount of radioactivity or the physical and/or chemical conditions. . The purpose of this study is to investigate the adsorption properties of nano Ca3Y2B4O12 (CYBO) material synthesized by the sol-gel combustion method for the adsorption of Thorium (IV) from an aqueous medium. We tested how pH (3-8), the concentration of Th (IV) (25-125 mg/L), amount of adsorbent value (0.005-0.08 g) and temperature (20-60 °C), affect adsorption efficiency. The best possible combinations of these parameters were examined by Response Surface Methodology (RSM) and Artificial Neural Network (ANN). R2 values for RSM and ANN were 0.9964 and 0.9666, respectively. According to the models, the adsorption capacity under the optimum conditions determined for the RSM and ANN model was found to be 134.62 mg/g and 125.12 mg/g, respectively.

Hippocampal shape alterations in healthy young women with familial risk for unipolar depression.

BACKGROUND: Although reduced hippocampal volume (HCV) is a common finding in depression, it is unclear whether the structural alterations leading to reduction of HCV are pre-existing risk factors before the onset of clinical symptoms or a cumulative process that begins with the onset of clinical symptoms. The aim of the present study was to understand the anatomical status of the hippocampus prior to the clinical symptoms in subjects with high familial risk for depression. METHODS: Twenty-seven young women (mean age: 22.3 ±â€¯2.1 years) who were at high risk for familial unipolar depression and 26 age- and gender-matched healthy controls (mean age: 22.1 ±â€¯2.1 years) with low familial risk for depression were included in the study. Total hippocampal volumes were measured by manual tracing. For 3D shape differences, the spherical harmonic basis functions (SPHARM) software was used. The segmented images were parameterized, and the point-to-point based group difference was compared by the Hotelling's T-squared test with total brain volume and Beck Depression Scale as covariates. RESULTS: Although there was no difference in overall HCVs, shape analyses revealed a contracted area on the Cornu Ammonis (CA) 1 region of the right hippocampus head in the high-risk group compared to the low-risk group. Cross-sectional design and small sample size, including only females, were the main limitations of this study. CONCLUSION: This study with shape analyses provided data suggesting that local structural hippocampal alterations in the CA1 region might be associated with depression vulnerability in women at high risk.

Altered hippocampal formation shape in first-episode depressed patients at 5-year follow-up.

It is generally accepted that patients with major depressive disorder have smaller hippocampus size compared to healthy people. However, it is still not known if this situation exists before the onset of the disease or is a result of the toxic mechanism created by the depression itself. The findings of the long-term follow-up studies of first-episode depressed patients might contribute to solve the ongoing problem. In this study, the hippocampus of 18 first-episode patients who were followed-up for 5 years, were compared with those of healthy controls. There were no volumetric differences among groups neither at the baseline nor after 5 years of follow-up. However, shape analyses, using high dimensional mapping methods, revealed regional structural changes in the head and tail of the hippocampal formation in CA1 and subiculum regions in patients at the follow-up. Furthermore, a significant negative correlation was found with the number of days in depression without antidepressant treatment in the CA1 region in the head and tail of the hippocampal formation bilaterally. The findings of this study support the hypothesis that pathophysiological processes of depression induce structural alterations in depressed patients.