Acevedobraun7097

Results indicate that (a) the relationship between RewP amplitude and depressive symptoms may, in part, depend upon positive affect regulation strategies and (b) the RewP elicited by reward appears sensitive to a savoring intervention. These findings suggest that mitigating depressive symptoms in emerging adults may depend on both top-down (i.e., savoring) and bottom-up (i.e., RewP) forms of positive affect regulation and have important implications for clinical prevention and intervention efforts for depressive symptoms and disorder. (PsycInfo Database Record (c) 2020 APA, all rights reserved).Persons with depression consistently report a different pattern of music preference, compared to nondepressed persons. Are such preferences maladaptive or beneficial? We tested this question in a study with 3 parts that examined 77 participants' (39 with and 38 without clinical depression) music choice in daily life, affective changes after music listening, and the reasons for music listening. During a 3-day ecological momentary assessment, participants chose a song from a preset music library of happy and sad songs and rated their affect before and after hearing the chosen song. In addition, we analyzed the characteristics (e.g., tempo) of songs participants listened to more than 5 times over 7 days (from participants' Spotfiy music streaming accounts; favorite songs). Finally, we analyzed the reasons for music listening in general when feeling happy and sad. Linderalactone mw Unlike nondepressed persons, persons with depression lacked a preference for happy over sad songs in daily contexts. Notably, both groups reported increased relaxedness as well as decreased happiness after hearing sad songs. Further, favorite songs of persons with depression had a slower tempo than nondepressed persons' ones. When reporting reasons to listen to music when feeling sad, both groups were less likely to report that they listened to music to increase high arousal positive affect, compared to other reasons. One reason that may attract persons with depression to sad music is a desire to feel calm. (PsycInfo Database Record (c) 2020 APA, all rights reserved).Significant inherent extra-articular varus angulation is associated with abnormal postoperative hip-knee-ankle (HKA) angle. At present, HKA is manually measured by orthopedic surgeons and it increases the doctors' workload. To automatically determine HKA, a deep learning-based automated method for measuring HKA on the unilateral lower limb X-rays was developed and validated. This study retrospectively selected 398 double lower limbs X-rays during 2018 and 2020 from Jilin University Second Hospital. The images (n = 398) were cropped into unilateral lower limb images (n = 796). The deep neural network was used to segment the head of hip, the knee, and the ankle in the same image, respectively. Then, the mean square error of distance between each internal point of each organ and the organ's boundary was calculated. The point with the minimum mean square error was set as the central point of the organ. HKA was determined using the coordinates of three organs' central points according to the law of cosines. In a quantitative analysis, HKA was measured manually by three orthopedic surgeons with a high consistency (176.90 °  ± 12.18°, 176.95 °  ± 12.23°, 176.87 °  ± 12.25°) as evidenced by the Kandall's W of 0.999 (p  less then  0.001). Of note, the average measured HKA by them (176.90 °  ± 12.22°) served as the ground truth. The automatically measured HKA by the proposed method (176.41 °  ± 12.08°) was close to the ground truth, showing no significant difference. In addition, intraclass correlation coefficient (ICC) between them is 0.999 (p  less then  0.001). The average of difference between prediction and ground truth is 0.49°. The proposed method indicates a high feasibility and reliability in clinical practice.Diabetes is a very common occurring disease, diagnosed by hyperglycemia. The established mode of diagnosis is the analysis of blood glucose level with the help of a hand-held glucometer. Nowadays, it is also known for affecting multi-organ functions, particularly the microvasculature of the cardiovascular system. In this work, an alternative diagnostic system based on the heart rate variability (HRV) analysis and artificial neural network (ANN) and support vector machine (SVM) have been proposed. The experiment and data recording has been performed on male Wister rats of 10-12 week of age and 200 ± 20 gm of weight. The digital lead-I electrocardiogram (ECG) data are recorded from control (n = 5) and Streptozotocin-induced diabetic rats (n = 5). Nine time-domain linear HRV parameters are computed from 60 s of ECG data epochs and used for the training and testing of backpropagation ANN and SVM. Total 526 (334 Control and 192 diabetics) such datasets are computed for the testing of ANN for the identification of the diabetic conditions. The ANN has been optimized for architecture 951 (Input hidden output neurons, respectively) with the optimized learning rate parameter at 0.02. With this network, a very good classification accuracy of 96.2% is achieved. While similar accuracy of 95.2% is attained using SVM. Owing to the successful implementation of HRV parameters based automated classifiers for diabetic conditions, a non-invasive, ECG based online prognostic system can be developed for accurate and non-invasive prediction of the diabetic condition.Recent technological advancements have led to the development and implementation of robotic surgery in several specialties, including neurosurgery. Our aim was to carry out a worldwide survey among neurosurgeons to assess the adoption of and attitude toward robotic technology in the neurosurgical operating room and to identify factors associated with use of robotic technology. The online survey was made up of nine or ten compulsory questions and was distributed via the European Association of the Neurosurgical Societies (EANS) and the Congress of Neurological Surgeons (CNS) in February and March 2018. From a total of 7280 neurosurgeons who were sent the survey, we received 406 answers, corresponding to a response rate of 5.6%, mostly from Europe and North America. Overall, 197 neurosurgeons (48.5%) reported having used robotic technology in clinical practice. The highest rates of adoption of robotics were observed for Europe (54%) and North America (51%). Apart from geographical region, only age under 30, female gender, and absence of a non-academic setting were significantly associated with clinical use of robotics.