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Levetiracetam-associated frustration and possible position involving vitamin and mineral B6 utilization in veterans using epilepsy.
Extensive experiments on several benchmark datasets demonstrate the superior performance of our method over existing state-of-the-art baselines for cross-domain object detection.Mobile phones offer an excellent low-cost alternative for Virtual Reality. However, the hardware constraints of these devices restrict the displayable visual complexity of graphics.Image-Based Rendering techniques arise as an alternative to solve this problem, but usually, the support of collisions and irregular surfaces (i.e. any surface that is not flat or even) represents a challenge. In this work, we present a technique suitable for both virtual and real-world environments that handle collisions and irregular surfaces for an Image-Based Rendering technique in low-cost virtual reality. We also conducted a user evaluation for finding the distance between images that presents a realistic and natural experience by maximizing the perceived virtual presence and minimizing the cybersickness effects. The results prove the benefits of our technique for both virtual and real-world environments.An effective person re-identification (re-ID) model should learn feature representations that are both discriminative, for distinguishing similar-looking people, and generalisable, for deployment across datasets without any adaptation. In this paper, we develop novel CNN architectures to address both challenges. First, we present a re-ID CNN termed omni-scale network (OSNet) to learn features that not only capture different spatial scales but also encapsulate a synergistic combination of multiple scales, namely omni-scale features. The basic building block consists of multiple convolutional streams, each detecting features at a certain scale. For omni-scale feature learning, a unified aggregation gate is introduced to dynamically fuse multi-scale features with channel-wise weights. OSNet is lightweight as its building blocks comprise factorised convolutions. Second, to improve generalisable feature learning, we introduce instance normalisation (IN) layers into OSNet to cope with cross-dataset discrepancies. Further, to determine the optimal placements of these IN layers in the architecture, we formulate an efficient differentiable architecture search algorithm. Extensive experiments show that, in the conventional same-dataset setting, OSNet achieves state-of-the-art performance, despite being much smaller than existing re-ID models. In the more challenging yet practical cross-dataset setting, OSNet beats most recent unsupervised domain adaptation methods without using any target data.This paper studies the problem of learning the conditional distribution of a high-dimensional output given an input, where the output and input belong to two different domains, e.g., the output is a photo image and the input is a sketch image. We solve this problem by cooperative training of a fast thinking initializer and slow thinking solver. The initializer generates the output directly by a non-linear transformation of the input as well as a noise vector that accounts for latent variability in the output. Vorapaxar solubility dmso The slow thinking solver learns an objective function in the form of a conditional energy function, so that the output can be generated by optimizing the objective function, or more rigorously by sampling from the conditional energy-based model. We propose to learn the two models jointly, where the fast thinking initializer serves to initialize the sampling of the slow thinking solver, and the solver refines the initial output by an iterative algorithm. The solver learns from the difference between the refined output and the observed output, while the initializer learns from how the solver refines its initial output. We demonstrate the effectiveness of the proposed method on various conditional learning tasks.Objective An optical imaging system is proposed for quantitatively assessing jugular venous response to altered central venous pressure. Methods The proposed system assesses sub-surface optical absorption changes from jugular venous waveforms with a spatial calibration procedure to normalize incident tissue illumination. Widefield frames of the right lateral neck were captured and calibrated using a novel flexible surface calibration method. A hemodynamic optical model was derived to quantify jugular venous optical attenuation (JVA) signals, and generate a spatial jugular venous pulsatility map. JVA was assessed in three cardiovascular protocols that altered central venous pressure acute central hypovolemia (lower body negative pressure), venous congestion (head-down tilt), and impaired cardiac filling (Valsalva maneuver). Results JVA waveforms exhibited biphasic wave properties consistent with jugular venous pulse dynamics when time-aligned with an electrocardiogram. JVA correlated strongly (median, interquartile range) with invasive central venous pressure during graded central hypovolemia (r=0.85, [0.72, 0.95]), graded venous congestion (r=0.94, [0.84, 0.99]), and impaired cardiac filling (r=0.94, [0.85, 0.99]). Reduced JVA during graded acute hypovolemia was strongly correlated with reductions in stroke volume (SV) (r=0.85, [0.76, 0.92]) from baseline (SV 7915 mL, JVA 0.560.10 a.u.) to -40 mmHg suction (SV 5918 mL, JVA 0.470.05 a.u.; p less then 0.01). Conclusion The proposed non-contact optical imaging system demonstrated jugular venous dynamics consistent with invasive central venous monitoring during three protocols that altered central venous pressure. Significance This system provides non-invasive monitoring of pressure-induced jugular venous dynamics in clinically relevant conditions where catheterization is traditionally required, enabling monitoring in non-surgical environments.
The present study aimed to investigate the intervening role of anxiety symptoms in relations between self-regulation and multiple forms of prosocial behaviors in U.S. Latino/a college students.

The sample is based on data from a cross-sectional study on college students' health and adjustment. Participants were 249 (62% women;
age =20 years; 86% U.S. born) college students who self-identified as Latino/a.

College students self-reported on their self-regulation, anxiety symptoms, and types and targets of prosocial behaviors using online surveys. Path analyses were conducted to test direct and indirect associations among the study variables.

Self-regulation was directly and indirectly associated with several types of prosocial behaviors via anxiety symptoms. The hypothesized associations also differed by the target of helping.

Our findings underscore a strengths-based view of the coping and mental health resources that predict positive well-being among U.S. Latino/a college students.
Our findings underscore a strengths-based view of the coping and mental health resources that predict positive well-being among U.S. Latino/a college students.Objective This study assessed the feasibility of capturing smartphone based digital phenotyping data in college students during the COVID-19 pandemic with the goal of understanding how digital biomarkers of behavior correlate with mental health. Participants Participants were 100 students enrolled in 4-year universities. Methods Each participant attended a virtual visit to complete a series of gold-standard mental health assessments, and then used a mobile app for 28 days to complete mood assessments and allow for passive collection of GPS, accelerometer, phone call, and screen time data. Students completed another virtual visit at the end of the study to collect a second round of mental health assessments. Results In-app daily mood assessments were strongly correlated with their corresponding gold standard clinical assessment. Sleep variance among students was correlated to depression scores (ρ = .28) and stress scores (ρ = .27). Conclusions Digital Phenotyping among college students is feasible on both an individual and a sample level. Studies with larger sample sizes are necessary to understand population trends, but there are practical applications of the data today.
Locomotive syndrome (LS) is the leading cause of persons needing long-term care in old age and is characterized by locomotive organ impairment including musculoskeletal pain. The aim was to examine the association between musculoskeletal pain and LS in young and middle-aged persons.

A total of 836 participants (male 667, female 169; mean age 44.4 years) were examined in this cross-sectional study. The LS was evaluated by three screening tools the two-step test, the stand-up test, and the 25-question Geriatric Locomotive Function Scale. Musculoskeletal pain, exercise habits, physical function (walkability and muscle strength), and physical activity were also assessed.

The LS was found in 22.8% of participants. The number with musculoskeletal pain was significantly higher in those with the LS. A significant correlation was found between the degree of musculoskeletal pain and exercise habits. Less regular exercise was significantly associated with higher LS prevalence. Physical activity and function were greater in participants with more regular exercise.

Musculoskeletal pain was significantly related to LS even in young and middle-aged persons. The present results suggest that control of musculoskeletal pain and improvement of exercise habits in young and middle-aged persons might help prevent the LS.
Musculoskeletal pain was significantly related to LS even in young and middle-aged persons. The present results suggest that control of musculoskeletal pain and improvement of exercise habits in young and middle-aged persons might help prevent the LS.In this study, researchers aimed to assess the situation of domestic violence against women during the pandemic. 332 women participated in the study. It was found that emotional, verbal and total violence scores of the literate ones were higher. The emotional violence scores of the women who do not work and whose partners do not work due to the pandemic are higher (p  less then  0.05). The researchers reached the conclusion that emotional violence is higher during the pandemic process, and that failing to work in an income-generating job triggers this situation.
To evaluate the visual and refractive outcomes of trifocal intraocular lens (IOL) implantation in eyes previously treated with myopic and hyperopic corneal refractive laser surgery.

Clinica Baviera-AIER-Eye group, Spain.

Retrospective comparative case series.

The series was divided into two groups according to the type of corneal laser refraction (myopic and hyperopic). The main visual and refractive outcome measures included corrected and uncorrected distance and near visual acuity (CDVA, UDVA, UNVA), safety, efficacy, and predictability. The secondary outcome measures were percentage of enhancement and NdYAG-capsulotomy, and influence of pre-laser magnitude of myopia and hyperopia on the outcome of trifocal IOL implantation.

The sample comprised 868 eyes (543 patients) myopic, n=319 eyes, 36.7%; and hyperopic, n=549 eyes, 63.2%. Three months postoperatively , visual outcomes were poorer in the hyperopic group than in the myopic group for mean-CDVA (0.06+/-0.05 vs 0.04+/-0.04, p<0.01) and safety (21% vs 12% of CDVA line-loss, p<0.
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