Subclinical Thyroid Disorders and Cognitive Performance Among Adolescents in the United States

Background: Thyroid hormone plays a crucial role in the growth and function of the central nervous system. The purpose of the study was to examine the relationships between the status of subclinical thyroid conditions and cognition among adolescents in the United States. Methods: Study sample included 1,327 adolescents 13 to 16 years old who participated in the Third National Health and Nutrition Examination Survey (NHANES III). Serum thyroxine (T4) and thyroid stimulating hormone (TSH) were measured and subclinical hypothyroidism, subclinical hyperthyroidism, and euthyroid groups were defined. Cognitive performance was assessed using the subscales of the Wide Range Achievement Test-Revised (WRAT-R) and the Wechsler Intelligence Scale for Children-Revised (WISC-R). The age-corrected scaled scores for arithmetic, reading, block design, and digit span were derived from the cognitive assessments. Results: Subclinical hypothyroidism was found in 1.7% and subclinical hyperthyroidism was found in 2.3% of the adolescents. Cognitive assessment scores on average tended to be lower in adolescents with subclinical hyperthyroidism and higher in those with subclinical hypothyroidism than the score for the euthyroid group. Adolescents with subclinical hypothyroidism had significantly better scores in block design and reading than the euthyroid subjects even after adjustment for a number of variables including sex, age, and family income level. Conclusion: Subclinical hypothyroidism was associated with better performance in some areas of cognitive functions while subclinical hyperthyroidism could be a potential risk factor

Dual encoding for abstractive text summarization

Recurrent Neural Network (RNN) based sequence-to-sequence attentional models have proven effective in abstractive text summarization. In this paper, we model abstractive text summarization using a dual encoding model. Different from the previous works only using a single encoder, the proposed method employs a dual encoder including the primary and the secondary encoders. Specifically, the primary encoder conducts coarse encoding in a regular way, while the secondary encoder models the importance of words and generates more fine encoding based on the input raw text and the previously generated output text summarization. The two level encodings are combined and fed into the decoder to generate more diverse summary that can decrease repetition phenomenon for long sequence generation. The experimental results on two challenging datasets (i.e., CNN/DailyMail and DUC 2004) demonstrate that our dual encoding model performs against existing methods

Scale Invariant Fully Convolutional Network: Detecting Hands Efficiently

Existing hand detection methods usually follow the pipeline of multiple stages with high computation cost, i.e., feature extraction, region proposal, bounding box regression, and additional layers for rotated region detection. In this paper, we propose a new Scale Invariant Fully Convolutional Network (SIFCN) trained in an end-to-end fashion to detect hands efficiently. Specifically, we merge the feature maps from high to low layers in an iterative way, which handles different scales of hands better with less time overhead comparing to concatenating them simply. Moreover, we develop the Complementary Weighted Fusion (CWF) block to make full use of the distinctive features among multiple layers to achieve scale invariance. To deal with rotated hand detection, we present the rotation map to get rid of complex rotation and derotation layers. Besides, we design the multi-scale loss scheme to accelerate the training process significantly by adding supervision to the intermediate layers of the network. Compared with the state-of-the-art methods, our algorithm shows comparable accuracy and runs a 4.23 times faster speed on the VIVA dataset and achieves better average precision on Oxford hand detection dataset at a speed of 62.5 fps.Comment: Accepted to AAAI201

Primary Care Practice Addressing Child Overweight and Obesity: A Survey of Primary Care Physicians at Four Clinics in Southern Appalachia

Objective: The prevalence of childhood overweight and obesity in southern Appalachia is among the highest in the United States (US). Primary care providers are in a unique position to address the problem; however, little is known about attitudes and practices in these settings. Methods: A 61-item healthcare provider questionnaire assessing current practices, attitudes, perceived barriers, and skill levels in managing childhood overweight and obesity was distributed to physicians in four primary care clinics. Questionnaires were obtained from 36 physicians. Results: Physicians\u27 practices to address childhood overweight and obesity were limited, despite the fact that most physicians shared the attitude that childhood overweight and obesity need attention. While 71% of physicians reported talking about eating and physical activity habits with parents of overweight or obese children, only 19% reported giving these parents the tools they needed to make changes. Approximately 42% determined the parents\u27 readiness to make small changes for their overweight or obese children. Physicians\u27 self-perceived skill level in managing childhood overweight and obesity was found to be a key factor for childhood overweight- and obesity- related practices. Conclusion: Primary care physicians in southern Appalachia currently play a limited role in the prevention or intervention of childhood overweight and obesity. Training physicians to improve their skills in managing childhood overweight and obesity may lead to an improvement in practice