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Chunhua Jin,1 Xiuqin Huang,2 Yanping Hu,1 Bing Xu,1 Jiasen Ma1 1Emergency Department, Shaoxing People’s Hospital, Shaoxing, Zhejiang Province, People’s Republic of China; 2Department of Plastic and Cosmetic Surgery, The 72nd Group Military Hospital of the People’s Liberation Army of China, Huzhou, Zhejiang Province, People’s Republic of ChinaCorrespondence: Jiasen Ma, Emergency Department, Shaoxing People’s Hospital, Shaoxing, Zhejiang Province, People’s Republic of China, Tel +86 575 88508888, Email sxrmyymjs@163.comBackground: Stanniocalcin-1 (STC1) may harbor anti-inflammatory and anti-oxidative properties, thereby exerting neuroprotective effects. This study was done with the intent to determine the role of serum STC1 in severity assessment and prognosis prediction of severe traumatic brain injury (sTBI).Methods: In this prospective longitudinal cohort study of 104 sTBI patients and 104 healthy individuals (controls), serum STC1 levels were quantified. Severity indicators were Glasgow Coma Scale (GCS) and Rotterdam computed tomography classification. Follow-up time was 180 days and extended Glasgow outcome scale (GOSE) score 1– 4 was deemed as poor prognosis. Multivariate analyses were applied to assess severity correlations and prognosis associations. Discriminative efficiencies were estimated in terms of area under receiver operating characteristic curve (AUC).Results: Patients exhibited significantly higher serum STC1 levels than controls. Serum STC1 levels were substantially elevated in order of GCS scores from 8 to 3, Rotterdam scores from 3 to 6 and 180-day GOSE scores from 8 to 1. Also, serum STC1 levels were independently correlated with GCS scores, Rotterdam scores and 180-day GOSE scores. Serum STC1 levels were independently associated with 180-day death, overall survival and poor prognosis, as well as were efficiently predictive of death and poor prognosis. Prediction model containing GCS scores, Rotterdam scores and serum STC1 levels, a

Dan Long,1 Ting Yang,1 Jie Chen,1 Ying Dai,1 Li Chen,1 Feiyong Jia,2 Lijie Wu,3 Yan Hao,4 Ling Li,5 Jie Zhang,6 Xiaoyan Ke,7 Mingji Yi,8 Qi Hong,9 Jinjin Chen,10 Shuanfeng Fang,11 Yichao Wang,12 Qi Wang,13 Chunhua Jin,14 Tingyu Li1 1Chongqing Key Laboratory of Child Nutrition and Health, Ministry of Education Key Laboratory of Child Development and Disorders, National Clinical Research Center of Child Health and Disorders, Children’s Hospital of Chongqing Medical University, Chongqing, People’s Republic of China; 2Department of Developmental and Behavioral Pediatrics, The First Hospital of Jilin University, Changchun, People’s Republic of China; 3Research Center for Child Development and Behavior, Harbin Medical University, Harbin, People’s Republic of China; 4Department of Pediatrics, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, People’s Republic of China; 5Department of Children Rehabilitation, Hainan Women and Children’s Medical Center, Haikou, People’s Republic of China; 6Children Health Care Center, Xi’an Children’s Hospital, Xi’an, People’s Republic of China; 7The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, People’s Republic of China; 8Department of Child Health Care, The Affiliated Hospital of Qingdao University, Qingdao, People’s Republic of China; 9Maternal and Child Health Hospital of Baoan, Shenzhen, People’s Republic of China; 10Department of Child Health Care, Shanghai Children’s Hospital, Shanghai, People’s Republic of China; 11Department of Child Health Care, Children’s Hospital Affiliated of Zhengzhou University, Zhengzhou, People’s Republic of China; 12National Health Commission Key Laboratory of Birth Defect for Research and Prevention, Hunan Provincial Maternal and Child Health Hospital, Changsha, People’s Republic of China; 13Deyang Maternal and Child Health Hospital, Deyang, People’s Republic of China; 14Department of Children Health C

Compared with facial emotion recognition on categorical model, the dimensional emotion recognition can describe numerous emotions of the real world more accurately. Most prior works of dimensional emotion estimation only considered laboratory data and used video, speech or other multi-modal features. The effect of these methods applied on static images in the real world is unknown. In this paper, a two-level attention with two-stage multi-task learning (2Att-2Mt) framework is proposed for facial emotion estimation on only static images. Firstly, the features of corresponding region(position-level features) are extracted and enhanced automatically by first-level attention mechanism. In the following, we utilize Bi-directional Recurrent Neural Network(Bi-RNN) with self-attention(second-level attention) to make full use of the relationship features of different layers(layer-level features) adaptively. Owing to the inherent complexity of dimensional emotion recognition, we propose a two-stage multi-task learning structure to exploited categorical representations to ameliorate the dimensional representations and estimate valence and arousal simultaneously in view of the correlation of the two targets. The quantitative results conducted on AffectNet dataset show significant advancement on Concordance Correlation Coefficient(CCC) and Root Mean Square Error(RMSE), illustrating the superiority of the proposed framework. Besides, extensive comparative experiments have also fully demonstrated the effectiveness of different components., Comment: 10 pages, 4 figures

This paper presents an efficient approach to simulate frequency and wave propagations in functionally graded material (FGM) bars by using novel weak form quadrature element method (WQEM). Based on Mindlin-Herrmann rod theory, a time domain N-node quadrature bar element is proposed. Detailed formulations are given. Dynamic problems of FGM bars are investigated by using the proposed weak form quadrature bar element. Comparisons are made with results obtained by using frequency domain spectral element method (SEM) and by using strong form differential quadrature method (DQM) to verify the developed quadrature bar element. It is shown that one 21-node bar element can yield very accurate frequencies and that the proposed element can efficiently simulate the wave propagation in FGM bars. Compared to results based on the simple rod theory, the results based on Mindlin-Herrmann rod theory should be more reliable, especially for the wave forms and group velocity.

This paper presents an efficient approach to simulate frequency and wave propagations in functionally graded material (FGM) bars by using novel weak form quadrature element method (WQEM). Based on Mindlin-Herrmann rod theory, a time domain N-node quadrature bar element is proposed. Detailed formulations are given. Dynamic problems of FGM bars are investigated by using the proposed weak form quadrature bar element. Comparisons are made with results obtained by using frequency domain spectral element method (SEM) and by using strong form differential quadrature method (DQM) to verify the developed quadrature bar element. It is shown that one 21-node bar element can yield very accurate frequencies and that the proposed element can efficiently simulate the wave propagation in FGM bars. Compared to results based on the simple rod theory, the results based on Mindlin-Herrmann rod theory should be more reliable, especially for the wave forms and group velocity.