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| 1 | s Early-onset in Major Depression a Predictor of Specific Clinical Features with More Impaired Social Function?显示文摘Background: Early-onset major depressive disorder (MDD) (EOD) is often particularly malignant due to its special clinical features, accompanying impaired social function, protracted recovery time, and frequent recurrence. This study aimed to observe the effects of age onset on clinical characteristics and social function in MDD patients in Asia. Methods: In total, 547 out-patients aged 18-5 years who were from 13 study sites in five Asian countries were included. These patients had MDD diagnose according to the Diagnostic and Statistical Manual of Mental Disorders, 4^th Edition criteria. Clinical features and social function were assessed using Symptom Checklist-90-revised (SCL-90-R) and Sheehan Disability Scale (SDS). Quality of life was assessed by a 36-item Short-form Health Survey (SF-36). Analyses were performed using a continuous or dichotomous (cut-off: 30 years) age-of-onset indicator. Results: Early-onset MDD (EOD, 〈30 years) was associated with longer illness (P - 0.003), unmarried status (P 〈 0.001), higher neuroticism (P 〈 0.002) based on the SCL-90-R, and more limited social function and mental health (P = 0.006, P = 0.007) based on the SF-36 and SDS. The impairment of social function and clinical severity were more prominent at in-patients with younger onset ages. Special clinical features and more impaired social function and quality of life were associated with EOD, as in western studies. Conclusions: EOD often follows higher levels of neuroticism. Age of onset of MDD may be a predictor of clinical features and impaired social function, allowing earlier diagnosis and treatment. | Yan-Hong Liu Lin Chen Yun-Ai Su Yi-Ru Fang Manit Srisurapanont Jin Pyo Hong Ahmad Hatim Hong Choon Chua Dianne Bautista Tian-Mei Si | 2015 | Chinese Medical Journal2015，，6： | 5 |
| 2 | Knowledge of first aid and basic life support amongst medical students:a com- parison between trained and un-trained students显示文摘 | Abbas A Bukhari SI Ahmad F | 2011 | J Pak Med Assoc2011，61，6： | 1 |
| 3 | Early rapid rise in intraocular pressure after intravitreal triamcinolone acetonide injection 显示文摘 | Singh I Ahmad SI Yeh D | 2004 | Am J Ophthalmol2004，138，2： | 1 |
| 4 | Early rapid rise in intraocular pressure after intravitreal triamcinolone acetonide injection显示文摘 | Singh IP Ahmad SI Yeh D | | 0，，： | 1 |
| 5 | Cytogenetic investigation of reduced pollen fertility in gamma irradiated barley 显示文摘 | Khan IM Ahmad H Khan SI 1993 | 1993 | Pakphyton1993，5，： | 1 |
| 6 | Early rapid rise in intraocular pressure after intravitreal triamcinolone acetonide injection显示文摘 | SinghI Ahmad SI Yeh D | | 0，，： | 1 |
| 7 | Early rapid rise in introcular pressure after intravitreal triamcinolone acetonide injection 显示文摘 | Singh I Ahmad SI Yeh D | 2004 | Am J Ophthalmol2004，138，2： | 1 |
| 8 | Early rapid rise in intraocular pressure after intravitreal triamcinolone acetonide injection 显示文摘 | Singh IP Ahmad SI Yeh D | 2004 | Am J Ophthalmol2004，138，2： | 1 |
| 9 | Partial-area method in bioequivalence assessment: naproxen显示文摘 | Niazi SK Alam SM Ahmad SI | 1997 | Biopharm Drug Disp1997，18，： | 1 |
| 10 | Introduction to diabetes mellitus显示文摘 | Kaul K Tarr JM Ahmad SI | 2012 | Adv Exp Med Biol2012，771，2： | 1 |
| 11 | Early rapid rise in intraocular pressureafter intravitreal triamcinolone acetonide injection显示文摘 | Singh IP Ahmad SI Yeh D | 2004 | Am J Ophthalmol2004，138，： | 1 |
| 12 | Knowledge of first aidand basic life support amongst medical students: acomparison between trained and un-trained students显示文摘 | Abbas A Bukhari SI Ahmad F | 2011 | JPak Med Assoc2011，61，6： | 1 |
| 13 | Partial-area method in bioequivalence assessment: naproxen显示文摘 | NIAZI SK ALAM SM AHMAD SI | 1997 | Biopharm Drug Dispos1997，18，2： | 1 |
| 14 | Hypotensive activity and toxicology of constituents from bombax ceiba stem bark 显示文摘 | Saleem R Ahmad SI Ahmed M | 2003 | Biol Pharm Bull2003，26，1： | 1 |
| 15 | Feasibility and physics potential of detecting ^(8)B solar neutrinos at JUNO显示文摘The Jiangmen Underground Neutrino Observatory(JUNO)features a 20 kt multi-purpose underground liquid scintillator sphere as its main detector.Some of JUNO's features make it an excellent location for^8B solar neutrino measurements,such as its low-energy threshold,high energy resolution compared with water Cherenkov detectors,and much larger target mass compared with previous liquid scintillator detectors.In this paper,we present a comprehensive assessment of JUNO's potential for detecting^8B solar neutrinos via the neutrino-electron elastic scattering process.A reduced 2 MeV threshold for the recoil electron energy is found to be achievable,assuming that the intrinsic radioactive background^(238)U and^(232)Th in the liquid scintillator can be controlled to 10^(-17)g/g.With ten years of data acquisition,approximately 60,000 signal and 30,000 background events are expected.This large sample will enable an examination of the distortion of the recoil electron spectrum that is dominated by the neutrino flavor transformation in the dense solar matter,which will shed new light on the inconsistency between the measured electron spectra and the predictions of the standard three-flavor neutrino oscillation framework.IfDelta m^(2)_(21)=4.8times10^(-5);(7.5times10^(-5))eV^(2),JUNO can provide evidence of neutrino oscillation in the Earth at approximately the 3sigma(2sigma)level by measuring the non-zero signal rate variation with respect to the solar zenith angle.Moreover,JUNO can simultaneously measureDelta m^2_(21)using^8B solar neutrinos to a precision of 20% or better,depending on the central value,and to sub-percent precision using reactor antineutrinos.A comparison of these two measurements from the same detector will help understand the current mild inconsistency between the value of Delta m^2_(21)reported by solar neutrino experiments and the KamLAND experiment. | Angel Abusleme Thomas Adam Shakeel Ahmad Sebastiano Aiello Muhammad Akram Nawab Ali Fengpeng An Guangpeng An Qi An Giuseppe Andronico Nikolay Anfimov Vito Antonelli Tatiana Antoshkina Burin Asavapibhop João Pedro Athayde Marcondes de André Didier Auguste Andrej Babic Wander Baldini Andrea Barresi Eric Baussan Marco Bellato Antonio Bergnoli Enrico Bernieri David Biare Thilo Birkenfeld Sylvie Blin David Blum Simon Blyth Anastasia Bolshakova Mathieu Bongrand Clément Bordereau Dominique Breton Augusto Brigatti Riccardo Brugnera Riccardo Bruno Antonio Budano Max Buesken Mario Buscemi Jose Busto Ilya Butorov Anatael Cabrera Hao Cai Xiao Cai Yanke Cai Zhiyan Cai Antonio Cammi Agustin Campeny Chuanya Cao Guofu Cao Jun Cao Rossella Caruso Cédric Cerna Jinfan Chang Yun Chang Pingping Chen Po-An Chen Shaomin Chen Shenjian Chen Xurong Chen Yi-Wen Chen Yixue Chen Yu Chen Zhang Chen Jie Cheng Yaping Cheng Alexander Chepurnov Davide Chiesa Pietro Chimenti Artem Chukanov Anna Chuvashova Gérard Claverie Catia Clementi Barbara Clerbaux Selma Conforti Di Lorenzo Daniele Corti Salvatore Costa Flavio Dal Corso Christophe De La Taille Jiawei Deng Zhi Deng Ziyan Deng Wilfried Depnering Marco Diaz Xuefeng Ding Yayun Ding Bayu Dirgantara Sergey Dmitrievsky Tadeas Dohnal Georgy Donchenko Jianmeng Dong Damien Dornic Evgeny Doroshkevich Marcos Dracos Frédéric Druillole Shuxian Du Stefano Dusini Martin Dvorak Timo Enqvist Heike Enzmann Andrea Fabbri Lukas Fajt Donghua Fan Lei Fan Can Fang Jian Fang Marco Fargetta Anna Fatkina Dmitry Fedoseev Vladko Fekete Li-Cheng Feng Qichun Feng Richard Ford Andrey Formozov Amélie Fournier Haonan Gan Feng Gao Alberto Garfagnini Alexandre Göttel Christoph Genster Marco Giammarchi Agnese Giaz Nunzio Giudice Franco Giuliani Maxim Gonchar Guanghua Gong Hui Gong Oleg Gorchakov Yuri Gornushkin Marco Grassi Christian Grewing Maxim Gromov Vasily Gromov Minghao Gu Xiaofei Gu Yu Gu Mengyun Guan Nunzio Guardone Maria Gul Cong Guo Jingyuan Guo Wanlei Guo Xinheng Guo Yuhang Guo Paul Hackspacher Caren Hagner Ran Han Yang Han Miao He Wei He Tobias Heinz Patrick Hellmuth Yuekun Heng Rafael Herrera Daojin Hong YuenKeung Hor Shaojing Hou Yee Hsiung Bei-Zhen Hu Hang Hu Jianrun Hu Jun Hu Shouyang Hu Tao Hu Zhuojun Hu Chunhao Huang Guihong Huang Hanxiong Huang Qinhua Huang Wenhao Huang Xingtao Huang Yongbo Huang Jiaqi Hui Wenju Huo Cédric Huss Safeer Hussain Antonio Insolia Ara Ioannisian Daniel Ioannisyan Roberto Isocrate Kuo-Lun Jen Xiaolu Ji Xingzhao Ji Huihui Jia Junji Jia Siyu Jian Di Jiang Xiaoshan Jiang Ruyi Jin Xiaoping Jing Cécile Jollet Jari Joutsenvaara Sirichok Jungthawan Leonidas Kalousis Philipp Kampmann Li Kang Michael Karagounis Narine Kazarian Amir Khan Waseem Khan Khanchai Khosonthongkee Patrick Kinz Denis Korablev Konstantin Kouzakov Alexey Krasnoperov Svetlana Krokhaleva Zinovy Krumshteyn Andre Kruth Nikolay Kutovskiy Pasi Kuusiniemi Tobias Lachenmaier Cecilia Landini Sébastien Leblanc Frederic Lefevre Liping Lei Ruiting Lei Rupert Leitner Jason Leung Demin Li Fei Li Fule Li Haitao Li Huiling Li Jiaqi Li Jin Li Kaijie Li Mengzhao Li Nan Li Nan Li Qingjiang Li Ruhui Li Shanfeng Li Shuaijie Li Tao Li Weidong Li Weiguo Li Xiaomei Li Xiaonan Li Xinglong Li Yi Li Yufeng Li Zhibing Li Ziyuan Li Hao Liang Hao Liang Jingjing Liang Jiajun Liao Daniel Liebau Ayut Limphirat Sukit Limpijumnong Guey-Lin Lin Shengxin Lin Tao Lin Jiajie Ling Ivano Lippi Fang Liu Haidong Liu Hongbang Liu Hongjuan Liu Hongtao Liu Hu Liu Hui Liu Jianglai Liu Jinchang Liu Min Liu Qian Liu Qin Liu Runxuan Liu Shuangyu Liu Shubin Liu Shulin Liu Xiaowei Liu Yan Liu Alexey Lokhov Paolo Lombardi Claudio Lombardo Kai Loo Chuan Lu Haoqi Lu Jingbin Lu Junguang Lu Shuxiang Lu Xiaoxu Lu Bayarto Lubsandorzhiev Sultim Lubsandorzhiev Livia Ludhova Fengjiao Luo Guang Luo Pengwei Luo Shu Luo Wuming Luo Vladimir Lyashuk Qiumei Ma Si Ma Xiaoyan Ma Xubo Ma Jihane Maalmi Yury Malyshkin Fabio Mantovani Francesco Manzali Xin Mao Yajun Mao Stefano MMari Filippo Marini Sadia Marium Cristina Martellini Gisele Martin-Chassard Agnese Martini Davit Mayilyan Axel Müller Ints Mednieks Yue Meng Anselmo Meregaglia Emanuela Meroni David Meyhöfer Mauro Mezzetto Jonathan Miller Lino Miramonti Salvatore Monforte Paolo Montini Michele Montuschi Nikolay Morozov Pavithra Muralidharan Massimiliano Nastasi Dmitry VNaumov Elena Naumova Igor Nemchenok Alexey Nikolaev Feipeng Ning Zhe Ning Hiroshi Nunokawa Lothar Oberauer Juan Pedro Ochoa-Ricoux Alexander Olshevskiy Domizia Orestano Fausto Ortica Hsiao-Ru Pan Alessandro Paoloni Nina Parkalian Sergio Parmeggiano Teerapat Payupol Yatian Pei Nicomede Pelliccia Anguo Peng Haiping Peng Frédéric Perrot Pierre-Alexandre Petitjean Fabrizio Petrucci Luis Felipe Piñeres Rico Oliver Pilarczyk Artyom Popov Pascal Poussot Wathan Pratumwan Ezio Previtali Fazhi Qi Ming Qi Sen Qian Xiaohui Qian Hao Qiao Zhonghua Qin Shoukang Qiu Muhammad Rajput Gioacchino Ranucci Neill Raper Alessandra Re Henning Rebber Abdel Rebii Bin Ren Jie Ren Taras Rezinko Barbara Ricci Markus Robens Mathieu Roche Narongkiat Rodphai Aldo Romani Bedřich Roskovec Christian Roth Xiangdong Ruan Xichao Ruan Saroj Rujirawat Arseniy Rybnikov Andrey Sadovsky Paolo Saggese Giuseppe Salamanna Simone Sanfilippo Anut Sangka Nuanwan Sanguansak Utane Sawangwit Julia Sawatzki Fatma Sawy Michaela Schever Jacky Schuler Cédric Schwab Konstantin Schweizer Dmitry Selivanov Alexandr Selyunin Andrea Serafini Giulio Settanta Mariangela Settimo Muhammad Shahzad Vladislav Sharov Gang Shi Jingyan Shi Yongjiu Shi Vitaly Shutov Andrey Sidorenkov FedorŠimkovic Chiara Sirignano Jaruchit Siripak Monica Sisti Maciej Slupecki Mikhail Smirnov Oleg Smirnov Thiago Sogo-Bezerra Julanan Songwadhana Boonrucksar Soonthornthum Albert Sotnikov Ondrej Sramek Warintorn Sreethawong Achim Stahl Luca Stanco Konstantin Stankevich DušanŠtefánik Hans Steiger Jochen Steinmann Tobias Sterr Matthias Raphael Stock Virginia Strati Alexander Studenikin Gongxing Sun Shifeng Sun Xilei Sun Yongjie Sun Yongzhao Sun Narumon Suwonjandee Michal Szelezniak Jian Tang Qiang Tang Quan Tang Xiao Tang Alexander Tietzsch Igor Tkachev Tomas Tmej Konstantin Treskov Andrea Triossi Giancarlo Troni Wladyslaw Trzaska Cristina Tuve Stefan van Waasen Johannes van den Boom Guillaume Vanroyen Nikolaos Vassilopoulos Vadim Vedin Giuseppe Verde Maxim Vialkov Benoit Viaud Cristina Volpe Vit Vorobel Lucia Votano Pablo Walker Caishen Wang Chung-Hsiang Wang En Wang Guoli Wang Jian Wang Jun Wang Kunyu Wang Lu Wang Meifen Wang Meng Wang Ruiguang Wang Siguang Wang Wei Wang Wenshuai Wang Xi Wang Xiangyue Wang Yangfu Wang Yaoguang Wang Yi Wang Yifang Wang Yuanqing Wang Yuman Wang Zhe Wang Zheng Wang Zhimin Wang Zongyi Wang Apimook Watcharangkool Lianghong Wei Wei Wei Yadong Wei Liangjian Wen Christopher Wiebusch Steven Chan-Fai Wong Bjoern Wonsak Diru Wu Fangliang Wu Qun Wu Wenjie Wu Zhi Wu Michael Wurm Jacques Wurtz Christian Wysotzki Yufei Xi Dongmei Xia Yuguang Xie Zhangquan Xie Zhizhong Xing Benda Xu Donglian Xu Fanrong Xu Jilei Xu Jing Xu Meihang Xu Yin Xu Yu Xu Baojun Yan Xiongbo Yan Yupeng Yan Anbo Yang Changgen Yang Huan Yang Jie Yang Lei Yang Xiaoyu Yang Yifan Yang Haifeng Yao Zafar Yasin Jiaxuan Ye Mei Ye Ugur Yegin Frédéric Yermia Peihuai Yi Xiangwei Yin Zhengyun You Boxiang Yu Chiye Yu Chunxu Yu Hongzhao Yu Miao Yu Xianghui Yu Zeyuan Yu Chengzhuo Yuan Ying Yuan Zhenxiong Yuan Ziyi Yuan Baobiao Yue Noman Zafar Andre Zambanini Pan Zeng Shan Zeng Tingxuan Zeng Yuda Zeng Liang Zhan Feiyang Zhang Guoqing Zhang Haiqiong Zhang Honghao Zhang Jiawen Zhang Jie Zhang Jingbo Zhang Peng Zhang Qingmin Zhang Shiqi Zhang Tao Zhang Xiaomei Zhang Xuantong Zhang Yan Zhang Yinhong Zhang Yiyu Zhang Yongpeng Zhang Yuanyuan Zhang Yumei Zhang Zhenyu Zhang Zhijian Zhang Fengyi Zhao Jie Zhao Rong Zhao Shujun Zhao Tianchi Zhao Dongqin Zheng Hua Zheng Minshan Zheng Yangheng Zheng Weirong Zhong Jing Zhou Li Zhou Nan Zhou Shun Zhou Xiang Zhou Jiang Zhu Kejun Zhu Honglin Zhuang Liang Zong Jiaheng Zou | 2021 | Chinese Physics C2021，45，2： | 0 |
| 16 | Development of deep neural network model to predict the compressive strength of FRCM confined columns显示文摘The present study describes a reliability analysis of the strength model for predicting concrete columns confinement influence with Fabric-Reinforced Cementitious Matrix(FRCM).through both physical models and Deep Neural Network model(artificial neural network(ANN)with double and triple hidden layers).The database of 330 samples collected for the training model contains many important parameters,i.e.,section type(circle or square),corner radius rc,unconfined concrete strength fco,thickness nt,the elastic modulus of fiber Ef,the elastic modulus of mortar Em.The results revealed that the proposed ANN models well predicted the compressive strength of FRCM with high prediction accuracy.The ANN model with double hidden layers(APDL-1)was shown to be the best to predict the compressive strength of FRCM confined columns compared with the ACI design code and five physical models.Furthermore,the results also reveal that the unconfined compressive strength of concrete,type of fiber mesh for FRCM,type of section,and the corner radius ratio,are the most significant input variables in the efficiency of FRCM confinement prediction.The performance of the proposed ANN models(including double and triple hidden layers)had high precision with R higher than 0.93 and RMSE smaller than 0.13,as compared with other models from the literature available. | Khuong LE-NGUYEN Quyen Cao MINH Afaq AHMAD Lanh Si HO | 2022 | Frontiers of Structural and Civil Engineering2022，16，10： | 0 |
| 17 | 高温高压下Saraline基超轻完井液黏度预测模型优选显示文摘以Saraline基超轻完井液为研究对象,分析其黏度在高温高压环境下的变化情况,并优选其高温高压黏度预测模型。测量黏度时温度为298.15~373.15 K,压力为0.10~4.48 MPa,分析测量数据后发现:随着温度的升高,低温下黏度快速下降,高温下黏度降幅较小,且黏度对压力变化不敏感。将实验测量数据与4个常用黏度-温度-压力模型进行拟合,发现利用改进型Mehrotra-Svreck模型和Ghaderi模型预测的黏度值与测量值吻合度较高,能够描述高温高压下Saraline基超轻完井液的黏度特性。与Sarapar基超轻完井液对比后发现,温度对Saraline基超轻完井液黏度的影响更小,压力对Saraline基超轻完井液黏度的影响可以忽略不计,改进型Mehrotra-Svreck模型和Ghaderi模型在各种温度和压力条件下对Saraline基超轻完井液的黏度预测精度优于对Sarapar基超轻完井液的黏度预测。 | AMIR Zulhelmi JAN Badrul Mohamed WAHAB Ahmad Khairi Abdul KHALIL Munawar ALI Brahim Si CHONG Wen Tong | 2016 | 石油勘探与开发2016，43，5： | 0 |
| 18 | Satellite precipitation product:Applicability and accuracy evaluation in diverse region显示文摘Satellite precipitation products,e.g.,Tropical Rainfall Measuring Mission version-07(hereafter TRMM)and its successor Integrated Multi-Satellite Retrievals for Global Precipitation Measurement(hereafter IMERG)are being used at a global scale for rainfall estimation.Recently,SM2RAIN-ASCAT(hereafter SM2RAIN)is a novel addition to satellite-based precipitation products which gives the rainfall estimates from the knowledge of soil moisture state and is based on‘bottom to top’approach.A comparative assessment of any newly developed product or a new version of the product is quite vital for algorithm developers and users.Hence,this research work was carried out to evaluate the accuracy and applicability of SM2RAIN,in comparison to in-situ data,TRMM,and IMERG in diverse regions of Pakistan.The comparative analysis was performed on a temporal scale(daily and monthly)and seasonal scale(spring,autumn,summer,and winter)using five performance metrics namely,root mean square error(RMSE),correlation coefficient(CC),false alarm ratio(FAR),the probability of detection(POD),and critical success index(CSI).The results showed that:(1)SM2RAIN is a better rainfall estimation product in the dry region(having avg.CC>0.35),however,less effective in hilly and mountainous terrain having high rainfall intensity;(2)SM2RAIN provides more satisfactory estimates in winter and autumn seasons,while relative poor in the summer season;(3)SM2RAIN performs better in terms of rainfall detection with an average POD of 0.61;(4)the overall performance of SM2RAIN is very convincing and it was concluded that SM2RAIN can be a feasible satellite product for most of the areas of Pakistan.It is noteworthy here to mention that this could be the preliminary assessment of SM2RAIN in diverse climatic zones of Pakistan. | MUHAMMAD Ehtsham MUHAMMAD Waseem AHMAD Ijaz MUHAMMAD KHAN Noor CHEN Si | 2020 | Science China(Technological Sciences)2020，63，5： | 0 |
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