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1. Interpersonal Relationship, Knowledge Characteristic, and Knowledge Sharing Behavior of Online Community Members: A TAM Perspective

Author

Wu Jiarui, Zhang Xiaoli, and Su Jiafu

Subjects
Technology, Article Subject, General Computer Science, General Mathematics, General Neuroscience, Humans, Interpersonal Relations, General Medicine
Abstract

With the rapid development of information science and technology, online communities are attracting an increasing number of participants, who can share information, create original content, and offer emotional support, thus communicating and spreading knowledge frequently within the community. To develop a model of influencing factors for the knowledge sharing behavior of online community members, this study employs the technology acceptance model (TAM) as a moderator variable based on the social exchange theory. In this study, the influencing factors model for knowledge sharing behavior of online community members was tested using PLS-SEM. The results show that knowledge sharing is motivated by trust and quality of knowledge; the interaction term of perceived usefulness and knowledge quality of the user has a significant negative correlation with the knowledge sharing behavior of online community users; perceived usefulness significantly positively moderates the correlation between knowledge tacitness and knowledge sharing behavior of users; perceived ease of use significantly positively moderates the relationship between knowledge quality and knowledge sharing behavior; perceived ease of use significantly negatively moderates the relationship between knowledge tacit and knowledge sharing behavior. In order to maximize the activity and stickiness of the online community platform, the platform must focus on maintaining and enhancing the platform’s credibility and knowledge quality. On the other hand, the online community platform extols its professional utility and ease of operation, which are conducive to the generation of behavior that is conducive to knowledge sharing.

Published
2022
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4. CFD-aided approach of modelling and dynamic characteristic optimization for a highly nonlinear auxiliary braking system

Author

Yang, Konghua, Guan, Xichun, Zhang, Xiaoli, and Liu, Chunbao

Subjects
General Computer Science, Modeling and Simulation
Abstract

It is a considerable challenge to develop a mathematical method for modelling the coupling relationship between the control signal and braking performance in the design and optimization, especially with a highly nonlinear system. The present study thus establishes an integrated CFD-aided method, elaborately proposed based on a 1D/3D co-simulation to accomplish a goal of collaborative calculation between multi-physics fields, and conducting the braking and thermal properties that changes dynamically with the control signal. In particular, the 3D model is actually a multi-domain coupling CFD transient calculation, and the 1D model provides the dynamic boundary conditions. These results manifest some interesting issues, such as modelling the prediction interaction between the isolated control signal and dynamic braking capability. The synergies of the blade agitation and centrifugal force in the multi-phases development affect the oil-filling control and braking generation. The control pressure (Pair) and throttle area (Aout) are predominated in enhancing braking torque, shortening response time, and improving heat dissipation efficiency. The operations of Pair ≤ 3.2 bar and Aout ≥ 64π mm2 ensure a safe and reliable design and then optimize braking and control characteristics. The resulting maximum braking torque is 4070Nm, and the outlet oil temperature is reduced to 138°C.

Published
2022
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6. Cervical Cancer Metastasis and Recurrence Risk Prediction Based on Deep Convolutional Neural Network

Author

Zhang Xiaoli, Xiao Man-sheng, Zang Guoliang, Yuan Dawei, Yang Jialiang, Liang Yuebin, Tian Geng, Lang Jidong, Ye Zixuan, and Zhang Yunxiang

Subjects
Oncology, Computational Mathematics, medicine.medical_specialty, Cervical cancer metastasis, business.industry, Internal medicine, Genetics, medicine, business, Molecular Biology, Biochemistry, Convolutional neural network, Recurrence risk
Abstract

Background: Evaluating the risk of metastasis and recurrence of a cervical cancer patient is critical for appropriate adjuvant therapy. However, current risk assessment models usually involve the testing of tens to thousands of genes from patients’ tissue samples, which is expensive and timeconsuming. Therefore, computer-aided diagnosis and prognosis prediction based on Hematoxylin and Eosin (H&E) pathological images have received much attention recently. Objective: The prognosis of whether patients will have metastasis and recurrence can support accurate treatment for patients in advance and help reduce patient loss. It is also important for guiding treatment after surgery to be able to quickly and accurately predict the risk of metastasis and recurrence of a cervical cancer patient. Method: To address this problem, we propose a hybrid method. Transfer learning is used to extract features, and it is combined with traditional machine learning in order to analyze and determine whether patients have the risks of metastasis and recurrence. First, the proposed model retrieved relevant patches using a color-based method from H&E pathological images, which were then subjected to image preprocessing steps such as image normalization and color homogenization. Based on the labeled patched images, the Xception model with good classification performance was selected, and deep features of patched pathological images were automatically extracted with transfer learning. After that, the extracted features were combined to train a random forest model to predict the label of a new patched image. Finally, a majority voting method was developed to predict the metastasis and recurrence risk of a patient based on the predictions of patched images from the whole-slide H&E image. Results: In our experiment, the proposed model yielded an area under the receiver operating characteristic curve of 0.82 for the whole-slide image. The experimental results showed that the high-level features extracted by the deep convolutional neural network from the whole-slide image can be used to predict the risk of recurrence and metastasis after surgical resection and help identify patients who might receive additional benefit from adjuvant therapy. Conclusion: This paper explored the feasibility of predicting the risk of metastasis and recurrence from cervical cancer whole slide H&E images through deep learning and random forest methods.

Published
2022
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7. Confidence-aware 3D Gaze Estimation and Evaluation Metric

Author

Zheng, Qiaojie, Zhang, Jiucai, Zhang, Amy, and Zhang, Xiaoli

Subjects
FOS: Computer and information sciences, Computer Vision and Pattern Recognition (cs.CV), Computer Science - Computer Vision and Pattern Recognition
Abstract

Deep learning appearance-based 3D gaze estimation is gaining popularity due to its minimal hardware requirements and being free of constraint. Unreliable and overconfident inferences, however, still limit the adoption of this gaze estimation method. To address the unreliable and overconfident issues, we introduce a confidence-aware model that predicts uncertainties together with gaze angle estimations. We also introduce a novel effectiveness evaluation method based on the causality between eye feature degradation and the rise in inference uncertainty to assess the uncertainty estimation. Our confidence-aware model demonstrates reliable uncertainty estimations while providing angular estimation accuracies on par with the state-of-the-art. Compared with the existing statistical uncertainty-angular-error evaluation metric, the proposed effectiveness evaluation approach can more effectively judge inferred uncertainties' performance at each prediction., 9 pages 12 figures

Published
2023

8. Alpha1-antitrypsin protects the immature mouse brain following hypoxic-ischemic injury

Author

Zhang, Shan, Li, Wendong, Xu, Yiran, Li, Tao, Ek, Joakim, Zhang, Xiaoli, Wang, Yafeng, Song, Juan, Zhu, Changlian, and Wang, Xiaoyang

Subjects
Cellular and Molecular Neuroscience
Abstract

Introduction: Preterm brain injury often leads to lifelong disabilities affecting both cognitive and motor functions, and effective therapies are limited. Alpha1-antitrypsin (AAT), an endogenous inhibitor of serine proteinases with anti-inflammatory, anti-apoptotic, and cytoprotective properties, might be beneficial in treating preterm brain injury. The aim of this study was to investigate whether AAT has neuroprotective effects in a mouse preterm brain injury model.Methods: Preterm brain injury was induced on postnatal day 5, and mouse pups’ right common carotid arteries were cut between two ligations followed by hypoxia induction. Brain injury was evaluated through immunohistochemistry staining and magnetic resonance imaging. Fluoro-Jade B and immunohistochemistry staining were performed to investigate the neuronal cell death and blood-brain barrier (BBB) permeability. The motor function and anxiety-like behaviors were revealed by CatWalk gait analysis and the open field test.Results: After hypoxia-ischemia (HI) insult, brain injury was alleviated by AAT treatment, and this was accompanied by reduced BBB permeability, reduced neuronal cell death and caspase-3 activation, and inhibition of microglia activation. In addition, AAT administration significantly improved HI-induced motor function deficiencies in mice. The neuroprotective effect of AAT was more pronounced in male mice.Conclusion: AAT treatment is neuroprotective against preterm brain injury in neonatal mice, and the effect is more pronounced in males.

Published
2023
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10. Mediating roles of perceived social support and sense of security in the relationship between negative life events and life satisfaction among left-behind children: A cross-sectional study

Author

Liu, Na, Li, Xinzheng, Ding, Xuemei, Liu, Haixia, and Zhang, Xiaoli

Subjects
General Psychology
Abstract

ObjectiveLife satisfaction is closely related to the quality of life. Previous studies showed that left-behind children have low life satisfaction levels due to their separation from their parents. Therefore, it is significant to explore the risk factors and protective factors of left-behind children’s life satisfaction to improve their life quality. Therefore, the purpose of this study was to determine the mediating roles of perceived social support and sense of security in the relationship between negative life events and life satisfaction among left-behind children.MethodsA survey was conducted on 281 left-behind children in rural Shandong, China using negative life events, perceived social support, a sense of security, and life satisfaction scales.ResultsNegative life events had a negative impact on life satisfaction (β = −0.34, p = 0.001). In addition, not only does social support mediate between negative life events and life satisfaction (β = −0.21, p = 0.001), but also in between, security (β = −0.05, p = 0.030). Moreover, social support and security act as a chain intermediary between negative life events and life satisfaction (β = −0.03, p = 0.010), with an indirect effect share of 4.76%.ConclusionNegative life events could directly or indirectly affect the life satisfaction of left-behind children through the chain-mediating effects of perceived social support or the sense of security alone. Perceived social support and the sense of security are two important targets for further improving the life satisfaction of LBC.

Published
2023
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11. Supplemental Material - Association of podocyte injury with clinical features and prognosis in patients with mesangial proliferative lupus nephritis

Author

Guo, Muyao, Xie, Xiaoyun, Lin, Wei, Zhou, Yaou, and Zhang, Xiaoli

Subjects
111702 Aged Health Care, FOS: Health sciences
Abstract

Supplemental Material for Association of podocyte injury with clinical features and prognosis in patients with mesangial proliferative lupus nephritis by Muyao Guo, Xiaoyun Xie, Wei Lin, Yaou Zhou and Xiaoli Zhang in Lupus

Published
2023
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12. Spatial uncertainty of the target influences the processing of task-irrelevant features – a behavioral measure

Author

Zhang, Xiaoli

Subjects
FOS: Psychology, Spatial uncertainty, Cognitive Psychology, Psychology, Attention, Social and Behavioral Sciences
Abstract

Recent behavioral evidence suggests that object representations more strongly constrain attention when spatial location of the target is uncertain, making a direct prediction that spatial uncertainty should modulate the strength of object representations. In this experiment we plan to test this modulation effect with behavioral measurement – using the attentional capture paradigm.

Published
2023
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14. Additional file 1 of Subarachnoid extension and unfavorable outcomes in patients with supratentorial intracerebral hemorrhage

Author

Wang, Jinjin, Wang, Dandan, Bian, Liheng, Wang, Anxin, Zhang, Xiaoli, Jiang, Ruixuan, Wang, Wenjuan, Ju, Yi, Lu, Jingjing, and Zhao, Xingquan

Abstract

Additional file 1: Supplementary table 1. Univariate and multivariate-adjusted OR and 95% CI for severe disability or death (mRS score = 4-6) according to the presence of SAHE. Supplementary table 2. Univariate and multivariate-adjusted HR and 95% CI for follow-up death according to the presence of SAHE.

Published
2023
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15. Safety of COVID-19 vaccination in women undergoing IVF/ICSI treatment - Clinical study and systematic review

Author

Chen, Huijun, Zhang, Xiaoli, Lin, Ge, Gong, Fei, and Hocher, Berthold

Subjects
fertilization rate, vaccine, IVF/ICSI, pregnancy outcomes, Immunology, Immunology and Allergy, COVID-19, 600 Technik, Medizin, angewandte Wissenschaften::610 Medizin und Gesundheit::615 Pharmakologie, Therapeutik
Abstract

Background: It was suggested that vaccination in general might affect reproductive health. Safety of COVID-19 vaccination in women undergoing assisted reproductive techniques (ART) treatment is not well established. Methods: We performed a retrospective study including 536 women undergoing fresh embryo transfer after IVF/ICSI treatment in a huge IVF center in southern China to investigate the effect of COVID-19 vaccination on oocyte maturation, fertilization rate, blastulation rate, implantation rate, clinical pregnancy rate and miscarriage rate. In addition, we performed a systematic review of existing studies on the safety of COVID-19 vaccination in women undergoing ART treatment. Results: In our study, 268 women received inactivated or recombinant COVID-19 vaccination and 268 controls were enrolled based on propensity score matching. We observed a decreased fertilization rate and signs for impaired oocyte maturation in vaccinated women. Besides our study, there were 15 studies analyzing the safety of COVID-19 vaccination in women undergoing ART treatment. For the mRNA vaccines, no adverse signals were reported concerning oocyte maturation, fertilization rate, blastulation rate, implantation rate, clinical pregnancy rate and miscarriage rate. In women being vaccinated with an inactivated vaccine, implantation rate, clinical pregnancy rate and miscarriage rate were not affected, whereas oocyte maturation and fertilization rate were impaired. Conclusions: Vaccination against COVID-19 in women undergoing ART treatment seems to be safe especially for women getting mRNA vaccines. The effects on oocyte maturation and fertilization rate of inactivated and recombinant COVID-19 vaccinations might be a safety signal and need further investigation and independent confirmation.rtilization rate, blastulation rate, implantation rate, clinical pregnancy rate and miscarriage rate. In addition, we performed a systematic review of existing studies on the safety of COVID-19 vaccination in women undergoing ART treatment. Results In our study, 268 women received inactivated or recombinant COVID-19 vaccination and 268 controls were enrolled based on propensity score matching. We observed a decreased fertilization rate and signs for impaired oocyte maturation in vaccinated women. Besides our study, there were 15 studies analyzing the safety of COVID-19 vaccination in women undergoing ART treatment. For the mRNA vaccines, no adverse signals were reported concerning oocyte maturation, fertilization rate, blastulation rate, implantation rate, clinical pregnancy rate and miscarriage rate. In women being vaccinated with an inactivated vaccine, implantation rate, clinical pregnancy rate and miscarriage rate were not affected, whereas oocyte maturation and fertilization rate were impaired. Conclusions Vaccination against COVID-19 in women undergoing ART treatment seems to be safe especially for women getting mRNA vaccines. The effects on oocyte maturation and fertilization rate of inactivated and recombinant COVID-19 vaccinations might be a safety signal and need further investigation and independent confirmation.

Published
2023
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16. Transferability-based Chain Motion Mapping from Humans to Humanoids for Teleoperation

Author

Stanley, Matthew, Jung, Yunsik, Bowman, Michael, Tao, Lingfeng, and Zhang, Xiaoli

Subjects
FOS: Computer and information sciences, Computer Science - Robotics, Computer Science - Human-Computer Interaction, Robotics (cs.RO), Human-Computer Interaction (cs.HC)
Abstract

Although data-driven motion mapping methods are promising to allow intuitive robot control and teleoperation that generate human-like robot movement, they normally require tedious pair-wise training for each specific human and robot pair. This paper proposes a transferability-based mapping scheme to allow new robot and human input systems to leverage the mapping of existing trained pairs to form a mapping transfer chain, which will reduce the number of new pair-specific mappings that need to be generated. The first part of the mapping schematic is the development of a Synergy Mapping via Dual-Autoencoder (SyDa) method. This method uses the latent features from two autoencoders to extract the common synergy of the two agents. Secondly, a transferability metric is created that approximates how well the mapping between a pair of agents will perform compared to another pair before creating the motion mapping models. Thus, it can guide the formation of an optimal mapping chain for the new human-robot pair. Experiments with human subjects and a Pepper robot demonstrated 1) The SyDa method improves the accuracy and generalizability of the pair mappings, 2) the SyDa method allows for bidirectional mapping that does not prioritize the direction of mapping motion, and 3) the transferability metric measures how compatible two agents are for accurate teleoperation. The combination of the SyDa method and transferability metric creates generalizable and accurate mapping need to create the transfer mapping chain.

Published
2022

17. The enhanced x-ray timing and polarimetry mission: eXTP: an update on its scientific cases, mission profile and development status

Author

Zhang, Shuang Nan, Santangelo, Andrea, Xu, Yupeng, Feroci, Marco, Hernanz, Margarita, Lu, Fangjun, Chen, Yong, Feng, Hua, Nandra, Kirpal, Jiang, Weichun, Svoboda, Jiri, Brandt, Søren, Schanne, Stéphane, Zand, Jean, Michalska, Malgorzata, Bozzo, Enrico, Kalemci, Emrah, Agudo, Ivan, Ahangarianabhari, Mahdi, Aitink-Kroes, Gabby, Ambrosi, Giovanni, Ambrosino, Filippo, An, Zhenghua, Perez Torres, Miguel Angel, Antonelli, Matias, Argan, Andrea, Babinec, Viktor, Baldini, Luca, Barbera, Marco, van Baren, Coen, Baudin, David, Bayer, Jörg, Bellazzini, Ronaldo, Bellutti, Pierluigi, Bertucci, Bruna, Bertuccio, Giuseppe, Bi, Xingzi, Boezio, Mirko, Bonvicini, Valter, Bonvicini, Walter, Bordas, Pol, Borghese, Alice, Borghi, Giacomo, Bouyjou, Florent, Bozkurt, Ayhan, Brez, Alessandro, Brienza, Daniele, Cadoux, Franck, Campana, Riccardo, Cao, Jiewei, Cao, Xuelei, Casares, Jorge, Cavazzuti, Elisabetta, Ceraudo, Francesco, Chen, Tianxiang, Chen, Wen, Chen, Can, Chen, Yupeng, Chen, Xin, Chen, Yehai, Chenevez, Jerome, Cheng, Yaodong, Cirrincione, Daniela, Civitani, Marta, Cong, Min, Zelati, Francesco Coti, Cui, Weiwei, Cui, Tao, Cui, Wei, Dai, Boyu, Dauser, Thomas, De Angelis, Nicolas, De Marco, Barbara, De Rosa, Alessandra, Monte, Ettore Del, Cosimo, Sergio Di, Diebold, Sebastian, Dilillo, Giuseppe, Ding, Fei, Dohnal, Roman, Dong, Zefang, Donnarumma, Immacolata, Dovciak, Michal, Du, Yuanyuan, Ducci, Lorenzo, Evangelista, Yuri, Fan, Qingmei, Favre, Yannick, Ferrés, Patrícia, Fiandrini, Emanuele, Ficorella, Francesco, Fuschino, Fabio, Gálvez, José Luis, Gao, Na, Gao, Min, Ge, Yuqiang, Ge, Mingyu, Gevin, Olivier, Grassi, Marco, Gu, Yudong, Gu, Quanying, Guan, Ju, Guedel, Manuel, Han, Xingbo, Han, Dawei, He, Huilin, He, Junwang, Hedderman, Paul, den Herder, Jan Willem, Hong, Bin, Hormaetxe, Ander, Hou, Dongjie, Hu, Zexun, Hu, Hao, Hu, Qingbao, Hu, Yu, Huang, Yue, Huang, Jiangjiang, Huang, Qiushi, Huo, Jia, Hynek, Richard, Iwasawa, Kazumi, Izzo, Lucca, Ji, Long, Jia, Shumei, Jiang, Bowen, Jiang, Wei, Jiang, Jiechen, Jiang, Xiaowei, Jiao, Yang, Jin, Ge, Jin, Fan, Jose, Jordi, Karas, Vladimir, Kennedy, Thomas, Kirsch, Christian, Kole, Merlin, Komarek, Martin, Kreykenbohm, Ingo, Kuiper, Lucien, Kuvvetli, Irfan, Labanti, Claudio, Latronico, Luca, Laubert, Phillip, Li, Tao, Li, Longhui, Li, Hong, Li, Duo, Li, Wei, Li, Maoshun, Li, Gang, Li, Xiaobo, Li, Chengkui, Li, Bing, Li, Haibo, Liang, Hong, Liao, Qiuyan, Liao, Jinyuan, Limousin, Olivier, Lin, Yanjian, Linares, Manuel, Liu, Rui, Liu, Yichen, Liu, Zhihao, Liu, Hongwei, Liu, Xiaojing, Liu, Xiaohua, Cicero, Ugo Lo, Loehring, Jens, Lombardi, Giovanni, Lorenz, Maximilian, Lu, Bing, Lucchesi, Leonardo, Luo, Tao, Luo, Laidan, Ma, Jia, Ma, Xiang, Macera, Daniele, Malcovati, Piero, Manfreda, Alberto, Paredes, Josep Maria, Marinucci, Andrea, Martindale, Adrian, Meidinger, Norbert, Mele, Filippo, Mendes, Vasco, Meng, Bin, Merkl, Martin, Meuris, Aline, Miniutti, Giovanni, Minuti, Massimo, Morbidini, Alfredo, Morgante, Gianluca, Muleri, Fabio, Munini, Riccardo, Mussolin, Lorenzo, Negri, Barbara, Nie, Jianyin, Novák, Petr, Nowosielski, Witold, Nuti, Alessio, Onat, Ahmet, Orleanski, Piotr, Orsini, Leonardo, Ottensamer, Roland, Ou, Ge, Pacciani, Luigi, Paltani, Stephane, Pan, Teng, Pan, Kai, Pareschi, Giovanni, Patruno, Alessandro, Pepponi, Giancarlo, Perinati, Emanuele, Piazzolla, Raffaele, Picciotto, Antonino, Piemonte, Claudio, Pinchera, Michele, Pliego, Samuel, Poutanen, Juri, Qi, Liqiang, Qi, Fazhi, Qiang, Pengfei, Qiao, Fangjian, Qiao, Zheng, Qiu, Xiangbiao, Qiu, Chengbo, Qu, Jinlu, Rachevski, Alexandre, Rashevskaia, Irina, Rea, Nanda, Ribo, Marc, de la Rie, Rob, Rodriguez, Jerome, Gil, Pablo Rodríguez, Sala, Gloria, Schanz, Thomas, Serafinelli, Roberto, Sgro, Carmelo, Shen, Zhengxiang, Sheng, Lizhi, Shi, Jingyan, Sironi, Giorgia, Skup, Konrad, Song, Liming, Song, Zeyu, Song, Jiangbo, Spandre, Gloria, Spiga, Daniele, Su, Yongquan, Sun, Liang, Sungur, Müberra, Svéda, Libor, Tagliaferri, Gianpiero, Tan, Ying, Tao, Lian, Tcherniak, Denis, Tenzer, Chris, Todaro, Michela, Tolos, Laura, Torok, Gabriel, Torres, Diego F., Trois, Alessio, Turhan, Onur, Uttley, Phil, Vacchi, Andrea, Virgilli, Enrico, Vogel, Channah, Walton, Dave, Wang, Jian, Wang, Xianqi, Wang, Bo, Wang, Langping, Wang, Xiaofeng, Wang, Dianlong, Wang, Yusa, Wang, Juan, Wang, Hao, Wang, Ruijie, Wang, Ping, Wang, Lingjun, Wang, Wenshuai, Wang, Tian, Wang, Haoyu, Wang, Zhanshan, Watts, Anna L., Wen, Xiangyang, Wilms, Jörn, Winter, Berend, Wu, Xin, Wu, Chao, Wu, Zhenyu, Wu, Qiong, Wu, Kaiji, Xiong, Hao, Xiong, Shaolin, Xu, Wei, Xu, Zhao, Xu, Jingjing, Xu, Xiongwei, Xue, Jiadai, Yan, Yongqing, Yan, Xingtao, Yang, Xianghui, Yang, Yanji, Yang, Xiongtao, Yang, Sheng, Yang, Jiawei, Yang, Yingquan, Yi, Shuxu, Yu, Yonggao, Yu, Ke, Zampa, Ganluigi, Zampa, Nicola, Zane, Silvia, Zdziarski, Andrzej A., Zhang, Long, Zhang, Shu, Zhang, Wenda, Zhang, Xiaoli, Zhang, Zhen, Zhang, Jiawei, Zhang, Fan, Zhang, Ziliang, Zhang, Dali, Zhang, Honglin, Zhang, Aimei, Zhang, Tong, Zhang, Hongmei, Zhang, Juan, Zhang, Liang, Zhang, Yueting, Zhao, Li, Zhao, Baosheng, Zhao, Zijian, Zhao, Xiaofan, Zhao, Haisheng, Zhao, Xiaoyun, Zheng, Shijie, Zhou, Yupeng, Zhou, Xiaohong, Zhu, Jiahuan, Zhu, Yuxuan, Zhu, Cheng, Zhu, Zhencai, Zorzi, Nicola, Zwart, Frans, den Herder, Jan-Willem A., Nikzad, Shouleh, Nakazawa, Kazuhiro, Chinese Academy of Sciences, Agenzia Spaziale Italiana, Ministerio de Ciencia e Innovación (España), Federal Ministry of Economics and Technology (Germany), and Foundation for Polish Science

Subjects
Equation of state, Extreme gravity, Black hole, X-ray timing, X-ray polarimetry, Neutron star, Extreme magnetism, Extreme density
Abstract

Space Telescopes and Instrumentation 2022: Ultraviolet to Gamma Ray (2022), Montreal, Jul 17-22, 2022.--Proceedings of SPIE - The International Society for Optical Engineering vol. 12181 Article number 121811W.-- Full list of authors: Zhang, Shuang-Nan; Santangelo, Andrea; Xu, Yupeng; Feroci, Marco; Hernanz, Margarita; Lu, Fangjun; Chen, Yong; Feng, Hua; Nandra, Kirpal; Jiang, Weichun; Svoboda, Jiri; Brandt, Soren; Schanne, Stephane; in't Zand, Jean; Michalska, Malgorzata; Bozzo, Enrico; Kalemci, Emrah; Agudo, Ivan; Ahangarianabhari, Mahdi; Aitink-Kroes, Gabby; Ambrosi, Giovanni; Ambrosino, Filippo; An, Zhenghua; Perez Torres, Miguel Angel; Antonelli, Matias; Argan, Andrea; Babinec, Viktor; Baldini, Luca; Barbera, Marco; van Baren, Coen; Baudin, David; Bayer, Joerg; Bellazzini, Ronaldo; Bellutti, Pierluigi; Bertucci, Bruna; Bertuccio, Giuseppe; Bi, Xingzi; Boezio, Mirko; Bonvicini, Valter; Bonvicini, Walter; Bordas, Pol; Borghese, Alice; Borghi, Giacomo; Bouyjou, Florent; Bozkurt, Ayhan; Brez, Alessandro; Brienza, Daniele; Cadoux, Franck; Campana, Riccardo; Cao, Jiewei; Cao, Xuelei; Casares, Jorge; Cavazzuti, Elisabetta; Ceraudo, Francesco; Chen, Tianxiang; Chen, Wen; Chen, Can; Chen, Yupeng; Chen, Xin; Chen, Yehai; Chenevez, Jerome; Cheng, Yaodong; Cirrincione, Daniela; Civitani, Marta; Cong, Min; Coti Zelati, Francesco; Cui, Weiwei; Cui, Tao; Cui, Wei; Dai, Boyu; Dauser, Thomas; De Angelis, Nicolas; De Marco, Barbara; De Rosa, Alessandra; Del Monte, Ettore; Di Cosimo, Sergio; Diebold, Sebastian; Dilillo, Giuseppe; Ding, Fei; Dohnal, Roman; Dong, Zefang; Donnarumma, Immacolata; Dovciak, Michal; Du, Yuanyuan; Ducci, Lorenzo; Evangelista, Yuri; Fan, Qingmei; Favre, Yannick; Ferres, Patricia; Fiandrini, Emanuele; Ficorella, Francesco; Fuschino, Fabio; Luis Galvez, Jose; Gao, Na; Gao, Min; Ge, Yuqiang; Ge, Mingyu; Gevin, Olivier; Grassi, Marco; Gu, Yudong; Gu, Quanying; Guan, Ju; Guedel, Manuel; Han, Xingbo; Han, Dawei; He, Huilin; He, Junwang; Hedderman, Paul; den Herder, Jan-Willem; Hong, Bin; Hormaetxe, Ander; Hou, Dongjie; Hu, Zexun; Hu, Hao; Hu, Qingbao; Hu, Yu; Huang, Yue; Huang, Jiangjiang; Huang, Qiushi; Huo, Jia; Hynek, Richard; Iwasawa, Kazumi; Izzo, Lucca; Ji, Long; Jia, Shumei; Jiang, Bowen; Jiang, Wei; Jiang, Jiechen; Jiang, Xiaowei; Jiao, Yang; Jin, Ge; Jin, Fan; Jose, Jordi; Karas, Vladimir; Kennedy, Thomas; Kirsch, Christian; Kole, Merlin; Komarek, Martin; Kreykenbohm, Ingo; Kuiper, Lucien; Kuvvetli, Irfan; Labanti, Claudio; Latronico, Luca; Laubert, Phillip; Li, Tao; Li, Longhui; Li, Hong; Li, Duo; Li, Wei; Li, Maoshun; Li, Gang; Li, Xiaobo; Li, Chengkui; Li, Bing; Li, Haibo; Liang, Hong; Liao, Qiuyan; Liao, Jinyuan; Limousin, Olivier; Lin, Yanjian; Linares, Manuel; Liu, Rui; Liu, Yichen; Liu, Zhihao; Liu, Hongwei; Liu, Xiaojing; Liu, Xiaohua; Lo Cicero, Ugo; Loehring, Jens; Lombardi, Giovanni; Lorenz, Maximilian; Lu, Bing; Lucchesi, Leonardo; Luo, Tao; Luo, Laidan; Ma, Jia; Ma, Xiang; Macera, Daniele; Malcovati, Piero; Manfreda, Alberto; Maria Paredes, Josep; Marinucci, Andrea; Martindale, Adrian; Meidinger, Norbert; Mele, Filippo; Mendes, Vasco; Meng, Bin; Merkl, Martin; Meuris, Aline; Miniutti, Giovanni; Minuti, Massimo; Morbidini, Alfredo; Morgante, Gianluca; Muleri, Fabio; Munini, Riccardo; Mussolin, Lorenzo; Negri, Barbara; Nie, Jianyin; Novak, Petr; Nowosielski, Witold; Nuti, Alessio; Onat, Ahmet; Orleanski, Piotr; Orsini, Leonardo; Ottensamer, Roland; Ou, Ge; Pacciani, Luigi; Paltani, Stephane; Pan, Teng; Pan, Kai; Pareschi, Giovanni; Patruno, Alessandro; Pepponi, Giancarlo; Perinati, Emanuele; Piazzolla, Raffaele; Picciotto, Antonino; Piemonte, Claudio; Pinchera, Michele; Pliego, Samuel; Poutanen, Juri; Qi, Liqiang; Qi, Fazhi; Qiang, Pengfei; Qiao, Fangjian; Qiao, Zheng; Qiu, Xiangbiao; Qiu, Chengbo; Qu, Jinlu; Rachevski, Alexandre; Rashevskaia, Irina; Rea, Nanda; Ribo, Marc; de la Rie, Rob; Rodriguez, Jerome; Rodriguez Gil, Pablo; Sala, Gloria; Schanz, Thomas; Serafinelli, Roberto; Sgro, Carmelo; Shen, Zhengxiang; Sheng, Lizhi; Shi, Jingyan; Sironi, Giorgia; Skup, Konrad; Song, Liming; Song, Zeyu; Song, Jiangbo; Spandre, Gloria; Spiga, Daniele; Su, Yongquan; Sun, Liang; Sungur, Muberra; Sveda, Libor; Tagliaferri, Gianpiero; Tan, Ying; Tao, Lian; Tcherniak, Denis; Tenzer, Chris; Todaro, Michela; Tolos, Laura; Torok, Gabriel; Torres, Diego F.; Trois, Alessio; Turhan, Onur; Uttley, Phil; Vacchi, Andrea; Virgilli, Enrico; Vogel, Channah; Walton, Dave; Wang, Jian; Wang, Xianqi; Wang, Bo; Wang, Langping; Wang, Xiaofeng; Wang, Dianlong; Wang, Yusa; Wang, Juan; Wang, Hao; Wang, Ruijie; Wang, Ping; Wang, Lingjun; Wang, Wenshuai; Wang, Tian; Wang, Haoyu; Wang, Zhanshan; Watts, Anna L.; Wen, Xiangyang; Wilms, Joern; Winter, Berend; Wu, Xin; Wu, Chao; Wu, Zhenyu; Wu, Qiong; Wu, Kaiji; Xiong, Hao; Xiong, Shaolin; Xu, Wei; Xu, Zhao; Xu, Jingjing; Xu, Xiongwei; Xue, Jiadai; Yan, Yongqing; Yan, Xingtao; Yang, Xianghui; Yang, Yanji; Yang, Xiongtao; Yang, Sheng; Yang, Jiawei; Yang, Yingquan; Yi, Shuxu; Yu, Yonggao; Yu, Ke; Zampa, Ganluigi; Zampa, Nicola; Zane, Silvia; Zdziarski, Andrzej A.; Zhang, Long; Zhang, Shu; Zhang, Wenda; Zhang, Xiaoli; Zhang, Zhen; Zhang, Jiawei; Zhang, Fan; Zhang, Ziliang; Zhang, Dali; Zhang, Honglin; Zhang, Aimei; Zhang, Tong; Zhang, Hongmei; Zhang, Juan; Zhang, Liang; Zhang, Yueting; Zhao, Li; Zhao, Baosheng; Zhao, Zijian; Zhao, Xiaofan; Zhao, Haisheng; Zhao, Xiaoyun; Zheng, Shijie; Zhou, Yupeng; Zhou, Xiaohong; Zhu, Jiahuan; Zhu, Yuxuan; Zhu, Cheng; Zhu, Zhencai; Zorzi, Nicola; Zwart, Frans, The enhanced X-ray Timing and Polarimetry mission (eXTP) is a flagship observatory for X-ray timing, spectroscopy and polarimetry developed by an International Consortium. Thanks to its very large collecting area, good spectral resolution and unprecedented polarimetry capabilities, eXTP will explore the properties of matter and the propagation of light in the most extreme conditions found in the Universe. eXTP will, in addition, be a powerful X-ray observatory. The mission will continuously monitor the X-ray sky, and will enable multiwavelength and multi-messenger studies. The mission is currently in phase B, which will be completed in the middle of 2022. © 2022 SPIE. All rights reserved., The Chinese team acknowledges support by the Chinese Academy of Sciences through the Strategic Priority Research Program, Grant No. XDA15020100. The Italian authors acknowledge funding support by the Italian Space Agency (under agreement n. 2020-3-HH.0 and ASI-INAF n.2017-14-H.O) and INFN (project XRO -X-ray Observatories). The Spanish authors acknowledge funding support from the MICIN/AEI grant PID2019-108709GB-I00. The German team acknowledges support from the Bundesministerium fur Wirtschaft und Technologie through the Deutsches Zentrum fur Luft-und Raumfahrt e.V. (DLR) under the grant number FKZ 50 OO 1902. The Polish team acknowledges FNP grant POIR.04.04.00-00-5C65/17. The Czech team acknowledges support from the GACR project 21-06825X. The Turkish team acknowledges support from the Turkish Space Agency., With funding from the Spanish government through the Severo Ochoa Centre of Excellence accreditation SEV-2017-0709

Published
2022
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18. Replication of semantic biasing on attention with mTurk participants

Author

Zhang, Xiaoli and Shomstein, Sarah

Subjects
FOS: Psychology, Cognitive Psychology, Psychology, Attention, Semantic bias, Social and Behavioral Sciences
Abstract

It has been shown that semantic information, even when task-irrelevant, may bias attention during visual search. This result has been shown in multiple in-lab experiments. The current pre-registered project aims at replicating this result pattern with mTurk participants, before further research questions and experiments are built on top of this result.

Published
2022
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19. The effect of task-irrelevant semantic information during salient distractor capture

Author

Zhang, Xiaoli

Subjects
Cognitive psychology, Attention, Social and Behavioral Sciences
Abstract

It has been shown that semantic information, even when task-irrelevant, may bias attention during visual search. To further test the hypothesis that task-irrelevant semantic information influences attentional allocation, we modify a paradigm that is based on a previous study (Malcolm et al., 2016), where a salient distractor will be added into each trial to capture attention. We will examine the effect of semantic information on distractor capture, and whether/how they combine to influence visual search.

Published
2022
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20. Attentional capture and semantic bias on attention

Author

Zhang, Xiaoli and Shomstein, Sarah

Subjects
FOS: Psychology, Cognitive Psychology, Psychology, Attention, Semantic bias, Social and Behavioral Sciences, Attentional capture
Abstract

It has been shown that semantic information, even when task-irrelevant, may bias attention during visual search. The current experiment aims to investigate when spatial attention is captured by a salient cue, whether and how task-irrelevant semantic information will still be processed and bias attention.

Published
2022
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21. Semantic information and bottom-up attentional capture (Expt 2a - 2b)

Author

Zhang, Xiaoli and Shomstein, Sarah

Subjects
FOS: Psychology, Psychology, Attention, Social and Behavioral Sciences
Abstract

It has been shown that semantic information, even when task-irrelevant, may bias attention during visual search. To further test the hypothesis that task-irrelevant semantic information influences attentional allocation, we modify a paradigm that is based on a previous study (Malcolm et al., 2016), where a salient distractor will be added into each trial to capture attention. We will examine the effect of semantic information on distractor capture, and whether/how they combine to influence visual search.

Published
2022
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22. Semantic information and bottom-up attentional capture (Expt 2c - 2f)

Author

Zhang, Xiaoli and Shomstein, Sarah

Subjects
FOS: Psychology, Psychology, Attention, Social and Behavioral Sciences
Abstract

It has been shown that semantic information, even when task-irrelevant, may bias attention during visual search. To further test the hypothesis that task-irrelevant semantic information influences attentional allocation, we modify a paradigm that is based on a previous study (Malcolm et al., 2016), where a salient distractor will be added into each trial to capture attention. We will examine the effect of semantic information on distractor capture, and whether/how they combine to influence visual search. This pre-registration follows the previous pre-registration on 8/5/2021. The cue delay time is manipulated across each experiment (marked red below).

Published
2022
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23. Semantic information and bottom-up attentional capture (Expt 2g - 2i)

Author

Zhang, Xiaoli and Shomstein, Sarah

Subjects
FOS: Psychology, Psychology, Attention, Social and Behavioral Sciences
Abstract

It has been shown that semantic information, even when task-irrelevant, may bias attention during visual search. To further test the hypothesis that task-irrelevant semantic information influences attentional allocation, we modify a paradigm that is based on a previous study (Malcolm et al., 2016), where a salient distractor will be added into each trial to capture attention. We will examine the effect of semantic information on distractor capture, and whether/how they combine to influence visual search.

Published
2022
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24. Replication of semantic biasing on attention

Author

Zhang, Xiaoli and Shomstein, Sarah

Subjects
FOS: Psychology, Cognitive Psychology, Psychology, Attention, Semantic bias, Social and Behavioral Sciences
Abstract

It has been shown that semantic information, even when task-irrelevant, may bias attention during visual search. This result has been shown in multiple in-lab experiments. The current pre-registered project aims at replicating this result pattern in lab before further research questions and experiments are built on top of this result.

Published
2022
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26. Replication semantic biasing on attention with mTurk participants

Author

Zhang, Xiaoli and Shomstein, Sarah

Subjects
FOS: Psychology, Cognitive Psychology, Psychology, semantic bias, Social and Behavioral Sciences, attention
Abstract

It has been shown that semantic information, even when task-irrelevant, may bias attention during visual search. This result has been shown in multiple in-lab experiments. The current pre-registered project aims at replicating this result pattern with mTurk participants, before further research questions and experiments are built on top of this result.

Published
2022
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27. Semantic information and bottom-up attentional capture (duo version)

Author

Zhang, Xiaoli and Shomstein, Sarah

Subjects
FOS: Psychology, semantic information, Cognitive Psychology, Psychology, Social and Behavioral Sciences, attention
Abstract

It has been shown that semantic information, even when task-irrelevant, may bias attention during visual search. To further test the hypothesis that task-irrelevant semantic information influences attentional allocation, we modify a paradigm that is based on a previous study (Malcolm et al., 2016), where a salient distractor will be added into each trial to capture attention. We will examine the effect of semantic information on distractor capture, and whether/how they combine to influence visual search.

Published
2022
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28. Research on the classification of lymphoma pathological images based on deep residual neural network

Author

Lin Yang, Mei Jiang, Zhang Xiaoli, and Kuixing Zhang

Subjects
Lymphoma, Computer science, Biomedical Engineering, Biophysics, Health Informatics, Bioengineering, automatic classification, Biomaterials, Set (abstract data type), 03 medical and health sciences, 0302 clinical medicine, Physicians, Genetic algorithm, Humans, Preprocessor, pathological images, 030304 developmental biology, Network model, 0303 health sciences, Artificial neural network, business.industry, Deep learning, deep learning, Pattern recognition, Data set, 030220 oncology & carcinogenesis, Softmax function, Disease Progression, Neural Networks, Computer, Artificial intelligence, business, Resnet-50, Research Article, Information Systems
Abstract

BACKGROUND: Malignant lymphoma is a type of tumor that originated from the lymphohematopoietic system, with complex etiology, diverse pathological morphology, and classification. It takes a lot of time and energy for doctors to accurately determine the type of lymphoma by observing pathological images. OBJECTIVE: At present, an automatic classification technology is urgently needed to assist doctors in analyzing the type of lymphoma. METHODS: In this paper, by comparing the training results of the BP neural network and BP neural network optimized by genetic algorithm (GA-BP), adopts a deep residual neural network model (ResNet-50), with 374 lymphoma pathology images as the experimental data set. After preprocessing the dataset by image flipping, color transformation, and other data enhancement methods, the data set is input into the ResNet-50 network model, and finally classified by the softmax layer. RESULTS: The training results showed that the classification accuracy was 98.63%. By comparing the classification effect of GA-BP and BP neural network, the accuracy of the network model proposed in this paper is improved. CONCLUSIONS: The network model can provide an objective basis for doctors to diagnose lymphoma types.

Published
2021
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29. Predictive Value of Tumor Mutation Burden for PD-1/PD-L1 Inhibitors Treatment on Non-small Cell Lung Cancer: A Meta-analysis

Author

SHEN Shijun, WANG Qiaoli, YANG Jinjiang, LI Guojian, LI Mengli, ZHANG Xiaoli, and GAN Ping

Subjects
meta-analysis, pd-1/pd-l1 inhibitors, mmunotherapy, tumor mutation burden, lcsh:Neoplasms. Tumors. Oncology. Including cancer and carcinogens, lcsh:RC254-282, non-small cell lung cancer
Abstract

Objective To investigate the relation between TMB and the efficiency of PD-1/PD-L1 inhibitors treatment for non-small-cell lung cancer. Methods Studies were searched from PubMed, Embase, Cochrane Library database, Chinese Biomedical Literature Database and Wanfang Database up to March 25, 2020. RevMan 5.3 software and STATA15.0 were used for analysis. Results Twelve literatures were involved, including 1209 patients. TMB significantly improved PFS (HR=0.54, 95%CI: 0.42-0.70, P < 0.001) but reduced the ORR (OR=4.41, 95%CI: 2.54-7.63, P < 0.001) of NSCLC patients treated with PD-1/PD-L1 inhibitors. The subgroup analyses showed that the predictive value of TMB was significant in non-small cell lung cancer treated by PD-1/PD-L1 inhibitors combined with anti-CTLA-4 therapy or chemotherapy. No significant publication bias was observed by the Begg's test and Egger's test. Conclusion High tumor mutation burden may predict the improved PFS of non-small cell lung cancer by PD-1/PD-L1 inhibitors treatment, but its predictive value for OS, ORR and long-term survival need more exploration.

Published
2021

30. Physics-Guided Hierarchical Reward Mechanism for Learning-Based Robotic Grasping

Author

Jung, Yunsik, Tao, Lingfeng, Bowman, Michael, Zhang, Jiucai, and Zhang, Xiaoli

Subjects
Computer Science - Robotics, Computer Science - Artificial Intelligence
Abstract

Learning-based grasping can afford real-time grasp motion planning of multi-fingered robotics hands thanks to its high computational efficiency. However, learning-based methods are required to explore large search spaces during the learning process. The search space causes low learning efficiency, which has been the main barrier to its practical adoption. In addition, the trained policy lacks a generalizable outcome unless objects are identical to the trained objects. In this work, we develop a novel Physics-Guided Deep Reinforcement Learning with a Hierarchical Reward Mechanism to improve learning efficiency and generalizability for learning-based autonomous grasping. Unlike conventional observation-based grasp learning, physics-informed metrics are utilized to convey correlations between features associated with hand structures and objects to improve learning efficiency and outcomes. Further, the hierarchical reward mechanism enables the robot to learn prioritized components of the grasping tasks. Our method is validated in robotic grasping tasks with a 3-finger MICO robot arm. The results show that our method outperformed the standard Deep Reinforcement Learning methods in various robotic grasping tasks.

Published
2022

31. Impact of COVID-19 on Cardiovascular Testing in the United States Versus the Rest of the World

Author

Hirschfeld, Cole B., Shaw, Leslee J., Williams, Michelle C., Lahey, Ryan, Villines, Todd C., Dorbala, Sharmila, Choi, Andrew D., Shah, Nishant R., Bluemke, David A., Berman, Daniel S., Blankstein, Ron, Ferencik, Maros, Narula, Jagat, Winchester, David, Malkovskiy, Eli, Goebel, Benjamin, Randazzo, Michael J., Lopez-Mattei, Juan, Parwani, Purvi, Vitola, Joao V., Cerci, Rodrigo J., Better, Nathan, Raggi, Paolo, Lu, Bin, Sergienko, Vladimir, Sinitsyn, Valentin, Kudo, Takashi, Nørgaard, Bjarne Linde, Maurovich-Horvat, Pál, Cohen, Yosef A., Pascual, Thomas N. B., Pynda, Yaroslav, Dondi, Maurizio, Paez, Diana, Einstein, Andrew J., Cerci, Rodrigo, Hinterleitner, Gerd, Lu, Yao, Morozova, Olga, Xu, Zhuoran, Cohen, Yosef, Randazzo, Michael, Choi, Andrew, Nasery, Mohammad Nawaz, Goda, Artan, Shirka, Ervina, Benlabgaa, Rabie, Bouyoucef, Salah, Medjahedi, Abdelkader, Nailli, Qais, Agolti, Mariela, Aguero, Roberto Nicolas, Alak, Maria del Carmen, Alberguina, Lucia Graciela, Arroñada, Guillermo, Astesiano, Andrea, Astesiano, Alfredo, Norton, Carolina Bas, Benteo, Pablo, Blanco, Juan, Bonelli, Juan Manuel, Bustos, Jose Javier, Cabrejas, Raul, Cachero, Jorge, Campisi, Roxana, Canderoli, Alejandro, Carames, Silvia, Carrascosa, Patrícia, Castro, Ricardo, Cendoya, Oscar, Cognigni, Luciano Martin, Collaud, Carlos, Cortes, Claudia, Courtis, Javier, Cragnolino, Daniel, Daicz, Mariana, De La Vega, Alejandro, De Maria, Silvia Teresa, Del Riego, Horacio, Dettori, Fernando, Deviggiano, Alejandro, Dragonetti, Laura, Embon, Mario, Enriquez, Ruben Emilio, Ensinas, Jorge, Faccio, Fernando, Facello, Adolfo, Garofalo, Diego, Geronazzo, Ricardo, Gonza, Natalia, Gutierrez, Lucas, Guzzo, Miguel Angel, Hasbani, Victor, Huerin, Melina, Jäger, Victor, Lewkowicz, Julio Manuel, Nieves A López De Munaín, Maria, Lotti, Jose Maria, Marquez, Alejandra, Masoli, Osvaldo, Masoli, Osvaldo Horacio, Mastrovito, Edgardo, Mayoraz, Matias, Melado, Graciela Eva, Mele, Anibal, Merani, Maria Fernanda, Meretta, Alejandro Horacio, Molteni, Susana, Montecinos, Marcos, Noguera, Eduardo, Novoa, Carlos, Sueldo, Claudio Pereyra, Ascani, Sebastian Perez, Pollono, Pablo, Pujol, Maria Paula, Radzinschi, Alejandro, Raimondi, Gustavo, Redruello, Marcela, Rodríguez, Marina, Rodríguez, Matías, Romero, Romina Lorena, Acuña, Arturo Romero, Rovaletti, Federico, San Miguel, Lucas, Solari, Lucrecia, Strada, Bruno, Traverso, Sonia, Traverzo, Sonia Simona, Espeche, Maria del Huerto Velazquez, Weihmuller, Juan Sebastian, Wolcan, Juan, Zeffiro, Susana, Sakanyan, Mari, Beuzeville, Scott, Boktor, Raef, Butler, Patrick, Calcott, Jennifer, Carr, Loretta, Chan, Virgil, Chao, Charles, Chong, Woon, Dobson, Mark, Downie, D'Arne, Dwivedi, Girish, Elison, Barry, Engela, Jean, Francis, Roslyn, Gaikwad, Anand, Basavaraj, Ashok Gangasandra, Goodwin, Bruce, Greenough, Robert, Hamilton-Craig, Christian, Hsieh, Victar, Joshi, Subodh, Lederer, Karin, Lee, Kenneth, Lee, Joseph, Magnussen, John, Mai, Nghi, Mander, Gordon, Murton, Fiona, Nandurkar, Dee, Neill, Johanne, O'Rourke, Edward, O'Sullivan, Patricia, Pandos, George, Pathmaraj, Kunthi, Pitman, Alexander, Poulter, Rohan, Premaratne, Manuja, Prior, David, Ridley, Lloyd, Rutherford, Natalie, Salehi, Hamid, Saunders, Connor, Scarlett, Luke, Seneviratne, Sujith, Shetty, Deepa, Shrestha, Ganesh, Shulman, Jonathan, Solanki, Vijay, Stanton, Tony, Stuart, Murch, Stubbs, Michael, Swainson, Ian, Taubman, Kim, Taylor, Andrew, Thomas, Paul, Unger, Steven, Upton, Anthony, Vamadevan, Shankar, Van Gaal, William, Verjans, Johan, Voutnis, Demetrius, Wayne, Victor, Wilson, Peter, Wong, David, Wong, Kirby, Younger, John, Feuchtner, Gudrun, Mirzaei, Siroos, Weiss, Konrad, Maroz-Vadalazhskaya, Natallia, Gheysens, Olivier, Homans, Filip, Moreno-Reyes, Rodrigo, Pasquet, Agnès, Roelants, Veronique, Van De Heyning, Caroline M., Ríos, Raúl Araujo, Soldat-Stankovic, Valentina, Stankovic, Sinisa, Albernaz Siqueira, Maria Helena, Almeida, Augusto, Alves Togni, Paulo Henrique, Andrade, Jose Henrique, Andrade, Luciana, Anselmi, Carlos, Araújo, Roberta, Azevedo, Guilherme, Bezerra, Sabbrina, Biancardi, Rodrigo, Grossman, Gabriel Blacher, Brandão, Simone, Pianta, Diego Bromfman, Carreira, Lara, Castro, Bruno, Chang, Tien, Cunali, Fernando, Cury, Roberto, Dantas, Roberto, de Amorim Fernandes, Fernando, De Lorenzo, Andrea, De Macedo Filho, Robson, Erthal, Fernanda, Fernandes, Fabio, Fernandes, Juliano, De Souza, Thiago Ferreira, Alves, Wilson Furlan, Ghini, Bruno, Goncalves, Luiz, Gottlieb, Ilan, Hadlich, Marcelo, Kameoka, Vinícius, Lima, Ronaldo, Lima, Adna, Lopes, Rafael Willain, Machado e Silva, Ricardo, Magalhães, Tiago, Silva, Fábio Martins, Mastrocola, Luiz Eduardo, Medeiros, Fábio, Meneghetti, José Claudio, Naue, Vania, Naves, Danilo, Nolasco, Roberto, Nomura, Cesar, Oliveira, Joao Bruno, Paixao, Eduardo, De Carvalho, Filipe Penna, Pinto, Ibraim, Possetti, Priscila, Quinta, Mayra, Nogueira Ramos, Rodrigo Rizzo, Rocha, Ricardo, Rodrigues, Alfredo, Rodrigues, Carlos, Romantini, Leila, Sanches, Adelina, Santana, Sara, Sara da Silva, Leonardo, Schvartzman, Paulo, Matushita, Cristina Sebastião, Senra, Tiago, Shiozaki, Afonso, Menezes de Siqueira, Maria Eduarda, Siqueira, Cristiano, Smanio, Paola, Soares, Carlos Eduardo, Junior, José Soares, Bittencourt, Marcio Sommer, Spiro, Bernardo, Mesquita, Cláudio Tinoco, Torreao, Jorge, Torres, Rafael, Uellendahl, Marly, Monte, Guilherme Urpia, Veríssimo, Otávia, Cabeda, Estevan Vieira, Pedras, Felipe Villela, Waltrick, Roberto, Zapparoli, Marcello, Naseer, Hamid, Garcheva-Tsacheva, Marina, Kostadinova, Irena, Theng, Youdaline, Abikhzer, Gad, Barette, Rene, Chow, Benjamin, Dabreo, Dominique, Friedrich, Matthias, Garg, Ria, Hafez, Mohammed Nassoh, Johnson, Chris, Kiess, Marla, Leipsic, Jonathon, Leung, Eugene, Miller, Robert, Oikonomou, Anastasia, Probst, Stephan, Roifman, Idan, Small, Gary, Tandon, Vikas, Trivedi, Adwait, White, James, Zukotynski, Katherine, Canessa, Jose, Muñoz, Gabriel Castro, Concha, Carmen, Hidalgo, Pablo, Lovera, Cesar, Massardo, Teresa, Vargas, Luis Salazar, Abad, Pedro, Arturo, Harold, Ayala, Sandra, Benitez, Luis, Cadena, Alberto, Caicedo, Carlos, Moncayo, Antonio Calderón, Gomez, Sharon, Gutierrez Villamil, Claudia T., Jaimes, Claudia, Londoño, Juan, Londoño Blair, Juan Luis, Pabon, Luz, Pineda, Mauricio, Rojas, Juan Carlos, Ruiz, Diego, Escobar, Manuel Valencia, Vasquez, Andres, Vergel, Damiana, Zuluaga, Alejandro, Gamboa, Isabel Berrocal, Castro, Gabriel, González, Ulises, Baric, Ana, Batinic, Tonci, Franceschi, Maja, Paar, Maja Hrabak, Jukic, Mladen, Medakovic, Petar, Persic, Viktor, Prpic, Marina, Punda, Ante, Batista, Juan Felipe, Gómez Lauchy, Juan Manuel, Gutierrez, Yamile Marcos, Menéndez, Rayner, Peix, Amalia, Rochela, Luis, Panagidis, Christoforos, Petrou, Ioannis, Engelmann, Vaclav, Kaminek, Milan, Kincl, Vladimír, Lang, Otto, Simanek, Milan, Abdulla, Jawdat, Bøttcher, Morten, Christensen, Mette, Gormsen, Lars Christian, Hasbak, Philip, Hess, Søren, Holdgaard, Paw, Johansen, Allan, Kyhl, Kasper, Norgaard, Bjarne Linde, Øvrehus, Kristian Altern, Rønnow Sand, Niels Peter, Steffensen, Rolf, Thomassen, Anders, Zerahn, Bo, Perez, Alfredo, Escorza Velez, Giovanni Alejandro, Velez, Mayra Sanchez, Abdel Aziz, Islam Shawky, Abougabal, Mahasen, Ahmed, Taghreed, Allam, Adel, Asfour, Ahmed, Hassan, Mona, Hassan, Alia, Ibrahim, Ahmed, Kaffas, Sameh, Kandeel, Ahmed, Ali, Mohamed Mandour, Mansy, Ahmad, Maurice, Hany, Nabil, Sherif, Shaaban, Mahmoud, Flores, Ana Camila, Poksi, Anne, Knuuti, Juhani, Kokkonen, Velipekka, Larikka, Martti, Uusitalo, Valtteri, Bailly, Matthieu, Burg, Samuel, Deux, Jean-François, Habouzit, Vincent, Hyafil, Fabien, Lairez, Olivier, Proffit, Franck, Regaieg, Hamza, Sarda-Mantel, Laure, Tacher, Vania, Schneider, Roman P., Ayetey, Harold, Angelidis, George, Archontaki, Aikaterini, Chatziioannou, Sofia, Datseris, Ioannis, Fragkaki, Christina, Georgoulias, Panagiotis, Koukouraki, Sophia, Koutelou, Maria, Kyrozi, Eleni, Repasos, Evangelos, Stavrou, Petros, Valsamaki, Pipitsa, Gonzalez, Carla, Gutierrez, Goleat, Maldonado, Alejandro, Buga, Klara, Garai, Ildiko, Schmidt, Erzsébet, Szilveszter, Balint, Várady, Edit, Banthia, Nilesh, Bhagat, Jinendra Kumar, Bhargava, Rishi, Bhat, Vivek, Bhatia, Mona, Choudhury, Partha, Chowdekar, Vijay Sai, Irodi, Aparna, Jain, Shashank, Joseph, Elizabeth, Kumar, Sukriti, Girijanandan Mahapatra, Prof Dr, Mitra, Deepanjan, Mittal, Bhagwant Rai, Ozair, Ahmad, Patel, Chetan, Patel, Tapan, Patel, Ravi, Patel, Shivani, Saxena, Sudhir, Sengupta, Shantanu, Singh, Santosh, Singh, Bhanupriya, Sood, Ashwani, Verma, Atul, Affandi, Erwin, Alam Edison, Padma Savenadia, Gunawan, Gani, Hapkido, Habusari, Hidayat, Basuki, Huda, Aulia, Mukti, Anggoro Praja, Prawiro, Djoko, Soeriadi, Erwin Affandi, Syawaluddin, Hilman, Albadr, Amjed, Assadi, Majid, Emami, Farshad, Houshmand, Golnaz, Maleki, Majid, Rostami, Maryam Tajik, Zakavi, Seyed Rasoul, Zaid, Eed Abu, Agranovich, Svetlana, Arnson, Yoav, Bar-Shalom, Rachel, Frenkel, Alex, Knafo, Galit, Lugassi, Rachel, Maor Moalem, Israel Shlomo, Mor, Maya, Muskal, Noam, Ranser, Sara, Shalev, Aryeh, Albano, Domenico, Alongi, Pierpaolo, Arnone, Gaspare, Bagatin, Elisa, Baldari, Sergio, Bauckneht, Matteo, Bertelli, Paolo, Bianco, Francesco, Bonfiglioli, Rachele, Boni, Roberto, Bruno, Andrea, Bruno, Isabella, Busnardo, Elena, Califaretti, Elena, Camoni, Luca, Carnevale, Aldo, Casoni, Roberta, Cavallo, Armando Ugo, Cavenaghi, Giorgio, Chierichetti, Franca, Chiocchi, Marcello, Cittanti, Corrado, Colletta, Mauro, Conti, Umberto, Cossu, Alberto, Cuocolo, Alberto, Cuzzocrea, Marco, De Rimini, Maria Luisa, De Vincentis, Giuseppe, Del Giudice, Eleonora, Del Torto, Alberico, Della Tommasina, Veronica, Durmo, Rexhep, Erba, Paola Anna, Evangelista, Laura, Faletti, Riccardo, Faragasso, Evelina, Farsad, Mohsen, Ferro, Paola, Florimonte, Luigia, Frantellizzi, Viviana, Fringuelli, Fabio Massimo, Gatti, Marco, Gaudiano, Angela, Gimelli, Alessia, Giubbini, Raffaele, Giuffrida, Francesca, Ialuna, Salvatore, Laudicella, Riccardo, Leccisotti, Lucia, Leva, Lucia, Liga, Riccardo, Liguori, Carlo, Longo, Giampiero, Maffione, Margherita, Mancini, Maria Elisabetta, Marcassa, Claudio, Milan, Elisa, Nardi, Barbara, Pacella, Sara, Pepe, Giovanna, Pontone, Gianluca, Pulizzi, Sabina, Quartuccio, Natale, Rampin, Lucia, Ricci, Fabrizio, Rossini, Pierluigi, Rubini, Giuseppe, Russo, Vincenzo, Sacchetti, Gian Mauro, Sambuceti, Gianmario, Scarano, Massimo, Sciagrà, Roberto, Sperandio, Massimiliano, Stefanelli, Antonella, Ventroni, Guido, Zoboli, Stefania, Baugh, Dainia, Chambers, Duane, Madu, Ernest, Nunura, Felix, Asano, Hiroshi, Chimura, Chimura Misato, Fujimoto, Shinichiro, Fujisue, Koichiro, Fukunaga, Tomohisa, Fukushima, Yoshimitsu, Fukuyama, Kae, Hashimoto, Jun, Ichikawa, Yasutaka, Iguchi, Nobuo, Imai, Masamichi, Inaki, Anri, Ishimura, Hayato, Isobe, Satoshi, Kadokami, Toshiaki, Kato, Takao, Kumita, Shinichiro, Maruno, Hirotaka, Mataki, Hiroyuki, Miyagawa, Masao, Morimoto, Ryota, Moroi, Masao, Nagamachi, Shigeki, Nakajima, Kenichi, Nakata, Tomoaki, Nakazato, Ryo, Nanasato, Mamoru, Naya, Masanao, Norikane, Takashi, Ohta, Yasutoshi, Okayama, Satoshi, Okizaki, Atsutaka, Otomi, Yoichi, Otsuka, Hideki, Saito, Masaki, Sakata, Sakata Yasushi, Sarai, Masayoshi, Sato, Daisuke, Shiraishi, Shinya, Suwa, Yoshinobu, Takanami, Kentaro, Takehana, Kazuya, Taki, Junichi, Tamaki, Nagara, Taniguchi, Yasuyo, Teragawa, Hiroki, Tomizawa, Nobuo, Tsujita, Kenichi, Umeji, Kyoko, Wakabayashi, Yasushi, Yamada, Shinichiro, Yamazaki, Shinya, Yoneyama, Tatsuya, Rawashdeh, Mohammad, Batyrkhanov, Daultai, Dautov, Tairkhan, Makhdomi, Khalid, Ombati, Kevin, Alkandari, Faridah, Garashi, Masoud, Coie, Tchoyoson Lim, Rajvong, Sonexay, Kalinin, Artem, Kalnina, Marika, Haidar, Mohamad, Komiagiene, Renata, Kviecinskiene, Giedre, Mataciunas, Mindaugas, Vajauskas, Donatas, Picard, Christian, Karim, Noor Khairiah A., Reichmuth, Luise, Samuel, Anthony, Allarakha, Mohammad Aaftaab, Naojee, Ambedhkar Shantaram, Alexanderson-Rosas, Erick, Barragan, Erika, González-Montecinos, Alejandro Becerril, Cabada, Manuel, Rodriguez, Daniel Calderon, Carvajal-Juarez, Isabel, Cortés, Violeta, Cortés, Filiberto, De La Peña, Erasmo, Gama-Moreno, Manlio, González, Luis, Ramírez, Nelsy Gonzalez, Jiménez-Santos, Moisés, Matos, Luis, Monroy, Edgar, Morelos, Martha, Ornelas, Mario, Ortga Ramirez, Jose Alberto, Preciado-Anaya, Andrés, Preciado-Gutiérrez, Óscar Ulises, Barragan, Adriana Puente, Rosales Uvera, Sandra Graciela, Sandoval, Sigelinda, Tomas, Miguel Santaularia, Sierra-Galan, Lilia M., Siu, Silvia, Vallejo, Enrique, Valles, Mario, Faraggi, Marc, Sereegotov, Erdenechimeg, Ilic, Srdja, Ben-Rais, Nozha, Alaoui, Nadia Ismaili, Taleb, Sara, Myo, Khin Pa, Thu, Phyo Si, Ghimire, Ram Kumar, Rajbanshi, Bijoy, Barneveld, Peter, Glaudemans, Andor, Habets, Jesse, Koopmans, Klaas Pieter, Manders, Jeroen, Pool, Stefan, Scholte, Arthur, Scholtens, Asbjørn, Slart, Riemer, Thimister, Paul, Van Asperen, Erik-Jan, Veltman, Niels, Verschure, Derk, Wagenaar, Nils, Edmond, John, Ellis, Chris, Johnson, Kerryanne, Keenan, Ross, Hua, Shaw, Occleshaw, Christopher, Sasse, Alexander, To, Andrew, Van Pelt, Niels, Young, Calum, Cuadra, Teresa, Roque Vanegas, Hector Bladimir, Soli, Idrissa Adamou, Issoufou, Djibrillou Moussa, Ayodele, Tolulope, Madu, Chibuzo, Onimode, Yetunde, Efros-Monsen, Elen, Forsdahl, Signe Helene, Hildre Dimmen, Jenni-Mari, Jørgensen, Arve, Krohn, Isabel, Løvhaugen, Pål, Bråten, Anders Tjellaug, Al Dhuhli, Humoud, Al Kindi, Faiza, Al-Bulushi, Naeema, Jawa, Zabah, Tag, Naima, Afzal, Muhammad Shehzad, Fatima, Shazia, Younis, Muhammad Numair, Riaz, Musab, Saadullah, Mohammad, Herrera, Yariela, Lenturut-Katal, Dora, Vázquez, Manuel Castillo, Ortellado, José, Akhter, Afroza, Cao, Dianbo, Cheung, Stephen, Dai, Xu, Gong, Lianggeng, Han, Dan, Hou, Yang, Li, Caiying, Li, Tao, Li, Dong, Li, Sijin, Liu, Jinkang, Liu, Hui, Ng, Ming Yen, Sun, Kai, Tang, Gongshun, Wang, Jian, Wang, Ximing, Wang, Zhao-Qian, Wang, Yining, Wang, Yifan, Wu, Jiang, Wu, Zhifang, Xia, Liming, Xiao, Jiangxi, Xu, Lei, Yang, Youyou, Yin, Wu, Yu, Jianqun, Yuan, Li, Zhang, Tong, Zhang, Longjiang, Zhang, Yong-Gao, Zhang, Xiaoli, Zhu, Li, Alfaro, Ana, Abrihan, Paz, Barroso, Asela, Cruz, Eric, Gomez, Marie Rhiamar, Magboo, Vincent Peter, Medina, John Michael, Obaldo, Jerry, Pastrana, Davidson, Pawhay, Christian Michael, Quinon, Alvin, Tang, Jeanelle Margareth, Tecson, Bettina, Uson, Kristine Joy, Uy, Mila, Kostkiewicz, Magdalena, Kunikowska, Jolanta, Bettencourt, Nuno, Cantinho, Guilhermina, Ferreira, Antonio, Syed, Ghulam, Arnous, Samer, Atyani, Said, Byrne, Angela, Gleeson, Tadhg, Kerins, David, Meehan, Conor, Murphy, David, Murphy, Mark, Murray, John, O'Brien, Julie, Bang, Ji-In, Bom, Henry, Cho, Sang-Geon, Hong, Chae Moon, Jang, Su Jin, Jeong, Yong Hyu, Kang, Won Jun, Kim, Ji-Young, Lee, Jaetae, Namgung, Chang Kyeong, So, Young, Won, Kyoung Sook, Majstorov, Venjamin, Vavlukis, Marija, Salobir, Barbara Gužic, Štalc, Monika, Benedek, Theodora, Benedek, Imre, Mititelu, Raluca, Stan, Claudiu Adrian, Ansheles, Alexey, Dariy, Olga, Drozdova, Olga, Gagarina, Nina, Gulyaev, Vsevolod Milyevich, Itskovich, Irina, Karalkin, Anatoly, Kokov, Alexander, Migunova, Ekaterina, Pospelov, Viktor, Ryzhkova, Daria, Saifullina, Guzaliya, Sazonova, Svetlana, Shurupova, Irina, Trifonova, Tatjana, Ussov, Wladimir Yurievich, Vakhromeeva, Margarita, Valiullina, Nailya, Zavadovsky, Konstantin, Zhuravlev, Kirill, Alasnag, Mirvat, Okarvi, Subhani, Saranovic, Dragana Sobic, Keng, Felix, Jason See, Jia Hao, Sekar, Ramkumar, Yew, Min Sen, Vondrak, Andrej, Bejai, Shereen, Bennie, George, Bester, Ria, Engelbrecht, Gerrit, Evbuomwan, Osayande, Gongxeka, Harlem, Vuuren, Magritha Jv, Kaplan, Mitchell, Khushica, Purbhoo, Lakhi, Hoosen, Louw, Lizette, Malan, Nico, Milos, Katarina, Modiselle, Moshe, More, Stuart, Naidoo, Mathava, Scholtz, Leonie, Vangu, Mboyo, Aguadé-Bruix, Santiago, Blanco, Isabel, Cabrera, Antonio, Camarero, Alicia, Casáns-Tormo, Irene, Cuellar-Calabria, Hug, Flotats, Albert, Fuentes Cañamero, Maria Eugenia, García, María Elia, Jimenez-Heffernan, Amelia, Leta, Rubén, Diaz, Javier Lopez, Lumbreras, Luis, Marquez-Cabeza, Juan Javier, Martin, Francisco, Martinez de Alegria, Anxo, Medina, Francisco, Canal, Maria Pedrera, Peiro, Virginia, Pubul-Nuñez, Virginia, Rayo Madrid, Juan Ignacio, Rey, Cristina Rodríguez, Perez, Ricardo Ruano, Ruiz, Joaquín, Hernández, Gertrudis Sabatel, Sevilla, Ana, Zeidán, Nahla, Nanayakkara, Damayanthi, Udugama, Chandraguptha, Simonsson, Magnus, Alkadhi, Hatem, Buechel, Ronny Ralf, Burger, Peter, Ceriani, Luca, De Boeck, Bart, Gräni, Christoph, Juillet de Saint Lager Lucas, Alix, Kamani, Christel H., Kawel-Boehm, Nadine, Manka, Robert, Prior, John O., Rominger, Axel, Vallée, Jean-Paul, Khiewvan, Benjapa, Premprabha, Teerapon, Thientunyakit, Tanyaluck, Sellem, Ali, Kir, Kemal Metin, Sayman, Haluk, Sebikali, Mugisha Julius, Muyinda, Zerida, Kmetyuk, Yaroslav, Korol, Pavlo, Mykhalchenko, Olena, Pliatsek, Volodymyr, Satyr, Maryna, Albalooshi, Batool, Ahmed Hassan, Mohamed Ismail, Anderson, Jill, Bedi, Punit, Biggans, Thomas, Bularga, Anda, Bull, Russell, Burgul, Rajesh, Carpenter, John-Paul, Coles, Duncan, Cusack, David, Deshpande, Aparna, Dougan, John, Fairbairn, Timothy, Farrugia, Alexia, Gopalan, Deepa, Gummow, Alistair, Ramkumar, Prasad Guntur, Hamilton, Mark, Harbinson, Mark, Hartley, Thomas, Hudson, Benjamin, Joshi, Nikhil, Kay, Michael, Kelion, Andrew, Khokhar, Azhar, Kitt, Jamie, Lee, Ken, Low, Chen, Mak, Sze Mun, Marousa, Ntouskou, Martin, Jon, Mcalindon, Elisa, Menezes, Leon, Morgan-Hughes, Gareth, Moss, Alastair, Murray, Anthony, Nicol, Edward, Patel, Dilip, Peebles, Charles, Pugliese, Francesca, Luis Rodrigues, Jonathan Carl, Rofe, Christopher, Sabharwal, Nikant, Schofield, Rebecca, Semple, Thomas, Sharma, Naveen, Strouhal, Peter, Subedi, Deepak, Topping, William, Tweed, Katharine, Weir-Mccall, Jonathan, Abbara, Suhny, Abbasi, Taimur, Abbott, Brian, Abohashem, Shady, Abramson, Sandra, Al-Abboud, Tarek, Al-Mallah, Mouaz, Almousalli, Omar, Ananthasubramaniam, Karthikeyan, Kumar, Mohan Ashok, Askew, Jeffrey, Attanasio, Lea, Balmer-Swain, Mallory, Bayer, Richard R., Bernheim, Adam, Bhatti, Sabha, Bieging, Erik, Bloom, Stephen, Blue, Sean, Bluemke, David, Borges, Andressa, Branch, Kelley, Bravo, Paco, Brothers, Jessica, Budoff, Matthew, Bullock-Palmer, Renée, Burandt, Angela, Burke, Floyd W., Bush, Kelvin, Candela, Candace, Capasso, Elizabeth, 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George Kinzfogl, Kircher, Jacqueline, Kirkbride, Rachael, Kontos, Michael, Kottam, Anupama, Krepp, Joseph, Layer, Jay, Lee, Steven H., Leppo, Jeffrey, Lesser, John, Leung, Steve, Lewin, Howard, Litmanovich, Diana, Liu, Yiyan, Magurany, Kathleen, Markowitz, Jeremy, Marn, Amanda, Matis, Stephen E., Mckenna, Michael, Mcrae, Tony, Mendoza, Fernando, Merhige, Michael, Min, David, Moffitt, Chanan, Moncher, Karen, Moore, Warren, Morayati, Shamil, Morris, Michael, Mossa-Basha, Mahmud, Mrsic, Zorana, Murthy, Venkatesh, Nagpal, Prashant, Napier, Kyle, Nelson, Katarina, Nijjar, Prabhjot, Osman, Medhat, Passen, Edward, Patel, Amit, Patil, Pravin, Paul, Ryan, Phillips, Lawrence, Polsani, Venkateshwar, Poludasu, Rajaram, Pomerantz, Brian, Porter, Thomas, Prentice, Ryan, Pursnani, Amit, Rabbat, Mark, Ramamurti, Suresh, Rich, Florence, Luna, Hiram Rivera, Robinson, Austin, Robles, Kim, Rodríguez, Cesar, Rorie, Mark, Rumberger, John, Russell, Raymond, Sabra, Philip, Sadler, Diego, Schemmer, Mary, Schoepf, U. Joseph, Shah, Samir, Shah, Nishant, Shanbhag, Sujata, Sharma, Gaurav, Shayani, Steven, Shirani, Jamshid, Shivaram, Pushpa, Sigman, Steven, Simon, Mitch, Slim, Ahmad, Smith, David, Smith, Alexandra, Soman, Prem, Sood, Aditya, Srichai-Parsia, Monvadi Barbara, Streeter, James, T, Albert, Tawakol, Ahmed, Thomas, Dustin, Thompson, Randall, Torbet, Tara, Trinidad, Desiree, Ullery, Shawn, Unzek, Samuel, Uretsky, Seth, Vallurupalli, Srikanth, Verma, Vikas, Waller, Alfonso, Wang, Ellen, Ward, Parker, Weissman, Gaby, Wesbey, George, White, Kelly, Wolinsky, David, Yost, Sandra, Zgaljardic, Michael, Alonso, Omar, Beretta, Mario, Ferrando, Rodolfo, Kapitan, Miguel, Mut, Fernando, Djuraev, Omoa, Rozikhodjaeva, Gulnora, Le Ngoc, Ha, Mai, Son Hong, Nguyen, Xuan Canh, Hirschfeld, Cole B., Shaw, Leslee J., Williams, Michelle C., Lahey, Ryan, Villines, Todd C., Dorbala, Sharmila, Choi, Andrew D., Shah, Nishant R., Bluemke, David A., Berman, Daniel S., Blankstein, Ron, Ferencik, Maro, Narula, Jagat, Winchester, David, Malkovskiy, Eli, Goebel, Benjamin, Randazzo, Michael J., Lopez-Mattei, Juan, Parwani, Purvi, Vitola, Joao V., Cerci, Rodrigo J., Better, Nathan, Raggi, Paolo, Lu, Bin, Sergienko, Vladimir, Sinitsyn, Valentin, Kudo, Takashi, Nørgaard, Bjarne Linde, Maurovich-Horvat, Pál, Cohen, Yosef A., Pascual, Thomas N. B., Pynda, Yaroslav, Dondi, Maurizio, Paez, Diana, Einstein, Andrew J., Cerci, Rodrigo, Hinterleitner, Gerd, Lu, Yao, Morozova, Olga, Xu, Zhuoran, Cohen, Yosef, Randazzo, Michael, Choi, Andrew, Nasery, Mohammad Nawaz, Goda, Artan, Shirka, Ervina, Benlabgaa, Rabie, Bouyoucef, Salah, Medjahedi, Abdelkader, Nailli, Qai, Agolti, Mariela, Aguero, Roberto Nicola, Alak, Maria del Carmen, Alberguina, Lucia Graciela, Arroñada, Guillermo, Astesiano, Andrea, Astesiano, Alfredo, Norton, Carolina Ba, Benteo, Pablo, Blanco, Juan, Bonelli, Juan Manuel, Bustos, Jose Javier, Cabrejas, Raul, Cachero, Jorge, Campisi, Roxana, Canderoli, Alejandro, Carames, Silvia, Carrascosa, Patrícia, Castro, Ricardo, Cendoya, Oscar, Cognigni, Luciano Martin, Collaud, Carlo, Cortes, Claudia, Courtis, Javier, Cragnolino, Daniel, Daicz, Mariana, De La Vega, Alejandro, De Maria, Silvia Teresa, Del Riego, Horacio, Dettori, Fernando, Deviggiano, Alejandro, Dragonetti, Laura, Embon, Mario, Enriquez, Ruben Emilio, Ensinas, Jorge, Faccio, Fernando, Facello, Adolfo, Garofalo, Diego, Geronazzo, Ricardo, Gonza, Natalia, Gutierrez, Luca, Guzzo, Miguel Angel, Hasbani, Victor, Huerin, Melina, Jäger, Victor, Lewkowicz, Julio Manuel, Nieves A López De Munaín, Maria, Lotti, Jose Maria, Marquez, Alejandra, Masoli, Osvaldo, Masoli, Osvaldo Horacio, Mastrovito, Edgardo, Mayoraz, Matia, Melado, Graciela Eva, Mele, Anibal, Merani, Maria Fernanda, Meretta, Alejandro Horacio, Molteni, Susana, Montecinos, Marco, Noguera, Eduardo, Novoa, Carlo, Sueldo, Claudio Pereyra, Ascani, Sebastian Perez, Pollono, Pablo, Pujol, Maria Paula, Radzinschi, Alejandro, Raimondi, Gustavo, Redruello, Marcela, Rodríguez, Marina, Rodríguez, Matía, Romero, Romina Lorena, Acuña, Arturo Romero, Rovaletti, Federico, San Miguel, Luca, Solari, Lucrecia, Strada, Bruno, Traverso, Sonia, Traverzo, Sonia Simona, Espeche, Maria del Huerto Velazquez, Weihmuller, Juan Sebastian, Wolcan, Juan, Zeffiro, Susana, Sakanyan, Mari, Beuzeville, Scott, Boktor, Raef, 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Irena, Theng, Youdaline, Abikhzer, Gad, Barette, Rene, Chow, Benjamin, Dabreo, Dominique, Friedrich, Matthia, Garg, Ria, Hafez, Mohammed Nassoh, Johnson, Chri, Kiess, Marla, Leipsic, Jonathon, Leung, Eugene, Miller, Robert, Oikonomou, Anastasia, Probst, Stephan, Roifman, Idan, Small, Gary, Tandon, Vika, Trivedi, Adwait, White, Jame, Zukotynski, Katherine, Canessa, Jose, Muñoz, Gabriel Castro, Concha, Carmen, Hidalgo, Pablo, Lovera, Cesar, Massardo, Teresa, Vargas, Luis Salazar, Abad, Pedro, Arturo, Harold, Ayala, Sandra, Benitez, Lui, Cadena, Alberto, Caicedo, Carlo, Moncayo, Antonio Calderón, Gomez, Sharon, Gutierrez Villamil, Claudia T., Jaimes, Claudia, Londoño, Juan, Londoño Blair, Juan Lui, Pabon, Luz, Pineda, Mauricio, Rojas, Juan Carlo, Ruiz, Diego, Escobar, Manuel Valencia, Vasquez, Andre, Vergel, Damiana, Zuluaga, Alejandro, Gamboa, Isabel Berrocal, Castro, Gabriel, González, Ulise, Baric, Ana, Batinic, Tonci, Franceschi, Maja, Paar, Maja Hrabak, Jukic, Mladen, Medakovic, Petar, Persic, Viktor, Prpic, Marina, Punda, Ante, Batista, Juan Felipe, Gómez Lauchy, Juan Manuel, Gutierrez, Yamile Marco, Menéndez, Rayner, Peix, Amalia, Rochela, Lui, Panagidis, Christoforo, Petrou, Ioanni, Engelmann, Vaclav, Kaminek, Milan, Kincl, Vladimír, Lang, Otto, Simanek, Milan, Abdulla, Jawdat, Bøttcher, Morten, Christensen, Mette, Gormsen, Lars Christian, Hasbak, Philip, Hess, Søren, Holdgaard, Paw, Johansen, Allan, Kyhl, Kasper, Norgaard, Bjarne Linde, Øvrehus, Kristian Altern, Rønnow Sand, Niels Peter, Steffensen, Rolf, Thomassen, Ander, Zerahn, Bo, Perez, Alfredo, Escorza Velez, Giovanni Alejandro, Velez, Mayra Sanchez, Abdel Aziz, Islam Shawky, Abougabal, Mahasen, Ahmed, Taghreed, Allam, Adel, Asfour, Ahmed, Hassan, Mona, Hassan, Alia, Ibrahim, Ahmed, Kaffas, Sameh, Kandeel, Ahmed, Ali, Mohamed Mandour, Mansy, Ahmad, Maurice, Hany, Nabil, Sherif, Shaaban, Mahmoud, Flores, Ana Camila, Poksi, Anne, Knuuti, Juhani, Kokkonen, Velipekka, Larikka, Martti, Uusitalo, Valtteri, Bailly, Matthieu, Burg, Samuel, Deux, Jean-Françoi, Habouzit, Vincent, Hyafil, Fabien, Lairez, Olivier, Proffit, Franck, Regaieg, Hamza, Sarda-Mantel, Laure, Tacher, Vania, Schneider, Roman P., Ayetey, Harold, Angelidis, George, Archontaki, Aikaterini, Chatziioannou, Sofia, Datseris, Ioanni, Fragkaki, Christina, Georgoulias, Panagioti, Koukouraki, Sophia, Koutelou, Maria, Kyrozi, Eleni, Repasos, Evangelo, Stavrou, Petro, Valsamaki, Pipitsa, Gonzalez, Carla, Gutierrez, Goleat, Maldonado, Alejandro, Buga, Klara, Garai, Ildiko, Schmidt, Erzsébet, Szilveszter, Balint, Várady, Edit, Banthia, Nilesh, Bhagat, Jinendra Kumar, Bhargava, Rishi, Bhat, Vivek, Bhatia, Mona, Choudhury, Partha, Chowdekar, Vijay Sai, Irodi, Aparna, Jain, Shashank, Joseph, Elizabeth, Kumar, Sukriti, Girijanandan Mahapatra, Prof Dr, Mitra, Deepanjan, Mittal, Bhagwant Rai, Ozair, Ahmad, Patel, Chetan, Patel, Tapan, Patel, Ravi, Patel, Shivani, Saxena, Sudhir, Sengupta, Shantanu, Singh, Santosh, Singh, Bhanupriya, Sood, Ashwani, Verma, Atul, Affandi, Erwin, Alam, Edison, Padma, Savenadia, Gunawan, Gani, Hapkido, Habusari, Hidayat, Basuki, Huda, Aulia, Mukti, Anggoro Praja, Prawiro, Djoko, Soeriadi, Erwin Affandi, Syawaluddin, Hilman, Albadr, Amjed, Assadi, Majid, Emami, Farshad, Houshmand, Golnaz, Maleki, Majid, Rostami, Maryam Tajik, Zakavi, Seyed Rasoul, Zaid, Eed Abu, Agranovich, Svetlana, Arnson, Yoav, Bar-Shalom, Rachel, Frenkel, Alex, Knafo, Galit, Lugassi, Rachel, Maor Moalem, Israel Shlomo, Mor, Maya, Muskal, Noam, Ranser, Sara, Shalev, Aryeh, Albano, Domenico, Alongi, Pierpaolo, Arnone, Gaspare, Bagatin, Elisa, Baldari, Sergio, Bauckneht, Matteo, Bertelli, Paolo, Bianco, Francesco, Bonfiglioli, Rachele, Boni, Roberto, Bruno, Andrea, Bruno, Isabella, Busnardo, Elena, Califaretti, Elena, Camoni, Luca, Carnevale, Aldo, Casoni, Roberta, Cavallo, Armando Ugo, Cavenaghi, Giorgio, Chierichetti, Franca, Chiocchi, Marcello, Cittanti, Corrado, Colletta, Mauro, Conti, Umberto, Cossu, Alberto, Cuocolo, Alberto, Cuzzocrea, Marco, De Rimini, Maria Luisa, De Vincentis, Giuseppe, Del Giudice, Eleonora, Del Torto, Alberico, Della Tommasina, Veronica, Durmo, Rexhep, Erba, Paola Anna, Evangelista, Laura, Faletti, Riccardo, Faragasso, Evelina, Farsad, Mohsen, Ferro, Paola, Florimonte, Luigia, Frantellizzi, Viviana, Fringuelli, Fabio Massimo, Gatti, Marco, Gaudiano, Angela, Gimelli, Alessia, Giubbini, Raffaele, Giuffrida, Francesca, Ialuna, Salvatore, Laudicella, Riccardo, Leccisotti, Lucia, Leva, Lucia, Liga, Riccardo, Liguori, Carlo, Longo, Giampiero, Maffione, Margherita, Mancini, Maria Elisabetta, Marcassa, Claudio, Milan, Elisa, Nardi, Barbara, Pacella, Sara, Pepe, Giovanna, Pontone, Gianluca, Pulizzi, Sabina, Quartuccio, Natale, Rampin, Lucia, Ricci, Fabrizio, Rossini, Pierluigi, Rubini, Giuseppe, Russo, Vincenzo, Sacchetti, Gian Mauro, Sambuceti, Gianmario, Scarano, Massimo, Sciagrà, Roberto, Sperandio, Massimiliano, Stefanelli, Antonella, Ventroni, Guido, Zoboli, Stefania, Baugh, Dainia, Chambers, Duane, Madu, Ernest, Nunura, Felix, Asano, Hiroshi, Chimura, Chimura Misato, Fujimoto, Shinichiro, Fujisue, Koichiro, Fukunaga, Tomohisa, Fukushima, Yoshimitsu, Fukuyama, Kae, Hashimoto, Jun, Ichikawa, Yasutaka, Iguchi, Nobuo, Imai, Masamichi, Inaki, Anri, Ishimura, Hayato, Isobe, Satoshi, Kadokami, Toshiaki, Kato, Takao, Kumita, Shinichiro, Maruno, Hirotaka, Mataki, Hiroyuki, Miyagawa, Masao, Morimoto, Ryota, Moroi, Masao, Nagamachi, Shigeki, Nakajima, Kenichi, Nakata, Tomoaki, Nakazato, Ryo, Nanasato, Mamoru, Naya, Masanao, Norikane, Takashi, Ohta, Yasutoshi, Okayama, Satoshi, Okizaki, Atsutaka, Otomi, Yoichi, Otsuka, Hideki, Saito, Masaki, Sakata, Sakata Yasushi, Sarai, Masayoshi, Sato, Daisuke, Shiraishi, Shinya, Suwa, Yoshinobu, Takanami, Kentaro, Takehana, Kazuya, Taki, Junichi, Tamaki, Nagara, Taniguchi, Yasuyo, Teragawa, Hiroki, Tomizawa, Nobuo, Tsujita, Kenichi, Umeji, Kyoko, Wakabayashi, Yasushi, Yamada, Shinichiro, Yamazaki, Shinya, Yoneyama, Tatsuya, Rawashdeh, Mohammad, Batyrkhanov, Daultai, Dautov, Tairkhan, Makhdomi, Khalid, Ombati, Kevin, Alkandari, Faridah, Garashi, Masoud, Coie, Tchoyoson Lim, Rajvong, Sonexay, Kalinin, Artem, Kalnina, Marika, Haidar, Mohamad, Komiagiene, Renata, Kviecinskiene, Giedre, Mataciunas, Mindauga, Vajauskas, Donata, Picard, Christian, Karim, Noor Khairiah A., Reichmuth, Luise, Samuel, Anthony, Allarakha, Mohammad Aaftaab, Naojee, Ambedhkar Shantaram, Alexanderson-Rosas, Erick, Barragan, Erika, González-Montecinos, Alejandro Becerril, Cabada, Manuel, Rodriguez, Daniel Calderon, Carvajal-Juarez, Isabel, Cortés, Violeta, Cortés, Filiberto, De La Peña, Erasmo, Gama-Moreno, Manlio, González, Lui, Ramírez, Nelsy Gonzalez, Jiménez-Santos, Moisé, Matos, Lui, Monroy, Edgar, Morelos, Martha, Ornelas, Mario, Ortga Ramirez, Jose Alberto, Preciado-Anaya, André, Preciado-Gutiérrez, Óscar Ulise, Barragan, Adriana Puente, Rosales Uvera, Sandra Graciela, Sandoval, Sigelinda, Tomas, Miguel Santaularia, Sierra-Galan, Lilia M., Siu, Silvia, Vallejo, Enrique, Valles, Mario, Faraggi, Marc, Sereegotov, Erdenechimeg, Ilic, Srdja, Ben-Rais, Nozha, Alaoui, Nadia Ismaili, Taleb, Sara, Myo, Khin Pa, Thu, Phyo Si, Ghimire, Ram Kumar, Rajbanshi, Bijoy, Barneveld, Peter, Glaudemans, Andor, Habets, Jesse, Koopmans, Klaas Pieter, Manders, Jeroen, Pool, Stefan, Scholte, Arthur, Scholtens, Asbjørn, Slart, Riemer, Thimister, Paul, Van Asperen, Erik-Jan, Veltman, Niel, Verschure, Derk, Wagenaar, Nil, Edmond, John, Ellis, Chri, Johnson, Kerryanne, Keenan, Ro, Hua, Shaw, Occleshaw, Christopher, Sasse, Alexander, To, Andrew, Van Pelt, Niel, Young, Calum, Cuadra, Teresa, Roque Vanegas, Hector Bladimir, Soli, Idrissa Adamou, Issoufou, Djibrillou Moussa, Ayodele, Tolulope, Madu, Chibuzo, Onimode, Yetunde, Efros-Monsen, Elen, Forsdahl, Signe Helene, Hildre Dimmen, Jenni-Mari, Jørgensen, Arve, Krohn, Isabel, Løvhaugen, Pål, Bråten, Anders Tjellaug, Al Dhuhli, Humoud, Al Kindi, Faiza, Al-Bulushi, Naeema, Jawa, Zabah, Tag, Naima, Afzal, Muhammad Shehzad, Fatima, Shazia, Younis, Muhammad Numair, Riaz, Musab, Saadullah, Mohammad, Herrera, Yariela, Lenturut-Katal, Dora, Vázquez, Manuel Castillo, Ortellado, José, Akhter, Afroza, Cao, Dianbo, Cheung, Stephen, Dai, Xu, Gong, Lianggeng, Han, Dan, Hou, Yang, Li, Caiying, Li, Tao, Li, Dong, Li, Sijin, Liu, Jinkang, Liu, Hui, Ng, Ming Yen, Sun, Kai, Tang, Gongshun, Wang, Jian, Wang, Ximing, Wang, Zhao-Qian, Wang, Yining, Wang, Yifan, Wu, Jiang, Wu, Zhifang, Xia, Liming, Xiao, Jiangxi, Xu, Lei, Yang, Youyou, Yin, Wu, Yu, Jianqun, Yuan, Li, Zhang, Tong, Zhang, Longjiang, Zhang, Yong-Gao, Zhang, Xiaoli, Zhu, Li, Alfaro, Ana, Abrihan, Paz, Barroso, Asela, Cruz, Eric, Gomez, Marie Rhiamar, Magboo, Vincent Peter, Medina, John Michael, Obaldo, Jerry, Pastrana, Davidson, Pawhay, Christian Michael, Quinon, Alvin, Tang, Jeanelle Margareth, Tecson, Bettina, Uson, Kristine Joy, Uy, Mila, Kostkiewicz, Magdalena, Kunikowska, Jolanta, Bettencourt, Nuno, Cantinho, Guilhermina, Ferreira, Antonio, Syed, Ghulam, Arnous, Samer, Atyani, Said, Byrne, Angela, Gleeson, Tadhg, Kerins, David, Meehan, Conor, Murphy, David, Murphy, Mark, Murray, John, O'Brien, Julie, Bang, Ji-In, Bom, Henry, Cho, Sang-Geon, Hong, Chae Moon, Jang, Su Jin, Jeong, Yong Hyu, Kang, Won Jun, Kim, Ji-Young, Lee, Jaetae, Namgung, Chang Kyeong, So, Young, Won, Kyoung Sook, Majstorov, Venjamin, Vavlukis, Marija, Salobir, Barbara Gužic, Štalc, Monika, Benedek, Theodora, Benedek, Imre, Mititelu, Raluca, Stan, Claudiu Adrian, Ansheles, Alexey, Dariy, Olga, Drozdova, Olga, Gagarina, Nina, Gulyaev, Vsevolod Milyevich, Itskovich, Irina, Karalkin, Anatoly, Kokov, Alexander, Migunova, Ekaterina, Pospelov, Viktor, Ryzhkova, Daria, Saifullina, Guzaliya, Sazonova, Svetlana, Shurupova, Irina, Trifonova, Tatjana, Ussov, Wladimir Yurievich, Vakhromeeva, Margarita, Valiullina, Nailya, Zavadovsky, Konstantin, Zhuravlev, Kirill, Alasnag, Mirvat, Okarvi, Subhani, Saranovic, Dragana Sobic, Keng, Felix, Jason See, Jia Hao, Sekar, Ramkumar, Yew, Min Sen, Vondrak, Andrej, Bejai, Shereen, Bennie, George, Bester, Ria, Engelbrecht, Gerrit, Evbuomwan, Osayande, Gongxeka, Harlem, Vuuren, Magritha Jv, Kaplan, Mitchell, Khushica, Purbhoo, Lakhi, Hoosen, Louw, Lizette, Malan, Nico, Milos, Katarina, Modiselle, Moshe, More, Stuart, Naidoo, Mathava, Scholtz, Leonie, Vangu, Mboyo, Aguadé-Bruix, Santiago, Blanco, Isabel, Cabrera, Antonio, Camarero, Alicia, Casáns-Tormo, Irene, Cuellar-Calabria, Hug, Flotats, Albert, Fuentes Cañamero, Maria Eugenia, García, María Elia, Jimenez-Heffernan, Amelia, Leta, Rubén, Diaz, Javier Lopez, Lumbreras, Lui, Marquez-Cabeza, Juan Javier, Martin, Francisco, Martinez de Alegria, Anxo, Medina, Francisco, Canal, Maria Pedrera, Peiro, Virginia, Pubul-Nuñez, Virginia, Rayo Madrid, Juan Ignacio, Rey, Cristina Rodríguez, Perez, Ricardo Ruano, Ruiz, Joaquín, Hernández, Gertrudis Sabatel, Sevilla, Ana, Zeidán, Nahla, Nanayakkara, Damayanthi, Udugama, Chandraguptha, Simonsson, Magnu, Alkadhi, Hatem, Buechel, Ronny Ralf, Burger, Peter, Ceriani, Luca, De Boeck, Bart, Gräni, Christoph, Juillet de Saint Lager Lucas, Alix, Kamani, Christel H., Kawel-Boehm, Nadine, Manka, Robert, Prior, John O., Rominger, Axel, Vallée, Jean-Paul, Khiewvan, Benjapa, Premprabha, Teerapon, Thientunyakit, Tanyaluck, Sellem, Ali, Kir, Kemal Metin, Sayman, Haluk, Sebikali, Mugisha Juliu, Muyinda, Zerida, Kmetyuk, Yaroslav, Korol, Pavlo, Mykhalchenko, Olena, Pliatsek, Volodymyr, Satyr, Maryna, Albalooshi, Batool, Ahmed Hassan, Mohamed Ismail, Anderson, Jill, Bedi, Punit, Biggans, Thoma, Bularga, Anda, Bull, Russell, Burgul, Rajesh, Carpenter, John-Paul, Coles, Duncan, Cusack, David, Deshpande, Aparna, Dougan, John, Fairbairn, Timothy, Farrugia, Alexia, Gopalan, Deepa, Gummow, Alistair, Ramkumar, Prasad Guntur, Hamilton, Mark, Harbinson, Mark, Hartley, Thoma, Hudson, Benjamin, Joshi, Nikhil, Kay, Michael, Kelion, Andrew, Khokhar, Azhar, Kitt, Jamie, Lee, Ken, Low, Chen, Mak, Sze Mun, Marousa, Ntouskou, Martin, Jon, Mcalindon, Elisa, Menezes, Leon, Morgan-Hughes, Gareth, Moss, Alastair, Murray, Anthony, Nicol, Edward, Patel, Dilip, Peebles, Charle, Pugliese, Francesca, Luis Rodrigues, Jonathan Carl, Rofe, Christopher, Sabharwal, Nikant, Schofield, Rebecca, Semple, Thoma, Sharma, Naveen, Strouhal, Peter, Subedi, Deepak, Topping, William, Tweed, Katharine, Weir-Mccall, Jonathan, Abbara, Suhny, Abbasi, Taimur, Abbott, Brian, Abohashem, Shady, Abramson, Sandra, Al-Abboud, Tarek, Al-Mallah, Mouaz, Almousalli, Omar, Ananthasubramaniam, Karthikeyan, Kumar, Mohan Ashok, Askew, Jeffrey, Attanasio, Lea, Balmer-Swain, Mallory, Bayer, Richard R., Bernheim, Adam, Bhatti, Sabha, Bieging, Erik, Bloom, Stephen, Blue, Sean, Bluemke, David, Borges, Andressa, Branch, Kelley, Bravo, Paco, Brothers, Jessica, Budoff, Matthew, Bullock-Palmer, Renée, Burandt, Angela, Burke, Floyd W., Bush, Kelvin, Candela, Candace, Capasso, Elizabeth, Cavalcante, Joao, Chang, Donald, Chatterjee, Saurav, Chatzizisis, Yianni, Cheezum, Michael, Chen, Tiffany, Chen, Jennifer, Chen, Marcu, Clarcq, Jame, Cordero, Ayreen, Crim, Matthew, Danciu, Sorin, Decter, Bruce, Dhruva, Nimish, Doherty, Neil, Doukky, Rami, Dunbar, Anjori, Duvall, William, Edwards, Rachael, Esquitin, Kerry, Farah, Husam, Fentanes, Emilio, Fisher, Daniel, Fitzpatrick, Daniel, Foster, Cameron, Fuisz, Tony, Gannon, Michael, Gastner, Lori, Gerson, Myron, Ghoshhajra, Brian, Goldberg, Alan, Goldner, Brian, Gonzalez, Jorge, Gore, Rosco, Gracia-López, Sandra, Hage, Fadi, Haider, Agha, Haider, Sofia, Hamirani, Yasmin, Hassen, Karen, Hatfield, Mallory, Hawkins, Carolyn, Hawthorne, Katie, Heath, Nichola, Hendel, Robert, Hernandez, Phillip, Hill, Gregory, Horgan, Stephen, Huffman, Jeff, Hurwitz, Lynne, Iskandrian, Ami, Janardhanan, Rajesh, Jellis, Christine, Jerome, Scott, Kalra, Dinesh, Kaviratne, Summanther, Kay, Fernando, Kelly, Faith, Khalique, Omar, Kinkhabwala, Mona, Iii, George Kinzfogl, Kircher, Jacqueline, Kirkbride, Rachael, Kontos, Michael, Kottam, Anupama, Krepp, Joseph, Layer, Jay, Lee, Steven H., Leppo, Jeffrey, Lesser, John, Leung, Steve, Lewin, Howard, Litmanovich, Diana, Liu, Yiyan, Magurany, Kathleen, Markowitz, Jeremy, Marn, Amanda, Matis, Stephen E., Mckenna, Michael, Mcrae, Tony, Mendoza, Fernando, Merhige, Michael, Min, David, Moffitt, Chanan, Moncher, Karen, Moore, Warren, Morayati, Shamil, Morris, Michael, Mossa-Basha, Mahmud, Mrsic, Zorana, Murthy, Venkatesh, Nagpal, Prashant, Napier, Kyle, Nelson, Katarina, Nijjar, Prabhjot, Osman, Medhat, Passen, Edward, Patel, Amit, Patil, Pravin, Paul, Ryan, Phillips, Lawrence, Polsani, Venkateshwar, Poludasu, Rajaram, Pomerantz, Brian, Porter, Thoma, Prentice, Ryan, Pursnani, Amit, Rabbat, Mark, Ramamurti, Suresh, Rich, Florence, Luna, Hiram Rivera, Robinson, Austin, Robles, Kim, Rodríguez, Cesar, Rorie, Mark, Rumberger, John, Russell, Raymond, Sabra, Philip, Sadler, Diego, Schemmer, Mary, Schoepf, U. Joseph, Shah, Samir, Shah, Nishant, Shanbhag, Sujata, Sharma, Gaurav, Shayani, Steven, Shirani, Jamshid, Shivaram, Pushpa, Sigman, Steven, Simon, Mitch, Slim, Ahmad, Smith, David, Smith, Alexandra, Soman, Prem, Sood, Aditya, Srichai-Parsia, Monvadi Barbara, Streeter, Jame, T, Albert, Tawakol, Ahmed, Thomas, Dustin, Thompson, Randall, Torbet, Tara, Trinidad, Desiree, Ullery, Shawn, Unzek, Samuel, Uretsky, Seth, Vallurupalli, Srikanth, Verma, Vika, Waller, Alfonso, Wang, Ellen, Ward, Parker, Weissman, Gaby, Wesbey, George, White, Kelly, Wolinsky, David, Yost, Sandra, Zgaljardic, Michael, Alonso, Omar, Beretta, Mario, Ferrando, Rodolfo, Kapitan, Miguel, Mut, Fernando, Djuraev, Omoa, Rozikhodjaeva, Gulnora, Le Ngoc, Ha, Mai, Son Hong, Nguyen, Xuan Canh, Hirschfeld, C, Shaw, L, Williams, M, Lahey, R, Villines, T, Dorbala, S, Choi, A, Shah, N, Bluemke, D, Berman, D, Blankstein, R, Ferencik, M, Narula, J, Winchester, D, Malkovskiy, E, Goebel, B, Randazzo, M, Lopez-Mattei, J, Parwani, P, Vitola, J, Cerci, R, Better, N, Raggi, P, Lu, B, Sergienko, V, Sinitsyn, V, Kudo, T, Nørgaard, B, Maurovich-Horvat, P, Cohen, Y, Pascual, T, Pynda, Y, Dondi, M, Paez, D, Einstein, A, and Erba, P

Subjects
Aging, Coronavirus disease 2019 (COVID-19), Clinical Sciences, coronavirus, Disease, 030204 cardiovascular system & hematology, Cardiorespiratory Medicine and Haematology, Cardiovascular, 030218 nuclear medicine & medical imaging, NO, 03 medical and health sciences, 0302 clinical medicine, COVID-19 Testing, Cardiovascular procedures, Predictive Value of Tests, cardiovascular disease, Clinical Research, Environmental health, INCAPS-COVID Investigators Group, Pandemic, Volume reduction, Medicine, Humans, Radiology, Nuclear Medicine and imaging, cardiovascular imaging, diagnostic cardiovascular procedure, Pandemics, business.industry, SARS-CoV-2, Prevention, COVID-19, Health Services, United States/epidemiology, United States, Invasive coronary angiography, coronaviru, Heart Disease, Good Health and Well Being, MYOCARDIAL-INFARCTION, Cardiovascular System & Hematology, Predictive value of tests, HOSPITAL ADMISSIONS, Cardiology and Cardiovascular Medicine, Early phase, business
Abstract

Objectives: This study sought to quantify and compare the decline in volumes of cardiovascular procedures between the United States and non-U.S. institutions during the early phase of the coronavirus disease-2019 (COVID-19) pandemic. Background: The COVID-19 pandemic has disrupted the care of many non-COVID-19 illnesses. Reductions in diagnostic cardiovascular testing around the world have led to concerns over the implications of reduced testing for cardiovascular disease (CVD) morbidity and mortality. Methods: Data were submitted to the INCAPS-COVID (International Atomic Energy Agency Non-Invasive Cardiology Protocols Study of COVID-19), a multinational registry comprising 909 institutions in 108 countries (including 155 facilities in 40 U.S. states), assessing the impact of the COVID-19 pandemic on volumes of diagnostic cardiovascular procedures. Data were obtained for April 2020 and compared with volumes of baseline procedures from March 2019. We compared laboratory characteristics, practices, and procedure volumes between U.S. and non-U.S. facilities and between U.S. geographic regions and identified factors associated with volume reduction in the United States. Results: Reductions in the volumes of procedures in the United States were similar to those in non-U.S. facilities (68% vs. 63%, respectively; p = 0.237), although U.S. facilities reported greater reductions in invasive coronary angiography (69% vs. 53%, respectively; p < 0.001). Significantly more U.S. facilities reported increased use of telehealth and patient screening measures than non-U.S. facilities, such as temperature checks, symptom screenings, and COVID-19 testing. Reductions in volumes of procedures differed between U.S. regions, with larger declines observed in the Northeast (76%) and Midwest (74%) than in the South (62%) and West (44%). Prevalence of COVID-19, staff redeployments, outpatient centers, and urban centers were associated with greater reductions in volume in U.S. facilities in a multivariable analysis. Conclusions: We observed marked reductions in U.S. cardiovascular testing in the early phase of the pandemic and significant variability between U.S. regions. The association between reductions of volumes and COVID-19 prevalence in the United States highlighted the need for proactive efforts to maintain access to cardiovascular testing in areas most affected by outbreaks of COVID-19 infection.

Published
2021
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32. Autolysis in Crustacean Tissues after Death: A Case Study Using the Procambarus clarkii Hepatopancreas

Author

Lili Ji, Yi Geng, Yangping Ou, Chen Xia, Defang Chen, Lizi Yin, Zhang Xiaoli, Liangyu Li, Xiong Guanqing, Ruisi Liu, Shiyong Yang, Xiaoli Huang, and Minghao Li

Subjects
Procambarus clarkii, Autolysis (biology), Article Subject, General Immunology and Microbiology, biology, 040301 veterinary sciences, Mesenchyme, 010401 analytical chemistry, 04 agricultural and veterinary sciences, General Medicine, biology.organism_classification, Crayfish, 01 natural sciences, Crustacean, General Biochemistry, Genetics and Molecular Biology, 0104 chemical sciences, 0403 veterinary science, Andrology, medicine.anatomical_structure, Apoptosis, Organelle, medicine, Medicine, Hepatopancreas
Abstract

Autolysis is an internal phenomenon following the death of an organism that leads to the degradation of tissues. In order to explore the initial stages of autolysis and attempt to establish reference standards for tissue changes after death, we studied the rapidly autolyzing tissue of the crayfish hepatopancreas. Samples from the hepatopancreas of crayfish were examined 0, 5, 10, 30, 60, and 120 minutes after death. Histological and ultrapathological examinations and evaluations and apoptotic cell counts were conducted to determine the initiation time and degree of autolysis. The results showed that autolysis in the hepatopancreas of crayfish began within 5 minutes. Initially, autolysis manifested in the swelling of hepatic tubular cells and the widening of mesenchyme. Cells undergoing autolysis showed severe organelle necrolysis. Based on these observations, tissue samples should be collected and preserved within five minutes to avoid interfering with histopathological diagnoses.

Published
2021
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33. Laccase-Mediated In Situ Oxidation of Dopamine for Dyeing of Human Hair

Author

Zhang Xiaoli, Zhijie Liang, Weini Jia, Aixue Dong, Hainan Yu, Zhengxin Luo, Shirong Li, Wenjun Zhu, and Mao Qinghui

Subjects
Laccase, chemistry.chemical_classification, Materials science, integumentary system, Polymers and Plastics, Scanning electron microscope, General Chemical Engineering, 02 engineering and technology, General Chemistry, Polymer, 010402 general chemistry, 021001 nanoscience & nanotechnology, Photochemistry, 01 natural sciences, 0104 chemical sciences, Catalysis, chemistry.chemical_compound, chemistry, Polymerization, Covalent bond, otorhinolaryngologic diseases, Hydroxyl radical, sense organs, Dyeing, 0210 nano-technology
Abstract

Due to the damage of scalp because of chemical dyes, increasing attention had been paid to the ecological dyeing of human hair. To address the need for ecological restoration and dyeing of human hair, herein, a novel approach has been proposed via laccase-mediated in situ oxidation of dihydroxy phenylalanine (dopamine). As laccase can catalyze dopamine oxidation and then polymerization to produce strongly colored polymers, different biological coloration processes of human hair based on this process were investigated: (i) Human hair was treated with laccase alone; (ii) The absorption of human hair with dopamine, followed by further catalysis of air; (iii) The absorption of human hair with dopamine, followed by further catalysis of laccase; (iv) Simultaneous laccase-mediated polymerization and dyeing of human hair; and (v) Polymerization of Dopamine with laccase and then dyeing human hair. The surface properties of dyed hair were evaluated in terms of ultraviolet (UV)-protection, color fastness, Fourier transforming infrared spectrum (FT-IR), optical microscope, and scanning electron microscopy (SEM). The results show that best hair dyeing effect was achieved via simultaneous laccase-mediated polymerization and dyeing. The dyed hair obtained K/S values of 28, light fastness of level 5, and UPF of 100+. The optical microscope and SEM observations show that the surface of colored hair fibers was covered with thin burrs, which can be attributed to polydopamine attached with physical adhesive force and covalent reaction. FT-IR analysis shows that functional groups were present on the surface of the dyed hair which can be considered as introduction of hydroxyl radical groups. This biological stain technique may be widely used in dyeing of hair with black color.

Published
2021
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34. Plant metabolomics for studying the effect of two insecticides on comprehensive constituents of Lonicerae Japonicae Flos

Author

Shen Ji, Shui Miao, Xiu-Hong Mao, Heng Zhou, Qing Hu, De-An Guo, Zhang Xiaoli, and Huiqin Pan

Subjects
Insecticides, Plants, Medicinal, Traditional medicine, biology, Quality assessment, Metabolite, food and beverages, Flos, Flowers, General Medicine, Pesticide, biology.organism_classification, Acetamiprid, Japonica, Lonicera, chemistry.chemical_compound, Ingredient, Metabolomics, Complementary and alternative medicine, chemistry, Drug Discovery, Chromatography, High Pressure Liquid, Drugs, Chinese Herbal
Abstract

Pesticides' overuse and misuse have been reported to induce ingredient variations in herbal medicine, which is now gaining attention in the medicinal field as a form of alternative medicine. To date, available studies on pesticide-induced ingredient variations of herbal medicine are limited only on a few compounds and remain most others unexamined. In this study, a plant metabolomics-based strategy was performed to systematically explore the effects of two frequently used insecticides on the comprehensive constituents of Lonicerae Japonicae Flos (LJF), the flower buds of Lonicera japonica Thunb. Field trials were designed on a cultivating plot of L. japonica with controls and treatments of imidacloprid (IMI) and compound flonicamid and acetamiprid (CFA). Unbiased metabolite profiling was conducted by ultra-high performance liquid chromatography/quadrupole-Orbitrap mass spectrometer. After data pretreatment by automatic extraction and screening, a data matrix of metabolite features was submitted for statistical analyses. Consequently, 29 metabolic markers, including chlorogenic acids, iridoids and organic acid-glucosides were obtained and characterized. The relative quantitative assay was subsequently performed to monitor their variations across flowering developments. This is the first study that systematically explored the insecticide-induced metabolite variations of LJF while taking into account the inherent variability of flowering development. The results were beneficial for holistic quality assessment of LJF and significant for guiding scientific use of pesticides in the large-scale cultivation.

Published
2021
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36. Glutathione Peroxidase 4 as a Therapeutic Target for Anti-Colorectal Cancer Drug-Tolerant Persister Cells

Author

Zhang, Xiaoli, Ma, Yiming, Ma, Jianhui, Yang, Lan, Song, Qingzhi, Wang, Hongying, and Lv, Guoqing

Subjects
Cancer Research, Oncology
Abstract

BackgroundDespite the effectiveness of chemotherapy and targeted therapy for colorectal cancer, drug resistance drives therapy failure and tumor relapse. Increasing evidence has suggested that cancer cells can enter a reversible drug-tolerant persister state to survive chemotherapy or targeted agents. However, the traits and treatable vulnerabilities of anti-colorectal cancer drug-tolerant persister cells is not yet known.MethodsIn this study, we established 5-fluorouracil and AZ628-tolerant persister cell models in two colorectal cancer cell lines, namely HCT116 and SW620, and revealed the characteristics of colorectal cancer persister cells by cell viability assay and flow cytometry. We investigated the efficacy and mechanism of ferroptosis inducers RSL3 and FIN56 on persister cells, which are glutathione peroxidase 4 inhibitors. In the xenograft mouse model, we further evaluated the inhibitory effect of RSL3 on tumor regrowth.ResultsColorectal cancer persister cells, which were enriched in the residual cancer cell population, exhibited reduced drug sensitivity, were largely quiescent and expressed high levels of stem cell-related genes and mesenchymal markers but not epithelial markers. The persister cells were more sensitive and underwent ferroptosis induced by glutathione peroxidase 4 inhibitors. Mechanistically, glutathione peroxidase 4 and ferrous iron, which are pivotal ferroptosis regulators, were upregulated in residual cells or tumors, and were hence potential therapeutic targets of persister cells. In the xenograft model, we confirmed that inhibition of glutathione peroxidase 4 restrained tumor regrowth after discontinuation of anti-cancer drug treatment. Moreover, biopsies obtained from patients with colorectal cancer undergoing neoadjuvant chemoradiotherapy revealed upregulated glutathione peroxidase 4 and ferritin heavy chain 1. High glutathione peroxidase 4 expression correlates with a worse prognosis in colorectal cancer patients.ConclusionsOur work reveals that the upregulated glutathione peroxidase 4 and ferrous iron in anti-colorectal cancer drug-tolerant persister cells were potential therapeutic targets. Glutathione peroxidase 4 inhibition combined with chemotherapy or targeted therapy may be a promising therapy for colorectal cancer.

Published
2022

37. Additional file 1 of Dysbiosis of vaginal microbiota associated with persistent high-risk human papilloma virus infection

Author

Mei, Ling, Wang, Tao, Chen, Yueyue, Wei, Dongmei, Zhang, Yueting, Cui, Tao, Meng, Jian, Zhang, Xiaoli, Liu, Yuqing, Ding, Lisha, and Niu, Xiaoyu

Abstract

Additional file 1. Rarefaction curves. The horizontal axis indicates the sampling depth, and the vertical axis indicates the index. All the curves become flat as the sampling depth increases, indicating that the high sampling coverage was achieved in all samples.

Published
2022
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38. A Multi-Agent Approach for Adaptive Finger Cooperation in Learning-based In-Hand Manipulation

Author

Tao, Lingfeng, Zhang, Jiucai, Bowman, Michael, and Zhang, Xiaoli

Subjects
FOS: Computer and information sciences, Computer Science - Robotics, Robotics (cs.RO)
Abstract

In-hand manipulation is challenging for a multi-finger robotic hand due to its high degrees of freedom and the complex interaction with the object. To enable in-hand manipulation, existing deep reinforcement learning based approaches mainly focus on training a single robot-structure-specific policy through the centralized learning mechanism, lacking adaptability to changes like robot malfunction. To solve this limitation, this work treats each finger as an individual agent and trains multiple agents to control their assigned fingers to complete the in-hand manipulation task cooperatively. We propose the Multi-Agent Global-Observation Critic and Local-Observation Actor (MAGCLA) method, where the critic can observe all agents' actions globally, and the actor only locally observes its neighbors' actions. Besides, conventional individual experience replay may cause unstable cooperation due to the asynchronous performance increment of each agent, which is critical for in-hand manipulation tasks. To solve this issue, we propose the Synchronized Hindsight Experience Replay (SHER) method to synchronize and efficiently reuse the replayed experience across all agents. The methods are evaluated in two in-hand manipulation tasks on the Shadow dexterous hand. The results show that SHER helps MAGCLA achieve comparable learning efficiency to a single policy, and the MAGCLA approach is more generalizable in different tasks. The trained policies have higher adaptability in the robot malfunction test compared to the baseline multi-agent and single-agent approaches., Comment: Submitted to ICRA 2023

Published
2022
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39. sj-pdf-10-jcb-10.1177_0271678X221098811 - Supplemental material for Temporal brain transcriptome analysis reveals key pathological events after germinal matrix hemorrhage in neonatal rats

Author

Song, Juan, Nilsson, Gisela, Xu, Yiran, Zelco, Aura, Rocha-Ferreira, Eridan, Wang, Yafeng, Zhang, Xiaoli, Zhang, Shan, Ek, Joakim, Hagberg, Henrik, Zhu, Changlian, and Wang, Xiaoyang

Subjects
animal structures, 110320 Radiology and Organ Imaging, FOS: Clinical medicine, FOS: Biological sciences, Medicine, Cell Biology, 110305 Emergency Medicine, 110306 Endocrinology, Biochemistry, 69999 Biological Sciences not elsewhere classified, 110904 Neurology and Neuromuscular Diseases, Neuroscience
Abstract

Supplemental material, sj-pdf-10-jcb-10.1177_0271678X221098811 for Temporal brain transcriptome analysis reveals key pathological events after germinal matrix hemorrhage in neonatal rats by Juan Song, Gisela Nilsson, Yiran Xu, Aura Zelco, Eridan Rocha-Ferreira, Yafeng Wang, Xiaoli Zhang, Shan Zhang, Joakim Ek, Henrik Hagberg, Changlian Zhu and Xiaoyang Wang in Journal of Cerebral Blood Flow & Metabolism

Published
2022
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40. sj-pdf-11-jcb-10.1177_0271678X221098811 - Supplemental material for Temporal brain transcriptome analysis reveals key pathological events after germinal matrix hemorrhage in neonatal rats

Author

Song, Juan, Nilsson, Gisela, Xu, Yiran, Zelco, Aura, Rocha-Ferreira, Eridan, Wang, Yafeng, Zhang, Xiaoli, Zhang, Shan, Ek, Joakim, Hagberg, Henrik, Zhu, Changlian, and Wang, Xiaoyang

Subjects
animal structures, 110320 Radiology and Organ Imaging, FOS: Clinical medicine, FOS: Biological sciences, Medicine, Cell Biology, 110305 Emergency Medicine, 110306 Endocrinology, Biochemistry, 69999 Biological Sciences not elsewhere classified, 110904 Neurology and Neuromuscular Diseases, Neuroscience
Abstract

Supplemental material, sj-pdf-11-jcb-10.1177_0271678X221098811 for Temporal brain transcriptome analysis reveals key pathological events after germinal matrix hemorrhage in neonatal rats by Juan Song, Gisela Nilsson, Yiran Xu, Aura Zelco, Eridan Rocha-Ferreira, Yafeng Wang, Xiaoli Zhang, Shan Zhang, Joakim Ek, Henrik Hagberg, Changlian Zhu and Xiaoyang Wang in Journal of Cerebral Blood Flow & Metabolism

Published
2022
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41. Physics-Guided Hierarchical Reward Mechanism for Learning-Based Object Grasping

Author

Jung, Yunsik, Tao, Lingfeng, Bowman, Michael, Zhang, Jiucai, and Zhang, Xiaoli

Subjects
FOS: Computer and information sciences, Artificial Intelligence (cs.AI), Robotics (cs.RO)
Abstract

Learning-based grasping can afford real-time motion planning of multi-fingered robotics hands thanks to its high computational efficiency. However, it needs to explore large search spaces during its learning process. The search space causes low learning efficiency, which has been the main barrier to its practical adoption. In addition, the generalizability of the trained policy is limited unless they are identical or similar to the trained objects. In this work, we develop a novel Physics-Guided Deep Reinforcement Learning with a Hierarchical Reward Mechanism to improve the learning efficiency and generalizability for learning-based autonomous grasping. Unlike conventional observation-based grasp learning, physics-informed metrics are utilized to convey correlations between features associated with hand structures and objects to improve learning efficiency and outcomes. Further, a hierarchical reward mechanism is developed to enable the robot to learn the grasping task in a prioritized way. It is validated in grasping tasks with a MICO robot arm in both simulation and physical experiments. The results show that our method outperformed the standard Deep Reinforcement learning method in task performance by 48% and learning efficiency by 40%.

Published
2022
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44. sj-pdf-11-jcb-10.1177_0271678X221098811 - Supplemental material for Temporal brain transcriptome analysis reveals key pathological events after germinal matrix hemorrhage in neonatal rats

Author

Song, Juan, Nilsson, Gisela, Xu, Yiran, Zelco, Aura, Rocha-Ferreira, Eridan, Wang, Yafeng, Zhang, Xiaoli, Zhang, Shan, Ek, Joakim, Hagberg, Henrik, Zhu, Changlian, and Wang, Xiaoyang

Subjects
animal structures, 110320 Radiology and Organ Imaging, FOS: Clinical medicine, FOS: Biological sciences, Medicine, Cell Biology, 110305 Emergency Medicine, 110306 Endocrinology, Biochemistry, 69999 Biological Sciences not elsewhere classified, 110904 Neurology and Neuromuscular Diseases, Neuroscience
Abstract

Supplemental material, sj-pdf-11-jcb-10.1177_0271678X221098811 for Temporal brain transcriptome analysis reveals key pathological events after germinal matrix hemorrhage in neonatal rats by Juan Song, Gisela Nilsson, Yiran Xu, Aura Zelco, Eridan Rocha-Ferreira, Yafeng Wang, Xiaoli Zhang, Shan Zhang, Joakim Ek, Henrik Hagberg, Changlian Zhu and Xiaoyang Wang in Journal of Cerebral Blood Flow & Metabolism

Published
2022
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45. Risk factors associated with preterm birth after IVF/ICSI

Author

Li, Jian, Shen, Jinhua, Zhang, Xiaoli, Peng, Yangqin, Zhang, Qin, Hu, Liang, Reichetzeder, Christoph, Zeng, Suimin, Li, Jing, and Tian, Mei

Subjects
Risk factors, Physiology, FOS: Biological sciences, Health care, 600 Technik, Medizin, angewandte Wissenschaften::610 Medizin und Gesundheit::610 Medizin und Gesundheit
Abstract

In vitro fertilization/intracytoplasmic sperm injection (IVF/ICSI) is associated with an increased risk of preterm (33rd–37th gestational week) and early preterm birth (20th–32nd gestational week). The underlying general and procedure related risk factors are not well understood so far. 4328 infertile women undergoing IVF/ICSI were entered into this study. The study population was divided into three groups: (a) early preterm birth group (n = 66), (b) preterm birth group (n = 675) and (c) full-term birth group (n = 3653). Odds for preterm birth were calculated by stepwise multivariate logistic regression analysis. We identified seven independent risk factors for preterm birth and four independent risk factors for early preterm birth. Older (> 39) or younger (< 25) maternal age (OR: 1.504, 95% CI 1.108–2.042, P = 0.009; OR: 2.125, 95% CI 1.049–4.304, P = 0.036, respectively), multiple pregnancy (OR: 9.780, 95% CI 8.014–11.935, P < 0.001; OR: 8.588, 95% CI 4.866–15.157, P < 0.001, respectively), placenta previa (OR: 14.954, 95% CI 8.053–27.767, P < 0.001; OR: 16.479, 95% CI 4.381–61.976, P < 0.001, respectively), and embryo reduction (OR: 3.547, 95% CI 1.736–7.249, P = 0.001; OR: 7.145, 95% CI 1.990–25.663, P = 0.003, respectively) were associated with preterm birth and early preterm birth, whereas gestational hypertension (OR: 2.494, 95% CI 1.770–3.514, P < 0.001), elevated triglycerides (OR: 1.120, 95% CI 1.011–1.240, P = 0.030) and shorter activated partial thromboplastin time (OR: 0.967, 95% CI 0.949–0.985, P < 0.001) were associated only with preterm birth. In conclusion, preterm and early preterm birth risk factors in patients undergoing assisted IVF/ICSI are in general similar to those in natural pregnancy. The lack of some associations in the early preterm group was most likely due to the lower number of early preterm birth cases. Only embryo reduction represents an IVF/ICSI specific risk factor.

Published
2022
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46. Additional file 1 of Association of triglyceride-glucose index and stroke recurrence among nondiabetic patients with acute ischemic stroke

Author

Yang, Xiaomeng, Wang, Guangyao, Jing, Jing, Wang, Anxin, Zhang, Xiaoli, Jia, Qian, Meng, Xia, Zhao, Xingquan, Liu, Liping, Li, Hao, Wang, Yongjun, and Wang, Yilong

Abstract

Additional file 1: Table1. Baseline characteristics of nondiabetic acute ischemic stroke patients included versus not included. Table 2. Characteristics of nondiabetic acute ischemic stroke patients without cardioembolism according to TyG index quartiles. Table 3. Adjusted hazard ratio of outcomes within 1 year according to TyG index quartiles in nondiabetic acute ischemic stroke patients without cardioembolism.

Published
2022
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47. sj-pdf-1-trj-10.1177_00405175221143070 - Supplemental material for Study of the surface properties of polypropylene by enzyme treatment

Author

Jia, Weini, Yao, Yiqing, Guo, Jianmin, Zhang, Xiaoli, and Liang, Zhijie

Subjects
FOS: Materials engineering, 91299 Materials Engineering not elsewhere classified
Abstract

Supplemental material, sj-pdf-1-trj-10.1177_00405175221143070 for Study of the surface properties of polypropylene by enzyme treatment by Weini Jia, Yiqing Yao, Jianmin Guo, Xiaoli Zhang, Zhijie Liang in Textile Research Journal

Published
2022
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48. sj-pdf-10-jcb-10.1177_0271678X221098811 - Supplemental material for Temporal brain transcriptome analysis reveals key pathological events after germinal matrix hemorrhage in neonatal rats

Author

Song, Juan, Nilsson, Gisela, Xu, Yiran, Zelco, Aura, Rocha-Ferreira, Eridan, Wang, Yafeng, Zhang, Xiaoli, Zhang, Shan, Ek, Joakim, Hagberg, Henrik, Zhu, Changlian, and Wang, Xiaoyang

Subjects
animal structures, 110320 Radiology and Organ Imaging, FOS: Clinical medicine, FOS: Biological sciences, Medicine, Cell Biology, 110305 Emergency Medicine, 110306 Endocrinology, Biochemistry, 69999 Biological Sciences not elsewhere classified, 110904 Neurology and Neuromuscular Diseases, Neuroscience
Abstract

Supplemental material, sj-pdf-10-jcb-10.1177_0271678X221098811 for Temporal brain transcriptome analysis reveals key pathological events after germinal matrix hemorrhage in neonatal rats by Juan Song, Gisela Nilsson, Yiran Xu, Aura Zelco, Eridan Rocha-Ferreira, Yafeng Wang, Xiaoli Zhang, Shan Zhang, Joakim Ek, Henrik Hagberg, Changlian Zhu and Xiaoyang Wang in Journal of Cerebral Blood Flow & Metabolism

Published
2022
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49. Additional file 1 of Time course of the triglyceride glucose index accumulation with the risk of cardiovascular disease and all-cause mortality

Author

Tian, Xue, Chen, Shuohua, Zhang, Yijun, Zhang, Xiaoli, Xu, Qin, Wang, Penglian, Wu, Shouling, Wang, Anxin, and Luo, Yanxia

Abstract

Additional file 1: Table S1. Baseline characteristics between included and excluded participants. Table S2. Baseline characteristics according to slope of the TyG index. Table S3. Association of time course of cumTyG accumulation with risk of CVD subtypes. Table S4. Association of cumulative accumulation and slope of the TyG index with CVD subtypes. Table S5. Sensitivity analysis by using competing risk model or excluding CVD event during the follow-up. Table S6. Sensitivity analysis by using 2-lagged analysis (n = 50652). Table S7. Sensitivity analysis by excluding participants were treated with antidiabetic or lipid-lowering agents (n = 46686). Table S8. Sensitivity analysis by excluding participants with abnormal FBG or TG levels (n = 33758). Figure S1. The flowchart of the study. Figure S2. Cumulative TyG index and TyG slope calculated across 3 examinations in 1 participants. Average TyG index between consecutive examinations as A1 and A2. Cumulative TyG index was calculated as (A1 × time06–08 + A2 × time08–10), showed by the dotted area, × year. Figure S3. Kaplan-Meier curve of cardiovascular disease subtypes incidence rate by the combination of cumTyG and time course of TyG index accumulation.

Published
2022
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50. Additional file 1 of Association of lipid, inflammatory, and metabolic biomarkers with age at onset for incident cardiovascular disease

Author

Tian, Xue, Chen, Shuohua, Zuo, Yingting, Zhang, Yijun, Zhang, Xiaoli, Xu, Qin, Luo, Yanxia, Wu, Shouling, and Wang, Anxin

Abstract

Additional file 1: Table S1. Baseline characteristics of participants with and without incident CVD. Table S2. Baseline characteristics of participants by different age. Table S3. Incidence rate of CVD in different age group. Table S4. Multivariable-adjusted associations of risk factors with incident CVD by age at onset. Table S5. PAR and 95% CI for cardiovascular disease by risk factors.

Published
2022
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