J.M.K. leads both clinical and research groups in the Department of Heart Rhythm Disorders at the Royal Melbourne Hospital and University of Melbourne, Melbourne, Australia. He has an international reputation as a leader in the field of atrial arrhythmia research and has authored 380 peer-reviewed publications. He serves on the editorial board of 12 international cardiology journals and is an associate editor of JACC Clinical Electrophysiology. He is the immediate past president of the Asia Pacific Heart Rhythm Society and served as scientific chair of the Cardiac Society of Australia and New Zealand for 6 years. S.L. is Professor in Biochemistry & Molecular Biology at the Faculty of Chemical & Pharmaceutical Sciences and Professor in Cell & Molecular Biology in the Faculty of Medicine, University of Chile in Santiago, Chile and adjunct professor in the Cardiology Division, University of Texas Southwestern Medical Center in Dallas, USA.
Bakas, Spyridon, Reyes, Mauricio, Jakab, Andras, Bauer, Stefan, Rempfler, Markus, Crimi, Alessandro, Shinohara, Russell Takeshi, Berger, Christoph, Ha, Sung Min, Rozycki, Martin, Prastawa, Marcel, Alberts, Esther, Lipkova, Jana, Freymann, John, Kirby, Justin, Bilello, Michel, Fathallah-Shaykh, Hassan, Wiest, Roland, Kirschke, Jan, Wiestler, Benedikt, Colen, Rivka, Kotrotsou, Aikaterini, Lamontagne, Pamela, Marcus, Daniel, Milchenko, Mikhail, Nazeri, Arash, Weber, Marc-Andre, Mahajan, Abhishek, Baid, Ujjwal, Kwon, Dongjin, Agarwal, Manu, Alam, Mahbubul, Albiol, Alberto, Albiol, Antonio, Alex, Varghese, Tran, Tuan Anh, Arbel, Tal, Avery, Aaron, B., Pranjal, Banerjee, Subhashis, Batchelder, Thomas, Batmanghelich, Kayhan, Battistella, Enzo, Bendszus, Martin, Benson, Eze, Bernal, Jose, Biros, George, Cabezas, Mariano, Chandra, Siddhartha, Chang, Yi-Ju, Chazalon, Joseph, Chen, Shengcong, Chen, Wei, Chen, Jefferson, Cheng, Kun, Christoph, Meinel, Chylla, Roger, Clérigues, Albert, Costa, Anthony, Cui, Xiaomeng, Dai, Zhenzhen, Dai, Lutao, Deutsch, Eric, Ding, Changxing, Dong, Chao, Dudzik, Wojciech, Estienne, Théo, Shin, Hyung Eun, Everson, Richard, Fabrizio, Jonathan, Fang, Longwei, Feng, Xue, Fidon, Lucas, Fridman, Naomi, Fu, Huan, Fuentes, David, Gering, David G, Gao, Yaozong, Gates, Evan, Gholami, Amir, Gong, Mingming, González-Villá, Sandra, Pauloski, J. Gregory, Guan, Yuanfang, Guo, Sheng, Gupta, Sudeep, Thakur, Meenakshi H, Maier-Hein, Klaus H., Han, Woo-Sup, He, Huiguang, Hernández-Sabaté, Aura, Herrmann, Evelyn, Himthani, Naveen, Hsu, Winston, Hsu, Cheyu, Hu, Xiaojun, Hu, Xiaobin, Hu, Yan, Hu, Yifan, Hua, Rui, Huang, Teng-Yi, Huang, Weilin, Huo, Quan, HV, Vivek, Isensee, Fabian, Islam, Mobarakol, Albiol, Francisco J., Wang, Chiatse J., Jambawalikar, Sachin, Jose, V Jeya Maria, Jian, Weijian, Jin, Peter, Jungo, Alain, Nuechterlein, Nicholas K, Kao, Po-Yu, Kermi, Adel, Keutzer, Kurt, Khened, Mahendra, Kickingereder, Philipp, King, Nik, Knapp, Haley, Knecht, Urspeter, Kohli, Lisa, Kong, Deren, Kong, Xiangmao, Koppers, Simon, Kori, Avinash, Krishnamurthi, Ganapathy, Kumar, Piyush, Kushibar, Kaisar, Lachinov, Dmitrii, Lee, Joon, Lee, Chengen, Lee, Yuehchou, Lefkovits, Szidonia, Lefkovits, Laszlo, Li, Tengfei, Li, Hongwei, Li, Wenqi, Li, Hongyang, Li, Xiaochuan, Lin, Zheng-Shen, Lin, Fengming, Liu, Chang, Liu, Boqiang, Liu, Xiang, Liu, Mingyuan, Liu, Ju, Lladó, Xavier, Luo, Lin, Iftekharuddin, Khan M., Tsai, Yuhsiang M., Ma, Jun, Ma, Kai, Mackie, Thomas, Mahmoudi, Issam, Marcinkiewicz, Michal, McKinley, Richard, Mehta, Sachin, Mehta, Raghav, Meier, Raphael, Merhof, Dorit, Meyer, Craig, Mitra, Sushmita, Moiyadi, Aliasgar, Mrukwa, Grzegorz, Monteiro, Miguel A. B., Myronenko, Andriy, Carver, Eric N, Nalepa, Jakub, Ngo, Thuyen, Niu, Chen, Oermann, Eric, Oliveira, Arlindo, Oliver, Arnau, Ourselin, Sebastien, French, Andrew P., Pound, Michael P., Pridmore, Tony P., Serrano-Rubio, Juan Pablo, Paragios, Nikos, Paschke, Brad, Pei, Linmim, Peng, Suting, Pham, Bao, Piella, Gemma, Pillai, G. N., Piraud, Marie, Popli, Anmol, Prčkovska, Vesna, Puch, Santi, Puybareau, Élodie, Qiao, Xu, Suter, Yannick R, Scott, Matthew R., Rane, Swapnil, Rebsamen, Michael, Ren, Hongliang, Ren, Xuhua, Rezaei, Mina, Lorenzo, Pablo Ribalta, Rippel, Oliver, Robert, Charlotte, Choudhury, Ahana Roy, Jackson, Aaron S., Manjunath, B. S., Salem, Mostafa, Salvi, Joaquim, Sánchez, Irina, Schellingerhout, Dawid, Shboul, Zeina, Shen, Haipeng, Shen, Dinggang, Shenoy, Varun, Shi, Feng, Shu, Hai, Snyder, James, Han, Il Song, Soni, Mehul, Stawiaski, Jean, Subramanian, Shashank, Sun, Li, Sun, Roger, Sun, Jiawei, Sun, Kay, Sun, Yu, Sun, Guoxia, Sun, Shuang, Park, Moo Sung, Szilagyi, Laszlo, Talbar, Sanjay, Tao, Dacheng, Tao, Dacheng, Khadir, Mohamed Tarek, Thakur, Siddhesh, Tochon, Guillaume, Tran, Tuan, Tseng, Kuan-Lun, Turlapov, Vadim, Tustison, Nicholas, Shankar, B. Uma, Vakalopoulou, Maria, Valverde, Sergi, Vanguri, Rami, Vasiliev, Evgeny, Vercauteren, Tom, Vidyaratne, Lasitha, Vivekanandan, Ajeet, Wang, Guotai, Wang, Qian, Wang, Weichung, Wen, Ning, Wen, Xin, Weninger, Leon, Wick, Wolfgang, Wu, Shaocheng, Wu, Qiang, Xia, Yong, Xu, Yanwu, Xu, Xiaowen, Xu, Peiyuan, Yang, Tsai-Ling, Yang, Xiaoping, Yang, Hao-Yu, Yang, Junlin, Yang, Haojin, Yao, Hongdou, Young-Moxon, Brett, Yue, Xiangyu, Zhang, Songtao, Zhang, Angela, Zhang, Kun, Zhang, Xuejie, Zhang, Lichi, Zhang, Xiaoyue, Zhao, Sicheng, Zhao, Yu, Zheng, Yefeng, Zhong, Liming, Zhou, Chenhong, Zhou, Xiaobing, Zhu, Hongtu, Zong, Weiwei, Kalpathy-Cramer, Jayashree, Farahani, Keyvan, Davatzikos, Christos, van Leemput, Koen, Menze, Bjoern
Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrotic core, active and non-enhancing core. This intrinsic heterogeneity is also portrayed in their radio-phenotype, as their sub-regions are depicted by varying intensity profiles disseminated across multi-parametric magnetic resonance imaging (mpMRI) scans, reflecting varying biological properties. Their heterogeneous shape, extent, and location are some of the factors that make these tumors difficult to resect, and in some cases inoperable. The amount of resected tumor is a factor also considered in longitudinal scans, when evaluating the apparent tumor for potential diagnosis of progression. Furthermore, there is mounting evidence that accurate segmentation of the various tumor sub-regions can offer the basis for quantitative image analysis towards prediction of patient overall survival. This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e. 2012-2018. Specifically, we focus on i) evaluating segmentations of the various glioma sub-regions in pre-operative mpMRI scans, ii) assessing potential tumor progression by virtue of longitudinal growth of tumor sub-regions, beyond use of the RECIST criteria, and iii) predicting the overall survival from pre-operative mpMRI scans of patients that undergone gross total resection. Finally, we investigate the challenge of identifying the best ML algorithms for each of these tasks, considering that apart from being diverse on each instance of the challenge, the multi-institutional mpMRI BraTS dataset has also been a continuously evolving/growing dataset.
In October 2016, the Council of the American Association for the Advancement of Science (AAAS) elected 391 members as Fellows of AAAS. These individuals will be recognized for their contributions to science and technology at the Fellows Forum to be held on 18 February 2017 during the AAAS Annual Meeting in Boston, MA. Herman B. Zimmerman, National Science Foundation (retired)
The implementation, data privacy, and operational challenges facing life science and healthcare professionals dedicated to integrating AI into their organization vastly differ by therapeutic area and patient population, size of the organization, and the number of resources available to them. Cookie-cutter solutions cannot address the unique challenges faced by a company. It is critical that the education available to these professionals meets the varying needs of the industry.
Understanding how human genetic variation affects phenotype requires tissue- or even cell type–specific measurements. Kim-Hellmuth et al. used computational methods to identify cell-type proportions within bulk tissues in the Genotype-Tissue Expression (GTEx) project dataset to identify cell-type interaction quantitative trait loci and map these to genetic variants correlated with expression or splicing differences between individuals. By characterizing the cellular context, this study illustrates how genetic variants that operate in a cell type–specific manner affect gene regulation and can be linked to complex traits. This deconvolution and analysis of cell types from bulk tissues allows greater precision in understanding how phenotypes are linked to genetic variation. Science , this issue p. [eaaz8528] ### INTRODUCTION Efforts to map quantitative trait loci (QTLs) across human tissues by the GTEx Consortium and others have identified expression and splicing QTLs (eQTLs and sQTLs, respectively) for a majority of genes. However, these studies were largely performed with gene expression measurements from bulk tissue samples, thus obscuring the cellular specificity of genetic regulatory effects and in turn limiting their functional interpretation. Identifying the cell type (or types) in which a QTL is active will be key to uncovering the molecular mechanisms that underlie complex trait variation. Recent studies demonstrated the feasibility of identifying cell type–specific QTLs from bulk tissue RNA-sequencing data by using computational estimates of cell type proportions. To date, such approaches have only been applied to a limited number of cell types and tissues. By applying this methodology to GTEx tissues for a diverse set of cell types, we aim to characterize the cellular specificity of genetic effects across human tissues and to describe the contribution of these effects to complex traits. ### RATIONALE A growing number of in silico cell type deconvolution methods and associated reference panels with cell type–specific marker genes enable the robust estimation of the enrichment of specific cell types from bulk tissue gene expression data. We benchmarked and used enrichment estimates for seven cell types (adipocytes, epithelial cells, hepatocytes, keratinocytes, myocytes, neurons, and neutrophils) across 35 tissues from the GTEx project to map QTLs that are specific to at least one cell type. We mapped such cell type–interaction QTLs for expression and splicing (ieQTLs and isQTLs, respectively) by testing for interactions between genotype and cell type enrichment. ### RESULTS Using 43 pairs of tissues and cell types, we found 3347 protein-coding and long intergenic noncoding RNA (lincRNA) genes with an ieQTL and 987 genes with an isQTL (at 5% false discovery rate in each pair). To validate these findings, we tested the QTLs for replication in available external datasets and applied an independent validation using allele-specific expression from eQTL heterozygotes. We analyzed the cell type–interaction QTLs for patterns of tissue sharing and found that ieQTLs are enriched for genes with tissue-specific eQTLs and are generally not shared across unrelated tissues, suggesting that tissue-specific eQTLs originate in tissue-specific cell types. Last, we tested the ieQTLs and isQTLs for colocalization with genetic associations for 87 complex traits. We show that cell type–interaction QTLs are enriched for complex trait associations and identify colocalizations for hundreds of loci that were undetected in bulk tissue, corresponding to an increase of >50% over colocalizations with standard QTLs. Our results also reveal the cellular specificity and potential origin for a similar number of colocalized standard QTLs. ### CONCLUSION The ieQTLs and isQTLs identified for seven cell types across GTEx tissues suggest that the large majority of cell type–specific QTLs remains to be discovered. Our colocalization results indicate that comprehensive mapping of cell type–specific QTLs will be highly valuable for gaining a mechanistic understanding of complex trait associations. We anticipate that the approaches presented here will complement studies mapping QTLs in single cells. ![Figure] Detection of cell type–specific effects on gene expression. The enrichment of seven cell types is calculated across GTEx tissues, enabling mapping of cell type–interaction QTLs for expression and splicing by testing for significant interactions between genotypes and cell type enrichments. Linking these QTLs to complex trait associations enables discovery of >50% more colocalizations compared with standard QTLs and reveals the cellular specificity of traits. The Genotype-Tissue Expression (GTEx) project has identified expression and splicing quantitative trait loci in cis (QTLs) for the majority of genes across a wide range of human tissues. However, the functional characterization of these QTLs has been limited by the heterogeneous cellular composition of GTEx tissue samples. We mapped interactions between computational estimates of cell type abundance and genotype to identify cell type–interaction QTLs for seven cell types and show that cell type–interaction expression QTLs (eQTLs) provide finer resolution to tissue specificity than bulk tissue cis-eQTLs. Analyses of genetic associations with 87 complex traits show a contribution from cell type–interaction QTLs and enables the discovery of hundreds of previously unidentified colocalized loci that are masked in bulk tissue. : /lookup/doi/10.1126/science.aaz8528 : pending:yes