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Sentiment analysis in Arabic is challenging due to the complex morphology of the language. The task becomes more challenging when considering Twitter data that contain significant amounts of noise such as the use of Arabizi, code-switching and different dialects that varies significantly across the Arab world, the use of non-Textual objects to express sentiments, and the frequent occurrence of misspellings and grammatical mistakes. Modeling sentiment in Twitter should become easier when we understand the characteristics of Twitter data and how its usage varies from one Arab region to another. We describe our effort to create the first Multi-Dialect Arabic Sentiment Twitter Dataset (MD-ArSenTD) that is composed of tweets collected from 12 Arab countries, annotated for sentiment and dialect. We use this dataset to analyze tweets collected from Egypt and the United Arab Emirates (UAE), with the aim of discovering distinctive features that may facilitate sentiment analysis. We also perform a comparative evaluation of different sentiment models on Egyptian and UAE tweets. These models are based on feature engineering and deep learning, and have already achieved state-of-The-Art accuracies in English sentiment analysis. Results indicate the superior performance of deep learning models, the importance of morphological features in Arabic NLP, and that handling dialectal Arabic leads to different outcomes depending on the country from which the tweets are collected. This work was made possible by NPRP 6-716-1-138 grant from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the authors. Scopus

Opinion mining in Arabic is a challenging task given the rich morphology of the language. The task becomes more challenging when it is applied to Twitter data, which contains additional sources of noise, such as the use of unstandardized dialectal variations, the non-conformation to grammatical rules, the use of Arabizi and code-switching, and the use of non-text objects such as images and URLs to express opinion. In this paper, we perform an analytical study to observe how such linguistic phenomena vary across different Arab regions. This study of Arabic Twitter characterization aims at providing better understanding of Arabic Tweets, and fostering advanced research on the topic. Furthermore, we explore the performance of the two schools of machine learning on Arabic Twitter, namely the feature engineering approach and the deep learning approach. We consider models that have achieved state-of-the-art performance for opinion mining in English. Results highlight the advantages of using deep learning-based models, and confirm the importance of using morphological abstractions to address Arabic's complex morphology. 2017 Association for Computational Linguistics This work was made possible by NPRP 6-716-1-138 grant from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the authors. Scopus

A clinical program in liver transplantation was begun at UCLA in 1984 after a period of laboratory investigation. The first 100 orthotopic liver transplants (OLT) were performed in 83 patients (43 adults and 40 children) between February 1, 1984 and November 1, 1986. Donors and recipients were matched only for size and ABO blood group compatibility, with OLT performed across blood groups in 28 patients. Standard operative techniques were used, including venous-venous bypass in adults. Arterial reconstruction was performed using an aortic Carrel patch or "branch patch" in 65% of cases and by end-to-end or aortic conduit techniques in the remainder. The hepatic artery thrombosis rate was 5%. Biliary reconstruction was choledochocholedochostomy in 67 OLT and Roux-en-Y choledochojejunostomy in 33 (complication rate of 24% and 24%, respectively). Average lengths and ranges of donor liver ischemia, operating time, and blood replacement were 4 hours (range: 1-10 hours), 7.6 hours (range: 4-15 hours), and 17 units packed cells (range: 2-220 units). Immunosuppressive regimen was cyclosporine-steroid combination, with monoclonal anti-T-cell antibody (OKT3) used for refractory rejection. All patients had one or more complications: pulmonary (78%), infectious (51%), renal dialysis (25%), neurologic (22%). All patients had at least one episode of acute rejection, and 3.6% had chronic rejection. Retransplantation was needed in nine patients once and in four patients twice. The overall retransplant survival rate was 54%, and two of four patients who received a second retransplant are alive. Sixty-three of the 83 patients (76%) are alive (adults 72%, children 80%). The 1- and 2-year actuarial survival rate is 73% (adults 68%, children 78%). Thirty-eight of 43 patients (88%) who had transplantation in the past year are alive. Of 14 perioperative variables assessed as predictors of early mortality, only postoperative dialysis (p less than 0.0005) and presence of severe rejection (p less than 0.01) had statistical significance. Seventy per cent of adults returned to work, and 84% of children had normal or accelerated growth. A new program in liver transplantation provides a dramatic option in patient care and an academic stimulus to the entire medical center.