Southeast University

About: Southeast University is a education organization based out in Nanjing, China. It is known for research contribution in the topics: Computer science & MIMO. The organization has 66363 authors who have published 79434 publications receiving 1170576 citations. The organization is also known as: SEU.

Link prediction in social networks: the state-of-the-art

Abstract: In social networks, link prediction predicts missing links in current networks and new or dissolution links in future networks, is important for mining and analyzing the evolution of social networks. In the past decade, many works have been done about the link prediction in social networks. The goal of this paper is to comprehensively review, analyze and discuss the state-of-the-art of the link prediction in social networks. A systematical category for link prediction techniques and problems is presented. Then link prediction techniques and problems are analyzed and discussed. Typical applications of link prediction are also addressed. Achievements and roadmaps of some active research groups are introduced. Finally, some future challenges of the link prediction in social networks are discussed.

MiPred: classification of real and pseudo microRNA precursors using random forest prediction model with combined features.

Abstract: To distinguish the real pre-miRNAs from other hairpin sequences with similar stem-loops (pseudo pre-miRNAs), a hybrid feature which consists of local contiguous structure-sequence composition, minimum of free energy (MFE) of the secondary structure and P-value of randomization test is used. Besides, a novel machine-learning algorithm, random forest (RF), is introduced. The results suggest that our method predicts at 98.21% specificity and 95.09% sensitivity. When compared with the previous study, Triplet-SVM-classifier, our RF method was nearly 10% greater in total accuracy. Further analysis indicated that the improvement was due to both the combined features and the RF algorithm. The MiPred web server is available at http://www.bioinf.seu.edu.cn/miRNA/. Given a sequence, MiPred decides whether it is a pre-miRNA-like hairpin sequence or not. If the sequence is a pre-miRNA-like hairpin, the RF classifier will predict whether it is a real pre-miRNA or a pseudo one.

Critical care crisis and some recommendations during the COVID-19 epidemic in China

Abstract: Lack of critical care resource in face of COVID‐19 epidemics Based on data reported by the National Health Commission of China, there have been about 2000 new confirmed cases and > 4000 suspected cases daily over the past week in Wuhan [3]. About 15% of the patients have developed severe pneumonia, and about 6% need noninvasive or invasive ventilatory support. Currently, there are about 1000 patients who need ventilatory support and another 120 new patients daily who require noninvasive or invasive ventilation support in Wuhan city; however, there are only about 600 ICU beds [4]. To address this shortfall, 70 ICU beds were created from general beds and the government quickly transformed three general hospitals to critical care hospitals with a total of about 2500 beds that specialize in patients with severe SARS-CoV-2 pneumonia (equipped with monitors and high-flow nasal cannula, noninvasive ventilator or invasive ventilators). An equally great (or potentially greater) problem is the shortage of trained personnel to treat these critically ill patients. Until the crisis, there were about 300 ICU physicians and 1000 ICU nurses in Wuhan city. By the end of January, more than 600 additional ICU doctors and 1500 ICU nurses were transferred to Wuhan from the rest of China. As well, an additional 3000 staff including infectious disease, respiratory, internal medicine physicians and nurses were transferred to Wuhan by the government. There are logistical issues which make care of the patients difficult. These include donning of personal protective equipment (e.g., gloves, gowns, respiratory and eye protection), lack of instruments and disposables, and shortages of supplemental oxygen. Many severe hypoxemic patients only receive high-flow nasal oxygen (HFNO) or noninvasive mechanical ventilation rather than invasive mechanical ventilation because of intubation delay or lack of mechanical ventilators (especially at early phase). Our preliminary data show that only about 25% of patients who died were intubated and received mechanical ventilation.