Who gets hired by top LIS schools in China?

In this study, we provide evidence and discuss findings regarding talent flow and intellectual diversity in library and information science (LIS) using a faculty hiring network of 274 full-time faculty members from 7 top LIS schools in China. We employ three groups of data items, including the universities they got Ph.D., their Ph.D. programs, and whether their graduation schools are iSchools. We use these to develop a descriptive analysis of the community's educational backgrounds. We show that faculty members in Nankai University are the most diverse, while Wuhan University, Nanjing University, Renmin University of China, and Peking University are experiencing intellectual inbreeding. Wuhan University has sent the largest number of talents to other LIS schools. Top LIS schools in China prefers those who graduated from LIS schools and more than half of the faculty members at each of the top 7 LIS schools graduated from iSchools. Overall, LIS faculty educational backgrounds analysis has considerable value in deriving credible academic hiring and revealing talent flow within the field

Robust saliency detection via regularized random walks ranking

In the field of saliency detection, many graph-based algorithms heavily depend on the accuracy of the pre-processed superpixel segmentation, which leads to significant sacrifice of detail information from the input image. In this paper, we propose a novel bottom-up saliency detection approach that takes advantage of both region-based features and image details. To provide more accurate saliency estimations, we first optimize the image boundary selection by the proposed erroneous boundary removal. By taking the image details and region-based estimations into account, we then propose the regularized random walks ranking to formulate pixel-wised saliency maps from the superpixel-based background and foreground saliency estimations. Experiment results on two public datasets indicate the significantly improved accuracy and robustness of the proposed algorithm in comparison with 12 state-of-the-art saliency detection approaches

Exploring the evolution and characteristics of the ischool movement in china

This study examines the evolution of current interests and emerging characteristics in library and information science (LIS) from Chinese iSchools, including an analysis of the LIS landscape, space distribution, citation, emerging characteristics, and collaborations. This study considers a non-parametric approach to outline the structure of the iSchool movement in China, while clustering analysis helped us obtain information about the descriptions generated within unsupervised learning groups. It was found that Chinese iSchools play an intermediary role in the international development of Chinese LIS, which further promotes the dissemination and exchange of knowledge and international cooperation in LIS.</p

DeepGene: an advanced cancer type classifier based on deep learning and somatic point mutations

BACKGROUND: With the developments of DNA sequencing technology, large amounts of sequencing data have become available in recent years and provide unprecedented opportunities for advanced association studies between somatic point mutations and cancer types/subtypes, which may contribute to more accurate somatic point mutation based cancer classification (SMCC). However in existing SMCC methods, issues like high data sparsity, small volume of sample size, and the application of simple linear classifiers, are major obstacles in improving the classification performance. RESULTS: To address the obstacles in existing SMCC studies, we propose DeepGene, an advanced deep neural network (DNN) based classifier, that consists of three steps: firstly, the clustered gene filtering (CGF) concentrates the gene data by mutation occurrence frequency, filtering out the majority of irrelevant genes; secondly, the indexed sparsity reduction (ISR) converts the gene data into indexes of its non-zero elements, thereby significantly suppressing the impact of data sparsity; finally, the data after CGF and ISR is fed into a DNN classifier, which extracts high-level features for accurate classification. Experimental results on our curated TCGA-DeepGene dataset, which is a reformulated subset of the TCGA dataset containing 12 selected types of cancer, show that CGF, ISR and DNN all contribute in improving the overall classification performance. We further compare DeepGene with three widely adopted classifiers and demonstrate that DeepGene has at least 24% performance improvement in terms of testing accuracy. CONCLUSIONS: Based on deep learning and somatic point mutation data, we devise DeepGene, an advanced cancer type classifier, which addresses the obstacles in existing SMCC studies. Experiments indicate that DeepGene outperforms three widely adopted existing classifiers, which is mainly attributed to its deep learning module that is able to extract the high level features between combinatorial somatic point mutations and cancer types

Cuff-Method Thigh Arterial Occlusion Counteracts Cerebral Hypoperfusion Against the Push–Pull Effect in Humans

Exposure to acute transition from negative (−Gz) to positive (+ Gz) gravity significantly impairs cerebral perfusion in pilots of high-performance aircraft during push—pull maneuver. This push—pull effect may raise the risk for loss of vision or consciousness. The aim of the present study was to explore effective countermeasures against cerebral hypoperfusion induced by the push—pull effect. Twenty healthy young volunteers (male, 21 ± 1 year old) were tested during the simulated push–pull maneuver by tilting. A thigh cuff (TC) pressure of 200 mmHg was applied before and during simulated push—pull maneuver (−0.87 to + 1.00 Gz). Beat-to-beat cerebral and systemic hemodynamics were measured continuously. During rapid −Gz to + Gz transition, mean cerebral blood flow velocity (CBFV) was decreased, but to a lesser extent, in the TC bout compared with the control bout (−3.1 ± 4.9 vs. −7.8 ± 4.4 cm/s, P &lt; 0.001). Similarly, brain-level mean blood pressure showed smaller reduction in the TC bout than in the control bout (−46 ± 12 vs. −61 ± 13 mmHg, P &lt; 0.001). The systolic CBFV was lower but diastolic CBFV was higher in the TC bout. The systemic blood pressure response was blunted in the TC bout, along with similar heart rate increase, smaller decrease, and earlier recovery of total peripheral resistance index than control during the gravitational transition. These data demonstrated that restricting thigh blood flow can effectively mitigate the transient cerebral hypoperfusion induced by rapid shift from −Gz to + Gz, characterized by remarkable improvement of cerebral diastolic flow