Association between the related characters of hemispatial neglect and prognosis in stroke patients

OBJECTIVE： To recognize the significances of the classification, evaluation, treatment, pathogenesis, etiological factors and related loci of hemispatial neglect （HSN） in the treatment and prognosis of stroke patients. DATA SOURCES： Articles related to HSN in stroke published in English from January 1985 to December 2002 were searched in Medline database by using the keywords of ＂stroke, hemispatial neglect （HSN）, rehabilitation＂. Chinese relevant professional works and articles were also referred to. STUDY SELECTION： The data were primarily checked. Inclusive criteria： ① articles about the HSN symptoms in stroke; ② articles about the classification, evaluation, treatment, pathogenesis, etiological factors and related loci of HSN, as well as the treatment and prognosis of stroke. The repetitive studies or reviews were excluded. DATA EXTRACTION ： Totally 474 articles related to HSN in stroke were collected, 43 of them were involved and 431 repetitive studies or reviews were excluded. DATA SYNTHESES： HSN can be classified as sensory neglect and motor neglect. Because HSN is caused by the injury of network structure which mediated directed attention, it is generally believed that the inferior parietal Iobule of dominant hemisphere is the most closely correlated with neglect. The main theories related to the pathogenesis of HSN at present include ＂internal instruction＂, ＂directed bradykinesia＂, ＂sensory attention＂, etc. The main clinical manifestations are setover in drawing lines, picture drawing towards one side, imitation towards one side, picture description towards one side, etc., which can be evaluated by the line bisection test, target cancellation test, picture drawing test and pegboard test. The most important thing for the treatment is to make the patients continuously concentrate on the neglected side. CONCLUSION： HSN is an indicator for the bad outcome of cerebrovascular disease, the symptoms take longer time to rehabilitate with slow recovery, but quite a few HSN patients will get good prognosis if timely treatment and proper program are given.

A Study on Genetic Variability, Character Association and Path Co-Efficient Analysis on Morphological and Yield Attributing Characters of Taro [Colocasia esculenta (L.) Schott]

The study investigated genotypic and phenotypic co-efficients of variation, heritability, genetic advance at 5% selection intensity and in percentage of population mean of nine characters (plant height, leaf number, length and width of leaf lamina, number and weight of cormels per plant, weight of corm per plant, dry matter percentage in the tubers and tuber yield per from 14 cultivars of taro [Colocasia esculenta (L.) Schott]). Results indicated highest genotypic co-efficient of variation for dry matter percentage (47.91), which was 95.78% of the phenotypic co-efficient of variation, whereas tuber yield per plant showed the widest range (819.37). Number of cormels per plant and dry matter percentage ehibited considerably higher heritability (84.90% and 91.70%, respectively) and genetic advance (81.19 and 79.00, respectively), indicating the potentiality of selection for improvement of such characters. These two characters were found to be positively correlated to tuber yield per plant. Path analysis revealed that weight of cormels per plant possessed the highest direct effect on tuber yield, indicating the importance of selection based on this character to increase tuber yield per plant.

Isolation and Characterization of the Cytoplasmic Male Sterility Associated Gene of Cotton(Gossypium harknessii)

Cytoplasmic male sterility(CMS) is a maternally inherited trait that results in the failure to produce functional pollen.It was identified in many plants,and it is widely used to exploit heterosis.

Droid Detector:Android Malware Characterization and Detection Using Deep Learning

Smartphones and mobile tablets are rapidly becoming indispensable in daily life. Android has been the most popular mobile operating system since 2012. However, owing to the open nature of Android, countless malwares are hidden in a large number of benign apps in Android markets that seriously threaten Android security. Deep learning is a new area of machine learning research that has gained increasing attention in artificial intelligence. In this study, we propose to associate the features from the static analysis with features from dynamic analysis of Android apps and characterize malware using deep learning techniques. We implement an online deep-learning-based Android malware detection engine（Droid Detector） that can automatically detect whether an app is a malware or not. With thousands of Android apps, we thoroughly test Droid Detector and perform an indepth analysis on the features that deep learning essentially exploits to characterize malware. The results show that deep learning is suitable for characterizing Android malware and especially effective with the availability of more training data. Droid Detector can achieve 96.76% detection accuracy, which outperforms traditional machine learning techniques. An evaluation of ten popular anti-virus softwares demonstrates the urgency of advancing our capabilities in Android malware detection.