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• Somatosensory system reorganization occurs in unilateral motor stroke. • The hemispheric shift of somatosensory processing provides feedback to post-stroke motor control. • The somatosensory system in the brain could be a new target for movement rehabilitation in stroke. Previous studies found that post-stroke motor impairments are associated with damage to the lesioned corticospinal tract and a maladaptive increase in indirect contralesional motor pathways. How the somatosensory system adapts to the change in the use of motor pathways and the role of adaptive sensory feedback to the abnormal movement control of the paretic arm remains largely unknown. We hypothesize that following a unilateral stroke, there is an adaptive hemispheric shift of somatosensory processing toward the contralesional sensorimotor areas to provide sensory feedback support to the contralesional indirect motor pathways. This research could provide new insights related to somatosensory reorganization after stroke, which could enrich future hypothesis-driven therapeutic rehabilitation strategies from a sensory or sensory-motor perspective. Understanding how somatosensory information shifts may provide a target for a novel method to therapeutically prevent and mitigate the emergence and expression of upper limb motor impairments, following a stroke. [ABSTRACT FROM AUTHOR]

Business Intelligence that applies data analytics to generate key information to support business decision making, has been an important area for more than two decades. In the last five years, the trend of “Big Data” has emerged and become a core element of Business Intelligence research. In this article, we review academic literature associated with “Big Data” and “Business Intelligence” to explore the development and research trends. We use bibliometric methods to analyze publications from 1990 to 2017 in journals indexed in Science Citation Index Expanded (SCIE), Social Science Citation Index (SSCI) and Arts & Humanities Citation Index (AHCI). We map the time trend, disciplinary distribution, high-frequency keywords to show emerging topics. The findings indicate that Computer Science and management information systems are two core disciplines that drive research associated with Big Data and Business Intelligence. “Data mining”, “social media” and “information system” are high frequency keywords, but “cloud computing”, “data warehouse” and “knowledge management” are more emphasized after 2016. [ABSTRACT FROM AUTHOR]

In this paper, two kinds of pulse feed including sinusoidal pulse flow (SPF) and intermittent pulse flow (IPF) were applied at the entrance of the ultrafiltration (UF) module with hollow fiber membrane, which were more effective for flux enhancement. As compared to compared to continuous flow at the same average velocity, the improvement of permeation flux could reach up to 80% for SPF and 44% for IPF, respectively. The mechanism of pulse feeding on flux enhancement was explored and a comprehensive analysis of the shear stress distribution along the membrane surface has been performed by a three-dimensional computational fluid dynamics (3D CFD) model. Additionally, comparison of pressure drop between pulse feed and steady flow was done to evaluate energy consumption. Results indicated that sinusoidal pulse flow (SPF) led to higher shear force and higher efficiency in UF process than IPF, which is consistent with experiments. [ABSTRACT FROM AUTHOR]

Deep sequencing of small RNAs (sRNA) is widely used in sRNAs studies in plants. In order to investigate the sequencing frequency variation of sRNAs, the same sRNA samples from rice grains were sequenced twice using deep sequencing technique. The sRNAs were classified into three categories, high abundance (> 100 RPM), medium abundance (10–100 RPM) and low abundance (1–10 RPM). According to the repeat sequencing data of the same sample, highly expressed sRNAs (> 100 RPM) were less subject to random drift, and 95% of the sRNAs Log 2 ratio between two samples fell between -0.649 and 0.558. The same trend was observed in mediumly expressed sRNAs (10–100 RPM), and 95% of the Log 2 ratio fell between -0.535 and 0.759. As to lowly expressed sRNAs (1–10 RPM), 95% of the Log 2 ratio varied between -1.009 and 1.011. These results can be used as a theoretical guide to find differentially expressed sRNAs in sRNA studies in plants. [ABSTRACT FROM AUTHOR]

Abstract: Data mining consists of a set of powerful methods that have been successfully applied to many different application domains, including business, engineering, and bioinformatics. In this paper, we propose an innovative approach that uses genetic algorithms to mine a set of temporal behavior data output by a biological system in order to determine the kinetic parameters of the system. Analyzing the behavior of a biological network is a complicated task. In our approach, the machine learning method is integrated with the framework of system dynamics so that its findings are expressed in a form of system dynamics model. An application of the method to the cell division cycle model has shown that the method can discover approximate parametric values of the system and reproduce the input behavior. [Copyright &y& Elsevier]

Three new compounds, including one daphnane diterpene (1), one sesquiterpene (6), and one lignan (7) have been isolated from the Stellera chamaejasme L., together with five other known compounds, including four daphnane diterpenenoids (2-5) and one lignan (8). The structures of the new compounds were elucidated by spectroscopic analysis. The cytotoxicities of compounds 1-8 towards human lung adenocarcinoma cells (A549 cells) were evaluated using a sulforhodamine B assay. All of the compounds displayed significant cytotoxicity, with IC50 values in the ranging of 0.2nM to 2.0μM. Mechanistic studies revealed that the antitumor activities of compounds 1-3 and 7 were derived from their inhibition of topoisomerase II (Topo II). Furthermore, as a Topo II inhibitor, compound 1 was found to effectively induced G2-M phase cell cycle arrest and apoptosis in cancer cells. [ABSTRACT FROM AUTHOR]

Abstract: Recommendation systems that provide appropriate solutions to users to reduce their decision complexity have become popular in the Internet world. Designing and evaluating such systems remain essential challenges to researchers and practitioners. Toward that end, a critical task is how to obtain user preferences. Mobile phones have become indispensable in everyday life, yet fierce market competition, characterized by rapid introductions of different models with novel designs and advanced features, have made consumers’ purchase decision making increasingly complex. As a well-established, multiple criteria decision technique, analytic hierarchy processing (AHP) provides an intuitive model of a hierarchical structure capable of supporting complex product comparisons and evaluations by consumers. In this paper, we illustrate the application of an AHP-based mechanism to develop a Web-based recommendation system and empirically evaluate the prototype by conducting a controlled experiment with 244 mobile phone users, focusing on both content and system satisfaction. Our evaluation includes benchmark systems built on rank-based analysis and an equal weight-based system as comparative baselines. Overall, the results suggest the viability and value of using AHP to construct effective recommendation systems. Subjects appear satisfied with the recommendations by the AHP-based system, though its relatively demanding input requirements may need mitigation and adequate interface designs. This study contributes to research and practice in recommender systems in general and helps develop mobile phone recommendation systems for online stores and consumers in particular. [ABSTRACT FROM AUTHOR]