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We investigated how elevated CO 2 affects the responses of Gracilariopsis lemaneiformis and Ulva lactuca to NH 4 + enrichments. All algae were incubated under four nutritional conditions (zero addition, 100, 500, and 2500 μM NH 4 +), and two CO 2 levels (390 ppm and 1000 ppm). The growth, photosynthesis, and soluble protein contents of both species increased under the eutrophication condition (100 μM NH 4 +). However, the growth and carotenoid contents of the two species declined when NH 4 + concentration increased. Under the super eutrophication condition (2500 μM NH 4 +), all indexes measured in G. lemaneiformis were suppressed, while the growth and photosynthesis in U. lactuca changed indistinctively, both compared with the control. Moreover, under the super eutrophication condition, elevated CO 2 reduced the suppression in the growth of G. lemaneiformis , but decreased the growth of U. lactuca. Nonetheless, G. lemaneiformis displayed much lower growth rates than U. lactuca under the super eutrophication and elevated CO 2 condition. • Responses of two algal species to NH 4 + and CO 2 enrichments were investigated. • 100 μmol L−1 NH 4 + enhanced growth and photosynthesis in both algal species. • 2500 μmol L−1NH 4 + was toxic to Gracilariopsis lemaneiformis but not Ulva lactuca. • Elevated CO 2 reduced the toxicity of 2500 μmol L−1 NH 4 + to G. lemaneiformis. [ABSTRACT FROM AUTHOR]

MicroRNAs (miRNAs) play a crucial role in the life processes of organisms and affect the occurrence of many complex human diseases. Discovering potential links between diseases and miRNAs can aid in the prevention, diagnosis, and therapy of complex human diseases. Identifying diseases and miRNA associations through existing biological experiments is expensive, while scientific computing approaches can quickly and efficiently predict the potential links between diseases and miRNAs. We propose SODNMF-DM, a SODNMF based Disease-MiRNA matrix factorisation algorithm. The SODNMF-DM model exhibited an AUC of 0.9051 and an AUPR of 0.4774, respectively. To directly verify the effectiveness of SODNMF-DM, we studied three common human cancers. The experimental results showed that 25 of the top 30 potential miRNAs predicted by our proposed SODNMF-DM model were confirmed by HMDDv3.0 database. Conclusion: SODNMF-DM outperformed four other approaches in AUC and AUPR. In addition, our proposed approach exhibited good performance in practical cases. The approach presented in this study is thus expected to complement the field of biological research. • We fully consider disease similarity and miRNA similarity from multiple perspectives. • We use the alternating gradient descent method to ensure the stability of the solution. • The SODNMF-DM model has low overall complexity, simple operation and easy replication. [ABSTRACT FROM AUTHOR]

App usage is now a ubiquitous phenomenon, but little is known about what types of psychological needs are met by which apps. We proposed a method to label automatically mobile apps in terms of whether and to what extent they can satisfy users’ particular psychological needs. First, using the grounded theory approach, we conducted semi-structured in-depth interviews to identify types of needs associated with app usage. Substantive and theoretical coding of the data from the interviews as well as data from samples of app reviews yielded eight types of psychological needs app users had: utilitarian, low-cost, security, health, hedonic, social, cognitive, and self-actualization needs. Second, using the needs corpus (words and phrases) generated above, a classifier was trained using latent Dirichlet allocation (LDA) and support vector machine (SVM) algorithms to filter reviews in terms of whether they included needs-related comments. The classifier showed good performance. Finally, Labeled-LDA was used to automatically provide each review with multiple labels of the types of needs mentioned and the apps were analyzed for the different types of needs they satisfied. [ABSTRACT FROM AUTHOR]

Acute pancreatitis is an inflammatory disorder of the pancreas. Medical imaging, such as computed tomography (CT), has been widely used to detect volume changes in the pancreas for acute pancreatitis diagnosis. Many pancreas segmentation methods have been proposed but no methods for pancreas segmentation from acute pancreatitis patients. The segmentation of an inflamed pancreas is more challenging than the normal pancreas due to the following two reasons. 1) The inflamed pancreas invades surrounding organs and causes blurry boundaries. 2) The inflamed pancreas has higher shape, size, and location variability than the normal pancreas. To overcome these challenges, we propose an automated CT pancreas segmentation approach for acute pancreatitis patients by combining a novel object detection approach and U-Net. Our approach includes a detector and a segmenter. Specifically, we develop an FCN-guided region proposal network (RPN) detector to localize the pancreatitis regions. The detector first uses a fully convolutional network (FCN) to reduce the background interference of medical images and generates a fixed feature map containing the acute pancreatitis regions. Then the RPN is employed on the feature map to precisely localize the acute pancreatitis regions. After obtaining the location of pancreatitis, the U-Net segmenter is used on the cropped image according to the bounding box. The proposed approach is validated using a collected clinical dataset with 89 abdominal contrast-enhanced 3D CT scans from acute pancreatitis patients. Compared with other start-of-the-art approaches for normal pancreas segmentation, our method achieves better performance on both localization and segmentation in acute pancreatitis patients. [ABSTRACT FROM AUTHOR]