Investigating the Impacts of Recommendation Agents on Impulsive Purchase Behaviour

The usage of recommendation agents (RAs) in the online marketplace can help consumers to locate their desired products. RAs can help consumers effectively obtain comprehensive product information and compare their candidate target products. As a result, RAs have affected consumers’ shopping behaviour. In this study, we investigate the usage and the influence of RAs in the online marketplace. Based on the Stimulus-Organism-Response (SOR) model, we propose that the stimulus of using RAs (informativeness, product search effectiveness and the lack of sociality stress) can affect consumers’ attitude (perceived control and satisfaction), which further affects their behavioural outcomes like impulsive purchase. We validate this research model with survey data from 157 users of RAs. The data largely support the proposed model and indicate that the RAs can significantly contribute to impulsive purchase behaviour in online marketplaces. Theoretical and practical contributions are discussed

Distributed functions of prefrontal and parietal cortices during sequential categorical decisions

Comparing sequential stimuli is crucial for guiding complex behaviors. To understand mechanisms underlying sequential decisions, we compared neuronal responses in the prefrontal cortex (PFC), the lateral intraparietal (LIP), and medial intraparietal (MIP) areas in monkeys trained to decide whether sequentially presented stimuli were from matching (M) or nonmatching (NM) categories. We found that PFC leads M/NM decisions, whereas LIP and MIP appear more involved in stimulus evaluation and motor planning, respectively. Compared to LIP, PFC showed greater nonlinear integration of currently visible and remembered stimuli, which correlated with the monkeys’ M/NM decisions. Furthermore, multi-module recurrent networks trained on the same task exhibited key features of PFC and LIP encoding, including nonlinear integration in the PFC-like module, which was causally involved in the networks’ decisions. Network analysis found that nonlinear units have stronger and more widespread connections with input, output, and within-area units, indicating putative circuit-level mechanisms for sequential decisions

LncRNA-Disease Association Prediction Using Two-Side Sparse Self-Representation

Evidences increasingly indicate the involvement of long non-coding RNAs (lncRNAs) in various biological processes. As the mutations and abnormalities of lncRNAs are closely related to the progression of complex diseases, the identification of lncRNA-disease associations has become an important step toward the understanding and treatment of diseases. Since only a limited number of lncRNA-disease associations have been validated, an increasing number of computational approaches have been developed for predicting potential lncRNA-disease associations. However, how to predict potential associations precisely through computational approaches remains challenging. In this study, we propose a novel two-side sparse self-representation (TSSR) algorithm for lncRNA-disease association prediction. By learning the self-representations of lncRNAs and diseases from known lncRNA-disease associations adaptively, and leveraging the information provided by known lncRNA-disease associations and the intra-associations among lncRNAs and diseases derived from other existing databases, our model could effectively utilize the estimated representations of lncRNAs and diseases to predict potential lncRNA-disease associations. The experiment results on three real data sets demonstrate that our TSSR outperforms other competing methods significantly. Moreover, to further evaluate the effectiveness of TSSR in predicting potential lncRNAs-disease associations, case studies of Melanoma, Glioblastoma, and Glioma are carried out in this paper. The results demonstrate that TSSR can effectively identify some candidate lncRNAs associated with these three diseases

N-(4-Chloro-2-nitro­phen­yl)-5-methyl­isoxazole-4-carboxamide

In the title compound, C11H8ClN3O4, the dihedral angle between benzene and isoxazole rings is 9.92 (1) °. The nitro group is almost coplanar with the benzene ring with an O—N—C—C torsion angle of 8.4 (3)°. The mol­ecular conformation is stabilized by an intra­molecular N—H⋯O hydrogen bond, closing a six-membered ring

5-Methyl-N-[2-(trifluoro­meth­yl)phen­yl]isoxazole-4-carboxamide

In the title compound, C12H9F3N2O2, the benzene ring is nearly perpendicular to the isoxazole ring, making a dihedral angle of 82.97 (2)°. In the crystal, mol­ecules are linked by N—H⋯O hydrogen bonds into a supra­molecular chain running along the c axis

RepLong - de novo repeat identification using long read sequencing data

Abstract Motivation The identification of repetitive elements is important in genome assembly and phylogenetic analyses. The existing de novo repeat identification methods exploiting the use of short reads are impotent in identifying long repeats. Since long reads are more likely to cover repeat regions completely, using long reads is more favorable for recognizing long repeats. Results In this study, we propose a novel de novo repeat elements identification method namely RepLong based on PacBio long reads. Given that the reads mapped to the repeat regions are highly overlapped with each other, the identification of repeat elements is equivalent to the discovery of consensus overlaps between reads, which can be further cast into a community detection problem in the network of read overlaps. In RepLong, we first construct a network of read overlaps based on pair-wise alignment of the reads, where each vertex indicates a read and an edge indicates a substantial overlap between the corresponding two reads. Secondly, the communities whose intra connectivity is greater than the inter connectivity are extracted based on network modularity optimization. Finally, representative reads in each community are extracted to form the repeat library. Comparison studies on Drosophila melanogaster and human long read sequencing data with genome-based and short-read-based methods demonstrate the efficiency of RepLong in identifying long repeats. RepLong can handle lower coverage data and serve as a complementary solution to the existing methods to promote the repeat identification performance on long-read sequencing data. Availability and implementation The software of RepLong is freely available at https://github.com/ruiguo-bio/replong. Supplementary information Supplementary data are available at Bioinformatics online. </jats:sec