Exploring demographic information in social media for product recommendation

In many e-commerce Web sites, product recommendation is essential to improve user experience and boost sales. Most existing product recommender systems rely on historical transaction records or Web-site-browsing history of consumers in order to accurately predict online users’ preferences for product recommendation. As such, they are constrained by limited information available on specific e-commerce Web sites. With the prolific use of social media platforms, it now becomes possible to extract product demographics from online product reviews and social networks built from microblogs. Moreover, users’ public profiles available on social media often reveal their demographic attributes such as age, gender, and education. In this paper, we propose to leverage the demographic information of both products and users extracted from social media for product recommendation. In specific, we frame recommendation as a learning to rank problem which takes as input the features derived from both product and user demographics. An ensemble method based on the gradient-boosting regression trees is extended to make it suitable for our recommendation task. We have conducted extensive experiments to obtain both quantitative and qualitative evaluation results. Moreover, we have also conducted a user study to gauge the performance of our proposed recommender system in a real-world deployment. All the results show that our system is more effective in generating recommendation results better matching users’ preferences than the competitive baselines

Effect of a semiconductor electrode on the tunneling electroresistance in ferroelectric tunneling junction

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Broad-Spectrum Antiviral Activity of RNA Interference against Four Genotypes of Japanese Encephalitis Virus Based on Single MicroRNA Polycistrons

Japanese encephalitis virus (JEV), a neurotropic mosquito-borne flavivirus, causes acute viral encephalitis and neurologic disease with a high fatality rate in humans and a range of animals. Small interfering RNA (siRNA) is a powerful antiviral agent able to inhibit JEV replication. However, the high rate of genetic variability between JEV strains (of four confirmed genotypes, genotypes I, II, III and IV) hampers the broad-spectrum application of siRNAs, and mutations within the targeted sequences could facilitate JEV escape from RNA interference (RNAi)-mediated antiviral therapy. To improve the broad-spectrum application of siRNAs and prevent the generation of escape mutants, multiple siRNAs targeting conserved viral sequences need to be combined. In this study, using a siRNA expression vector based on the miR-155 backbone and promoted by RNA polymerase II, we initially identified nine siRNAs targeting highly conserved regions of seven JEV genes among strains of the four genotypes of JEV to effectively block the replication of the JEV vaccine strain SA14-14-2. Then, we constructed single microRNA-like polycistrons to simultaneously express these effective siRNAs under a single RNA polymerase II promoter. Finally, these single siRNAs or multiple siRNAs from the microRNA-like polycistrons showed effective anti-virus activity in genotype I and genotype III JEV wild type strains, which are the predominant genotypes of JEV in mainland China. The anti-JEV effect of these microRNA-like polycistrons was also predicted in other genotypes of JEV (genotypes II and IV), The inhibitory efficacy indicated that siRNAs×9 could theoretically inhibit the replication of JEV genotypes II and IV

Texture analysis-and support vector machine-assisted diffusional kurtosis imaging may allow in vivo gliomas grading and IDH-mutation status prediction:a preliminary study

We sought to investigate, whether texture analysis of diffusional kurtosis imaging (DKI) enhanced by support vector machine (SVM) analysis may provide biomarkers for gliomas staging and detection of the IDH mutation. First-order statistics and texture feature extraction were performed in 37 patients on both conventional (FLAIR) and mean diffusional kurtosis (MDK) images and recursive feature elimination (RFE) methodology based on SVM was employed to select the most discriminative diagnostic biomarkers. The first-order statistics demonstrated significantly lower MDK values in the IDH-mutant tumors. This resulted in 81.1% accuracy (sensitivity = 0.96, specificity = 0.45, AUC 0.59) for IDH mutation diagnosis. There were non-significant differences in average MDK and skewness among the different tumour grades. When texture analysis and SVM were utilized, the grading accuracy achieved by DKI biomarkers was 78.1% (sensitivity 0.77, specificity 0.79, AUC 0.79); the prediction accuracy for IDH mutation reached 83.8% (sensitivity 0.96, specificity 0.55, AUC 0.87). For the IDH mutation task, DKI outperformed significantly the FLAIR imaging. When using selected biomarkers after RFE, the prediction accuracy achieved 83.8% (sensitivity 0.92, specificity 0.64, AUC 0.88). These findings demonstrate the superiority of DKI enhanced by texture analysis and SVM, compared to conventional imaging, for gliomas staging and prediction of IDH mutational status

Altered Small-World Brain Networks in Schizophrenia Patients during Working Memory Performance

Impairment of working memory (WM) performance in schizophrenia patients (SZ) is well-established. Compared to healthy controls (HC), SZ patients show aberrant blood oxygen level dependent (BOLD) activations and disrupted functional connectivity during WM performance. In this study, we examined the small-world network metrics computed from functional magnetic resonance imaging (fMRI) data collected as 35 HC and 35 SZ performed a Sternberg Item Recognition Paradigm (SIRP) at three WM load levels. Functional connectivity networks were built by calculating the partial correlation on preprocessed time courses of BOLD signal between task-related brain regions of interest (ROIs) defined by group independent component analysis (ICA). The networks were then thresholded within the small-world regime, resulting in undirected binarized small-world networks at different working memory loads. Our results showed: 1) at the medium WM load level, the networks in SZ showed a lower clustering coefficient and less local efficiency compared with HC; 2) in SZ, most network measures altered significantly as the WM load level increased from low to medium and from medium to high, while the network metrics were relatively stable in HC at different WM loads; and 3) the altered structure at medium WM load in SZ was related to their performance during the task, with longer reaction time related to lower clustering coefficient and lower local efficiency. These findings suggest brain connectivity in patients with SZ was more diffuse and less strongly linked locally in functional network at intermediate level of WM when compared to HC. SZ show distinctly inefficient and variable network structures in response to WM load increase, comparing to stable highly clustered network topologies in HC

Measurement of the Bottom-Strange Meson Mixing Phase in the Full CDF Data Set

We report a measurement of the bottom-strange meson mixing phase \beta_s using the time evolution of B0_s -> J/\psi (->\mu+\mu-) \phi (-> K+ K-) decays in which the quark-flavor content of the bottom-strange meson is identified at production. This measurement uses the full data set of proton-antiproton collisions at sqrt(s)= 1.96 TeV collected by the Collider Detector experiment at the Fermilab Tevatron, corresponding to 9.6 fb-1 of integrated luminosity. We report confidence regions in the two-dimensional space of \beta_s and the B0_s decay-width difference \Delta\Gamma_s, and measure \beta_s in [-\pi/2, -1.51] U [-0.06, 0.30] U [1.26, \pi/2] at the 68% confidence level, in agreement with the standard model expectation. Assuming the standard model value of \beta_s, we also determine \Delta\Gamma_s = 0.068 +- 0.026 (stat) +- 0.009 (syst) ps-1 and the mean B0_s lifetime, \tau_s = 1.528 +- 0.019 (stat) +- 0.009 (syst) ps, which are consistent and competitive with determinations by other experiments.Comment: 8 pages, 2 figures, Phys. Rev. Lett 109, 171802 (2012

Influence of calcination temperature on structural and magnetic properties of nanocomposites formed by Co-ferrite dispersed in sol-gel silica matrix using tetrakis(2-hydroxyethyl) orthosilicate as precursor

Effects of calcination temperatures varying from 400 to 1000°C on structural and magnetic properties of nanocomposites formed by Co-ferrite dispersed in the sol-gel silica matrix using tetrakis(2-hydroxyethyl) orthosilicate (THEOS) as water-soluble silica precursor have been investigated. Studies carried out using XRD, FT-IR, TEM, STA (TG-DTG-DTA) and VSM techniques. Results indicated that magnetic properties of samples such as superparamagnetism and ferromagnetism showed great dependence on the variation of the crystallinity and particle size caused by the calcination temperature. The crystallization, saturation magnetization Ms and remenant magnetization Mr increased as the calcination temperature increased. But the variation of coercivity Hc was not in accordance with that of Ms and Mr, indicating that Hc is not determined only by the crystallinity and size of CoFe2O4 nanoparticles. TEM images showed spherical nanoparticles dispersed in the silica network with sizes of 10-30 nm. Results showed that the well-established silica network provided nucleation locations for CoFe2O4 nanoparticles to confinement the coarsening and aggregation of nanoparticles. THEOS as silica matrix network provides an ideal nucleation environment to disperse CoFe2O4 nanoparticles and thus to confine them to aggregate and coarsen. By using THEOS as water-soluble silica precursor over the currently used TEOS and TMOS, the organic solvents are not needed owing to the complete solubility of THEOS in water. Synthesized nanocomposites with adjustable particle sizes and controllable magnetic properties make the applicability of Co-ferrite even more versatile

JAK-STAT and AKT pathway-coupled genes in erythroid progenitor cells through ontogeny

Background: It has been reported that the phosphatidylinositol 3-kinase (PI3K)-AKT signaling pathway regulates erythropoietin (EPO)-induced survival, proliferation, and maturation of early erythroid progenitors. Erythroid cell proliferation and survival have also been related to activation of the JAK-STAT pathway. The goal of this study was to observe the function of EPO activation of JAK-STAT and PI3K/AKT pathways in the development of erythroid progenitors from hematopoietic CD34(+) progenitor cells, as well as to distinguish early EPO target genes in human erythroid progenitors during ontogeny. Methods: Hematopoietic CD34(+) progenitor cells, isolated from fetal and adult hematopoietic tissues, were differentiated into erythroid progenitor cells. We have used microarray analysis to examine JAK-STAT and PI3K/AKT related genes, as well as broad gene expression modulation in these human erythroid progenitor cells. Results: In microarray studies, a total of 1755 genes were expressed in fetal liver, 3844 in cord blood, 1770 in adult bone marrow, and 1325 genes in peripheral blood-derived erythroid progenitor cells. The erythroid progenitor cells shared 1011 common genes. Using the Ingenuity Pathways Analysis software, we evaluated the network pathways of genes linked to hematological system development, cellular growth and proliferation. The KITLG, EPO, GATA1, PIM1 and STAT3 genes represent the major connection points in the hematological system development linked genes. Some JAK-STAT signaling pathway-linked genes were steadily upregulated throughout ontogeny (PIM1, SOCS2, MYC, PTPN11), while others were downregulated (PTPN6, PIAS, SPRED2). In addition, some JAK-STAT pathway related genes are differentially expressed only in some stages of ontogeny (STATs, GRB2, CREBB). Beside the continuously upregulated (AKT1, PPP2CA, CHUK, NFKB1) and downregulated (FOXO1, PDPK1, PIK3CG) genes in the PI3K-AKT signaling pathway, we also observed intermittently regulated gene expression (NFKBIA, YWHAH). Conclusions: This broad overview of gene expression in erythropoiesis revealed transcription factors differentially expressed in some stages of ontogenesis. Finally, our results show that EPO-mediated proliferation and survival of erythroid progenitors occurs mainly through modulation of JAK-STAT pathway associated STATs, GRB2 and PIK3 genes, as well as AKT pathway-coupled NFKBIA and YWHAH genes