TIMME: Twitter Ideology-detection via Multi-task Multi-relational Embedding

We aim at solving the problem of predicting people's ideology, or political tendency. We estimate it by using Twitter data, and formalize it as a classification problem. Ideology-detection has long been a challenging yet important problem. Certain groups, such as the policy makers, rely on it to make wise decisions. Back in the old days when labor-intensive survey-studies were needed to collect public opinions, analyzing ordinary citizens' political tendencies was uneasy. The rise of social medias, such as Twitter, has enabled us to gather ordinary citizen's data easily. However, the incompleteness of the labels and the features in social network datasets is tricky, not to mention the enormous data size and the heterogeneousity. The data differ dramatically from many commonly-used datasets, thus brings unique challenges. In our work, first we built our own datasets from Twitter. Next, we proposed TIMME, a multi-task multi-relational embedding model, that works efficiently on sparsely-labeled heterogeneous real-world dataset. It could also handle the incompleteness of the input features. Experimental results showed that TIMME is overall better than the state-of-the-art models for ideology detection on Twitter. Our findings include: links can lead to good classification outcomes without text; conservative voice is under-represented on Twitter; follow is the most important relation to predict ideology; retweet and mention enhance a higher chance of like, etc. Last but not least, TIMME could be extended to other datasets and tasks in theory.Comment: In proceedings of KDD'20, Applied Data Science Track; 9 pages, 2 supplementary page

Cyclohexadione-aniline conjugate inhibits proliferation of melanoma cells via upregulation of Mek 1/2 kinase activity

Purpose: To investigate the antiproliferative effect of cyclohexadione-aniline conjugate (CHAC) on melanoma cells, and the mechanism of action involved. Methods: Human melanoma cell lines (B16 F1 and A375) were used in this study. The cells were cultured in RPMI 1640 medium supplemented with 10 % fetal bovine serum (FBS) and 1 % penicillin/streptomycin at 37 °C in a humidified atmosphere of 5 % CO2 and 95 % air. After attaining 70 - 80 % confluency, the cells were treated with serum-free medium and graded concentrations of CHAC (10 – 60 μM) for 24 h. Normal cell culture without CHAC served as control group. B16 F1 and A375 cells were used in logarithmic growth phase in this study. Cell viability and apoptosis were assessed using 3-(4, 5-dimethylthiazol-2-yl) 2, 5-diphe¬nyltetrazolium bromide (MTT) and flow cytometric assays, respectively. Western blotting was used to assess the levels of protein expression of X linked inhibitor of apoptosis (XIAP), survivin, p-Erk 1/2, and p-Mek 1/2. Results: Treatment of B16 F1 and A375 cells with CHAC led to significant and concentrationdependent reductions in their viability (p < 0.05). The proliferation of B16 F1 cells decreased from 93.41 to 32.87 %, while that of A375 cells was reduced from 95.23 to 36.50 %. Treatment of B16 F1 cells with CHAC significantly and concentration-dependently increased the population of cells in G0/G1 phase, and significantly reduced cell proportion in S and G2/M phases (p < 0.05). It also significantly and concentration-dependently promoted apoptosis in B16 F1 cells (p < 0.05). CHAC treatment significantly and concentration-dependently down-regulated the expressions of XIAP and survivin proteins (p < 0.05). Exposure of B16 F1 cells to CHAC significantly and concentration-dependently upregulated the expression of p-Mek 1/2, but down-regulated p-Erk 1/2 protein expression (p < 0.05). Densitometric analysis revealed that the expression of p-Mek 1/2 was increased from 12 to 91 %. Conclusion: The results of this study indicate that CHAC inhibits the proliferation of melanoma cells via upregulation of Mek 1/2 kinase activity, and therefore may find application in the management of melanoma

Revisiting Initializing Then Refining: An Incomplete and Missing Graph Imputation Network

With the development of various applications, such as social networks and knowledge graphs, graph data has been ubiquitous in the real world. Unfortunately, graphs usually suffer from being absent due to privacy-protecting policies or copyright restrictions during data collection. The absence of graph data can be roughly categorized into attribute-incomplete and attribute-missing circumstances. Specifically, attribute-incomplete indicates that a part of the attribute vectors of all nodes are incomplete, while attribute-missing indicates that the whole attribute vectors of partial nodes are missing. Although many efforts have been devoted, none of them is custom-designed for a common situation where both types of graph data absence exist simultaneously. To fill this gap, we develop a novel network termed Revisiting Initializing Then Refining (RITR), where we complete both attribute-incomplete and attribute-missing samples under the guidance of a novel initializing-then-refining imputation criterion. Specifically, to complete attribute-incomplete samples, we first initialize the incomplete attributes using Gaussian noise before network learning, and then introduce a structure-attribute consistency constraint to refine incomplete values by approximating a structure-attribute correlation matrix to a high-order structural matrix. To complete attribute-missing samples, we first adopt structure embeddings of attribute-missing samples as the embedding initialization, and then refine these initial values by adaptively aggregating the reliable information of attribute-incomplete samples according to a dynamic affinity structure. To the best of our knowledge, this newly designed method is the first unsupervised framework dedicated to handling hybrid-absent graphs. Extensive experiments on four datasets have verified that our methods consistently outperform existing state-of-the-art competitors

What experiments on pinned nanobubbles can tell about the critical nucleus for bubble nucleation.

The process of homogeneous bubble nucleation is almost impossible to probe experimentally, except near the critical point or for liquids under large negative tension. Elsewhere in the phase diagram, the bubble nucleation barrier is so high as to be effectively insurmountable. Consequently, there is a severe lack of experimental studies of homogenous bubble nucleation under conditions of practical importance (e.g., cavitation). Here we use a simple geometric relation to show that we can obtain information about the homogeneous nucleation process from Molecular Dynamics studies of bubble formation in solvophobic nanopores on a solid surface. The free energy of pinned nanobubbles has two extrema as a function of volume: one state corresponds to a free-energy maximum ("the critical nucleus"), the other corresponds to a free-energy minimum (the metastable, pinned nanobubble). Provided that the surface tension does not depend on nanobubble curvature, the radius of the curvature of the metastable surface nanobubble is independent of the radius of the pore and is equal to the radius of the critical nucleus in homogenous bubble nucleation. This observation opens the way to probe the parameters that determine homogeneous bubble nucleation under experimentally accessible conditions, e.g. with AFM studies of metastable nanobubbles. Our theoretical analysis also indicates that a surface with pores of different sizes can be used to determine the curvature corrections to the surface tension. Our conclusions are not limited to bubble nucleation but suggest that a similar approach could be used to probe the structure of critical nuclei in crystal nucleation

Detecting Political Biases of Named Entities and Hashtags on Twitter

Ideological divisions in the United States have become increasingly prominent in daily communication. Accordingly, there has been much research on political polarization, including many recent efforts that take a computational perspective. By detecting political biases in a corpus of text, one can attempt to describe and discern the polarity of that text. Intuitively, the named entities (i.e., the nouns and phrases that act as nouns) and hashtags in text often carry information about political views. For example, people who use the term "pro-choice" are likely to be liberal, whereas people who use the term "pro-life" are likely to be conservative. In this paper, we seek to reveal political polarities in social-media text data and to quantify these polarities by explicitly assigning a polarity score to entities and hashtags. Although this idea is straightforward, it is difficult to perform such inference in a trustworthy quantitative way. Key challenges include the small number of known labels, the continuous spectrum of political views, and the preservation of both a polarity score and a polarity-neutral semantic meaning in an embedding vector of words. To attempt to overcome these challenges, we propose the Polarity-aware Embedding Multi-task learning (PEM) model. This model consists of (1) a self-supervised context-preservation task, (2) an attention-based tweet-level polarity-inference task, and (3) an adversarial learning task that promotes independence between an embedding's polarity dimension and its semantic dimensions. Our experimental results demonstrate that our PEM model can successfully learn polarity-aware embeddings. We examine a variety of applications and we thereby demonstrate the effectiveness of our PEM model. We also discuss important limitations of our work and stress caution when applying the PEM model to real-world scenarios.Comment: Submitted to EPJ -- Data Science, under revie

A benchmark and an algorithm for detecting germline transposon insertions and measuring de novo transposon insertion frequencies

Transposons are genomic parasites, and their new insertions can cause instability and spur the evolution of their host genomes. Rapid accumulation of short-read whole-genome sequencing data provides a great opportunity for studying new transposon insertions and their impacts on the host genome. Although many algorithms are available for detecting transposon insertions, the task remains challenging and existing tools are not designed for identifying de novo insertions. Here, we present a new benchmark fly dataset based on PacBio long-read sequencing and a new method TEMP2 for detecting germline insertions and measuring de novo \u27singleton\u27 insertion frequencies in eukaryotic genomes. TEMP2 achieves high sensitivity and precision for detecting germline insertions when compared with existing tools using both simulated data in fly and experimental data in fly and human. Furthermore, TEMP2 can accurately assess the frequencies of de novo transposon insertions even with high levels of chimeric reads in simulated datasets; such chimeric reads often occur during the construction of short-read sequencing libraries. By applying TEMP2 to published data on hybrid dysgenic flies inflicted by de-repressed P-elements, we confirmed the continuous new insertions of P-elements in dysgenic offspring before they regain piRNAs for P-element repression. TEMP2 is freely available at Github: https://github.com/weng-lab/TEMP2

Comprehensive identification of alternative back-splicing in human tissue transcriptomes

Circular RNAs (circRNAs) are covalently closed RNAs derived from back-splicing of genes across eukaryotes. Through alternative back-splicing (ABS), a single gene produces multiple circRNAs sharing the same back-splice site. Although many ABS events have recently been discovered, to what extent ABS involves in circRNA biogenesis and how it is regulated in different human tissues still remain elusive. Here, we reported an in-depth analysis of ABS events in 90 human tissue transcriptomes. We observed that ABS occurred for about 84% circRNAs. Interestingly, alternative 5\u27 back-splicing occurs more prevalently than alternative 3\u27 back-splicing, and both of them are tissue-specific, especially enriched in brain tissues. In addition, the patterns of ABS events in different brain regions are similar to each other and are more complex than the patterns in non-brain tissues. Finally, the intron length and abundance of Alu elements positively correlated with ABS event complexity, and the predominant circRNAs had longer flanking introns and more Alu elements than other circRNAs in the same ABS event. Together, our results represent a resource for circRNA research-we expanded the repertoire of ABS events of circRNAs in human tissue transcriptomes and provided insights into the complexity of circRNA biogenesis, expression, and regulation

Granulocyte colony-stimulating factor affects the distribution and clonality of TRGV and TRDV repertoire of T cells and graft-versus-host disease

<p>Abstract</p> <p>Background</p> <p>The immune modulatory effect of granulocyte colony-stimulating factor (G-CSF) on T cells resulted in an unexpected low incidence of graft-versus-host disease (GVHD) in allogeneic peripheral blood stem cell transplantation (allo-PBSCT). Recent data indicated that gamma delta⁺T cells might participate in mediating graft-versus-host disease (GVHD) and graft-versus-leukemia (GVL) effect after allogeneic hematopoietic stem cell transplantation. However, whether G-CSF could influence the T cell receptors (TCR) of gamma delta⁺T cells (<it>TRGV </it>and <it>TRDV </it>repertoire) remains unclear. To further characterize this feature, we compared the distribution and clonality of <it>TRGV </it>and <it>TRDV </it>repertoire of T cells before and after G-CSF mobilization and investigated the association between the changes of TCR repertoire and GVHD in patients undergoing G-CSF mobilized allo-PBSCT.</p> <p>Methods</p> <p>The complementarity-determining region 3 (CDR3) sizes of three <it>TRGV </it>and eight <it>TRDV </it>subfamily genes were analyzed in peripheral blood mononuclear cells (PBMCs) from 20 donors before and after G-CSF mobilization, using RT-PCR and genescan technique. To determine the expression levels of <it>TRGV </it>subfamily genes, we performed quantitative analysis of <it>TRGV</it>I~III subfamilies by real-time PCR.</p> <p>Results</p> <p>The expression levels of three <it>TRGV </it>subfamilies were significantly decreased after G-CSF mobilization (<it>P </it>= 0.015, 0.009 and 0.006, respectively). The pattern of <it>TRGV </it>subfamily expression levels was <it>TRGV</it>II ><it>TRGV </it>I ><it>TRGV </it>III before mobilization, and changed to <it>TRGV </it>I ><it>TRGV </it>II ><it>TRGV </it>III after G-CSF mobilization. The expression frequencies of <it>TRGV </it>and <it>TRDV </it>subfamilies changed at different levels after G-CSF mobilization. Most <it>TRGV </it>and <it>TRDV </it>subfamilies revealed polyclonality from pre-G-CSF-mobilized and G-CSF-mobilized samples. Oligoclonality was detected in <it>TRGV </it>and <it>TRDV </it>subfamilies in 3 donors before mobilization and in another 4 donors after G-CSF mobilization, distributed in <it>TRGV</it>II, <it>TRDV</it>1, <it>TRDV</it>3 and <it>TRDV</it>6, respectively. Significant positive association was observed between the invariable clonality of <it>TRDV</it>1 gene repertoire after G-CSF mobilization and low incidence of GVHD in recipients (<it>P </it>= 0.015, <it>OR </it>= 0.047).</p> <p>Conclusions</p> <p>G-CSF mobilization not only influences the distribution and expression levels of <it>TRGV </it>and <it>TRDV </it>repertoire, but also changes the clonality of gamma delta⁺T cells. This alteration of <it>TRGV </it>and <it>TRDV </it>repertoire might play a role in mediating GVHD in G-CSF mobilized allo-PBSCT.</p

Scorpion Toxins from <em>Buthus martensii</em> Karsch (BmK) as Potential Therapeutic Agents for Neurological Disorders: State of the Art and Beyond

Scorpions are fascinating creatures which became residents of the planet well before human beings dwelled on Earth. Scorpions are always considered as a figure of fear, causing notable pain or mortality throughout the world. Their venoms are cocktails of bioactive molecules, called toxins, which are responsible for their toxicity. Fortunately, medical researchers have turned the life-threatening toxins into life-saving therapeutics. From Song Dynasty in ancient China, scorpions and their venoms have been applied in traditional medicine for treating neurological disorders, such as pain, stroke, and epilepsy. Neurotoxins purified from Chinese scorpion Buthus Martensii Karsch (BmK) are considered as the main active ingredients, which act on membrane ion channels. Long-chain toxins of BmK, composed of 58–76 amino acids, could specifically recognize voltage-gated sodium channels (VGSCs). Short-chain BmK toxins, containing 28–40 amino acids, are found to modulate the potassium or chloride channels. These components draw attention as useful scaffolds for drug-design in order to tackle the emerging global medical threats. In this chapter, we aim to summarize the most promising candidates that have been isolated from BmK venoms for drug development