Keyword Aware Influential Community Search in Large Attributed Graphs

We introduce a novel keyword-aware influential community query KICQ that finds the most influential communities from an attributed graph, where an influential community is defined as a closely connected group of vertices having some dominance over other groups of vertices with the expertise (a set of keywords) matching with the query terms (words or phrases). We first design the KICQ that facilitates users to issue an influential CS query intuitively by using a set of query terms, and predicates (AND or OR). In this context, we propose a novel word-embedding based similarity model that enables semantic community search, which substantially alleviates the limitations of exact keyword based community search. Next, we propose a new influence measure for a community that considers both the cohesiveness and influence of the community and eliminates the need for specifying values of internal parameters of a network. Finally, we propose two efficient algorithms for searching influential communities in large attributed graphs. We present detailed experiments and a case study to demonstrate the effectiveness and efficiency of the proposed approaches.Comment: Under review for VLDB 202

A Prospective Study of Arsenic Exposure, Arsenic Methylation Capacity, and Risk of Cardiovascular Disease in Bangladesh

Millions of persons worldwide, including 13 million Americans (U.S. Environmental Protection Agency 2009) and over 50 million in Bangladesh (British Geological Survey 2007), have been chronically exposed to arsenic, a group 1 human carcinogen (International Agency for Research on Cancer 2004), through contaminated drinking water. Arsenic exposure from drinking water has been associated with cardiovascular disease (CVD) (Chen CJ et al. 1996; Chen Y et al. 2011; Chiou et al. 1997; Liao et al. 2012; Tseng et al. 2003; Yuan et al. 2007). However, prospective studies assessing susceptibility to CVD due to arsenic exposure are rare. Arsenic in drinking water is present as inorganic arsenic (iAS). Once ingested, iAs is methylated to monomethylarsonic acid (MMA) and dimethylarsinic acid (DMA). The relative distribution of urinary arsenic metabolites varies from person to person and has been interpreted to reflect arsenic methylation capacity (Hopenhayn-Rich et al. 1996; Vahter 1999). Mechanistic studies have shown that MMAIII is more toxic than iAs or any of the pentavalent metabolites (Petrick et al. 2000; Styblo et al. 2000). Incomplete methylation, indicated by a high percentage of urinary MMA (MMA%), has been consistently related to cancers (Chen YC et al. 2003; Pu et al. 2007; Steinmaus et al. 2006; Yu et al. 2000), and there is some evidence of stronger associations among smokers than nonsmokers (Pu et al. 2007; Steinmaus et al. 2006). However, the association between urinary MMA% and CVD risk is unknown, and research on the combined effects of arsenic and biomarkers of arsenic susceptibility on CVD risk is needed. We conducted a prospective case–cohort study nested in a large prospective cohort to assess associations of arsenic exposure from drinking water and arsenic methylation capacity, indicated using relative distribution of urinary arsenic metabolites, with CVD risk

Ribonucleotide reductase regulatory subunit M2 drives glioblastoma TMZ resistance through modulation of dNTP production

During therapy, adaptations driven by cellular plasticity are partly responsible for driving the inevitable recurrence of glioblastoma (GBM). To investigate plasticity-induced adaptation during standard-of-care chemotherapy temozolomide (TMZ), we performed in vivo single-cell RNA sequencing in patient-derived xenograft (PDX) tumors of GBM before, during, and after therapy. Comparing single-cell transcriptomic patterns identified distinct cellular populations present during TMZ therapy. Of interest was the increased expression of ribonucleotide reductase regulatory subunit M2 (RRM2), which we found to regulate dGTP and dCTP production vital for DNA damage response during TMZ therapy. Furthermore, multidimensional modeling of spatially resolved transcriptomic and metabolomic analysis in patients’ tissues revealed strong correlations between RRM2 and dGTP. This supports our data that RRM2 regulates the demand for specific dNTPs during therapy. In addition, treatment with the RRM2 inhibitor 3-AP (Triapine) enhances the efficacy of TMZ therapy in PDX models. We present a previously unidentified understanding of chemoresistance through critical RRM2-mediated nucleotide production

Nonmalignant Respiratory Effects of Chronic Arsenic Exposure from Drinking Water among Never-Smokers in Bangladesh

BACKGROUND: Arsenic from drinking water has been associated with malignant and nonmalignant respiratory illnesses. The association with nonmalignant respiratory illnesses has not been well established because the assessments of respiratory symptoms may be influenced by recall bias or interviewer bias because participants had visible skin lesions. OBJECTIVES: We examined the relationship of the serum level of Clara cell protein CC 16-a novel biomarker for respiratory illnesses-with well As, total urinary As, and urinary As methylation indices. METHODS: We conducted a cross-sectional study in nonsmoking individuals (n = 241) selected from a large cohort with a wide range of As exposure (0.1-761 mu g/L) from drinking water in Bangladesh. Total urinary As, urinary As metabolites, and serum CC16 were measured in urine and serum samples collected at baseline of the parent cohort study. RESULTS: We observed an inverse association between urinary As and serum CC 16 among persons with skin lesions (beta = -0. 13, p = 0.01). We also observed a positive association between secondary methylation index in urinary As and CC16 levels (beta = 0. 12,,P = 0.05) in the overall study population; the association was stronger among people without skin lesions (beta = 0. 18, p = 0.04), indicating that increased methylation capability may be protective against As-induced respiratory damage. In a subsample of study participants undergoing spirometric measures (n = 3 1), we observed inverse associations between urinary As and predictive FEV1 (forced expiratory volume measured in 1 sec) (r = -0.37; FEV1/forced vital capacity ratio and primary methylation index (r = -0.42, p = 0.01). CONCLUSIONS: The findings suggest that serum CC 16 may be a useful biomarker of epithelial lung damage in individuals with arsenical skin lesions. Also, we observed the deleterious respiratory effects of As exposure at concentrations lower than reported in earlier studies

Thyroid hormones and neurobehavioral functions among adolescents chronically exposed to groundwaterwith geogenic arsenic in Bangladesh

Groundwater, the major source of drinking water in Bengal Delta Plain, is contaminated with geogenic arsenic (As) enrichment affecting millions of people. Children exposed to tubewell water containing As may be associated with thyroid dysfunction, which in turn may impact neurodevelopmental outcomes. However, data to support such relationship is sparse. The purpose of this study was to examine if chronic water As (WAs) from Holocene alluvial aquifers in this region was associated with serum thyroid hormone (TH) and if TH biomarkers were related to neurobehavioral (NB) performance in a group of adolescents. A sample of 32 healthy adolescents were randomly drawn from a child cohort in the Health Effects of Arsenic Longitudinal Study (HEALS) in Araihazar, Bangladesh. Half of these participants were consistently exposed to low WAs (<10 μg/L) and the remaining half had high WAs exposure (≥10 μg/L) since birth. Measurements included serum total triiodothyronine (tT3), free thyroxine (fT4), thyrotropin (TSH) and thyroperoxidase antibodies (TPOAb); concurrent WAs and urinary arsenic (UAs); and adolescents' NB performance. WAs and UAs were positively and significantly correlated with TPOAb but were not correlated with TSH, tT3 and fT4. After accounting for covariates, both WAs and UAs demonstrated positive but non-significant relationships with TSH and TPOAb and negative but non-significant relationships with tT3 and fT4. TPOAb was significantly associated with reduced NB performance indicated by positive associations with latencies in simple reaction time (b = 82.58; p < 0.001) and symbol digit (b = 276.85; p = 0.005) tests. TSH was significantly and negatively associated with match-to-sample correct count (b = −0.95; p = 0.05). Overall, we did not observe significant associations between arsenic exposure and TH biomarkers although the relationships were in the expected directions. We observed TH biomarkers to be related to reduced NB performance as hypothesized. Our study indicated a possible mechanism of As-induced neurotoxicity, which requires further investigations for confirmatory findings

Identifying Insomnia From Social Media Posts: Psycholinguistic Analyses of User Tweets

BackgroundMany people suffer from insomnia, a sleep disorder characterized by difficulty falling and staying asleep during the night. As social media have become a ubiquitous platform to share users’ thoughts, opinions, activities, and preferences with their friends and acquaintances, the shared content across these platforms can be used to diagnose different health problems, including insomnia. Only a few recent studies have examined the prediction of insomnia from Twitter data, and we found research gaps in predicting insomnia from word usage patterns and correlations between users’ insomnia and their Big 5 personality traits as derived from social media interactions. ObjectiveThe purpose of this study is to build an insomnia prediction model from users’ psycholinguistic patterns, including the elements of word usage, semantics, and their Big 5 personality traits as derived from tweets. MethodsIn this paper, we exploited both psycholinguistic and personality traits derived from tweets to identify insomnia patients. First, we built psycholinguistic profiles of the users from their word choices and the semantic relationships between the words of their tweets. We then determined the relationship between a users’ personality traits and insomnia. Finally, we built a double-weighted ensemble classification model to predict insomnia from both psycholinguistic and personality traits as derived from user tweets. ResultsOur classification model showed strong prediction potential (78.8%) to predict insomnia from tweets. As insomniacs are generally ill-tempered and feel more stress and mental exhaustion, we observed significant correlations of certain word usage patterns among them. They tend to use negative words (eg, “no,” “not,” “never”). Some people frequently use swear words (eg, “damn,” “piss,” “fuck”) with strong temperament. They also use anxious (eg, “worried,” “fearful,” “nervous”) and sad (eg, “crying,” “grief,” “sad”) words in their tweets. We also found that the users with high neuroticism and conscientiousness scores for the Big 5 personality traits likely have strong correlations with insomnia. Additionally, we observed that users with high conscientiousness scores have strong correlations with insomnia patterns, while negative correlation between extraversion and insomnia was also found. ConclusionsOur model can help predict insomnia from users’ social media interactions. Thus, incorporating our model into a software system can help family members detect insomnia problems in individuals before they become worse. The software system can also help doctors to diagnose possible insomnia in patients

Characterization of the complete mitochondrial genome of Gangetic ailia, Ailia coila (Siluriformes: Ailiidae)

The first complete mitochondrial genome sequence of Ailia coila from Bangladesh was determined by the bioinformatic assembly of the next generation sequencing (NGS) reads. The constructed circular mitogenome for A. coila was 16,565 bp in length which harbored the canonical 13 protein-coding genes, 22 tRNAs, 2 rRNAs. Two non-coding regions, control region, D-loop (927 bp), and origin of light strand replication, OL (30 bp) were also well conserved in the mitogenome. Among the currently reported mitochondrial genomes in the order Siluriformes, A. coila was most closely related to Eutropiichthys vacha (AB919123) with 85.63% sequence identity