A note on the colorability of mixed hypergraph using k colors

The colorability problem on mixed hypergraphs is discussed. A criterion of colorability of mixed hypergraph with k colors is given

Noname manuscript No. (will be inserted by the editor) Maximum Series-Parallel Subgraph

Abstract Consider the NP-hard problem of, given a simple graph G, to find a seriesparallel subgraph of G with the maximum number of edges. The algorithm that, given a connected graph G, outputs a spanning tree of G, is a 1 2-approximation. Indeed, if n is the number of vertices in G, any spanning tree in G has n−1 edges and any seriesparallel graph on n vertices has at most 2n−3 edges. We present a 7 12-approximation for this problem and results showing the limits of our approach

Predicting Opioid Epidemic by Using Twitter Data

Opioid crisis was declared as a public health emergency in 2017 by the President of USA. According to the Centers for Disease Control and Prevention, more than 91 Americans die every day from an opioid overdose. Nearly $4B is provided to address the opioid epidemic in the 2018 spending bill and help fulfill the President’s Opioid Initiative. How to monitor and predict the opioid epidemic accurately and in real time? The traditional methods mainly use the hospital data and usually have a lag of several years. Even though they are accurate, the long lag period prevents us from monitoring and predicting the epidemic in real time. We observe that people discuss things related to the epidemic a lot in social media platforms. These user behavior data collected from social media platforms can potentially help us monitor and predict the epidemic in real time. In this paper, we study how to use Twitter to monitor the epidemic. We collect the historic tweets containing the set of keywords related to the epidemic. We count the frequency of the tweets posted at each month and each state. We compare the frequency values with the real-world death rates at each month and each state. We identify high correlation between tweet frequency values and real-world death rates. The statistical significance demonstrates that the Twitter data can be used for predicting the death rate and epidemic in future

Detecting Illicit Drug Ads in Google+ Using Machine Learning

Opioid abuse epidemics is a major public health emergency in the US. Social media platforms have facilitated illicit drug trading, with significant amount of drug advertisement and selling being carried out online. In order to understand dynamics of drug abuse epidemics and design efficient public health interventions, it is essential to extract and analyze data from online drug markets. In this paper, we present a computational framework for automatic detection of illicit drug ads in social media, with Google+ being used for a proof-of-concept. The proposed SVM- and CNN-based methods have been extensively validated on the large dataset containing millions of posts collected using Google+ API. Experimental results demonstrate that our methods can efficiently identify illicit drug ads with high accuracy. Both approaches have been extensively validated using the dataset containing millions of posts collected using Google+ API. Experimental results demonstrate that both methods allow for accurate identification of illicit drug ads

Antibody Response to Lyme Disease Spirochetes in the Context of VlsE-Mediated Immune Evasion

Lyme disease (LD), the most prevalent tick-borne illness in North America, is caused by Borrelia burgdorferi. The long-term survival of B. burgdorferi spirochetes in the mammalian host is achieved though VlsE-mediated antigenic variation. It is mathematically predicted that a highly variable surface antigen prolongs bacterial infection sufficiently to exhaust the immune response directed toward invariant surface antigens. If the prediction is correct, it is expected that the antibody response to B. burgdorferi invariant antigens will become nonprotective as B. burgdorferi infection progresses. To test this assumption, changes in the protective efficacy of the immune response to B. burgdorferi surface antigens were monitored via a superinfection model over the course of 70 days. B. burgdorferi-infected mice were subjected to secondary challenge by heterologous B. burgdorferi at different time points postinfection (p.i.). When the infected mice were superinfected with a VlsE-deficient clone (ΔVlsE) at day 28 p.i., the active anti-B. burgdorferi immune response did not prevent ΔVlsE-induced spirochetemia. In contrast, most mice blocked culture-detectable spirochetemia induced by wild-type B. burgdorferi (WT), indicating that VlsE was likely the primary target of the antibody response. As the B. burgdorferi infection further progressed, however, reversed outcomes were observed. At day 70 p.i. the host immune response to non-VlsE antigens became sufficiently potent to clear spirochetemia induced by ΔVlsE and yet failed to prevent WT-induced spirochetemia. To test if any significant changes in the anti-B. burgdorferi antibody repertoire accounted for the observed outcomes, global profiles of antibody specificities were determined. However, comparison of mimotopes revealed no major difference between day 28 and day 70 antibody repertoires

Technology dictates algorithms: Recent developments in read alignment

Massively parallel sequencing techniques have revolutionized biological and medical sciences by providing unprecedented insight into the genomes of humans, animals, and microbes. Modern sequencing platforms generate enormous amounts of genomic data in the form of nucleotide sequences or reads. Aligning reads onto reference genomes enables the identification of individual-specific genetic variants and is an essential step of the majority of genomic analysis pipelines. Aligned reads are essential for answering important biological questions, such as detecting mutations driving various human diseases and complex traits as well as identifying species present in metagenomic samples. The read alignment problem is extremely challenging due to the large size of analyzed datasets and numerous technological limitations of sequencing platforms, and researchers have developed novel bioinformatics algorithms to tackle these difficulties. Importantly, computational algorithms have evolved and diversified in accordance with technological advances, leading to todays diverse array of bioinformatics tools. Our review provides a survey of algorithmic foundations and methodologies across 107 alignment methods published between 1988 and 2020, for both short and long reads. We provide rigorous experimental evaluation of 11 read aligners to demonstrate the effect of these underlying algorithms on speed and efficiency of read aligners. We separately discuss how longer read lengths produce unique advantages and limitations to read alignment techniques. We also discuss how general alignment algorithms have been tailored to the specific needs of various domains in biology, including whole transcriptome, adaptive immune repertoire, and human microbiome studies

SARS-CoV-2 Wastewater Genomic Surveillance: Approaches, Challenges, and Opportunities

During the SARS-CoV-2 pandemic, wastewater-based genomic surveillance (WWGS) emerged as an efficient viral surveillance tool that takes into account asymptomatic cases and can identify known and novel mutations and offers the opportunity to assign known virus lineages based on the detected mutations profiles. WWGS can also hint towards novel or cryptic lineages, but it is difficult to clearly identify and define novel lineages from wastewater (WW) alone. While WWGS has significant advantages in monitoring SARS-CoV-2 viral spread, technical challenges remain, including poor sequencing coverage and quality due to viral RNA degradation. As a result, the viral RNAs in wastewater have low concentrations and are often fragmented, making sequencing difficult. WWGS analysis requires advanced computational tools that are yet to be developed and benchmarked. The existing bioinformatics tools used to analyze wastewater sequencing data are often based on previously developed methods for quantifying the expression of transcripts or viral diversity. Those methods were not developed for wastewater sequencing data specifically, and are not optimized to address unique challenges associated with wastewater. While specialized tools for analysis of wastewater sequencing data have also been developed recently, it remains to be seen how they will perform given the ongoing evolution of SARS-CoV-2 and the decline in testing and patient-based genomic surveillance. Here, we discuss opportunities and challenges associated with WWGS, including sample preparation, sequencing technology, and bioinformatics methods.Comment: V Munteanu and M Saldana contributed equally to this work A Smith and S Mangul jointly supervised this work For correspondence: [email protected]