Using Similarity Metrics on Real World Data and Patient Treatment Pathways to Recommend the Next Treatment

Non-small-cell lung cancer (NSCLC) is one of the most prevalent types of lung cancer and continues to have an ominous five year survival rate. Considerable work has been accomplished in analyzing the viability of the treatments offered to NSCLC patients; however, while many of these treatments have performed better over populations of diagnosed NSCLC patients, a specific treatment may not be the most effective therapy for a given patient. Coupling both patient similarity metrics using the Gower similarity metric and prior treatment knowledge, we were able to demonstrate how patient analytics can complement clinical efforts in recommending the next best treatment. Our retrospective and exploratory results indicate that a majority of patients are not recommended the best surviving therapy once they require a new therapy. This investigation lays the groundwork for treatment recommendation using analytics, but more investigation is required to analyze patient outcomes beyond survival

Exaggerated CpH methylation in the autism-affected brain.

BackgroundThe etiology of autism, a complex, heritable, neurodevelopmental disorder, remains largely unexplained. Given the unexplained risk and recent evidence supporting a role for epigenetic mechanisms in the development of autism, we explored the role of CpG and CpH (H = A, C, or T) methylation within the autism-affected cortical brain tissue.MethodsReduced representation bisulfite sequencing (RRBS) was completed, and analysis was carried out in 63 post-mortem cortical brain samples (Brodmann area 19) from 29 autism-affected and 34 control individuals. Analyses to identify single sites that were differentially methylated and to identify any global methylation alterations at either CpG or CpH sites throughout the genome were carried out.ResultsWe report that while no individual site or region of methylation was significantly associated with autism after multi-test correction, methylated CpH dinucleotides were markedly enriched in autism-affected brains (~2-fold enrichment at p < 0.05 cutoff, p = 0.002).ConclusionsThese results further implicate epigenetic alterations in pathobiological mechanisms that underlie autism

RNA-Seq optimization with eQTL gold standards.

BackgroundRNA-Sequencing (RNA-Seq) experiments have been optimized for library preparation, mapping, and gene expression estimation. These methods, however, have revealed weaknesses in the next stages of analysis of differential expression, with results sensitive to systematic sample stratification or, in more extreme cases, to outliers. Further, a method to assess normalization and adjustment measures imposed on the data is lacking.ResultsTo address these issues, we utilize previously published eQTLs as a novel gold standard at the center of a framework that integrates DNA genotypes and RNA-Seq data to optimize analysis and aid in the understanding of genetic variation and gene expression. After detecting sample contamination and sequencing outliers in RNA-Seq data, a set of previously published brain eQTLs was used to determine if sample outlier removal was appropriate. Improved replication of known eQTLs supported removal of these samples in downstream analyses. eQTL replication was further employed to assess normalization methods, covariate inclusion, and gene annotation. This method was validated in an independent RNA-Seq blood data set from the GTEx project and a tissue-appropriate set of eQTLs. eQTL replication in both data sets highlights the necessity of accounting for unknown covariates in RNA-Seq data analysis.ConclusionAs each RNA-Seq experiment is unique with its own experiment-specific limitations, we offer an easily-implementable method that uses the replication of known eQTLs to guide each step in one's data analysis pipeline. In the two data sets presented herein, we highlight not only the necessity of careful outlier detection but also the need to account for unknown covariates in RNA-Seq experiments

Light Quark Masses with an O(a)-Improved Action

We present the recent Fermilab calculations of the masses of the light quarks, using tadpole-improved Sheikholeslami-Wohlert (SW) quarks. Various sources of systematic errors are studied. Our final result for the average light quark mass in the quenched approximation evaluated in the $\bar{MS}$ scheme is $\bar{m}_q(\mu=2 GeV;n_f=0)= (m_u+m_d)/2=3.6 \pm 0.6 MeV$.Comment: 3 pgs. 3 figures. espcrc2.sty included. Talk presented at LATTICE96(phenomenology

Leadership curricula and assessment in Australian and New Zealand medical schools

Background: The Australian Medical Council, which accredits Australian medical schools, recommends medical leadership graduate outcomes be taught, assessed and accredited. In Australia and New Zealand (Australasia) there is a significant research gap and no national consensus on how to educate, assess, and evaluate leadership skills in medical professional entry degree/programs. This study aims to investigate the current curricula, assessment and evaluation of medical leadership in Australasian medical degrees, with particular focus on the roles and responsibilities of medical leadership teachers, frameworks used and competencies taught, methods of delivery, and barriers to teaching leadership. Methods: A self-administered cross-sectional survey was distributed to senior academics and/or heads or Deans of Australasian medical schools. Data for closed questions and ordinal data of each Likert scale response were described via frequency analysis. Content analysis was undertaken on free text responses and coded manually. Results: Sixteen of the 22 eligible (73%) medical degrees completed the full survey and 100% of those indicate that leadership is taught in their degree. In most degrees (11, 69%) leadership is taught as a common theme integrated throughout the curricula across several subjects. There is a variety of leadership competencies taught, with strengths being communication (100%), evidence based practice (100%), critical reflective practice (94%), self-management (81%), ethical decision making (81%), critical thinking and decision making (81%). Major gaps in teaching were financial management (20%), strategic planning (31%) and workforce planning (31%). The teaching methods used to deliver medical leadership within the curricula are diverse, with many degrees providing opportunities for leadership teaching for students outside the curricula. Most degrees (10, 59%) assess the leadership education, with one-third (6, 35%) evaluating it. Conclusions: Medical leadership competencies are taught in most degrees, but key leadership competencies are not being taught and there appears to be no continuous quality improvement process for leadership education. There is much more we can do as medical educators, academics and leaders to shape professional development of academics to teach medical leadership, and to agree on required leadership skills set for our students so they can proactively shape the future of the health care system

Taxonomy Induction using Hypernym Subsequences

We propose a novel, semi-supervised approach towards domain taxonomy induction from an input vocabulary of seed terms. Unlike all previous approaches, which typically extract direct hypernym edges for terms, our approach utilizes a novel probabilistic framework to extract hypernym subsequences. Taxonomy induction from extracted subsequences is cast as an instance of the minimumcost flow problem on a carefully designed directed graph. Through experiments, we demonstrate that our approach outperforms stateof- the-art taxonomy induction approaches across four languages. Importantly, we also show that our approach is robust to the presence of noise in the input vocabulary. To the best of our knowledge, no previous approaches have been empirically proven to manifest noise-robustness in the input vocabulary

Pathway-based analysis using reduced gene subsets in genome-wide association studies

<p>Abstract</p> <p>Background</p> <p>Single Nucleotide Polymorphism (SNP) analysis only captures a small proportion of associated genetic variants in Genome-Wide Association Studies (GWAS) partly due to small marginal effects. Pathway level analysis incorporating prior biological information offers another way to analyze GWAS's of complex diseases, and promises to reveal the mechanisms leading to complex diseases. Biologically defined pathways are typically comprised of numerous genes. If only a subset of genes in the pathways is associated with disease then a joint analysis including all individual genes would result in a loss of power. To address this issue, we propose a pathway-based method that allows us to test for joint effects by using a pre-selected gene subset. In the proposed approach, each gene is considered as the basic unit, which reduces the number of genetic variants considered and hence reduces the degrees of freedom in the joint analysis. The proposed approach also can be used to investigate the joint effect of several genes in a candidate gene study.</p> <p>Results</p> <p>We applied this new method to a published GWAS of psoriasis and identified 6 biologically plausible pathways, after adjustment for multiple testing. The pathways identified in our analysis overlap with those reported in previous studies. Further, using simulations across a range of gene numbers and effect sizes, we demonstrate that the proposed approach enjoys higher power than several other approaches to detect associated pathways.</p> <p>Conclusions</p> <p>The proposed method could increase the power to discover susceptibility pathways and to identify associated genes using GWAS. In our analysis of genome-wide psoriasis data, we have identified a number of relevant pathways for psoriasis.</p

Postmortem cardiac tissue maintains gene expression profile even after late harvesting

<p>Abstract</p> <p>Background</p> <p>Gene expression studies can be used to help identify disease-associated genes by comparing the levels of expressed transcripts between cases and controls, and to identify functional genetic variants (expression quantitative loci or eQTLs) by comparing expression levels between individuals with different genotypes. While many of these studies are performed in blood or lymphoblastoid cell lines due to tissue accessibility, the relevance of expression differences in tissues that are not the primary site of disease is unclear. Further, many eQTLs are tissue specific. Thus, there is a clear and compelling need to conduct gene expression studies in tissues that are specifically relevant to the disease of interest. One major technical concern about using autopsy-derived tissue is how representative it is of physiologic conditions, given the effect of postmortem interval on tissue degradation.</p> <p>Results</p> <p>In this study, we monitored the gene expression of 13 tissue samples harvested from a rapid autopsy heart (non-failed heart) and 7 from a cardiac explant (failed heart) through 24 hours of autolysis. The 24 hour autopsy simulation was designed to reflect a typical autopsy scenario where a body may begin cooling to ambient temperature for ~12 hours, before transportation and storage in a refrigerated room in a morgue. In addition, we also simulated a scenario wherein the body was left at room temperature for up to 24 hours before being found. A small fraction (< 2.5%) of genes showed fluctuations in expression over the 24 hr period and largely belong to immune and signal response and energy metabolism-related processes. Global expression analysis suggests that RNA expression is reproducible over 24 hours of autolysis with 95% genes showing < 1.2 fold change. Comparing the rapid autopsy to the failed heart identified 480 differentially expressed genes, including several types of collagens, lumican (<it>LUM</it>), natriuretic peptide A (<it>NPPA</it>) and connective tissue growth factor (<it>CTGF</it>), which allows for the clear separation between failing and non-failing heart based on gene expression profiles.</p> <p>Conclusions</p> <p>Our results demonstrate that RNA from autopsy-derived tissue, even up to 24 hours of autolysis, can be used to identify biologically relevant expression pattern differences, thus serving as a practical source for gene expression experiments.</p

Synchronic Curation for Assessing Reuse and Integration Fitness of Multiple Data Collections

Data driven applications often require using data integrated from different, large, and continuously updated collections. Each of these collections may present gaps, overlapping data, have conflicting information, or complement each other. Thus, a curation need is to continuously assess if data from multiple collections are fit for integration and reuse. To assess different large data collections at the same time, we present the Synchronic Curation (SC) framework. SC involves processing steps to map the different collections to a unifying data model that represents research problems in a scientific area. The data model, which includes the collections' provenance and a data dictionary, is implemented in a graph database where collections are continuously ingested and can be queried. SC has a collection analysis and comparison module to track updates, and to identify gaps, changes, and irregularities within and across collections. Assessment results can be accessed interactively through a web-based interactive graph. In this paper we introduce SC as an interdisciplinary enterprise, and illustrate its capabilities through its implementation in ASTRIAGraph, a space sustainability knowledge system

Triangulated sentiment analysis of tweets for social CRM

High resolution data from social media platforms like Twitter presents an unprecedented opportunity to organisations for social customer relationship management (Social CRM) by analysing the ongoing discussion about business events such as a service outage. Text based sentiment analysis has been widely researched utilising mainly lexicon-based and machine learning approaches to uncover customers' opinions. They are similar in the sense that the machine learning approach relies on an initial lexical model on which the learning is based. Both methods view sentiment as either positive, neutral, or negative. This is not the case for the psycholinguistic approach following which text sentiment is more continuous. We compare these three approaches with a Twitter dataset collected during a service outage. Contrary to our expectation, we find that the language used in tweets is not very negative or emotionally intense. This research therefore contributes to the sentiment analysis discussion by dissecting three methods and illustrating how and why they arrive at differing results. The selected research context provides an illuminating case about service failure and recovery