Estimating Stellar Parameters from Spectra using a Hierarchical Bayesian Approach

A method is developed for fitting theoretically predicted astronomical spectra to an observed spectrum. Using a hierarchical Bayesian principle, the method takes both systematic and statistical measurement errors into account, which has not been done before in the astronomical literature. The goal is to estimate fundamental stellar parameters and their associated uncertainties. The non-availability of a convenient deterministic relation between stellar parameters and the observed spectrum, combined with the computational complexities this entails, necessitate the curtailment of the continuous Bayesian model to a reduced model based on a grid of synthetic spectra. A criterion for model selection based on the so-called predictive squared error loss function is proposed, together with a measure for the goodness-of-fit between observed and synthetic spectra. The proposed method is applied to the infrared 2.38--2.60 \mic ISO-SWS data (Infrared Space Observatory - Short Wavelength Spectrometer) of the star $\alpha$ Bootis, yielding estimates for the stellar parameters: effective temperature \Teff = 4230 $\pm$ 83 K, gravity $\log$ g = 1.50 $\pm$ 0.15 dex, and metallicity [Fe/H] = $-0.30 \pm 0.21$ dex.Comment: 15 pages, 8 figures, 5 tables. Accepted for publication in MNRA

Epidemiology of Mycobacterium tuberculosis lineages and strain clustering within urban and peri-urban settings in Ethiopia

BACKGROUND: Previous work has shown differential predominance of certain Mycobacterium tuberculosis (M. tb) lineages and sub-lineages among different human populations in diverse geographic regions of Ethiopia. Nevertheless, how strain diversity is evolving under the ongoing rapid socio-economic and environmental changes is poorly understood. The present study investigated factors associated with M. tb lineage predominance and rate of strain clustering within urban and peri-urban settings in Ethiopia. METHODS: Pulmonary Tuberculosis (PTB) and Cervical tuberculous lymphadenitis (TBLN) patients who visited selected health facilities were recruited in the years of 2016 and 2017. A total of 258 M. tb isolates identified from 163 sputa and 95 fine-needle aspirates (FNA) were characterized by spoligotyping and compared with international M.tb spoligotyping patterns registered at the SITVIT2 databases. The molecular data were linked with clinical and demographic data of the patients for further statistical analysis. RESULTS: From a total of 258 M. tb isolates, 84 distinct spoligotype patterns that included 58 known Shared International Type (SIT) patterns and 26 new or orphan patterns were identified. The majority of strains belonged to two major M. tb lineages, L3 (35.7%) and L4 (61.6%). The observed high percentage of isolates with shared patterns (n = 200/258) suggested a substantial rate of overall clustering (77.5%). After adjusting for the effect of geographical variations, clustering rate was significantly lower among individuals co-infected with HIV and other concomitant chronic disease. Compared to L4, the adjusted odds ratio and 95% confidence interval (AOR; 95% CI) indicated that infections with L3 M. tb strains were more likely to be associated with TBLN [3.47 (1.45, 8.29)] and TB-HIV co-infection [2.84 (1.61, 5.55)]. CONCLUSION: Despite the observed difference in strain diversity and geographical distribution of M. tb lineages, compared to earlier studies in Ethiopia, the overall rate of strain clustering suggests higher transmission and warrant more detailed investigations into the molecular epidemiology of TB and related factors

Predominance of null mutations in ataxia-telangiectasia

Ataxia-telangiectasia (A-T) is an autosomal recessive disorder involving cerebellar degeneration, immunodeficiency, chromosomal instability, radiosensitivity and cancer predisposition. The responsible gene, ATM, was recently identified by positional cloning and found to encode a putative 350 kDa protein with a PI 3-kinase-like domain, presumably involved in mediating cell cycle arrest in response to radiation-induced DNA damage. The nature and location of A-T mutations should provide insight into the function of the ATM protein and the molecular basis of this pleiotropic disease. Of 44 A-T mutations identified by us to date, 39 (89%) are expected to inactivate the ATM protein by truncating it, by abolishing correct initiation or termination of translation, or by deleting large segments. Additional mutations are four smaller in-frame deletions and insertions, and one substitution of a highly conserved amino acid at the PI 3-kinase domain. The emerging profile of mutations causing A-T is thus dominated by those expected to completely inactivate the ATM protein. ATM mutations with milder effects may result in phenotypes related, but not identical, to A-T

Epidemiology of Mycobacterium tuberculosis lineages and strain clustering within urban and peri-urban settings in Ethiopia

Background Previous work has shown differential predominance of certain Mycobacterium tuberculosis (M. tb) lineages and sub-lineages among different human populations in diverse geographic regions of Ethiopia. Nevertheless, how strain diversity is evolving under the ongoing rapid socio-economic and environmental changes is poorly understood. The present study investigated factors associated with M. tb lineage predominance and rate of strain clustering within urban and peri-urban settings in Ethiopia. Methods Pulmonary Tuberculosis (PTB) and Cervical tuberculous lymphadenitis (TBLN) patients who visited selected health facilities were recruited in the years of 2016 and 2017. A total of 258 M. tb isolates identified from 163 sputa and 95 fine-needle aspirates (FNA) were characterized by spoligotyping and compared with international M.tb spoligotyping patterns registered at the SITVIT2 databases. The molecular data were linked with clinical and demographic data of the patients for further statistical analysis. Results From a total of 258 M. tb isolates, 84 distinct spoligotype patterns that included 58 known Shared International Type (SIT) patterns and 26 new or orphan patterns were identified. The majority of strains belonged to two major M. tb lineages, L3 (35.7%) and L4 (61.6%). The observed high percentage of isolates with shared patterns (n = 200/258) suggested a substantial rate of overall clustering (77.5%). After adjusting for the effect of geographical variations, clustering rate was significantly lower among individuals co-infected with HIV and other concomitant chronic disease. Compared to L4, the adjusted odds ratio and 95% confidence interval (AOR; 95% CI) indicated that infections with L3 M. tb strains were more likely to be associated with TBLN [3.47 (1.45, 8.29)] and TB-HIV co-infection [2.84 (1.61, 5.55)]. Conclusion Despite the observed difference in strain diversity and geographical distribution of M. tb lineages, compared to earlier studies in Ethiopia, the overall rate of strain clustering suggests higher transmission and warrant more detailed investigations into the molecular epidemiology of TB and related factors

Community assessment to advance computational prediction of cancer drug combinations in a pharmacogenomic screen

The effectiveness of most cancer targeted therapies is short-lived. Tumors often develop resistance that might be overcome with drug combinations. However, the number of possible combinations is vast, necessitating data-driven approaches to find optimal patient-specific treatments. Here we report AstraZeneca’s large drug combination dataset, consisting of 11,576 experiments from 910 combinations across 85 molecularly characterized cancer cell lines, and results of a DREAM Challenge to evaluate computational strategies for predicting synergistic drug pairs and biomarkers. 160 teams participated to provide a comprehensive methodological development and benchmarking. Winning methods incorporate prior knowledge of drug-target interactions. Synergy is predicted with an accuracy matching biological replicates for >60% of combinations. However, 20% of drug combinations are poorly predicted by all methods. Genomic rationale for synergy predictions are identified, including ADAM17 inhibitor antagonism when combined with PIK3CB/D inhibition contrasting to synergy when combined with other PI3K-pathway inhibitors in PIK3CA mutant cells.Peer reviewe

Mixture modeling

This chapter is concerned with methods for the analysis of data on outbreaks of infectious disease in which additional genomic information is available on the pathogen. Molecular typing data from viruses or bacteria isolated from individual patients can contain additional information on possible links in the contact network that have led to transmission. Higher similarity between isolates is likely indicative of a closer link in the transmission chain. Different approaches to modeling such data are reviewed, all of which have associated R packages

Development of a generic and open source Incidence-Prevalence-Mortality model for the assessment of chronic disease epidemiology

&lt;p&gt;It is often a problem for researchers when the epidemiology of a disease is unreliable or altogether lacking. This thesis was created in order to make an R package called DisModR that accounts for this problem. In particular, the package was made for applications on chronic diseases.&lt;/p&gt; &lt;p&gt;The compartmental model used was taken from previous iterations of a software called DisMod. This model gives the logical relationship between di erent epidemiological parameters of a disease. In particular, inputs on disease incidence, remission, and case fatality were needed to determine the disease-speci c mortality and prevalence. However, an iterative optimization procedure called the Nelder-Mead method was employed in order to accept more inputs. This was done through the fminsearch() function of the neldermead package. Likewise, in order to make the values of the input variables vary continuously as a function of age, cubic smoothing splines were incorporated in DisModR.&lt;/p&gt; &lt;p&gt;The DisMod() function in DisModR allows the user to compute the di erent epidemiological parameters of a disease. It accepts data on three of the following ve inputs: incidence, remission, case fatality, mortality, and prevalence. Likewise, it allows users to manipulate options on the cubic smoothing splines and the tolerance limits of the optimization procedure. Using the breast cancer data set of the Netherlands, the properties of the DisMod() function was shown. Additional outputs on case fatality, prevalence, and relative risk mortality were produced using only inputs on incidence, remission, and mortality. In addition, the function also produced various objects that contained lists of&lt;br&gt; input and output values, as well as their corresponding graphs.&lt;/p&gt; &lt;p&gt;There are several uses to DisModR. The first, which is perhaps the most obvious, is that it provides values for epidemiological parameters, which are otherwise unavailable such as mortality and prevalence of particular diseases. The second is that it allows users to compare its results with existing data. This is to check if the present data is consistent with the logical relationship of the other input variables. The third gives researchers the opportunity to work in the R environment, which is a unique feature of this package compared to other versions of DisMod. Furthermore, users are advised that they should not only rely on the R package alone, but they must combine the results with expert&lt;br&gt; knowledge of the epidemiology of the disease.&lt;/p&gt;</p

Prediction of gene expression in human using rat in vivo gene expression in Japanese Toxicogenomics Project

The Japanese Toxicogenomics Project (TGP) provides large amount of data for the toxicology and safety framework. We focus on gene expression data of rat in vivo and human in vitro. We consider two different analyses for the TGP data. The first analysis is based on two-way analysis of variance model and the goal is to detect genes with significant dose-response relationship in both humans and rats. The second analysis consists of a trend analysis at each time point and the goal is to detect genes in the rat in order to predict gene expression in humans. The first analysis leads us to conclusions about the heterogeneity of the compound set and will suggest how to address this issue to improve future analyses. In the second part, we identify, for particular compounds, groups of genes that are translatable from rats to humans, so they can be used for prediction of human in vitro data based on rat in vivo data