Toxin-antitoxin system promoter binding by type IV antitoxins of Mycobacterium tuberculosis

Tuberculosis remains a global health concern that requires difficult and lengthy treatment regimens for latent and persistent infection. Toxin-antitoxin systems have been linked to controlling bacterial growth rate and also implicated in bacterial persistence. Mycobacterium tuberculosis carries three uncharacterised type IV toxin-antitoxin systems, rv0837c/rv0836c, rv1044/rv1045, and rv2827c/rv2826c, which are regulated during macrophage infection. This work characterises the DNA-binding capabilities of Mycobacterium tuberculosis protein antitoxins Rv0837c, Rv1044, Rv2827c, and a homologue AbiEi from Streptococcus agalactiae, with the aim of developing the autoregulatory paradigm for type IV antitoxins. Biochemical analysis of AbiEi corroborated already published work and structural characterisation of AbiEi was begun. Rv2827c demonstrated an ability to bind four sites within the rv2827c cognate promoter with differing affinities, and is the first example of a negatively cooperative autoregulatory response within toxin-antitoxin systems. Rv0837c could not be expressed in E. coli and Rv1044 could only bind to the abiEi promoter indicating a structural relationship to AbiEi. The functional relevance of these data are unclear, but given the essentiality of Rv2827c these results prompt further study into this family of systems

How Should the State Interact Constitutionally with Corporations which have significant power and influence over its population? Lessons from the Impeachment of Warren Hastings, 1788-1795

How to maintain constitutional accountability over large corporations is an increasing theme in contemporary politics. The impeachment trial of Warren Hastings in 1788-1795 addressed this directly with the behaviour of the East India Trading Company. What lessons for today are illustrated by this historical trial

Beyond Active Learning: Leveraging the Full Potential of Human Interaction via Auto-Labeling, Human Correction, and Human Verification

Active Learning (AL) is a human-in-the-loop framework to interactively and adaptively label data instances, thereby enabling significant gains in model performance compared to random sampling. AL approaches function by selecting the hardest instances to label, often relying on notions of diversity and uncertainty. However, we believe that these current paradigms of AL do not leverage the full potential of human interaction granted by automated label suggestions. Indeed, we show that for many classification tasks and datasets, most people verifying if an automatically suggested label is correct take $3\times$ to $4\times$ less time than they do changing an incorrect suggestion to the correct label (or labeling from scratch without any suggestion). Utilizing this result, we propose CLARIFIER (aCtive LeARnIng From tIEred haRdness), an Interactive Learning framework that admits more effective use of human interaction by leveraging the reduced cost of verification. By targeting the hard (uncertain) instances with existing AL methods, the intermediate instances with a novel label suggestion scheme using submodular mutual information functions on a per-class basis, and the easy (confident) instances with highest-confidence auto-labeling, CLARIFIER can improve over the performance of existing AL approaches on multiple datasets -- particularly on those that have a large number of classes -- by almost 1.5$\times$ to 2$\times$ in terms of relative labeling cost.Comment: 14 pages, 8 figure

Common Pitfalls in Analysis of Tissue Scores

Histopathology remains an important source of descriptive biological data in biomedical research. Recent petitions for enhanced reproducibility in scientific studies have elevated the role of tissue scoring (semiquantitative and quantitative) in research studies. Effective tissue scoring requires appropriate statistical analysis to help validate the group comparisons and give the pathologist confidence in interpreting the data. Each statistical test is typically founded on underlying assumptions regarding the data. If the underlying assumptions of a statistical test do not match the data, then these tests can lead to increased risk of erroneous interpretations of the data. The choice of appropriate statistical test is influenced by the study’s experimental design and resultant data (eg, paired vs unpaired, normality, number of groups, etc). Here, we identify 3 common pitfalls in the analysis of tissue scores: shopping for significance, overuse of paired t-tests, and misguided analysis of multiple groups. Finally, we encourage pathologists to use the full breadth of resources available to them, such as using statistical software, reading key publications about statistical approaches, and identifying a statistician to serve as a collaborator on the multidisciplinary research team. These collective resources can be helpful in choosing the appropriate statistical test for tissue-scoring data to provide the most valid interpretation for the pathologist

Powerful Method for Including Genotype Uncertainty in Tests of Hardy-Weinberg Equilibrium

The use of posterior probabilities to summarize genotype uncertainty is pervasive across genotype, sequencing and imputation platforms. Prior work in many contexts has shown the utility of incorporating genotype uncertainty (posterior probabilities) in downstream statistical tests. Typical approaches to incorporating genotype uncertainty when testing Hardy-Weinberg equilibrium tend to lack calibration in the type I error rate, especially as genotype uncertainty increases. We propose a new approach in the spirit of genomic control that properly calibrates the type I error rate, while yielding improved power to detect deviations from Hardy-Weinberg Equilibrium. We demonstrate the improved performance of our method on both simulated and real genotypes

Influence of Warping on Stress for Restrained Concrete Slabs: For Application to CRCP

Continuously-reinforced concrete pavement (CRCP) is widely used in transportation system because of its low maintenance requirement. However, the need for large volumes of steel creates a high cost for new construction. The Illinois Tollway is preparing to substantially renovate highways in and around Chicago and this work seeks to understand how concretes of varying mixture designs can be made thinner by reducing the amount of built-in stress. The experiment examines warping in beams subjected to various degrees of restraint, in an effort to assess effectiveness at reducing warping in continuously reinforced concrete pavements. Value added methodologies such as internal curing with fine lightweight aggregate and topically-applied shrinkage-reducing admixture (SRA) were applied to a controlled concrete mixture design. The experimental program examines the warping of a composite concrete-steel beam with differing degrees of restraint, accomplished through using a 1/4 and ½” steel plate with a 2.5 concrete section . Each beam undergoes seven days of uni-axial warping, subjected to a controlled temperature and humidity environment (23+/-2C and 50 +/- 2 % RH), with a linear variable differential transformer to monitor endpoint deflection. Results indicate that as degree of restraint increases, the associated, or built-in , stress increases too; however, the deflection decreases by as much as a factor of two between unrestrained and 1/2 restraint. These findings potentially serve as a solution for effectively reducing the amount of concrete necessary for sustained loading associated with CRCP while mitigating warping and stresses associated

Supporting health and well-being among infants born to First Nations parents experiencing incarceration: a partnership-based whole-population administrative data study.

Objectives Generations of racist and colonial policies have resulted in First Nations (FN) people being systematically over-represented in Canada’s legal system. FN researchers partnered with data scientists at the University of Manitoba to document the birth outcomes associated with experiences of prenatal incarceration among FN families. Approach This retrospective cohort study linked whole-population administrative data from (i) Manitoba’s legal system to identify infants born to people incarcerated while pregnant, (ii) the First Nations research file to identify FN families, (iii) hospital records for birth outcomes, (iii) health and social services data for measured confounders. All Manitoba residents with a live birth (Jan 2004 - Dec 2017), and their infants, were eligible. Generalized linear models tested for differences in birth outcomes associated with experiencing incarceration while pregnant. Propensity score weights adjusted for measured confounders. Effect modification analyses tested whether associations differed between FN and all other Manitobans (AOM). Results FN people were more likely to experience incarceration while pregnant (n=1449) than AOM (n=278). Before propensity score adjustment, incarcerated pregnant people differed on important sociodemographic confounding characteristics from pregnant people who were not incarcerated – e.g., lower socioeconomic status, higher prevalence of pre-existing mental disorders, higher prevalence of having a previous child taken into care of family services, more likely to live in an urban setting. After propensity score adjustment, confounding characteristics were balanced between exposure groups. After adjustment, infants born to people incarcerated while pregnant were more likely to be low birth weight at term (aRR 1.76; 95% CI 1.41-2.18), be born preterm (aRR 1.44; 1.33-1.56), be small for gestational age (aRR 1.40; 1.28-1.54). Associations did not differ between FN and AOM families. Conclusion Incarceration of pregnant people compromises their infant’s birth outcomes and perpetuates intergenerational systems of oppression that exacerbate health inequities. To improve the health and well-being of FN people, we must implement Calls to Action outlined by the Truth and Reconciliation Commission to redress these harms experienced by FN people

Application of family-based tests of association for rare variants to pathways

Pathway analysis approaches for sequence data typically either operate in a single stage (all variants within all genes in the pathway are combined into a single, very large set of variants that can then be analyzed using standard gene-based test statistics) or in 2-stages (gene-based p values are computed for all genes in the pathway, and then the gene-based p values are combined into a single pathway p value). To date, little consideration has been given to the performance of gene-based tests (typically designed for a smaller number of single-nucleotide variants [SNVs]) when the number of SNVs in the gene or in the pathway is very large and the genotypes come from sequence data organized in large pedigrees. We consider recently proposed gene-based tests for rare variants from complex pedigrees that test for association between a large set of SNVs and a qualitative phenotype of interest (1-stage analyses) as well as 2-stage approaches. We find that many of these methods show inflated type I errors when the number of SNVs in the gene or the pathway is large (\u3e200 SNVs) and when using standard approaches to estimate the genotype covariance matrix. Alternative methods are needed when testing very large sets of SNVs in 1-stage approaches

Evaluation of the Power and Type 1 Error of Recently Proposed Family-based Tests of Assocations for Rare Variants

Until very recently, few methods existed to analyze rare-variant association with binary phenotypes in complex pedigrees. We consider a set of recently proposed methods applied to the simulated and real hypertension phenotype as part of the Genetic Analysis Workshop 18. Minimal power of the methods is observed for genes containing variants with weak effects on the phenotype. Application of the methods to the real hypertension phenotype yielded no genes meeting a strict Bonferroni cutoff of significance. Some prior literature connects 3 of the 5 most associated genes (p \u3c1 × 10−4) to hypertension or related phenotypes. Further methodological development is needed to extend these methods to handle covariates, and to explore more powerful test alternatives