Discrete Optimization for Interpretable Study Populations and Randomization Inference in an Observational Study of Severe Sepsis Mortality

Motivated by an observational study of the effect of hospital ward versus intensive care unit admission on severe sepsis mortality, we develop methods to address two common problems in observational studies: (1) when there is a lack of covariate overlap between the treated and control groups, how to define an interpretable study population wherein inference can be conducted without extrapolating with respect to important variables; and (2) how to use randomization inference to form confidence intervals for the average treatment effect with binary outcomes. Our solution to problem (1) incorporates existing suggestions in the literature while yielding a study population that is easily understood in terms of the covariates themselves, and can be solved using an efficient branch-and-bound algorithm. We address problem (2) by solving a linear integer program to utilize the worst case variance of the average treatment effect among values for unobserved potential outcomes that are compatible with the null hypothesis. Our analysis finds no evidence for a difference between the sixty day mortality rates if all individuals were admitted to the ICU and if all patients were admitted to the hospital ward among less severely ill patients and among patients with cryptic septic shock. We implement our methodology in R, providing scripts in the supplementary material

Randomization Inference and Sensitivity Analysis for Composite Null Hypotheses With Binary Outcomes in Matched Observational Studies

We present methods for conducting hypothesis testing and sensitivity analyses for composite null hypotheses in matched observational studies when outcomes are binary. Causal estimands discussed include the causal risk difference, causal risk ratio, and the effect ratio. We show that inference under the assumption of no unmeasured confounding can be performed by solving an integer linear program, while inference allowing for unmeasured confounding of a given strength requires solving an integer quadratic program. Through simulation studies and data examples, we demonstrate that our formulation allows these problems to be solved in an expedient manner even for large datasets and for large strata. We further exhibit that through our formulation, one can assess the impact of various assumptions about the potential outcomes on the performed inference. R scripts are provided that implement our methods. Supplementary materials for this article are available online. Keywords: Causal inference; Causal risk; Effect ratio; Integer programming; Sensitivity analysi

The stomach acts as a barrier against Salmonella in pigs fed a meal diet

Finishing pigs fed a coarsely ground meal (CGM) diet showed increased in vitro death rate of Salmonella in the gastric content and a reduced number of enterobacteria in the small intestine and caecum compared with a finely ground and pelleted diet (FGP). The CGM diet resulted moreover in a slower gastric emptying rate, increased the DM content and established a pH-gradient in the stomach. This affected the microbiota in the gastric digesta resulting in more lactic acid bacteria and fewer enterobacteria. Consequently Salmonella bacteria are killed in the stomach and do not enter and proliferate in other parts of the gastrointestinal tract. Furthermore the time after feeding a meal is of importance to whether or not Salmonella bacteria will survive transit through the stomach

Analysis of the Effects of Five Factors Relevant to In Vitro Chondrogenesis of Human Mesenchymal Stem Cells Using Factorial Design and High Throughput mRNA-Profiling

The in vitro process of chondrogenic differentiation of mesenchymal stem cells for tissue engineering has been shown to require three-dimensional culture along with the addition of differentiation factors to the culture medium. In general, this leads to a phenotype lacking some of the cardinal features of native articular chondrocytes and their extracellular matrix. The factors used vary, but regularly include members of the transforming growth factor β superfamily and dexamethasone, sometimes in conjunction with fibroblast growth factor 2 and insulin-like growth factor 1, however the use of soluble factors to induce chondrogenesis has largely been studied on a single factor basis. In the present study we combined a factorial quality-by-design experiment with high-throughput mRNA profiling of a customized chondrogenesis related gene set as a tool to study in vitro chondrogenesis of human bone marrow derived mesenchymal stem cells in alginate. 48 different conditions of transforming growth factor β 1, 2 and 3, bone morphogenetic protein 2, 4 and 6, dexamethasone, insulin-like growth factor 1, fibroblast growth factor 2 and cell seeding density were included in the experiment. The analysis revealed that the best of the tested differentiation cocktails included transforming growth factor β 1 and dexamethasone. Dexamethasone acted in synergy with transforming growth factor β 1 by increasing many chondrogenic markers while directly downregulating expression of the pro-osteogenic gene osteocalcin. However, all factors beneficial to the expression of desirable hyaline cartilage markers also induced undesirable molecules, indicating that perfect chondrogenic differentiation is not achievable with the current differentiation protocols

Balancing competing policy demands: the case of sustainable public sector food procurement.

A focus on market-based green growth strategies to pursue sustainability goals neglects the pursuit of understanding how human health is interwoven with the health of eco-systems to deliver sustainability goals. The article argues that clarifying the difference between green and sustainable public sector food procurement, with political continuity that supports and enables policymakers and practitioners to take an incremental approach to change, makes an important contribution to delivering more sustainable food systems and better public health nutrition. Five European case studies demonstrate the reality of devising and implementing innovative approaches to sustainable public sector food procurement and the effects of cultural and political framings. How legislation is enacted at the national level and interpreted at the local level is a key driver for sustainable procurement. Transition is dependent on political will and leadership and an infrastructure that can balance the economic, environmental and social drivers to effect change. The development of systems and indicators to measure change, reforms to EU directives on procurement, and the relationship between green growth strategies and sustainable diets are also discussed. The findings show the need to explore how consistent definitions for green public procurement and sustainable public procurement can be refined and standardized in order to support governments at all levels in reviewing and analyzing their current food procurement strategies and practices to improve sustainability

Source control options for reducing emission of priority pollutants from urban areas.

The overall aim of the ScorePP project is to develop comprehensive and appropriate source control strategies that authorities, cities, water utilities and the chemical industry can employ to reduce emissions of priority pollutants (PPs) from urban areas into the receiving water environment. Focus is on the 33 priority and priority hazardous substances and substance groups identified in the European Water Framework Directive. However, this list may be expanded to include emerging pollutants or reduced if appropriate model compounds can be identified. The initial work focuses on 67 substances, including substances identified in the proposed European environmental quality standard (EQS) directive as well as the defined example compounds and several organometallic derivatives. Information on inherent properties, environmental presence and fate, and legislative issues is made available in open database format, and a data management system combining chemical identification (CAS#), NACE economic activity classifications and NOSE-P emission source classifications has been developed as a basis for spatial characterisation of PP sources using GIS. Further work will focus on dynamic urban scale source-flux models, identifying emission patterns and optimising monitoring programmes in case studies and multi-criteria comparison of source control versus end-of-pipe mitigation options in relation to their economic, social and environmental impacts

Genome-Scale CRISPR-Cas9 Knockout Screening in Human Cells

The simplicity of programming the CRISPR (clustered regularly interspaced short palindromic repeats)–associated nuclease Cas9 to modify specific genomic loci suggests a new way to interrogate gene function on a genome-wide scale. We show that lentiviral delivery of a genome-scale CRISPR-Cas9 knockout (GeCKO) library targeting 18,080 genes with 64,751 unique guide sequences enables both negative and positive selection screening in human cells. First, we used the GeCKO library to identify genes essential for cell viability in cancer and pluripotent stem cells. Next, in a melanoma model, we screened for genes whose loss is involved in resistance to vemurafenib, a therapeutic RAF inhibitor. Our highest-ranking candidates include previously validated genes NF1 and MED12, as well as novel hits NF2, CUL3, TADA2B, and TADA1. We observe a high level of consistency between independent guide RNAs targeting the same gene and a high rate of hit confirmation, demonstrating the promise of genome-scale screening with Cas9.National Institutes of Health (U.S.) (Award 1DP1-MH100706)National Institutes of Health (U.S.) (1R01-DK097768