Testing weak nulls in matched observational studies.

We develop sensitivity analyses for the sample average treatment effect in matched observational studies while allowing unit-level treatment effects to vary. The methods may be applied to studies using any optimal without-replacement matching algorithm. In contrast to randomized experiments and to paired observational studies, we show for general matched designs that over a large class of test statistics, any procedure bounding the worst-case expectation while allowing for arbitrary effect heterogeneity must be unnecessarily conservative if treatment effects are actually constant across individuals. We present a sensitivity analysis which bounds the worst-case expectation while allowing for effect heterogeneity, and illustrate why it is generally conservative if effects are constant. An alternative procedure is presented that is asymptotically sharp if treatment effects are constant, and that is valid for testing the sample average effect under additional restrictions which may be deemed benign by practitioners. Simulations demonstrate that this alternative procedure results in a valid sensitivity analysis for the weak null hypothesis under a host of reasonable data-generating processes. The procedures allow practitioners to assess robustness of estimated sample average treatment effects to hidden bias while allowing for effect heterogeneity in matched observational studies.

Diversity and composition of the gut microbiota of Atlantic salmon (Salmo salar) farmed in Irish waters.

AIMS: Information on the gut microbiota of salmon is essential for optimizing nutrition while maintaining host health and welfare. This study's objectives were to characterize the microbiota in the GI tract of Atlantic salmon (Salmo salar) farmed in waters off the west coast of Ireland and to investigate whether there is a difference in microbiota diversity between the proximal and distal regions of the intestine. METHODS AND RESULTS: The microbiota from the proximal and distal intestine (PI and DI, respectively) of Atlantic salmon was examined using MiSeq Illumina high-throughput sequencing of the 16S ribosomal RNA gene. The PI region had greater bacterial diversity than the DI region. Six phyla were present in the DI samples, dominated by Tenericutes and Firmicutes. These six phyla were also amongst the 12 phyla detected in the PI samples. The PI microbiota was dominated by Tenericutes, Firmicutes, Bacteroidetes and Proteobacteria. A core microbiota of 20 operational taxonomic units (OTUs) common to both regions was observed. CONCLUSIONS: It was concluded that Tenericutes were the dominant phylum in both PI and DI samples, and the PI region had greater Shannon and Simpson diversity of bacteria. However, further work is required to identify the functionality of the salmon microbiota. SIGNIFICANCE AND IMPACT OF THE STUDY: Our study determined the composition and diversity of the intestinal microbiota in adult salmon from a commercial fishery and provides data to improve our understanding of their contributions to the nutrition, health and welfare of Atlantic salmon farmed in Irish waters.

Malaria parasite clearance rate regression: an R software package for a Bayesian hierarchical regression model.

BACKGROUND: Emerging resistance to anti-malarial drugs has led malaria researchers to investigate what covariates (parasite and host factors) are associated with resistance. In this regard, investigation of how covariates impact malaria parasites clearance is often performed using a two-stage approach in which the WWARN Parasite Clearance Estimator or PCE is used to estimate parasite clearance rates and then the estimated parasite clearance is regressed on the covariates. However, the recently developed Bayesian Clearance Estimator instead leads to more accurate results for hierarchial regression modelling which motivated the authors to implement the method as an R package, called "bhrcr". METHODS: Given malaria parasite clearance profiles of a set of patients, the "bhrcr" package performs Bayesian hierarchical regression to estimate malaria parasite clearance rates along with the effect of covariates on them in the presence of "lag" and "tail" phases. In particular, the model performs a linear regression of the log clearance rates on covariates to estimate the effects within a Bayesian hierarchical framework. All posterior inferences are obtained by a "Markov Chain Monte Carlo" based sampling scheme which forms the core of the package. RESULTS: The "bhrcr" package can be utilized to study malaria parasite clearance data, and specifically, how covariates affect parasite clearance rates. In addition to estimating the clearance rates and the impact of covariates on them, the "bhrcr" package provides tools to calculate the WWARN PCE estimates of the parasite clearance rates as well. The fitted Bayesian model to the clearance profile of each individual, as well as the WWARN PCE estimates, can also be plotted by this package. CONCLUSIONS: This paper explains the Bayesian Clearance Estimator for malaria researchers including describing the freely available software, thus making these methods accessible and practical for modelling covariates' effects on parasite clearance rates.

Spoilage indicator bacteria in farmed Atlantic salmon (Salmo salar) stored on ice for 10 days.

This study investigated the growth of indicator and spoilage bacteria on whole Atlantic salmon (Salmo salar) stored aerobically at 2 °C. On days 0, 2, 3, 6, 8 and 10 microbiological analysis was carried out on inner flesh and outer skin samples as well as outer skin swabs (25 cm2 surface areas). Mesophilic total viable counts (TVCm) on skin, flesh and swab samples increased from 1.9, 1.1 and 2.7 log10 CFUcm2 to 6.0, 5.1 and 5.7 log10 CFU/cm2 after 10 days, respectively. Psychrotrophic counts (TVCp), increased from 2.2, 1.8 and 3.1 log10 CFU/cm2 to 6.2, 5.3 and 5.9 log10 CFU/cm2, for skin, flesh and swab samples respectively. Hydrogen sulphide producing bacteria (HSPB), lactic acid bacteria (LAB), Pseudomonas spp., Brochothrix thermosphacta and Photobacterium spp. grew well with similar growth rates (mean generation times of 17.2-26 h). It was concluded that the shelf-life of salmon at 2 °C was approximately 10 days and that HSPB, LAB, Pseudomonas spp., Br. thermosphacta and Photobacterium spp. may be a better indicator of fish spoilage rather than TVC growth, with a count of 5-6 log10 CFU/cm2 indicating the end of shelf-life.

The Effect of Organic Acid, Trisodium Phosphate and Essential Oil Component Immersion Treatments on the Microbiology of Cod (Gadus morhua) during Chilled Storage.

Spoilage is a major issue for the seafood sector with the sale and exportation of fish limited by their short shelf-life. The immediate and storage effects of immersion (30 s at 20 °C) with 5% (w/v) citric acid (CA), 5% (v/v) lactic acid (LA), 5% (w/v) capric acid (CP) and 12% trisodium phosphate (TSP) (experiment 1) and essential oil components (EOC) (1% (v/v) citral (CIT), 1% (v/v) carvacrol (CAR), 1% (w/v) thymol (THY) and 1% (v/v) eugenol (EUG)) (experiment 2) on the concentrations of indicator (total viable counts (TVC) (mesophilic and psychrophilic) and total Enterobacteriaceae counts (TEC)), and spoilage organisms (Pseudomonas spp., lactic acid bacteria (LAB), Brochothrix thermosphacta, Photobacterium spp. and hydrogen sulphide producing bacteria (HSPB)) on cod (Gadus morhua) (stored aerobically at 2 °C) was investigated. There was no significant reduction for most treatment-bacteria combinations, with the following exceptions; TSP and TVCm (time t = 6), TSP and TVCp (t = 6), CP and LAB (t = 6, 8 and 10), CP and Br. thermosphacta (t = 4, 6, 8, 10, 14 and 16), TSP and Photobacterium spp. (t = 4), CAR and Br. thermosphacta (t = 6) and CAR and HSPB (t = 3, 6, 9, 12, 15 and 18). Although the majority of treatments did not significantly (P > 0.05) reduce bacterial counts, the limited success with CP and CAR warrants further investigation.

Bayesian hierarchical regression on clearance rates in the presence of "lag" and "tail" phases with an application to malaria parasites.

We present a principled technique for estimating the effect of covariates on malaria parasite clearance rates in the presence of "lag" and "tail" phases through the use of a Bayesian hierarchical linear model. The hierarchical approach enables us to appropriately incorporate the uncertainty in both estimating clearance rates in patients and assessing the potential impact of covariates on these rates into the posterior intervals generated for the parameters associated with each covariate. Furthermore, it permits us to incorporate information about individuals for whom there exists only one observation time before censoring, which alleviates a systematic bias affecting inference when these individuals are excluded. We use a changepoint model to account for both lag and tail phases, and hence base our estimation of the parasite clearance rate only on observations within the decay phase. The Bayesian approach allows us to treat the delineation between lag, decay, and tail phases within an individual's clearance profile as themselves being random variables, thus taking into account the additional uncertainty of boundaries between phases. We compare our method to existing methodology used in the antimalarial research community through a simulation study and show that it possesses desirable frequentist properties for conducting inference. We use our methodology to measure the impact of several covariates on Plasmodium falciparum clearance rate data collected in 2009 and 2010. Though our method was developed with this application in mind, it can be easily applied to any biological system exhibiting these hindrances to estimation.