Data splitting as a countermeasure against hypothesis fishing: with a case study of predictors for low back pain

There is growing concern in the scientific community that many published scientific findings may represent spurious patterns that are not reproducible in independent data sets. A reason for this is that significance levels or confidence intervals are often applied to secondary variables or sub-samples within the trial, in addition to the primary hypotheses (multiple hypotheses). This problem is likely to be extensive for population-based surveys, in which epidemiological hypotheses are derived after seeing the data set (hypothesis fishing). We recommend a data-splitting procedure to counteract this methodological problem, in which one part of the data set is used for identifying hypotheses, and the other is used for hypothesis testing. The procedure is similar to two-stage analysis of microarray data. We illustrate the process using a real data set related to predictors of low back pain at 14-year follow-up in a population initially free of low back pain. “Widespreadness” of pain (pain reported in several other places than the low back) was a statistically significant predictor, while smoking was not, despite its strong association with low back pain in the first half of the data set. We argue that the application of data splitting, in which an independent party handles the data set, will achieve for epidemiological surveys what pre-registration has done for clinical studies

Field validation of the southern rock lobster paralytic shellfish toxin monitoring program in Tasmania, Australia

Paralytic shellfish toxins (PST) are found in the hepatopancreas of Southern Rock Lobster Jasus edwardsii from the east coast of Tasmania in association with blooms of the toxic dinoflagellate Alexandrium catenella. Tasmania’s rock lobster fishery is one of the state’s most important wild capture fisheries, supporting a significant commercial industry (AUD 97M) and recreational fishing sector. A comprehensive 8 years of field data collected across multiple sites has allowed continued improvements to the risk management program protecting public health and market access for the Tasmanian lobster fishery. High variability was seen in toxin levels between individuals, sites, months, and years. The highest risk sites were those on the central east coast, with July to January identified as the most at-risk months. Relatively high uptake rates were observed (exponential rate of 2% per day), similar to filter-feeding mussels, and meant that lobster accumulated toxins quickly. Similarly, lobsters were relatively fast detoxifiers, losing up to 3% PST per day, following bloom demise. Mussel sentinel lines were effective in indicating a risk of elevated PST in lobster hepatopancreas, with annual baseline monitoring costing approximately 0.06% of the industry value. In addition, it was determined that if the mean hepatopancreas PST levels in five individual lobsters from a site were −1, there is a 97.5% probability that any lobster from that site would be below the bivalve maximum level of 0.8 mg STX equiv. kg−1. The combination of using a sentinel species to identify risk areas and sampling five individual lobsters at a particular site, provides a cost-effective strategy for managing PST risk in the Tasmanian commercial lobster fishery

A fresh look at the evolution and diversification of photochemical reaction centers

In this review, I reexamine the origin and diversification of photochemical reaction centers based on the known phylogenetic relations of the core subunits, and with the aid of sequence and structural alignments. I show, for example, that the protein folds at the C-terminus of the D1 and D2 subunits of Photosystem II, which are essential for the coordination of the water-oxidizing complex, were already in place in the most ancestral Type II reaction center subunit. I then evaluate the evolution of reaction centers in the context of the rise and expansion of the different groups of bacteria based on recent large-scale phylogenetic analyses. I find that the Heliobacteriaceae family of Firmicutes appears to be the earliest branching of the known groups of phototrophic bacteria; however, the origin of photochemical reaction centers and chlorophyll synthesis cannot be placed in this group. Moreover, it becomes evident that the Acidobacteria and the Proteobacteria shared a more recent common phototrophic ancestor, and this is also likely for the Chloroflexi and the Cyanobacteria. Finally, I argue that the discrepancies among the phylogenies of the reaction center proteins, chlorophyll synthesis enzymes, and the species tree of bacteria are best explained if both types of photochemical reaction centers evolved before the diversification of the known phyla of phototrophic bacteria. The primordial phototrophic ancestor must have had both Type I and Type II reaction centers

Using Stochastic Causal Trees to Augment Bayesian Networks for Modeling eQTL Datasets

<p>Abstract</p> <p>Background</p> <p>The combination of genotypic and genome-wide expression data arising from segregating populations offers an unprecedented opportunity to model and dissect complex phenotypes. The immense potential offered by these data derives from the fact that genotypic variation is the sole source of perturbation and can therefore be used to reconcile changes in gene expression programs with the parental genotypes. To date, several methodologies have been developed for modeling eQTL data. These methods generally leverage genotypic data to resolve causal relationships among gene pairs implicated as associates in the expression data. In particular, leading studies have augmented Bayesian networks with genotypic data, providing a powerful framework for learning and modeling causal relationships. While these initial efforts have provided promising results, one major drawback associated with these methods is that they are generally limited to resolving causal orderings for transcripts most proximal to the genomic loci. In this manuscript, we present a probabilistic method capable of learning the causal relationships between transcripts at all levels in the network. We use the information provided by our method as a prior for Bayesian network structure learning, resulting in enhanced performance for gene network reconstruction.</p> <p>Results</p> <p>Using established protocols to synthesize eQTL networks and corresponding data, we show that our method achieves improved performance over existing leading methods. For the goal of gene network reconstruction, our method achieves improvements in recall ranging from 20% to 90% across a broad range of precision levels and for datasets of varying sample sizes. Additionally, we show that the learned networks can be utilized for expression quantitative trait loci mapping, resulting in upwards of 10-fold increases in recall over traditional univariate mapping.</p> <p>Conclusions</p> <p>Using the information from our method as a prior for Bayesian network structure learning yields large improvements in accuracy for the tasks of gene network reconstruction and expression quantitative trait loci mapping. In particular, our method is effective for establishing causal relationships between transcripts located both proximally and distally from genomic loci.</p

Could Work Be a Source of Behavioural Disorders? A Study in Horses

Stress at work, as shown by a number of human studies, may lead to a variety of negative and durable effects, such as impaired psychological functioning (anxiety, depression…). Horses share with humans this characteristic of working on a daily basis and are submitted then to work stressors related to physical constraints and/or more “psychological” conflicts, such as potential controversial orders from the riders or the requirement to suppress emotions. On another hand, horses may perform abnormal repetitive behaviour (“stereotypies”) in response to adverse life conditions. In the present study, we investigated whether the type of work the horses are used for may have an impact on their tendency to show stereotypic behaviour (and its type) outside work. Observations in their box of 76 horses all living in the same conditions, belonging to one breed and one sex, revealed that the prevalence and types of stereotypies performed strongly depended upon the type of work they were used for. The stereotypies observed involved mostly mouth movements and head tossing/nodding. Work constraints probably added to unfavourable living conditions, favouring the emergence of chronic abnormal behaviours. This is especially remarkable as the 23 hours spent in the box were influenced by the one hour work performed every day. To our knowledge, this is the first evidence of potential effects of work stressors on the emergence of abnormal behaviours in an animal species. It raises an important line of thought on the chronic impact of the work situation on the daily life of individuals

Growth Parameter Components of Adaptive Specificity during Experimental Evolution of the UVR-Inducible Mutator Pseudomonas cichorii 302959

BACKGROUND: Mutagenic DNA repair (MDR) transiently increases mutation rate through the activation of low-fidelity repair polymerases in response to specific, DNA-damaging environmental stress conditions such as ultraviolet radiation (UVR) exposure. These repair polymerases also confer UVR tolerance, intimately linking mutability and survival in bacteria that colone habitats subject to regular UVR exposure. METHODOLOGY/PRINCIPAL FINDINGS: Here, we investigate adaptive specificity in experimental lineages of the highly UVR-mutable epiphytic plant pathogen Pseudomonas cichorii 302959. Relative fitness measurements of isolates and population samples from replicate lineages indicated that adaptive improvements emerged early in all lineages of our evolution experiment and specific increases in relative fitness correlated with distinct improvements in doubling and lag times. Adaptive improvements gained under UVR and non-UVR conditions were acquired preferentially, and differentially contributed to relative fitness under varied growth conditions. CONCLUSIONS: These results support our earlier observations that MDR activation may contribute to gains in relative fitness without impeding normal patterns of adaptive specificity in P. cichorii 302959

Evaluation of Intereye Corneal Asymmetry in Patients with Keratoconus. A Scheimpflug Imaging Study

Purpose: To assess the correlation between keratoconus severity and intereye asymmetry of pachymetric data and posterior elevation values and to evaluate their combined accuracy in discriminating normal corneas from those with keratoconus. Methods: This study included 97 patients: 65 subjects with bilateral normal corneas (NC) and 32 with keratoconus (KC). Central corneal thickness (CCT), thinnest corneal thickness (ThCT) and posterior elevation (PE) at the thinnest point of the cornea were measured in both eyes using Scheimpflug imaging. Intereye asymmetry and its correlation with keratoconus severity were calculated for each variable. The area under the receiver operating characteristic curve (AUROC) was used to compare predictive accuracy of different variables for keratoconus. Results: In normal eyes, intereye differences were significantly lower compared with the keratoconus eyes (p<0.001, for CCT, ThCT and PE). There was a significant exponential correlation between disease severity and intereye asymmetry of steep keratometry (r(2) = 0.55, p<0.001), CCT (r(2) = 0.39, p<0.001), ThCT (r(2) = 0.48, p<0.001) and PE (r(2) = 0.64, p<0.001). After adjustment for keratoconus severity, asymmetry in thinnest pachymetry proved to be the best parameter to characterize intereye corneal asymmetry in keratoconus. This variable had high accuracy and significantly better discriminating ability (AUROC: 0.99) for KC than posterior elevation (AUROC: 0.96), ThCT (AUROC: 0.94) or CCT (AUROC: 0.92) alone. Conclusions: There is an increased intereye asymmetry in keratometry, pachymetry and posterior corneal elevation values in keratoconic patients compared to subjects with normal corneas. Keratoconus patients with more severe disease are also more asymmetric in their disease status which should be taken into account during clinical care

Quantitative utilization of prior biological knowledge in the Bayesian network modeling of gene expression data

<p>Abstract</p> <p>Background</p> <p>Bayesian Network (BN) is a powerful approach to reconstructing genetic regulatory networks from gene expression data. However, expression data by itself suffers from high noise and lack of power. Incorporating prior biological knowledge can improve the performance. As each type of prior knowledge on its own may be incomplete or limited by quality issues, integrating multiple sources of prior knowledge to utilize their consensus is desirable.</p> <p>Results</p> <p>We introduce a new method to incorporate the quantitative information from multiple sources of prior knowledge. It first uses the Naïve Bayesian classifier to assess the likelihood of functional linkage between gene pairs based on prior knowledge. In this study we included cocitation in PubMed and schematic similarity in Gene Ontology annotation. A candidate network edge reservoir is then created in which the copy number of each edge is proportional to the estimated likelihood of linkage between the two corresponding genes. In network simulation the Markov Chain Monte Carlo sampling algorithm is adopted, and samples from this reservoir at each iteration to generate new candidate networks. We evaluated the new algorithm using both simulated and real gene expression data including that from a yeast cell cycle and a mouse pancreas development/growth study. Incorporating prior knowledge led to a ~2 fold increase in the number of known transcription regulations recovered, without significant change in false positive rate. In contrast, without the prior knowledge BN modeling is not always better than a random selection, demonstrating the necessity in network modeling to supplement the gene expression data with additional information.</p> <p>Conclusion</p> <p>our new development provides a statistical means to utilize the quantitative information in prior biological knowledge in the BN modeling of gene expression data, which significantly improves the performance.</p