Comparison of two sampling protocols and four home-range estimators using radio-tracking data from urban badgers Meles meles

Radio-telemetry is often the method of choice for studies of species whose behaviour is difficult to observe directly. However, considerable debate has ensued about the best way of deriving home-range estimates. In recent years, kernel estimators have become the most widely used method, together with the oldest and simplest method, the minimum convex polygon (MCP). More recently, it has been suggested that the local convex hull (LCH) might be more appropriate than kernel methods in cases where an animal’s home range includes a priori inaccessible areas. Yet another method, the Brownian bridge (BB), explicitly uses autocorrelated data to determine movement paths and, ultimately, home ranges or migration routes of animals. Whereas several studies have used simulation techniques to compare these different methods, few have used data from real animals. We used radio-telemetric data from urban badgers Meles meles to compare two sampling protocols (10-minute vs at least 30-minute inter-fix intervals) and four home-range estimators (MCP, fixed kernels (FK), LCH and BB). We used a multi-response permutation procedure and randomisation tests to compare overall patterns of fixes and degree of overlap of home ranges estimated using data from different sampling protocols, and a general linear model to compare the influence of sampling protocols and home-range estimator on the size of habitat patches. The shape of the estimated home ranges was influenced by sampling protocol in some cases. By contrast, the sizes and proportions of different habitats within home ranges were influenced by estimator type but not by sampling protocol. LCH performed consistently better than FK, and is especially appropriate for patchy study areas containing frequent no-go zones. However, we recommend using LCH in combination with other methods to estimate total range size, because LCH tended to produce smaller estimates than any other method. Results relating to BB are preliminary but suggest that this method is unsuitable for species in which range size is small compared to average travel speed.Marie-Curie Intra-European Fellowship (BSSUB - 24007); Defra WSC contract WM0304; Wildlife Biology granted the permit to upload the article to this repositor

Model selection for prognostic time-to-event gene signature discovery with applications in early breast cancer data

Model selection between competing models is a key consideration in the discovery of prognostic multigene signatures. The use of appropriate statistical performance measures as well as verification of biological significance of the signatures is imperative to maximise the chance of external validation of the generated signatures. Current approaches in time-to-event studies often use only a single measure of performance in model selection, such as logrank test p-values, or dichotomise the follow-up times at some phase of the study to facilitate signature discovery. In this study we improve the prognostic signature discovery process through the application of the multivariate partial Cox model combined with the concordance index, hazard ratio of predictions, independence from available clinical covariates and biological enrichment as measures of signature performance. The proposed framework was applied to discover prognostic multigene signatures from early breast cancer data. The partial Cox model combined with the multiple performance measures were used in both guiding the selection of the optimal panel of prognostic genes and prediction of risk within cross validation without dichotomising the follow-up times at any stage. The signatures were successfully externally cross validated in independent breast cancer datasets, yielding a hazard ratio of 2.55 [1.44, 4.51] for the top ranking signature.</p

Clinical evaluation and treatment of phaeochromocytoma

Phaeochromocytoma and extra adrenal paraganglioma are rare neuroendocrine tumours and have the potential to secrete adrenaline, noradrenaline and dopamine causing a myriad of clinical symptoms. Prompt diagnosis is essential for clinicians and requires a multidisciplinary specialist approach for the clinical and laboratory investigation, diagnosis, treatment and follow-up of patients. This paper is an integrated review of the clinical and laboratory evaluation and treatment of patients suspected to have phaeochromocytoma or paraganglioma, highlighting recent developments and best practices from recent published clinical guidelines.</jats:p

Association Between Results of a Gene Expression Signature Assay and Recurrence-Free Interval in Patients With Stage II Colon Cancer in Cancer and Leukemia Group B 9581 (Alliance)

PURPOSE: Conventional staging methods are inadequate to identify patients with stage II colon cancer (CC) who are at high risk of recurrence after surgery with curative intent. ColDx is a gene expression, microarray-based assay shown to be independently prognostic for recurrence-free interval (RFI) and overall survival in CC. The objective of this study was to further validate ColDx using formalin-fixed, paraffin-embedded specimens collected as part of the Alliance phase III trial, C9581. PATIENTS AND METHODS: C9581 evaluated edrecolomab versus observation in patients with stage II CC and reported no survival benefit. Under an initial case-cohort sampling design, a randomly selected subcohort (RS) comprised 514 patients from 901 eligible patients with available tissue. Forty-nine additional patients with recurrence events were included in the analysis. Final analysis comprised 393 patients: 360 RS (58 events) and 33 non-RS events. Risk status was determined for each patient by ColDx. The Self-Prentice method was used to test the association between the resulting ColDx risk score and RFI adjusting for standard prognostic variables. RESULTS: Fifty-five percent of patients (216 of 393) were classified as high risk. After adjustment for prognostic variables that included mismatch repair (MMR) deficiency, ColDx high-risk patients exhibited significantly worse RFI (multivariable hazard ratio, 2.13; 95% CI, 1.3 to 3.5; P < .01). Age and MMR status were marginally significant. RFI at 5 years for patients classified as high risk was 82% (95% CI, 79% to 85%), compared with 91% (95% CI, 89% to 93%) for patients classified as low risk. CONCLUSION: ColDx is associated with RFI in the C9581 subsample in the presence of other prognostic factors, including MMR deficiency. ColDx could be incorporated with the traditional clinical markers of risk to refine patient prognosis

Genome-scale resources for Thermoanaerobacterium saccharolyticum

Background Thermoanaerobacterium saccharolyticum is a hemicellulose-degrading thermophilic anaerobe that was previously engineered to produce ethanol at high yield. A major project was undertaken to develop this organism into an industrial biocatalyst, but the lack of genome information and resources were recognized early on as a key limitation. Results Here we present a set of genome-scale resources to enable the systems level investigation and development of this potentially important industrial organism. Resources include a complete genome sequence for strain JW/SL-YS485, a genome-scale reconstruction of metabolism, tiled microarray data showing transcription units, mRNA expression data from 71 different growth conditions or timepoints and GC/MS-based metabolite analysis data from 42 different conditions or timepoints. Growth conditions include hemicellulose hydrolysate, the inhibitors HMF, furfural, diamide, and ethanol, as well as high levels of cellulose, xylose, cellobiose or maltodextrin. The genome consists of a 2.7 Mbp chromosome and a 110 Kbp megaplasmid. An active prophage was also detected, and the expression levels of CRISPR genes were observed to increase in association with those of the phage. Hemicellulose hydrolysate elicited a response of carbohydrate transport and catabolism genes, as well as poorly characterized genes suggesting a redox challenge. In some conditions, a time series of combined transcription and metabolite measurements were made to allow careful study of microbial physiology under process conditions. As a demonstration of the potential utility of the metabolic reconstruction, the OptKnock algorithm was used to predict a set of gene knockouts that maximize growth-coupled ethanol production. The predictions validated intuitive strain designs and matched previous experimental results. Conclusion These data will be a useful asset for efforts to develop T. saccharolyticum for efficient industrial production of biofuels. The resources presented herein may also be useful on a comparative basis for development of other lignocellulose degrading microbes, such as Clostridium thermocellum. Electronic supplementary material The online version of this article (doi:10.1186/s12918-015-0159-x) contains supplementary material, which is available to authorized users