Wavelet Analysis and Lognormal Distributions in GRBs

A wavelet analysis has been performed on 80 intense gamma-ray bursts GRBs) from the BATSE 3B catalog with durations longer than 2 seconds. The wavelet analysis applied novel features developed for edge detection in image processing and this filtering process was used to extract a fit to the irregular GRB profile from the background. A straightforward algorithm was subsequently used to identify statistically significant peaks in this profile. The areas and FWHM of 270 peaks that were characterised as isolated were found to be consistent with lognormal distributions. The distribution of time intervals between peak maxima for all 963 identified peaks in the GRBs is also presented.Comment: 5 pages, 4 figure

Violence brief interventions: a rapid review

Provision of a Violence Brief Intervention (VBI) to young men undergoing treatment for a violent injury may represent a teachable moment for the prevention of future interpersonal violence in Scotland. Prior to intervention design, a rapid review of the research literature was necessary to examine existing programmes. After title and abstract screening, eight distinct VBIs were identified from full texts. Whilst none of the programmes were a perfect match for our intervention goals, they did demonstrate the potential effectiveness of brief interventions for violence prevention at both cognitive and behavioural levels. Key themes of successful interventions included brief motivational interviewing as an effective method of engaging with at-risk participants and encouraging change, the utility of social norms approaches for correcting peer norm misperceptions, the usefulness of working with victims of violence in medical settings (particularly oral and maxillofacial surgeries), the importance of addressing the role of alcohol after violent injury, the advantages of a computer-therapist hybrid model of delivery, and the need for adequate follow-up evaluation as part of a randomised control trial. This information has been used to design a VBI which is currently under evaluation

The bronze serpent: abuse, trauma and the lifted healer in the wilderness.

Many Christian groups and churches have been forced to recognize that they have been complicit in behaviour which has betrayed the gospel. How then is the church to address the historical reality of being an abusive healer? The image of the bronze serpent (Num. 21.4-9; 2 Kgs 18.4; Jn 3.14) offers an ambiguous image which may reveal the reality of the church as both a source of abuse and trauma as well as an instrument of healing within a pattern of restorative justice

A window opening algorithm and UK office temperature field results and thermal simulation

This investigation of the window opening data from extensive field surveys in UK office buildings investigates 1) how people control the indoor environment by opening windows, 2) the cooling potential of opening windows, and 3) the use of an “adaptive algorithm” for predicting window opening behaviour for thermal simulation in ESP-r. We found that the mean indoor and outdoor temperatures when the window was open were higher than when it was closed, but show that nonetheless there was a useful cooling effect from opening a window. The adaptive algorithm for window opening behaviour was then used in thermal simulation studies for some typical office designs. The thermal simulation results were in general agreement with the findings of the field surveys

The thermal simulation of an office building implementing a new behavioural algorithm for window opening and the use of ceiling fans

This investigation of the window opening data from extensive field surveys in UK office buildings investigates 1) how people control the indoor environment by opening windows, 2) the cooling potential of opening windows, and 3) the use of an “adaptive algorithm” for predicting window opening behaviour for thermal simulation in ESP-r. We found that the mean indoor and outdoor temperatures when the window was open were higher than when it was closed, but show that nonetheless there was a useful cooling effect from opening a window. The adaptive algorithm for window opening behaviour was then used in thermal simulation studies for some typical office designs. The thermal simulation results were in general agreement with the findings of the field surveys

Identification of four novel susceptibility loci for oestrogen receptor negative breast cancer

Common variants in 94 loci have been associated with breast cancer including 15 loci with genome-wide significant associations (P<5 × 10−8) with oestrogen receptor (ER)-negative breast cancer and BRCA1-associated breast cancer risk. In this study, to identify new ER-negative susceptibility loci, we performed a meta-analysis of 11 genome-wide association studies (GWAS) consisting of 4,939 ER-negative cases and 14,352 controls, combined with 7,333 ER-negative cases and 42,468 controls and 15,252 BRCA1 mutation carriers genotyped on the iCOGS array. We identify four previously unidentified loci including two loci at 13q22 near KLF5, a 2p23.2 locus near WDR43 and a 2q33 locus near PPIL3 that display genome-wide significant associations with ER-negative breast cancer. In addition, 19 known breast cancer risk loci have genome-wide significant associations and 40 had moderate associations (P<0.05) with ER-negative disease. Using functional and eQTL studies we implicate TRMT61B and WDR43 at 2p23.2 and PPIL3 at 2q33 in ER-negative breast cancer aetiology. All ER- negative loci combined account for ~11% of familial relative risk for ER- negative disease and may contribute to improved ER-negative and BRCA1 breast cancer risk prediction

A critical look at studies applying over-sampling on the TPEHGDB dataset

Preterm birth is the leading cause of death among young children and has a large prevalence globally. Machine learning models, based on features extracted from clinical sources such as electronic patient files, yield promising results. In this study, we review similar studies that constructed predictive models based on a publicly available dataset, called the Term-Preterm EHG Database (TPEHGDB), which contains electrohysterogram signals on top of clinical data. These studies often report near-perfect prediction results, by applying over-sampling as a means of data augmentation. We reconstruct these results to show that they can only be achieved when data augmentation is applied on the entire dataset prior to partitioning into training and testing set. This results in (i) samples that are highly correlated to data points from the test set are introduced and added to the training set, and (ii) artificial samples that are highly correlated to points from the training set being added to the test set. Many previously reported results therefore carry little meaning in terms of the actual effectiveness of the model in making predictions on unseen data in a real-world setting. After focusing on the danger of applying over-sampling strategies before data partitioning, we present a realistic baseline for the TPEHGDB dataset and show how the predictive performance and clinical use can be improved by incorporating features from electrohysterogram sensors and by applying over-sampling on the training set