Grasses and the resource availability hypothesis: the importance of silica-based defences

The resource availability hypothesis (RAH) predicts that allocation of resources to anti-herbivore defences differs between species according to their growth rate. We tested this hypothesis by assessing the growth and defence investment strategies of 18 grass species and comparing them against vole feeding preferences. In addition, we assessed the effectiveness of silica, the primary defence in many grasses, in influencing vole feeding behaviour. Across species, we found that there was a strong negative relationship between the overall investment in defence and growth rate, thus supporting predictions of the RAH. However, no such relationship was found when assessing the various individual anti-herbivore defences, suggesting that different grass species show significant variation in their relative investment in strategies such as phenolic concentration, silica concentration and leaf toughness. Silica was the most influential defensive factor in determining vole feeding preference. Experimentally induced increases in leaf silica concentration deterred vole feeding in three of the five species tested, and altered feeding preference ranks between species. The strong positive relationship between silica concentration and leaf abrasiveness, when assessed both within and between species, suggests that increased abrasiveness is the mechanism by which silica deters feeding. Although grasses are often considered to be tolerant of herbivore damage rather then defended against it, they do follow predictions of defence allocation strategy based on their growth rates, and this affects the feeding behaviour of generalist grass-feeding herbivores

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

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

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

Motion Deblurring in the Wild

The task of image deblurring is a very ill-posed problem as both the image and the blur are unknown. Moreover, when pictures are taken in the wild, this task becomes even more challenging due to the blur varying spatially and the occlusions between the object. Due to the complexity of the general image model we propose a novel convolutional network architecture which directly generates the sharp image.This network is built in three stages, and exploits the benefits of pyramid schemes often used in blind deconvolution. One of the main difficulties in training such a network is to design a suitable dataset. While useful data can be obtained by synthetically blurring a collection of images, more realistic data must be collected in the wild. To obtain such data we use a high frame rate video camera and keep one frame as the sharp image and frame average as the corresponding blurred image. We show that this realistic dataset is key in achieving state-of-the-art performance and dealing with occlusions