A Ligand predication tool based on modeling and reasoning with imprecise probabilistic knowledge

Ligand prediction has been driven by a fundamental desire to understand more about how biomolecules recognize their ligands and by the commercial imperative to develop new drugs. Most of the current available software systems are very complex and time-consuming to use. Therefore, developing simple and efficient tools to perform initial screening of interesting compounds is an appealing idea. In this paper, we introduce our tool for very rapid screening for likely ligands (either substrates or inhibitors) based on reasoning with imprecise probabilistic knowledge elicited from past experiments. Probabilistic knowledge is input to the system via a user-friendly interface showing a base compound structure. A prediction of whether a particular compound is a substrate is queried against the acquired probabilistic knowledge base and a probability is returned as an indication of the prediction. This tool will be particularly useful in situations where a number of similar compounds have been screened experimentally, but information is not available for all possible members of that group of compounds. We use two case studies to demonstrate how to use the tool.

Clinical significance of sperm DNA damage in assisted reproduction outcome

Background: Sperm DNA damage shows great promise as a biomarker of infertility. The study aim is to determine the usefulness of DNA fragmentation (DF), including modified bases (MB), to predict assisted reproduction treatment (ART) outcomes. Methods: DF in 360 couples (230 IVF and 130 ICSI) was measured by the alkaline Comet assay in semen and in sperm following density gradient centrifugation (DGC) and compared with fertilization rate (FR), embryo cumulative scores (ECS1) for the total number of embryos/treatment, embryos transferred (ECS2), clinical pregnancy (CP) and spontaneous pregnancy loss. MB were also measured using formamidopyrimidine DNA glycosylase to convert them into strand breaks. Results: In IVF, FR and ECS decreased as DF increased in both semen and DGC sperm, and couples who failed to achieve a CP had higher DF than successful couples (+12.2 semen, P = 0.004; +9.9 DGC sperm, P = 0.010). When MB were added to existing strand breaks, total DF was markedly higher (+17.1 semen, P = 0.009 and +13.8 DGC sperm, P = 0.045). DF was not associated with FR, ECS or CP in either semen or DGC sperm following ISCI. In contrast, by including MB, there was significantly more DNA damage (+16.8 semen, P = 0.008 and +15.5 DGC sperm, P = 0.024) in the group who did not achieve CP. Conclusion: SDF can predict ART outcome for IVF. Converting MB into further DNA strand breaks increased the test sensitivity, giving negative correlations between DF and CP for ICSI as well as IVF. © 2010 The Author.

The effect of androgen deprivation therapy on body composition in men with prostate cancer systematic review and meta analysis

The use of androgen deprivation therapy (ADT) in the treatment of prostate cancer is associated with changes in body composition including increased fat and decreased lean mass. Limited information exists regarding the rate and extent of these changes. This systematic review was conducted to determine the effects of ADT on body composition in prostate cancer patients.

Resting energy expenditure in non-ventilated, non-sedated patients recovering from serious traumatic brain injury: Comparison of prediction equations with indirect calorimetry values

Background & aims: Little is known about energy requirements in brain injured (TBI) patients, despite evidence suggesting adequate nutritional support can improve clinical outcomes. The study aim was to compare predicted energy requirements with measured resting energy expenditure (REE) values, in patients recovering from TBI.

Methods: Indirect calorimetry (IC) was used to measure REE in 45 patients with TBI. Predicted energy requirements were determined using FAO/WHO/UNU and Harris–Benedict (HB) equations. Bland– Altman and regression analysis were used for analysis.

Results: One-hundred and sixty-seven successful measurements were recorded in patients with TBI. At an individual level, both equations predicted REE poorly. The mean of the differences of standardised areas of measured REE and FAO/WHO/UNU was near zero (9 kcal) but the variation in both directions was substantial (range 591 to þ573 kcal). Similarly, the differences of areas of measured REE and HB demonstrated a mean of 1.9 kcal and range 568 to þ571 kcal. Glasgow coma score, patient status, weight and body temperature were signi?cant predictors of measured REE (p < 0.001; R²⁼ 0.47).

Conclusions: Clinical equations are poor predictors of measured REE in patients with TBI. The variability in REE is substantial. Clinicians should be aware of the limitations of prediction equations when estimating energy requirements in TBI patients.

Trophic interactions and consequent impacts of the invasive fish Pseudorasbora parva in a native aquatic foodweb: a field investigation in the UK

Introduction of the invasive Asian cyprinid fish Pseudorasbora parva into a 0.3 ha pond in England with a fish assemblage that included Cyprinus carpio, Rutilus rutilus and Scardinius erythrophthalmus resulted in their establishment of a numerically dominant population in only 2 years; density estimates exceeded 60 ind. m(-2) and they comprised > 99% of fish present. Stable isotope analysis (SIA) revealed significant trophic overlap between P. parva, R. rutilus and C. carpio, a shift associated with significantly depressed somatic growth in R. rutilus. Despite these changes, fish community composition remained similar between the ponds. Comparison with SIA values collected from an adjacent pond free of P. parva revealed a simplified food web in P. parva presence, but with an apparent trophic position shift for several fishes, including S. erythrophthalmus which appeared to assimilate energy at a higher trophic level, probably through P. parva consumption. The marked isotopic shifts shown in all taxa in the P. parva invaded pond (C-13-enriched, N-15 depleted) were indicative of a shift to a cyanobacteria-dominated phytoplankton community. These findings provide an increased understanding of the ecological consequences of the ongoing P. parva invasion of European freshwater ecosystems.

Short, medium and long term load forecasting model and virtual load forecaster based on radial basis neural networks

Artificial neural networks (ANNs) can be easily applied to short-term load forecasting (STLF) models for electric power distribution applications. However, they are not typically used in medium and long term load forecasting (MLTLF) electric power models because of the difficulties associated with collecting and processing the necessary data. Virtual instrument (VI) techniques can be applied to electric power load forecasting but this is rarely reported in the literature. In this paper, we investigate the modelling and design of a VI for short, medium and long term load forecasting using ANNs. Three ANN models were built for STLF of electric power. These networks were trained using historical load data and also considering weather data which is known to have a significant affect of the use of electric power (such as wind speed, precipitation, atmospheric pressure, temperature and humidity). In order to do this a V-shape temperature processing model is proposed. With regards MLTLF, a model was developed using radial basis function neural networks (RBFNN). Results indicate that the forecasting model based on the RBFNN has a high accuracy and stability. Finally, a virtual load forecaster which integrates the VI and the RBFNN is presented.

On-line Emotion Recognition in a 3-D Activation-Valence-Time Continuum using Acoustic and Linguistic Cues

For many applications of emotion recognition, such as virtual agents, the system must select responses while the user is speaking. This requires reliable on-line recognition of the user’s affect. However most emotion recognition systems are based on turnwise processing. We present a novel approach to on-line emotion recognition from speech using Long Short-Term Memory Recurrent Neural Networks. Emotion is recognised frame-wise in a two-dimensional valence-activation continuum. In contrast to current state-of-the-art approaches, recognition is performed on low-level signal frames, similar to those used for speech recognition. No statistical functionals are applied to low-level feature contours. Framing at a higher level is therefore unnecessary and regression outputs can be produced in real-time for every low-level input frame. We also investigate the benefits of including linguistic features on the signal frame level obtained by a keyword spotter.

On the derived category of a regular toric scheme

Let X be a quasi-compact scheme, equipped with an open covering by affine schemes U s = Spec A s . A quasi-coherent sheaf on X gives rise, by taking sections over the U s , to a diagram of modules over the coordinate rings A s , indexed by the intersection poset S of the covering. If X is a regular toric scheme over an arbitrary commutative ring, we prove that the unbounded derived category of quasi-coherent sheaves on X can be obtained from a category of Sop-diagrams of chain complexes of modules by inverting maps which induce homology isomorphisms on hyper-derived inverse limits. Moreover, we show that there is a finite set of weak generators, one for each cone in the fan S. The approach taken uses the machinery of Bousfield–Hirschhorn colocalisation of model categories. The first step is to characterise colocal objects; these turn out to be homotopy sheaves in the sense that chain complexes over different open sets U s agree on intersections up to quasi-isomorphism. In a second step it is shown that the homotopy category of homotopy sheaves is equivalent to the derived category of X.

SoC memory hierarchy derivation from dataflow graphs

Hardware synthesis from dataflow graphs of signal processing systems is a growing research area as focus shifts to high level design methodologies. For data intensive systems, dataflow based synthesis can lead to an inefficient usage of memory due to the restrictive nature of synchronous dataflow and its inability to easily model data reuse. This paper explores how dataflow graph changes can be used to drive both the on-chip and off-chip memory organisation and how these memory architectures can be mapped to a hardware implementation. By exploiting the data reuse inherent to many image processing algorithms and by creating memory hierarchies, off-chip memory bandwidth can be reduced by a factor of a thousand from the original dataflow graph level specification of a motion estimation algorithm, with a minimal increase in memory size. This analysis is verified using results gathered from implementation of the motion estimation algorithm on a Xilinx Virtex-4 FPGA, where the delay between the memories and processing elements drops from 14.2 ns down to 1.878 ns through the refinement of the memory architecture. Care must be taken when modeling these algorithms however, as inefficiencies in these models can be easily translated into overuse of hardware resources.