Software-based study of a non-sorting method for median calculation on a set of integer numbers

This paper presents a software-based study of a hardware-based non-sorting median calculation method on a set of integer numbers. The method divides the binary representation of each integer element in the set into bit slices in order to find the element located in the middle position. The method exhibits a linear complexity order and our analysis shows that the best performance in execution time is obtained when slices of 4-bit in size are used for 8-bit and 16-bit integers, in mostly any data set size. Results suggest that software implementation of bit slice method for median calculation outperforms sorting-based methods with increasing improvement for larger data set size. For data set sizes of N > 5, our simulations show an improvement of at least 40%.

Tensor regression based on linked multiway parameter analysis

Classical regression methods take vectors as covariates and estimate the corresponding vectors of regression parameters. When addressing regression problems on covariates of more complex form such as multi-dimensional arrays (i.e. tensors), traditional computational models can be severely compromised by ultrahigh dimensionality as well as complex structure. By exploiting the special structure of tensor covariates, the tensor regression model provides a promising solution to reduce the model’s dimensionality to a manageable level, thus leading to efficient estimation. Most of the existing tensor-based methods independently estimate each individual regression problem based on tensor decomposition which allows the simultaneous projections of an input tensor to more than one direction along each mode. As a matter of fact, multi-dimensional data are collected under the same or very similar conditions, so that data share some common latent components but can also have their own independent parameters for each regression task. Therefore, it is beneficial to analyse regression parameters among all the regressions in a linked way. In this paper, we propose a tensor regression model based on Tucker Decomposition, which identifies not only the common components of parameters across all the regression tasks, but also independent factors contributing to each particular regression task simultaneously. Under this paradigm, the number of independent parameters along each mode is constrained by a sparsity-preserving regulariser. Linked multiway parameter analysis and sparsity modeling further reduce the total number of parameters, with lower memory cost than their tensor-based counterparts. The effectiveness of the new method is demonstrated on real data sets.

Distribution analysis of the putative cancer marker S100A4 across invasive squamous cell carcinoma penile tissue

MS-based proteomic methods were utilised for the first time in the discovery of novel penile cancer biomarkers. MALDI MS imaging was used to obtain the in situ biomolecular MS profile of squamous cell carcinoma of the penis which was then compared to benign epithelial MS profiles. Spectra from cancerous and benign tissue areas were examined to identify MS peaks that best distinguished normal epithelial cells from invasive squamous epithelial cells, providing crucial evidence to suggest S100A4 to be differentially expressed. Verification by immunohistochemistry resulted in positive staining for S100A4 in a sub-population of invasive but not benign epithelial cells.

Mobile sink based fault diagnosis scheme for wireless sensor networks

Network diagnosis in Wireless Sensor Networks (WSNs) is a difficult task due to their improvisational nature, invisibility of internal running status, and particularly since the network structure can frequently change due to link failure. To solve this problem, we propose a Mobile Sink (MS) based distributed fault diagnosis algorithm for WSNs. An MS, or mobile fault detector is usually a mobile robot or vehicle equipped with a wireless transceiver that performs the task of a mobile base station while also diagnosing the hardware and software status of deployed network sensors. Our MS mobile fault detector moves through the network area polling each static sensor node to diagnose the hardware and software status of nearby sensor nodes using only single hop communication. Therefore, the fault detection accuracy and functionality of the network is significantly increased. In order to maintain an excellent Quality of Service (QoS), we employ an optimal fault diagnosis tour planning algorithm. In addition to saving energy and time, the tour planning algorithm excludes faulty sensor nodes from the next diagnosis tour. We demonstrate the effectiveness of the proposed algorithms through simulation and real life experimental results.

Problems with determining the particle size distribution of chalk soil and some of their implications

Particle size distribution (psd) is one of the most important features of the soil because it affects many of its other properties, and it determines how soil should be managed. To understand the properties of chalk soil, psd analyses should be based on the original material (including carbonates), and not just the acid-resistant fraction. Laser-based methods rather than traditional sedimentation methods are being used increasingly to determine particle size to reduce the cost of analysis. We give an overview of both approaches and the problems associated with them for analyzing the psd of chalk soil. In particular, we show that it is not appropriate to use the widely adopted 8 pm boundary between the clay and silt size fractions for samples determined by laser to estimate proportions of these size fractions that are equivalent to those based on sedimentation. We present data from field and national-scale surveys of soil derived from chalk in England. Results from both types of survey showed that laser methods tend to over-estimate the clay-size fraction compared to sedimentation for the 8 mu m clay/silt boundary, and we suggest reasons for this. For soil derived from chalk, either the sedimentation methods need to be modified or it would be more appropriate to use a 4 pm threshold as an interim solution for laser methods. Correlations between the proportions of sand- and clay-sized fractions, and other properties such as organic matter and volumetric water content, were the opposite of what one would expect for soil dominated by silicate minerals. For water content, this appeared to be due to the predominance of porous, chalk fragments in the sand-sized fraction rather than quartz grains, and the abundance of fine (<2 mu m) calcite crystals rather than phyllosilicates in the clay-sized fraction. This was confirmed by scanning electron microscope (SEM) analyses. "Of all the rocks with which 1 am acquainted, there is none whose formation seems to tax the ingenuity of theorists so severely, as the chalk, in whatever respect we may think fit to consider it". Thomas Allan, FRS Edinburgh 1823, Transactions of the Royal Society of Edinburgh. (C) 2009 Natural Environment Research Council (NERC) Published by Elsevier B.V. All rights reserved.

Sensitivity analysis of a catchment-scale nitrogen model

There are now considerable expectations that semi-distributed models are useful tools for supporting catchment water quality management. However, insufficient attention has been given to evaluating the uncertainties inherent to this type of model, especially those associated with the spatial disaggregation of the catchment. The Integrated Nitrogen in Catchments model (INCA) is subjected to an extensive regionalised sensitivity analysis in application to the River Kennet, part of the groundwater-dominated upper Thames catchment, UK The main results are: (1) model output was generally insensitive to land-phase parameters, very sensitive to groundwater parameters, including initial conditions, and significantly sensitive to in-river parameters; (2) INCA was able to produce good fits simultaneously to the available flow, nitrate and ammonium in-river data sets; (3) representing parameters as heterogeneous over the catchment (206 calibrated parameters) rather than homogeneous (24 calibrated parameters) produced a significant improvement in fit to nitrate but no significant improvement to flow and caused a deterioration in ammonium performance; (4) the analysis indicated that calibrating the flow-related parameters first, then calibrating the remaining parameters (as opposed to calibrating all parameters together) was not a sensible strategy in this case; (5) even the parameters to which the model output was most sensitive suffered from high uncertainty due to spatial inconsistencies in the estimated optimum values, parameter equifinality and the sampling error associated with the calibration method; (6) soil and groundwater nutrient and flow data are needed to reduce. uncertainty in initial conditions, residence times and nitrogen transformation parameters, and long-term historic data are needed so that key responses to changes in land-use management can be assimilated. The results indicate the general, difficulty of reconciling the questions which catchment nutrient models are expected to answer with typically limited data sets and limited knowledge about suitable model structures. The results demonstrate the importance of analysing semi-distributed model uncertainties prior to model application, and illustrate the value and limitations of using Monte Carlo-based methods for doing so. (c) 2005 Elsevier B.V. All rights reserved.

Identifying appropriate construction procurement strategies

The origins of the various procurement methods in construction are considered, followed by a discussion of the circumstances for which each procurement system is best suited. The likely future development sin management-based methods are discussed in the context of current research, showing that co-ordination of trade and specialist contractors , more flexible contracting, constructive conflict management and a potential polarization of views about project management in the industry are likely.

Prediction of global and local model quality in CASP8 using the ModFOLD server

The development of effective methods for predicting the quality of three-dimensional (3D) models is fundamentally important for the success of tertiary structure (TS) prediction strategies. Since CASP7, the Quality Assessment (QA) category has existed to gauge the ability of various model quality assessment programs (MQAPs) at predicting the relative quality of individual 3D models. For the CASP8 experiment, automated predictions were submitted in the QA category using two methods from the ModFOLD server-ModFOLD version 1.1 and ModFOLDclust. ModFOLD version 1.1 is a single-model machine learning based method, which was used for automated predictions of global model quality (QMODE1). ModFOLDclust is a simple clustering based method, which was used for automated predictions of both global and local quality (QMODE2). In addition, manual predictions of model quality were made using ModFOLD version 2.0-an experimental method that combines the scores from ModFOLDclust and ModFOLD v1.1. Predictions from the ModFOLDclust method were the most successful of the three in terms of the global model quality, whilst the ModFOLD v1.1 method was comparable in performance to other single-model based methods. In addition, the ModFOLDclust method performed well at predicting the per-residue, or local, model quality scores. Predictions of the per-residue errors in our own 3D models, selected using the ModFOLD v2.0 method, were also the most accurate compared with those from other methods. All of the MQAPs described are publicly accessible via the ModFOLD server at: http://www.reading.ac.uk/bioinf/ModFOLD/. The methods are also freely available to download from: http://www.reading.ac.uk/bioinf/downloads/.

Aligning sequences to structures

Most newly sequenced proteins are likely to adopt a similar structure to one which has already been experimentally determined. For this reason, the most successful approaches to protein structure prediction have been template-based methods. Such prediction methods attempt to identify and model the folds of unknown structures by aligning the target sequences to a set of representative template structures within a fold library. In this chapter, I discuss the development of template-based approaches to fold prediction, from the traditional techniques to the recent state-of-the-art methods. I also discuss the recent development of structural annotation databases, which contain models built by aligning the sequences from entire proteomes against known structures. Finally, I run through a practical step-by-step guide for aligning target sequences to known structures and contemplate the future direction of template-based structure prediction.

Benchmarking consensus model quality assessment for protein fold recognition

Background: Selecting the highest quality 3D model of a protein structure from a number of alternatives remains an important challenge in the field of structural bioinformatics. Many Model Quality Assessment Programs (MQAPs) have been developed which adopt various strategies in order to tackle this problem, ranging from the so called "true" MQAPs capable of producing a single energy score based on a single model, to methods which rely on structural comparisons of multiple models or additional information from meta-servers. However, it is clear that no current method can separate the highest accuracy models from the lowest consistently. In this paper, a number of the top performing MQAP methods are benchmarked in the context of the potential value that they add to protein fold recognition. Two novel methods are also described: ModSSEA, which based on the alignment of predicted secondary structure elements and ModFOLD which combines several true MQAP methods using an artificial neural network. Results: The ModSSEA method is found to be an effective model quality assessment program for ranking multiple models from many servers, however further accuracy can be gained by using the consensus approach of ModFOLD. The ModFOLD method is shown to significantly outperform the true MQAPs tested and is competitive with methods which make use of clustering or additional information from multiple servers. Several of the true MQAPs are also shown to add value to most individual fold recognition servers by improving model selection, when applied as a post filter in order to re-rank models. Conclusion: MQAPs should be benchmarked appropriately for the practical context in which they are intended to be used. Clustering based methods are the top performing MQAPs where many models are available from many servers; however, they often do not add value to individual fold recognition servers when limited models are available. Conversely, the true MQAP methods tested can often be used as effective post filters for re-ranking few models from individual fold recognition servers and further improvements can be achieved using a consensus of these methods.

Recent developments in genetic data analysis: what can they tell us about human demographic history?

Over the last decade, a number of new methods of population genetic analysis based on likelihood have been introduced. This review describes and explains the general statistical techniques that have recently been used, and discusses the underlying population genetic models. Experimental papers that use these methods to infer human demographic and phylogeographic history are reviewed. It appears that the use of likelihood has hitherto had little impact in the field of human population genetics, which is still primarily driven by more traditional approaches. However, with the current uncertainty about the effects of natural selection, population structure and ascertainment of single-nucleotide polymorphism markers, it is suggested that likelihood-based methods may have a greater impact in the future.

Mass spectrometry in clinical proteomics - from the present to the future

MS is an important analytical tool in clinical proteomics, primarily in the disease-specific discovery identification and characterisation of proteomic biomarkers and patterns. MS-based proteomics is increasingly used in clinical validation and diagnostic method development. The latter departs from the typical application of MS-based proteomics by exchanging some of the high performance of analysis for the throughput, robustness and simplicity required for clinical diagnostics. Although conventional MS-based proteomics has become an important field in clinical applications, some of the most recent MS technologies have not yet been extensively applied in clinical proteomics. in this review, we will describe the current state of MS in clinical proteomics and look to the future of this field.

Measles vaccination and antibody response in autism spectrum disorders

Objective: To test the hypothesis that measles vaccination was involved in the pathogenesis of autism spectrum disorders (ASD) as evidenced by signs of a persistent measles infection or abnormally persistent immune response shown by circulating measles virus or raised antibody titres in children with ASD who had been vaccinated against measles, mumps and rubella (MMR) compared with controls. Design: Case-control study, community based. Methods: A community sample of vaccinated children aged 10-12 years in the UK with ASD (n = 98) and two control groups of similar age, one with special educational needs but no ASD (n = 52) and one typically developing group (n = 90), were tested for measles virus and antibody response to measles in the serum. Results: No difference was found between cases and controls for measles antibody response. There was no dose-response relationship between autism symptoms and antibody concentrations. Measles virus nucleic acid was amplified by reverse transcriptase-PCR in peripheral blood mononuclear cells from one patient with autism and two typically developing children. There was no evidence of a differential response to measles virus or the measles component of the MMR in children with ASD, with or without regression, and controls who had either one or two doses of MMR. Only one child from the control group had clinical symptoms of possible enterocolitis. Conclusion: No association between measles vaccination and ASD was shown.

Hybrid fault detection and isolation in an heater bank of an HVAC system

In this work a hybrid technique that includes probabilistic and optimization based methods is presented. The method is applied, both in simulation and by means of real-time experiments, to the heating unit of a Heating, Ventilation Air Conditioning (HVAC) system. It is shown that the addition of the probabilistic approach improves the fault diagnosis accuracy.

Characterisation of soils containing naturally occurring radioactive material (NORM) using in situ and rapid laboratory techniques. Case study: South Terras, Cornwall

In situ analysis has become increasingly important for contaminated land investigation and remediation. At present, portable techniques are used mainly as scanning tools to assess the spread and magnitude of the contamination, and are an adjunct to conventional laboratory analyses. A site in Cornwall, containing naturally occurring radioactive material (NORM), provided an opportunity for Reading University PhD student Anna Kutner to compare analytical data collected in situ with data generated by laboratory-based methods. The preliminary results in this paper extend the author‟s poster presentation at last September‟s GeoSpec2010 conference held in Lancaster.