A method for matching patients to advanced prostate cancer clinical trials

Objective: To illustrate a new method for simplifying patient recruitment for advanced prostate cancer clinical trials using natural language processing techniques. Background: The identification of eligible participants for clinical trials is a critical factor to increase patient recruitment rates and an important issue for discovery of new treatment interventions. The current practice of identifying eligible participants is highly constrained due to manual processing of disparate sources of unstructured patient data. Informatics-based approaches can simplify the complex task of evaluating patient’s eligibility for clinical trials. We show that an ontology-based approach can address the challenge of matching patients to suitable clinical trials. Methods: The free-text descriptions of clinical trial criteria as well as patient data were analysed. A set of common inclusion and exclusion criteria was identified through consultations with expert clinical trial coordinators. A research prototype was developed using Unstructured Information Management Architecture (UIMA) that identified SNOMED CT concepts in the patient data and clinical trial description. The SNOMED CT concepts model the standard clinical terminology that can be used to represent and evaluate patient’s inclusion/exclusion criteria for the clinical trial. Results: Our experimental research prototype describes a semi-automated method for filtering patient records using common clinical trial criteria. Our method simplified the patient recruitment process. The discussion with clinical trial coordinators showed that the efficiency in patient recruitment process measured in terms of information processing time could be improved by 25%. Conclusion: An UIMA-based approach can resolve complexities in patient recruitment for advanced prostate cancer clinical trials.

VIP Barcoding: Composition vector-based software for rapid species identification based on DNA barcoding

Species identification based on short sequences of DNA markers, that is, DNA barcoding, has emerged as an integral part of modern taxonomy. However, software for the analysis of large and multilocus barcoding data sets is scarce. The Basic Local Alignment Search Tool (BLAST) is currently the fastest tool capable of handling large databases (e.g. >5000 sequences), but its accuracy is a concern and has been criticized for its local optimization. However, current more accurate software requires sequence alignment or complex calculations, which are time-consuming when dealing with large data sets during data preprocessing or during the search stage. Therefore, it is imperative to develop a practical program for both accurate and scalable species identification for DNA barcoding. In this context, we present VIP Barcoding: a user-friendly software in graphical user interface for rapid DNA barcoding. It adopts a hybrid, two-stage algorithm. First, an alignment-free composition vector (CV) method is utilized to reduce searching space by screening a reference database. The alignment-based K2P distance nearest-neighbour method is then employed to analyse the smaller data set generated in the first stage. In comparison with other software, we demonstrate that VIP Barcoding has (i) higher accuracy than Blastn and several alignment-free methods and (ii) higher scalability than alignment-based distance methods and character-based methods. These results suggest that this platform is able to deal with both large-scale and multilocus barcoding data with accuracy and can contribute to DNA barcoding for modern taxonomy. VIP Barcoding is free and available at http://msl.sls.cuhk.edu.hk/vipbarcoding/.

Frequency response function based damage identification using principal component analysis and pattern recognition technique

Pattern recognition is a promising approach for the identification of structural damage using measured dynamic data. Much of the research on pattern recognition has employed artificial neural networks (ANNs) and genetic algorithms as systematic ways of matching pattern features. The selection of a damage-sensitive and noise-insensitive pattern feature is important for all structural damage identification methods. Accordingly, a neural networks-based damage detection method using frequency response function (FRF) data is presented in this paper. This method can effectively consider uncertainties of measured data from which training patterns are generated. The proposed method reduces the dimension of the initial FRF data and transforms it into new damage indices and employs an ANN method for the actual damage localization and quantification using recognized damage patterns from the algorithm. In civil engineering applications, the measurement of dynamic response under field conditions always contains noise components from environmental factors. In order to evaluate the performance of the proposed strategy with noise polluted data, noise contaminated measurements are also introduced to the proposed algorithm. ANNs with optimal architecture give minimum training and testing errors and provide precise damage detection results. In order to maximize damage detection results, the optimal architecture of ANN is identified by defining the number of hidden layers and the number of neurons per hidden layer by a trial and error method. In real testing, the number of measurement points and the measurement locations to obtain the structure response are critical for damage detection. Therefore, optimal sensor placement to improve damage identification is also investigated herein. A finite element model of a two storey framed structure is used to train the neural network. It shows accurate performance and gives low error with simulated and noise-contaminated data for single and multiple damage cases. As a result, the proposed method can be used for structural health monitoring and damage detection, particularly for cases where the measurement data is very large. Furthermore, it is suggested that an optimal ANN architecture can detect damage occurrence with good accuracy and can provide damage quantification with reasonable accuracy under varying levels of damage.

Inverse Sensitivity Analysis of Singular Solutions of FRF matrix in Structural System Identification

The problem of structural damage detection based on measured frequency response functions of the structure in its damaged and undamaged states is considered. A novel procedure that is based on inverse sensitivity of the singular solutions of the system FRF matrix is proposed. The treatment of possibly ill-conditioned set of equations via regularization scheme and questions on spatial incompleteness of measurements are considered. The application of the method in dealing with systems with repeated natural frequencies and (or) packets of closely spaced modes is demonstrated. The relationship between the proposed method and the methods based on inverse sensitivity of eigensolutions and frequency response functions is noted. The numerical examples on a 5-degree of freedom system, a one span free-free beam and a spatially periodic multi-span beam demonstrate the efficacy of the proposed method and its superior performance vis-a-vis methods based on inverse eigensensitivity.

targetTB: A target identification pipeline for Mycobacterium tuberculosis through an interactome, reactome and genome-scale structural analysis

Background: Tuberculosis still remains one of the largest killer infectious diseases, warranting the identification of newer targets and drugs. Identification and validation of appropriate targets for designing drugs are critical steps in drug discovery, which are at present major bottle-necks. A majority of drugs in current clinical use for many diseases have been designed without the knowledge of the targets, perhaps because standard methodologies to identify such targets in a high-throughput fashion do not really exist. With different kinds of 'omics' data that are now available, computational approaches can be powerful means of obtaining short-lists of possible targets for further experimental validation. Results: We report a comprehensive in silico target identification pipeline, targetTB, for Mycobacterium tuberculosis. The pipeline incorporates a network analysis of the protein-protein interactome, a flux balance analysis of the reactome, experimentally derived phenotype essentiality data, sequence analyses and a structural assessment of targetability, using novel algorithms recently developed by us. Using flux balance analysis and network analysis, proteins critical for survival of M. tuberculosis are first identified, followed by comparative genomics with the host, finally incorporating a novel structural analysis of the binding sites to assess the feasibility of a protein as a target. Further analyses include correlation with expression data and non-similarity to gut flora proteins as well as 'anti-targets' in the host, leading to the identification of 451 high-confidence targets. Through phylogenetic profiling against 228 pathogen genomes, shortlisted targets have been further explored to identify broad-spectrum antibiotic targets, while also identifying those specific to tuberculosis. Targets that address mycobacterial persistence and drug resistance mechanisms are also analysed. Conclusion: The pipeline developed provides rational schema for drug target identification that are likely to have high rates of success, which is expected to save enormous amounts of money, resources and time in the drug discovery process. A thorough comparison with previously suggested targets in the literature demonstrates the usefulness of the integrated approach used in our study, highlighting the importance of systems-level analyses in particular. The method has the potential to be used as a general strategy for target identification and validation and hence significantly impact most drug discovery programmes.

Identification of parameters in unconfined aquifers

A method is presented for identification of parameters in unconfined aquifers from pumping tests, based on the optimisation of the objective function using the least squares approach. Four parameters are to be evaluated, namely: The hydraulic conductivity in the radial and the vertical directions, the storage coefficient and the specific yield. The sensitivity analysis technique is used for solving the optimisation problem. Besides eliminating the subjectivity involved in the graphical procedure, the method takes into account the field data at all time intervals without classifying them into small and large time intervals and does not use the approximation that the ratio of the storage coefficient to the specific yield tends to zero. Two illustrative examples are presented and it is found that the parameter estimates from the computational and graphical procedures differ fairly significantly.

Cloning Of Rice Dna And Identification Of Transfer-Rna Gene Clones

THE rapid development of recombinant DNA technology has brought forth a revolution in biology'>", it aids us to have a closer look at the 'way genes are organized, eS11 ecially in the complex eucaryotic genornes'<", Although many animal and yeast genes have been studied in detail using recombinant DNA technology, plant genes have seldom been targets for such studie., Germination is an ideal process to study gene expression .because it effects a . shift in the metabolic status of seeds from a state of 'dormancy to an active one. AJ;l understanding of gene organization and regulation darin.g germination can be accomplblted by molecular cloning of DNA from seeds lik.e rice. To study the status of histone, rRNA tRNA and other genes in the rice genome, a general method was developed to clone eucarvotic DNA in a' plasmid vector pBR 322. This essentially ~ involves the following steps. The rice embryo and plasmid pBR 322 DNAs were cut witll restriction endonuclease Bam Hi to generate stick.Y ends, The plasmid DNA was puosphatased, the DNA~ ware a~·tnealed and joined 'by T4 phage DNA ligase. The recombinant DNA molecules thus produced were transjerred into E. coli and colonies containing them Were selected by their sensitivity to tetracycline and resistance to ampicillin, Two clones were identified . 2S haVing tRNA genes by hybridization of the DNA in the clones \vitl1 32P-la.belled rice tRNAs.

Rapid and efficient screening of a Representational Difference Analysis library using reverse Southern hybridisation: Identification of genetic differences between Haemophilus parasuis isolates

Representational Difference Analysis (RDA) is an established technique used for isolation of specific genetic differences between or within bacterial species. This method was used to investigate the genetic basis of serovar-specificity and the relationship between serovar and virulence in Haemophilus parasuis. An RDA clone library of 96 isolates was constructed using H. parasuis strains H425(P) (serovar 12) and HS1967 (serovar 4). To screen such a large clone library to determine which clones are strain-specific would typically involved separately labelling each clone for use in Southern hybridisation against genomic DNA from each of the strains. In this study, a novel application of reverse Southern hybridisation was used to screen the RDA library: genomic DNA from each strain was labelled and used to probe the library to identify strain-specific clones. This novel approach represents a significant improvement in methodology that is rapid and efficient.

Identification of genes involved with tick infestation in Bos taurus and Bos indicus

Tick resistant cattle could provide a potentially sustainable and environmentally sound method of controlling cattle ticks. Advances in genomics and the availability of the bovine genome sequence open up opportunities to identify useful and selectable genes controlling cattle tick resistance. Using quantitative real-time PCR and the Affymetrix bovine array platform, differences in gene expression of skin biopsies from tick resistant Bos indicus (Brahman) and tick susceptible Bos taurus (Holstein-Friesian) cattle following tick challenge were examined. We identified 138 significant differentially-expressed genes, including several immunological/host defence genes, extracellular matrix proteins, and transcription factors as well as genes involved in lipid metabolism. Three key pathways, represented by genes differentially expressed in resistant Brahmans, were identified; the development of the cell-mediated immune response, structural integrity of the dermis and intracellular Ca 2+ levels. Ca2+, which is implicated in host responses to microbial stimuli, may be required for the enhancement or fine-tuning of transcriptional activation of Ca2+- dependant host defence signalling pathways. Animal Genomics for Animal Health International Symposium, Paris, October 2007: (Proceedings)

Identification of small juvenile scombrids from northwest tropical Australia using mitochondrial DNA cytochrome b sequences

Small juveniles of the nine species of scombrids in Australian waters are morphologically similar to one another and, consequently, difficult to identify to species level. We show that the sequence of the mitochondrial DNA cytochrome b gene region is a powerful tool for identification of these young fish. Using this method, we identified 50 juvenile scombrids collected from Exmouth Bay, Western Australia. Six species of scombrids were apparent in this sample of fish: narrow-barred Spanish mackerel (Scomberomorus commerson), Indian mackerel (Rastrelliger kanagurta), frigate tuna (Auxis thazard), bullet tuna (Auxis rochei), leaping bonito (Cybiosarda elegans), and kawakawa (Euthynnus affinis). The presence of Indian mackerel, frigate tuna, leaping bonito, and kawakawa is the first indication that coastal waters may be an important spawning habitat for these species, although offshore spawning may also occur. The occurrence of small juvenile S. commerson was predicted from the known spawning patterns of that species, but other mackerel species (Scomberomorus munroi, Scomberomorus queenslandicus, Scomberomorus semifasiciatus) likely to be spawning during the sampling period were not detected among the 50 small juveniles analyzed here.

Identification of dynamical systems with fractional derivative damping models using inverse sensitivity analysis

The problem of identifying parameters of time invariant linear dynamical systems with fractional derivative damping models, based on a spatially incomplete set of measured frequency response functions and experimentally determined eigensolutions, is considered. Methods based on inverse sensitivity analysis of damped eigensolutions and frequency response functions are developed. It is shown that the eigensensitivity method requires the development of derivatives of solutions of an asymmetric generalized eigenvalue problem. Both the first and second order inverse sensitivity analyses are considered. The study demonstrates the successful performance of the identification algorithms developed based on synthetic data on one, two and a 33 degrees of freedom vibrating systems with fractional dampers. Limited studies have also been conducted by combining finite element modeling with experimental data on accelerances measured in laboratory conditions on a system consisting of two steel beams rigidly joined together by a rubber hose. The method based on sensitivity of frequency response functions is shown to be more efficient than the eigensensitivity based method in identifying system parameters, especially for large scale systems.

Cyanide poisoning in cattle from Dysphania glomulifera (red crumbweed): Using the internet for rapid plant identification and diagnostic advice: Case Report

A 300-strong Angus-Brahman cattle herd near Springsure, central Queensland, was being fed Acacia shirleyi (lancewood) browse during drought and crossed a 5-hectare, previously burnt area with an almost pure growth of Dysphania glomulifera subspecies glomulifera (red crumbweed) on their way to drinking water. Forty cows died of cyanide poisoning over 2 days before further access to the plant was prevented. A digital image of a plant specimen made on a flat-bed scanner and transmitted by email was used to identify D glomulifera. Specific advice on the plant's poisonous properties and management of the case was then provided by email within 2 hours of an initial telephone call by the field veterinarian to the laboratory some 600 km away. The conventional method using physical transport of a pressed dried plant specimen to confirm the identification took 5 days. D glomulifera was identified in the rumen of one of two cows necropsied. The cyanogenic potential of D glomulifera measured 4 days after collection from the site of cattle deaths was 18,600 mg HCN/kg in dry matter. The lethal dose of D glomulifera for a 420 kg cow was estimated as 150 to 190 g wet weight. The plant also contained 4.8% KNO3 equivalent in dry matter, but nitrate-nitrite poisoning was not involved in the deaths.

Oral Actinomyces Species in Health and Disease: Identification, Occurence and Importance of Early Colonization

The genus Actinomyces consists of a heterogeneous group of gram-positive, mainly facultatively anaerobic or microaerobic rods showing various degrees of branching. In the oral cavity, streptococci and Actinomyces form a fundamental component of the indigenous microbiota, being among initial colonizers in polymicrobial biofilms. The significance of the genus Actinomyces is based on the capability of species to adhere to surfaces such as on teeth and to co-aggregate with other bacteria. Identification of Actinomyces species has mainly been based on only a few biochemical characteristics, such as pigmentation and catalase production, or on the use of a single commercial kit. The limited identification of oral Actinomyces isolates to species level has hampered knowledge of their role both in health and disease. In recent years, Actinomyces and related organisms have attracted the attention of clinical microbiologists because of a growing awareness of their presence in clinical specimens and their association with disease. This series of studies aimed to amplify the identification methods for Actinomyces species. With the newly developed identification scheme, the age-related occurrence of Actinomyces in healthy mouths of infants and their distribution in failed dental implants was investigated. Adhesion of Actinomyces species to titanium surfaces processed in various ways was studied in vitro. The results of phenotypic identification methods indicated a relatively low applicability of commercially available test kits for reliable identification within the genus Actinomyces. However, in the study of conventional phenotypic methods, it was possible to develop an identification scheme that resulted in accurate differentiation of Actinomyces and closely related species, using various different test methods. Genotypic methods based on 16S rRNA sequence analysis of Actinomyces proved to be a useful method for genus level identification and further clarified the species level identification with phenotypic methods. The results of the study of infants showed that the isolation frequency of salivary Actinomyces species increased according to age: thirty-one percent of the infants at 2 months but 97% at 2 years of age were positive for Actinomyces. A. odontolyticus was the most prominent Actinomyces colonizer during the study period followed in frequency by A. naeslundii and A. viscosus. In the study of explanted dental implants, Actinomyces was the most prevalent bacterial genus, colonizing 94% of the fixtures. Also in the implants A. odontolyticus was revealed as the most common Actinomyces species. It was present in 84% of Actinomyces -positive fixtures followed in frequency by A. naeslundii, A. viscosus and A. israelii. In an in vitro study of titanium surfaces, different Actinomyces species showed variation regarding their adhesion to titanium. Surface roughness as well as albumin coating of titanium had significant effects on adhesion. The use of improved phenotypic and molecular diagnostic methods increased the accuracy of the identification of the Actinomyces to species level. This facilitated an investigation of their occurrence and distribution in oral specimens in both health and disease.

Identification and analysis of extended strands and beta-sheets in globular proteins

A method to identify β-sheets in globular proteins from extended strands, using only α-carbon positions, has been developed. The strands that form β-sheets are picked up by means of simple distance criteria. The method has been tested by applying it to three proteins with accurately known secondary structures. It has also been applied to ten other proteins wherein only α-carbon coordinates are available, and the list of β-sheets obtained. The following points are worth noting: (i) The sheets identified by the algorithm are found to agree satisfactorily with the reported ones based on backbone hydrogen bonding, wherever this information is available. (ii) β-Strands that do not form parts of any sheet are a common feature of protein structures. (iii) Such isolated β-strands tend to be short. (iv) The conformation corresponding to the preferred right-handed twist of the sheet is overwhelmingly observed in both the sheet-forming and isolated β-strands.

Prevention of type 2 diabetes with lifestyle intervention - emphasis on dietary composition and identification of high-risk individuals

Type 2 diabetes is an increasing, serious, and costly public health problem. The increase in the prevalence of the disease can mainly be attributed to changing lifestyles leading to physical inactivity, overweight, and obesity. These lifestyle-related risk factors offer also a possibility for preventive interventions. Until recently, proper evidence regarding the prevention of type 2 diabetes has been virtually missing. To be cost-effective, intensive interventions to prevent type 2 diabetes should be directed to people at an increased risk of the disease. The aim of this series of studies was to investigate whether type 2 diabetes can be prevented by lifestyle intervention in high-risk individuals, and to develop a practical method to identify individuals who are at high risk of type 2 diabetes and would benefit from such an intervention. To study the effect of lifestyle intervention on diabetes risk, we recruited 522 volunteer, middle-aged (aged 40 - 64 at baseline), overweight (body mass index > 25 kg/m2) men (n = 172) and women (n = 350) with impaired glucose tolerance to the Diabetes Prevention Study (DPS). The participants were randomly allocated either to the intensive lifestyle intervention group or the control group. The control group received general dietary and exercise advice at baseline, and had annual physician's examination. The participants in the intervention group received, in addition, individualised dietary counselling by a nutritionist. They were also offered circuit-type resistance training sessions and were advised to increase overall physical activity. The intervention goals were to reduce body weight (5% or more reduction from baseline weight), limit dietary fat (< 30% of total energy consumed) and saturated fat (< 10% of total energy consumed), and to increase dietary fibre intake (15 g / 1000 kcal or more) and physical activity (≥ 30 minutes/day). Diabetes status was assessed annually by a repeated 75 g oral glucose tolerance testing. First analysis on end-points was completed after a mean follow-up of 3.2 years, and the intervention phase was terminated after a mean duration of 3.9 years. After that, the study participants continued to visit the study clinics for the annual examinations, for a mean of 3 years. The intervention group showed significantly greater improvement in each intervention goal. After 1 and 3 years, mean weight reductions were 4.5 and 3.5 kg in the intervention group and 1.0 kg and 0.9 kg in the control group. Cardiovascular risk factors improved more in the intervention group. After a mean follow-up of 3.2 years, the risk of diabetes was reduced by 58% in the intervention group compared with the control group. The reduction in the incidence of diabetes was directly associated with achieved lifestyle goals. Furthermore, those who consumed moderate-fat, high-fibre diet achieved the largest weight reduction and, even after adjustment for weight reduction, the lowest diabetes risk during the intervention period. After discontinuation of the counselling, the differences in lifestyle variables between the groups still remained favourable for the intervention group. During the post-intervention follow-up period of 3 years, the risk of diabetes was still 36% lower among the former intervention group participants, compared with the former control group participants. To develop a simple screening tool to identify individuals who are at high risk of type 2 diabetes, follow-up data of two population-based cohorts of 35-64 year old men and women was used. The National FINRISK Study 1987 cohort (model development data) included 4435 subjects, with 182 new drug-treated cases of diabetes identified during ten years, and the FINRISK Study 1992 cohort (model validation data) included 4615 subjects, with 67 new cases of drug-treated diabetes during five years, ascertained using the Social Insurance Institution's Drug register. Baseline age, body mass index, waist circumference, history of antihypertensive drug treatment and high blood glucose, physical activity and daily consumption of fruits, berries or vegetables were selected into the risk score as categorical variables. In the 1987 cohort the optimal cut-off point of the risk score identified 78% of those who got diabetes during the follow-up (= sensitivity of the test) and 77% of those who remained free of diabetes (= specificity of the test). In the 1992 cohort the risk score performed equally well. The final Finnish Diabetes Risk Score (FINDRISC) form includes, in addition to the predictors of the model, a question about family history of diabetes and the age category of over 64 years. When applied to the DPS population, the baseline FINDRISC value was associated with diabetes risk among the control group participants only, indicating that the intensive lifestyle intervention given to the intervention group participants abolished the diabetes risk associated with baseline risk factors. In conclusion, the intensive lifestyle intervention produced long-term beneficial changes in diet, physical activity, body weight, and cardiovascular risk factors, and reduced diabetes risk. Furthermore, the effects of the intervention were sustained after the intervention was discontinued. The FINDRISC proved to be a simple, fast, inexpensive, non-invasive, and reliable tool to identify individuals at high risk of type 2 diabetes. The use of FINDRISC to identify high-risk subjects, followed by lifestyle intervention, provides a feasible scheme in preventing type 2 diabetes, which could be implemented in the primary health care system.