Electrochemical oxidation of landfill leachate in a flow reactor: optimization using response surface methodology

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Validating the functional capacity index : a comparison of predicted versus observed total body scores

Background: The Functional Capacity Index (FCI) was designed to predict physical function 12 months after injury. We report a validation study of the FCI. Methods: This was a consecutive case series registered in the Queensland Trauma Registry who consented to the prospective 12-month telephone-administered follow-up study. FCI scores measured at 12 months were compared with those originally predicted. Results: Complete Abbreviated Injury Scale score information was available for 617 individuals, of whom 587 (95%) could be assigned at least one FCI score (range, 1-17). Agreement between the largest predicted FCI and observed FCI score was poor ([kappa] = 0.05; 95% confidence interval, 0.00-0.10) and explained only 1% of the variability in observed FCI. Using an encompassing model that included all FCI assignments, agreement remained poor ([kappa] = 0.05; 95% confidence interval, -0.02-0.12), and the model explained only 9% of the variability in observed FCI. Conclusion: The predicted functional capacity poorly agrees with actual functional outcomes. Further research should consider including other (noninjury) explanatory factors in predicting FCI at 12 months.

Patient-specific computational biomechanics for simulating adolescent scoliosis surgery : predicted vs clinical correction for a preliminary series of six patients

Scoliosis is a spinal deformity that requires surgical correction in progressive cases. In order to optimize surgical outcomes, patient-specific finite element models are being developed by our group. In this paper, a single rod anterior correction procedure is simulated for a group of six scoliosis patients. For each patient, personalised model geometry was derived from low-dose CT scans, and clinically measured intra-operative corrective forces were applied. However, tissue material properties were not patient-specific, being derived from existing literature. Clinically, the patient group had a mean initial Cobb angle of 47.3 degrees, which was corrected to 17.5 degrees after surgery. The mean simulated post-operative Cobb angle for the group was 18.1 degrees. Although this represents good agreement between clinical and simulated corrections, the discrepancy between clinical and simulated Cobb angle for individual patients varied between -10.3 and +8.6 degrees, with only three of the six patients matching the clinical result to within accepted Cobb measurement error of +-5 degrees. The results of this study suggest that spinal tissue material properties play an important role in governing the correction obtained during surgery, and that patient-specific modelling approaches must address the question of how to prescribe patient-specific soft tissue properties for spine surgery simulation.

Measured and predicted total body water in children with myelomeningocele

Objective: Children with myelomeningocele (MMC) have an altered body composition and an atypical distribution of total body water (TBW). The aim of the present study was to determine the accuracy of current predictive equations, based on bioelectrical impedance analysis (BIA), in determining TBW when compared with measured TBW using deuterium dilution. Methods: Fourteen children with MMC were measured for whole body BIA and TBW (using deuterium dilution and the Plateau method). Total body water was predicted using equations based on the resistance and characteristic frequency from BIA measurements and heights of subjects. Results: The mean measured TBW was 15.46 ± 8.28 L and the mean predictions for TBW using equations based on the resistance and characteristic frequency from BIA measurements and heights of subjects were 18.29 ± 8.41 L, 17.72 ± 11.42 L and 12.51 ± 7.59 L, respectively. The best correlation was found using characteristic frequency. The limits of agreement between measured and predicted TBW values using Bland-Altman analysis were large. Conclusions: The present study suggests that the prediction of TBW in children with MMC can be made accurately using the equation of Cornish et al. based on BIA measurements of characteristic frequency.

Comparison of measured sleeping metabolic rate and predicted basal metabolic rate during the first year of life : evidence of a bias changing with increasing metabolic rate

Objective: To compare measurements of sleeping metabolic rate (SMR) in infancy with predicted basal metabolic rate (BMR) estimated by the equations of Schofield. Methods: Some 104 serial measurements of SMR by indirect calorimetry were performed in 43 healthy infants at 1.5, 3, 6, 9 and 12 months of age. Predicted BMR was calculated using the weight only (BMR-wo) and weight and height (BMR-wh) equations of Schofield for 0-3-y-olds. Measured SMR values were compared with both predictive values by means of the Bland-Altman statistical test. Results: The mean measured SMR was 1.48 MJ/day. The mean predicted BMR values were 1.66 and 1.47 MJ/day for the weight only and weight and height equations, respectively. The Bland-Altman analysis showed that BMR-wo equation on average overestimated SMR by 0.18 MJ/day (11%) and the BMR-wh equation underestimated SMR by 0.01 MJ/day (1%). However the 95% limits of agreement were wide: -0.64 to + 0.28 MJ/day (28%) for the former equation and -0.39 to + 0.41 MJ/day (27%) for the latter equation. Moreover there was a significant correlation between the mean of the measured and predicted metabolic rate and the difference between them. Conclusions: The wide variation seen in the difference between measured and predicted metabolic rate and the bias probably with age indicates there is a need to measure actual metabolic rate for individual clinical care in this age group.

Validation of in silico-predicted genic SNPs in white clover (Trifolium repens L.), an outbreeding allopolyploid species

White clover (Trifolium repens L.) is an obligate outbreeding allotetraploid forage legume. Gene-associated SNPs provide the optimum genetic system for improvement of such crop species. An EST resource obtained from multiple cDNA libraries constructed from numerous genotypes of a single cultivar has been used for in silico SNP discovery and validation. A total of 58 from 236 selected sequence clusters (24.5%) were fully validated as containing polymorphic SNPs by genotypic analysis across the parents and progeny of several two-way pseudo-testcross mapping families. The clusters include genes belonging to a broad range of predicted functional categories. Polymorphic SNP-containing ESTs have also been used for comparative genomic analysis by comparison with whole genome data from model legume species, as well as Arabidopsis thaliana. A total of 29 (50%) of the 58 clusters detected putative ortholoci with known chromosomal locations in Medicago truncatula, which is closely related to white clover within the Trifolieae tribe of the Fabaceae. This analysis provides access to translational data from model species. The efficiency of in silico SNP discovery in white clover is limited by paralogous and homoeologous gene duplication effects, which are resolved unambiguously by the transmission test. This approach will also be applicable to other agronomically important cross-pollinating allopolyploid plant species.

Seed persistence in the field may be predicted by laboratory-controlled aging

Weed management is complicated by the presence of soil seed banks. The complexity of soil-seed interactions means that seed persistence in the field is often difficult to measure, let alone predict. Field trials, although accurate in their context, are time-consuming and expensive to conduct for individual species. Some ex situ techniques for estimating seed life expectancy have been proposed, but these fail to simulate the environmental complexity of the field. Also, it has been questioned whether techniques such as the controlled aging test (CAT) are useful indicators of field persistence. This study aimed to test the validity of the standard CAT (seed aging at 45 C and 60% relative humidity) in use at the Royal Botanic Gardens, Kew, U.K., for predicting field seed-persistence. Comparison of seed persistence and CAT data for 27 northwest European species suggested a significant positive correlation of 0.31. Subsequently, 13 species of emerging and common weeds of Queensland were assessed for their seed longevity using the CAT. The seed longevity data of these species in the CAT were linked with field seed-persistence data according to three broad seed-persistence categories: <1 yr, 1 to 3 yr, and >3 yr. We discuss the scope for using the CAT as a tool for rapid assignment of species to these categories. There is a need for further studies that compare predictions of seed persistence based on the CAT with seed persistence in the field for a larger range of species and environments.

Evaluation of polymorphisms in predicted target sites for micro RNAs differentially expressed in endometriosis

Previous microarray analyses identified 22 microRNAs (miRNAs) differentially expressed in paired ectopic and eutopic endometrium of women with and without endometriosis. To investigate further the role of these miRNAs in women with endometriosis, we conducted an association study aiming to explore the relationship between endometriosis risk and single-nucleotide polymorphisms (SNPs) in miRNA target sites for these differentially expressed miRNAs. A panel of 102 SNPs in the predicted miRNA binding sites were evaluated for an endometriosis association study and an ingenuity pathway analysis was performed. Fourteen rare variants were identified in this study. We found SNP rs14647 in the Wolf-Hirschhorn syndrome candidate gene1 (WHSC1) 3'UTR (untranslated region) was associated with endometriosis-related infertility presenting an odds ratio of 12.2 (95% confidence interval = 2.4-60.7, P = 9.03 x 10(-5)). SNP haplotype AGG in the solute carrier family 22, member 23 (SLC22A23) 3'UTR was associated with endometriosis-related infertility and more severe disease. With the individual genotyping data, ingenuity pathways analysis identified the tumour necrosis factor and cyclin-dependant kinase inhibitor as major factors in the molecular pathways. Significant associations between WHSC1 alleles and endometriosis-related infertility and SLC22A23 haplotypes and the disease severe stage were identified. These findings may help focus future research on subphenotypes of this disease. Replication studies in independent large sample sets to confirm and characterize the involvement of the gene variation in the pathogenesis of endometriosis are needed.

Resistance to Fusarium oxysporum f. sp fragariae and Predicted Breeding Values in Strawberry

Fusarium wilt of strawberry, incited by Fusarium oxysporum f. sp. fragariae (Fof), is a major disease of the cultivated strawberry (Fragaria xananassa) worldwide. An increase in disease outbreaks of the pathogen in Western Australia and Queensland plus the search for alternative disease management strategies place emphasis on the development of resistant cultivars. In response, a partial incomplete diallel cross involving four parents was performed for use in glasshouse resistance screenings. The resulting progeny were evaluated for their susceptibility to Fof. Best-performing progeny and suitability of progenies as parents were determined using data from disease severity ratings and analyzed using a linear mixed model incorporating a pedigree to produce best linear unbiased predictions of breeding values. Variation in disease response, ranging from highly susceptible to resistant, indicates a quantitative effect. The estimate of the narrow-sense heritability was 0.49 +/- 0.04 (SE), suggesting the population should be responsive to phenotypic recurrent selection. Several progeny genotypes have predicted breeding values higher than any of the parents. Knowledge of Fof resistance derived from this study can help select best parents for future crosses for the development of new strawberry cultivars with Fof resistance.

Search for Higgs bosons predicted in two-Higgs-doublet models via decays to tau lepton pairs in 1.96 TeV proton-antiproton collisions

We present the results of a search for Higgs bosons predicted in two-Higgs-doublet models, in the case where the Higgs bosons decay to tau lepton pairs, using 1.8 inverse fb of integrated luminosity of proton-antiproton collisions recorded by the CDF II experiment at the Fermilab Tevatron. Studying the observed mass distribution in events where one or both tau leptons decay leptonically, no evidence for a Higgs boson signal is observed. The result is used to infer exclusion limits in the two-dimensional parameter space of tan beta versus m(A).

Search for Higgs bosons predicted in two-Higgs-doublet models via decays to tau lepton pairs in 1.96 TeV proton-antiproton collisions

We present the results of a search for Higgs bosons predicted in two-Higgs-doublet models, in the case where the Higgs bosons decay to tau lepton pairs, using 1.8 inverse fb of integrated luminosity of proton-antiproton collisions recorded by the CDF II experiment at the Fermilab Tevatron. Studying the observed mass distribution in events where one or both tau leptons decay leptonically, no evidence for a Higgs boson signal is observed. The result is used to infer exclusion limits in the two-dimensional parameter space of tan beta versus m(A).

Network properties of decoys and CASP predicted models: a comparison with native protein structures

Protein structure space is believed to consist of a finite set of discrete folds, unlike the protein sequence space which is astronomically large, indicating that proteins from the available sequence space are likely to adopt one of the many folds already observed. In spite of extensive sequence-structure correlation data, protein structure prediction still remains an open question with researchers having tried different approaches (experimental as well as computational). One of the challenges of protein structure prediction is to identify the native protein structures from a milieu of decoys/models. In this work, a rigorous investigation of Protein Structure Networks (PSNs) has been performed to detect native structures from decoys/ models. Ninety four parameters obtained from network studies have been optimally combined with Support Vector Machines (SVM) to derive a general metric to distinguish decoys/models from the native protein structures with an accuracy of 94.11%. Recently, for the first time in the literature we had shown that PSN has the capability to distinguish native proteins from decoys. A major difference between the present work and the previous study is to explore the transition profiles at different strengths of non-covalent interactions and SVM has indeed identified this as an important parameter. Additionally, the SVM trained algorithm is also applied to the recent CASP10 predicted models. The novelty of the network approach is that it is based on general network properties of native protein structures and that a given model can be assessed independent of any reference structure. Thus, the approach presented in this paper can be valuable in validating the predicted structures. A web-server has been developed for this purpose and is freely available at http://vishgraph.mbu.iisc.ernet.in/GraProStr/PSN-QA.html.

Synconset Waves and Chains: Spiking Onsets in Synchronous Populations Predict and Are Predicted by Network Structure

Synfire waves are propagating spike packets in synfire chains, which are feedforward chains embedded in random networks. Although synfire waves have proved to be effective quantification for network activity with clear relations to network structure, their utilities are largely limited to feedforward networks with low background activity. To overcome these shortcomings, we describe a novel generalisation of synfire waves, and define `synconset wave' as a cascade of first spikes within a synchronisation event. Synconset waves would occur in `synconset chains', which are feedforward chains embedded in possibly heavily recurrent networks with heavy background activity. We probed the utility of synconset waves using simulation of single compartment neuron network models with biophysically realistic conductances, and demonstrated that the spread of synconset waves directly follows from the network connectivity matrix and is modulated by top-down inputs and the resultant oscillations. Such synconset profiles lend intuitive insights into network organisation in terms of connection probabilities between various network regions rather than an adjacency matrix. To test this intuition, we develop a Bayesian likelihood function that quantifies the probability that an observed synfire wave was caused by a given network. Further, we demonstrate it's utility in the inverse problem of identifying the network that caused a given synfire wave. This method was effective even in highly subsampled networks where only a small subset of neurons were accessible, thus showing it's utility in experimental estimation of connectomes in real neuronal-networks. Together, we propose synconset chains/waves as an effective framework for understanding the impact of network structure on function, and as a step towards developing physiology-driven network identification methods. Finally, as synconset chains extend the utilities of synfire chains to arbitrary networks, we suggest utilities of our framework to several aspects of network physiology including cell assemblies, population codes, and oscillatory synchrony.