Post Hoc Analysis Of The Patricia Randomized Trial Of The Efficacy Of Human Papillomavirus Type 16 (hpv-16)/hpv-18 As04-adjuvanted Vaccine Against Incident And Persistent Infection With Nonvaccine Oncogenic Hpv Types Using An Alternative Multiplex Type-specific Pcr Assay For Hpv Dna.

The efficacy of the human papillomavirus type 16 (HPV-16)/HPV-18 AS04-adjuvanted vaccine against cervical infections with HPV in the Papilloma Trial against Cancer in Young Adults (PATRICIA) was evaluated using a combination of the broad-spectrum L1-based SPF10 PCR-DNA enzyme immunoassay (DEIA)/line probe assay (LiPA25) system with type-specific PCRs for HPV-16 and -18. Broad-spectrum PCR assays may underestimate the presence of HPV genotypes present at relatively low concentrations in multiple infections, due to competition between genotypes. Therefore, samples were retrospectively reanalyzed using a testing algorithm incorporating the SPF10 PCR-DEIA/LiPA25 plus a novel E6-based multiplex type-specific PCR and reverse hybridization assay (MPTS12 RHA), which permits detection of a panel of nine oncogenic HPV genotypes (types 16, 18, 31, 33, 35, 45, 52, 58, and 59). For the vaccine against HPV types 16 and 18, there was no major impact on estimates of vaccine efficacy (VE) for incident or 6-month or 12-month persistent infections when the MPTS12 RHA was included in the testing algorithm versus estimates with the protocol-specified algorithm. However, the alternative testing algorithm showed greater sensitivity than the protocol-specified algorithm for detection of some nonvaccine oncogenic HPV types. More cases were gained in the control group than in the vaccine group, leading to higher point estimates of VE for 6-month and 12-month persistent infections for the nonvaccine oncogenic types included in the MPTS12 RHA assay (types 31, 33, 35, 45, 52, 58, and 59). This post hoc analysis indicates that the per-protocol testing algorithm used in PATRICIA underestimated the VE against some nonvaccine oncogenic HPV types and that the choice of the HPV DNA testing methodology is important for the evaluation of VE in clinical trials. (This study has been registered at ClinicalTrials.gov under registration no. NCT00122681.).

Identification of the biodiesel source using an electronic nose

Biodiesel is an important new alternative fuel. The feedstock used and the process employed determines whether it fulfills the required specifications. In this work, an identification method is proposed using an electronic nose (e-nose). Four samples of biodiesel from different sources and one of petrodiesel were analyzed and well-recognized by the e-nose. Both pure biodiesel and B20 blends were studied. Furthermore, an innovative semiquantitative method is proposed on the basis of the smellprints correlated by a feed-forward artificial neural network. The results have demonstrated that the e-nose can be used to identify the biodiesel source and as a preliminary quantitative assay in place of expensive equipment.

The treatment of sulfate-rich wastewater using an anaerobic sequencing batch biofilm pilot-scale reactor

This paper presents the results from 92 cycles of an anaerobic sequencing batch biofilm reactor containing biomass immobilized on inert support (mineral coal) applied for the treatment of an industrial wastewater containing high sulfate concentration. The pilot-scale reactor, with a total volume of 1.2 m(3), was operated at sulfate loading rates ranging from 0.15 to 1.90 kgSO(4)(2-)/cycle (48 It - cycle) corresponding to sulfate concentrations of 0.25 to 3.0 gSO(4)(2-) l(-1). Domestic sewage and ethanol were utilized as electron donors for sulfate reduction. Influent sulfate concentrations were increased in order to evaluate the minimum COD/sulfate ratio at which high reactor performance could be maintained. The mean sulfate removal efficiency remained between the range of 88 to 92% at several sulfate concentrations. Temporal profiles along the 48 h cycles were carried out under stable operation at sulfate concentrations of 1.0, 2.0 and 3.0 gSO(4)(2-) l(-1). Sulfate removal reached 99% for cycle times of 15, 25, and 30 h, and the effluents sulfate concentrations were lower than 8 mgSO(4)(2-) l(-1). The results demonstrate the potential applicability of the anaerobic configuration for the biological treatment of sulfate-rich wastewaters. (C) 2009 Elsevier B.V. All rights reserved.

A 2-D Delaunay refinement algorithm using an initial prerefinement from the boundary mesh

An improvement to the quality bidimensional Delaunay mesh generation algorithm, which combines the mesh refinement algorithms strategy of Ruppert and Shewchuk is proposed in this research. The developed technique uses diametral lenses criterion, introduced by L. P. Chew, with the purpose of eliminating the extremely obtuse triangles in the boundary mesh. This method splits the boundary segment and obtains an initial prerefinement, and thus reducing the number of necessary iterations to generate a high quality sequential triangulation. Moreover, it decreases the intensity of the communication and synchronization between subdomains in parallel mesh refinement.

Hardware/firmware implementation of a soft sensor using an improved version of a fuzzy identification algorithm

This paper presents the design and implementation of an embedded soft sensor, i. e., a generic and autonomous hardware module, which can be applied to many complex plants, wherein a certain variable cannot be directly measured. It is implemented based on a fuzzy identification algorithm called ""Limited Rules"", employed to model continuous nonlinear processes. The fuzzy model has a Takagi-Sugeno-Kang structure and the premise parameters are defined based on the Fuzzy C-Means (FCM) clustering algorithm. The firmware contains the soft sensor and it runs online, estimating the target variable from other available variables. Tests have been performed using a simulated pH neutralization plant. The results of the embedded soft sensor have been considered satisfactory. A complete embedded inferential control system is also presented, including a soft sensor and a PID controller. (c) 2007, ISA. Published by Elsevier Ltd. All rights reserved.

Speciation of arsenic metabolites in the urine of occupational workers and experimental rats using an optimised hydride cold-trapping method

A hydride cold-trapping technique was developed and optimised for the measurement of urinary arsenic metabolites. The analytical precision of the method was found to be 6.1, 4.0 and 4.8% (n = 5) for inorganic arsenic (As-i), monomethylarsonate (MMA) and dimethylarsinate (DMA), respectively, with recoveries close to 100%, The detection limits were 1.0, 1.3 and 3 ng for As-i, MMA and DMA, respectively. The method was then used to analyse urine samples obtained from three groups of workers for occupational exposure in three companies where copper chrome arsenate was used for timber treatment. The results were compared with those for a normal control group of laboratory workers. Arsenic and its metabolites were also measured in experimental rats given 5 mg As kg(-1) body mass by oral gavage in the form of sodium arsenite, calcium arsenite or sodium arsenate. Occupational workers showed a significantly higher excretion of As-i, Up to two fold increases of urinary As-i excretion in rats compared with control rats were also observed in animals dosed with various forms of arsenicals. The method is suitable for the measurement of arsenic metabolites in urine of both humans and experimental animals.

Prediction of MHC class II-binding peptides using an evolutionary algorithm and artificial neural network

Motivation: Prediction methods for identifying binding peptides could minimize the number of peptides required to be synthesized and assayed, and thereby facilitate the identification of potential T-cell epitopes. We developed a bioinformatic method for the prediction of peptide binding to MHC class II molecules. Results: Experimental binding data and expert knowledge of anchor positions and binding motifs were combined with an evolutionary algorithm (EA) and an artificial neural network (ANN): binding data extraction --> peptide alignment --> ANN training and classification. This method, termed PERUN, was implemented for the prediction of peptides that bind to HLA-DR4(B1*0401). The respective positive predictive values of PERUN predictions of high-, moderate-, low- and zero-affinity binder-a were assessed as 0.8, 0.7, 0.5 and 0.8 by cross-validation, and 1.0, 0.8, 0.3 and 0.7 by experimental binding. This illustrates the synergy between experimentation and computer modeling, and its application to the identification of potential immunotheraaeutic peptides.

Analysis of immunosuppressive proteins in serum of liver-transplanted rats by using an anti-LSF-1 affinity column

Liver suppressor factor one (LSF-1) is a 40-kDa immunosuppressive protein in the serum of rats 60 days after orthotopic liver transplantation (OLT) between the nonrejector combination of DA donors into PVG; recipients. In the present study, the purification of proteins from rat OLT serum taken 60 days after transplantation Mras performed by affinity chromatography using the anti-LSF-1 polyclonal antibody (pAb). The assessment of column eluates using anti-LSF-1 and OLT serum was studied using rat heart and liver transplantation models. Rejection was not suppressed by the administration of OLT serum in heart or liver allografts. However, heart allografts treated with peak eluates (450 mu g single shot im, dissolved in Intralipos) taken from the affinity OLT serum survived significantly longer than untreated rats (median = 36.5 days; n = 7 vs 6.5 days; n = 5, respectively, P = 0.011). The same treatment with anti-LSF-1 column eluates also prolonged liver allografts significantly (>200 days) than those in either the untreated group (median = 11 days; n = 7) or those which received only Intralipos (median = 10.5 days; n = 5, P = 0.019). Subsequent analysis of the N-terminal sequences of some of the proteins which were eluted from the affinity column revealed that the homology of a 30-kDa protein was identical to hemoglobin alpha-chain, a 59-kDa protein to granulocyte inhibitory factor, a 70-kDa and a 90-kDa to albumin and its precursor, respectively. Although the specific immunosuppressive component has not been isolated, our results suggested that the anti-LSF-1 column can extract immunosuppressive moiety of LSF-1 from OLT serum. (C) 1998 Academic Press.

Determining forest structural attributes using an inverted geometric-optical model in mixed eucalypt forests, Southeast Queensland, Australia

The Montreal Process indicators are intended to provide a common framework for assessing and reviewing progress toward sustainable forest management. The potential of a combined geometrical-optical/spectral mixture analysis model was assessed for mapping the Montreal Process age class and successional age indicators at a regional scale using Landsat Thematic data. The project location is an area of eucalyptus forest in Emu Creek State Forest, Southeast Queensland, Australia. A quantitative model relating the spectral reflectance of a forest to the illumination geometry, slope, and aspect of the terrain surface and the size, shape, and density, and canopy size. Inversion of this model necessitated the use of spectral mixture analysis to recover subpixel information on the fractional extent of ground scene elements (such as sunlit canopy, shaded canopy, sunlit background, and shaded background). Results obtained fron a sensitivity analysis allowed improved allocation of resources to maximize the predictive accuracy of the model. It was found that modeled estimates of crown cover projection, canopy size, and tree densities had significant agreement with field and air photo-interpreted estimates. However, the accuracy of the successional stage classification was limited. The results obtained highlight the potential for future integration of high and moderate spatial resolution-imaging sensors for monitoring forest structure and condition. (C) Elsevier Science Inc., 2000.

Measuring the interaction forces between protein inclusion bodies and an air bubble using an atomic force microscope

Interaction forces between protein inclusion bodies and an air bubble have been quantified using an atomic force microscope (AFM). The inclusion bodies were attached to the AFM tip by covalent bonds. Interaction forces measured in various buffer concentrations varied from 9.7 nN to 25.3 nN (+/- 4-11%) depending on pH. Hydrophobic forces provide a stronger contribution to overall interaction force than electrostatic double layer forces. It also appears that the ionic strength affects the interaction force in a complex way that cannot be directly predicted by DLVO theory. The effects of pH are significantly stronger for the inclusion body compared to the air bubble. This study provides fundamental information that will subsequently facilitate the rational design of flotation recovery system for inclusion bodies. It has also demonstrated the potential of AFM to facilitate the design of such processes from a practical viewpoint.

Modelling the Distribution of Ischaemic Stroke-Specific Survival Time Using an EM-based Mixture Approach with Random Effects Adjustment

A two-component survival mixture model is proposed to analyse a set of ischaemic stroke-specific mortality data. The survival experience of stroke patients after index stroke may be described by a subpopulation of patients in the acute condition and another subpopulation of patients in the chronic phase. To adjust for the inherent correlation of observations due to random hospital effects, a mixture model of two survival functions with random effects is formulated. Assuming a Weibull hazard in both components, an EM algorithm is developed for the estimation of fixed effect parameters and variance components. A simulation study is conducted to assess the performance of the two-component survival mixture model estimators. Simulation results confirm the applicability of the proposed model in a small sample setting. Copyright (C) 2004 John Wiley Sons, Ltd.

Prediction of impact forces in a vibratory ball mill using an inverse technique

To understand the dynamic mechanisms of the mechanical milling process in a vibratory mill, it is necessary to determine the characteristics of the impact forces associated with the collision events. However, it is difficult to directly measure the impact force in an operating mill. This paper describes an inverse technique for the prediction of impact forces from acceleration measurements on a vibratory ball mill. The characteristics of the vibratory mill have been investigated by the modal testing technique, and its system modes have been identified. In the modelling of the system vibration response to the impact forces, two modal equations have been used to describe the modal responses. The superposition of the modal responses gives rise to the total response of the system. A method based on an optimisation approach has been developed to predict the impact forces by minimising the difference between the measured acceleration of the vibratory ball mill and the predicted acceleration from the solution of the modal equations. The predicted and measured impact forces are in good agreement. Copyright (C) 1996 Elsevier Science Ltd.

Investigating the influence of achievement on self-concept using an intra-class design and a comparison of the PASS and SDQ-1 self-concept tests

Background. The formation and measurement of self-concept were the foci of this research. Aims. The study aimed to investigate the influence of achievement on academic self-concept and to compare the Perception of Ability Scale for Students (PASS, Boersma & Chapman, 1992) with the Self-Description Questionnaire-1 (SDQ-1, Marsh, 1988). Sample. The participants were 479 grade 5 (mean age 126.6 months) coeducational Australian students, located in 18 schools. Method. An intra-class research design was used to investigate the influence of frame-of-reference on self-concept development. Results. As students' academic scores rose above their class mean their self-concepts increased and as students' academic scores fell below their class mean their self-concepts decreased. Students' difference from class mean predicted their self-concept scores. This finding was consistently shown across the reading, spelling, and mathematics domains using test and teaching rating data. A comparison between the PASS and the SDQ-1 demonstrated concurrent validity across self-concept domains. Conclusion. The findings support the notions that the social environment is a significant agent that influences self-concept, and that teacher ratings and standardised tests of achievement and the PASS and the SDQ-1 are valid measures for self-concept research.