Reliability of current estimates of schistosomiasis prevalence in the Rainforest Zone of the state of Pernambuco, Northeastern Brazil

The Program for Schistosomiasis Control within the Unified Health System (PCE-SUS) was implemented by 1999 in the Rainforest Zone or "Zona da Mata" of Pernambuco (ZMP) aiming to carry out biennial stool surveys of whole populations through municipal health organs followed by treatment of the positives through the local units of the Family Health Program (PSF). Yearly reports from the Health Department of Pernambuco State (SES/PE) from 2002 to 2004 on the PCE-SUS surveys were assessed to evaluate whether the current estimates of prevalence in the municipalities of the ZMP are based on reliable samples so as to allow considerations on the real situation of schistosomiasis in that area. The surveys carried out in that period did not follow the major principles underlying sampling design, thus posing problems in both precision and validity of the estimates. Only 12 out of 43 municipalities had minimally reliable estimates: five with moderate prevalence (10-50%) and seven with low prevalence (< 10%). Surveys with appropriate sampling procedures aimed either at representative target groups (school-aged children) or communities are recommended for the ZMP and other endemic areas not only to provide reliable information on the current situation of schistosomiasis but also to plan adequate control strategies.     

Relationship between 5-aminoimidazole-4-carboxamide-ribotide and AMP-activated protein kinase activity in the perfused mouse heart

AMP-activated protein kinase (AMPK) is a cellular energy sensor whose activity responds to AMP concentration ([AMP]). An agent that activates AMPK in cells is 5-aminoimidazole-4-carboxamide-1-riboside (AICA-riboside). Phosphorylated AICA-riboside or AICA-ribotide (ZMP) is an AMP analog. It is generally assumed that ZMP accumulation does not alter [AMP]. Additionally, the effect of AICA-riboside on AMPK activity of the heart is uncertain. Two hypotheses were tested in the isolated mouse heart: 1) sufficient ZMP concentration ([ZMP]) forms to increase AMPK activity, and 2) [ZMP] accumulation increases [AMP]. Perfusion of isolated mouse hearts with Krebs-Henseleit buffer containing 0.15-2 mM AICA-riboside concentration resulted in [ZMP] of 2-8 mM. ZMP accumulation reduced phosphocreatine concentration, which increased cytosolic [AMP]. In hearts with [ZMP] less than approximately 3 mM, in vivo AMPK allosteric activity effects of ZMP were observed; AMPK phosphorylation and [AMP] were not increased. With [ZMP] between 3 and 5 mM, in vitro AMPK activity and phosphorylation increased with unchanged [AMP]. This occurred in hearts perfused with 0.25 mM AICA-riboside for 48 min and 0.5 mM AICA-riboside for 24 min. The [ZMP] resulting in 50% AMPK activity (covalent phosphorylation of AMPK) was 4.1 +/- 0.6 mM. Hearts with [ZMP] >5 mM displayed increased [AMP] and AMPK activity that was not different from hearts with similar [AMP] with no [ZMP]; the half-maximal activity of AMP was 5.6 +/- 1.6 microM. Thus, in mouse hearts, AICA-riboside was metabolized to [ZMP] adequately to increase AMPK activity. Higher [ZMP] also increased cytosolic [AMP], which affects AMPK activity.     

A Fault Diagnosis Methodology for Gear Pump Based on EEMD and Bayesian Network

This paper proposes a fault diagnosis methodology for a gear pump based on the ensemble empirical mode decomposition (EEMD) method and the Bayesian network. Essentially, the presented scheme is a multi-source information fusion based methodology. Compared with the conventional fault diagnosis with only EEMD, the proposed method is able to take advantage of all useful information besides sensor signals. The presented diagnostic Bayesian network consists of a fault layer, a fault feature layer and a multi-source information layer. Vibration signals from sensor measurement are decomposed by the EEMD method and the energy of intrinsic mode functions (IMFs) are calculated as fault features. These features are added into the fault feature layer in the Bayesian network. The other sources of useful information are added to the information layer. The generalized three-layer Bayesian network can be developed by fully incorporating faults and fault symptoms as well as other useful information such as naked eye inspection and maintenance records. Therefore, diagnostic accuracy and capacity can be improved. The proposed methodology is applied to the fault diagnosis of a gear pump and the structure and parameters of the Bayesian network is established. Compared with artificial neural network and support vector machine classification algorithms, the proposed model has the best diagnostic performance when sensor data is used only. A case study has demonstrated that some information from human observation or system repair records is very helpful to the fault diagnosis. It is effective and efficient in diagnosing faults based on uncertain, incomplete information.     

Survival analysis using auxiliary variables via multiple imputation, with application to AIDS clinical trial data

We develop an approach, based on multiple imputation, to using auxiliary variables to recover information from censored observations in survival analysis. We apply the approach to data from an AIDS clinical trial comparing ZDV and placebo, in which CD4 count is the time-dependent auxiliary variable. To facilitate imputation, a joint model is developed for the data, which includes a hierarchical change-point model for CD4 counts and a time-dependent proportional hazards model for the time to AIDS. Markov chain Monte Carlo methods are used to multiply impute event times for censored cases. The augmented data are then analyzed and the results combined using standard multiple-imputation techniques. A comparison of our multiple-imputation approach to simply analyzing the observed data indicates that multiple imputation leads to a small change in the estimated effect of ZDV and smaller estimated standard errors. A sensitivity analysis suggests that the qualitative findings are reproducible under a variety of imputation models. A simulation study indicates that improved efficiency over standard analyses and partial corrections for dependent censoring can result. An issue that arises with our approach, however, is whether the analysis of primary interest and the imputation model are compatible.     

A Bayesian multi-risks survival (MRS) model in the presence of double censorings

Semi-competing risks data include the time to a nonterminating event and the time to a terminating event, while competing risks data include the time to more than one terminating event. Our work is motivated by a prostate cancer study, which has one nonterminating event and two terminating events with both semi-competing risks and competing risks present as well as two censoring times. In this paper, we propose a new multi-risks survival (MRS) model for this type of data. In addition, the proposed MRS model can accommodate noninformative right-censoring times for nonterminating and terminating events. Properties of the proposed MRS model are examined in detail. Theoretical and empirical results show that the estimates of the cumulative incidence function for a nonterminating event may be biased if the information on a terminating event is ignored. A Markov chain Monte Carlo sampling algorithm is also developed. Our methodology is further assessed using simulations and also an analysis of the real data from a prostate cancer study. As a result, a prostate-specific antigen velocity greater than 2.0 ng/mL per year and higher biopsy Gleason scores are positively associated with a shorter time to death due to prostate cancer.     

Combining genetic, historical and geographical data to reconstruct the dynamics of bioinvasions: application to the cane toad Bufo marinus

We developed a spatially explicit model of a bioinvasion and used an approximate Bayesian computation (ABC) framework to make various inferences from a combination of genetic (microsatellite genotypes), historical (first observation dates) and geographical (spatial coordinates of introduction and sampled sites) information. Our method aims to discriminate between alternative introduction scenarios and to estimate posterior densities of demographically relevant parameters of the invasive process. The performance of our landscape-ABC method is assessed using simulated data sets differing in their information content (genetic and/or historical data). We apply our methodology to the recent introduction and spatial expansion of the cane toad, Bufo marinus, in northern Australia. We find that, at least in the context of cane toad invasion, historical data are more informative than genetic data for discriminating between introduction scenarios. However, the combination of historical and genetic data provides the most accurate estimates of demographic parameters. For the cane toad, we find some evidence for a strong bottleneck prior to introduction, a small initial number of founder individuals (about 15), a large population growth rate (about 400% per generation), a standard deviation of dispersal distance of 19 km per generation and a high invasion speed at equilibrium (50 km per year). Our approach strengthens the application of the ABC method to the field of bioinvasion by allowing statistical inferences to be made on the introduction and the spatial expansion dynamics of invasive species using a combination of various relevant sources of information.     

The tripartite architecture of the eukaryotic integral membrane protein zinc metalloprotease Ste24

Ste24 enzymes, a family of eukaryotic integral membrane proteins, are zinc metalloproteases (ZMPs) originally characterized as “CAAX proteases” targeting prenylated substrates, including a -factor mating pheromone in yeast and prelamin A in humans. Recently, Ste24 was shown to also cleave nonprenylated substrates. Reduced activity of the human ortholog, HsSte24, is linked to multiple disease states (laminopathies), including progerias and lipid disorders. Ste24 possesses a unique “α-barrel” structure consisting of seven transmembrane (TM) α-helices encircling a large intramembranous cavity (~14 000 ų). The catalytic zinc, coordinated via a HExxH…E/H motif characteristic of gluzincin ZMPs, is positioned at one of the cavity's bases. The interrelationship between Ste24 as a gluzincin, a long-studied class of soluble ZMPs, and as a novel cavity-containing integral membrane protein protease has been minimally explored to date. Informed by homology to well-characterized soluble, gluzincin ZMPs, we develop a model of Ste24 that provides a conceptual framework for this enzyme family, suitable for development and interpretation of structure/function studies. The model consists of an interfacial, zinc-containing “ZMP Core” module surrounded by a “ZMP Accessory” module, both capped by a TM helical “α-barrel” module of as yet unknown function. Multiple sequence alignment of 58 Ste24 orthologs revealed 38 absolutely conserved residues, apportioned unequally among the ZMP Core (18), ZMP Accessory (13), and α-barrel (7) modules. This Tripartite Architecture representation of Ste24 provides a unified image of this enzyme family.     

Development of a Relative Ranking Risk Index for Risk Assessment in the Transportation of Liquefied Petroleum Gas in Mexico City's Metropolitan Area

The development of a relative ranking methodology created specifically for the assessment of the risk due transportation of Liquefied Petroleum Gas (LPG) in Mexico City is reported. Mexico City has a number of conditions that present specific challenges: Large population, varied geography, large consumption of LPG, and all sorts of vehicles, including those for LPG. Given Mexico's status as “emerging economy,” it is reasonable to assume that the main causes that promote accidents during transportation of LPG are quite different from those in developed countries. This issue was corroborated through information from official sources and from news media. Due to the lack of official data, a systematic collection of information on the media was performed. A relative ranking methodology was developed based on a systematic study to elucidate the main causes of accidents in Mexico and on the results derived from the integrated database from official data and the media. The weighting that made up the Risk Index for Transportation of LPG is based on the same database. A classification guide was created in order to harmonize criteria in the assessment of the parameters of the proposed risk index.     

Joint Spatial Modeling of Recurrent Infection and Growth with Processes under Intermittent Observation

Summary In this article, we present a new statistical methodology for longitudinal studies in forestry, where trees are subject to recurrent infection, and the hazard of infection depends on tree growth over time. Understanding the nature of this dependence has important implications for reforestation and breeding programs. Challenges arise for statistical analysis in this setting with sampling schemes leading to panel data, exhibiting dynamic spatial variability, and incomplete covariate histories for hazard regression. In addition, data are collected at a large number of locations, which poses computational difficulties for spatiotemporal modeling. A joint model for infection and growth is developed wherein a mixed nonhomogeneous Poisson process, governing recurring infection, is linked with a spatially dynamic nonlinear model representing the underlying height growth trajectories. These trajectories are based on the von Bertalanffy growth model and a spatially varying parameterization is employed. Spatial variability in growth parameters is modeled through a multivariate spatial process derived through kernel convolution. Inference is conducted in a Bayesian framework with implementation based on hybrid Monte Carlo. Our methodology is applied for analysis in an 11‐year study of recurrent weevil infestation of white spruce in British Columbia.     

A hybrid model for nonignorable dropout in longitudinal binary responses

This article presents a likelihood-based method for handling nonignorable dropout in longitudinal studies with binary responses. The methodology developed is appropriate when the target of inference is the marginal distribution of the response at each occasion and its dependence on covariates. A "hybrid" model is formulated, which is designed to retain advantageous features of the selection and pattern-mixture model approaches. This formulation accommodates a variety of assumed forms of nonignorable dropout, while maintaining transparency of the constraints required for identifying the overall model. Once appropriate identifying constraints have been imposed, likelihood-based estimation is conducted via the EM algorithm. The article concludes by applying the approach to data from a randomized clinical trial comparing two doses of a contraceptive.     

Generalized linear mixture models for handling nonignorable dropouts in longitudinal studies

This paper presents a method for analysing longitudinal data when there are dropouts. In particular, we develop a simple method based on generalized linear mixture models for handling nonignorable dropouts for a variety of discrete and continuous outcomes. Statistical inference for the model parameters is based on a generalized estimating equations (GEE) approach (Liang and Zeger, 1986). The proposed method yields estimates of the model parameters that are valid when nonresponse is nonignorable under a variety of assumptions concerning the dropout process. Furthermore, the proposed method can be implemented using widely available statistical software. Finally, an example using data from a clinical trial of contracepting women is used to illustrate the methodology.     

Nonparametric mixed effects models for unequally sampled noisy curves

We propose a method of analyzing collections of related curves in which the individual curves are modeled as spline functions with random coefficients. The method is applicable when the individual curves are sampled at variable and irregularly spaced points. This produces a low-rank, low-frequency approximation to the covariance structure, which can be estimated naturally by the EM algorithm. Smooth curves for individual trajectories are constructed as best linear unbiased predictor (BLUP) estimates, combining data from that individual and the entire collection. This framework leads naturally to methods for examining the effects of covariates on the shapes of the curves. We use model selection techniques--Akaike information criterion (AIC), Bayesian information criterion (BIC), and cross-validation--to select the number of breakpoints for the spline approximation. We believe that the methodology we propose provides a simple, flexible, and computationally efficient means of functional data analysis.     

Multiscale modelling as a tool to prescribe realistic boundary conditions for the study of surgical procedures

This work was motivated by the problems of analysing detailed 3D models of vascular districts with complex anatomy. It suggests an approach to prescribing realistic boundary conditions to use in order to obtain information on local as well as global haemodynamics. A method was developed which simultaneously solves Navier-Stokes equations for local information and a non-linear system of ordinary differential equations for global information. This is based on the principle that an anatomically detailed 3D model of a cardiovascular district can be achieved by using the finite element method. In turn the finite element method requires a specific boundary condition set. The approach outlined in this work is to include the system of ordinary differential equations in the boundary condition set. Such a multiscale approach was first applied to two controls: (i) a 3D model of a straight tube in a simple hydraulic network and (ii) a 3D model of a straight coronary vessel in a lumped-parameter model of the cardiovascular system. The results obtained are very close to the solutions available for the pipe geometry. This paper also presents preliminary results from the application of the methodology to a particular haemodynamic problem: namely the fluid dynamics of a systemic-to-pulmonary shunt in paediatric cardiac surgery.     

Combining a Geographical Information System and Process Engineering to Design an Agricultural-Industrial Ecosystem

A methodology to systematically construct an industrial ecosystem is presented in the context of a specific biomass input of peanut hulls and a process complex of activated carbon, adhesive precursor, and hydrogen. The methodology consists of three phases: locating sources and sinks of potential materials for exchange or upgrading; filtering feasible exchanges, the exchange candidates, and locations for new infrastructure; and generating optimal configurations for the industrial ecosystem based on the objective of maximizing the system benefit, defined in the particular case study as the revenue that is generated by the system. To implement the methodology two computer tools are combined: geographic information systems (GIS) and mathematical programming. A screening problem formulation is developed that uses feasibility contours to help reduce the number of candidate material and energy linkages. A case study describing a prospective agro-industrial complex in the state of Georgia, U.S.A., is given.     

Artificial neural networks: fundamentals, computing, design, and application

Artificial neural networks (ANNs) are relatively new computational tools that have found extensive utilization in solving many complex real-world problems. The attractiveness of ANNs comes from their remarkable information processing characteristics pertinent mainly to nonlinearity, high parallelism, fault and noise tolerance, and learning and generalization capabilities. This paper aims to familiarize the reader with ANN-based computing (neurocomputing) and to serve as a useful companion practical guide and toolkit for the ANNs modeler along the course of ANN project development. The history of the evolution of neurocomputing and its relation to the field of neurobiology is briefly discussed. ANNs are compared to both expert systems and statistical regression and their advantages and limitations are outlined. A bird's eye review of the various types of ANNs and the related learning rules is presented, with special emphasis on backpropagation (BP) ANNs theory and design. A generalized methodology for developing successful ANNs projects from conceptualization, to design, to implementation, is described. The most common problems that BPANNs developers face during training are summarized in conjunction with possible causes and remedies. Finally, as a practical application, BPANNs were used to model the microbial growth curves of S. flexneri. The developed model was reasonably accurate in simulating both training and test time-dependent growth curves as affected by temperature and pH.     

Screening stochastic Life Cycle assessment inventory models

A screening methodology is presented that utilizes the linear structure within the deterministic life cycle inventory (LCI) model. The methodology ranks each input data element based upon the amount it contributes toward the final output. The identified data elements along with their position in the deterministic model are then sorted into descending order based upon their individual contributions. This enables practitioners and model users to identify those input data elements that contribute the most in the inventory stage. Percentages of the top ranked data elements are then selected, and their corresponding data quality index (DQI) value is upgraded in the stochastic LCI model. Monte Carlo computer simulations are obtained and used to compare the output variance of the original stochastic model with modified stochastic model. The methodology is applied to four real-world beverage delivery system LCA inventory models for verification. This research assists LCA practitioners by streamlining the conversion process when converting a deterministic LCI model to a stochastic model form. Model users and decision-makers can benefit from the reduction in output variance and the increase in ability to discriminate between product system alternatives.     

5-Amino-4-imidazolecarboxamide riboside (Z-riboside) metabolism in eukaryotic cells

Metabolites of 5-amino-4-imidazolecarboxamide riboside (Z-riboside) have potential roles in the regulation of cellular metabolism and as pharmacological agents in several pathological situations. Before studying Z-riboside metabolism it was necessary to develop methods for identifying and quantitating 5(4)-amino-4(5)-imidazolecarboxamide metabolites. These studies utilized Chinese hamster ovary fibroblast auxotrophic mutants to identify and isolate compounds relevant to Z-riboside metabolism by a combination of high performance liquid chromatographic procedures. In order to study Z-riboside metabolism wild-type and mutant cells were cultured in Z-riboside. This ribosyl precursor to a purine de novo intermediate does not undergo any detectable phosphorolysis but rather is phosphorylated by adenosine kinase in an unregulated manner. This results in the intracellular accumulation of 5-amino-4-imidazolecarboxamide ribotide (ZMP), the levels of which control flow from Z-riboside to the following metabolites: 1) IMP and other purine nucleotides, 2) 5-amino-4-imidazole-N-succinocarboxamide ribotide (sZMP), and 3) 5-amino-4-imidazolecarboxamide riboside 5'-triphosphate (ZTP). At low ZMP concentrations, the predominant metabolic fate is IMP. Initially, IMP enters the adenylate and guanylate pools, but subsequently is hydrolyzed to inosine and this phosphorolyzed to hypoxanthine. At intermediate ZMP concentrations there is net retrograde flux through the bifunctional enzyme adenylosuccinate AMP lyase resulting in sZMP synthesis and antegrade flux leads to the accumulation of adenylosuccinate. At high ZMP concentrations, ZTP accumulates. In addition to these effects on purine metabolism, pyrimidine nucleotide pools are depleted when ZMP accumulates. These results are discussed in relation to the regulation of purine nucleotide synthesis and the use of Z-riboside as a pharmacological intervention in pathophysiological situations.     

A flexible hierarchical framework for improving inference in area-referenced environmental health studies

Study designs where data have been aggregated by geographical areas are popular in environmental epidemiology. These studies are commonly based on administrative databases and, providing a complete spatial coverage, are particularly appealing to make inference on the entire population. However, the resulting estimates are often biased and difficult to interpret due to unmeasured confounders, which typically are not available from routinely collected data. We propose a framework to improve inference drawn from such studies exploiting information derived from individual-level survey data. The latter are summarized in an area-level scalar score by mimicking at ecological level the well-known propensity score methodology. The literature on propensity score for confounding adjustment is mainly based on individual-level studies and assumes a binary exposure variable. Here, we generalize its use to cope with area-referenced studies characterized by a continuous exposure. Our approach is based upon Bayesian hierarchical structures specified into a two-stage design: (i) geolocated individual-level data from survey samples are up-scaled at ecological level, then the latter are used to estimate a generalized ecological propensity score (EPS) in the in-sample areas; (ii) the generalized EPS is imputed in the out-of-sample areas under different assumptions about the missingness mechanisms, then it is included into the ecological regression, linking the exposure of interest to the health outcome. This delivers area-level risk estimates, which allow a fuller adjustment for confounding than traditional areal studies. The methodology is illustrated by using simulations and a case study investigating the risk of lung cancer mortality associated with nitrogen dioxide in England (UK).     

Multivariate Data Analysis Methodology to Solve Data Challenges Related to Scale‐Up Model Validation and Missing Data on a Micro‐Bioreactor System

Multivariate data analysis (MVDA) is a highly valuable and significantly underutilized resource in biomanufacturing. It offers the opportunity to enhance understanding and leverage useful information from complex high‐dimensional data sets, recorded throughout all stages of therapeutic drug manufacture. To help standardize the application and promote this resource within the biopharmaceutical industry, this paper outlines a novel MVDA methodology describing the necessary steps for efficient and effective data analysis. The MVDA methodology is followed to solve two case studies: a “small data” and a “big data” challenge. In the “small data” example, a large‐scale data set is compared to data from a scale‐down model. This methodology enables a new quantitative metric for equivalence to be established by combining a two one‐sided test with principal component analysis. In the “big data” example, this methodology enables accurate predictions of critical missing data essential to a cloning study performed in the ambr15 system. These predictions are generated by exploiting the underlying relationship between the off‐line missing values and the on‐line measurements through the generation of a partial least squares model. In summary, the proposed MVDA methodology highlights the importance of data pre‐processing, restructuring, and visualization during data analytics to solve complex biopharmaceutical challenges.     

Optimization of bioreactor using metabolic control analysis approach

A new methodology based on a metabolic control analysis (MCA) approach is developed for the optimization of continuous cascade bioreactor system. A general framework for representation of a cascade bioreactor system consisting of a large number of reactors as a single network is proposed. The kinetic and transport processes occurring in the system are represented as a reaction network with appropriate stoichiometry. Such representation of the bioreactor systems makes it amenable to the direct application of the MCA approach. The process sensitivity information is extracted using MCA methodology in the form of flux and concentration control coefficients. The process sensitivity information is shown to be a useful guide for determining the choice of decision variables for the purpose of optimization. A generalized problem of optimization of the bioreactor is formulated in which the decision variables are the operating conditions and kinetic parameters. The gradient of the objective function to be maximized with respect to all decision variables is obtained in the form of response coefficients. This gradient information can be used in any gradient-based optimization algorithm. The efficiency of the proposed technique is demonstrated with two examples taken from literature: biotransformation of crotonobetaine and alcohol fermentation in cascade bioreactor system.