A General Latent Class Model for Performance Evaluation of Diagnostic Tests in the Absence of a Gold Standard: An Application to Chagas Disease

We propose a new general Bayesian latent class model for evaluation of the performance of multiple diagnostic tests in situations in which no gold standard test exists based on a computationally intensive approach. The modeling represents an interesting and suitable alternative to models with complex structures that involve the general case of several conditionally independent diagnostic tests, covariates, and strata with different disease prevalences. The technique of stratifying the population according to different disease prevalence rates does not add further marked complexity to the modeling, but it makes the model more flexible and interpretable. To illustrate the general model proposed, we evaluate the performance of six diagnostic screening tests for Chagas disease considering some epidemiological variables. Serology at the time of donation (negative, positive, inconclusive) was considered as a factor of stratification in the model. The general model with stratification of the population performed better in comparison with its concurrents without stratification. The group formed by the testing laboratory Biomanguinhos FIOCRUZ-kit (c-ELISA and rec-ELISA) is the best option in the confirmation process by presenting false-negative rate of 0.0002% from the serial scheme. We are 100% sure that the donor is healthy when these two tests have negative results and he is chagasic when they have positive results.

IDM - A New Parallel Methodology to Calculate the Determinant of Matrices of the Order n, with Computational Complexity O(n)

This paper presents a new parallel methodology for calculating the determinant of matrices of the order n, with computational complexity O(n), using the Gauss-Jordan Elimination Method and Chio's Rule as references. We intend to present our step-by-step methodology using clear mathematical language, where we will demonstrate how to calculate the determinant of a matrix of the order n in an analytical format. We will also present a computational model with one sequential algorithm and one parallel algorithm using a pseudo-code.

Complexity of inferences in polytree-shaped semi-qualitative probabilistic networks

Semi-qualitative probabilistic networks (SQPNs) merge two important graphical model formalisms: Bayesian networks and qualitative probabilistic networks. They provade a very Complexity of inferences in polytree-shaped semi-qualitative probabilistic networks and qualitative probabilistic networks. They provide a very general modeling framework by allowing the combination of numeric and qualitative assessments over a discrete domain, and can be compactly encoded by exploiting the same factorization of joint probability distributions that are behind the bayesian networks. This paper explores the computational complexity of semi-qualitative probabilistic networks, and takes the polytree-shaped networks as its main target. We show that the inference problem is coNP-Complete for binary polytrees with multiple observed nodes. We also show that interferences can be performed in time linear in the number of nodes if there is a single observed node. Because our proof is construtive, we obtain an efficient linear time algorithm for SQPNs under such assumptions. To the best of our knowledge, this is the first exact polynominal-time algorithm for SQPn. Together these results provide a clear picture of the inferential complexity in polytree-shaped SQPNs.

Numerical simulation of an ethanol turbulent spray flame with RANS and diffusion combustion model

A detailed numerical simulation of ethanol turbulent spray combustion on a rounded jet flame is pre- sented in this article. The focus is to propose a robust mathematical model with relatively low complexity sub- models to reproduce the main characteristics of the cou- pling between both phases, such as the turbulence modulation, turbulent droplets dissipation, and evaporative cooling effect. A RANS turbulent model is implemented. Special features of the model include an Eulerian– Lagrangian procedure under a fully two-way coupling and a modified flame sheet model with a joint mixture fraction– enthalpy b -PDF. Reasonable agreement between measured and computed mean profiles of temperature of the gas phase and droplet size distributions is achieved. Deviations found between measured and predicted mean velocity profiles are attributed to the turbulent combustion modeling adopted

Effects of environmental complexity on neurogenesis in the hippocampal dentate gyrus

Introduction. Postnatal neurogenesis in the hippocampal dentate gyrus, can be modulated by numerous determinants, such as hormones, transmitters and stress. Among the factors positively interfering with neurogenesis, the complexity of the environment appears to play a particularly striking role. Adult mice reared in an enriched environment produce more neurons and exhibit better performance in hippocampus-specific learning tasks. While the effects of complex environments on hippocampal neurogenesis are well documented, there is a lack of information on the effects of living under socio-sensory deprivation conditions. Due to the immaturity of rats and mice at birth, studies dealing with the effects of environmental enrichment on hippocampal neurogenesis were carried out in adult animals, i.e. during a period of relatively low rate of neurogenesis. The impact of environment is likely to be more dramatic during the first postnatal weeks, because at this time granule cell production is remarkably higher than at later phases of development. The aim of the present research was to clarify whether and to what extent isolated or enriched rearing conditions affect hippocampal neurogenesis during the early postnatal period, a time window characterized by a high rate of precursor proliferation and to elucidate the mechanisms underlying these effects. The experimental model chosen for this research was the guinea pig, a precocious rodent, which, at 4-5 days of age can be independent from maternal care. Experimental design. Animals were assigned to a standard (control), an isolated, or an enriched environment a few days after birth (P5-P6). On P14-P17 animals received one daily bromodeoxyuridine (BrdU) injection, to label dividing cells, and were sacrificed either on P18, to evaluate cell proliferation or on P45, to evaluate cell survival and differentiation. Methods. Brain sections were processed for BrdU immunhistochemistry, to quantify the new born and surviving cells. The phenotype of the surviving cells was examined by means of confocal microscopy and immunofluorescent double-labeling for BrdU and either a marker of neurons (NeuN) or a marker of astrocytes (GFAP). Apoptotic cell death was examined with the TUNEL method. Serial sections were processed for immunohistochemistry for i) vimentin, a marker of radial glial cells, ii) BDNF (brain-derived neurotrofic factor), a neurotrophin involved in neuron proliferation/survival, iii) PSA-NCAM (the polysialylated form of the neural cell adhesion molecule), a molecule associated with neuronal migration. Total granule cell number in the dentate gyrus was evaluated by stereological methods, in Nissl-stained sections. Results. Effects of isolation. In P18 isolated animals we found a reduced cell proliferation (-35%) compared to controls and a lower expression of BDNF. Though in absolute terms P45 isolated animals had less surviving cells than controls, they showed no differences in survival rate and phenotype percent distribution compared to controls. Evaluation of the absolute number of surviving cells of each phenotype showed that isolated animals had a reduced number of cells with neuronal phenotype than controls. Looking at the location of the new neurons, we found that while in control animals 76% of them had migrated to the granule cell layer, in isolated animals only 55% of the new neurons had reached this layer. Examination of radial glia cells of P18 and P45 animals by vimentin immunohistochemistry showed that in isolated animals radial glia cells were reduced in density and had less and shorter processes. Granule cell count revealed that isolated animals had less granule cells than controls (-32% at P18 and -42% at P45). Effects of enrichment. In P18 enriched animals there was an increase in cell proliferation (+26%) compared to controls and a higher expression of BDNF. Though in both groups there was a decline in the number of BrdU-positive cells by P45, enriched animals had more surviving cells (+63) and a higher survival rate than controls. No differences were found between control and enriched animals in phenotype percent distribution. Evaluation of the absolute number of cells of each phenotype showed that enriched animals had a larger number of cells of each phenotype than controls. Looking at the location of cells of each phenotype we found that enriched animals had more new neurons in the granule cell layer and more astrocytes and cells with undetermined phenotype in the hilus. Enriched animals had a higher expression of PSA-NCAM in the granule cell layer and hilus Vimentin immunohistochemistry showed that in enriched animals radial glia cells were more numerous and had more processes.. Granule cell count revealed that enriched animals had more granule cells than controls (+37% at P18 and +31% at P45). Discussion. Results show that isolation rearing reduces hippocampal cell proliferation but does not affect cell survival, while enriched rearing increases both cell proliferation and cell survival. Changes in the expression of BDNF are likely to contribute to he effects of environment on precursor cell proliferation. The reduction and increase in final number of granule neurons in isolated and enriched animals, respectively, are attributable to the effects of environment on cell proliferation and survival and not to changes in the differentiation program. As radial glia cells play a pivotal role in neuron guidance to the granule cell layer, the reduced number of radial glia cells in isolated animals and the increased number in enriched animals suggests that the size of radial glia population may change dynamically, in order to match changes in neuron production. The high PSA-NCAM expression in enriched animals may concur to favor the survival of the new neurons by facilitating their migration to the granule cell layer. Conclusions. By using a precocious rodent we could demonstrate that isolated/enriched rearing conditions, at a time window during which intense granule cell proliferation takes place, lead to a notable decrease/increase of total granule cell number. The time-course and magnitude of postnatal granule cell production in guinea pigs are more similar to the human and non-human primate condition than in rats and mice. Translation of current data to humans would imply that exposure of children to environments poor/rich of stimuli may have a notably large impact on dentate neurogenesis and, very likely, on hippocampus dependent memory functions.

Complexity of N-Myc transcriptional function in childhood neuroblastoma

Myc is a transcription factor that can activate transcription of several hundreds genes by direct binding to their promoters at specific DNA sequences (E-box). However, recent studies have also shown that it can exert its biological role by repressing transcription. Such studies collectively support a model in which c-Myc-mediated repression occurs through interactions with transcription factors bound to promoter DNA regions but not through direct recognition of typical E-box sequences. Here, we investigated whether N-Myc can also repress gene transcription, and how this is mechanistically achieved. We used human neuroblastoma cells as a model system in that N-MYC amplification/over-expression represents a key prognostic marker of this tumour. By means of transcription profile analyses we could identify at least 5 genes (TRKA, p75NTR, ABCC3, TG2, p21) that are specifically repressed by N-Myc. Through a dual-step-ChIP assay and genetic dissection of gene promoters, we found that N-Myc is physically associated with gene promoters in vivo, in proximity of the transcription start site. N-Myc association with promoters requires interaction with other proteins, such as Sp1 and Miz1 transcription factors. Furthermore, we found that N-Myc may repress gene expression by interfering directly with Sp1 and/or with Miz1 activity (i.e. TRKA, p75NTR, ABCC3, p21) or by recruiting Histone Deacetylase 1 (Hdac1) (i.e. TG2). In vitro analyses show that distinct N-Myc domains can interact with Sp1, Miz1 and Hdac1, supporting the idea that Myc may participate in distinct repression complexes by interacting specifically with diverse proteins. Finally, results show that N-Myc, through repressed genes, affects important cellular functions, such as apoptosis, growth, differentiation and motility. Overall, our results support a model in which N-Myc, like c-Myc, can repress gene transcription by direct interaction with Sp1 and/or Miz1, and provide further lines of evidence on the importance of transcriptional repression by Myc factors in tumour biology.

Model predictive control in thermal management of multiprocessor systems-on-chip

MultiProcessor Systems-on-Chip (MPSoC) are the core of nowadays and next generation computing platforms. Their relevance in the global market continuously increase, occupying an important role both in everydaylife products (e.g. smartphones, tablets, laptops, cars) and in strategical market sectors as aviation, defense, robotics, medicine. Despite of the incredible performance improvements in the recent years processors manufacturers have had to deal with issues, commonly called “Walls”, that have hindered the processors development. After the famous “Power Wall”, that limited the maximum frequency of a single core and marked the birth of the modern multiprocessors system-on-chip, the “Thermal Wall” and the “Utilization Wall” are the actual key limiter for performance improvements. The former concerns the damaging effects of the high temperature on the chip caused by the large power densities dissipation, whereas the second refers to the impossibility of fully exploiting the computing power of the processor due to the limitations on power and temperature budgets. In this thesis we faced these challenges by developing efficient and reliable solutions able to maximize performance while limiting the maximum temperature below a fixed critical threshold and saving energy. This has been possible by exploiting the Model Predictive Controller (MPC) paradigm that solves an optimization problem subject to constraints in order to find the optimal control decisions for the future interval. A fully-distributedMPC-based thermal controller with a far lower complexity respect to a centralized one has been developed. The control feasibility and interesting properties for the simplification of the control design has been proved by studying a partial differential equation thermal model. Finally, the controller has been efficiently included in more complex control schemes able to minimize energy consumption and deal with mixed-criticalities tasks

Model reduction of stochastic groundwater flow and transport processes

This work presents a comprehensive methodology for the reduction of analytical or numerical stochastic models characterized by uncertain input parameters or boundary conditions. The technique, based on the Polynomial Chaos Expansion (PCE) theory, represents a versatile solution to solve direct or inverse problems related to propagation of uncertainty. The potentiality of the methodology is assessed investigating different applicative contexts related to groundwater flow and transport scenarios, such as global sensitivity analysis, risk analysis and model calibration. This is achieved by implementing a numerical code, developed in the MATLAB environment, presented here in its main features and tested with literature examples. The procedure has been conceived under flexibility and efficiency criteria in order to ensure its adaptability to different fields of engineering; it has been applied to different case studies related to flow and transport in porous media. Each application is associated with innovative elements such as (i) new analytical formulations describing motion and displacement of non-Newtonian fluids in porous media, (ii) application of global sensitivity analysis to a high-complexity numerical model inspired by a real case of risk of radionuclide migration in the subsurface environment, and (iii) development of a novel sensitivity-based strategy for parameter calibration and experiment design in laboratory scale tracer transport.

A SMO inhibitor drug reduces the progenitor cell's maintenance in the Drosophila hematopoietic organ: a novel model to study Chronic Myelogenous Leukemia relapse

The chronic myeloid leukemia complexity and the difficulties of disease eradication have recently led to the development of drugs which, together with the inhibitors of TK, could eliminate leukemia stem cells preventing the occurrence of relapses in patients undergoing transplantation. The Hedgehog (Hh) signaling pathway positively regulates the self-renewal and the maintenance of leukemic stem cells and not, and this function is evolutionarily conserved. Using Drosophila as a model, we studied the efficacy of the SMO inhibitor drug that inhibit the human protein Smoothened (SMO). SMO is a crucial component in the signal transduction of Hh and its blockade in mammals leads to a reduction in the disease induction. Here we show that administration of the SMO inhibitor to animals has a specific effect directed against the Drosophila ortholog protein, causing loss of quiescence and hematopoietic precursors mobilization. The SMO inhibitor induces in L3 larvae the appearance of melanotic nodules generated as response by Drosophila immune system to the increase of its hemocytes. The same phenotype is induced even by the dsRNA:SMO specific expression in hematopoietic precursors of the lymph gland. The drug action is also confirmed at cellular level. The study of molecular markers has allowed us to demonstrate that SMO inhibitor leads to a reduction of the quiescent precursors and to an increase of the differentiated cells. Moreover administering the inhibitor to heterozygous for a null allele of Smo, we observe a significant increase in the phenotype penetrance compared to administration to wild type animals. This helps to confirm the specific effect of the drug itself. These data taken together indicate that the study of inhibitors of Smo in Drosophila can represent a useful way to dissect their action mechanism at the molecular-genetic level in order to collect information applicable to the studies of the disease in humans.

Projected 21st century decrease in marine productivity: a multi-model analysis

Changes in marine net primary productivity (PP) and export of particulate organic carbon (EP) are projected over the 21st century with four global coupled carbon cycle-climate models. These include representations of marine ecosystems and the carbon cycle of different structure and complexity. All four models show a decrease in global mean PP and EP between 2 and 20% by 2100 relative to preindustrial conditions, for the SRES A2 emission scenario. Two different regimes for productivity changes are consistently identified in all models. The first chain of mechanisms is dominant in the low- and mid-latitude ocean and in the North Atlantic: reduced input of macro-nutrients into the euphotic zone related to enhanced stratification, reduced mixed layer depth, and slowed circulation causes a decrease in macro-nutrient concentrations and in PP and EP. The second regime is projected for parts of the Southern Ocean: an alleviation of light and/or temperature limitation leads to an increase in PP and EP as productivity is fueled by a sustained nutrient input. A region of disagreement among the models is the Arctic, where three models project an increase in PP while one model projects a decrease. Projected changes in seasonal and interannual variability are modest in most regions. Regional model skill metrics are proposed to generate multi-model mean fields that show an improved skill in representing observation-based estimates compared to a simple multi-model average. Model results are compared to recent productivity projections with three different algorithms, usually applied to infer net primary production from satellite observations.

The Complexity of Quest in Emerging Adults' Religiosity, Well-Being, and Identity

A growing body of literature indicates a modestly positive association between religiosity and spirituality as predictors of psychological health (anxiety and depression), suggesting they serve as personal resiliency factors. The purpose of this study was to expand our understanding of the relationships among these constructs. Using Lazarus's Transactional Model of Stress as a theoretical framework, we examined: (a) the extent to which spirituality and religiosity mediated and/or moderated the association between perceived stress and psychological health and (b) whether there was a moderated (religiosity) mediation (spirituality) between stress and health. The Perceived Stress Scale, Daily Spiritual Experiences Scale, Religious Commitment Inventory, and Hospital Anxiety and Depression Scale were administered to measure the following constructs: stress, spirituality, religiosity, and psychological health. This study utilized a nonexperimental, quantitative, correlational, cross-sectional, moderated-mediation design, and included a convenience sample of 331 research participants. Both spirituality and religiosity moderated stress and health. However, only spirituality partially mediated the relationship. In addition, religiosity did not moderate the mediating effects of spirituality. Overall, this study confirmed the role of both religiosity and spirituality as effective resiliency resources.

Predicting asthma control and exacerbations: chronic asthma as a complex dynamic model

PURPOSE OF REVIEW: Predicting asthma episodes is notoriously difficult but has potentially significant consequences for the individual, as well as for healthcare services. The purpose of this review is to describe recent insights into the prediction of acute asthma episodes in relation to classical clinical, functional or inflammatory variables, as well as present a new concept for evaluating asthma as a dynamically regulated homeokinetic system. RECENT FINDINGS: Risk prediction for asthma episodes or relapse has been attempted using clinical scoring systems, considerations of environmental factors and lung function, as well as inflammatory and immunological markers in induced sputum or exhaled air, and these are summarized here. We have recently proposed that newer mathematical methods derived from statistical physics may be used to understand the complexity of asthma as a homeokinetic, dynamic system consisting of a network comprising multiple components, and also to assess the risk for future asthma episodes based on fluctuation analysis of long time series of lung function. SUMMARY: Apart from the classical analysis of risk factor and functional parameters, this new approach may be used to assess asthma control and treatment effects in the individual as well as in future research trials.

EEG reactivity and EEG activity in never-treated acute schizophrenics, measured with spectral parameters and dimensional complexity

Our approaches to the use of EEG studies for the understanding of the pathogenesis of schizophrenic symptoms are presented. The basic assumptions of a heuristic and multifactorial model of the psychobiological brain mechanisms underlying the organization of normal behavior is described and used in order to formulate and test hypotheses about the pathogenesis of schizophrenic behavior using EEG measures. Results from our studies on EEG activity and EEG reactivity (= EEG components of a memory-driven, adaptive, non-unitary orienting response) as analyzed with spectral parameters and "chaotic" dimensionality (correlation dimension) are summarized. Both analysis procedures showed a deviant brain functional organization in never-treated first-episode schizophrenia which, within the framework of the model, suggests as common denominator for the pathogenesis of the symptoms a deviation of working memory, the nature of which is functional and not structural.

Model based experimental investigation on Powered Gait Orthosis (PGO)

Research on rehabilitation showed that appropriate and repetitive mechanical movements can help spinal cord injured individuals to restore their functional standing and walking. The objective of this paper was to achieve appropriate and repetitive joint movements and approximately normal gait through the PGO by replicating normal walking, and to minimize the energy consumption for both patients and the device. A model based experimental investigative approach is presented in this dissertation. First, a human model was created in Ideas and human walking was simulated in Adams. The main feature of this model was the foot ground contact model, which had distributed contact points along the foot and varied viscoelasticity. The model was validated by comparison of simulated results of normal walking and measured ones from the literature. It was used to simulate current PGO walking to investigate the real causes of poor function of the current PGO, even though it had joint movements close to normal walking. The direct cause was one leg moving at a time, which resulted in short step length and no clearance after toe off. It can not be solved by simply adding power on both hip joints. In order to find a better answer, a PGO mechanism model was used to investigate different walking mechanisms by locking or releasing some joints. A trade-off between energy consumption, control complexity and standing position was found. Finally a foot release PGO virtual model was created and simulated and only foot release mechanism was developed into a prototype. Both the release mechanism and the design of foot release were validated through the experiment by adding the foot release on the current PGO. This demonstrated an advancement in improving functional aspects of the current PGO even without a whole physical model of foot release PGO for comparison.

Development of an optimization model for biofuel facility size and location and a simulation model for design of a biofuel supply chain

To mitigate greenhouse gas (GHG) emissions and reduce U.S. dependence on imported oil, the United States (U.S.) is pursuing several options to create biofuels from renewable woody biomass (hereafter referred to as “biomass”). Because of the distributed nature of biomass feedstock, the cost and complexity of biomass recovery operations has significant challenges that hinder increased biomass utilization for energy production. To facilitate the exploration of a wide variety of conditions that promise profitable biomass utilization and tapping unused forest residues, it is proposed to develop biofuel supply chain models based on optimization and simulation approaches. The biofuel supply chain is structured around four components: biofuel facility locations and sizes, biomass harvesting/forwarding, transportation, and storage. A Geographic Information System (GIS) based approach is proposed as a first step for selecting potential facility locations for biofuel production from forest biomass based on a set of evaluation criteria, such as accessibility to biomass, railway/road transportation network, water body and workforce. The development of optimization and simulation models is also proposed. The results of the models will be used to determine (1) the number, location, and size of the biofuel facilities, and (2) the amounts of biomass to be transported between the harvesting areas and the biofuel facilities over a 20-year timeframe. The multi-criteria objective is to minimize the weighted sum of the delivered feedstock cost, energy consumption, and GHG emissions simultaneously. Finally, a series of sensitivity analyses will be conducted to identify the sensitivity of the decisions, such as the optimal site selected for the biofuel facility, to changes in influential parameters, such as biomass availability and transportation fuel price. Intellectual Merit The proposed research will facilitate the exploration of a wide variety of conditions that promise profitable biomass utilization in the renewable biofuel industry. The GIS-based facility location analysis considers a series of factors which have not been considered simultaneously in previous research. Location analysis is critical to the financial success of producing biofuel. The modeling of woody biomass supply chains using both optimization and simulation, combing with the GIS-based approach as a precursor, have not been done to date. The optimization and simulation models can help to ensure the economic and environmental viability and sustainability of the entire biofuel supply chain at both the strategic design level and the operational planning level. Broader Impacts The proposed models for biorefineries can be applied to other types of manufacturing or processing operations using biomass. This is because the biomass feedstock supply chain is similar, if not the same, for biorefineries, biomass fired or co-fired power plants, or torrefaction/pelletization operations. Additionally, the research results of this research will continue to be disseminated internationally through publications in journals, such as Biomass and Bioenergy, and Renewable Energy, and presentations at conferences, such as the 2011 Industrial Engineering Research Conference. For example, part of the research work related to biofuel facility identification has been published: Zhang, Johnson and Sutherland [2011] (see Appendix A). There will also be opportunities for the Michigan Tech campus community to learn about the research through the Sustainable Future Institute.