TBS reflects trabecular microarchitecture in premenopausal women and men with idiopathic osteoporosis and low-traumatic fractures.

Transiliac bone biopsies, while widely considered to be the standard for the analysis of bone microstructure, are typically restricted to specialized centers. The benefit of Trabecular Bone Score (TBS) in addition to areal bone mineral density (aBMD) for fracture risk assessment has been documented in cross-sectional and prospective studies. The aim of this study was to test if TBS may be useful as a surrogate to histomorphometric trabecular parameters of transiliac bone biopsies. Transiliac bone biopsies from 80 female patients (median age 39.9years-interquartile range, IQR 34.7; 44.3) and 43 male patients (median age 42.7years-IQR 38.9; 49.0) with idiopathic osteoporosis and low traumatic fractures were included. Micro-computed tomography values of bone volume fraction (BV/TV), trabecular thickness (Tb.Th), trabecular number (Tb.N), trabecular separation (Tb.Sp), structural model index (SMI) as well as serum bone turnover markers (BTMs) sclerostin, intact N-terminal type 1 procollagen propeptide (P1NP) and cross-linked C-telopeptide (CTX) were investigated. TBS values were higher in females (1.282 vs 1.169, p< 0.0001) with no differences in spine aBMD, whereas sclerostin levels (45.5 vs 33.4pmol/L) and aBMD values at the total hip (0.989 vs 0.971g/cm(2), p<0.001 for all) were higher in males. Multiple regression models including: gender, aBMD and BTMs revealed TBS as an independent, discriminative variable with adjusted multiple R(2) values of 69.1% for SMI, 79.5% for Tb.N, 68.4% for Tb.Sp, and 83.3% for BV/TV. In univariate regression models, BTMs showed statistically significant results, whereas in the multiple models only P1NP and CTX were significant for Tb.N. TBS is a practical, non-invasive, surrogate technique for the assessment of cancellous bone microarchitecture and should be implemented as an additional tool for the determination of trabecular bone properties.

Adaptive MCMC methods with applications in environmental and geophysical models

This work presents new, efficient Markov chain Monte Carlo (MCMC) simulation methods for statistical analysis in various modelling applications. When using MCMC methods, the model is simulated repeatedly to explore the probability distribution describing the uncertainties in model parameters and predictions. In adaptive MCMC methods based on the Metropolis-Hastings algorithm, the proposal distribution needed by the algorithm learns from the target distribution as the simulation proceeds. Adaptive MCMC methods have been subject of intensive research lately, as they open a way for essentially easier use of the methodology. The lack of user-friendly computer programs has been a main obstacle for wider acceptance of the methods. This work provides two new adaptive MCMC methods: DRAM and AARJ. The DRAM method has been built especially to work in high dimensional and non-linear problems. The AARJ method is an extension to DRAM for model selection problems, where the mathematical formulation of the model is uncertain and we want simultaneously to fit several different models to the same observations. The methods were developed while keeping in mind the needs of modelling applications typical in environmental sciences. The development work has been pursued while working with several application projects. The applications presented in this work are: a winter time oxygen concentration model for Lake Tuusulanjärvi and adaptive control of the aerator; a nutrition model for Lake Pyhäjärvi and lake management planning; validation of the algorithms of the GOMOS ozone remote sensing instrument on board the Envisat satellite of European Space Agency and the study of the effects of aerosol model selection on the GOMOS algorithm.

Anthropomorphic model observer performance in three-dimensional detection task for low-contrast computed tomography.

X-ray medical imaging is increasingly becoming three-dimensional (3-D). The dose to the population and its management are of special concern in computed tomography (CT). Task-based methods with model observers to assess the dose-image quality trade-off are promising tools, but they still need to be validated for real volumetric images. The purpose of the present work is to evaluate anthropomorphic model observers in 3-D detection tasks for low-contrast CT images. We scanned a low-contrast phantom containing four types of signals at three dose levels and used two reconstruction algorithms. We implemented a multislice model observer based on the channelized Hotelling observer (msCHO) with anthropomorphic channels and investigated different internal noise methods. We found a good correlation for all tested model observers. These results suggest that the msCHO can be used as a relevant task-based method to evaluate low-contrast detection for CT and optimize scan protocols to lower dose in an efficient way.

Genomics of clinal variation in Drosophila: disentangling the interactions of selection and demography.

Clines in phenotypes and genotype frequencies across environmental gradients are commonly taken as evidence for spatially varying selection. Classical examples include the latitudinal clines in various species of Drosophila, which often occur in parallel fashion on multiple continents. Today, genomewide analysis of such clinal systems provides a fantastic opportunity for unravelling the genetics of adaptation, yet major challenges remain. A well-known but often neglected problem is that demographic processes can also generate clinality, independent of or coincident with selection. A closely related issue is how to identify true genic targets of clinal selection. In this issue of Molecular Ecology, three studies illustrate these challenges and how they might be met. Bergland et al. report evidence suggesting that the well-known parallel latitudinal clines in North American and Australian D. melanogaster are confounded by admixture from Africa and Europe, highlighting the importance of distinguishing demographic from adaptive clines. In a companion study, Machado et al. provide the first genomic comparison of latitudinal differentiation in D. melanogaster and its sister species D. simulans. While D. simulans is less clinal than D. melanogaster, a significant fraction of clinal genes is shared between both species, suggesting the existence of convergent adaptation to clinaly varying selection pressures. Finally, by drawing on several independent sources of evidence, Bo?ičević et al. identify a functional network of eight clinal genes that are likely involved in cold adaptation. Together, these studies remind us that clinality does not necessarily imply selection and that separating adaptive signal from demographic noise requires great effort and care.

Aplicação de algoritmos genéticos na seleção de variáveis em espectroscopia no infravermelho médio: determinação simultânea de glicose, maltose e frutose

Genetic algorithm was used for variable selection in simultaneous determination of mixtures of glucose, maltose and fructose by mid infrared spectroscopy. Different models, using partial least squares (PLS) and multiple linear regression (MLR) with and without data pre-processing, were used. Based on the results obtained, it was verified that a simpler model (multiple linear regression with variable selection by genetic algorithm) produces results comparable to more complex methods (partial least squares). The relative errors obtained for the best model was around 3% for the sugar determination, which is acceptable for this kind of determination.

International research partnerships: establishing partner selection criteria in the university-university context

The main objective of the study was to identify and evaluate criteria for international partner selection in university-university context. This study attempted at promoting better understanding of how universities should proceed in selecting partners for producing joint research publications. Thus, the aim of the study was to gain an understanding of how research collaborations can be developed and how partners can be selected. The choice of a right partner has been identified as a precondition for partnership success. In international research collaborations partnering scientists with different skills and backgrounds bring together complementary knowledge into research projects, which in most cases results in a higher quality output. Therefore, prior to selecting a partner, the set of criteria should be established. This research examined twelve Russian universities with the status of national research university as potential partners for Lappeenranta University of Technology, and selected the most appropriate universities based on established set of criteria. Potential partners’ evaluation was done using secondary sources by tracking partners’ academic success during the period 2005 – 2010. Based on established criteria, the study calculated the partnership index for each university. The results of the research reveal that among twelve examined universities there are four potential partners who have been rather active in publishing scientific articles during 2005 – 2010.

Bartlett-Lewis of rectangular pulse modified model: estimate of parameters for simulation of precipitation in sub-hourly duration

The Bartlett-Lewis Rectangular Pulse Modified (BLPRM) model simulates the precipitous slide in the hourly and sub-hourly and has six parameters for each of the twelve months of the year. This study aimed to evaluate the behavior of precipitation series in the duration of 15 min, obtained by simulation using the model BLPRM in situations: (a) where the parameters are estimated from a combination of statistics, creating five different sets; (b) suitability of the model to generate rain. To adjust the parameters were used rain gauge records of Pelotas/RS/Brazil, which statistics were estimated - mean, variance, covariance, autocorrelation coefficient of lag 1, the proportion of dry days in the period considered. The results showed that the parameters related to the time of onset of precipitation (λ) and intensities (μx) were the most stable and the most unstable were ν parameter, related to rain duration. The BLPRM model adequately represented the mean, variance, and proportion of the dry period of the series of precipitation lasting 15 min and, the time dependence of the heights of rain, represented autocorrelation coefficient of the first retardation was statistically less simulated series suitability for the duration of 15 min.

Analysis and validation of space averaged drag model for numerical simulations of gas-solid flows in fluidized beds

This thesis presents an approach for formulating and validating a space averaged drag model for coarse mesh simulations of gas-solid flows in fluidized beds using the two-fluid model. Proper modeling for fluid dynamics is central in understanding any industrial multiphase flow. The gas-solid flows in fluidized beds are heterogeneous and usually simulated with the Eulerian description of phases. Such a description requires the usage of fine meshes and small time steps for the proper prediction of its hydrodynamics. Such constraint on the mesh and time step size results in a large number of control volumes and long computational times which are unaffordable for simulations of large scale fluidized beds. If proper closure models are not included, coarse mesh simulations for fluidized beds do not give reasonable results. The coarse mesh simulation fails to resolve the mesoscale structures and results in uniform solids concentration profiles. For a circulating fluidized bed riser, such predicted profiles result in a higher drag force between the gas and solid phase and also overestimated solids mass flux at the outlet. Thus, there is a need to formulate the closure correlations which can accurately predict the hydrodynamics using coarse meshes. This thesis uses the space averaging modeling approach in the formulation of closure models for coarse mesh simulations of the gas-solid flow in fluidized beds using Geldart group B particles. In the analysis of formulating the closure correlation for space averaged drag model, the main parameters for the modeling were found to be the averaging size, solid volume fraction, and distance from the wall. The closure model for the gas-solid drag force was formulated and validated for coarse mesh simulations of the riser, which showed the verification of this modeling approach. Coarse mesh simulations using the corrected drag model resulted in lowered values of solids mass flux. Such an approach is a promising tool in the formulation of appropriate closure models which can be used in coarse mesh simulations of large scale fluidized beds.

Particle model for simulating limestone reactions in novel fluidised bed energy applications

The development of carbon capture and storage (CCS) has raised interest towards novel fluidised bed (FB) energy applications. In these applications, limestone can be utilized for S02 and/or CO2 capture. The conditions in the new applications differ from the traditional atmospheric and pressurised circulating fluidised bed (CFB) combustion conditions in which the limestone is successfully used for SO2 capture. In this work, a detailed physical single particle model with a description of the mass and energy transfer inside the particle for limestone was developed. The novelty of this model was to take into account the simultaneous reactions, changing conditions, and the effect of advection. Especially, the capability to study the cyclic behaviour of limestone on both sides of the calcination-carbonation equilibrium curve is important in the novel conditions. The significances of including advection or assuming diffusion control were studied in calcination. Especially, the effect of advection in calcination reaction in the novel combustion atmosphere was shown. The model was tested against experimental data; sulphur capture was studied in a laboratory reactor in different fluidised bed conditions. Different Conversion levels and sulphation patterns were examined in different atmospheres for one limestone type. The Conversion curves were well predicted with the model, and the mechanisms leading to the Conversion patterns were explained with the model simulations. In this work, it was also evaluated whether the transient environment has an effect on the limestone behaviour compared to the averaged conditions and in which conditions the effect is the largest. The difference between the averaged and transient conditions was notable only in the conditions which were close to the calcination-carbonation equilibrium curve. The results of this study suggest that the development of a simplified particle model requires a proper understanding of physical and chemical processes taking place in the particle during the reactions. The results of the study will be required when analysing complex limestone reaction phenomena or when developing the description of limestone behaviour in comprehensive 3D process models. In order to transfer the experimental observations to furnace conditions, the relevant mechanisms that take place need to be understood before the important ones can be selected for 3D process model. This study revealed the sulphur capture behaviour under transient oxy-fuel conditions, which is important when the oxy-fuel CFB process and process model are developed.

Two point deterministic model for acquisition of in vitro pollen grain androgenetic capacity based on wheat studies

This article discusses, from the standpoint of cellular biology, the deterministic and indeterministic androgenesis theories. The role of the vacuole and of various types of stresses on deviation of the microspore from normal development and the point where androgenetic competence is acquired are examined. Based on extensive literature review and data on wheat studies from our laboratory, a model for androgenetic capacity of pollen grain is proposed. A two point deterministic model for in vitro androgenesis is our proposal for acquisition of androgenetic potential of the pollen grain: the first switch point would be early meiosis and the second switch point the uninucleate pollen stage, because the elimination of cytoplasmatic sporophytic determinants takes place at those two strategic moments. Any abnormality in this process allowing the maintenance of sporophytic informational molecules results in the absence of establishment of a gametophytic program, allowing the reactivation of the embryogenic process

Psychiatric disorders and cardiac anxiety in exercising and sedentary coronary artery disease patients: a case-control study

Regular physical exercise has been shown to favorably influence mood and anxiety; however, there are few studies regarding psychiatric aspects of physically active patients with coronary artery disease (CAD). The objective of the present study was to compare the prevalence of psychiatric disorders and cardiac anxiety in sedentary and exercising CAD patients. A total sample of 119 CAD patients (74 men) were enrolled in a case-control study. The subjects were interviewed to identify psychiatric disorders and responded to the Cardiac Anxiety Questionnaire. In the exercise group (N = 60), there was a lower prevalence (45 vs 81%; P < 0.001) of at least one psychiatric diagnosis, as well as multiple comorbidities, when compared to the sedentary group (N = 59). Considering the Cardiac Anxiety Questionnaire, sedentary patients presented higher scores compared to exercisers (mean ± SEM = 55.8 ± 1.9 vs 37.3 ± 1.6; P < 0.001). In a regression model, to be attending a medically supervised exercise program presented a relevant potential for a 35% reduction in cardiac anxiety. CAD patients regularly attending an exercise program presented less current psychiatric diagnoses and multiple mental-related comorbidities and lower scores of cardiac anxiety. These salutary mental effects add to the already known health benefits of exercise for CAD patients.

Scalable Feature Selection Applications for Genome-Wide Association Studies of Complex Diseases

Personalized medicine will revolutionize our capabilities to combat disease. Working toward this goal, a fundamental task is the deciphering of geneticvariants that are predictive of complex diseases. Modern studies, in the formof genome-wide association studies (GWAS) have afforded researchers with the opportunity to reveal new genotype-phenotype relationships through the extensive scanning of genetic variants. These studies typically contain over half a million genetic features for thousands of individuals. Examining this with methods other than univariate statistics is a challenging task requiring advanced algorithms that are scalable to the genome-wide level. In the future, next-generation sequencing studies (NGS) will contain an even larger number of common and rare variants. Machine learning-based feature selection algorithms have been shown to have the ability to effectively create predictive models for various genotype-phenotype relationships. This work explores the problem of selecting genetic variant subsets that are the most predictive of complex disease phenotypes through various feature selection methodologies, including filter, wrapper and embedded algorithms. The examined machine learning algorithms were demonstrated to not only be effective at predicting the disease phenotypes, but also doing so efficiently through the use of computational shortcuts. While much of the work was able to be run on high-end desktops, some work was further extended so that it could be implemented on parallel computers helping to assure that they will also scale to the NGS data sets. Further, these studies analyzed the relationships between various feature selection methods and demonstrated the need for careful testing when selecting an algorithm. It was shown that there is no universally optimal algorithm for variant selection in GWAS, but rather methodologies need to be selected based on the desired outcome, such as the number of features to be included in the prediction model. It was also demonstrated that without proper model validation, for example using nested cross-validation, the models can result in overly-optimistic prediction accuracies and decreased generalization ability. It is through the implementation and application of machine learning methods that one can extract predictive genotype–phenotype relationships and biological insights from genetic data sets.

Effect of dietary fiber on the bioaccessibility of phenolic compounds of mango, papaya and pineapple fruits by an in vitro digestion model

The presence of dietary fiber (DF) in the food matrix of some tropical fruits plays an important role in the release and absorption of its bioactive compounds, such as phenolic compounds (PCs). The aim of this study was to evaluate the effect of the DF fractions in mango cv. ‘Ataulfo’, papaya cv. ‘Maradol’ and pineapple cv. ‘Esmeralda’, on the bioaccessibility of their PCs and antioxidant capacity (AOXC) under an in vitro digestion model. The highest PCs content and AOXC was found in mango (274.30 mg GAE/100 g FW), followed by papaya (212 mg GAE//100 g FW), and pineapple (107.63 mg GAE/100 g FW), respectively. About 50% of the total PCs in all fruits was released at gastric phase, increasing closer to 60% at intestinal phase in mango and pineapple. However, the highest content of PCs associated to DF was found in mango (2.48 mg GAE/100 g FW) compared with papaya DF fractions (0.96 GAE/100 g FW) and pineapple (0.52 GAE/100 g FW). The presence of DF in mango, papaya and pineapple did not represent a major limitation on the bioaccessibility of its PCs according to the in vitro digestion model used in this study.

Sexual selections in the northern fall field cricket, Gryllus pennsylvanicus: a manipulation of the adult sex ratio

The operational sex ratio has long been considered an important constraint on the structure of mating systems. The effects of an experimentally manipulated sex ratio on mating behavior and selection were investigated in a polygynous species, Gryllus pennsylvanicus, where the potential exists for spatial/temporal fluctuations in sex ratio of field populations. Four different sex ratios (males: females, 5:0, 5:2, 5:5, 5:10) were investigated. Observations were conducted in late summer over two field seasons, from 2400 h , to 1000 h EST. Several male characters thought to be associated with male reproduc.tive success were studied: calling duration, searching distance, weight, fighting behavior, courtship frequency, and mating success. Variance in male mating success was used as the indicator for the opportunity for sexual selection. Total selection was estimated as the univariate regression coefficient between relative fitness and the character of interest, while direct selection was estimated as standardized partial regression coefficients generated from a multiple regression of relative fitness on each character. The opportunity for sexual selection was highest at 5:2 and lowest at 5:10. The frequency of fighting behavior was highest at 5:2 and 5:5. Fighting ability (% wins) was determined to be an important correlate of male body weight. Direct selection for increased male body weight was detected at 5:2, while total selection for body weight was seen at 5:5. Selection on male body weight was not detected at 5: 10. Calling duration decreased as sex ratio became more female-biased. Total and direct selection were detected for increased calling at 5:2, only total selection for calling was seen at 5:5, whereas direct selection against calling was detected at 5: 10. Searching distance also decreased as sex ratio became more female-biased, however no form of selection was detected for searching at any of the sex ratios. Data are discussed in terms of sexual selection on male reproductive tactics, the mating system and maintenance of genetic variation in male reproductive behavior.

Computation and Analysis of Multiple Structural-Change Models

In a recent paper, Bai and Perron (1998) considered theoretical issues related to the limiting distribution of estimators and test statistics in the linear model with multiple structural changes. In this companion paper, we consider practical issues for the empirical applications of the procedures. We first address the problem of estimation of the break dates and present an efficient algorithm to obtain global minimizers of the sum of squared residuals. This algorithm is based on the principle of dynamic programming and requires at most least-squares operations of order O(T 2) for any number of breaks. Our method can be applied to both pure and partial structural-change models. Secondly, we consider the problem of forming confidence intervals for the break dates under various hypotheses about the structure of the data and the errors across segments. Third, we address the issue of testing for structural changes under very general conditions on the data and the errors. Fourth, we address the issue of estimating the number of breaks. We present simulation results pertaining to the behavior of the estimators and tests in finite samples. Finally, a few empirical applications are presented to illustrate the usefulness of the procedures. All methods discussed are implemented in a GAUSS program available upon request for non-profit academic use.