Long maximal incremental tests accurately assess aerobic fitness in class II and III obese men.

This study aimed to compare two different maximal incremental tests with different time durations [a maximal incremental ramp test with a short time duration (8-12 min) (STest) and a maximal incremental test with a longer time duration (20-25 min) (LTest)] to investigate whether an LTest accurately assesses aerobic fitness in class II and III obese men. Twenty obese men (BMI≥35 kg.m-2) without secondary pathologies (mean±SE; 36.7±1.9 yr; 41.8±0.7 kg*m-2) completed an STest (warm-up: 40 W; increment: 20 W*min-1) and an LTest [warm-up: 20% of the peak power output (PPO) reached during the STest; increment: 10% PPO every 5 min until 70% PPO was reached or until the respiratory exchange ratio reached 1.0, followed by 15 W.min-1 until exhaustion] on a cycle-ergometer to assess the peak oxygen uptake [Formula: see text] and peak heart rate (HRpeak) of each test. There were no significant differences in [Formula: see text] (STest: 3.1±0.1 L*min-1; LTest: 3.0±0.1 L*min-1) and HRpeak (STest: 174±4 bpm; LTest: 173±4 bpm) between the two tests. Bland-Altman plot analyses showed good agreement and Pearson product-moment and intra-class correlation coefficients showed a strong correlation between [Formula: see text] (r=0.81 for both; p≤0.001) and HRpeak (r=0.95 for both; p≤0.001) during both tests. [Formula: see text] and HRpeak assessments were not compromised by test duration in class II and III obese men. Therefore, we suggest that the LTest is a feasible test that accurately assesses aerobic fitness and may allow for the exercise intensity prescription and individualization that will lead to improved therapeutic approaches in treating obesity and severe obesity.

Mult-class differentiation of cannabis seedlings in a forensic context

This article presents an experimental study about the classification ability of several classifiers for multi-classclassification of cannabis seedlings. As the cultivation of drug type cannabis is forbidden in Switzerland lawenforcement authorities regularly ask forensic laboratories to determinate the chemotype of a seized cannabisplant and then to conclude if the plantation is legal or not. This classification is mainly performed when theplant is mature as required by the EU official protocol and then the classification of cannabis seedlings is a timeconsuming and costly procedure. A previous study made by the authors has investigated this problematic [1]and showed that it is possible to differentiate between drug type (illegal) and fibre type (legal) cannabis at anearly stage of growth using gas chromatography interfaced with mass spectrometry (GC-MS) based on therelative proportions of eight major leaf compounds. The aims of the present work are on one hand to continueformer work and to optimize the methodology for the discrimination of drug- and fibre type cannabisdeveloped in the previous study and on the other hand to investigate the possibility to predict illegal cannabisvarieties. Seven classifiers for differentiating between cannabis seedlings are evaluated in this paper, namelyLinear Discriminant Analysis (LDA), Partial Least Squares Discriminant Analysis (PLS-DA), Nearest NeighbourClassification (NNC), Learning Vector Quantization (LVQ), Radial Basis Function Support Vector Machines(RBF SVMs), Random Forest (RF) and Artificial Neural Networks (ANN). The performance of each method wasassessed using the same analytical dataset that consists of 861 samples split into drug- and fibre type cannabiswith drug type cannabis being made up of 12 varieties (i.e. 12 classes). The results show that linear classifiersare not able to manage the distribution of classes in which some overlap areas exist for both classificationproblems. Unlike linear classifiers, NNC and RBF SVMs best differentiate cannabis samples both for 2-class and12-class classifications with average classification results up to 99% and 98%, respectively. Furthermore, RBFSVMs correctly classified into drug type cannabis the independent validation set, which consists of cannabisplants coming from police seizures. In forensic case work this study shows that the discrimination betweencannabis samples at an early stage of growth is possible with fairly high classification performance fordiscriminating between cannabis chemotypes or between drug type cannabis varieties.

Promoter IV of the class II transactivator gene is essential for positive selection of CD4+ T cells.

Major histocompatibility complex class II (MHCII) expression is regulated by the transcriptional coactivator CIITA. Positive selection of CD4(+) T cells is abrogated in mice lacking one of the promoters (pIV) of the Mhc2ta gene. This is entirely due to the absence of MHCII expression in thymic epithelia, as demonstrated by bone marrow transfer experiments between wild-type and pIV(-/-) mice. Medullary thymic epithelial cells (mTECs) are also MHCII(-) in pIV(-/-) mice. Bone marrow-derived, professional antigen-presenting cells (APCs) retain normal MHCII expression in pIV(-/-) mice, including those believed to mediate negative selection in the thymic medulla. Endogenous retroviruses thus retain their ability to sustain negative selection of the residual CD4(+) thymocytes in pIV(-/-) mice. Interestingly, the passive acquisition of MHCII molecules by thymocytes is abrogated in pIV(-/-) mice. This identifies thymic epithelial cells as the source of this passive transfer. In peripheral lymphoid organs, the CD4(+) T-cell population of pIV(-/-) mice is quantitatively and qualitatively comparable to that of MHCII-deficient mice. It comprises a high proportion of CD1-restricted natural killer T cells, which results in a bias of the V beta repertoire of the residual CD4(+) T-cell population. We have also addressed the identity of the signal that sustains pIV expression in cortical epithelia. We found that the Jak/STAT pathways activated by the common gamma chain (CD132) or common beta chain (CDw131) cytokine receptors are not required for MHCII expression in thymic cortical epithelia.

Brief on Branstad-Reynolds administration Recommendations for World-Class School, January 6, 2012

In advance of the 2012 legislative session, I am pleased to provide for your review this legislative brief on Gov. Terry E. Branstad’s and Lt. Gov. Kim Reynolds’ education reform package. The purpose is to provide a broad overview of the components of the package, give some examples of where similar approaches are in place, and provide cost estimates. In collaboration with the Governor’s Office, the staff at the Iowa Department of Education and I have worked intensively to prepare a set of legislative proposals worthy of careful consideration. I believe this package puts us on the path to our unshakable vision of having one of the best school systems in the world. Iowa’s children deserve nothing less.

Evaluation of Fly Ash Concrete Durability Containing Class II Durability Aggregates, July 1986

Fly ash was used in this evaluation study to replace 15% of the cement in Class C-3 concrete paving mixes. One Class "c" ash from Iowa approved sources was examined in each mix. Substitution rate was based on 1 to 1 basis, for each pound of cement removed 1.0 pound of ash was added. The freeze/thaw durability of the concrete studied was not adversely affected by the presence of fly ash. This study reveals that the durability of the concrete test specimens made with Class II durability aggregates was slightly increased in all cases by the substitution of cement with 15% Class "c" fly ash. In all cases durability factors either remained the same or slightly improved except for one case where the durability factor decreased from 36 to 34. The expansion decreased in all cases.

"We are all middle class: On the subjective status in Chile"

Chile is one of the countries with the highest level of income inequality worldwide. Given the association between economic inequality and patterns of social stratification, in such context it would be expected to find a large dispersion of subjective social status' indicators in the population. Nevertheless, analyses from international surveys reveal a strong tendency to the mean of the subjective status i.e. there would be downward and upward biases of the subjective status regarding the objective status. The present research attempts to tackle this phenomenon for the Chilean case, with a focus on the influence of status and class variables on the subjective status. For the analysis we use Chilean data from the social inequality module of the International Social Survey Programme 2009. The results indicate a strong tendency to the mean of the subjective status in the population, particularly from the side of those with higher objective status.

Crash Rates and Crash Densities on Secondary Roads in Iowa by Functional Class 2001 – 2009, July 6, 2010

Crash Rates and Crash Densities on Secondary Roads in Iowa by Functional Class produced by the Iowa Department of Transportation.

n=1/4 domain-growth universality class: Crossover to the n=1/2 class

The kinetic domain-growth exponent is studied by Monte Carlo simulation as a function of temperature for a nonconserved order-parameter model. In the limit of zero temperature, the model belongs to the n=(1/4 slow-growth unversality class. This is indicative of a temporal pinning in the domain-boundary network of mixed-, zero-, and finite-curvature boundaries. At finite temperature the growth kinetics is found to cross over to the Allen-Cahn exponent n=(1/2. We obtain that the pinning time of the zero-curvature boundary decreases rapidly with increasing temperature.

Positive impact of inhibitory Ly49 receptor-MHC class I interaction on NK cell development.

NK cells can kill MHC-different or MHC-deficient but not syngeneic MHC-expressing target cells. This MHC class I-specific tolerance is acquired during NK cell development. MHC recognition by murine NK cells largely depends on clonally distributed Ly49 family receptors, which inhibit NK cell function upon ligand engagement. We investigated whether these receptors play a role for the development of NK cells and provide evidence that the expression of a Ly49 receptor transgene on developing NK cells endowed these cells with a significant developmental advantage over NK cells lacking such a receptor, but only if the relevant MHC ligand was present in the environment. The data suggest that the transgenic Ly49 receptor accelerates and/or rescues the development of NK cells which would otherwise fail to acquire sufficient numbers of self-MHC-specific receptors. Interestingly, the positive effect on NK cell development is most prominent when the MHC ligand is simultaneously present on both hemopoietic and nonhemopoietic cells. These findings correlate with functional data showing that MHC class I ligand on all cells is required to generate functionally mature NK cells capable of reacting to cells lacking the respective MHC ligand. We conclude that the engagement of inhibitory MHC receptors during NK cell development provides signals that are important for further NK cell differentiation and/or maturation.

Artificial neural networks applied for soil class prediction in mountainous landscape of the Serra do Mar¹

Soil information is needed for managing the agricultural environment. The aim of this study was to apply artificial neural networks (ANNs) for the prediction of soil classes using orbital remote sensing products, terrain attributes derived from a digital elevation model and local geology information as data sources. This approach to digital soil mapping was evaluated in an area with a high degree of lithologic diversity in the Serra do Mar. The neural network simulator used in this study was JavaNNS and the backpropagation learning algorithm. For soil class prediction, different combinations of the selected discriminant variables were tested: elevation, declivity, aspect, curvature, curvature plan, curvature profile, topographic index, solar radiation, LS topographic factor, local geology information, and clay mineral indices, iron oxides and the normalized difference vegetation index (NDVI) derived from an image of a Landsat-7 Enhanced Thematic Mapper Plus (ETM+) sensor. With the tested sets, best results were obtained when all discriminant variables were associated with geological information (overall accuracy 93.2 - 95.6 %, Kappa index 0.924 - 0.951, for set 13). Excluding the variable profile curvature (set 12), overall accuracy ranged from 93.9 to 95.4 % and the Kappa index from 0.932 to 0.948. The maps based on the neural network classifier were consistent and similar to conventional soil maps drawn for the study area, although with more spatial details. The results show the potential of ANNs for soil class prediction in mountainous areas with lithological diversity.

Class of correlated random networks with hidden variables

We study a class of models of correlated random networks in which vertices are characterized by hidden variables controlling the establishment of edges between pairs of vertices. We find analytical expressions for the main topological properties of these models as a function of the distribution of hidden variables and the probability of connecting vertices. The expressions obtained are checked by means of numerical simulations in a particular example. The general model is extended to describe a practical algorithm to generate random networks with an a priori specified correlation structure. We also present an extension of the class, to map nonequilibrium growing networks to networks with hidden variables that represent the time at which each vertex was introduced in the system.

On a class of exact solutions to the Fokker-Planck equations

In this paper we study under which circumstances there exists a general change of gross variables that transforms any FokkerPlanck equation into another of the OrnsteinUhlenbeck class that, therefore, has an exact solution. We find that any FokkerPlanck equation will be exactly solvable by means of a change of gross variables if and only if the curvature tensor and the torsion tensor associated with the diffusion is zero and the transformed drift is linear. We apply our criteria to the Kubo and Gompertz models.

Rgtsp: a generalized top scoring pairs package for class prediction.

SUMMARY: A top scoring pair (TSP) classifier consists of a pair of variables whose relative ordering can be used for accurately predicting the class label of a sample. This classification rule has the advantage of being easily interpretable and more robust against technical variations in data, as those due to different microarray platforms. Here we describe a parallel implementation of this classifier which significantly reduces the training time, and a number of extensions, including a multi-class approach, which has the potential of improving the classification performance. AVAILABILITY AND IMPLEMENTATION: Full C++ source code and R package Rgtsp are freely available from http://lausanne.isb-sib.ch/~vpopovic/research/. The implementation relies on existing OpenMP libraries.