Biometrical analysis and selection of tetraploid progenies of Panicum maximum using mixed model methods

The objectives of this work were to estimate the genetic and phenotypic parameters and to predict the genetic and genotypic values of the selection candidates obtained from intraspecific crosses in Panicum maximum as well as the performance of the hybrid progeny of the existing and projected crosses. Seventy-nine intraspecific hybrids obtained from artificial crosses among five apomictic and three sexual autotetraploid individuals were evaluated in a clonal test with two replications and ten plants per plot. Green matter yield, total and leaf dry matter yields and leaf percentage were evaluated in five cuts per year during three years. Genetic parameters were estimated and breeding and genotypic values were predicted using the restricted maximum likelihood/best linear unbiased prediction procedure (REML/BLUP). The dominant genetic variance was estimated by adjusting the effect of full-sib families. Low magnitude individual narrow sense heritabilities (0.02-0.05), individual broad sense heritabilities (0.14-0.20) and repeatability measured on an individual basis (0.15-0.21) were obtained. Dominance effects for all evaluated characteristics indicated that breeding strategies that explore heterosis must be adopted. Less than 5% increase in the parameter repeatability was obtained for a three-year evaluation period and may be the criterion to determine the maximum number of years of evaluation to be adopted, without compromising gain per cycle of selection. The identification of hybrid candidates for future cultivars and of those that can be incorporated into the breeding program was based on the genotypic and breeding values, respectively. The prediction of the performance of the hybrid progeny, based on the breeding values of the progenitors, permitted the identification of the best crosses and indicated the best parents to use in crosses.

Decision curve analysis revisited: overall net benefit, relationships to ROC curve analysis, and application to case-control studies.

ABSTRACT: BACKGROUND: Decision curve analysis has been introduced as a method to evaluate prediction models in terms of their clinical consequences if used for a binary classification of subjects into a group who should and into a group who should not be treated. The key concept for this type of evaluation is the "net benefit", a concept borrowed from utility theory. METHODS: We recall the foundations of decision curve analysis and discuss some new aspects. First, we stress the formal distinction between the net benefit for the treated and for the untreated and define the concept of the "overall net benefit". Next, we revisit the important distinction between the concept of accuracy, as typically assessed using the Youden index and a receiver operating characteristic (ROC) analysis, and the concept of utility of a prediction model, as assessed using decision curve analysis. Finally, we provide an explicit implementation of decision curve analysis to be applied in the context of case-control studies. RESULTS: We show that the overall net benefit, which combines the net benefit for the treated and the untreated, is a natural alternative to the benefit achieved by a model, being invariant with respect to the coding of the outcome, and conveying a more comprehensive picture of the situation. Further, within the framework of decision curve analysis, we illustrate the important difference between the accuracy and the utility of a model, demonstrating how poor an accurate model may be in terms of its net benefit. Eventually, we expose that the application of decision curve analysis to case-control studies, where an accurate estimate of the true prevalence of a disease cannot be obtained from the data, is achieved with a few modifications to the original calculation procedure. CONCLUSIONS: We present several interrelated extensions to decision curve analysis that will both facilitate its interpretation and broaden its potential area of application.

Iowa Highway Research and Development Activities FY2014

This report, entitled Iowa Highway Research and Development Activities FY2014 is submitted in compliance with Iowa Code sections 310.36 and 312.3A, which direct the submission of a report of the Secondary Road Research Fund and the Street Research Fund, respectively. It is a report of the status of research and development projects in process on June 20, 2014. It is also a report on projects completed during the fiscal year beginning July 1, 2013 and ending June 30, 2014. Detailed information on each of the research and development projects mentioned in this report is available from the office of Research and Analytics, Performance and Technology Division, Iowa Department of Transportation. All approved reports are also online for viewing at: http://www.iowadot.gov/research/pdf/IHRBAnnualReport.pdf.

Detailed analysis and follow-up studies of a high-throughput screening for indoleamine 2,3-dioxygenase 1 (IDO1) inhibitors.

Indoleamine 2,3-dioxygenase 1 (IDO1) is a key regulator of immune responses and therefore an important therapeutic target for the treatment of diseases that involve pathological immune escape, such as cancer. Here, we describe a robust and sensitive high-throughput screen (HTS) for IDO1 inhibitors using the Prestwick Chemical Library of 1200 FDA-approved drugs and the Maybridge HitFinder Collection of 14,000 small molecules. Of the 60 hits selected for follow-up studies, 14 displayed IC50 values below 20 μM under the secondary assay conditions, and 4 showed an activity in cellular tests. In view of the high attrition rate we used both experimental and computational techniques to identify and to characterize compounds inhibiting IDO1 through unspecific inhibition mechanisms such as chemical reactivity, redox cycling, or aggregation. One specific IDO1 inhibitor scaffold, the imidazole antifungal agents, was chosen for rational structure-based lead optimization, which led to more soluble and smaller compounds with micromolar activity.

Four essays on economic geography, trade and development

RESUME Cette thèse se situe à la frontière de la recherche en économie du développement et du commerce international et vise à intégrer les apports de l'économie géographique. Le premier chapitre s'intéresse aux effets de création et de détournement de commerce au sein des accords régionaux entre pays en développement et combine une approche gravitaire et une estimation non paramétrique des effets de commerce. Cette analyse confirme un effet de commerce non monotone pour six accords régionaux couvrant l'Afrique, l'Amérique Latine et l'Asie (AFTA, CAN, CACM, CEDEAO, MERCO SUR et SADC) sur la période 1960-1996. Les accords signés dans les années 90 (AFTA, CAN, MERCOSUR et SADC) semblent avoir induis une amélioration du bien-être de leurs membres mais avec un impact variable sur le reste du monde, tandis que les accords plus anciens (CEDEAO et CACM) semblent montrer que les effets de commerce et de bien-être se réduisent pour finir par s'annuler à mesure que le nombre d'années de participation des Etats membres augmente. Le deuxième chapitre pose la question de l'impact de la géographie sur les échanges Sud-Sud. Ce chapitre innove par rapport aux méthodes classiques d'estimation en dérivant une équation de commerce à partir de l'hypothèse d'Armington et en intégrant une fonction de coût de transport qui prend en compte la spécificité des pays de l'UEMOA. Les estimations donnent des effets convaincants quant au rôle de l'enclavement et des infrastructures: deux pays enclavés de l'UEMOA commercent 92% moins que deux autres pays quelconques, tandis que traverser un pays de transit au sein de l'espace UEMOA augmente de 6% les coûts de transport, et que bitumer toutes les routes inter-Etat de l'Union induirait trois fois plus de commerce intra-UEMOA. Le chapitre 3 s'intéresse à la persistance des différences de développement au sein des accords régionaux entre pays en développement. Il montre que la géographie différenciée des pays du Sud membres d'un accord induit un impact asymétrique de celui-ci sur ses membres. Il s'agit d'un modèle stylisé de trois pays dont deux ayant conclu un accord régional. Les résultats obtenus par simulation montrent qu'une meilleure dotation en infrastructure d'un membre de l'accord régional lui permet d'attirer une plus grande part industrielle à mesure que les coûts de transport au sein de l'accord régional sont baissés, ce qui conduit à un développement inégal entre les membres. Si les niveaux d'infrastructure domestique de transport sont harmonisés au sein des pays membres de l'accord d'intégration, leurs parts industrielles peuvent converger au détriment des pays restés hors de l'union. Le chapitre 4 s'intéresse à des questions d'économie urbaine en étudiant comment l'interaction entre rendements croissants et coûts de transport détermine la localisation des activités et des travailleurs au sein d'un pays ou d'une région. Le modèle développé reproduit un fait stylisé observé à l'intérieur des centres métropolitains des USA: sur une période longue (1850-1990), on observe une spécialisation croissante des centres urbains et de leurs périphéries associée à une évolution croissante puis décroissante de la population des centres urbains par rapport à leurs périphéries. Ce résultat peut se transférer dans un contexte en développement avec une zone centrale et une zone périphérique: à mesure que l'accessibilité des régions s'améliore, ces régions se spécialiseront et la région principale, d'abord plus importante (en termes de nombre de travailleurs) va finir par se réduire à une taille identique à celle de la région périphérique.

Spatial statistical analysis and selection of genotypes in plant breeding

The objective of this study was to evaluate the efficiency of spatial statistical analysis in the selection of genotypes in a plant breeding program and, particularly, to demonstrate the benefits of the approach when experimental observations are not spatially independent. The basic material of this study was a yield trial of soybean lines, with five check varieties (of fixed effect) and 110 test lines (of random effects), in an augmented block design. The spatial analysis used a random field linear model (RFML), with a covariance function estimated from the residuals of the analysis considering independent errors. Results showed a residual autocorrelation of significant magnitude and extension (range), which allowed a better discrimination among genotypes (increase of the power of statistical tests, reduction in the standard errors of estimates and predictors, and a greater amplitude of predictor values) when the spatial analysis was applied. Furthermore, the spatial analysis led to a different ranking of the genetic materials, in comparison with the non-spatial analysis, and a selection less influenced by local variation effects was obtained.

On Automatic Diagnosis of Alzheimer's Disease based on Spontaneous Speech Analysis and Emotional Temperature

Alzheimer's disease is the most prevalent form of progressive degenerative dementia; it has a high socio-economic impact in Western countries. Therefore it is one of the most active research areas today. Alzheimer's is sometimes diagnosed by excluding other dementias, and definitive confirmation is only obtained through a post-mortem study of the brain tissue of the patient. The work presented here is part of a larger study that aims to identify novel technologies and biomarkers for early Alzheimer's disease detection, and it focuses on evaluating the suitability of a new approach for early diagnosis of Alzheimer’s disease by non-invasive methods. The purpose is to examine, in a pilot study, the potential of applying Machine Learning algorithms to speech features obtained from suspected Alzheimer sufferers in order help diagnose this disease and determine its degree of severity. Two human capabilities relevant in communication have been analyzed for feature selection: Spontaneous Speech and Emotional Response. The experimental results obtained were very satisfactory and promising for the early diagnosis and classification of Alzheimer’s disease patients.

Predictive analysis and mapping of indoor radon concentrations in a complex environment using kernel estimation: an application to Switzerland.

PURPOSE: The aim of this study was to develop models based on kernel regression and probability estimation in order to predict and map IRC in Switzerland by taking into account all of the following: architectural factors, spatial relationships between the measurements, as well as geological information. METHODS: We looked at about 240,000 IRC measurements carried out in about 150,000 houses. As predictor variables we included: building type, foundation type, year of construction, detector type, geographical coordinates, altitude, temperature and lithology into the kernel estimation models. We developed predictive maps as well as a map of the local probability to exceed 300 Bq/m(3). Additionally, we developed a map of a confidence index in order to estimate the reliability of the probability map. RESULTS: Our models were able to explain 28% of the variations of IRC data. All variables added information to the model. The model estimation revealed a bandwidth for each variable, making it possible to characterize the influence of each variable on the IRC estimation. Furthermore, we assessed the mapping characteristics of kernel estimation overall as well as by municipality. Overall, our model reproduces spatial IRC patterns which were already obtained earlier. On the municipal level, we could show that our model accounts well for IRC trends within municipal boundaries. Finally, we found that different building characteristics result in different IRC maps. Maps corresponding to detached houses with concrete foundations indicate systematically smaller IRC than maps corresponding to farms with earth foundation. CONCLUSIONS: IRC mapping based on kernel estimation is a powerful tool to predict and analyze IRC on a large-scale as well as on a local level. This approach enables to develop tailor-made maps for different architectural elements and measurement conditions and to account at the same time for geological information and spatial relations between IRC measurements.

Examination of Curing Criteria for Cold In-Place Recycling (Phase 3: Calibration of Moisture Loss Indices and Development of Stiffness Gain Model), TR-609, 2011

In the previous study, moisture loss indices were developed based on the field measurements from one CIR-foam and one CIR-emulsion construction sites. To calibrate these moisture loss indices, additional CIR construction sites were monitored using embedded moisture and temperature sensors. In addition, to determine the optimum timing of an HMA overlay on the CIR layer, the potential of using the stiffness of CIR layer measured by geo-gauge instead of the moisture measurement by a nuclear gauge was explored. Based on the monitoring the moisture and stiffness from seven CIR project sites, the following conclusions are derived: 1. In some cases, the in-situ stiffness remained constant and, in other cases, despite some rainfalls, stiffness of the CIR layers steadily increased during the curing time. 2. The stiffness measured by geo-gauge was affected by a significant amount of rainfall. 3. The moisture indices developed for CIR sites can be used for predicting moisture level in a typical CIR project. The initial moisture content and temperature were the most significant factors in predicting the future moisture content in the CIR layer. 4. The stiffness of a CIR layer is an extremely useful tool for contractors to use for timing their HMA overlay. To determine the optimal timing of an HMA overlay, it is recommended that the moisture loss index should be used in conjunction with the stiffness of the CIR layer.

The knock-out of ARP3a gene affects F-actin cytoskeleton organization altering cellular tip growth, morphology and development in moss Physcomitrella patens.

The seven subunit Arp2/3 complex is a highly conserved nucleation factor of actin microfilaments. We have isolated the genomic sequence encoding a putative Arp3a protein of the moss Physcomitrella patens. The disruption of this ARP3A gene by allele replacement has generated loss-of-function mutants displaying a complex developmental phenotype. The loss-of function of ARP3A gene results in shortened, almost cubic chloronemal cells displaying affected tip growth and lacking differentiation to caulonemal cells. In moss arp3a mutants, buds differentiate directly from chloronemata to form stunted leafy shoots having differentiated leaves similar to wild type. Yet, rhizoids never differentiate from stem epidermal cells. To characterize the F-actin organization in the arp3a-mutated cells, we disrupted ARP3A gene in the previously described HGT1 strain expressing conditionally the GFP-talin marker. In vivo observation of the F-actin cytoskeleton during P. patens development demonstrated that loss-of-function of Arp3a is associated with the disappearance of specific F-actin cortical structures associated with the establishment of localized cellular growth domains. Finally, we show that constitutive expression of the P. patens Arp3a and its Arabidopsis thaliana orthologs efficiently complement the mutated phenotype indicating a high degree of evolutionary conservation of the Arp3 function in land plants.

Child Care and Development Fund (CCDF) Plan for Iowa FFY 2014-2015, December 16, 2013

Chief Executive Officer of the State (or Territory), to represent the State (or Territory) as the Lead Agency. The Lead Agency agrees to administer the program in accordance with applicable Federal laws and regulations and the provisions of this Plan, including the assurances and certifications appended hereto. **********Names, phone numbers, emails addresses may not be current or correct any longer****************

Diagnosing Patients Combining Principal Components Analysis and Case Based Reasoning

This paper addresses the application of a PCA analysis on categorical data prior to diagnose a patients data set using a Case-Based Reasoning (CBR) system. The particularity is that the standard PCA techniques are designed to deal with numerical attributes, but our medical data set contains many categorical data and alternative methods as RS-PCA are required. Thus, we propose to hybridize RS-PCA (Regular Simplex PCA) and a simple CBR. Results show how the hybrid system produces similar results when diagnosing a medical data set, that the ones obtained when using the original attributes. These results are quite promising since they allow to diagnose with less computation effort and memory storage

Spectral analysis and limit behaviours in a spring-mass system

We consider a model for a damped spring-mass system that is a strongly damped wave equation with dynamic boundary conditions. In a previous paper we showed that for some values of the parameters of the model, the large time behaviour of the solutions is the same as for a classical spring-mass damper ODE. Here we use spectral analysis to show that for other values of the parameters, still of physical relevance and related to the effect of the spring inner viscosity, the limit behaviours are very different from that classical ODE