Miracle-dz/UniFAR-FTUs huggingface dataset:
Quantifying genetic effects of ground cover on soil water evaporation using digital imaging

Rapid development of leaf area and/or aboveground biomass has the potential to improve water harvest of rain fed wheat in Mediterranean-type environments through reduced soil evaporation. However, quantitative relationships between genetic differences in early ground cover and soil water evaporation have not been established. Furthermore, accurate phenotyping of ground cover and early vigour have typically been achieved via destructive sampling methods, which are too time-consuming to undertake within breeding programs. Digital image analysis has previously been identified as an alternative indirect method of analysis, whereby computer analysis is ued to determine percentage ground cover. This study uses a digital ground cover (DGC) analysis tool for high throughput screening of four large wheat populations. The DGC methodology was validated via comparisons with alternative measures of canopy cover, such as normalised difference vegetation index (NDVI) (r2 = 0.69), biomass (r2 = 0.63), leaf area index (r2 = 0.80) and light penetration through the canopy (r2 = 0.70). The wheat populations were utilised to estimate the potential variation in soil evaporation associated with genetic differences in early ground cover, which was validated using established models. Estimates of genetic differences in soil evaporation within the four populations (6.90–24.8 mm) suggest that there is sufficient genetic variation to increase water harvest through targeting faster ground cover. Implications for improved wheat yields and breeding are discussed.
CAViaR

Value at risk (VaR) is the standard measure of market risk used by financial institutions. Interpreting the VaR as the quantile of future portfolio values conditional on current information, the conditional autoregressive value at risk (CAViaR) model specifies the evolution of the quantile over time using an autoregressive process and estimates the parameters with regression quantiles. Utilizing the criterion that each period the probability of exceeding the VaR must be independent of all the past information, we introduce a new test of model adequacy, the dynamic quantile test. Applications to real data provide empirical support to this methodology.
Computational Statistics

in which the authors discuss common misconceptions regarding Bonferroni’s correction, false discovery rate, and cross-validation. The only fault of the chapter is its length of three pages. I think the authors have given an excellent treatment of the subject. Comparing the book to its cohort group, it is at a higher level than Draghici (2003) and Simon et al. (2003), but on par with Amaratunga and Cabrera (2004). Unfortunately, it lacks the color graphics found in Draghici (2003), which is almost a necessity for books on this topic. I wish the next edition would provide color graphics. For those considering this book as a course text, be aware that it does not provide exercises at end of each chapter, as do Amaratunga and Cabrera (2004), and to a more limited degree, Draghici (2003). However, textbook exercises in this area are of debatable utility. Although the authors provide their own R package, do not expect much explicit demonstration of R code as in Parmigiani et al. (2003). Nevertheless, software implementation is presented more consistently than Speed (2003) and in much greater detail than Amaratunga and Cabrera (2004). Overall, I liked this book and would recommend it to any statistician new to microarray data analysis. The book has a unique combination of features that make it a contender among the standard textbooks on microarray data analysis. It is suitable as a text for an introductory course on microarray data analysis. I feel students would enjoy the authors’ straightforward style and their easy-toread presentation.
Comment

requires a large IC Phase I dataset. In determining the IC ARL’s, the authors need to remove the effect of the initial conditions for the EWMA. As the EWMA weight for τ profiles in the past is (1−λ)τ using τ = 30 is unlikely to be sufficient. With λ = 0.02 (the smallest recommended value), (1 − λ)30 = 0.55, which is the weight of the initial value in the EWMA statistic. In Table 1, the authors compare random and fixed effect modeling. The fixed effect model does not have the desired incontrol ARL. To make the comparison fair, we see no reason why the control limits for the fixed effects approach cannot be set to achieve the desired IC ARL.
Comment

Again, using bλ = . 7 as done by the authors gives γ ∗ = . 282 (Fig. 3). This value is smaller than the optimal γ obtained in the previous section, but the minimum is not as sharp. When D is not fully isotropic, the calculations are complicated by the fact that they depend on the particular orientation in space of the anisotropic axes. However, it can be argued that if r is sampled uniformly, then the estimation variance should not depend on those axes. Then, without loss of generality, one may assume that = I3. Then again, we have that U = D , and the required submatrices U11 and U22 can be taken as the appropriate submatrices of (8). Note that because of the symmetry, we get that in fact, U11 = U22 . Similarly, the variance σii in the authors’ corollary 2 is equal to any one of the upper three diagonal entries of (8) and thus is also proportional to (9). Interestingly, the minimization problem in terms of γ turns out to be the same as in the isotropic case. The distribution of the eigenvector matrices C, C11, and C22 in theorems 3 and 4 and corollaries 1 and 2 are more difficult to assess directly from the written densities. However, intuition dictates that C should be uniformly distributed on the orthogonal group O(3), whereas C11 and C22 should be uniformly distributed on the orthogonal group O(2) in the oblate and prolate cases. The authors did not write this observation explicitly, but their figures 1 and 2 support this conjecture. Figures 2(b) and 2(f) make this clear for the oblate and prolate case. Similarly, the angle histogram plots in figure 1 indicate a uniform distribution on the sphere, because the uniform density in spherical coordinates is sin(φ)/(4π).
Newcomer's attitudes to further tourism development: the case of Alcúdia (Mallorca)

ABSTRACT One of the potential outputs derived from large-scale tourism development in a certain area or region can be the attraction of large segments of immigrant population. This has been the case in Alcudia, a town located in the North of Mallorca, where a large proportion of its current population are “newcomers” fairly recently arrived as a consequence of impressive tourism development in the area. The advent of the twenty-first century has raised concerns about long-term potential negative impacts derived from tourism overdevelopment among residents and tourism planning authorities. Two rounds of surveys on residents’ attitudes towards tourism were conducted as part of the city's strategic planning. Findings show that attitudes towards further or additional tourism development vary according to a fundamental variable: the degree of integration into the host community which is correlated with the length of residence and concern about culture preservation: immigrant population show a lower degree of integration. Therefore, an excess of tourism development attracting large numbers of immigrants can result in a potential conflict, not between the host community and the tourists, but among residents: between those who want to carry on overdeveloping and those who want to keep nature, culture and self-identity.
Comment

μT(x) can be very different from μX(x) = E(Y|X = x), causing a lot of biases. Left panel of Figure 3 shows the difference between the two functions for three noise level σ = 0, σ = 0.1, and σ = 0.2 with normal distribution. Suppose that we wish to predict Y based on X = x and only the function μT(x) is available. One naturally generates many Ti = x + εi, gets prediction μT(x + εi) and uses their average as the prediction of Y . This approach essentially computes μa(x) = EμT(x + ε).
TRANSMISSSION DE L'IMMUNITÉ MATERNELLE CHEZ LE PORC ET LES RUMINANTS APRÈS LA NAISSANCE

In swine and ruminants, maternal immunoglobulins (Ig) do not reach the foetus before birth. In these species, passive immunity is transferred via the colostrum rich in IgG, which cross the newborn’s intestinal wall due to a temporary non-selective permeability. The newborn’s protection against pathogens is therefore provided initially by these colostral immunoglobulins, then by milk immunoglobulins, mainly IgG1 in ruminants and IgA in swine. In swine, IgA plasmocytes are generated by inducer sites in the intestine (Peyer’s patches) and in the upper airways (nasal mucosa, tonsils). These cells migrate to the mammary glands where they are recruited by vascular addressins and chemokines. This process provides a far greater excretion of IgA than the simple filtration of serum IgA. In ruminants, the lack of such addressins in the mammary gland explains the low levels of IgA plasmocytes in this organ. In these animals, both colostral and lactogenic immunity are provided by IgG1 filtered from the blood into the milk. Consequently, the improvement of colostral immunity in both species (IgG in swine and IgG1 in ruminants), and of lactogenic immunity in ruminants (IgG1) depends on the stimulation of the maternal systemic immune system, whereas the improvement of lactogenic immunity in swine (IgA) depends on the stimulation of the maternal mucosal inducer sites.
Instruction for Authors

The International Journal of Medical Sciences [IJMS], ISSN 2522-7386, is a peer-reviewed, 3 issues published annually. Authors are invited to submit for publication articles with a wide spectrum of coverage reporting original work, in the fields of medicine, nursery, dentistry, and pharmacy sciences. Review articles are usually by invitation only. However, Review articles of current interest and high standard will be considered. Prospective work should not be back dated. There are also sections for Case Reports, Brief Communication, correspondence and medical news items. Authors should read the editorial policy and publication ethics before submitting their manuscripts. Authors should also use the appropriate reporting guidelines in preparing their manuscripts.
Missing Data Due to Culling of Pigs Before Testing and the Effects on the Estimation of (Co)Variance Components

Abstract A stochastic simulation of a pig breeding unit with 100 sows and 15 boars was used to examine the effects of culling before testing on the estimates of (co)variance components and parameters. The traits were age at test (AGE) and sidefat thickness (FAT) measured on farm. Data were simulated over 10 years and missing data were generated by deletion of records based on the phenotypic value of AGE. A situation with no culling (C-NO) was compared with culling at random (C-RAND), within litters (C-W/IN) and over litters (C-OVER). A multivariate restricted maximum likelihood procedure was applied to estimate (co)variance components, using the same model as that for the simulation of records. The heritabilities of AGE were over estimated in C-W/IN and C-OVER, and genetic, residual and phenotypic correlations became weaker. The litter variance ratio of AGE was seriously overestimated in C-W/IN and underestimated in C-OVER. A reduced model, without the litter effect included, resulted in even larger diffe.
Caracterização de ambientes aquáticos no rio de Ondas, cerrado baiano

This work sought to characterize the types of environments present in the De Ondas River, with the aim of obtaining information on their structural complexity and supporting public policies for water resource management that will allow the shared water use and will protect the biodiversity of the river basin. The De Ondas River watershed is located in the Cerrado Region, in the western part of Bahia State, and covers an area of 5.559 km². In order to perform the characterization, two stretches were demarcated, one in the upper part of the basin and another in the lower. Afterwards, a topobathymetric survey was conducted, and characterizations of the bottom and of hydraulic properties were done for both the rainy and dry seasons. The topobathymetric survey in the upper course showed altitudes ranging from 699.82 m to 714.06, whereas in the lower course these varied from 439.45 m to 446.91 m. Bathymetric profiles allowed the visualization of the structure and distribution of the bottom of the channel environments. The granulometric analysis of the higher part of the basin showed that sand was predominant (88.94%), while in the lower part boulders (76.9%) and cobble (23.1%) predominated. In the higher part of the basin, the water flow varied from 5.20 m³s-1 (dry period) to 9.07 m³s-1 (rainy period); and in the lower part, from 25.73 m³s-1 (dry period) to 66.44 m³s-1 (rainy period). The complexity of the environments of these stretches favors biological diversity in the river, resulting in different environments that aid aquatic species in adapting to the channel structure. The study of the physical characteristics of the De Ondas River produced a great amount of information regarding the micro-environments of the stretches examined, and will support studies related to aquatic biota and water resource management.
Discussion

This insightful article by van Dyk and Meng (vDM) makes the important point that advances in data augmentation algorithms offer a wide variety of tools for statistical inference. Time series methods are no exception and mixture modeling within this context may help to improve forecasting and to detect changes in structure across time. The time series modelingapproach that we adopt is based on the idea of mixing models through the neural network paradigm known as hierarchical mixtures-of-experts (HME)— see Jordan and Jacobs (1994). The HME approach easily allows for model comparison and permits one to represent the mixture weights as a function of time or other covariables. With the additional hierarchy, it is possible to localize the comparisons to speci c regions or regimes. Furthermore, the de ning elements of the mixture do not have to be restricted to a particular class of models permitting very general comparisons. In this comment, parameters are estimated via maximum likelihood using the EM-algorithm—extensions to a full Bayesian approach using MCMC may follow one or more of the many lines outlined by vDM. We see this comment as a call to the Chagalls of this world to use their artist abilities to develop quick mixing stochastic algorithms for this important, yet complex class of HME models. Let fytg0 be a time series of endogenous or response variables, and fxtg0 be a time series of exogenous variables or covariates. Suppose the main interest is to draw inference on fytg0 conditional on fxtg0 . Let the conditional probability density function (pdf) of yt be ft(ytjFt 1; X ; 3 ), where 3 is a parameter vector; X is the 1⁄4 - eld generated by fxtg0 , representing the external information; and for each t, Ft 1 is the 1⁄4 - eld generated by fysg 1 0 representing “the previous history” at time t 1. Typically, the conditional pdf ft
Comment

Chernozhukov, V. , Hong, H. , and Tamer, E. (2007), “Inference on Parameter Sets in Econometric Models,” Econometrica, 75, 1243–1284. Galichon, A. , and Henry, M. (2007), “A Test of Non-Identifying Restrictions and Confidence Regions for Partially Identified Parameters,” unpublished manuscript, submitted to Journal of Economics. Leamer, E. E. (1987), “Errors in Variables in Linear Systems,” Econometrica, 55, 893–909. Magnac, T. , and Thesmar, D. (2002), “Identifying Dynamic Discrete Choice Processes,” Econometrica, 70, 801–816. Manski, C. F. (2004), “Measuring Expectations,” Econometrica, 72, 1329– 1376. Rust, J., Traub, J. F. , and Wozniakowski, H. (2002), “Is There a Curse of Dimensionality for Contraction Fixed Points in the Worst Case? ” Econometrica, 70, 285–329.
Rejoinder

McKibbin, W. J., and Sachs, J. D. (1991), Global Linkages: Macroeconomic Interdependence and Cooperation in the World Economy, Washington, DC: The Brookings Institution. McKibbin, W. J., and Wilcoxen, P. J. (1999), “The Theoretical and Empirical Structure of the G-Cubed Model,” Economic Modelling, 16, 123–148. Mitchell, P. R. , Sault, J. E. , Smith, P. N. , and Wallis, K. F. (1998), “Comparing Global Economic Models,” Economic Modelling, 15, 1–48. Roeger, W. , and in’t Veld, J. (1997), “QUEST II: A Multicountry Business Cycle and Growth Model,” Economic Papers No. 123, European Commission, Brussels. Sims, C. A. (1993), “A Nine-Variable Probabilistic Macroeconomic Forecasting Model,” in Business Cycles, Indicators, and Forecasting, eds. J. H. Stock and M. W. Watson, Chicago: University of Chicago Press for NBER, pp. 179–204. Wallis, K. F. (2003), “Comparing Empirical Models of the Euro Economy,” Economic Modelling, forthcoming. Watson, M. W. (2003), “Macroeconomic Forecasting Using Many Predictors,” in Advances in Economics and Econometrics: Theory and Applications, Eighth World Congress, Vol. 3, eds. M. Dewatripont, L. P. Hansen, and S. J. Turnovsky, Cambridge, U.K.: Cambridge University Press, pp. 87–114.
The Reality of Evaluating Community-Based Sport and Physical Activity Programs to Enhance the Development of Underserved Youth: Challenges and Potential Strategies

In recent years, underserved communities have received much attention within the field of positive youth development (PYD). While there are an increasing number of opportunities for underserved youth to join PYD-based programs, there is growing concern surrounding the lack of critical evaluation of these programs (Kidd, 2008). Unfortunately, there are numerous challenges related to evaluation within underserved communities and in international settings that often prevent or disrupt the research process. These challenges include difficulty building trust with the target population, power relations, obtaining parental consent, demonstrating cultural competence, and attrition. Moreover, these challenges are rarely discussed in the literature (Knight, Roosa, & Umaña-Taylor, 2009). The primary goal of this article is to present an open discussion of such challenges using case studies from four distinct programs implemented with four different underserved communities. In addition, the limited empirical evidence regarding these challenges is presented, along with potential solutions for consideration in future research efforts.
Mitosis in the intact and regenerating planarian Dugesia mediterranea n.sp. I. Mitotic studies during growth, feeding and starvation

Planarian neoblasts, the only cell type in this organism endowed with mitotic power, decrease in density as total body volume, total cell number, and length increase. Studies on mitosis show that the neoblast mitotic rate is a function of body length, being lower as the organism increases in length. The cephalo-caudal distribution of mitotic figures in animals of different sizes shows a change towards a more even distribution of mitosis jointly with a mitotic increase in the caudal regions as length increases. However, in the sagittal and dorso-ventral axes, the distribution of mitosis is always nonuniform, probably due to the uneven distribution of parenchymal tissue along them. The pharynx of animals in all size groups examined shows no mitotic figures except some near its base. This may suggest for this organ a proximal type of growth with some cell migration. Feeding elicits a very rapid increase in mitosis which lasts for almost a week, whereas long periods of starvation do not cause a significant increase or decrease in the basal mitotic rate. All these results stress the need of looking at the planarian neoblast mainly as a stem or replacement cell of most differentiated cell types and not as a mere “regeneration cell. ” The implications of the results obtained for the mechanisms of growth and shrinkage are discussed.
A new model system for tomato genetics

The purpose of this study was to develop a model system for studying tomato genetics. Agronomic, genetic, and molecular data are presented which show that the miniature Lycopersicon esculentum cultivar, Micro-Tom (Micro tomato), fulfills the requirements for such a model. It grows at high density (up to 1357 plants/m−2); it has a short life cycle (70–90 days from sowing to fruit ripening); and it can be transformed at frequencies of up to 80% through Agrobacterium-mediated transformation of cotyledons. Moreover, it differs from standard tomato cultivars by only two major genes. Therefore, any mutation or transgene can be conveniently studied in Micro-Tom’s background and, when needed, transferred into a standard background. We took advantage of Micro-Tom’s features to improve the infrastructure for mutagenesis in tomato. A screening of 9000 M1 and 20 000 M2 EMS mutagenized plants is described. Mutants with altered pigmentation or modified shape of leaves, flowers and fruits were found. In addition, an enhancer trapping and a gene trapping system, based on the Ac/Ds maize transposable elements, were transformed into Micro-Tom and found to be active. In summary, Micro-Tom opens new prospects to achieve saturated mutagenesis in tomato, and facilitates the application of transposon-based technologies such as gene tagging, trapping and knockout.
Occurrence of dark septate endophytes in Nothofagus seedlings from Patagonia, Argentina

Temperate forests of southern Argentina and Chile cover a wide latitudinal and altitudinal range on both sides of the Andes, with Nothofagus species being one of the main tree species. Most of the larger remnants are protected within the limits of national parks and forest reserves. However, some of these are invaded by exotic conifers such as Pseudotsuga menziesii. In order to better understand the mycorrhizal condition within the Nothofagus–P. menziesii interaction, transects were established at six study sites along the effective recruitment area. Three treatments were established: Nothofagus forest without P. menziesii invasion (Forest), Nothofagus–P. menziesii matrix (Interface) and P. menziesii plantation lacking Nothofagus specimens (Plantation). A 2 kg mixed soil sample was taken at each treatment site and kept in brand-new plastic bags. A soil bioassay with Nothofagus seedlings acting as baits was set to evaluate the mycorrhizal inoculum potential and soil-fungi composition at each sampling treatment. Forty percent of the evaluated seedlings were found to be colonised by dark septate endophytes (DSE) fungi. Ectomycorrhizal colonisation percentage was significantly higher in plants growing in Forest soils (mean = 57.77%) when compared with Interface (mean = 42.53%) and Plantation (mean = 44.65%). The high incidence of DSE in young roots of Nothofagus seedlings in this study supports the hypothesis that DSE might be pioneering colonisers of young tree seedlings in secondary successional environments. For future interventions in the forest, either with productive or protective intentions, it should be taken into account the incidence of these fungi, which may have an important positive role in Nothofagus forest post-invasion recovery.