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Objective: In this study, we assessed college students’ eating behaviors and determined the association between their eating behaviors and body image perception (feelings about body, shape, and weight). Methods: We surveyed college students (N = 184) enrolled at least parttime and actively living on campus during the 2019-2020 academic year. We used 4 validated surveys (EDE-Q, NEMS-P, SATAQ-3 and SATAQ-4) to collect all data. Two trained facilitators aided students in completing the surveys in the campus’s nutrition lab. We used weighted linear regression to assess the association between eating behaviors (frequency of fruit, vegetables, candy, and chips consumption available in dorm rooms, and meal consumption frequency at restaurants) (independent variables) and body image perception (dependent variable). We also calculated odds ratios. Results: We found statistically significant associations between students reducing their consumption of available fruit, vegetables, candy, soda, and chips in their dorm rooms to control weight and shape, choosing meals more frequently at fast-food versus sit-down restaurants, and unhappiness with weight and shape. Conclusion: Our results may assist college campus personnel in providing students with the necessary skills to develop better eating habits and improved body image. Students with a positive body image and a healthy relationship with food are more likely to have better mental and physical health.

Although treated effluent is being increasingly used to irrigate mature turfgrass, information on its use to establish grass is limited. Greenhouse experiments were conducted in 2015 and 2017 to examine establishment and nitrate leaching from three warm-season grasses: buffalograss [Buchloe dactyloides (Natt.) Eng.] 'SWI 2000', inland saltgrass [Distichlis spicata (L.) Greene], and bermudagrass [Cynodon dactylon (L.) Pers.] 'Princess77'. All grasses were grown with tailored (tertiary treated effluent with 15 mg L

Complex dynamic phenomena in which dynamics is related to events (modes) that cause structural changes over time, are well described by the switching linear dynamical system (SLDS). We extend the SLDS by allowing the measurement noise to be mode-specific, a flexible way to model non stationary data. Additionally, for models that are functions of explanatory variables, we adapt a variable selection method to identify which of them are significant in each mode. Our proposed model is a flexible Bayesian nonparametric model that allows to learn about the number of modes and their location, and within each mode, it identifies the significant variables and estimates the regression coefficients. The model performance is evaluated by simulation and two application examples from a dataset of meteorological time series of Barranquilla, Colombia are presented.

Lutescens, or lutescent, plant mutants produce leaves that are abnormally light yellow-green compared with normal plants, and are observed in multiple species of Capsicum as well as other genera such as Zea, Oryza, and Oenothera. Previous investigations into the lutescent phenotype in Capsicum have focused on genetic and transcriptomic analyses, and comparatively little is known about the phytochemical constituents of the lutescent leaf phenotype. Previous research in similar lutescent mutants in Capsicum and Oryza species has attributed their pale yellow-green leaf color and poor vigor to deficient chloroplast development. A total of 25 accessions of Capsicum lutescens mutants were phenotyped and analyzed based on a multivariate approach, using ‘Jupiter’ bell pepper (Capsicum annuum) with normal green leaves as a contextual benchmark. Photosynthetic pigments from mutant leaves were extracted and analyzed using high-performance liquid chromatography (HPLC); reflectance of the leaf material was measured with a chromameter using the L*a*b* color space. The chlorophyll a (Chl a)/b (Chl b) ratio was greater in leaves of lutescens mutants than in ‘Jupiter’. Multivariate statistical analyses revealed all lutescent mutant accessions could be distinguished from the ‘Jupiter’ contextual benchmark by variables indicating poor chloroplast development and increased photooxidative stress in lutescent mutant accessions. The lutescent leaf phenotype was not found to be caused by elevated xanthophyll or decreased chlorophyll concentrations. Furthermore, multivariate analysis revealed the lutescent mutant phenotype to be variable, with a wide range of phenotypes clustered into four major groups.

Model-based learning of organism dynamics is challenging, particularly when modeling count correlated data. In this paper, we adapt the multivariate Poisson distribution to model nematode dynamics. This distribution relaxes the mean-equal-variance property of the univariate Poisson distribution and allows recovery of the correlation among nematode genera. An observational dataset with 68 soil samples, 11 nematode genera, and 12 soil parameters is analyzed. The Spike and Slab Variable Selection procedure is adapted to obtain parsimonious models for the nematode occurrence. Nematode genus to genus interaction is assessed through the correlation matrix of the model. A simulation study validated the model's implementation. As a result, the model determined the most important covariates for each nematode and classified pairs of nematodes as: sympathetic, antagonistic or neutral, based on their estimated correlations. The model is useful for researchers and practitioners interested in studying population dynamics. In particular, the current results are important inputs when planning strategies for improving or managing soil health regarding nematodes.

Para determinar la genética de la resistencia a Phytophthora parasitica en jamaica, se analizaron las medias generacionales de cinco líneas resistentes y cinco susceptibles para estimar los parámetros genéticos de la resistencia en cruzas de jamaica. El análisis mostró que los efectos aditivos fueron más importantes que efectos de dominancia para la resistencia a P. parasitica. La heredabilidad, en sentido amplio, fue de 37%. Los resultados obtenidos indican que un programa de pedigree puede ser efectivo y el más adecuado para incrementar la resistencia genética a P. parasitica.

We present an application of the switching linear dynamical model (SLDM) to model the nominal Mexican peso-USD exchange rate time series from January 01, 1970 to January 09, 2019. Two main features exhibited by the time series are observed: dynamical behavior with upward and downward movements after moving to the floating exchange rate regime, and most recently a persistent increasing trend. The SLDM is a flexible method for learning about dynamic models for time series with complex and uncertain behavior patterns, such as those exhibited by the exchange rate. The SLDM allows us to account for four novel results not obtained before: (1) we are able to identify four regimes; (2) we cluster the observations into regimes; (3) we provide the estimated probability of a change point at each time instead of estimating a regime constant persistence; and (4) we find that the change points match with some internal and external events associated with crisis periods.

Roselle (Hibiscus sabdariffa L.) leaf and calyx spot induced by Corynespora cassiicola is one of the most important diseases that limit production, processing, and commercial use of this crop. The temporal progress of this disease was followed in four production locations in Guerrero, Mexico, during the 2014 and 2015 cycles. Disease incidence and severity were evaluated every 10 days through the 173-day production cycles. Epidemics were evaluated using the area under disease progress curves (AUDPCs) and Weibull model parameters. The highest severity and epidemic rate in calyces and leaves was registered in San Miguel, while the lowest was recorded in Xalpatlahuac in both cycles. Disease incidence in calyces was lower in Xalpatlahuac during both evaluation cycles and in leaves only in the 2015 cycle. The Weibull model properly described the epidemics (r = 0.93–0.99). The highest periods of epidemic growth in leaves were registered during flowering, development of calyces and foliar senescence; and in calyces during their physiological maturation and harvest. Severity on leaves and calyces was correlated with temperature from 20 to 30 °C (r = 0.80–1.0) and relative humidity higher than 90% (r = 0.80–1.0) (p

RESUMEN El manchado de hojas y cálices de la jamaica, causado por Corynespora cassiicola, se considera la principal limitante fitosanitaria del cultivo de jamaica (Hibiscus sabdariffa) en el Estado de Guerrero, México, causando pérdidas significativas de la producción. Para su control, se evaluó la efectividad biológica de diversas substancias químicas (convencionales y alternativas) durante los ciclos de producción 2014 y 2015. El estudio se realizó en los municipios de Ayutla y Tecoanapa, Guerrero, bajo condiciones de campo y presión natural de inóculo. Las substancias químicas (tratamientos), se aplicaron a intervalos de 7 y 14 días. En cada tratamiento, se registró la severidad en hojas y cálices, así como el peso fresco y seco de cada unidad experimental. El ensayo se estableció bajo un diseño bloques al azar con cuatro repeticiones. Para el análisis de los datos se realizó un análisis de varianza y pruebas de separación de medias (Tukey-Kramer, α=0.05). La menor severidad de la enfermedad y mayor peso fresco y seco de cálices se obtuvo en plantas tratadas con mancozeb, clorotalonil y benomilo alternado con oxicloruro de cobre.

Greenhouse experiments were conducted in 2015 and 2017 to assess the feasibility of establishing three warm-season grasses-buffalograss [Buchloe dactyloides (Natt.) Eng.] 'SWI 2000', inland saltgrass (Distichlis spicata L.), and bermudagrass (Cynodon dactylon L.) 'Princess77'-with tailored water (tertiary treated effluent with 15 mg L

Regulatory restrictions on herbicide use for managing turfgrass weeds has prompted the search for alternative control strategies. Fescue (Festuca) species were identified for their potential to interfere with growth of annual and perennial weeds. In a study conducted in 2018 and 2019, six fescue cultivars were tested from five different species for interference with the growth of three common turfgrass weeds: white clover (Trifolium repens L., WC), daisy (Bellis perennis L.) and yarrow (Achillea millefolium L.). Fine (Festuca L. spp.) and tall fescues (Schedonorus arundinaceus (Schreb.) Dumort.) were sown and grown in a field trial for 14 days before overseeding with different weeds. vigor and visual quality of grasses, weed cover, and vegetation cover was recorded regularly for 84 days. Differences in mean temperatures and precipitation between the two years of the study resulted in differences in growth of grasses and weeds, as well as in the extent of weed interference of fescue cultivars. Cultivars Musica (F. rubra L. ssp. commutata Gaudin) and Barpearl (F. rubra L. ssp. littoralis) were least affected by weed growth during both years, but there was overlap with other cultivars for the measured parameters. Melyane (tall fescue) was deemed unsuitable for natural weed suppression because growth and vigor declined after first mowing, ultimately leading to unacceptable visual quality. Turfgrass visual scores were moderately negatively correlated to weed cover in both years. Future research should focus on F. rubra L. ssp. rubra Gaudin and F. rubra L. ssp. littoralis subspecies and identify the mechanisms used to interfere with weed growth.

The current practice used to evaluate broadleaf weed cover in turfgrass is visual assessment, which is time consuming and often leads to inconsistencies among evaluators. In this study, we investigated the effectiveness of constructing Random Forest models (RF), either pixel-, object-based (OBIA) or a combination of both to detect and quantify broadleaf weed cover. High resolution multispectral images were captured of 136 turfgrass plots, seeded with five species of Festuca L. and overseeded with either clover (Trifolium repens L.), daisy (Bellis perennis L.), yarrow (Achillea millefolium L.), or a mixture of all three weeds. Ground measurements of vegetation cover and bare soil were taken with a point quadrat and digital image analysis. Weeds were detected with 99% accuracy by OBIA, followed by the combined approach (98%) and Pixel-based approach (93%). Accuracy at distinguishing among weed species was somewhat lower (89%, 81% and 90%, respectively), with yarrow contributing most to the decrease in accuracy. The predictions based on ground measurements were further compared to field measurements. For both soil and weed classification, models that used shape features (OBIA and combined) resulted in better agreement with field measurements compared to Pixel- based classifications. Our study suggests that broadleaf weed cover comprised of species such as clover and daisy can be accurately quantified with high resolution multispectral images; however, quantifying yarrow cover remains challenging.

El estudio de los sistemas agrícolas tradicionales representa una opción ante la necesidad de conservar los ecosistemas naturales e incrementar la producción de alimentos. Una mejor comprensión de la dinámica ecológica de la roza-tumba-quema (RTQ) puede permitir la toma de decisiones encaminadas al manejo racional de los ecosistemas donde esta se practica. Por lo anterior, el objetivo fue describir la relación de las comunidades microbianas con la materia orgánica y la condición nutrimental del suelo. Se calcularon los índices de diversidad de Shannon (H’) a partir del perfil metabólico de las poblaciones con el uso de placas BIOLOG-ECO™, el contenido de materia orgánica (MO), nitrógeno total, nitratos, amonio, fósforo extractable, textura, densidad aparente, capacidad de campo y punto de marchitez permanente del suelo. Las propiedades físicas no fueron significativamente diferentes. El pH, contenido de MO y de nitratos se modificó con el tiempo de descanso. No se encontró relación entre la diversidad microbiana (H’) y el tiempo de descanso (t), pero sí diferencias significativas (p < 0.05) entre las parcelas con mayor y menor tiempo de descanso, siendo más diversa esta última.

Genomic selection (GS) has become a tool for selecting candidates in plant and animal breeding programs. In the case of quantitative traits, it is common to assume that the distribution of the response variable can be approximated by a normal distribution. However, it is known that the selection process leads to skewed distributions. There is vast statistical literature on skewed distributions, but the skew normal distribution is of particular interest in this research. This distribution includes a third parameter that drives the skewness, so that it generalizes the normal distribution. We propose an extension of the Bayesian whole-genome regression to skew normal distribution data in the context of GS applications, where usually the number of predictors vastly exceeds the sample size. However, it can also be applied when the number of predictors is smaller than the sample size. We used a stochastic representation of a skew normal random variable, which allows the implementation of standard Markov Chain Monte Carlo (MCMC) techniques to efficiently fit the proposed model. The predictive ability and goodness of fit of the proposed model were evaluated using simulated and real data, and the results were compared to those obtained by the Bayesian Ridge Regression model. Results indicate that the proposed model has a better fit and is as good as the conventional Bayesian Ridge Regression model for prediction, based on the DIC criterion and cross-validation, respectively. A computing program coded in the R statistical package and C programming language to fit the proposed model is available as supplementary material.

El consumo de frutas y vegetales reduce el riesgo de desarrollar enfermedades crónico-degenerativas, por su alto contenido de sustancias bioactivas. Durante 2013-2014 se cosecharon frutos de frambuesa (Rubus idaeus L.) cvs. Adelita, Erika, Lupita y Polka, en Michoacán y Jalisco, México, para determinar color (L*, a*, b*, C* y h*), firmeza, sólidos solubles totales (SST), acidez titulable (AT), pH del jugo, capacidad antioxidante (CA) y concentración de ácido elágico (AE). Como resultado se encontró que todas las variables físicas y químicas, excepto CA, presentaron diferencias significativas para distintas épocas de cosecha. Se realizó un análisis de regresión para evaluar el efecto del promedio mensual de la temperatura (T) y la humedad relativa (HR) de los sitios, y se encontró que existe una relación positiva significativa entre la T promedio y SST, pH, AE y CA, pero negativa y significativa con AT. La HR sólo tuvo efecto negativo sobre las variables firmeza y SST. Se observaron diferencias significativas en la concentración de AE entre cultivares. Las frutas cosechadas en junio y octubre presentaron mayor concentración de AE. La mayor concentración de AE se encontró en Erika (3.69 μg g-1 de peso fresco de la fruta) y la menor concentración en Adelita (2.26 μg g-1). La capacidad antioxidante de todos los cultivares fue mayor a 60 %, que es aceptable en la neutralización de los radicales libres.

Vineyard applications of foliar nitrogen (N) and sulfur were evaluated in the Yadkin Valley of North Carolina in 2011 and 2012 as a means to modify grape composition of Vitis vinifera cv. petit manseng. Four treatments were applied each year in a randomized complete block design with six replicates of six vines each: untreated control, soil N at 30 kg/ha, foliar N at 15 kg/ha and foliar N at 15 kg/ha plus sulfur at 5 kg/ha. Fruit ammonia and yeast assimilable nitrogen (YAN) concentrations increased in foliar-treated vines both seasons compared to control vines. Total glycosides (TGGs) increased ∼80% in foliar N/sulfur-treated vines compared to control vines in 2011, and TGGs increased ∼157% in foliar N/sulfur-treated vines relative to glycoside level in foliar N alone in 2012. Foliar N and foliar N/sulfur applications increased the majority of amino acid concentrations analyzed in 2011. Measured concentrations of various amino acids, partly responsible for aroma and flavor of petit manseng wines, ...

In some finite sampling situations, there is a primary variable that is sampled, and there are measurements on covariates for the entire population. A Bayesian hierarchical model for estimating totals for finite populations is proposed. A nonparametric linear model is assumed to explain the relationship between the dependent variable of interest and covariates. The regression coefficients in the linear model are allowed to vary as a function of a subset of covariates nonparametrically based on B-splines. The generality of this approach makes it robust and applicable to data collected using a variety of sampling techniques, provided the sample is representative of the finite population. A simulation study is carried out to evaluate the performance of the proposed model for the estimation of the population total. Results indicate accurate estimation of population totals using the approach. The modeling approach is used to estimate the total production of avocado for a large group of groves in Mexico.

Root biomass, root intercepts, and root length density of SO4 rootstock grafted to Cabernet Sauvignon and exposed to under-trellis cover cropping (CC) and with or without root pruning (RP or NRP, respectively) were evaluated over three years. The CC treatments included tall fescue (Festuca arundinacea Shreb.) varieties KY-31, with and without RP, Elite II without RP, and an 0.9 m wide, under-trellis herbicide strip with KY-31 fescue inter-rows, with and without RP. Around 70% of grapevine root biomass was observed at ≤60 cm soil depth, regardless of treatment or year. KY-31 fescue/NRP vines had the most (96%) root biomass at ≤60 cm soil depth in 2008. Rooting depth distributions were fitted to the model Y = (1 − βd), where d is soil depth (cm), Y is the cumulative root fraction from the soil surface to depth d, and the unknown parameter β is a measure of soil vertical root distribution used as a response variable to test for treatment differences. All root distributions across treatments and years, except KY-31 fescue/NRP in 2008, generated β values greater than 0.970, similar to β values from other root distribution studies. The modest treatment impact on root biomass and distribution suggests that these mature grapevines adapted to CC and annual RP in this environment, and partially explains the minimal impact on soil water content and vine water potential previously reported from this experiment.

Complete vineyard floor cover cropping (inter- and intrarow) and vine root pruning were evaluated as tools to restrict vegetative growth of Vitis vinifera cv. Cabernet Sauvignon grapevines. Treatments were arranged in a split-plot, randomized, complete block design with cover crop schemes as main plots and annual vine root pruning (RP), or not (NRP), as subplots. Five perennial grasses as complete floor cover crops were compared to a more conventional under-trellis herbicide strip combined with KY-31 fescue interrows. KY-31 fescue and orchardgrass each reduced shoot growth rate by >30% in 2006 and >20% in 2007, below that of the herbicide-strip control vines. Root pruning independently reduced shoot growth rates. The combination of cover crop and RP decreased dormant pruning weights more than did the additive effects of either factor applied alone. Pruning weights in 2010 were reduced 8% below the control by RP, by 15% by cover crop, but by 38% when both treatments were applied. Leaf petiole N concentration at bloom was ~11% lower in RP vines in two of three years evaluated, but did not differ among vines grown with different cover crops. Stem water potential (Ψstem) was not affected by treatments. Cover cropping did not reduce crop yield with the exception of reduced yield due to KY-31 fescue in 2006. Berry weights were slightly reduced by a RP × year interaction from 2007 to 2009 and by a year effect in 2011 compared to 2010. While complete vineyard floor cover cropping and root pruning were effective tools to reduce vine size and vigor, effects on canopy architecture and primary fruit chemistry were minimal and more influenced by seasonal variation.

Complete vineyard floor cover crops were evaluated in a long-term study for their ability to regulate excessive vegetative growth of the grapevine (Vitis vinifera L.) variety ‘Cabernet Sauvignon’. Treatments were: tall fescue (Festuca arundinacea Shreb.) ‘KY-31′ and ‘Elite II’, hard fescue (Festuca ovina L.) ‘Aurora Gold’, perennial ryegrass (Lolium perenne L.), orchardgrass (Dactylis glomerata L.), and an under-trellis herbicide strip combined with KY-31 fescue interrows. Compared to herbicide-treated soil strip, Elite II fescue reduced vine pruning weights (kg/vine) 28%, individual cane weight (g) 20%, and canopy leaf layer number 25%. KY-31 fescue produced the greatest biomass and stand density, while perennial ryegrass produced the least biomass and Aurora Gold hard fescue produced the lowest stand density. Elite II fescue produced less biomass but equivalent stand density compared to KY-31 fescue. Treatments minimally impacted vine water potential (Ψmd, Ψstem), indicating that the grasses were not overly competitive with grapevines for soil moisture. All grass treatments tended to depress grapevine nitrogen levels relative to the under-trellis herbicide strip treatment, but no treatment resulted in vine nitrogen levels below the acceptable sufficiency range. Because of its establishment and growth characteristics, desired suppression of vine vegetative growth, and its low impact on crop yield, we considered Elite II fescue the optimal cover crop evaluated.

Decisions often need to be made about the ecological status of regions based on limited or variable quantities of data. The United States Forest Service, for example, has developed a subwatershed condition classification approach to classify status of National Forest subwatersheds (level 6 hydrological units) based on a variety of indicators including biological indicators. For forests in Western Virginia, benthic macroinvertebrate data are commonly collected and used to estimate biological status. Available data varies in sample size from 1 to 20 samples per HUC. One approach to improve estimation of the biological status of a region using the biological indicator is to use a model based estimate. A hierarchical model is proposed to aid in estimating the status of regions based on a HUC level 6 classification. The hierarchical model involves components associated with regressors at both the subwatershed and sampling level, a model for the error that allows for extreme values and for a spatial component. The model based estimates have lower mean square error than the estimates using the mean and the model may be used to estimate the status as well as the associated uncertainty or variability. The distribution of the multimetric is used to predict the risk category associated with each subwatershed.

Understanding the relationship between air temperature and water temperature is a critical component in the management of aquatic resources. One important application is for stream fisheries that have temperature-sensitive fish. Co-located devices were used to obtain air and water temperature for summer periods from 100 locations in Virginia that have native brook trout populations. We develop a dynamic spatiotemporal model that accounts for the relationship between air and water temperature, and the spatial and temporal correlation in the data. Our model allows for the inclusion of land use, solar gain and other site level characteristics that might influence the relationship. Our model also allows for predictive forecasts of the risk to fish at individual sites and one can track how the risk changes over time. The model may be used to rank sites with regard to risk, which aids management in prioritizing decisions about restoration and preservation.