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We tested if the movement slowness of individuals with Parkinson's disease is related to their decreased ability to generate adequate net torques and linearly coordinate them between joints. This cross-sectional study included ten individuals with Parkinson's disease and ten healthy individuals. They performed planar movements with a reversal over three target distances. We calculated joint kinematics of the elbow and shoulder using spatial orientation. The muscle, interaction, and net torques were integrated into the acceleration/deceleration phases of the fingertip speed. We calculated the linear correlations of those torques between joints. Both groups modulated the elbow and shoulder net torques with target distances. They linearly coupled the production of torques. Both groups did not modulate the interaction torques. The movement slowness in Parkinson's disease was related to the difficulty in generating the appropriate muscle and net torques in the task. The interaction torques do not seem to play any role in movement control.

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Computing the agreement between 2 continuous sequences is of great interest in statistics when comparing 2 instruments or one instrument with a gold standard. The probability of agreement quantifies the similarity between 2 variables of interest, and it is useful for determining what constitutes a practically important difference. In this article, we introduce a generalization of the PA for the treatment of spatial variables. Our proposal makes the PA dependent on the spatial lag. We establish the conditions for which the PA decays as a function of the distance lag for isotropic stationary and nonstationary spatial processes. Estimation is addressed through a first-order approximation that guarantees the asymptotic normality of the sample version of the PA. The sensitivity of the PA with respect to the covariance parameters is studied for finite sample size. The new method is described and illustrated with real data involving autumnal changes in the green chromatic coordinate (Gcc), an index of "greenness" that captures the phenological stage of tree leaves, is associated with carbon flux from ecosystems, and is estimated from repeated images of forest canopies.

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Measurements of ecosystem carbon (C) fluxes in temperate forests are concentrated in the Northern Hemisphere, leaving the functionally diverse temperate forests in the Southern Hemisphere underrepresented. Here, we report three years (February 2018-January 2021) of C fluxes, studied with eddy-covariance and closed chamber techniques, in an endangered temperate evergreen rainforest of the long-lived paleoendemic South American conifer Fitzroya cupressoides. Using classification and regression trees we analyzed the most relevant drivers and thresholds of daily net ecosystem exchange (NEE) and soil respiration. The annual NEE showed that the forest was a moderate C sink during the period analyzed (-287±38 g C m-2 year -1). We found that the capacity to capture C of the Fitzroya rainforests in the Coastal Range of southern Chile is optimal under cool and rainy conditions in the early austral spring (October-November) and decreases rapidly towards the summer dry season (January-February) and autumn. Although the studied forest type has a narrow geographical coverage, the gross primary productivity measured at the tower was highly representative of Fitzroya and other rainforests in the region. Our results suggest that C fluxes in paleoendemic cool F. cupressoides forests may be negatively affected by the warming and drying predicted by climate change models, reinforcing the importance of maintaining this and other long-term ecological research sites in the Southern Hemisphere.

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Rectal temperature (RT), heart rate (HR), and respiratory rate (RR), determined as repeated measurements over time in female goats, were used to identify covariance matrices that best fit the data for residual modeling on these three traits. Then, based on this result, the goats' responses to heat were evaluated. Five matrices were found with convergence for the three traits. The Heterogeneous Compound Symmetry matrix showed a good fit for modeling the residual associated with RT, whereas the Heterogeneous Autoregressive matrix had a better fit for RR and HR, according to the Akaike Information Criteria (AIC), corrected AIC (AICc), and Schwarz Bayesian Information Criterion (BIC) used. After adjusting the residual data for these three traits, a mixed-model analysis was used to evaluate collection period (3), physiological stage (3), and animal age (3) as fixed effects. Residual modeling interfered differently with the p-value associated with the fixed effects studied. Collection period and interactions did not influence the variation in RT (P>0.761), which was within the standard range for goats in the tropics, while the physiological stage of the goats affected it (P<0.05). Rectal temperature, HR, and RR tend to show covariance structures that can be modeled using specific residual covariance matrices, that is, the heterogeneous compound symmetry matrix best suits RT data, whereas the heterogeneous autoregressive matrix is better suited for HR and RR, which are usually correlated. The goats of the evaluated breed maintain RT within the range of variation displayed by breeds adapted to a hot environment, regardless of their physiological condition. Variations occur in RR and HR, without, however, exceeding the normal range for goats. Pregnancy causes goats to raise their RR in the rainy season of the year in the region in order to maintain RT within the normal range for the species.(AU)

Utilizou-se a Temperatura retal (TR), Frequências cardíaca (FC) e respiratória (FR) aferidas como medidas repetidas no tempo em fêmeas caprinas, objetivando-se identificar matrizes de estruturas de covariância que melhor se ajustou aos dados para modelagem do resíduo nessas três características e, em seguida, avaliou-se a respostas de cabras ao calor, com base nesse resultado. Constatou-se cinco matrizes com convergência nas três características. A Simétrica composta heterogênea ajustou-se bem para modelagem do resíduo associado a TR, enquanto a Autorregressiva heterogênea ajustou-se melhor para a FR e FC, de acordo com os critérios de informação de Akaike (AIC), Akaike corrigido (AICc) e o Bayesiano de Schwarz (BIC) utilizados. Com o resíduos de dados dessas três características ajustados, utilizou-se uma análise com modelos mistos para avaliar a Época de coleta (3), Estado fisiológico (3) e Idade do animal (3) foram como efeitos fixos. Constatou-se que a modelagem do resíduo interferiu de modo diferenciado no p valor associado aos efeitos fixos estudados. A época da coleta e interações não influenciaram a variação da TR (P>0,761), que oscilou dentro da faixa padrão para caprinos nos trópicos, mas o Estágio fisiológico da cabra sim (P<0,05). A Temperatura retal e as Frequências cardíaca e respiratória tendem a apresentar estruturas de covariâncias modeláveis com utilização de matrizes de covariâncias residuais especificas, ou seja, a matriz Simétrica composta heterogênea mais adequada para dados da Temperatura retal, enquanto a Autorregressiva heterogênea para as Frequências cardíaca e respiratória, geralmente correlacionas. As cabras da raça avaliadas mantêm a temperatura retal dentro da amplitude de variação apresentada por raças adaptadas a ambiente quente. Isso ocorre independente da condição fisiológica que se encontra, mas com ocorrência de variação na frequência respiratória e cardíaca, não excedendo, no entanto, a faixa normal para caprinos. A gestação condiciona a cabra a elevar a FR na época chuvosa do ano na região para manter a TR na faixa de amplitude normal para caprinos.(AU)

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The use of longitudinal measurements is an essential practice both in Psidium guajava L. breeding and in other perennial crops in which covariance structures can be introduced to explain the form of dependence between measurements. Hence, this study aimed to analyze six covariance structures to identify one that best described the correlation between the repeated measurements in time in traits of guava full-sib families. The repeatability coefficient for each trait was estimated and the minimum number of evaluations required for estimates representing the population was determined. The work was performed based on average data of three yield-related variables from nine harvests of a guava tree population evaluated from 2011 to 2018. The best model was chosen based on the Akaike and Schwarz Bayesian information criterion. The autoregressive covariance structure best represented the dependencies among families between crops for all traits. The number of variables of fruits and total yield per plant presented repeatability estimates higher than 0.5 and may be essential traits for indirect selection of others, such as fruit mass, which had an estimated repeatability of 0.24, proving low regularity in the repetition of the character from one cycle to another. It was also possible to define four harvests as the minimum acceptable number of observations necessary on the same individual for these traits; therefore, the repetitions represented the individuals.(AU)

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Background: Agriculture is essential for food security. However, conventional agriculture alters the water and carbon cycle and soil properties. We investigated the effect of conventional management (CM) and sustainable management (SM) on the carbon and water cycle in crops of nopal (Np) and wheat (Wh). Methods: A micrometeorological eddy covariance tower was installed to measure water use through evapotranspiration (ET) and the net exchange of CO2 during the crop's development. Gross primary productivity (GPP), water use efficiency (WUE), and soil properties were obtained. Results: The results showed that both agricultural managements influenced the carbon flux of the ecosystem, with a lower GPP and Reco in the nopal field (1.85 and 0.99 mmol C m-2 s-1, respectively), compared to the wheat field (6.34 and 1.8 mmol C m-2 s-1, respectively). It was mainly attributed to the metabolic plant differences, phenological stages, and wheat biomass developed during the winter. On the other hand, the accumulated ET in the SM-Wh plots was lower than SM-Np. Therefore, the crops subjected to sustainable practices use water more efficiently with 1.42 and 1.03 g C m-3 H2O for nopal and wheat, respectively. In regard to soil properties, it was observed that tillage alters microbial activity affecting organic matter and carbon. It can be concluded that the differences in agricultural management for both crops altered the carbon and water cycle and soil quality. In addition, implementing good agricultural practices allows more efficient use of water by the plant, higher retention of water in the soil, and less ET.

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Arid and semiarid environments are characterized by low water availability (e.g., in soil and atmosphere), high air temperature, and irregularity in the spatio-temporal distribution of rainfall. In addition to the economic and environmental consequences, drought also causes physiological damage to crops and compromises their survival in ecosystems. The removal of vegetation is responsible for altering the energy exchange of heat and water in natural ecosystems and agricultural areas. The fluxes of CO2 are also changed, and environments with characteristics of sinks, which can be sources of CO2 after anthropic disturbances. These changes can be measured through methods such as sap flow, eddy covariance, remote sensing, and energy balance. Despite the relevance of each method mentioned above, there are limitations in their applications that must be respected. Thus, this review aims to quantify the processes and changes of energy fluxes, CO2, and their interactions with the surfaces of terrestrial ecosystems in dry environments. Studies report that the use of methods that integrate data from climate monitoring towers and remote sensing products helps to improve the accuracy of the determination of energy fluxes on a global scale, also helping to reduce the dissimilarity of results obtained individually. Through the collection of works in the literature, it is reported that several areas of the Brazilian Caatinga biome, which is a Seasonally Dry Tropical Forest have been suffering from changes in land use and land cover. Similar fluxes of sensible heat in areas with cacti and Caatinga can be observed in studies. On the other hand, one of the variables influenced mainly by air temperature is net radiation. In dry forest areas, woody species can store large amounts of carbon in their biomass above and belowground. The use of cacti can modify the local carbon budget when using tree crops together. Therefore, the study highlights the complexity and severity of land degradation and changes in CO2, water, and energy fluxes in dry environments with areas of forest, grassland, and cacti. Vegetation energy balance is also a critical factor, as these simulations are helpful for use in forecasting weather or climate change. We also highlight the need for more studies that address environmental conservation techniques and cactus in the conservation of degraded areas.

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An increasing interest in models for multivariate spatio-temporal processes has been noted in the last years. Some of these models are very flexible and can capture both marginal and cross spatial associations amongst the components of the multivariate process. In order to contribute to the statistical analysis of these models, this paper deals with the estimation and prediction of multivariate spatio-temporal processes by using multivariate state-space models. In this context, a multivariate spatio-temporal process is represented through the well-known Wold decomposition. Such an approach allows for an easy implementation of the Kalman filter to estimate linear temporal processes exhibiting both short and long range dependencies, together with a spatial correlation structure. We illustrate, through simulation experiments, that our method offers a good balance between statistical efficiency and computational complexity. Finally, we apply the method for the analysis of a bivariate dataset on average daily temperatures and maximum daily solar radiations from 21 meteorological stations located in a portion of south-central Chile. Supplementary Information: The online version contains supplementary material available at 10.1007/s00477-022-02266-3.

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The spatio-temporal assessment of water and carbon fluxes in Brazil's Northeast region (NEB) allows for a better understanding of these surface flux patterns in areas with different vegetation physiognomies. The NEB is divided into four biomes: Amazon, Cerrado, Caatinga, and Atlantic Forest. Land degradation is a growing problem, particularly in susceptible areas of the Caatinga biome, such as the seasonally dry tropical forest. Furthermore, this region has experienced climatic impacts, such as severe droughts. Due to increasing human pressure, the Caatinga's natural land cover undergoes drastic changes, making it a region particularly vulnerable to desertification. In this study, the Moderate Resolution Imaging Spectroradiometer (MODIS) estimates of evapotranspiration (ET) and gross primary production (GPP) were validated in two contrasting areas, dense Caatinga and sparse Caatinga, using eddy covariance (EC) data and then investigated their behavior over 21 years (2000-2021) for the NEB. MODIS products explained around 60% of the variations in ET and GPP, showing higher accuracy in dense Caatinga, while areas of sparse Caatinga presented the lowest GPP, indicating that land degradation has reduced the photosynthetic activity of the vegetation in this area. Based on the analysis of ET and GPP over 21 years, we observed a greater dependence of the sparse Caatinga on climate variability, demonstrating a stronger resilience of dense Caatinga to climate effects. In comparison with the other biomes of the NEB region, we found lower rates of ET and GPP in the Caatinga biome, with averages similar to the Sparse Caatinga. In comparison with the other biomes in the NEB region, we found the lowest averages of ET and GPP in the Caatinga biome, similar to values found in the sparse Caatinga. In forest areas, similar to the monitored DC, they allowed the Caatinga to behave closer to the other biomes present in the region.

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This paper describes a new model for portfolio optimization (PO), using entropy and mutual information instead of variance and covariance as measurements of risk. We also compare the performance in and out of sample of the original Markowitz model against the proposed model and against other state of the art shrinkage methods. It was found that ME (mean-entropy) models do not always outperform their MV (mean-variance) and robust counterparts, although presenting an edge in terms of portfolio diversity measures, especially for portfolio weight entropy. It further shows that when increasing return constraints on portfolio optimization, ME models were more stable overall, showing dampened responses in cumulative returns and Sharpe indexes in comparison to MV and robust methods, but concentrated their portfolios more rapidly as they were more evenly spread initially. Finally, the results suggest that it was also shown that, depending on the market, increasing return constraints may have positive or negative impacts on the out-of-sample performance.

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We investigated the use of different Legendre polynomial orders to estimate genetic parameters for milk production and fatty acid (FA) traits in the first lactation Walloon Holstein cows. The data set comprised 302,684 test-day records of milk yield, fat and protein contents, and FAs generated by mid-infrared (MIR) spectroscopy, C16:0 (palmitic acid), C18:1 cis-9 (oleic acid), LCFAs (long-chain FAs), SFAs (saturated FAs) and UFAs (unsaturated FAs) were studied. The models included random regression coefficients for herd-year of calving (h), additive genetic (a) and permanent environment (p) effects. The selection of the best random regression model (RRM) was based on the deviance information criterion (DIC), and genetic parameters were estimated via a Bayesian approach. For all analysed random effects, DIC values decreased as the order of the Legendre polynomials increased. Best-fit models had fifth-order (degree 4) for the p effect and ranged from second- to fifth-order (degree 1-4) for the a and h effects (LEGhap: LEG555 for milk yield and protein content; LEG335 for fat content and SFA; LEG545 for C16:0 and UFA; and LEG535 for C18:1 cis-9 and LCFA). Based on the best-fit models, an effect of overcorrection was observed in early lactation (5-35 days in milk [DIM]). On the contrary, third-order (LEG333; degree 2) models showed flat residual trajectories throughout lactation. In general, the estimates of genetic variance tended to increase over DIM, for all traits. Heritabilities for milk production traits ranged from 0.11 to 0.58. Milk FA heritabilities ranged from low-to-high magnitude (0.03-0.56). High Spearman correlations (>0.90 for all bulls and >0.97 for top 100) were found among breeding values for 155 and 305 DIM between the best RRM and LEG333 model. Therefore, third-order Legendre polynomials seem to be most parsimonious and sufficient to describe milk production and FA traits in Walloon Holstein cows.

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BACKGROUND AND OBJECTIVE: Parkinson's disease (PD) is a motor neurodegenerative disease principally manifested by motor disabilities, such as postural instability, bradykinesia, tremor, and stiffness. In clinical practice, there exist several diagnostic rating scales that coarsely allow the measurement, characterization and classification of disease progression. These scales, however, are only based on strong changes in kinematic patterns, and the classification remains subjective, depending on the expertise of physicians. In addition, even for experts, disease analysis based on independent classical motor patterns lacks sufficient sensitivity to establish disease progression. Consequently, the disease diagnosis, stage, and progression could be affected by misinterpretations that lead to incorrect or inefficient treatment plans. This work introduces a multimodal non-invasive strategy based on video descriptors that integrate patterns from gait and eye fixation modalities to assist PD quantification and to support the diagnosis and follow-up of the patient. The multimodal representation is achieved from a compact covariance descriptor that characterizes postural and time changes of both information sources to improve disease classification. METHODS: A multimodal approach is introduced as a computational method to capture movement abnormalities associated with PD. Two modalities (gait and eye fixation) are recorded in markerless video sequences. Then, each modality sequence is represented, at each frame, by primitive features composed of (1) kinematic measures extracted from a dense optical flow, and (2) deep features extracted from a convolutional network. The spatial distributions of these characteristics are compactly coded in covariance matrices, making it possible to map each particular dynamic in a Riemannian manifold. The temporal mean covariance is then computed and submitted to a supervised Random Forest algorithm to obtain a disease prediction for a particular patient. The fusion of the covariance descriptors and eye movements integrating deep and kinematic features is evaluated to assess their contribution to disease quantification and prediction. In particular, in this study, the gait quantification is associated with typical patterns observed by the specialist, while ocular fixation, associated with early disease characterization, complements the analysis. RESULTS: In a study conducted with 13 control subjects and 13 PD patients, the fusion of gait and ocular fixation, integrating deep and kinematic features, achieved an average accuracy of 100% for early and late fusion. The classification probabilities show high confidence in the prediction diagnosis, the control subjects probabilities being lower than 0.27 with early fusion and 0.3 with late fusion, and those of the PD patients, being higher than 0.62 with early fusion and 0.51 with late fusion. Furthermore, it is observed that higher probability outputs are correlated with more advanced stages of the disease, according to the H&Y scale. CONCLUSIONS: A novel approach for fusing motion modalities captured in markerless video sequences was introduced. This multimodal integration had a remarkable discrimination performance in a study conducted with PD and control patients. The representation of compact covariance descriptors from kinematic and deep features suggests that the proposed strategy is a potential tool to support diagnosis and subsequent monitoring of the disease. During fusion it was observed that devoting major attention to eye fixational patterns may contribute to a better quantification of the disease, especially at stage 2.

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The Brazilian semiarid region presents a physical water scarcity and high seasonal and interannual irregularities of precipitation, known as a region with periodic droughts. This region is mainly covered by the Caatinga biome, recognized as a Seasonally Dry Tropical Forest (SDTF). Soil water availability directly impacts the ecosystem's functioning, characterized by low fertility and sparse vegetation cover during the dry season, making it a fragile ecosystem vulnerable to climatic variations. Additionally, this region has been suffering from several issues due to human activities over the centuries, which has resulted in extensive areas being severely degraded, which aggravates the impacts from climatic variations and the susceptibility to desertification. Thus, studying the soil-plant-atmosphere continuum in this region can help better understand the seasonal and annual behavior of the water and carbon fluxes. This study investigated the dynamics of water and carbon fluxes during four years (2013-2016) by using eddy covariance (EC) measurements within two areas of Caatinga (dense Caatinga (DC) and sparse Caatinga (SC)) that suffered anthropic pressures. The two study areas showed similar behavior in relation to physical parameters (air temperature, incoming radiation, vapor pressure deficit, and relative humidity), except for soil temperature. The SC area presented a surface temperature of 3 °C higher than the DC, related to their vegetation cover differences. The SC area had higher annual evapotranspiration, representing 74% of the precipitation for the DC area and 90% for the SC area. The two areas acted as a carbon sink during the study period, with the SC area showing a lower CO2 absorption capacity. On average, the DC area absorbs 2.5 times more carbon than the SC area, indicating that Caatinga deforestation affects evaporative fluxes, reducing atmospheric carbon fixation and influencing the ability to mitigate the effects of increased greenhouse gas concentrations in the atmosphere.

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This study developed a method to approximate the covariance matrix associated with the simulation of water molecular diffusion inside the brain tissue. The computation implements the Discontinuous Galerkin method of the diffusion equation. A physically consistent numerical flux is applied to model the interaction between the axon walls and extracellular regions. This numerical flux yields an efficient GPU-CUDA implementation. We consider the two-dimensional case of high axon pack density, valid, for instance, in the brain's corpus callosum region.

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The problem of finding covariance matrices that remain constant in time for arbitrary multi-dimensional quadratic Hamiltonians (including those with time-dependent coefficients) is considered. General solutions are obtained.

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Risk for autism can be influenced by genetic mutations in hundreds of genes. Based on findings showing that genes with highly correlated gene expressions are functionally interrelated, "guilt by association" methods such as DAWN have been developed to identify these autism risk genes. Previous research analyze the BrainSpan dataset, which contains gene expression of brain tissues from varying regions and developmental periods. Since the spatiotemporal properties of brain tissue is known to affect the gene expression's covariance, previous research have focused only on a specific subset of samples to avoid the issue of heterogeneity. This analysis leads to a potential loss of power when detecting risk genes. In this article, we develop a new method called COBS (COvariance-Based sample Selection) to find a larger and more homogeneous subset of samples that share the same population covariance matrix for the downstream DAWN analysis. To demonstrate COBS's effectiveness, we use genetic risk scores from two sequential data freezes obtained in 2014 and 2020. We show COBS improves DAWN's ability to predict risk genes detected in the newer data freeze when using the risk scores of the older data freeze as input.

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Abstract Background: Meat goat breeding programs should prioritize the identification and selection of genetically superior animals for traits related to meat quality and carcass yield in order to increase the value of the final product. Objective: To estimate (co)variance components and genetic parameters for ultrasound-measured carcass traits, body size and body weight in Anglo- Nubian breed goats raised in the Mid-North region of Brazil. Methods: (Co)variance components and genetic parameters were estimated using the single and two-trait animal model analyses via Bayesian inference for loin eye dimensions (area, length, and depth), sternal fat thickness, rump height, chest circumference and depth, leg perimeter, and body weight. Results: Heritability estimates were higher when two-trait analyses were used. This finding implies that it is possible to recover part of the additive genetic variance included in the residual variance due to the correlation between traits. Genetic correlations between carcass and body size traits showed different magnitudes. On the other hand, genetic correlations between the traits related to muscularity showed high magnitudes. Conclusions: Body weight was not a good indicator of muscularity; therefore, it is not recommended as a criterion for indirect selection to improve carcass traits of Anglo-Nubian goats. Leg perimeter and chest circumference may be important to construct selection indexes in meat goat breeding programs.

Resumen Antecedentes: los programas de mejoramiento de caprinos de carne deben priorizar la identificación y selección de animales genéticamente superiores para características relacionadas con la calidad de la carne y rendimiento de la canal, con el fin de agregar valor al producto final. Objetivo: estimar los componentes de (co)varianza y parámetros genéticos para características de canal obtenidas por ultrasonografía, características de tamaño y peso corporal en caprinos de la raza Anglonubiana, criados en la región medio-norte de Brasil. Métodos: los componentes de (co)varianza y parámetros genéticos fueron estimados mediante un modelo animal usando análisis uni y bi-carácter vía metodología Bayesiana para las dimensiones del ojo de lomo (área, profundidad y longitud), grosor de la grasa esternal, altura de la grupa, circunferencia y profundidad torácica, perímetro de la pierna y peso corporal. Resultados: las estimativas de heredabilidad obtenidas a partir del análisis bi-carácteristico fueron mayores que las obtenidas a partir del análisis uni-carácteristico. Este supuesto implica que es posible recuperar parte de la variancia genética aditiva incluida en la variancia residual, debido a la correlación entre las características. Las correlaciones genéticas entre las características de canal y las medidas corporales presentaron diferentes magnitudes. Por otro lado, las correlaciones genéticas entre las características relacionadas con musculatura presentaron alta magnitud. Conclusiones: el peso corporal no fue un buen indicador de musculatura; por eso no es recomendado como criterio de selección indirecta para mejorar la canal de caprinos Anglonubianos. El perímetro de la pierna y la circunferencia del pecho pueden ser importantes para la construcción de índices de selección en programas de mejoramiento de carne caprina.

Resumo Antecedentes: programas de melhoramento de caprinos de corte devem priorizar a identificação e seleção de animais geneticamente superiores para características relacionadas à qualidade da carne e rendimento de carcaça, para aumentar o valor ao produto final. Objetivo: estimar componentes de (co)variância e parâmetros genéticos para características de carcaça obtidas por ultrassonografia, características de tamanho e peso corporal em caprinos da raça Anglo-Nubiana criados na região Meio-Norte do Brasil. Métodos: os componentes de (co)variância e parâmetros genéticos foram estimados usando análises uni e bicaracterísticas de um modelo animal via metodologia Bayesiana para área, profundidade e comprimento de olho de lombo, espessura da gordura esternal, altura da garupa, circunferência e profundidade torácica, perímetro da perna e peso corporal. Resultados: as estimativas de herdabilidade obtidas a partir das análises bicaracterísticas foram maiores que as obtidas a partir das análises unicaracterísticas. Esse resultado implica que é possível recuperar parte da variância genética aditiva incluída na variância residual devido à correlação entre as características. As correlações genéticas entre as características de carcaça e as medidas corporais apresentaram magnitudes variáveis. Por outro lado, as correlações genéticas entre as características relacionadas à musculosidade apresentaram altas magnitudes. Conclusões: o peso corporal não se mostrou um bom indicador de muscularidade, de modo que não é recomendado como critério de seleção indireta para melhorar a carcaça de caprinos Anglo-Nubiano. O perímetro de perna e a circunferência torácica podem ser importantes para a construção de índices de seleção em programas de melhoramento de carne caprina.

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Tropical forests are an important part of global water and energy cycles, but the mechanisms that drive seasonality of their land-atmosphere exchanges have proven challenging to capture in models. Here, we (1) report the seasonality of fluxes of latent heat (LE), sensible heat (H), and outgoing short and longwave radiation at four diverse tropical forest sites across Amazonia-along the equator from the Caxiuanã and Tapajós National Forests in the eastern Amazon to a forest near Manaus, and from the equatorial zone to the southern forest in Reserva Jaru; (2) investigate how vegetation and climate influence these fluxes; and (3) evaluate land surface model performance by comparing simulations to observations. We found that previously identified failure of models to capture observed dry-season increases in evapotranspiration (ET) was associated with model overestimations of (1) magnitude and seasonality of Bowen ratios (relative to aseasonal observations in which sensible was only 20%-30% of the latent heat flux) indicating model exaggerated water limitation, (2) canopy emissivity and reflectance (albedo was only 10%-15% of incoming solar radiation, compared to 0.15%-0.22% simulated), and (3) vegetation temperatures (due to underestimation of dry-season ET and associated cooling). These partially compensating model-observation discrepancies (e.g., higher temperatures expected from excess Bowen ratios were partially ameliorated by brighter leaves and more interception/evaporation) significantly biased seasonal model estimates of net radiation (Rn ), the key driver of water and energy fluxes (LE ~ 0.6 Rn and H ~ 0.15 Rn ), though these biases varied among sites and models. A better representation of energy-related parameters associated with dynamic phenology (e.g., leaf optical properties, canopy interception, and skin temperature) could improve simulations and benchmarking of current vegetation-atmosphere exchange and reduce uncertainty of regional and global biogeochemical models.

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In the differential approach elaborated, we study the evolution of the parameters of Gaussian, mixed, continuous variable density matrices, whose dynamics are given by Hermitian Hamiltonians expressed as quadratic forms of the position and momentum operators or quadrature components. Specifically, we obtain in generic form the differential equations for the covariance matrix, the mean values, and the density matrix parameters of a multipartite Gaussian state, unitarily evolving according to a Hamiltonian H ^ . We also present the corresponding differential equations, which describe the nonunitary evolution of the subsystems. The resulting nonlinear equations are used to solve the dynamics of the system instead of the Schrödinger equation. The formalism elaborated allows us to define new specific invariant and quasi-invariant states, as well as states with invariant covariance matrices, i.e., states were only the mean values evolve according to the classical Hamilton equations. By using density matrices in the position and in the tomographic-probability representations, we study examples of these properties. As examples, we present novel invariant states for the two-mode frequency converter and quasi-invariant states for the bipartite parametric amplifier.

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Based on the application of the conditional mean rule, a sampling-recovery algorithm is studied for a Gaussian two-dimensional process. The components of such a process are the input and output processes of an arbitrary linear system, which are characterized by their statistical relationships. Realizations are sampled in both processes, and the number and location of samples in the general case are arbitrary for each component. As a result, general expressions are found that determine the optimal structure of the recovery devices, as well as evaluate the quality of recovery of each component of the two-dimensional process. The main feature of the obtained algorithm is that the realizations of both components or one of them is recovered based on two sets of samples related to the input and output processes. This means that the recovery involves not only its own samples of the restored realization, but also the samples of the realization of another component, statistically related to the first one. This type of general algorithm is characterized by a significantly improved recovery quality, as evidenced by the results of six non-trivial examples with different versions of the algorithms. The research method used and the proposed general algorithm for the reconstruction of multidimensional Gaussian processes have not been discussed in the literature.