Impact of changes of horse movement regulations on the risks of equine infectious anemia: A risk assessment approach.

Equine infectious anemia virus (EIAV) is a transboundary disease affecting a large number of equines worldwide. In this study, we assessed the transmission risk of EIAV in Rio Grande do Sul, Brazil. Serum samples from 1010 animals from 341 farms were initially analyzed using agar gel immunodiffusion to detect viral antibodies, and no antibody-positive animals were found. A risk assessment stochastic model was applied to generate the expected number of potential infections per month and to estimate the time to new infections. Our results estimated 6.5 months as the interval for new infections in the worst-case scenario. Among the variables evaluated, the number of transported animals and the test sensitivity influenced the model the most. These results were then used to revisit the impact of EIAV control regulations, which triggered a change in the diagnostic testing required for animal movement, in which the validity of a negative test for EIAV was extended from 60 to 180 days. Finally, revisiting the annual average of infected farms before and after the new regulation, the number of infected farms increased pre-implementation, and then, the number of culled animals increased, which should impact future EIAV incidence in this region. Our results demonstrated the importance of constant reviews of disease control programs and provided quantitative-based knowledge for decision-makers in official veterinary services.

Solar radiation synthetic series for power purchase agreements.

The present paper proposes a methodology based on the implementation and assessment of autoregressive (AR) solar radiation models for generating synthetic series and providing guidance on bidding strategies for power purchase agreements. The work considered conventional and periodic AR models with different lag orders, assessing the models against real solar radiation measurements. The synthetic series generation process developed 1000 1-year monthly solar radiation scenarios that were later employed for simulating electric energy production and power purchase agreement models. This application allowed one to evaluate the risk associated with the energy supply security, supporting bidding strategies in energy auctions. A real study case is also illustrated in detail, referring to a spot in the Brazilian best irradiance area.

Interspecific variation in conspecific negative density dependence can make species less likely to coexist.

Conspecific negative density dependence (CNDD) is thought to promote plant species diversity. Theoretical studies showing the importance of CNDD often assumed that all species are equally susceptible to CNDD; however, recent empirical studies have shown species can differ greatly in their susceptibility to CNDD. Using a theoretical model, we show that interspecific variation in CNDD can dramatically alter its impact on diversity. First, if the most common species are the least regulated by CNDD, then the stabilising benefit of CNDD is reduced. Second, when seed dispersal is limited, seedlings that are susceptible to CNDD are at a competitive disadvantage. When parameterised with estimates of CNDD from a tropical tree community in Panama, our model suggests that the competitive inequalities caused by interspecific variation in CNDD may undermine many species' ability to persist. Thus, our model suggests that variable CNDD may make communities less stable, rather than more stable.

General Linear Models for Pain Prediction in Knee Osteoarthritis: Data from the Osteoarthritis Initiative / Modelos Lineales Generales para Predecir Dolor en Osteoartritis de Rodilla: Datos de la "Osteoarthritis Initiative"

Abstract: Knee pain is the most common and disabling symptom in Osteoarthritis (OA). Joint pain is a late manifestation of the OA. In earlier stages of the disease changes in joint structures are shown. Also, formation of bony osteophytes, cartilage degradation, and joint space reduction which are some of the most common, among others. The main goal of this study is to associate radiological features with the joint pain symptom. Univariate and multivariate studies were performed using Bioinformatics tools to determine the relationship of future pain with early radiological evidence of the disease. All data was retrieved from the Osteoarthritis Initiative repository (OAI). A case-control study was done using available data from participants in OAI database. Radiological data was assessed with different OAI radiology groups. We have used quantitative and semi-quantitative scores to measure two different relations between radiological data in three different time points. The goal was to track the appearance and prevalence of pain as a symptom. All predictive models were statistically significant (P ≤ 0,05), obtaining the receiving operating characteristic (ROC) curves with their respective area under the curves (AUC) of 0.6516, 0.6174, and 0.6737 for T-0, T-1 and T-2 in quantitative analysis. For semi-quantitative an AU C of 0.6865, 0.6486, and 0.6406 for T-0, T-1 and T-2. The models obtained in the Bioinformatics study suggest that early joint structure changes can be associated with future joint pain. An image-based biomarker that could predict future pain, measured in early OA stages, could become a useful tool to improve the quality of life of people dealing OA.

Resumen: El dolor de rodilla es el síntoma más común y limitante de la Osteoartritis (OA), además de presentarse como una manifestación tardía de la enfermedad. Los cambios que ocurren en las estructuras de las articulaciones se presentan en las primeras etapas de la OA. Algunos de los cambios más comunes son la formación de osteofitos óseos, degradación del cartílago, y la reducción del espacio en la articulación, entre otros. El principal objetivo de este estudio es la asociación de características radiológicas con el síntoma de dolor de las articulaciones, para lo que fueron realizados dos estudios: univariado y multivariado, usando herramientas bioinformáticas para determinar la relación de futuro dolor con la evidencia radiológica temprana de la enfermedad. Todos los datos fueron recuperados de la Osteoarthritis Initiative repository (OAI). Este estudio de caso-control se llevó a cabo utilizando los datos disponibles de los participantes de la base de datos de la OAI. Los datos radiológicos fueron evaluados con diferentes grupos de radiología de la OAI. Fueron usadas puntuaciones cuantitativas y semi- cuantitativas para medir las dos diferentes relaciones entre los datos radiológicos en tres diferentes puntos de tiempo. El objetivo fue seguir la trayectoria de la aparición y prevalencia del dolor como síntoma. Todos los modelos predictivos fueron estadísticamente significativos (P ≤ 0,05). Para el análisis cuantitativo se calcularon las áreas bajo la curva (AUC): 0.6516, 0.6174, y 0.6737 para T-0, T-1 y T-2, y para el análisis semi-cuantitativo se calcularon las AU C: 0.6865, 0.6486, y 0.6406 para T-0, T-1 y T-2. Los modelos obtenidos en el estudio bioinformático sugieren que los cambios tempranos en la estructura de las articulaciones pueden estar asociados con el futuro dolor de rodilla. Un biomarcador basado en imágenes que pueda predecir el futuro dolor, medido en las primeras etapas de OA, podría convertirse en una herramienta útil para mejorar la calidad de vida de la gente que padece OA.

Self-Consistent Scheme for Spike-Train Power Spectra in Heterogeneous Sparse Networks.

Recurrent networks of spiking neurons can be in an asynchronous state characterized by low or absent cross-correlations and spike statistics which resemble those of cortical neurons. Although spatial correlations are negligible in this state, neurons can show pronounced temporal correlations in their spike trains that can be quantified by the autocorrelation function or the spike-train power spectrum. Depending on cellular and network parameters, correlations display diverse patterns (ranging from simple refractory-period effects and stochastic oscillations to slow fluctuations) and it is generally not well-understood how these dependencies come about. Previous work has explored how the single-cell correlations in a homogeneous network (excitatory and inhibitory integrate-and-fire neurons with nearly balanced mean recurrent input) can be determined numerically from an iterative single-neuron simulation. Such a scheme is based on the fact that every neuron is driven by the network noise (i.e., the input currents from all its presynaptic partners) but also contributes to the network noise, leading to a self-consistency condition for the input and output spectra. Here we first extend this scheme to homogeneous networks with strong recurrent inhibition and a synaptic filter, in which instabilities of the previous scheme are avoided by an averaging procedure. We then extend the scheme to heterogeneous networks in which (i) different neural subpopulations (e.g., excitatory and inhibitory neurons) have different cellular or connectivity parameters; (ii) the number and strength of the input connections are random (Erdos-Rényi topology) and thus different among neurons. In all heterogeneous cases, neurons are lumped in different classes each of which is represented by a single neuron in the iterative scheme; in addition, we make a Gaussian approximation of the input current to the neuron. These approximations seem to be justified over a broad range of parameters as indicated by comparison with simulation results of large recurrent networks. Our method can help to elucidate how network heterogeneity shapes the asynchronous state in recurrent neural networks.

Economic and financial viability of small-scale dairy systems in central Mexico: economic scenario 2010-2018.

A simulation Monte Carlo model was used to assess the economic and financial viability of 130 small-scale dairy farms in central Mexico, through a Representative Small-Scale Dairy Farm. Net yields were calculated for a 9-year planning horizon by means of simulated values for the distribution of input and product prices taking 2010 as base year and considering four scenarios which were compared against the scenario of actual production. The other scenarios were (1) total hiring in of needed labour; (2) external purchase of 100 % of inputs and (3) withdrawal of subsidies to production. A stochastic modelling approach was followed to determine the scenario with the highest economic and financial viability. Results show a viable economic and financial situation for the real production scenario, as well as the scenarios for total hiring of labour and of withdrawal of subsidies, but the scenario when 100 % of feed inputs for the herd are bought-in was not viable.