ALBERT

Description: The occurrence of neurotoxicity caused by xenobiotics such as pesticides (dichlorodiphenyltrichloroethane, organophosphates, pyrethroids, etc.) or metals (mercury, lead, aluminum, arsenic, etc.) is a growing concern around the world, particularly in vulnerable populations with difficulties on both detection and symptoms treatment, due to low economic status, remote access, poor infrastructure, and low educational level, among others features. Despite the numerous molecular markers and questionnaires/clinical evaluations, studying neurotoxicity and its effects on cognition in these populations faces problems with samples collection and processing, and information accuracy. Assessing cognitive changes caused by neurotoxicity, especially those that are subtle in the initial stages, is fundamentally challenging. Finding accurate, non-invasive, and low-cost strategies to detect the first signals of brain injury has the potential to support an accelerated development of the research with these populations. Saliva emerges as an ideal pool of biomarkers (with interleukins and neural damage-related proteins, among others) and potential alternative diagnostic fluid to molecularly investigate neurotoxicity. As a source of numerous neurological biomarkers, saliva has several advantages compared to blood, such as easier storage, requires less manipulation, and the procedure is cheaper, safer and well accepted by patients compared with drawing blood. Regarding cognitive dysfunction, neuropsychological batteries represent, with their friendly interface, a feasible and accurate method to evaluate the eventual cognitive deficits associated with neurotoxicity in people from diverse cultural and educational backgrounds. The association of these two tools, saliva and neuropsychological batteries, to cover the molecular and cognitive aspects of neurotoxicity in vulnerable populations, could potentially increase the prevalence of early intervention and successful treatment.

Description: Viticulture and winemaking are important socioeconomic sectors in many European regions. Climate plays a vital role in the terroir of a given wine region, as it strongly controls canopy microclimate, vine growth, vine physiology, yield, and berry composition, which together determine wine attributes and typicity. New challenges are, however, predicted to arise from climate change, as grapevine cultivation is deeply dependent on weather and climate conditions. Changes in viticultural suitability over the last decades, for viticulture in general or the use of specific varieties, have already been reported for many wine regions. Despite spatially heterogeneous impacts, climate change is anticipated to exacerbate these recent trends on suitability for wine production. These shifts may reshape the geographical distribution of wine regions, while wine typicity may also be threatened in most cases. Changing climates will thereby urge for the implementation of timely, suitable, and cost-effective adaptation strategies, which should also be thoroughly planned and tuned to local conditions for an effective risk reduction. Although the potential of the different adaptation options is not yet fully investigated, deserving further research activities, their adoption will be of utmost relevance to maintain the socioeconomic and environmental sustainability of the highly valued viticulture and winemaking sector in Europe.

Description: We present the result of the third Marine Ice Sheet Model Intercomparison Project, MISMIP+. MISMIP+ is intended to be a benchmark for ice-flow models which include fast sliding marine ice streams and floating ice shelves and in particular a treatment of viscous stress that is sufficient to model buttressing, where upstream ice flow is restrained by a downstream ice shelf. A set of idealized experiments first tests that models are able to maintain a steady state with the grounding line located on a retrograde slope due to buttressing and then explore scenarios where a reduction in that buttressing causes ice stream acceleration, thinning, and grounding line retreat. The majority of participating models passed the first test and then produced similar responses to the loss of buttressing. We find that the most important distinction between models in this particular type of simulation is in the treatment of sliding at the bed, with other distinctions – notably the difference between the simpler and more complete treatments of englacial stress but also the differences between numerical methods – taking a secondary role.

Description: For dynamic computable general equilibrium (CGE) modeling, long-term baseline construction is key and depends on the applied methods and the sources of projections considered. For dynamic CGE models, baseline assumptions and base data are both important determinants of results. This paper reviews the assumptions made by 24 modeling teams on baseline macroeconomic drivers, understood as factor accumulation and gross domestic product (GDP) growth. We critically review the various methods, identifying state-of-the-art practices and propose simple guidelines, particularly focusing on consistency between data sources and models, which is intended to help dynamic CGE modelers build their own baselines.

Description: Budbreak date in grapevine is strictly dependent on temperature, and the correct simulation of its occurrence is of great interest since it may have major consequences on the final yield and quality. In this study, we evaluated the reliability for budbreak simulation of two modeling approaches, the chilling-forcing (CF), which describes the entire dormancy period (endo- and eco-dormancy) and the forcing approach (F), which only describes the eco-dormancy. For this, we selected six phenological models that apply CF and F in different ways, which were tested on budbreak simulation of eight grapevine varieties cultivated at different latitudes in Europe. Although none of the compared models showed a clear supremacy over the others, models based on CF showed a generally higher estimation accuracy than F where fixed starting dates were adopted. In the latter models, the accurate simulation of budbreak was dependent on the selection of the starting date for forcing accumulation that changes according to the latitude, whereas CF models were independent. Indeed, distinct thermal requirements were found for the grapevine varieties cultivated in Northern and Southern Europe. This implies the need to improve modeling of the dormancy period to avoid under- or over-estimations of budbreak date under different environmental conditions. View Full-Text

Description: Reliable estimations of parameter values and associated uncertainties are crucial for crop model applications in agro-environmental research. However, estimating many parameters simultaneously for different types of response variables is difficult. This becomes more complicated for grapevines with different phenotypes between varieties and training systems. Our study aims to evaluate how a standard least square approach can be used to calibrate a complex grapevine model for simulating both the phenology (flowering and harvest date) and yield of four different variety–training systems in the Douro Demarcated Region, northern Portugal. An objective function is defined to search for the best-fit parameters that result in the minimum value of the unweighted sum of the normalized Root Mean Squared Error (nRMSE) of the studied variables. Parameter uncertainties are estimated as how a given parameter value can determine the total prediction variability caused by variations in the other parameter combinations. The results indicate that the best-estimated parameters show a satisfactory predictive performance, with a mean bias of −2 to 4 days for phenology and −232 to 159 kg/ha for yield. The corresponding variance in the observed data was generally well reproduced, except for one occasion. These parameters are a good trade-off to achieve results close to the best possible fit of each response variable. No parameter combinations can achieve minimum errors simultaneously for phenology and yield, where the best fit to one variable can lead to a poor fit to another. The proposed parameter uncertainty analysis is particularly useful to select the best-fit parameter values when several choices with equal performance occur. A global sensitivity analysis is applied where the fruit-setting parameters are identified as key determinants for yield simulations. Overall, the approach (including uncertainty analysis) is relatively simple and straightforward without specific pre-conditions (e.g., model continuity), which can be easily applied for other models and crops. However, a challenge has been identified, which is associated with the appropriate assumption of the model errors, where a combination of various calibration approaches might be essential to have a more robust parameter estimation

Description: A fundamental question of data analysis is how to distinguish noise corrupted deterministic chaotic dynamics from time-(un)correlated stochastic fluctuations when just short length data is available. Despite its importance, direct tests of chaos vs stochasticity in finite time series still lack of a definitive quantification. Here we present a novel approach based on recurrence analysis, a nonlinear approach to deal with data. The main idea is the identification of how recurrence microstates and permutation patterns are affected by time reversibility of data, and how its behavior can be used to distinguish stochastic and deterministic data. We demonstrate the efficiency of the method for a bunch of paradigmatic systems under strong noise influence, as well as for real-world data, covering electronic circuit, sound vocalization and human speeches, neuronal activity, heart beat data, and geomagnetic indexes. Our results support the conclusion that the method distinguishes well deterministic from stochastic fluctuations in simulated and empirical data even under strong noise corruption, finding applications involving various areas of science and technology. In particular, for deterministic signals, the quantification of chaotic behavior may be of fundamental importance because it is believed that chaotic properties of some systems play important functional roles, opening doors to a better understanding and/or control of the physical mechanisms behind the generation of the signals.