Rosana Ferrero

Introduction: Alzheimer’s disease (AD), and more specifically late-onset Alzheimer’s disease (LOAD), represents a considerable challenge in terms of early and timely diagnosis and treatment. Early diagnosis is crucial to improve the efficacy of the therapies and patients’ quality of life. The current challenge is to accurately identify at-risk individuals before the manifestations of the first symptoms of AD. Methods and results: Here, we present an improved model for LOAD risk prediction, which applies the k-nearest neighbors (KNN) algorithm. We have achieved a sensitivity of 0.80 and an area under the curve (AUC) of 0.71, which represents a high performance especially when compared to an AUC of 0.66 reported previously in 2019 using a KNN model. Discussion: The application of a mathematical model that combines genetic and clinical covariates showed a good prediction of the AD/LOAD risk, with the higher weight being the polygenic genetic risk, APOE haplotype, and age. Compared to previous studies, our model integrates and correlates genetic prediction together with phenotypic information by fine-tuning the parameters of the model in order to achieve the best performance. This algorithm can be used in the general population and does not require the manifestation of any symptoms for its effective application. Thus, we present here an advanced model for risk prediction of LOAD.

Southern South America is expected to play an increasingly important role in global food production, but climate change could seriously threaten it. Here we have analysed long‐term historical data for major crops (rice, oats, barley, sunflower, soybean, sorghum, wheat, maize) at subnational scale to (a) look for common features among crop yield dynamics, evaluating their structure and implications for the persistence of that crop; (b) address complex crop responses to changes in environmental growing conditions; and (c) identify climate impact hotspots that are crucial for adaptation and mitigation. We have proposed a novel methodological approach based on dynamics systems in order to understand the processes behind annual crop yield fluctuations. We report the results of general patterns in the internal process (biophysical adjustments by rapid negative feedbacks) regulating crop production and analyse how it influences crop persistence and yield ceilings. The structure of a crop yield dynamic system defines its behaviour, but climate variations could displace it from yield equilibrium and affect its stability. Our findings suggest that weather conditions have a stronger impact on yield growth at high rather than at low yield levels (non‐additive impacts). This allows agriculture management to be refined and applied more efficiently, weakening the relationship between crop productivity and climate change and predicting the response of crop production to yield‐improvement strategies. We have identified those crops and regions which are most vulnerable to the current climate change trends in southern South American agroecosystems. Our results allow us to point to new ways to enhance self‐regulatory success, maximising the efficiency of crop production and reducing climate impacts. We have discussed important implications for crop management and climate change mitigation in an area where agriculture plays a key role in its socioeconomic and ecologic dimensions.

Managing production environments in ways that promote weed community diversity may enhance both crop production and the development of a more sustainable agriculture. This study analyzed data of productivity of maize (corn) and soybean in plots in a Main Cropping System Experiment (MCSE) at the W. K. Kellogg Biological Station Long‐Term Ecological Research (KBS‐LTER) in Michigan, USA, from 1996 to 2011. We used models derived from population ecology to explore how weed diversity, temperature and precipitation interact with crop yields. Using three types of models that considered internal and external (climate and weeds) factors, with additive or non‐linear variants, we found that changes in weed diversity were associated with changes in rates of crop yield increase over time for both maize and soybeans. The intrinsic capacity for soybean yield increase in response to the environment was greater under more diverse weed communities. Soybean production risks were greatest in the least weed diverse systems, in which each weed species lost was associated with progressively greater crop yield losses. Managing for weed community diversity, while suppressing dominant, highly competitive weeds, may be a helpful strategy for supporting long term increases in soybean productivity. In maize, there was a negative and non-additive response of yields to the interaction between weed diversity and minimum air temperatures. When cold temperatures constrained potential maize productivity through limited resources, negative interactions with weed diversity became more pronounced. We suggest that: 1) maize was less competitive in cold years allowing higher weed diversity and the dominance of some weed species; or 2) that cold years resulted in increased weed richness and prevalence of competitive weeds, thus reducing crop yields. Therefore, we propose to control dominant weed species especially in the years of low yield and extreme minimum temperatures to improve maize yields. Results of our study indicate that through the proactive management of weed diversity, it may be possible to promote both high productivity of crops and environmental sustainability.

Many studies have analyzed the impact of climate change on crop productivity, but comparing the performance of water management systems has rarely been explored. Because water supply and crop demand in agro-systems may be affected by global climate change in shaping the spatial patterns of agricultural production, we should evaluate how and where irrigation practices are effective in mitigating climate change effects. Here we have constructed simple, general models, based on biological mechanisms and a theoretical framework, which could be useful in explaining and predicting crop productivity dynamics. We have studied maize in irrigated and rain-fed systems at a provincial scale, from 1996 to 2009 in Spain, one of the most prominent “hot-spots” in future climate change projections. Our new approach allowed us to: (1) evaluate new structural properties such as the stability of crop yield dynamics, (2) detect nonlinear responses to climate change (thresholds and discontinuities), challenging the usual linear way of thinking, and (3) examine spatial patterns of yield losses due to water constraints and identify clusters of provinces that have been negatively affected by warming. We have reduced the uncertainty associated with climate change impacts on maize productivity by improving the understanding of the relative contributions of individual factors and providing a better spatial comprehension of the key processes. We have identified water stress and water management systems as being key causes of the yield gap, and detected vulnerable regions where efforts in research and policy should be prioritized in order to increase maize productivity.

Scinax aromothyella is a recently described hylid frog. Its distribution is not well known. There are records for Misiones, Argentina and for southeastern Uruguay. Here we report a new record of S. aromothyella from Aceguá, Cerro Largo Department, Uruguay (31° 53' 39" S and 54° 9' 17" W), based in tadpoles collected on the 15 of August, 2007. This new record extends the distribution of S. aromothyella in approximately 100 km north from previous reports in Uruguay. The samples were collected in a site located 2 km away from the border line with Brazil, reinforcing the idea that S. aromothyella might occur in Rio Grande do Sul.