Using linear algebra for protein structural comparison and classification.

In this article, we describe a novel methodology to extract semantic characteristics from protein structures using linear algebra in order to compose structural signature vectors which may be used efficiently to compare and classify protein structures into fold families. These signatures are built from the pattern of hydrophobic intrachain interactions using Singular Value Decomposition (SVD) and Latent Semantic Indexing (LSI) techniques. Considering proteins as documents and contacts as terms, we have built a retrieval system which is able to find conserved contacts in samples of myoglobin fold family and to retrieve these proteins among proteins of varied folds with precision of up to 80%. The classifier is a web tool available at our laboratory website. Users can search for similar chains from a specific PDB, view and compare their contact maps and browse their structures using a JMol plug-in.

Probabilistic methods for seasonal forecasting in a changing climate: Cox-type regression models.

For climate risk management, cumulative distribution functions (CDFs) are an important source of information. They are ideally suited to compare probabilistic forecasts of primary (e.g. rainfall) or secondary data (e.g. crop yields). Summarised as CDFs, such forecasts allow an easy quantitative assessment of possible, alternative actions. Although the degree of uncertainty associated with CDF estimation could influence decisions, such information is rarely provided. Hence, we propose Cox-type regression models (CRMs) as a statistical framework for making inferences on CDFs in climate science. CRMs were designed for modelling probability distributions rather than just mean or median values. This makes the approach appealing for risk assessments where probabilities of extremes are often more informative than central tendency measures. CRMs are semi-parametric approaches originally designed for modelling risks arising from time-to-event data. Here we extend this original concept beyond time-dependent measures to other variables of interest. We also provide tools for estimating CDFs and surrounding uncertainty envelopes from empirical data. These statistical techniques intrinsically account for non-stationarities in time series that might be the result of climate change. This feature makes CRMs attractive candidates to investigate the feasibility of developing rigorous global circulation model (GCM)-CRM interfaces for provision of user-relevant forecasts. To demonstrate the applicability of CRMs, we present two examples for El Ni ? no/Southern Oscillation (ENSO)-based forecasts: the onset date of the wet season (Cairns, Australia) and total wet season rainfall (Quixeramobim, Brazil). This study emphasises the methodological aspects of CRMs rather than discussing merits or limitations of the ENSO-based predictors.

Energy balance model applied to pasture experimental areas in São Paulo State, Brazil.

The Simple Algorithm for Evapotranspiration Retrieving (SAFER) was used to estimate biophysical parameters and theenergy balance components in two different pasture experimental areas, in the São Paulo state, Brazil. The experimentalpastures consist in six rotational (RGS) and three continuous grazing systems (CGS) paddocks. Landsat-8 images from2013 and 2015 dry and rainy seasons were used, as these presented similar hydrological cycle, with 1,600 mm and 1,613mm of annual precipitation, resulting in 19 cloud-free images. Bands 1 to 7 and thermal bands 10 and 11 were used withweather data from a station located nearthe experimental area. NDVI, biomass, evapotranspiration and latent heat flux(λE) temporal values statistically differ CGS from RGS areas. Grazing systems influences the energy partition and theseresults indicate that RGS benefits biomass production, evapotranspiration and the microclimate, due higher LE values.SAFER is a feasible tool to estimate biophysical parameters and energy balance components in pasture and has potentialto discriminate continuous and rotation grazing systems in a temporal analysis.