Parallel factor analysis enables quantification and identification of highly-convolved data independent-acquired protein spectra

High-throughput data-independent acquisition (DIA) is the method of choice for quantitative proteomics, combining the best practices of targeted and shotgun proteomics approaches. The resultant DIA spectra are, however, highly convolved and with no direct precursor-fragment correspondence, complicating the analysis of biological samples. Here we present PARADIAS (PARAllel factor analysis of Data Independent Acquired Spectra), a GPU-powered unsupervised multiway factor analysis framework that deconvolves multispectral scans to individual analyte spectra, chromatographic profiles, and sample abundances, using the PARAFAC tensor decomposition method based on variation of informative spectral features. The deconvolved spectra can be annotated with traditional database search engines or used as a high-quality input for de novo sequencing methods. We demonstrate that spectral libraries generated with PARADIAS substantially reduce the false discovery rate underlying the validation of spectral quantification. PARADIAS covers up to 33 times more total ion current than library-based approaches, which typically use less than 5 % of total recorded ions, thus allowing the quantification and identification of signals from unexplored DIA spectra.

Agricultural Land-Use Changes and Soil Quality: Evaluating Long-Term Trends in a Rural Mediterranean Region

Land-Use Changes (LUCs) are the result of interacting environmental and socioeconomic factors. Although in southern Europe traditional agroforestry systems are an important component of the Mediterranean landscape, intensification and simplification of the rural space coupled with the increasing sensitivity to soil degradation are potentially harmful for the integrity of natural resources and biodiversity stock. The present study introduced a quantitative assessment of rural LUCs that occurred in a region devoted to agriculture and experiencing a progressively higher human impact from both urbanization and land abandonment. The assessment was carried out at the municipality scale along forty years (1970–2010) using data collected every ten years in the framework of the National Census of Agriculture. The Maximum potential Water Capacity (MWC) in the soil, taken as a proxy for agricultural soil quality, and an index of crop intensity have been introduced in the analysis as supplementary variables. A Multiway Factor Analysis (MFA) was developed to evaluate stability or dynamics in the investigated land-use classes. Results illustrate relevant changes in the rural landscape by identifying the classes “moving” towards better soils. An integrated evaluation of rural LUCs and soil resources based on long-time inventories available at an adequate spatial scale is a tool informing policies against soil degradation.