Multivariate geostatistics for assessing and predicting soil compaction

The aim of this research is to investigate the potential of geostatistical techniques for understanding and evaluating the spatial variability of soil compaction, caused by the traffic of agricultural machines and/or the action of tillage implements. Soil cone penetrometer resistance was measured in a field of inland Sicily, along a transect of 3 m length, from the soil surface until 70 cm depth. The 3D mean maps showed a random variation on the surface and a high spatial correlation among penetrometer resistance data measured at different depths. The map corresponding to five tractor passes showed the largest extension of the areas characterised by the highest values of penetrometer resistance. The probability maps showed that at least 20% of the monitored soil volume can exceed the critical penetrometer resistance for root growth

A gene expression atlas for different kinds of stress in the mouse brain

Stressful experiences are part of everyday life and animals have evolved physiological and behavioral responses aimed at coping with stress and maintaining homeostasis. However, repeated or intense stress can induce maladaptive reactions leading to behavioral disorders. Adaptations in the brain, mediated by changes in gene expression, have a crucial role in the stress response. Recent years have seen a tremendous increase in studies on the transcriptional effects of stress. The input raw data are freely available from public repositories and represent a wealth of information for further global and integrative retrospective analyses. We downloaded from the Sequence Read Archive 751 samples (SRA-experiments), from 18 independent BioProjects studying the effects of different stressors on the brain transcriptome in mice. We performed a massive bioinformatics re-analysis applying a single, standardized pipeline for computing differential gene expression. This data mining allowed the identification of novel candidate stress-related genes and specific signatures associated with different stress conditions. The large amount of computational results produced was systematized in the interactive “Stress Mice Portal”

Molecular Lego of Human Cytochrome P450: The Key Role of Heme Domain Flexibility for the Activity of the Chimeric Proteins

The cytochrome P450 superfamily are heme-thiolate enzymes able to carry out monooxygenase reactions. Several studies have demonstrated the feasibility of using a soluble bacterial reductase from Bacillus megaterium, BMR, as an artificial electron transfer partner fused to the human P450 domain in a single polypeptide chain in an approach known as ‘molecular Lego’. The 3A4-BMR chimera has been deeply characterized biochemically for its activity, coupling efficiency, and flexibility by many different biophysical techniques leading to the conclusion that an extension of five glycines in the loop that connects the two domains improves all the catalytic parameters due to improved flexibility of the system. In this work, we extend the characterization of 3A4-BMR chimeras using differential scanning calorimetry to evaluate stabilizing role of BMR. We apply the ‘molecular Lego’ approach also to CYP19A1 (aromatase) and the data show that the activity of the chimeras is very low (<0.003 min−1) for all the constructs tested with a different linker loop length: ARO-BMR, ARO-BMR-3GLY, and ARO-BMR-5GLY. Nevertheless, the fusion to BMR shows a remarkable effect on thermal stability studied by differential scanning calorimetry as indicated by the increase in Tonset by 10 °C and the presence of a cooperative unfolding process driven by the BMR protein domain. Previously characterized 3A4-BMR constructs show the same behavior of ARO-BMR constructs in terms of thermal stabilization but a higher activity as a function of the loop length. A comparison of the ARO-BMR system to 3A4-BMR indicates that the design of each P450-BMR chimera should be carefully evaluated not only in terms of electron transfer, but also for the biophysical constraints that cannot always be overcome by chimerization

Nitrogen replenishment using variable rate application technique in a small hand-harvested pear orchard

Precision agriculture is a management approach for sustainable agriculture. It can be applied even in small fields. It aims to optimize inputs, improve profits, and reduce adverse environmental impacts. In this study, a series of measurements were conducted over three growing seasons to assess variability in a 0.55 ha pear orchard located in central Greece. Soil ECa was measured using EM38 sensor, while soil samples were taken from a grid 17 × 8 m and analysed for texture, pH, P, K, Mg, CaCO3, and organic matter content. Data analysis indicated that most of the nutrients were at sufficient levels. Soil and yield maps showed considerable variability while fruit quality presented small variations across the orchard. Yield fluctuations were observed, possibly due to climatic conditions. Prescription maps were developed for nitrogen variable rate application (VRA) for two years based on the replacement of the nutrients removed by the crop. VRA application resulted in 56% and 50% reduction of N fertiliser compared to uniform application

HPC-REDItools: A novel HPC-aware tool for improved large scale RNA-editing analysis

Background: RNA editing is a widespread co-/post-transcriptional mechanism that alters primary RNA sequences through the modification of specific nucleotides and it can increase both the transcriptome and proteome diversity. The automatic detection of RNA-editing from RNA-seq data is computational intensive and limited to small data sets, thus preventing a reliable genome-wide characterisation of such process. Results: In this work we introduce HPC-REDItools, an upgraded tool for accurate RNA-editing events discovery from large dataset repositories. Availability: https://github.com/BioinfoUNIBA/REDItools2. Conclusions: HPC-REDItools is dramatically faster than the previous version, REDItools, enabling big-data analysis by means of a MPI-based implementation and scaling almost linearly with the number of available cores

Assessment of the hyperspectral data analysis as a tool to diagnose xylella fastidiosa in the asymptomatic leaves of olive plants

Xylella fastidiosa is a bacterial pathogen affecting many plant species worldwide. Recently, the subspecies pauca (Xfp) has been reported as the causal agent of a devastating disease on olive trees in the Salento area (Apulia region, southeastern Italy), where centenarian and millenarian plants constitute a great agronomic, economic, and landscape trait, as well as an important cultural heritage. It is, therefore, important to develop diagnostic tools able to detect the disease early, even when infected plants are still asymptomatic, to reduce the infection risk for the surrounding plants. The reference analysis is the quantitative real time-Polymerase-Chain-Reaction (qPCR) of the bacterial DNA. The aim of this work was to assess whether the analysis of hyperspectral data, using different statistical methods, was able to select with sufficient accuracy, which plants to analyze with PCR, to save time and economic resources. The study area was selected in the Municipality of Oria (Brindisi). Partial Least Square Regression (PLSR) and Canonical Discriminant Analysis (CDA) indicated that the most important bands were those related to the chlorophyll function, water, lignin content, as can also be seen from the wilting symptoms in Xfp-infected plants. The confusion matrix of CDA showed an overall accuracy of 0.67, but with a better capability to discriminate the infected plants. Finally, an unsupervised classification, using only spectral data, was able to discriminate the infected plants at a very early stage of infection. Then, in phase of testing qPCR should be performed only on the plants predicted as infected from hyperspectral data, thus, saving time and financial resources

A geostatistical fusion approach using UAV data for probabilistic estimation of Xylella fastidiosa subsp. pauca infection in olive trees

Xylella fastidiosa is one of the most destructive plant pathogenic bacteria worldwide, affecting more than 500 plant species. In Apulia region (southeastern Italy), X. fastidiosa subsp. pauca (Xfp) is responsible for a severe disease, the olive quick decline syndrome (OQDS), spreading epidemically and with dramatic impact on the agriculture, the landscape, the tourism, and the cultural heritage of this region. An early detection of the infected plants would hinder the rapid spread of the disease. The main objective of this paper was to define a geostatistical approach of data fusion, which combines remote (radiometric), and proximal (geophysical) sensor data and visual inspections with plant diagnostic tests, to provide probabilistic maps of Xfp infection risk. The study site was an olive grove located at Oria (province of Brindisi, Italy), where at the time of monitoring (September 2017) only few plants showed initial symptoms of the disease. The measurements included: 1) acquisitions of reflected electromagnetic radiation with UAV (Unmanned Aerial Vehicle) equipped with a multi-spectral camera; 2) geophysical surveys on the trunks of 49 plants with Ground Penetrating Radar (GPR); 3) disease severity rating, by visual inspection of the proportion of canopy with symptoms; 4) qPCR (real time-quantitative Polymerase Chain Reaction) data from tests on 61 plants. The data were submitted to a set of processing techniques to define a “data fusion” procedure, based on non-parametric multivariate geostatistics. The approach allowed marking those areas where the risk of infection was higher, and identifying the possible infection entry routes into the field. The probability map of infection risk could be used as an effective tool for a preventive action and for a better organization of the monitoring plans

ASPicDB: a database of annotated transcript and protein variants generated by alternative splicing

Alternative splicing is emerging as a major mechanism for the expansion of the transcriptome and proteome diversity, particularly in human and other vertebrates. However, the proportion of alternative transcripts and proteins actually endowed with functional activity is currently highly debated. We present here a new release of ASPicDB which now provides a unique annotation resource of human protein variants generated by alternative splicing. A total of 256 939 protein variants from 17 191 multi-exon genes have been extensively annotated through state of the art machine learning tools providing information of the protein type (globular and transmembrane), localization, presence of PFAM domains, signal peptides, GPI-anchor propeptides, transmembrane and coiled-coil segments. Furthermore, full-length variants can be now specifically selected based on the annotation of CAGE-tags and polyA signal and/or polyA sites, marking transcription initiation and termination sites, respectively. The retrieval can be carried out at gene, transcript, exon, protein or splice site level allowing the selection of data sets fulfilling one or more features settled by the user. The retrieval interface also enables the selection of protein variants showing specific differences in the annotated features. ASPicDB is available at http://www.caspur.it/ASPicDB/