OBJECTIVE: Digitalization is a pillar of reproducible research and a mandatory requirement for Research Infrastructures. Biobanks must ensure a fully engineered and digitalized process towards data FAIRification. To this aim, the first step is to assess the current level of digitalization using quantitative metrics, which is particularly challenging given the multi-faceted regulatory and logistical nature of biobanking. METHODS: We developed a Biobanking digital assessment maturity framework, BB4FAIR, comprising a survey divided into three macro areas, namely IT infrastructure, personnel, and data annotation richness. Furthermore, we implemented an automated R/Shiny system to analyse survey responses and generate visual data representations. We piloted the tool on 46 Italian biobanks that in 2023 had signed the partner charter with BBMRI. A scoring table facilitated the tiering of digital maturity, highlighting areas requiring corrective action. RESULTS: The assessment revealed significant heterogeneity across the three macro-areas of digitalization: almost half of the biobanks feature adequate IT infrastructure and personnel, and a smaller proportion have robust data annotation capabilities. Notably, most biobanks reported having a Biobank IT Management System (BIMS) or an alternative that serves their purposes, yet they still collect the consent to biobanking for future purposes in paper format; the digitalization of informed consent is generally lacking. These findings highlight the need for targeted improvements in Biobank digitalization to enhance overall data FAIRness. CONCLUSION: The survey results underscore a pressing need for enhanced IT training and improved data annotation resources within the BBMRI.it. Corrective actions on many lacking features and desiderata are ongoing in the context of the #NextGenerationEu "Strengthening BBMRI.it" project.

The Albula Region in the canton Grisons in Switzerland is prone to a large number of landslides. In order to spatially estimate the surface deformation of these phenomena, we performed a multi-temporal interferometric analysis on point (i.e., persistent scatterers) phases by jointly exploiting ALOS-2 PALSAR-2 ScanSAR and StripMap data acquired along the same orbit. Our results indicate a widespread presence of large-scale rock slope instabilities with linear LOS displacement rates in the range of a few cm/year. In comparison with a PSI Sentinel-1 analysis, valid information is retrieved with L-Band also over forests, where only very limited information is available at C-Band. Over built-up regions and for alpine areas above the tree line, the density of points is higher from Sentinel-1 than from ALOS-2 PALSAR-2 because of the moderate resolution of the ALOS-2 PALSAR-2 ScanSAR data.

The increase of social media use in recent years has shown potential also for the identification of specific trends in the data that could be used to locate earthquakes. In this work, we implemented a pipeline that uses Twitter data to identify locations of earthquakes and use the information to trigger EO data analysis. We tested the pipeline for almost a year over Japan, an area where earthquake events are frequent, as well as the use of social media in the population. Here, we show the results and discuss the potential development of such procedures. In the future, considering the rapid development and the increase of satellite constellations aimed at global coverage with short revisit times, algorithms of this kind could be used to prioritize satellite acquisitions for the detection of the areas most affected by earthquake damages.

In this paper, we present examples of DInSAR-based measurement of surface displacements using a novel compact L-band SAR system that can be mounted on mobile mapping platforms such as a UAV or a car. The good DInSAR system performance is demonstrated and, particularly, we also show a use case in which a car-borne system setup is employed to map surface displacements of a fast-moving landslide and the surrounding area in Switzerland. Our results show that car-borne and UAV-borne interferometric displacement measurements at L-band are feasible with high quality over various natural terrain. This novel compact DInSAR system for agile platforms complements existing terrestrial, airborne, and space-borne radar interferometry systems in terms of its new combination of (1) radar wavelength (sensitivity to displacement/decorrelation properties), (2) spatial resolution, (3) (near-) terrestrial observation geometry, and (4) mobile mapping capability.

On 2ndand 3rdOctober 2020, Storm Alex hit northern Italy and southern France regions with 500 mm of rainfall in about 24 hours. This triggered devastating flash floods and landslides, causing severe damages and 15 fatalities. This study presents a landslide inventory map obtained by using a generalized deep-learning model, avoiding human interaction in the workflow by skipping the time-consuming training step. A total of 1,249 landslides have been mapped with this approach in minutes after a suitable post-event satellite image was available for processing. Our results show how deep-learning strategies applied to remote sensing data can help in the aftermath of catastrophic events for the rapid detection and mapping of landslide phenomena.

This paper implements a novel approach to automatically detect and classify rockfalls in Lunar Reconnaissance Orbiter narrow angle camera (NAC) images using a single-stage dense object detector (RetinaNet). The convolutional neural network has been trained with a data set of 2932 original rockfall images. In order to avoid overfitting, the initial training data set has been augmented during training using random image rotation, scaling, and flipping. Testing images have been labelled by human operators and have been used for RetinaNet performance evaluation. Testing shows that RetinaNet is capable to reach recall values between 0.98 and 0.39, precision values between 1 and 0.25, and average precisions ranging from 0.89 to 0.69, depending on the used confidence threshold and intersection-over-union values. Mean processing time of a single NAC image in RetinaNet is around 10 s using a GeForce GTX 1080 Ti and GeForce Titan Xp, which is in orders of magnitudes faster than a human operator. The processing speed allows to efficiently exploit the currently available NAC data stack with more than 1 million images in a reasonable timeframe. The combination of speed and detection performance can be used to produce lunar rockfall distribution maps on large spatial scales for utilization by the scientific and engineering community.

This preface introduces the content of the BioMed Central journal Supplement related to the 14th annual meeting of the Bioinformatics Italian Society, held in Cagliari, Italy, from the 5th to the 7th of July, 2017.

BACKGROUND: Nowadays, the increasing availability of omics data, due to both the advancements in the acquisition of molecular biology results and in systems biology simulation technologies, provides the bases for precision medicine. Success in precision medicine depends on the access to healthcare and biomedical data. To this end, the digitization of all clinical exams and medical records is becoming a standard in hospitals. The digitization is essential to collect, share, and aggregate large volumes of heterogeneous data to support the discovery of hidden patterns with the aim to define predictive models for biomedical purposes. Patients' data sharing is a critical process. In fact, it raises ethical, social, legal, and technological issues that must be properly addressed. RESULTS: In this work, we present an infrastructure devised to deal with the integration of large volumes of heterogeneous biological data. The infrastructure was applied to the data collected between 2010-2016 in one of the major diagnostic analysis laboratories in Italy. Data from three different platforms were collected (i.e., laboratory exams, pathological anatomy exams, biopsy exams). The infrastructure has been designed to allow the extraction and aggregation of both unstructured and semi-structured data. Data are properly treated to ensure data security and privacy. Specialized algorithms have also been implemented to process the aggregated information with the aim to obtain a precise historical analysis of the clinical activities of one or more patients. Moreover, three Bayesian classifiers have been developed to analyze examinations reported as free text. Experimental results show that the classifiers exhibit a good accuracy when used to analyze sentences related to the sample location, diseases presence and status of the illnesses. CONCLUSIONS: The infrastructure allows the integration of multiple and heterogeneous sources of anonymized data from the different clinical platforms. Both unstructured and semi-structured data are processed to obtain a precise historical analysis of the clinical activities of one or more patients. Data aggregation allows to perform a series of statistical assessments required to answer complex questions that can be used in a variety of fields, such as predictive and precision medicine. In particular, studying the clinical history of patients that have developed similar pathologies can help to predict or individuate markers able to allow an early diagnosis of possible illnesses.

BACKGROUND: During library construction polymerase chain reaction is used to enrich the DNA before sequencing. Typically, this process generates duplicate read sequences. Removal of these artifacts is mandatory, as they can affect the correct interpretation of data in several analyses. Ideally, duplicate reads should be characterized by identical nucleotide sequences. However, due to sequencing errors, duplicates may also be nearly-identical. Removing nearly-identical duplicates can result in a notable computational effort. To deal with this challenge, we recently proposed a GPU method aimed at removing identical and nearly-identical duplicates generated with an Illumina platform. The method implements an approach based on prefix-suffix comparison. Read sequences with identical prefix are considered potential duplicates. Then, their suffixes are compared to identify and remove those that are actually duplicated. Although the method can be efficiently used to remove duplicates, there are some limitations that need to be overcome. In particular, it cannot to detect potential duplicates in the event that prefixes are longer than 27 bases, and it does not provide support for paired-end read libraries. Moreover, large clusters of potential duplicates are split into smaller with the aim to guarantees a reasonable computing time. This heuristic may affect the accuracy of the analysis. RESULTS: In this work we propose GPU-DupRemoval, a new implementation of our method able to (i) cluster reads without constraints on the maximum length of the prefixes, (ii) support both single- and paired-end read libraries, and (iii) analyze large clusters of potential duplicates. CONCLUSIONS: Due to the massive parallelization obtained by exploiting graphics cards, GPU-DupRemoval removes duplicate reads faster than other cutting-edge solutions, while outperforming most of them in terms of amount of duplicates reads.

Summary Sorting is a very important task in computer science and becomes a critical operation for programs making heavy use of sorting algorithms. General‐purpose computing has been successfully used on Graphics Processing Units (GPUs) to parallelize some sorting algorithms. Two GPU‐based implementations of the quicksort were presented in literature: the GPU‐quicksort, a compute‐unified device architecture (CUDA) iterative implementation, and the CUDA dynamic parallel (CDP) quicksort, a recursive implementation provided by NVIDIA Corporation. We propose CUDA‐quicksort an iterative GPU‐based implementation of the sorting algorithm. CUDA‐quicksort has been designed starting from GPU‐quicksort. Unlike GPU‐quicksort, it uses atomic primitives to perform inter‐block communications while ensuring an optimized access to the GPU memory. Experiments performed on six sorting benchmark distributions show that CUDA‐quicksort is up to four times faster than GPU‐quicksort and up to three times faster than CDP‐quicksort. An in‐depth analysis of the performance between CUDA‐quicksort and GPU‐quicksort shows that the main improvement is related to the optimized GPU memory access rather than to the use of atomic primitives. Moreover, in order to assess the advantages of using the CUDA dynamic parallelism, we implemented a recursive version of the CUDA‐quicksort. Experimental results show that CUDA‐quicksort is faster than the CDP‐quicksort provided by NVIDIA, with better performance achieved using the iterative implementation. Copyright © 2015 John Wiley & Sons, Ltd.

In this work, we analyze the characteristics of snow cover using two space borne products (Level 1C and Level 2A) provided by the SPOT4 Take 5 Initiative. The principal aim of this initiative is to evaluate the benefits of Sentinel 2 acquisition mode before its launch. We processed SPOT4 images acquired from February 2013 to June 2013 over the site of Midi-Pyrénées (South West). The Normalized Difference Snow Index (NDSI) and Fraction of Snow Cover (SCF) are calculated on all dataset and the snow surface map and its evolution over the time is obtained. The distribution of snow cover is slightly larger using the Level 1C than in the Level 2A data. This information will be merged with climatic data and snow depth ground measures in order to derive the melt rate, the snow cover depletion rate and the snow water equivalent, and to analyze the groundwater level variations and their interaction with the landslide activity.

The civilian use of small inexpensive mini- and micro-UAVs has increased dramatically in the past few years. UAVs can be used for natural hazards management. In this context, UAVs can be very useful for surveying and monitoring of active small landslides in urban environments. In this paper, a methodology for the data acquisition and processing that considers the landslide typology is presented and discussed. Two case studies from the northwest part of Italy are also described to illustrate the presented methodology.

This work presents the Differential SAR Interferometry and pixel-offset analysis on the event landslide that struck Montescaglioso town (Matera, southern Italy) on December 3rd, 2013. The event occurred after adverse weather conditions that produced a ground displacement of several meters, causing a severe emergency situation. The analysis has shown the presence of two main directions of motion: a major and a minor movement along the South-SouthWest and South-SouthEast directions. The pixel-offset results are well in agreement with both the magnitude and the deformation mechanisms that have been identified and mapped during field observations.

BACKGROUND: Single Nucleotide Polymorphism (SNP) genotyping analysis is very susceptible to SNPs chromosomal position errors. As it is known, SNPs mapping data are provided along the SNP arrays without any necessary information to assess in advance their accuracy. Moreover, these mapping data are related to a given build of a genome and need to be updated when a new build is available. As a consequence, researchers often plan to remap SNPs with the aim to obtain more up-to-date SNPs chromosomal positions. In this work, we present G-SNPM a GPU (Graphics Processing Unit) based tool to map SNPs on a genome. METHODS: G-SNPM is a tool that maps a short sequence representative of a SNP against a reference DNA sequence in order to find the physical position of the SNP in that sequence. In G-SNPM each SNP is mapped on its related chromosome by means of an automatic three-stage pipeline. In the first stage, G-SNPM uses the GPU-based short-read mapping tool SOAP3-dp to parallel align on a reference chromosome its related sequences representative of a SNP. In the second stage G-SNPM uses another short-read mapping tool to remap the sequences unaligned or ambiguously aligned by SOAP3-dp (in this stage SHRiMP2 is used, which exploits specialized vector computing hardware to speed-up the dynamic programming algorithm of Smith-Waterman). In the last stage, G-SNPM analyzes the alignments obtained by SOAP3-dp and SHRiMP2 to identify the absolute position of each SNP. RESULTS AND CONCLUSIONS: To assess G-SNPM, we used it to remap the SNPs of some commercial chips. Experimental results shown that G-SNPM has been able to remap without ambiguity almost all SNPs. Based on modern GPUs, G-SNPM provides fast mappings without worsening the accuracy of the results. G-SNPM can be used to deal with specialized Genome Wide Association Studies (GWAS), as well as in annotation tasks that require to update the SNP mapping probes.

We exploit the amplitude information of a sequence of synthetic aperture radar (SAR) images, acquired at different times, in order to generate displacement time-series in areas characterized by large and/or rapid deformation, the size of which is on the order of the image's pixel dimensions. We follow the same rationale of the Small BAseline Subset (SBAS) differential SAR interferometry (DInSAR) approach, by coupling the available SAR images into pairs characterized by a small separation between the acquisition orbits. We exploit the amplitudes of the selected image pairs in order to calculate the relative across-track (range) and along-track (azimuth) pixel-offsets (PO). Finally, we apply the SBAS inversion strategy to retrieve the range and azimuth displacement time-series. This approach, referred to as pixel-offset (PO-) SBAS technique, has been applied to a set of 25 ENVISAT SAR observations of the Sierra Negra caldera, Galápagos Islands, spanning the 2003-2007 time interval. The retrieved deformation time-series show the capability of the technique to detect and measure the large displacements affecting the inner part of the caldera that, in correspondence to the October 2005 eruption, reached several meters. Moreover, by comparing the PO-SBAS results to continuous GPS measurements, we estimate that the accuracy of the PO-SBAS time-series is on the order of 1/30th of a pixel for both range and azimuth directions.

We propose advances on the generation of deformation time series in areas affected by large deformation dynamics, where the exploitation of the differential SAR phase can be strongly limited by severe misregistration errors or by very high fringe rates. First, to overcome the former issue, we present an extension of the amplitude-based Pixel-Offset (PO) analyses by applying the Small BAseline Subset (SBAS) strategy, in order to move from the investigation of single (large) deformation events to that of dynamic phenomena. Secondly, to handle the high fringe rate interferograms, we subtract from them properly generated synthetic deformation models allowing us to reduce the fringe rate, thus helping the phase unwrapping step. The proposed approaches have been tested on ASAR-ENVISAT data acquired on Galápagos Islands and validated via continuous GPS measurements.

We perform a full exploitation of the Differential SAR Interferometry (DInSAR) algorithm referred to as Small BAseline Subset (SBAS) technique to investigate long term surface deformation occurring in extended, seismogenetic areas. To this aim we benefit of the SBAS technique capability to work in multi-frame and multi-sensor scenarios in order to improve the spatial and temporal coverage, as well as to employ new generation SAR sensors to increase the temporal sampling of the retrieved time series. In this work we apply the SBAS algorithm to analyze the temporal evolution of the detected displacements affecting three different seismogenetic scenarios by means of deformation time series retrieved through data acquired by European (ERS-1/2, ENVISAT) and Italian (COSMO-SkyMed) satellites. In particular, we focus on the analysis of the deformation patterns associated with the activity of the San Andreas (SAF, California, USA), the North Anatolian (NAF, Turkey) and the Paganica (PF, Abruzzo, Central Italy) Faults. The achieved results provide a clear idea of the surface deformation retrieval capability of the SBAS procedure.