90 research outputs found

    Samples and data accessibility in research biobanks. An explorative survey

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    Biobanks, which contain human biological samples and/or data, provide a crucial contribution to the progress of biomedical research. However, the effective and efficient use of biobank resources depends on their accessibility. In fact, making bio-resources promptly accessible to everybody may increase the benefits for society. Furthermore, optimizing their use and ensuring their quality will promote scientific creativity and, in general, contribute to the progress of bio-medical research. Although this has become a rather common belief, several laboratories are still secretive and continue to withhold samples and data. In this study, we conducted a questionnairebased survey in order to investigate sample and data accessibility in research biobanks operating all over the world. The survey involved a total of 46 biobanks. Most of them gave permission to access their samples (95.7%) and data (85.4%), but free and unconditioned accessibility seemed not to be common practice. The analysis of the guidelines regarding the accessibility to resources of the biobanks that responded to the survey highlights three issues: (i) the request for applicants to explain what they would like to do with the resources requested; (ii) the role of funding, public or private, in the establishment of fruitful collaborations between biobanks and research labs; (iii) the request of co-authorship in order to give access to their data. These results suggest that economic and academic aspects are involved in determining the extent of sample and data sharing stored in biobanks. As a second step of this study, we investigated the reasons behind the high diversity of requirements to access biobank resources. The analysis of informative answers suggested that the different modalities of resource accessibility seem to be largely influenced by both social context and legislation of the countries where the biobanks operate

    Effect of different lignocellulosic fibres on poly(ε-caprolactone)-based composites for potential applications in orthotics

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    This work compares the mechanical and thermal behaviour of fully biodegradable biocomposites based on polycaprolactone reinforced with three different natural fibres, namely hemp, sisal and coir, for potential applications in the field of orthoses. The same properties were further compared to those of two commercially available materials commonly used in the same prospective field. The results confirmed that the addition of natural fibres, irrespective of the origin of the fibres (leaf, bast or fruit) to a biodegradable matrix allows for significant improvement of the mechanical behaviour of the ensuing composites compared to traditional thermoplastic materials used in orthotics

    Land Cover Segmentation with Sparse Annotations from Sentinel-2 Imagery

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    Land cover (LC) segmentation plays a critical role in various applications, including environmental analysis and natural disaster management. However, generating accurate LC maps is a complex and time-consuming task that requires the expertise of multiple annotators and regular updates to account for environmental changes. In this work, we introduce SPADA, a framework for fuel map delineation that addresses the challenges associated with LC segmentation using sparse annotations and domain adaptation techniques for semantic segmentation. Performance evaluations using reliable ground truths, such as LUCAS and Urban Atlas, demonstrate the technique's effectiveness. SPADA outperforms state-of-the-art semantic segmentation approaches as well as third-party products, achieving a mean Intersection over Union (IoU) score of 42.86 and an F1 score of 67.93 on Urban Atlas and LUCAS, respectively.Comment: 4 pages, short paper. Accepted to IGARSS 202

    Data from GNSS-Based Passive Radar to Support Flood Monitoring Operations

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    Signals transmitted by Global Navigation Satellite Systems can be exploited as signals of opportunity for remote sensing applications. Satellites can be seen as spread sources of electromagnetic radiation, whose signals reflected back from ground can be processed to detect and monitor geophysical properties of the Earth’s surface. In the past years, several experiments of GNSS-based passive radars have been demonstrated successfully, mainly from piloted aircraft. Then, the proliferation of small UAVs enabled new applications where GNSS-based passive radars can provide useful geospatial information for environmental monitoring. Thanks to the availability of commercial Radio Frequency front ends and the enhanced processing capabilities of embedded platforms, it is possible to develop GNSS-based passive radars at moderated cost. These can be mounted on Unmanned Aerial Vehicles, and be used to support the sensing of environmental parameters. This paper presents the results of an experimental campaign based on the use of a UAV for GNSS reflectometry, tailored to the detection of the presence of water on ground after floods. The work is part of wider project, which intends to develop solutions to support rescuers and decision makers to manage operations after natural disasters, through the integration and modelling of geospatial data coming from multiple sources

    Land Cover Segmentation with Sparse Annotations from Sentinel-2 Imagery

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    Land cover (LC) segmentation plays a critical role in various applications, including environmental analysis and natural disaster management. However, generating accurate LC maps is a complex and time-consuming task that requires the expertise of multiple annotators and regular updates to account for environmental changes. In this work, we introduce SPADA, a framework for fuel map delineation that addresses the challenges associated with LC segmentation using sparse annotations and domain adaptation techniques for semantic segmentation. Performance evaluations using reliable ground truths, such as LUCAS and Urban Atlas, demonstrate the technique's effectiveness. SPADA outperforms state-of-the-art semantic segmentation approaches as well as third-party products, achieving a mean Intersection over Union (IoU) score of 42.86 and an F1 score of 67.93 on Urban Atlas and LUCAS, respectively

    Celecoxib inhibits proliferation and survival of chronic myelogeous leukemia (CML) cells via AMPK-dependent regulation of β-catenin and mTORC1/2.

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    CML is effectively treated with tyrosine kinase inhibitors (TKIs). However, the efficacy of these drugs is confined to the chronic phase of the disease and development of resistance to TKIs remains a pressing issue. The anti-inflammatory COX2 inhibitor celecoxib has been utilized as anti-tumour drug due to its anti-proliferative activity. However, its effects in hematological malignancies, in particular CML, have not been investigated yet. Thus, we tested biological effects and mechanisms of action of celecoxib in Philadelphia-positive (Ph+) CML and ALL cells.We show here that celecoxib suppresses the growth of Ph+ cell lines by increasing G1-phase and apoptotic cells and reducing S- and G2-phase cells. These effects were independent of COX2 inhibition but required the rapid activation of AMP-activated protein kinase (AMPK) and the consequent inhibition mTORC1 and 2. Treatment with celecoxib also restored GSK3β function and led to down-regulation of β-catenin activity through transcriptional and post-translational mechanisms, two effects likely to contribute to Ph+ cell growth suppression by celecoxib.Celecoxib inhibited colony formation of TKI-resistant Ph+ cell lines including those with the T315I BCR-ABL mutation and acted synergistically with imatinib in suppressing colony formation of TKI-sensitive Ph+ cell lines. Finally, it suppressed colony formation of CD34+ cells from CML patients, while sparing most CD34+ progenitors from healthy donors, and induced apoptosis of primary Ph+ ALL cells.Together, these findings indicate that celecoxib may serve as a COX2-independent lead compound to simultaneously target the mTOR and β-catenin pathways, key players in the resistance of CML stem cells to TKIs

    GNSS Radio Frequency Interference Monitoring from LEO Satellites: An In-Laboratory Prototype

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    The disruptive effect of radio frequency interference (RFI) on global navigation satellite system (GNSS) signals is well known, and in the last four decades, many have been investigated as countermeasures. Recently, low-Earth orbit (LEO) satellites have been looked at as a good opportunity for GNSS RFI monitoring, and the last five years have seen the proliferation of many commercial and academic initiatives. In this context, this paper proposes a new spaceborne system to detect, classify, and localize terrestrial GNSS RFI signals, particularly jamming and spoofing, for civil use. This paper presents the implementation of the RFI detection software module to be hosted on a nanosatellite. The whole development work is described, including the selection of both the target platform and the algorithms, the implementation, the detection performance evaluation, and the computational load analysis. Two are the implemented RFI detectors: the chi-square goodness-of-fit (GoF) algorithm for non-GNSS-like interference, e.g., chirp jamming, and the snapshot acquisition for GNSS-like interference, e.g., spoofing. Preliminary testing results in the presence of jamming and spoofing signals reveal promising detection capability in terms of sensitivity and highlight room to optimize the computational load, particularly for the snapshot-acquisition-based RFI detector

    A Comparative Evaluation of Deep Learning Techniques for Photovoltaic Panel Detection From Aerial Images

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    Solar energy production has significantly increased in recent years in the European Union (EU), accounting for 12% of the total in 2022. The growth in solar energy production can be attributed to the increasing adoption of solar photovoltaic (PV) panels, which have become cost-effective and efficient means of energy production, supported by government policies and incentives. The maturity of solar technologies has also led to a decrease in the cost of solar energy, making it more competitive with other energy sources. As a result, there is a growing need for efficient methods for detecting and mapping the locations of PV panels. Automated detection can in fact save time and resources compared to manual inspection. Moreover, the resulting information can also be used by governments, environmental agencies and other companies to track the adoption of renewable sources or to optimize energy distribution across the grid. However, building effective models to support the automated detection and mapping of solar photovoltaic (PV) panels presents several challenges, including the availability of high-resolution aerial imagery and high-quality, manually-verified labels and annotations. In this study, we address these challenges by first constructing a dataset of PV panels using very-high-resolution (VHR) aerial imagery, specifically focusing on the region of Piedmont in Italy. The dataset comprises 105 large-scale images, providing more than 9,000 accurate and detailed manual annotations, including additional attributes such as the PV panel category. We first conduct a comprehensive evaluation benchmark on the newly constructed dataset, adopting various well-established deep-learning techniques. Specifically, we experiment with instance and semantic segmentation approaches, such as Rotated Faster RCNN and Unet, comparing strengths and weaknesses on the task at hand. Second, we apply ad-hoc modifications to address the specific issues of this task, such as the wide range of scales of the installations and the sparsity of the annotations, considerably improving upon the baseline results. Last, we introduce a robust and efficient post-processing polygonization algorithm that is tailored to PV panels. This algorithm converts the rough raster predictions into cleaner and more precise polygons for practical use. Our benchmark evaluation shows that both semantic and instance segmentation techniques can be effective for detecting and mapping PV panels. Instance segmentation techniques are well-suited for estimating the localization of panels, while semantic solutions excel at surface delineation. We also demonstrate the effectiveness of our ad-hoc solutions and post-processing algorithm, which can provide an improvement up to +10% on the final scores, and can accurately convert coarse raster predictions into usable polygons

    an explorative survey

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    Biobanks, which contain human biological samples and/or data, provide a crucial contribution to the progress of biomedical research. However, the effective and efficient use of biobank resources depends on their accessibility. In fact, making bio-resources promptly accessible to everybody may increase the benefits for society. Furthermore, optimizing their use and ensuring their quality will promote scientific creativity and, in general, contribute to the progress of bio-medical research. Although this has become a rather common belief, several laboratories are still secretive and continue to withhold samples and data. In this study, we conducted a questionnaire-based survey in order to investigate sample and data accessibility in research biobanks operating all over the world. The survey involved a total of 46 biobanks. Most of them gave permission to access their samples (95.7%) and data (85.4%), but free and unconditioned accessibility seemed not to be common practice. The analysis of the guidelines regarding the accessibility to resources of the biobanks that responded to the survey highlights three issues: (i) the request for applicants to explain what they would like to do with the resources requested; (ii) the role of funding, public or private, in the establishment of fruitful collaborations between biobanks and research labs; (iii) the request of co-authorship in order to give access to their data. These results suggest that economic and academic aspects are involved in determining the extent of sample and data sharing stored in biobanks. As a second step of this study, we investigated the reasons behind the high diversity of requirements to access biobank resources. The analysis of informative answers suggested that the different modalities of resource accessibility seem to be largely influenced by both social context and legislation of the countries where the biobanks operate

    Machine learning approach to the safety assessment of a prestressed concrete railway bridge

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    Early structural anomalies identification allows to hold maintenance activities that avoid loss of both economic resources and human life. This is extremely important for crucial infrastructures like railway bridges. This paper illustrates the structural health monitoring approach applied to a simply supported prestressed concrete railway bridge. In the framework of long-term monitoring, both static quantities (displacements, strains, and rotations) and environmental measurements (temperatures) have been recorded. Machine learning techniques, Extreme Gradient boosting machine and Multi-Layer Perceptron, have been exploited to build regression correlation models associated with the undamaged structural condition after adequate pre-processing operations. In this way, alarm thresholds based on the expected residuals between the predicted structural quantities and the measured ones, have been defined. The thresholds turned out to be able to catch early-stage anomalies not pointed out by traditional damage thresholds based on the design values. The proposed damage index is chosen as the moving median of the residuals, allowing a significant reduction of false alarms. The used correlation models and the obtained results represent a starting point for the generalization of this approach to the bridges belonging to the same static typology
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