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Objective: Left atrial appendage closure (LAAC) concomitant to heart surgery in patients with underlying atrial fibrillation (AF) has gained attention because of long-term reduction of thromboembolic complications. As of mortality benefits in the setting of non-AF, data from both observational studies and randomized controlled trials are conflicting. Methods: On-line databases were screened for studies comparing LAAC versus no LAAC concomitant to other heart surgery. End points assessed were all-cause mortality and stroke at early and longest-available follow-up. Subgroup analyses stratified on preoperative AF were performed. Risk ratios (RR) with 95% CIs served as primary statistics. Results: Electronic search yielded 25 studies (N = 660 [158 patients]). There was no difference between LAAC and no LAAC in terms of early mortality. In the overall population analysis, LAAC reduced long-term mortality (RR, 0.86; 95% CI, 0.74-1.00; P = .05; I2 = 88%), reduced early stroke risk by 19% (RR, 0.81; 95% CI, 0.72-0.93; P = .002; I2 = 57%), and reduced late stroke risk by 13% (RR, 0.87; 95% CI, 0.84-0.90; P

Abstract Despite evidence suggesting the benefit of prophylactic regional antibiotic delivery (RAD) to sternal edges during cardiac surgery, it is seldom performed in clinical practice. The value of topical vancomycin and gentamicin for sternal wound infections (SWI) prophylaxis was further questioned by recent studies including randomized controlled trials (RCTs). The aim of this systematic review and meta-analysis was to comprehensively assess the safety and effectiveness of RAD to reduce the risk of SWI.We screened multiple databases for RCTs assessing the effectiveness of RAD (vancomycin, gentamicin) in SWI prophylaxis. Random effects meta-analysis was performed. The primary endpoint was any SWI; other wound complications were also analysed. Odds Ratios served as the primary statistical analyses. Trial sequential analysis (TSA) was performed.Thirteen RCTs (N = 7,719 patients) were included. The odds of any SWI were significantly reduced by over 50% with any RAD: OR (95%CIs): 0.49 (0.35–0.68); p

Abstract Background The anatomic variants of the intercostobrachial nerve (ICBN) represent a potential risk of injuries during surgical procedure such as axillary lymph node dissection and sentinel lymph node biopsy in breast cancer and melanoma patients. The aim of this systematic review and meta-analysis was to investigate the different origins and branching patterns of the intercostobrachial nerve also providing an analysis of the prevalence, through the analysis of the literature available up to September 2023. Materials and methods The protocol for this study was registered on PROSPERO (ID: CRD42023447932), an international prospective database for reviews. The PRISMA guideline was respected throughout the meta-analysis. A systematic literature search was performed using PubMed, Scopus and Web of Science. A search was performed in grey literature through google. Results We included a total of 23 articles (1,883 patients). The prevalence of the ICBN in the axillae was 98.94%. No significant differences in prevalence were observed during the analysis of geographic subgroups or by study type (cadaveric dissections and in intraoperative dissections). Only five studies of the 23 studies reported prevalence of less than 100%. Overall, the PPE was 99.2% with 95% Cis of 98.5% and 99.7%. As expected from the near constant variance estimates, the heterogeneity was low, I2 = 44.3% (95% CI 8.9%−65.9%), Q = 39.48, p = .012. When disaggregated by evaluation type, the difference in PPEs between evaluation types was negligible. For cadaveric dissection, the PPE was 99.7% (95% CI 99.1%–100.0%) compared to 99.0% (95% CI 98.1%–99.7%). Conclusions The prevalence of ICBN variants was very high. The dissection of the ICBN during axillary lymph-node harvesting, increases the risk of sensory disturbance. The preservation of the ICBN does not modify the oncological radicality in axillary dissection for patients with cutaneous metastatic melanoma or breast cancer. Therefore, we recommend to operate on these patients in high volume center to reduce post-procedural pain and paresthesia associated with a lack of ICBN variants recognition.

Digital Twin has emerged as a promising paradigm for accurately representing wireless communication electromagnetic environments. The resulting virtual representation of reality facilitates comprehensive insights into the propagation environment, empowering multi-layer decision-making processes at the physical communication level. This paper investigates the impact of ray-based model simulation within real-time Digital Twins. A benchmark for ray-based propagation simulations is presented to evaluate computational time, considering two urban scenarios characterized by different mesh complexity, single and multiple wireless link configurations, and simulations with/without diffuse scattering. Exhaustive empirical analyses are performed showing the behavior of different ray-based solutions. By offering standardized simulations and scenarios, this work provides a technical benchmark for practitioners involved in the implementation of real-time Digital Twins and optimization of ray-based propagation models.

BackgroundThe rise in life expectancy is associated with an increase in long-term and gradual cognitive decline. Treatment effectiveness is enhanced at the early stage of the disease. Therefore, there is a need to find low-cost and ecological solutions for mass screening of community-dwelling older adults. ObjectiveThis work aims to exploit automatic analysis of free speech to identify signs of cognitive function decline. MethodsA sample of 266 participants older than 65 years were recruited in Italy and Spain and were divided into 3 groups according to their Mini-Mental Status Examination (MMSE) scores. People were asked to tell a story and describe a picture, and voice recordings were used to extract high-level features on different time scales automatically. Based on these features, machine learning algorithms were trained to solve binary and multiclass classification problems by using both mono- and cross-lingual approaches. The algorithms were enriched using Shapley Additive Explanations for model explainability. ResultsIn the Italian data set, healthy participants (MMSE score≥27) were automatically discriminated from participants with mildly impaired cognitive function (20≤MMSE score≤26) and from those with moderate to severe impairment of cognitive function (11≤MMSE score≤19) with accuracy of 80% and 86%, respectively. Slightly lower performance was achieved in the Spanish and multilanguage data sets. ConclusionsThis work proposes a transparent and unobtrusive assessment method, which might be included in a mobile app for large-scale monitoring of cognitive functionality in older adults. Voice is confirmed to be an important biomarker of cognitive decline due to its noninvasive and easily accessible nature.

Hyperspectral imaging holds significant promise in remote sensing applications, particularly for land cover and land-use classification, thanks to its ability to capture rich spectral information. However, leveraging hyperspectral data for accurate segmentation poses critical challenges, including the curse of dimensionality and the scarcity of ground truth data, that hinder the accuracy and efficiency of machine learning approaches. This paper presents a holistic approach for adaptive optimized hyperspectral-based land cover and land-use segmentation using automated machine learning (AutoML). We address the challenges of high-dimensional hyperspectral data through a revamped machine learning pipeline, thus emphasizing feature engineering tailored to hyperspectral classification tasks. We propose a framework that dissects feature engineering into distinct steps, thus allowing for comprehensive model generation and optimization. This framework incorporates AutoML techniques to streamline model selection, hyperparameter tuning, and data versioning, thus ensuring robust and reliable segmentation results. Our empirical investigation demonstrates the efficacy of our approach in automating feature engineering and optimizing model performance, even without extensive ground truth data. By integrating automatic optimization strategies into the segmentation workflow, our approach offers a systematic, efficient, and scalable solution for hyperspectral-based land cover and land-use classification.

BackgroundLeft ventricular free-wall rupture (LVFWR) is a catastrophic complication of acute myocardial infarction (AMI). Historically, cardiac surgery is considered the treatment of choice. However, because of the rarity of this entity, little is known regarding the efficacy and safety of surgical treatment for post-infarction LVFWR. The aim of this study was to report a single-center experience in this field over a period of 30 years.MethodsPatients who developed LVFWR following AMI and underwent surgical repair at our Institution from January 1990 to December 2019 were considered. The primary end-point was in-hospital morality rate; secondary outcomes were long-term survival and postoperative complications. Multivariate analysis was carried out by constructing a logistic regression model to identify risk factors for early mortality.ResultsA total of 35 patients were enrolled in this study. The mean age was 68.9 years; 65.7% were male. The oozing type of LVFWR was encountered in 29 individuals, and the blowout type in 6 subjects. Sutured repair was used in 77.1% of patients, and sutureless repair in the remaining cases. The in-hospital mortality rate was 28.6%. Low cardiac output syndrome was the main cause of postoperative death. Multivariable analysis identified age >75 years at operation, preoperative cardiac arrest, concurrent ventricular septal rupture (VSR) as independent predictors of in-hospital death. Follow-up was complete in 100% of patients who survived surgery (mean follow-up: 9.3 ± 7.8 years); among the survivors, 16 patients died during the follow-up with a 3-year and 12-year overall survival rate of 82.5% and 55.2%, respectively.ConclusionsSurgical treatment of LVFWR following AMI is possible with acceptable in-hospital mortality and excellent long-term results. Advanced age, concurrent VSR and cardiac arrest at presentation are independent risk factors of poor early outcome.

Abstract Handwriting learning delays should be addressed early to prevent their exacerbation and long-lasting consequences on whole children’s lives. Ideally, proper training should start even before learning how to write. This work presents a novel method to disclose potential handwriting problems, from a pre-literacy stage, based on drawings instead of words production analysis. Two hundred forty-one kindergartners drew on a tablet, and we computed features known to be distinctive of poor handwriting from symbols drawings. We verified that abnormal features patterns reflected abnormal drawings, and found correspondence in experts’ evaluation of the potential risk of developing a learning delay in the graphical sphere. A machine learning model was able to discriminate with 0.75 sensitivity and 0.76 specificity children at risk. Finally, we explained why children were considered at risk by the algorithms to inform teachers on the specific weaknesses that need training. Thanks to this system, early intervention to train specific learning delays will be finally possible.

Automatic weed identification is becoming increasingly important in the Precision Agriculture field as a fundamental capability for targeted spraying or mechanical weed destruction. Targeted weed elimination reduces herbicides' use and thus lowers the environmental impact of treatments. Convolutional Neural Networks are one of the most successful techniques to automatically detect weeds on RGB images. Such models require a high amount of labeled data to obtain satisfying detection performance. The agricultural context presents a high degree of variability, and it is thus unfeasible to expect a representative dataset for each specific condition that can appear in the fields. Domain Adaptation techniques are exploited to maintain high detection performance in different field conditions, lowering the need for labeled data. This study presents a comparison of the two main style transfer techniques for performing domain adaptation, that is, the Fourier Transform and the CycleGAN architecture. We used these techniques to reduce the domain gap in two use cases: one with images collected by different robots with different cameras and another with images collected by the same platform in different years. We show how, in the first case, the CycleGAN architecture attains satisfying performance and beats the simpler Fourier Transform. Instead, in the second case, all the tested DA techniques struggle to reach baseline performance. We also show how introducing a loss based on phase discrepancy in the CycleGAN architecture stabilizes the training and improves the performance. Moreover, we release a new dataset of labeled agricultural images and the code of our experiments for the reproducibility of the results and comparison with future works.

In the last decades, Simultaneous Localization and Mapping (SLAM) proved to be a fundamental topic in the field of robotics, due to the many applications, ranging from autonomous driving to 3D reconstruction. Many systems have been proposed in literature exploiting a heterogeneous variety of sensors. State-of-the-art methods build their own map from scratch, using only data coming from the equipment of the robot, and not exploiting possible reconstructions of the environment. Moreover, temporary loss of data proves to be a challenge for SLAM systems, as it demands efficient re-localization to continue the localization process. In this paper, we present a SLAM system that exploits additional information coming from mapping services like OpenStreetMaps, hence the name OSM-SLAM, to face these issues. We extend an existing LiDAR-based Graph SLAM system, ART-SLAM, making it able to integrate the 2D geometry of buildings in the trajectory estimation process, by matching a prior OpenStreetMaps map with a single LiDAR scan. Each estimated pose of the robot is then associated with all buildings surrounding it. This association allows to improve localization accuracy, but also to adjust possible mistakes in the prior map. The pose estimates coming from SLAM are then jointly optimized with the constraints associated with the various OSM buildings, which can assume one of the following types: Buildings are always fixed (Prior SLAM); buildings surrounding a robot are movable in chunks, for every scan (Rigid SLAM); and every single building is free to move independently from the others (Non-rigid SLAM). Lastly, OSM maps can also be used to re-localize the robot when sensor data is lost. We compare the accuracy of the proposed system with existing methods for LiDAR-based SLAM, including the baseline, also providing a visual inspection of the results. The comparison is made by evaluating the estimated trajectory displacement using the KITTI odometry dataset. Moreover, the experimental campaign, along with an ablation study on the re-localization capabilities of the proposed system and its accuracy in loop detection-denied scenarios, allow a discussion about how the quality of prior maps influences the SLAM procedure, which may lead to worse estimates than the baseline.

HD-maps are one of the core components of the self-driving pipeline. Despite the effort of many companies to develop a completely independent vehicle, many state-of-the-art solutions rely on high-definition maps of the environment for localization and navigation. Nevertheless, the creation process of such maps can be complex and error-prone or expensive if performed via ad-hoc surveys. For this reason, robust automated solutions are required. One fundamental component of an high-definition map is traffic lights. In particular, traffic light detection has been a well-known problem in the autonomous driving field. Still, the focus has always been on the light state, not the features (i.e., shape, orientation, pictogram). This work presents a pipeline for lights HD-map creation designed to provide accurate georeferenced position and description of all traffic lights seen by a camera mounted on a surveying vehicle. Our algorithm considers consecutive detection of the same light and uses Kalman filtering techniques to provide each target’s smoother and more precise position. Our pipeline has been validated for the detection and mapping task using the state-of-the-art dataset DriveU Traffic Light Dataset. The results show that our model is robust even with noisy GPS data. Moreover, for the detection task, we highlight how our model can correctly identify even far-away targets which are not labeled in the original dataset.

Abstract Background Histone Mark Modifications (HMs) are crucial actors in gene regulation, as they actively remodel chromatin to modulate transcriptional activity: aberrant combinatorial patterns of HMs have been connected with several diseases, including cancer. HMs are, however, reversible modifications: understanding their role in disease would allow the design of ‘epigenetic drugs’ for specific, non-invasive treatments. Standard statistical techniques were not entirely successful in extracting representative features from raw HM signals over gene locations. On the other hand, deep learning approaches allow for effective automatic feature extraction, but at the expense of model interpretation. Results Here, we propose ShallowChrome, a novel computational pipeline to model transcriptional regulation via HMs in both an accurate and interpretable way. We attain state-of-the-art results on the binary classification of gene transcriptional states over 56 cell-types from the REMC database, largely outperforming recent deep learning approaches. We interpret our models by extracting insightful gene-specific regulative patterns, and we analyse them for the specific case of the PAX5 gene over three differentiated blood cell lines. Finally, we compare the patterns we obtained with the characteristic emission patterns of ChromHMM, and show that ShallowChrome is able to coherently rank groups of chromatin states w.r.t. their transcriptional activity. Conclusions In this work we demonstrate that it is possible to model HM-modulated gene expression regulation in a highly accurate, yet interpretable way. Our feature extraction algorithm leverages on data downstream the identification of enriched regions to retrieve gene-wise, statistically significant and dynamically located features for each HM. These features are highly predictive of gene transcriptional state, and allow for accurate modeling by computationally efficient logistic regression models. These models allow a direct inspection and a rigorous interpretation, helping to formulate quantifiable hypotheses.

Positioning and navigation represent relevant topics in the field of robotics, due to their multiple applications in real-world scenarios, ranging from autonomous driving to harsh environment exploration. Despite localization in outdoor environments is generally achieved using a Global Navigation Satellite System (GNSS) receiver, global navigation satellite system-denied environments are typical of many situations, especially in indoor settings. Autonomous robots are commonly equipped with multiple sensors, including laser rangefinders, IMUs, and odometers, which can be used for mapping and localization, overcoming the need for global navigation satellite system data. In literature, almost no information can be found on the positioning accuracy and precision of 6 Degrees of Freedom Light Detection and Ranging (LiDAR) localization systems, especially for real-world scenarios. In this paper, we present a short review of state-of-the-art light detection and ranging localization methods in global navigation satellite system-denied environments, highlighting their advantages and disadvantages. Then, we evaluate two state-of-the-art Simultaneous Localization and Mapping (SLAM) systems able to also perform localization, one of which implemented by us. We benchmark these two algorithms on manually collected dataset, with the goal of providing an insight into their attainable precision in real-world scenarios. In particular, we present two experimental campaigns, one indoor and one outdoor, to measure the precision of these algorithms. After creating a map for each of the two environments, using the simultaneous localization and mapping part of the systems, we compute a custom localization error for multiple, different trajectories. Results show that the two algorithms are comparable in terms of precision, having a similar mean translation and rotation errors of about 0.01 m and 0.6°, respectively. Nevertheless, the system implemented by us has the advantage of being modular, customizable and able to achieve real-time performance.

Abstract Background In-depth analysis of regulation networks of genes aberrantly expressed in cancer is essential for better understanding tumors and identifying key genes that could be therapeutically targeted. Results We developed a quantitative analysis approach to investigate the main biological relationships among different regulatory elements and target genes; we applied it to Ovarian Serous Cystadenocarcinoma and 177 target genes belonging to three main pathways (DNA REPAIR, STEM CELLS and GLUCOSE METABOLISM) relevant for this tumor. Combining data from ENCODE and TCGA datasets, we built a predictive linear model for the regulation of each target gene, assessing the relationships between its expression, promoter methylation, expression of genes in the same or in the other pathways and of putative transcription factors. We proved the reliability and significance of our approach in a similar tumor type (basal-like Breast cancer) and using a different existing algorithm (ARACNe), and we obtained experimental confirmations on potentially interesting results. Conclusions The analysis of the proposed models allowed disclosing the relations between a gene and its related biological processes, the interconnections between the different gene sets, and the evaluation of the relevant regulatory elements at single gene level. This led to the identification of already known regulators and/or gene correlations and to unveil a set of still unknown and potentially interesting biological relationships for their pharmacological and clinical use.

Background: The axilla is a region of fundamental importance for the implications during oncological surgery, and there are many classifications of axillary lymph node subdivision: on the basis of studies on women with breast cancer, we used Clough’s and Li’s classification. However, currently we do not have a gold-standard classification regarding axillary lymphatic drainage in melanoma patients. Purpose: Our aim was to evaluate how these classifications could be adapted to sentinel lymph node evaluation in skin-melanoma patients and to look for a possible correlation between the most recent classifications of axillary lymph node location and Oeslner’s classification, one of the most common anatomical classifications still widespread today. Methods: We analyzed data from 21 patients who underwent sentinel lymph node biopsy between January 2021 and January 2022. Results: Our study demonstrates that, to an extent, there is a possible difference in the use of the various classifications, hinting at possible limits of each. The data we obtained underline how cutaneous melanoma presents extremely heterogenous lymphatic drainage at the level of the axillary cavity. However, the limited data in our possession do not allow us to obtain, at the moment, results that are statistically significant, although we are continuing to enroll patients and collect data. Conclusions: Results of this study support the evidence that the common classifications used for breast cancer do not seem to be exhaustive. Therefore, a specific axillary lymph node classification is necessary in skin melanoma patients.

ObjectivesAlthough endorsed by international guidelines, complete revascularization (CR) with Coronary Artery Bypass Grafting (CABG) remains underused. In higher-risk patients such as those with pre-operative atrial fibrillation (AF), the effects of CR are not well studied.MethodsWe analyzed patients’ data from the HEIST (HEart surgery In AF and Supraventricular Tachycardia) registry. Between 2012 and 2020 we identified 4770 patients with pre-operative AF and multivessel coronary artery disease who underwent isolated CABG. We divided the cohort according to the completeness of the revascularization and used propensity score matching (PSM) to minimize differences between baseline characteristics. The primary endpoint was all-cause mortality.ResultsMedian follow-up was 4.7 years [interquartile range (IQR) 2.3–6.9]. PSM resulted in 1,009 pairs of complete and incomplete revascularization. Number of distal anastomoses varied, accounting for 3.0 + –0.6 vs. 1.7 + –0.6, respectively. Although early (< 24 h) and 30-day post-operative mortalities were not statistically different between non-CR and CR patients [Odds Ratio (OR) and 95% Confidence Intervals (CIs): 1.34 (0.46–3.86); P = 0.593, Hazard Ratio (HR) and 95% CIs: 0.88 (0.59–1.32); P = 0.542, respectively] the long term mortality was nearly 20% lower in the CR cohort [HR (95% CIs) 0.83 (0.71–0.96); P = 0.011]. This benefit was sustained throughout subgroup analyses, yet most accentuated in low-risk patients (younger i.e., < 70 year old, with a EuroSCORE II < 2%, non-diabetic) and when off-pump CABG was performed.ConclusionComplete revascularization in patients with pre-operative AF is safe and associated with improved survival. Particular survival benefit with CR was observed in low-risk patients undergoing off-pump CABG.

The application of digital technologies in agriculture can improve traditional practices to adapt to climate change, reduce Greenhouse Gases (GHG) emissions, and promote a sustainable intensification for food security. Some authors argued that we are experiencing a Digital Agricultural Revolution (DAR) that will boost sustainable farming. This study aims to find evidence of the ongoing DAR process and clarify its roots, what it means, and where it is heading. We investigated the scientific literature with bibliometric analysis tools to produce an objective and reproducible literature review. We retrieved 4995 articles by querying the Web of Science database in the timespan 2012-2019, and we analyzed the obtained dataset to answer three specific research questions: i) what is the spectrum of the DAR-related terminology?; ii) what are the key articles and the most influential journals, institutions, and countries?; iii) what are the main research streams and the emerging topics? By grouping the authors’ keywords reported on publications, we identified five main research streams: Climate-Smart Agriculture (CSA), Site-Specific Management (SSM), Remote Sensing (RS), Internet of Things (IoT), and Artificial Intelligence (AI). To provide a broad overview of each of these topics, we analyzed relevant review articles, and we present here the main achievements and the ongoing challenges. Finally, we showed the trending topics of the last three years (2017, 2018, 2019).

Dysgraphia is a neurodevelopmental disorder specific to handwriting. Classical diagnosis is based on the evaluation of speed and quality of the final handwritten text: it is therefore delayed as it is conducted only when handwriting is mastered, in addition to being highly language-dependent and not always easily accessible. This work presents a solution able to anticipate dysgraphia screening when handwriting has not been learned yet, in order to prevent negative consequences on the individuals’ academic and daily life. To quantitatively measure handwriting-related characteristics and monitor their evolution over time, we leveraged the Play-Draw-Write iPad application to collect data produced by children from the last year of kindergarten through the second year of elementary school. We developed a meta-model based on deep learning techniques (ensemble techniques and Quasi-SVM) which receives as input raw signals collected after a processing phase based on dimensionality reduction techniques (autoencoder and Time2Vec) and mathematical tools for high-level feature extraction (Procrustes Analysis). The final dysgraphia classifier can identify “at-risk” children with 84.62% Accuracy and 100% Precision more than two years earlier than current diagnostic techniques.

Dysgraphia is a learning disability that causes handwritten production below expectations. Its diagnosis is delayed until the completion of handwriting development. To allow a preventive training program, abilities not directly related to handwriting should be evaluated, and one of them is visual perception. To investigate the role of visual perception in handwriting skills, we gamified standard clinical visual perception tests to be played while wearing an eye tracker at three difficulty levels. Then, we identified children at risk of dysgraphia through the means of a handwriting speed test. Five machine learning models were constructed to predict if the child was at risk, using the CatBoost algorithm with Nested Cross-Validation, with combinations of game performance, eye-tracking, and drawing data as predictors. A total of 53 children participated in the study. The machine learning models obtained good results, particularly with game performances as predictors (F1 score: 0.77 train, 0.71 test). SHAP explainer was used to identify the most impactful features. The game reached an excellent usability score (89.4 ± 9.6). These results are promising to suggest a new tool for dysgraphia early screening based on visual perception skills.

Cardiogenic shock (CS) is a challenging syndrome, associated with significant morbidity and mortality. Although pharmacological therapies are successful and can successfully control this acute cardiac illness, some patients remain refractory to drugs. Therefore, a more aggressive treatment strategy is needed. Temporary mechanical circulatory support (TCS) can be used to stabilise patients with decompensated heart failure. In the last two decades, the increased use of TCS has led to several kinds of devices becoming available. However, indications for TCS and device selection are part of a complex process. It is necessary to evaluate the severity of CS, any early and prompt haemodynamic resuscitation, prior TCS, specific patient risk factors, technical limitations and adequacy of resources and training, as well as an assessment of whether care would be futile. This article examines options for commonly used TCS devices, including intra-aortic balloon pumps, a pulsatile percutaneous ventricular assist device (the iVAC), veno-arterial extra-corporeal membrane oxygenation and Impella (Abiomed) and TandemHeart (LivaNova) percutaneous ventricular assist device.

Abstract Background With the wide-spreading of public repositories of NGS processed data, the availability of user-friendly and effective tools for data exploration, analysis and visualization is becoming very relevant. These tools enable interactive analytics, an exploratory approach for the seamless “sense-making” of data through on-the-fly integration of analysis and visualization phases, suggested not only for evaluating processing results, but also for designing and adapting NGS data analysis pipelines. Results This paper presents abstractions for supporting the early analysis of NGS processed data and their implementation in an associated tool, named GenoMetric Space Explorer (GeMSE). This tool serves the needs of the GenoMetric Query Language, an innovative cloud-based system for computing complex queries over heterogeneous processed data. It can also be used starting from any text files in standard BED, BroadPeak, NarrowPeak, GTF, or general tab-delimited format, containing numerical features of genomic regions; metadata can be provided as text files in tab-delimited attribute-value format. GeMSE allows interactive analytics, consisting of on-the-fly cycling among steps of data exploration, analysis and visualization that help biologists and bioinformaticians in making sense of heterogeneous genomic datasets. By means of an explorative interaction support, users can trace past activities and quickly recover their results, seamlessly going backward and forward in the analysis steps and comparative visualizations of heatmaps. Conclusions GeMSE effective application and practical usefulness is demonstrated through significant use cases of biological interest. GeMSE is available at http://www.bioinformatics.deib.polimi.it/GeMSE/ , and its source code is available at https://github.com/Genometric/GeMSE under GPLv3 open-source license.

Detecting buildings in the surroundings of an urban vehicle and matching them to building models available on map services is an emerging trend in robotics localization for urban vehicles. In this paper, we present a novel technique, which improves a previous work by detecting building façade, their positions, and finding the correspondences with their 3D models, available in OpenStreetMap. The proposed technique uses segmented point clouds produced using stereo images, processed by a convolutional neural network. The point clouds of the façades are then matched against a reference point cloud, produced extruding the buildings’ outlines, which are available on OpenStreetMap (OSM). In order to produce a lane-level localization of the vehicle, the resulting information is then fed into our probabilistic framework, called Road Layout Estimation (RLE). We prove the effectiveness of this proposal, testing it on sequences from the well-known KITTI dataset and comparing the results concerning a basic RLE version without the proposed pipeline.

Incorrect pesticide use in plant protection often involve a risk to the health of operators and consumers and can have negative impacts on the environment and the crops. The application of artificial intelligence techniques can help the reduction of the volume sprayed, decreasing these impacts. In Italy, the production of ready-to-eat salad in greenhouses requires usually from 8 to 12 treatments per year. Moreover, inappropriate sprayers are frequently used, being originally designed for open-field operations. To solve this problem, a small vehicle suitable for moving over rough ground (named “rover”), was designed, able to carry out treatments based on a single row pass in the greenhouse, devoted to reduce significantly the sprayed product amount. To ascertain its potential, the prototype has been tested at two growth stages of some salad cultivars, adopting different nozzles and boom settings. Parameters such as boom height, nozzle spacing and inclination, pump pressure and rover traveling speed were studied. To assess the effectiveness of the spraying coverage, for each run several water-sensitive papers were placed throughout the vegetation. Compared to the commonly distributed mixture volume (1000 L/ha), the prototype is able to reduce up to 55% of product sprayed, but still assure an excellent crop coverage.

Lately, with deep learning outpacing the other machine learning techniques in classifying images, we have witnessed a growing interest of the remote sensing community in employing these techniques for the land use and land cover classification based on multispectral and hyperspectral images; the number of related publications almost doubling each year since 2015 is an attest to that. The advances in remote sensing technologies, hence the fast-growing volume of timely data available at the global scale, offer new opportunities for a variety of applications. Deep learning being significantly successful in dealing with Big Data, seems to be a great candidate for exploiting the potentials of such complex massive data. However, there are some challenges related to the ground-truth, resolution, and the nature of data that strongly impact the performance of classification. In this paper, we review the use of deep learning in land use and land cover classification based on multispectral and hyperspectral images and we introduce the available data sources and datasets used by literature studies; we provide the readers with a framework to interpret the-state-of-the-art of deep learning in this context and offer a platform to approach methodologies, data, and challenges of the field.

Speckle filtering is an unavoidable step when dealing with applications that involve amplitude or intensity images acquired by coherent systems, such as Synthetic Aperture Radar (SAR). Speckle is a target-dependent phenomenon; thus, its estimation and reduction require the individuation of specific properties of the image features. Speckle filtering is one of the most prominent topics in the SAR image processing research community, who has first tackled this issue using handcrafted feature-based filters. Even if classical algorithms have slowly and progressively achieved better and better performance, the more recent Convolutional-Neural-Networks (CNNs) have proven to be a promising alternative, in the light of the outstanding capabilities in efficiently learning task-specific filters. Currently, only simplistic CNN architectures have been exploited for the speckle filtering task. While these architectures outperform classical algorithms, they still show some weakness in the texture preservation. In this work, a deep encoder−decoder CNN architecture, focused in the specific context of SAR images, is proposed in order to enhance speckle filtering capabilities alongside texture preservation. This objective has been addressed through the adaptation of the U-Net CNN, which has been modified and optimized accordingly. This architecture allows for the extraction of features at different scales, and it is capable of producing detailed reconstructions through its system of skip connections. In this work, a two-phase learning strategy is adopted, by first pre-training the model on a synthetic dataset and by adapting the learned network to the real SAR image domain through a fast fine-tuning procedure. During the fine-tuning phase, a modified version of the total variation (TV) regularization was introduced to improve the network performance when dealing with real SAR data. Finally, experiments were carried out on simulated and real data to compare the performance of the proposed method with respect to the state-of-the-art methodologies.

People with mobility impairment must travel through cities with extra care. An intelligent database of Maps for Easy Paths (MEP) project for storing and retrieving accessibility information about a particular path by using PostGIS may enable the public to enjoy the privilege of the technology that alleviates the problem of city accessibility through the Web or, more recently, through smartphones/tablet applications. The methods and techniques for intelligent database to storeand retrieve accessibility information by using a relational intelligent PostGIS database are described. The conceptual data model is designed for an intelligent database (PostGIS). Users will be able to actively participate in the process of taking pictures of obstacles, uploading them to the MEP server and giving some comments on the attributes of the picture of the barrier. The resulting data will help others to locate and evaluate paths. This paper presents the conceptual data model design as well as a review of features of the data of the existing systems. Therefore, the novelty of this approach lies in developing an intelligent database for the enrichment of geographical maps with information about the accessibility of urban pedestrian areas for people with mobility problems.

Home Automation and Smart Homes diffusion are providing an interesting opportunity to implement elderly monitoring. This is a new valid technological support to allow in-place aging of seniors by means of a detection system to notify potential anomalies. Monitoring has been implemented by means of Complex Event Processing on live streams of home automation data: this allows the analysis of the behavior of the house inhabitant through quantitative indicators. Different kinds of quantitative indicators for monitoring and behavior drift detection have been identified and implemented using the Esper complex event processing engine. The chosen solution permits us not only to exploit the queries when run “online”, but enables also “offline” (re-)execution for testing and a posteriori analysis. Indicators were developed on both real world data and on realistic simulations. Tests were made on a dataset of 180 days: the obtained results prove that it is possible to evidence behavior changes for an evaluation of a person’s condition.

We investigated whether the covert orienting of visuospatial attention can be effectively used in a brain-computer interface guided by event-related potentials. Three visual interfaces were tested: one interface that activated voluntary orienting of visuospatial attention and two interfaces that elicited automatic orienting of visuospatial attention. We used two epoch classification procedures. The online epoch classification was performed via Independent Component Analysis, and then it was followed by fixed features extraction and support vector machines classification. The offline epoch classification was performed by means of a genetic algorithm that permitted us to retrieve the relevant features of the signal, and then to categorise the features with a logistic classifier. The offline classification, but not the online one, allowed us to differentiate between the performances of the interface that required voluntary orienting of visuospatial attention and those that required automatic orienting of visuospatial attention. The offline classification revealed an advantage of the participants in using the "voluntary" interface. This advantage was further supported, for the first time, by neurophysiological data. Moreover, epoch analysis was performed better with the "genetic algorithm classifier" than with the "independent component analysis classifier". We suggest that the combined use of voluntary orienting of visuospatial attention and of a classifier that permits feature extraction ad personam (i.e., genetic algorithm classifier) can lead to a more efficient control of visual BCIs.