Multi-Stream 1D CNN for EEG Motor Imagery Classification of Limbs Activation

Determining the motor intentions of an individual through the analysis of electroencephalograms (EEGs) is a challenging task that concurrently holds considerable potential in aiding subjects with motor dysfunctions. Moreover, thanks to the recent advances in artificial intelligence models and EEG acquisition devices, such analyses can be carried out with ever higher accuracy. The latter aspect covers great importance, since the EEG analysis of subjects whose mental efforts are focused on moving limbs is frequently used for crucial tasks, including the control of interactive interfaces and prosthetic devices. In this paper, a novel multi-stream 1D Convolutional Neural Network (CNN) architecture is proposed. The input EEG signal is processed by four convolutional streams, which differ in the size of convolutional kernels, thus allowing the extraction of information at different time scales. The resulting 1D feature maps are then fused together and provided to a dense classifier to identify which limb the subject intended to move. Comprehensive experiments conducted on PhysioNet EEG motor movement/imagery dataset, which remains the reference collection of data in this application context, have demonstrated that the proposed model surpasses the key works in the current state-of-the-art in both cross-subject and intra-subject settings

A Novel Transformer-Based IMU Self-Calibration Approach through On-Board RGB Camera for UAV Flight Stabilization

During flight, unmanned aerial vehicles (UAVs) need several sensors to follow a predefined path and reach a specific destination. To this aim, they generally exploit an inertial measurement unit (IMU) for pose estimation. Usually, in the UAV context, an IMU entails a three-axis accelerometer and a three-axis gyroscope. However, as happens for many physical devices, they can present some misalignment between the real value and the registered one. These systematic or occasional errors can derive from different sources and could be related to the sensor itself or to external noise due to the place where it is located. Hardware calibration requires special equipment, which is not always available. In any case, even if possible, it can be used to solve the physical problem and sometimes requires removing the sensor from its location, which is not always feasible. At the same time, solving the problem of external noise usually requires software procedures. Moreover, as reported in the literature, even two IMUs from the same brand and the same production chain could produce different measurements under identical conditions. This paper proposes a soft calibration procedure to reduce the misalignment created by systematic errors and noise based on the grayscale or RGB camera built-in on the drone. Based on the transformer neural network architecture trained in a supervised learning fashion on pairs of short videos shot by the UAV’s camera and the correspondent UAV measurements, the strategy does not require any special equipment. It is easily reproducible and could be used to increase the trajectory accuracy of the UAV during the flight

Role of heme oxygenase-1 (HSP32) and HSP90 in glioblastoma

Glioblastoma (GBM) is the most common and malignant primary brain tumor in adults. The current treatment regimes for glioblastoma demonstrated a low efficiency and offer a poor prognosis. Advancements in conventional treatment strategies have only yielded modest improvements in overall survival. The heat shockproteins, heme oxygenase-1 (HO-1) and Hsp90, serve these pivotal roles in tumor cells and have been identified as effective targets for developing therapeutics. This topic review summarizes the current preclinical and clinical evidences and rationale to define the potential of HO-1 and Hsp90 in GBM progression and chemoresistance

Spatio-Temporal Image-Based Encoded Atlases for EEG Emotion Recognition

Emotion recognition plays an essential role in human-human interaction since it is a key to understanding the emotional states and reactions of human beings when they are subject to events and engagements in everyday life. Moving towards human-computer interaction, the study of emotions becomes fundamental because it is at the basis of the design of advanced systems to support a broad spectrum of application areas, including forensic, rehabilitative, educational, and many others. An effective method for discriminating emotions is based on ElectroEncephaloGraphy (EEG) data analysis, which is used as input for classification systems. Collecting brain signals on several channels and for a wide range of emotions produces cumbersome datasets that are hard to manage, transmit, and use in varied applications. In this context, the paper introduces the Empátheia system, which explores a different EEG representation by encoding EEG signals into images prior to their classification. In particular, the proposed system extracts spatio-temporal image encodings, or atlases, from EEG data through the Processing and transfeR of Interaction States and Mappings through Image-based eNcoding (PRISMIN) framework, thus obtaining a compact representation of the input signals. The atlases are then classified through the Empátheia architecture, which comprises branches based on convolutional, recurrent, and transformer models designed and tuned to capture the spatial and temporal aspects of emotions. Extensive experiments were conducted on the Shanghai Jiao Tong University (SJTU) Emotion EEG Dataset (SEED) public dataset, where the proposed system significantly reduced its size while retaining high performance. The results obtained highlight the effectiveness of the proposed approach and suggest new avenues for data representation in emotion recognition from EEG signals

A Novel GAN-Based Anomaly Detection and Localization Method for Aerial Video Surveillance at Low Altitude

The last two decades have seen an incessant growth in the use of Unmanned Aerial Vehicles (UAVs) equipped with HD cameras for developing aerial vision-based systems to support civilian and military tasks, including land monitoring, change detection, and object classification. To perform most of these tasks, the artificial intelligence algorithms usually need to know, a priori, what to look for, identify. or recognize. Actually, in most operational scenarios, such as war zones or post-disaster situations, areas and objects of interest are not decidable a priori since their shape and visual features may have been altered by events or even intentionally disguised (e.g., improvised explosive devices (IEDs)). For these reasons, in recent years, more and more research groups are investigating the design of original anomaly detection methods, which, in short, are focused on detecting samples that differ from the others in terms of visual appearance and occurrences with respect to a given environment. In this paper, we present a novel two-branch Generative Adversarial Network (GAN)-based method for low-altitude RGB aerial video surveillance to detect and localize anomalies. We have chosen to focus on the low-altitude sequences as we are interested in complex operational scenarios where even a small object or device can represent a reason for danger or attention. The proposed model was tested on the UAV Mosaicking and Change Detection (UMCD) dataset, a one-of-a-kind collection of challenging videos whose sequences were acquired between 6 and 15 m above sea level on three types of ground (i.e., urban, dirt, and countryside). Results demonstrated the effectiveness of the model in terms of Area Under the Receiving Operating Curve (AUROC) and Structural Similarity Index (SSIM), achieving an average of 97.2% and 95.7%, respectively, thus suggesting that the system can be deployed in real-world applications

Data integration by two-sensors in a LEAP-based Virtual Glove for human-system interaction

Virtual Glove (VG) is a low-cost computer vision system that utilizes two orthogonal LEAP motion sensors to provide detailed 4D hand tracking in real-time. VG can find many applications in the field of human-system interaction, such as remote control of machines or tele-rehabilitation. An innovative and efficient data-integration strategy, based on the velocity calculation, for selecting data from one of the LEAPs at each time, is proposed for VG. The position of each joint of the hand model, when obscured to a LEAP, is guessed and tends to flicker. Since VG uses two LEAP sensors, two spatial representations are available each moment for each joint: the method consists of the selection of the one with the lower velocity at each time instant. Choosing the smoother trajectory leads to VG stabilization and precision optimization, reduces occlusions (parts of the hand or handling objects obscuring other hand parts) and/or, when both sensors are seeing the same joint, reduces the number of outliers produced by hardware instabilities. The strategy is experimentally evaluated, in terms of reduction of outliers with respect to a previously used data selection strategy on VG, and results are reported and discussed. In the future, an objective test set has to be imagined, designed, and realized, also with the help of an external precise positioning equipment, to allow also quantitative and objective evaluation of the gain in precision and, maybe, of the intrinsic limitations of the proposed strategy. Moreover, advanced Artificial Intelligence-based (AI-based) real-time data integration strategies, specific for VG, will be designed and tested on the resulting dataset. (c) 2021, The Author(s)

Italian Society of Rheumatology recommendations for the management of gout.

Objective: Gout is the most common arthritis in adults. Despite the availability of valid therapeutic options, the management of patients with gout is still suboptimal. The Italian Society of Rheumatology (SIR) aimed to update, adapt to national contest and disseminate the 2006 EULAR recommendations for the management of gout. Methods: The multidisciplinary group of experts included rheumatologists, general practitioners, internists, geriatricians, nephrologists, cardiologists and evidence-based medicine experts. To maintain consistency with EULAR recommendations, a similar methodology was utilized by the Italian group. The original propositions were translated in Italian and priority research queries were identified through a Delphi consensus approach. A systematic search was conducted for selected queries. Efficacy and safety data on drugs reported in RCTs were combined in a meta-analysis where feasible. The strength of recommendation was measured by utilising the EULAR ordinal and visual analogue scales. Results: The original 12 propositions were translated and adapted to Italian context. Further evidences were collected about the role of diet in the non-pharmacological treatment of gout and the efficacy of oral corticosteroids and low-dose colchicine in the management of acute attacks. Statements concerning uricosuric treatments were withdrawn and replaced with a proposition focused on a new urate lowering agent, febuxostat. A research agenda was developed to identify topics still not adequately investigated concerning the management of gout. Conclusions: The SIR has developed updated recommendations for the management of gout adapted to the Italian healthcare system. Their implementation in clinical practice is expected to improve the management of patients with gout

Guideline Application in Real world: multi-Institutional Based survey of Adjuvant and first-Line pancreatic Ductal adenocarcinoma treatment in Italy. Primary analysis of the GARIBALDI survey

Background: Information about the adherence to scientific societies guidelines in the ‘real-world’ therapeutic management of oncological patients are lacking. This multicenter, prospective survey was aimed to improve the knowledge relative to 2017-2018 recommendations of the Italian Association of Medical Oncology (AIOM). Patients and methods: Treatment-naive adult patients with pancreatic adenocarcinoma were enrolled. Group A received adjuvant therapy, group B received primary chemotherapy, and group C had metastatic disease. The results on patients accrued until 31 October 2019 with a mature follow-up were presented. Results: Since July 2017, 833 eligible patients of 923 (90%) were enrolled in 44 Italian centers. The median age was 69 years (range 36-89 years; 24% >75 years); 48% were female; 93% had Eastern Cooperative Oncology Group (ECOG) performance status (PS) score of 0 or 1; group A: 16%, group B: 30%; group C: 54%; 72% Nord, 13% Center, 15% South. In group A, guidelines adherence was 68% [95% confidence interval (CI) 59% to 76%]; 53% of patients received gemcitabine and 15% gemcitabine + capecitabine; median CA19.9 was 29 (range 0-7300; not reported 15%); median survival was 36.4 months (95% CI 27.5-47.3 months). In group B, guidelines adherence was 96% (95% CI 92% to 98%); 55% of patients received nab-paclitaxel + gemcitabine, 27% FOLFIRINOX, 12% gemcitabine, and 3% clinical trial; median CA19.9 was 337 (range 0-20220; not reported 9%); median survival was 18.1 months (95% CI 15.6-19.9 months). In group C, guidelines adherence was 96% (95% CI 94% to 98%); 71% of patients received nab-paclitaxel + gemcitabine, 16% gemcitabine, 8% FOLFIRINOX, and 4% clinical trial; liver and lung metastases were reported in 76% and 23% of patients, respectively; median CA19.9 value was 760 (range 0-1374500; not reported 9%); median survival was 10.0 months (95% CI 9.1-11.1 months). Conclusions: The GARIBALDI survey shows a very high rate of adherence to guidelines and survival outcome in line with the literature. CA19.9 testing should be enhanced; nutritional and psychological counseling represent an unmet need. Enrollment to assess adherence to updated AIOM guidelines is ongoing

Rural World, Migration, and Agriculture in Mediterranean EU: An Introduction

AbstractThis book investigates the dynamics that are reshaping human and natural landscapes in the European agrarian world, with a specific focus on Mediterranean Europe. We focus here on more marginal rural settings, where the potential for agricultural intensification is structurally limited. These areas in particular have suffered from the geographical and socio-economic polarization of development patterns and have paid a relevant burden to the recent crisis