Tools for Brain-Computer Interaction: A General Concept for a Hybrid BCI

The aim of this work is to present the development of a hybrid Brain-Computer Interface (hBCI) which combines existing input devices with a BCI. Thereby, the BCI should be available if the user wishes to extend the types of inputs available to an assistive technology system, but the user can also choose not to use the BCI at all; the BCI is active in the background. The hBCI might decide on the one hand which input channel(s) offer the most reliable signal(s) and switch between input channels to improve information transfer rate, usability, or other factors, or on the other hand fuse various input channels. One major goal therefore is to bring the BCI technology to a level where it can be used in a maximum number of scenarios in a simple way. To achieve this, it is of great importance that the hBCI is able to operate reliably for long periods, recognizing and adapting to changes as it does so. This goal is only possible if many different subsystems in the hBCI can work together. Since one research institute alone cannot provide such different functionality, collaboration between institutes is necessary. To allow for such a collaboration, a new concept and common software framework is introduced. It consists of four interfaces connecting the classical BCI modules: signal acquisition, preprocessing, feature extraction, classification, and the application. But it provides also the concept of fusion and shared control. In a proof of concept, the functionality of the proposed system was demonstrated

Preface of the 31st Italian Symposium on Advanced Database Systems

This volume contains the proceedings of the 31st Italian Symposium on Advanced Database Systems (SEBD - Sistemi Evoluti per Basi di Dati), held in Galzinagno Terme (Padua, Italy) from 2 to 5 July 2023.</p

Preface of the 31st Italian Symposium on Advanced Database Systems

This volume contains the proceedings of the 31st Italian Symposium on Advanced Database Systems (SEBD - Sistemi Evoluti per Basi di Dati), held in Galzinagno Terme (Padua, Italy) from 2 to 5 July 2023.</p

Common and rare variant association analyses in amyotrophic lateral sclerosis identify 15 risk loci with distinct genetic architectures and neuron-specific biology

A cross-ancestry genome-wide association meta-analysis of amyotrophic lateral sclerosis (ALS) including 29,612 patients with ALS and 122,656 controls identifies 15 risk loci with distinct genetic architectures and neuron-specific biology. Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease with a lifetime risk of one in 350 people and an unmet need for disease-modifying therapies. We conducted a cross-ancestry genome-wide association study (GWAS) including 29,612 patients with ALS and 122,656 controls, which identified 15 risk loci. When combined with 8,953 individuals with whole-genome sequencing (6,538 patients, 2,415 controls) and a large cortex-derived expression quantitative trait locus (eQTL) dataset (MetaBrain), analyses revealed locus-specific genetic architectures in which we prioritized genes either through rare variants, short tandem repeats or regulatory effects. ALS-associated risk loci were shared with multiple traits within the neurodegenerative spectrum but with distinct enrichment patterns across brain regions and cell types. Of the environmental and lifestyle risk factors obtained from the literature, Mendelian randomization analyses indicated a causal role for high cholesterol levels. The combination of all ALS-associated signals reveals a role for perturbations in vesicle-mediated transport and autophagy and provides evidence for cell-autonomous disease initiation in glutamatergic neurons

Endovascular Bail Out for a Failed Transcatheter Aortic Valve Implantation in High Surgical Risk Patient

The endovascular treatment for acute type A dissection (ATAD) represents an alternative and emerging option in selected high surgical risk patients. We report a successful total endovascular ATAD repair occurred intraoperatively during transcatheter aortic valve implantation (TAVI) placement in 82 years old female, not fit for surgery in emergency setting. The presentation, the diagnostic evaluation, and the technique are discussed. This case would support the feasibility and efficacy of the stent graft technology to treat ATADs after evaluation of clinical, anatomical, and radiological parameters

Continuous Intraoperative Neuromonitoring (C-IONM) Technique with the Automatic Periodic Stimulating (APS) Accessory for Conventional and Endoscopic Thyroid Surgery

One of the most important trends in intraoperative neural monitoring (IONM) in thyroid surgery is currently the real-time monitoring of the vagus nerve (VN) in order to prevent recurrent laryngeal nerve (RLN) iatrogenic damages. Notably, continuous intraoperative neuromonitoring (C-IONM) seems to be superior to intermitted intraoperative neural monitoring (I-IONM) because it enhances standardization by permanent vagus nerve (VN) stimulation, and it provides entire and constant RLN function monitoring as the surgeon dissects and removes the thyroid gland. It also has to be highlighted that the surgical maneuvers for the automatic periodic stimulating (APS) placement must be accurate and standardized in order to avoid a potential iatrogenic morbidity on the VN function. We recommend the experienced surgeon be very careful in each step, with cautious dissection. With this review article we provide a comprehensive analyses of C-IONM technique with the APS accessory for conventional and endoscopic thyroid surger

Electrospindle 4.0: Towards Zero Defect Manufacturing of Spindles

Industry 4.0 represents the last evolution of manufacturing. With respect to Industry 3.0, which introduced the digital interconnection of machinery with monitoring and control systems, the fourth industrial revolution extends this concept to sensors, products and any kind of object or actor (thing) involved in the process. The tremendous amount of data produced is intended to be analyzed by applying methods from artificial intelligence, machine learning and data mining. One of the objectives of such analysis is Zero Defect Manufacturing, i.e., a manufacturing process where acquired data during the entire life cycle of products are used to continuously improve the product design in order to provide customers with unprecedented quality guarantees. In this paper, we discuss the goals of the Electrospindle 4.0 project, which aims at applying Zero Defect Manufacturing principles to the production of spindles