Does combined training of biofeedback and neurofeedback affect smoking status, behavior, and longitudinal brain plasticity?

Introduction: Investigations of biofeedback (BF) and neurofeedback (NF) training for nicotine addiction have been long documented to lead to positive gains in smoking status, behavior and to changes in brain activity. We aimed to: (a) evaluate a multi-visit combined BF/NF intervention as an alternative smoking cessation approach, (b) validate training-induced feedback learning, and (c) document effects on resting-state functional connectivity networks (rsFCN); considering gender and degree of nicotine dependence in a longitudinal design.Methods: We analyzed clinical, behavioral, and electrophysiological data from 17 smokers who completed five BF and 20 NF sessions and three evaluation stages. Possible neuroplastic effects were explored comparing whole-brain rsFCN by phase-lag index (PLI) for different brain rhythms. PLI connections with significant change across time were investigated according to different resting-state networks (RSNs).Results: Improvements in smoking status were observed as exhaled carbon monoxide levels, Total Oxidative Stress, and Fageström scores decreased while Vitamin E levels increased across time. BF/NF promoted gains in anxiety, self-esteem, and several aspects of cognitive performance. BF learning in temperature enhancement was observed within sessions. NF learning in theta/alpha ratio increase was achieved across baselines and within sessions. PLI network connections significantly changed across time mainly between or within visual, default mode and frontoparietal networks in theta and alpha rhythms, while beta band RSNs mostly changed significantly after BF sessions.Discussion: Combined BF/NF training positively affects the clinical and behavioral status of smokers, displays benefit in smoking harm reduction, plays a neuroprotective role, leads to learning effects and to positive reorganization of RSNs across time.Clinical Trial Registration:https://clinicaltrials.gov/ct2/show/NCT02991781

Crowdsourced mapping of unexplored target space of kinase inhibitors

Despite decades of intensive search for compounds that modulate the activity of particular protein targets, a large proportion of the human kinome remains as yet undrugged. Effective approaches are therefore required to map the massive space of unexplored compound-kinase interactions for novel and potent activities. Here, we carry out a crowdsourced benchmarking of predictive algorithms for kinase inhibitor potencies across multiple kinase families tested on unpublished bioactivity data. We find the top-performing predictions are based on various models, including kernel learning, gradient boosting and deep learning, and their ensemble leads to a predictive accuracy exceeding that of single-dose kinase activity assays. We design experiments based on the model predictions and identify unexpected activities even for under-studied kinases, thereby accelerating experimental mapping efforts. The open-source prediction algorithms together with the bioactivities between 95 compounds and 295 kinases provide a resource for benchmarking prediction algorithms and for extending the druggable kinome. The IDG-DREAM Challenge carried out crowdsourced benchmarking of predictive algorithms for kinase inhibitor activities on unpublished data. This study provides a resource to compare emerging algorithms and prioritize new kinase activities to accelerate drug discovery and repurposing efforts

Collection, characterisation and evaluation of greek faba bean ( vicia faba L.) landraces

The object of this thesis was: a) the collection of faba bean landraces, b) their classification in an inexpensive way giving more weight to yield traits, c) the proposal of a new handling model for faba bean landraces, d) their characterization and evaluation and e) the creation of a new parameter in order to explore the rarest levels of trait in a germplasm collection or in group of the collection (after its classification). The data were obtained from four environments during a two-year experiment. No fertilizers or any other soil additives were applied. Finally, three multi-variate statistical methods were evaluated in order to find the method most suitable for classification purposes within faba bean using RAPD data. Fifty six faba bean landraces were collected. Their classification was carried out using data from the first year of experiment. Neighbor joining method for twenty two traits was combined with PCA for seven yield traits. Data from all environments were used for characterization and evaluation. The parameter R was created and Neighbor joining and Principal Coordinate Analysis were the most suitable methods for classification purposes within faba bean using RAPD data.Στόχος της διατριβής ήταν: α) η συλλογή εγχώριων πληθυσμών κουκιών, β) η ομαλοποίηση των πληθυσμών με τρόπο οικονομικό και δίνοντας βάρος στην απόδοση, γ) η πρόταση ενός προτύπου για τη διαχείριση εγχώριων πληθυσμών κουκιών, δ) ο χαρακτηρισμός και η αξιολόγησή τους και ε) η δημιουργία μιας παραμέτρου που θα βοηθά τους βελτιωτές να βρουν πληθυσμούς πλούσιους σε βαθμούς έκφρασης χαρακτηριστικών που είναι συχνά στη συλλογή ή σε ομάδα της συλλογής (που προέκυψαν μετά από ταξινόμηση). Έγιναν πειράματα σε περιβάλλοντα σε συνθήκες ξηρασίας και χωρίς την χρήση αγροχημικών. Τέλος η εύρεση της καταλληλότερης μεθόδου πολυμεταβλητής στατιστικής για τη ταξινόμηση γονοτύπων κουκιών με RAPD δεδομένα. Επιλέχθηκαν 56 εγχώριοι πληθυσμοί. Για την ομαδοποίηση χρησιμοποιήθηκαν δεδομένα μόνο από την πρώτη χρονιά και έγινε συνδυασμός της Σύνθεσης Γειτόνων για 22 χαρακτηριστικά και της Ανάλυσης Κυρίων Συστατικών για 7 αγρονομικά που σχετίζονται με την απόδοση. Οι πληθυσμοί χαρακτηρίστηκαν χρησιμοποιώντας μορφολογικά χαρακτηριστικά ενώ αξιολογήθηκαν με αγρονομικά (από 4 περιβάλλοντα δεδομένα). Δημιουργήθηκε η παράμετρος R και βρέθηκε ως καταλληλότερη για RAPD δεδομένα η Σύνθεση Γειτόνων και Ανάλυση Κύριων Συντεταγμένων

A Promising Technological Approach to Improve Indoor Air Quality

Indoor Air quality (IAQ) in private or public environments is progressively recognized as a critical issue for human health. For that purpose the poor IAQ needs to be mitigated and immediate drastic measures must be taken. In environmental science and especially in advanced oxidation processes and technologies (AOPs-AOTs), photocatalysis has gained considerable interest among scientists as a tool for IAQ improvement. In the current study an innovative paint material was developed which exhibits intense photocatalytic activity under direct and diffused visible light for the degradation of air pollutants, suitable for indoor use. A laboratory and a real scale study were performed using the above innovative photo-paint. The lab test was performed in a special design photo-reactor while the real scale in a military’s medical building. Nitrogen Oxide (NO) and Toluene concentration was monitored between “reference” rooms (without photo paint) and “green” rooms (with photo-paint) in order to estimate the photocatalytic efficiency of the photo-paint to degrade the above pollutants. Results of the study showed a decrease up to 60% and 16% for NO and toluene respectively under lab scale tests while an improvement of air quality up to 19% and 5% under real world conditions was achieved

Functional Brain Connectivity during Multiple Motor Imagery Tasks in Spinal Cord Injury

Reciprocal communication of the central and peripheral nervous systems is compromised during spinal cord injury due to neurotrauma of ascending and descending pathways. Changes in brain organization after spinal cord injury have been associated with differences in prognosis. Changes in functional connectivity may also serve as injury biomarkers. Most studies on functional connectivity have focused on chronic complete injury or resting-state condition. In our study, ten right-handed patients with incomplete spinal cord injury and ten age- and gender-matched healthy controls performed multiple visual motor imagery tasks of upper extremities and walking under high-resolution electroencephalography recording. Directed transfer function was used to study connectivity at the cortical source space between sensorimotor nodes. Chronic disruption of reciprocal communication in incomplete injury could result in permanent significant decrease of connectivity in a subset of the sensorimotor network, regardless of positive or negative neurological outcome. Cingulate motor areas consistently contributed the larger outflow (right) and received the higher inflow (left) among all nodes, across all motor imagery categories, in both groups. Injured subjects had higher outflow from left cingulate than healthy subjects and higher inflow in right cingulate than healthy subjects. Alpha networks were less dense, showing less integration and more segregation than beta networks. Spinal cord injury patients showed signs of increased local processing as adaptive mechanism. This trial is registered with NCT02443558

Production, Validation and Morphometric Analysis of a Digital Terrain Model for Lake Trichonis Using Geospatial Technologies and Hydroacoustics

Covering an area of approximately 97 km2 and with a maximum depth of 58 m, Lake Trichonis is the largest and one of the deepest natural lakes in Greece. As such, it constitutes an important ecosystem and freshwater reserve at the regional scale, whose qualitative and quantitative properties ought to be monitored. Depth is a crucial parameter, as it is involved in both qualitative and quantitative monitoring aspects. Thus, the availability of a bathymetric model and a reliable DTM (Digital Terrain Model) of such an inland water body is imperative for almost any systematic observation scenario or ad hoc measurement endeavor. In this context, the purpose of this study is to produce a DTM from the only official cartographic source of relevant information available (dating back approximately 70 years) and evaluate its performance against new, independent, high-accuracy hydroacoustic recordings. The validation procedure involves the use of echosoundings coupled with GPS, and is followed by the production of a bathymetric model for the assessment of the discrepancies between the DTM and the measurements, along with the relevant morphometric analysis. Both the production and validation of the DTM are conducted in a GIS environment. The results indicate substantial discrepancies between the old DTM and contemporary acoustic data. A significant overall deviation of 3.39 ± 5.26 m in absolute bottom elevation differences and 0.00 ± 7.26 m in relative difference residuals (0.00 ± 2.11 m after 2nd polynomial model corrector surface fit) of the 2019 bathymetric dataset with respect to the ~1950 lake DTM and overall morphometry appear to be associated with a combination of tectonics, subsidence and karstic phenomena in the area. These observations could prove useful for the tectonics, geodynamics and seismicity with respect to the broader Corinth Rift region, as well as for environmental management and technical interventions in and around the lake. This dictates the necessity for new, extensive bathymetric measurements in order to produce an updated DTM of Lake Trichonis, reflecting current conditions and tailored to contemporary accuracy standards and state-of-the-art research in various disciplines in and around the lake

A Non-Invasive Photonics-Based Device for Monitoring of Diabetic Foot Ulcers: Architectural/Sensorial Components & Technical Specifications

This paper proposes a new photonic-based non-invasive device for managing of Diabetic Foot Ulcers (DFUs) for people suffering from diabetes. DFUs are one of the main severe complications of diabetes, which may lead to major disabilities, such as foot amputation, or even to the death. The proposed device exploits hyperspectral (HSI) and thermal imaging to measure the status of an ulcer, in contrast to the current practice where invasive biopsies are often applied. In particular, these two photonic-based imaging techniques can estimate the biomarkers of oxyhaemoglobin (HbO2) and deoxyhaemoglobin (Hb), through which the Peripheral Oxygen Saturation (SpO2) and Tissue Oxygen Saturation (StO2) is computed. These factors are very important for the early prediction and prognosis of a DFU. The device is implemented at two editions: the in-home edition suitable for patients and the PRO (professional) edition for the medical staff. The latter is equipped with active photonic tools, such as tuneable diodes, to permit detailed diagnosis and treatment of an ulcer and its progress. The device is enriched with embedding signal processing tools for noise removal and enhancing pixel accuracy using super resolution schemes. In addition, a machine learning framework is adopted, through deep learning structures, to assist the doctors and the patients in understanding the effect of the biomarkers on DFU. The device is to be validated at large scales at three European hospitals (Charité–University Hospital in Berlin, Germany; Attikon in Athens, Greece, and Victor Babes in Timisoara, Romania) for its efficiency and performance

A non-invasive photonics-based device for monitoring of diabetic foot ulcers: Architectural/sensorial components & technical specifications

This paper proposes a new photonic-based non-invasive device for managing of Diabetic Foot Ulcers (DFUs) for people suffering from diabetes. DFUs are one of the main severe complications of diabetes, which may lead to major disabilities, such as foot amputation, or even to the death. The proposed device exploits hyperspectral (HSI) and thermal imaging to measure the status of an ulcer, in contrast to the current practice where invasive biopsies are often applied. In particular, these two photonic-based imaging techniques can estimate the biomarkers of oxyhaemoglobin (HbO2) and deoxyhaemoglobin (Hb), through which the Peripheral Oxygen Saturation (SpO2) and Tissue Oxygen Saturation (StO2) is computed. These factors are very important for the early prediction and prognosis of a DFU. The device is implemented at two editions: the in-home edition suitable for patients and the PRO (professional) edition for the medical staff. The latter is equipped with active photonic tools, such as tuneable diodes, to permit detailed diagnosis and treatment of an ulcer and its progress. The device is enriched with embedding signal processing tools for noise removal and enhancing pixel accuracy using super resolution schemes. In addition, a machine learning framework is adopted, through deep learning structures, to assist the doctors and the patients in understanding the effect of the biomarkers on DFU. The device is to be validated at large scales at three European hospitals (Charité–University Hospital in Berlin, Germany; Attikon in Athens, Greece, and Victor Babes in Timisoara, Romania) for its efficiency and performance

Neurorehabilitation Through Synergistic Man-Machine Interfaces Promoting Dormant Neuroplasticity in Spinal Cord Injury: Protocol for a Nonrandomized Controlled Trial

Background: Spinal cord injury (SCI) constitutes a major sociomedical problem, impacting approximately 0.32-0.64 million people each year worldwide; particularly, it impacts young individuals, causing long-term, often irreversible disability. While effective rehabilitation of patients with SCI remains a significant challenge, novel neural engineering technologies have emerged to target and promote dormant neuroplasticity in the central nervous system. Objective: This study aims to develop, pilot test, and optimize a platform based on multiple immersive man-machine interfaces offering rich feedback, including (1) visual motor imagery training under high-density electroencephalographic recording, (2) mountable robotic arms controlled with a wireless brain-computer interface (BCI), (3) a body-machine interface (BMI) consisting of wearable robotics jacket and gloves in combination with a serious game (SG) application, and (4) an augmented reality module. The platform will be used to validate a self-paced neurorehabilitation intervention and to study cortical activity in chronic complete and incomplete SCI at the cervical spine. Methods: A 3-phase pilot study (clinical trial) was designed to evaluate the NeuroSuitUp platform, including patients with chronic cervical SCI with complete and incomplete injury aged over 14 years and age-/sex-matched healthy participants. Outcome measures include BCI control and performance in the BMI-SG module, as well as improvement of functional independence, while also monitoring neuropsychological parameters such as kinesthetic imagery, motivation, self-esteem, depression and anxiety, mental effort, discomfort, and perception of robotics. Participant enrollment into the main clinical trial is estimated to begin in January 2023 and end by December 2023. Results: A preliminary analysis of collected data during pilot testing of BMI-SG by healthy participants showed that the platform was easy to use, caused no discomfort, and the robotics were perceived positively by the participants. Analysis of results from the main clinical trial will begin as recruitment progresses and findings from the complete analysis of results are expected in early 2024. Conclusions: Chronic SCI is characterized by irreversible disability impacting functional independence. NeuroSuitUp could provide a valuable complementary platform for training in immersive rehabilitation methods to promote dormant neural plasticity