Low Frequency Brain Oscillations during the execution and imagination of simple hand movements for Brain-Computer Interface applications.

Low Frequency Brain Oscillations (LFOs) are brief periods of oscillatory activity in delta and lower theta band that appear at motor cortical areas before and around movement onset. It has been shown that LFO power decreases in post-stroke patients and re-emerges with motor functional recovery. To date, LFOs have not yet been explored during the motor execution (ME) and imagination (MI) of simple hand movements, often used in BCI-supported motor rehabilitation protocols post-stroke. This study aims at analyzing the LFOs during the ME and MI of the finger extension task in a sample of 10 healthy subjects and 2 stroke patients in subacute phase. The results showed that LFO power peaks occur in the preparatory phase of both ME and MI tasks on the sensorimotor channels in healthy subjects and their alterations in stroke patients. Clinical Relevance- Results suggest that LFOs could be explored as biomarker of the motor function recovery in rehabilitative protocols based on the movement imagination.

Cortico-Muscular Coupling Allows to Discriminate Different Types of Hand Movements.

Cortico-muscular coupling (CMC) could be used as potential input of a novel hybrid Brain-Computer Interface (hBCI) for motor re-learning after stroke. Here, we aim of addressing the design of a hBCI able to classify different movement tasks taking into account the interplay between the cerebral and residual or recovered muscular activity involved in a given movement. Hence, we compared the performances of four classification methods based on CMC features to evaluate their ability in discriminating finger extension from grasping movements executed by 17 healthy subjects. We also explored how the variation in the dimensionality of the feature domain would influence the different classifier performances. Results showed that, regardless of the model, few CMC features (up to 10) allow for a successful classification of two different movements type. Moreover, support vector machine classifier with linear kernel showed the best trade-off between performances and system usability (few electrodes). Thus, these results suggest that a hBCI based on brain-muscular interplay holds the potential to enable more informed neural plasticity and functional motor recovery after stroke. Furthermore, this CMC-based BCI could also allow for a more "natural control" (l.e., that resembling physiological control) of prosthetic devices.

Distinctive physiological muscle synergy patterns define the Box and Block Task execution as revealed by electromyographic features.

Stroke survivors experience muscular pattern alterations of the upper limb that decrease their ability to perform daily-living activities. The Box and Block test (BBT) is widely used to assess the unilateral manual dexterity. Although BBT provides insights into functional performance, it returns limited information about the mechanisms contributing to the impaired movement. This study aims at exploring the BBT by means of muscle synergies analysis during the execution of BBT in a sample of 12 healthy participants with their dominant and non-dominant upper limb. Results revealed that: (i) the BBT can be described by 1 or 2 synergies; the number of synergies (ii) does not differ between dominant and non-dominant sides and (iii) varies considering each phase of the task; (iv) the transfer phase requires more synergies. Clinical Relevance- This preliminary study characterizes muscular synergies during the BBT task in order to establish normative patterns that could assist in understanding the neuromuscular demands and support future evaluations of stroke deficits.

Untangling the physics of water transport in boron nitride nanotubes.

Carbon nanotubes (CNTs) have long been heralded as the material of choice for next-generation membranes. Some studies have suggested that boron nitride nanotubes (BNNTs) may offer higher transport of pure water than CNTs, while others conclude otherwise. In this work, we use a combination of simulations and experimental data to uncover the causes of this discrepancy and investigate the flow resistance through BNNT membranes in detail. By dividing the resistance of the nanotube membranes into their contributing components, we study the effects of pore end configuration, membrane length, and BNNT atom partial charges. Most molecular simulation studies of BNNT membranes use short membranes connected to high and low pressure reservoirs. Here we find that flow resistances in these short membranes are dominated by the resistance at the pore ends, which can obscure the understanding of water transport performance through the nanotubes and comparison between different nanotube materials. In contrast, it is the flow resistance inside the nanotubes that dominates microscale-thick laboratory membranes, and end resistances tend to be negligible. Judged by the nanotube flow resistance alone, we therefore find that CNTs are likely to consistently outperform BNNTs. Furthermore, we find a large role played by the choice of partial charges on the BN atoms in the flow resistance measurements in our molecular simulations. This paper highlights a way forward for comparing molecular simulations and experimental results.

SARS-CoV-2 multi-variant graphene biosensor based on engineered dimeric ACE2 receptor

Fast, reliable and point-of-care systems to detect the SARS-CoV-2 infection are crucial to contain viral spreading and to adopt timely clinical treatments. Many of the rapid detection tests currently in use are based on antibodies that bind viral proteins1. However, newly appearing virus variants accumulate mutations in their RNA sequence and produce proteins, such as Spike, that may show reduced binding affinity to these diagnostic antibodies, resulting in less reliable tests and in the need for continuous update of the sensing systems2. Here we propose a graphene field-effect transistor (gFET) biosensor which exploits the key interaction between the Spike protein and the human ACE2 receptor. This interaction is one of the determinants of host infections and indeed recently evolved Spike variants were shown to increase affinity for ACE2 receptor3. Through extensive computational analyses we show that a chimeric ACE2-Fc construct mimics the ACE2 dimer, normally present on host cells membranes, better than its soluble truncated form. We demonstrate that ACE2-Fc functionalized gFET is effective for in vitro detection of Spike and outperforms the same chip functionalized with either a diagnostic antibody or the soluble ACE2. Our sensor is implemented in a portable, wireless, point-of-care device and successfully detected both alpha and gamma virus variants in patients clinical samples. As incomplete immunization, due to vaccine roll-out, may offer new selective grounds for antibody-escaping virus variants4, our biosensor opens to a class of highly sensitive, rapid and variant-robust SARS-CoV-2 detection systems.

A guide to plasma membrane solute carrier proteins.

This review aims to serve as an introduction to the solute carrier proteins (SLC) superfamily of transporter proteins and their roles in human cells. The SLC superfamily currently includes 458 transport proteins in 65 families that carry a wide variety of substances across cellular membranes. While members of this superfamily are found throughout cellular organelles, this review focuses on transporters expressed at the plasma membrane. At the cell surface, SLC proteins may be viewed as gatekeepers of the cellular milieu, dynamically responding to different metabolic states. With altered metabolism being one of the hallmarks of cancer, we also briefly review the roles that surface SLC proteins play in the development and progression of cancer through their influence on regulating metabolism and environmental conditions.

TASL is the SLC15A4-associated adaptor for IRF5 activation by TLR7-9.

Toll-like receptors (TLRs) have a crucial role in the recognition of pathogens and initiation of immune responses1-3. Here we show that a previously uncharacterized protein encoded by CXorf21-a gene that is associated with systemic lupus erythematosus4,5-interacts with the endolysosomal transporter SLC15A4, an essential but poorly understood component of the endolysosomal TLR machinery also linked to autoimmune disease4,6-9. Loss of this type-I-interferon-inducible protein, which we refer to as 'TLR adaptor interacting with SLC15A4 on the lysosome' (TASL), abrogated responses to endolysosomal TLR agonists in both primary and transformed human immune cells. Deletion of SLC15A4 or TASL specifically impaired the activation of the IRF pathway without affecting NF-κB and MAPK signalling, which indicates that ligand recognition and TLR engagement in the endolysosome occurred normally. Extensive mutagenesis of TASL demonstrated that its localization and function relies on the interaction with SLC15A4. TASL contains a conserved pLxIS motif (in which p denotes a hydrophilic residue and x denotes any residue) that mediates the recruitment and activation of IRF5. This finding shows that TASL is an innate immune adaptor for TLR7, TLR8 and TLR9 signalling, revealing a clear mechanistic analogy with the IRF3 adaptors STING, MAVS and TRIF10,11. The identification of TASL as the component that links endolysosomal TLRs to the IRF5 transcription factor via SLC15A4 provides a mechanistic explanation for the involvement of these proteins in systemic lupus erythematosus12-14.

Targeted Degradation of SLC Transporters Reveals Amenability of Multi-Pass Transmembrane Proteins to Ligand-Induced Proteolysis.

With more than 450 members, the solute carrier (SLC) group of proteins represents the largest class of transporters encoded in the human genome. Their several-pass transmembrane domain structure and hydrophobicity contribute to the orphan status of many SLCs, devoid of known cargos or chemical inhibitors. We report that SLC proteins belonging to different families and subcellular compartments are amenable to induced degradation by heterobifunctional ligands. Engineering endogenous alleles via the degradation tag (dTAG) technology enabled chemical control of abundance of the transporter protein, SLC38A2. Moreover, we report the design of d9A-2, a chimeric compound engaging several members of the SLC9 family and leading to their degradation. d9A-2 impairs cellular pH homeostasis and promotes cell death in a range of cancer cell lines. These findings open the era of SLC-targeting chimeric degraders and demonstrate potential access of multi-pass transmembrane proteins of different subcellular localizations to the chemically exploitable degradation machinery.

Distribution of Mediastinal Lesions Across Multi-Institutional, International, Radiology Databases.

INTRODUCTION: Mediastinal lesions are uncommon; studies on their distribution are, in general, small and from a single institution. Furthermore, these studies are usually based on pathology or surgical databases and, therefore, miss many lesions that did not undergo biopsy or resection. Our aim was to identify the distribution of lesions in the mediastinum in a large international, multi-institutional cohort. METHODS: At each participating institution, a standardized retrospective radiology database search was performed for interpretations of computed tomography, positron emission tomography-computed tomography, and magnetic resonance imaging scans including any of the following terms: "mediastinal nodule," "mediastinal lesion," "mediastinal mass," or "mediastinal abnormality" (2011-2014). Standardized data were collected. Statistical analysis was performed. RESULTS: Among 3308 cases, thymomas (27.8%), benign mediastinal cysts (20.0%), and lymphomas (16.1%) were most common. The distribution of lesions varied among mediastinal compartments; thymomas (38.3%), benign cysts (16.8%), and neurogenic tumors (53.9%) were the most common lesions in the prevascular, visceral, and paravertebral mediastinum, respectively (p < 0.001). Mediastinal compartment was associated with age; patients with paravertebral lesions were the youngest (p < 0.0001). Mediastinal lesions differed by continent or country, with benign cysts being the most common mediastinal lesions in the People's Republic of China, thymomas in Europe, and lymphomas in North America and Israel (p < 0.001). Benign cysts, thymic carcinomas, and metastases were more often seen in larger hospitals, whereas lymphomas and thymic hyperplasia occurred more often in smaller hospitals (p < 0.01). CONCLUSIONS: Our study confirmed that the spectrum and frequency of mediastinal lesions depend on mediastinal compartment and age. This information provides helpful demographic data and is important when considering the differential diagnosis of a mediastinal lesion.

The Optimal Setting for Multilayer Modularity Optimization in Multilayer Brain Networks.

Community detection plays a key role in the study of brain networks, as mechanisms of modular integration and segregation are known to characterize the brain functioning. Moreover, brain networks are intrinsically multilayer: they can vary across time, frequency, subjects, conditions, and meaning, according to different definitions of connectivity. Several algorithms for the multilayer community detection were defined to identify communities in time-varying networks. The most used one is based on the optimization of a multilayer formulation of the modularity, in which two parameters linked to the spatial (γ) and temporal (ω) resolution of the uncovered communities can be set. While the effect of different γ-values has been largely explored, which ω-values are most suitable to different purposes and conditions is still an open issue. In this work, we test the algorithm performances under different values of ω by means of ad hoc implemented benchmark graphs that cover a wide range of conditions. Results provide a guide to the choice of the ω-values according to the network features. Finally, we show an application of the algorithm to real functional brain networks estimated from electro-encephalographic (EEG) signals collected at rest with closed and open eyes. The application to real data agrees with the results of the simulation study and confirms the conclusion drawn from it.

Adaptive learning in the detection of Movement Related Cortical Potentials improves usability of associative Brain-Computer Interfaces.

Brain-computer interfaces have increasingly found applications in motor function recovery in stroke patients. In this context, it has been demonstrated that associative-BCI protocols, implemented by means the movement related cortical potentials (MRCPs), induce significant cortical plasticity. To date, no methods have been proposed to deal with brain signal (i.e. MRCP feature) non-stationarity. This study introduces adaptive learning methods in MRCP detection and aims at comparing a no-adaptive approach based on the Locality Sensitive Discriminant Analysis (LSDA) with three LSDA-based adaptive approaches. As a proof of concept, EEG and force data were collected from six healthy subjects while performing isometric ankle dorsiflexion. Results revealed that adaptive algorithms increase the number of true detections and decrease the number of false positives per minute. Moreover, the markedly reduction of BCI system calibration time suggests that these methods have the potential to improve the usability of associative-BCI in post-stroke motor recovery.

Multiscale design of ZnO nanostructured photocatalysts.

A systematic investigation of the photocatalytic activity (PCA) of nanostructured ZnO films showed how this is directly affected by the films' morphology at different scales, from the macroscale morphology of films (e.g. thickness and surface area), to the microscale feature arrangement (e.g. aligned vs. randomly oriented structures or interpenetrated ones), to the nanoscale structure (e.g. crystal size and orientation). The interest in immobilizing photocatalysts in water treatment stems from concerns about the potential toxicity of their slurry form, which requires expensive downstream removal. Immobilisation, though, leads to a reduction in PCA, generally attributed to a lower surface area. By reducing the films' feature size to the nanoscale, an immobilized photocatalyst with high surface area can be achieved. At this scale, however, feature structuring and morphology become important as they determine the interaction between light and the photocatalytic material. In this work, nanostructured ZnO films with different morphology, arrangement and structure were produced by electrochemical anodization of zinc and were tested using the degradation of phenol in a batch reactor as a model system. Results show that the PCA for immobilized catalysts can be optimised by controlling microscale arrangement (light absorbance capacity) and nanoscale structure (crystal size and orientation) rather than macroscale morphology (surface area). These results provide a clear direction to maximising the photocatalytic activity of immobilised photocatalysts for the removal of organic pollutants from water.

EEG source estimation accuracy in presence of simulated cortical lesions.

Methods to reconstruct the neuroelectrical activity in the brain source space can be used to improve the spatial resolution of scalp-recorded EEG and to estimate the locations of electrical sources in the brain. This procedure can improve the investigation of the functional organization of the human brain, exploiting the high temporal resolution of EEG to follow the temporal dynamics of information processing. As for today, the uncertainties about the effects of inhomogeneities due to brain lesions preclude the adoption of EEG functional mapping on patients with lesioned brain. The aim of this work is to quantify the accuracy of a distributed source localization method in recovering extended sources of activated cortex when cortical lesions of different dimensions are introduced in simulated data. For this purpose, EEG source-distributed activity estimated from real data was modified including silent lesion areas. Then, for each simulated lesion, forward and inverse calculations were carried out to localize the produced scalp activity and the reconstructed cortical activity. Finally, the error induced in the reconstruction by the presence of the lesion was computed and analyzed in relation to the number of electrodes and to the size of the simulated lesion. Results returned values of global error in the whole cortex and of error in the non-lesioned area which are strongly dependent on the number of recorded scalp sensors, as they increase when a lower spatial sampling is performed on the scalp (64 versus 32 EEG channels). For increasing spatial sampling frequencies, the accuracy of the source reconstruction improves and even the presence of small lesions induces significantly higher error levels with respect to the lesion-free condition.

Brain connectivity networks at the basis of human attention components: An EEG study.

The Attention Network Task (ANT) was developed to disentangle the three components of attention identified in the Posner's theoretical model (alerting, orienting and executive control) and to measure the corresponding behavioral efficiency. Several fMRI studies have already provided evidences on the anatomical separability and interdependency of these three networks, and EEG studies have also unveiled the associated brain rhythms. What is still missing is a characterization of the brain circuits subtending the attentional components in terms of directed relationships between the brain areas and their frequency content. Here, we want to exploit the high temporal resolution of the EEG, improving its spatial resolution by means of advanced source localization methods, and to integrate the resulting information by a directed connectivity analysis. The results showed in the present study demonstrate the possibility to associate a specific directed brain circuit to each attention component and to identify synthetic indices able to selectively describe their neurophysiological, spatial and spectral properties.

Community detection: Comparison among clustering algorithms and application to EEG-based brain networks.

Community structure is a feature of complex networks that can be crucial for the understanding of their internal organization. This is particularly true for brain networks, as the brain functioning is thought to be based on a modular organization. In the last decades, many clustering algorithms were developed with the aim to identify communities in networks of different nature. However, there is still no agreement about which one is the most reliable, and to test and compare these algorithms under a variety of conditions would be beneficial to potential users. In this study, we performed a comparative analysis between six different clustering algorithms, analyzing their performances on a ground-truth consisting of simulated networks with properties spanning a wide range of conditions. Results show the effect of factors like the noise level, the number of clusters, the network dimension and density on the performances of the algorithms and provide some guidelines about the use of the more appropriate algorithm according to the different conditions. The best performances under a wide range of conditions were obtained by Louvain and Leicht & Newman algorithms, while Ronhovde and Infomap proved to be more appropriate in very noisy conditions. Finally, as a proof of concept, we applied the algorithms under exam to brain functional connectivity networks obtained from EEG signals recorded during a sustained movement of the right hand, obtaining a clustering of scalp electrodes which agrees with the results of the simulation study conducted.

Transcranial cerebellar direct current stimulation: Effects on brain resting state oscillatory and network activity.

Transcranial cerebellar direct current stimulation (tcDCS) can offer new insights into the cerebellar function and disorders, by modulating noninvasively the activity of cerebellar networks. Taking into account the functional interplay between the cerebellum and the cerebral cortex, we addressed the effects of unilateral tcDCS (active electrode positioned over the right cerebellar hemisphere) on the electroencephalographic (EEG) oscillatory activity and on the cortical network organization at resting state. Effects on spectral (de)synchronizations and functional connectivity after anodal and cathodal stimulation were assessed with respect to a sham condition. A lateralized synchronization over the sensorimotor area in gamma band, as well as an increase of the network segregation in sensory-motor rhythms and a higher communication between hemispheres in gamma band, were detected after anodal stimulation. The same measures after cathodal tcDCS returned responses similar to the sham condition.

Identifying Functional Cysteine Residues in the Mitochondria.

The mitochondria are dynamic organelles that regulate oxidative metabolism and mediate cellular redox homeostasis. Proteins within the mitochondria are exposed to large fluxes in the surrounding redox environment. In particular, cysteine residues within mitochondrial proteins sense and respond to these redox changes through oxidative modifications of the cysteine thiol group. These oxidative modifications result in a loss in cysteine reactivity, which can be monitored using cysteine-reactive chemical probes and quantitative mass spectrometry (MS). Analysis of cell lysates treated with cysteine-reactive probes enable the identification of hundreds of cysteine residues, however, the mitochondrial proteome is poorly represented (<10% of identified peptides), due to the low abundance of mitochondrial proteins and suppression of mitochondrial peptide MS signals by highly abundant cytosolic peptides. Here, we apply a mitochondrial isolation and purification protocol to substantially increase coverage of the mitochondrial cysteine proteome. Over 1500 cysteine residues from ∼450 mitochondrial proteins were identified, thereby enabling interrogation of an unprecedented number of mitochondrial cysteines. Specifically, these mitochondrial cysteines were ranked by reactivity to identify hyper-reactive cysteines with potential catalytic and regulatory functional roles. Furthermore, analyses of mitochondria exposed to nitrosative stress revealed previously uncharacterized sites of protein S-nitrosation on mitochondrial proteins. Together, the mitochondrial cysteine enrichment strategy presented herein enables detailed characterization of protein modifications that occur within the mitochondria during (patho)physiological fluxes in the redox environment.

Interfacing brain with computer to improve communication and rehabilitation after brain damage.

Communication and control of the external environment can be provided via brain-computer interfaces (BCIs) to replace a lost function in persons with severe diseases and little or no chance of recovery of motor abilities (ie, amyotrophic lateral sclerosis, brainstem stroke). BCIs allow to intentionally modulate brain activity, to train specific brain functions, and to control prosthetic devices, and thus, this technology can also improve the outcome of rehabilitation programs in persons who have suffered from a central nervous system injury (ie, stroke leading to motor or cognitive impairment). Overall, the BCI researcher is challenged to interact with people with severe disabilities and professionals in the field of neurorehabilitation. This implies a deep understanding of the disabled condition on the one hand, and it requires extensive knowledge on the physiology and function of the human brain on the other. For these reasons, a multidisciplinary approach and the continuous involvement of BCI users in the design, development, and testing of new systems are desirable. In this chapter, we will focus on noninvasive EEG-based systems and their clinical applications, highlighting crucial issues to foster BCI translation outside laboratories to eventually become a technology usable in real-life realm.

Measuring the agreement between brain connectivity networks.

Investigating the level of similarity between two brain networks, resulting from measures of effective connectivity in the brain, can be of interest from many respects. In this study, we propose and test the idea to borrow measures of association used in machine learning to provide a measure of similarity between the structure of (un-weighted) brain connectivity networks. The measures here explored are the accuracy, Cohen's Kappa (K) and Area Under Curve (AUC). We implemented two simulation studies, reproducing two contexts of application that can be particularly interesting for practical applications, namely: i) in methodological studies, performed on surrogate data, aiming at comparing the estimated network with the corresponding ground-truth network; ii) in applications to real data, when it is necessary to compare the structure of a network obtained in a specific subject with a reference (e.g. a baseline condition or normative data). In the simulations, the level of similarity between two networks was manipulated through different factors. We then investigated the effect of such manipulations on the measures of association. Results showed how the three parameters modulated their values according to the level of similarity between the two networks. In particular, the AUC provided the better performances in terms of its capability to synthetize the similarity between two networks, showing high dynamic and sensitivity.

Graph theory in brain-to-brain connectivity: A simulation study and an application to an EEG hyperscanning experiment.

Hyperscanning consists in the simultaneous recording of hemodynamic or neuroelectrical signals from two or more subjects acting in a social context. Well-established methodologies for connectivity estimation have already been adapted to hyperscanning purposes. The extension of graph theory approach to multi-subjects case is still a challenging issue. In the present work we aim to test the ability of the currently used graph theory global indices in describing the properties of a network given by two interacting subjects. The testing was conducted first on surrogate brain-to-brain networks reproducing typical social scenarios and then on real EEG hyperscanning data recorded during a Joint Action task. The results of the simulation study highlighted the ability of all the investigated indexes in modulating their values according to the level of interaction between subjects. However, only global efficiency and path length indexes demonstrated to be sensitive to an asymmetry in the communication between the two subjects. Such results were, then, confirmed by the application on real EEG data. Global efficiency modulated, in fact, their values according to the inter-brain density, assuming higher values in the social condition with respect to the non-social condition.