Authentication of Satellite Navigation Signals by Wiretap Coding and Artificial Noise

In order to combat the spoofing of global navigation satellite system (GNSS) signals we propose a novel approach for satellite signal authentication based on information-theoretic security. In particular we superimpose to the navigation signal an authentication signal containing a secret message corrupted by artificial noise (AN), still transmitted by the satellite. We impose the following properties: a) the authentication signal is synchronous with the navigation signal, b) the authentication signal is orthogonal to the navigation signal and c) the secret message is undecodable by the attacker due to the presence of the AN. The legitimate receiver synchronizes with the navigation signal and stores the samples of the authentication signal with the same synchronization. After the transmission of the authentication signal, through a separate public asynchronous authenticated channel (e.g., a secure Internet connection) additional information is made public allowing the receiver to a) decode the secret message, thus overcoming the effects of AN, and b) verify the secret message. We assess the performance of the proposed scheme by the analysis of both the secrecy capacity of the authentication message and the attack success probability, under various attack scenarios. A comparison with existing approaches shows the effectiveness of the proposed scheme

Authentication of GNSS signal by Information-theoretic security

In this work a new authentication protocol for global navigation satellite system (GNSS) signals is proposed. The protocol uses artificial noise to confuse the adversary and send an initially hidden verification message. Correctness is based on information-theoretic security and performances are evaluated in terms of secrecy capacityope

Machine Learning For In-Region Location Verification In Wireless Networks

In-region location verification (IRLV) aims at verifying whether a user is inside a region of interest (ROI). In wireless networks, IRLV can exploit the features of the channel between the user and a set of trusted access points. In practice, the channel feature statistics is not available and we resort to machine learning (ML) solutions for IRLV. We first show that solutions based on either neural networks (NNs) or support vector machines (SVMs) and typical loss functions are Neyman-Pearson (N-P)-optimal at learning convergence for sufficiently complex learning machines and large training datasets . Indeed, for finite training, ML solutions are more accurate than the N-P test based on estimated channel statistics. Then, as estimating channel features outside the ROI may be difficult, we consider one-class classifiers, namely auto-encoders NNs and one-class SVMs, which however are not equivalent to the generalized likelihood ratio test (GLRT), typically replacing the N-P test in the one-class problem. Numerical results support the results in realistic wireless networks, with channel models including path-loss, shadowing, and fading

Location-Verification and Network Planning via Machine Learning Approaches

In-region location verification (IRLV) in wireless networks is the problem of deciding if user equipment (UE) is transmitting from inside or outside a specific physical region (e.g., a safe room). The decision process exploits the features of the channel between the UE and a set of network access points (APs). We propose a solution based on machine learning (ML) implemented by a neural network (NN) trained with the channel features (in particular, noisy attenuation values) collected by the APs for various positions both inside and outside the specific region. The output is a decision on the UE position (inside or outside the region). By seeing IRLV as an hypothesis testing problem, we address the optimal positioning of the APs for minimizing either the area under the curve (AUC) of the receiver operating characteristic (ROC) or the cross entropy (CE) between the NN output and ground truth (available during the training). In order to solve the minimization problem we propose a twostage particle swarm optimization (PSO) algorithm. We show that for a long training and a NN with enough neurons the proposed solution achieves the performance of the Neyman-Pearson (N-P) lemma.Comment: Accepted for Workshop on Machine Learning for Communications, June 07 2019, Avignon, Franc

Identification of Molecular and Functional Mechanisms behind Cell Volume Regulation in Astrocytes

Aquaporin 4 (AQP4) is a highly conserved protein in mammals, since point mutation usually lead to a reduce water permeability. A recent study investigated the effect of a mutation in AQP4 gene in AQP4 membrane protein expression and water permeability. Chloride channels are also involved in the maintenance of homeostasis in the brain and in particular, volume-regulated anion channels (VRAC) mainly address this task. VRACmolecular identity, partially solved in 2014 consist of an essential sub-unit termed as leucine rich repeat containing 8 family member A. Hence, the aim of this study was to investigate the molecular and functional interactions between the mutant D184E of aquaporin 4 protein, with the ion channels TRPV4 and VRAC. Moreover, three antibody raised against LRRC8A antigenic polypeptides have been produced to investigate the expression of VRAC underpinning subunits in cortical astrocytes and mouse brain. The main findings and contribution to theory of my PhD project are the following: 1-the functional antagonism between TRPV4 and VRAC in a heterologous system, previously reported in HEK-293 cells (98), can be overcome by co-expression of AQP4 and TRPV4. 2-The evidence that a D184E mutation of the M1 isoform of AQP4 gene impair the molecular interaction between TRPV4 and AQP4. 3-The definition of the expression pattern of LRRC8A subunits, underpinning VRAC conductance (REF), in COS-7 heterologous system, in astrocytes and in the mouse brain. In conclusion, the results of my PhD studies provide new insights into key molecular and functional player involved central nervous system homeostatic volume regulation. In particular they suggested and reinforce the tenet that a macromolecular complex or a straight functional interaction between TRPV4, AQP4 and VRAC might be essential for the cell volume regulation

Magnetoencephalography in Stroke Recovery and Rehabilitation

Magnetoencephalography (MEG) is a non-invasive neurophysiological technique used to study the cerebral cortex. Currently, MEG is mainly used clinically to localize epileptic foci and eloquent brain areas in order to avoid damage during neurosurgery. MEG might, however, also be of help in monitoring stroke recovery and rehabilitation. This review focuses on experimental use of MEG in neurorehabilitation. MEG has been employed to detect early modifications in neuroplasticity and connectivity, but there is insufficient evidence as to whether these methods are sensitive enough to be used as a clinical diagnostic test. MEG has also been exploited to derive the relationship between brain activity and movement kinematics for a motor-based brain-computer interface. In the current body of experimental research, MEG appears to be a powerful tool in neurorehabilitation, but it is necessary to produce new data to confirm its clinical utility

Assessment of Event-Related EEG Power After Single-Pulse TMS in Unresponsive Wakefulness Syndrome and Minimally Conscious State Patients

In patients without a behavioral response, non-invasive techniques and new methods of data analysis can complement existing diagnostic tools by providing a method for detecting covert signs of residual cognitive function and awareness. The aim of this study was to investigate the brain oscillatory activities synchronized by single-pulse transcranial magnetic stimulation (TMS) delivered over the primary motor area in the time\u2013frequency domain in patients with the unresponsive wakefulness syndrome or in a minimally conscious state as compared to healthy controls. A time\u2013frequency analysis based on the wavelet transform was used to characterize rapid modifications of oscillatory EEG rhythms induced by TMS in patients as compared to healthy controls. The pattern of EEG changes in the patients differed from that of healthy controls. In the controls there was an early synchronization of slow waves immediately followed by a desynchronization of alpha and beta frequency bands over the frontal and centro-parietal electrodes, whereas an opposite early synchronization, particularly over motor areas for alpha and beta and over the frontal and parietal electrodes for beta power, was seen in the patients. In addition, no relevant modification in slow rhythms (delta and theta) after TMS was noted in patients. The clinical impact of these findings could be relevant in neurorehabilitation settings for increasing the awareness of these patients and defining new treatment procedures

L-Acetylcarnitine causes analgesia in mice modeling Fabry disease by up-regulating type-2 metabotropic glutamate receptors

Fabry disease (FD) is a X-linked lysosomal storage disorder caused by deficient function of the alpha-galactosidase A (α-GalA) enzyme. α-GalA deficiency leads to multisystemic clinical manifestations caused by the preferential accumulation of globotriaosylceramide (Gb3). A hallmark symptom of FD patients is neuropathic pain that appears in the early stage of the disease as a result of peripheral small fiber damage. Previous studies have shown that Acetyl-L-carnitine (ALC) has neuroprotective, neurotrophic, and analgesic activity in animal models of neuropathic pain. To study the action of ALC on neuropathic pain associated with FD, we treated α-GalA gene null mice (α-GalA(-/0)) with ALC for 30 days. In α-Gal KO mice ALC treatment induced acute and long-lasting analgesia, which persisted 1 month after drug withdrawal. This effect was antagonized by single administration of LY341495, an orthosteric antagonist of mGlu2/3 metabotropic glutamate receptors. We also found an up-regulation of mGlu2 receptors in cultured DRG neurons isolated from 30-day ALC treated α-GalA KO mice. However, the up-regulation of mGlu2 receptors was no longer present in DRG neurons isolated 30 days after the end of treatment. Taken together, these findings suggest that ALC induces analgesia in an animal model of FD by up-regulating mGlu2 receptors, and that analgesia is maintained by additional mechanisms after ALC withdrawal. ALC might represent a valuable pharmacological strategy to reduce pain in FD patients

Neurophysiological and BOLD signal uncoupling of giant somatosensory evoked potentials in progressive myoclonic epilepsy: a case-series study

In progressive myoclonic epilepsy (PME), a rare epileptic syndrome caused by a variety of genetic disorders, the combination of peripheral stimulation and functional magnetic resonance imaging (fMRI) can shed light on the mechanisms underlying cortical dysfunction. The aim of the study is to investigate sensorimotor network modifications in PME by assessing the relationship between neurophysiological findings and blood oxygen level dependent (BOLD) activation. Somatosensory-evoked potential (SSEP) obtained briefly before fMRI and BOLD activation during median-nerve electrical stimulation were recorded in four subjects with typical PME phenotype and compared with normative data. Giant scalp SSEPs with enlarger N20-P25 complex compared to normal data (mean amplitude of 26.2\u2009\ub1\u20098.2\u2009\u3bcV after right stimulation and 27.9\u2009\ub1\u20093.7\u2009\u3bcV after left stimulation) were detected. Statistical group analysis showed a reduced BOLD activation in response to median nerve stimulation in PMEs compared to controls over the sensorimotor (SM) areas and an increased response over subcortical regions (p\u2009\u20092.3, corrected). PMEs show dissociation between neurophysiological and BOLD findings of SSEPs (giant SSEP with reduced BOLD activation over SM). A direct pathway connecting a highly restricted area of the somatosensory cortex with the thalamus can be hypothesized to support the higher excitability of these areas

Detection of subtype-specific breast cancer surface protein biomarkers via a novel transcriptomics approach

Background: Cell-surface proteins have been widely used as diagnostic and prognostic markers in cancer research and as targets for the development of anticancer agents. So far, very few attempts have been made to characterize the surfaceome of patients with breast cancer, particularly in relation with the current molecular breast cancer (BRCA) classification. In this view, we developed a new computational method to infer cell-surface protein activities from transcriptomics data, termed ‘SURFACER’. Methods: Gene expression data from GTEx were used to build a normal breast network model as input to infer differential cell-surface proteins activity in BRCA tissue samples retrieved from TCGA versus normal samples. Data were stratified according to the PAM50 transcriptional subtypes (Luminal A, Luminal B, HER2 and Basal), while unsupervised clustering techniques were applied to define BRCA subtypes according to cell-surface proteins activity. Results: Our approach led to the identification of 213 PAM50 subtypes-specific deregulated surface genes and the definition of five BRCA subtypes, whose prognostic value was assessed by survival analysis, identifying a cell-surface activity configuration at increased risk. The value of the SURFACER method in BRCA genotyping was tested by evaluating the performance of 11 different machine learning classification algorithms. Conclusions: BRCA patients can be stratified into five surface activity-specific groups having the potential to identify subtype-specific actionable targets to design tailored targeted therapies or for diagnostic purposes. SURFACER-defined subtypes show also a prognostic value, identifying surface-activity profiles at higher risk