Assessing REM sleep behaviour disorder: from machine learning classification to the definition of a continuous dissociation index

Objectives: Rapid Eye Movement Sleep Behaviour Disorder (RBD) is regarded as a pro-drome of neurodegeneration, with a high conversion rate to α–synucleinopathies such as Parkinson’s Disease (PD). The clinical diagnosis of RBD co–exists with evidence of REM Sleep Without Atonia (RSWA), a parasomnia that features loss of physiological muscular atonia during REM sleep. The objectives of this study are to implement an automatic detection of RSWA from polysomnographic traces, and to propose a continuous index (the Dissociation Index) to assess the level of dissociation between REM sleep stage and atonia. This is performed using Euclidean distance in proper vector spaces. Each subject is assigned a dissociation degree based on their distance from a reference, encompassing healthy subjects and clinically diagnosed RBD patients at the two extremes. Methods: Machine Learning models were employed to perform automatic identification of patients with RSWA through clinical polysomnographic scores, together with variables derived from electromyography. Proper distance metrics are proposed and tested to achieve a dissociation measure. Results: The method proved efficient in classifying RSWA vs. not-RSWA subjects, achieving an overall accuracy, sensitivity and precision of 87%, 93% and 87.5%, respectively. On its part, the Dissociation Index proved to be promising in measuring the impairment level of patients. Conclusions: The proposed method moves a step forward in the direction of automatically identifying REM sleep disorders and evaluating the impairment degree. We believe that this index may be correlated with the patients’ neurodegeneration process; this assumption will undergo a robust clinical validation process involving healthy, RSWA, RBD and PD subjects

An isoform of the giant protein titin is a master regulator of human T lymphocyte trafficking

Response to multiple microenvironmental cues and resilience to mechanical stress are essential features of trafficking leukocytes. Here, we describe unexpected role of titin (TTN), the largest protein encoded by the human genome, in the regulation of mechanisms of lymphocyte trafficking. Human T and B lymphocytes express five TTN isoforms, exhibiting cell-specific expression, distinct localization to plasma membrane microdomains, and different distribution to cytosolic versus nuclear compartments. In T lymphocytes, the LTTN1 isoform governs the morphogenesis of plasma membrane microvilli independently of ERM protein phosphorylation status, thus allowing selectin-mediated capturing and rolling adhesions. Likewise, LTTN1 controls chemokine-triggered integrin activation. Accordingly, LTTN1 mediates rho and rap small GTPases activation, but not actin polymerization. In contrast, chemotaxis is facilitated by LTTN1 degradation. Finally, LTTN1 controls resilience to passive cell deformation and ensures T lymphocyte survival in the blood stream. LTTN1 is, thus, a critical and versatile housekeeping regulator of T lymphocyte trafficking

Towards Explainable Quantum Machine Learning for Mobile Malware Detection and Classification

Through the years, the market for mobile devices has been rapidly increasing, and as a result of this trend, mobile malware has become sophisticated. Researchers are focused on the design and development of malware detection systems to strengthen the security and integrity of sensitive and private information. In this context, deep learning is exploited, also in cybersecurity, showing the ability to build models aimed at detecting whether an application is Trusted or malicious. Recently, with the introduction of quantum computing, we have been witnessing the introduction of quantum algorithms in Machine Learning. In this paper, we provide a comparison between five state-of-the-art Convolutional Neural Network models (i.e., AlexNet, MobileNet, EfficientNet, VGG16, and VGG19), one network developed by the authors (called Standard-CNN), and two quantum models (i.e., a hybrid quantum model and a fully quantum neural network) to classify malware. In addition to the classification, we provide explainability behind the model predictions, by adopting the Gradient-weighted Class Activation Mapping to highlight the areas of the image obtained from the application symptomatic of a certain prediction, to the convolutional and to the quantum models obtaining the best performances in Android malware detection. Real-world experiments were performed on a dataset composed of 8446 Android malicious and legitimate applications, obtaining interesting results

Towards Explainable Quantum Machine Learning for Mobile Malware Detection and Classification

Through the years, the market for mobile devices has been rapidly increasing, and as a result of this trend, mobile malware has become sophisticated. Researchers are focused on the design and development of malware detection systems to strengthen the security and integrity of sensitive and private information. In this context, deep learning is exploited, also in cybersecurity, showing the ability to build models aimed at detecting whether an application is Trusted or malicious. Recently, with the introduction of quantum computing, we have been witnessing the introduction of quantum algorithms in Machine Learning. In this paper, we provide a comparison between five state-of-the-art Convolutional Neural Network models (i.e., AlexNet, MobileNet, EfficientNet, VGG16, and VGG19), one network developed by the authors (called Standard-CNN), and two quantum models (i.e., a hybrid quantum model and a fully quantum neural network) to classify malware. In addition to the classification, we provide explainability behind the model predictions, by adopting the Gradient-weighted Class Activation Mapping to highlight the areas of the image obtained from the application symptomatic of a certain prediction, to the convolutional and to the quantum models obtaining the best performances in Android malware detection. Real-world experiments were performed on a dataset composed of 8446 Android malicious and legitimate applications, obtaining interesting results

Sleep fragmentation affects glymphatic system through the different expression of AQP4 in wild type and 5xFAD mouse models

Abstract Alzheimer’s disease (AD) is characterized by genetic and multifactorial risk factors. Many studies correlate AD to sleep disorders. In this study, we performed and validated a mouse model of AD and sleep fragmentation, which properly mimics a real condition of intermittent awakening. We noticed that sleep fragmentation induces a general acceleration of AD progression in 5xFAD mice, while in wild type mice it affects cognitive behaviors in particular learning and memory. Both these events may be correlated to aquaporin-4 (AQP4) modulation, a crucial player of the glymphatic system activity. In particular, sleep fragmentation differentially affects aquaporin-4 channel (AQP4) expression according to the stage of the disease, with an up-regulation in younger animals, while such change cannot be detected in older ones. Moreover, in wild type mice sleep fragmentation affects cognitive behaviors, in particular learning and memory, by compromising the glymphatic system through the decrease of AQP4. Nevertheless, an in-depth study is needed to better understand the mechanism by which AQP4 is modulated and whether it could be considered a risk factor for the disease development in wild type mice. If our hypotheses are going to be confirmed, AQP4 modulation may represent the convergence point between AD and sleep disorder pathogenic mechanisms

Deciphering the proteome profile of rice (Oryza sativa) bran: a pilot study.

The exact knowledge of the qualitative and quantitative protein components of rice bran is an essential aspect to be considered for a better understanding of the functional properties of this resource. Aim of the present investigation was to extract the largest number of rice bran proteins and to obtain their qualitative characterization. For this purpose, three different extraction protocols have been applied either on full-fat or on defatted rice bran. Likewise, to identify the highest number of proteins, MS data collected from 1-DE, 2-DE and gel-free procedures have been combined. These approaches allowed to unambiguously identify 43 proteins that were classified as signalling/regulation proteins (30%), proteins with enzymatic activity (30%), storage proteins (30%), transfer (5%) and structural (5%) proteins. The fact that all extraction and identification procedures have been performed in triplicate with an excellent reproducibility provides a rationale for considering the platform of proteins shown in this study as the potential proteome profile of rice bran. It also represents a source of information to evaluate better the qualities of rice bran as food resource