Optimal therapy for HIV infection containment and virions inhibition.

Prevention and early diagnosis are the best and most effective ways for defeating HIV. There is still no vaccine, but treatments with antiretroviral drugs are now available which, in many cases, allow the infection to become chronic. However, research has highlighted side effects of these drugs and the fact that a flare-up of the infection occurs if the therapy is stopped. In recent years, the presence of virus reserves located in various parts of the body, including the brain, has been hypothesized. The possibility of controlling the infection of healthy cells and of interrupting the proliferation of virions inside the brain has been studied, proposing optimal control strategies.

Early estimation of the number of hidden HIV infected subjects: An extended Kalman filter approach.

In the last decades several epidemic emergencies have been affecting the world, influencing the social relationships, the economics and the habits. In particular, starting in the early '80, the Acquired Immunodeficiency Syndrome, AIDS, is representing one of the most worrying sanitary emergency, that has caused up to now more than 25 million of dead patients. The infection is caused by the Human Immunodeficiency Virus, HIV, that may be transmitted by body fluids; therefore with wise behaviours the epidemic spread could rapidly be contained. This sanitary emergency is peculiar for the long incubation time: it can reach even 10 years, a long period in which the individual can unconsciously infect other subjects. The identification of the number of infected unaware people, mandatory to define suitable containment measures, is here obtained by using the extended Kalman filter applied to a noisy model in which, reasonably, only the number of infected diagnosed patients is available. Numerical simulations and real data analysis support the effectiveness of the approach.

Temperature Influence on Brake Pad Friction Coefficient Modelisation.

Brake pad linings are an essential part of the correct functioning of braking systems based on the use of pads and discs. Generally, the compounds used to make the gaskets are characterised by the use of over 20 sintered components, which allow friction coefficients that vary between 0.2 and 0.6 at temperatures up to 200 °C. In this work, a traditional compound was investigated under close-to-real conditions in order to evaluate the tribological behaviour at different temperatures. Finally, a model based on the proportionality between temperature increase and relative variation of the friction coefficient was proposed. From the experimental test, it was evident that the friction coefficient increased with the temperature, passing from 0.4 to 0.6 in the temperature range of 100 °C to 180 °C; however, a further temperature increment until 350 °C caused a reduction in the friction coefficient to 0.2. The proposed model was able to anticipate the abovementioned trend, especially at high temperatures.

Time-frequency analysis of brain activity in response to directional and non-directional visual stimuli: an event related spectral perturbations (ERSP) study.

Objective.A large part of the cerebral cortex is dedicated to the processing of visual stimuli and there is still much to understand about such processing modalities and hierarchies. The main aim of the present study is to investigate the differences between directional visual stimuli (DS) and non-directional visual stimuli (n-DS) processing by time-frequency analysis of brain electroencephalographic activity during a visuo-motor task. Electroencephalography (EEG) data were divided into four regions of interest (ROIs) (frontal, central, parietal, occipital).Approach.The analysis of the visual stimuli processing was based on the combination of electroencephalographic recordings and time-frequency analysis. Event related spectral perturbations (ERSPs) were computed with spectrum analysis that allow to obtain the average time course of relative changes induced by the stimulus presentation in spontaneous EEG amplitude spectrum.Main results.Visual stimuli processing enhanced the same pattern of spectral modulation in all investigated ROIs with differences in amplitudes and timing. Additionally, statistically significant differences in occipital ROI between the DS and n-DS visual stimuli processing in theta, alpha and beta bands were found.Significance.These evidences suggest that ERSPs could be a useful tool to investigate the encoding of visual information in different brain regions. Because of their simplicity and their capability in the representation of brain activity, the ERSPs might be used as biomarkers of functional recovery for example in the rehabilitation of visual dysfunction and motor impairment following a stroke, as well as diagnostic tool of anomalies in brain functions in neurological diseases tailored to personalized treatments in clinical environment.

A data-driven model of the COVID-19 spread among interconnected populations: epidemiological and mobility aspects following the lockdown in Italy.

An epidemic multi-group model formed by interconnected SEIR-like structures is formulated and used for data fitting to gain insight into the COVID-19 dynamics and into the role of non-pharmaceutical control actions implemented to limit the infection spread since its outbreak in Italy. The single submodels provide a rather accurate description of the COVID-19 evolution in each subpopulation by an extended SEIR model including the class of asymptomatic infectives, which is recognized as a determinant for disease diffusion. The multi-group structure is specifically designed to investigate the effects of the inter-regional mobility restored at the end of the first strong lockdown in Italy (June 3, 2020). In its time-invariant version, the model is shown to enjoy some analytical stability properties which provide significant insights on the efficacy of the implemented control measurements. In order to highlight the impact of human mobility on the disease evolution in Italy between the first and second wave onset, the model is applied to fit real epidemiological data of three geographical macro-areas in the period March-October 2020, including the mass departure for summer holidays. The simulation results are in good agreement with the data, so that the model can represent a useful tool for predicting the effects of the combination of containment measures in triggering future pandemic scenarios. Particularly, the simulation shows that, although the unrestricted mobility alone appears to be insufficient to trigger the second wave, the human transfers were crucial to make uniform the spatial distribution of the infection throughout the country and, combined with the restart of the production, trade, and education activities, determined a time advance of the contagion increase since September 2020.

Evaluation of the effect of different policies in the containment of epidemic spreads for the COVID-19 case.

The paper presents a new mathematical model for the SARS-CoV-2 virus propagation, designed to include all the possible actions to prevent the spread and to help in the healing of infected people. After a discussion on the equilibrium and stability properties of the model, the effects of each different control actions on the evolution of the epidemic spread are analysed, through numerical evaluations for a more intuitive and immediate presentation, showing the consequences on the classes of the population.

Dynamical Evolution of COVID-19 in Italy With an Evaluation of the Size of the Asymptomatic Infective Population.

The present work deals with an Ordinary Differential Equation (ODE) model specifically designed to describe the COVID-19 evolution in Italy. The model is particularised on the basis of National data about the infection status of the Italian population to obtain numerical solutions that effectively reproduce the real data. Our epidemic model is a classical SEIR model that incorporates two compartments of infected subpopulations, representing diagnosed and undiagnosed individuals respectively, and an additional quarantine compartment. Possible control actions representing social, political, and medical interventions are also included. The numerical results of the proposed model identification by least square fitting are analysed and commented with special emphasis on the estimation of the number of asymptomatic infective individuals. Our fitting results are in good agreement with the epidemiological data. Short and long-term predictions on the evolution of the disease are also given.

A Modular Framework for EEG Web Based Binary Brain Computer Interfaces to Recover Communication Abilities in Impaired People.

A Brain Computer Interface (BCI) allows communication for impaired people unable to express their intention with common channels. Electroencephalography (EEG) represents an effective tool to allow the implementation of a BCI. The present paper describes a modular framework for the implementation of the graphic interface for binary BCIs based on the selection of symbols in a table. The proposed system is also designed to reduce the time required for writing text. This is made by including a motivational tool, necessary to improve the quality of the collected signals, and by containing a predictive module based on the frequency of occurrence of letters in a language, and of words in a dictionary. The proposed framework is described in a top-down approach through its modules: signal acquisition, analysis, classification, communication, visualization, and predictive engine. The framework, being modular, can be easily modified to personalize the graphic interface to the needs of the subject who has to use the BCI and it can be integrated with different classification strategies, communication paradigms, and dictionaries/languages. The implementation of a scenario and some experimental results on healthy subjects are also reported and discussed: the modules of the proposed scenario can be used as a starting point for further developments, and application on severely disabled people under the guide of specialized personnel.

A Classification Algorithm for Electroencephalography Signals by Self-Induced Emotional Stimuli.

The aim of this paper is to propose a real-time classification algorithm for the low-amplitude electroencephalography (EEG) signals, such as those produced by remembering an unpleasant odor, to drive a brain-computer interface. The peculiarity of these EEG signals is that they require ad hoc signals preprocessing by wavelet decomposition, and the definition of a set of features able to characterize the signals and to discriminate among different conditions. The proposed method is completely parameterized, aiming at a multiclass classification and it might be considered in the framework of machine learning. It is a two stages algorithm. The first stage is offline and it is devoted to the determination of a suitable set of features and to the training of a classifier. The second stage, the real-time one, is to test the proposed method on new data. In order to avoid redundancy in the set of features, the principal components analysis is adapted to the specific EEG signal characteristics and it is applied; the classification is performed through the support vector machine. Experimental tests on ten subjects, demonstrating the good performance of the algorithm in terms of both accuracy and efficiency, are also reported and discussed.

EEG-detected olfactory imagery to reveal covert consciousness in minimally conscious state.

PRIMARY OBJECTIVE: To reveal covert abilities in a minimally conscious state (MCS) through an innovative activation paradigm based on olfactory imagery. RESEARCH DESIGN: Case study. METHODS AND PROCEDURES: A patient in MCS was asked to 'imagine an unpleasant odour' or to 'relax' in response to the appearance on a screen of a downward pointing arrow or a cross, respectively. Electrophysiological responses to stimuli were investigated by means of an 8-channel EEG equipment and analysed using a specific threshold algorithm. The protocol was repeated for 10 sessions separated from each other by 2 weeks. Accuracy, defined as the number of successes with respect to the total number of trials, was used to evaluate the number of times in which the classification strategy was successful. MAIN OUTCOMES AND RESULTS: Analyses of accuracy showed that the patient was able to activate and to relax himself purposefully and that he optimized his performances with the number of sessions, probably as a result of training-related improvements. CONCLUSIONS: Subtle signs of consciousness may be under-estimated and need to be revealed through specific activation tasks. This paradigm may be useful to detect covert signs of consciousness, especially when patients are precluded from carrying out more complex cognitive tasks.

A real-time classification algorithm for EEG-based BCI driven by self-induced emotions.

BACKGROUND AND OBJECTIVE: The aim of this paper is to provide an efficient, parametric, general, and completely automatic real time classification method of electroencephalography (EEG) signals obtained from self-induced emotions. The particular characteristics of the considered low-amplitude signals (a self-induced emotion produces a signal whose amplitude is about 15% of a really experienced emotion) require exploring and adapting strategies like the Wavelet Transform, the Principal Component Analysis (PCA) and the Support Vector Machine (SVM) for signal processing, analysis and classification. Moreover, the method is thought to be used in a multi-emotions based Brain Computer Interface (BCI) and, for this reason, an ad hoc shrewdness is assumed. METHOD: The peculiarity of the brain activation requires ad-hoc signal processing by wavelet decomposition, and the definition of a set of features for signal characterization in order to discriminate different self-induced emotions. The proposed method is a two stages algorithm, completely parameterized, aiming at a multi-class classification and may be considered in the framework of machine learning. The first stage, the calibration, is off-line and is devoted at the signal processing, the determination of the features and at the training of a classifier. The second stage, the real-time one, is the test on new data. The PCA theory is applied to avoid redundancy in the set of features whereas the classification of the selected features, and therefore of the signals, is obtained by the SVM. RESULTS: Some experimental tests have been conducted on EEG signals proposing a binary BCI, based on the self-induced disgust produced by remembering an unpleasant odor. Since in literature it has been shown that this emotion mainly involves the right hemisphere and in particular the T8 channel, the classification procedure is tested by using just T8, though the average accuracy is calculated and reported also for the whole set of the measured channels. CONCLUSIONS: The obtained classification results are encouraging with percentage of success that is, in the average for the whole set of the examined subjects, above 90%. An ongoing work is the application of the proposed procedure to map a large set of emotions with EEG and to establish the EEG headset with the minimal number of channels to allow the recognition of a significant range of emotions both in the field of affective computing and in the development of auxiliary communication tools for subjects affected by severe disabilities.

A stochastic delay differential model of cerebral autoregulation.

Mathematical models of the cardiovascular system and of cerebral autoregulation (CAR) have been employed for several years in order to describe the time course of pressures and flows changes subsequent to postural changes. The assessment of the degree of efficiency of cerebral auto regulation has indeed importance in the prognosis of such conditions as cerebro-vascular accidents or Alzheimer. In the quest for a simple but realistic mathematical description of cardiovascular control, which may be fitted onto non-invasive experimental observations after postural changes, the present work proposes a first version of an empirical Stochastic Delay Differential Equations (SDDEs) model. The model consists of a total of four SDDEs and two ancillary algebraic equations, incorporates four distinct delayed controls from the brain onto different components of the circulation, and is able to accurately capture the time course of mean arterial pressure and cerebral blood flow velocity signals, reproducing observed auto-correlated error around the expected drift.

A low-cost real time virtual system for postural stability assessment at home.

BACKGROUND AND OBJECTIVE: The degeneration of the balance control system in the elderly and in many pathologies requires measuring the equilibrium conditions very often. In clinical practice, equilibrium control is commonly evaluated by using a force platform (stabilometric platform) in a clinical environment. In this paper, we demonstrate how a simple movement analysis system, based on a 3D video camera and a 3D real time model reconstruction of the human body, can be used to collect information usually recorded by a physical stabilometric platform. METHODS: The algorithm used to reconstruct the human body model as a set of spheres is described and discussed. Moreover, experimental measurements and comparisons with data collected by a physical stabilometric platform are also reported. The measurements were collected on a set of 6 healthy subjects to whom a change in equilibrium condition was stimulated by performing an equilibrium task. RESULTS: The experimental results showed that more than 95% of data collected by the proposed method were not significantly different from those collected by the classic platform, thus confirming the usefulness of the proposed system. CONCLUSIONS: The proposed virtual balance assessment system can be implemented at low cost (about 500$) and, for this reason, can be considered a home use medical device. On the contrary, astabilometric platform has a cost of about 10,000$ and requires periodical calibration. The proposed system does not require periodical calibration, as is necessary for stabilometric force platforms, and it is easy to use. In future, the proposed system with little integration can be used, besides being an emulator of a stabilometric platform, also to recognize and track, in real time, head, legs, arms and trunk, that is to collect information actually obtained by sophisticated optoelectronic systems.

Optimal bone density distributions: numerical analysis of the osteocyte spatial influence in bone remodeling.

In this paper a control and optimization procedure for bone remodeling simulations was adopted to study the effect of the osteocyte influence range on the predicted density distribution. In order to reach this goal, the osteocyte network regulating bone remodeling process in a 2-D bone sample was numerically simulated. The assumed proportional-integral-derivative (PID) bone remodeling rule was related to the error signal between the strain energy density and a selected target. Furthermore the control parameters and the target were optimally determined minimizing a suitable cost index: the goal was to minimize the final mass and the energy thus maximizing the stiffness. The continuum model results show that the developed and adapted trabecular structure was consistent with the applied loads and only depended on the external forces, the value of the cost index, the maximum attainable elastic modulus value (hence, the maximum density value) and the value of the energy target. The remodeling phenomenon determined the number and thickness of the trabeculae which are formed from a uniform distribution of mass density in the considered domain; this number and these thicknesses are controlled by the values assigned to the parameters of the model. In particular, the osteocyte decay distance (D) of the influence range affected the trabecular patterns formation, showing an important effect in the adaptive capacity of the optimization numerical model.

Overall design and implementation of the virtual glove.

Post-stroke patients and people suffering from hand diseases often need rehabilitation therapy. The recovery of original skills, when possible, is closely related to the frequency, quality, and duration of rehabilitative therapy. Rehabilitation gloves are tools used both to facilitate rehabilitation and to control improvements by an evaluation system. Mechanical gloves have high cost, are often cumbersome, are not re-usable and, hence, not usable with the healthy hand to collect patient-specific hand mobility information to which rehabilitation should tend. The approach we propose is the virtual glove, a system that, unlike tools based on mechanical haptic interfaces, uses a set of video cameras surrounding the patient hand to collect a set of synchronized videos used to track hand movements. The hand tracking is associated with a numerical hand model that is used to calculate physical, geometrical and mechanical parameters, and to implement some boundary constraints such as joint dimensions, shape, joint angles, and so on. Besides being accurate, the proposed system is aimed to be low cost, not bulky (touch-less), easy to use, and re-usable. Previous works described the virtual glove general concepts, the hand model, and its characterization including system calibration strategy. The present paper provides the virtual glove overall design, both in real-time and in off-line modalities. In particular, the real-time modality is described and implemented and a marker-based hand tracking algorithm, including a marker positioning, coloring, labeling, detection and classification strategy, is presented for the off-line modality. Moreover, model based hand tracking experimental measurements are reported, discussed and compared with the corresponding poses of the real hand. An error estimation strategy is also presented and used for the collected measurements. System limitations and future work for system improvement are also discussed.

Optimal control for SIRC epidemic outbreak.

In this paper the mathematical SIRC epidemic model is considered. It efficiently describes diseases in which a cross immune class (C) is present, along with the susceptible (S), the infected (I) and the removed (R) ones. Controlling epidemic diseases corresponds to the introduction of vaccination, quarantine and treatment strategies; generally only one of these actions is considered. In this paper the possibility of optimal controls both over the susceptible and the infected subjects is assumed, taking into account also limitations of resources. A suitable cost index is introduced and via the Pontryagin's Minimum Principle the optimal control strategy is determined and the existence of the optimal solution is assessed. Numerical results are developed analyzing the effects of different control strategies.

Mobile phone emission modulates event-related desynchronization of α rhythms and cognitive-motor performance in healthy humans.

OBJECTIVES: It has been shown that electromagnetic fields of Global System for Mobile Communications phone (GSM-EMFs) affect human brain rhythms (Vecchio et al., 2007, 2010), but it is not yet clear whether these effects are related to alterations of cognitive functions. METHODS: Eleven healthy adults underwent two electroencephalographic (EEG) sessions separated by 1 week, following a cross-over, placebo-controlled, double-blind paradigm. In both sessions, they performed a visual go/no-go task before real exposure to GSM-EMFs or after a sham condition with no EMF exposure. In the GSM real session, temporal cortex was continuously exposed to GSM-EMFs for 45 min. In the sham session, the subjects were not aware that the EMFs had been switched off for the duration of the experiment. In the go/no-go task, a central fixation stimulus was followed by a green (50% of probability) or red visual stimulus. Subjects had to press the mouse button after the green stimuli (go trials). With reference to a baseline period, power decrease of low- (about 8-10 Hz) and high-frequency (about 10-12 Hz) alpha rhythms indexed the cortical activity. RESULTS: It was found less power decrease of widely distributed high-frequency alpha rhythms and faster reaction time to go stimuli in the post- than pre-exposure period of the GSM session. No effect was found in the sham session. CONCLUSIONS: These results suggest that the peak amplitude of alpha ERD and the reaction time to the go stimuli are modulated by the effect of the GSM-EMFs on the cortical activity. SIGNIFICANCE: Exposure to GSM-EMFs for 45 min may enhance human cortical neural efficiency and simple cognitive-motor processes in healthy adults.

Robust real time eye tracking for computer interface for disabled people.

Gaze is a natural input for a Human Computer Interface (HCI) for disabled people, who have of course an acute need for a communication system. An efficient eye tracking procedure is presented providing a non-invasive method for real time detection of a subject eyes in a sequence of frames captured by low cost equipment. The procedure can be easily adapted to any subject and is adequately insensitive to changing of the illumination. The eye identification is performed on a piece-wise constant approximation of the frames. It is based on a discrete level set formulation of the variational approach to the optimal segmentation problem. This yields a simplified version of the original data retaining all the information relevant to the application. Tracking is obtained by a fast update of the optimal segmentation between successive frames. No eye movement model is required being the procedure fast enough to obtain the current frame segmentation as one step update from the previous frame segmentation.

Parametric characterization of the form of the human pupil from blurred noisy images.

The fluctuation of the human pupil is an important parameter in order to make non-invasive diagnosis of many different diseases and in several clinical applications. The relevant measurement device, the pupillometer, consists in a CCD camera, which shoots the pupil. We suppose that the measured image is blurred by a Gaussian kernel and corrupted by an additive white noise; moreover an elliptic shape for the pupil is assumed. We here present the extension of a multiscale approach for edge detection to identify some parameters of the pupil: the location of its centre, the length of the semi-axes and the orientation of the corresponding ellipse. The chosen method requires knowledge about the degradation parameters of the assumed model; so we first present a simple but efficient method to determine such quantities for the measured image. Then we apply the edge detection procedure to identify points close to the pupil edge, within a chosen probability. Finally we find the optimal ellipse fitting a suitable subset of the previously detected edge points. Results are presented, with comparisons to other approaches for edge finding.