Exploring the Connected Brain by fNIRS: Human-to-Human Interactions Engineering

Functional Near Infrared Spectroscopy (fNIRS) is a relatively new neuroimaging technique adequate and useful for exploring neural activity in social contexts involving human interactions. Compared to functional Magnetic Resonance Imaging (fMRI), fNIRS is easy-to-use safe, noninvasive, silent, relatively low cost and portable, and applicable to subjects of all ages, thus resulting in a good option for ecological studies involving humans in their real-life context. Moreover, by using hyperscanning technique, fNIRS allows recording the hemodynamic cerebral activity of two interacting subjects in an ecological context or during a shared performance. Thus, moving from a simple analysis about each subject\u2019s neural response during joint actions towards more complex computations makes possible to investigate brain synchrony, that is the if and how one\u2019s brain activity is related to that of another interacting partner simultaneously recorded. Here, we discuss how connectivity analyses, with respect to both time and frequency domain procedures, permitted to deepen some aspects of inter-brain synchrony in relation to emotional closeness, and to highlight how concurrent, cooperative actions can lead to interpersonal synchrony and bond construction

A CAUSAL HIERARCHICAL MARKOV FRAMEWORK FOR THE CLASSIFICATION OF MULTIRESOLUTION AND MULTISENSOR REMOTE SENSING IMAGES

Abstract. In this paper, a multiscale Markov framework is proposed in order to address the problem of the classification of multiresolution and multisensor remotely sensed data. The proposed framework makes use of a quadtree to model the interactions across different spatial resolutions and a Markov model with respect to a generic total order relation to deal with contextual information at each scale in order to favor applicability to very high resolution imagery. The methodological properties of the proposed hierarchical framework are investigated. Firstly, we prove the causality of the overall proposed model, a particularly advantageous property in terms of computational cost of the inference. Secondly, we prove the expression of the marginal posterior mode criterion for inference on the proposed framework. Within this framework, a specific algorithm is formulated by defining, within each layer of the quadtree, a Markov chain model with respect to a pixel scan that combines both a zig-zag trajectory and a Hilbert space-filling curve. Data collected by distinct sensors at the same spatial resolution are fused through gradient boosted regression trees. The developed algorithm was experimentally validated with two very high resolution datasets including multispectral, panchromatic and radar satellite images. The experimental results confirm the effectiveness of the proposed algorithm as compared to previous techniques based on alternate approaches to multiresolution fusion

Quantitative assessment of HCC wash-out on CT is a predictor of early complete response to TACE

OBJECTIVES: To investigate the predictive value of four-phase contrast-enhanced CT (CECT) for early complete response (CR) to drug-eluting-bead transarterial chemoembolization (DEB-TACE), with a particular focus on the quantitatively assessed wash-in and wash-out. METHODS: A retrospective analysis of preprocedural CECTs was performed for 129 HCC nodules consecutively subjected to DEB-TACE as first-line therapy. Lesion size, location, and margins were recorded. For the quantitative analysis, the following parameters were computed: contrast enhancement ratio (CER) and lesion-to-liver contrast ratio (LLC) as estimates of wash-in; absolute and relative wash-out (WO(abs) and WO(rel)) and delayed percentage attenuation ratio (DPAR) as estimates of wash-out. The early radiological response of each lesion was assessed by the mRECIST criteria and dichotomized in CR versus others (partial response, stable disease, and progressive disease). RESULTS: All quantitatively assessed wash-out variables had significantly higher rates for CR lesions (WO(abs) p = 0.01, WO(rel) p = 0.01, and DPAR p = 0.00002). However, only DPAR demonstrated an acceptable discriminating ability, quantified by AUC = 0.80 (95% CI0.73–0.88). In particular, nodules with DPAR ≥ 120 showed an odds ratio of 3.3(1.5–7.2) for CR (p = 0.0026). When accompanied by smooth lesion margins, DPAR ≥ 120 lesions showed a 78% CR rate at first follow-up imaging. No significative association with CR was found for quantitative wash-in estimates (CER and LLC). CONCLUSIONS: Based on preprocedural CECT, the quantitative assessment of HCC wash-out is useful in predicting early CR after DEB-TACE. Among the different formulas for wash-out quantification, DPAR has the best discriminating ability. When associated, DPAR ≥ 120 and smooth lesion margins are related to relatively high CR rates. KEY POINTS: • A high wash-out rate, quantitatively assessed during preprocedural four-phase contrast-enhanced CT (CECT), is a favorable predictor for early radiological complete response of HCC to drug-eluting-bead chemoembolization (DEB-TACE). • The arterial phase of CECT shows great dispersion of attenuation values among different lesions, even when a standardized protocol is used, limiting its usefulness for quantitative analyses. • Among the different formulas used to quantify the wash-out rate (absolute wash-out, relative wash-out, and delayed percentage attenuation ratio), the latter (DPAR), based only on the delayed phase, is the most predictive (AUC = 0.80), showing a significant association with complete response for values above 120

Primi dati del monitoraggio di Pinna nobilis nell'AMP "Isole di Ventotene e Santo Stefano"

A study on the distribution, abundance and morphometric variables of Pinna nobilis Linnaeus, 1758 (Mollusca, Bivalvia) has been carried out in the ‘Isole di Ventotene e Santo Stefano’ Marine Protected Area, in the Central Tyrrhenian Sea. A total of 39 individuals was recorded (0.7 ind./100 m2), 37 of them were dead (94.87%) and 2 were alive (5.13%), with a shell length of 57.97±12.84 cm and 40.69±4.65 cm respectively. This study represents a first investigation about the current status of the P. nobilis population around Ventotene Island

Modeling the Line Interruption Issue in a Railway Network

Rail line interruptions are rare but very costly events, as they require a complete re-definition not only of the timetable of the trains, but also of their path, with major variations at least in the hit area. To the best of our knowledge, the literature is rich of documentation on timetable re-scheduling in case of delays and/or disruption of train lines, but without considering path deviations. The Flatland initiative has published a 2D railway-world toolkit that allows developers devise and experiment with different solutions to deal with the train re-scheduling issue particularly, but not exclusively, through reinforcement learning (RL) with multi-agent path finding (MAPF). While the approach looks very promising to deal with the complexity of the issue, the platform still has some limitations in terms of the modeling of a realistic railway network scenario. This article proposes the integration of some key features in order to make Flatland a valuable platform for training RL agents for supporting decision making in real-world train re-scheduling

Survey of Positioning Technologies for In-Tunnel Railway Maintenance

Maintenance plays a fundamental role for the safety and efficiency of the railway infrastructure. This document analyzes the state of the art of technological solutions for indoor positioning, which has recently had significant developments, particularly with ultra-wide band (UWB), and can be taken into account to manage the positioning of teams of workers inside a tunnel. Based on our analysis, we argue that there is not sufficient information about the performance achievable by state of the art technologies in a railway tunnel maintenance scenario. We thus propose a set of research questions for an experimental measurement campaign in the authentic context of use