Information redundancy neglect versus overconfidence: a social learning experiment

We study social learning in a continuous action space experiment. Subjects, acting in sequence, state their belief about the value of a good, after observing their predecessors' statements and a private signal. We compare the behavior in the laboratory with the Perfect Bayesian Equilibrium prediction and the predictions of bounded rationality models of decision making: the redundancy of information neglect model and the overconfidence model. The results of our experiment are in line with the predictions of the overconfidence model and at odds with the others'

A Wearable Brain-Computer Interface Instrument for Augmented Reality-Based Inspection in Industry 4.0

This paper proposes a wearable monitoring system for inspection in the framework of Industry 4.0. The instrument integrates augmented reality (AR) glasses with a noninvasive single-channel brain-computer interface (BCI), which replaces the classical input interface of AR platforms. Steady-state visually evoked potentials (SSVEP) are measured by a single-channel electroencephalography (EEG) and simple power spectral density analysis. The visual stimuli for SSVEP elicitation are provided by AR glasses while displaying the inspection information. The real-time metrological performance of the BCI is assessed by the receiver operating characteristic curve on the experimental data from 20 subjects. The characterization was carried out by considering stimulation times from 10.0 down to 2.0 s. The thresholds for the classification were found to be dependent on the subject and the obtained average accuracy goes from 98.9% at 10.0 s to 81.1% at 2.0 s. An inspection case study of the integrated AR-BCI device shows encouraging accuracy of about 80% of lab values

Metrological performance of a single-channel brain-computer interface based on motor imagery

In this paper, the accuracy in classifying Motor Imagery (MI) tasks for a Brain-Computer Interface (BCI) is analyzed. Electroencephalographic (EEG) signals were taken into account, notably by employing one channel per time. Four classes were to distinguish, i.e. imagining the movement of left hand, right hand, feet, or tongue. The dataset '2a' of BCI Competition IV (2008) was considered. Brain signals were processed by applying a short-time Fourier transform, a common spatial pattern filter for feature extraction, and a support vector machine for classification. With this work, the aim is to give a contribution to the development of wearable MI-based BCIs by relying on single channel EEG

JCV-specific T-cells producing IFN-gamma are differently associated with PmL occurrence in HIV patients and liver transplant recipients

Aim of this work was to investigate a possible correlation between the frequency of JCV-specific T-cells and PML occurrence in HIV-infected subjects and in liver transplant recipients. A significant decrease of JCV-specific T-cells was observed in HIV-PML subjects, highlighting a close relation between JCV-specific T-cell immune impairment and PML occurrence in HIV-subjects. Interestingly, liver-transplant recipients (LTR) showed a low frequency of JCV-specific T-cells, similar to HIV-PML subjects. Nevertheless, none of the enrolled LTR developed PML, suggesting the existence of different immunological mechanisms involved in the maintenance of a protective immune response in LT

A dynamic link between H/ACA snoRNP components and cytoplasmic stress granules

Many cell stressors block protein translation, inducing formation of cytoplasmic aggregates. These aggregates, named stress granules (SGs), are composed by translationally stalled ribonucleoproteins and their assembly strongly contributes to cell survival. Composition and dynamics of SGs are thus important starting points for identifying critical factors of the stress response. In the present study we link components of the H/ACA snoRNP complexes, highly concentrated in the nucleoli and the Cajal bodies, to SG composition. H/ACA snoRNPs are composed by a core of four highly conserved proteins -dyskerin, Nhp2, Nop10 and Gar1- and are involved in several fundamental processes, including ribosome biogenesis, RNA pseudouridylation, stabilization of small nucleolar RNAs and telomere maintenance. By taking advantage of cells overexpressing a dyskerin splice variant undergoing a dynamic intracellular trafficking, we were able to show that H/ACA snoRNP components can participate in SG formation, this way contributing to the stress response and perhaps transducing signals from the nucleus to the cytoplasm. Collectively, our results show for the first time that H/ACA snoRNP proteins can have additional non-nuclear functions, either independently or interacting with each other, thus further strengthening the close relationship linking nucleolus to SG composition

Fine-needle cytology in the follow-up of breast carcinoma

The postoperative follow-up strategies for breast carcinoma (BC) utilize different procedures; the aim of this study was to investigate the role of fine-needle cytology (FNC) in the follow-up of BC patients. Two hundred sixty-six FNC samples from 190 BC patients have been reviewed. The target anatomical sites were 190 breast including 155 ipsilateral and 145 contralateral breast lesions and 76 extra-mammary nodules. Extra-mammary lesions included lymph nodes, thyroidal nodules, soft tissue lesions, (subcutaneous and sub-scars), salivary glands and deep located masses. Diagnostic distribution of the breast lesions was as follows: 51 positive, 15 indeterminate/suspicious, 119 negative and 5 inadequate. Positive cases included 43 ipsilateral and 8 contralateral BC, 9 BC in different quadrants from those of onset of the first BC. Sensitivity, specificity and accuracy have been 90, 91 and 90&, respectively. FNC, in a correct setting, is a reliable and effective method for the follow-up management of BC patients

A ML-based Approach to Enhance Metrological Performance of Wearable Brain-Computer Interfaces

In this paper, the adoption of Machine Learning (ML) classifiers is addressed to improve the performance of highly wearable, single-channel instrumentation for Brain-Computer Interfaces (BCIs). The proposed BCI is based on the classification of Steady-State Visually Evoked Potentials (SSVEPs). In this setup, Augmented Reality Smart Glasses are used to generate and display the flickering stimuli for the SSVEP elicitation. An experimental campaign was conducted on 20 adult volunteers. Successively, a Leave-One-Subject-Out Cross Validation was performed to validate the proposed algorithm. The obtained experimental results demonstrate that suitable ML-based processing strategies outperform the state-of-the-art techniques in terms of classification accuracy. Furthermore, it was also shown that the adoption of an inter-subjective model successfully led to a decrease in the 3-σ uncertainty: this can facilitate future developments of ready-to-use systems