High-wearable EEG-based distraction detection in motor rehabilitation

A method for EEG-based distraction detection during motor-rehabilitation tasks is proposed. A wireless cap guarantees very high wearability with dry electrodes and a low number of channels. Experimental validation is performed on a dataset from 17 volunteers. Different feature extractions from spatial, temporal, and frequency domain and classification strategies were evaluated. The performances of five supervised classifiers in discriminating between attention on pure movement and with distractors were compared. A k-Nearest Neighbors classifier achieved an accuracy of 92.8 ± 1.6%. In this last case, the feature extraction is based on a custom 12 pass-band Filter-Bank (FB) and the Common Spatial Pattern (CSP) algorithm. In particular, the mean Recall of classification (percentage of true positive in distraction detection) is higher than 92% and allows the therapist or an automated system to know when to stimulate the patient’s attention for enhancing the therapy effectiveness

CONTAINER LOCALISATION AND MASS ESTIMATION WITH AN RGB-D CAMERA

In the research area of human-robot interactions, the automatic estimation of the mass of a container manipulated by a person leveraging only visual information is a challenging task. The main challenges consist of occlusions, different filling materials and lighting conditions. The mass of an object constitutes key information for the robot to correctly regulate the force required to grasp the container. We propose a single RGB-D camera-based method to locate a manipulated container and estimate its empty mass i.e., independently of the presence of the content. The method first automatically selects a number of candidate containers based on the distance with the fixed frontal view, then averages the mass predictions of a lightweight model to provide the final estimation. Results on the CORSMAL Containers Manipulation dataset show that the proposed method estimates empty container mass obtaining a score of 71.08% under different lighting or filling conditions

Genetic variability detected at the lactoferrin locus (LTF) in the Italian Mediterranean river buffalo

Lactoferrin (LTF) is multi-functional protein belonging to the whey protein fractions of the milk. The gene LTF encoding for such protein is considered a potential candidate for body measurement, milk composition and yield. This study reports on the genetic variability at LTF locus in the Italian Mediterranean river buffalo and its possible association with milk yield. Eleven polymorphic sites were found in the DNA fragment spanning the exons 15-16. In particular, the intron 15 was extremely polymorphic with 9 SNPs detected, whereas the remaining 2 SNPs were exonic mutations (g.88G>A at the exon 15 and g.1351G>A at the exon 16) and both synonymous. The genotyping of the informative samples evidenced 3 haplotypes, whose frequencies were 0.6; 0.3 and 0.1 respectively, whereas the analysis of the exonic SNPs showed a perfect condition of linkage disequilibrium (g.88A/g.1351G and g.88G/g.1351A). The association study carried out by using the SNP g.88G>A showed that buffalo LTF gene has no statistically significant influence on daily milk yield. This study adds knowledge to the genetic variability of a species less investigated than the other ruminant species, that may serve as a useful tool for large-scale screening of buffalo populations

Enhancement of SSVEPs Classification in BCI-based Wearable Instrumentation Through Machine Learning Techniques

This work addresses the adoption of Machine Learning classifiers and Convolutional Neural Networks to improve the performance of highly wearable, single-channel instrumentation for Brain-Computer Interfaces. The proposed measurement system is based on the classification of Steady-State Visually Evoked Potentials (SSVEPs). In particular, Head-Mounted Displays for Augmented Reality are used to generate and display the flickering stimuli for the SSVEPs elicitation. Four experiments were conducted by employing, in turn, a different Head-Mounted Display. For each experiment, two different algorithms were applied and compared with the state-of-the-art-techniques. Furthermore, the impact of different Augmented Reality technologies in the elicitation and classification of SSVEPs was also explored. The experimental metrological characterization demonstrates (i) that the proposed Machine Learning-based processing strategies provide a significant enhancement of the SSVEP classification accuracy with respect to the state of the art, and (ii) that choosing an adequate Head-Mounted Display is crucial to obtain acceptable performance. Finally, it is also shown that the adoption of inter-subjective validation strategies such as the Leave-One-Subject-Out Cross Validation successfully leads to an increase in the inter-individual 1-σ reproducibility: this, in turn, anticipates an easier development of ready-to-use systems

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

Prolyl 3‐hydroxylase 2 is a molecular player of angiogenesis

Prolyl 3‐hydroxylase 2 (P3H2) catalyzes the post‐translational formation of 3‐ hydroxyproline on collagens, mainly on type IV. Its activity has never been directly associated to angiogenesis. Here, we identified P3H2 gene through a deep‐sequencing transcriptome analysis of human umbilical vein endothelial cells (HUVECs) stimulated with vascular endothelial growth factor A (VEGF‐A). Differently from many previous studies we carried out the stimulation not on starved HUVECs, but on cells grown to maintain the best condition for their in vitro survival and propagation. We showed that P3H2 is induced by VEGF‐A in two primary human endothelial cell lines and that its transcription is modulated by VEGF‐A/VEGF receptor 2 (VEGFR‐2) signaling pathway through p38 mitogen‐activated protein kinase (MAPK). Then, we demonstrated that P3H2, through its activity on type IV Collagen, is essential for angiogenesis properties of endothelial cells in vitro by performing experiments of gain‐ and loss‐of‐function. Immunofluorescence studies showed that the overexpression of P3H2 induced a more condensed status of Collagen IV, accompanied by an alignment of the cells along the Collagen IV bundles, so towards an evident pro‐angiogenic status. Finally, we found that P3h2 knockdown prevents pathological angiogenesis in vivo, in the model of laser‐induced choroid neovascularization. Together these findings reveal that P3H2 is a new molecular player involved in new vessels formation and could be considered as a potential target for anti‐angiogenesis therapy

ABC messages for HIV prevention in Kenya: Clarity and confusion, barriers and facilitators

The Horizons Program and FHI/IMPACT developed a collaborative research study to explore how adults and youth in Kenya define and perceive the ABC (abstinence/being faithful/consistent condom use) terms and behaviors. Additional objectives of the study were to identify attitudes and norms around the ABC behaviors that influence perceptions of them, and the role of important actors in transmitting messages about them. Findings highlight potential challenges in promoting each of the ABC behaviors, as well as some positive elements that can be built upon when developing programs. HIV prevention programs that incorporate ABC messages—both in Kenya and elsewhere—should consider a number of lessons highlighted in this study

Mediterranean River Buffalo CSN1S1 gene: search for polymorphisms and association studies.

The aim of the present work was to study the variability at CSN1S1 locus of the Italian Mediterranean river buffalo and to investigate possible allele effects on milk yield and its composition. Effects of parity, calving season and month of production were also evaluated. Three SNPs were detected. The first mutation, located at position 89 of 17th exon (c.628C>T), is responsible for the amino acid change (p.Ser178Leu). The other two polymorphisms, detected at the positions 144 (c.882G>A) and 239 (c.977A>G) of 19th exon respectively, are silent (3’ UTR). Associations between the CSN1S1 genotypes and milk production traits were investigated using 4,122 test day records of 503 lactations from 175 buffalo cows. Milk yield, fat and protein percentages were analyzed using a mixed linear model. A significant association between the c.628C>T SNP and the protein percentage was found. In particular, the CC genotype showed an average value of about 0.04% higher than the CT and TT genotypes. The allele substitution effect of the cytosine into the thymine was -0.014, with a quite low (0.3%) protein percentage (PP) contribution on total phenotypic variance. A large dominance effect was detected. Furthermore, a characterization of the CSN1S1 transcripts and a method based on MboI-ACRS-PCR for a rapid genotyping of c.628C>T were provided