A motivational approach to support healthy habits in long-term child–robot interaction

We examine the use of role-switching as an intrinsic motivational mechanism to increase engagement in long-term child–robot interaction. The present study describes a learning framework where children between 9 and 11-years-old interact with a robot to improve their knowledge and habits with regards to healthy life-styles. Experiments were carried out in Italy where 41 children were divided in three groups interacting with: (i) a robot with a role-switching mechanism, (ii) a robot without a role-switching mechanism and (iii) an interactive video. Additionally, a control group composed of 43 more children, who were not exposed to any interactive approach, was used as a baseline of the study. During the intervention period, the three groups were exposed to three interactive sessions once a week. The aim of the study was to find any difference in healthy-habits acquisition based on alternative interactive systems, and to evaluate the effectiveness of the role-switch approach as a trigger for engagement and motivation while interacting with a robot. The results provide evidence that the rate of children adopting healthy habits during the intervention period was higher for those interacting with a robot. Moreover, alignment with the robot behaviour and achievement of higher engagement levels were also observed for those children interacting with the robot that used the role-switching mechanism. This supports the notion that role-switching facilitates sustained long-interactions between a child and a robot

Exploring the role of RNASET2 in the immune response of black soldier fly larvae

T2 RNases are transferase-type enzymes distributed across phyla, crucial for breaking down single-stranded RNA molecules. In addition to their canonical function, several T2 enzymes exhibit pleiotropic roles, contributing to various biological processes, such as the immune response in invertebrates and vertebrates. This study aims at characterizing RNASET2 in the larvae of black soldier fly (BSF), Hermetia illucens, which are used for organic waste reduction and the production of valuable insect biomolecules for feed formulation and other applications. Given the exposure of BSF larvae to pathogens present in the feeding substrate, it is likely that the mechanisms of their immune response have undergone significant evolution and increased complexity. After in silico characterization of HiRNASET2, demonstrating the high conservation of this T2 homolog, we investigated the expression pattern of the enzyme in the fat body and hemocytes, two districts mainly involved in the insect immune response, in larvae challenged with bacterial infection. While no variation in HiRNASET2 expression was observed in the fat body following infection, a significant upregulation of HiRNASET2 synthesis occurred in hemocytes shortly after the injection of bacteria in the larva. The intracellular localization of HiRNASET2 in lysosomes of plasmatocytes, its extracellular association with bacteria, and the presence of a putative antimicrobial domain in the molecule, suggest its potential role in RNA clean-up and as an alarm molecule promoting phagocytosis activation by hemocytes. These insights contribute to the characterization of the immune response of Hermetia illucens larvae and may facilitate the development of animal feedstuff enriched with highly valuable BSF bioactive compounds

Modeling the cumulative genetic risk for multiple sclerosis from genome-wide association data

Background: Multiple sclerosis (MS) is the most common cause of chronic neurologic disability beginning in early to middle adult life. Results from recent genome-wide association studies (GWAS) have substantially lengthened the list of disease loci and provide convincing evidence supporting a multifactorial and polygenic model of inheritance. Nevertheless, the knowledge of MS genetics remains incomplete, with many risk alleles still to be revealed. Methods: We used a discovery GWAS dataset (8,844 samples, 2,124 cases and 6,720 controls) and a multi-step logistic regression protocol to identify novel genetic associations. The emerging genetic profile included 350 independent markers and was used to calculate and estimate the cumulative genetic risk in an independent validation dataset (3,606 samples). Analysis of covariance (ANCOVA) was implemented to compare clinical characteristics of individuals with various degrees of genetic risk. Gene ontology and pathway enrichment analysis was done using the DAVID functional annotation tool, the GO Tree Machine, and the Pathway-Express profiling tool. Results: In the discovery dataset, the median cumulative genetic risk (P-Hat) was 0.903 and 0.007 in the case and control groups, respectively, together with 79.9% classification sensitivity and 95.8% specificity. The identified profile shows a significant enrichment of genes involved in the immune response, cell adhesion, cell communication/ signaling, nervous system development, and neuronal signaling, including ionotropic glutamate receptors, which have been implicated in the pathological mechanism driving neurodegeneration. In the validation dataset, the median cumulative genetic risk was 0.59 and 0.32 in the case and control groups, respectively, with classification sensitivity 62.3% and specificity 75.9%. No differences in disease progression or T2-lesion volumes were observed among four levels of predicted genetic risk groups (high, medium, low, misclassified). On the other hand, a significant difference (F = 2.75, P = 0.04) was detected for age of disease onset between the affected misclassified as controls (mean = 36 years) and the other three groups (high, 33.5 years; medium, 33.4 years; low, 33.1 years). Conclusions: The results are consistent with the polygenic model of inheritance. The cumulative genetic risk established using currently available genome-wide association data provides important insights into disease heterogeneity and completeness of current knowledge in MS genetics

Genotype-Phenotype correlations in multiple sclerosis: HLA genes influence disease severity inferred by 1HMR spectroscopy and MRI measures

Genetic susceptibility to multiple sclerosis (MS) is associated with the human leukocyte antigen (HLA) DRB1*1501 allele. Here we show a clear association between DRB1*1501 carrier status and four domains of disease severity in an investigation of genotype-phenotype associations in 505 robust, clinically well characterized MS patients evaluated cross-sectionally: (i) a reduction in the N-acetyl-aspartate (NAA) concentration within normal appearing white matter (NAWM) via 1HMR spectroscopy (P = 0.025), (ii) an increase in the volume of white matter (WM) lesions utilizing conventional anatomical MRI techniques (1,127 mm3; P = 0.031), (iii) a reduction in normalized brain parenchymal volume (nBPV) (P = 0.023), and (iv) impairments in cognitive function as measured by the Paced Auditory Serial Addition Test (PASAT-3) performance (Mean Z Score: DRB1*1501+: 0.110 versus DRB1*1501-: 0.048; P = 0.004). In addition, DRB1*1501+ patients had significantly more women (74% versus 63%; P = 0.009) and a younger mean age at disease onset (32.4 years versus 34.3 years; P = 0.025). Our findings suggest that DRB1*1501 increases disease severity in MS by facilitating the development of more T2-foci, thereby increasing the potential for irreversible axonal compromise and subsequent neuronal degeneration, as suggested by the reduction of NAA concentrations in NAWM, ultimately leading to a decline in brain volume. These structural aberrations may explain the significant differences in cognitive performance observed between DRB1*1501 groups. The overall goal of a deep phenotypic approach to MS is to develop an array of meaningful biomarkers to monitor the course of the disease, predict future disease behaviour, determine when treatment is necessary, and perhaps to more effectively recommend an available therapeutic interventio

Parallel tracks towards a global treaty on carbon pricing

We argue that a global carbon price is the only way to effectively tackle free riding in international climate policy, required to substantially reduce greenhouse gas emissions. We briefly review the main reasons behind the essential role of carbon pricing, address common misunderstandings and scepticism, and identify key complementary policy instruments. Negotiating global carbon pricing is argued to be much easier than negotiating binding country-level targets, especially if it includes equitable revenue recycling. Moreover, a global carbon price can be more readily adapted to new data and insights of climate science. We propose a political strategy towards a global carbon price that consists of two tracks. The first entails assembly of a carbon-pricing club, a specific case of a climate club, to gradually move towards a full participatory agreement on carbon pricing. The second track involves putting time and energy into re-focusing UNFCCC negotiations on a carbon-pricing agreement. The two tracks reinforce one another, increasing the likelihood of a successful outcome

Infectious Disease Ontology

Technological developments have resulted in tremendous increases in the volume and diversity of the data and information that must be processed in the course of biomedical and clinical research and practice. Researchers are at the same time under ever greater pressure to share data and to take steps to ensure that data resources are interoperable. The use of ontologies to annotate data has proven successful in supporting these goals and in providing new possibilities for the automated processing of data and information. In this chapter, we describe different types of vocabulary resources and emphasize those features of formal ontologies that make them most useful for computational applications. We describe current uses of ontologies and discuss future goals for ontology-based computing, focusing on its use in the field of infectious diseases. We review the largest and most widely used vocabulary resources relevant to the study of infectious diseases and conclude with a description of the Infectious Disease Ontology (IDO) suite of interoperable ontology modules that together cover the entire infectious disease domain

Circulating extracellular vesicles release oncogenic miR-424 in experimental models and patients with aggressive prostate cancer

Extracellular vesicles (EVs) are relevant means for transferring signals across cells and facilitate propagation of oncogenic stimuli promoting disease evolution and metastatic spread in cancer patients. Here, we investigated the release of miR-424 in circulating small EVs or exosomes from prostate cancer patients and assessed the functional implications in multiple experimental models. We found higher frequency of circulating miR-424 positive EVs in patients with metastatic prostate cancer compared to patients with primary tumors and BPH. Release of miR-424 in small EVs was enhanced in cell lines (LNCaPabl), transgenic mice (Pb-Cre4;Ptenflox/flox;Rosa26ERG/ERG) and patient-derived xenograft (PDX) models of aggressive disease. EVs containing miR-424 promoted stem-like traits and tumor-initiating properties in normal prostate epithelial cells while enhanced tumorigenesis in transformed prostate epithelial cells. Intravenous administration of miR-424 positive EVs to mice, mimicking blood circulation, promoted miR-424 transfer and tumor growth in xenograft models. Circulating miR-424 positive EVs from patients with aggressive primary and metastatic tumors induced stem-like features when supplemented to prostate epithelial cells. This study establishes that EVs-mediated transfer of miR-424 across heterogeneous cell populations is an important mechanism of tumor self-sustenance, disease recurrence and progression. These findings might indicate novel approaches for the management and therapy of prostate cancer

Genetic, environmental and stochastic factors in monozygotic twin discordance with a focus on epigenetic differences

PMCID: PMC3566971This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited

Optimization of cognitive assessment in Parkinsonisms by applying artificial intelligence to a comprehensive screening test.

The assessment of cognitive deficits is pivotal for diagnosis and management in patients with parkinsonisms. Low levels of correspondence are observed between evaluations assessed with screening cognitive tests in comparison with those assessed with in-depth neuropsychological batteries. A new tool, we named CoMDA (Cognition in Movement Disorders Assessment), was composed by merging Mini-Mental State Examination (MMSE), Montreal Cognitive Assessment (MoCA), and Frontal Assessment Battery (FAB). In total, 500 patients (400 with Parkinson's disease, 41 with vascular parkinsonism, 31 with progressive supranuclear palsy, and 28 with multiple system atrophy) underwent CoMDA (level 1-L1) and in-depth neuropsychological battery (level 2-L2). Machine learning was developed to classify the CoMDA score and obtain an accurate prediction of the cognitive profile along three different classes: normal cognition (NC), mild cognitive impairment (MCI), and impaired cognition (IC). The classification accuracy of CoMDA, assessed by ROC analysis, was compared with MMSE, MoCA, and FAB. The area under the curve (AUC) of CoMDA was significantly higher than that of MMSE, MoCA and FAB (p < 0.0001, p = 0.028 and p = 0.0007, respectively). Among 15 different algorithmic methods, the Quadratic Discriminant Analysis algorithm (CoMDA-ML) showed higher overall-metrics performance levels in predictive performance. Considering L2 as a 3-level continuous feature, CoMDA-ML produces accurate and generalizable classifications: micro-average ROC curve, AUC = 0.81; and AUC = 0.85 for NC, 0.67 for MCI, and 0.83 for IC. CoMDA and COMDA-ML are reliable and time-sparing tools, accurate in classifying cognitive profile in parkinsonisms.This study has been registered on ClinicalTrials.gov (NCT04858893)