Why Robots Should Be Social: Enhancing Machine Learning through Social Human-Robot Interaction.

Social learning is a powerful method for cultural propagation of knowledge and skills relying on a complex interplay of learning strategies, social ecology and the human propensity for both learning and tutoring. Social learning has the potential to be an equally potent learning strategy for artificial systems and robots in specific. However, given the complexity and unstructured nature of social learning, implementing social machine learning proves to be a challenging problem. We study one particular aspect of social machine learning: that of offering social cues during the learning interaction. Specifically, we study whether people are sensitive to social cues offered by a learning robot, in a similar way to children's social bids for tutoring. We use a child-like social robot and a task in which the robot has to learn the meaning of words. For this a simple turn-based interaction is used, based on language games. Two conditions are tested: one in which the robot uses social means to invite a human teacher to provide information based on what the robot requires to fill gaps in its knowledge (i.e. expression of a learning preference); the other in which the robot does not provide social cues to communicate a learning preference. We observe that conveying a learning preference through the use of social cues results in better and faster learning by the robot. People also seem to form a "mental model" of the robot, tailoring the tutoring to the robot's performance as opposed to using simply random teaching. In addition, the social learning shows a clear gender effect with female participants being responsive to the robot's bids, while male teachers appear to be less receptive. This work shows how additional social cues in social machine learning can result in people offering better quality learning input to artificial systems, resulting in improved learning performance

The Shortest Isoform of Dystrophin (Dp40) Interacts with a Group of Presynaptic Proteins to Form a Presumptive Novel Complex in the Mouse Brain

Duchenne muscular dystrophy (DMD) causes cognitive impairment in one third of the patients, although the underlying mechanisms remain to be elucidated. Recent studies showed that mutations in the distal part of the dystrophin gene correlate well with the cognitive impairment in DMD patients, which is attributed to Dp71. The study on the expression of the shortest isoform, Dp40, has not been possible due to the lack of an isoform specific antibody. Dp40 has the same promoter as that found in Dp71 and lacks the normal C-terminal end of Dp427. In the present study, we have raised polyclonal antibody against the N-terminal sequence common to short isoforms of dystrophin, including Dp40, and investigated the expression pattern of Dp40 in the mouse brain. Affinity chromatography with this antibody and the consecutive LC-MS/MS analysis on the interacting proteins revealed that Dp40 was abundantly expressed in synaptic vesicles and interacted with a group of presynaptic proteins, including syntaxin1A and SNAP25, which are involved in exocytosis of synaptic vesicles in neurons. We thus suggest that Dp40 may form a novel protein complex and play a crucial role in presynaptic function. Further studies on these aspects of Dp40 function might provide more insight into the molecular mechanisms of cognitive impairment found in patients with DMD

Virtual Partner Interaction (VPI): Exploring Novel Behaviors via Coordination Dynamics

Inspired by the dynamic clamp of cellular neuroscience, this paper introduces VPI—Virtual Partner Interaction—a coupled dynamical system for studying real time interaction between a human and a machine. In this proof of concept study, human subjects coordinate hand movements with a virtual partner, an avatar of a hand whose movements are driven by a computerized version of the Haken-Kelso-Bunz (HKB) equations that have been shown to govern basic forms of human coordination. As a surrogate system for human social coordination, VPI allows one to examine regions of the parameter space not typically explored during live interactions. A number of novel behaviors never previously observed are uncovered and accounted for. Having its basis in an empirically derived theory of human coordination, VPI offers a principled approach to human-machine interaction and opens up new ways to understand how humans interact with human-like machines including identification of underlying neural mechanisms

Expression of methylation-related genes is associated with overall survival in patients with non-small cell lung cancer

The abnormality of DNA methylation is involved in tumour progression, and thus has a modulating effect on clinical outcome of cancer patients. In this study, we measured the mRNA expression levels of three methylation-regulating genes (DNMT1, DNMT3b, and MBD2) in 148 tumour samples from patients with non-small cell lung cancer (NSCLC) using quantitative real-time polymerase chain reaction and then determined their prognostic values. Our data showed that the high level of DNMT1 expression was significantly associated with an increased risk of death in all NSCLC patients (hazard ratio (HR), 1.74; 95% confidence interval (95% CI), 1.04–2.90). However, the high level of DNMT3b expression was significantly associated with poor prognosis only in young patients (<65 years). The high level of MBD2 expression had a significantly reduced risk for death only in male patients and in squamous cell lung carcinoma (SQLC) patients. All three combination groups with DNMT1 and DNMT3b, DNMT1 and MBD2 or DNMT3b and MBD2 revealed significant combined effects in male patients and SQLC patients. Our results suggest that DNMT1, DNMT3b, and MBD2 may play important roles in modulating NSCLC patient survival and thus be useful for identifying NSCLC patients who would benefit most from aggressive therapy

HIV-1 assembly in macrophages

The molecular mechanisms involved in the assembly of newly synthesized Human Immunodeficiency Virus (HIV) particles are poorly understood. Most of the work on HIV-1 assembly has been performed in T cells in which viral particle budding and assembly take place at the plasma membrane. In contrast, few studies have been performed on macrophages, the other major target of HIV-1. Infected macrophages represent a viral reservoir and probably play a key role in HIV-1 physiopathology. Indeed macrophages retain infectious particles for long periods of time, keeping them protected from anti-viral immune response or drug treatments. Here, we present an overview of what is known about HIV-1 assembly in macrophages as compared to T lymphocytes or cell lines

Inhibition of Soluble Tumor Necrosis Factor Ameliorates Synaptic Alterations and Ca2+ Dysregulation in Aged Rats

The role of tumor necrosis factor α (TNF) in neural function has been investigated extensively in several neurodegenerative conditions, but rarely in brain aging, where cognitive and physiologic changes are milder and more variable. Here, we show that protein levels for TNF receptor 1 (TNFR1) are significantly elevated in the hippocampus relative to TNF receptor 2 (TNFR2) in aged (22 months) but not young adult (6 months) Fischer 344 rats. To determine if altered TNF/TNFR1 interactions contribute to key brain aging biomarkers, aged rats received chronic (4–6 week) intracranial infusions of XPro1595: a soluble dominant negative TNF that preferentially inhibits TNFR1 signaling. Aged rats treated with XPro1595 showed improved Morris Water Maze performance, reduced microglial activation, reduced susceptibility to hippocampal long-term depression, increased protein levels for the GluR1 type glutamate receptor, and lower L-type voltage sensitive Ca2+ channel (VSCC) activity in hippocampal CA1 neurons. The results suggest that diverse functional changes associated with brain aging may arise, in part, from selective alterations in TNF signaling

Brucella abortus Uses a Stealthy Strategy to Avoid Activation of the Innate Immune System during the Onset of Infection

To unravel the strategy by which Brucella abortus establishes chronic infections, we explored its early interaction with innate immunity. Methodology/Principal Findings Brucella did not induce proinflammatory responses as demonstrated by the absence of leukocyte recruitment, humoral or cellular blood changes in mice. Brucella hampered neutrophil (PMN) function and PMN depletion did not influence the course of infection. Brucella barely induced proinflammatory cytokines and consumed complement, and was strongly resistant to bactericidal peptides, PMN extracts and serum. Brucella LPS (BrLPS), NH-polysaccharides, cyclic glucans, outer membrane fragments or disrupted bacterial cells displayed low biological activity in mice and cells. The lack of proinflammatory responses was not due to conspicuous inhibitory mechanisms mediated by the invading Brucella or its products. When activated 24 h post-infection macrophages did not kill Brucella, indicating that the replication niche was not fusiogenic with lysosomes. Brucella intracellular replication did not interrupt the cell cycle or caused cytotoxicity in WT, TLR4 and TLR2 knockout cells. TNF-α-induction was TLR4- and TLR2-dependent for live but not for killed B. abortus. However, intracellular replication in TLR4, TLR2 and TLR4/2 knockout cells was not altered and the infection course and anti-Brucella immunity development upon BrLPS injection was unaffected in TLR4 mutant mice. Conclusion/Significance We propose that Brucella has developed a stealth strategy through PAMPs reduction, modification and hiding, ensuring by this manner low stimulatory activity and toxicity for cells. This strategy allows Brucella to reach its replication niche before activation of antimicrobial mechanisms by adaptive immunity. This model is consistent with clinical profiles observed in humans and natural hosts at the onset of infection and could be valid for those intracellular pathogens phylogenetically related to Brucella that also cause long lasting infections

The poly-omics of ageing through individual-based metabolic modelling

Abstract Background Ageing can be classified in two different ways, chronological ageing and biological ageing. While chronological age is a measure of the time that has passed since birth, biological (also known as transcriptomic) ageing is defined by how time and the environment affect an individual in comparison to other individuals of the same chronological age. Recent research studies have shown that transcriptomic age is associated with certain genes, and that each of those genes has an effect size. Using these effect sizes we can calculate the transcriptomic age of an individual from their age-associated gene expression levels. The limitation of this approach is that it does not consider how these changes in gene expression affect the metabolism of individuals and hence their observable cellular phenotype. Results We propose a method based on poly-omic constraint-based models and machine learning in order to further the understanding of transcriptomic ageing. We use normalised CD4 T-cell gene expression data from peripheral blood mononuclear cells in 499 healthy individuals to create individual metabolic models. These models are then combined with a transcriptomic age predictor and chronological age to provide new insights into the differences between transcriptomic and chronological ageing. As a result, we propose a novel metabolic age predictor. Conclusions We show that our poly-omic predictors provide a more detailed analysis of transcriptomic ageing compared to gene-based approaches, and represent a basis for furthering our knowledge of the ageing mechanisms in human cells