The development of companion animal robots is of growing interest. These robots have recently been marketed to older adults with dementia as a means of encouraging social engagement and reducing behavioural and psychological symptoms of dementia. This paper outlines the results of a pilot study that sought to assess the feasibility and effect of using a robotic companion animal called CuDDler on engagement and emotional states of five older adults with dementia living in nursing home care. CuDDler is a prototype robot developed in Singapore. Despite their cognitive decline, the study participants raised a number of concerns regarding the feasibility and tolerability of CuDDler. The effectiveness of CuDDler was also limited in these participants, although one participant with visual agnosia benefited greatly from the one-on-one experience. The findings demonstrate the importance of companion robots being developed that are of an appropriate size, weight and shape for older people, including those with dementia, and a realistic animal shape that does not encourage thoughts of it being a toy. Our conclusions indicate the need for further studies on the development and use of companion robots, and investigation of the comparative benefits of social robots both compared to and in association with human interactions.

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A new mechanistic principle by which protein tyrosine kinase substrates fluorescently report the introduction of a phosphate moiety has been developed. NMR was used to establish that tyrosine phosphorylation induces the disruption of pi-pi stacking interactions of the tyrosine moiety with a proximal fluorophore on the peptide substrate. We have demonstrated that (1) the peptide substrates described in this study are useful for a wide variety of different tyrosine kinases, (2) physiological concentrations of ATP can be employed (unlike the standard radioactive ATP kinase assays), thus providing a more realistic assessment of inhibitor potency, and (3) protein kinase self-activation can be observed in real-time.

Taking Control! The binary catalyst system composed of MoO3 and an organic phoshponium salt [Bu4P]X proved very efficient to produce oleochemical cyclic carbonates from renewables.

Video-based human action recognition has become one of the most popular research areas in the field of computer vision and pattern recognition in recent years. It has a wide variety of applications such as surveillance, robotics, health care, video searching and human-computer interaction. There are many challenges involved in human action recognition in videos, such as cluttered backgrounds, occlusions, viewpoint variation, execution rate, and camera motion. A large number of techniques have been proposed to address the challenges over the decades. Three different types of datasets namely, single viewpoint, multiple viewpoint and RGB-depth videos, are used for research. This paper presents a review of various state-of-theart deep learning-based techniques proposed for human action recognition on the three types of datasets. In light of the growing popularity and the recent developments in video-based human action recognition, this review imparts details of current trends and potential directions for future work to assist researchers.