No abstract

With increasing reports of textiles serving as vectors for the transmission of infections, it is crucial to study the factors influencing such transfers. This is of concern in various sectors, including healthcare and hospitality, where fabrics constitute an integral part. A better understanding of the fabric types that discourage microbial transfer could help to formulate guidelines for uniforms and other apparels in these sectors. This study aimed to assess the transferability of bacteria from fabrics considering the following factors: fibre and fabric type, moisture and friction. The transfer of bacterial genera important in healthcare settings was quantified with and without application of friction: Escherichia coli and Acinetobacter calcoaceticus from seven different fabrics (cotton, silk, viscose, wool, polyester, polypropylene and a polyester-cotton blend). Amongst the fabrics, transferability was observed to be maximal in polyester followed by viscose, while polypropylene showed the least transfer. Transfer of bacteria was favored by moisture and the application of friction. The study brings forth a correlation between fabric type and the transfer of bacterial cells between fabrics.

No abstract

Diagnostic and intervention methodologies for skill assessment of autism typically requires a clinician repetitively initiating several stimuli and recording the child's response. In this paper, we propose to automate the response measurement through video recording of the scene following the use of Deep Neural models for human action recognition from videos. However, supervised learning of neural networks demand large amounts of annotated data that is hard to come by. This issue is addressed by leveraging the ‘similarities’ between the action categories in publicly available large-scale video action (source) datasets and the dataset of interest. A technique called Guided Weak Supervision is proposed, where every class in the target data is matched to a class in the source data using the principle of posterior likelihood maximization. Subsequently, classifier on the target data is re-trained by augmenting samples from the matched source classes, along with a new loss encouraging inter-class separability. The proposed method is evaluated on two skill assessment autism datasets, SSBD (Sundar Rajagopalan, Dhall, and Goecke 2013) and a real world Autism dataset comprising 37 children of different ages and ethnicity who are diagnosed with autism. Our proposed method is found to improve the performance of the state-of-the-art multi-class human action recognition models in-spite of supervision with scarce data.

No abstract