PEDOT–CNT Composite Microelectrodes for Recording and Electrostimulation Applications: Fabrication, Morphology, and Electrical Properties

Composites of carbon nanotubes and poly(3,4-ethylenedioxythiophene, PEDOT) and layers of PEDOT are deposited onto microelectrodes by electropolymerization of ethylenedioxythiophene in the presence of a suspension of carbon nanotubes and polystyrene sulfonate. Analysis by FIB and SEM demonstrates that CNT–PEDOT composites exhibit a porous morphology whereas PEDOT layers are more compact. Accordingly, capacitance and charge injection capacity of the composite material exceed those of pure PEDOT layers. In vitro cell culture experiments reveal excellent biocompatibility and adhesion of both PEDOT and PEDOT–CNT electrodes. Signals recorded from heart muscle cells demonstrate the high S/N ratio achievable with these electrodes. Long-term pulsing experiments confirm stability of charge injection capacity. In conclusion, a robust fabrication procedure for composite PEDOT–CNT electrodes is demonstrated and results show that these electrodes are well suited for stimulation and recording in cardiac and neurophysiological research

ChoreoNet: Towards Music to Dance Synthesis with Choreographic Action Unit

Dance and music are two highly correlated artistic forms. Synthesizing dance motions has attracted much attention recently. Most previous works conduct music-to-dance synthesis via directly music to human skeleton keypoints mapping. Meanwhile, human choreographers design dance motions from music in a two-stage manner: they firstly devise multiple choreographic dance units (CAUs), each with a series of dance motions, and then arrange the CAU sequence according to the rhythm, melody and emotion of the music. Inspired by these, we systematically study such two-stage choreography approach and construct a dataset to incorporate such choreography knowledge. Based on the constructed dataset, we design a two-stage music-to-dance synthesis framework ChoreoNet to imitate human choreography procedure. Our framework firstly devises a CAU prediction model to learn the mapping relationship between music and CAU sequences. Afterwards, we devise a spatial-temporal inpainting model to convert the CAU sequence into continuous dance motions. Experimental results demonstrate that the proposed ChoreoNet outperforms baseline methods (0.622 in terms of CAU BLEU score and 1.59 in terms of user study score).Comment: 10 pages, 5 figures, Accepted by ACM MM 202

DeepFacePencil: Creating Face Images from Freehand Sketches

In this paper, we explore the task of generating photo-realistic face images from hand-drawn sketches. Existing image-to-image translation methods require a large-scale dataset of paired sketches and images for supervision. They typically utilize synthesized edge maps of face images as training data. However, these synthesized edge maps strictly align with the edges of the corresponding face images, which limit their generalization ability to real hand-drawn sketches with vast stroke diversity. To address this problem, we propose DeepFacePencil, an effective tool that is able to generate photo-realistic face images from hand-drawn sketches, based on a novel dual generator image translation network during training. A novel spatial attention pooling (SAP) is designed to adaptively handle stroke distortions which are spatially varying to support various stroke styles and different levels of details. We conduct extensive experiments and the results demonstrate the superiority of our model over existing methods on both image quality and model generalization to hand-drawn sketches.Comment: ACM MM 2020 (oral

FABIA: factor analysis for bicluster acquisition

Motivation: Biclustering of transcriptomic data groups genes and samples simultaneously. It is emerging as a standard tool for extracting knowledge from gene expression measurements. We propose a novel generative approach for biclustering called ‘FABIA: Factor Analysis for Bicluster Acquisition’. FABIA is based on a multiplicative model, which accounts for linear dependencies between gene expression and conditions, and also captures heavy-tailed distributions as observed in real-world transcriptomic data. The generative framework allows to utilize well-founded model selection methods and to apply Bayesian techniques

An international effort towards developing standards for best practices in analysis, interpretation and reporting of clinical genome sequencing results in the CLARITY Challenge

There is tremendous potential for genome sequencing to improve clinical diagnosis and care once it becomes routinely accessible, but this will require formalizing research methods into clinical best practices in the areas of sequence data generation, analysis, interpretation and reporting. The CLARITY Challenge was designed to spur convergence in methods for diagnosing genetic disease starting from clinical case history and genome sequencing data. DNA samples were obtained from three families with heritable genetic disorders and genomic sequence data were donated by sequencing platform vendors. The challenge was to analyze and interpret these data with the goals of identifying disease-causing variants and reporting the findings in a clinically useful format. Participating contestant groups were solicited broadly, and an independent panel of judges evaluated their performance. RESULTS: A total of 30 international groups were engaged. The entries reveal a general convergence of practices on most elements of the analysis and interpretation process. However, even given this commonality of approach, only two groups identified the consensus candidate variants in all disease cases, demonstrating a need for consistent fine-tuning of the generally accepted methods. There was greater diversity of the final clinical report content and in the patient consenting process, demonstrating that these areas require additional exploration and standardization. CONCLUSIONS: The CLARITY Challenge provides a comprehensive assessment of current practices for using genome sequencing to diagnose and report genetic diseases. There is remarkable convergence in bioinformatic techniques, but medical interpretation and reporting are areas that require further development by many groups

The impact of ageing on natural killer cell function and potential consequences for health in older adults

Forming the first line of defence against virally infected and malignant cells, natural killer (NK) cells are critical effector cells of the innate immune system. With age, significant impairments have been reported in the two main mechanisms by which NK cells confer host protection: direct cytotoxicity and the secretion of immunoregulatory cytokines and chemokines. In elderly subjects, decreased NK cell activity has been shown to be associated with an increased incidence and severity of viral infection, highlighting the clinical implications that age-associated changes in NK cell biology have on the health of older adults. However, is an increased susceptibility to viral infection the only consequence of these age-related changes in NK cell function? Recently, evidence has emerged that has shown that in addition to eliminating transformed cells, NK cells are involved in many other biological processes such as immune regulation, anti-microbial immune responses and the recognition and elimination of senescent cells, novel functions that involve NK-mediated cytotoxicity and/or cytokine production. Thus, the decrease in NK cell function that accompanies physiological ageing is likely to have wider implications for the health of older adults than originally thought. Here, we give a detailed description of the changes in NK cell biology that accompany human ageing and propose that certain features of the ageing process such as: (i) the increased reactivation rates of latent Mycobacterium tuberculosis, (ii) the slower resolution of inflammatory responses and (iii) the increased incidence of bacterial and fungal infection are attributable in part to an age-associated decline in NK cell function