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These results can provide design guidelines for haptic grippers that elicit natural grasping in virtual and bilateral teleoperation applications.A person's behavior significantly influences their health and well-being. It also contributes to the social environment in which humans interact, with cascading impacts to the health and behaviors of others. During social interactions, our understanding and awareness of vital nonverbal messages expressing beliefs, emotions, and intentions can be obstructed by a variety of factors including greatly flawed self-awareness. For these reasons, human behavior is a very important topic to study using the most advanced technology. Moreover, technology offers a breakthrough opportunity to improve people's social awareness and self-awareness through machine-enhanced recognition and interpretation of human behaviors. This paper reviews (1) the social psychology theories that have established the framework to study human behaviors and their manifestations during social interactions and (2) the technologies that have contributed to the monitoring of human behaviors. State-of-the-art in sensors, signal features, and computational models are categorized, summarized, and evaluated from a comprehensive transdisciplinary perspective. This review focuses on assessing technologies most suitable for real-time monitoring while highlighting their challenges and opportunities in near-future applications. Although social behavior monitoring has been highly reported in psychology and engineering literature, this paper uniquely aims to serve as a disciplinary convergence bridge and a guide for engineers capable of bringing new technologies to bear against the current challenges in real-time human behavior monitoring.Size and absolute concentration of suspensions of nanoparticles are important information for the study and development of new materials and products in different industrial applications spanning from biotechnology and pharmaceutics to food preparation and conservation. Laser Transmission Spectroscopy (LTS) is the only methodology able to measure nanoparticle size and concentration by performing a single measurement. In this paper we report on a new variable gain calibration procedure for LTS-based instruments allowing to decrease of an order of magnitude the experimental indetermination of the particle size respect to the conventional LTS based on the double ratio technique. The variable gain calibration procedure makes use of a specifically designed tunable-gain, dual-channel, dual-phase Lock-In Amplifier (LIA) whose input voltage signals are those ones generated by two Si photodiodes that measure the laser beam intensities passing through the sample containing the nanoparticles and a reference optical path. The LTS variable gain calibration procedure has been validated by firstly using a suspension of NIST standard polystyrene nanoparticles even 36 hours after the calibration procedure was accomplished. JG98 cell line The paper reports in detail the LIA implementation describing the design methodologies and the electronic circuits. As a case example of the characterization of biological nanostructures, we demonstrate that a single LTS measurement allowed to determine size density distribution of a population of extracellular vesicles extracted from orange juice (25 nm in size) with the presence of their aggregates having a size of 340 nm and a concentration smaller than 3 orders of magnitude.

Drug response prediction is an important problem in computational personalized medicine. Many machine-learning-based methods, especially deep learning-based ones, have been proposed for this task. However, these methods often represent the drugs as strings, which are not a natural way to depict molecules. Also, interpretation (e.g., what are the mutation or copy number aberration contributing to the drug response) has not been considered thoroughly.

In this study, we propose a novel method, GraphDRP, based on graph convolutional network for the problem. In GraphDRP, drugs were represented in molecular graphs directly capturing the bonds among atoms, meanwhile cell lines were depicted as binary vectors of genomic aberrations. Representative features of drugs and cell lines were learned by convolution layers, then combined to represent for each drug-cell line pair. Finally, the response value of each drug-cell line pair was predicted by a fully-connected neural network. Four variants of graph convolutional networks were used for learning the features of drugs.

We found that GraphDRP outperforms tCNNS in all performance measures for all experiments. Also, through saliency maps of the resulting GraphDRP models, we discovered the contribution of the genomic aberrations to the responses.

Representing drugs as graphs can improve the performance of drug response prediction. Availability of data and materials Data and source code can be downloaded athttps//github.com/hauldhut/GraphDRP.

Representing drugs as graphs can improve the performance of drug response prediction. Availability of data and materials Data and source code can be downloaded athttps//github.com/hauldhut/GraphDRP.The design space for user interfaces for Immersive Analytics applications is vast. Designers can combine navigation and manipulation to enable data exploration with ego- or exocentric views, have the user operate at different scales, or use different forms of navigation with varying levels of physical movement. This freedom results in a multitude of different viable approaches. Yet, there is no clear understanding of the advantages and disadvantages of each choice. Our goal is to investigate the affordances of several major design choices, to enable both application designers and users to make better decisions. In this work, we assess two main factors, exploration mode and frame of reference, consequently also varying visualization scale and physical movement demand. To isolate each factor, we implemented nine different conditions in a Space-Time Cube visualization use case and asked 36 participants to perform multiple tasks. We analyzed the results in terms of performance and qualitative measures and correlated them with participants' spatial abilities.