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1. Trends in Haptic Communication of Human-Human Dyads: Toward Natural Human-Robot Co-manipulation

   https://doi.org/10.3389/fnbot.2021.626074  

   Jensen, Spencer W. ; Salmon, John L. ; Killpack, Marc D. ( February 2021 , Frontiers in Neurorobotics)

   In this paper, we analyze and report on observable trends in human-human dyads performing collaborative manipulation (co-manipulation) tasks with an extended object (object with significant length). We present a detailed analysis relating trends in interaction forces and torques with other metrics and propose that these trends could provide a way of improving communication and efficiency for human-robot dyads. We find that the motion of the co-manipulated object has a measurable oscillatory component. We confirm that haptic feedback alone represents a sufficient communication channel for co-manipulation tasks, however we find that the loss of visual and auditory channels has a significant effect on interaction torque and velocity. The main objective of this paper is to lay the essential groundwork in defining principles of co-manipulation between human dyads. We propose that these principles could enable effective and intuitive human-robot collaborative manipulation in future co-manipulation research. 

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2. Research on the Human Proteome Reaches a Major Milestone: >90% of Predicted Human Proteins Now Credibly Detected, According to the HUPO Human Proteome Project

   https://doi.org/10.1021/acs.jproteome.0c00485  

   Omenn, Gilbert S. ; Lane, Lydie ; Overall, Christopher M. ; Cristea, Ileana M. ; Corrales, Fernando J. ; Lindskog, Cecilia ; Paik, Young-Ki ; Van Eyk, Jennifer E. ; Liu, Siqi ; Pennington, Stephen R. ; et al ( December 2020 , Journal of Proteome Research)
3. Modeling, Analysis and Physics Informed Neural Network approaches for studying the dynamics of COVID-19 involving human-human and human-pathogen interaction

   https://doi.org/10.1515/cmb-2022-0001  

   Nguyen, Long ; Raissi, Maziar ; Seshaiyer, Padmanabhan ( January 2022 , Computational and Mathematical Biophysics)

   Abstract In this work, the dynamics of the spread of COVID-19 is considered in the presence of both human-to-human transmission as well as environment-to-human transmission. Specifically, we expand and modify traditional epidemiological model for COVID-19 by incorporating a compartment to study the dynamics of pathogen concentration in the environmental reservoir, for instance concentration of droplets in closed spaces. We perform a mathematical analysis for the model proposed including an endemic equilibrium analysis as well as a next-generation approach both of which help to derive the basic reproduction number. We also study the e˚cacy of wearing a facemask through this model. Another important contribution of this work is the introduction to physics informed deep learning methods (PINNs) to study the dynamics. We propose this as an alternative to traditional numerical methods for solving system of differential equations used to describe dynamics of infectious diseases. Our results show that the proposed PINNs approach is a reliable candidate for both solving such systems and for helping identify important parameters that control the disease dynamics. 

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4. Detection of human fecal pollution in environmental waters using human mitochondrial DNA and correlation with general and human-associated fecal genetic markers

   https://doi.org/10.2166/wh.2019.197  

   Tanvir Pasha, A. B. ; Hinojosa, Jessica ; Phan, Duc ; Lopez, Adrianne ; Kapoor, Vikram ( December 2019 , Journal of Water and Health)

   Abstract Human mitochondrial DNA (mtDNA) genetic markers are abundant in sewage and highly human-specific, suggesting a great potential for the environmental application as human fecal pollution indicators. Limited data are available on the occurrence and co-occurrence of human mtDNA with fecal bacterial markers in surface waters, and how the abundance of these markers is influenced by rain events. A 1-year sampling study was conducted in a suburban watershed impacted by human sewage contamination to evaluate the performance of a human mtDNA-based marker along with the bacterial genetic markers for human-associated Bacteroidales (BacHum and HF183) and Escherichia coli. Additionally, the human mtDNA-based assay was correlated with rain events and other markers. The mtDNA marker was detected in 92% of samples (n = 140) with a mean concentration of 2.96 log10 copies/100 ml throughout the study period. Human mtDNA was detected with greater abundance than human-associated Bacteroidales that could be attributed to differences in the decay of these markers in the environment. The abundance of all markers was positively correlated with rain events, and human mtDNA abundance was significantly correlated with various bacterial markers. In general, these results should support future risk assessment for impacted watersheds, particularly those affected by human fecal pollution, by evaluating the performance of these markers during rain events. 

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5. An investigation into human-autonomous vs. human-human vehicle interaction in time-critical situations

   https://doi.org/10.1145/3316782.3321544  

   Trende, Alexander ; Unni, Anirudh ; Weber, Lars ; Rieger, Jochem W. ; Luedtke, Andreas ( January 2019 , Proceedings of the 12th ACM International Conference on PErvasive Technologies Related to Assistive Environments)

   We performed a driving simulator study to investigate merging decisions with respect to an interaction partner in time-critical situations. The experimental paradigm was a two-alternative forced choice, where the subjects could choose to merge before human vehicles or highly automated vehicles (HAV). Under time pressure, subjects showed a significantly higher gap acceptance during merging situations when interacting with HAV. This confirmed our original hypothesis that when interacting with HAV, drivers would exploit the HAV's technological advantages and defensive programming in time-critical situations. 

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