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human machine interaction

Shanshan Xu, Lanlan Dong, Yingying Shi, Liujun Chen, Peipei Yuan, Shuang Wang, Zhi Li, Yan Sun, Song Han, Jun Yin, Biwen Peng, Xiaohua He, Wanhong Liu
Human foamy virus (HFV) is a complex and unique retrovirus with the longest genomes among retroviruses used as vectors for gene therapy. Long non-coding RNAs (lncRNAs) are regarded as key regulators that involved in diverse biological processes during viral infection. However, the role of lncRNAs in HFV infection remains unknown. In this study, we utilized next-generation sequencing to first characterize lncRNAs in 293T cells after HFV infection, evaluating length distribution, exon number distribution, volcano picture and lncRNA class distribution...
October 18, 2016: AIDS Research and Human Retroviruses
Andreas Holzinger
Machine learning (ML) is the fastest growing field in computer science, and health informatics is among the greatest challenges. The goal of ML is to develop algorithms which can learn and improve over time and can be used for predictions. Most ML researchers concentrate on automatic machine learning (aML), where great advances have been made, for example, in speech recognition, recommender systems, or autonomous vehicles. Automatic approaches greatly benefit from big data with many training sets. However, in the health domain, sometimes we are confronted with a small number of data sets or rare events, where aML-approaches suffer of insufficient training samples...
June 2016: Brain Informatics
Seid Muhie Yimam, Chris Biemann, Ljiljana Majnaric, Šefket Šabanović, Andreas Holzinger
In this article, we demonstrate the impact of interactive machine learning: we develop biomedical entity recognition dataset using a human-into-the-loop approach. In contrary to classical machine learning, human-in-the-loop approaches do not operate on predefined training or test sets, but assume that human input regarding system improvement is supplied iteratively. Here, during annotation, a machine learning model is built on previous annotations and used to propose labels for subsequent annotation. To demonstrate that such interactive and iterative annotation speeds up the development of quality dataset annotation, we conduct three experiments...
September 2016: Brain Informatics
Sean Mendez, Louis Watanabe, Rachel Hill, Meredith Owens, Jason Moraczewski, Glenn C Rowe, Nicole C Riddle, Laura K Reed
Obesity is one of the dramatic health issues affecting developed and developing nations, and exercise is a well-established intervention strategy. While exercise-by-genotype interactions have been shown in humans, overall little is known. Using the natural negative geotaxis of Drosophila melanogaster, an important model organism for the study of genetic interactions, a novel exercise machine, the TreadWheel, can be used to shed light on this interaction. The mechanism for inducing exercise with the TreadWheel is inherently gentle, thus minimizing possible confounding effects of other stressors...
2016: PloS One
Ji-Yong An, Zhu-Hong You, Xing Chen, De-Shuang Huang, Zheng-Wei Li, Gang Liu, Yin Wang
Self-interacting Proteins (SIPs) play an essential role in a wide range of biological processes, such as gene expression regulation, signal transduction, enzyme activation and immune response. Because of the limitations for experimental self-interaction proteins identification, developing an effective computational method based on protein sequence to detect SIPs is much important. In the study, we proposed a novel computational approach called RVMBIGP that combines the Relevance Vector Machine (RVM) model and Bi-gram probability (BIGP) to predict SIPs based on protein sequence...
October 8, 2016: Oncotarget
Sameer Saproo, Victor Shih, David C Jangraw, Paul Sajda
OBJECTIVE: We investigated the neural correlates of workload buildup in a fine visuomotor task called the boundary avoidance task (BAT). The BAT has been known to induce naturally occurring failures of human-machine coupling in high performance aircraft that can potentially lead to a crash-these failures are termed pilot induced oscillations (PIOs). APPROACH: We recorded EEG and pupillometry data from human subjects engaged in a flight BAT simulated within a virtual 3D environment...
October 5, 2016: Journal of Neural Engineering
Amy M Savage, Justin Hills, Katherine Driscoll, Daniel J Fergus, Amy M Grunden, Robert R Dunn
High-throughput sequencing techniques have opened up the world of microbial diversity to scientists, and a flurry of studies in the most remote and extreme habitats on earth have begun to elucidate the key roles of microbes in ecosystems with extreme conditions. These same environmental extremes can also be found closer to humans, even in our homes. Here, we used high-throughput sequencing techniques to assess bacterial and archaeal diversity in the extreme environments inside human homes (e.g., dishwashers, hot water heaters, washing machine bleach reservoirs, etc...
2016: PeerJ
Jose M González-Calabozo, Francisco J Valverde-Albacete, Carmen Peláez-Moreno
BACKGROUND: Gene Expression Data (GED) analysis poses a great challenge to the scientific community that can be framed into the Knowledge Discovery in Databases (KDD) and Data Mining (DM) paradigm. Biclustering has emerged as the machine learning method of choice to solve this task, but its unsupervised nature makes result assessment problematic. This is often addressed by means of Gene Set Enrichment Analysis (GSEA). RESULTS: We put forward a framework in which GED analysis is understood as an Exploratory Data Analysis (EDA) process where we provide support for continuous human interaction with data aiming at improving the step of hypothesis abduction and assessment...
2016: BMC Bioinformatics
Ghazaleh Taherzadeh, Yaoqi Zhou, Alan Wee-Chung Liew, Yuedong Yang
Carbohydrate-binding proteins play significant roles in many diseases including cancer. How and where these proteins interact with carbohydrates is of fundamental importance and practical interest. Experimental studies of binding mechanisms are costly and labour intensive because of low binding affinity between proteins and carbohydrates. As a result, developing an effective computational method becomes increasingly important. Here, we established a machine-learning-based method (called Sequence-based Prediction of Residue-level INTeraction sites of carbohydrates, SPRINT-CBH) to predict carbohydrate-binding sites in proteins by using Support Vector Machines (SVM)...
September 13, 2016: Journal of Chemical Information and Modeling
Aritra Dasgupta, Joon-Yong Lee, Ryan Wilson, Robert Lafrance, Nick Cramer, Kristin Cook, Samuel Payne
Combining interactive visualization with automated analytical methods like statistics and data mining facilitates data-driven discovery. These visual analytic methods are beginning to be instantiated within mixed-initiative systems, where humans and machines collaboratively influence evidence-gathering and decision-making. But an open research question is that, when domain experts analyze their data, can they completely trust the outputs and operations on the machine-side? Visualization potentially leads to a transparent analysis process, but do domain experts always trust what they see? To address these questions, we present results from the design and evaluation of a mixed-initiative, visual analytics system for biologists, focusing on analyzing the relationships between familiarity of an analysis medium and domain experts' trust...
August 8, 2016: IEEE Transactions on Visualization and Computer Graphics
Laureen Logger, Marie-Stéphanie Aschtgen, Marie Guérin, Eric Cascales, Eric Durand
The type VI secretion system (T6SS) is a multi-protein complex that catalyses toxin secretion through the bacterial cell envelope of various Gram-negative bacteria including important human pathogens. This machine uses a bacteriophage-like contractile tail to puncture the prey cell and inject armful toxins. The T6SS tail comprises an inner tube capped by the cell-puncturing spike and wrapped by the contractile sheath. This structure is built on an assembly platform, the baseplate, which is anchored to the bacterial cell envelope by the TssJLM membrane complex...
September 3, 2016: Journal of Molecular Biology
Juan-Miguel López-Gil, Jordi Virgili-Gomá, Rosa Gil, Roberto García
Technical advances, particularly the integration of wearable and embedded sensors, facilitate tracking of physiological responses in a less intrusive way. Currently, there are many devices that allow gathering biometric measurements from human beings, such as EEG Headsets or Health Bracelets. The massive data sets generated by tracking of EEG and physiology may be used, among other things, to infer knowledge about human moods and emotions. Apart from direct biometric signal measurement, eye tracking systems are nowadays capable of determining the point of gaze of the users when interacting in ICT environments, which provides an added value research on many different areas, such as psychology or marketing...
2016: Frontiers in Computational Neuroscience
P Aricò, G Borghini, G Di Flumeri, A Colosimo, S Pozzi, F Babiloni
In the last decades, it has been a fast-growing concept in the neuroscience field. The passive brain-computer interface (p-BCI) systems allow to improve the human-machine interaction (HMI) in operational environments, by using the covert brain activity (eg, mental workload) of the operator. However, p-BCI technology could suffer from some practical issues when used outside the laboratories. In particular, one of the most important limitations is the necessity to recalibrate the p-BCI system each time before its use, to avoid a significant reduction of its reliability in the detection of the considered mental states...
2016: Progress in Brain Research
Dingfang Li, Longqiang Luo, Wen Zhang, Feng Liu, Fei Luo
BACKGROUND: Predicting piwi-interacting RNA (piRNA) is an important topic in the small non-coding RNAs, which provides clues for understanding the generation mechanism of gamete. To the best of our knowledge, several machine learning approaches have been proposed for the piRNA prediction, but there is still room for improvements. RESULTS: In this paper, we develop a genetic algorithm-based weighted ensemble method for predicting transposon-derived piRNAs. We construct datasets for three species: Human, Mouse and Drosophila...
2016: BMC Bioinformatics
Christina P Keravnou, Ine De Cock, Ine Lentacker, Maria-Louisa Izamis, Michalakis A Averkiou
Localized drug delivery and uptake can benefit from the combined action of ultrasound and microbubbles at a specific site. Some of the possible mechanisms suggested are vessel poration and/or cell poration, but the exact acoustic parameters that trigger those phenomena remain unknown. Ex vivo machine perfusion of human-sized organs is a technique that provides an ideal environment for pre-clinical investigations with high physiologic relevance not possible with in vitro experiments. In this work, ex vivo machine-perfused pig livers were combined with an image-guided therapy system to investigate microvascular flow changes caused by the interaction of ultrasound-driven microbubbles with the vasculature...
November 2016: Ultrasound in Medicine & Biology
Anthony Mathelier, Beibei Xin, Tsu-Pei Chiu, Lin Yang, Remo Rohs, Wyeth W Wasserman
Interactions of transcription factors (TFs) with DNA comprise a complex interplay between base-specific amino acid contacts and readout of DNA structure. Recent studies have highlighted the complementarity of DNA sequence and shape in modeling TF binding in vitro. Here, we have provided a comprehensive evaluation of in vivo datasets to assess the predictive power obtained by augmenting various DNA sequence-based models of TF binding sites (TFBSs) with DNA shape features (helix twist, minor groove width, propeller twist, and roll)...
August 18, 2016: Cell Systems
Ewart J de Visser, Samuel S Monfort, Ryan McKendrick, Melissa A B Smith, Patrick E McKnight, Frank Krueger, Raja Parasuraman
We interact daily with computers that appear and behave like humans. Some researchers propose that people apply the same social norms to computers as they do to humans, suggesting that social psychological knowledge can be applied to our interactions with computers. In contrast, theories of human-automation interaction postulate that humans respond to machines in unique and specific ways. We believe that anthropomorphism-the degree to which an agent exhibits human characteristics-is the critical variable that may resolve this apparent contradiction across the formation, violation, and repair stages of trust...
September 2016: Journal of Experimental Psychology. Applied
Shengming Li, Wenbo Peng, Jie Wang, Long Lin, Yunlong Zi, Gong Zhang, Zhong Lin Wang
The drastic expansion of consumer electronics (like personal computers, touch pads, smart phones, etc.) creates many human-machine interfaces and multiple types of interactions between human and electronics. Considering the high frequency of such operations in our daily life, an extraordinary amount of biomechanical energy from typing or pressing buttons is available. In this study, we have demonstrated a highly flexible triboelectric nanogenerator (TENG) solely made from elastomeric materials as a cover on a conventional keyboard to harvest biomechanical energy from typing...
August 23, 2016: ACS Nano
Jongsoo Keum, Sunyong Yoo, Doheon Lee, Hojung Nam
BACKGROUND: Verifying the proteins that are targeted by compounds of natural herbs will be helpful to select natural herb-based drug candidates. However, this entails a great deal of effort to clarify the interaction throughout in vitro or in vivo experiments. In this light, in silico prediction of the interactions between compounds and target proteins can help ease the efforts. RESULTS: In this study, we performed in silico predictions of herbal compound target identification...
2016: BMC Bioinformatics
Jorge I Padilla-Buritica, Juan D Martinez-Vargas, German Castellanos-Dominguez
Lately, research on computational models of emotion had been getting much attention due to their potential for understanding the mechanisms of emotions and their promising broad range of applications that potentially bridge the gap between human and machine interactions. We propose a new method for emotion classification that relies on features extracted from those active brain areas that are most likely related to emotions. To this end, we carry out the selection of spatially compact regions of interest that are computed using the brain neural activity reconstructed from Electroencephalography data...
2016: Frontiers in Computational Neuroscience
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