Fibromine is a multi-omics database and mining tool for target discovery in pulmonary fibrosis

Idiopathic pulmonary fibrosis is a lethal lung fibroproliferative disease with limited therapeutic options. Differential expression profiling of affected sites has been instrumental for involved pathogenetic mechanisms dissection and therapeutic targets discovery. However, there have been limited efforts to comparatively analyse/mine the numerous related publicly available datasets, to fully exploit their potential on the validation/creation of novel research hypotheses. In this context and towards that goal, we present Fibromine, an integrated database and exploration environment comprising of consistently re-analysed, manually curated transcriptomic and proteomic pulmonary fibrosis datasets covering a wide range of experimental designs in both patients and animal models. Fibromine can be accessed via an R Shiny application (http://www.fibromine.com/Fibromine) which offers dynamic data exploration and real-time integration functionalities. Moreover, we introduce a novel benchmarking system based on transcriptomic datasets underlying characteristics, resulting to dataset accreditation aiming to aid the user on dataset selection. Cell specificity of gene expression can be visualised and/or explored in several scRNA-seq datasets, in an effort to link legacy data with this cutting-edge methodology and paving the way to their integration. Several use case examples are presented, that, importantly, can be reproduced on-the-fly by a non-specialist user, the primary target and potential user of this endeavour.

Bio-Inspired Take-Off Maneuver and Control in Vertical Jumping for Quadruped Robot with Manipulator

The jumping motion of legged robots is an effective way to overcome obstacles in the rugged microgravity planetary exploration environment. At the same time, a quadruped robot with a manipulator can achieve operational tasks during movement, which is more practical. However, the additional manipulator will restrict the jumping ability of the quadruped robot due to the increase in the weight of the system, and more active degrees of freedom will increase the control complexity. To improve the jumping height of a quadruped robot with a manipulator, a bio-inspired take-off maneuver based on the coordination of upper and lower limbs is proposed in this paper. The kinetic energy and potential energy of the system are increased by driving the manipulator-end (ME) to swing upward, and the torso driven by the legs will delay reaching the required peak speed due to the additional load caused by the accelerated ME. When the acceleration of ME is less than zero, it will pull the body upward, which reduces the peak power of the leg joints. Therefore, the jumping ability of the system is improved. To realize continuous and stable jumping, a control framework based on whole-body control was established, in which the quadruped robot with a manipulator was a simplified floating seven-link model, and the hierarchical optimization was used to solve the target joint torques. This method greatly simplifies the dynamic model and is convenient for calculation. Finally, the jumping simulations in different gravity environments and a 15° slope were performed. The jump heights have all been improved after adding the arm swing, which verified the superiority of the bio-inspired take-off maneuver proposed in this paper. Furthermore, the stability of the jumping control method was testified by the continuous and stable jumping.

Wearable 3-Lead Electrocardiogram Placement Model for Fleet Sizing of Medical Devices

BACKGROUND: Electrocardiography (ECG) provides valuable information on astronaut physiological and psychological health. ECG monitoring has been conducted during crewed missions since the beginning of human spaceflight and will continue during astronauts upcoming long-duration exploration missions (LDEMs) in support of automated health monitoring systems. ECG monitoring is traditionally performed in clinical environments with single-use, adhesive electrodes in a 3, 6, or 12-lead configuration placed by a trained clinician. In the space exploration environment, astronauts self-place electrodes without professional assistance. Wearable ECG systems are an attractive option for automated health monitoring, but electrode placement has not been quantified to a high enough degree to avoid artifacts within the data due to position changes. This variability presents challenges for physician-limited, autonomous health monitoring, so quantifying electrode placement is key in the development of reliable, wearable ECG monitoring systems.METHODS: We present a method of quantifying electrode placement for 3-lead, chest-mounted ECG using easy-to-measure, two-dimensional chest measurements.RESULTS: We find that male and female dimensions require different electrode positioning computations, but there is overlap in positioning between men and women. The distribution of electrodes vertical positions is wider than their horizontal positions.DISCUSSION: These results can be translated directly to ECG wearable design for the individual and for the size range and adjustability required for the astronaut fleet. Implementation of this method will improve the reliability in placement and fit of future wearables, increasing comfort and usability of these systems and subsequently augmenting autonomous health monitoring capabilities for exploration medicine.Arquilla K, Leary S, Webb AK, Anderson AP. Wearable 3-lead electrocardiogram placement model for fleet sizing of medical devices. Aerosp Med Hum Perform. 2020; 91(11):868875.

G-PARE: A visual analytic tool for comparative analysis of uncertain graphs

There are a growing number of machine learning algorithms which operate on graphs. Example applications for these algorithms include predicting which customers will recommend products to their friends in a viral marketing campaign using a customer network, predicting the topics of publications in a citation network, or predicting the political affiliations of people in a social network. It is important for an analyst to have tools to help compare the output of these machine learning algorithms. In this work, we present G-PARE, a visual analytic tool for comparing two uncertain graphs, where each uncertain graph is produced by a machine learning algorithm which outputs probabilities over node labels. G-PARE provides several different views which allow users to obtain a global overview of the algorithms output, as well as focused views that show subsets of nodes of interest. By providing an adaptive exploration environment, G-PARE guides the users to places in the graph where two algorithms predictions agree and places where they disagree. This enables the user to follow cascades of misclassifications by comparing the algorithms outcome with the ground truth. After describing the features of G-PARE, we illustrate its utility through several use cases based on networks from different domains.