Human-Robot Team Performance Compared to Full Robot Autonomy in 16 Real-World Search and Rescue Missions: Adaptation of the DARPA Subterranean Challenge

Human operators in human-robot teams are commonly perceived to be critical for mission success. To explore the direct and perceived impact of operator input on task success and team performance, 16 real-world missions (10 hrs) were conducted based on the DARPA Subterranean Challenge. These missions were to deploy a heterogeneous team of robots for a search task to locate and identify artifacts such as climbing rope, drills and mannequins representing human survivors. Two conditions were evaluated: human operators that could control the robot team with state-of-the-art autonomy (Human-Robot Team) compared to autonomous missions without human operator input (Robot-Autonomy). Human-Robot Teams were often in directed autonomy mode (70% of mission time), found more items, traversed more distance, covered more unique ground, and had a higher time between safety-related events. Human-Robot Teams were faster at finding the first artifact, but slower to respond to information from the robot team. In routine conditions, scores were comparable for artifacts, distance, and coverage. Reasons for intervention included creating waypoints to prioritise high-yield areas, and to navigate through error-prone spaces. After observing robot autonomy, operators reported increases in robot competency and trust, but that robot behaviour was not always transparent and understandable, even after high mission performance.Comment: Submitted to Transactions on Human-Robot Interactio

Developing non-response weights to account for attrition-related bias in a longitudinal pregnancy cohort

Abstract Background Prospective cohorts may be vulnerable to bias due to attrition. Inverse probability weights have been proposed as a method to help mitigate this bias. The current study used the “All Our Families” longitudinal pregnancy cohort of 3351 maternal-infant pairs and aimed to develop inverse probability weights using logistic regression models to predict study continuation versus drop-out from baseline to the three-year data collection wave. Methods Two methods of variable selection took place. One method was a knowledge-based a priori variable selection approach, while the second used Least Absolute Shrinkage and Selection Operator (LASSO). The ability of each model to predict continuing participation through discrimination and calibration for both approaches were evaluated by examining area under the receiver operating curve (AUROC) and calibration plots, respectively. Stabilized inverse probability weights were generated using predicted probabilities. Weight performance was assessed using standardized differences of baseline characteristics for those who continue in study and those that do not, with and without weights (unadjusted estimates). Results The a priori and LASSO variable selection method prediction models had good and fair discrimination with AUROC of 0.69 (95% Confidence Interval [CI]: 0.67–0.71) and 0.73 (95% CI: 0.71–0.75), respectively. Calibration plots and non-significant Hosmer-Lemeshow Goodness of Fit Tests indicated that both the a priori (p = 0.329) and LASSO model (p = 0.242) were well-calibrated. Unweighted results indicated large (> 10%) standardized differences in 15 demographic variables (range: 11 − 29%), when comparing those who continued in the study with those that did not. Weights derived from the a priori and LASSO models reduced standardized differences relative to unadjusted estimates, with the largest differences of 13% and 5%, respectively. Additionally, when applying the same LASSO variable selection method to develop weights in future data collection waves, standardized differences remained below 10% for each demographic variable. Conclusion The LASSO variable selection approach produced robust weights that addressed non-response bias more than the knowledge-driven approach. These weights can be applied to analyses across multiple longitudinal waves of data collection to reduce bias

Prevalence of Common Child Mental Health Disorders Using Administrative Health Data and Parent Report in a Prospective Community-Based Cohort from Alberta, Canada: Prévalence des troubles communs de santé mentale de l’enfant à l’aide des données de santé administratives et des rapports des parents dans une cohorte prospective communautaire d’Alberta, Canada

Objective. Knowing the prevalence of mental health difficulties in young children is critical for early identification and intervention. In the current study, we examine the agreement among three different data sources estimating the prevalence of diagnoses for attention deficit hyperactivity disorder (ADHD) and emotional disorders (i.e., anxiety or mood disorder) for children between birth and 9 years of age. Methods. Data from a prospective pregnancy cohort was linked with provincial administrative health data for children in Alberta, Canada. We report the positive agreement, negative agreement, and Cohen's Kappa of parent-reported child diagnoses provided by a health professional (“parent report”), exceeding a clinical cut-off on a standardized questionnaire completed by parents (the Behavior Assessment System for Children, 3rd edition [“BASC-3”]), and cumulative inpatient, outpatient, or physician claims diagnoses (“administrative data”). Results. Positive and negative agreement for administrative data and parent-reported ADHD diagnoses were 70.8% and 95.6%, respectively, and 30.5% and 94.9% for administrative data and the BASC-3, respectively. For emotional disorders, administrative data and parent-reported diagnoses had a positive agreement of 35.7% and negative agreement of 96.30%. Positive and negative agreement for emotional disorders using administrative data and the BASC-3 were 20.0% and 87.4%, respectively. Kappa coefficients were generally low, indicating poor chance-corrected agreement between these data sources. Conclusions. The data sources highlighted in this study provide disparate agreement for the prevalence of ADHD and emotional disorder diagnoses in young children. Low Kappa coefficients suggest that parent-reported diagnoses, clinically elevated symptoms using a standardized questionnaire, and diagnoses from administrative data serve different purposes and provide discrete estimates of mental health difficulties in early childhood

Epilepsy due to PNPO mutations: genotype, environment and treatment affect presentation and outcome

Mutations in PNPO are a known cause of neonatal onset seizures that are resistant to pyridoxine but responsive to pyridoxal phosphate (PLP). Mills etal. show that PNPO mutations can also cause neonatal onset seizures that respond to pyridoxine but worsen with PLP, as well as PLP-responsive infantile spasm

Neuroprotective Effect of Combination Therapy of Glatiramer Acetate and Epigallocatechin-3-Gallate in Neuroinflammation

Multiple sclerosis (MS) is an inflammatory autoimmune disease of the central nervous system. However, studies of MS and the animal model, experimental autoimmune encephalomyelitis (EAE), indicate that neuronal pathology is the principle cause of clinical disability. Thus, there is need to develop new therapeutic strategies that not only address immunomodulation but also neuroprotection. Here we show that the combination therapy of Glatiramer acetate (GA), an immunomodulatory MS therapeutic, and the neuroprotectant epigallocatechin-3-gallate (EGCG), the main phenol in green tea, have synergistic protective effects in vitro and in the EAE model. EGCG and GA together led to increased protection from glutamate- and TRAIL-induced neuronal cell death in vitro. EGCG combined with GA induced regeneration of hippocampal axons in an outgrowth assay. The combined application of EGCG and GA did not result in unexpected adverse events in vivo. Neuroprotective and neuroregenerative effects could be translated in the in vivo model, where combination treatment with EGCG and GA significantly delayed disease onset, strongly reduced clinical severity, even after onset of symptoms and reduced inflammatory infiltrates. These results illustrate the promise of combining neuroprotective and anti-inflammatory treatments and strengthen the prospects of EGCG as an adjunct therapy for neuroinflammatory and neurodegenerative diseases

Filovirus RefSeq Entries: Evaluation and Selection of Filovirus Type Variants, Type Sequences, and Names

Sequence determination of complete or coding-complete genomes of viruses is becoming common practice for supporting the work of epidemiologists, ecologists, virologists, and taxonomists. Sequencing duration and costs are rapidly decreasing, sequencing hardware is under modification for use by non-experts, and software is constantly being improved to simplify sequence data management and analysis. Thus, analysis of virus disease outbreaks on the molecular level is now feasible, including characterization of the evolution of individual virus populations in single patients over time. The increasing accumulation of sequencing data creates a management problem for the curators of commonly used sequence databases and an entry retrieval problem for end users. Therefore, utilizing the data to their fullest potential will require setting nomenclature and annotation standards for virus isolates and associated genomic sequences. The National Center for Biotechnology Information’s (NCBI’s) RefSeq is a non-redundant, curated database for reference (or type) nucleotide sequence records that supplies source data to numerous other databases. Building on recently proposed templates for filovirus variant naming [ ()////-], we report consensus decisions from a majority of past and currently active filovirus experts on the eight filovirus type variants and isolates to be represented in RefSeq, their final designations, and their associated sequences

Design and baseline characteristics of the finerenone in reducing cardiovascular mortality and morbidity in diabetic kidney disease trial

Background: Among people with diabetes, those with kidney disease have exceptionally high rates of cardiovascular (CV) morbidity and mortality and progression of their underlying kidney disease. Finerenone is a novel, nonsteroidal, selective mineralocorticoid receptor antagonist that has shown to reduce albuminuria in type 2 diabetes (T2D) patients with chronic kidney disease (CKD) while revealing only a low risk of hyperkalemia. However, the effect of finerenone on CV and renal outcomes has not yet been investigated in long-term trials. Patients and Methods: The Finerenone in Reducing CV Mortality and Morbidity in Diabetic Kidney Disease (FIGARO-DKD) trial aims to assess the efficacy and safety of finerenone compared to placebo at reducing clinically important CV and renal outcomes in T2D patients with CKD. FIGARO-DKD is a randomized, double-blind, placebo-controlled, parallel-group, event-driven trial running in 47 countries with an expected duration of approximately 6 years. FIGARO-DKD randomized 7,437 patients with an estimated glomerular filtration rate >= 25 mL/min/1.73 m(2) and albuminuria (urinary albumin-to-creatinine ratio >= 30 to <= 5,000 mg/g). The study has at least 90% power to detect a 20% reduction in the risk of the primary outcome (overall two-sided significance level alpha = 0.05), the composite of time to first occurrence of CV death, nonfatal myocardial infarction, nonfatal stroke, or hospitalization for heart failure. Conclusions: FIGARO-DKD will determine whether an optimally treated cohort of T2D patients with CKD at high risk of CV and renal events will experience cardiorenal benefits with the addition of finerenone to their treatment regimen. Trial Registration: EudraCT number: 2015-000950-39; ClinicalTrials.gov identifier: NCT02545049

Surface Modification of 3D Printed Microfluidic Devices for Controlled Wetting in Two-Phase Flow

Microfluidic devices (MFDs) printed in 3-D geometry using digital light projection to polymerize monomers often have surfaces that are not as hydrophobic as MFDs made from polydimethylsiloxane. Droplet microfluidics in these types of devices are subject to droplet adhesion and aqueous spreading on less hydrophobic MFD surfaces. We have developed a post-processing technique using hydrophobic monomers that renders the surfaces of these devices much more hydrophobic. The technique is fast and easy, and involves flowing monomer without initiator into the channels and then exposing the entire device to UV light that generates radicals from the initiator molecules remaining in the original 3-D polymerization. After treatment the channels can be cleared and the surface is more hydrophobic, as evidenced by higher contact angles with aqueous droplets. We hypothesize that radicals generated near the previously printed surfaces initiate polymerization of the hydrophobic monomers on the surfaces without bulk polymerization extending into the channels. The most hydrophobic surfaces were produced by treatment with an alkyl acrylate and a fluorinated acrylate. This technique could be used for surface treatment with other types of monomers to impart unique characteristics to channels in MFDs

Drivers and levers of the double burden of malnutrition in South Africa: protocol for a complex systems mapping exercise

This is the registration of the protocol for a study which aims to use group model building to create complex systems maps of the drivers, outcomes, and levers of the double burden of malnutrition in the Western Cape region of South Africa