A C.elegans inspired robotic model for pothole detection

Animals navigate complex and variable environments, but often use only limited sensory information. Here we present a simulated robot system using a C. elegans inspired sensory model and navigation strategy and demonstrate its ability to successfully identify specific, discretely located cues. We show a range of conditions under which this approach has performance benefits over other search strategies

FUSED SALT HEAT TRANSFER. PART II. FORCED CONVECTION HEAT TRANSFER IN CIRCULAR TUBES CONTAINING NaF-Kf-LiF EUTECTIC

Heat transfer coefficients were determined for the eutectic mixture LiF- KF-NaF (Flinak) flowing in forced convection through circular tubes. Heat, electrically generated in the tube wall was transferred uniformly to the fluid during passage through small-diameter tubes of nickel, Inconel, and 316 stainless steel. The variables involved: Reynolds modulus (N/sub R//sub e/), 2300 to 9500; Prandtl modulus (N/sub P//sub r/, 1.6 to 4.0; average fluid temperatures, 980 to 1370 deg F; and heat flux, 9,000 to 192,000 Btu/hr-ft/sup 2/. Forced-convection heat transfer with Flinak can be represented by the general correlation for heat transfer with ordinary fluids (0.5 < N /sub P//sub r/< 100). The existence of an interfacial resistance in Flinak-Inconel systems was established and its composition determined. Preliminary measurements of thermal conductivity and thickness of film were made. The results verify the effect of the film on Flinak heat transfer in small-diameter Inconel tubes. Thermal entry lengths, determined from variations of local heat transfer coefficients in the entrance of the heated section, were correlated with the Peclet modulus. (auth

Bayesian Centroid Estimation for Motif Discovery

Biological sequences may contain patterns that are signal important biomolecular functions; a classical example is regulation of gene expression by transcription factors that bind to specific patterns in genomic promoter regions. In motif discovery we are given a set of sequences that share a common motif and aim to identify not only the motif composition, but also the binding sites in each sequence of the set. We present a Bayesian model that is an extended version of the model adopted by the Gibbs motif sampler, and propose a new centroid estimator that arises from a refined and meaningful loss function for binding site inference. We discuss the main advantages of centroid estimation for motif discovery, including computational convenience, and how its principled derivation offers further insights about the posterior distribution of binding site configurations. We also illustrate, using simulated and real datasets, that the centroid estimator can differ from the maximum a posteriori estimator.Comment: 24 pages, 9 figure

Computational approaches for understanding the diagnosis and treatment of Parkinson's disease

This study describes how the application of evolutionary algorithms (EAs) can be used to study motor function in humans with Parkinson's disease (PD) and in animal models of PD. Human data is obtained using commercially available sensors via a range of non-invasive procedures that follow conventional clinical practice. EAs can then be used to classify human data for a range of uses, including diagnosis and disease monitoring. New results are presented that demonstrate how EAs can also be used to classify fruit flies with and without genetic mutations that cause Parkinson's by using measurements of the proboscis extension reflex. The case is made for a computational approach that can be applied across human and animal studies of PD and lays the way for evaluation of existing and new drug therapies in a truly objective way

Computational approaches for understanding the diagnosis and treatment of Parkinson's disease

This study describes how the application of evolutionary algorithms (EAs) can be used to study motor function in humans with Parkinson’s disease (PD) and in animal models of PD. Human data is obtained using commercially available sensors via a range of non-invasive procedures that follow conventional clinical practice. EAs can then be used to classify human data for a range of uses, including diagnosis and disease monitoring. New results are presented that demonstrate how EAs can also be used to classify fruit flies with and without genetic mutations that cause Parkinson’s by using measurements of the proboscis extension reflex. The case is made for a computational approach that can be applied across human and animal studies of PD and lays the way for evaluation of existing and new drug therapies in a truly objective way

A New Evolutionary Algorithm-Based Home Monitoring Device for Parkinson’s Dyskinesia

Parkinson’s disease (PD) is a neurodegenerative movement disorder. Although there is no cure, symptomatic treatments are available and can significantly improve quality of life. The motor, or movement, features of PD are caused by reduced production of the neurotransmitter dopamine. Dopamine deficiency is most often treated using dopamine replacement therapy. However, this therapy can itself lead to further motor abnormalities referred to as dyskinesia. Dyskinesia consists of involuntary jerking movements and muscle spasms, which can often be violent. To minimise dyskinesia, it is necessary to accurately titrate the amount of medication given and monitor a patient’s movements. In this paper, we describe a new home monitoring device that allows dyskinesia to be measured as a patient goes about their daily activities, providing information that can assist clinicians when making changes to medication regimens. The device uses a predictive model of dyskinesia that was trained by an evolutionary algorithm, and achieves AUC>0.9 when discriminating clinically significant dyskinesia

Outcomes of Cardiac Transplantation in Highly Sensitized Pediatric Patients

Despite aggressive immunosuppressive therapy, pediatric orthotopic heart transplant (OHT) candidates with elevated pre-transplant panel reactive antibody (PRA) carry an increased risk of rejection and early graft failure following transplantation. This study has aimed to more specifically evaluate the outcomes of transplant candidates stratified by PRA values. Records of pediatric patients listed for OHT between April 2004 and July 2008 were reviewed (n = 101). Survival analysis was performed comparing patients with PRA < 25 to those with PRA > 25, as well as patients with PRA < 80 and PRA > 80. Patients with PRA > 25 had decreased survival compared with those with PRA < 25 after listing (P = 0.004). There was an even greater difference in survival between patients with PRA > 80 and those with PRA < 80 (P = 0.002). Similar analyses for the patients who underwent successful transplantation showed no significant difference in post-transplant survival between patients with a pre-transplant PRA > 25 and those with PRA < 25 (P = 0.23). A difference approaching significance was noted for patients with PRA > 80 compared with PRA < 80 (P = 0.066). Patients with significantly elevated pre-transplant PRAs at the time of listing have a significantly worse outcome compared to those with moderately increased PRA values or non-sensitized patients. Further study is necessary to guide physician and family treatment decisions at the time of listing

REFORMS: Reporting Standards for Machine Learning Based Science

Machine learning (ML) methods are proliferating in scientific research. However, the adoption of these methods has been accompanied by failures of validity, reproducibility, and generalizability. These failures can hinder scientific progress, lead to false consensus around invalid claims, and undermine the credibility of ML-based science. ML methods are often applied and fail in similar ways across disciplines. Motivated by this observation, our goal is to provide clear reporting standards for ML-based science. Drawing from an extensive review of past literature, we present the REFORMS checklist ($\textbf{Re}$porting Standards $\textbf{For}$ $\textbf{M}$achine Learning Based $\textbf{S}$cience). It consists of 32 questions and a paired set of guidelines. REFORMS was developed based on a consensus of 19 researchers across computer science, data science, mathematics, social sciences, and biomedical sciences. REFORMS can serve as a resource for researchers when designing and implementing a study, for referees when reviewing papers, and for journals when enforcing standards for transparency and reproducibility