Naive Bayes texture classification applied to whisker data from a moving robot

Many rodents use their whiskers to distinguish objects by surface texture. To examine possible mechanisms for this discrimination, data from an artificial whisker attached to a moving robot was used to test texture classification algorithms. This data was examined previously using a template-based classifier of the whisker vibration power spectrum [1]. Motivated by a proposal about the neural computations underlying sensory decision making [2], we classified the raw whisker signal using the related ‘naive Bayes’ method. The integration time window is important, with roughly 100ms of data required for good decisions and 500ms for the best decisions. For stereotyped motion, the classifier achieved hit rates of about 80% using a single (horizontal or vertical) stream of vibration data and 90% using both streams. Similar hit rates were achieved on natural data, apart from a single case in which the performance was only about 55%. Therefore this application of naive Bayes represents a biologically motivated algorithm that can perform well in a real-world robot task

Evaluation of a COVID-19 convalescent plasma program at a U.S. academic medical center

Amidst the therapeutic void at the onset of the COVID-19 pandemic, a critical mass of scientific and clinical interest coalesced around COVID-19 convalescent plasma (CCP). To date, the CCP literature has focused largely on safety and efficacy outcomes, but little on implementation outcomes or experience. Expert opinion suggests that if CCP has a role in COVID-19 treatment, it is early in the disease course, and it must deliver a sufficiently high titer of neutralizing antibodies (nAb). Missing in the literature are comprehensive evaluations of how local CCP programs were implemented as part of pandemic preparedness and response, including considerations of the core components and personnel required to meet demand with adequately qualified CCP in a timely and sustained manner. To address this gap, we conducted an evaluation of a local CCP program at a large U.S. academic medical center, the University of North Carolina Medical Center (UNCMC), and patterned our evaluation around the dimensions of the Reach, Effectiveness, Adoption, Implementation, and Maintenance (RE-AIM) framework to systematically describe key implementation-relevant metrics. We aligned our evaluation with program goals of reaching the target population with severe or critical COVID-19, integrating into the structure of the hospital-wide pandemic response, adapting to shifting landscapes, and sustaining the program over time during a compassionate use expanded access program (EAP) era and a randomized controlled trial (RCT) era. During the EAP era, the UNCMC CCP program was associated with faster CCP infusion after admission compared with contemporaneous affiliate hospitals without a local program: median 29.6 hours (interquartile range, IQR: 21.2–48.1) for the UNCMC CCP program versus 47.6 hours (IQR 32.6–71.6) for affiliate hospitals; (P<0.0001). Sixty-eight of 87 CCP recipients in the EAP (78.2%) received CCP containing the FDA recommended minimum nAb titer of ≥1:160. CCP delivery to hospitalized patients operated with equal efficiency regardless of receiving treatment via a RCT or a compassionate-use mechanism. It was found that in a highly resourced academic medical center, rapid implementation of a local CCP collection, treatment, and clinical trial program could be achieved through redeployment of highly trained laboratory and clinical personnel. These data provide important pragmatic considerations critical for health systems considering the use of CCP as part of an integrated pandemic response