Reflexiones sobre el marco conceptual de la integración económica centroamericana

Incluye Bibliografí

Evaluation of a Low-Cost, PC-Based Driving Simulator to Assess Persons with Cognitive Impairments Due to Brain Injury

Brain injury due to accident or stroke frequently results in cognitive impairment, reducing an individual’s ability to judge driving situations accurately. Rehabilitation professionals typically use a combination of clinical and on-road tests to determine whether an individual is safe to drive. Weighing the safety of the community, the candidate, and the driving evaluator, these on-road tests are often conducted under road, traffic and weather conditions less demanding than those that a driver might face in the “real world,” and thus may offer less than complete information regarding the candidate’s responses to such real-world driving challenges. Indeed, individuals with mild cognitive deficits may perform adequately under such testing conditions but unsafely when driving challenges increase. Complicating this situation further, those with mild to moderate acquired cognitive impairments may be largely unaware of their own limitations, and thus more intolerant of perceived delays or challenges to their desire to drive again. Although continuing advances have improved performance and fidelity while significantly reducing costs, most interactive driving simulators remain too expensive for widespread clinical application. In a project funded by the National Center for Medical Rehabilitation Research, National Institutes of Health, we sought to determine, on a pilot basis, whether a low-cost, PC-based driving simulator could provide clinicians with information useful to their efforts to assess the safe ability to drive of individuals with cognitive impairments. We developed two comprehensive simulator-based driving scenarios, one quite basic and one more challenging, and pilot-tested them on ten subjects – five with moderate cognitive impairments, and five age and sex matched-controls without impairment. The “simple” scenario was developed to match the essential demands of the first half of an existing on-road driving evaluation; the “complex” scenario was based on the second half of the on-road evaluation into which more demanding, but still common, driving challenges were integrated. Road types, lane widths, pavement markings, traffic signals, horizontal and vertical curvature, and the proximal built environment were all created in simulation to provide a convincing generic representation of the on-road test. Challenges incorporated into the “complex” phase of the scenario, which were absent from the “simple” phase, included traffic events such as: cross-traffic failing to stop at a STOP sign; pedestrians crossing the driver’s path; vehicles suddenly pulling out in front of the subject from the road shoulder; opposing thru traffic appearing suddenly from behind slower moving vehicles as the subject attempted to turn left; slower moving lead vehicles causing passing decisions; traffic streams forcing gap acceptance decisions; etc. Results from the simulator were compared to results from the on-road evaluation. In addition, data gathered from subject exit interviews was used to judge simulator verisimilitude and efficacy in changing self-awareness of deficit. Because the cognitive impairments associated with brain injury often reduce the individual’s awareness of his or her own limitations, we looked at evidence that performance on the simulator could contribute to an individual’s own understanding of his or her driving strengths and weaknesses. The results of the pilot study will lead to an enhancement of simulator capabilities, and to a comprehensive clinical trial at multiple sites. This paper will present the findings of this pilot investigation and an overview of the expanded clinical study

Automated satellite remote sensing of giant kelp at the Falkland Islands (Islas Malvinas)

© The Author(s), 2022. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Houskeeper, H. F., Rosenthal, I. S., Cavanaugh, K. C., Pawlak, C., Trouille, L., Byrnes, J. E. K., Bell, T. W., & Cavanaugh, K. C. Automated satellite remote sensing of giant kelp at the Falkland Islands (Islas Malvinas). Plos One, 17(1), (2022): e0257933, https://doi.org/10.1371/journal.pone.0257933.Giant kelp populations that support productive and diverse coastal ecosystems at temperate and subpolar latitudes of both hemispheres are vulnerable to changing climate conditions as well as direct human impacts. Observations of giant kelp forests are spatially and temporally uneven, with disproportionate coverage in the northern hemisphere, despite the size and comparable density of southern hemisphere kelp forests. Satellite imagery enables the mapping of existing and historical giant kelp populations in understudied regions, but automating the detection of giant kelp using satellite imagery requires approaches that are robust to the optical complexity of the shallow, nearshore environment. We present and compare two approaches for automating the detection of giant kelp in satellite datasets: one based on crowd sourcing of satellite imagery classifications and another based on a decision tree paired with a spectral unmixing algorithm (automated using Google Earth Engine). Both approaches are applied to satellite imagery (Landsat) of the Falkland Islands or Islas Malvinas (FLK), an archipelago in the southern Atlantic Ocean that supports expansive giant kelp ecosystems. The performance of each method is evaluated by comparing the automated classifications with a subset of expert-annotated imagery (8 images spanning the majority of our continuous timeseries, cumulatively covering over 2,700 km of coastline, and including all relevant sensors). Using the remote sensing approaches evaluated herein, we present the first continuous timeseries of giant kelp observations in the FLK region using Landsat imagery spanning over three decades. We do not detect evidence of long-term change in the FLK region, although we observe a recent decline in total canopy area from 2017–2021. Using a nitrate model based on nearby ocean state measurements obtained from ships and incorporating satellite sea surface temperature products, we find that the area of giant kelp forests in the FLK region is positively correlated with the nitrate content observed during the prior year. Our results indicate that giant kelp classifications using citizen science are approximately consistent with classifications based on a state-of-the-art automated spectral approach. Despite differences in accuracy and sensitivity, both approaches find high interannual variability that impedes the detection of potential long-term changes in giant kelp canopy area, although recent canopy area declines are notable and should continue to be monitored carefully.This work was funded by the National Aeronautics and Space Administration as part of the Citizen Science for Earth Systems Program (https://earthdata.nasa.gov/esds/competitive-programs/csesp) with grant #80NSSC18M0103 (awarded to JEKB), which also provided salary to HFH, and by the National Science Foundation through the Santa Barbara Coastal Long-Term Environmental Research (https://sbclter.msi.ucsb.edu) program with grants #OCE 0620276 and 1232779. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript

Circulating HPV DNA as a Biomarker for Pre-Invasive and Early Invasive Cervical Cancer: A Feasibility Study

BACKGROUND: High-risk HPV infection is responsible for >99% of cervix cancers (CC). In persistent infections that lead to cancer, the tumour breaches the basement membrane, releasing HPV-DNA into the bloodstream (cHPV-DNA). A next-generation sequencing assay (NGS) for detection of plasma HPV circulating DNA (cHPV-DNA) has demonstrated high sensitivity and specificity in patients with locally advanced cervix cancers. We hypothesised that cHPV-DNA is detectable in early invasive cervical cancers but not in pre-invasive lesions (CIN). METHODS: Blood samples were collected from patients with CIN (n = 52) and FIGO stage 1A-1B CC (n = 12) prior to treatment and at follow-up. DNA extraction from plasma, followed by NGS, was used for the detection of cHPV-DNA. RESULTS: None of the patients with pre-invasive lesions were positive for CHPV-DNA. In invasive tumours, plasma from one patient (10%) reached the threshold of positivity for cHPV-DNA in plasma. CONCLUSION: Low detection of cHPV-DNA in early CC may be explained by small tumour size, poorer access to lymphatics and circulation, and therefore little shedding of cHPV-DNA in plasma at detectable levels. The detection rate of cHPV-DNA in patients with early invasive cervix cancer using even the most sensitive of currently available technologies lacks adequate sensitivity for clinical utility

Avidity of anti-malarial antibodies inversely related to transmission intensity at three sites in Uganda.

BACKGROUND: People living in malaria endemic areas acquire protection from severe malaria quickly, but protection from clinical disease and control of parasitaemia is acquired only after many years of repeated infections. Antibodies play a central role in protection from clinical disease; however, protective antibodies are slow to develop. This study sought to investigate the influence of Plasmodium falciparum exposure on the acquisition of high-avidity antibodies to P. falciparum antigens, which may be associated with protection. METHODS: Cross-sectional surveys were performed in children and adults at three sites in Uganda with varied P. falciparum transmission intensity (entomological inoculation rates; 3.8, 26.6, and 125 infectious bites per person per year). Sandwich ELISA was used to measure antibody responses to two P. falciparum merozoite surface antigens: merozoite surface protein 1-19 (MSP1-19) and apical membrane antigen 1 (AMA1). In individuals with detectable antibody levels, guanidine hydrochloride (GuHCl) was added to measure the relative avidity of antibody responses by ELISA. RESULTS: Within a site, there were no significant differences in median antibody levels between the three age groups. Between sites, median antibody levels were generally higher in the higher transmission sites, with differences more apparent for AMA-1 and in ≥5 year group. Similarly, median avidity index (proportion of high avidity antibodies) showed no significant increase with increasing age but was significantly lower at sites of higher transmission amongst participants ≥5 years of age. Using 5 M GuHCl, the median avidity indices in the ≥5 year group at the highest and lowest transmission sites were 19.9 and 26.8, respectively (p = 0.0002) for MSP1-19 and 12.2 and 17.2 (p = 0.0007) for AMA1. CONCLUSION: Avidity to two different P. falciparum antigens was lower in areas of high transmission intensity compared to areas with lower transmission. Appreciation of the mechanisms behind these findings as well as their clinical consequences will require additional investigation, ideally utilizing longitudinal data and investigation of a broader array of responses

From fat droplets to floating forests: cross-domain transfer learning using a PatchGAN-based segmentation model

Many scientific domains gather sufficient labels to train machine algorithms through human-in-the-loop techniques provided by the Zooniverse.org citizen science platform. As the range of projects, task types and data rates increase, acceleration of model training is of paramount concern to focus volunteer effort where most needed. The application of Transfer Learning (TL) between Zooniverse projects holds promise as a solution. However, understanding the effectiveness of TL approaches that pretrain on large-scale generic image sets vs. images with similar characteristics possibly from similar tasks is an open challenge. We apply a generative segmentation model on two Zooniverse project-based data sets: (1) to identify fat droplets in liver cells (FatChecker; FC) and (2) the identification of kelp beds in satellite images (Floating Forests; FF) through transfer learning from the first project. We compare and contrast its performance with a TL model based on the COCO image set, and subsequently with baseline counterparts. We find that both the FC and COCO TL models perform better than the baseline cases when using >75% of the original training sample size. The COCO-based TL model generally performs better than the FC-based one, likely due to its generalized features. Our investigations provide important insights into usage of TL approaches on multi-domain data hosted across different Zooniverse projects, enabling future projects to accelerate task completion.Comment: 5 pages, 4 figures, accepted for publication at the Proceedings of the ACM/CIKM 2022 (Human-in-the-loop Data Curation Workshop