Semi-Automatic Data Annotation guided by Feature Space Projection

Data annotation using visual inspection (supervision) of each training sample can be laborious. Interactive solutions alleviate this by helping experts propagate labels from a few supervised samples to unlabeled ones based solely on the visual analysis of their feature space projection (with no further sample supervision). We present a semi-automatic data annotation approach based on suitable feature space projection and semi-supervised label estimation. We validate our method on the popular MNIST dataset and on images of human intestinal parasites with and without fecal impurities, a large and diverse dataset that makes classification very hard. We evaluate two approaches for semi-supervised learning from the latent and projection spaces, to choose the one that best reduces user annotation effort and also increases classification accuracy on unseen data. Our results demonstrate the added-value of visual analytics tools that combine complementary abilities of humans and machines for more effective machine learning.Comment: 28 pages, 10 figure

High prevalence of blastocystis Spp. infection in children and staff members attending public urban schools in São Paulo stare, Brazil

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)After a gastroenteritis outbreak of unknown etiology in the municipality of Sebastiao da Grama, Sao Paulo, Brazil, we conducted a parasitological survey to establish the epidemiological profile of enteroparasitosis in children and staff members attending the public urban schools in operation in town. The cross-sectional study evaluated 172 children aged 11 months to 6 years old and 33 staff members aged 19 to 58 years old. Overall, 96 (55.81%) children and 20 (60.61%) staff members were mono-parasitized, while 58 (33.72%) children and 4 (12.12%) workers were poly-parasitized. Protozoa (88.37%; 72.73%) was more prevalent than helminthes (3.48%; 0%) in children and staff members respectively. Blastocystis spp. was the most prevalent parasite in children (86.63%) and staff members (66.67%). The age of 1 year old or less was found to be associated with increased prevalence of giardiasis [OR = 13.04; 95% CI 2.89-58.91; p = 0.00] and public garbage collection was identified as a protective factor against intestinal helminth infections [OR = 0.06; 95% CI 0.00-0.79; p = 0.03]. Although most of the children tested positive for Blastocystis spp. and also presented clinical signs/symptoms (62.2%), this association was not statistically significant [OR = 1.35; 95% CI 0.53-3.44; p = 0.51]. Intestinal parasites still represent a public health concern and this study underscores the importance of further investigations to better understand the pathogenic role of Blastocystis spp.After a gastroenteritis outbreak of unknown etiology in the municipality of Sebastiao da Grama, Sao Paulo, Brazil, we conducted a parasitological survey to establish the epidemiological profile of enteroparasitosis in children and staff members attending58CNPQ - CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICOConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)sem informaçã

Recent Advances on Optimum-Path Forest for Data Classification: Supervised, Semi-Supervised and Unsupervised Learning

Although one can find several pattern recognition techniques out there, there is still room for improvements and new approaches. In this book chapter, we revisited the Optimum-Path Forest (OPF) classifier, which has been evaluated over the last years in a number of applications that consider supervised, semi-supervised and unsupervised learning problems. We also presented a brief compilation of a number of previous works that employed OPF in different research fields, that range from remote sensing image classification to medical data analysis

The Antinociceptive and Anti-Inflammatory Activities of Caulerpin, a Bisindole Alkaloid Isolated from Seaweeds of the Genus Caulerpa

The antinociceptive and anti-inflammatory activity of caulerpin was investigated. This bisindole alkaloid was isolated from the lipoid extract of Caulerpa racemosa and its structure was identified by spectroscopic methods, including IR and NMR techniques. The pharmacological assays used were the writhing and the hot plate tests, the formalin-induced pain, the capsaicin-induced ear edema and the carrageenan-induced peritonitis. Caulerpin was given orally at a concentration of 100 μmol/kg. In the abdominal constriction test caulerpin showed reduction in the acetic acid-induced nociception at 0.0945 μmol (0.0103–1.0984) and for dypirone it was 0.0426 μmol (0.0092–0.1972). In the hot plate test in vivo the inhibition of nociception by caulerpin (100 μmol/kg, p.o.) was also favorable. This result suggests that this compound exhibits a central activity, without changing the motor activity (seen in the rotarod test). Caulerpin (100 μmol/kg, p.o.) reduced the formalin effects in both phases by 35.4% and 45.6%, respectively. The possible anti-inflammatory activity observed in the second phase in the formalin test of caulerpin (100 μmol/kg, p.o.) was confirmed on the capsaicin-induced ear edema model, where an inhibition of 55.8% was presented. Indeed, it was also observed in the carrageenan-induced peritonitis that caulerpin (100 μmol/kg, p.o.) exhibited anti-inflammatory activity, reducing significantly the number of recruit cells by 48.3%. Pharmacological studies are continuing in order to characterize the mechanism(s) responsible for the antinociceptive and anti-inflammatory actions and also to identify other active principles present in Caulerpa racemosa

Federated learning enables big data for rare cancer boundary detection.

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio