Employment Loss in Informal Settlements during the Covid-19 Pandemic: Evidence from Chile.

The Covid-19 pandemic has reached almost every corner of the world. Despite the historical development, approval, and distribution of vaccines in some countries, non-pharmaceutical interventions will remain an essential strategy to control the pandemic until a substantial proportion of the population has immunity. There is increasing evidence of the devastating social and economic effects of the pandemic, particularly on vulnerable communities. Individuals living in urban informal settlements are in a structurally disadvantaged position to cope with a health crisis such as the Covid-19 pandemic. Estimates of this impact are needed to inform and prioritize policy decisions and actions. We study employment loss in informal settlements before and during the Covid-19 pandemic in Chile, using a longitudinal panel study of households living in Chile's informal settlements before and during the health crisis. We show that before the pandemic, 75% of respondents reported being employed. There is a decrease of 30 and 40 percentage points in May and September 2020, respectively. We show that the employment loss is substantially higher for individuals in informal settlements than for the general population and has particularly affected the immigrant population. We also show that the pandemic has triggered neighborhood cooperation within the settlements and that targeted government assistance programs have reached these communities in a limited way. Our results suggest that individuals living in informal settlements are facing severe hardship as a consequence of the pandemic. In addition to providing much-needed support, this crisis presents a unique opportunity for long-term improvements in these marginalized communities.

BraNet: a mobil application for breast image classification based on deep learning algorithms.

Mobile health apps are widely used for breast cancer detection using artificial intelligence algorithms, providing radiologists with second opinions and reducing false diagnoses. This study aims to develop an open-source mobile app named "BraNet" for 2D breast imaging segmentation and classification using deep learning algorithms. During the phase off-line, an SNGAN model was previously trained for synthetic image generation, and subsequently, these images were used to pre-trained SAM and ResNet18 segmentation and classification models. During phase online, the BraNet app was developed using the react native framework, offering a modular deep-learning pipeline for mammography (DM) and ultrasound (US) breast imaging classification. This application operates on a client-server architecture and was implemented in Python for iOS and Android devices. Then, two diagnostic radiologists were given a reading test of 290 total original RoI images to assign the perceived breast tissue type. The reader's agreement was assessed using the kappa coefficient. The BraNet App Mobil exhibited the highest accuracy in benign and malignant US images (94.7%/93.6%) classification compared to DM during training I (80.9%/76.9%) and training II (73.7/72.3%). The information contrasts with radiological experts' accuracy, with DM classification being 29%, concerning US 70% for both readers, because they achieved a higher accuracy in US ROI classification than DM images. The kappa value indicates a fair agreement (0.3) for DM images and moderate agreement (0.4) for US images in both readers. It means that not only the amount of data is essential in training deep learning algorithms. Also, it is vital to consider the variety of abnormalities, especially in the mammography data, where several BI-RADS categories are present (microcalcifications, nodules, mass, asymmetry, and dense breasts) and can affect the API accuracy model.

Paired gill slits in a fossil with a calcite skeleton.

The chordates, hemichordates (such as acorn worms) and echinoderms (such as starfish) comprise the group Deuterostomia, well established as monophyletic. Among extant deuterostomes, a skeleton in which each plate has the crystallographic structure of a single crystal of calcite is characteristic of echinoderms and is always associated with radial symmetry and never with gill slits. Among fossils, however, such a skeleton sometimes occurs without radial symmetry. This is true of Jaekelocarpus oklahomensis, from the Upper Carboniferous of Oklahoma, USA, which, being externally almost bilaterally symmetrical, is traditionally placed in the group Mitrata (Ordovician to Carboniferous periods, 530-280 million years ago), by contrast with the bizarrely asymmetrical Cornuta (Cambrian to Ordovician periods, 540 to 440 million years ago). Using computer X-ray microtomography, we describe the anatomy of Jaekelocarpus in greater detail than formerly possible, reveal evidence of paired gill slits internally and interpret its functional anatomy. On this basis we suggest its phylogenetic position within the deuterostomes.