Validation of artificial intelligence prediction models for skin cancer diagnosis using dermoscopy images: the 2019 International Skin Imaging Collaboration Grand Challenge

Previous studies of artificial intelligence (AI) applied to dermatology have shown AI to have higher diagnostic classification accuracy than expert dermatologists; however, these studies did not adequately assess clinically realistic scenarios, such as how AI systems behave when presented with images of disease categories that are not included in the training dataset or images drawn from statistical distributions with significant shifts from training distributions. We aimed to simulate these real-world scenarios and evaluate the effects of image source institution, diagnoses outside of the training set, and other image artifacts on classification accuracy, with the goal of informing clinicians and regulatory agencies about safety and real-world accuracy.We designed a large dermoscopic image classification challenge to quantify the performance of machine learning algorithms for the task of skin cancer classification from dermoscopic images, and how this performance is affected by shifts in statistical distributions of data, disease categories not represented in training datasets, and imaging or lesion artifacts. Factors that might be beneficial to performance, such as clinical metadata and external training data collected by challenge participants, were also evaluated. 25?331 training images collected from two datasets (in Vienna [HAM10000] and Barcelona [BCN20000]) between Jan 1, 2000, and Dec 31, 2018, across eight skin diseases, were provided to challenge participants to design appropriate algorithms. The trained algorithms were then tested for balanced accuracy against the HAM10000 and BCN20000 test datasets and data from countries not included in the training dataset (Turkey, New Zealand, Sweden, and Argentina). Test datasets contained images of all diagnostic categories available in training plus other diagnoses not included in training data (not trained category). We compared the performance of the algorithms against that of 18 dermatologists in a simulated setting that reflected intended clinical use.64 teams submitted 129 state-of-the-art algorithm predictions on a test set of 8238 images. The best performing algorithm achieved 58·8% balanced accuracy on the BCN20000 data, which was designed to better reflect realistic clinical scenarios, compared with 82·0% balanced accuracy on HAM10000, which was used in a previously published benchmark. Shifted statistical distributions and disease categories not included in training data contributed to decreases in accuracy. Image artifacts, including hair, pen markings, ulceration, and imaging source institution, decreased accuracy in a complex manner that varied based on the underlying diagnosis. When comparing algorithms to expert dermatologists (2460 ratings on 1269 images), algorithms performed better than experts in most categories, except for actinic keratoses (similar accuracy on average) and images from categories not included in training data (26% correct for experts vs 6% correct for algorithms, p<0·0001). For the top 25 submitted algorithms, 47·1% of the images from categories not included in training data were misclassified as malignant diagnoses, which would lead to a substantial number of unnecessary biopsies if current state-of-the-art AI technologies were clinically deployed.We have identified specific deficiencies and safety issues in AI diagnostic systems for skin cancer that should be addressed in future diagnostic evaluation protocols to improve safety and reliability in clinical practice.Melanoma Research Alliance and La Marató de TV3.Copyright © 2022 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY-NC-ND 4.0 license. Published by Elsevier Ltd.. All rights reserved

Intra- and Inter-clade Cross-reactivity by HIV-1 Gag Specific T-Cells Reveals Exclusive and Commonly Targeted Regions: Implications for Current Vaccine Trials

The genetic diversity of HIV-1 across the globe is a major challenge for developing an HIV vaccine. To facilitate immunogen design, it is important to characterize clusters of commonly targeted T-cell epitopes across different HIV clades. To address this, we examined 39 HIV-1 clade C infected individuals for IFN-γ Gag-specific T-cell responses using five sets of overlapping peptides, two sets matching clade C vaccine candidates derived from strains from South Africa and China, and three peptide sets corresponding to consensus clades A, B, and D sequences. The magnitude and breadth of T-cell responses against the two clade C peptide sets did not differ, however clade C peptides were preferentially recognized compared to the other peptide sets. A total of 84 peptides were recognized, of which 19 were exclusively from clade C, 8 exclusively from clade B, one peptide each from A and D and 17 were commonly recognized by clade A, B, C and D. The entropy of the exclusively recognized peptides was significantly higher than that of commonly recognized peptides (p = 0.0128) and the median peptide processing scores were significantly higher for the peptide variants recognized versus those not recognized (p = 0.0001). Consistent with these results, the predicted Major Histocompatibility Complex Class I IC50 values were significantly lower for the recognized peptide variants compared to those not recognized in the ELISPOT assay (p<0.0001), suggesting that peptide variation between clades, resulting in lack of cross-clade recognition, has been shaped by host immune selection pressure. Overall, our study shows that clade C infected individuals recognize clade C peptides with greater frequency and higher magnitude than other clades, and that a selection of highly conserved epitope regions within Gag are commonly recognized and give rise to cross-clade reactivities

Cognitive testing of physical activity and acculturation questions in recent and long-term Latino immigrants

<p>Abstract</p> <p>Background</p> <p>We ascertained the degree to which language (English versus Spanish), and residence time in the US influence responses to survey questions concerning two topics: self-reported acculturation status, and recent physical activity (PA). This topic is likely to be of general interest because of growing numbers of immigrants in countries worldwide.</p> <p>Methods</p> <p>We carried out qualitative (cognitive) interviews of survey items on acculturation and physical activity on 27 Latino subjects from three groups: (a) In Spanish, of those of low residence time (less than five years living in the U.S.) (n = 9); (b) In Spanish, of those of high residence time (15 or more years in the U.S) (n = 9); and (c) in English, of those of high residence time (n = 9).</p> <p>Results</p> <p>There were very few language translation problems; general question design defects and socio-cultural challenges to survey responses were more common. Problems were found for both acculturation and PA questions, with distinct problem types for the two question areas. Residence time/language group was weakly associated with overall frequency of problems observed: low residence time/Spanish (86%), high residence time/Spanish (67%), and English speaking groups (62%).</p> <p>Conclusions</p> <p>Standardized survey questions related to acculturation and physical activity present somewhat different cognitive challenges. For PA related questions, problems with such questions were similar regardless of subject residence time or language preference. For acculturation related questions, residence time/language or education level influenced responses to such questions. These observations should help in the interpretation of survey results for culturally diverse populations.</p

A multimodal cell census and atlas of the mammalian primary motor cortex

ABSTRACT We report the generation of a multimodal cell census and atlas of the mammalian primary motor cortex (MOp or M1) as the initial product of the BRAIN Initiative Cell Census Network (BICCN). This was achieved by coordinated large-scale analyses of single-cell transcriptomes, chromatin accessibility, DNA methylomes, spatially resolved single-cell transcriptomes, morphological and electrophysiological properties, and cellular resolution input-output mapping, integrated through cross-modal computational analysis. Together, our results advance the collective knowledge and understanding of brain cell type organization: First, our study reveals a unified molecular genetic landscape of cortical cell types that congruently integrates their transcriptome, open chromatin and DNA methylation maps. Second, cross-species analysis achieves a unified taxonomy of transcriptomic types and their hierarchical organization that are conserved from mouse to marmoset and human. Third, cross-modal analysis provides compelling evidence for the epigenomic, transcriptomic, and gene regulatory basis of neuronal phenotypes such as their physiological and anatomical properties, demonstrating the biological validity and genomic underpinning of neuron types and subtypes. Fourth, in situ single-cell transcriptomics provides a spatially-resolved cell type atlas of the motor cortex. Fifth, integrated transcriptomic, epigenomic and anatomical analyses reveal the correspondence between neural circuits and transcriptomic cell types. We further present an extensive genetic toolset for targeting and fate mapping glutamatergic projection neuron types toward linking their developmental trajectory to their circuit function. Together, our results establish a unified and mechanistic framework of neuronal cell type organization that integrates multi-layered molecular genetic and spatial information with multi-faceted phenotypic properties

Prey captured and used in Polistes versicolor (Olivier) (Hymenoptera: Vespidae) Nourishment

As vespas sociais são predadoras de muitas espécies de insetos e o estudo de suas presas pode revelar seu potencial para programas de controle biológico de pragas. Foram realizadas 240h de coleta de presas em 32 colônias de Polistes versicolor (Olivier) no município de Juiz de Fora, MG, de março de 2000 a fevereiro de 2001. As presas capturadas por P. versicolor foram, principalmente, das ordens Lepidoptera (95,4%) e Coleoptera (1,1%) além de 3,4% de indivíduos não identificados. A espécie mais coletada foi Chlosyne lacinia saundersii Doubleday & Hewitson (13,5%) (Lepidoptera: Nymphalidae) e o número total estimado de presas capturadas por colônia de P. versicolor foi de 4.015 indivíduos por ano. Isso mostra que a espécie pode ser utilizada em programas de manejo integrado de pragas de insetos herbívoros, principalmente lagartas desfolhadoras.The social wasps are predators of many insect species and the study of their preys can reveal the potential of these natural enemies in biological control programs. A total of 240h of collections of preys in 32 nests of Polistes versicolor (Olivier) was carried on in Juiz de Fora, Minas Gerais State, Brazil, from March 2000 to February 2001. The preys captured by P. versicolor were mainly those from the orders Lepidoptera (95.4%) and Coleoptera (1.1%) while 3.4% of them were not identified. Chlosyne lacinia saundersii Doubleday & Hewitson (Lepidoptera: Nymphalidae) was the most collected prey (13.5%). The total of 4,015 preys was estimated to be captured per colony of P. versicolor during one year. The species can be used in integrated pest management of herbivorous insects, especially defoliating caterpillars