Transformer-based normative modelling for anomaly detection of early schizophrenia

Despite the impact of psychiatric disorders on clinical health, early-stage diagnosis remains a challenge. Machine learning studies have shown that classifiers tend to be overly narrow in the diagnosis prediction task. The overlap between conditions leads to high heterogeneity among participants that is not adequately captured by classification models. To address this issue, normative approaches have surged as an alternative method. By using a generative model to learn the distribution of healthy brain data patterns, we can identify the presence of pathologies as deviations or outliers from the distribution learned by the model. In particular, deep generative models showed great results as normative models to identify neurological lesions in the brain. However, unlike most neurological lesions, psychiatric disorders present subtle changes widespread in several brain regions, making these alterations challenging to identify. In this work, we evaluate the performance of transformer-based normative models to detect subtle brain changes expressed in adolescents and young adults. We trained our model on 3D MRI scans of neurotypical individuals (N=1,765). Then, we obtained the likelihood of neurotypical controls and psychiatric patients with early-stage schizophrenia from an independent dataset (N=93) from the Human Connectome Project. Using the predicted likelihood of the scans as a proxy for a normative score, we obtained an AUROC of 0.82 when assessing the difference between controls and individuals with early-stage schizophrenia. Our approach surpassed recent normative methods based on brain age and Gaussian Process, showing the promising use of deep generative models to help in individualised analyses.Comment: 10 pages, 2 figures, 2 tables, presented at NeurIPS22@PAI4M

Extração de amido de pinhão.

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Discriminating among Earth composition models using geo-antineutrinos

It has been estimated that the entire Earth generates heat corresponding to about 40 TW (equivalent to 10,000 nuclear power plants) which is considered to originate mainly from the radioactive decay of elements like U, Th and K, deposited in the crust and mantle of the Earth. Radioactivity of these elements produce not only heat but also antineutrinos (called geo-antineutrinos) which can be observed by terrestrial detectors. We investigate the possibility of discriminating among Earth composition models predicting different total radiogenic heat generation, by observing such geo-antineutrinos at Kamioka and Gran Sasso, assuming KamLAND and Borexino (type) detectors, respectively, at these places. By simulating the future geo-antineutrino data as well as reactor antineutrino background contributions, we try to establish to which extent we can discriminate among Earth composition models for given exposures (in units of kt$\cdot$ yr) at these two sites on our planet. We use also information on neutrino mixing parameters coming from solar neutrino data as well as KamLAND reactor antineutrino data, in order to estimate the number of geo-antineutrino induced events.Comment: 24 pages, 10 figures, final version to appear in JHE

Alimentação popular em São Paulo (1920 a 1950): políticas públicas, discursos técnicos e práticas profissionais

This article discusses how the concept of lower-class eating habits came about and developed in the intellectual circles of São Paulo during the first half of the 20th century. It starts by reconstructing the elements of the debate around the income and ignorance of the underprivileged as the main reasons behind their bad eating habits. Then, it looks at the focal points for interventions and public policies proposed by the government to deal with the problem thus identified, namely: training methods to produce sanitation counselors capable of offering dietary guidance as well; popular educational campaigns and new learning sites in addition to schools (e.g. healthcare centers and households); lunch and other means of offering food at schools; and diagnostic studies about food intake and eating habits among laborers. Because they were translated into technical and scientific language, the proposals and policies implemented in São Paulo left traces in a variety of supporting documents and media (photographs, primers, posters, inquiry notebooks, and academic literature).O artigo discute a construção da idéia de alimentação popular nos meios intelectuais em São Paulo, na primeira metade do século XX. Para isso, reconstitui, como motivos da má alimentação, elementos do debate em torno da renda e da ignorância dos mais pobres. Identificado o problema, as propostas de intervenção e as políticas públicas concentraram-se em alguns setores, abordados neste trabalho: métodos para a formação de educadores sanitários aptos a atuar também na educação alimentar; campanhas de instrução popular e criação de novos lugares de aprendizado (além das escolas, os centros de saúde e os lares); merenda escolar e outras alternativas de alimentação nas escolas; e diagnósticos referentes ao conteúdo e à forma da alimentação dos operários. Traduzidas em discurso técnico-científicos, as propostas e políticas implementadas na cidade deixaram indícios em documentação de suporte e tipologia variados (fotografias, cartilhas, cartazes, cadernetas de inquéritos e textos acadêmicos).Universidade Federal de São Paulo (UNIFESP)UNIFESPSciEL

Generative AI for Medical Imaging: extending the MONAI Framework

Recent advances in generative AI have brought incredible breakthroughs in several areas, including medical imaging. These generative models have tremendous potential not only to help safely share medical data via synthetic datasets but also to perform an array of diverse applications, such as anomaly detection, image-to-image translation, denoising, and MRI reconstruction. However, due to the complexity of these models, their implementation and reproducibility can be difficult. This complexity can hinder progress, act as a use barrier, and dissuade the comparison of new methods with existing works. In this study, we present MONAI Generative Models, a freely available open-source platform that allows researchers and developers to easily train, evaluate, and deploy generative models and related applications. Our platform reproduces state-of-art studies in a standardised way involving different architectures (such as diffusion models, autoregressive transformers, and GANs), and provides pre-trained models for the community. We have implemented these models in a generalisable fashion, illustrating that their results can be extended to 2D or 3D scenarios, including medical images with different modalities (like CT, MRI, and X-Ray data) and from different anatomical areas. Finally, we adopt a modular and extensible approach, ensuring long-term maintainability and the extension of current applications for future features

SOD2 immunoexpression predicts lymph node metastasis in penile cancer

BACKGROUND: Superoxide dismutase-2 (SOD2) is considered one of the most important antioxidant enzymes that regulate cellular redox state in normal and tumorigenic cells. Overexpression of this enzyme in lung, gastric, colorectal, breast cancer and cervical cancer malignant tumors has been observed. Its relationship with inguinal lymph node metastasis in penile cancer is unknown. METHODS: SOD2 protein expression levels were determined by immunohistochemistry in 125 usual type squamous cell carcinomas of the penis from a Brazilian cancer center. The casuistic has been characterized by means of descriptive statistics. An exploratory logistic regression has been proposed to evaluate the independent predictive factors of lymph node metastasis. RESULTS: SOD2 expression in more than 50% of cells was observed in 44.8% of primary penile carcinomas of the usual type. This expression pattern was associated with lymph node metastasis both in the uni and multivariate analysis. CONCLUSIONS: Our results indicate that SOD2 expression predicts regional lymph node metastasis. The potential clinical implication of this observation warrants further studies.Dr. Lara Termini (FAPESP 2005/57274-9); Dr. Luisa Lina Villa (FAPESP 2008/57889-1 and CNPq 573799/2008-3)

Obtenção de polpa de coco verde congelada com e sem uso de aditivo químico.

O fluxograma de processamento da polpa de coco verde congelada é composto das seguintes etapas: recepção e seleção; lavagem e sanitização; abertura do fruto e extração de água de coco; extração da polpa e imersão; separação e desintegração da polpa; embalagem e armazenamento sob congelamento. O Comunicado Técnico apresenta recomendações para obtenção da polpa de coco verde e sua conservação com e sem o uso de aditivos químicos.bitstream/item/224387/1/CT-241-15abr2021.pd