Guidelines for the management of neuroendocrine tumours by the Brazilian gastrointestinal tumour group

Neuroendocrine tumours are a heterogeneous group of diseases with a significant variety of diagnostic tests and treatment modalities. Guidelines were developed by North American and European groups to recommend their best management. However, local particularities and relativisms found worldwide led us to create Brazilian guidelines. Our consensus considered the best feasible strategies in an environment involving more limited resources. We believe that our recommendations may be extended to other countries with similar economic standards.Univ Sao Paulo, Inst Canc Estado Sao Paulo, BR-01246000 Sao Paulo, BrazilUniv Sao Paulo, Fac Med, Dept Radiol & Oncol, BR-01246903 Sao Paulo, BrazilHosp Sirio Libanes, BR-01308050 Sao Paulo, BrazilHosp Moinhos de Vento Porto Alegre, BR-90035000 Porto Alegre, RS, BrazilOncoctr, BR-30360680 Belo Horizonte, MG, BrazilUniv Fed Rio Grande do Sul, Dept Cirurgia, BR-90040060 Porto Alegre, RS, BrazilHosp Clin Porto Alegre, BR-90035903 Porto Alegre, RS, BrazilUniv Fed Ceara, Fac Med, Dept Fisiol & Farmacol, BR-60020180 Fortaleza, Ceara, BrazilHosp Univ Walter Cantidio, BR-60430370 Fortaleza, Ceara, BrazilInst Nacl Canc, BR-20230240 Rio De Janeiro, BrazilUniv Sao Paulo, Fac Med, Disciplina Endocrinol & Metabol, BR-01246903 Sao Paulo, BrazilAC Camargo Canc Ctr, Dept Surg, BR-01509010 Sao Paulo, BrazilUniv Sao Paulo, Fac Med, Dept Gastroenterol, Sao Paulo, BrazilUniv Fed Ciencias Saude Porto Alegre, BR-90050170 Porto Alegre, RS, BrazilHosp Albert Einstein, BR-05652900 Sao Paulo, BrazilHosp Base, Fac Med Sao Jose do Rio Preto, BR-15090000 Sao Paulo, BrazilSanta Casa Sao Jose do Rio Preto, BR-15025500 Sao Jose Do Rio Preto, BrazilPontificia Univ Catolica Parana, Hosp Erasto Gaertner, BR-81520060 Curitiba, Parana, BrazilUniv Fed Rio Grande do Norte, BR-59300000 Natal, RN, BrazilUniv Sao Paulo, Inst Coracao, BR-05403900 Sao Paulo, BrazilAC Camargo Canc Ctr, Med Oncol, BR-01509010 Sao Paulo, BrazilUniv Fed Sao Paulo, Disciplina Gastroenterol, BR-04021001 Sao Paulo, BrazilHosp Sao Rafael, BR-41253190 Salvador, BA, BrazilHosp Canc Barretos, Dept Cirurgia Aparelho Digest Alto & Hepatobiliop, BR-14784400 Sao Paulo, BrazilUniv Sao Paulo, Fac Med, Dept Patol, BR-01246903 Sao Paulo, BrazilClin AMO, BR-1950640 Salvador, BA, BrazilHosp Sao Jose, BR-01323001 Sao Paulo, BrazilUniv Nove de Julho, BR-02111030 Sao Paulo, BrazilUniv Fed Sao Paulo, Disciplina Gastroenterol, BR-04021001 Sao Paulo, BrazilWeb of Scienc

Catálogo Taxonômico da Fauna do Brasil: setting the baseline knowledge on the animal diversity in Brazil

The limited temporal completeness and taxonomic accuracy of species lists, made available in a traditional manner in scientific publications, has always represented a problem. These lists are invariably limited to a few taxonomic groups and do not represent up-to-date knowledge of all species and classifications. In this context, the Brazilian megadiverse fauna is no exception, and the Catálogo Taxonômico da Fauna do Brasil (CTFB) (http://fauna.jbrj.gov.br/), made public in 2015, represents a database on biodiversity anchored on a list of valid and expertly recognized scientific names of animals in Brazil. The CTFB is updated in near real time by a team of more than 800 specialists. By January 1, 2024, the CTFB compiled 133,691 nominal species, with 125,138 that were considered valid. Most of the valid species were arthropods (82.3%, with more than 102,000 species) and chordates (7.69%, with over 11,000 species). These taxa were followed by a cluster composed of Mollusca (3,567 species), Platyhelminthes (2,292 species), Annelida (1,833 species), and Nematoda (1,447 species). All remaining groups had less than 1,000 species reported in Brazil, with Cnidaria (831 species), Porifera (628 species), Rotifera (606 species), and Bryozoa (520 species) representing those with more than 500 species. Analysis of the CTFB database can facilitate and direct efforts towards the discovery of new species in Brazil, but it is also fundamental in providing the best available list of valid nominal species to users, including those in science, health, conservation efforts, and any initiative involving animals. The importance of the CTFB is evidenced by the elevated number of citations in the scientific literature in diverse areas of biology, law, anthropology, education, forensic science, and veterinary science, among others

GCOOD: A Generic Coupled Out-of-Distribution Detector for Robust Classification

International audienceNeural networks have achieved high degrees of accuracy in classification tasks. However, when an out-of-distribution (OOD) sample (i.e., entries from unknown classes) is submitted to the classification process, the result is the association of the sample to one or more of the trained classes with different degrees of confidence. If any of these confidence values are more significant than the user-defined threshold, the network will mislabel the sample, affecting the model credibility. The definition of the acceptance threshold itself is a sensitive issue in the face of the classifier’s overconfidence. This paper presents the Generic Coupled OOD Detector (GCOOD), a novel Convolutional Neural Network (CNN) tailored to detect whether an entry submitted to a trained classification model is an OOD sample for that model. From the analysis of the Softmax output of any classifier, our approach can indicate whether the resulting classification should be considered or not as a sample of some of the trained classes. To train our CNN, we had to develop a novel training strategy based on Voronoi diagrams of the location of representative entries in the latent space of the classification model and graph coloring. We evaluated our approach using ResNet, VGG, DenseNet, and SqueezeNet classifiers with images from the CIFAR-10 dataset

Heuristics to reduce linear combinations of activation functions to improve image classification

International audienceImage classification is one of the classical problems in computer vision, and CNNs (Convolutional Neural Networks) are widely used for this task. However, the choice of a CNN can vary depending on the chosen dataset. In this context, we have trainable activation functions that are crucial in CNNs and adapt to the data. One technique for constructing these functions is to write them as a linear combination of other activation functions, where the coefficients of this combination are learned during training. However, if we have a large number of activation functions to combine, the computational cost can be very high, and manually testing and choosing these functions may be impractical, depending on the number of available activation functions. To alleviate the difficulty of choosing which activation functions should be part of the linear combination, we propose two heuristics: Linear Combination Approximator by Coefficients (LCAC) and Major and Uniform Coefficient Extractor (MUCE). Our heuristics provide an efficient selection of a subset of activation functions so that their results are better or equivalent to the linear combination that uses all 34 available activation functions in our experiments (C34), considering the image classification problem. Compared to the C34 function, the LCAC function was better or equivalent in 62.5%, and the MUCE function in 87.5% of the conducted experiments