Pandemônio Durante a Pandemia: Qual o Papel dos Profissionais da Saúde e a Ciência?

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Quintais Orgânicos de Frutas: diversificação da matriz produtiva e geração de renda familiar.

Quintais Orgânicos de Frutas é uma iniciativa de transferência de tecnologia desenvolvida pela Embrapa Clima Temperado que leva, a públicos em situação de vulnerabilidade social, econômica e alimentar (agricultores familiares, assentados da reforma agrária, comunidades indígenas, quilombolas, alunos de escolas rurais e instituições assistencialistas), as últimas soluções tecnológicas desenvolvidas e validadas pela Embrapa, buscando a sustentabilidade; as práticas compreendem desde o preparo do solo até o pós-colheita

Neural Networks for State Evaluation in General Game Playing

Abstract. Unlike traditional game playing, General Game Playing is concerned with agents capable of playing classes of games. Given the rules of an unknown game, the agent is supposed to play well without human intervention. For this purpose, agent systems that use deterministic game tree search need to automatically construct a state value function to guide search. Successful systems of this type use evaluation functions derived solely from the game rules, thus neglecting further improvements by experience. In addition, these functions are fixed in their form and do not necessarily capture the game’s real state value function. In this work we present an approach for obtaining evaluation functions on the basis of neural networks that overcomes the aforementioned problems. A network initialization extracted from the game rules ensures reasonable behavior without the need for prior training. Later training, however, can lead to significant improvements in evaluation quality, as our results indicate.

Speaker recognition with hybrid features from a deep belief network

Learning representation from audio data has shown advantages over the handcrafted features such as mel-frequency cepstral coefficients (MFCCs) in many audio applications. In most of the representation learning approaches, the connectionist systems have been used to learn and extract latent features from the fixed length data. In this paper, we propose an approach to combine the learned features and the MFCC features for speaker recognition task, which can be applied to audio scripts of different lengths. In particular, we study the use of features from different levels of deep belief network for quantizing the audio data into vectors of audio word counts. These vectors represent the audio scripts of different lengths that make them easier to train a classifier. We show in the experiment that the audio word count vectors generated from mixture of DBN features at different layers give better performance than the MFCC features. We also can achieve further improvement by combining the audio word count vector and the MFCC features

Projeto Quintais Orgânicos de Frutas: Contribuição para a segurança alimentar em áreas rurais, indígenas e urbanas.

O Projeto Quintais tem como objetivo contribuir com a sustentabilidade social, econômica e ambiental de públicos em situação de vulnerabilidade e de risco social, econômico e alimentar. Para a composição dos quintais, são adotadas três plantas frutíferas, provenientes de um conjunto de 20 espécies, sementes de feijão e milho, mudas de três cultivares de batata doce, mudas de uma espécie forrageira e de doze espécies de plantas medicinais, totalizando 38 produtos cultivados no interior de cada Quintal. As tecnologias desenvolvidas no Projeto, tais como novas cultivares, conhecimento das propriedades funcionais dos alimentos que compõem o Quintal, assim como o processo de verticalização ou transformação e agregação de valor aos alimentos deverão promover a capacitação e a inclusão social de beneficiários, assim como, viabilizar a geração de emprego e renda.Anais da Reunião Técnica sobre Agroecologia: Agroecologia, Resiliência e Bem Viver, 2021, Pelotas

Fat-Fast VG-RAM WNN: A high performance approach

The Virtual Generalizing Random Access Memory Weightless Neural Network (VGRAM WNN) is a type of WNN that only requires storage capacity proportional to the training set. As such, it is an effective machine learning technique that offers simple implementation and fast training – it can be made in one shot. However, the VG-RAM WNN test time for applications that require many training samples can be large, since it increases with the size of the memory of each neuron. In this paper, we present Fat-Fast VG-RAM WNNs. Fat-Fast VG-RAM WNNs employ multi-index chained hashing for fast neuron memory search. Our chained hashing technique increases the VG-RAM memory consumption (fat) but reduces test time substantially (fast), while keeping most of its machine learning performance. To address the memory consumption problem, we employ a data clustering technique to reduce the overall size of the neurons’ memory. This can be achieved by replacing clusters of neurons’ memory by their respective centroid values. With our approach, we were able to reduce VG-RAM WNN test time and memory footprint, while maintaining a high and acceptable machine learning performance. We performed experiments with the Fat-Fast VG-RAM WNN applied to two recognition problems: (i) handwritten digit recognition, and (ii) traffic sign recognition. Our experimental results showed that, in both recognition problems, our new VG-RAM WNN approach was able to run three orders of magnitude faster and consume two orders of magnitude less memory than standard VG-RAM, while experiencing only a small reduction in recognition performance

Hepatitis C in Brazil: lessons learned with boceprevir and telaprevir

In 2012, the first-generation protease inhibitors telaprevir (TVR) and boceprevir (BOC) were introduced in the Brazilian health system for treatment of chronic hepatitis C, after their approval by the National Committee for Health Technology Incorporation (CONITEC). However, these medicines were discontinued in 2015. The short period of use in therapy and their high cost require a discussion about the consequences for patients and for the health system of the early incorporation of new therapies. The article presents a qualitative analysis of the incorporation process of both medications in Brazil and the results of a multicenter study that included patients treated with BOC or TVR between January 2011 and December 2015 in five Brazilian cities. The study included 855 patients (BOC: n=247) and (TVR: n=608). The document analysis showed that CONITEC’s decision to incorporate BOC and TVR was based on results of phase III clinical trials that compared sustained virologic response (SVR) rates of patients treated with BOC and TVR with rates of those that received placebo. However, these studies included a low percentage of cirrhotic patients. The SVR rates observed in this multicenter study were worse than clinical trials pointed out (BOC: 45.6%; TVR: 51.8%), but similar to those achieved with previously adopted therapies. The discontinuation rate due to adverse events was (BOC: 15.4%; TVR: 12.7%). Based on these unsatisfactory results, the study brings a discussion that goes beyond the therapy outcomes, exploring the incorporation of these high-cost medicines and the related decision-making process, contributing to future decisions in medicine policies and in the treatment of chronic hepatitis C

A Multicenter, Long-Term Study on Arrhythmias in Children with Ebstein Anomaly

To assess the prevalence, history, and treatment of arrhythmias, in particular preexcitation and Wolff–Parkinson–White (WPW) syndrome, in patients with Ebstein anomaly (EA) during childhood and adolescence, we performed a multicenter retrospective study of all consecutive live-born patients with EA, diagnosed, and followed by pediatric cardiologists between 1980 and 2005 in The Netherlands. During a follow-up after EA diagnosis of 13 years 3 months (range: 6 days to 28 years 2 months), 16 (17%) of the 93 pediatric EA patients exhibited rhythm disturbances. Nine patients showed arrhythmic events starting as of the neonatal period. Supraventricular tachycardia was noted in 11 patients. One patient died in the neonatal period due to intractable supraventricular tachycardia resulting in heart failure and one patient died at 5 weeks of age most probably due to an arrhythmic event. The 14 surviving patients all show preexcitation, albeit 4 of them intermittently, and all have a right-sided accessory pathway location. Nine patients underwent catheter ablation of an accessory pathway. Only four patients are currently on antiarrhythmic drugs. The 17% prevalence of rhythm disturbances in pediatric EA patients, most commonly supraventricular arrhythmias, is significantly lower than in adult EA patients. Life-threatening rhythm disturbances are not frequent early in life. Symptomatic patients are well treated with radiofrequency catheter ablation

Fast relational learning using bottom clause propositionalization with artificial neural networks

Relational learning can be described as the task of learning first-order logic rules from examples. It has enabled a number of new machine learning applications, e.g. graph mining and link analysis. Inductive Logic Programming (ILP) performs relational learning either directly by manipulating first-order rules or through propositionalization, which translates the relational task into an attribute-value learning task by representing subsets of relations as features. In this paper, we introduce a fast method and system for relational learning based on a novel propositionalization called Bottom Clause Propositionalization (BCP). Bottom clauses are boundaries in the hypothesis search space used by ILP systems Progol and Aleph. Bottom clauses carry semantic meaning and can be mapped directly onto numerical vectors, simplifying the feature extraction process. We have integrated BCP with a well-known neural-symbolic system, C-IL2P, to perform learning from numerical vectors. C-IL2P uses background knowledge in the form of propositional logic programs to build a neural network. The integrated system, which we call CILP++, handles first-order logic knowledge and is available for download from Sourceforge. We have evaluated CILP++ on seven ILP datasets, comparing results with Aleph and a well-known propositionalization method, RSD. The results show that CILP++ can achieve accuracy comparable to Aleph, while being generally faster, BCP achieved statistically significant improvement in accuracy in comparison with RSD when running with a neural network, but BCP and RSD perform similarly when running with C4.5. We have also extended CILP++ to include a statistical feature selection method, mRMR, with preliminary results indicating that a reduction of more than 90 % of features can be achieved with a small loss of accuracy

Lipid bodies containing oxidatively truncated lipids block antigen cross-presentation by dendritic cells in cancer

Tumor-associated dendritic cells are defective in their ability to cross-present antigens, and they accumulate lipid bodies. Here the authors show that this defect is due to an impaired trafficking of peptide-MHC class I caused by the interaction of electrophilic lipids with chaperone heat shock protein 70