Population dynamic of porcine parvovirus indicates decrease of variability

Projeto/Plano de Ação: 02.11.10.600-03

Neural Collaborative Filtering

In recent years, deep neural networks have yielded immense success on speech recognition, computer vision and natural language processing. However, the exploration of deep neural networks on recommender systems has received relatively less scrutiny. In this work, we strive to develop techniques based on neural networks to tackle the key problem in recommendation -- collaborative filtering -- on the basis of implicit feedback. Although some recent work has employed deep learning for recommendation, they primarily used it to model auxiliary information, such as textual descriptions of items and acoustic features of musics. When it comes to model the key factor in collaborative filtering -- the interaction between user and item features, they still resorted to matrix factorization and applied an inner product on the latent features of users and items. By replacing the inner product with a neural architecture that can learn an arbitrary function from data, we present a general framework named NCF, short for Neural network-based Collaborative Filtering. NCF is generic and can express and generalize matrix factorization under its framework. To supercharge NCF modelling with non-linearities, we propose to leverage a multi-layer perceptron to learn the user-item interaction function. Extensive experiments on two real-world datasets show significant improvements of our proposed NCF framework over the state-of-the-art methods. Empirical evidence shows that using deeper layers of neural networks offers better recommendation performance.Comment: 10 pages, 7 figure

Learning evolving relations for multivariate time series forecasting

Multivariate time series forecasting is essential in various fields, including healthcare and traffic management, but it is a challenging task due to the strong dynamics in both intra-channel relations (temporal patterns within individual variables) and inter-channel relations (the relationships between variables), which can evolve over time with abrupt changes. This paper proposes ERAN (Evolving Relational Attention Network), a framework for multivariate time series forecasting, that is capable to capture such dynamics of these relations. On the one hand, ERAN represents inter-channel relations with a graph which evolves over time, modeled using a recurrent neural network. On the other hand, ERAN represents the intra-channel relations using a temporal attentional convolution, which captures the local temporal dependencies adaptively with the input data. The elvoving graph structure and the temporal attentional convolution are intergrated in a unified model to capture both types of relations. The model is experimented on a large number of real-life datasets including traffic flows, energy consumption, and COVID-19 transmission data. The experimental results show a significant improvement over the state-of-the-art methods in multivariate time series forecasting particularly for non-stationary data

Проблема взаємозв’язку громадянського суспільства і державної бюрократії в Україні: деякі сучасні аспекти історіографії дослідження

Розглядається взаємодія громадянського суспільства і державної бюрократії. Зроблено висновок, що позиції українських дослідників відповідають напрацюванням західної наукової традиції стосовно теоретичних, а також практичних способів забезпечення взаємодії інститутів громадянського суспільства і державного апарату.In this article the interrelation between civil society and state bureaucracy is analysed. The conclusion is made that the views of the Ukrainian scientists correlate with the western traditional scientific opinion concerning theoretical and practical ways of ensuring interaction between the civil society institutions and state apparatus

ABCD : Update of the 2009 guidelines on prevention and management of feline infectious diseases

In this article, the ABCD guidelines published in the JFMS Special Issue of July 2009 (Volume 11, Issue 7, pages 527-620) are updated by including previously unavailable and novel information. For a better picture, the reader is advised to consult that issue before focusing on the novel features

Anthropogenic infection of cats during the 2020 covid-19 pandemic

COVID-19 is a severe acute respiratory syndrome (SARS) caused by a new coronavirus (CoV), SARS-CoV-2, which is closely related to SARS-CoV that jumped the animal–human species bar-rier and caused a disease outbreak in 2003. SARS-CoV-2 is a betacoronavirus that was first described in 2019, unrelated to the commonly occurring feline coronavirus (FCoV) that is an alphacoronavirus associated with feline infectious peritonitis (FIP). SARS-CoV-2 is highly contagious and has spread globally within a few months, resulting in the current pandemic. Felids have been shown to be susceptible to SARS-CoV-2 infection. Particularly in the Western world, many people live in very close contact with their pet cats, and natural infections of cats in COVID-19-positive households have been described in several countries. In this review, the European Advisory Board on Cat Diseases (ABCD), a scientifically independent board of experts in feline medicine from 11 European Countries, discusses the current status of SARS-CoV infections in cats. The review examines the host range of SARS-CoV-2 and human-to-animal transmissions, including infections in domestic and non-domestic felids, as well as mink-to-human/-cat transmission. It summarises current data on SARS-CoV-2 prevalence in domestic cats and the results of experimental infections of cats and provides expert opinions on the clinical relevance and prevention of SARS-CoV-2 infection in cats

Influenza virus infections in cats

In the past, cats were considered resistant to influenza. Today, we know that they are susceptible to some influenza A viruses (IAVs) originating in other species. Usually, the outcome is only subclinical infection or a mild fever. However, outbreaks of feline disease caused by canine H3N2 IAV with fever, tachypnoea, sneezing, coughing, dyspnoea and lethargy are occasionally noted in shelters. In one such outbreak, the morbidity rate was 100% and the mortality rate was 40%. Recently, avian H7N2 IAV infection occurred in cats in some shelters in the USA, inducing mostly mild respiratory disease. Furthermore, cats are susceptible to experimental infection with the human H3N2 IAV that caused the pandemic in 1968. Several studies indicated that cats worldwide could be infected by H1N1 IAV during the subsequent human pandemic in 2009. In one shelter, severe cases with fatalities were noted. Finally, the highly pathogenic avian H5N1 IAV can induce a severe, fatal disease in cats, and can spread via cat-to-cat contact. In this review, the Advisory Board on Cat Diseases (ABCD), a scientifically independent board of experts in feline medicine from 11 European countries, summarises current data regarding the aetiology, epidemiology, pathogenesis, clinical picture, diagnostics, and control of feline IAV infections, as well as the zoonotic risks