d=3 Bosonic Vector Models Coupled to Chern-Simons Gauge Theories

We study three dimensional O(N)_k and U(N)_k Chern-Simons theories coupled to a scalar field in the fundamental representation, in the large N limit. For infinite k this is just the singlet sector of the O(N) (U(N)) vector model, which is conjectured to be dual to Vasiliev's higher spin gravity theory on AdS_4. For large k and N we obtain a parity-breaking deformation of this theory, controlled by the 't Hooft coupling lambda = 4 \pi N / k. For infinite N we argue (and show explicitly at two-loop order) that the theories with finite lambda are conformally invariant, and also have an exactly marginal (\phi^2)^3 deformation. For large but finite N and small 't Hooft coupling lambda, we show that there is still a line of fixed points parameterized by the 't Hooft coupling lambda. We show that, at infinite N, the interacting non-parity-invariant theory with finite lambda has the same spectrum of primary operators as the free theory, consisting of an infinite tower of conserved higher-spin currents and a scalar operator with scaling dimension \Delta=1; however, the correlation functions of these operators do depend on lambda. Our results suggest that there should exist a family of higher spin gravity theories, parameterized by lambda, and continuously connected to Vasiliev's theory. For finite N the higher spin currents are not conserved.Comment: 34 pages, 29 figures. v2: added reference

Covid-19 Dynamic Monitoring and Real-Time Spatio-Temporal Forecasting

Background: Periodically, humanity is often faced with new and emerging viruses that can be a significant global threat. It has already been over a century post—the Spanish Flu pandemic, and we are witnessing a new type of coronavirus, the SARS-CoV-2, which is responsible for Covid-19. It emerged from the city of Wuhan (China) in December 2019, and within a few months, the virus propagated itself globally now resulting more than 50 million cases with over 1 million deaths. The high infection rates coupled with dynamic population movement demands for tools, especially within a Brazilian context, that will support health managers to develop policies for controlling and combating the new virus. / Methods: In this work, we propose a tool for real-time spatio-temporal analysis using a machine learning approach. The COVID-SGIS system brings together routinely collected health data on Covid-19 distributed across public health systems in Brazil, as well as taking to under consideration the geographic and time-dependent features of Covid-19 so as to make spatio-temporal predictions. The data are sub-divided by federative unit and municipality. In our case study, we made spatio-temporal predictions of the distribution of cases and deaths in Brazil and in each federative unit. Four regression methods were investigated: linear regression, support vector machines (polynomial kernels and RBF), multilayer perceptrons, and random forests. We use the percentage RMSE and the correlation coefficient as quality metrics. / Results: For qualitative evaluation, we made spatio-temporal predictions for the period from 25 to 27 May 2020. Considering qualitatively and quantitatively the case of the State of Pernambuco and Brazil as a whole, linear regression presented the best prediction results (thematic maps with good data distribution, correlation coefficient >0.99 and RMSE (%) <4% for Pernambuco and around 5% for Brazil) with low training time: [0.00; 0.04 ms], CI 95%. / Conclusion: Spatio-temporal analysis provided a broader assessment of those in the regions where the accumulated confirmed cases of Covid-19 were concentrated. It was possible to differentiate in the thematic maps the regions with the highest concentration of cases from the regions with low concentration and regions in the transition range. This approach is fundamental to support health managers and epidemiologists to elaborate policies and plans to control the Covid-19 pandemics

COVID-SGIS: A Smart Tool for Dynamic Monitoring and Temporal Forecasting of Covid-19

Background: The global burden of the new coronavirus SARS-CoV-2 is increasing at an unprecedented rate. The current spread of Covid-19 in Brazil is problematic causing a huge public health burden to its population and national health-care service. To evaluate strategies for alleviating such problems, it is necessary to forecast the number of cases and deaths in order to aid the stakeholders in the process of making decisions against the disease. We propose a novel system for real-time forecast of the cumulative cases of Covid-19 in Brazil. / Methods: We developed the novel COVID-SGIS application for the real-time surveillance, forecast and spatial visualization of Covid-19 for Brazil. This system captures routinely reported Covid-19 information from 27 federative units from the Brazil.io database. It utilizes all Covid-19 confirmed case data that have been notified through the National Notification System, from March to May 2020. Time series ARIMA models were integrated for the forecast of cumulative number of Covid-19 cases and deaths. These include 6-days forecasts as graphical outputs for each federative unit in Brazil, separately, with its corresponding 95% CI for statistical significance. In addition, a worst and best scenarios are presented. / Results: The following federative units (out of 27) were flagged by our ARIMA models showing statistically significant increasing temporal patterns of Covid-19 cases during the specified day-to-day period: Bahia, Maranhão, Piauí, Rio Grande do Norte, Amapá, Rondônia, where their day-to-day forecasts were within the 95% CI limits. Equally, the same findings were observed for Espírito Santo, Minas Gerais, Paraná, and Santa Catarina. The overall percentage error between the forecasted values and the actual values varied between 2.56 and 6.50%. For the days when the forecasts fell outside the forecast interval, the percentage errors in relation to the worst case scenario were below 5%. / Conclusion: The proposed method for dynamic forecasting may be used to guide social policies and plan direct interventions in a cost-effective, concise, and robust manner. This novel tools can play an important role for guiding the course of action against the Covid-19 pandemic for Brazil and country neighbors in South America

Tumor Necrosis Factor-α +489G/A gene polymorphism is associated with chronic obstructive pulmonary disease

BACKGROUND: Chronic obstructive pulmonary disease (COPD) is characterized by a chronic inflammatory process, in which the pro-inflammatory cytokine Tumor Necrosis Factor (TNF)-α is considered to play a role. In the present study the putative involvement of TNF-α gene polymorphisms in pathogenesis of COPD was studied by analysis of four TNF-α gene polymorphisms in a Caucasian COPD population. METHODS: TNF-α gene polymorphisms at positions -376G/A, -308G/A, -238G/A, and +489G/A were examined in 169 Dutch COPD patients, who had a mean forced expiratory volume in one second (FEV1) of 37 ± 13%, and compared with a Dutch population control group of 358 subjects. RESULTS: The data showed that the TNF-α +489G/A genotype frequency tended to be different in COPD patients as compared to population controls, which was due to an enhanced frequency of the GA genotype. In line herewith, carriership of the minor allele was associated with enhanced risk of development of COPD (odds ratio = 1.9, p = 0.009). The other TNF-α gene polymorphisms studied revealed no discrimination between patients and controls. No differences in the examined four TNF-α polymorphisms were found between subtypes of COPD, which were stratified for the presence of radiological emphysema. However, comparison of the COPD subtypes with controls showed a significant difference in the TNF-α +489G/A genotype in patients without radiological emphysema (χ(2)-test: p < 0.025 [Bonferroni adjusted]), while no differences between COPD patients with radiological emphysema and controls were observed. CONCLUSION: Based on the reported data, it is concluded that COPD, and especially a subgroup of COPD patients without radiological emphysema, is associated with TNF-α +489G/A gene polymorphism

Institutionalisation of Social Movements: Co-option And Democratic Policy-making

Over the past 30 years, urban policy in Brazil has undergone a major transformation, both in terms of regulatory frameworks and the involvement of citizens in the process of policy-making. As an intense process of institutional innovation and mobilisation for decent publicservices took place, academics started to consider the impact of institutionalisation on the autonomy of social movements. Using empirical evidence from a city in the northeast of Brazil, this article addresses the wider literature on citizen participation and social movements to examine specifically the problem with co-optation. I examine the risks linked to co-optation, risks that can undermine the credibility of social movements as agents of change, and explore the tensions that go beyond the ‘co-optation versus autonomy’ divide, an issue frequently found in the practices of social movements, in their dealings with those in power. In particular, this article explores the learning processes and contentious relationships between mainly institutionally oriented urban movements and local government. This study found that the learning of deliberative skills not only led to changes in the objectives and repertoires of housing movements, but also to the inclusion of new components in their objectives that provide room for creative agency and which, in some cases, might allow them to maintain their autonomy from the state

Neuronal circuitry for pain processing in the dorsal horn

Neurons in the spinal dorsal horn process sensory information, which is then transmitted to several brain regions, including those responsible for pain perception. The dorsal horn provides numerous potential targets for the development of novel analgesics and is thought to undergo changes that contribute to the exaggerated pain felt after nerve injury and inflammation. Despite its obvious importance, we still know little about the neuronal circuits that process sensory information, mainly because of the heterogeneity of the various neuronal components that make up these circuits. Recent studies have begun to shed light on the neuronal organization and circuitry of this complex region