Building the Brazilian Academic Genealogy Tree

Along the history, many researchers provided remarkable contributions to science, not only advancing knowledge but also in terms of mentoring new scientists. Currently, identifying and studying the formation of researchers over the years is a challenging task as current repositories of theses and dissertations are cataloged in a decentralized way through many local digital libraries. Following our previous work in which we created and analyzed a large collection of genealogy trees extracted from NDLTD, in this paper we focus our attention on building such trees for the Brazilian research community. For this, we use data from the Lattes Platform, an internationally renowned initiative from CNPq, the Brazilian National Council for Scientific and Technological Development, for managing information about individual researchers and research groups in Brazil

History of early life adversity is associated with increased food addiction and sex-specific alterations in reward network connectivity in obesity.

Background:Neuroimaging studies have identified obesity-related differences in the brain's resting state activity. An imbalance between homeostatic and reward aspects of ingestive behaviour may contribute to obesity and food addiction. The interactions between early life adversity (ELA), the reward network and food addiction were investigated to identify obesity and sex-related differences, which may drive obesity and food addiction. Methods:Functional resting state magnetic resonance imaging was acquired in 186 participants (high body mass index [BMI]: ≥25: 53 women and 54 men; normal BMI: 18.50-24.99: 49 women and 30 men). Participants completed questionnaires to assess ELA (Early Traumatic Inventory) and food addiction (Yale Food Addiction Scale). A tripartite network analysis based on graph theory was used to investigate the interaction between ELA, brain connectivity and food addiction. Interactions were determined by computing Spearman rank correlations, thresholded at q < 0.05 corrected for multiple comparisons. Results:Participants with high BMI demonstrate an association between ELA and food addiction, with reward regions playing a role in this interaction. Among women with high BMI, increased ELA was associated with increased centrality of reward and emotion regulation regions. Men with high BMI showed associations between ELA and food addiction with somatosensory regions playing a role in this interaction. Conclusions:The findings suggest that ELA may alter brain networks, leading to increased vulnerability for food addiction and obesity later in life. These alterations are sex specific and involve brain regions influenced by dopaminergic or serotonergic signalling

Output-only full-field modal testing

© 2017 The Authors. Published by Elsevier Ltd. Operational modal analysis has become the focus of much research attention in the last two decades. Instead of an artificial force, the ambient excitation is considered as white-noise input to the structure and modal properties are calculated only from measured responses. In terms of the measurement technique, full-field optical methods, for example: electronic speckle pattern interferometry and digital image correlation have become popular and there is now much interest in applying these methods in structural dynamics. In this case the generated data is a full displacement map of the object, therefore there is no necessity to select specific measurement locations in order to visualise the deformation. However, there are generally large volumes of data to be processed, which makes the computation expensive and time-consuming, especially for engineering structures with large surface areas. Thanks to image decomposition techniques, huge amounts of data can be compressed into tens of shape descriptors with acceptably small distortion. In this paper, operational modal analysis and full-field methods are combined together, and the analysis is done in the shape descriptor domain to reduce the required computation time. Simulated responses from a finite element model of a clamped plate (under random excitation) serve to illustrate the methodology. Several different operational modal analysis methods are applied to analyse the data, and results are provided for purposes of comparison

Inflammasomes contributing to inflammation in arthritis.

Inflammasomes are intracellular multiprotein signaling platforms that initiate inflammatory responses in response to pathogens and cellular damage. Active inflammasomes induce the enzymatic activity of caspase-1, resulting in the induction of inflammatory cell death, pyroptosis, and the maturation and secretion of inflammatory cytokines IL-1β and IL-18. Inflammasomes are activated in many inflammatory diseases, including autoinflammatory disorders and arthritis, and inflammasome-specific therapies are under development for the treatment of inflammatory conditions. In this review, we outline the different inflammasome platforms and recent findings contributing to our knowledge about inflammasome biology in health and disease. In particular, we discuss the role of the inflammasome in the pathogenesis of arthritic diseases, including rheumatoid arthritis, gout, ankylosing spondylitis, and juvenile idiopathic arthritis, and the potential of newly developed therapies that specifically target the inflammasome or its products for the treatment of inflammatory diseases

Coherent spinor dynamics in a spin-1 Bose condensate

Collisions in a thermal gas are perceived as random or incoherent as a consequence of the large numbers of initial and final quantum states accessible to the system. In a quantum gas, e.g. a Bose-Einstein condensate or a degenerate Fermi gas, the phase space accessible to low energy collisions is so restricted that collisions be-come coherent and reversible. Here, we report the observation of coherent spin-changing collisions in a gas of spin-1 bosons. Starting with condensates occupying two spin states, a condensate in the third spin state is coherently and reversibly created by atomic collisions. The observed dynamics are analogous to Josephson oscillations in weakly connected superconductors and represent a type of matter-wave four-wave mixing. The spin-dependent scattering length is determined from these oscillations to be -1.45(18) Bohr. Finally, we demonstrate coherent control of the evolution of the system by applying differential phase shifts to the spin states using magnetic fields.Comment: 19 pages, 3 figure

Mortality risks associated with empirical antibiotic activity in E. coli bacteraemia: an analysis of electronic health records

Background: Reported bacteraemia outcomes following inactive empirical antibiotics (based on in vitro testing) are conflicting, potentially reflecting heterogeneity in causative species, MIC breakpoints defining resistance/susceptibility, and times to rescue therapy. Methods: We investigated adult inpatients with Escherichia coli bacteraemia at Oxford University Hospitals, UK, from 4 February 2014 to 30 June 2021 who were receiving empirical amoxicillin/clavulanate with/without other antibiotics. We used Cox regression to analyse 30 day all-cause mortality by in vitro amoxicillin/clavulanate susceptibility (activity) using the EUCAST resistance breakpoint (>8/2 mg/L), categorical MIC, and a higher resistance breakpoint (>32/2 mg/L), adjusting for other antibiotic activity and confounders including comorbidities, vital signs and blood tests. Results: A total of 1720 E. coli bacteraemias (1626 patients) were treated with empirical amoxicillin/clavulanate. Thirty-day mortality was 193/1400 (14%) for any active baseline therapy and 52/320 (16%) for inactive baseline therapy (P = 0.17). With EUCAST breakpoints, there was no evidence that mortality differed for inactive versus active amoxicillin/clavulanate [adjusted HR (aHR) = 1.27 (95% CI 0.83–1.93); P = 0.28], nor of an association with active aminoglycoside (P = 0.93) or other active antibiotics (P = 0.18). Considering categorical amoxicillin/clavulanate MIC, MICs > 32/2 mg/L were associated with mortality [aHR = 1.85 versus MIC = 2/2 mg/L (95% CI 0.99–3.73); P = 0.054]. A higher resistance breakpoint (>32/2 mg/L) was independently associated with higher mortality [aHR = 1.82 (95% CI 1.07–3.10); P = 0.027], as were MICs > 32/2 mg/L with active empirical aminoglycosides [aHR = 2.34 (95% CI 1.40–3.89); P = 0.001], but not MICs > 32/2 mg/L with active non-aminoglycoside antibiotic(s) [aHR = 0.87 (95% CI 0.40–1.89); P = 0.72]. Conclusions: We found no evidence that EUCAST-defined amoxicillin/clavulanate resistance was associated with increased mortality, but a higher resistance breakpoint (MIC > 32/2 mg/L) was. Additional active baseline non-aminoglycoside antibiotics attenuated amoxicillin/clavulanate resistance-associated mortality, but aminoglycosides did not. Granular phenotyping and comparison with clinical outcomes may improve AMR breakpoints

Non-Redundant Spectral Dimensionality Reduction

Spectral dimensionality reduction algorithms are widely used in numerous domains, including for recognition, segmentation, tracking and visualization. However, despite their popularity, these algorithms suffer from a major limitation known as the "repeated Eigen-directions" phenomenon. That is, many of the embedding coordinates they produce typically capture the same direction along the data manifold. This leads to redundant and inefficient representations that do not reveal the true intrinsic dimensionality of the data. In this paper, we propose a general method for avoiding redundancy in spectral algorithms. Our approach relies on replacing the orthogonality constraints underlying those methods by unpredictability constraints. Specifically, we require that each embedding coordinate be unpredictable (in the statistical sense) from all previous ones. We prove that these constraints necessarily prevent redundancy, and provide a simple technique to incorporate them into existing methods. As we illustrate on challenging high-dimensional scenarios, our approach produces significantly more informative and compact representations, which improve visualization and classification tasks