Exercise-Induced Changes in Exhaled NO Differentiates Asthma With or Without Fixed Airway Obstruction From COPD With Dynamic Hyperinflation.

Asthmatic patients with fixed airway obstruction (FAO) and patients with chronic obstructive pulmonary disease (COPD) share similarities in terms of irreversible pulmonary function impairment. Exhaled nitric oxide (eNO) has been documented as a marker of airway inflammation in asthma, but not in COPD. To examine whether the basal eNO level and the change after exercise may differentiate asthmatics with FAO from COPD, 27 normal subjects, 60 stable asthmatics, and 62 stable COPD patients were studied. Asthmatics with FAO (n = 29) were defined as showing a postbronchodilator FEV(1)/forced vital capacity (FVC) ≤70% and FEV(1) less than 80% predicted after inhaled salbutamol (400 μg). COPD with dynamic hyperinflation (n = 31) was defined as a decrease in inspiratory capacity (ΔIC%) after a 6 minute walk test (6MWT). Basal levels of eNO were significantly higher in asthmatics and COPD patients compared to normal subjects. The changes in eNO after 6MWT were negatively correlated with the percent change in IC (r = −0.380, n = 29, P = 0.042) in asthmatics with FAO. Their levels of basal eNO correlated with the maximum mid-expiratory flow (MMEF % predicted) before and after 6MWT. In COPD patients with air-trapping, the percent change of eNO was positively correlated to ΔIC% (rs = 0.404, n = 31, P = 0.024). We conclude that asthma with FAO may represent residual inflammation in the airways, while dynamic hyperinflation in COPD may retain NO in the distal airspace. eNO changes after 6MWT may differentiate the subgroups of asthma or COPD patients and will help toward delivery of individualized therapy for airflow obstruction

Deep Adaptive Attention for Joint Facial Action Unit Detection and Face Alignment

Facial action unit (AU) detection and face alignment are two highly correlated tasks since facial landmarks can provide precise AU locations to facilitate the extraction of meaningful local features for AU detection. Most existing AU detection works often treat face alignment as a preprocessing and handle the two tasks independently. In this paper, we propose a novel end-to-end deep learning framework for joint AU detection and face alignment, which has not been explored before. In particular, multi-scale shared features are learned firstly, and high-level features of face alignment are fed into AU detection. Moreover, to extract precise local features, we propose an adaptive attention learning module to refine the attention map of each AU adaptively. Finally, the assembled local features are integrated with face alignment features and global features for AU detection. Experiments on BP4D and DISFA benchmarks demonstrate that our framework significantly outperforms the state-of-the-art methods for AU detection.Comment: This paper has been accepted by ECCV 201

Conformative Filtering for Implicit Feedback Data

Implicit feedback is the simplest form of user feedback that can be used for item recommendation. It is easy to collect and is domain independent. However, there is a lack of negative examples. Previous work tackles this problem by assuming that users are not interested or not as much interested in the unconsumed items. Those assumptions are often severely violated since non-consumption can be due to factors like unawareness or lack of resources. Therefore, non-consumption by a user does not always mean disinterest or irrelevance. In this paper, we propose a novel method called Conformative Filtering (CoF) to address the issue. The motivating observation is that if there is a large group of users who share the same taste and none of them have consumed an item before, then it is likely that the item is not of interest to the group. We perform multidimensional clustering on implicit feedback data using hierarchical latent tree analysis (HLTA) to identify user `tastes' groups and make recommendations for a user based on her memberships in the groups and on the past behavior of the groups. Experiments on two real-world datasets from different domains show that CoF has superior performance compared to several common baselines

Knocking down 10-formyltetrahydrofolate dehydrogenase increased oxidative stress and impeded zebrafish embryogenesis by obstructing morphogenetic movement

[[incitationindex]]SC

Capacitated Center Problems with Two-Sided Bounds and Outliers

In recent years, the capacitated center problems have attracted a lot of research interest. Given a set of vertices $V$, we want to find a subset of vertices $S$, called centers, such that the maximum cluster radius is minimized. Moreover, each center in $S$ should satisfy some capacity constraint, which could be an upper or lower bound on the number of vertices it can serve. Capacitated $k$-center problems with one-sided bounds (upper or lower) have been well studied in previous work, and a constant factor approximation was obtained. We are the first to study the capacitated center problem with both capacity lower and upper bounds (with or without outliers). We assume each vertex has a uniform lower bound and a non-uniform upper bound. For the case of opening exactly $k$ centers, we note that a generalization of a recent LP approach can achieve constant factor approximation algorithms for our problems. Our main contribution is a simple combinatorial algorithm for the case where there is no cardinality constraint on the number of open centers. Our combinatorial algorithm is simpler and achieves better constant approximation factor compared to the LP approach

Selection for Replicases in Protocells

PMCID: PMC3649988This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited

Structured Landmark Detection via Topology-Adapting Deep Graph Learning

Image landmark detection aims to automatically identify the locations of predefined fiducial points. Despite recent success in this field, higher-ordered structural modeling to capture implicit or explicit relationships among anatomical landmarks has not been adequately exploited. In this work, we present a new topology-adapting deep graph learning approach for accurate anatomical facial and medical (e.g., hand, pelvis) landmark detection. The proposed method constructs graph signals leveraging both local image features and global shape features. The adaptive graph topology naturally explores and lands on task-specific structures which are learned end-to-end with two Graph Convolutional Networks (GCNs). Extensive experiments are conducted on three public facial image datasets (WFLW, 300W, and COFW-68) as well as three real-world X-ray medical datasets (Cephalometric (public), Hand and Pelvis). Quantitative results comparing with the previous state-of-the-art approaches across all studied datasets indicating the superior performance in both robustness and accuracy. Qualitative visualizations of the learned graph topologies demonstrate a physically plausible connectivity laying behind the landmarks.Comment: Accepted to ECCV-20. Camera-ready with supplementary materia

A Technique for Obtaining True Approximations for kk-Center with Covering Constraints

There has been a recent surge of interest in incorporating fairness aspects into classical clustering problems. Two recently introduced variants of the $k$-Center problem in this spirit are Colorful $k$-Center, introduced by Bandyapadhyay, Inamdar, Pai, and Varadarajan, and lottery models, such as the Fair Robust $k$-Center problem introduced by Harris, Pensyl, Srinivasan, and Trinh. To address fairness aspects, these models, compared to traditional $k$-Center, include additional covering constraints. Prior approximation results for these models require to relax some of the normally hard constraints, like the number of centers to be opened or the involved covering constraints, and therefore, only obtain constant-factor pseudo-approximations. In this paper, we introduce a new approach to deal with such covering constraints that leads to (true) approximations, including a $4$-approximation for Colorful $k$-Center with constantly many colors---settling an open question raised by Bandyapadhyay, Inamdar, Pai, and Varadarajan---and a $4$-approximation for Fair Robust $k$-Center, for which the existence of a (true) constant-factor approximation was also open. We complement our results by showing that if one allows an unbounded number of colors, then Colorful $k$-Center admits no approximation algorithm with finite approximation guarantee, assuming that $\mathrm{P} \neq \mathrm{NP}$. Moreover, under the Exponential Time Hypothesis, the problem is inapproximable if the number of colors grows faster than logarithmic in the size of the ground set

UMA PROPOSTA DE OTIMIZAÇÃO NA CONVERSÃO DE ENERGIA SOLAR EM ENERGIA ELÉTRICA POR PAINEIS FOTOVOLTAICOS

O crescimento da demanda de energia observado nas últimas décadas vem sendo um estímulo para a utilização de novas fontes de energias, anteriormente consideradas caras. Sabe-se que a energia solar que chega à superfície terrestre supera a demanda de energia mundial, porém os equipamentos que convertem a radiação solar em algum tipo de energia útil possuem baixos rendimentos. Com base nesta perspectiva, esta dissertação de mestrado tem por objetivo conseguir a maior extração de energia elétrica de um painel fotovoltaico utilizando circuitos eletrônicos que buscam o ponto de máxima potência de operação da célula. Para tanto, implementou-se experimentalmente um circuito eletrônico que utiliza, como plano de controle, um algoritmo de busca do máximo ponto de potência (MPPT Maximum Power Point Tracking) com passo variável que é acoplado a um painel fotovoltaico. Desenvolveu-se também um sistema de gerenciamento microcontrolado que é capaz de coletar parâmetros do painel fotovoltaico para análise de resultados. Estudos de caso na avaliação do tempo de resposta do circuito e rendimento do arranjo proposto também foram realizados, conseguindo situações onde o ganho de potência foi superior a 100% e tempos de resposta do circuito variando de 0,88 a 11 segundos

L-Carnitine and extendin-4 improve outcomes following moderate brain contusion injury

© 2018, The Author(s). There is a need for pharmaceutical agents that can reduce neuronal loss and improve functional deficits following traumatic brain injury (TBI). Previous research suggests that oxidative stress and mitochondrial dysfunction play a major role in neuronal damage after TBI. Therefore, this study aimed to investigate two drugs known to have antioxidant effects, L-carnitine and exendin-4, in rats with moderate contusive TBI. L-carnitine (1.5 mM in drinking water) or exendin-4 (15 µg/kg/day, ip) were given immediately after the injury for 2 weeks. Neurological function and brain histology were examined (24 h and 6 weeks post injury). The rats with TBI showed slight sensory, motor and memory functional deficits at 24 h, but recovered by 6 weeks. Both treatments improved sensory and motor functions at 24 h, while only exendin-4 improved memory. Both treatments reduced cortical contusion at 24 h and 6 weeks, however neither affected gliosis and inflammatory cell activation. Oxidative stress was alleviated and mitochondrial reactive oxygen species was reduced by both treatments, however only mitochondrial functional marker protein transporter translocase of outer membrane 20 was increased at 24 h post injury. In conclusion, L-carnitine and exendin-4 treatments immediately after TBI can improve neurological functional outcome and tissue integrity by reducing oxidative stress