The Efficacy and Safety of Budesonide/Glycopyrronium/Formoterol in the Treatment of COPD in the Elderly.

Objective: Chronic obstructive pulmonary disease (COPD) is a major and difficult disease of the chronic respiratory system that is common and frequent, with a huge disease burden. The aim of this study was to investigate the efficacy and safety of budesonide/glyburide/formoterol fumarate (BGF) in the treatment of COPD. Methods: A comprehensive literature search was conducted in PubMed, Embase, Cochrane Library, and Web of Science. The basic features of the seven pieces of literature were identified using the search strategy. The sample size range was 130∼1264. Results: The effects of BGF increased FEV1 in patients with COPD (mean difference = 2.86, 95%CI: 2.71-3.01, p < 0.00001). The effects of BGF improved in patients with ≥1 TEAE in patients with COPD, and was not statistically significant after treatment (Odds rate = 1.00, 95%CI: 0.85-1.17, p=0.97). The effects of BGF increased in patients with TEAEs related a to study treatment in patients with COPD (odds rate = 1.27, 95% CI: 1.03-1.57, p=0.02). The effects of BGF in decreased patients with serious TEAEs in patients with COPD (odds rate = -0.02, 95% CI: -0.03--0.00, p=0.04). The effects of BGF decreased the death rate in patients with COPD, and were not statistically significant after treatment (odds rate = 0.77, 95% CI: 0.31-1.97, p=0.59). The effects of BGF decreased the hypertension rate in patients with COPD (odds rate = 0.92, 95% CI: 0.44-1.89, p=0.81), and was not statistically significant after treatment. The effects of BGF increased pneumonia in patients with COPD (odds rate = 1.55, 95% CI: 0.81-2.97, p=0.19), and were not statistically significant after treatment. The effects of BGF increased FEV1, increased patients with TEAEs related a to study treatment, and decreased patients with serious TEAEs in patients with COPD. Conclusion: This study elucidates the efficacy and safety of BGF in the treatment of COPD with a view to providing a clinical reference.

Why Do People Who Belong to the Same Clan Engage in the Same Entrepreneurial Activities?-A Case Study on the Influence of Clan Networks on the Content of Farmers' Entrepreneurship.

Farmers' entrepreneurship is a powerful breakthrough for solving the problems associated with "agriculture, rural areas and farmers." Although studies have commonly used the same entrepreneurial activities to analyze farmers' entrepreneurship, its deep economic roots have rarely been investigated. Investigating the internal development mechanism within the same industry is helpful for understanding farmers' entrepreneurship motivation and decision making and is an important point at which to implement regional research and enrich the overall research on farmers' entrepreneurship in the Chinese context. Based on a single-case study in Q Village, Fujian Province, this work identifies the key role played by relational contracts in entrepreneurship groups: reducing transaction costs, promoting investment in asset specificity, and improving contract flexibility. Moreover, this approach is conducive for different action groups in terms of stimulating entrepreneurial motivation in the initial entrepreneurship period and improving entrepreneurship learning ability in the long term. Primary Action Group transforms exploratory intuitive learning into exploratory compilation learning, and Secondary Action Group triggers the learning effect and makes a proprietary investment by utilizing intuitive formulaic learning and compiled formulaic learning, thus reducing unforeseen, contracting and verification costs. During the pattern maturity period, Primary Action Group rationally integrates the supply chain and forms a stable entrepreneurial paradigm, while Secondary Action Group does so to maintain prior information reserves and lower information search, supervised execution, and bargaining decision costs. The value cocreation ability of the same type of commercial modularity is formed, and the whole process of farmers' entrepreneurship is completed. Our results have important implications for policymakers in China and other countries with clans.

Deep Attention Network for Egocentric Action Recognition.

Recognizing a camera wearer's actions from videos captured by an egocentric camera is a challenging task. In this paper, we employ a two-stream deep neural network composed of an appearance-based stream and a motion-based stream to recognize egocentric actions. Based on the insight that human action and gaze behavior are highly coordinated in object manipulation tasks, we propose a spatial attention network to predict human gaze in the form of attention map. The attention map helps each of the two streams to focus on the most relevant spatial region of the video frames to predict actions. To better model the temporal structure of the videos, a temporal network is proposed. The temporal network incorporates bi-directional long short-term memory to model the long-range dependencies to recognize egocentric actions. The experimental results demonstrate that our method is able to predict attention maps that are consistent with human attention and achieve competitive action recognition performance with the state-of-the-art methods on the GTEA Gaze and GTEA Gaze+ datasets.

Continuous Depth Map Reconstruction From Light Fields.

In this paper, we investigate how the recently emerged photography technology--the light field--can benefit depth map estimation, a challenging computer vision problem. A novel framework is proposed to reconstruct continuous depth maps from light field data. Unlike many traditional methods for the stereo matching problem, the proposed method does not need to quantize the depth range. By making use of the structure information amongst the densely sampled views in light field data, we can obtain dense and relatively reliable local estimations. Starting from initial estimations, we go on to propose an optimization method based on solving a sparse linear system iteratively with a conjugate gradient method. Two different affinity matrices for the linear system are employed to balance the efficiency and quality of the optimization. Then, a depth-assisted segmentation method is introduced so that different segments can employ different affinity matrices. Experiment results on both synthetic and real light fields demonstrate that our continuous results are more accurate, efficient, and able to preserve more details compared with discrete approaches.

Optimizing nondecomposable loss functions in structured prediction.

We develop an algorithm for structured prediction with nondecomposable performance measures. The algorithm learns parameters of Markov Random Fields (MRFs) and can be applied to multivariate performance measures. Examples include performance measures such as Fß score (natural language processing), intersection over union (object category segmentation), Precision/Recall at k (search engines), and ROC area (binary classifiers). We attack this optimization problem by approximating the loss function with a piecewise linear function. The loss augmented inference forms a Quadratic Program (QP), which we solve using LP relaxation. We apply this approach to two tasks: object class-specific segmentation and human action retrieval from videos. We show significant improvement over baseline approaches that either use simple loss functions or simple scoring functions on the PASCAL VOC and H3D Segmentation datasets, and a nursing home action recognition dataset.

Matching by linear programming and successive convexification.

We present a novel convex programming scheme to solve matching problems, focusing on the challenging problem of matching in a large search range and with cluttered background. Matching is formulated as metric labeling with L1 regularization terms, for which we propose a novel linear programming relaxation method and an efficient successive convexification implementation. The unique feature of the proposed relaxation scheme is that a much smaller set of basis labels is used to represent the original label space. This greatly reduces the size of the searching space. A successive convexification scheme solves the labeling problem in a coarse to fine manner. Importantly, the original cost function is reconvexified at each stage, in the new focus region only, and the focus region is updated so as to refine the searching result. This makes the method well-suited for large label set matching. Experiments demonstrate successful applications of the proposed matching scheme in object detection, motion estimation, and tracking.