Chronic obstructive pulmonary disease (COPD) is a global disease characterised by chronic obstruction of lung airflow interfering with normal breathing. Although the microbiota of respiratory tract ...is established to be associated with COPD, the causality of gut microbiota in COPD development is not yet established. We aimed to address the connection between gut microbiota composition and lung COPD development, and characterise bacteria and their derived active components for COPD amelioration.
A murine cigarette smoking (CS)-based model of COPD and strategies evaluating causal effects of microbiota were performed. Gut microbiota structure was analysed, followed by isolation of target bacterium. Single cell RNA sequencing, together with sera metabolomics analyses were performed to identify host responsive molecules. Bacteria derived active component was isolated, followed by functional assays.
Gut microbiota composition significantly affects CS-induced COPD development, and faecal microbiota transplantation restores COPD pathogenesis. A commensal bacterium
was isolated and shown to ameliorate COPD. Reduction of intestinal inflammation and enhancement of cellular mitochondrial and ribosomal activities in colon, systematic restoration of aberrant host amino acids metabolism in sera, and inhibition of lung inflammations act as the important COPD ameliorative mechanisms. Besides, the lipopolysaccharide derived from
is anti-inflammatory, and significantly ameliorates COPD by acting as an antagonist of toll-like receptor 4 signalling pathway.
The gut microbiota-lung COPD axis was connected. A potentially benefial bacterial strain and its functional component may be developed and used as alternative agents for COPD prevention or treatment.
A Survey on Deep Learning Pouyanfar, Samira; Sadiq, Saad; Yan, Yilin ...
ACM computing surveys,
09/2019, Letnik:
51, Številka:
5
Journal Article
Recenzirano
The field of machine learning is witnessing its golden era as deep learning slowly becomes the leader in this domain. Deep learning uses multiple layers to represent the abstractions of data to build ...computational models. Some key enabler deep learning algorithms such as generative adversarial networks, convolutional neural networks, and model transfers have completely changed our perception of information processing. However, there exists an aperture of understanding behind this tremendously fast-paced domain, because it was never previously represented from a multiscope perspective. The lack of core understanding renders these powerful methods as black-box machines that inhibit development at a fundamental level. Moreover, deep learning has repeatedly been perceived as a silver bullet to all stumbling blocks in machine learning, which is far from the truth. This article presents a comprehensive review of historical and recent state-of-the-art approaches in visual, audio, and text processing; social network analysis; and natural language processing, followed by the in-depth analysis on pivoting and groundbreaking advances in deep learning applications. It was also undertaken to review the issues faced in deep learning such as unsupervised learning, black-box models, and online learning and to illustrate how these challenges can be transformed into prolific future research avenues.
The explosive growth and widespread accessibility of digital health data have led to a surge of research activity in the healthcare and data sciences fields. The conventional approaches for health ...data management have achieved limited success as they are incapable of handling the huge amount of complex data with high volume, high velocity, and high variety. This article presents a comprehensive overview of the existing challenges, techniques, and future directions for computational health informatics in the big data age, with a structured analysis of the historical and state-of-the-art methods. We have summarized the challenges into four Vs (i.e., volume, velocity, variety, and veracity) and proposed a systematic data-processing pipeline for generic big data in health informatics, covering data capturing, storing, sharing, analyzing, searching, and decision support. Specifically, numerous techniques and algorithms in machine learning are categorized and compared. On the basis of this material, we identify and discuss the essential prospects lying ahead for computational health informatics in this big data age.
A highly efficient blue‐light emitter, 2‐tert‐butyl‐9,10‐bis4′‐(diphenyl‐phosphoryl)phenylanthracene (POAn) is synthesized, and comprises electron‐deficient triphenylphosphine oxide side groups ...appended to the 9‐ and 10‐positions of a 2‐tert‐butylanthracene core. This sophisticated anthracene compound possesses a non‐coplanar configuration that results in a decreased tendency to crystallize and weaker intermolecular interactions in the solid state, leading to its pronounced morphological stability and high quantum efficiency. In addition to serving as an electron‐transporting blue‐light‐emitting material, POAn also facilitates electron injection from the Al cathode to itself. Consequently, simple double‐layer devices incorporating POAn as the emitting, electron‐transporting, and ‐injecting material produce bright deep‐blue lights having Commission Internationale de L'Eclairage coordinates of (0.15,0.07). The peak electroluminescence performance was 4.3% (2.9 cd A−1). For a device lacking an electron‐transport layer or alkali fluoride, this device displays the best performance of any such the deep‐blue organic light‐emitting diodes reported to date.
A blue‐light‐emitting material POAn, which contains a 2‐tert‐butyl‐anthracene core functionalized with two triphenylphos phine oxide units, exhibits excellent electroluminescence (EL) performance when incorporated into a simple double‐layer EL device possessing high‐work‐function Al as the cathode. The EL efficiency reaches as high as 4.3% (2.9 cd A−1), together with a satisfying blue emission (CIE coordinates: (0.15,0.07)).
This paper presents a framework that applies a series of algorithms to automatically extract building footprints from airborne light detection and ranging (LIDAR) measurements. In the proposed ...framework, the ground and nonground LIDAR measurements are first separated using a progressive morphological filter. Then, building measurements are identified from nonground measurements using a region-growing algorithm based on the plane-fitting technique. Finally, raw footprints for segmented building measurements are derived by connecting boundary points, and the raw footprints are further simplified and adjusted to remove noise caused by irregularly spaced LIDAR measurements. Data sets from urbanized areas including large institutional, commercial, and small residential buildings were employed to test the proposed framework. A quantitative analysis showed that the total of omission and commission errors for extracted footprints for both institutional and residential areas was about 12%. The results demonstrated that the proposed framework identified building footprints well
Background: Head and neck cancers (HNCs) and their treatment may cause oral function impairment. Purpose: This study was designed to identify oral dysfunction in patients receiving treatment for HNCs ...using a systematic review. Methods: The PubMed, Embase, and CINAHL databases were searched for studies on oral function impairment in patients receiving treatment for HNCs published between January 2014 and May 2019. Only descriptive, correlational, and interventional quantitative studies that included patients receiving treatment for HNCs who presented with oral dysfunction, were published in English during the aforementioned time frame, and were accessible in full-text versions were selected. Results: Twenty-eight studies (13 cross-sectional, two longitudinal, 12 randomized controlled trial, and one retrospective chart review) fulfilled the inclusion criteria. Oral mucositis, dysphagia, xerostomia, trismus, and chewing and speech problems were the most common oral dysfunctions. Age, cancer stage, tumor location, treatment modalities, treatment status, treatment dose, and pretreatment oral function were factors associated with oral dysfunction. Although individual interventions were shown to improve oral dysfunction, the related evidence was inconclusive. Conclusions/Implications for Practice: Primary HNCs and their treatment significantly deteriorate oral function. A holistic and interdisciplinary approach may maximize oral function.
This study aims to examine the relation between customer satisfaction and loyalty through the introduction of commitment, trust, involvement and perceived value as mediators in the e-service context. ...An online consumer survey on selected web auction sites was used for the data collection. The findings suggest that customer satisfaction is an essential ingredient for service loyalty, but that mediators exist between satisfaction and loyalty. Commitment, trust and involvement are each proven to be partial mediators between satisfaction and loyalty, while perceived value is proven to be a complete mediator. The results have implications for the retention of satisfied customers in an interactive e-service setting.
With the proliferation of online services and mobile technologies, the world has stepped into a multimedia big data era. A vast amount of research work has been done in the multimedia area, targeting ...different aspects of big data analytics, such as the capture, storage, indexing, mining, and retrieval of multimedia big data. However, very few research work provides a complete survey of the whole pine-line of the multimedia big data analytics, including the management and analysis of the large amount of data, the challenges and opportunities, and the promising research directions. To serve this purpose, we present this survey, which conducts a comprehensive overview of the state-of-the-art research work on multimedia big data analytics. It also aims to bridge the gap between multimedia challenges and big data solutions by providing the current big data frameworks, their applications in multimedia analyses, the strengths and limitations of the existing methods, and the potential future directions in multimedia big data analytics. To the best of our knowledge, this is the first survey that targets the most recent multimedia management techniques for very large-scale data and also provides the research studies and technologies advancing the multimedia analyses in this big data era.
Aim and objectives
This correlation study examined the relationship among recently workplace violence, depressive tendency, social support, and resilience of victimised nurses, and we also tried to ...identify protective factors and potential targets for preventive interventions for these nurses.
Background
Workplace violence in hospitals negatively affects occupational health and safety of medical professionals, especially for emergency department nurses.
Design
A cross‐sectional, correlation research design was applied.
Methods
Hierarchical regression was used to examine data which were collected from June 2013 to December 2013 from emergency departments in Taiwan. One hundred and eighty nurses were recruited from two hospitals. Structured interviews and questionnaires were applied to collect data, including the Social Support Scale, the Resilience Scale and the Center for Epidemiologic Studies Depression.
Results
A total of 159 (88·33%) nurses had suffered from physical or verbal violence by patients or their family. Resilience and peer support were significantly higher in the group without depressive tendency. Components of resilience of personal strength, social competence, structure style and religious beliefs were significant factors which accounted for 46·0% of variance in depressive tendency. Three of the five components of resilience: personal strength, social competence and structured style were found to have profounder effects against depressive tendency than peer support.
Conclusions
Hospital managers should establish a safer working environment for emergency department nurses and reinforce their resilience against depression when they encounter workplace violence.
Relevance to clinical practice
This study showed that three of the five components of resilience: personal strength, social competence and structured style are protective factors against depressive tendency in victimised nurses. Improving these three components with coping and problem‐solving skills by healthcare manager would be effective measures for enhancing their resilience in situations of workplace violence.
PDF
