oalib

Publish in OALib Journal

ISSN: 2333-9721

APC: Only $99

Submit

Any time

2020 ( 2 )

2019 ( 281 )

2018 ( 1906 )

2017 ( 1802 )

Custom range...

Search Results: 1 - 10 of 120472 matches for " Xiangdong Wang "
All listed articles are free for downloading (OA Articles)
Page 1 /120472
Display every page Item
Recognition of Greenhouse Cucumber Disease Based on Image Processing Technology  [PDF]
Dong Pixia, Wang Xiangdong
Open Journal of Applied Sciences (OJAppS) , 2013, DOI: 10.4236/ojapps.2013.31B006
Abstract: This paper mainly studies the disease of cucumber downy mildew, powdery mildew and anthracnose leaf image processing and recognition technologies. Application of median filtering method of filtering noise, leaf spot disease of cucumber leaf color range segmentation part extract color feature parameters of the lesion site, characteristic parameters of the shape; extraction texture parameters by using gray level co-occurrence matrix. Based on the shortest distance methods to identify diseases of images. The experimental result showed that the current method on disease recognition accuracy rates more than 96%.
A new vision of definition, commentary, and understanding in clinical and translational medicine
Xiangdong Wang
Clinical and Translational Medicine , 2012, DOI: 10.1186/2001-1326-1-5
Abstract: There is growing evidence to the importance of translational science and medicine in the improvement of patient outcome, even though the definitions of translational science, translational medicine, and clinical and translational medicine need to be further clarified. Clinical and translational medicine is expected to include scientific and regulatory investigations to translate preclinical researches to clinical application with a specific emphasis on new biotechnologies, biomaterials, bioengineering, disease-specific biomarkers, cellular and molecular medicine, omics science, bioinformatics, applied immunology, molecular imaging, drug discovery and development, and regulation and health policy. It is believed that clinical and translational medicine will benefit and improve novel diagnostics/prognostics and therapeutics for clinical use, post-genomic knowledge and experience, and/or new disciplines that reflect additional levels of complexity. We should clarify the bioethics at the interface and paradigms between technology and society, academies and industries, as well as publics and private models. Translational medicine should meet the demands of maintaining or expanding the biomedical workforce and education programs that attract and retain young people in the translational and biomedical sciences. In the present perspective, we collected commentaries and descriptions about clinical and translational medicine from some members of Clinical and Translational Medicine editorial board to stimulate the discussion and help the understanding better.Barry S. Coller (David Rockefeller Professor of Medicine; Head, Allen and Frances Adler Laboratory of Blood and Vascular Biology; Physician-in Chief of the Rockefeller University Hospital; and Vice President for Medical Affairs, Rockefeller University) defines translational science as, “The application of the scientific method to address a health need.” In contrast to basic investigation, which has the generation of new kn
Role of clinical bioinformatics in the development of network-based Biomarkers
Xiangdong Wang
Journal of Clinical Bioinformatics , 2011, DOI: 10.1186/2043-9113-1-28
Abstract: Disease is a disordered or incorrectly functioning cells, tissue, organ, or system of the body, involved in multiple proteins, cells, organs and systems with the complexity. There still remains the poor understanding of molecular mechanisms by which diseases occur, even though biotechnologies and knowledge on diseases have been improved tremendously. Variations of protein-based biomarkers appear on basis of applications, e.g. functional neuro-imaging biomarkers can play in detecting, diagnosing, assessing treatment response and investigating neurodegenerative disorders [1], which may why the emphasis of much recent work has shifted to network-based biomarkers. The most of preclinical and clinical studies measure systemic levels of one or a few inflammatory proteins as an indicator of pathological alterations or disease severity, while molecular network-based approaches can describe associations between network properties, disease biology and capacity to distinguish between prognostic categories. It was suggested that information encoded in a network of inflammation proteins could predict clinical outcome after myocardial infarction [2].Biomarkers can be gene-, protein-, peptide-, chemical- or physic-based variables. Of those biomarkers, gene- and protein-based ones have been focused and explored mostly from a single gene or protein to multiple genes or proteins, from the expression to functional indication, and from the network to dynamic network, in order to understand a multi-factorial basis responsible for the pathogenesis of diseases. Protein-protein interactions play a central and critical role in many biological functions, mediating the signaling pathways. Network biomarker as a new type of biomarkers with protein-protein interactions was initiated and investigated with the integration of knowledge on protein annotations, interaction, and signaling pathway. It was found that network biomarkers discovered on basis of protein knowledge on the SELDI-TOF-MS data w
Significance of bioinformatics in research of chronic obstructive pulmonary disease
Hong Chen, Xiangdong Wang
Journal of Clinical Bioinformatics , 2011, DOI: 10.1186/2043-9113-1-35
Abstract: Chronic obstructive pulmonary disease (COPD) is an inflammatory disease characterized by the progressive deterioration of pulmonary function and increasing airway obstruction [1,2]. It can be caused by inflammatory responses triggered by noxious particles or gas, most commonly from tobacco smoking and is accompanied by chronic bronchitis and emphysema [3,4]. Some patients go on to require long-term oxygen therapy or even lung transplantation [3]. COPD was ranked as fourth leading cause of death worldwide and is estimated to become the top third cause of mortality by 2020 [5]. According to the data in China, COPD ranks as the fourth leading cause of death in urban areas and third in rural areas[6]. The high mortality and morbidity with COPD, and its chronic progressive nature, have promoted the need to investigate the underlying mechanisms and identify biomarkers for diagnosis, prognosis and drug target.The understanding of COPD increased by advanced molecular biology approaches, genetically modified animals, virally administered genes, and high-throughput transcriptional profiling approaches. High-throughput methodologies, such as genomics and proteomics, are commonly used. The variety of data from biology, mainly in the form of DNA, RNA and protein sequences is putting heavy demand in computer sciences and computational biology. Bioinformatics, including many sub-disciplines, such as genomics, proteomics and system biology, is an integration of mathematical, statistical, and computational methods to analyze biological, biochemical, and biophysical data. Compared to wet-lab method, bioinformatics focused on data mining via computational means. Sophisticated bioinformatics techniques are developed to analyze the vast amount of data generated from genomics and proteomics studies, such as gene and protein function, interactions and metabolic and regulatory pathways. However, there is still a great challenge to combine the computer figures with clinical data for both be
Clinical bioinformatics: a new emerging science
Xiangdong Wang, Lance Liotta
Journal of Clinical Bioinformatics , 2011, DOI: 10.1186/2043-9113-1-1
Abstract: Clinical bioinformatics is a new emerging science combining clinical informatics, bioinformatics, medical informatics, information technology, mathematics, and omics science together. At the beginning of the 20th century, clinical physicians needed to be informed and open to advances in omics technology despite the barriers which existed for physicians applying genetic tests, for example the low tolerance for uncertainty, negative attitudes about their responsibility for genetic counseling and testing, and unfamiliarity with ethical issues raised by testing [1]. Since the middle of the 20th century, bioinformatics was suggested to be applied for clinical toxicology [2] and cancer [3]. One of the early studies on expressed sequence tags in human stem cells by bioinformatics was performed in 1998 [4], where near 10000 sequences were analyzed. Of these, 48% showed the identity to known genes in the GenBank database, 26.4% matched to the previously deposited in a public domain database, 14% were previously undescribed sequences, and the remaining 12% were mitochondrial DNA, ribosomal RNA, or repetitive sequences. At the beginning of the 21st century, gene expression profiles in 60 human cancer cell lines used in a drug discovery screen were evaluated by cDNA microarrays and corrected with drug activity patterns by combining bioinformatics and chemoinformatics [5]. Clinical bioinformatics was initially proposed to provide biological and medical information for individualized healthcare, enable researchers to search online biological databases and use bioinformatics in medical practice, select appropriate software to analyze the microarray data for medical decision-making, optimize the development of disease-specific biomarkers, and supervise drug target identification and clinical validation [6].Clinical bioinformatics plays an important role in a number of clinical applications, including omics technology, metabolic and signaling pathways, biomarker discovery and develo
Curvature and Entropy Perturbations in Generalized Gravity
Xiangdong Ji,Tower Wang
Physics , 2009, DOI: 10.1103/PhysRevD.79.103525
Abstract: We investigate the cosmological perturbations in generalized gravity, where the Ricci scalar and a scalar field are non-minimally coupled via an arbitrary function. In the Friedmann-Lemaitre-Robertson-Walker background, by studying the linear perturbation theory, we separate the scalar type perturbations into the curvature perturbation and the entropy perturbation, whose evolution equations are derived. Then we apply this framework to inflation. We consider the generalized slow-roll conditions and the quantization initial condition. Under these conditions, two special examples are studied analytically. One example is the case with no entropy perturbation. The other example is a model with the entropy perturbation large initially but decaying significantly after crossing the horizon.
A decade plus of translation: what do we understand?
Xiangdong Wang, Francesco M Marincola
Clinical and Translational Medicine , 2012, DOI: 10.1186/2001-1326-1-3
Abstract: It is expected that life expectancy will progressively increase as it has in the last decades [1]. As a consequence, the prevalence of chronic diseases, which affect predominantly the elderly, is expected to grow [2]. This will result in a formidable challenge for the health care system as chronic conditions last for a long time and require, as a consequence, prolonged spending with marginal benefit for the patient. Although the gross domestic product of a given Country is directly correlated with prolonged life expectancy [3] the relationship plateaus at the upper end of GDP values. Thus, and it is inaccurate to assume that the more it is spent in Health the better the results. A recent analysis from the United States of America Congressional Budget Office in fact suggested an inverse relationship between spending for treatment and a composite measure of quality of care [4]. Yet, the United States spending for health care continues to expand and it is projected to reach the paradoxical figure of almost 100% of the gross domestic product by the end of the this century. It could be argued that better patient selection and enhancement of treatment effectiveness may decrease the costs. Thus, it is not only in the patient interest but in the interest of the health care provider to spend a meager proportion of the astronomic health care spending in research and development relevant to these goals: this is one of the fundamental values of translational sciences.It has been suggested that translational sciences are just a "fad"; a passing spree of introspection toward an utopist goal. In reality, translational sciences reflects quite specific needs that will stay relevant (independently of the term used) till they will be met. The need for translational sciences resides in 1) the need to find cost/effective solutions to the treatment of chronic diseases which in turn represent 2/3 of heath care spending in most countries; 2) the high throughput of modern biotechnology expo
A PRIORI ESTIMATES OF THE MAXIMUM MODULUS OF GENERALIZED SOLUTIONS TO A CLASS OF QUASILINEAR ELLIPTIC EQUATIONS WITH ANISOTROPIC GROWTH CONDITIONS
临界增长的椭圆型方程广义解的有界性及先验估计

Wang Xiangdong,
王向东

系统科学与数学 , 1997,
Abstract: In this paper, we give the a priori estimates of the maximum modulus of generalized solutions to quasilinear elliptic equations with anisotropic growth conditions.
Opportunities and challenges of disease biomarkers: a new section in the journal of translational medicine
Wang Xiangdong,Ward Peter A
Journal of Translational Medicine , 2012, DOI: 10.1186/1479-5876-10-220
Abstract:
Opportunities and challenges of disease biomarkers: a new section in the journal of translational medicine
Wang Xiangdong,Ward Peter A
Journal of Translational Medicine , 2012, DOI: 10.1186/1479-5876-10-240
Abstract: Disease biomarkers are defined to diagnose various phases of diseases, monitor severities of diseases and responses to therapies, or predict prognosis of patients. Disease-specific biomarkers should benefit drug discovery and development, integrate multidisciplinary sciences, be validated by molecular imaging. The opportunities and challenges in biomarker development are emphasized and considered. The Journal of Translational Medicine opens a new Section of Disease Biomarkers to bridge identification and validation of gene or protein-based biomarkers, network biomarkers, dynamic network biomarkers in human diseases, patient phenotypes, and clinical applications. Disease biomarkers are also important for determining drug effects, target specificities and binding, dynamic metabolism and pharmacological kinetics, or toxicity profiles.
Page 1 /120472
Display every page Item


Home
Copyright © 2008-2017 Open Access Library. All rights reserved.