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Search Results: 1 - 10 of 61067 matches for " Bolan Yu "
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The Effects of Cigarette Smoke Extract on Ovulation, Oocyte Morphology and Ovarian Gene Expression in Mice
Zixin Mai, Ming Lei, Bolan Yu, Hongzi Du, Jianqiao Liu
PLOS ONE , 2014, DOI: 10.1371/journal.pone.0095945
Abstract: Cigarette smoking can harm fertility, but the existing research has targeted primarily on ovarian follicles, embryos or sex hormone. In this study, we tested cigarette smoke extract on ovulation, oocyte morphology and ovarian gene expression associated with inhibition of oxidative stress using C57BL/6 mice. Mice in the experimental group were administered a cigarette smoke extract (CSE) solution (2 mg/ml) orally daily, while the blank control group was given dimethylsulfoxide (DMSO). A positive control group (menadione) was used that received an intraperitoneal injection of 15 mg/kg menadione in oil solution daily. We found that the CSE group manifested a reduced diameter of zona pellucida-free oocyte (ZP-free OD) and a morphologically misshapen first polar body (PB). Our results suggest that CSE exposure is associated with a shrink size and poor quality of oocytes. Quitting smoking is a wise choice to ensure good fertility.
Genome-wide prioritization of disease genes and identification of disease-disease associations from an integrated human functional linkage network
Bolan Linghu, Evan S Snitkin, Zhenjun Hu, Yu Xia, Charles DeLisi
Genome Biology , 2009, DOI: 10.1186/gb-2009-10-9-r91
Abstract: Recently, a number of computational approaches have been developed to predict or prioritize candidate disease genes [1-34]. Most approaches are based on the idea that genes associated with the same or related disease phenotypes tend to participate in common functional modules (such as protein complexes, metabolic pathways, developmental or organogenesis processes, and so on) [1-16]. This concept is supported by functional analysis of genes associated with diverse diseases [1-4], and by the success of various disease gene prioritization studies based on the concept [5-7,9-17,19,20,23,24,29].Network-based approaches have also been employed to infer new candidate disease genes based upon network linkages with known disease genes [15,17-23]. These methods typically first construct a gene-gene association network based on one or more types of genomic and proteomic data, and subsequently rank candidate genes based on network proximity to known disease associated genes. Although some of these methods perform well using just one specific type of evidence for functional association, such as protein-protein physical-interaction data or co-expression data, the restriction to only one type of functional association potentially limits their predictive ability [17,20-23]. To address this issue, Franke et al. [15] constructed a functional linkage network (FLN) by integrating multiple types of data, and utilized the FLN for disease gene prioritization. However, their results indicate that the performance was highly dependent on Gene Ontology (GO) annotations, in addition to functional associations from curated databases such as the Kyoto Encyclopaedia of Genes and Genomes (KEGG) and Reactome [15,35,36]. As a result, the predictions tend to be biased towards well-characterized genes, and thus limit potential inferences. In a second study, Kohler et al. [19] constructed an FLN from heterogeneous data sources, and used a random walk algorithm for disease gene prioritization. However,
High-precision high-coverage functional inference from integrated data sources
Bolan Linghu, Evan S Snitkin, Dustin T Holloway, Adam M Gustafson, Yu Xia, Charles DeLisi
BMC Bioinformatics , 2008, DOI: 10.1186/1471-2105-9-119
Abstract: We first apply this framework to Saccharomyces cerevisiae. In the first step, we demonstrate that four commonly used machine learning methods, Linear SVM, Linear Discriminant Analysis, Na?ve Bayes, and Neural Network, all combine heterogeneous data to produce reliable and high-coverage FLNs, in which the linkage weight more accurately estimates functional coupling of linked proteins than use individual data sources alone. In the second step, empirical tuning of an adjustable decision rule on the constructed FLN reveals that basing annotation on maximum edge weight results in the most precise annotation at high coverages. In particular at low coverage all rules evaluated perform comparably. At coverage above approximately 50%, however, they diverge rapidly. At full coverage, the maximum weight decision rule still has a precision of approximately 70%, whereas for other methods, precision ranges from a high of slightly more than 30%, down to 3%. In addition, a scoring scheme to estimate the precisions of individual predictions is also provided. Finally, tests of the robustness of the framework indicate that our framework can be successfully applied to less studied organisms.We provide a general two-step function-annotation framework, and show that high coverage, high precision annotations can be achieved by constructing a high-coverage and reliable FLN via data integration followed by applying a maximum weight decision rule.Computationally based functional inference can be considered a two step process: (1) finding functional correlation between annotated and unannotated proteins by one or more experimental or computational methods, and (2) formulating a decision rule for transferring the function of annotated proteins to unannotated proteins. In general one can expect that the stronger the correlation, the greater the precision of transfer; similarly, one expects that the more data sources by which a correlation is found, the greater the precision. The integration of
Polycystic ovary syndrome resembling histopathological alterations in ovaries from prenatal androgenized female rats
Fang Wang, Bolan Yu, Wenjing Yang, Jianqiao Liu, Jiachun Lu, Xuefeng Xia
Journal of Ovarian Research , 2012, DOI: 10.1186/1757-2215-5-15
Abstract: A rat model of PCOS was established by injecting dehydroepiandrosterone sulfoconjugate (DHEAS) into pregnant females. Rats were administered with DHEAS (60?mg/kg/d) subcutaneously (s.c.) for all 20?days of pregnancy (Group A), or for the first 10?days (Group B), or from day 11 to day 20 (Group C). Controls were administered with injection oil (0.2?ml/day) s.c. throughout pregnancy (Group D). The litter rate, abortion rate, and offspring survival rate in each group were recorded. Serum androgen and estrogen were measured and the morphological features of the ovaries were examined by light and electron microscopy in the offspring of each group.We found that rats injected with DHEAS throughout pregnancy (group A) lost fertility. Rats injected with DHEAS during early pregnancy (group B) exhibited more serious aberrations in fertility than both Group C, in which rats were injected with DHEAS during late pregnancy (P?<?0.05), and Group D (controls). There was a statistical difference of ovarian weight among female offspring in Group B, C and D (P?<?0.01). By light and electron microscopy, a significant morphological difference among the female offspring in the three groups was observed.Our results indicate that androgen excess during pregnancy can decrease rat fertility. Excess androgen at the early stage of pregnancy causes high reproductive toxicity, leading to abnormality of ovarian morphology and functions in female offspring.
Functional Polymorphisms of Interferon-gamma Affect Pneumonia-Induced Sepsis
Ding Wang, Xuan Zhong, Dongjian Huang, Rui Chen, Guibin Bai, Qing Li, Bolan Yu, Yong Fan, Xiaofang Sun
PLOS ONE , 2014, DOI: 10.1371/journal.pone.0087049
Abstract: Objective Sepsis is an inflammatory syndrome caused by infection, and both its incidence and mortality are high. Because interferon-gamma (IFN-γ) plays an important role in inflammation, this work assessed IFN-γ single nucleotide polymorphism (SNPs) that may be associated with sepsis. Methods A total of 196 patients with pneumonia-induced sepsis and 213 age- and sex-matched healthy volunteers participated in our study from July 2012 to July 2013 in Guangzhou, China. Patient clinical information was collected. Clinical pathology was assessed in subgroups defined based on clinical criteria, APACHE II (acute physiology and chronic health evaluation) and SOFA (sepsis-related organ failure assessment) scores and discharge rate. Four functional SNPs, ?1616T/C (rs2069705), ?764G/C (rs2069707), +874A/T (rs2430561) and +3234C/T (rs2069718), were genotyped by Snapshot in both sepsis patients and healthy controls. Pearson’s chi-square test or Fisher’s exact test were used to analyze the distribution of the SNPs, and the probability values (P values), odds ratios (OR) and 95% confidence intervals (CIs) were calculated. Results No mutations in the IFN-γ ?764G/C SNP were detected among the participants in our study. The +874A/T and +3234C/T SNPs were in strong linkage disequilibrium (LD) (r2 = 0.894). The ?1616 TC+TT, +874 AT+AA genotype and the TAC haplotype were significantly associated with sepsis susceptibility, while the CTT haplotype was associated with protection against sepsis incidence. Genotype of ?1616 TT wasn’t only protective against severity of sepsis, but also against higher APACHE II and SOFA scores as +874 AA and +3234 CC. The TAC haplotype was was protective against progression to severe sepsis either. Conclusion Our results suggest that functional IFN-γ SNPs and their haplotypes are associated with pneumonia-induced sepsis.
Phenotypic connections in surprising places
Bolan Linghu, Charles DeLisi
Genome Biology , 2010, DOI: 10.1186/gb-2010-11-4-116
Abstract: The past few years have witnessed a growing number of well documented connections between and among human disease phenotypes, whose relationship would not have been obvious within the current disease classification framework. The evidence stems from a variety of sources, spanning clinical epidemiology, computational genomics and various model systems [1-5]. The implications are potentially so fundamental to disease etiology, drug development and the general diagnostic paradigm that there have been calls for an NIH Roadmap (large trans-institute transformational grants) focused on delineating the human disease landscape - a quantitative bipartite correlation map relating disease phenotypes and their genetic structures. A new study of phenotype correlations [6] now takes this idea further.One of the earliest comprehensive studies of human phenotype correlations was by Rzhetsky et al. [5], who established a correlation network between 161 diseases and disorders using evidence of comorbidities obtained from some 1.5 million patient records. Among other results, they found suggestive evidence for genetic relations between autism, which manifests in childhood, and several late onset diseases, including bipolar disorder and schizophrenia.A different approach was taken by Goh et al. [1], who constructed a human disease network by linking genetic disorders that are known to share causative genes. The network was based on an analysis of the Online Mendelian Inheritance in Man (OMIM) database, the most comprehensive compendium of well established associations between human disorders and their associated genes [7]. Among their findings was a large subnet formed by 516 of the 1,284 disorders studied, clearly showing many-to-many relationships between phenotypes and genes. For example, KRAS (encoding a small GTPase), BRCA1 and BRCA2 (both encoding tumor suppressors) are all involved in breast cancer, but KRAS is also implicated in pancreatic cancer, whereas BRCA1 and BRCA2 are as
Flexographic Printing and Integral Approach to Pollution Prevention and Control
Ivana Bolan?a Mirkovi?
Acta Graphica : Journal for Printing Science and Graphic Communications , 2010,
Abstract: The awareness of environmental, economic and sociological problems is rising. The purpose of such an approach is to reconcile industrial development with environmental capacities. This approach to environmental protection dates back to the time when the devastation of environment brought on by human factor, the intensification of production process and a build-up of harmful pollutants first became visible. The measures aiming at protecting the environment are a part of the organization system within the production. They are carried out by means of the bat-Best Available Techniques and are based on contemporary technology, which guarantee a high degree of environmental, energetic and economic efficiency.
Carbon Footprint - Application in Graphic Art Technology
Ivana Bolan?a Mirkovi?
Acta Graphica : Journal for Printing Science and Graphic Communications , 2010,
Abstract: The need for more sustainable products and processes has triggered the develop-ment of a large number of environmental assessment tools. These tools measure environmental performance and identify improvement potentials from the envi-ronmental point of view. The life cycle assessment (lca) methods take into ac-count all effects on the environment, direct and indirect resource inputs and/or emissions during the whole life cycle of products. The carbon footprint is a sub-set of data covered by life cycle assessment.The aim of this paper is to describe the potential environmental impacts (green-house gases, carbon footprint) of printed paper and new media.
Obavezno pravo, posebni dio I. - Pojedini ugovori - ; Ur.: Vilim Gorenc, et al., Novi informator, Zagreb 2012. Pravni fakultet Sveu ili ta u Zagrebu., Zagreb, 2011.
?ur?a Bolan?a
Zbornik Radova Pravnog Fakulteta u Splitu , 2013,
Abstract:
Koncesije na pomorskom dobru - novine u hrvatskom zakonodavstvu
Dragan Bolan?a
Zbornik Radova Pravnog Fakulteta u Splitu , 2009,
Abstract: Zakon o pomorskom dobru i morskim lukama Republike Hrvatske iz 2003. god. odre uje da je pomorsko dobro op e dobro od interesa za Republiku Hrvatsku, ima njezinu osobitu za titu, a upotrebljava se i koristi pod uvjetima i na na in propisan zakonom. U ovom radu, autor opisuje sve vrste koncesija za gospodarsko kori tenje i posebnu upotrebu (uklju uju i koncesije za lu ke djelatnosti).
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