918 resultados para Small groups interaction
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Motility responses of the small intestine of iNOS deficient mice (iNOS −/−) and their wildtype littermates (iNOS+/+) to the inflammatory challenge of lipopolysaccharide (LPS) were investigated. LPS administration failed to attenuate intestinal transit in iNOS−/− mice but depressed transit in their iNOS+/+ littermates. Supporting an inhibitory role for sustained nitric oxide (NO) synthesis in the regulation of intestinal motility during inflammation, iNOS immunoreactivity was upregulated in all regions of the small intestine of iNOS+/+ mice. In contrast, neuronal NOS was barely affected. Cyclooxygenase activation was determined by prostaglandin E2 (PGE2) concentration. Following LPS challenge, PGE2 levels were elevated in all intestinal segments in both animal groups. Moreover, COX-1 and COX-2 protein levels were elevated in iNOS+/+ mice in response to LPS, while COX-2 levels were similarly increased in iNOS −/− intestine. However, no apparent relationship was observed between increased prostaglandin concentrations and attenuated intestinal transit. The presence of heme oxygenase 1 (HO-1) in the murine small intestine was also investigated. In both animal groups HO-1 immunoreactivity in the proximal intestine increased in response to treatment, while the constitutive protein levels detected in the middle and distal intestine were unresponsive to LPS administration. No apparent correlation of HO-1 to the suppression of small intestinal motility induced by LPS administration was detected. The presence of S-nitrosylated contractile proteins in the small intestine was determined. γ-smooth muscle actin was basally nitrosylated as well as in response to LPS, but myosin light chain kinase and myosin regulatory chain (MLC20) were not. In conclusion, in a model of acute intestinal inflammation, iNOS-produced NO plays a significant role in suppressing small intestinal motility while nNOS, COX-1, COX-2 and HO-1 do not participate in this event. S-nitrosylation of γ-smooth muscle actin is associated with elevated levels of nitric oxide in the smooth muscle of murine small intestine. ^
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Background. The rise in survival rates along with more detailed follow-up using sophisticated imaging studies among non-small lung cancer (NSCLC) patients has led to an increased risk of second primary tumors (SPT) among these cases. Population and hospital based studies of lung cancer patients treated between 1974 and 1996 have found an increasing risk over time for the development of all cancers following treatment of non-small cell lung cancer (NSCLC). During this time the primary modalities for treatment were surgery alone, radiation alone, surgery and post-operative radiation therapy, or combinations of chemotherapy and radiation (sequentially or concurrently). There is limited information in the literature about the impact of treatment modalities on the development of second primary tumors in these patients. ^ Purpose. To investigate the impact of treatment modalities on the risk of second primary tumors in patients receiving treatment with curative intent for non-metastatic (Stage I–III) non-small cell lung cancer (NSCLC). ^ Methods. The hospital records of 1,095 NSCLC patients who were diagnosed between 1980–2001 and received treatment with curative intent at M.D. Anderson Cancer Center with surgery alone, radiation alone (with a minimum total radiation dose of at least 45Gy), surgery and post-operative radiation therapy, radiation therapy in combination with chemotherapy or surgery in combination with chemotherapy and radiation were retrospectively reviewed. A second primary malignancy was be defined as any tumor histologically different from the initial cancer, or of another anatomic location, or a tumor of the same location and histology as the initial tumor having an interval between cancers of at least five years. Only primary tumors occurring after treatment for NSCLC will qualified as second primary tumors for this study. ^ Results. The incidence of second primary tumor was 3.3%/year and the rate increased over time following treatment. The type of NSCLC treatment was not found to have a striking effect upon SPT development. Increased rates were observed in the radiation only and chemotherapy plus radiation treatment groups; but, these increases did not exceed expected random variation. Higher radiation treatment dose, patient age and weight loss prior to index NSCLC treatment were associated with higher SPT development. ^
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This study explored the health, education, social assets, needs, attitudes, and behaviors of residents of Ferrocarril #4, a small urban community in Tamaulipas, Mexico. A collaborative Participatory Action Research approach was used to emphasize community involvement. Using Triangulation to ensure validity, qualitative methods included key informant in depth interviews, participant observation and participatory discussion groups with women and men. A personal interview with a probability sample of women was done. The median age of interviewees was 37 years. The majority was married or had a partner. Over half of respondents completed grades 6-9. Employed women (25%) earned a median weekly salary equivalent to ∼56 USD. Women with health insurance (67.7%) were covered mainly through Social Security and Seguro Popular. One in 5 reported bad health. Barriers to care were primarily money and transportation. To improve health care, women wanted a full service clinic in or close to the community and affordable health care. Socially, 28% of respondents had no close friends in the community and most did not participate in beneficial community activities. Many women did not socialize with others and help from neighbors was situational. Primary school teachers lacked parental support and it interfered with classroom efforts. Healthy community discussion groups focused on personal and environmental hygiene and safety. Valuable assets exist in the community. To date, collaborative efforts resulted in a school First Aid station, a school nurse visit weekly, posting of emergency contact phone numbers in the school and community center, and development of a student health information form. ^
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This retrospective cohort study examined the association between nativity status and very preterm birth, preterm birth, and small-for-gestational-age (SGA) among Asian subgroups using Texas birth certificate data with no personal identifiers. A total of 877,322 birth certificates of Asian and US-born white women with a singleton birth in Texas from 2001-2004 were analyzed. Birth certificate records of US-born white, Chinese, Japanese, Korean, Vietnamese, Filipino, and Asian Indian women with a singleton birth were included in the analysis. Logistic regressions models were used to explore and understand the differences of the effect of nativity status on birth outcomes in Asian subgroups with US-born whites as the reference group. Most of the Asian subgroups had a lower risk of preterm births compared with US born whites, with reductions in risk ranging from 19% to 49% and the lowest risk of preterm birth observed among foreign-born Chinese mothers. Only Filipino mothers had a higher risk of preterm birth compared to US-born whites. Overall, foreign-born Asians had lower risks for very preterm birth and preterm birth than US-born Asians and US-born whites. US-born Asians were at higher risk for preterm birth than US-born whites. For SGA, all Asian subgroups and Asian subgroups by nativity status were at higher risk of SGA than US-born whites. Asian Indians and Japanese were at highest risk for SGA infants with 2.5 to 3 times the risk of SGA present in US-born whites. Foreign-born Asian women were at higher risk for SGA than their US-born counterparts. This study showed that health disparities among Asian subgroups are hidden by classifying Asians into a single group. By examining Asian subgroups separately and looking at nativity status, the differences in risk of SGA and preterm birth can be revealed so prevention efforts can focus on high risk groups. ^
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The purpose of this study was to compare the financial performance of small rural hospitals to that of small urban hospitals in Texas. Hospital-specific and environmental factors were studied as control variables.^ Small rural hospitals were found to be financially stronger on measures of liquidity but weaker on measures of profitability. Small urban hospitals performed better on measures of profitability and long-range solvency. When all measures in the five dimensions of financial performance were analyzed, no significant difference was found between the two groups of hospitals. None of the control variables included in the study was significantly associated with financial performance both for rural and urban hospitals. Conclusions were that small rural hospitals in Texas are experiencing a deterioration in financial condition but small, rural hospitals are not doing any worse than small urban hospitals; and that the financial hardship which rural hospitals suffer may be inherent in the nature of the institutions themselves, and not as a result of their smallness nor their rural settings. ^
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The purpose of this study was to investigate whether an incongruence between personality characteristics of individuals and concomitant charcteristics of health professional training environments on salient dimensions contributes to aspects of mental health. The dimensions examined were practical-theoretical orientation and the degree of structure-unstructure. They were selected for study as they are particularly important attributes of students and of learning environments. It was proposed that when the demand of the environment is disparate from the proclivities of the individual, strain arises. This strain was hypothesized to contribute to anxiety, depression, and subjective distress.^ Select subscales on the Omnibus Personality Inventory (OPI) were the operationalized measures for the personality component of the dimensions studied. An environmental index was developed to assess students' perceptions of the learning environment on these same dimensions. The Beck Depression Inventory, State-Trait Anxiety Inventory and General Well-Being schedule measured the outcome variables.^ A congruence model was employed to determine person-environment (P-E) interaction. Scores on the scales of the OPI and the environmental index were divided into high, medium, and low based on the range of scores. Congruence was defined as a match between the level of personality need and the complementary level of the perception of the environment. Alternatively, incongruence was defined as a mismatch between the person and the environment. The consistent category was compared to the inconsistent categories by an analysis of variance procedure. Furthermore, analyses of covariance were conducted with perceived supportiveness of the learning environment and life events external to the learning environment as the covariates. These factors were considered critical influences affecting the outcome measures.^ One hundred and eighty-five students (49% of the population) at the College of Optometry at the University of Houston participated in the study. Students in all four years of the program were equally represented in the study. However, the sample differed from the total population on representation by sex, marital status, and undergraduate major.^ The results of the study did not support the hypotheses. Further, after having adjusted for perceived supportiveness and life events external to the learning environment, there were no statistically significant differences between the congruent category and incongruent categories. Means indicated than the study sample experienced significantly lower depression and subjective distress than the normative samples.^ Results are interpreted in light of their utility for future study design in the investigation of the effects of P-E interaction. Emphasized is the question of the feasibility of testing a P-E interaction model with extant groups. Recommendations for subsequent research are proposed in light of the exploratory nature of the methodology. ^
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Schizophrenia (SZ) is a complex disorder with high heritability and variable phenotypes that has limited success in finding causal genes associated with the disease development. Pathway-based analysis is an effective approach in investigating the molecular mechanism of susceptible genes associated with complex diseases. The etiology of complex diseases could be a network of genetic factors and within the genes, interaction may occur. In this work we argue that some genes might be of small effect that by itself are neither sufficient nor necessary to cause the disease however, their effect may induce slight changes to the gene expression or affect the protein function, therefore, analyzing the gene-gene interaction mechanism within the disease pathway would play crucial role in dissecting the genetic architecture of complex diseases, making the pathway-based analysis a complementary approach to GWAS technique. ^ In this study, we implemented three novel linkage disequilibrium based statistics, the linear combination, the quadratic, and the decorrelation test statistics, to investigate the interaction between linked and unlinked genes in two independent case-control GWAS datasets for SZ including participants of European (EA) and African (AA) ancestries. The EA population included 1,173 cases and 1,378 controls with 729,454 genotyped SNPs, while the AA population included 219 cases and 288 controls with 845,814 genotyped SNPs. We identified 17,186 interacting gene-sets at significant level in EA dataset, and 12,691 gene-sets in AA dataset using the gene-gene interaction method. We also identified 18,846 genes in EA dataset and 19,431 genes in AA dataset that were in the disease pathways. However, few genes were reported of significant association to SZ. ^ Our research determined the pathways characteristics for schizophrenia through the gene-gene interaction and gene-pathway based approaches. Our findings suggest insightful inferences of our methods in studying the molecular mechanisms of common complex diseases.^
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High-resolution, small-bore PET systems suffer from a tradeoff between system sensitivity, and image quality degradation. In these systems long crystals allow mispositioning of the line of response due to parallax error and this mispositioning causes resolution blurring, but long crystals are necessary for high system sensitivity. One means to allow long crystals without introducing parallax errors is to determine the depth of interaction (DOI) of the gamma ray interaction within the detector module. While DOI has been investigated previously, newly available solid state photomultipliers (SSPMs) well-suited to PET applications and allow new modules for investigation. Depth of interaction in full modules is a relatively new field, and so even if high performance DOI capable modules were available, the appropriate means to characterize and calibrate the modules are not. This work presents an investigation of DOI capable arrays and techniques for characterizing and calibrating those modules. The methods introduced here accurately and reliably characterize and calibrate energy, timing, and event interaction positioning. Additionally presented is a characterization of the spatial resolution of DOI capable modules and a measurement of DOI effects for different angles between detector modules. These arrays have been built into a prototype PET system that delivers better than 2.0 mm resolution with a single-sided-stopping-power in excess of 95% for 511 keV g's. The noise properties of SSPMs scale with the active area of the detector face, and so the best signal-to-noise ratio is possible with parallel readout of each SSPM photodetector pixel rather than multiplexing signals together. This work additionally investigates several algorithms for improving timing performance using timing information from multiple SSPM pixels when light is distributed among several photodetectors.
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Complex diseases such as cancer result from multiple genetic changes and environmental exposures. Due to the rapid development of genotyping and sequencing technologies, we are now able to more accurately assess causal effects of many genetic and environmental factors. Genome-wide association studies have been able to localize many causal genetic variants predisposing to certain diseases. However, these studies only explain a small portion of variations in the heritability of diseases. More advanced statistical models are urgently needed to identify and characterize some additional genetic and environmental factors and their interactions, which will enable us to better understand the causes of complex diseases. In the past decade, thanks to the increasing computational capabilities and novel statistical developments, Bayesian methods have been widely applied in the genetics/genomics researches and demonstrating superiority over some regular approaches in certain research areas. Gene-environment and gene-gene interaction studies are among the areas where Bayesian methods may fully exert its functionalities and advantages. This dissertation focuses on developing new Bayesian statistical methods for data analysis with complex gene-environment and gene-gene interactions, as well as extending some existing methods for gene-environment interactions to other related areas. It includes three sections: (1) Deriving the Bayesian variable selection framework for the hierarchical gene-environment and gene-gene interactions; (2) Developing the Bayesian Natural and Orthogonal Interaction (NOIA) models for gene-environment interactions; and (3) extending the applications of two Bayesian statistical methods which were developed for gene-environment interaction studies, to other related types of studies such as adaptive borrowing historical data. We propose a Bayesian hierarchical mixture model framework that allows us to investigate the genetic and environmental effects, gene by gene interactions (epistasis) and gene by environment interactions in the same model. It is well known that, in many practical situations, there exists a natural hierarchical structure between the main effects and interactions in the linear model. Here we propose a model that incorporates this hierarchical structure into the Bayesian mixture model, such that the irrelevant interaction effects can be removed more efficiently, resulting in more robust, parsimonious and powerful models. We evaluate both of the 'strong hierarchical' and 'weak hierarchical' models, which specify that both or one of the main effects between interacting factors must be present for the interactions to be included in the model. The extensive simulation results show that the proposed strong and weak hierarchical mixture models control the proportion of false positive discoveries and yield a powerful approach to identify the predisposing main effects and interactions in the studies with complex gene-environment and gene-gene interactions. We also compare these two models with the 'independent' model that does not impose this hierarchical constraint and observe their superior performances in most of the considered situations. The proposed models are implemented in the real data analysis of gene and environment interactions in the cases of lung cancer and cutaneous melanoma case-control studies. The Bayesian statistical models enjoy the properties of being allowed to incorporate useful prior information in the modeling process. Moreover, the Bayesian mixture model outperforms the multivariate logistic model in terms of the performances on the parameter estimation and variable selection in most cases. Our proposed models hold the hierarchical constraints, that further improve the Bayesian mixture model by reducing the proportion of false positive findings among the identified interactions and successfully identifying the reported associations. This is practically appealing for the study of investigating the causal factors from a moderate number of candidate genetic and environmental factors along with a relatively large number of interactions. The natural and orthogonal interaction (NOIA) models of genetic effects have previously been developed to provide an analysis framework, by which the estimates of effects for a quantitative trait are statistically orthogonal regardless of the existence of Hardy-Weinberg Equilibrium (HWE) within loci. Ma et al. (2012) recently developed a NOIA model for the gene-environment interaction studies and have shown the advantages of using the model for detecting the true main effects and interactions, compared with the usual functional model. In this project, we propose a novel Bayesian statistical model that combines the Bayesian hierarchical mixture model with the NOIA statistical model and the usual functional model. The proposed Bayesian NOIA model demonstrates more power at detecting the non-null effects with higher marginal posterior probabilities. Also, we review two Bayesian statistical models (Bayesian empirical shrinkage-type estimator and Bayesian model averaging), which were developed for the gene-environment interaction studies. Inspired by these Bayesian models, we develop two novel statistical methods that are able to handle the related problems such as borrowing data from historical studies. The proposed methods are analogous to the methods for the gene-environment interactions on behalf of the success on balancing the statistical efficiency and bias in a unified model. By extensive simulation studies, we compare the operating characteristics of the proposed models with the existing models including the hierarchical meta-analysis model. The results show that the proposed approaches adaptively borrow the historical data in a data-driven way. These novel models may have a broad range of statistical applications in both of genetic/genomic and clinical studies.
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Lung cancer is the leading cause of cancer death in both men and women in the United States and worldwide. Despite improvement in treatment strategies, the 5-year survival rate of lung cancer patients remains low. Thus, effective chemoprevention and treatment approaches are sorely needed. Mutations and activation of KRAS occur frequently in tobacco users and the early stage of development of non-small cell lung cancers (NSCLC). So they are thought to be the primary driver for lung carcinogenesis. My work showed that KRAS mutations and activations modulated the expression of TNF-related apoptosis-inducing ligand (TRAIL) receptors by up-regulating death receptors and down-regulating decoy receptors. In addition, we showed that KRAS suppresses cellular FADD-like IL-1β-converting enzyme (FLICE)-like inhibitory protein (c-FLIP) expression through activation of ERK/MAPK-mediated activation of c-MYC which means the mutant KRAS cells could be specifically targeted via TRAIL induced apoptosis. The expression level of Inhibitors of Apoptosis Proteins (IAPs) in mutant KRAS cells is usually high which could be overcome by the second mitochondria-derived activator of caspases (Smac) mimetic. So the combination of TRAIL and Smac mimetic induced the synthetic lethal reaction specifically in the mutant-KRAS cells but not in normal lung cells and wild-type KRAS lung cancer cells. Therefore, a synthetic lethal interaction among TRAIL, Smac mimetic and KRAS mutations could be used as an approach for chemoprevention and treatment of NSCLC with KRAS mutations. Further data in animal experiments showed that short-term, intermittent treatment with TRAIL and Smac mimetic induced apoptosis in mutant KRAS cells and reduced tumor burden in a KRAS-induced pre-malignancy model and mutant KRAS NSCLC xenograft models. These results show the great potential benefit of a selective therapeutic approach for the chemoprevention and treatment of NSCLC with KRAS mutations.
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Multi-center clinical trials are very common in the development of new drugs and devices. One concern in such trials, is the effect of individual investigational sites enrolling small numbers of patients on the overall result. Can the presence of small centers cause an ineffective treatment to appear effective when treatment-by-center interaction is not statistically significant?^ In this research, simulations are used to study the effect that centers enrolling few patients may have on the analysis of clinical trial data. A multi-center clinical trial with 20 sites is simulated to investigate the effect of a new treatment in comparison to a placebo treatment. Twelve of these 20 investigational sites are considered small, each enrolling less than four patients per treatment group. Three clinical trials are simulated with sample sizes of 100, 170 and 300. The simulated data is generated with various characteristics, one in which treatment should be considered effective and another where treatment is not effective. Qualitative interactions are also produced within the small sites to further investigate the effect of small centers under various conditions.^ Standard analysis of variance methods and the "sometimes-pool" testing procedure are applied to the simulated data. One model investigates treatment and center effect and treatment-by-center interaction. Another model investigates treatment effect alone. These analyses are used to determine the power to detect treatment-by-center interactions, and the probability of type I error.^ We find it is difficult to detect treatment-by-center interactions when only a few investigational sites enrolling a limited number of patients participate in the interaction. However, we find no increased risk of type I error in these situations. In a pooled analysis, when the treatment is not effective, the probability of finding a significant treatment effect in the absence of significant treatment-by-center interaction is well within standard limits of type I error. ^
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Contraction of cardiac muscle is regulated through the Ca2+ dependent protein-protein interactions of the troponin complex (Tn). The critical role cardiac troponin C (cTnC) plays as the Ca2+ receptor in this complex makes it an attractive target for positive inotropic compounds. In this study, the ten Met methyl groups in cTnC, [98% 13C ϵ]-Met cTnC, are used as structural markers to monitor conformational changes in cTnC and identify sites of interaction between cTnC and cardiac troponin I (cTnI) responsible for the Ca2+ dependent interactions. In addition the structural consequences that a number of Ca2+-sensitizing compounds have on free cTnC and the cTnC·cTnI complex were characterized. Using heteronuclear NMR experiments and monitoring chemical shift changes in the ten Met methyl 1H-13C correlations in 3Ca2+ cTnC when bound to cTnI revealed an anti-parallel arrangement for the two proteins such that the N-domain of cTnI interacts with the C-domain of cTnC. The large chemical shifts in Mets-81, -120, and -157 identified points of contact between the proteins that include the C-domain hydrophobic surface in cTnC and the A, B, and D helical interface located in the regulatory N-domain of cTnC. TnI association [cTnI(33–80), cTnI(86–211), or cTnI(33–211)] was found also to dramatically reduce flexibility in the D/E central linker of cTnC as monitored by line broadening in the Met 1H- 13C correlations of cTnC induced by a nitroxide spin label, MTSSL, covalently attached to cTnC at Cys 84. TnI association resulted in an extended cTnC that is unlike the compact structure observed for free cTnC. The Met 1H-13C correlations also allowed the binding characteristics of bepridil, TFP, levosimendan, and EMD 57033 to the apo, 2Ca2+, and Ca2+ saturated forms of cTnC to be determined. In addition, the location of drug binding on the 3Ca2+cTnC·cTnI complex was identified for bepridil and TFP. Use of a novel spin-labeled phenothiazine, and detection of isotope filtered NOEs, allowed identification of drug binding sites in the shallow hydrophobic cup in the C-terminal domain, and on two hydrophobic surfaces on N-regulatory domain in free 3Ca2+ cTnC. In contrast, only one N-domain drug binding site exists in 3Ca2+ cTnC·cTnI complex. The methyl groups of Met 45, 60 and 80, which are grouped in a hydrophobic patch near site II in cTnC, showed the greatest change upon titration with bepridil or TFP, suggesting that this is a critical site of drug binding in both free cTnC and when associated with cTnI. The strongest NOEs were seen for Met-60 and -80, which are located on helices C and D, respectively, of Ca2+ binding site II. These results support the conclusion that the small hydrophobic patch which includes Met-45, -60, and -80 constitutes a drug binding site, and that binding drugs to this site will lead to an increase in Ca2+ binding affinity of site II while preserving maximal cTnC activity. Thus, the subregion in cTnC makes a likely target against which to design new and selective Ca2+-sensitizing compounds. ^
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Decorin, a dermatan/chondroitin sulfate proteoglycan, is ubiquitously distributed in the extracellular matrix (ECM) of mammals. Decorin belongs to the small leucine rich proteoglycan (SLRP) family, a proteoglycan family characterized by a core protein dominated by Leucine Rich Repeat motifs. The decorin core protein appears to mediate the binding of decorin to ECM molecules, such as collagens and fibronectin. It is believed that the interactions of decorin with these ECM molecules contribute to the regulation of ECM assembly, cell adhesions, and cell proliferation. These basic biological processes play critical roles during embryonic development and wound healing and are altered in pathological conditions such as fibrosis and tumorgenesis. ^ In this dissertation, we discover that decorin core protein can bind to Zn2+ ions with high affinity. Zinc is an essential trace element in mammals. Zn2+ ions play a catalytic role in the activation of many enzymes and a structural role in the stabilization of protein conformation. By examining purified recombinant decorin and its core protein fragments for Zn2+ binding activity using Zn2+-chelating column chromatography and Zn2+-equilibrium dialysis approaches, we have located the Zn2+ binding domain to the N-terminal sequence of the decorin core protein. The decorin N-terminal domain appears to contain two Zn2+ binding sites with similar high binding affinity. The sequence of the decorin N-terminal domain does not resemble any other reported zinc-binding motifs and, therefore, represents a novel Zn 2+ binding motif. By investigating the influence of Zn2+ ions on decorin binding interactions, we found a novel Zn2+ dependent interaction with fibrinogen, the major plasma protein in blood clots. Furthermore, a recombinant peptide (MD4) consisting of a 41 amino acid sequence of mouse decorin N-terminal domain can prolong thrombin induced fibrinogen/fibrin clot formation. This suggests that in the presence of Zn2+ the decorin N-terminal domain has an anticoagulation activity. The changed Zn2+-binding activities of the truncated MD4 peptides and site-directed mutagenesis generated mutant peptides revealed that the functional MD4 peptide might contain both a structural zinc-binding site in the cysteine cluster region and a catalytic zinc site that could be created by the flanking sequences of the cysteine cluster region. A model of a loop-like structure for MD4 peptide is proposed. ^
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Molecular methods provide promising tools for routine detection and quantification of toxic microalgae in plankton samples. To this end, novel TaqMan minor groove binding probes and primers targeting the small (SSU) or large (LSU) ribosomal subunit (rRNA) were developed for two species of the marine dinoflagellate genus Alexandrium (A. minutum, A. tamutum) and for three groups/ribotypes of the A. tamarense species complex: Group I/North American (NA), Group II/Mediterranean (ME) and Group III/Western European (WE). Primers and probes for real-time quantitative PCR (qPCR) were species-specific and highly efficient when tested in qPCR assays for cross-validation with pure DNA from cultured Alexandrium strains. Suitability of the qPCR assays as molecular tools for the detection and estimation of relative cell abundances of Alexandrium species and groups was evaluated from samples of natural plankton assemblages along the Scottish east coast. The results were compared with inverted microscope cell counts (Utermöhl technique) of Alexandrium spp. and associated paralytic shellfish poisoning (PSP) toxin concentrations. The qPCR assays indicated that A. tamarense (Group I) and A. tamutum were the most abundant Alexandrium taxa and both were highly positively correlated with PSP toxin content of plankton samples. Cells of A. tamarense (Group III) were present at nearly all stations but in low abundance. Alexandrium minutum and A. tamarense (Group II) cells were not detected in any of the samples, thereby arguing for their absence from the specific North Sea region, at least at the time of the survey. The sympatric occurrence of A. tamarense Group I and Group III gives further support to the hypothesis that the groups/ribotypes of the A. tamarense species complex are cryptic species rather than variants belonging to the same species.
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The first data set contains the mean and cofficient of variation (standard deviation divided by mean) of a multi-frequency indicator I derived from ER60 acoustic information collected at five frequencies (18, 38, 70, 120, and 200 kHz) in the Bay of Biscay in May of the years 2006, 2008, 2009 and 2010 (Pelgas surveys). The multi-frequency indicator was first calculated per voxel (20 m long × 5 m deep sampling unit) and then averaged on a spatial grid (approx. 20 nm × 20 nm) for five 5-m depth layers in the surface waters (10-15m, 15-20m, 20-25m, 25-30m below sea surface); there are missing values in particular in the shallowest layer. The second data set provides for each grid cell and depth layer the proportion of voxels for which the multi-frequency indicator I was indicative of a certain group of organisms. For this the following interpretation was used: I < 0.39 swim bladder fish or large gas bubbles, I = 0.39-0.58 small resonant bubbles present in gas bearing organisms such as larval fish and phytoplankton, I = 0.7-0.8 fluidlike zooplankton such as copepods and euphausiids, and I > 0.8 mackerel. These proportions can be interpreted as a relative abundance index for each of the four organism groups.