Troponin-based risk stratification of patients with acute nonmassive pulmonary embolism: systematic review and metaanalysis.

BACKGROUND: Controversy exists regarding the usefulness of troponin testing for the risk stratification of patients with acute pulmonary embolism (PE). We conducted an updated systematic review and a metaanalysis of troponin-based risk stratification of normotensive patients with acute symptomatic PE. The sources of our data were publications listed in Medline and Embase from 1980 through April 2008 and a review of cited references in those publications. METHODS: We included all studies that estimated the relation between troponin levels and the incidence of all-cause mortality in normotensive patients with acute symptomatic PE. Two reviewers independently abstracted data and assessed study quality. From the literature search, 596 publications were screened. Nine studies that consisted of 1,366 normotensive patients with acute symptomatic PE were deemed eligible. Pooled results showed that elevated troponin levels were associated with a 4.26-fold increased odds of overall mortality (95% CI, 2.13 to 8.50; heterogeneity chi(2) = 12.64; degrees of freedom = 8; p = 0.125). Summary receiver operating characteristic curve analysis showed a relationship between the sensitivity and specificity of troponin levels to predict overall mortality (Spearman rank correlation coefficient = 0.68; p = 0.046). Pooled likelihood ratios (LRs) were not extreme (negative LR, 0.59 [95% CI, 0.39 to 0.88]; positive LR, 2.26 [95% CI, 1.66 to 3.07]). The Begg rank correlation method did not detect evidence of publication bias. CONCLUSIONS: The results of this metaanalysis indicate that elevated troponin levels do not adequately discern normotensive patients with acute symptomatic PE who are at high risk for death from those who are at low risk for death.

Tissue-based immune monitoring I: tumor core needle biopsies allow in-depth interrogation of the tumor microenvironment.

We sought to assess the feasibility and reproducibility of performing tissue-based immune characterization of the tumor microenvironment using CT-compatible needle biopsy material. Three independent biopsies were obtained intraoperatively from one metastatic epithelial ovarian cancer lesion of 7 consecutive patients undergoing surgical cytoreduction using a 16-gauge core biopsy needle. Core specimens were snap-frozen and subjected to immunohistochemistry (IHC) against human CD3, CD4, CD8, and FoxP3. A portion of the cores was used to isolate RNA for 1) real-time quantitative (q)PCR for CD3, CD4, CD8, FoxP3, IL-10 and TGF-beta, 2) multiplexed PCR-based T cell receptor (TCR) CDR3 Vβ region spectratyping, and 3) gene expression profiling. Pearson's correlations were examined for immunohistochemistry and PCR gene expression, as well as for gene expression array data obtained from different tumor biopsies. Needle biopsy yielded sufficient tissue for all assays in all patients. IHC was highly reproducible and informative. Significant correlations were seen between the frequency of CD3+, CD8+ and FoxP3+ T cells by IHC with CD3ε, CD8A, and FoxP3 gene expression, respectively, by qPCR (r=0.61, 0.86, and 0.89; all p< 0.05). CDR3 spectratyping was feasible and highly reproducible in each tumor, and indicated a restricted repertoire for specific TCR Vβ chains in tumor-infiltrating T cells. Microarray gene expression revealed strong correlation between different biopsies collected from the same tumor. Our results demonstrate a feasible and reproducible method of immune monitoring using CT-compatible needle biopsies from tumor tissue, thereby paving the way for sophisticated translational studies during tumor biological therapy.

Use of LIDAR-based Elevation Data for Highway Drainage Analysis: A Qualitative Assessment, 2003

US Geological Survey (USGS) based elevation data are the most commonly used data source for highway hydraulic analysis; however, due to the vertical accuracy of USGS-based elevation data, USGS data may be too “coarse” to adequately describe surface profiles of watershed areas or drainage patterns. Additionally hydraulic design requires delineation of much smaller drainage areas (watersheds) than other hydrologic applications, such as environmental, ecological, and water resource management. This research study investigated whether higher resolution LIDAR based surface models would provide better delineation of watersheds and drainage patterns as compared to surface models created from standard USGS-based elevation data. Differences in runoff values were the metric used to compare the data sets. The two data sets were compared for a pilot study area along the Iowa 1 corridor between Iowa City and Mount Vernon. Given the limited breadth of the analysis corridor, areas of particular emphasis were the location of drainage area boundaries and flow patterns parallel to and intersecting the road cross section. Traditional highway hydrology does not appear to be significantly impacted, or benefited, by the increased terrain detail that LIDAR provided for the study area. In fact, hydrologic outputs, such as streams and watersheds, may be too sensitive to the increased horizontal resolution and/or errors in the data set. However, a true comparison of LIDAR and USGS-based data sets of equal size and encompassing entire drainage areas could not be performed in this study. Differences may also result in areas with much steeper slopes or significant changes in terrain. LIDAR may provide possibly valuable detail in areas of modified terrain, such as roads. Better representations of channel and terrain detail in the vicinity of the roadway may be useful in modeling problem drainage areas and evaluating structural surety during and after significant storm events. Furthermore, LIDAR may be used to verify the intended/expected drainage patterns at newly constructed highways. LIDAR will likely provide the greatest benefit for highway projects in flood plains and areas with relatively flat terrain where slight changes in terrain may have a significant impact on drainage patterns.

Remote Sensing Change Analysis Methodology to Support Traffic Monitoring Programs: Phase 2, 2004

The Center for Transportation Research and Education (CTRE) issued a report in July 2003, based on a sample study of the application of remote sensed image land use change detection to the methodology of traffic monitoring in Blackhawk County, Iowa. In summary, the results indicated a strong correlation and a statistically significant regression coefficient between the identification of built-up land use change areas from remote sensed data and corresponding changes in traffic patterns, expressed as vehicle miles traveled (VMT). Based on these results, the Iowa Department of Transportation (Iowa DOT) requested that CTRE expand the study area to five counties in the southwest quadrant of the state. These counties are scheduled for traffic counts in 2004, and the Iowa DOT desired the data to 1) evaluate the current methodology used to place the devices; 2) potentially influence the placement of traffic counting devices in areas of high built-up land use change; and 3) determine if opportunities exist to reduce the frequency and/or density of monitoring activity in lower trafficked rural areas of the state. This project is focused on the practical application of built-up land use change data for placement of traffic count data recording devices in five southwest Iowa counties.

Diagnosing Patients Combining Principal Components Analysis and Case Based Reasoning

This paper addresses the application of a PCA analysis on categorical data prior to diagnose a patients data set using a Case-Based Reasoning (CBR) system. The particularity is that the standard PCA techniques are designed to deal with numerical attributes, but our medical data set contains many categorical data and alternative methods as RS-PCA are required. Thus, we propose to hybridize RS-PCA (Regular Simplex PCA) and a simple CBR. Results show how the hybrid system produces similar results when diagnosing a medical data set, that the ones obtained when using the original attributes. These results are quite promising since they allow to diagnose with less computation effort and memory storage

GIS-Based Accident Location and Analysis System (GIS-ALAS) Project Report: Phase 1, 1998

This report summarizes progress made in Phase 1 of the GIS-based Accident Location and Analysis System (GIS-ALAS) project. The GIS-ALAS project builds on several longstanding efforts by the Iowa Department of Transportation (DOT), law enforcement agencies, Iowa State University, and several other entities to create a locationally-referenced highway accident database for Iowa. Most notable of these efforts is the Iowa DOT’s development of a PC-based accident location and analysis system (PC-ALAS), a system that has been well received by users since it was introduced in 1989. With its pull-down menu structure, PC-ALAS is more portable and user-friendly than its mainframe predecessor. Users can obtain accident statistics for locations during specified time periods. Searches may be refined to identify accidents of specific types or involving drivers with certain characteristics. Output can be viewed on a computer screen, sent to a file, or printed using pre-defined formats.

Correlation of Locally-Based Performance of Asphalts with Their Physicochemical Parameters, Progress Report 2, HR-298, 1989

This project was undertaken to study the relationships between the performance of locally available asphalts and their physicochemical properties under Iowa conditions with the ultimate objective of development of a locally and performance-based asphalt specification for durable pavements. Physical and physicochemical tests were performed on three sets of asphalt samples including: (a) twelve samples from local asphalt suppliers and their TFOT residues, (b) six core samples of known service records, and (c) a total of 79 asphalts from 10 pavement projects including original, lab aged and recovered asphalts from field mixes, as well as from lab aged mixes. Tests included standard rheological tests, HP-GPC and TMA. Some specific viscoelastic tests (at 5 deg C) were run on b samples and on some a samples. DSC and X-ray diffraction studies were performed on a and b samples. Furthermore, NMR techniques were applied to some a, b and c samples. Efforts were made to identify physicochemical properties which are correlated to physical properties known to affect field performance. The significant physicochemical parameters were used as a basis for an improved performance-based trial specification for Iowa to ensure more durable pavements.

Nutrient-based dietary patterns and the risk of head and neck cancer: a pooled analysis in the International Head and Neck Cancer Epidemiology consortium.

Background The association between dietary patterns and head and neck cancer has rarely been addressed. Patients and methods We used individual-level pooled data from five case-control studies (2452 cases and 5013 controls) participating in the International Head and Neck Cancer Epidemiology consortium. A posteriori dietary patterns were identified through a principal component factor analysis carried out on 24 nutrients derived from study-specific food-frequency questionnaires. Odds ratios (ORs) and corresponding 95% confidence intervals (CIs) were estimated using unconditional logistic regression models on quintiles of factor scores. Results We identified three major dietary patterns named 'animal products and cereals', 'antioxidant vitamins and fiber', and 'fats'. The 'antioxidant vitamins and fiber' pattern was inversely related to oral and pharyngeal cancer (OR = 0.57, 95% CI 0.43-0.76 for the highest versus the lowest score quintile). The 'animal products and cereals' pattern was positively associated with laryngeal cancer (OR = 1.54, 95% CI 1.12-2.11), whereas the 'fats' pattern was inversely associated with oral and pharyngeal cancer (OR = 0.78, 95% CI 0.63-0.97) and positively associated with laryngeal cancer (OR = 1.69, 95% CI 1.22-2.34). Conclusions These findings suggest that diets rich in animal products, cereals, and fats are positively related to laryngeal cancer, and those rich in fruit and vegetables inversely related to oral and pharyngeal cancer.

Stability and adaptability of runner peanut genotypes based on nonlinear regression and AMMI analysis

The objective of this work was to estimate the stability and adaptability of pod and seed yield in runner peanut genotypes based on the nonlinear regression and AMMI analysis. Yield data from 11 trials, distributed in six environments and three harvests, carried out in the Northeast region of Brazil during the rainy season were used. Significant effects of genotypes (G), environments (E), and GE interactions were detected in the analysis, indicating different behaviors among genotypes in favorable and unfavorable environmental conditions. The genotypes BRS Pérola Branca and LViPE‑06 are more stable and adapted to the semiarid environment, whereas LGoPE‑06 is a promising material for pod production, despite being highly dependent on favorable environments.

A cophenetic correlation coefficient for Tocher's method

The objective of this work was to propose a way of using the Tocher's method of clustering to obtain a matrix similar to the cophenetic one obtained for hierarchical methods, which would allow the calculation of a cophenetic correlation. To illustrate the obtention of the proposed cophenetic matrix, we used two dissimilarity matrices - one obtained with the generalized squared Mahalanobis distance and the other with the Euclidean distance - between 17 garlic cultivars, based on six morphological characters. Basically, the proposal for obtaining the cophenetic matrix was to use the average distances within and between clusters, after performing the clustering. A function in R language was proposed to compute the cophenetic matrix for Tocher's method. The empirical distribution of this correlation coefficient was briefly studied. For both dissimilarity measures, the values of cophenetic correlation obtained for the Tocher's method were higher than those obtained with the hierarchical methods (Ward's algorithm and average linkage - UPGMA). Comparisons between the clustering made with the agglomerative hierarchical methods and with the Tocher's method can be performed using a criterion in common: the correlation between matrices of original and cophenetic distances.

DFMA- Analysis and Aspects of Applying Internet Based Collaborative Design for Axial Eccentric Oil-Pump Design

The integrated system of design for manufacturing and assembly (DFMA) and internet based collaborative design are presented to support product design, manufacturing process, and assembly planning for axial eccentric oil-pump design. The presented system manages and schedules group oriented collaborative activities. The design guidelines of internet based collaborative design & DFMA are expressed. The components and the manufacturing stages of axial eccentric oil-pump are expressed in detail. The file formats of the presented system include the data types of collaborative design of the product, assembly design, assembly planning and assembly system design. Product design and assembly planning can be operated synchronously and intelligently and they are integrated under the condition of internet based collaborative design and DFMA. The technologies of collaborative modelling, collaborative manufacturing, and internet based collaborative assembly for the specific pump construction are developed. A seven-security level is presented to ensure the security of the internet based collaborative design system.

Self-Organising Maps and Correlation Analysis as a Tool to Explore Patterns in Excitation-Emission Matrix Data Sets and to Discriminate Dissolved Organic Matter Fluorescence Components

Dissolved organic matter (DOM) is a complex mixture of organic compounds, ubiquitous in marine and freshwater systems. Fluorescence spectroscopy, by means of Excitation-Emission Matrices (EEM), has become an indispensable tool to study DOM sources, transport and fate in aquatic ecosystems. However the statistical treatment of large and heterogeneous EEM data sets still represents an important challenge for biogeochemists. Recently, Self-Organising Maps (SOM) has been proposed as a tool to explore patterns in large EEM data sets. SOM is a pattern recognition method which clusterizes and reduces the dimensionality of input EEMs without relying on any assumption about the data structure. In this paper, we show how SOM, coupled with a correlation analysis of the component planes, can be used both to explore patterns among samples, as well as to identify individual fluorescence components. We analysed a large and heterogeneous EEM data set, including samples from a river catchment collected under a range of hydrological conditions, along a 60-km downstream gradient, and under the influence of different degrees of anthropogenic impact. According to our results, chemical industry effluents appeared to have unique and distinctive spectral characteristics. On the other hand, river samples collected under flash flood conditions showed homogeneous EEM shapes. The correlation analysis of the component planes suggested the presence of four fluorescence components, consistent with DOM components previously described in the literature. A remarkable strength of this methodology was that outlier samples appeared naturally integrated in the analysis. We conclude that SOM coupled with a correlation analysis procedure is a promising tool for studying large and heterogeneous EEM data sets.