Detecting Outlier Microarray Arrays by Correlation and Percentage of Outliers Spots

We developed a quality assurance (QA) tool, namely microarray outlier filter (MOF), and have applied it to our microarray datasets for the identification of problematic arrays. Our approach is based on the comparison of the arrays using the correlation coefficient and the number of outlier spots generated on each array to reveal outlier arrays. For a human universal reference (HUR) dataset, which is used as a technical control in our standard hybridization procedure, 3 outlier arrays were identified out of 35 experiments. For a human blood dataset, 12 outlier arrays were identified from 185 experiments. In general, arrays from human blood samples displayed greater variation in their gene expression profiles than arrays from HUR samples. As a result, MOF identified two distinct patterns in the occurrence of outlier arrays. These results demonstrate that this methodology is a valuable QA practice to identify questionable microarray data prior to downstream analysis

Expanding the perspective of translational medicine: the value of observational data

In 2003, the Journal of Translational Medicine was launched to foster the publication of high quality research in both "bench-to-bedside" as well as ex vivo human observation. In spite of the success of several large-scale observational studies, e.g. Framingham Heart Study, the opportunity to expand upon the ex vivo human observation has remained limited within the field of translational medicine. We believe that this presents a significant opportunity that merits consideration in both the planning and analysis of large scale observational studies and can contribute greatly to expanding our approaches in translational medicin

Characterization of adjacent breast tumors using oligonucleotide microarrays

BACKGROUND: Current methodology often cannot distinguish second primary breast cancers from multifocal disease, a potentially important distinction for clinical management. In the present study we evaluated the use of oligonucleotide-based microarray analysis in determining the clonality of tumors by comparing gene expression profiles. METHOD: Total RNA was extracted from two tumors with no apparent physical connection that were located in the right breast of an 87-year-old woman diagnosed with invasive ductal carcinoma (IDC). The RNA was hybridized to the Affymetrix Human Genome U95A Gene Chip(®) (12,500 known human genes) and analyzed using the Gene Chip Analysis Suite(®) 3.3 (Affymetrix, Inc, Santa Clara, CA, USA) and JMPIN(®) 3.2.6 (SAS Institute, Inc, Cary, NC, USA). Gene expression profiles of tumors from five additional patients were compared in order to evaluate the heterogeneity in gene expression between tumors with similar clinical characteristics. RESULTS: The adjacent breast tumors had a pairwise correlation coefficient of 0.987, and were essentially indistinguishable by microarray analysis. Analysis of gene expression profiles from different individuals, however, generated a pairwise correlation coefficient of 0.710. CONCLUSION: Transcriptional profiling may be a useful diagnostic tool for determining tumor clonality and heterogeneity, and may ultimately impact on therapeutic decision making

The international effort: building the bridge for Translational Medicine: Report of the 1st International Conference of Translational Medicine (ICTM)

Background: Supported by the International Society for Translational Medicine (ISTM), Wenzhou Medical College and the First Affiliated Hospital of Wenzhou Medical College, the International Conference on Translational Medicine (ICTM) was held on October 22–23, 2011 in Wenzhou, China. Nearly 800 registrants attended the meeting, primarily representing institutes and hospitals in Europe, The United States of America, And Asia, and China. The meeting was chaired and organized by Dr. Xiangdong Wang, Xiaoming Chen, Richard Coico, Jeffrey M. Drazen, Richard Horton, Francesco M. Marincola, Laurentiu M. Popescu, Jia Qu and Aamir Shahzad. Findings: The meeting focused on the communication of the need to foster translational medicine (TM) by building and broadening bridges between basic research and clinical studies at the international level. The meeting included distinguished TM experts from academia, the pharmaceutical and diagnostics industries, government agencies, regulators, and clinicians and provided the opportunity to identify shared interests and efforts for collaborative approaches utilizing cutting edge technologies, innovative approaches and novel therapeutic interventions. The meeting defined the concept of TM in its two-way operational scheme and emphasized the need for bed to bench efforts based directly on clinical observation. Conclusions: It was the meeting participants’ realization that the shared main goals of TM include breaking the separation between clinic practice and basic research, establishing positive feedback by understanding the basis of expected and unexpected clinical outcomes and accelerating basic research relevant to human suffering. The primary objectives of the meeting were two-fold: to accelerate the two-way translation by informing the participants representing the different disciplines about the state of art activities around TM approaches; and to identify areas that need to be supported by redirecting limited resources as well as identifying new sources of funding. This report summarizes key concepts presented during the meeting representing the state-of-art translational research and salient aspects of the ensuing discussions

Predicting crop yields and soil‐plant nitrogen dynamics in the US Corn Belt

We used the Agricultural Production Systems sIMulator (APSIM) to predict and explain maize and soybean yields, phenology, and soil water and nitrogen (N) dynamics during the growing season in Iowa, USA. Historical, current and forecasted weather data were used to drive simulations, which were released in public four weeks after planting. In this paper, we (1) describe the methodology used to perform forecasts; (2) evaluate model prediction accuracy against data collected from 10 locations over four years; and (3) identify inputs that are key in forecasting yields and soil N dynamics. We found that the predicted median yield at planting was a very good indicator of end‐of‐season yields (relative root mean square error [RRMSE] of ∼20%). For reference, the prediction at maturity, when all the weather was known, had a RRMSE of 14%. The good prediction at planting time was explained by the existence of shallow water tables, which decreased model sensitivity to unknown summer precipitation by 50–64%. Model initial conditions and management information accounted for one‐fourth of the variation in maize yield. End of season model evaluations indicated that the model simulated well crop phenology (R2 = 0.88), root depth (R2 = 0.83), biomass production (R2 = 0.93), grain yield (R2 = 0.90), plant N uptake (R2 = 0.87), soil moisture (R2 = 0.42), soil temperature (R2 = 0.93), soil nitrate (R2 = 0.77), and water table depth (R2 = 0.41). We concluded that model set‐up by the user (e.g. inclusion of water table), initial conditions, and early season measurements are very important for accurate predictions of soil water, N and crop yields in this environment

Circulating microparticles: square the circle

Background: The present review summarizes current knowledge about microparticles (MPs) and provides a systematic overview of last 20 years of research on circulating MPs, with particular focus on their clinical relevance. Results: MPs are a heterogeneous population of cell-derived vesicles, with sizes ranging between 50 and 1000 nm. MPs are capable of transferring peptides, proteins, lipid components, microRNA, mRNA, and DNA from one cell to another without direct cell-to-cell contact. Growing evidence suggests that MPs present in peripheral blood and body fluids contribute to the development and progression of cancer, and are of pathophysiological relevance for autoimmune, inflammatory, infectious, cardiovascular, hematological, and other diseases. MPs have large diagnostic potential as biomarkers; however, due to current technological limitations in purification of MPs and an absence of standardized methods of MP detection, challenges remain in validating the potential of MPs as a non-invasive and early diagnostic platform. Conclusions: Improvements in the effective deciphering of MP molecular signatures will be critical not only for diagnostics, but also for the evaluation of treatment regimens and predicting disease outcomes

Linking soil microbial community structure to potential carbon mineralization: A continental scale assessment of reduced tillage

Potential carbon mineralization (Cmin) is a commonly used indicator of soil health, with greater Cmin values interpreted as healthier soil. While Cmin values are typically greater in agricultural soils managed with minimal physical disturbance, the mechanisms driving the increases remain poorly understood. This study assessed bacterial and archaeal community structure and potential microbial drivers of Cmin in soils maintained under various degrees of physical disturbance. Potential carbon mineralization, 16S rRNA sequences, and soil characterization data were collected as part of the North American Project to Evaluate Soil Health Measurements (NAPESHM). Results showed that type of cropping system, intensity of physical disturbance, and soil pH influenced microbial sensitivity to physical disturbance. Furthermore, 28% of amplicon sequence variants (ASVs), which were important in modeling Cmin, were enriched under soils managed with minimal physical disturbance. Sequences identified as enriched under minimal disturbance and important for modeling Cmin, were linked to organisms which could produce extracellular polymeric substances and contained metabolic strategies suited for tolerating environmental stressors. Understanding how physical disturbance shapes microbial communities across climates and inherent soil properties and drives changes in Cmin provides the context necessary to evaluate management impacts on standardized measures of soil microbial activity