BackgroundIdiopathic Pulmonary Fibrosis (IPF) is a lethal lung disease of unknown etiology. A major limitation in transcriptomic profiling of lung tissue in IPF has been a dependence on snap-frozen fresh tissues (FF). In this project we sought to determine whether genome scale transcript profiling using RNA Sequencing (RNA-Seq) could be applied to archived Formalin-Fixed Paraffin-Embedded (FFPE) IPF tissues.ResultsWe isolated total RNA from 7 IPF and 5 control FFPE lung tissues and performed 50 base pair paired-end sequencing on Illumina 2000 HiSeq. TopHat2 was used to map sequencing reads to the human genome. On average ~62 million reads (53.4% of ~116 million reads) were mapped per sample. 4,131 genes were differentially expressed between IPF and controls (1,920 increased and 2,211 decreased (FDR < 0.05). We compared our results to differentially expressed genes calculated from a previously published dataset generated from FF tissues analyzed on Agilent microarrays (GSE47460). The overlap of differentially expressed genes was very high (760 increased and 1,413 decreased, FDR < 0.05). Only 92 differentially expressed genes changed in opposite directions. Pathway enrichment analysis performed using MetaCore confirmed numerous IPF relevant genes and pathways including extracellular remodeling, TGF-beta, and WNT. Gene network analysis of MMP7, a highly differentially expressed gene in both datasets, revealed the same canonical pathways and gene network candidates in RNA-Seq and microarray data. For validation by NanoString nCounter® we selected 35 genes that had a fold change of 2 in at least one dataset (10 discordant, 10 significantly differentially expressed in one dataset only and 15 concordant genes). High concordance of fold change and FDR was observed for each type of the samples (FF vs FFPE) with both microarrays (r = 0.92) and RNA-Seq (r = 0.90) and the number of discordant genes was reduced to four.ConclusionsOur results demonstrate that RNA sequencing of RNA obtained from archived FFPE lung tissues is feasible. The results obtained from FFPE tissue are highly comparable to FF tissues. The ability to perform RNA-Seq on archived FFPE IPF tissues should greatly enhance the availability of tissue biopsies for research in IPF.Electronic supplementary materialThe online version of this article (doi:10.1186/s12890-016-0356-4) contains supplementary material, which is available to authorized users.
We offer an algorithm that can identify aircraft categories from Inverse Synthetic Aperture Radar (ISAR) images that use both the radar reflection pulse shape, which includes the duration or size of the radar pulse that is reflected, and the Doppler shifts of different parts of the aircraft caused by rotational motions of the aircraft as it maneuvers. We investigated the practicality of determining which of seven different aircraft categories a radar return indicates. The object of this research is to very quickly tell from an ISAR return how an aircraft compares to the seven different categories where the aircraft is in any position of a prescribed holding pattern. We propose a new method in which we compare each ISAR image to unions of images of the different aircraft categories. This method gave us results that are superior to the results we obtained in [8].
SUMMARYDiabetic retinopathy is the progressive pathological alterations in the retinal microvasculature that very often causes blindness. Because of its clinical significance, it will be helpful to have regular cost-effective eye screening for diabetic patients by developing algorithms to perform retinal image analysis, fundus image enhancement, and monitoring. The two cost-effective algorithms are proposed for exudates detection and optic disk extraction aimed for retinal images classification and diagnosis assistance. They represent the effort made to offer a cost-effective algorithm for optic disk identification, which will enable easier exudates extraction, exudates detection and retinal images classification aimed to assist ophthalmologists while making diagnoses. The proposed algorithms apply mathematical modeling, which enables light intensity levels emphasis, easier optic disk and exudates detection, efficient and correct classification of retinal images. The algorithm is robust to various appearance changes of retinal fundus images and shows very promising results. Fundus images are classified into those that are healthy and those affected by diabetes, based on the detected optic disk and exudates. The obtained results indicate that the proposed algorithm successfully and correctly classifies more than 98% of the observed retinal images because of the changes in the appearance of retinal fundus images typically encountered in clinical environments.
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