Improving Gene-finding in Chlamydomonas reinhardtii:GreenGenie2

<p>Abstract</p> <p>Background</p> <p>The availability of whole-genome sequences allows for the identification of the entire set of protein coding genes as well as their regulatory regions. This can be accomplished using multiple complementary methods that include ESTs, homology searches and <it>ab initio </it>gene predictions. Previously, the Genie gene-finding algorithm was trained on a small set of <it>Chlamydomonas </it>genes and shown to improve the accuracy of gene prediction in this species compared to other available programs. To improve <it>ab initio </it>gene finding in <it>Chlamydomonas</it>, we assemble a new training set consisting of over 2,300 cDNAs by assembling over 167,000 <it>Chlamydomonas </it>EST entries in GenBank using the EST assembly tool PASA.</p> <p>Results</p> <p>The prediction accuracy of our cDNA-trained gene-finder, GreenGenie2, attains 83% sensitivity and 83% specificity for exons on short-sequence predictions. We predict about 12,000 genes in the version <it>v3 Chlamydomonas </it>genome assembly, most of which (78%) are either identical to or significantly overlap the published catalog of <it>Chlamydomonas </it>genes <abbrgrp><abbr bid="B1">1</abbr></abbrgrp>. 22% of the published catalog is absent from the GreenGenie2 predictions; there is also a fraction (23%) of GreenGenie2 predictions that are absent from the published gene catalog. Randomly chosen gene models were tested by RT-PCR and most support the GreenGenie2 predictions.</p> <p>Conclusion</p> <p>These data suggest that training with EST assemblies is highly effective and that GreenGenie2 is a valuable, complementary tool for predicting genes in <it>Chlamydomonas reinhardtii</it>.</p

The accuracy of coronary CT angiography in patients with coronary calcium score above 1000 Agatston Units:Comparison with quantitative coronary angiography: Coronary CT Angiography in High Coronary Calcium

BACKGROUND: High amounts of coronary artery calcium (CAC) pose challenges in interpretation of coronary CT angiography (CCTA). The accuracy of stenosis assessment by CCTA in patients with very extensive CAC is uncertain. METHODS: Retrospective study was performed including patients who underwent clinically directed CCTA with CAC score >1000 and invasive coronary angiography within 90 days. Segmental stenosis on CCTA was graded by visual inspection with two-observer consensus using categories of 0%, 1–24%, 25–49%, 50–69%, 70–99%, 100% stenosis, or uninterpretable. Blinded quantitative coronary angiography (QCA) was performed on all segments with stenosis ≥25% by CCTA. The primary outcome was vessel-based agreement between CCTA and QCA, using significant stenosis defined by diameter stenosis ≥ 70%. Secondary analyses on a per-patient basis and inclusive of uninterpretable segments were performed. RESULTS: 726 segments with stenosis ≥25% in 346 vessels within 119 patients were analyzed. Median coronary calcium score was 1616 (1221–2118). CCTA identification of QCA-based stenosis resulted in a per-vessel sensitivity of 79%, specificity of 75%, positive predictive value (PPV) of 45%, negative predictive value (NPV) of 93%, and accuracy 76% (68 false positive and 15 false negative). Per-patient analysis had sensitivity 94%, specificity 55%, PPV 63%, NPV 92%, and accuracy 72% (30 false-positive and 3 false-negative). Inclusion of uninterpretable segments had variable effect on sensitivity and specificity, depending on whether they are considered as significant or non-significant stenosis. CONCLUSIONS: In patients with very extensive CAC (>1000 Agatston units), CCTA retained a negative predictive value > 90% to identify lack of significant stenosis on a per-vessel and per-patient level, but frequently overestimated stenosis

Radiation tolerance of the CMS forward pixel detector

In this paper we present some results on the radiation tolerance of the CMS forward pixel detector. They were obtained from a beam test at Fermilab of a pixel-detector module, which was previously irradiated up to a maximum dose of 45 Mrad of protons at 200 MeV. It is shown that CMS forward pixel detector can tolerate this radiation dose without any major deterioration of its performance. © 2008 Elsevier B.V

High-Throughput Precision Phenotyping of Left Ventricular Hypertrophy with Cardiovascular Deep Learning

Left ventricular hypertrophy (LVH) results from chronic remodeling caused by a broad range of systemic and cardiovascular disease including hypertension, aortic stenosis, hypertrophic cardiomyopathy, and cardiac amyloidosis. Early detection and characterization of LVH can significantly impact patient care but is limited by under-recognition of hypertrophy, measurement error and variability, and difficulty differentiating etiologies of LVH. To overcome this challenge, we present EchoNet-LVH - a deep learning workflow that automatically quantifies ventricular hypertrophy with precision equal to human experts and predicts etiology of LVH. Trained on 28,201 echocardiogram videos, our model accurately measures intraventricular wall thickness (mean absolute error [MAE] 1.4mm, 95% CI 1.2-1.5mm), left ventricular diameter (MAE 2.4mm, 95% CI 2.2-2.6mm), and posterior wall thickness (MAE 1.2mm, 95% CI 1.1-1.3mm) and classifies cardiac amyloidosis (area under the curve of 0.83) and hypertrophic cardiomyopathy (AUC 0.98) from other etiologies of LVH. In external datasets from independent domestic and international healthcare systems, EchoNet-LVH accurately quantified ventricular parameters (R2 of 0.96 and 0.90 respectively) and detected cardiac amyloidosis (AUC 0.79) and hypertrophic cardiomyopathy (AUC 0.89) on the domestic external validation site. Leveraging measurements across multiple heart beats, our model can more accurately identify subtle changes in LV geometry and its causal etiologies. Compared to human experts, EchoNet-LVH is fully automated, allowing for reproducible, precise measurements, and lays the foundation for precision diagnosis of cardiac hypertrophy. As a resource to promote further innovation, we also make publicly available a large dataset of 23,212 annotated echocardiogram videos

Direct Measurements of the Convective Recycling of the Upper Troposphere

We present a statistical representation of the aggregate effects of deep convection on the chemistry and dynamics of the Upper Troposphere (UT) based on direct aircraft observations of the chemical composition of the UT over the Eastern United States and Canada during summer. These measurements provide new and unique observational constraints on the chemistry occurring downwind of convection and the rate at which air in the UT is recycled, previously only the province of model analyses. These results provide quantitative measures that can be used to evaluate global climate and chemistry models

A Global View of Cancer-Specific Transcript Variants by Subtractive Transcriptome-Wide Analysis

BACKGROUND: Alternative pre-mRNA splicing (AS) plays a central role in generating complex proteomes and influences development and disease. However, the regulation and etiology of AS in human tumorigenesis is not well understood. METHODOLOGY/PRINCIPAL FINDINGS: A Basic Local Alignment Search Tool database was constructed for the expressed sequence tags (ESTs) from all available databases of human cancer and normal tissues. An insertion or deletion in the alignment of EST/EST was used to identify alternatively spliced transcripts. Alignment of the ESTs with the genomic sequence was further used to confirm AS. Alternatively spliced transcripts in each tissue were then subtractively cross-screened to obtain tissue-specific variants. We systematically identified and characterized cancer/tissue-specific and alternatively spliced variants in the human genome based on a global view. We identified 15,093 cancer-specific variants of 9,989 genes from 27 types of human cancers and 14,376 normal tissue-specific variants of 7,240 genes from 35 normal tissues, which cover the main types of human tumors and normal tissues. Approximately 70% of these transcripts are novel. These data were integrated into a database HCSAS (http://202.114.72.39/database/human.html, pass:68756253). Moreover, we observed that the cancer-specific AS of both oncogenes and tumor suppressor genes are associated with specific cancer types. Cancer shows a preference in the selection of alternative splice-sites and utilization of alternative splicing types. CONCLUSIONS/SIGNIFICANCE: These features of human cancer, together with the discovery of huge numbers of novel splice forms for cancer-associated genes, suggest an important and global role of cancer-specific AS during human tumorigenesis. We advise the use of cancer-specific alternative splicing as a potential source of new diagnostic, prognostic, predictive, and therapeutic tools for human cancer. The global view of cancer-specific AS is not only useful for exploring the complexity of the cancer transcriptome but also widens the eyeshot of clinical research

Deep learning-enabled coronary CT angiography for plaque and stenosis quantification and cardiac risk prediction: an international multicentre study

BACKGROUND: Atherosclerotic plaque quantification from coronary CT angiography (CCTA) enables accurate assessment of coronary artery disease burden and prognosis. We sought to develop and validate a deep learning system for CCTA-derived measures of plaque volume and stenosis severity. METHODS: This international, multicentre study included nine cohorts of patients undergoing CCTA at 11 sites, who were assigned into training and test sets. Data were retrospectively collected on patients with a wide range of clinical presentations of coronary artery disease who underwent CCTA between Nov 18, 2010, and Jan 25, 2019. A novel deep learning convolutional neural network was trained to segment coronary plaque in 921 patients (5045 lesions). The deep learning network was then applied to an independent test set, which included an external validation cohort of 175 patients (1081 lesions) and 50 patients (84 lesions) assessed by intravascular ultrasound within 1 month of CCTA. We evaluated the prognostic value of deep learning-based plaque measurements for fatal or non-fatal myocardial infarction (our primary outcome) in 1611 patients from the prospective SCOT-HEART trial, assessed as dichotomous variables using multivariable Cox regression analysis, with adjustment for the ASSIGN clinical risk score. FINDINGS: In the overall test set, there was excellent or good agreement, respectively, between deep learning and expert reader measurements of total plaque volume (intraclass correlation coefficient [ICC] 0·964) and percent diameter stenosis (ICC 0·879; both p<0·0001). When compared with intravascular ultrasound, there was excellent agreement for deep learning total plaque volume (ICC 0·949) and minimal luminal area (ICC 0·904). The mean per-patient deep learning plaque analysis time was 5·65 s (SD 1·87) versus 25·66 min (6·79) taken by experts. Over a median follow-up of 4·7 years (IQR 4·0–5·7), myocardial infarction occurred in 41 (2·5%) of 1611 patients from the SCOT-HEART trial. A deep learning-based total plaque volume of 238·5 mm(3) or higher was associated with an increased risk of myocardial infarction (hazard ratio [HR] 5·36, 95% CI 1·70–16·86; p=0·0042) after adjustment for the presence of deep learning-based obstructive stenosis (HR 2·49, 1·07–5·50; p=0·0089) and the ASSIGN clinical risk score (HR 1·01, 0·99–1·04; p=0·35). INTERPRETATION: Our novel, externally validated deep learning system provides rapid measurements of plaque volume and stenosis severity from CCTA that agree closely with expert readers and intravascular ultrasound, and could have prognostic value for future myocardial infarction

Hundreds of variants clustered in genomic loci and biological pathways affect human height

Most common human traits and diseases have a polygenic pattern of inheritance: DNA sequence variants at many genetic loci influence the phenotype. Genome-wide association (GWA) studies have identified more than 600 variants associated with human traits, but these typically explain small fractions of phenotypic variation, raising questions about the use of further studies. Here, using 183,727 individuals, we show that hundreds of genetic variants, in at least 180 loci, influence adult height, a highly heritable and classic polygenic trait. The large number of loci reveals patterns with important implications for genetic studies of common human diseases and traits. First, the 180 loci are not random, but instead are enriched for genes that are connected in biological pathways (P = 0.016) and that underlie skeletal growth defects (P < 0.001). Second, the likely causal gene is often located near the most strongly associated variant: in 13 of 21 loci containing a known skeletal growth gene, that gene was closest to the associated variant. Third, at least 19 loci have multiple independently associated variants, suggesting that allelic heterogeneity is a frequent feature of polygenic traits, that comprehensive explorations of already-discovered loci should discover additional variants and that an appreciable fraction of associated loci may have been identified. Fourth, associated variants are enriched for likely functional effects on genes, being over-represented among variants that alter amino-acid structure of proteins and expression levels of nearby genes. Our data explain approximately 10% of the phenotypic variation in height, and we estimate that unidentified common variants of similar effect sizes would increase this figure to approximately 16% of phenotypic variation (approximately 20% of heritable variation). Although additional approaches are needed to dissect the genetic architecture of polygenic human traits fully, our findings indicate that GWA studies can identify large numbers of loci that implicate biologically relevant genes and pathways.