Normal Dataset Research Articles

Evaluation of Epithelial-Mesenchymal Transition Genes Involved in Iranian Gastric Cancer Patients via Transcriptome Analysis

Background: Gastric cancer is one of the most frequent cancers among men and women. Patients with gastric cancer are mostly diagnosed in advanced stages because of asymptomatic progression. Due to the heterogeneity and poor prognosis of this cancer, a comprehensive study at the transcriptome level gene expression is needed to find the various genes and mechanisms involved in gastric cancer. Differentially expressed genes (DEG) derived from high-throughput RNA-sequencing could lead to the achievement of new molecular biomarkers. Objectives: In this study, after transcriptome reanalysis, we focused on the genes involved in epithelial-mesenchymal transition (EMT) via extracellular matrix (ECM). We have aimed at finding mRNA level changes in new candidate genes among Iranian patients with gastric cancer. Methods: Six gastric cancer and two normal sequencing raw sample datasets were collected from the European Nucleotide Archive (ENA). The bioinformatic pipeline was used to reanalyze raw datasets and get DEGs, using the DESeq2 package. After analyzing, THBS2, OSMR, and CHI3L1 genes were selected for validation and verification in 25 confirmed adenocarcinoma gastric cancer patients and non-malignant normal tissues from the Iranian population by real-time polymerase chain reaction (PCR). Results: The bioinformatic analysis of raw datasets revealed many upregulated and downregulated genes in gastric cancer tissues compared with normal samples. Then, real-time PCR verified the upregulation of THBS2, OSMR, and CHI3L1 genes in a group of Iranian patients with gastric cancer. Analyzing graphs showed a significant increase in the expression of targeted genes in patients with gastric cancer (P < 0.0001, P = 0.0016, and P = 0.0002, respectively). Conclusions: The results validated an obvious increase in the expression of THBS2, OSMR, and CHI3L1 genes in gastric cancer of Iranian patients. These genes are involved in EMT and may have a role in cancer invasion if tested further for their diagnostic and prognostic value in larger sample sizes.

Support vector machine approach for model-plant mismatch detection

We develop a model-plant mismatch (MPM) detection strategy based on a novel closed-loop identification approach and one-class support vector machine (SVM) learning technique. With this scheme we can monitor MPM and noise model change separately, thus separating the MPM from noise model changes. Another advantage of this approach is that it is applicable to routine operating data that may lack any external excitation signals. Theoretical analysis of the proposed closed-loop identification is provided in this paper, showing that it can give a consistent parameter estimate for the process model even in the case where a priori knowledge about the true noise model structure is not available. A set of normal operation data with satisfactory performance is collected as the training data. We build SVM models based on process and noise model estimates from training data to predict the occurrence of MPM in the test data. The proposed technique can be applied to both single-input-single-output (SISO) and multi-input-multi-output (MIMO) systems. Two examples from paper machine control are provided to verify the effectiveness of the proposed MPM detection framework.

Enhancement of Plant Disease Detection Framework Using Cloud Computing and GPU Computing

GPUs are very useful in high performance computing. With the emerging new trend of cloud environments with GPU instances are now gaining popularity in many real time applications. GPUs in a cloud environment still needs a long way to initiate various challenges in a cloud .The gap for making this as a shared resource in the cloud is still at preliminary level and still limited to many real life problems like plant disease detection at a grass root level. Timely information to farmers about diseases is still a great bottleneck for farmers. Due to this, farmers pour many rounds of pesticides to prevent their crops from diseases. But due to lack of proper ICT, farmers are not informed properly about their crop diseases which result in high loss to ecological balance and community people. Thus, to solve the problem of delayed updation about their crop diseases to farmers, GPU processing is used instead of Normal CPU processing on the cloud. This research paper focuses on the applications of GPU Computing within cloud and to study the performance of GPU image processing and Normal CPU image processing with respect to plant disease detection framework. This paper also addresses the problem of throttling in normal CPU when used for large datasets. GPU processors had shown a four-fold increase in performance as compared to normal datasets. GPU results shown 63 times faster as compared to normal CPU for analyzing 52,486 images of healthy and diseased leaf images. This include 16 plant types and 55 leaf diseases.

Finding Grade Fake and Virus Identifying in Play Store

Deceitful behaviors in Google Play, the leading favored robot application market, fuel search ranking misuse as well as malware proliferation. to spot malware, previous job has actually centred on application practicable and also permission analysis. during this paper, we often tend to introduce Justice, a distinct system that finds and leverages traces left by scammers, to sight each malware as well as applications based on go looking rank fraudulence. Fair game correlates review tasks as well as unambiguously incorporates identified testimonial relations with linguistic and also task signals obtained from Google Play application expertise (87 K applications, 2.9 M evaluations, and 2.4 M customers, gathered over 0.5 a year), to spot suspicious applications. Fair game attains over ninety-five plc. accuracy in categorizing gold normal datasets of malware, unethical as well as Bonafede apps. we tend to reveal that seventy-five plc. of the identified malware apps have interaction in search rank fraud. Fair Play discovers numerous dishonest applications that presently escape Google Baby bouncer's detection innovation. Fair Play furthermore helped the creation of rather one,000 reviews, reportable for 193 apps, that reveal a new sort of "forceful" review project: users are troubled into composing favorable evaluations and also mount as well as assess alternative applications.

Morphometric measurement and applicable feature analysis of sacral alar-iliac screw fixation using forward engineering.

To evaluate S1AI-S4AI screw channels with three-dimensional digital technology simulation analysis and to study the feasibility and applicable features of sacral alar-iliac screw fixation in adults. Forty (20 men and 20 women) normal adult's pelvic CT scan data sets were selected to reconstruct the three-dimensional pelvic model. The ideal S1AI-S4AI screw channels were simulated, followed by precise measurement of their parameters. The results showed that there were no significant differences in the transverse angles, sagittal angles, radiuses of the maximal inscribed circles, or lengths of the screw channels in S1AI-S2AI screws between genders (P > 0.05). In contrast, the radiuses of the maximal inscribed circles on the left and right, respectively, were 5.93 ± 1.02mm and 5.92 ± 1.04mm in males and 4.64 ± 0.98mm and 4.59 ± 0.95mm in females, and there was a significant difference in S3AI screws between genders (P < 0.05). With a radius of 2.50mm considered to be standard, there were 25 cases (62.5%) with an S4AI screw channel radius ≤ 2.50mm in 40 adults, and 15 cases (37.5%; 9 males and 6 females) with a radius > 2.50mm. Furthermore, the transverse angles, the sagittal angles, the lengths of the screw channels, and the radiuses of the maximal inscribed circles were significantly different between genders in 15 cases (P < 0.05). Only one maximum ideal screw can be placed on one side at a time. With a radius of 2.50mm considered to be standard, it is feasible to place S1AI-S3AI screws with a radius > 2.50mm in the entire adult population and S4AI screws with a radius > 2.50mm in some of the adult population. Furthermore, preoperative three-dimensional reconstruction and three-matic research software can effectively simulate the sacral alar-iliac screw channels, and they can provide accurate data for clinical applications.

Identification and Classification of Cataract Stages in Old Age People Using Deep Learning Algorithm

Cataract is a dense cloudy area that forms in a lens of the eye because of which many people are going blind. More than 50% of people in old age suffer due to cataract and will not have a clear vision. In the convolutional neural network, there are many trained models which help in the classification of the object. We use transfer learning technology to train the model for the data set we have. The image feature extraction model with the inception V3 architecture trained on image net. Cataract and normal image dataset are collected. A cataract is further divided into a mature and immature cataract. The result shows whether the image is either a normal eye or cataract eye with the model accuracy being 87.5%. If in the presence of cataract, the model will identify the stage of cataract.

How we are misinterpreting physical activity intention \u2013 behavior relations and what to do about it

BackgroundStudies of the physical activity intention-behavior gap, and factors that may moderate the gap (e.g., habit, perceived behavioral control), can inform physical activity promotion efforts. Yet, these studies typically apply linear modeling procedures, and so conclusions rely on linearity and homoscedasticity assumptions, which may not hold.MethodsWe modelled and plotted physical activity intention-behavior associations and the moderation effects of habit using simulated data based on (a) normal distributions with no shared variance, (b) correlated parameters with normal distribution, and (c) realistically correlated and non-normally distributed parameters.ResultsIn the uncorrelated and correlated normal distribution datasets, no violations were unmet, and the moderation effects applied across the entire data range. However, because in the realistic dataset, few people who engaged in physical activity behavior had low intention scores, the intention-behavior association was non-linear, resulting in inflated linear moderation estimations of habit. This finding was replicated when tested with intention-behavior moderation of perceived behavioral control.ConclusionsComparisons of the three scenarios illustrated how an identical correlation coefficient may mask different types of intention-behavior association and moderation effects. These findings highlight the risk of misinterpreting tests of the intention-behavior gap and its moderators for physical activity due to unfounded statistical assumptions. The previously well-documented moderating effects of habit, whereby the impact of intention on behavior weakens as habit strength increases, may be based on statistical byproducts of unmet model assumptions.

Abstract 567: Comparative mining of normal and tumor tissue RNAseq gene expression datasets to define expression-based neo-antigens

Abstract Background: Mutational neo-antigens (MuNeoAgs) are attractive targets for cancer vaccines because they are unique to tumors. However, limited epitope potential of point mutations, inherent difficulties predicting immunogenicity, and lack of shared mutations across patient tumors has hindered vaccine development. Alternatively, we elected to identify non-mutated, expression-based tumor-specific neo-antigens (EbNeoAgs) defined by their overexpression in tumors and negligible expression in healthy tissue. EbNeoAgs have a low probability of cross-reactivity, while allowing epitope creation across the entire protein. Additionally, they should be present in a higher proportion of patients and could be used to make modular off-the-shelf vaccines. Methods: RNAseq data from the NIH Genotype-Tissue Expression (GTEx) and The Cancer Genome Atlas (TCGA) projects, previously harmonized with a common calling/ mapping algorithm and available from the University of California Santa Cruz TOIL RNAseq recompute project, were used to calculate upper quartile FPKM gene expression values for all samples based on protein coding RNAs. RNAseq data from 27 normal tissue types (excluding testis) from GTEx and TCGA normal tissue was analyzed to identify genes with negligible expression across all healthy tissue. Genes with negligible expression were then analyzed to determine if they were elevated (i.e. expressed at intermediate or higher levels) in samples across 32 different cancer types (excluding testicular tumors). Thresholds for intermediate expression were calculated individually for each tumor type based upon the median FPKM expression values for all protein coding genes with 95% confidence intervals. Results: 1296 genes were expressed below threshold in all healthy tissues, and 107 of these genes were elevated in 5% or more of samples in at least one cancer. 40/107 EbNeoAgs (37%) are known Cancer Testis Antigens (CTAs), with the remaining 67 representing potentially novel EbNeoAgs. If the threshold for elevated expression is lowered to just 1% of samples for at least one cancer, 58 of the known 276 CTAs are detected. The remaining CTAs are overexpressed in at least one healthy tissue or were not elevated in cancer. In melanoma (cancer with the most EbNeoAgs), 53 (52.0%), 69 (67.6%), and 88 (86.3%) samples expressed at least 5, 3, or 1 EbNeoAg respectively. Comparatively, in lung adenocarcinoma, 81 (15.8%), 97 (18.9%), and 228 (44.4%) samples expressed at least 5, 3, or 1 EbNeoAg respectively. Conclusion: We leveraged publicly available tumor and normal RNAseq datasets to comprehensively identify EbNeoAgs in an unbiased fashion across 32 tumor types. Many of the EbNeoAgs were known CTAs, but a significant number of EbNeoAgs were novel. Approximately half the cancers exhibited EbNeoAg expression in a large proportion of patients making them broad targets for cancer vaccine development. Citation Format: Jan O. Kemnade, Maria Cardenas, David Wheeler, Andrew G. Sikora, Mitchell J. Frederick. Comparative mining of normal and tumor tissue RNAseq gene expression datasets to define expression-based neo-antigens [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 567.

Abstract 1817: Differential expression of long non-coding RNA in colon adenocarcinoma RNA-sequence data set

Abstract Introduction: Colon cancer is the fourth most common cancer in the United States and the third leading cause of cancer-related death. High throughput genomic sequencing has led to a number of significant advances in tumor biology and in identifying novel signaling molecules such as long non-coding RNA (lncRNA). The aim of this study was to identify differentially expressed lncRNA in colon cancer from a large RNA-sequencing (RNA-seq) data set. Methods: The raw RNA-seq files of 398 patients with colon cancer were downloaded from The Cancer Genome Atlas (TCGA). Sequencing files were aligned using STAR (Spliced Transcripts Alignment to a Reference) to the most recent genome annotation. Using a subset of patients with paired colon cancer and normal colon epithelium RNA-seq data (n=40), an exploratory binomial regression model was used to calculate differential RNA expression. The most differentially expressed lncRNA were identified from the exploratory analysis and verified by comparing the larger colon cancer RNA-seq data set (n=358) with the normal colon epithelium RNA-seq data set (n=40). Results: 33,514 genes were identified from the comparative analysis, and using differential expression cut off values of &amp;gt; +1.5 or &amp;lt; -1.5 log fold change and a false discovery rate of &amp;lt;0.05; 543 were upregulated and 1822 were downregulated. Within this dysregulated group, 60 lncRNAs were identified. Using the larger data set (n=358 vs n=40), 41/60 lncRNA remained differentially expressed, of which 15 were downregulated and 26 were upregulated. Twenty-four of these lncRNAs have not previously been described in colon cancer, 12 of which are upregulated and 12 of which are downregulated (Table 1). Conclusions: This analysis of RNA-seq data from TCGA has identified dysregulated lncRNAs which have not previously been described in human colon cancer. These lncRNAs may have significant roles in colon cancer tumor signaling. Table 1.Differential expression of lncRNA not described in colon cancerlncRNALog Fold ChangeP-valueDescription in other cancersKRT16P14.1041.36E-21Increased in lung squamous cell carcinomaBOK-AS13.7784.98E-09Increased in oral squamous cell carcinomaCELP3.1151.30E-09Not availableCLDN10-AS12.3944.61E-12Not availablePOU6F2-AS12.2678.25E-09Not availableSLC7A11-AS12.1931.60E-13Decreased in gastric adenocarcinomaCSAG42.1083.59E-10Not availableLUCAT12.0224.21E-11Increased in Ovarian cancer, renal clear cell carcinoma, head and neck squamous cell carcinoma, Non-small cell cancer, glioma, osteosarcoma, esophageal squamous cell carcinomaVPS9D1-AS11.9795.27E-21Decreased in gastric adenocarcinoma, Increased in non-small cell lung cancerLEF1-AS11.8464.48E-13Increased in glioblastomaCASC81.6485.64E-06Single nucleotide polymorphisms in colorectal adenocarcinomaLINC004911.5903.11E-04Not availableLINC00648-1.5021.02E-15Not availableLINC00163-1.6751.03E-22Not availableSATB2-AS1-1.8074.18E-09Increased in osteosarcomaLINC00702-2.1424.81E-18Not availableTRHDE-AS1-2.2623.44E-24Not availableLINC00908-2.3082.76E-103Not availableLINC00483-2.4012.87E-39Increased in gastric adenocarcinomaLINC00461-2.4401.42E-41Increased in glioma, multiple myelomaFENDRR-2.6872.12E-58Decreased in breast cancer, prostate cancer, osteosarcoma, gastric adenocarcinoma,ARHGEF26-AS1-2.7148.55E-32Not availableADAMTS9-AS2-2.9041.78E-74Increased in lung cancer salivary adenoid cystic carcinoma, Decreased in glioma, gastric adenocarcinomaMT1JP-4.4601.08E-114Decreased in retinoblastoma, Decreased in gastric adenocarcinoma Citation Format: Stephen J. O'Brien, Theodore Kalbfleisch, Sudhir Srivastava, Shesh Rai, Susan Galandiuk. Differential expression of long non-coding RNA in colon adenocarcinoma RNA-sequence data set [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 1817.

Abstract 2572: Integrated viral sequences in esophageal cancer tissue

Abstract The human genome exhibits a significant degree of invasion by viral genomes. During the course of evolution, several RNA and DNA viruses have become integrated into the vertebrate genome and there is no reason to believe that the process has ended. Integrated viral DNA may account for as much as 10% of the human genome and it is very likely that they may act as carcinogens by altering normal cellular gene expression. In this study we have compared the viral DNA sequences present in normal and tumor tissues from esophageal squamous cell carcinoma (ESCC) samples. DNA was extracted from paired normal and tumor biopsies and subjected to whole genome sequencing. Raw data run folders were converted to FASTQ files and progressed through a QC pipeline. Normal and tumor data sets were then blasted and aligned against an extensive viral sequence database using specialized DRAGEN software, so that all integrated viral DNA fragment sequences were identified. The identified viral sequences were then compared between the paired normal and tumor samples. Using this approach, a large number of viral DNA sequences were identified; these included human papilloma viruses, Herpes simplex viruses, adenoviruses and Hepatitis C viruses. Furthermore, some unexpected viruses of interest were also identified. These include the Autographa Californica Nucleopolyhedrovirus, the Emiliana Huxleyi virus 86, the Gryllus bimaculatus nudivirus and the Trichodysplasia spinulosa-associated polyomavirus, present in different tumour DNA samples. Amplification and rearrangements of endogenous retroviruses such as the Human Endogenous Retrovirus K113 were also observed. Endogenous retroviruses (ERV) can serve as long-lasting viral reservoirs and provide evidence for the coexistence between retroviruses and their hosts over many generations. Defective ERVs can also recombine to affect the expression of adjacent genes. It has been proposed that integrated viral sequences may play a possible role in the etiology and promotion of diseases such as cancer, thus targeted studies of specific viral sequences may elucidate the mechanisms of gene disruption and may ultimately lead to the development of more effective therapeutic interventions. Note: This abstract was not presented at the meeting. Citation Format: Victoria A. Patten, Lamech Mwaphaga, Christopher G. Mathew, Denver T. Hendricks, M. Iqbal Parker. Integrated viral sequences in esophageal cancer tissue [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 2572.

Abstract SY25-01: Identification of enhancer elements at kidney cancer susceptibility loci using genome-wide approaches in which post-GWAS functional studies implicate the SWI/SNF DPF3 gene for the 14q24 risk locus

Abstract We investigated the set of common single nucleotide polymorphisms (SNPs) that are highly correlated with 20 renal cancer (RCC) susceptibility regions identified by Genome-wide Association Study (GWAS). Together extensive fine-mapping and in silico functional analyses, based on publicly available resources, we used an integrated approach to pursue regulatory elements in which one or more variant allele could confer differential functional activity. To this end, we conducted an initial screening Massively Parallel Reporter Assay (MPRA), followed by three additional approaches: 1. Assay for Transposase-Accessible Chromatin (ATAC-seq); 2. RCC eQTL analysis;3. Capture-HiC. We confirmed the effects reported for three regions (8q24, 11q13 and 12p12). We selected 784 SNPs with an r2&amp;gt;0.4 or D’&amp;gt;0.5 and MAF&amp;lt;0.05 that had evidence for enhancer activity based on available ENCODE transcription factor or histone ChIP-seq, FAIRE, and DNase I hypersensitive data. The MPRA library included 47,461 oligonucleotides of which, 145 bps contained the SNP in either forward or reverse orientation followed by an identification barcode. The set of 784 SNPs tested were tagged 10 separate times for both orientations, while controls were constructed by randomizing the 10 nucleotides centered on the SNP. The MPRA library was the cloned into a pMPRA reporter vector that was transfected into HEK293T and ACHN cell lines, under normoxia and hypoxia conditions; next generation sequence was performed on 5 replicates per condition and analyzed using regression for allele, direction, cell line and condition effects. For 18 of 20 RCC regions, we identified one or more functional variants while Cis-eQTL identified 16 RCC regions that mapped to alterations in expression of 198 genes. Our integrated approach underscores the spectrum of effects of genetic variation on regulation of genes critical for RCC, each notable for determining functional allelic effects on nearby (but not always the closest gene). Our work illustrates the importance of regulatory variants and RCC risk. We further pursued the region on 14q24 and confirmed the strongest signal on MPRA, the GWAS tag SNP, rs4903064, by luciferase assay. In a series of electromobility shift assays (EMSA), we showed higher binding to the rs4903064 T-allele, associated with lower risk (compared to the C-risk allele). Interestingly, the T-allele creates a binding site for IRX2/IRX5, which can act as a transcriptional repressor, whereas the C-allele generates a HIF1a binding site. By eQTL analysis, we observed higher expression of the nearby gene DPF3 (on 14q24) associated with the C-allele in both the TCGA RCC and a normal kidney eQTL data set. In four stable renal cell lines that included a DOX-dependency for overexpression of DPF3 isoforms (DPF3a or DPF3b), we performed RNA-seq analysis and observed over 500 genes deregulated when either isoform is overexpressed. To investigate chromatin accessibility changes due to DPF3 overexpression, we used ATAC-seq and found 86 regions with altered accessibility and 15% of the RNA-seq deregulated genes are located within 2 MB of one of the regions, including the promoter of the top deregulated gene (CEMIP). In growth curve assays, DPF3 increased the growth rate of renal cell lines. Moreover, knockdown of the top two DPF3-deregulated genes (CEMIP and IL23R) by siRNA, reduced the effect of DPF3, suggesting that CEMIP and IL23R could be important cofactors for growth along with DPF3. Since CEMIP is important for apoptosis and in our dox-inducible-DPF3 cell lines, DPF3 overexpression decreased the percentage of apoptotic cells, measured by flow cytometry, and reduced Caspase-3 and PARP cleavage, as determined by Western blot (WB). The second deregulated gene, IL23R, interacts with STAT3, an important RCC oncogene, which has increased phosphorylation following DPF3 overexpression and leads to increased STAT3 phosphorylation. While the SWI/SNF complex genes have been shown to influence response to immunotherapy, we extended our studies to look at co-cultured of tumor cells with stimulated fresh peripheral blood mononuclear cells (PBMCs); DPF3 overexpression in RRC cells leads to increased PBMC-cytotoxicity, mainly due to an increased proportion of T CD8+ cells. In summary, we integrated RCC post-GWAS functional studies to pinpoint notable variants on 14q24 and suggest that altering DPF3 regulation can perturb the SWI/SNF complex by reduction of apoptosis and STAT3 pathway activation. Further confirmation and follow-up studies are required to explain the functional underpinnings of the additional promising regions identified by GWAS and pursued by large-scale screening approaches and fine-mapping. Citation Format: Leandro M. Colli, Lea Jessop J. Chanock, Timothy Myers, Mitchell Machiela, Jiyeon Choi, Mark Purdue, Kevin Brown, Stephen J. Chanock. Identification of enhancer elements at kidney cancer susceptibility loci using genome-wide approaches in which post-GWAS functional studies implicate the SWI/SNF DPF3 gene for the 14q24 risk locus [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr SY25-01.

Abstract 1699: Accurate detection of low AF variants relevant to AML by Anchored Multiplex PCR and next generation sequencing

Abstract Introduction: Acute Myeloid Leukemia (AML) is clinically and biologically heterogeneous, requiring the detection of multiple mutations for characterization. For instance, FLT3-ITDs and CEBPA mutations represent important markers in AML, however they are difficult to detect by NGS due to the highly variable nature of ITDs, the high GC content of CEBPA, and the difficulty in mapping repeated sequences to a wild-type reference. Tracking low frequency mutations is also of growing importance. The ability to accurately detect variants at low allele fractions (AFs) using a single test can be used to assess treatment efficacy and potential relapse. Methods: We developed Archer® VariantPlex® myeloid assays based on Anchored Multiplex PCR (AMP™) to detect important mutations in myeloid malignancies. AMP is a target enrichment strategy that uses molecular-barcoded adapters and gene-specific primers for amplification, permitting open-ended capture of DNA fragments from a single end. This approach enables flexible and strand-specific primer design to provide better coverage of ITD-containing and GC-rich regions. We also developed a method to assess SNV sensitivity taking into account both unique coverage depth and noise for single base substitutions. This strategy enables utilization of position-specific detection thresholds and maximizes sensitivity and specificity. We tested this approach using the VariantPlex® Core Myeloid panel, by titrating reference inputs into background normal samples to examine detection of low AF variants. Results: Our assay enables calling of a 30bp FLT3-ITD down to sub-0.05% allele frequencies. Using optimized low AF conditions improves coverage depth, consistency of low AF FLT3-ITD detection, and sensitivity (98.5% of bases are powered to call a true variant at an allele frequency of 3.0% with 1M reads and 200ng of input). We show &amp;gt;1000X unique molecule coverage across the coding region of CEBPA and use this challenging region to visualize the minimum detectable AF (MDAF) at which a variant has a &amp;gt;95% probability of being detected above the noise (95MDAF). Finally, we show consistent single nucleotide variant (SNV), insertion and deletion (indel), and ITD calling at sub-0.5% allele frequencies, and demonstrate the utility of reporting variant-specific MDAFs and normal dataset P-values when analyzing low AF variants. Conclusion: AMP provides NGS-based detection of complex mutation types that are relevant in AML. We demonstrate robust calling of FLT3-ITDs and other variants at low AFs. We also demonstrate full coverage of CEBPA with high depth and low noise such that the 95MDAF predicts confident variant calling at low AFs. This approach is accurate and scalable, enabling simultaneous detection of multiple mutation types across multiple target genes in a single assay. Citation Format: Verity Johnson, Kaitlyn E. Moore, Laura M. Griffin, Aaron Berlin, Abel Licon, Ryan Walters. Accurate detection of low AF variants relevant to AML by Anchored Multiplex PCR and next generation sequencing [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 1699.

Business strategy for sustainable development: Impact of life cycle inventory and life cycle impact assessment steps in supply chain design and planning

AbstractSustainability reporting has become a central instrument in displaying a company's strategy and engagement with a more sustainable society. Life cycle assessment (LCA) is an important tool in this context as it is aimed to analyze the entire life cycle of the product/service, instead of focusing on one focal firm. However, the lack of standardized guidelines in its application raises questions regarding the usefulness and validity of results. This study contributes to the improvement of sustainability reporting through the identification of the uncertainty sources in life cycle methodologies that have the biggest impact in the definition of business and environmental strategies, exploring a supply chain‐oriented analysis. The focus is on life cycle inventory (LCI) and life cycle impact assessment (LCIA). A multiobjective optimization‐based methodology is applied to a supply chain case study in the pulp and paper industry. Environmental and economic objectives are considered. Results show that different LCIA methods and different normalization data sets result in significantly different business and environmental strategies. However, different system models and cultural perspectives at the LCI step and variations in the characterization factors tend to lead to the same decisions. Important insights are gained regarding the impact of nonstandardized analysis of the application of LCA in supply chain design and planning decisions and consequently on business strategy definition and sustainability reporting. Furthermore, the importance of operations research methods to improve LCA‐based studies and of bridging the gap between LCA and supply chain management bodies of knowledge is highlighted in this work.

Hypoxia Regulated Gene Network in Glioblastoma Has Special Algebraic Topology Structures and Revealed Communications Involving Warburg Effect and Immune Regulation.

Hypoxia regulated genes (HRGs) formed a complex molecular interaction network (MINW), contributing to many aspects of glioblastoma (GBM) tumor biology. However, little is known about the intrinsic structures of the HRGs-MINW, mainly due to a lack of analysis tools to decipher MINWs. By introducing general hyper-geometric distribution, we obtained a statistically reliable gene set of HRGs (SR-HRGs) from several datasets. Next, MINWs were reconstructed from several independent GBM expression datasets. Algebraic topological analysis was performed to quantitatively analyze the amount of equivalence classes of cycles in various dimensions by calculating the Betti numbers. Persistent homology analysis of a filtration of growing networks was further performed to examine robust topological structures in the network by investigating the Betti curves, life length of the cycles. Random networks with the same number of node and edge and degree distribution were produced as controls. As a result, GBM-HRGs-MINWs reconstructed from different datasets exhibited great consistent Betti curves to each other, which were significantly different from that of random networks. Furthermore, HRGs-MINWs reconstructed from normal brain expression datasets exhibited topological structures significantly different from that of GBM-HRGs-MINWs. Analysis of cycles in GBM-HRGs-MINWs revealed genes that had clinical implications, and key parts of the cycles were also identified in reconstructed protein-protein interaction networks. In addition, the cycles are composed by genes involved in the Warburg effect, immune regulation, and angiogenesis. In summary, GBM-HRGs-MINWs contained abundant molecular interacting cycles in different dimensions, which are composed by genes involved in multiple programs essential for the tumorigenesis of GBM, revealing novel interaction diagrams in GBM and providing novel potential therapeutic targets.

Assessment of Robustness of some Measures of Variation with Normal and Non-Normal Data Sets

In any investigation where numerical values are obtained, it is always desirable to have a typical value for all the observations, and the mean as a measure of central tendency is commonly used. Reliability of the value of the mean is strengthened when a corresponding measure of variation (also known as dispersion) for the data is obtained. This paper therefore, presents a study of robustness of some measures of dispersion namely, the variance, standard deviation, absolute mean deviation with divisor ‘n’ (AMD(n)), and absolute mean deviation with divisor ‘n-1’ (AMD(n-1)). The level of robustness of the measures of dispersion in this paper was facilitated by the adoption of simulation technique that utilized the following: small sample sizes; and large sample sizes, for both normal and non-normal data sets of different specifications. Overall, the results obtained showed that AMD (n-1) gave values that were closest in magnitude to standard deviation. The implication of the findings herein is that all the three measures of spread proved to be robust, however AMD (n-1) is a better substitute for the standard deviation.

An expanded variant list and assembly annotation identifies multiple novel coding and noncoding genes for prostate cancer risk using a normal prostate tissue eQTL data set.

Prostate cancer (PrCa) is highly heritable; 284 variants have been identified to date that are associated with increased prostate cancer risk, yet few genes contributing to its development are known. Expression quantitative trait loci (eQTL) studies link variants with affected genes, helping to determine how these variants might regulate gene expression and may influence prostate cancer risk. In the current study, we performed eQTL analysis on 471 normal prostate epithelium samples and 249 PrCa-risk variants in 196 risk loci, utilizing RNA sequencing transcriptome data based on ENSEMBL gene definition and genome-wide variant data. We identified a total of 213 genes associated with known PrCa-risk variants, including 141 protein-coding genes, 16 lncRNAs, and 56 other non-coding RNA species with differential expression. Compared to our previous analysis, where RefSeq was used for gene annotation, we identified an additional 130 expressed genes associated with known PrCa-risk variants. We detected an eQTL signal for more than half (n = 102, 52%) of the 196 loci tested; 52 (51%) of which were a Group 1 signal, indicating high linkage disequilibrium (LD) between the peak eQTL variant and the PrCa-risk variant (r2>0.5) and may help explain how risk variants influence the development of prostate cancer.

Transposable elements drive widespread expression of oncogenes in human cancers.

Transposable elements (TEs) are an abundant and rich genetic resource of regulatory sequences1–3. Cryptic regulatory elements within TEs can be epigenetically reactivated in cancer to influence oncogenesis in a process termed onco-exaptation4. However, the prevalence and impact of TE onco-exaptation events across cancer types are poorly characterized. Here, we analyzed 7,769 tumors and 625 normal datasets from 15 cancer types, identifying 129 TE cryptic promoter activation events involving 106 oncogenes across 3,864 tumors. Furthermore, we interrogated the AluJb-LIN28B candidate: the genetic deletion of the TE eliminated oncogene expression, while dynamic DNA methylation modulated promoter activity, illustrating the necessity and sufficiency of a TE for oncogene activation. Collectively, our results characterize the global profile of TE onco-exaptation and highlight this prevalent phenomenon as an important mechanism for promiscuous oncogene activation and ultimately tumorigenesis.

Perceptual Judgment of Voice Quality in Nondysphonic French Speakers: Effect of Task-, Speaker- and Listener-Related Variables

Several perceptual scales have been developed to assess voice quality in dysphonic voices, among which the Grade Roughness Breathiness Asthenia Strain and a Rate of Dysphonia scale is probably the most frequently used. However, this clinical tool has not been properly validated with a normophonic population yet. The aim of the present study was to provide a first set of reference data gathered from a normal population, to serve as a basis of comparison for vocologists and laryngologists working with French-speaking patients. A second goal was to investigate the influence on this normal voice dataset, of variables known to affect perceptual judgments of pathological voice. Sustained vowels and sentences produced by 80 healthy, normophonic French native speakers were perceptually assessed by a panel of 18 raters (nine students, nine experts) using the Grade Roughness Breathiness Asthenia Strain and a Rate of Dysphonia scale. The average overall grade was close to 1 on the (0 to 3) scale, questioning the notion of "normal" voice as opposed to dysphonic voice. Rating reliability as well as perceptual scores were affected by task-, speaker-, and listener-related factors: speech stimuli led to better rating reliability and were judged less severely than voice stimuli; experts were slightly more reliable and less severe than students; older speakers were unanimously considered as more dysphonic. Multiple interactions between these factors were observed, confirming the multidimensional nature of voice quality.

VAMPIRE® fundus image analysis algorithms: Validation and diagnostic relevance in hypertensive cats.

To validate a retinal imaging software named VAMPIRE® (Vascular Assay and Measurement Platform for Images of the Retina) in feline patients and test the clinical utility in hypertensive cats. One hundred and five healthy cats were enrolled. They represented the normal dataset used in the validation (group 1). Forty-three hypertensive cats with no noticeable retinal abnormalities were enrolled for the clinical validity of the software (group 2). Eleven points (4 veins, 4 arteries, and 3 arterial bifurcations) were measured for each digital image. Repeatability and reproducibility of measurements were assessed using two independent operators. Data were statistically analyzed by the Mann-Whiney and Tukey box plot. Significance was considered when P<0.05. Two hundred and ten retinal images were analyzed for a total of 2310 measurements. Total mean was 9.1 and 6.1 pixels for veins and arteries, respectively. First, second, and third arteriolar bifurcations angles were 73.6°, 76.9°, and 85.4°, respectively. A comparison between groups 1 and 2 showed a statistically significant reduction in arteriolar diameter (mean 3.3 pixels) and branch angle (55°, 47.8° and 59.9°) associated with increasing vein diameter (mean 24.15 pixels). Current image analysis techniques used in human medicine were investigated in terms of extending their use to veterinary medicine. The VAMPIRE® algorithm proved useful for an objective diagnosis of retinal vasculature changes secondary to systemic hypertension in cats, and could be an additional diagnostic test for feline systemic hypertension.

Principal component analysis-based control charts using support vector machines for multivariate non-normal distributions

The growing demand for statistical process monitoring has led to the vast utilization of multivariate control charts. Complicated structure of the measured variables associated with highly correlated characteristics, has given rise to daily increasing urge for reliable substitutes of conventional methods. In this regard, projection methods have been developed to address the issue of high correlation among characteristics by transforming them to an uncorrelated set of variables. Principal component analysis (PCA)-based control charts are widely used to overcome the issue of correlation among measured variables by defining linear transformations of the existing variables to a new uncorrelated space. Newly transformed variables explain different amount of variations in the measured variables with the first PC explaining the highest amount, the second PC explains the second highest one, and so on. PCA, also gives the opportunity of dimension reduction to the researcher, in cost of losing a part of information extracted from observed variation, yet using all the original measured variables. In spite of the mentioned strength of the PCA based methods, the underlying assumption of observations to be normally distributed, has limited the applicability of PCA-based schemes, as the normality assumption is widely violated in real practices. With this in regard, a distribution-free method to establish the limits of PCA-based control charts can be a good modification to keep the scheme reliable when the normality assumption is not met. The proposed method presented in this article is based on support vector machines (SVM) as a substitute for conventional methods to construct control limits for PCA-based control charts. As SVM uses real-world observations of the process, no distributional assumption is required to construct control limits. Extensive simulation experiments are conducted using normal and non-normal datasets to compare the performance of the proposed method with those of the conventional and some non-parametric methods existing in the literature. The results show a relatively good performance of the proposed method compared to others in terms of the average and the standard deviation of run lengths.