Activation of Transcriptional Activities of AP-1 and SRE by a New Zinc
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A Computational Approach for Defining a Signature of Β-Cell Golgi Stress in Diabetes Mellitus
Page 1 of 781 Diabetes A Computational Approach for Defining a Signature of β-Cell Golgi Stress in Diabetes Mellitus Robert N. Bone1,6,7, Olufunmilola Oyebamiji2, Sayali Talware2, Sharmila Selvaraj2, Preethi Krishnan3,6, Farooq Syed1,6,7, Huanmei Wu2, Carmella Evans-Molina 1,3,4,5,6,7,8* Departments of 1Pediatrics, 3Medicine, 4Anatomy, Cell Biology & Physiology, 5Biochemistry & Molecular Biology, the 6Center for Diabetes & Metabolic Diseases, and the 7Herman B. Wells Center for Pediatric Research, Indiana University School of Medicine, Indianapolis, IN 46202; 2Department of BioHealth Informatics, Indiana University-Purdue University Indianapolis, Indianapolis, IN, 46202; 8Roudebush VA Medical Center, Indianapolis, IN 46202. *Corresponding Author(s): Carmella Evans-Molina, MD, PhD ([email protected]) Indiana University School of Medicine, 635 Barnhill Drive, MS 2031A, Indianapolis, IN 46202, Telephone: (317) 274-4145, Fax (317) 274-4107 Running Title: Golgi Stress Response in Diabetes Word Count: 4358 Number of Figures: 6 Keywords: Golgi apparatus stress, Islets, β cell, Type 1 diabetes, Type 2 diabetes 1 Diabetes Publish Ahead of Print, published online August 20, 2020 Diabetes Page 2 of 781 ABSTRACT The Golgi apparatus (GA) is an important site of insulin processing and granule maturation, but whether GA organelle dysfunction and GA stress are present in the diabetic β-cell has not been tested. We utilized an informatics-based approach to develop a transcriptional signature of β-cell GA stress using existing RNA sequencing and microarray datasets generated using human islets from donors with diabetes and islets where type 1(T1D) and type 2 diabetes (T2D) had been modeled ex vivo. To narrow our results to GA-specific genes, we applied a filter set of 1,030 genes accepted as GA associated. -
A Flexible Microfluidic System for Single-Cell Transcriptome Profiling
www.nature.com/scientificreports OPEN A fexible microfuidic system for single‑cell transcriptome profling elucidates phased transcriptional regulators of cell cycle Karen Davey1,7, Daniel Wong2,7, Filip Konopacki2, Eugene Kwa1, Tony Ly3, Heike Fiegler2 & Christopher R. Sibley 1,4,5,6* Single cell transcriptome profling has emerged as a breakthrough technology for the high‑resolution understanding of complex cellular systems. Here we report a fexible, cost‑efective and user‑ friendly droplet‑based microfuidics system, called the Nadia Instrument, that can allow 3′ mRNA capture of ~ 50,000 single cells or individual nuclei in a single run. The precise pressure‑based system demonstrates highly reproducible droplet size, low doublet rates and high mRNA capture efciencies that compare favorably in the feld. Moreover, when combined with the Nadia Innovate, the system can be transformed into an adaptable setup that enables use of diferent bufers and barcoded bead confgurations to facilitate diverse applications. Finally, by 3′ mRNA profling asynchronous human and mouse cells at diferent phases of the cell cycle, we demonstrate the system’s ability to readily distinguish distinct cell populations and infer underlying transcriptional regulatory networks. Notably this provided supportive evidence for multiple transcription factors that had little or no known link to the cell cycle (e.g. DRAP1, ZKSCAN1 and CEBPZ). In summary, the Nadia platform represents a promising and fexible technology for future transcriptomic studies, and other related applications, at cell resolution. Single cell transcriptome profling has recently emerged as a breakthrough technology for understanding how cellular heterogeneity contributes to complex biological systems. Indeed, cultured cells, microorganisms, biopsies, blood and other tissues can be rapidly profled for quantifcation of gene expression at cell resolution. -
High-Resolution Analysis of Chromosomal Breakpoints and Genomic Instability Identifies PTPRD As a Candidate Tumor Suppressor Gene in Neuroblastoma
Research Article High-Resolution Analysis of Chromosomal Breakpoints and Genomic Instability Identifies PTPRD as a Candidate Tumor Suppressor Gene in Neuroblastoma Raymond L. Stallings,1 Prakash Nair,1 John M. Maris,2 Daniel Catchpoole,3 Michael McDermott,4 Anne O’Meara,5 and Fin Breatnach5 1Children’s Cancer Research Institute and Department of Pediatrics, University of Texas Health Science Center at San Antonio, San Antonio, Texas; 2Division of Oncology, Children’s Hospital of Philadelphia and Department of Pediatrics, University of Pennsylvania School of Medicine, Philadelphia, Pennsylvania; 3The Tumor Bank, Children’s Hospital at Westmead, Sydney, New South Wales, Australia; and Departments of 4Pathology and 5Oncology, Our Lady’s Hospital for Sick Children, Dublin, Ireland Abstract and death from disease. Patient age, tumor stage, and several Although neuroblastoma is characterized by numerous different genetic abnormalities are important factors that influence clinical outcome. Loss of 1p and 11q, gain of 17q, and amplification recurrent, large-scale chromosomal imbalances, the genes MYCN targeted by such imbalances have remained elusive. We have of the oncogene are particularly strong genetic indicators of poor disease outcome (2–5). Two of these abnormalities, loss of 11q applied whole-genome oligonucleotide array comparative MYCN genomic hybridization (median probe spacing 6 kb) to 56 and amplification, form the basis for dividing advanced- stage neuroblastomas into genetic subtypes due to their rather neuroblastoma tumors and cell lines to identify genes involved with disease pathogenesis. This set oftumors was selected for striking inverse distribution in tumors (6, 7). Many other recurrent having either 11q loss or MYCN amplification, abnormalities partial chromosomal imbalances, including loss of 3p, 4p, 9p, and that define the two most common genetic subtypes of 14q and gain of 1q, 2p, 7q, and 11p, have been identified by metastatic neuroblastoma. -
Supplementary Table S4. FGA Co-Expressed Gene List in LUAD
Supplementary Table S4. FGA co-expressed gene list in LUAD tumors Symbol R Locus Description FGG 0.919 4q28 fibrinogen gamma chain FGL1 0.635 8p22 fibrinogen-like 1 SLC7A2 0.536 8p22 solute carrier family 7 (cationic amino acid transporter, y+ system), member 2 DUSP4 0.521 8p12-p11 dual specificity phosphatase 4 HAL 0.51 12q22-q24.1histidine ammonia-lyase PDE4D 0.499 5q12 phosphodiesterase 4D, cAMP-specific FURIN 0.497 15q26.1 furin (paired basic amino acid cleaving enzyme) CPS1 0.49 2q35 carbamoyl-phosphate synthase 1, mitochondrial TESC 0.478 12q24.22 tescalcin INHA 0.465 2q35 inhibin, alpha S100P 0.461 4p16 S100 calcium binding protein P VPS37A 0.447 8p22 vacuolar protein sorting 37 homolog A (S. cerevisiae) SLC16A14 0.447 2q36.3 solute carrier family 16, member 14 PPARGC1A 0.443 4p15.1 peroxisome proliferator-activated receptor gamma, coactivator 1 alpha SIK1 0.435 21q22.3 salt-inducible kinase 1 IRS2 0.434 13q34 insulin receptor substrate 2 RND1 0.433 12q12 Rho family GTPase 1 HGD 0.433 3q13.33 homogentisate 1,2-dioxygenase PTP4A1 0.432 6q12 protein tyrosine phosphatase type IVA, member 1 C8orf4 0.428 8p11.2 chromosome 8 open reading frame 4 DDC 0.427 7p12.2 dopa decarboxylase (aromatic L-amino acid decarboxylase) TACC2 0.427 10q26 transforming, acidic coiled-coil containing protein 2 MUC13 0.422 3q21.2 mucin 13, cell surface associated C5 0.412 9q33-q34 complement component 5 NR4A2 0.412 2q22-q23 nuclear receptor subfamily 4, group A, member 2 EYS 0.411 6q12 eyes shut homolog (Drosophila) GPX2 0.406 14q24.1 glutathione peroxidase -
Genome-Wide Methylation Sequencing Identifies Progression
Zhou et al. Cell Death and Disease (2020) 11:997 https://doi.org/10.1038/s41419-020-03213-2 Cell Death & Disease ARTICLE Open Access Genome-wide methylation sequencing identifies progression-related epigenetic drivers in myelodysplastic syndromes Jing-dong Zhou1,2,3, Ting-juan Zhang1,2,3, Zi-jun Xu2,3,4,Zhao-qunDeng2,3,4,YuGu1,2,3,Ji-chunMa2,3,4, Xiang-mei Wen2,3,4, Jia-yan Leng1,2,3,JiangLin2,3,4, Su-ning Chen5,6 and Jun Qian1,2,3 Abstract The potential mechanism of myelodysplastic syndromes (MDS) progressing to acute myeloid leukemia (AML) remains poorly elucidated. It has been proved that epigenetic alterations play crucial roles in the pathogenesis of cancer progression including MDS. However, fewer studies explored the whole-genome methylation alterations during MDS progression. Reduced representation bisulfite sequencing was conducted in four paired MDS/secondary AML (MDS/ sAML) patients and intended to explore the underlying methylation-associated epigenetic drivers in MDS progression. In four paired MDS/sAML patients, cases at sAML stage exhibited significantly increased methylation level as compared with the matched MDS stage. A total of 1090 differentially methylated fragments (DMFs) (441 hypermethylated and 649 hypomethylated) were identified involving in MDS pathogenesis, whereas 103 DMFs (96 hypermethylated and 7 hypomethylated) were involved in MDS progression. Targeted bisulfite sequencing further identified that aberrant GFRA1, IRX1, NPY, and ZNF300 methylation were frequent events in an additional group of de novo MDS and AML patients, of which only ZNF300 methylation was associated with ZNF300 expression. Subsequently, ZNF300 fi 1234567890():,; 1234567890():,; 1234567890():,; 1234567890():,; hypermethylation in larger cohorts of de novo MDS and AML patients was con rmed by real-time quantitative methylation-specific PCR. -
Most Variable Genes and Transcription Factors in Acute Lymphoblastic Leukemia Patients
Interdisciplinary Sciences: Computational Life Sciences https://doi.org/10.1007/s12539-019-00325-y ORIGINAL RESEARCH ARTICLE Most Variable Genes and Transcription Factors in Acute Lymphoblastic Leukemia Patients Anil Kumar Tomar1 · Rahul Agarwal2 · Bishwajit Kundu1 Received: 24 September 2018 / Revised: 21 January 2019 / Accepted: 26 February 2019 © International Association of Scientists in the Interdisciplinary Areas 2019 Abstract Acute lymphoblastic leukemia (ALL) is a hematologic tumor caused by cell cycle aberrations due to accumulating genetic disturbances in the expression of transcription factors (TFs), signaling oncogenes and tumor suppressors. Though survival rate in childhood ALL patients is increased up to 80% with recent medical advances, treatment of adults and childhood relapse cases still remains challenging. Here, we have performed bioinformatics analysis of 207 ALL patients’ mRNA expression data retrieved from the ICGC data portal with an objective to mark out the decisive genes and pathways responsible for ALL pathogenesis and aggression. For analysis, 3361 most variable genes, including 276 transcription factors (out of 16,807 genes) were sorted based on the coefcient of variance. Silhouette width analysis classifed 207 ALL patients into 6 subtypes and heat map analysis suggests a need of large and multicenter dataset for non-overlapping subtype classifcation. Overall, 265 GO terms and 32 KEGG pathways were enriched. The lists were dominated by cancer-associated entries and highlight crucial genes and pathways that can be targeted for designing more specifc ALL therapeutics. Diferential gene expression analysis identifed upregulation of two important genes, JCHAIN and CRLF2 in dead patients’ cohort suggesting their pos- sible involvement in diferent clinical outcomes in ALL patients undergoing the same treatment. -
The Three-Dimensional Structure of the Cystic Fibrosis Locus: a Dissertation
University of Massachusetts Medical School eScholarship@UMMS GSBS Dissertations and Theses Graduate School of Biomedical Sciences 2014-11-18 The Three-Dimensional Structure of the Cystic Fibrosis Locus: A Dissertation Emily M. Smith University of Massachusetts Medical School Let us know how access to this document benefits ou.y Follow this and additional works at: https://escholarship.umassmed.edu/gsbs_diss Part of the Genetic Processes Commons, Genomics Commons, and the Structural Biology Commons Repository Citation Smith EM. (2014). The Three-Dimensional Structure of the Cystic Fibrosis Locus: A Dissertation. GSBS Dissertations and Theses. https://doi.org/10.13028/M2SK51. Retrieved from https://escholarship.umassmed.edu/gsbs_diss/744 This material is brought to you by eScholarship@UMMS. It has been accepted for inclusion in GSBS Dissertations and Theses by an authorized administrator of eScholarship@UMMS. For more information, please contact [email protected]. THE THREE-DIMENSIONAL STRUCTURE OF THE CYSTIC FIBROSIS LOCUS A Dissertation Presented By EMILY MALINDA SMITH Submitted to the Faculty of the University of Massachusetts Graduate School of Biomedical Sciences, Worcester in partial fulfillment of the requirements for the degree of DOCTOR OF PHILOSOPHY NOVEMBER 18, 2014 BIOMEDICAL SCIENCES THE THREE-DIMENSIONAL STRUCTURE OF THE CYSTIC FIBROSIS LOCUS Dissertation Presented By Emily Malinda Smith The signatures of the ee signify completion and approval as t the Dissertation The signature of the Chair of the Committee signifies that the written dissertation meets the requirements of the Dissertation Committee Sean omm1ttee The signature of the Dean of the Graduate School of Biomedical Sciences signifies that the student has met al aduation requirements of the school. -
Table SII. Significantly Differentially Expressed Mrnas of GSE23558 Data Series with the Criteria of Adjusted P<0.05 And
Table SII. Significantly differentially expressed mRNAs of GSE23558 data series with the criteria of adjusted P<0.05 and logFC>1.5. Probe ID Adjusted P-value logFC Gene symbol Gene title A_23_P157793 1.52x10-5 6.91 CA9 carbonic anhydrase 9 A_23_P161698 1.14x10-4 5.86 MMP3 matrix metallopeptidase 3 A_23_P25150 1.49x10-9 5.67 HOXC9 homeobox C9 A_23_P13094 3.26x10-4 5.56 MMP10 matrix metallopeptidase 10 A_23_P48570 2.36x10-5 5.48 DHRS2 dehydrogenase A_23_P125278 3.03x10-3 5.40 CXCL11 C-X-C motif chemokine ligand 11 A_23_P321501 1.63x10-5 5.38 DHRS2 dehydrogenase A_23_P431388 2.27x10-6 5.33 SPOCD1 SPOC domain containing 1 A_24_P20607 5.13x10-4 5.32 CXCL11 C-X-C motif chemokine ligand 11 A_24_P11061 3.70x10-3 5.30 CSAG1 chondrosarcoma associated gene 1 A_23_P87700 1.03x10-4 5.25 MFAP5 microfibrillar associated protein 5 A_23_P150979 1.81x10-2 5.25 MUCL1 mucin like 1 A_23_P1691 2.71x10-8 5.12 MMP1 matrix metallopeptidase 1 A_23_P350005 2.53x10-4 5.12 TRIML2 tripartite motif family like 2 A_24_P303091 1.23x10-3 4.99 CXCL10 C-X-C motif chemokine ligand 10 A_24_P923612 1.60x10-5 4.95 PTHLH parathyroid hormone like hormone A_23_P7313 6.03x10-5 4.94 SPP1 secreted phosphoprotein 1 A_23_P122924 2.45x10-8 4.93 INHBA inhibin A subunit A_32_P155460 6.56x10-3 4.91 PICSAR P38 inhibited cutaneous squamous cell carcinoma associated lincRNA A_24_P686965 8.75x10-7 4.82 SH2D5 SH2 domain containing 5 A_23_P105475 7.74x10-3 4.70 SLCO1B3 solute carrier organic anion transporter family member 1B3 A_24_P85099 4.82x10-5 4.67 HMGA2 high mobility group AT-hook 2 A_24_P101651 -
Supplementary Table 1
Supplementary Table 1. 492 genes are unique to 0 h post-heat timepoint. The name, p-value, fold change, location and family of each gene are indicated. Genes were filtered for an absolute value log2 ration 1.5 and a significance value of p ≤ 0.05. Symbol p-value Log Gene Name Location Family Ratio ABCA13 1.87E-02 3.292 ATP-binding cassette, sub-family unknown transporter A (ABC1), member 13 ABCB1 1.93E-02 −1.819 ATP-binding cassette, sub-family Plasma transporter B (MDR/TAP), member 1 Membrane ABCC3 2.83E-02 2.016 ATP-binding cassette, sub-family Plasma transporter C (CFTR/MRP), member 3 Membrane ABHD6 7.79E-03 −2.717 abhydrolase domain containing 6 Cytoplasm enzyme ACAT1 4.10E-02 3.009 acetyl-CoA acetyltransferase 1 Cytoplasm enzyme ACBD4 2.66E-03 1.722 acyl-CoA binding domain unknown other containing 4 ACSL5 1.86E-02 −2.876 acyl-CoA synthetase long-chain Cytoplasm enzyme family member 5 ADAM23 3.33E-02 −3.008 ADAM metallopeptidase domain Plasma peptidase 23 Membrane ADAM29 5.58E-03 3.463 ADAM metallopeptidase domain Plasma peptidase 29 Membrane ADAMTS17 2.67E-04 3.051 ADAM metallopeptidase with Extracellular other thrombospondin type 1 motif, 17 Space ADCYAP1R1 1.20E-02 1.848 adenylate cyclase activating Plasma G-protein polypeptide 1 (pituitary) receptor Membrane coupled type I receptor ADH6 (includes 4.02E-02 −1.845 alcohol dehydrogenase 6 (class Cytoplasm enzyme EG:130) V) AHSA2 1.54E-04 −1.6 AHA1, activator of heat shock unknown other 90kDa protein ATPase homolog 2 (yeast) AK5 3.32E-02 1.658 adenylate kinase 5 Cytoplasm kinase AK7 -
Placenta DNA Methylation at ZNF300 Is Associated with Fetal Sex and Placental Morphology
bioRxiv preprint doi: https://doi.org/10.1101/2021.03.05.433992; this version posted March 7, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. Placenta DNA methylation at ZNF300 is associated with fetal sex and placental morphology Christine Ladd-Acosta1,2,*,‡, Shan V. Andrews1,2,‡, Kelly M. Bakulski3, Jason I. Feinberg2,4, Rakel Tryggvadottir5, Ruofan Yao6, Lisa A. Croen7, Irva Hertz-Picciotto8,9, Craig J. Newschaffer10,11, Carolyn M. Salafia12, Andrew P. Feinberg4,5,13, Kasper D. Hansen14,15, M. Daniele Fallin2,4,5 1Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, 615 N. Wolfe Street, Baltimore, MD 21205, USA 2Wendy Klag Center for Autism and Developmental Disabilities, Johns Hopkins Bloomberg School of Public Health, 615 N. Wolfe Street, Baltimore, MD 21205, USA 3Department of Epidemiology, University of Michigan School of Public Health, 1415 Washington Heights, Ann Arbor, MI 48109, USA 4Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, 624 N. Broadway, Baltimore, MD, 21205, USA 5Center for Epigenetics, Institute for Basic Biomedical Sciences, Johns Hopkins School of Medicine, 733 N. Broadway, Baltimore, MD 21205, USA 6Department of Obstetrics and Gynecology, Loma Linda University School of Medicine, 11234 Anderson St, Loma Linda, CA 92354, USA 7Division of Research, Kaiser Permanente Northern California, 2000 Broadway, Oakland, CA 94612, USA 8Department of Public Health Sciences, School of Medicine, University of California Davis, 4610 X St, Sacramento, CA 95817, USA 9MIND Institute, University of California Davis, 2825 50th St, Sacramento, CA 95817, USA 10AJ Drexel Autism Institute, Drexel University, 3020 Market St #560, Philadelphia, PA 19104, USA. -
Discovery of Biased Orientation of Human DNA Motif Sequences
bioRxiv preprint doi: https://doi.org/10.1101/290825; this version posted January 27, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. 1 Discovery of biased orientation of human DNA motif sequences 2 affecting enhancer-promoter interactions and transcription of genes 3 4 Naoki Osato1* 5 6 1Department of Bioinformatic Engineering, Graduate School of Information Science 7 and Technology, Osaka University, Osaka 565-0871, Japan 8 *Corresponding author 9 E-mail address: [email protected], [email protected] 10 1 bioRxiv preprint doi: https://doi.org/10.1101/290825; this version posted January 27, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. 11 Abstract 12 Chromatin interactions have important roles for enhancer-promoter interactions 13 (EPI) and regulating the transcription of genes. CTCF and cohesin proteins are located 14 at the anchors of chromatin interactions, forming their loop structures. CTCF has 15 insulator function limiting the activity of enhancers into the loops. DNA binding 16 sequences of CTCF indicate their orientation bias at chromatin interaction anchors – 17 forward-reverse (FR) orientation is frequently observed. DNA binding sequences of 18 CTCF were found in open chromatin regions at about 40% - 80% of chromatin 19 interaction anchors in Hi-C and in situ Hi-C experimental data. -
SUPPLEMENTAL TABLE 1: List of Shrnas Used for Validation
Singleton, Kim, Hinz, Marek, Casas-Selves, Hatheway, Tan, DeGregori and Heasley. A Receptor Tyrosine Kinase Network Comprised of FGFRs, EGFR, ERBB2 and MET Drives Growth and Survival of Head and Neck Squamous Carcinoma Cell Lines. MOLPHARM #084111 SUPPLEMENTAL TABLE 1: List of shRNAs used for validation. Singleton, Kim, Hinz, Marek, Casas-Selves, Hatheway, Tan, DeGregori and Heasley. A Receptor Tyrosine Kinase Network Comprised of FGFRs, EGFR, ERBB2 and MET Drives Growth and Survival of Head and Neck Squamous Carcinoma Cell Lines. MOLPHARM #084111 SUPPLEMENTAL TABLE 2: SLATs (E-value cut off of E value ≤2) in UMSCC25, 584-A2, and CCL30. UMSCC25 Rank Gene Symbol p-value [p(wZ)] E value Rank Gene Symbol p-value [p(wZ)] E value 1 CLUAP1 5.15E-07 5.15E-07 48 FHL1 4.84E-04 2.32E-02 2 ZFR 8.78E-07 1.76E-06 49 RABGGTB 5.03E-04 2.46E-02 3 MET 1.94E-06 5.83E-06 50 THPO 5.28E-04 2.64E-02 4 MEMO1 1.95E-06 7.79E-06 50 BCAP29 5.28E-04 2.64E-02 5 NPAT 3.00E-06 1.50E-05 52 KBTBD4 5.28E-04 2.75E-02 6 EPB41L4B 3.18E-06 1.91E-05 53 MEX3B 5.28E-04 2.80E-02 7 G2E3 3.43E-06 2.40E-05 54 EML4 5.29E-04 2.85E-02 8 PSMD13 4.10E-06 3.28E-05 55 C20orf7 5.29E-04 2.91E-02 9 MUC7 5.60E-06 5.04E-05 56 GPR126 5.29E-04 2.96E-02 10 ARSD 5.67E-06 5.67E-05 56 PNPO 5.29E-04 2.96E-02 11 TMEM163 1.13E-05 1.24E-04 58 TJAP1 5.29E-04 3.07E-02 12 PCTK2 1.38E-05 1.65E-04 58 SLC26A2 5.29E-04 3.07E-02 13 ANP32E 1.38E-05 1.80E-04 58 NETO2 5.29E-04 3.07E-02 14 ITGA6 2.62E-05 3.66E-04 61 MAML3 5.29E-04 3.23E-02 15 C14orf147 2.74E-05 4.11E-04 62 HLA-DRB3 5.30E-04 3.29E-02 16 MDH1B 3.18E-05