Determinants of secondary alterations in WWTR1-CAMTA1 fusion epithelioid hemangioendothelioma.

2019 ◽  
Vol 37 (15_suppl) ◽  
pp. 11045-11045
Author(s):  
Nathan David Seligson ◽  
Achal Awasthi ◽  
Sherri Z. Millis ◽  
David A. Liebner ◽  
John L. Hays ◽  
...  
Keyword(s):  
Cell Cycle ◽  
Epithelioid Hemangioendothelioma ◽  
Gene Variants ◽  
Chi Square ◽  
Genomic Variants ◽  
Variants Of Unknown Significance ◽  
Genomic Copy ◽  
Copy Numbers ◽  
Log Normal ◽  
Genomic Variant

11045 Background: Epithelioid hemangioendothelioma (EHE) is a rare vascular sarcoma characterized by the WWTR1- CAMTA1 fusion ( WC-F) in a majority of cases. EHE demonstrates a biphasic clinical course; remaining indolent for many years before suddenly demonstrating aggressive progression. Cell cycle mutations have been previously noted to account for some secondary alterations; however, little is known regarding the chronicity of these secondary alterations or their clinical implications. Here we present the largest assessment of secondary genomic variants and their clinical import. Methods: Comprehensive genomic profiling from 45 WC-F positive EHE patients (pts) were provided by Foundation Medicine (FMI). 8 of these 45 pts were treated at The Ohio State University (OSU) and were evaluated retrospectively through chart review. Known deleterious alterations, variants of unknown significance (VUS), and genomic copy quantification for the WC-F was included in our analysis. Targetable gene variants were defined by OncoKB. Chi-square and student’s t-tests were used as appropriate. Results: Genomic copy number of the WC-F best fit a log-normal distribution (range: 13-2,131 copies). 20 pts (44%) did not exhibit any secondary genomic variants. The most commonly altered genes included: CDKN2A/B (7 variants), RB1 (3 variants), and ATRX (3 variants). Commonly identified pathways included: cell cycle (9 pts, 20.0%), epigenetic modulators (7 pts, 15.6%), and DNA damage repair (7 pts, 15.6%). Eight pts exhibited targetable gene variants (18%) as defined by OncoKB. Subjects ≥50 years of age exhibited a greater proportion of clinically targetable variants (27.6% vs 0%; p = 0.02). Pts with a secondary genomic variant exhibited elevated WC-F copy numbers (p < 0.001). OSU pts with aggressive EHE were more likely to have a second genomic variant (80% vs 0%; p = 0.03) when compared to indolent EHE, with trends toward higher WC-F copy numbers (809±315 vs 207±147; p = 0.2) and older age at diagnosis (59.5±5.5 vs 36.7±8.8; p = 0.1). Conclusions: In this study, secondary genomic variants in WC-F driven EHE are more common in older patients ( > 50 yo). Further, the presence of secondary genomic variants is associated with an aggressive phenotype and may drive poor prognosis. Prospective research is needed to confirm these findings.

Prediction of Haemoglobin Level by Some Probability Distribution

2020 ◽  
Vol 9 (1) ◽  
pp. 84-88
Author(s):  
Govinda Prasad Dhungana ◽  
Laxmi Prasad Sapkota
Keyword(s):  
Probability Distribution ◽  
Normal Distribution ◽  
Goodness Of Fit ◽  
Continuous Variable ◽  
Hemoglobin Level ◽  
Chi Square ◽  
Chi Square Test ◽  
Log Normal ◽  
Two Parameters ◽  
Best Fit

 Hemoglobin level is a continuous variable. So, it follows some theoretical probability distribution Normal, Log-normal, Gamma and Weibull distribution having two parameters. There is low variation in observed and expected frequency of Normal distribution in bar diagram. Similarly, calculated value of chi-square test (goodness of fit) is observed which is lower in Normal distribution. Furthermore, plot of PDFof Normal distribution covers larger area of histogram than all of other distribution. Hence Normal distribution is the best fit to predict the hemoglobin level in future.

scholarly journals Management of Gene Variants of Unknown Significance: Analysis Method and Risk Assessment of the VHL Mutation p.P81S (c.241C>T)

2016 ◽  
Vol 18 (1) ◽  
pp. 93-103
Author(s):  
Daniela Alosi ◽  
Marie Bisgaard ◽  
Sophie Hemmingsen ◽  
Lotte Krogh ◽  
Hanne Mikkelsen ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Gene Variants ◽  
Analysis Method ◽  
Variants Of Unknown Significance ◽  
Significance Analysis ◽  
Unknown Significance

scholarly journals Estimating the Best-Fitted Probability Distribution for Monthly Maximum Temperature at the Sylhet Station in Bangladesh

2021 ◽  
Vol 2 (2) ◽  
pp. 60-67
Author(s):  
Rashidul Hasan Rashidul Hasan
Keyword(s):  
Probability Distribution ◽  
Goodness Of Fit ◽  
Probability Distributions ◽  
Probability Model ◽  
Temperature Data ◽  
Maximum Temperature ◽  
Chi Square ◽  
Goodness Of Fit Tests ◽  
Anderson Darling ◽  
Log Normal

The estimation of a suitable probability model depends mainly on the features of available temperature data at a particular place. As a result, existing probability distributions must be evaluated to establish an appropriate probability model that can deliver precise temperature estimation. The study intended to estimate the best-fitted probability model for the monthly maximum temperature at the Sylhet station in Bangladesh from January 2002 to December 2012 using several statistical analyses. Ten continuous probability distributions such as Exponential, Gamma, Log-Gamma, Beta, Normal, Log-Normal, Erlang, Power Function, Rayleigh, and Weibull distributions were fitted for these tasks using the maximum likelihood technique. To determine the model’s fit to the temperature data, several goodness-of-fit tests were applied, including the Kolmogorov-Smirnov test, Anderson-Darling test, and Chi-square test. The Beta distribution is found to be the best-fitted probability distribution based on the largest overall score derived from three specified goodness-of-fit tests for the monthly maximum temperature data at the Sylhet station.

scholarly journals PEMILIHAN DISTRIBUSI PROBABILITAS PADA ANALISA HUJAN DENGAN METODE GOODNESS OF FIT TEST

2016 ◽  
Vol 18 (2) ◽  
pp. 139-148
Author(s):  
Togani Cahyadi Upomo ◽  
Rini Kusumawardani
Keyword(s):  
Normal Distribution ◽  
Goodness Of Fit ◽  
Gumbel Distribution ◽  
Rainfall Event ◽  
Goodness Of Fit Test ◽  
Chi Square ◽  
Chi Square Test ◽  
Log Normal ◽  
Log Normal Distribution ◽  
Pearson Iii Distribution

Rainfall event is a stochastic process, so to explain and analyze this processes the probability theory and frequency analysisare used. There are four types of probability distributions.They are normal, log normal, log Pearson III and Gumbel. To find the best probabilities distribution, it will used goodness of fit test. The tests consist of chi-square and smirnov-kolmogorov. Results of the chi-square test for normal distribution, log normal and log Pearson III was 0.200, while for the Gumbel distribution was 2.333. Results of Smirnov Kolmogorov test for normal distribution D = 0.1554, log-normal distribution D = 0.1103, log Pearson III distribution D = 0.1177 and Gumbel distribution D = 0.095. All of the distribution can be accepted with a confidence level of 95%, but the best distribution is log normal distribution.Kejadian hujan merupakan proses stokastik, sehingga untuk keperluan analisa dan menjelaskan proses stokastik tersebut digunakan teori probabilitas dan analisa frekuensi. Terdapat empat jenis distribusi probabilitas yaitu distribusi normal, log normal, log pearson III dan gumbel. Untuk mencari distribusi probabilitas terbaik maka akan digunakan pengujian metode goodness of fit test. Pengujian tersebut meliputi uji chi-kuadrat dan uji smirnov kolmogorov. Hasil pengujian chi kuadrat untuk distribusi normal, log normal dan log pearson III adalah 0.200, sedangkan untuk distribusi gumbel 2.333. Hasil pengujian smirnov kolmogorov untuk distribusi normal dengan nilai D = 0.1554, distribusi log normal dengan nilai D = 0.1103, distribusi log pearson III dengan nilai D = 0.1177 dan distribusi gumbel dengan nilai D = 0.095. Seluruh distribusi dapat diterima dengan tingkat kepercayaan 95%, tetapi distribusi terbaik adalah distribusi log normal.

scholarly journals Chromosome segregation in B. subtilis is highly heterogeneous

2020 ◽  
Vol 13 (1) ◽  
Author(s):  
Nina El Najjar ◽  
Peter L. Graumann
Keyword(s):  
Cell Cycle ◽  
Dna Damage ◽  
Chromosome Segregation ◽  
Growth Rates ◽  
Slow Growth ◽  
Growth Conditions ◽  
Single Chromosome ◽  
Copy Numbers ◽  
Initiation Of Replication ◽  
Bacterial Cell Cycle

Abstract Objective The bacterial cell cycle comprises initiation of replication and ensuing elongation, concomitant chromosome segregation (in some organisms with a delay termed cohesion), and finally cell division. By quantifying the number of origin and terminus regions in exponentially growing Bacillus subtilis cells, and after induction of DNA damage, we aimed at determining cell cycle parameters at different growth rates at a single cell level. Results B. subtilis cells are mostly mero-oligoploid during fast growth and diploid during slow growth. However, we found that the number of replication origins and of termini is highly heterogeneous within the cell population at two different growth rates, and that even at slow growth, a majority of cells attempts to maintain more than a single chromosome at all times of the cell cycle. Heterogeneity of chromosome copy numbers may reflect different subpopulations having diverging growth rates even during exponential growth conditions. Cells continued to initiate replication and segregate chromosomes after induction of DNA damage, as judged by an increase in origin numbers per cell, showing that replication and segregation are relatively robust against cell cycle perturbation.

scholarly journals DeepVar: An End-to-End Deep Learning Approach for Genomic Variant Recognition in Biomedical Literature

2020 ◽  
Vol 34 (01) ◽  
pp. 598-605
Author(s):  
Chaoran Cheng ◽  
Fei Tan ◽  
Zhi Wei
Keyword(s):  
Deep Learning ◽  
Large Data ◽  
Biomedical Literature ◽  
Entity Recognition ◽  
Learning Approach ◽  
Learning Approaches ◽  
Genomic Variants ◽  
Low Resource ◽  
End To End ◽  
Genomic Variant

We consider the problem of Named Entity Recognition (NER) on biomedical scientific literature, and more specifically the genomic variants recognition in this work. Significant success has been achieved for NER on canonical tasks in recent years where large data sets are generally available. However, it remains a challenging problem on many domain-specific areas, especially the domains where only small gold annotations can be obtained. In addition, genomic variant entities exhibit diverse linguistic heterogeneity, differing much from those that have been characterized in existing canonical NER tasks. The state-of-the-art machine learning approaches heavily rely on arduous feature engineering to characterize those unique patterns. In this work, we present the first successful end-to-end deep learning approach to bridge the gap between generic NER algorithms and low-resource applications through genomic variants recognition. Our proposed model can result in promising performance without any hand-crafted features or post-processing rules. Our extensive experiments and results may shed light on other similar low-resource NER applications.

scholarly journals Statistical Analysis of Natural Events in the United States

1991 ◽  
Vol 21 (2) ◽  
pp. 253-276 ◽  
Author(s):  
Charles Levi ◽  
Christian Partrat
Keyword(s):  
United States ◽  
Statistical Analysis ◽  
Negative Binomial ◽  
The United States ◽  
Normal Model ◽  
Chi Square ◽  
Expected Frequency ◽  
Kolmogorov Smirnov ◽  
Anderson Darling ◽  
Log Normal

AbstractA statistical analysis is performed on natural events which can produce important damages to insurers. The analysis is based on hurricanes which have been observed in the United States between 1954 et 1986.At first, independence between the number and the amount of the losses is examined. Different distributions (Poisson and negative binomial for frequency and exponential, Pareto and lognormal for severity) are tested. Along classical tests as chi-square, Kolmogorov-Smirnov and non parametric tests, a test with weights on the upper tail of the distribution is used: the Anderson – Darling test.Confidence intervals for the probability of occurrence of a claim and expected frequency for different potential levels of claims are derived. The Poisson Log-normal model gives a very good fit to the data.

scholarly journals A study of SeqA subcellular localization in Escherichia coli using photo-activated localization microscopy

2015 ◽  
Vol 184 ◽  
pp. 425-450 ◽  
Author(s):  
Jacek T. Mika ◽  
Aster Vanhecke ◽  
Peter Dedecker ◽  
Toon Swings ◽  
Jeroen Vangindertael ◽  
...  
Keyword(s):  
Escherichia Coli ◽  
Cell Cycle ◽  
Bacterial Genome ◽  
Genetically Engineered ◽  
Replication Initiation ◽  
Cell Protein ◽  
Experimental Conditions ◽  
Localization Microscopy ◽  
E Coli ◽  
Copy Numbers

Escherichia coli (E. coli) cells replicate their genome once per cell cycle to pass on genetic information to the daughter cells. The SeqA protein binds the origin of replication, oriC, after DNA replication initiation and sequesters it from new initiations in order to prevent overinitiation. Conventional fluorescence microscopy studies of SeqA localization in bacterial cells have shown that the protein is localized to discrete foci. In this study we have used photo-activated localization microscopy (PALM) to determine the localization of SeqA molecules, tagged with fluorescent proteins, with a localization precision of 20–30 nm with the aim to visualize the SeqA subcellular structures in more detail than previously possible. SeqA–PAmCherry was imaged in wild type E. coli, expressed from plasmid or genetically engineered into the bacterial genome, replacing the native seqA gene. Unsynchronized cells as well as cells with a synchronized cell cycle were imaged at various time points, in order to investigate the evolution of SeqA localization during the cell cycle. We found that SeqA indeed localized into discrete foci but these were not the only subcellular localizations of the protein. A significant amount of SeqA–PAmCherry molecules was localized outside the foci and in a fraction of cells we saw patterns indicating localization at the membrane. Using quantitative PALM, we counted protein copy numbers per cell, protein copy numbers per focus, the numbers of foci per cell and the sizes of the SeqA clusters. The data showed broad cell-to-cell variation and we did not observe a correlation between SeqA–PAmCherry protein numbers and the cell cycle under the experimental conditions of this study. The numbers of SeqA–PAmCherry molecules per focus as well as the foci sizes also showed broad distributions indicating that the foci are likely not characterized by a fixed number of molecules. We also imaged an E. coli strain devoid of the dam methylase (Δdam) and observed that SeqA–PAmCherry no longer formed foci, and was dispersed throughout the cell and localized to the plasma membrane more readily. We discuss our results in the context of the limitations of the technique.

Down-regulation of cell growth and cell cycle regulated genes during chick osteoblast differentiation with the reciprocal expression of histone gene variants

Biochemistry ◽  
1989 ◽  
Vol 28 (13) ◽  
pp. 5318-5322 ◽  
Author(s):  
Victoria Shalhoub ◽  
Louis C. Gerstenfeld ◽  
David Collart ◽  
Jane B. Lian ◽  
Gary S. Stein
Keyword(s):  
Cell Cycle ◽  
Cell Growth ◽  
Osteoblast Differentiation ◽  
Histone Gene ◽  
Gene Variants ◽  
Down Regulation ◽  
Reciprocal Expression

scholarly journals Toxic effects of excess cloned centromeres.

1986 ◽  
Vol 6 (6) ◽  
pp. 2213-2222 ◽  
Author(s):  
B Futcher ◽  
J Carbon
Keyword(s):  
Saccharomyces Cerevisiae ◽  
Cell Cycle ◽  
Copy Number ◽  
Spindle Pole Body ◽  
Spindle Pole ◽  
Host Cells ◽  
Selectable Markers ◽  
Pole Body ◽  
Copy Numbers ◽  
Yeast Plasmids

Plasmids carrying a Saccharomyces cerevisiae centromere have a copy number of one or two, whereas other yeast plasmids have high copy numbers. The number of CEN plasmids per yeast cell was made artificially high by transforming cells simultaneously with several different CEN plasmids carrying different, independently selectable markers. Some host cells carried five different CEN plasmids and an average total of 13 extra copies of CEN3. Several effects were noted. The copy number of each plasmid was unexpectedly high. The plasmids were mutually unstable. Cultures contained many dead cells. The viable host cells grew more slowly than control cells, even in nonselective medium. There was a pause in the cell cycle at or just before mitosis. We conclude that an excess of centromeres is toxic and that the copy number of centromere plasmids is low partly because of selection against cells carrying multiple centromere plasmids. The toxicity may be caused by competition between the centromeres for some factor present in limiting quantities, e.g., centromere-binding proteins, microtubules, or space on the spindle pole body.

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