Data Safety and Cybersecurity

Author(s):  
Luka Murn
Keyword(s):  
Author(s):  
Marsha Meyer ◽  
Susan Enguidanos ◽  
Yujun Zhu ◽  
Denise Likar ◽  
Romilla Batra

2014 ◽  
pp. 193-202
Author(s):  
Stephen P. Glasser ◽  
O. Dale Williams
Keyword(s):  

2011 ◽  
Vol 17 (7) ◽  
pp. 574-579 ◽  
Author(s):  
Karen A. Kovach ◽  
Jill Ann Aubrecht ◽  
Mary Amanda Dew ◽  
Brad Myers ◽  
Annette DeVito Dabbs

Blood ◽  
2019 ◽  
Vol 134 (Supplement_1) ◽  
pp. 2692-2692
Author(s):  
Xueyan Chen ◽  
Megan Othus ◽  
Brent L Wood ◽  
Roland B. Walter ◽  
Pamela S. Becker ◽  
...  

Introduction: The World Health Organization (WHO) diagnoses acute myeloid leukemia (AML) if ≥20% myeloid blasts are present in peripheral blood or bone marrow. Consequently a patient with even 19% blasts is often ineligible for an "AML study". A less arbitrary means to define "AML" and myelodysplastic syndromes ("MDS") emphasizes biologic features. Here, focusing on patients with WHO-defined MDS with excess (5-19%) blasts (MDS-EB) or AML with myelodysplasia-related changes (AML-MRC) or therapy-related (t-AML) (WHO defined secondary AML), we compared morphologic blast percentage (MBP) with the frequency of mutations in genes belonging to different functional groups, and with the variant allele frequency (VAF) for individually mutated genes. Methods: 328 adults with WHO-defined AML (de novo and secondary; n=149) or MDS (n=179) and with mutational analysis by next-generation sequencing (NGS) performed at the University of Washington Hematopathology Laboratory between 2015-2017 were included. Of these, 86 had MDS-EB and 49 had secondary AML. Mutational analysis was performed using a customized, amplicon-based assay, TruSeq Custom Amplicon (Illumina, San Diego, CA). Custom oligonucleotide probes targeted specific mutational hotspots in ASXL1, CBL, CEBPA, CSF3R, EZH2, FBXW7, FGFR1, FLT3, GATA1, GATA2, HRAS, IDH1, IDH2, JAK2, KIT, KMT2A, KRAS, MAP2K1, MPL, NOTCH1, NPM1, NRAS, PDGFRA, PHF6, PTEN, RB1, RUNX1, SF3B1, SRSF2, STAG2, STAT3, TET2, TP53, U2AF1, WT1, and ZRSR2. VAF ≥5% was required to identify point mutations. Spearman's correlation coefficient was used to examine the relation between VAF of individually mutated genes and MBP. The Mann Whitney test served to compare the distribution of VAF in AML (≥20% blasts) vs. MDS (<20% blasts), before and after exclusion of subgroups as described below. Fisher's exact test was used to compare incidence of mutations. Results: 96% of cases had ≥one mutation in the 36 genes tested using NGS. Considering all 328 patients, mutations in tumor suppressor and cohesin complex genes were similarly frequent in MDS and AML, whereas spliceosomal genes, in particular SF3B1 and SRSF2, were more frequently mutated in MDS than in AML (46% vs. 26%, p<0.001). Mutations in epigenetic modifiers were more common in AML than MDS (54% vs. 42%, p= 0.035) as were transcription factor mutations (52% vs. 28%, p<0.001). However comparisons limited to MDS-EB vs. AML-MRC/t-AML, indicated the differences observed when comparing all MDS and all AML were less apparent, both statistically and more perhaps importantly with respect to observed frequencies. For example, spliceosomal gene mutations were found in 35% in MDS-EB and 27% in AML-MRC/t-AML (p=0.34) vs. 46% and 26% in all MDS and all AML. NPM1 mutations were detected in only 8% of AML-MRC/t-AML vs. 3% in MDS-EB but 29% for all AML. Results were analogous with FLT3 ITD, FLT3 TKD, and JAK2 mutations. Examining 20 individually mutated genes detected in ≥ 10 patients only with SRSF2 (p=0.04), did distribution of VAF differ statistically according to whether blast percentage was <20% versus ≥20%. Conclusions: The similar prevalence of mutations in different functional categories in MDS-EB and AML-MRC/t-AML suggests these entities are two manifestations of the same disease. We believe it appropriate to combine these WHO entities allowing patients in each to be eligible for both AML and MDS trials. Disclosures Othus: Glycomimetics: Other: Data Safety and Monitoring Committee; Celgene: Other: Data Safety and Monitoring Committee. Walter:Amgen: Consultancy; Boston Biomedical: Consultancy; Agios: Consultancy; Argenx BVBA: Consultancy; Astellas: Consultancy; BioLineRx: Consultancy; BiVictriX: Consultancy; Covagen: Consultancy; Daiichi Sankyo: Consultancy; Jazz Pharmaceuticals: Consultancy; Kite Pharma: Consultancy; New Link Genetics: Consultancy; Pfizer: Consultancy, Research Funding; Race Oncology: Consultancy; Seattle Genetics: Research Funding; Amphivena Therapeutics: Consultancy, Equity Ownership; Boehringer Ingelheim: Consultancy; Aptevo Therapeutics: Consultancy, Research Funding. Becker:Accordant Health Services/Caremark: Consultancy; AbbVie, Amgen, Bristol-Myers Squibb, Glycomimetics, Invivoscribe, JW Pharmaceuticals, Novartis, Trovagene: Research Funding; The France Foundation: Honoraria.


Author(s):  
Jing Yang ◽  
Quan Zhang ◽  
Kunpeng Liu ◽  
Peng Jin ◽  
Guoyi Zhao

In recent years, electricity big data has extensive applications in the grid companies across the provinces. However, certain problems are encountered including, the inability to generate an ideal model using the isolated data possessed by each company, and the priority concerns for data privacy and safety during big data application and sharing. In this pursuit, the present research envisaged the application of federated learning to protect the local data, and to build a uniform model for different companies affiliated to the State Grid. Federated learning can serve as an essential means for realizing the grid-wide promotion of the achievements of big data applications, while ensuring the data safety.


Author(s):  
Dharmpal Singh ◽  
Ira Nath ◽  
Pawan Kumar Singh

Big data refers to enormous amount of information which may be in planned and unplanned form. The huge capacity of data creates impracticable situation to handle with conventional database and traditional software skills. Thousands of servers are needed for its processing purpose. Big data gathers and examines huge capacity of data from various resources to determine exceptional novel awareness and recognizing the technical and commercial circumstances. However, big data discloses the endeavor to several data safety threats. Various challenges are there to maintain the privacy and security in big data. Protection of confidential and susceptible data from attackers is a vital issue. Therefore, the goal of this chapter is to discuss how to maintain security in big data to keep your organization robust, operational, flexible, and high performance, preserving its digital transformation and obtaining the complete benefit of big data, which is safe and secure.


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