Academic literature on the topic 'Spotfires'
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Journal articles on the topic "Spotfires"
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Hall, James, Peter F. Ellis, Geoffrey J. Cary, Glenys Bishop, and Andrew L. Sullivan. "Long-distance spotting potential of bark strips of a ribbon gum (Eucalyptus viminalis)." International Journal of Wildland Fire 24, no. 8 (2015): 1109. http://dx.doi.org/10.1071/wf15031.
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Firebrands of ribbon bark eucalypt are notorious for igniting spotfires many kilometres ahead of a bushfire. However, no research to date has demonstrated that this bark type can sustain combustion at its terminal velocity for the travel time required. Fifty samples of shed bark of Eucalyptus viminalis of three distinct morphologies were ignited at one end and burned tethered in a vertical wind tunnel at air velocities approximating their terminal velocity. Mean terminal velocity and burnout time for ‘flat plates’, ‘simple cylinders’ and ‘internally convoluted cylinders’ were 5.4 m s–1 and 251 s; 5.2 m s–1 and 122 s; and 5.8 m s–1 and 429 s. The corresponding maximum burnout times were 785 s, 353 s and 1304 s. One internally convoluted cylinder flamed continuously and consumed its length of 368 mm in 271 s. The maximum burnout time for the internally convoluted cylinders is commensurate with a potential spotting distance exceeding 20 km given a mean wind speed during transport of 60 km h–1. This is the first study in which combustion times exceeding a few minutes have been recorded for this bark morphology, and thus provides some corroboration of the notoriety for long-distance spotting.
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Ahlberg, Christopher. "Spotfire." ACM SIGMOD Record 25, no. 4 (December 1996): 25–29. http://dx.doi.org/10.1145/245882.245893.
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Wilkins, Charles L. "Books and Software: Data mining with Spotfire Pro 4.0." Analytical Chemistry 72, no. 15 (August 2000): 550 A. http://dx.doi.org/10.1021/ac0028797.
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Gomez, H. L., M. A. Chavez, D. C. Doval, S. Franco, M. Arbushites, M. S. Berger, M. A. Casey, S. Stein, T. Zaks, and A. M. Martin. "Investigation of tumor biomarkers as response predictors in a monotherapy study with lapatinib (L) as a first line treatment in ErbB2 amplified women with breast cancer." Journal of Clinical Oncology 25, no. 18_suppl (June 20, 2007): 10562. http://dx.doi.org/10.1200/jco.2007.25.18_suppl.10562.
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10562 Background: Lapatinib is a potent inhibitor of both EGFR and HER2 tyrosine kinase activity. Results of the phase II trial EGF20009 in advanced or metastatic breast cancer (MBC) indicated an overall response rate of 24% to monotherapy L as first line (1L) treatment in 138 patients. In an effort to better define predictive biomarkers of response, we evaluated correlations between baseline tumor biomarker expression levels and clinical response to L. Methods: Response rate (RR) was defined as either complete response (CR) or partial response (PR). Tumor blocks were available on 118 patients from the time of most recent biopsy, and mRNA was extracted from 65 individual patient blocks for this preliminary analysis. Using qRT-PCR Taqman analysis, tumors were evaluated for expression of ERBB1- 4, PTEN, c-MYC, as well as two comparator genes for normalization. DecisionTree analysis using SpotFire software was performed to determine the genes most significantly associated with RR. Results: For the initial 65 patient samples analyzed, an elevation of ERBB2 expression was significantly associated with response to treatment with L (p=0.02) and a longer time to progression following treatment with L (p<0.0025). Furthermore, of the 17/65 responders in this preliminary study, SpotFire Decision Tree Analysis demonstrated that 16/17 (94%) who responded to L had a gene expression signature combining ERBB1, 2, and 3, which is currently being confirmed. No association was observed with ERBB4, PTEN and c-MYC in this preliminary analysis. Conclusion: This analysis suggests a correlation between elevated ERBB2 mRNA levels and both RR and TTP in patients treated with L as a 1L treatment. Analysis of the full set of samples (including genome-wide microarray analysis) is ongoing in an effort to determine biomarkers predictive of clinical activity beyond HER2. No significant financial relationships to disclose.
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Vaca, Mireya, Louie Naumovski, Chang-Heok Soh, and Jailene Leal. "Oncology early development technology to facilitate early analysis of safety and efficacy data." Journal of Clinical Oncology 38, no. 15_suppl (May 20, 2020): e14116-e14116. http://dx.doi.org/10.1200/jco.2020.38.15_suppl.e14116.
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e14116 Background: The lack of a standard data review platform for the surveillance of early oncology trials led to the formation of a cross-functional initiative with the objective of creating a data surveillance framework that would provide clinical study teams with access to near real-time data in order to be able to generate fast insights into the safety and efficacy findings of oncology early development trials so that early signal detection or clinical proof-of-concept could take place. Methods: To determine the requirements that would drive the framework, the cross-functional team first conducted user interviews with study physicians to gather information on the types of tables, graphs, and visual outputs that would be most useful to the clinical study team, as well as most useful to re-create as a standard across the early oncology portfolio, focusing at this stage, on solid tumor trials using RECIST 1.1 for the efficacy output. Once these requirements were determined, specification details, including mock-ups of the visual output and guidelines to follow, were created. Programming and development of the pilot dashboard then began using R and Spotfire as the tools of choice. Results: The resulting dashboard branded “OED Fast Insights” included a standard set of pages composed of dynamic tables and graphs organized around 3 components: study disposition, disease response, and treatment-emergent adverse events. The output closely resembled tables and graphs the clinical study team were familiar with through statistical programming output using SAS. However, leveraging the interactive functionality of Spotfire allowed the team to produce dynamic tables and graphs that could be readily filtered by different pre-specified criteria (e.g. cohort, dose level, gender, lines of therapy, best overall response, type of cancer, biomarker status, etc.) or by subject or aggregate-level views, changed by different color indication, or adjusted in other ways by the user. The dynamic aspect of spider plots, waterfall plots, and swimmer lanes were of special interest in identifying early signal detection. Conclusions: The cross-functional initiative began in 2018 and by the end of 2019 the data surveillance framework was successfully scaled and rolled-out to the entire early oncology portfolio of existing trials utilizing RECIST 1.1 solid tumor criteria. The next phase of the initiative will be to extend the framework beyond RECIST 1.1 efficacy surveillance to include hematologic cancers.
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Wilson, Aaron C., Ioannis K. Moutsatsos, Gary Yu, Javier J. Pineda, Yan Feng, and Douglas S. Auld. "A Scalable Pipeline for High-Throughput Flow Cytometry." SLAS DISCOVERY: Advancing the Science of Drug Discovery 23, no. 7 (May 16, 2018): 708–18. http://dx.doi.org/10.1177/2472555218774770.
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Flow cytometry (FC) provides high-content data for a variety of applications, including phenotypic analysis of cell surface and intracellular markers, characterization of cell supernatant or lysates, and gene expression analysis. Historically, sample preparation, acquisition, and analysis have presented as a bottleneck for running such types of assays at scale. This article will outline the solutions that have been implemented at Novartis which have allowed high-throughput FC to be successfully conducted and analyzed for a variety of cell-based assays. While these experiments were generally conducted to measure phenotypic responses from a well-characterized and information-rich small molecular probe library known as the Mechanism-of-Action (MoA) Box, they are broadly applicable to any type of test sample. The article focuses on application of automated methods for FC sample preparation in 384-well assay plates. It also highlights a pipeline for analyzing large volumes of FC data, covering a visualization approach that facilitates review of screen-level data by dynamically embedding FlowJo (FJ) workspace images for each sample into a Spotfire file, directly linking them to the metric being observed. Finally, an application of these methods to a screen for MHC-I expression upregulators is discussed.
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Allen, E., Maikke Ohlson, David Finkelstein, Carrie Rosenberger, and Paul Thomas. "Transcriptional landscape of immune responses to naturally acquired influenza (HUM6P.248)." Journal of Immunology 194, no. 1_Supplement (May 1, 2015): 190.7. http://dx.doi.org/10.4049/jimmunol.194.supp.190.7.
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Abstract Several human population studies have sought to determine what components of the immune system confer protection to influenza infection. The aim of our study was to detect transcriptional changes contributing to the illness outcome in patients with naturally acquired influenza infection. Patients with naturally acquired influenza infection were recruited into a five year prospective, longitudinal cohort. Whole blood was sampled at day of enrollment (Day 0) and Days 3, 7, 10, and 28. Additionally, clinical data was collected during the course of infection including symptoms (upper respiratory, lower respiratory, systemic, and gastrointestinal symptoms). RNA was extracted from PBMCs collected at Day 0 in a subset of influenza infected patients (N=45) and was analyzed by RNA-Seq. Genes were ranked using Median Absolute Deviation (MAD) and then hierarchical clustering analysis was conducted using Spotfire and WGCNA. We detected two distinct clusters of individuals with large differences in their transcriptional landscapes falling into four clusters of genes. Principal components analysis was unable to define the clusters using any clinical traits including age, race, gender, and illness severity. One of the four clusters was characterized as an interferon response, where the other clusters have more complex annotations. Ongoing analyses are exploring upstream mechanisms that might lead to the observed divergent transcriptional outcomes.
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Zou, Min, Yves Barmaz, Melissa Preovolos, Leszek Popko, and Timothé Ménard. "Using Statistical Modeling for Enhanced and Flexible Pharmacovigilance Audit Risk Assessment and Planning." Therapeutic Innovation & Regulatory Science 55, no. 1 (August 17, 2020): 190–96. http://dx.doi.org/10.1007/s43441-020-00205-4.
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Abstract Background The European Medicines Agency Good Pharmacovigilance Practices (GVP) guidelines provide a framework for pharmacovigilance (PV) audits, including limited guidance on risk assessment methods. Quality assurance (QA) teams of large and medium sized pharmaceutical companies generally conduct annual risk assessments of the PV system, based on retrospective review of data and pre-defined impact factors to plan for PV audits which require a high volume of manual work and resources. In addition, for companies of this size, auditing the entire “universe” of individual entities on an annual basis is generally prohibitive due to sheer volume. A risk assessment approach that enables efficient, temporal, and targeted PV audits is not currently available. Methods In this project, we developed a statistical model to enable holistic and efficient risk assessment of certain aspects of the PV system. We used findings from a curated data set from Roche operational and quality assurance PV data, covering a span of over 8 years (2011–2019) and we modeled the risk with a logistic regression on quality PV risk indicators defined as data stream statistics over sliding windows. Results We produced a model for each PV impact factor (e.g. 'Compliance to Individual Case Safety Report') for which we had enough features. For PV impact factors where modeling was not feasible, we used descriptive statistics. All the outputs were consolidated and displayed in a QA dashboard built on Spotfire®. Conclusion The model has been deployed as a quality decisioning tool available to Roche Quality professionals. It is used, for example, to inform the decision on which affiliates (i.e. pharmaceutical company commercial entities) undergo audit for PV activities. The model will be continuously monitored and fine-tuned to ensure its reliability.
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Geyer, C. E., A. Martin, B. Newstat, M. A. Casey, M. S. Berger, C. R. Oliva, S. D. Rubin, S. Stein, and D. Cameron. "Lapatinib (L) plus capecitabine (C) in HER2+ advanced breast cancer (ABC): Genomic and updated efficacy data." Journal of Clinical Oncology 25, no. 18_suppl (June 20, 2007): 1035. http://dx.doi.org/10.1200/jco.2007.25.18_suppl.1035.
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1035 Background: EGF100151 demonstrated L+C improved TTP relative to C alone in women with HER2 positive, trastuzumab- exposed ABC (Geyer, NEJM;355(26):2006). We report updated efficacy data and results of correlative studies to determine if gene expression levels in the 5-FU and HER pathways are associated with clinical benefit in L+C. Methods: Tumor blocks are available on 217/399 patients, and so far sufficient mRNA has been extracted from 64/217 blocks using qRT-PCR. Tumors were evaluated for expression of HER1–4, TS, TP, PTEN, c-MYC. In addition to univariate and multivariate analyses, a SpotFire™ DecisionTree analysis was performed to determine the genes most significantly associated with RR; these genes were further analyzed for association with TTP. Results: Updated efficacy results through April 3, 2006 demonstrate: TTP L+C (27 wks)vs C (19 wks) HR 0.57 [0.43,0.77] p=0.00013; ORR L+C (24%)vs C (14%), Odds Ratio 1.9[1, 1.34] p=0.017; OS L+C vs C HR 0.78 [0.55,1.12] p=0.177; progression in CNS L+C (2%)vs C (11%) p=0.0445. Preliminary analysis of the first 64 samples indicates that elevated baseline mRNA expression of HER2 is associated with a higher RR and longer TTP (p<0.0001) with L+C. The opposite was seen in the C arm, where elevated HER2 mRNA expression seemed to predict for a poorer outcome. Analyses of ErbB3,4, PTEN and MYC as well as TS did not correlate with RR. Conclusion: The updated efficacy results confirm the prior demonstrated benefit of L+Cvs C and provide the first evidence for a systemic anti-HER2 therapy to have an effect on the development of CNS metastases. This preliminary analysis suggests a correlation between elevated HER2 mRNA levels and RR/TTP. Further investigation, including full genome microarray analysis of samples is ongoing. [Table: see text]
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Fabro, F., E. Tóth, L. J. M. Dekker, T. M. Luider, T. M. Pierson, and S. Leenstra. "P13.07 Multi-omics as a tool for elucidating temozolomide resistance in glioblastoma multiforme." Neuro-Oncology 21, Supplement_3 (August 2019): iii63. http://dx.doi.org/10.1093/neuonc/noz126.228.
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Abstract BACKGROUND Glioblastoma multiforme is the most common and aggressive brain tumor in adults, with an average overall survival of 14 months. Current standard of care consists of tumor resection followed by radiotherapy with concomitant temozolomide and adjuvant temozolomide. However, glioblastoma recurs in all patients. The causes reside in the enhanced invasiveness and resistance to treatment, giving a clear indication that recurrent and resistant glioblastoma biology must be understood better in order to achieve future treatment strategies to benefit the patients. The complex nature of recurrent glioblastoma makes its understanding still a challenging achievement in the field. Nowadays multi-omics approaching is developing further and further and it may be used to unravel, by combining different layers of biological information, a comprehensive view of the changes occurring during the treatment. MATERIAL AND METHODS A discovery set of 13 primary patient-derived glioblastoma stem-like cultures were analysed, comprising selected resistant, induced resistant and with pre-existing resistance conditions. A characterization of transcriptome, proteome and phosphoproteome was performed using RNAseq and liquid chromatography mass spectrometry. Additional 10 paired primary and recurrent tumor tissues were utilized as a validation set. The data obtained was visualised, explored and integrated through TIBCO Spotfire, Ingenuity Pathway Analysis, STRING and COREMINE medical software. RESULTS Genetic regulatory processes such as DNA repair mechanism, mRNA splicing and chromatin assembly were shown to be common over-represented trends in resistant and recurrent glioblastomas as a result of increased genomic instability and stress deriving from acute an repeated temozolomide exposure. Due to the immense heterogeneity of glioblastomas, other proteins and genes here identified as differentially expressed need a further investigation as they also may play an important role in relevant biological processes in a patient-specific way. CONCLUSION This study provides further understanding of glioblastoma biology revealing an association with processes of recurrence and temozolomide resistance, moreover offering potential therapeutic targets for better treatment options for glioblastoma patients.
Dissertations / Theses on the topic "Spotfires"
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Ellis, Peter Francis. "The aerodynamic and combustion characteristics of eucalypt bark : a firebrand study." Phd thesis, 2000. http://hdl.handle.net/1885/49422.
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The process of spotting whereby burning firebrands are transported by convection and wind to ignite new fires ahead of the source fire is significant both economically and in terms of exposure of fire crews to dangerous situations. Spotting behaviour recorded in Australia is the worst in the world in terms of spotfire distance and concentration and this has been attributed to features of eucalypt bark types. This thesis is the first comprehensive firebrand investigation of any bark. It briefly examines selected firebrand characteristics of Eucalyptus diversicolor, E. marginata and E. bicostata and examines in detail the aerodynamic and combustion characteristics and fuel bed ignition potential of Eucalyptus obliqua...Mayne Nickless Limited, CSIRO Division of Forestry and Forest Products
Books on the topic "Spotfires"
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TIBCO Spotfire - a Comprehensive Primer. Packt Publishing, Limited, 2015.
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Dettman, Paul. Learn Spotfire for Clinical Trials. Independently Published, 2020.
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Dettman, Paul. Learn Spotfire Fast: Skills for Business Authors. Independently Published, 2020.
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Press, Spotfire Guru Book. Don't Panic! I'm a Professional Spotfire Developer : Customized 100 Page Lined Notebook Journal Gift for a Busy Spotfire Developer: Far Better Than a Throw Away Greeting Card. Independently Published, 2020.
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Phillips, Michael, and Andrew Berridge. TIBCO Spotfire : a Comprehensive Primer: Building Enterprise-Grade Data Analytics and Visualization Solutions, 2nd Edition. Packt Publishing, Limited, 2019.
Find full textConference papers on the topic "Spotfires"
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Tasgaonkar, Mugdha, Maneesh Shrivastav, and Michael Brandt. "Fueling Innovation For Medical Devices: An Interactive Market Visualization Studio For Rapid Assessment Of Healthcare Opportunities." In 2022 Design of Medical Devices Conference. American Society of Mechanical Engineers, 2022. http://dx.doi.org/10.1115/dmd2022-1005.
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Abstract An essential component of innovation for medical devices is a comprehensive market sizing assessment. This evaluation is useful for creating a solid business case and understanding market potential. This project involves the development of an online medical information dashboard for rapid market sizing assessments using business intelligence tools. A Spotfire front end was paired with a robust SQL database with market information. A visually appealing, simple, and clickable user interface was developed. This tool is designed with the technologist in mind and has value in the early prototyping stages of innovative new medical therapies.
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"Spotfire Utilisation Project: Development of a mapping tool for regions prone to vorticity-driven lateral spread." In 24th International Congress on Modelling and Simulation. Modelling and Simulation Society of Australia and New Zealand, 2021. http://dx.doi.org/10.36334/modsim.2021.g3.badlan.
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Srinivasan, Ashwin, Gaurav Modi, Rahul Agrawal, and Viren Kumar. "Application of Advanced Data Analytics for Gas Reservoirs and Wells Management." In SPE Trinidad and Tobago Section Energy Resources Conference. SPE, 2021. http://dx.doi.org/10.2118/200927-ms.
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Abstract Objectives/Scope The amount of time and effort required to access and integrate Subsurface data from multiple sources is significant. Using Advanced Data Analytics, mainly python, an integrated subsurface dashboard titled Hybrid Integrated Visualization Environment (HIVE) was created using Spotfire to empower the integrated Exploration, Development and Well Reservoir and Facilities Management (WRFM) subsurface teams in: Professionalizing data and knowledge management to have "one" version of the truth. Data consolidation and preparation to avoid repetitive manual work & Enhancing opportunity identification to optimize production and value Methods, procedure, process The approach of subsurface data integration can be broken down into 4 major steps, namely: Step 1: Python programming was used to pre-process, restructure and create unified data frames. Use of python significantly reduces the time required to pre-process a diverse number of subsurface data sources consisting of static, dynamic reservoir models, log data, historical production & pressure data and wells & completion data to name a few. Step 2: - Standard diagnostic industry recognized diagnostic plots were automated using advanced analytic techniques in HIVE with the help of unified data frames. Step 3: HIVE was created to link various internal corporate data stores like pressure, temperature, rate data from PI System (stores real time measured data), Energy Components (EC) and Oil Field Manager (OFM) in real time. This was done to ensure that data from various petroleum engineering disciplines could now be visualized and analyzed in a structured manner to make integrated business decisions. Step 4: One of the key objectives of pursuing this initiative was to ensure that subsurface professionals in Shell Trinidad and Tobago were trained and upskilled in the use of python as well visualization tools like Spotfire and Power BI to ensure the maintenance and improvement of HIVE going forward. Results, Observations, Conclusions The development of HIVE has made it easier and more efficient to access and visualize subsurface data, which was extremely time consuming earlier while using older conventional techniques. Standard diagnostic plots and visuals were developed and are now used to drive integrated decision making, with key focus being water and sand production management from a production management perspective. Consequently, HIVE also drives enhanced integration between disciplines (Petrophysics, Petroleum Geology, Production Technology, Reservoir Engineering and Production operations) and departments (Developments, Upstream and Exploration). Novel/Additive Information The petroleum industry has started to embrace the application of advanced data analytics in our day-to-day work. A successful application of these techniques results in transforming the ways of working by increasing efficiency, transparency and integration among teams.
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Cely, Alexandra, Morten Hammer, Hilde Andersen, Tao Yang, Petter Nekså, and Øivind Wilhelmsen. "Thermodynamic Model Evaluations for Hydrogen Pipeline Transportation." In SPE EuropEC - Europe Energy Conference featured at the 83rd EAGE Annual Conference & Exhibition. SPE, 2022. http://dx.doi.org/10.2118/209626-ms.
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Abstract Equations of state and transport property models for hydrogen and hydrogen mixed with natural gas components relevant for pipeline transport have been evaluated by comparing to experimental data and reference equations of state. The evaluated properties are density, speed of sound, Joule-Thompson coefficient, the isobaric and isochoric heat capacity, viscosity, and thermal conductivity. A temperature span of −10 to 50 °C and a pressure span of 1-300 bara has been set as the target range for pipeline transport. Viscosity and thermal conductivity models have been evaluated for binaries where experimental data are available. The goal of this work was to determine if models already available in commercial simulators can predict fluid properties accurate enough for engineering purposes. The classical cubic equations of state of Soave-Redich-Kwong (SRK) and Peng-Robinson (PR) with van der Waals mixing rules have been tested with parameters extracted from the common commercial simulations tools Hysys, Unisim, PVTsim Nova and Multifash. In general, the different parameter sets give similar performance. One exception is the H2-CH4 binary, where both Unisim and Multifash use a non-optimal kij interaction parameter. Neither the SRK, nor PR can describe the Joule-Thompson coefficient of hydrogen, and the error lies in the range 50%-100%. The Joule-Thompson coefficient is however small, and the effect of this misprediction on pipeline simulations might not be significant. For viscosity and thermal conductivity predictions, the SuperTRAPP model in REFPROP 10 as well as simpler viscosity models LGE, LBC and a corresponding-state-principle model of PVTsim Nova have been evaluated. The SuperTRAPP model in REFPROP 10 was found to predict viscosity and thermal conductivity within a reasonable accuracy for pure hydrogen. The simpler viscosity models LGE and LBC overestimate the viscosity of hydrogen by 65% to 90% in the transport domain of interest. For the hydrogen binary systems studied, the SuperTRAPP model for the thermal conductivity and viscosity had errors around 20% at high pressures. Comparing corresponding-state-principle viscosity models with the SuperTRAPP model gave relative deviations in the range 3.9% to 13%. The SRK equation of state is found to perform better than the PR equation of state, with a relative density error below 1% for hydrogen rich systems. A TIBCO Spotfire® visualization dashboard has been developed for easy access to the evaluation results of the large amount of data and thermodynamic models. A steady-state thermohydraulic analysis for Europipe (Norway to Europe) has been performed to evaluate the effect that using different equations of state and viscosity models have on the thermohydraulic estimations. After a review of the thermodynamic model's performance, clear guidance on model selection for hydrogen transportation is provided.