New class of hybrid EoS and Bayesian M - R data analysis

The data management industry has matured over the last three decades, primarily based on Relational Data Base Management Systems (RDBMS) technology. The amount of data collected and analyzed in enterprises has increased several folds in volume, variety and velocity of generation and consumption, organizations have started struggling with architectural limitations of traditional RDBMS architecture. As a result a new class of systems had to be designed and implemented, giving rise to the new phenomenon of “Big Data”. The data-driven world has the potential to improve the efficiencies of enterprises and improve the quality of our lives. There are a number of challenges that must be addressed to allow us to exploit the full potential of Big Data. This article highlights the key technical challenges of Big Data.

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Multivariate dependent renewal processes

Advances in Applied Probability ◽

10.1017/s0001867800022564 ◽

1984 ◽

Vol 16 (02) ◽

pp. 347-362

Author(s):

Eric Slud

Keyword(s):

Asymptotic Behavior ◽

Data Analysis ◽

Survival Data ◽

Process Models ◽

Partial Likelihood ◽

Renewal Processes ◽

Likelihood Methods ◽

New Class ◽

New Models ◽

Point Process Models

A new class of reliability point-process models for dependent components is introduced. The dependence is expressed through a regression, following a form suggested by Cox (1972) for survival data analysis involving the current life-length of the components. After formulating the current-life process as a Markov process with stationary transitions and stating some general results on asymptotic behavior, we describe the stationary distributions in some bivariate examples. Finally, we discuss statistical inference for the new models, exhibiting and justifying full- and partial-likelihood methods for their analysis.

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Multivariate dependent renewal processes

Advances in Applied Probability ◽

10.2307/1427073 ◽

1984 ◽

Vol 16 (2) ◽

pp. 347-362 ◽

Cited By ~ 5

Author(s):

Eric Slud

Keyword(s):

Asymptotic Behavior ◽

Data Analysis ◽

Survival Data ◽

Process Models ◽

Partial Likelihood ◽

Renewal Processes ◽

Likelihood Methods ◽

New Class ◽

New Models ◽

Point Process Models

A new class of reliability point-process models for dependent components is introduced. The dependence is expressed through a regression, following a form suggested by Cox (1972) for survival data analysis involving the current life-length of the components. After formulating the current-life process as a Markov process with stationary transitions and stating some general results on asymptotic behavior, we describe the stationary distributions in some bivariate examples. Finally, we discuss statistical inference for the new models, exhibiting and justifying full- and partial-likelihood methods for their analysis.

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Semiparametric models for antagonistic drug interactions

Journal of Applied Physiology ◽

10.1152/jappl.1994.76.5.2224 ◽

1994 ◽

Vol 76 (5) ◽

pp. 2224-2233 ◽

Cited By ~ 11

Author(s):

I. F. Troconiz ◽

L. B. Sheiner ◽

D. Verotta

Keyword(s):

Data Analysis ◽

Drug Interactions ◽

Exploratory Data Analysis ◽

Real Data ◽

Data Set ◽

Semiparametric Modeling ◽

New Class ◽

Semiparametric Approach ◽

Exploratory Data ◽

Model Determination

A new class of models to describe antagonistic drug interactions are presented. They are semiparametric in that they use nonparametric functions (splines) but are forced to obey certain constraints corresponding to reasonable assumptions. We propose the models primarily for exploratory data analysis, but they may also be definitive models for such purposes as predicting future responses. Certain problems that arise in semiparametric modeling, such as model selection, are addressed so that we can propose a relatively automatic and objective approach to model determination. We demonstrate the applicability of the class of models we propose to two real data set examples involving pain relief response to opioid agonists/antagonists. The results suggest that the semiparametric approach is particularly useful when unusual shapes link dose to response.

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High-Parameter Single-Cell Analysis

Annual Review of Analytical Chemistry ◽

10.1146/annurev-anchem-061417-125927 ◽

2019 ◽

Vol 12 (1) ◽

pp. 411-430 ◽

Cited By ~ 9

Author(s):

Pratip K. Chattopadhyay ◽

Aidan F. Winters ◽

Woodrow E. Lomas ◽

Andressa S. Laino ◽

David M. Woods

Keyword(s):

Data Analysis ◽

Single Cell ◽

Cell Biology ◽

Single Cell Analysis ◽

Cell Types ◽

The Body ◽

New Class ◽

Analysis Process ◽

A Cell ◽

New Biomarkers

Thousands of transcripts and proteins confer function and discriminate cell types in the body. Using high-parameter technologies, we can now measure many of these markers at once, and multiple platforms are now capable of analysis on a cell-by-cell basis. Three high-parameter single-cell technologies have particular potential for discovering new biomarkers, revealing disease mechanisms, and increasing our fundamental understanding of cell biology. We review these three platforms (high-parameter flow cytometry, mass cytometry, and a new class of technologies called integrated molecular cytometry platforms) in this article. We describe the underlying hardware and instrumentation, the reagents involved, and the limitations and advantages of each platform. We also highlight the emerging field of high-parameter single-cell data analysis, providing an accessible overview of the data analysis process and choice of tools.

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Blockchain

International Journal of Healthcare Information Systems and Informatics ◽

10.4018/ijhisi.20210401.oa1 ◽

2021 ◽

Vol 16 (2) ◽

pp. 1-20

Author(s):

Riya Sapra ◽

Parneeta Dhaliwal

Keyword(s):

Information Technology ◽

Data Analysis ◽

Real Time ◽

Personal Data ◽

Central Unit ◽

Application Area ◽

New Class ◽

Distributed Ledger ◽

Real Time Applications ◽

Ease Of Access

Many applications are being built using the immutability and robustness of blockchain. Blockchain is a new class of information technology that combines cryptography and a distributed ledger that already exists. The model is composed of a group of computers that collaborate towards maintaining a secured database without storing the data at any central unit. It is the technology behind all the crypto currencies like Bitcoin, Litecoin, Ethereum, and now finding its way to record everything possible. This paper focuses on the basic framework of blockchain model, its pre-requisites, and challenges of blockchain. Various current real-time applications of the technology are also discussed. Finally, an application area has been proposed that can be used to create a huge database of the citizens of the country and facilitate them with ease of access to their personal data. It will open new ways of data analysis at a nationwide scale.

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In Situ microscopy of the anodic etching of silicon

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100137537 ◽

1995 ◽

Vol 53 ◽

pp. 232-233

Author(s):

Frances M. Ross ◽

Peter C. Searson

Keyword(s):

Composite Structures ◽

High Surface ◽

High Surface Area ◽

Chemical Properties ◽

Selective Etching ◽

Silicon On Insulator ◽

Second Phase ◽

Porous Layers ◽

New Class ◽

Porous Semiconductors

Porous semiconductors represent a relatively new class of materials formed by the selective etching of a single or polycrystalline substrate. Although porous silicon has received considerable attention due to its novel optical properties1, porous layers can be formed in other semiconductors such as GaAs and GaP. These materials are characterised by very high surface area and by electrical, optical and chemical properties that may differ considerably from bulk. The properties depend on the pore morphology, which can be controlled by adjusting the processing conditions and the dopant concentration. A number of novel structures can be fabricated using selective etching. For example, self-supporting membranes can be made by growing pores through a wafer, films with modulated pore structure can be fabricated by varying the applied potential during growth, composite structures can be prepared by depositing a second phase into the pores and silicon-on-insulator structures can be formed by oxidising a buried porous layer. In all these applications the ability to grow nanostructures controllably is critical.

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Microchemical imaging in biomedical research

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100130225 ◽

1992 ◽

Vol 50 (2) ◽

pp. 1118-1119

Author(s):

P. Ingram

Keyword(s):

Data Analysis ◽

Electron Probe ◽

The Other ◽

X Ray ◽

Cell Element ◽

Physiological Information ◽

Functional States ◽

Elemental Maps ◽

Electron Energy Loss ◽

Secondary Ion

It is well established that unique physiological information can be obtained by rapidly freezing cells in various functional states and analyzing the cell element content and distribution by electron probe x-ray microanalysis. (The other techniques of microanalysis that are amenable to imaging, such as electron energy loss spectroscopy, secondary ion mass spectroscopy, particle induced x-ray emission etc., are not addressed in this tutorial.) However, the usual processes of data acquisition are labor intensive and lengthy, requiring that x-ray counts be collected from individually selected regions of each cell in question and that data analysis be performed subsequent to data collection. A judicious combination of quantitative elemental maps and static raster probes adds not only an additional overall perception of what is occurring during a particular biological manipulation or event, but substantially increases data productivity. Recent advances in microcomputer instrumentation and software have made readily feasible the acquisition and processing of digital quantitative x-ray maps of one to several cells.

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The microtubule associated protein (MAP) tau forms a new class of triple-stranded left-hand helical fibrous protein polymer

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100123106 ◽

1992 ◽

Vol 50 (1) ◽

pp. 540-541

Author(s):

G. C. Ruben ◽

K. Iqbal ◽

I. Grundke-Iqbal ◽

H. Wisniewski ◽

T. L. Ciardelli ◽

...

Keyword(s):

Axial Ratio ◽

Normal Structure ◽

Paired Helical Filaments ◽

Left Hand ◽

New Class ◽

Protein Polymer ◽

Fibrous Protein ◽

Protein Map ◽

Neurotransmitter Transport ◽

Alzheimer Neurofibrillary Tangles

In neurons, the microtubule associated protein, tau, is found in the axons. Tau stabilizes the microtubules required for neurotransmitter transport to the axonal terminal. Since tau has been found in both Alzheimer neurofibrillary tangles (NFT) and in paired helical filaments (PHF), the study of tau's normal structure had to preceed TEM studies of NFT and PHF. The structure of tau was first studied by ultracentrifugation. This work suggested that it was a rod shaped molecule with an axial ratio of 20:1. More recently, paraciystals of phosphorylated and nonphosphoiylated tau have been reported. Phosphorylated tau was 90-95 nm in length and 3-6 nm in diameter where as nonphosphorylated tau was 69-75 nm in length. A shorter length of 30 nm was reported for undamaged tau indicating that it is an extremely flexible molecule. Tau was also studied in relation to microtubules, and its length was found to be 56.1±14.1 nm.

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