47 research outputs found
Hierarchical Nearest-Neighbor Gaussian Process Models for Large Geostatistical Datasets
Spatial process models for analyzing geostatistical data entail computations
that become prohibitive as the number of spatial locations become large. This
manuscript develops a class of highly scalable Nearest Neighbor Gaussian
Process (NNGP) models to provide fully model-based inference for large
geostatistical datasets. We establish that the NNGP is a well-defined spatial
process providing legitimate finite-dimensional Gaussian densities with sparse
precision matrices. We embed the NNGP as a sparsity-inducing prior within a
rich hierarchical modeling framework and outline how computationally efficient
Markov chain Monte Carlo (MCMC) algorithms can be executed without storing or
decomposing large matrices. The floating point operations (flops) per iteration
of this algorithm is linear in the number of spatial locations, thereby
rendering substantial scalability. We illustrate the computational and
inferential benefits of the NNGP over competing methods using simulation
studies and also analyze forest biomass from a massive United States Forest
Inventory dataset at a scale that precludes alternative dimension-reducing
methods
A Review on the epidemiology and characteristics of COVID-19
In December 2019, there was a health emergency worldwide named novel coronavirus or COVID-19 by the world health organization (WHO). It originated from the Wuhan seafood market, Hubei Province, China. Till now Severe Acute Respiratory Syndrome Coronavirus-2 or SARS-CoV-2 spread over 216 countries with 177,108,695 confirmed cases and 3,840,223 confirmed death cases has been reported (5:31 pm CEST, 18 June 2021; WHO). Analyzing the risk factor of this pandemic situation, different government health organizations of all the countries including WHO are taking several preventive measures with ongoing research works, even the vaccination process started. In this study, we tried to analyze all the available information on pandemic COVID-19, which includes the origin of COVID-19, pathogenic mechanism, transmission, diagnosis, treatment, and control-preventive measures, also the additional treatment and prevention taken by the Indian government is being studied here
Cell-Laden alginate biomaterial modelling using three-dimensional (3D) microscale finite element technique
A novel modelling technique using finite element analysis to mimic the
mechanoresponse of cell-laden biomaterial is proposed for the use in bioinks
and other tissue engineering applications. Here a hydrogel-based composite
biomaterial specimen was used consisting of 5% (V/V) HeLa cells added to
alginate solution (4% W/V) and another specimen with no living cell present in
alginate solution (4% W/V). Tensile test experiments were performed on both the
specimens with a load cell of 25 N. The specimens were bioprinted using an
in-house developed three-dimensional (3D) bioprinter. To allow for the
nonlinear hyperelastic behavior of the specimen, the specimens were loaded very
slowly, at rates of 0.1 mm/min and 0.5 mm/min, during the tensile test. The
microscale finite element models developed in Ansys were loaded with similar
load rates and their responses were recorded. Both the model results were
validated with the experiment results. A very good agreement between the finite
element model and the tensile test experiment was observed under the same
mechanical stimuli. Hence, the study reveals that bioprinted scaffold can be
virtually modeled to obtain its mechanical characteristics beforehand.Comment: 4 pages, 7 figure
Importance of Alginate Bioink for 3D Bioprinting in Tissue Engineering and Regenerative Medicine
Among many bioinks used for extrusion 3D bioprinting, the most commonly used bioink is the polysaccharide alginate because of its various cellular-friendly property like gelation. Erratic degradation and cell-binding motifs are not present in alginate which are the limitations of alginate bioinks, which can be improved by blending various low concentrations of natural or artificial polymers. Here in this chapter, we will discuss the various important properties of the alginate which make it as the bioink for almost all bioprinting scaffold designs as well as how improve the cellular properties like its cell-material interaction by blending it with other polymer solutions
A finite element analysis model to predict and optimize the mechanical behaviour of bioprinted scaffolds
Bioprinting is an enabling biofabrication technique to create heterogeneous
tissue constructs according to patient-specific geometries and compositions.
Optimization of bioinks as per requirements for specific tissue applications is
a critical exercise in ensuring clinical translation of the bioprinting
technologies. Most notably, optimum hydrogel polymer concentrations are
required to ensure adequate mechanical properties of bioprinted constructs
without causing significant shear stresses on cells. However, experimental
iterations are often tedious for optimizing the bioink properties. In this
work, a finite element modelling approach has been undertaken to determine the
effect of different bioink parameters like composition, concentration on the
range of stresses being experienced by the cells in a bioprinting process. The
stress distribution of the cells at different parts of the constructs has also
been modelled. It is found that both bioink chemical compositions and
stoichiometric concentrations can substantially alter the stress effects
experienced by the cells. Similarly, concentrated regions of soft cells near
the pore regions were found to increase stress concentrations by almost three
times compared to the Von-Mises stress generated around the region of cells
away from the pores. The study outlines the importance of finite element models
in the rapid development of bioinks.Comment: 21 pages, 10 figure
Application of Artificial Intelligence in Modern Healthcare System
Artificial intelligence (AI) has the potential of detecting significant interactions in a dataset and also it is widely used in several clinical conditions to expect the results, treat, and diagnose. Artificial intelligence (AI) is being used or trialed for a variety of healthcare and research purposes, including detection of disease, management of chronic conditions, delivery of health services, and drug discovery. In this chapter, we will discuss the application of artificial intelligence (AI) in modern healthcare system and the challenges of this system in detail. Different types of artificial intelligence devices are described in this chapter with the help of working mechanism discussion. Alginate, a naturally available polymer found in the cell wall of the brown algae, is used in tissue engineering because of its biocompatibility, low cost, and easy gelation. It is composed of α-L-guluronic and β-D-manuronic acid. To improve the cell-material interaction and erratic degradation, alginate is blended with other polymers. Here, we discuss the relationship of artificial intelligence with alginate in tissue engineering fields
Graphical Gaussian Process Models for Highly Multivariate Spatial Data
For multivariate spatial Gaussian process (GP) models, customary
specifications of cross-covariance functions do not exploit relational
inter-variable graphs to ensure process-level conditional independence among
the variables. This is undesirable, especially for highly multivariate
settings, where popular cross-covariance functions such as the multivariate
Mat\'ern suffer from a "curse of dimensionality" as the number of parameters
and floating point operations scale up in quadratic and cubic order,
respectively, in the number of variables. We propose a class of multivariate
"Graphical Gaussian Processes" using a general construction called "stitching"
that crafts cross-covariance functions from graphs and ensures process-level
conditional independence among variables. For the Mat\'ern family of functions,
stitching yields a multivariate GP whose univariate components are Mat\'ern
GPs, and conforms to process-level conditional independence as specified by the
graphical model. For highly multivariate settings and decomposable graphical
models, stitching offers massive computational gains and parameter dimension
reduction. We demonstrate the utility of the graphical Mat\'ern GP to jointly
model highly multivariate spatial data using simulation examples and an
application to air-pollution modelling