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

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

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

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

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

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