A Simple Flood Forecasting Scheme Using Wireless Sensor Networks

This paper presents a forecasting model designed using WSNs (Wireless Sensor Networks) to predict flood in rivers using simple and fast calculations to provide real-time results and save the lives of people who may be affected by the flood. Our prediction model uses multiple variable robust linear regression which is easy to understand and simple and cost effective in implementation, is speed efficient, but has low resource utilization and yet provides real time predictions with reliable accuracy, thus having features which are desirable in any real world algorithm. Our prediction model is independent of the number of parameters, i.e. any number of parameters may be added or removed based on the on-site requirements. When the water level rises, we represent it using a polynomial whose nature is used to determine if the water level may exceed the flood line in the near future. We compare our work with a contemporary algorithm to demonstrate our improvements over it. Then we present our simulation results for the predicted water level compared to the actual water level.Comment: 16 pages, 4 figures, published in International Journal Of Ad-Hoc, Sensor And Ubiquitous Computing, February 2012; V. seal et al, 'A Simple Flood Forecasting Scheme Using Wireless Sensor Networks', IJASUC, Feb.201

Efficient SNP based Heritability Estimation and Multiple Phenotype-Genotype Association Analysis in Large Scale Cohort studies

University of Minnesota Ph.D. dissertation. August 2020. Major: Biostatistics. Advisors: Saonli Basu, Cavan Reilly. 1 computer file (PDF); xi, 128 pages.Recent developments in genotyping technologies have opened up many new possibilities of unraveling the genetic basis of common diseases. The past decade has seen an advent of a bunch of large scale cohort studies giving us, the researchers, access to an unprecedented wealth of data providing information on millions of genetic variants and numerous diseases/traits on millions of individuals. But, efficient analysis of such high-dimensional data demands non-traditional yet novel statistical techniques. The development of a complex human disease is an intricate interplay of genetic and environmental factors. In order to better understand such traits, we are often interested in estimating the overall trait heritability: the proportion of total trait variance due to genetic factors within a given population. Accurate estimation and inference of heritability give us some basic understanding of disease risk and etiology. Traits with high estimated heritability incite interest among the researchers for a further Genome-Wide Association Study (GWAS) to pinpoint significant genetic variants. As we move into the era of genome editing and personalized medicine, addressing the shared genetic basis of multiple diseases/traits or the genetic basis of a single disease/trait over multiple time-points becomes more and more important. In light of these exciting statistical problems, my thesis focuses on developing robust tools for estimating heritability and performing GWAS in large scale cohort studies both in a univariate and multivariate context

Efficient estimation of SNP heritability using Gaussian predictive process in large scale cohort studies.

With the advent of high throughput genetic data, there have been attempts to estimate heritability from genome-wide SNP data on a cohort of distantly related individuals using linear mixed model (LMM). Fitting such an LMM in a large scale cohort study, however, is tremendously challenging due to its high dimensional linear algebraic operations. In this paper, we propose a new method named PredLMM approximating the aforementioned LMM motivated by the concepts of genetic coalescence and Gaussian predictive process. PredLMM has substantially better computational complexity than most of the existing LMM based methods and thus, provides a fast alternative for estimating heritability in large scale cohort studies. Theoretically, we show that under a model of genetic coalescence, the limiting form of our approximation is the celebrated predictive process approximation of large Gaussian process likelihoods that has well-established accuracy standards. We illustrate our approach with extensive simulation studies and use it to estimate the heritability of multiple quantitative traits from the UK Biobank cohort

SMASH: Scalable Method for Analyzing Spatial Heterogeneity of genes in spatial transcriptomics data.

In high-throughput spatial transcriptomics (ST) studies, it is of great interest to identify the genes whose level of expression in a tissue covaries with the spatial location of cells/spots. Such genes, also known as spatially variable genes (SVGs), can be crucial to the biological understanding of both structural and functional characteristics of complex tissues. Existing methods for detecting SVGs either suffer from huge computational demand or significantly lack statistical power. We propose a non-parametric method termed SMASH that achieves a balance between the above two problems. We compare SMASH with other existing methods in varying simulation scenarios demonstrating its superior statistical power and robustness. We apply the method to four ST datasets from different platforms uncovering interesting biological insights

Inhibition of stemness and EMT by taxifolin ruthenium-p-cymene complex via downregulating the SOX2 and OCT4 expression on lung cancer

Lung carcinoma is perhaps the most often reported cancer incidence throughout the world. The flavonoid metal complexes have exhibited a potential chemotherapeutic effect. This study investigated the chemotherapeutic effect of taxifolin ruthenium-p-cymene complex against lung carcinoma through MTT assay, transwell migration assay, sphere formation assay, western blot, histopathology, immunohistochemistry, and TUNEL assay. The in silico study determined the target proteins of the complex. The synthesized organometallic complex was characterized via various spectroscopical techniques. The complex exhibited strong binding affinity for PI3K, EGFR, and β-catenin. The complex promoted apoptosis and inhibited the colony formation of the cancer cells. Moreover, the cancer cell migration and cancer stemness were reduced with decreased SOX2 and OCT4 expression. The complex induced cell cycle arrest at sub G0/G1 phase, S phase and G2/M phase and promoted caspase-3 mediated apoptosis. The in vivo study demonstrated successful restoration of lung tissue due to the treatment with the complex in benzo-α-pyrene induced lung cancer model. Additionally, the expression of p53 and caspase-3 was increased with significant downregulation of Akt, mTOR and β-catenin. In conclusion, the complex inhibited cancer cell viability, stemness, and EMT in both in vitro and in vivo model systems through the alteration of PI3K/Akt/mTOR/EGFR pathway and expression of stem cell markers including SOX2 and OCT4 that eventually abrogated the EMT mediated metastasis of cancer cells

RCFGL: Rapid Condition adaptive Fused Graphical Lasso and application to modeling brain region co-expression networks.

Inferring gene co-expression networks is a useful process for understanding gene regulation and pathway activity. The networks are usually undirected graphs where genes are represented as nodes and an edge represents a significant co-expression relationship. When expression data of multiple (p) genes in multiple (K) conditions (e.g., treatments, tissues, strains) are available, joint estimation of networks harnessing shared information across them can significantly increase the power of analysis. In addition, examining condition-specific patterns of co-expression can provide insights into the underlying cellular processes activated in a particular condition. Condition adaptive fused graphical lasso (CFGL) is an existing method that incorporates condition specificity in a fused graphical lasso (FGL) model for estimating multiple co-expression networks. However, with computational complexity of O(p2K log K), the current implementation of CFGL is prohibitively slow even for a moderate number of genes and can only be used for a maximum of three conditions. In this paper, we propose a faster alternative of CFGL named rapid condition adaptive fused graphical lasso (RCFGL). In RCFGL, we incorporate the condition specificity into another popular model for joint network estimation, known as fused multiple graphical lasso (FMGL). We use a more efficient algorithm in the iterative steps compared to CFGL, enabling faster computation with complexity of O(p2K) and making it easily generalizable for more than three conditions. We also present a novel screening rule to determine if the full network estimation problem can be broken down into estimation of smaller disjoint sub-networks, thereby reducing the complexity further. We demonstrate the computational advantage and superior performance of our method compared to two non-condition adaptive methods, FGL and FMGL, and one condition adaptive method, CFGL in both simulation study and real data analysis. We used RCFGL to jointly estimate the gene co-expression networks in different brain regions (conditions) using a cohort of heterogeneous stock rats. We also provide an accommodating C and Python based package that implements RCFGL

The run-times of different methods (in seconds) with the genes left after pruning based on different CV cut-offs.

λ1 and λ2 were respectively kept at 0.01 and 0.02. The mark “X” means that we could not run those methods due to inordinate amount of time required.</p

The networks between the hub-genes whose degree changed from IL and PL to LHB.

The top row corresponds to the genes whose degree decreased from IL and PL to LHB, while the bottom row corresponds to the genes whose degree increased.</p

Normalized root mean squared errors (NRMSE) across three different models, with four sample sizes (N = 100, 200, 500, 1000), and at three levels of additive noise (small, medium, and large).

Corresponding standard deviations are recorded in parentheses.</p