Liquid biopsies have advanced rapidly in recent years for use in diagnostic and prognostic applications. One important aspect of this advancement is the growth in our understanding of microRNA (miRNA) biology. The measurement of miRNAs packaged within exosomes, which are constantly released into the blood stream, may reflect pathological changes within the body. The current study performed miRNA profiling using plasma and plasma-derived exosome samples from two animal models of kidney disease, the 5/6th partial nephrectomy (PNx) and two-kidney-one-clip (2K1C) models. The RT-qPCR-based profiling results revealed that the overall miRNA expression level was much higher in plasma than in plasma-derived exosomes. With 200 µl of either plasma or exosomes derived from the same volume of plasma, 629 out of 665 total miRNAs analyzed were detectable in plasma samples from sham-operated rats, while only 403 were detectable in exosomes with a cutoff value set at 35 cycles. Moreover, the average miRNA expression level in plasma was about 16-fold higher than that in exosomes. We also found a select subset of miRNAs that were enriched within exosomes. The number of detectable miRNAs from plasma-derived exosomes was increased in rats subjected to PNx or 2K1C surgery compared to sham-operated animals. Importantly, we found that the changes of individual miRNAs measured in plasma had very poor concordance with that measured in plasma-derived exosomes in both animal models, suggesting that miRNAs in plasma and plasma-derived exosomes are differentially regulated in these disease conditions. Interestingly, PNx and 2K1C surgeries induced similar changes in miRNA expression, implying that common pathways were activated in these two disease models. Pathway analyses using DIANA-miRPath v3.0 showed that significantly changed exosomal miRNAs were associated with extracellular matrix (ECM) receptor interaction and mucin type-O-glycan synthesis pathways, which are related with tissue fibrosis and kidney injury, respectively. In conclusion, our results demonstrated that due to the differential changes in miRNAs, the measurement of exosomal miRNAs cannot be replaced by the measurement of miRNAs in plasma, or vice versa. We also showed that a set of miRNAs related with kidney injury and organ fibrosis were dysregulated in plasma-derived exosomes from animal models of kidney disease.

A daily precipitation generator based on a hidden Markov model with Gaussian copulas (HMM-GC) is constructed using remote sensing data from GPM-IMERG for the Potomac river basin on the East Coast of the USA. Daily precipitation over the basin from 2001-2018 for the wet season months of July to September is modeled using a 4-state HMM, and correlated precipitation amounts are generated from a mixture of Gamma distributions using Gaussian copulas for each state. Synthetic data from a model using a mixture of two Gamma distributions for the non-zero precipitation is shown to replicate the historical data better than a model using a single Gamma distribution.

The underlying market trends that drive stock price fluctuations are often referred to in terms of bull and bear markets. Optimal stock portfolio selection methods need to take into account these market trends; however, the bull and bear market states tend to be unobserved and can only be assigned retrospectively. We fit a linked hidden Markov model (LHMM) to relative stock price changes for S&P 500 stocks from 2011-2016 based on weekly closing values. The LHMM consists of a multivariate state process whose individual components correspond to HMMs for each of the 12 sectors of the S&P 500 stocks. The state processes are linked using a Gaussian copula so that the states of the component chains are correlated at any given time point. The LHMM allows us to capture more heterogeneity in the underlying market dynamics for each sector. In this study, stock performances are evaluated in terms of capital gains using the LHMM by utilizing historical stock price data. Based on the fitted LHMM, optimal stock portfolios are constructed to maximize capital gain while balancing reward and risk. Under out-of-sample testing, the annual capital gain for the portfolios for 2016-2017 are calculated. Portfolios constructed using the LHMM are able to generate returns comparable to the S&P 500 index.

Quantifying changes in the probability and magnitude of extreme flooding events is key to mitigating their impacts. While hydrodynamic data are inherently spatially dependent, traditional spatial models such as Gaussian processes are poorly suited for modeling extreme events. Spatial extreme value models with more realistic tail dependence characteristics are under active development. They are theoretically justified, but give intractable likelihoods, making computation challenging for small datasets and prohibitive for continental-scale studies. We propose a process mixture model which specifies spatial dependence in extreme values as a convex combination of a Gaussian process and a max-stable process, yielding desirable tail dependence properties but intractable likelihoods. To address this, we employ a unique computational strategy where a feed-forward neural network is embedded in a density regression model to approximate the conditional distribution at one spatial location given a set of neighbors. We then use this univariate density function to approximate the joint likelihood for all locations by way of a Vecchia approximation. The process mixture model is used to analyze changes in annual maximum streamflow within the US over the last 50 years, and is able to detect areas which show increases in extreme streamflow over time.

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