Time-Specific Associations of Wearable, Sensor-Based Cardiovascular and Behavioral Readouts with Disease Phenotypes in the Outpatient Setting of the Chronic Renal Insufficiency Cohort (CRIC)

Patients with chronic kidney disease (CKD) are at risk of developing cardiovascular disease. To facilitate out-of-clinic evaluation, we piloted wearable device-based analysis of heart rate variability and behavioral readouts in patients with CKD participating in the Chronic Renal Insufficiency Cohort and (n=49) controls. Time-specific partitioning of HRV readouts indicate higher parasympathetic nervous activity during the night (mean RR at night 14.4+/-1.9 ms versus 12.8+/-2.1 ms during active hours; n=47, ANOVA q=0.001). The alpha2 long-term fluctuations in the detrended fluctuation analysis, a parameter predictive of cardiovascular mortality, significantly differentiated between diabetic and non-diabetic patients (prominent at night with 0.58+/-0.2 versus 0.45+/-0.12, respectively, adj. p=0.004). Both diabetic and nondiabetic CKD patients showed loss of rhythmic organization compared to controls, with diabetic CKD patients exhibiting deconsolidation of peak phases between their activity and SDNN (standard deviation of interbeat intervals) rhythms (mean phase difference CKD 8.3h, CKD/T2DM 4h, controls 6.8h). This work provides a roadmap toward deriving actionable clinical insights from the data collected by wearable devices outside of highly controlled clinical environments.

Characterizing the effect of incrementally increasing dry bulb temperature on linear and nonlinear measures of heart rate variability in non-pregnant, mid-gestation, and late-gestation sows

Characterizing the sow physiological response to an increased heat load is essential for effective heat stress mitigation. The study objective was to characterize the effects of a 400-min heating episode on sow heart rate variability (HRV) at different reproductive stages. Heart rate variability is a commonly used non-invasive proxy measure of autonomic function. Twenty-seven sows were enrolled in the study according to their gestation stage at time of selection: 1) non-pregnant (NP; n = 7), 2) mid-gestation (MID; 57.3 ± 11.8 d gestation; n = 11), and 3) late-gestation (LATE; 98.8 ± 4.9 d gestation; n = 8). The HRV data utilized in the study were collected from each pig as the dry bulb temperature in the room increased incrementally from 19.84 ± 2.15 °C to 35.54 ± 0.43 °C (range: 17.1 – 37.5 °C) over a 400-min period. After data collection, one 5-min set of continuous heart rate data were identified per pig for each of nine temperature intervals (19 to 20.99, 21 to 22.99, 23 to 24.99, 25 to 26.99, 27 to 28.99, 29 to 30.99, 31 to 32.99, 33 to 34.99, 35 to 36.99 °C). Mean inter-beat interval length (RR), standard deviation of r-r intervals (SDNN), root mean square of successive differences (RMSSD), high frequency spectral power (HF), sample entropy (SampEn), short-term detrended fluctuation analysis (DFAα1), and three measures (%REC, DET, LMEAN) derived from recurrence quantification analysis were calculated for each data set. All data were analyzed using the PROC GLIMMIX procedure in SAS 9.4. Overall, LATE sows exhibited lower RR than NP sows (P < 0.01). The standard deviation of r-r intervals and RMSSD differed between each group (P < 0.01), with LATE sows exhibiting the lowest SDNN and RMSSD and NP sows exhibiting the greatest SDNN and RMSSD. Late-gestation sows exhibited lower HF than both MID and NP sows (P < 0.0001), greater DFA values than NP sows (P = 0.05), and greater DET compared to MID sows (P = 0.001). Late-gestation also sows exhibited greater %REC and LMEAN compared to MID (P < 0.01) and NP sows (all P < 0.01). In conclusion, LATE sows exhibited indicators of greater autonomic stress throughout the heating period compared to MID and NP sows. However, temperature by treatment interactions were not detected as dry bulb increased. Future studies are needed to fully elucidate the effect of gestational stage and increasing dry bulb temperature on sow HRV.

Scale-invariance in miniature coarse-grained red blood cells by fluctuation analysis

The scale-invariance of a coarse-grained molecular dynamics model of a red blood cell is investigated through fluctuation analysis, justifying the use of “miniature cells” in silico.

On the Validity of Detrended Fluctuation Analysis at Short Scales

Detrended Fluctuation Analysis (DFA) has become a standard method to quantify the correlations and scaling properties of real-world complex time series. For a given scale ℓ of observation, DFA provides the function F(ℓ), which quantifies the fluctuations of the time series around the local trend, which is substracted (detrended). If the time series exhibits scaling properties, then F(ℓ)∼ℓα asymptotically, and the scaling exponent α is typically estimated as the slope of a linear fitting in the logF(ℓ) vs. log(ℓ) plot. In this way, α measures the strength of the correlations and characterizes the underlying dynamical system. However, in many cases, and especially in a physiological time series, the scaling behavior is different at short and long scales, resulting in logF(ℓ) vs. log(ℓ) plots with two different slopes, α1 at short scales and α2 at large scales of observation. These two exponents are usually associated with the existence of different mechanisms that work at distinct time scales acting on the underlying dynamical system. Here, however, and since the power-law behavior of F(ℓ) is asymptotic, we question the use of α1 to characterize the correlations at short scales. To this end, we show first that, even for artificial time series with perfect scaling, i.e., with a single exponent α valid for all scales, DFA provides an α1 value that systematically overestimates the true exponent α. In addition, second, when artificial time series with two different scaling exponents at short and large scales are considered, the α1 value provided by DFA not only can severely underestimate or overestimate the true short-scale exponent, but also depends on the value of the large scale exponent. This behavior should prevent the use of α1 to describe the scaling properties at short scales: if DFA is used in two time series with the same scaling behavior at short scales but very different scaling properties at large scales, very different values of α1 will be obtained, although the short scale properties are identical. These artifacts may lead to wrong interpretations when analyzing real-world time series: on the one hand, for time series with truly perfect scaling, the spurious value of α1 could lead to wrongly thinking that there exists some specific mechanism acting only at short time scales in the dynamical system. On the other hand, for time series with true different scaling at short and large scales, the incorrect α1 value would not characterize properly the short scale behavior of the dynamical system.

Time-resolved analysis of transcription kinetics in single live mammalian cells

In eukaryotic cells, RNA polymerase II synthesizes mRNA in three stages, initiation, elongation, and termination, and numerous factors determine how quickly a gene is transcribed to produce mRNA molecules through these steps. However, there are few techniques available to measure the rate of each step in living cells, which prevents a better understanding of transcriptional regulation. Here, we present a quantitative analysis method to extract kinetic rates of transcription from time-lapse imaging data of fluorescently labeled mRNA in live cells. Using embryonic fibroblasts cultured from two knock-in mouse models, we monitored transcription of β-actin and Arc mRNA labeled with MS2 and PP7 stem-loop systems, respectively. After inhibiting transcription initiation, we measured the elongation rate and the termination time by fitting the time trace of transcription intensity with a mathematical model function. We validated our results by comparing them with steady-state fluctuation analysis and stochastic simulations. This live-cell transcription analysis method will be useful for studying the regulation of elongation and termination steps and may provide insight into the diverse mechanisms of transcriptional processes.

Price efficiency and safe-haven property of Bitcoin in relation to stocks in the pandemic era

Purpose When investors experience extreme uncertainty, they seek “safe havens” to reduce their risk, to limit their losses and to protect the value of their portfolios. The purpose of this paper is to examine the safe-haven properties of Bitcoin compared to the stock market. Design/methodology/approach Based on intraday data, this study compares the price efficiencies of Bitcoin and Morgan Stanley Capital Index (MSCI) using Multifractal Detrended Fluctuation Analysis for the second half of 2020. This study then evaluates Bitcoin’s safe-haven property using Detrended Partial-Cross-Correlation Analysis (DPCCA). Findings This study finds that the price efficiency of Bitcoin is lower than that of MSCI. Further, Bitcoin was not a safe haven at any time for the MSCI index. The net cross-correlations between Bitcoin and MSCI are weak and they vary at different time scales. Research limitations/implications The behavior of market prices varies over time. Therefore, it is important to replicate this study for other time periods. Social implications The paper sheds light on the price behavior of Bitcoin during a period of instability. The results suggest that the construction of portfolios should differ based on the time horizons of the investors. Originality/value The authors compare Bitcoin against a global equity index instead of a specific country index or commodity. They also demonstrate the applicability of DPCCA in finance research.

Live Cancer Cell Classification Based on Quantitative Phase Spatial Fluctuations and Deep Learning With a Small Training Set

We present an analysis method that can automatically classify live cancer cells from cell lines based on a small data set of quantitative phase imaging data without cell staining. The method includes spatial image analysis to extract the cell phase spatial fluctuation map, derived from the quantitative phase map of the cell measured without cell labeling, thus without prior knowledge on the biomarker. The spatial fluctuations are indicative of the cell stiffness, where cancer cells change their stiffness as cancer progresses. In this paper, the quantitative phase spatial fluctuations are used as the basis for a deep-learning classifier for evaluating the cell metastatic potential. The spatial fluctuation analysis performed on the quantitative phase profiles before inputting them to the neural network was proven to increase the classification results in comparison to inputting the quantitative phase profiles directly, as done so far. We classified between primary and metastatic cancer cells and obtained 92.5% accuracy, in spite of using a small training set, demonstrating the method potential for objective automatic clinical diagnosis of cancer cells in vitro.