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Register a new userAssessment of long-range correlation in animal behaviour time series: the temporal pattern of locomotor activity of Japanese quail (Coturnix coturnix) and mosquito larva (Culex quinquefasciatus)
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The aim of this study was to evaluate the performance of a classical method of fractal analysis, Detrended Fluctuation Analysis (DFA), in the analysis of the dynamics of animal behavior time series. In order to correctly use DFA to assess the presence of long-range correlation, previous authors using statistical model systems have stated that different aspects should be taken into account such as: 1) the establishment by hypothesis testing of the absence of short term correlation, 2) an accurate estimation of a straight line in the log-log plot of the fluctuation function, 3) the elimination of artificial crossovers in the fluctuation function, and 4) the length of the time series. Taking into consideration these factors, herein we evaluated the presence of long-range correlation in the temporal pattern of locomotor activity of Japanese quail ({sl Coturnix coturnix}) and mosquito larva ({sl Culex quinquefasciatus}). In our study, modeling the data with the general ARFIMA model, we rejected the hypothesis of short range correlations (d=0) in all cases. We also observed that DFA was able to distinguish between the artificial crossover observed in the temporal pattern of locomotion of Japanese quail, and the crossovers in the correlation behavior observed in mosquito larvae locomotion. Although the test duration can slightly influence the parameter estimation, no qualitative differences were observed between different test durations.
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Background The morphological and biochemical impact of a short-period of starvation on Japanese quail was investigated. Materials and methods Ten adult male Japanese quail were divided into two groups; control fed and starved. The control-fed group was offered food and water ad libitum and the starved group was subjected to a short-period of food deprivation. After 2.5 days, the serum was obtained and different parameters including the total protein, AST, ALT, triglyceride, HDL, LDL, creatinine and urea were assessed. Gastrointestinal tract, stomach and liver were excised and their masses were estimated. Paraffin and resin embedded sections from the proventriculus, gizzard, liver, duodenum, kidney and pancreas were examined with a light microscopy. Results Significant decreases in the masses of body, gastrointestinal tract, stomach and liver of the starved group were recorded. The liver and duodenum were the most affected organs. The liver showed depletion of glycogen, vacuolation, hyperemia and cellular infiltrations. Duodenal villi showed degenerative changes in lamina epithelialis and cellular infiltrations in the lamina propria. Biochemical analysis revealed a decreased level of total protein, AST and ALT, increased cholesterol, triglycerides and LDL, and unchanged HDL, urea and creatinine by starvation. Conclusion The current study described in details the effect of short time starvation on quail organs. Time-point adaptive responses of male quail to starvation and refeeding on quail organs will be investigated in future studies.
The dynamics of a mosquito population depends heavily on climatic variables such as temperature and precipitation. Since climate change models predict that global warming will impact on the frequency and intensity of rainfall, it is important to understand how these variables affect the mosquito populations. We present a model of the dynamics of a {it Culex quinquefasciatus} mosquito population that incorporates the effect of rainfall and use it to study the influence of the number of rainy days and the mean monthly precipitation on the maximum yearly abundance of mosquitoes $M_{max}$. Additionally, using a fracturing process, we investigate the influence of the variability in daily rainfall on $M_{max}$. We find that, given a constant value of monthly precipitation, there is an optimum number of rainy days for which $M_{max}$ is a maximum. On the other hand, we show that increasing daily rainfall variability reduces the dependence of $M_{max}$ on the number of rainy days, leading also to a higher abundance of mosquitoes for the case of low mean monthly precipitation. Finally, we explore the effect of the rainfall in the months preceding the wettest season, and we obtain that a regimen with high precipitations throughout the year and a higher variability tends to advance slightly the time at which the peak mosquito abundance occurs, but could significantly change the total mosquito abundance in a year.
A method for estimating the cross-correlation $C_{xy}(tau)$ of long-range correlated series $x(t)$ and $y(t)$, at varying lags $tau$ and scales $n$, is proposed. For fractional Brownian motions with Hurst exponents $H_1$ and $H_2$, the asymptotic expression of $C_{xy}(tau)$ depends only on the lag $tau$ (wide-sense stationarity) and scales as a power of $n$ with exponent ${H_1+H_2}$ for $tauto 0$. The method is illustrated on (i) financial series, to show the leverage effect; (ii) genomic sequences, to estimate the correlations between structural parameters along the chromosomes.
Background: During the early stages of hospital admission, clinicians must use limited information to make diagnostic and treatment decisions as patient acuity evolves. However, it is common that the time series vital sign information from patients to be both sparse and irregularly collected, which poses a significant challenge for machine / deep learning techniques to analyze and facilitate the clinicians to improve the human health outcome. To deal with this problem, We propose a novel deep interpolation network to extract latent representations from sparse and irregularly sampled time-series vital signs measured within six hours of hospital admission. Methods: We created a single-center longitudinal dataset of electronic health record data for all (n=75,762) adult patient admissions to a tertiary care center lasting six hours or longer, using 55% of the dataset for training, 23% for validation, and 22% for testing. All raw time series within six hours of hospital admission were extracted for six vital signs (systolic blood pressure, diastolic blood pressure, heart rate, temperature, blood oxygen saturation, and respiratory rate). A deep interpolation network is proposed to learn from such irregular and sparse multivariate time series data to extract the fixed low-dimensional latent patterns. We use k-means clustering algorithm to clusters the patient admissions resulting into 7 clusters. Findings: Training, validation, and testing cohorts had similar age (55-57 years), sex (55% female), and admission vital signs. Seven distinct clusters were identified. M Interpretation: In a heterogeneous cohort of hospitalized patients, a deep interpolation network extracted representations from vital sign data measured within six hours of hospital admission. This approach may have important implications for clinical decision-support under time constraints and uncertainty.
This paper presents two approaches to quantifying and visualizing variation in datasets of trees. The first approach localizes subtrees in which significant population differences are found through hypothesis testing and sparse classifiers on subtree features. The second approach visualizes the global metric structure of datasets through low-distortion embedding into hyperbolic planes in the style of multidimensional scaling. A case study is made on a dataset of airway trees in relation to Chronic Obstructive Pulmonary Disease.
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