The role of annual periodic behavior of water quality parameters in primary production – Chlorophyll-a estimation

Since phytoplankton is an autochthonous primary producer, it plays a vital role in driving the water quality of rivers and lakes. Therefore, in cases where measurements are lacking, its estimation is of the essence. In the present study, Morlet wavelet spectrum (periodicity) and multiple regression analyses were conducted on 15 chemical, biological and physical water quality variables sampled at 14 sites along the Hungarian section of the River Tisza and 4 sites from artificial tributary channels for 1993–2005. Results show that annual periodicity was not always to be found in the water quality parameters, at least at certain sampling sites. Periodicity was found to vary over space and time, but in general, an increase was observed in the company of higher trophic states of the river heading downstream. Based on the spatial distribution of the periodic behavior of the water quality parameters (runoff, ions, and nutrients given in so-called periodicity indices), an improved model was constructed which was capable of explaining about half (adjusted R² = 0.5) of the phytoplankton variance in the study area.     

Weather and regional crop composition variation drive spatial synchrony of lepidopteran agricultural pests

1. Spatial synchrony, the tendency for temporal population fluctuations to be correlated across multiple locations at regional scales, is common and contributes to the severity of outbreaks and epidemics, but is little studied in agricultural pests. 2. This study analysed spatial synchrony from 1974 to 2008 in 16 lepidopteran agricultural pests in Maryland, U.S.A., and investigated whether pest synchrony is driven by interannual variability in seasonal weather and the areas planted in different crop types. 3. Lepidopteran pests exhibited high degrees of spatial synchrony, which was driven by environmental variation, a phenomenon known as the Moran effect. Region-wide variation in the areas planted in major crops drove spatially synchronous abundance fluctuations in more than half of studied species. The combination of weather and crop composition explained large fractions of synchrony in black cutworm, corn earworm, European corn borer, and spotted cutworm populations. Other pests, including forage looper and variegated cutworm, displayed a high degree of spatial synchrony, but without dependence on the tested drivers. 4. The study finding that synchronous variation in the area planted in different crop types contributed to synchronous pest abundance fluctuations suggests that strategies to reduce synchrony in changes in crop type across a region could reduce the severity of pest outbreaks and enhance the stability of agricultural systems.     

Self‐organizing cicada choruses respond to the local sound and light environment

1. Periodical cicadas exhibit an extraordinary capacity for self‐organizing spatially synchronous breeding behavior. The regular emergence of periodical cicada broods across the United States is a phenomenon of longstanding public and scientific interest, as the cicadas of each brood emerge in huge numbers and briefly dominate their ecosystem. During the emergence, the 17‐year periodical cicada species Magicicada cassini is found to form synchronized choruses, and we investigated their chorusing behavior from the standpoint of spatial synchrony.
2. Cicada choruses were observed to form in trees, calling regularly every five seconds. In order to determine the limits of this self‐organizing behavior, we set out to quantify the spatial synchronization between cicada call choruses in different trees, and how and why this varies in space and time.
3. We performed 20 simultaneous recordings in Clinton State Park, Kansas, in June 2015 (Brood IV), with a team of citizen‐science volunteers using consumer equipment (smartphones). We use a wavelet approach to show in detail how spatially synchronous, self‐organized chorusing varies across the forest.
4. We show how conditions that increase the strength of audio interactions between cicadas also increase the spatial synchrony of their chorusing. Higher forest canopy light levels increase cicada activity, corresponding to faster and higher‐amplitude chorus cycling and to greater synchrony of cycles across space. We implemented a relaxation‐oscillator‐ensemble model of interacting cicadas, finding that a tendency to call more often, driven by light levels, results in all these effects.
5. Results demonstrate how the capacity to self‐organize in ecology depends sensitively on environmental conditions. Spatially correlated modulation of cycling rate by an external driver can also promote self‐organization of phase synchrony.

     

Predicting homo-oligomers and hetero-oligomers by pseudo-amino acid composition: an approach from discrete wavelet transformation

Many proteins exist in vivo as oligomers with different quaternary structural attributes rather than as individual chains. These proteins are the structural components of various biological functions, including cooperative effects, allosteric mechanisms and ion-channel gating. With the dramatic increase in the number of protein sequences submitted to the public databank, it is important for both basic research and drug discovery research to acquire the knowledge about possible quaternary structural attributes of their interested proteins in a timely manner. A high-throughput method (DWT_SVM), fusing discrete wavelet transform (DWT) and support vector machine (SVM) classifier algorithm with various physicochemical features, has been developed to predict protein quaternary structure. The accuracy in distinguishing candidate proteins as homo-oligomer or hetero-oligomer using the dataset R₂₇₂₀ was 85.95% and 85.49% respectively by jackknife, showing that DWT_SVM is guide promising in predicting protein quaternary structures. The online service is available at http://bioinfo.ncu.edu.cn/Services.aspx. Protein sequences in FASTA format can be directly fed to the system OligoPred. The processed results will be presented in a diagram that includes the information of feature extraction and the classification error rate.     

Decadal changes in mean annual rainfall drive long‐term changes in bush‐encroached southern African savannas

Bush encroachment can have profound effects on the ability of savanna ecosystems to provide goods and services to society. It is therefore crucial to understand the key drivers of bush encroachment in savannas. In this study, we test whether decadal changes in mean annual rainfall significantly explain changes in the dominant patch size as well as the density of bush patches at six protected savanna sites located along a rainfall gradient in Zimbabwe. We first performed Maximal Overlap Discrete Wavelet transform within the intensity‐dominant scale theoretical framework on multi‐temporal aerial photographs and high spatial resolution satellite imagery to objectively detect changes in the dominant patch dimension as well as the intensity of bush cover over a 40‐year period at six test sites. We then pooled the data and performed regression analysis relating changes in dominant scale and intensity to decadal changes in mean annual rainfall in order to deduce a possible connection between dynamics of bush encroachment and rainfall variability. Our results indicate a significant nonlinear relationship between changes in the dominant scale and decadal changes in mean annual rainfall (R² = 0.85, F₁₃ = 35.96, P < 0.01). In contrast, the relationship between decadal changes in mean annual rainfall and changes in intensity was weak and not significant (R² = 0.29, F₁₃ = 2.69, P = 0.106). These results imply the importance of annual rainfall in explaining long‐term changes in the dominant scale of woody patches. However, mechanisms other than rainfall probably explain changes in the intensity of bush cover, and this needs further investigation.     

A modified resonant recognition model to predict protein-protein interaction

Proteins are fundamental components of all living cells and the protein-protein interaction plays an important role in vital movement.This paper briefly introduced the original Resonant Recognition Model (RRM),and then modified it by using the wavelet transform to acquire the Modified Resonant Recognition Model (MRRM).The key characteristic of the new model is that it can predict directly the proteinprotein interaction from the primary sequence,and the MRRM is more suitable than the RRM for this prediction.The results of numerical experiments show that the MRRM is effective for predicting the protein-protein interaction.     

A Comparison of ECG Signal Pre-processing Using FrFT,FrWT and IPCA for Improved Analysis

ObjectiveElectrocardiogram (ECG) is a diagnostic tool for recording electrical activities of the human heart non-invasively. It is detected by electrodes placed on the surface of the skin in a conductive medium. In medical applications, ECG is used by cardiologists to observe heart anomalies (cardiovascular diseases) such as abnormal heart rhythms, heart attacks, effects of drug dosage on subject's heart and knowledge of previous heart attacks. Recorded ECG signal is generally corrupted by various types of noise/distortion such as cardiac (isoelectric interval, prolonged depolarization and atrial flutter) or extra cardiac (respiration, changes in electrode position, muscle contraction and power line noise). These factors hide the useful information and alter the signal characteristic due to low Signal-to-Noise Ratio (SNR). In such situations, any failure to judge the ECG signal correctly may result in a delay in the treatment and harm a subject (patient) health. Therefore, appropriate pre-processing technique is necessary to improve SNR to facilitate better treatment to the subject. Effects of different pre-processing techniques on ECG signal analysis (based on R-peaks detection) are compared using various Figures of Merit (FoM) such as sensitivity (Se), accuracy (Acc) and detection error rate (DER) along with SNR.MethodsIn this research article, a new fractional wavelet transform (FrWT) has been proposed as a pre-processing technique in order to overcome the disadvantages of other existing commonly used techniques viz. wavelet transform (WT) and the fractional Fourier transform (FrFT). The proposed FrWT technique possesses the properties of multiresolution analysis and represents signal in the fractional domain which consists of representation in terms of rotation of signals in the time–frequency plane. In the literature, ECG signal analysis has been improvised using statistical pre-processing techniques such as principal component analysis (PCA), and independent component analysis (ICA). However, both PCA and ICA are prone to suffer from slight alterations in either signal or noise, unless the basis functions are prepared with a worldwide set of ECG. Independent Principal Component Analysis (IPCA) has been used to overcome this shortcoming of PCA and ICA. Therefore, in this paper three techniques viz. FrFT, FrWT and IPCA are selected for comparison in pre-processing of ECG signals.ResultsThe selected methods have been evaluated on the basis of SNR, Se, Acc and DER of the detected ECG beats. FrWT yields the best results among all the methods considered in this paper; 34.37dB output SNR, 99.98% Se, 99.96% Acc, and 0.036% DER. These results indicate the quality of biology-related information retained from the pre-processed ECG signals for identifying different heart abnormalities.ConclusionCorrect analysis of the acquired ECG signal is the main challenge for cardiologist due to involvement of various types of noises (high and low frequency). Twenty two real time ECG records have been evaluated based on various FoM such as SNR, Se, Acc and DER for the proposed FrWT and existing FrFT and IPCA preprocessing techniques. Acquired real-time ECG database in normal and disease situations is used for the purpose. The values of FoMs indicate high SNR and better detection of R-peaks in a ECG signal which is important for the diagnosis of cardiovascular disease. The proposed FrWT outperforms all other techniques and holds both analytical attributes of the actual ECG signal and alterations in the amplitudes of various ECG waveforms adequately. It also provides signal portrayals in the time-fractional-frequency plane with low computational complexity enabling their use practically for versatile applications.     

线性随机系统的小波性质

本文分析了线性随机系统解过程的小波特征，得到若干性质，并分析了小波展开系统的统计特征及相关程度．     

Regeneration pattern analysis of Quercus Iiaotungensis in a temperate forest using two-dimensional wavelet analysis

This paper introduces the two-dimensional (2D) wavelet analysis as a general interrogative technique for the detection of spatial structure in lattice data. The 2D wavelet analysis detects components of hierarchical structure and displays the locational information of the components. Patches and gaps of different spatial scales in graphical presentation of wavelet coefficients can be linked to the local ecological processes that determine patterns at stand or landscape scales. Derived from the 2D wavelet transform function, the calculation of wavelet variance can reduce the four-dimensional data of wavelet coefficients to a two-dimensional wavelet variance function and quantify the contribution of the given scale to the overall pattern. We illustrate the use of the 2D wavelet analysis by analyzing two simulated patterns and identifying the regeneration pattern of the Quercus liaotungensis in a warm temperate forest in north China. Our results indicate that the recruitment of Q. liaotungensis occurs in an overlapping area between the patch of adult and canopy gap at scales of 45m×45m-70m×70 m and 20m×20m-30m×30m. The regeneration pattern of Q. liaotungensis can be mainly ascribed to a trade-off between two ecological processes: recruitment around parent trees and the physiological light requirements of seedlings and saplings. Our results provide a general portrayal of the regeneration pattern for the dispersal-limited and shade-intolerant Quercus species. We find that the two-dimensional wavelet analysis efficiently characterizes the scale-specific pattern of Q. liaotungensis at different life-history stages.     

Multiscale analysis of temporal variability of soil CO2 production as influenced by weather and vegetation

Ecosystem processes are influenced by weather and climatic perturbations at multiple temporal scales with a large range of amplitudes and phases. Technological advances of automated biometeorological measurements provide the opportunity to apply spectral methods on continuous time series to identify differences in amplitudes and phases and relationships with weather variation. Here we used wavelet coherence analysis to study the temporal covariance between soil CO₂ production and soil temperature, soil moisture, and photosynthetically active radiation (PAR). Continuous (hourly average) data were acquired over 2 years among three vegetation types in a semiarid mixed temperate forest. We showed that soil temperature and soil moisture influence soil CO₂ production differently at multiple periods (e.g. hours, days, weeks, months, years), especially after rain pulse events. Our results provide information about the periodicity of soil CO₂ production among vegetation types, and provide insights about processes controlling CO₂ production through the study of phase relationships between two time series (e.g. soil CO₂ production and PAR). We tested the performance of empirical models of soil CO₂ production using the continuous wavelet transform. These models, built around soil temperature and moisture, failed at multiple periods across the measured dates, suggesting that empirical models should include other factors that regulate soil CO₂ production at different temporal scales. Our results add a new dimension for the analysis of continuous time series of biometeorological measurements and model testing, which will prove useful for analysis of increasing sensor data obtained by environmental networks.