Wavelet meets actogram.
Ontology highlight
ABSTRACT:
SUBMITTER: Leise TL
PROVIDER: S-EPMC4487858 | biostudies-literature | 2013 Feb
REPOSITORIES: biostudies-literature
Publications
Wavelet meets actogram.
Leise Tanya L TL Indic Premananda P Paul Matthew J MJ Schwartz William J WJ
Journal of biological rhythms 20130201 1
A variety of methods to determine phase markers and period length from experimental data sets have traditionally assumed a rhythm of fixed period and amplitude. But most biological oscillations exhibit fluctuations in both period and amplitude, leading to the recent interest in the application of wavelet transforms that can measure how rhythms vary over time. Here we examine how wavelet-based methods can be extended to the analysis of conventional actograms, including the detection of onsets in ...[more]
Similar Datasets
Project description:This case report describes a 67-year-old African-American woman with homozygous familial hypercholesterolemia caused by 2 pathogenic variants in the LDLR gene. Initial surgical, pharmacological, and low-density lipoprotein apheresis interventions were insufficient; the addition of proprotein convertase subtilisin-kexin type 9 and angiopoietin-like 3 inhibitors lowered her low-density lipoprotein cholesterol to <70 mg/dL. (Level of Difficulty: Advanced.).
| S-EPMC9730161 | biostudies-literature
Project description:Senescence represents the final stage of leaf development but is often induced prematurely following exposure to biotic and abiotic stresses. Leaf senescence is manifested by color change from green to yellow (due to chlorophyll degradation) or to red (due to de novo synthesis of anthocyanins coupled with chlorophyll degradation) and frequently culminates in programmed death of leaves. However, the breakdown of chlorophyll and macromolecules such as proteins and RNAs that occurs during leaf senescence does not necessarily represent a one-way road to death but rather a reversible process whereby senescing leaves can, under certain conditions, re-green and regain their photosynthetic capacity. This phenomenon essentially distinguishes senescence from programmed cell death, leading researchers to hypothesize that changes occurring during senescence might represent a process of trans-differentiation, that is the conversion of one cell type to another. In this review, we highlight attributes common to senescence and dedifferentiation including chromatin structure and activation of transposable elements and provide further support to the notion that senescence is not merely a deterioration process leading to death but rather a unique developmental state resembling dedifferentiation.
| S-EPMC4844402 | biostudies-literature
Project description:As high-throughput molecular dynamics simulations of proteins become more common and the databases housing the results become larger and more prevalent, more sophisticated methods to quickly and accurately mine large numbers of trajectories for relevant information will have to be developed. One such method, which is only recently gaining popularity in molecular biology, is the continuous wavelet transform, which is especially well-suited for time course data such as molecular dynamics simulations. We describe techniques for the calculation and analysis of wavelet transforms of molecular dynamics trajectories in detail and present examples of how these techniques can be useful in data mining. We demonstrate that wavelets are sensitive to structural rearrangements in proteins and that they can be used to quickly detect physically relevant events. Finally, as an example of the use of this approach, we show how wavelet data mining has led to a novel hypothesis related to the mechanism of the protein γδ resolvase.
| S-EPMC4255908 | biostudies-literature
Project description:UnlabelledOscillations in mRNA and protein of circadian clock components can be continuously monitored in vitro using synchronized cell lines. These rhythms can be highly variable due to culture conditions and are non-stationary due to baseline trends, damping and drift in period length. We present a technique for characterizing the modal frequencies of oscillation using continuous wavelet decomposition to non-parametrically model changes in amplitude and period while removing baseline effects and noise.AvailabilityThe method has been implemented as the package waveclock for the free statistical software program R and is available for download from http://cran.r-project.org/
| S-EPMC2639275 | biostudies-literature
Project description:Generative adversarial networks and variational autoencoders (VAEs) provide impressive image generation from Gaussian white noise, but both are difficult to train, since they need a generator (or encoder) and a discriminator (or decoder) to be trained simultaneously, which can easily lead to unstable training. To solve or alleviate these synchronous training problems of generative adversarial networks (GANs) and VAEs, researchers recently proposed generative scattering networks (GSNs), which use wavelet scattering networks (ScatNets) as the encoder to obtain features (or ScatNet embeddings) and convolutional neural networks (CNNs) as the decoder to generate an image. The advantage of GSNs is that the parameters of ScatNets do not need to be learned, while the disadvantage of GSNs is that their ability to obtain representations of ScatNets is slightly weaker than that of CNNs. In addition, the dimensionality reduction method of principal component analysis (PCA) can easily lead to overfitting in the training of GSNs and, therefore, affect the quality of generated images in the testing process. To further improve the quality of generated images while keeping the advantages of GSNs, this study proposes generative fractional scattering networks (GFRSNs), which use more expressive fractional wavelet scattering networks (FrScatNets), instead of ScatNets as the encoder to obtain features (or FrScatNet embeddings) and use similar CNNs of GSNs as the decoder to generate an image. Additionally, this study develops a new dimensionality reduction method named feature-map fusion (FMF) instead of performing PCA to better retain the information of FrScatNets,; it also discusses the effect of image fusion on the quality of the generated image. The experimental results obtained on the CIFAR-10 and CelebA datasets show that the proposed GFRSNs can lead to better generated images than the original GSNs on testing datasets. The experimental results of the proposed GFRSNs with deep convolutional GAN (DCGAN), progressive GAN (PGAN), and CycleGAN are also given.
| S-EPMC8577828 | biostudies-literature
Project description:Cavitating and bubbly flows involve a host of physical phenomena and processes ranging from nucleation, surface and interfacial effects, mass transfer via diffusion and phase change to macroscopic flow physics involving bubble dynamics, turbulent flow interactions and two-phase compressible effects. The complex physics that result from these phenomena and their interactions make for flows that are difficult to investigate and analyse. From an experimental perspective, evolving sensing technology and data processing provide opportunities for gaining new insight and understanding of these complex flows, and the continuous wavelet transform (CWT) is a powerful tool to aid in their elucidation. Five case studies are presented involving many of these phenomena in which the CWT was key to data analysis and interpretation. A diverse set of experiments are presented involving a range of physical and temporal scales and experimental techniques. Bubble turbulent break-up is investigated using hydroacoustics, bubble dynamics and high-speed imaging; microbubbles are sized using light scattering and ultrasonic sensing, and large-scale coherent shedding driven by various mechanisms are analysed using simultaneous high-speed imaging and physical measurement techniques. The experimental set-up, aspect of cavitation being addressed, how the wavelets were applied, their advantages over other techniques and key findings are presented for each case study.This paper is part of the theme issue 'Redundancy rules: the continuous wavelet transform comes of age'.
| S-EPMC6048584 | biostudies-literature
Project description:BackgroundThe ever-growing need for cheap, simple, fast, and accurate healthcare solutions spurred a lot of research activities which are aimed at the reliable deployment of artificial intelligence in the medical fields. However, this has proved to be a daunting task especially when looking to make automated diagnoses using biomedical image data. Biomedical image data have complex patterns which human experts find very hard to comprehend. Against this backdrop, we applied a representation or feature learning algorithm: Invariant Scattering Convolution Network or Wavelet scattering Network to retinal fundus images and studied the the efficacy of the automatically extracted features therefrom for glaucoma diagnosis/detection. The influence of wavelet scattering network parameter settings as well as 2-D channel image type on the detection correctness is also examined. Our work is a distinct departure from the usual method where wavelet transform is applied to pre-processed retinal fundus images and handcrafted features are extracted from the decomposition results. Here, the RIM-ONE DL image dataset was fed into a wavelet scattering network developed in the Matlab environment to achieve a stage-wise decomposition process called wavelet scattering of the retinal fundus images thereby, automatically learning features from the images. These features were then used to build simple and computationally cheap classification algorithms.ResultsMaximum detection correctness of 98% was achieved on the held-out test set. Detection correctness is highly sensitive to scattering network parameter setting and 2-D channel image type.ConclusionA superficial comparison of the classification results obtained from our work and those obtained using a convolutional neural network underscores the potentiality of the proposed method for glaucoma detection.
| S-EPMC9979468 | biostudies-literature
Project description:We present a small molecule pK a prediction tool entirely written in Python. It predicts the macroscopic pK a value and is trained on a literature compilation of monoprotic compounds. Different machine learning models were tested and random forest performed best given a five-fold cross-validation (mean absolute error=0.682, root mean squared error=1.032, correlation coefficient r 2 =0.82). We test our model on two external validation sets, where our model performs comparable to Marvin and is better than a recently published open source model. Our Python tool and all data is freely available at https://github.com/czodrowskilab/Machine-learning-meets-pKa.
| S-EPMC7096188 | biostudies-literature
Project description:Vaccinating women in pregnancy (i.e., maternal immunization) has emerged as a promising tool to tackle infant morbidity and mortality worldwide. This approach nurtures a 'gift of nature,' whereby antibody is transferred from mother to fetus transplacentally during pregnancy, or postnatally in breast milk, thereby providing passive, antigen-specific protection against infections in the first few months of life, a period of increased immune vulnerability for the infant. In this review, we briefly summarize the rationale for maternal immunization programs and the landscape of vaccines currently in use or in the pipeline. We then direct the focus to the underlying biological phenomena, including the main mechanisms by which maternally derived antibody is transferred efficiently to the infant, at the placental interface or in breast milk; important research models and methodological approaches to interrogate these processes, particularly in the context of recent advances in systems vaccinology; the potential biological and clinical impact of high maternal antibody titres on neonatal ontogeny and subsequent infant vaccine responses; and key vaccine- and host-related factors influencing the maternal-infant dyad across different environments. Finally, we outline important gaps in knowledge and suggest future avenues of research on this topic, proposing potential strategies to ensure optimal testing, delivery and implementation of maternal vaccination programs worldwide.
| S-EPMC7396522 | biostudies-literature
Project description:The past several years have witnessed an increased presence of control theoretic concepts in synthetic biology. This review presents an organized summary of how these control design concepts have been applied to tackle a variety of problems faced when building synthetic biomolecular circuits in living cells. In particular, we describe success stories that demonstrate how simple or more elaborate control design methods can be used to make the behaviour of synthetic genetic circuits within a single cell or across a cell population more reliable, predictable and robust to perturbations. The description especially highlights technical challenges that uniquely arise from the need to implement control designs within a new hardware setting, along with implemented or proposed solutions. Some engineering solutions employing complex feedback control schemes are also described, which, however, still require a deeper theoretical analysis of stability, performance and robustness properties. Overall, this paper should help synthetic biologists become familiar with feedback control concepts as they can be used in their application area. At the same time, it should provide some domain knowledge to control theorists who wish to enter the rising and exciting field of synthetic biology.
| S-EPMC4971224 | biostudies-literature