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Plants (Basel, Switzerland) Sep 2023The quantitative description of growth rings is yet incomplete, including the functional division into earlywood and latewood. Methods developed to date, such as the...
The quantitative description of growth rings is yet incomplete, including the functional division into earlywood and latewood. Methods developed to date, such as the Mork criterion for conifers, can be biased and arbitrary depending on species and growth conditions. We proposed the use of modeling of the statistical distribution of tracheids to determine a universal criterion applicable to all conifer species. Thisstudy was based on 50-year anatomical measurements of L., Du Tour, and Ledeb. near the upper tree line in the Western Sayan Mountains (South Siberia). Statistical distributions of the cell wall thickness (CWT)-to-radial-diameter (D) ratio and its slope were investigated for raw and standardized data (divided by the mean). The bimodal distribution of the slope for standardized CWT and D was modeled with beta distributions for earlywood and latewood tracheids and a generalized normal distribution for transition wood to account for the gradual shift in cell traits. The modelcan describe with high accuracy the growth ring structure for species characterized by various proportions of latewood, histometric traits, and gradual or abrupt transition. The proportion of two (or three, including transition wood) zones in the modeled distribution is proposed as a desired criterion.
PubMed: 37836196
DOI: 10.3390/plants12193454 -
PloS One 2022We propose a stochastic generative model to represent a directed graph constructed by citations among academic papers, where nodes and directed edges represent papers...
We propose a stochastic generative model to represent a directed graph constructed by citations among academic papers, where nodes and directed edges represent papers with discrete publication time and citations respectively. The proposed model assumes that a citation between two papers occurs with a probability based on the type of the citing paper, the importance of cited paper, and the difference between their publication times, like the existing models. We consider the out-degrees of citing paper as its type, because, for example, survey paper cites many papers. We approximate the importance of a cited paper by its in-degrees. In our model, we adopt three functions: a logistic function for illustrating the numbers of papers published in discrete time, an inverse Gaussian probability distribution function to express the aging effect based on the difference between publication times, and an exponential distribution (or a generalized Pareto distribution) for describing the out-degree distribution. We consider that our model is a more reasonable and appropriate stochastic model than other existing models and can perform complete simulations without using original data. In this paper, we first use the Web of Science database and see the features used in our model. By using the proposed model, we can generate simulated graphs and demonstrate that they are similar to the original data concerning the in- and out-degree distributions, and node triangle participation. In addition, we analyze two other citation networks derived from physics papers in the arXiv database and verify the effectiveness of the model.
Topics: Databases, Factual; Likelihood Functions; Normal Distribution; Organizations; Physics
PubMed: 35767539
DOI: 10.1371/journal.pone.0269845 -
PloS One 2023Gul and Mohsin 2021 developed a new modified form of renowned "Half logistic" distribution introduced by Balakrishnan (1991) and named it half logistic-truncated...
Gul and Mohsin 2021 developed a new modified form of renowned "Half logistic" distribution introduced by Balakrishnan (1991) and named it half logistic-truncated exponential distribution (HL-TEXPD). Some mathematical characteristics are studied, including hazard function, Pth percentile, moment generating function and Shannon entropy. Simulation study is performed to examine the behaviour of parameter estimates. The proposed model is fitted on three real data sets to check its efficacy. Additionally, TTT (total time on test) plot is drawn to study the failure rate of the three data sets. The results verdict that HL-TEXPD can be efficiently utilized in the field of engineering and medical sciences based on the data sets under study contrary to the classical and baseline models.
Topics: Computer Simulation; Statistical Distributions; Entropy
PubMed: 37963157
DOI: 10.1371/journal.pone.0285992 -
Neural Networks : the Official Journal... Jan 2022In this paper we introduce a biologically inspired Convolutional Neural Network (CNN) architecture called LGN-CNN that has a first convolutional layer composed of a...
In this paper we introduce a biologically inspired Convolutional Neural Network (CNN) architecture called LGN-CNN that has a first convolutional layer composed of a single filter that mimics the role of the Lateral Geniculate Nucleus (LGN). The first layer of the neural network shows a rotational symmetric pattern justified by the structure of the net itself that turns up to be an approximation of a Laplacian of Gaussian (LoG). The latter function is in turn a good approximation of the receptive field profiles (RFPs) of the cells in the LGN. The analogy with the visual system is established, emerging directly from the architecture of the neural network. A proof of rotation invariance of the first layer is given on a fixed LGN-CNN architecture and the computational results are shown. Thus, contrast invariance capability of the LGN-CNN is investigated and a comparison between the Retinex effects of the first layer of LGN-CNN and the Retinex effects of a LoG is provided on different images. A statistical study is done on the filters of the second convolutional layer with respect to biological data. In conclusion, the model we have introduced approximates well the RFPs of both LGN and V1 attaining similar behavior as regards long range connections of LGN cells that show Retinex effects.
Topics: Geniculate Bodies; Neural Networks, Computer; Normal Distribution; Visual Cortex; Visual Pathways
PubMed: 34715534
DOI: 10.1016/j.neunet.2021.09.024 -
Biochimica Et Biophysica Acta. Gene... Jun 2020Gaussian Graphical Models (GGMs) are tools to infer dependencies between biological variables. Popular applications are the reconstruction of gene, protein, and... (Review)
Review
Gaussian Graphical Models (GGMs) are tools to infer dependencies between biological variables. Popular applications are the reconstruction of gene, protein, and metabolite association networks. GGMs are an exploratory research tool that can be useful to discover interesting relations between genes (functional clusters) or to identify therapeutically interesting genes, but do not necessarily infer a network in the mechanistic sense. Although GGMs are well investigated from a theoretical and applied perspective, important extensions are not well known within the biological community. GGMs assume, for instance, multivariate normal distributed data. If this assumption is violated Mixed Graphical Models (MGMs) can be the better choice. In this review, we provide the theoretical foundations of GGMs, present extensions such as MGMs or multi-class GGMs, and illustrate how those methods can provide insight in biological mechanisms. We summarize several applications and present user-friendly estimation software. This article is part of a Special Issue entitled: Transcriptional Profiles and Regulatory Gene Networks edited by Dr. Dr. Federico Manuel Giorgi and Dr. Shaun Mahony.
Topics: Gene Regulatory Networks; Genomics; Humans; Metabolomics; Models, Genetic; Models, Statistical; Normal Distribution; Software
PubMed: 31639475
DOI: 10.1016/j.bbagrm.2019.194418 -
Computational Intelligence and... 2021In reliability studies, the best fitting of lifetime models leads to accurate estimates and predictions, especially when these models have nonmonotone hazard functions....
In reliability studies, the best fitting of lifetime models leads to accurate estimates and predictions, especially when these models have nonmonotone hazard functions. For this purpose, the new Exponential-X Fréchet (NEXF) distribution that belongs to the new exponential-X (NEX) family of distributions is proposed to be a superior fitting model for some reliability models with nonmonotone hazard functions and beat the competitive distribution such as the exponential distribution and Frechet distribution with two and three parameters. So, we concentrated our effort to introduce a new novel model. Throughout this research, we have studied the properties of its statistical measures of the NEXF distribution. The process of parameter estimation has been studied under a complete sample and Type-I censoring scheme. The numerical simulation is detailed to asses the proposed techniques of estimation. Finally, a Type-I censoring real-life application on leukaemia patient's survival with a new treatment has been studied to illustrate the estimation methods, which are well fitted by the NEXF distribution among all its competitors. We used for the fitting test the novel modified Kolmogorov-Smirnov (KS) algorithm for fitting Type-I censored data.
Topics: Computer Simulation; Humans; Leukemia; Models, Statistical; Reproducibility of Results; Statistical Distributions
PubMed: 34497637
DOI: 10.1155/2021/2167670 -
Nature Communications Sep 2019The recent decline in energy, size and complexity scaling of traditional von Neumann architecture has resurrected considerable interest in brain-inspired computing....
The recent decline in energy, size and complexity scaling of traditional von Neumann architecture has resurrected considerable interest in brain-inspired computing. Artificial neural networks (ANNs) based on emerging devices, such as memristors, achieve brain-like computing but lack energy-efficiency. Furthermore, slow learning, incremental adaptation, and false convergence are unresolved challenges for ANNs. In this article we, therefore, introduce Gaussian synapses based on heterostructures of atomically thin two-dimensional (2D) layered materials, namely molybdenum disulfide and black phosphorus field effect transistors (FETs), as a class of analog and probabilistic computational primitives for hardware implementation of statistical neural networks. We also demonstrate complete tunability of amplitude, mean and standard deviation of the Gaussian synapse via threshold engineering in dual gated molybdenum disulfide and black phosphorus FETs. Finally, we show simulation results for classification of brainwaves using Gaussian synapse based probabilistic neural networks.
Topics: Disulfides; Molybdenum; Nanotechnology; Neural Networks, Computer; Normal Distribution; Transistors, Electronic
PubMed: 31519885
DOI: 10.1038/s41467-019-12035-6 -
BMC Bioinformatics Feb 2021The search for statistically significant relationships between molecular markers and outcomes is challenging when dealing with high-dimensional, noisy and collinear...
BACKGROUND
The search for statistically significant relationships between molecular markers and outcomes is challenging when dealing with high-dimensional, noisy and collinear multivariate omics data, such as metabolomic profiles. Permutation procedures allow for the estimation of adjusted significance levels without assuming independence among metabolomic variables. Nevertheless, the complex non-normal structure of metabolic profiles and outcomes may bias the permutation results leading to overly conservative threshold estimates i.e. lower than those from a Bonferroni or Sidak correction.
METHODS
Within a univariate permutation procedure we employ parametric simulation methods based on the multivariate (log-)Normal distribution to obtain adjusted significance levels which are consistent across different outcomes while effectively controlling the type I error rate. Next, we derive an alternative closed-form expression for the estimation of the number of non-redundant metabolic variates based on the spectral decomposition of their correlation matrix. The performance of the method is tested for different model parametrizations and across a wide range of correlation levels of the variates using synthetic and real data sets.
RESULTS
Both the permutation-based formulation and the more practical closed form expression are found to give an effective indication of the number of independent metabolic effects exhibited by the system, while guaranteeing that the derived adjusted threshold is stable across outcome measures with diverse properties.
Topics: Genetic Markers; Metabolome; Metabolomics; Models, Biological; Statistical Distributions
PubMed: 33579202
DOI: 10.1186/s12859-021-03975-2 -
Scientific Reports Jul 2021The number of secondary cases, i.e. the number of new infections generated by an infectious individual, is an important parameter for the control of infectious diseases....
The number of secondary cases, i.e. the number of new infections generated by an infectious individual, is an important parameter for the control of infectious diseases. When individual variation in disease transmission is present, like for COVID-19, the distribution of the number of secondary cases is skewed and often modeled using a negative binomial distribution. However, this may not always be the best distribution to describe the underlying transmission process. We propose the use of three other offspring distributions to quantify heterogeneity in transmission, and we assess the possible bias in estimates of the mean and variance of this distribution when the data generating distribution is different from the one used for inference. We also analyze COVID-19 data from Hong Kong, India, and Rwanda, and quantify the proportion of cases responsible for 80% of transmission, [Formula: see text], while acknowledging the variation arising from the assumed offspring distribution. In a simulation study, we find that variance estimates may be biased when there is a substantial amount of heterogeneity, and that selection of the most accurate distribution from a set of distributions is important. In addition we find that the number of secondary cases for two of the three COVID-19 datasets is better described by a Poisson-lognormal distribution.
Topics: COVID-19; Computer Simulation; Hong Kong; Humans; India; Infectious Disease Transmission, Vertical; Poisson Distribution; Rwanda; SARS-CoV-2
PubMed: 34238978
DOI: 10.1038/s41598-021-93578-x -
Statistics in Medicine Jan 2023Many real data analyses involve two-sample comparisons in location or in distribution. Most existing methods focus on problems where observations are independently and...
Many real data analyses involve two-sample comparisons in location or in distribution. Most existing methods focus on problems where observations are independently and identically distributed in each group. However, in some applications the observed data are not identically distributed but associated with some unobserved parameters which are identically distributed. To address this challenge, we propose a novel two-sample testing procedure as a combination of the -modeling density estimation introduced by Efron and the two-sample Kolmogorov-Smirnov test. We also propose efficient bootstrap algorithms to estimate the statistical significance for such tests. We demonstrate the utility of the proposed approach with two biostatistical applications: the analysis of surgical nodes data with binomial model and differential expression analysis of single-cell RNA sequencing data with zero-inflated Poisson model.
Topics: Humans; Poisson Distribution; Models, Statistical; Algorithms
PubMed: 36412978
DOI: 10.1002/sim.9603