403 research outputs found
sj-docx-2-tag-10.1177_17562848231154101 – Supplemental material for Characteristics of fecal microbiota in different constipation subtypes and association with colon physiology, lifestyle factors, and psychological status
No full textSupplemental material, sj-docx-2-tag-10.1177_17562848231154101 for Characteristics of fecal microbiota in different constipation subtypes and association with colon physiology, lifestyle factors, and psychological status by Ting Yu, Yu Ding, Dong Qian, Lin Lin and Yurong Tang in Therapeutic Advances in Gastroenterology</p
sj-docx-1-tag-10.1177_17562848231154101 – Supplemental material for Characteristics of fecal microbiota in different constipation subtypes and association with colon physiology, lifestyle factors, and psychological status
No full textSupplemental material, sj-docx-1-tag-10.1177_17562848231154101 for Characteristics of fecal microbiota in different constipation subtypes and association with colon physiology, lifestyle factors, and psychological status by Ting Yu, Yu Ding, Dong Qian, Lin Lin and Yurong Tang in Therapeutic Advances in Gastroenterology</p
sj-docx-1-wmr-10.1177_0734242X231168051 – Supplemental material for Selective leaching process for efficient and rapid recycling of spent lithium iron phosphate batteries
No full textSupplemental material, sj-docx-1-wmr-10.1177_0734242X231168051 for Selective leaching process for efficient and rapid recycling of spent lithium iron phosphate batteries by Yuchuan Xiong, Zhenzhen Guo, Tao Mei, Yurong Han, Yueyue Wang, Xin Xiong, Yifan Tang and Xianbao Wang in Waste Management & Research</p
Joint sparsity pattern learning based channel estimation for massive MIMO-OTFS systems
No full textWe propose a channel estimation scheme based on joint sparsity pattern learning (JSPL) for massive multi-input multi-output (MIMO) orthogonal time-frequency-space (OTFS) modulation aided systems. By exploiting the potential joint sparsity of the delay-Doppler-angle (DDA) domain channel, the channel estimation problem is transformed into a sparse recovery problem. To solve it, we first apply the spike and slab prior model to iteratively estimate the support set of the channel matrix, and a higher-accuracy parameter update rule relying on the identified support set is introduced into the iteration. Then the specific values of the channel elements corresponding to the support set are estimated by the orthogonal matching pursuit (OMP) method. Both our simulation results and analysis demonstrate that the proposed JSPL channel estimation scheme achieves an improved performance over the representative state-of-the-art baseline schemes, despite its reduced pilot overhead
Phylogenetic model selection for non-stationary Markov processes
No full text<p>PhyloTree.zip archive with the implementation of Inherited Rate Matrix algorithm for model selection in molecular evolutionary analysis. Also the PhyloTreeManuscript.zip archive contains all supplementary scripts and data used in the study.</p>
Algorithms for estimating rates of nucleotide change
No full textThe rapidly reducing cost of high throughput sequencing allows for the acquisition of genome-wide data for estimating nucleotide rates not only including mutation rates within species, but including substitution rates between species from multiple sequence alignments.
To address the problem of estimating mutation rates within species, Vogl (2014) has developed a general algorithm for the case of bi-allelic neutral evolution. A necessary first step in generalizing the Vogl estimator to the multi-allele case is the generalisation of Wright's stationary beta distribution to higher dimensions. This involves finding a stationary solution to the multi-allelic forward Kolmogorov equation. We present an approximate analytic solution to the neutrally evolving multi-allelic forward Kolmogorov equation in the form of a set of line densities defined on the edges of the solution simplex for the general case of K alleles. The solution is obtained in terms of a parameterisation in which the rate matrix Q is decomposed into the sum of a time-reversible part and a non-reversible `flux' part. The accuracy of the approximate solution with k = 3 and k = 4 alleles is illustrated using simulation data. The result shows that the agreement between simulation and theory is very close. Based on the approximate analytic solution, we address the problem of estimating a mutation rate matrix from site frequency data. The data is assumed to take the form of a multiple alignment of independent, neutrally evolving genomic sites sequenced from a moderate number of individuals chosen independently from a large effective population. We have demonstrated that it is possible, in principle, to estimate an evolutionary rate matrix from the site frequency spectrum of an alignment of genomes sampled from a population, provided certain conditions are met.
Nucleotide substitution rate matrices between species are generally used to calculate the likelihood of phylogenetic trees. In phylogenetic reconstruction, the assumption of heterogeneity in the substitution process across lineages is supported by evidence of compositional heterogeneity between the sequences. However, the total number of possible ways of reducing heterogeneity among lineages is enormous for even a modest number of taxa (Jayaswal et al., 2011). An efficient strategy for model selection is required to identify an 'optimal' model for a data set. In this study, we address these issues with a novel model selection algorithm we term the Inherited Rate Matrix algorithm (hereafter IRM). This approach is based on the notion that a species inherits the substitution tendencies of its ancestor. We further present the non-stationary heterogeneous across lineages model (hereafter ns-HAL algorithm) which extends the HAL algorithm of Jayaswal et al. [2014] to the general nucleotide Markov process. The IRM algorithm substantially reduces the complexity of identifying a sufficient solution to the problem of time-heterogeneous substitution processes across lineages. We also address the issue of reducing the computing time with development of a constrained-optimisation approach for the IRM algorithm (fast-IRM). From a simulation study based on 2nd codon position genome sequences of yeast, we establish that IRM is significantly more accurate than both ns-HAL and heterogeneity in the substitution process across lineages (hereafter HAL) for close and dispersed sequences. IRM and fast-IRM are faster than ns-HAL. fast-IRM also showed a marked speed improvement over a C++ implementation of HAL. Our comparison of the accuracy of IRM with fast-IRM showed no difference with identical inferences made for all data sets. These two algorithms greatly improve the compute time for model selection of a non-stationary process, increasing the suite of problems to which this important substitution model class can be applied
Cooperation in the sphere of regional security strengthening – priority task of SCO
No full textThe author insists that cooperation in the sphere of security remains the main task of SCO. The achievements of recent 10 years as well as new threats and challenges for security are considered, the author argues for necessity to provide common for all members of SCO legal basis for further approaches to security issues in the region of Central Asia
A hybrid EKF and switching PSO algorithm for joint state and parameter estimation of lateral flow immunoassay models
No full textThis is the post-print version of the Article. The official published can be accessed from the link below - Copyright @ 2012 IEEEIn this paper, a hybrid extended Kalman filter (EKF) and switching particle swarm optimization (SPSO) algorithm is proposed for jointly estimating both the parameters and states of the lateral flow immunoassay model through available short time-series measurement. Our proposed method generalizes the well-known EKF algorithm by imposing physical constraints on the system states. Note that the state constraints are encountered very often in practice that give rise to considerable difficulties in system analysis and design. The main purpose of this paper is to handle the dynamic modeling problem with state constraints by combining the extended Kalman filtering and constrained optimization algorithms via the maximization probability method. More specifically, a recently developed SPSO algorithm is used to cope with the constrained optimization problem by converting it into an unconstrained optimization one through adding a penalty term to the objective function. The proposed algorithm is then employed to simultaneously identify the parameters and states of a lateral flow immunoassay model. It is shown that the proposed algorithm gives much improved performance over the traditional EKF method.This work was supported in part by the International Science and Technology Cooperation Project of China under Grant
2009DFA32050, Natural Science Foundation of China under Grants 61104041, International Science and Technology Cooperation Project of Fujian Province of China under Grant
2009I0016
- …
