JOINT IDENTIFICATION OF SPATIALLY VARIABLE GENES VIA A NETWORK-ASSISTED BAYESIAN REGULARIZATION APPROACH

Empirical and fully Bayesian approaches for the identification of vibration sources from transverse displacement measurements

Colchicum autumnale L. (Colchicaceae) is classed as an endangered plant species in Ireland, with a highly localised distribution centred on the Nore Valley in the southeast. The high resolution Amplified Fragment Length Polymorphism genetic fingerprinting technique was used to clarify a question over the native status of the species and to assess its Irish conservation status through comparison with populations throughout northwestern Europe. The analysis of 20 populations with three selective primer pairs yielded 155 markers, 90% of which displayed polymorphism. Allele frequency was estimated using a Bayesian approach and based on this, total gene diversity (Ht) was 0.305 and mean within-population gene diversity (Hw) was 0.142. Irish populations contain comparatively moderate levels of genetic diversity and are unlikely to be susceptible to the deleterious effects of inbreeding depression. Analysis of Molecular Variance revealed that populations were significantly differentiated, with 68% of variation partitioned among populations and 32% within. Neighbour-joining and ordination analyses revealed a major biogeographic division between populations, suggesting that post-glacial migration in northwest Europe was driven from at least two separate glacial refugia and that the re-colonisation of Britain and Ireland is likely to have occurred via a western seaboard route from an Iberian glacial refugium. The total evidence points to C. autumnale being a native component of the Irish flora and its endangered IUCN (World Conservation Union) country status should therefore make it a priority for national conservation efforts. The study illustrates the point that a broader geographical perspective is fundamental to the assessment of the conservation status of regionally threatened plant species.

Modern individual clustering methods utilising hypervariable nuclear microsatellite DNA polymorphisms are being increasingly applied in the field of population genetics. This study explores the efficiency of the clustering methods in identifying the breeds of origin of 250 domestic dog (Canis familiaris) individuals based on 10 microsatellite loci. An allele sharing distance (DAS) matrix and the corresponding neighbour-joining tree of individuals revealed monophyletic assemblages that corresponded perfectly with the breeds of origin of the dogs. Individual assignment tests using a Bayesian statistical approach, an allele frequency based method, and a DCE genetic distance based method were all extremely powerful. Most strikingly, the Bayesian method provided 100% assignment success of individuals into their correct breeds of origin and 100% exclusion success of individuals from all alternate reference populations with a high level of statistical confidence (P < 0.0001). A Bayesian Markov Chain Monte Carlo clustering approach revealed clear distinction of individuals into groups according to their breeds of origin, with a near-zero level of 'genetic admixture' among breeds. The results demonstrate that an FST of 0.18, mean expected gene diversity of 0.6 across 10 loci, and approximately 50 individuals per reference population suffice to provide maximum individual assignment success in C. familiaris. This refutes the traditional view that DNA based dog breed identification is not feasible at the individual level of resolution.

This study attempts to provide a comprehensive insight into the prevailing genetic status of Indian sheep breeds using microsatellite markers. Seventeen Indian sheep breeds from 3 agroecological zones were analysed using a panel of 25 microsatellite markers. All of the sheep breeds investigated were genetically diverse, as evident from the high allele (>6) and gene (>0.6) diversity values. The gene diversity values for all breeds ranged from 0.621 to 0.780. The within-population heterozygote deficit (F(IS)) varied from -0.098 to 0.234, reflecting significant levels for 12 of the 17 breeds investigated. The average genetic differentiation between all breeds (F(ST)) was 11.1%, revealing moderate discrimination between the indigenous sheep breeds. The genetic distance and principal component analysis revealed a separation of sheep breeds based on geographical propinquity. The Bayesian clustering approach suggested poor breed differentiation in the north-western arid and semi-arid region when compared to the breeds from the eastern and southern peninsular regions. The observed results mirror the divergent management strategies in the different agroecological regions, lack of specific selection policies, and intermixing of breeds in close proximity. Immediate steps to curb the intermixing and erosion of breed purity for some of these breeds need to be implemented, for example, by introducing measures like making proven rams available and ensuring their frequent exchange between flocks. The data generated here provides valuable information about the genetic structure of the 17 Indian sheep breeds and this can be used for designating priorities for their conservation.

The population structure of cloudberry (Rubus chamaemorus L.), collected from Krkonose Mountains (the Czech Republic), continental Norway and Spitsbergen, was examined using microsatellite analyses (SSR). Among 184 individuals, 162 different genotypes were identified. The overall unbiased gene diversity was high (h^=0.463). A high level of genetic differentiation among populations (FST = 0.45; p < .01) indicated restricted gene flow between populations. Using a Bayesian approach, six clusters were found which represented the genetic structure of the studied cloudberry populations. The value of correlation index between genetic and geographical distances (r = .44) indicates that gene flow, even over a long distance, could exist. An exact test of population differentiation showed that Rubus chamaemorus populations from regions (Krkonose Mountains, continental Norway and Spitsbergen) are differentiated although some individuals within populations share common alleles even among regions. These results were confirmed by AMOVA, where the highest level of diversity was found within populations (70.8%). There was no difference between 87 pairs of populations (18.7%) mostly within cloudberry populations from continental Norway and from Spitsbergen. Based on obtained results, it is possible to conclude that Czech and Norwegian cloudberry populations are undergoing differentiation, which preserves unique allele compositions most likely from original populations during the last glaciation period. This knowledge will be important for the creation and continuation of in situ and ex situ conservation of cloudberry populations within these areas.

In the current era, the government consistently emphasizes the pursuit of high-quality development, as evidenced by the ongoing increase in the tertiary industry’s GDP share. As a crucial component of the modern service sector, logistics plays a pivotal role in determining the operational efficiency and overall quality of the industrial economy. This study focuses on constructing a Chongqing logistics express demand prediction index system. It employs an improved BP neural network model to forecast the logistics express demand for Chongqing over the next five years. Given the limited express demand data sequence and the normalized characteristics of the data, the selected training method is the Bayesian regularization approach, with the LeCun Tanh function serving as the hidden layer activation function. Additionally, a genetic algorithm is designed to optimize the initial weights and thresholds of the BP neural network, thereby enhancing prediction accuracy and reducing the number of iterations. The experimental results of the improved GA-BP network are analyzed and compared, demonstrating that the improved BP neural network, utilizing GA optimization, can more reliably and accurately predict regional logistics express demand. According to the findings, the forecast indicates that the logistics express demand for Chongqing in 2026 will be 2,171,642,700 items.

Many reduced-order models are neither robust with respect to parameter changes nor cost-effective enough for handling the nonlinear dependence of complex dynamical systems. In this study, we put forth a robust machine learning framework for projection-based reduced-order modeling of such nonlinear and nonstationary systems. As a demonstration, we focus on a nonlinear advection-diffusion system given by the viscous Burgers equation, which is a prototypical setting of more realistic fluid dynamics applications due to its quadratic nonlinearity. In our proposed methodology the effects of truncated modes are modeled using a single layer feed-forward neural network architecture. The neural network architecture is trained by utilizing both the Bayesian regularization and extreme learning machine approaches, where the latter one is found to be more computationally efficient. A significant emphasis is laid on the selection of basis functions through the use of both Fourier bases and proper orthogonal decomposition. It is shown that the proposed model yields significant improvements in accuracy over the standard Galerkin projection methodology with a negligibly small computational overhead and provide reliable predictions with respect to parameter changes.

This paper proposes an extended model for a Permanent Magnet Assisted Synchronous Reluctance Motor (PMaSynRM) to address the torque fluctuation component in the modeling procedure in addition to the conventional average term. In many studies, the PMaSynRM mathematical model is provided. To increase the modeling accuracy, some frequencies of cogging torque were considered in the modeling procedure. However, these models do not satisfy precision for model-based control methods or torque ripple reduction algorithms. A trained shallow Artificial Neural Network (ANN) based on the Bayesian Regularization backpropagation approach is used in this work to model the torque fluctuations accurately, thanks to the advanced Finite Element Analysis (FEA) results. This method accurately results in fluctuation modeling with low computational time and is a practical solution for real-time applications, model-based control methods, and torque ripple reduction strategies. The experimental and simulation results verify the performance of the proposed extended model.

Applying artificial neural networks for systematic estimation of degree of fouling in heat exchangers

Spatiotemporally resolved ambient particulate matter concentration by fusing observational data and ensemble chemical transport model simulations

Diffusion tensor magnetic resonance imaging (DT-MRI) is a non-invasive imaging technique allowing to estimate the molecular self-diffusion tensors of water within surrounding tissue. Due to the low signal-to-noise ratio of magnetic resonance images, reconstructed tensor images usually require some sort of regularization in a post-processing step. Previous approaches are either suboptimal with respect to the reconstruction or regularization step. This paper presents a Bayesian approach for simultaneous reconstruction and regularization of DT-MR images that allows to resolve the disadvantages of previous approaches. To this end, estimation theoretical concepts are generalized to tensor valued images that are considered as Riemannian manifolds. Doing so allows us to derive a maximum a posteriori estimator of the tensor image that considers both the statistical characteristics of the Rician noise occurring in MR images as well as the nonlinear structure of tensor valued images. Experiments on synthetic data as well as real DT-MRI data validate the advantage of considering both statistical as well as geometrical characteristics of DT-MRI.

The photon counting image collected under 10− 4 lux environment has a degraded image quality due to background noises and other problems. Bayesian estimation is a classical approach for photon counting image restoration and regularization has also been widely used in image processing. However, the regularization method is not suitable for photon counting images with extremely lack of information, and the recovery effect of Bayesian estimation in images mixed with unknown noise is not ideal. The main contribution of this paper is that on the basis of Bayesian estimation, the regularization method is introduced to solve the problem of restoring photon counting images mixed with unknown noise under 10− 4 lux environment. The original part is that the gamma distribution of the expected value of photon counting is used as its prior condition, and the error function is expressed as the form of the norm to establish the objective function. Through an approximate iterative solution, the optimal estimation of the photon counting expectation is carried out to achieve the optimal restoration of the photon counting image. Experiments demonstrate that the background noise is effectively removed and the image quality is improved after restoring photon counting images. Also, the final result of the proposed method is superior to other comparative methods in multiple evaluation indexes and achieved better effects.