Propagation Approach Research Articles

Explainable Medical Image Segmentation via Generative Adversarial Networks and Layer-wise Relevance Propagation

This paper contributes in automating medical image segmentation by proposing generative adversarial network based models to segment both polyps and instruments in endoscopy images. A main contribution of this paper is providing explanations for the predictions using layer-wise relevance propagation approach, showing which pixels in the input image are more relevant to the predictions. The models achieved 0.46 and 0.70, on Jaccard index and 0.84 and 0.96 accuracy, on the polyp segmentation and the instrument segmentation, respectively.

Recent Progress in the Simulation of Chiral Systems with Real Time Propagation Methods

AbstractIn this brief review, an overview about recent efforts to simulate the spectroscopic signatures of chiral molecules is given with focus on real time propagation approaches to solve the time‐dependentSchrödingerequation. In particular the simulation of electric circular dichroism spectra and vibrationalRamanoptical activity is discussed. In comparison to linear absorption spectra, where only the response of the electric dipole moment is necessary, the response of the magnetic dipole moment and electric quadrupole moment is more intricate. Issues such as gauge origin dependence, basis set dependence, non‐local potentials and the dipole approximation are addressed.

Monte Carlo-Based Error Propagation for a More Reliable Regression Analysis across Specific Rates in Bioprocesses.

During process development, bioprocess data need to be converted into applicable knowledge. Therefore, it is crucial to evaluate the obtained data under the usage of transparent and reliable data reduction and correlation techniques. Within this contribution, we show a generic Monte Carlo error propagation and regression approach applied to two different, industrially relevant cultivation processes. Based on measurement uncertainties, errors for cell-specific growth, uptake, and production rates were determined across an evaluation chain, with interlinked inputs and outputs. These uncertainties were subsequently included in regression analysis to derive the covariance of the regression coefficients and the confidence bounds for prediction. The usefulness of the approach is shown within two case studies, based on the relations across biomass-specific rate control limits to guarantee high productivities in E. coli, and low lactate formation in a CHO cell fed-batch could be established. Besides the possibility to determine realistic errors on the evaluated process data, the presented approach helps to differentiate between reliable and unreliable correlations and prevents the wrong interpretations of relations based on uncertain data.

Network propagation-based prioritization of long tail genes in 17 cancer types

BackgroundThe diversity of genomic alterations in cancer poses challenges to fully understanding the etiologies of the disease. Recent interest in infrequent mutations, in genes that reside in the “long tail” of the mutational distribution, uncovered new genes with significant implications in cancer development. The study of cancer-relevant genes often requires integrative approaches pooling together multiple types of biological data. Network propagation methods demonstrate high efficacy in achieving this integration. Yet, the majority of these methods focus their assessment on detecting known cancer genes or identifying altered subnetworks. In this paper, we introduce a network propagation approach that entirely focuses on prioritizing long tail genes with potential functional impact on cancer development.ResultsWe identify sets of often overlooked, rarely to moderately mutated genes whose biological interactions significantly propel their mutation-frequency-based rank upwards during propagation in 17 cancer types. We call these sets “upward mobility genes” and hypothesize that their significant rank improvement indicates functional importance. We report new cancer-pathway associations based on upward mobility genes that are not previously identified using driver genes alone, validate their role in cancer cell survival in vitro using extensive genome-wide RNAi and CRISPR data repositories, and further conduct in vitro functional screenings resulting in the validation of 18 previously unreported genes.ConclusionOur analysis extends the spectrum of cancer-relevant genes and identifies novel potential therapeutic targets.

Semiclassical propagator approach for emission processes from deformed nuclei

We describe a more rigorous formulation of the 3D semiclassical approach, corresponding to deformed potentials, which leads to the exact results and we also compare them with the much simpler expressions given by the angular Wentzel–Krames–Brillouin (WKB) and linearized WKB with its approximation, known as Fröman WKB method. We define the semiclassical fundamental matrix of solutions, describing the effect of the deformed field. Its inverse, proportional to the so-called propagator matrix, connects the solution in two points. A comparison between the coupled channels fundamental matrix of solutions to various semiclassical counterparts evidences close results in the barrier region. We express the scattering amplitudes in terms of the propagator matrix multiplied by the ratio between internal wave function components and irregular Coulomb waves. The relative influence of nuclear and Coulomb action terms together with nuclear and Coulomb centrifugal barriers upon the penetration process reveals the dominance of the Coulombian parts. An analysis of alpha-emission processes between ground states and to excited states of even–even nuclei in terms of the propagator matrix evidenced the important role played by deformation of the Coulomb field and the shape of the nuclear interaction.

An Analytical Solution for the Vibration and Far-Field Sound Radiation Analysis of Finite, Semisubmerged Cylindrical Shells

The vibration response and far-field sound radiation of a semisubmerged, finite cylindrical shell with low-frequency excitation are studied. The solution to this problem can be divided into two steps. The first step is to apply the wave propagation approach to determine the vibration response of the cylindrical shell. In the cylindrical coordinate system, the Flügge shell equations and Laplace equation are used to describe the cylindrical shell and surrounding fluid so that the vibration responses of the shell can be addressed analytically. The fluid free surface effect is taken into account by applying the sine series to force the velocity potential on the free surface to be zero. Furthermore, compared with the FEM (the finite element method), the present method is not only reliable but also effective. In the second step, the far-field sound radiation is solved by the Fourier transform technique and the stationary phase method in accordance with the vibration responses of the shell from the previous step. The boundary element method is applied to validate the reliability of the acoustical radiation calculation. The circumferential directivity of far-field sound pressure is discussed, and it is found that the maximum value of the sound pressure always appears directly under the structure when the driving frequencies are relatively low. Besides, in consideration of simplicity and less computation effort, the present method can be used for the rapid prediction of the vibration and far-field sound pressure of a semisubmerged cylindrical shell with low-frequency excitation.

Affinity propagation approach for catchment classification applied to arid catchments

One of the major issues in the arid region is the availability of hydrological data for hydrological studies of the catchments for water resources projects. Since the Kingdom of Saudi Arabia (KSA) is a huge country and contains many arid catchments it is awfully expensive and time-consuming to make hydrological networks for studying all these catchments. Therefore, the Affinity Propagation (AP) clustering technique is proposed to cluster catchments into groups that are similar in morphological, hydrological, and land cover characteristics and defining an exemplar (a representative catchment) to each group. This catchment is utilized for the installation of a detailed hydrological network. The hydrological response of that catchment can be transferred and scaled appropriately to other catchments in the cluster since they are hydrologically and morphologically similar. A pilot study is performed on 18 sub-catchments in the southwestern part of KSA. GIS software is used to extract catchment attributes and the clustering process is performed using the AP cluster packages in R software. The results show that four clusters are obtained based on the morphological attributes (twenty-eight attributes), five clusters based on hydrological attributes (twelve attributes), and three clusters based on land cover and CN (three kinds of land cover as attributes). The AP clustering technique was evaluated by the construction of a correlation matrix that shows a high correlation of 0.817–0.999. This study provides a robust technique that is effective and efficient to identify the similarity of catchments and can help hydrologists to develop a catchment management application in arid regions.

Simulating electronic excitation and dynamics with real-time propagation approach to TDDFT within plane-wave pseudopotential formulation.

We give a perspective on simulating electronic excitation and dynamics using the real-time propagation approach to time-dependent density functional theory (RT-TDDFT) in the plane-wave pseudopotential formulation. RT-TDDFT is implemented in various numerical formalisms in recent years, and its practical application often dictates the most appropriate implementation of the theory. We discuss recent developments and challenges, emphasizing numerical aspects of studying real systems. Several applications of RT-TDDFT simulation are discussed to highlight how the approach is used to study interesting electronic excitation and dynamics phenomena in recent years.

Towards the NASA UQ Challenge 2019: Systematically forward and inverse approaches for uncertainty propagation and quantification

This paper is dedicated to exploring the NASA Langley Challenge on Optimization under Uncertainty by proposing a series of approaches for both forward and inverse treatment of uncertainty propagation and quantification. The primary effort is placed on the categorization of the subproblems as to be forward or inverse procedures, such that dedicated techniques are proposed for the two directions, respectively. The sensitivity analysis and reliability analysis are categorized as forward procedures, while modal calibration & uncertainty reduction, reliability-based optimization, and risk-based design are regarded as inverse procedures. For both directions, the overall approach is based on imprecise probability characterization where both aleatory and epistemic uncertainties are investigated for the inputs, and consequently, the output is described as the probability-box (P-box). Theoretic development is focused on the definition of comprehensive uncertainty quantification criteria from limited and irregular time-domain observations to extract as much as possible uncertainty information, which will be significant for the inverse procedure to refine uncertainty models. Furthermore, a decoupling approach is proposed to investigate the P-box along two directions such that the epistemic and aleatory uncertainties are decoupled, and thus a two-loop procedure is designed to propagate both epistemic and aleatory uncertainties through the systematic model. The key for successfully addressing this challenge is in obtaining on the balance among an appropriate hypothesis of the input uncertainty model, a comprehensive criterion of output uncertainty quantification, and a computational viable approach for both forward and inverse uncertainty treatment.

Wave Propagation Approach for Elastic Transient Responses of Transversely Isotropic Asphalt Pavement under an Impact Load: A Semianalytical Solution

The conventional transfer matrix method, adopted formerly in a semianalytical solution for layered elastic or viscoelastic asphalt pavement structures, has inherent deficiencies, such as ill-condition matrix, numerical overflow, and error accumulation, due to exponential items. This phenomenon is more evident in multilayered dynamic analysis with imperfect interfaces. Moreover, several studies revealed that pavement materials exhibit transverse isotropy in service. Consequently, a novel semianalytical solution methodology, wave propagation approach, was proposed herein to calculate the dynamic responses of asphalt pavement under an impact load considering the transversely isotropic material model and the imperfect interfaces. First, the transfer matrix was established based on matrix theory and wave propagation approach, while the relation between the state vector and wave vector in the transformed domain was constructed simultaneously. Then, combined with the boundary conditions and interface contact conditions, the solution of the wave vector in the transformed domain was derived. Finally, based on Laplace–Hankel inverse transform, the state vector in the time domain was obtained, followed by numerical computation with programming. The accuracy and efficiency of the proposed semianalytical solution, together with the influence regularities of several variables, were discussed. Results showed that due to the absence of positive exponential functions and a large-dimensional matrix, accuracy and efficiency requirements were satisfied during calculation. Moreover, the variation induced by the transversely isotropic properties and interface conditions, presented in the dynamic responses, reiterated that these factors should be considered during the design and analysis of asphalt pavement structures.

Fatigue Life Prediction of Welded Girders with Flange Thickness Transitions and Cope Hole

AbstractThe paper presents an analytical and numerical approach for the fatigue life prediction using local concepts. A Two‐Stage‐Model is applied to a welded girder with thickness transition and cope hole to determine its fatigue life. The model follows the distinction between crack initiation and crack propagation according to the phenomenological background. Using the Strain‐Life‐Approach and the Crack Propagation Approach the local geometry and likely failure position influence the determined fatigue life for the initiation and propagation phase individually. The results are presented in terms of S‐N curves for initiation, propagation, and total fatigue life.

A modular methodology for time-domain stochastic seismic wave propagation

Presented here is a modular methodology for time-domain stochastic seismic wave propagation analysis. Presented methodology is designed to analyse uncertain seismic motions as an input, propagating through uncertain material. Traditional approach for uncertain wave propagation relies on models that include deep bedrock, local soil site, and their random process and random field information. Such models can become quite large and computationally intractable. The modular approach proposed herein features two step approach that allows separate consideration of the deep bedrock and local site along with corresponding random field information. The first step considers an auxiliary stochastic motions problem in the bedrock. Stochastic local site response can then be simulated in a reduced domain within certain depth from the surface. Application of uncertain seismic motions at depth, for local uncertain site response is done using stochastic effective forces developed through the Domain Reduction Method. By using Hermite polynomial chaos expansion to represent the non-Gaussian random field of material parameters and non-stationary random process of seismic motion, the proposed modular methodology is formulated using intrusive stochastic Galerkin approach, as seen in the Stochastic Elastic–Plastic Finite Element Method (SEPFEM).Developed modular methodology is illustrated using a 1-D stochastic seismic wave propagation analysis with three cases, and simulation results are also verified with results from conventional approach.

A non-probabilistic uncertainty analysis method based on ellipsoid possibility model and its applications in multi-field coupling systems

Non-probabilistic methods are usually more appropriate and feasible than probabilistic methods to quantify and propagate uncertainty with small samples in many engineering problems. However, some non-probabilistic methods are easily affected by an outlier in uncertainty quantification and only estimate the bounds of output responses in a feasible region in uncertainty propagation. Moreover, some such methods ignore the situation that its sample points have the characteristic of clustering to the center, which significantly affects the accuracy of uncertainty quantification and propagation. In this study, a novel non-probabilistic uncertainty analysis method is proposed to solve the situation and obtain detailed information within the response interval during the process of uncertainty propagation for some problems with small samples. First, an ellipsoid possibility model (EPM) including multiple ellipsoid domains is established using the estimated covariance matrix and predefined scaling rule, which takes into consideration the distribution characteristic of the sample points. The possibilities of all established ellipsoid domains can be calculated by transforming the oblique ellipsoid space into the standard ellipsoid space. Then, two uncertainty propagation approaches based on the established EPM are presented to obtain the interval of an uncertain response and the possibility on all the possible values of the system response, respectively. Finally, the calculation results based on the proposed uncertainty analysis method are in good agreement with those of the MCS in three numerical examples and two engineering applications. The comparison results with three traditional non-probabilistic methods demonstrate that the proposed non-probabilistic uncertainty analysis method is accurate and practical.

IceR improves proteome coverage and data completeness in global and single-cell proteomics

Label-free proteomics by data-dependent acquisition enables the unbiased quantification of thousands of proteins, however it notoriously suffers from high rates of missing values, thus prohibiting consistent protein quantification across large sample cohorts. To solve this, we here present IceR (Ion current extraction Re-quantification), an efficient and user-friendly quantification workflow that combines high identification rates of data-dependent acquisition with low missing value rates similar to data-independent acquisition. Specifically, IceR uses ion current information for a hybrid peptide identification propagation approach with superior quantification precision, accuracy, reliability and data completeness compared to other quantitative workflows. Applied to plasma and single-cell proteomics data, IceR enhanced the number of reliably quantified proteins, improved discriminability between single-cell populations, and allowed reconstruction of a developmental trajectory. IceR will be useful to improve performance of large scale global as well as low-input proteomics applications, facilitated by its availability as an easy-to-use R-package.

Biotechnological Approaches for In-Vitro Propagation of a Valuable Medicinal Plant - Vitex Negundo L

Biotechnological Approaches for In-Vitro Propagation of a Valuable Medicinal Plant - Vitex Negundo L

Propagation of waves with nonlocal effects for vibration response of armchair double-walled CNTs

In this paper, vibration characteristics of double-walled carbon nanotubes (CNTs) are studied based upon nonlocal elastic shell theory. The significance of small scale is being perceived by developing nonlocal Love shell model. The wave propagation approach has been utilized to frame the governing equations as eigen value system. The influence of nonlocal parameter subjected to diverse end supports has been overtly analyzed. An appropriate selection of material properties and nonlocal parameter has been considered. The influence of changing mechanical parameter Poisson's ratio has been investigated in detail. It is found that the frequencies decrease as nonlocal parameter increases and for the certain values of nonlocal parameter against range of Poisson ratio rise slowly with length double-walled CNTs. The dominance of boundary conditions via nonlocal parameter is shown graphically. The results generated furnish the evidence regarding applicability of nonlocal shell model and also verified by earlier published literature.

Wave propagation approach for dynamic responses of transversely isotropic viscoelastic pavement under impact load

Recently studies reveal that the viscoelasticity of the asphalt layer, the transverse isotropy of each structure layer and the foundation condition of the pavement structure are important factors when analysing the data from falling weight deflectors. However, these factors have not been considered in present analytical solutions simultaneously. To fill this gap, the wave propagation approach was extended to establish a new analytical solution. It was shown that the wave propagation approach could eliminate the positive exponential functions in matrix and only matrix with four elements was adopted in calculation, which contributed to an immense improvement in computational stability and efficiency. A subroutine for transversely isotropic viscoelastic material in finite element software ABAQUS was developed to verify the accuracy of the proposed analytical solution. Several numerical examples were also presented to discuss the influences of the aforementioned factors on the dynamic responses of asphalt pavements.

Genetic algorithm based rumor mitigation in online social networks through counter-rumors: A multi-objective optimization

Immense use of social media platforms results in quicker and wider dissemination of not only legitimate information, but also rumors which may cause mental stress and reduce the productivity of the society. One of the important ways to counteract the rumors in online social networks (OSNs) is to spread counter-rumors. In this work, we propose a Precedence based Competitive Cascade (PCC) model for the propagation of competing rumor and counter-rumor cascades. We have presented a model to compute a belief based precedence value by which a user chooses to believe the rumor or counter-rumor received during information propagation. The influence of the rumor and the counter-rumor cascades in the OSNs is analyzed by considering a neighborhood based propagation approach. Another challenging issue which has been addressed in this work is to select the minimal seed set of users for the initiation of the counter-rumor so as to reduce the message overhead in the application. This has been formulated as a multi-objective optimization problem to select the minimal set of seed users for counter-rumor to minimize the effect of the rumor. We design a Decomposition based Multi-objective Genetic (DMOG) algorithm to solve the problem. Experiments are conducted on real-world data sets to evaluate the efficacy of the proposed PCC model and DMOG algorithm by considering important parameters such as the precedence, budget and time delay.

A Fast Semi-Supervised Clustering Framework for Large-Scale Time Series Data

Semi-supervised clustering algorithms have several limitations: 1) the computation complexity of them is very high, because calculating the similarity distances of pairs of examples is time-consuming; 2) traditional semi-supervised clustering methods have not considered how to make full use of must-link and cannot-link constraints. In the clustering, the contribution of a few pairwise constraints to the clustering performance is very limited, and some may negatively affect the outcome; and 3) these methods are not effective to handle high dimensional data, especially for time series data. Up to now, few work touched semi-supervised clustering on time series data. To efficiently cluster large-scale time series data, we first tackle contract time series clustering to produce the most accurate clustering results under a contracted time. We propose a semi-supervised time series clustering framework (STSC), which integrates a fast similarity measure and a constraint propagation approach. Based on the proposed framework, two valid semi-supervised clustering algorithms including fssK-means and fssDBSCAN are designed. Experiments on 11 datasets show that our proposed method is efficient and effective for clustering large-scale time series data.

Micropropagation and in vitro conservation of Ruta graveolens plants

Ruta graveolens is a widespread plant in moderate and semiarid regions with several medicinal properties that are overturning and is threatened due to destruction and overuse for the pharmaceutical and food industry. Moreover, conventional propagation through seeds is not sufficient enough to produce due to seed dormancy and poor seed viability for which clonal propagation is the need of the hour. Therefore, the current study outlines a simple approach for clonal propagation and germplasm conservation of R. graveolens. Seeds were firstly surface decontaminated and germinated on agar water media, then micro shoots were cultivated on Murashige and Skoog (MS) medium contains 3% sucrose. The addition of 1.5 mg/L 6-Benzylaminopurine (BAP) produced a maximum multiplication rate (4.2 shoots per explant). Root formation was developed at the base of micro shoot on MS containing Indole-3- Butyric acid (IBA), Indole Acetic Acid (IAA), or Naphthalene Acetic Acid (NAA). Maximum root number and root length produced at 0.9 mg/L NAA (2.25 roots per explants, and 2.69 mm length). The survival rate after acclimatization reached 90% under the greenhouse conditions when rooted plantlets were adopted to ex vitro employing in 1 soil: 1 perlite: 1 peat mixture. Micro shoots were kept for up to 16 weeks without serious losses at 6 ± 2 °C utilizing MS medium supplemented with sucrose, glucose, fructose, or sorbitol at different concentrations. Media supplemented with 6% sucrose resulted in the maximum number of shoots (2.69 shoots per explants) in the two methods of conservation. Further studies are still needed on medium-term conservation to enhance the survival percentages of different plant material types.