Neighbor Parameters Research Articles

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Programable peptide nucleic acid (PNA) probes assisted DNA variants detection method demonstrates exceptional sensitivity and efficiency in detecting low‐frequency mutations. We presented a computational method for calculating the stacking energy of PNA and DNA hybrids, leveraging nearest neighbor parameters. Utilizing this computational framework, we designed PNA probes, which improve amplification efficiency by more than 10‐fold and detect mutations as low as 0.5% variant allele frequency. More details are discussed in the article by Song et al. on pages 2572—2580.image

An integrated MCDM model with enhanced decision support in transport safety using machine learning optimization

Having the capacity to effectively manage multi-criteria decision-making (MCDM) issues with considerable reliability is a crucial prerequisite for any MCDM model. This study introduces an advanced MCDM model that integrates logarithmic percentage change-driven objective weighting (LOPCOW), multi-objective optimization on the basis of a ratio analysis plus the full multiplicative form (MULTIMOORA), and density-based spatial clustering of applications with noise (DBSCAN). The goal is to provide robust decision support. Specifically, the model incorporates a machine learning tool, namely, grid search, to optimize the parameters of neighborhood radius (ε) and the minimum number of points, which significantly influence the clustering quality of DBSCAN. Subsequently, the cluster centers determined by DBSCAN serve as initial points for k-means clustering to negate the impact of outliers. This approach addresses the failures of DBSCAN in nearest neighbor search and parameter selection, and it also resolves the limitations of k-means related to the uncertainty in cluster outcomes due to varying initial centroid selections. Utilizing a real case study from the European Union, comprehensive comparisons of the model results demonstrate the adaptability, reliability, and stability of the proposed model, affirming its effectiveness in providing credible policy recommendations and practical decisions. Overall, this study presents policymakers, professionals, and administrators with a reliable decision-making system for conducting real-world MCDM tasks with enhanced confidence and robustness.

Quantitative study of random walk parameters in node2vec model

In this paper, the Monte Carlo simulation method is used to investigate a generalized random walk model based on node2vec which is a popular algorithm in network embedding and has been widely applied in various domains such as link prediction, node classification, recommendation systems, etc. The aim is to quantitatively study the impact of the random walk parameters(including the number of walks per initial node r, the length of each walk l, the return parameter α, the common neighbor parameter β, and the outgoing parameter γ) on the embedding results. Specifically, the cross entropy is utilized as an observation to compare the difference between the frequency of nodes after random walks and the normalized degree sequence of nodes. The results show that the clustering coefficient significantly impacts the cross entropy. For networks with high clustering coefficient, the value of β should closely approximate that of γ, whereas for networks with low clustering coefficient, the value of β should be significantly smaller than that of γ. The value of α should be less than or equal to the minimum values between β and γ. Finally, the embedding effects of different random walk parameters are tested using node classification and link prediction tasks in real-world networks, and the results indicate that cross entropy can provide guidance for obtaining high-quality node embedding.

Adaptive locally weighted support vector algorithm with asymmetrically parametric insensitive/margin model

In many fields such as finance and electrical load, locally weighted support vector regression (LWSVR) method can achieve better prediction results by constructing different sub-models for each prediction target. However, it selected a fixed and single nearest neighbor parameter of kglobal for each query, and adopted the traditional k-nearest neighbor (KNN) algorithm, which is suboptimal. The weights in LWSVR assigned to the neighboring sample points also affected the accuracy of the model predictions. In this paper, we propose an adaptive locally weighted support vector regression with asymmetrically parametric insensitive/margin model, called Asy-par-v-ALWSVR. Since the selection of similar sample for query points is significant. We propose a novel locally q-neighbour selection algorithm that adaptively selects the most suitable number of neighbours for each query point based on Mahalanobis distance, rather than using a fixed kglobal. This significantly improves prediction accuracy. The weights of neighbours for each query point in Asy-par-v-ALWSVR is based on the distance within the kernel-induced feature space. This effectively discerns the importance of samples during the training process. In particular, Asy-par-v-ALWSVR can handle skewed noise with heteroscedastic structure in regression problems. Finally, we evaluate the proposed model on a synthetic sinc dataset and 14 real datasets. The results demonstrate that Asy-par-v-ALWSVR outperforms six state-of-the-art SVR-based models.

NNDB: An Expanded Database of Nearest Neighbor Parameters for Predicting Stability of Nucleic Acid Secondary Structures

Nearest neighbor thermodynamic parameters are widely used for RNA and DNA secondary structure prediction and to model thermodynamic ensembles of secondary structures. The Nearest Neighbor Database (NNDB) is a freely available web resource (https://rna.urmc.rochester.edu/NNDB) that provides the functional forms, parameter values, and example calculations. The NNDB provides the 1999 and 2004 set of RNA folding nearest neighbor parameters. We expanded the database to include a set of DNA parameters and a set of RNA parameters that includes m6A in addition to the canonical RNA nucleobases. The site was redesigned using the Quarto open-source publishing system. A downloadable PDF version of the complete resource and downloadable sets of nearest neighbor parameters are available.

Spin dynamic soliton in ferromagnetic materials over the (2 + 1)-dimensional beta fractional HFSC model

Ferromagnetic materials have important exploitation practice in permanent information in electromagnets, electric motors, generators, recording in thin film etc. This research explores spin dynamic soliton in Ferromagnetic materials via the (2 + 1)-D Heisenberg ferro-magnetic Spin Chains (HFSC) model. The Extended (I,R) and Extended Simple schemes treatment are applied to integrate the model. Through the technique, various novel dynamical wave solutions afford as optical dark and bright soliton envelope, spin wave envelope Black soliton, optical bell-shock interaction, optical bell-anti-shock wave, optical Dark-bright bell soliton and optical bright periodic waves from modulus plots, besides these real and imaginary profiles exhibits bright periodic wave with dark bell wave, periodically increasing–decreasing carrier wave, periodic carrier wave increase its amplitude by shock, periodic carrier wave suddenly decrease its amplitude by shock waves. The effect of fractional derivative, neighboring interaction as well as uniaxial crystal field anisotropy parameters is explored on the envelope soliton and amplitude of carrier waves. The changing value of neighboring interaction parameters are exhibited as increases wave height with the increases of parametric values, but while increasing values of the uni-axial crystal arena anisotropy factor causes the reduction of wave heights.

Estimating RNA Secondary Structure Folding Free Energy Changes with efn2.

A number of analyses require estimates of the folding free energy changes of specific RNA secondary structures. These predictions are often based on a set of nearest neighbor parameters that models the folding stability of a RNA secondary structure as the sum of folding stabilities of the structural elements that comprise the secondary structure. In the software suite RNAstructure, the free energy change calculation is implemented in the program efn2. The efn2 program estimates the folding free energy change and the experimental uncertainty in the folding free energy change. It can be run through the graphical user interface for RNAstructure, from the command line, or a web server. This chapter provides detailed protocols for using efn2.

Stability and spin solitonic dynamics of the HFSC model: effects of neighboring interactions and crystal field anisotropy parameters

Stability and spin solitonic dynamics of the HFSC model: effects of neighboring interactions and crystal field anisotropy parameters

In vivo-like nearest neighbor parameters improve prediction of fractional RNA base-pairing in cells.

We conducted a thermodynamic analysis of RNA stability in Eco80 artificial cytoplasm, which mimics in vivo conditions, and compared it to transcriptome-wide probing of mRNA. Eco80 contains 80% of Escherichia coli metabolites, with biological concentrations of metal ions, including 2 mM free Mg2+ and 29 mM metabolite-chelated Mg2+. Fluorescence-detected binding isotherms (FDBI) were used to conduct a thermodynamic analysis of 24 RNA helices and found that these helices, which have an average stability of -12.3 kcal/mol, are less stable by ΔΔGo37 ∼1 kcal/mol. The FDBI data was used to determine a set of Watson-Crick free energy nearestneighbor parameters (NNPs), which revealed that Eco80 reduces the stability of three NNPs. This information was used to adjust the NN model using the RNAstructure package. The in vivo-like adjustments have minimal effects on the prediction of RNA secondary structures determined in vitro and in silico, but markedly improve prediction of fractional RNA base pairing in E. coli, as benchmarked with our in vivo DMS and EDC RNA chemical probing data. In summary, our thermodynamic and chemical probing analyses of RNA helices indicate that RNA secondary structures are less stable in cells than in artificially stable in vitro buffer conditions.

Ultrasonic and Thermophysical Properties of Potassium Halides Crystals

In this communication, author is presenting ultrasonic and thermophysical properties of potassium fluoride (KF) and potassium chloride (KCl) by van der Waals three body force shell model with the help of second and third order elastic constants at 100 K–300 K. The elastic constants have been calculated by using Coulomb and Born– Mayer potential with the help of two parameters-distance of nearest neighbour and hardness parameter. We evaluated ultrasonic properties such as elastic constants, wave velocities, Debye temperature- average velocity, conductivity, and ultrasonic attenuation due to phonon–phonon interaction along in different direction [100], [110], and [111] orientations. The achieved results are hold good and are given very important information of these crystal for further investigation.

Self-training algorithm based on density peaks combining globally adaptive multi-local noise filter

Self-training algorithm highlights the speed of training a supervised classifier through small labeled samples and large unlabeled samples. Despite its long considerable success, self-training algorithm has suffered from mislabeled samples. Local noise filters are designed to detect mislabeled samples. However, two major problem with this kind of application are: (a) Current local noise filters have not treated the spatial distribution of the nearest neighbors in different classes in much detail. (b) They are being disadvantaged when mislabeled samples are located in overlapping areas of different classes. Here, we develop an integrated architecture – self-training algorithm based on density peaks combining globally adaptive multi-local noise filter (STDP-GAMLNF), to improve detecting efficiency. Firstly, the spatial structure of the data set is revealed by density peak clustering, and it is used for empowering self-training to label unlabeled samples. In the meantime, after each epoch of labeling, GAMLNF can comprehensively judge whether a sample is a mislabeled sample from multiple classes or not, and it will reduce the influence of edge samples effectively. The corresponding experimental results conducted on eighteen UCI data sets demonstrate that GAMLNF is not sensitive to the value of the neighbor parameter k, and it is capable of adaptively finding the appropriate number of neighbors of each class.

Analisis Performa Algoritma K-Nearest Neighbor dan Reduksi Dimensi Menggunakan Principal Component Analysis

This paper discusses the performance of the K-Nearest Neighbor Algorithm with dimension reduction using Principal Component Analysis (PCA) in the case of diabetes disease classification. A large number of variables and data on the diabetes dataset requires a relatively long computation time, so dimensional reduction is needed to speed up the computational process. The dimension reduction method used in this study is PCA. After dimension reduction is done, it is continued with classification using the K-Nearest Neighbor Algorithm. The results on diabetes case studies show that dimension reduction using PCA produces 3 main components of the 8 variables in the original data, namely PC1, PC2, and PC3. Then classification result using K-Nearest Neighbor shows that by choosing 3 closest neighbor parameters (K), for K = 3, K = 5, and K = 7. The result for K = 3 has an accuracy of 67,53%, for K = 5 had an accuracy is 72,72%, and for K=7 had an accuracy of 77,92%. Thus, it was concluded that the best accuracy performance for the classification of diabetes was achieved at K=7 with an accuracy of 77.92%.

Thermodynamic determination of RNA duplex stability in magnesium solutions

The prediction of RNA secondary structure and thermodynamics from sequence relies on free energy minimization and nearest neighbor parameters. Currently, algorithms used to make these predictions are based on parameters from optical melting studies performed in 1 M NaCl. However, many physiological and biochemical buffers containing RNA include much lower concentrations of monovalent cations and the presence of divalent cations. In order to improve these algorithms, thermodynamic data was previously collected for RNA duplexes in solutions containing 71, 121, 221, and 621 mM Na+. From this data, correction factors for free energy (ΔG°37) and melting temperature (Tm) were derived. Despite these newly derived correction factors for sodium, the stabilizing effects of magnesium have been ignored. Here, the same RNA duplexes were melted in solutions containing 0.5, 1.5, 3.0, and 10.0 mM Mg2+ in the absence of monovalent cations. Correction factors for Tm and ΔG°37 were derived to scale the current parameters to a range of magnesium concentrations. The Tm correction factor predicts the melting temperature within 1.2°C, and the ΔG°37 correction factor predicts the free energy within 0.30 kcalmol. These newly derived magnesium correction factors can be incorporated into algorithms that predict RNA secondary structure and stability from sequence.

A Test and Refinement of Folding Free Energy Nearest Neighbor Parameters for RNA Including N6-Methyladenosine

RNA folding free energy change parameters are widely used to predict RNA secondary structure and to design RNA sequences. These parameters include terms for the folding free energies of helices and loops. Although the full set of parameters has only been traditionally available for the four common bases and backbone, it is well known that covalent modifications of nucleotides are widespread in natural RNAs. Covalent modifications are also widely used in engineered sequences. We recently derived a full set of nearest neighbor terms for RNA that includes N6-methyladenosine (m6A). In this work, we test the model using 98 optical melting experiments, matching duplexes with or without N6-methylation of A. Most experiments place RRACH, the consensus site of N6-methylation, in a variety of contexts, including helices, bulge loops, internal loops, dangling ends, and terminal mismatches. For matched sets of experiments that include either A or m6A in the same context, we find that the parameters for m6A are as accurate as those for A. Across all experiments, the root mean squared deviation between estimated and experimental free energy changes is 0.67 kcal/mol. We used the new experimental data to refine the set of nearest neighbor parameter terms for m6A. These parameters enable prediction of RNA secondary structures including m6A, which can be used to model how N6-methylation of A affects RNA structure.

Nearest neighbor rules for RNA helix folding thermodynamics: improved end effects.

Nearest neighbor parameters for estimating the folding stability of RNA secondary structures are in widespread use. For helices, current parameters penalize terminal AU base pairs relative to terminal GC base pairs. We curated an expanded database of helix stabilities determined by optical melting experiments. Analysis of the updated database shows that terminal penalties depend on the sequence identity of the adjacent penultimate base pair. New nearest neighbor parameters that include this additional sequence dependence accurately predict the measured values of 271 helices in an updated database with a correlation coefficient of 0.982. This refined understanding of helix ends facilitates fitting terms for base pair stacks with GU pairs. Prior parameter sets treated 5′GGUC3′ paired to 3′CUGG5′ separately from other 5′GU3′/3′UG5′ stacks. The improved understanding of helix end stability, however, makes the separate treatment unnecessary. Introduction of the additional terms was tested with three optical melting experiments. The average absolute difference between measured and predicted free energy changes at 37°C for these three duplexes containing terminal adjacent AU and GU pairs improved from 1.38 to 0.27 kcal/mol. This confirms the need for the additional sequence dependence in the model.

Determining How Stem‐loop Structure Thermodynamic Stability Influences Frameshift Efficiency at the HTLV‐1 gag‐proFrameshift Site

The human T‐lymphotropic virus type 1 (HTLV‐1) RNA genome includes two programmed ‐1 ribosomal frameshift (‐1 PRF) sites. These sites allow ribosomes access to alternate reading frames encoding critical viral enzymes. The gag‐proframeshift site includes a slippery sequence, spacer, and stem‐loop structure. How the stem‐loop acts to promote frameshifting is unclear. Previous HTLV‐2 research showed that changes to the gag‐pro frameshift site stem‐loop thermodynamic stability influenced its frameshift efficiency to a modest degree. There is substantial conservation between the HTLV‐1 and HTLV‐2 gag‐pro frameshift site sequences (86%) and structures. We hypothesized that the HTLV‐1 gag‐pro frameshift efficiency would be similarly influenced by its stem‐loop thermodynamic stability. To test this hypothesis, we designed 15 stem‐loop mutants (SLMs) with varied base‐pair composition. These mutations decoupled changes in overall thermodynamic stability from those localized to the stem‐loop base. The SLM thermodynamic stabilities were calculated using nearest neighbor parameters and the in vitro frameshift efficiencies were measured with a dual‐luciferase assay. Correlations between frameshift efficiency and thermodynamic stability were subsequently assessed. Preliminarily results reveal a moderate correlation between the SLM stem‐loop overall thermodynamic stability and frameshifting efficiency. No correlation was observed between the thermodynamic stability of the stem‐loop base and frameshifting efficiency. While the overall thermodynamic stability does impact the frameshift efficiency, it cannot be used exclusively to predict it. This reflects a complex interplay between the frameshift site elements. Overall, our preliminary results suggest a conserved function for the gag‐pro frameshift site stem‐loop between the HTLV‐1 and HTLV‐2 retroviruses.

Secondary structure prediction for RNA sequences including N6-methyladenosine

There is increasing interest in the roles of covalently modified nucleotides in RNA. There has been, however, an inability to account for modifications in secondary structure prediction because of a lack of software and thermodynamic parameters. We report the solution for these issues for N6-methyladenosine (m6A), allowing secondary structure prediction for an alphabet of A, C, G, U, and m6A. The RNAstructure software now works with user-defined nucleotide alphabets of any size. We also report a set of nearest neighbor parameters for helices and loops containing m6A, using experiments. Interestingly, N6-methylation decreases folding stability for adenosines in the middle of a helix, has little effect on folding stability for adenosines at the ends of helices, and increases folding stability for unpaired adenosines stacked on a helix. We demonstrate predictions for an N6-methylation-activated protein recognition site from MALAT1 and human transcriptome-wide effects of N6-methylation on the probability of adenosine being buried in a helix.

FRF of a coupled geared system using the frequency based substructuring and condensation methods

Power transmission systems are complex systems and their dynamic investigation requires much more CPU and computation time. For the purpose of studying the complicated global dynamics, the reduction approach is applied to simplify the complexity of connected problem. This work details the coupling technique and the identification of dynamic compartment of transmission system, from a known dynamic response of distinct multibody subsystems. The identification of transmission system was built by joining subsystem using the Frequency Response Function (FRF). This paper discusses the Frequency Based Sub-structuring (FBS) and the condensation methods. A description of these techniques and their importance were displayed. This approach was tested on a discrete system describing a dual stage reducer using receptance coupling. A parametric study was made to determine the sensitivity of the sub-structuring method by varying different parameters. In addition, the sensibility of random perturbation of coupling stiffness on the sub-structuring method was studied. The achieved results obtained by the coupling and the condensation approach were evaluated. The impact of reducer properties on subsystem response and the effect of neighbor parameter system on condensation methods were discussed. A great correspondence is shown between the simulated coupling technique and the full system results.

Monitoring Algorithm of Stress Point of Concrete Penstock in Large Construction Engineering

With the rapid development of the national construction industry, cracks and other problems often appear in the concrete structure during the initial and subsequent construction. When these problems develop further, the structural safety of the entire building may be compromised. Therefore, it is necessary to analyze the causes of cracks and other problems in concrete buildings, and be able to monitor and analyze these problems in time, and then propose reasonable solutions. This is already a problem that the entire construction technicians urgently need to solve. This paper studies the algorithm for monitoring stress points of concrete penstocks in large construction projects. Firstly, it uses literature research to explain the form of stress nodes in large-scale construction projects and the deficiencies in the research on the stress nodes of concrete penstocks in large-scale construction projects. In the experiment, the existing 3 algorithms are used to detect the force points, and compare their detection degree and false alarm rate. The experimental results show that the detection effect of the KNN algorithm is obviously inferior to the other two algorithms with the same neighbor parameters. Its detection rate is only 91%, and the false alarm rate reaches 30%. The other two algorithms are equivalent. The detection effect of the KNN algorithm is obviously inferior to the other two algorithms, the detection rate is poor, the outlier force points that are obviously deviating from the whole around the dense force points are not recognized, and the data of many normal force points located at the edge of the sparse area Instead, it was recognized as abnormal. Among the three algorithms, the detection rate of the NLOF algorithm is better, reaching 99%, which is significantly higher than the other two algorithms.

Ordinal patterns in the Duffing oscillator: Analyzing powers of characterization.

Ordinal patterns are a time-series data analysis tool used as a preliminary step to construct the permutation entropy, which itself allows the same characterization of dynamics as chaotic or regular as more theoretical constructs such as the Lyapunov exponent. However, ordinal patterns store strictly more information than permutation entropy or Lyapunov exponents. We present results working with the Duffing oscillator showing that ordinal patterns reflect changes in dynamical symmetry that is invisible to other measures, even permutation entropy. We find that these changes in symmetry at given parameter values are correlated with a change in stability at neighboring parameters, which suggests a novel predictive capability for this analysis technique.