scholarly journals Correction to ‘Improved nearest-neighbor parameters for the stability of RNA/DNA hybrids under a physiological condition’

2021 ◽  
Vol 49 (18) ◽  
pp. 10796-10799
Author(s):  
Dipanwita Banerjee ◽  
Hisae Tateishi-Karimata ◽  
Tatsuya Ohyama ◽  
Saptarshi Ghosh ◽  
Tamaki Endoh ◽  
...  
Keyword(s):  
Nearest Neighbor ◽  
Physiological Condition ◽  
The Stability

scholarly journals Improved nearest-neighbor parameters for the stability of RNA/DNA hybrids under a physiological condition

2020 ◽  
Vol 48 (21) ◽  
pp. 12042-12054 ◽  
Author(s):  
Dipanwita Banerjee ◽  
Hisae Tateishi-Karimata ◽  
Tatsuya Ohyama ◽  
Saptarshi Ghosh ◽  
Tamaki Endoh ◽  
...  
Keyword(s):  
Thermodynamic Parameters ◽  
Target Gene ◽  
Nearest Neighbor ◽  
Physiological Condition ◽  
Specific Binding ◽  
Published Data ◽  
Nearest Neighbour ◽  
Nacl Solution ◽  
Duplex Stability ◽  
The Stability

Abstract The stability of Watson–Crick paired RNA/DNA hybrids is important for designing optimal oligonucleotides for ASO (Antisense Oligonucleotide) and CRISPR (Clustered Regularly Interspaced Short Palindromic Repeats)–Cas9 techniques. Previous nearest-neighbour (NN) parameters for predicting hybrid stability in a 1 M NaCl solution, however, may not be applicable for predicting stability at salt concentrations closer to physiological condition (e.g. ∼100 mM Na+ or K+ in the presence or absence of Mg2+). Herein, we report measured thermodynamic parameters of 38 RNA/DNA hybrids at 100 mM NaCl and derive new NN parameters to predict duplex stability. Predicted ΔG°37 and Tm values based on the established NN parameters agreed well with the measured values with 2.9% and 1.1°C deviations, respectively. The new results can also be used to make precise predictions for duplexes formed in 100 mM KCl or 100 mM NaCl in the presence of 1 mM Mg2+, which can mimic an intracellular and extracellular salt condition, respectively. Comparisons of the predicted thermodynamic parameters with published data using ASO and CRISPR–Cas9 may allow designing shorter oligonucleotides for these techniques that will diminish the probability of non-specific binding and also improve the efficiency of target gene regulation.

Stability and Hopf Bifurcation of Nearest-Neighbor Coupled Neural Networks With Delays

2020 ◽  
Vol 15 (11) ◽  
Author(s):  
Lu Wang ◽  
Min Xiao ◽  
Shuai Zhou ◽  
Yurong Song ◽  
Jinde Cao
Keyword(s):  
Neural Networks ◽  
Hopf Bifurcation ◽  
Nearest Neighbor ◽  
Multiple Delays ◽  
Dimensional System ◽  
Practical Applications ◽  
Coupled Neural Networks ◽  
The Stability ◽  
Related Characteristic ◽  
Theoretical Analyses

Abstract In this paper, a high-dimensional system of nearest-neighbor coupled neural networks with multiple delays is proposed. Nowadays, most present researches about neural networks have studied the connection between adjacent nodes. However, in practical applications, neural networks are extremely complicated. This paper further considers that there are still connection relationships between nonadjacent nodes, which reflect the intrinsic characteristics of neural networks more accurately because of the complexity of its topology. The influences of multiple delays on the local stability and Hopf bifurcation of the system are explored by selecting the sum of delays as bifurcation parameter and discussing the related characteristic equations. It is found that the dynamic behaviors of the system depend on the critical value of bifurcation. In addition, the conditions that ensure the stability of the system and the criteria of Hopf bifurcation are given. Finally, the correctness of the theoretical analyses is verified by numerical simulation.

scholarly journals Laws and Trends of the Evolution of Traditional Villages in Plane Pattern

2020 ◽  
Vol 12 (7) ◽  
pp. 3005
Author(s):  
Xi Yang ◽  
Ke Song ◽  
Fuan Pu
Keyword(s):  
Spatial Data ◽  
Nearest Neighbor ◽  
Morphological Evolution ◽  
Temporal Analysis ◽  
Mean Value ◽  
Significant Shift ◽  
Spatial Processes ◽  
Common Mean ◽  
Multi Temporal ◽  
The Stability

This study collected and analyzed dynamic spatial data of eight traditional villages scattered in different regions of China. A multi-temporal analysis of morphological metrics of spatial patterns and a regression analysis of the morphological evolution were used to analyze and contrast the historical spatial processes of different villages. These were then compared using patch texture and rural macro-morphology perspectives. This led to an assessment of the general laws and trends associated with rural spatial processes. (1) There has been a significant shift in the stability of rural spatial development since the founding of the People’s Republic of China (PRC). (2) Most small and medium-sized villages have maintained a relatively stable spatial texture, while large villages have changed significantly. (3) The mean and variance of the patch area, and the Euclidean nearest-neighbor distance, are correlated in some cases. (4) The mode of rural expansion may be relevant to limitations in the total area of growth. (5) The fractal dimension of the rural macro-morphology may follow a morphological order of oscillation around the equilibrium level. (6) The common mean value of the projected area of rural building patches is expected to be 100 m2.

scholarly journals Evaluating the Stability of Embedding-based Word Similarities

2018 ◽  
Vol 6 ◽  
pp. 107-119 ◽  
Author(s):  
Maria Antoniak ◽  
David Mimno
Keyword(s):  
Nearest Neighbor ◽  
Word Embeddings ◽  
Training Set ◽  
Word Associations ◽  
Training Corpus ◽  
Study Word ◽  
Highly Sensitive ◽  
The Stability ◽  
Nearest Neighbor Distances

Word embeddings are increasingly being used as a tool to study word associations in specific corpora. However, it is unclear whether such embeddings reflect enduring properties of language or if they are sensitive to inconsequential variations in the source documents. We find that nearest-neighbor distances are highly sensitive to small changes in the training corpus for a variety of algorithms. For all methods, including specific documents in the training set can result in substantial variations. We show that these effects are more prominent for smaller training corpora. We recommend that users never rely on single embedding models for distance calculations, but rather average over multiple bootstrap samples, especially for small corpora.

scholarly journals Fault Diagnosis Method for Rolling Bearings Based on Composite Multiscale Fluctuation Dispersion Entropy

Entropy ◽  
2019 ◽  
Vol 21 (3) ◽  
pp. 290 ◽  
Author(s):  
Xiong Gan ◽  
Hong Lu ◽  
Guangyou Yang
Keyword(s):  
Fault Diagnosis ◽  
Nearest Neighbor ◽  
Coarse Grained ◽  
K Nearest Neighbor ◽  
Rolling Bearings ◽  
Entropy Estimation ◽  
Fault Recognition ◽  
Knn Classifier ◽  
Diagnosis Method ◽  
The Stability

This paper proposes a new method named composite multiscale fluctuation dispersion entropy (CMFDE), which measures the complexity of time series under different scale factors and synthesizes the information of multiple coarse-grained sequences. A simulation validates that CMFDE could improve the stability of entropy estimation. Meanwhile, a fault recognition method for rolling bearings based on CMFDE, the minimum redundancy maximum relevancy (mRMR) method, and the k nearest neighbor (kNN) classifier (CMFDE-mRMR-kNN) is developed. For the CMFDE-mRMR-kNN method, the CMFDE method is introduced to extract the fault characteristics of the rolling bearings. Then, the sensitive features are obtained by utilizing the mRMR method. Finally, the kNN classifier is used to recognize the different conditions of the rolling bearings. The effectiveness of the proposed CMFDE-mRMR-kNN method is verified by analyzing the standard experimental dataset. The experimental results show that the proposed fault diagnosis method can effectively classify the conditions of rolling bearings.

The Sedimentation Velocity of Erythrocytes in the Psychoses: a Study of Seventy-Three Cases

1929 ◽  
Vol 75 (308) ◽  
pp. 80-100 ◽  
Author(s):  
E. W. Anderson
Keyword(s):  
Physiological Condition ◽  
Sedimentation Velocity ◽  
Buffy Coat ◽  
Suspension Stability ◽  
Historical Survey ◽  
The Stability ◽  
Cellular Pathology ◽  
Morbid Conditions

At the outset it may be desirable to give a brief outline of the phenomena upon which this work is based.A complete historical survey takes us back to very early times, to the days of Hippocrates and in more recent times to the humoral pathologists, when the formation of the “crust phiogistica” or “buffy coat” was practically the only blood symptom known. Until the old humoral theories were discarded, that is until the rise of the doctrines of cellular pathology, the problem loomed large, but after this period interest in it declined, until the whole matter was revived about ten years ago by R. Fahraeus, of Stockholm, who published a series of researches into what he termed the “suspension stability” of the blood. By this term is meant the power which the plasma has of holding the erythrocytes in suspension. When the stability is lowered, the corpuscles sink with increased rapidity. This author found that a lowered suspension stability occurred in a large number of morbid conditions, and also in one physiological condition, namely pregnancy. Since the publication of this work the phenomenon in question has been made the basis of a test which has found considerable application in certain departments of medicine, notably in gynæcology and tuberculosis.

scholarly journals Machine-Learning-Based Classification Approaches toward Recognizing Slope Stability Failure

2019 ◽  
Vol 9 (21) ◽  
pp. 4638 ◽  
Author(s):  
Moayedi ◽  
Bui ◽  
Kalantar ◽  
Foong
Keyword(s):  
Machine Learning ◽  
Slope Stability ◽  
Nearest Neighbor ◽  
Support Vector ◽  
Sequential Minimal Optimization ◽  
Stability Assessment ◽  
K Nearest Neighbor ◽  
The Stability ◽  
Intelligent Models ◽  
Improved Support Vector Machine

In this paper, the authors investigated the applicability of combining machine-learning-based models toward slope stability assessment. To do this, several well-known machine-learning-based methods, namely multiple linear regression (MLR), multi-layer perceptron (MLP), radial basis function regression (RBFR), improved support vector machine using sequential minimal optimization algorithm (SMO-SVM), lazy k-nearest neighbor (IBK), random forest (RF), and random tree (RT), were selected to evaluate the stability of a slope through estimating the factor of safety (FOS). In the following, a comparative classification was carried out based on the five stability categories. Based on the respective values of total scores (the summation of scores obtained for the training and testing stages) of 15, 35, 48, 15, 50, 60, and 57, acquired for MLR, MLP, RBFR, SMO-SVM, IBK, RF, and RT, respectively, it was concluded that RF outperformed other intelligent models. The results of statistical indexes also prove the excellent prediction from the optimized structure of the ANN and RF techniques.

Atomic Interactions and the Stability of Surface Clusters

1998 ◽  
Vol 528 ◽  
Author(s):  
Seong Jin Koh ◽  
Gert Ehrlich
Keyword(s):  
Simple Model ◽  
Nearest Neighbor ◽  
Equilibrium Shape ◽  
Model System ◽  
Palladium Clusters ◽  
Atomic Interactions ◽  
Surface Yield ◽  
Simple Model System ◽  
The Stability ◽  
Field Ion Microscope

AbstractInteractions dictating the shape and stability of surface islands have been examined for a simple model system, palladium clusters on the W(110) plane. Observations in a field ion microscope of the distribution of two Pd adatoms over the surface yield quantitative values for pair interactions. These are found to be complex, extending over distances longer than 10 A and to vary strongly with the orientation of the pair axis on the (110) surface. Using the measured pair energies it has been possible to infer the magnitude of many-atom effects necessary to account for the equilibrium shape of the clusters. Many-atom interactions turn out to make by far the largest contribution to cluster cohesion; modeling of growth phenomena in terms of nearest-neighbor bonds only is clearly problematic.

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