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Resource flow supports the delivery of products and services and plays a vital role in the low-carbon urban distribution grid. Therefore, reasonable forecasting of the resource flow is essential for financial decision-making. Through the trained model, the resource flow forecasting process can be simplified and oneclick forecasting can be realized. However, this method relies on the selection and optimization of model parameters, where poor parameter choices can significantly impact the forecasting accuracy. This paper first introduces a model for identifying key influencing factors in resource flow data, incorporating an elastic network and gray correlation analysis. Subsequently, a resource flow forecasting method based on improved support vector machines-longand short term memory (SVM-LSTM) is proposed. Finally, the superior performance of the proposed method is validated through simulations. [ABSTRACT FROM AUTHOR]

Resource flow supports the delivery of products and services and plays a vital role in the low-carbon urban distribution grid. Therefore, reasonable forecasting of the resource flow is essential for financial decision-making. Through the trained model, the resource flow forecasting process can be simplified and one-click forecasting can be realized. However, this method relies on the selection and optimization of model parameters, where poor parameter choices can significantly impact the forecasting accuracy. This paper first introduces a model for identifying key influencing factors in resource flow data, incorporating an elastic network and gray correlation analysis. Subsequently, a resource flow forecasting method based on improved support vector machines–long- and short term memory (SVM-LSTM) is proposed. Finally, the superior performance of the proposed method is validated through simulations.

The rapid development of internet technology has brought convenience to people’s lives, increasingly making their work and personal activities inseparable from the internet. However, the various types of information data in the Internet are very large, and accurately obtaining useful information from them has become particularly important. Therefore, clustering analysis technology is widely applied in various fields. The elastic network algorithm is vulnerable to noise and other factors in clustering analysis, resulting in low accuracy of clustering results. To solve this problem, this study proposes an elastic network algorithm based on weighted properties for clustering analysis. After comparative experiments, the results showed that the accuracy of the proposed algorithm was 98%. The area under the precision recall curve was 0.86. The maximum F-value was 0.93. The area under the working characteristic curve of the subject was 0.94. The precision ultimately stabilized at 97%. All values were higher than the comparison algorithm. In the experiment, the running time of the algorithm was 11 seconds and the average error was 7%, both of which were lower than the comparison algorithm. When the algorithm was affected by Statistical dispersion and threshold parameters, the accuracy of the proposed algorithm was 71%, 69% and 67% respectively, which were better than the comparison algorithm. In conclusion, the proposed elastic network algorithm based on the weighted characteristics has high accuracy, strong stability and advantages in clustering.

Tactical communication networks has high dynamic, weak connection, low bandwidth, multi-link backup, SDN (software defined network) technology decouples from hardware and software through traditional network control devices, software the core control functions, get a global view through centralized control policies, so as to realize the flexible scheduling of resources and the efficient utilization of channel resources, promote the development of tactical communication networks more intelligent and flexible adaptive.The development status and advantages of software defined network were firstly introduced, the problems of traditional architecture of tactical communication networks were analyzed, including the application of SDN in the field of foreign military tactical communication.Then, the feasible idea of the application of SDN in the field of military communication was put forward, as well as the application scenarios were conceived.Finally, the feasibility of the application of SDN in military communication was summarized.

Copyright of Telecommunications Science is the property of Beijing Xintong Media Co., Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Linear regression is the use of linear functions to model the relationship between a dependent variable and one or more independent variables. Linear regression models have been widely used in various fields such as finance, industry, and medicine. To address the problem that the traditional linear regression model is difficult to handle uncertain data, we propose a granule-based elastic network regression model. First we construct granules and granular vectors by granulation methods. Then, we define multiple granular operation rules so that the model can effectively handle uncertain data. Further, the granular norm and the granular vector norm are defined to design the granular loss function and construct the granular elastic network regression model. After that, we conduct the derivative of the granular loss function and design the granular elastic network gradient descent optimization algorithm. Finally, we performed experiments on the UCI datasets to verify the validity of the granular elasticity network. We found that the granular elasticity network has the advantage of good fit compared with the traditional linear regression model. [ABSTRACT FROM AUTHOR]

The creep-recovery test was used to analyze the rheological properties of black bean-wheat flour blends in linear and nonlinear viscoelastic regions. After adding black bean powder to wheat flour, the water absorption rate and development time of the dough were both increased, and the stable time of the dough showed a trend of increasing first and then decreasing. Creep and recovery data fitted well with the 4-element Burgers model (R2>0.94). During the creep time of 3、5、10 and 15 min, the λc、μc、Jcm、and Jmax of the dough gradually increased, the Jro and the recovery rate gradually decreased. Jco、Jro、Jcm generally showed a decreasing trend. After the deformation, the recovery rate of the dough in the recovery time of 3、5、10 and 15 min was 63.51%～73.6%. Under the shear stress of 150、250、500 and 1 000 Pa, the Jco、Jcm and Jmax of the dough gradually increased, μc、Jro、λr and recovery rate dropped from 89.99% to 32.36%. When the amount of black bean flour added in wheat flour was 10%, the weakening of the dough was the smallest and the stable time was relatively longer. The Linear creep-response analysis results showed that the dough had reached steady shear during the creep time of 3 min, prolonging of the creep time affected the elastic recovery and recovery speed of the dough, and the elastic network of the dough was broken partially; the recovery rate of the dough was 73.6% at the recovery time of 10 min. The nonlinear creep-analysis results showed that the dough was sensitive to shear stress outside the linear viscoelastic region, but it still had typical viscoelastic properties,improved the shear stress, reduced the steady shear viscosity of the dough, and accelerated the delayed elastic recovery.

This paper proposed a novel image interpolation algorithm with an arbitrary upscaling factor based on the spatial general autoregressive model. First, to accommodate arbitrary scale factors, a non-integer mapping method was modulated into the spatial general autoregressive model, which was employed to model the piecewise stationary pattern with a higher description capacity than autoregressive models. A gradient angle guided extension method was utilized to extend the spatial general autoregressive model, and more pixels in the neighborhood were included to estimate the parameters of the spatial general autoregressive model. To realize the high-accuracy estimation of the model parameters, a regularization method via an elastic network was adopted to maintain the complexity of the object function in a reasonable state and address the overfitting problem. We also introduced an iterative curvature method to refine the interpolation result of those image blocks with large variances of gray intensities. Experiments on 25 images were conducted with integer and non-integer magnification factors to systematically verify the objective and subjective measures of the proposed method. The visual artifacts were effectively suppressed by the proposed method, and a flexible interpolation method for arbitrary scale factors was implemented.

Electric fuses are protection devices with a long history. They are widely used in both high voltage and low voltage power systems. The pre-arc I-t (current versus time) characteristic is an important parameter of electric fuse. The traditional method of obtaining this parameter is designing the electrical fuse by experience, then conducting experiments to obtain the I-t characteristic. However, customers request specific of I-t characters first, and then the engineers have to design electric fuses based on the requirements, this is an inverse design problem of electric fuses. There is no research in this area yet. The objective of this paper is to propose a new method to solve the inverse design problem of electrical fuse with machine learning. The method contains two steps. First, finite element analysis is used to obtain training samples for machine learning. A total of 252 samples (pre-arc current-time result) are obtained by simulations. Second, an elastic network is used to solve the probabilistic inference model by utilizing 217 samples from the total of 252. The other 35 samples are used to evaluate inverse design problem solution results. The results show that this method can accurately predict the design parameters of the restricted zone of fuse element, including the thickness (T1) and radius (R1) of restricted zone. The average relative errors value of T1 and R1 are 15.7% and 3.4%, respectively. Increasing the penalty factor and elastic error decreases the relative error. By changing the penalty error to 0.8, the average relative errors value of T1 and R1 are reduced by 14.1% and 1.7%, respectively. By changing the elastic error to 0.8, the average relative errors value of T1 and R1 are reduced by 13.1% and 2.1%, respectively. The research results provide a new solution for the electrical fuse design problem.

In this paper, we conduct quantitative research on the atmospheric environmental corrosion of aluminum alloy based on the atmospheric environmental factors. we apply the elastic network regression method to construct a regression model for corrosion rates based on the identified damage factors, which allows us to calculate the dependent variable as long as we know the main environmental factors in a specific year. Finally, we introduce a measure of atmospheric environment corrosion based on the established regression which can characterize the severity of corrosion with different transformations.

Crop growth and development is a dynamic and complex process, and the essence of yield formation is the continuous accumulation of photosynthetic products from multiple fertility stages. In this study, a new stacking method for integrating multiple growth stages information was proposed to improve the performance of the winter wheat grain yield (GY) prediction model. For this purpose, crop canopy hyperspectral reflectance and leaf area index (LAI) data were obtained at the jointing, flagging, anthesis and grain filling stages. In this case, 15 vegetation indices and LAI were used as input features of the elastic network to construct GY prediction models for single growth stage. Based on Stacking technique, the GY prediction results of four single growth stages were integrated to construct the ensemble learning framework. The results showed that vegetation indices coupled LAI could effectively overcome the spectral saturation phenomenon, the validated R2 of each growth stage was improved by 10%, 22.5%, 3.6% and 10%, respectively. The stacking method provided more stable information with higher prediction accuracy than the individual fertility results (R2 = 0.74), and the R2 of the model validation phase improved by 236%, 51%, 27.6%, and 12.1%, respectively. The study can provide a reference for GY prediction of other crops. [ABSTRACT FROM AUTHOR]

Linear regression is the use of linear functions to model the relationship between a dependent variable and one or more independent variables. Linear regression models have been widely used in various fields such as finance, industry, and medicine. To address the problem that the traditional linear regression model is difficult to handle uncertain data, we propose a granule-based elastic network regression model. First we construct granules and granular vectors by granulation methods. Then, we define multiple granular operation rules so that the model can effectively handle uncertain data. Further, the granular norm and the granular vector norm are defined to design the granular loss function and construct the granular elastic network regression model. After that, we conduct the derivative of the granular loss function and design the granular elastic network gradient descent optimization algorithm. Finally, we performed experiments on the UCI datasets to verify the validity of the granular elasticity network. We found that the granular elasticity network has the advantage of good fit compared with the traditional linear regression model.

Background: At present, the main diagnostic methods for Alzheimer's disease (AD) are positron emission tomography (PET) scanning of the brain and analysis of cerebrospinal fluid (CSF) sample, but these methods are expensive and harmful to patients. Recently, more researchers focus on diagnosing AD by detecting biomarkers in blood, which is a cheaper and harmless way. Therefore, identifying AD-related proteins in blood can help treatment and diagnosis.Methods: We proposed a hypothesis that similar diseases share similar proteins. Diseases with similar symptoms are caused by abnormalities of similar proteins. Assuming that the similarities between AD and other diseases obey the normal distribution, we developed an iterative method based on disease similarity (IBDS). We combined Elastic Network (EN) with Minimum angle regression (MAR) to find the optimal solution. Finally, we used case studies and Summary data Mendelian Random (SMR) to verify our method.Results: We selected 39 diseases which are highly related to AD. They correspond 1,481 kinds of proteins. One hundred and eighty-four proteins are reported to be related to AD in Uniprot and the number would be 284 with our method. The AUC of our method by cross-validation is 0.9251 which is much higher than previous methods.Conclusion: In this paper, we presented a novel method for prioritizing AD-related proteins. Seven proteins have tissue specificity in blood among these 284 proteins, which could be used to diagnose AD in future. Case studies and SMR have been used to prove the relationship between these 7 proteins and AD.Availability and Implementation:https://github.com/zty2009/Identifying-Protein-Biomarkers-in-Blood-for-Alzheimer-s-Disease

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Mild cognitive impairment (MCI) is a pre-existing state of Alzheimer's disease (AD). An accurate prediction on the conversion from MCI to AD is of vital clinical significance for potential prevention and treatment of AD. Longitudinal studies received widespread attention for investigating the disease progression, though most studies did not sufficiently utilize the evolution information. In this paper, we proposed a cerebral similarity network with more progression information to predict the conversion from MCI to AD efficiently. First, we defined the new dynamic morphological feature to mine longitudinal information sufficiently. Second, based on the multiple dynamic morphological features the cerebral similarity network was constructed by sparse regression algorithm with optimized parameters to obtain better prediction performance. Then, leave-one-out cross-validation and support vector machine (SVM) were employed for the training and evaluation of the classifiers. The proposed methodology obtained a high accuracy of 92.31% (Sensitivity = 100%, Specificity = 82.86%) in a three-year ahead prediction of MCI to AD conversion. Experiment results suggest the effectiveness of the dynamic morphological feature, serving as a more sensitive biomarker in the prediction of MCI conversion. [ABSTRACT FROM AUTHOR]

To ease the traffic pressure on the plain urban road net work and reduce t he traffic accident risk an annular radial pattern was applied, with elastic topology network and the topology theory as the reference, in determining the road network path at the same time, the aggregation coefficient of the main road was calculated to complete the study of urban road network on the basic form. On this basis, the actual planning capacity of urban road was calculated according to the construction demand of step-by-step network, and then the planning layout model of urban road network was designed by balancing supply and demand of layout. It was found that after the application of the layout model, the TPI value matching with the trunk road decreased to 0.15, and the traffic flow at the fixed junction per unit time also increased effectively. The results show that compared with the original road network layout model, this model alleviates the traffic congestion pressure of urban road network effectively. [ABSTRACT FROM AUTHOR]

Forage intake is one of the most important indicators of health and productivity of ruminant livestock, such as sheep. Forage intake estimation can also make an important contribution to the design and implementation of rotational grazing systems. In this paper, sheep forage intake estimation models were developed based on acoustic analysis from data gathered by an audio recorder mounted on a sheep. A data set with 114 pieces of audio was constructed by collecting ingestion sound of eight female sheep (2 years old with 50 ± 3 kg body mass). A Gaussian kernel-based support vector machine (SVM) classifier was trained to identify chewing sound segments from sheep ingestion audio. Seven explanatory variables were extracted from each chewing sound segment. These variables were used to establish single variable and multiple variable-based forage estimation models. Least squares regression and elastic network approach were employed to determine the coefficients of the single variable and multiple variable-based forage intake estimation models, respectively. Validation results showed that the best single variable and multiple variable-based model could explain 71.02% and 80.94% of forage intake changes, respectively. The average accuracy of the two best models was 86.13% and 89.32%, respectively. The results suggested that an automatic system to estimate the forage intake of sheep based on a wearable audio-recorder could be developed in the future. This could contribute to sheep health disorder identification, decisions on the number of sheep for a given grassland unit, and so on. The latter is the foundation of sheep rotational grazing. • Two types of models were established to estimate sheep forage intake. • Trained SVM classifier to identify chewing sound segment with an accuracy of 95.34%. • Model coefficients of correlated variables were determined by using elastic network. • The best model can explain 80.94% of the forage intake changes. [ABSTRACT FROM AUTHOR]

Crop growth and development is a dynamic and complex process, and the essence of yield formation is the continuous accumulation of photosynthetic products from multiple fertility stages. In this study, a new stacking method for integrating multiple growth stages information was proposed to improve the performance of the winter wheat grain yield (GY) prediction model. For this purpose, crop canopy hyperspectral reflectance and leaf area index (LAI) data were obtained at the jointing, flagging, anthesis and grain filling stages. In this case, 15 vegetation indices and LAI were used as input features of the elastic network to construct GY prediction models for single growth stage. Based on Stacking technique, the GY prediction results of four single growth stages were integrated to construct the ensemble learning framework. The results showed that vegetation indices coupled LAI could effectively overcome the spectral saturation phenomenon, the validated R2 of each growth stage was improved by 10%, 22.5%, 3.6% and 10%, respectively. The stacking method provided more stable information with higher prediction accuracy than the individual fertility results (R2 = 0.74), and the R2 of the model validation phase improved by 236%, 51%, 27.6%, and 12.1%, respectively. The study can provide a reference for GY prediction of other crops.

Globular proteins are held together by interacting networks of amino acid residues. A number of different structural and computational methods have been developed to interrogate these amino acid networks. In this review, we describe some of these methods, including analyses of X-ray crystallographic data and structures, computer simulations, NMR data, and covariation among protein sequences, and indicate the critical insights that such methods provide into protein function. This information can be leveraged towards the design of new allosteric drugs, and the engineering of new protein function and protein regulation strategies.

Ubiquitin, discovered less than 50 years ago, tags thousands of diseased proteins for destruction. It is small (only 76 amino acids), and is found unchanged in mammals, birds, fish and even worms. Key features of its functionality are identified here using critical point thermodynamic scaling theory. These include Fano interference between first- and second-order elements of correlated long-range globular surface shape transitions. Comparison with its closest relative, 76 amino acid Nedd8, shows that the latter lacks these features. A cracked elastic network model is proposed for the common target shared by many diseased proteins. [ABSTRACT FROM AUTHOR]

Xylella fastidiosa is a xylem-limited bacterium that infects a wide variety of plants. Stationary phase survival protein E is classified as a nucleotidase, which is expressed when bacterial cells are in the stationary growth phase and subjected to environmental stresses. Here, we report four refined X-ray structures of this protein from X. fastidiosa in four different crystal forms in the presence and/or absence of the substrate 3′-AMP. In all chains, the conserved loop verified in family members assumes a closed conformation in either condition. Therefore, the enzymatic mechanism for the target protein might be different of its homologs. Two crystal forms exhibit two monomers whereas the other two show four monomers in the asymmetric unit. While the biological unit has been characterized as a tetramer, differences of their sizes and symmetry are remarkable. Four conformers identified by Small-Angle X-ray Scattering (SAXS) in a ligand-free solution are related to the low frequency normal modes of the crystallographic structures associated with rigid body-like protomer arrangements responsible for the longitudinal and symmetric adjustments between tetramers. When the substrate is present in solution, only two conformers are selected. The most prominent conformer for each case is associated to a normal mode able to elongate the protein by moving apart two dimers. To our knowledge, this work was the first investigation based on the normal modes that analyzed the quaternary structure variability for an enzyme of the SurE family followed by crystallography and SAXS validation. The combined results raise new directions to study allosteric features of XfSurE protein. [ABSTRACT FROM AUTHOR]

The present study compares fibroblasts extracted from intact and ruptured human anterior cruciate ligaments (ACL) for creation of a tissue engineered ACL-construct, made of porcine small intestinal submucosal extracellular matrix (SIS-ECM) seeded with these ACL cells. The comparison is based on histological, immunohistochemical and RT-PCR analyses. Differences were observed between cells in a ruptured ACL (rACL) and cells in an intact ACL (iACL), particularly with regard to the expression of integrin subunits and smooth muscle actin (SMA). Despite these differences in the cell source, both cell populations behaved similarly when seeded on an SIS-ECM scaffold, with similar cell morphology, connective tissue organization and composition, SMA and integrin expression. This study shows the usefulness of naturally occurring scaffolds such as SIS-ECM for the study of cell behaviour in vitro, and illustrates the possibility to use autologous cells extracted from ruptured ACL biopsies as a source for tissue engineered ACL constructs.

We present results on spatially localized oscillations in some inhomogeneous nonlinear lattices of Fermi–Pasta–Ulam (FPU) type derived from phenomenological nonlinear elastic network models proposed to study localized protein vibrations. The main feature of the FPU lattices we consider is that the number of interacting neighbors varies from site to site, and we see numerically that this spatial inhomogeneity leads to spatially localized normal modes in the linearized problem. This property is seen in 1-D models, and in a 3-D model with a geometry obtained from protein data. The spectral analysis of these examples suggests some non-resonance assumptions that we use to show the existence of invariant subspaces of spatially localized solutions in quartic Birkhoff normal forms of the FPU systems. The invariant subspaces have an additional symmetry and this fact allows us to compute periodic orbits of the quartic normal form in a relatively simple way. [ABSTRACT FROM AUTHOR]

Diffusion in colloidal suspensions can be very slow due to the cage effect, which confines each particle within a short radius on one hand, and involves large-scale cooperative motions on the other. In search of insight into this cooperativity, here the authors develop a formalism to calculate the displacement correlation in colloidal systems, mainly in the two-dimensional (2D) case. To clarify the idea for it, studies are reviewed on cooperativity among the particles in the one-dimensional (1D) case, i.e. the single-file diffusion (SFD). As an improvement over the celebrated formula by Alexander and Pincus on the mean-square displacement (MSD) in SFD, it is shown that the displacement correlation in SFD can be calculated from Lagrangian correlation of the particle interval in the one-dimensional case, and also that the formula can be extended to higher dimensions. The improved formula becomes exact for large systems. By combining the formula with a nonlinear theory for correlation, a correction to the asymptotic law for the MSD in SFD is obtained. In the 2D case, the linear theory gives description of vortical cooperative motion. [ABSTRACT FROM AUTHOR]

Improving energy efficiency can simultaneously contribute to reducing energy shortages while achieving sustainable economic development. With the expansion of the renewable energy sector, improving energy efficiency requires further investigation. This study used the data envelopment analysis (DEA) game cross-efficiency model to calculate the clean energy efficiency (CEE) and fossil energy efficiency (FEE) of China at the provincial level; it identified the key factors affecting energy efficiency using an elastic network model, and clustered the 31 regions. The results show that: (1) the average level of CEE is 0.77, which is higher than the overall FEE; (2) in the geographical distribution, clean energy sources show a high trend in the middle and low trend in the east and west, while FEE shows a low trend in the west and high trend in the east; and (3) the main influencing factors influencing CEE are urbanization levels and secondary industry shares, while the main factors influencing FEE are electrification levels and urbanization level. Finally, suggestions on how to improve energy efficiency were proposed based on the clustering results. • Analyze energy efficiency from clean energy and fossil energy. • The average efficiency of clean energy is higher than fossil energy. • Urbanization level and industrial structure mainly affect clean energy efficiency. • Electrification level mainly affect fossil energy efficiency. • Perform cluster analysis according to efficiency and coupling coordination degree. [ABSTRACT FROM AUTHOR]

Voltage-dependent potassium channels open in response to changes in membrane potential and become partially inactivated upon binding of inhibitors. Here we calculate normal mode motion of two voltage-dependent K channels, KvAP and Shaker, and their complexes with inhibitors and address the gating principle, opening mechanism, and inhibition. The normal modes indicate that pore expansion and channel opening is correlated with a displacement of the arginine gating charges and a tilting of the voltage-sensor paddles. Normal modes of Shaker in complex with agitoxin, which blocks the central pore, do not display significantly altered paddle tilting and pore expansion. In contrast, normal modes of Shaker in complex with hanatoxin, which binds to the voltage sensor paddle, display decreased paddle tilting and pore expansion. This study presents a unified motion for the gating principle and channel opening, and offers insight into the voltage sensor paddle motion and its inhibition. [ABSTRACT FROM AUTHOR]

ABSTRACT Citrate synthase (CS) catalyses the entry of carbon into the citric acid cycle and is highly-conserved structurally across the tree of life. Crystal structures of dimeric CSs are known in both 'open' and 'closed' forms, which differ by a substantial domain motion that closes the substrate-binding clefts. We explore both the static rigidity and the dynamic flexibility of CS structures from mesophilic and extremophilic organisms from all three evolutionary domains. The computational expense of this wide-ranging exploration is kept to a minimum by the use of rigidity analysis and rapid all-atom simulations of flexible motion, combining geometric simulation and elastic network modeling. CS structures from thermophiles display increased structural rigidity compared with the mesophilic enzyme. A CS structure from a psychrophile, stabilized by strong ionic interactions, appears to display likewise increased rigidity in conventional rigidity analysis; however, a novel modified analysis, taking into account the weakening of the hydrophobic effect at low temperatures, shows a more appropriate decreased rigidity. These rigidity variations do not, however, affect the character of the flexible dynamics, which are well conserved across all the structures studied. Simulation trajectories not only duplicate the crystallographically observed symmetric open-to-closed transitions, but also identify motions describing a previously unidentified antisymmetric functional motion. This antisymmetric motion would not be directly observed in crystallography but is revealed as an intrinsic property of the CS structure by modeling of flexible motion. This suggests that the functional motion closing the binding clefts in CS may be independent rather than symmetric and cooperative. Proteins 2014; 82:2657-2670. © 2014 Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR]

A quality-related fault monitoring method of multi-phase batch process based on multiway weighted elastic network is proposed in this paper. Firstly, to make the phase division for batch process more accurately, an improved affinity propagation clustering algorithm is developed. Secondly, a multiway weighted elastic network model is developed in each phase. On the one hand, quality-related subspace and quality-unrelated subspace are constructed in each phase to achieve dual monitoring of process fault and quality anomalies. On the other hand, kernel density estimation is used to measure the contribution of each element in each subspace to the fault. According to the difference of the contribution of each element to the fault, different weight is assigned to enhance the fault features and eliminate irrelevant features such as noise. Finally, support vector data description is used to establish monitoring indexes in both quality-related subspace and quality-unrelated subspace. Compared with traditional methods, the superiority and effectiveness of the proposed method have been verified by monitoring the penicillin fermentation process and the hot strip mill process. • An improved AP clustering algorithm is developed to make the phase division for batch process more accurately. • A multiway weighted elastic network model is developed in each phase to improve the fault detection effects. • The statistics based on SVDD are constructed to achieve dual monitoring of process fault and quality anomalies. [ABSTRACT FROM AUTHOR]

Periplasmic binding proteins are the initial receptors for the transport of various substrates over the inner membrane of gram-negative bacteria. The binding proteins are composed of two domains, and the substrate is entrapped between these domains. For several of the binding proteins it has been established that a closed-up conformation exists even without substrate present, suggesting a highly flexible apo-structure which would compete with the ligand-bound protein for the transporter interaction. For the leucine binding protein (LBP), structures of both open and closed conformations are known, but no closed-up structure without substrate has been reported. Here we present molecular dynamics simulations exploring the conformational flexibility of LBP. Coarse grained models based on the MARTINI force field are used to access the microsecond timescale. We show that a standard MARTINI model cannot maintain the structural stability of the protein whereas the ELNEDIN extension to MARTINI enables simulations showing a stable protein structure and nanosecond dynamics comparable to atomistic simulations, but does not allow the simulation of conformational flexibility. A modification to the MARTINI-ELNEDIN setup, referred to as domELNEDIN, is therefore presented. The domELNEDIN setup allows the protein domains to move independently and thus allows for the simulation of conformational changes. Microsecond domELNEDIN simulations starting from either the open or the closed conformations consistently show that also for LBP, the apo-structure is flexible and can exist in a closed form. [Figure not available: see fulltext.] [ABSTRACT FROM AUTHOR]

Molecular Dynamics is a method of choice for membrane simulations and the rising of coarse-grained forcefields has opened the way to longer simulations with reduced calculations times. Here, we present an elastic network, SAHBNET (Surface Accessibility Hydrogen-Bonds elastic NETwork), that will maintain the structure of soluble or membrane proteins based on the hydrogen bonds present in the atomistic structure and the proximity between buried residues. This network is applied on the coarse-grained beads defined by the MARTINI model, and was designed to be more physics-based than a simple elastic network. The SAHBNET model is evaluated against atomistic simulations, and compared with ELNEDYN models. The SAHBNET is then used to simulate two membrane proteins inserted in complex lipid bilayers. These bilayers are formed by self-assembly and the use of a modified version of the GROMACS tool genbox (which is accessible through the gcgs.gembloux.ulg.ac.be website). The results show that SAHBNET keeps the structure close to the atomistic one and is successfully used for the simulation of membrane proteins. [ABSTRACT FROM AUTHOR]

Abstract: The actin microfilament (F-actin) is a structural and functional component of the cell cytoskeleton. Notwithstanding the primary role it plays for the mechanics of the cell, the mechanical behaviour of F-actin is still not totally explored. In particular, the relationship between the mechanics of F-actin and its molecular architecture is not completely understood. In this study, the mechanical properties of F-actin were related to the molecular topology of its building monomers (G-actin) by employing a computational multi-level approach. F-actins with lengths up to 500nm were modelled and characterized, using a combination of equilibrium molecular dynamics (MD) simulations and normal mode analysis (NMA). MD simulations were performed to analyze the molecular rearrangements of G-actin in physiological conditions; NMA was applied to compute the macroscopic properties of F-actin from its vibrational modes of motion. Results from this multi-level approach showed that bending stiffness, bending modulus and persistence length are independent from the length of F-actin. On the contrary, the orientations and motions of selected groups of residues of G-actin play a primary role in determining the filament flexibility. In conclusion, this study (i) demonstrated that a combined computational approach of MD and NMA allows to investigate the biomechanics of F-actin taking into account the molecular topology of the filament (i.e., the molecular conformations of G-actin) and (ii) that this can be done using only crystallographic G-actin, without the need of introducing experimental parameters nor of reducing the number of residues. [Copyright &y& Elsevier]

The elastic network of articular cartilage was investigated by immunohistochemistry using specific antibodies to elastin and fibrillin-1. Articular cartilage was dissected from defined regions of bovine metacarpophalangeal joints. Elastin fibres and microfibrils were dual-immunostained by labelling with distinct fluorescent dyes. A conventional fluorescence microscope combined with a polarized light filter was used to study the organization and degree of colocalization of elastin fibres, microfibrils and of the collagen network. We observed an elaborately organized elastic network. In the uppermost superficial zone, where few cells were present, elastin fibres and microfibrils formed a dense three dimensional network showing some degree of colocalization. The thickness and organization of this elastic network varied dramatically from region to region and was most extensive in the metacarpal palmar region. In the middle and deep zones, very few elastin fibres were observed but microfibrils formed a network in the inter-territorial matrix and dense network around the cells. Our finding of a three dimensional network of dense, well organized elastin fibres and microfibrils in the surface zone of the articular cartilage matrix, and a dense network of microfibrils around the cells deeper into the tissue suggests the elastic network could play both a mechanical and a biological role in articular cartilage. [ABSTRACT FROM AUTHOR]

We developed a software package (RedMD) to perform molecular dynamics simulations and normal mode analysis of reduced models of proteins, nucleic acids, and their complexes. With RedMD one can perform molecular dynamics simulations in a microcanonical ensemble, with Berendsen and Langevin thermostats, and with Brownian dynamics. We provide force field and topology generators which are based on the one-bead per residue/nucleotide elastic network model and its extensions. The user can change the force field parameters with the command line options that are passed to generators. Also, the generators can be modified, for example, to add new potential energy functions. Normal mode analysis tool is available for elastic or anisotropic network models. The program is written in C and C++ languages and the structure/topology of a molecule is based on an XML format. OpenMP technology for shared-memory architectures was used for code parallelization. The code is distributed under GNU public licence and available at http://bionano.icm.edu.pl/software/. © 2009 Wiley Periodicals, Inc. J Comput Chem, 2009 [ABSTRACT FROM AUTHOR]

• The maximum R2 of the intake regression model is 0.9734. • Adding test condition variables improves model accuracy. • Introducing sheep weight into intake improves model accuracy. • The effect of individual differences is more extensive than grass moisture content. Accurate estimation of sheep feed intake is essential for optimal pasture management and understanding the patterns of grass-livestock ecosystems. Although many studies have been reported, fewer studies have comprehensively considered animal body weight, grass species, and grass moisture content. This study designed a 29-test experiment with three classes of sheep weight, two grass species, and three levels of grass moisture content combinations. Segment samples were constructed from each test, and many explanatory variables (44 in total) associated with chewing were extracted from each segment sample. Correlations between each explanatory variable and intake were examined under every test, and the slopes of their regression lines were also recorded. A statistical analysis was then used to reveal the effects of sheep bodyweight class, grass species, and grass moisture content class on the slopes of the explanatory variables. On the test sample set (one test sample corresponding to one test), intake was predicted using a single explanatory variable, all explanatory variables, all explanatory variables and factor variables (sheep weight, grass moisture content, grass NDF content, grass ADF content). The results showed that the chew_ZCTOverLegth variable (number of chewing waveforms over the mean divided by waveform length and then accumulated over all chews) and intake had the strongest correlation with a mean R2 score of 0.8796. The influence of grass moisture content class on the slope of the variable was approximately equal to that of the sheep weight class and less than that of the differences in individual sheep. In the test sample set, the LogMel_power variable (sum of squares of waveform log-Mel) was the best single explanatory variable for predicting intake (when the type of intake is fresh grass matter). Adding factor variables significantly increased the R2 score of the model and reduced the number of variables used. Introducing sheep bodyweight to intake further increased the R2 score of the model and reduced the number of variables used. When the type of intake was FMI_W (fresh matter multiplied by the square of the sheep bodyweight value), intake was accurately estimated using all explanatory and factor variables with an R2 score of 0.9734. This result demonstrates that chewing-derived acoustic variables acoustically can accurately estimate intake despite the complexity of the experimental conditions. This study will make an outstanding contribution to the development of general and accurate models for estimating intake. [ABSTRACT FROM AUTHOR]

In this work, the Gaussian Network Model (GNM) and Anisotropic Network Model (ANM) approaches are applied to describe the dynamics of protein structure graphs built from calculated promolecular electron density (ED) distribution functions. A first set of analyses is carried out on results obtained from ED maxima calculated at various smoothing levels. A second set is achieved for ED networks whose edges are weighted by ED overlap integral values. Results are compared with those obtained through the classical GNM and ANM approaches applied to networks of Cα atoms. It is shown how the network model and the consideration of crystal packing as well as of the side chains may lead to various improvements dependent upon the structure under study. The selected protein structures are Crambin and Pancreatic Trypsin Inhibitor because of their small size and numerous dynamical data obtained by other authors. © 2008 Wiley Periodicals, Inc. J Comput Chem, 2008 [ABSTRACT FROM AUTHOR]

Computational studies of large macromolecular assemblages have come a long way during the past 10 years. With the explosion of computer power and parallel computing, timescales of molecular dynamics simulations have been extended far beyond the hundreds of picoseconds timescale. However, limitations remain for studies of large-scale conformational changes occurring on timescales beyond nanoseconds, especially for large macromolecules. In this review, we describe recent methods based on normal mode analysis that have enabled us to study dynamics on the microsecond timescale for large macromolecules using different levels of coarse graining, from atomically detailed models to those employing only low-resolution structural information. Emerging from such studies is a control principle for robustness in Nature's machines. We discuss this idea in the context of large-scale functional reorganization of the ribosome, virus particles, and the muscle protein myosin. [ABSTRACT FROM AUTHOR]

The plastic network model (PNM) is used to generate a conformational change pathway for Escherichia coli adenylate kinase based on two crystal structures, namely that of an open and a closed conformer. In this model, the energy basins corresponding to known conformers are connected at their lowest common energies. The results are used to evaluate and analyze the minimal energy pathways between these basins. The open to closed transition analysis provides an identification of hinges that is in agreement with the existing definitions based on the available X-ray structures. The elastic energy distribution and the Cα pseudo-dihedral variation provide similar information on these hinges. The ensemble of the 45 published structures for this protein and closely related proteins is shown to always be within 3.0Å of the pathway, which corresponds to a conformational change between two end structures that differ by a Cα-atom root-mean-squared deviation of 7.1Å. [Copyright &y& Elsevier]

Abstract: The structure–function relationship is critical to understanding the biologically relevant functions of protein structures. Various experimental techniques and numerical modeling methods, normal mode analysis (NMA) in particular, have been employed to gain insight into this relationship. Experimental methods are often unable to provide all the desired information and comprehensive modeling techniques are often too computationally expensive. The authors build upon and optimize their cluster normal mode analysis (cNMA) tool, which uses embedded rigid-bodies and harmonic potentials to capture the biologically significant, low-frequency, oscillations of protein structures. cNMA represents atomic details with a scalable number of degrees-of-freedom, which can be chosen independent of structure size. This representation overcomes the otherwise quadratic order memory requirements and cubic order computational complexity associated with traditional all-atom NMA. cNMA is two orders of magnitude faster than traditional all-atom NMA when clustering by residue (very high resolution) and in the more traditional application using a fixed number of clusters, cNMA computationally scales as , which is two orders of complexity faster than all-atom NMA. cNMA is presented and very large example structures with up to atoms are analyzed on a notebook PC in the time scale of minutes/hours. The resulting mode shapes help identify biologically significant, conformational pathways. [Copyright &y& Elsevier]

A combination of experimental structural data, homology modelling and elastic network normal mode analysis is used to explore how coupled motions between the two myosin heads and the dimerization domain (S2) in smooth muscle myosin II determine the domain movements required to achieve the inhibited state of this ATP-dependent molecular motor. These physical models rationalize the empirical requirement for at least two heptads of non-coiled α-helix at the junction between the myosin heads and S2, and the dependence of regulation on S2 length. The results correlate well with biochemical data regarding altered conformational-dependent solubility and stability. Structural models of the conformational transition between putative active states and the inhibited state show that torsional flexibility of the S2 α-helices is a key mechanical requirement for myosin II regulation. These torsional motions of the myosin heads about their coiled coil α-helices affect the S2 domain structure, which reciprocally affects the motions of the myosin heads. This inter-relationship may explain a large body of data on function of molecular motors that form dimers through a coiled-coil domain. [Copyright &y& Elsevier]

We analyze the mechanical properties and putative dynamical fluctuations of a variety of viral capsids comprising different sizes and quasi-equivalent symmetries by performing normal mode analysis using the elastic network model. The expansion of the capsid to a swollen state is studied using normal modes and is compared with the experimentally observed conformational change for three of the viruses for which experimental data exist. We show that a combination of one or two normal modes captures remarkably well the overall translation that dominates the motion between the two conformational states, and reproduces the overall conformational change. We observe for all of the viral capsids that the nature of the modes is different. In particular for the T=7 virus, HK97, for which the shape of the capsid changes from spherical to faceted polyhedra, two modes are necessary to accomplish the conformational transition. In addition, we extend our study to viral capsids with other T numbers, and discuss the similarities and differences in the features of virus capsid conformational dynamics. We note that the pentamers generally have higher flexibility and propensity to move freely from the other capsomers, which facilitates the shape adaptation that may be important in the viral life cycle. [Copyright &y& Elsevier]

Pyruvate dehydrogenase complex (PDC) is one of the largest multienzyme complexes known and consists of a dodecahedral E2 core to which other components are attached. We report the results of applying a new computational method, quantized elastic deformational model, to simulating the conformational fluctuations of the truncated E2 core, using low-resolution electron cryomicroscopy density maps. The motional features are well reproduced; especially, the symmetric breathing mode revealed in simulation is nearly identical with what was observed experimentally. Structural details of the motions of the trimeric building blocks, which are critical to facilitating the global expansion and contraction of the complex, were revealed. Using the low-resolution maps from electron cryomicroscopy reconstructions, the simulations showed a picture of the motional mechanism of the PDC core, which is an example without precedent of thermally activated global dynamics. Moreover, the current results support an earlier suggestion that, at low resolution and without the use of amino acid sequence and atomic coordinates, it is possible for computer simulations to provide an accurate description of protein dynamics. [Copyright &y& Elsevier]

Poor water resistance and curing layer brittleness are significant challenges of greener soybean meal (SM) adhesive promotion and application. In this study an elastic network was built to crosslink the SM adhesive and improve the cured layer toughness of the resultant adhesive. Long-chain organo-sepiolite (OSEP) was first prepared by compound modification using KH-560 and KH-602 silane coupling agents. Triglycidylamine (TGA), a greener crosslinking agent with a large number of effective epoxy functional groups, was then synthesized. SM-based adhesives were fabricated using SM, OSEP and TGA. Results indicated that an elastic network was fabricated by the cross-linked reaction of SM, OSEP, and TGA. The elastic network effectively improved the toughness of the resultant adhesive. The wet shear strength of the plywood (PlyW) bonded by SM with 1% OSEP (SM/TGA/OSEP-1 adhesive) increased by 60% to 1.25 MPa relative to that of the SM/TGA adhesive. This study provided a greener, simple and cheap method improving the toughness and wet shear strength of SM-based adhesive, which contributes to the industrialized application of the product. Image 1 • A greener soybean-meal based bioadhesive was prepared. • Long-chain OSEP was first prepared by compound modification using KH-560 and KH-602 silane coupling agents. • A greener crosslinking agent TGA with a large number of effective epoxy functional was prepared. • A flexible network was fabricated by the reaction of soy protein molecules, OSEP, and TGA. • The wet shear strength and toughness of the soybean-meal based bioadhesive was improved. [ABSTRACT FROM AUTHOR]

Elastic networks have been used as simple models of proteins to study their slow structural dynamics. They consist of point-like particles connected by linear Hookean springs and hence are convenient for linear normal mode analysis around a given reference structure. Furthermore, dynamic simulations using these models can provide new insights. As the computational cost associated with these models is considerably lower compared to that of all-atom models, they are also convenient for comparative studies between multiple protein structures. In this review, we introduce examples of coarse-grained molecular dynamics studies using elastic network models and their derivatives, focusing on the nonlinear phenomena, and discuss their applicability to large-scale macromolecular assemblies. [ABSTRACT FROM AUTHOR]