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We propose a framework employing stochastic differential equations to facilitate the long-term stability analysis of power grids with intermittent wind power generations. This framework takes into account the discrete dynamics which play a critical role in the long-term stability analysis, incorporates the model of wind speed with different probability distributions, and also develops an approximation methodology (by a deterministic hybrid model) for the stochastic hybrid model to reduce the computational burden brought about by the uncertainty of wind power. The theoretical and numerical studies show that a deterministic hybrid model can provide an accurate trajectory approximation and stability assessments for the stochastic hybrid model under mild conditions. In addition, we discuss the critical cases that the deterministic hybrid model fails and discover that these cases are caused by a violation of the proposed sufficient conditions. Such discussion complements the proposed framework and methodology and also reaffirms the importance of the stochastic hybrid model when the system operates close to its stability limit., The paper has been accepted by IEEE Journal on Emerging and Selected Topics in Circuits and Systems

The multidisciplinary design optimization is suitable for modern large aircraft, and it has the potential in conceptual phase of aircraft design especially. An integrated optimization method considering the disciplines of aerodynamics, structure and stability for large aircraft design in conceptual phase is presented. The objective is the minimum stiffness of a beam-frame wing structure subject to aeroelasticity, aerodynamics, and stability constraints. The aeroelastic responses are computed by commercial software MSC. Nastran, and the cruise stability is evaluated by the linear small-disturbance equations. A viscous-inviscid iteration method, which is composed of a computational fluid dynamics tool solving the Euler equations and a viscous correction method, is used for computing the flow over the model. The method ensures effective and rapid computation. In this paper, a complete aircraft model is optimized, and all the responses are computed in the trim condition with a fixed maximum takeoff weight. Genetic algorithm is utilized for global optimizations, and the optimal jig shape, the elastic axis positions and the stiffness distribution can be attained adequately. The results show that the method has a value of application in engineering optimizations. For the satisfaction of the total drag and stability constraints, the structure weight usually needs a price to pay. The integrated optimization captures the tradeoff between aerodynamics, structure and stability, and the repeated design can be avoided.

The increasing demands for travelling comfort and reduction of carbon dioxide emissions have been considered substantially in the stage of conceptual aircraft design. However, the design of a modern aircraft is a multidisciplinary process, which requires the coordination of information from several specific disciplines, such as structures, aerodynamics, control, etc. To address this problem with adequate accuracy, the multidisciplinary analysis and optimization (MAO) method is usually applied as a systematic and robust approach to solve such complex design issues arising from industries. Since MAO method is tedious and computationally expensive, genetic programming (GP)-based metamodeling techniques incorporating MAO are proposed as an effective approach to minimize the wing stiffness of a large aircraft subject to aerodynamic, aeroelastic and stability constraints in the conceptual design phase. Based on the linear small-disturbance theory, the state-space equation is employed for stability analysis. In the process of multidisciplinary analysis, aeroelastic response simulations are performed using Nastran. To construct metamodels representing the responses of the interests with high accuracy as well as less computational burden, optimal Latin hypercube design of experiments (DoE) is applied to determine the optimized distribution of sampling points. Following that, parametric optimization is carried out on metamodels to obtain the optimal wing geometry shape, elastic axis positions and stiffness distribution, and then the solution is verified by finite element simulations. Finally, the superiority of the GP-based metamodel technique over genetic algorithm is demonstrated by multidisciplinary design optimization of a representative beam-frame wing structure in terms of accuracy and efficiency. The results also show that GP metamodel-based strategy for solving MAO problems can provide valuable insights to tailoring parameters for the effective design of a large aircraft in the conceptual phase.

In this paper, the cascaded H-bridge STATCOM parallel system is taken as the research object to analyze the instability of STATCOM system caused by the interaction between parallel STATCOMs in wind farm afflux station. Firstly, the inherent relation of harmonic circulation and low-frequency disturbance will be revealed by CPS-PWM theory. Then, the output impedance and transfer function of STATCOM give an insight into the interaction between STATCOMs and low-frequency resonance, and the virtual impedance method is proposed to shape the resonant peak. Finally, the correctness of theoretical analysis is verified by real-time simulation.

The detailed physics model of the modular multilevel converters (MMCs) consumes excessive computational resources because of massive submodules (SMs). To deal with this problem, several simplified modeling methods have been proposed. However, the startup process modeling techniques attract little attention. In this paper, two common startup processes of MMC are analyzed, including the ac-side and dc-side pre-charge. Firstly, a charging index is defined to represent the charging state of the arm capacitors. Based on the charging index, the ordinary differential equations of MMC under startup process are derived. Furthermore, an average value model (AVM) of MMC is proposed based on the ordinary differential equations. The proposed model is compared and validated by the IGBT-based MMC model in PSCAD/EMTDC.

In this paper, a purely measurement-based method is proposed to estimate the dynamic system state matrix by applying the regression theorem of the multivariate Ornstein-Uhlenbeck process. The proposed method employs a recursive algorithm to minimize the required computational effort, making it applicable to the real-time environment. One main advantage of the proposed method is model independence, i.e., it is independent of the network model and the dynamic model of generators. Among various applications of the estimated matrix, detecting and locating unexpected network topology change is illustrated in details. Simulation studies have shown that the proposed measurement-based method can provide an accurate and efficient estimation of the dynamic system state matrix under the occurrence of unexpected topology change. Besides, various implementation conditions are tested to show that the proposed method can provide accurate approximation despite measurement noise, missing PMUs, and the implementation of higher-order generator models with control devices., 11 pages, 13 figures

In the future, household electricity consumption is used as part of the smart grid. It is of great significance to study the optimal operation of household intelligent electrical equipment. Firstly, the mathematical model of typical household intelligent electrical equipment in the future is established, and then based on the time-of-use electricity price, an optimized operation model of intelligent electrical equipment in the future home with the aim of economic and user comfort is proposed, which is convenient for users to formulate a power consumption plan that meets their own needs. Finally, taking a typical home user as an example, the genetic algorithm is used to optimize the operation of the home intelligent electrical equipment by establishing a typical daily simulation scenario. The simulation results show the effectiveness of the established optimization model.

This paper studies the impacts of stochastic load fluctuations, namely the fluctuation intensity and the load power variation speed, on power system dynamic voltage stability. Additionally, the trade-off relationship between the two parameters is revealed, which provides important insights regarding the potential of using energy storage to maintain voltage stability under high uncertainty. To this end, Stochastic Differential-Algebraic Equations (SDAEs) are used to model the stochastic load variation; bifurcation analysis is carried out to explain the influence of stochasticity. Numerical study and Monte Carlo simulations on the IEEE 14-bus system demonstrate that a larger fluctuation intensity or a slower load power variation speed may lead to a smaller voltage stability margin. To the best of authors' knowledge, this work uncovers the impacts of the time evolution property of the driving parameters, i.e., the load power variation speed and its trade off effect with the fluctuation intensity on the size of the dynamic voltage stability margin., Comment: 10 pages, Accept by IEEE Transactions on Circuits and Systems-I: Regular Papers on September 17, 2019

In this paper, a novel non-intrusive probabilistic power flow (PPF) analysis method based on the low-rank approximation (LRA) is proposed, which can accurately and efficiently estimate the probabilistic characteristics (e.g., mean, variance, probability density function) of the PPF solutions. This method aims at building up a statistically-equivalent surrogate for the PPF solutions through a small number of power flow evaluations. By exploiting the retained tensor-product form of the univariate polynomial basis, a sequential correction-updating scheme is applied, making the total number of unknowns to be linear rather than exponential to the number of random inputs. Consequently, the LRA method is particularly promising for dealing with high-dimensional problems with a large number of random inputs. Numerical studies on the IEEE 39-bus, 118-bus, and 1354-bus systems show that the proposed method can achieve accurate probabilistic characteristics of the PPF solutions with much less computational effort compared to the Monte Carlo simulations. Even compared to the polynomial chaos expansion method, the LRA method can achieve comparable accuracy, while the LRA method is more capable of handling higher-dimensional problems. Moreover, numerical results reveal that the randomness brought about by the renewable energy resources and loads may inevitably affect the feasibility of dispatch/planning schemes., Comment: 12 pages, 5 figures, Accepted by IEEE Transactions on Power Systems on January 27, 2019. arXiv admin note: text overlap with arXiv:1810.08156

Sustained oscillations observed in power systems can damage equipment, degrade the power quality and increase the risks of cascading blackouts. There are several mechanisms that can give rise to oscillations, each requiring different countermeasure to suppress or eliminate the oscillation. This work develops mathematical framework for analysis of sustained oscillations and identifies statistical signatures of each mechanism, based on which a novel oscillation diagnosis method is developed via real-time processing of phasor measurement units (PMUs) data. Case studies show that the proposed method can accurately identify the exact mechanism for sustained oscillation, and meanwhile provide insightful information to locate the oscillation sources.

RUNX2 is an important osteo-specific factor with crucial functions in bone formation and remodelling as well as resorption of teeth. Heterozygous mutation of RUNX2 can cause cleidocranial dysplasia (CCD), a systemic disease with extensive skeletal dysplasia and abnormality of tooth growth. In our study, dental follicle cells (DFCs) and periodontal ligament cells (PDLCs) were isolated, cultured and identified from one patient with CCD and compared with normal controls. This CCD patient was confirmed to have a heterozygous frameshift mutation of RUNX2 (c.514delT, p.Ser172fs) in the previous study. The results showed that the proliferation abilities of DFCs and PDLCs were both disturbed by the RUNX2 mutation in the CCD patient compared with the normal control. A co-culture system of these cells with human peripheral blood mononuclear cells was then used to investigate the effect of RUNX2 mutation on osteoclastogenesis. We found that the RUNX2 mutation in CCD reduced the expression of osteoclast-related genes, such as RUNX2, CTR, CTSK, RANKL and OPG The ability of osteoclastogenesis in DFCs and PDLCs detected by tartrate-resistant acid phosphatase staining in the co-culture system was also reduced by the RUNX2 mutation compared with the normal control. These outcomes indicate that the RUNX2 mutation disturbs the modulatory effects of DFCs and PDLCs on the differentiation of osteoclasts and osteoblasts, thereby interfering with bone remodelling. These effects may contribute in part to the pathological manifestations of retention of primary teeth and delayed eruption of permanent teeth in patients with CCD.

The gut microbiome plays an important role in the health and disease status of the host. Research on the effect of sex on animal intestinal microorganisms is still limited, and the effect of castration on the gut microbiome of male pigs has not been fully investigated. In this study, 30 Hainan special wild boars at the same growth stage were divided into three groups (10 entire males, 10 females, and 10 castrated males). High-throughput 16S rRNA sequencing was used to investigate the fecal microbiota of the Hainan special wild boar. Firmicutes, Bacteroidetes, Actinobacteria, Spirochaetes, and Proteobacteria were the five dominant phyla found in the specimens. The relative abundance of Bacteroidetes was higher in the microbiota of female pigs than in male pigs, while Firmicutes was on the contrary. The percentage of Streptococcus and Lactobacillus was higher in males than females. The microbial diversity of females was significantly higher compared to males, castration increased the intestinal microbial diversity of males. Functional prediction showed that male fecal microorganisms were rich in membrane transport and carbohydrate metabolism, energy metabolism, glycan biosynthesis, and metabolism of cofactors and vitamins were rich in the female group, the fecal microorganisms of castrated males had higher membrane transport abundance.

With the continuous expansion of wind farm clusters, the multi-terminal modular multilevel converter based high voltage direct current (MMC-HVDC) transmission has been employed for wind power delivery. To improve the control flexibility, dynamic voltage adjustment speed and rationality of power allocation, various coordination control methods have been recently investigated in multi-terminal MMC-HVDC transmission system. An attempt is made in this paper to contribute to the optimization of coordination control in the multi-terminal MMC-HVDC system connected to wind farms. Based on the analysis of DC voltage, power deviation and droop coefficient, an improved droop control strategy with adjustable coefficients is first proposed and thereby applied to multi-terminal MMC-HVDC transmission system with wind farm clusters. Arguably, the droop coefficient correction should be an effective method for optimizing power allocation and reducing DC voltage deviation between the receiving converters. In addition, a lag compensator is introduced to improve the response speed of the inner loop controller when the improved droop control is adopted. Furthermore, the influence of droop coefficients on the stability of the control system is discussed, and the dynamic response of the improved control system is also analyzed under the different droop and time coefficients, which can provide guidelines for the control optimization of multi-terminal MMC-HVDC system. Finally, a model of a five-terminal MMC-HVDC transmission system is built based on the RT-LAB simulator, and the simulation results show that the proposed method can not only optimize the voltage control and power allocation, but also improve the system response speed.

In this paper, we present an integration subtraction technique to model holes interactively in a predesigned domain for adaptive problems. This technique involves two approaches, the normal subtraction technique and the moving subtraction technique. In the basic normal subtraction technique, the predesigned domain can be meshed using any methods as an initial integration background cell for meshfree analysis. Holes are described using closed non-uniform rational B-spline (NURBS) curves to preserve the exact computer-aided design (CAD) geometry and are meshed alone using the homotopic method, so they can easily be subtracted from the predesigned domain with no refinement. On the other hand, when the hole size is varying, the moving subtraction technique, in which only the changing part between the new and old boundaries needs to be integrated and subtracted, is more efficient. Compared with the standard radial point interpolation method (RPIM) and finite element method (FEM) in three linear elastic examples with different holes, the excellent accuracy and good efficiency of the proposed method are demonstrated, and its feasibility in complex topology problems is verified.

Power distribution systems require continuous monitoring as the integration of renewable energy resources is increasing and the load demand is growing. The implementation of synchrophasors in distribution systems enhances the situational awareness of the system and provides a unique opportunity for developing new monitoring algorithms. This paper proposes a voltage monitoring algorithm based on the synchrophasor-based linear state estimation method. Particularly, the voltage monitoring algorithm combines a set of early warning indicators and the BDS independence test which can detect the voltage instability in a timely manner while avoiding false alarms when the system is still away from the stability boundary. Numerical study has been conducted in the Quebec test feeder to show the effectiveness of the method.

Objectives To explore the role of dental follicle cells (DFCs) with a novel cleidocranial dysplasia (CCD) causative gene RUNX2 mutation (DFCsRUNX2+/m ) in delayed permanent tooth eruption. Materials and methods A CCD patient with typical clinical features was involved in this study. DFCsRUNX2+/m were cultured and DNA was extracted for RUNX2 mutation screening. Measurements of cell proliferation, alkaline phosphatase (ALP) activity, alizarin red staining and osteoblast-specific genes expression were performed to assess osteogenesis of DFCsRUNX2+/m . Co-culture of DFCs and peripheral blood mononuclear cells (PBMCs), followed tartrate-resistant acid phosphatase (TRAP) staining, real-time PCR and western blot were performed to evaluate osteoclast-inductive capacity of DFCsRUNX2+/m . Results A missense RUNX2 mutation (c. 557G>C) was found in DFCsRUNX2+/m from the CCD patient. Compared with normal controls, this mutation did not affect the proliferation of DFCsRUNX2+/m , but down-regulated the expression of osteogenesis-related genes, leading to a decrease in ALP activity and mineralisation. Co-culture results showed that DFCsRUNX2+/m reduced the formation of TRAP+ multinucleated cells and the expression of osteoclastogenesis-associated genes. Furthermore, the mutation reduced the ratio of RANKL/OPG in DFCsRUNX2+/m . Conclusions DFCsRUNX2+/m disturbs bone remodelling activity during tooth eruption through RANK/RANKL/OPG signalling pathway and may thus be responsible for impaired permanent tooth eruption in CCD patients.

Objectives Cleidocranial dysplasia (CCD), mainly caused by RUNX2 mutation, is a dominantly inherited skeletal disorder with many dental abnormalities, characterized by delayed permanent tooth eruption. In this study, we explored a novel RUNX2 mutation and the effect of RUNX2 mutation on osteogenic differentiation of dental follicle cells (DFCs). Design A CCD patient with typical clinical features was involved in this study. Conservation and secondary structural analysis of the RUNX2 mutation was first performed. Then DFCs that stably expressing wild-type or mutant RUNX2 were established using lentiviruses. Cell Counting Kit 8 (CCK8) assays were performed to test the proliferation of DFCs. Measurement of alkaline phosphatase (ALP) activity, ALP staining, alizarin red staining and determination of osteoblast-specific genes expression were performed to assess osteogenic capacity of DFCs. Results A missense mutation (c.674 G > T, p. R225 L) of RUNX2 gene was identified in the CCD patient. Conservation and secondary structural analysis revealed that the mutation was located in highly conserved Runt domain and altered secondary structure of RUNX2. CCK8 assays showed that mutant RUNX2 increased the proliferation rate of DFCs compared to wild-type RUNX2. ALP activity, ALP staining and alizarin red staining results indicated that mutant RUNX2 decreased the mineralization ability of DFCs. In addition, mutant RUNX2 significantly down-regulated the expression of osteoblast-associated genes. Conclusions RUNX2 mutation can reduce the osteogenic capacity of DFCs by inhibiting osteoblast-associated genes and then affecting bone formation, which participates in bone remodeling during tooth eruption. These effects may be partly responsible for the defects in permanent tooth eruption of CCD patients.

Considering the increasing penetration of renewable energy sources and electrical vehicles in utility distribution feeders, it is imperative to study the impacts of the resulting increasing uncertainty on the delivery capability of a distribution network. In this paper, probabilistic available delivery capability (ADC) is formulated for a general distribution network integrating various RES and load variations. To reduce the computational efforts by using conventional Monte Carlo simulations, we develop and employ a computationally efficient method to assess the probabilistic ADC, which combines the up-to-date sparse polynomial chaos expansion (PCE) and the continuation method. Particularly, the proposed method is able to handle a large number of correlated random inputs with different marginal distributions. Numerical examples in the IEEE 13 and IEEE 123 node test feeders are presented, showing that the proposed method can achieve accuracy and efficiency simultaneously. Numerical results also demonstrate that the randomness brought about by the RES and loads indeed leads to a reduction in the delivery capability of a distribution network., Comment: 10 pages, 4 figures, journal paper accepted by IEEE Transactions on Power Systems

In this paper, a new technique is applied to conduct mode identification using ambient measurement data. The proposed hybrid measurement- and model-based method can accurately estimate the system state matrix in ambient conditions, the eigenvalues and eigenvectors of which readily provide all the modal knowledge including frequencies, damping ratios, mode shapes, and more importantly, participation factors. Numerical simulations show that the proposed technique is able to provide accurate estimation of modal knowledge for all modes. In addition, the discrepancy between the participation factor and the mode shape is shown through a numerical example, demonstrating that using the mode shape may not effectively pinpoint the best location for damping control. Therefore, the proposed technique capable of estimating participation factors may greatly facilitate designing damping controls.

Rapid increase of renewable energy sources and electric vehicles in utility distribution feeders introduces more and more uncertainties. To investigate how such uncertainties may affect the available delivery capability (ADC) of the distribution network, it is imperative to employ a probabilistic analysis framework. In this paper, a formulation for probabilistic ADC incorporating renewable generators and load variations is proposed; a computationally efficient method to solve the probabilistic ADC is presented, which combines the up-to-date sparse polynomial chaos expansion (PCE) and the continuation method. A numerical example in the IEEE 13 node test feeder is given to demonstrate the accuracy and efficiency of the proposed method., 6 pages, 3 figures, Conference paper, Published in the IEEE Canada Electrical Power and Energy Conference 2017

In this paper, a hybrid measurement- and model-based method is proposed which can estimate the dynamic state Jacobian matrix and the dynamic system state matrix in near real-time utilizing statistical properties extracted from PMU measurements. The proposed method can be used to detect and identify network topology changes that have not been reflected in an assumed network model. Additionally, an application of the estimated system state matrix in online dynamic stability monitoring is presented., To appear in IEEE Transactions on Power Systems

Fanconi anemia (FA) is a genome instability syndrome characterized by bone marrow failure and cellular hypersensitivity to DNA cross-linking agents. In response to DNA damage, the FA pathway is activated through the cooperation of 16 FA proteins. A central player in the pathway is a multisubunit E3 ubiquitin ligase complex or the FA core complex, which monoubiquitinates its substrates FANCD2 and FANCI. FANCE, a subunit of the FA core complex, plays an essential role by promoting the integrity of the complex and by directly recognizing FANCD2. To delineate its role in substrate ubiquitination from the core complex assembly, we analyzed a series of mutations within FANCE. We report that a phenylalanine located at the highly conserved extreme C terminus, referred to as Phe-522, is a critical residue for mediating the monoubiquitination of the FANCD2-FANCI complex. Using the FANCE mutant that specifically disrupts the FANCE-FANCD2 interaction as a tool, we found that the interaction-deficient mutant conferred cellular sensitivity in reconstituted FANCE-deficient cells to a similar degree as FANCE null cells, suggesting the significance of the FANCE-FANCD2 interaction in promoting cisplatin resistance. Intriguingly, ectopic expression of the FANCE C terminus fragment alone in FA normal cells disrupts DNA repair, consolidating the importance of the FANCE-FANCD2 interaction in the DNA cross-link repair.

Multivariate timeseries become a popular data form to represent images, that are used as suitable inputs to higher-level recognition processes. We present a novel cluster analysis based on timeseries structure to identify similar human motion sequences. To clustering sequences, the movement silhouettes from video were transformed into low-dimensional multivariate timeseries, then further converted into vectors based on their structure in a finite-dimensional Euclidean space. The identification and selection of structural metrics for human motion sequences were highlighted to demonstrate that these statistical features are generic but also problem dependent. Various clustering algorithms were used to demonstrate the effectiveness and simplicity using real data sets.

Introduction Calcium hydroxide is used in direct pulp capping of uncontaminated exposed vital pulps caused by mechanical or traumatic injury. Calcium hydroxide creates a high alkaline pH environment and initiates a mineralized tissue formation in the pulp. The exact mechanism by which calcium hydroxide induces the reparative dentin formation is unknown. Because Eph receptors and ephrin ligands play a role in pulp stem cell migration and proliferation, our hypothesis is that calcium hydroxide–related odontogenic/osteogenic differentiation may be associated with Eph-ephrin interaction. The aim of this study was to investigate whether Eph-ephrin interaction regulates odontogenic/osteogenic differentiation with calcium hydroxide. Methods Primary pulp cells were harvested from the molars of C57BL/6 mice. The cells were treated with calcium hydroxide. Immunofluorescence was used to detect protein expression. A knockout of the ephrinB1 or EphB2 gene was performed with short hairpin RNAs. Cell migration, proliferation, and gene expression were then analyzed. Results Calcium hydroxide stimulated EphB2 gene expression but suppressed ephrinB1 gene expression at the proliferation stage. However, calcium hydroxide stimulated both ephrinB1 and EphB2 gene expression at the differentiation stage. In addition, EphB2 localized at ephrinB1–positive cells at the area of Dentin sialoprotein (DSP) staining, which increased with calcium hydroxide treatment. Knockdown of ephrinB1–EphB2 significantly suppressed cell proliferation. Additionally, knockdown of the ephrinB1 gene caused cell migration, whereas a lack of the EphB2 gene suppressed calcium hydroxide–induced mineralization from primary pulp cells. Conclusions EphrinB1–EphB2 interaction contributes to calcium hydroxide–induced odontogenic/osteogenic differentiation. This observation is the first finding of the mechanism of calcium hydroxide–induced odontogenic/osteogenic differentiation.

Objectives: The aim of this study was to evaluate the epidemiology of tooth wear in Beijing and to establish appropriate preventive measures. Materials and Methods: This cross-sectional analysis involved a questionnaire survey conducted for 1,812 individuals aged 12–74 years in Beijing. Subjects were local residents living in the region for >6 months before the survey. Subjects were evaluated using clinical examinations with the basic erosive wear examination index and a self-administered questionnaire. Statistical analyses were performed using SPSS software. Results: The prevalence of maxillary tooth wear was 84.9% for the molars, 68.9% for the premolars, 74.1% for the canines and 97% for the incisors. In the mandible, the corresponding prevalence rates were 85.2%, 59.3%, 78.6% and 97.4%, respectively. The occlusal, incisal and cervical surfaces showed more frequent wear compared with the other surfaces. Age, acidic beverages, xerostomia and brushing habits were identified as risk factors for tooth wear (P Conclusions: Tooth wear is common in Beijing. Specific preventive measures should be recommended for individuals reporting excessive consumption of fruits and/or acidic beverages, and those with xerostomia. In particular, incisor wear should be carefully monitored in individuals of all age groups.

Background Severe early childhood caries (s-ECC), which has quite high prevalence among children, is a widespread problem with significant impacts among both developing and developed countries. At present, it is widely known that no early detective techniques and diagnostic tests could have high sensitivity and specificity when using for clinical screening of s-ECC. In this study, we had applied magnetic bead (MB)-based matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) to screen distinctive candidate biomarkers of this disease, so as to establish protein profiles and diagnostic models of s-ECC. Methods Firstly, we used the technique mentioned above to detect specifically expressed peptides in saliva samples from ten children with s-ECC, separately at the time point of before, 1 and 4 weeks after dental treatment. Then a diagnostic model for s-ECC was established with the K nearest-neighbour method, which was validated in another six children in the next stage of study. After that, linear ion trap-orbitrap-mass spectrometry (LTQ-Orbitrap-MS) was performed to identify which of the proteins in saliva might be the origination of these peptides. Results We found that seven peptide peaks were significantly different when comparing the three time points, among them two were higher, while other five were lower in the pre-treatment s-ECC group compared with post-treatment. The sensitivity and specificity of the diagnostic model we built were both 83.3 %. Two of these peptides were identified to be segments of histatin-1, which was one important secretory protein in saliva. Conclusions Hereby we confirmed that MB-based MALDI-TOF MS is an effective method for screening distinctive peptides from the saliva of junior patients with s-ECC, and histatin-1 may probably be one important candidate biomarker of this common dental disease. These findings might have bright prospect in future in establishing new diagnostic methods for s-ECC. Electronic supplementary material The online version of this article (doi:10.1186/s12967-016-0996-4) contains supplementary material, which is available to authorized users.

Additional file 1. Detailed information of the participants and differentially expressed peptide peaks before and after caries treatment.

All variables used in six modules designed for capturing key clinical information from treating lung cancer patients. (DOCX 57 kb)

Following genotoxic stress, cells activate a complex kinase-based signaling network to arrest the cell cycle and initiate DNA repair. p53-defective tumor cells rewire their checkpoint response and become dependent on the p38/MK2 pathway for survival after DNA damage, despite a functional ATR-Chk1 pathway. We used functional genetics to dissect the contributions of Chk1 and MK2 to checkpoint control. We show that nuclear Chk1 activity is essential to establish a G(2)/M checkpoint, while cytoplasmic MK2 activity is critical for prolonged checkpoint maintenance through a process of post-transcriptional mRNA stabilization. Following DNA damage, the p38/MK2 complex relocalizes from nucleus to cytoplasm where MK2 phosphorylates hnRNPA0, to stabilize Gadd45 alpha mRNA, while p38 phosphorylates and releases the translational inhibitor TIAR. In addition, MK2 phosphorylates PARN, blocking Gadd45 alpha mRNA degradation. Gadd45a functions within a positive feedback loop, sustaining the MK2-dependent cytoplasmic sequestration of Cdc25B/C to block mitotic entry in the presence of unrepaired DNA damage. Our findings demonstrate a critical role for the MK2 pathway in the posttranscriptional regulation of gene expression as part of the DNA damage response in cancer cells.

For univariate forecasting, there are various statistical models and computational algorithms available. In real-world exercises, too many choices can create difficulties in selecting the most appropriate technique, especially for users lacking sufficient knowledge of forecasting. This study focuses on rule induction for forecasting method selection by understanding the nature of historical forecasting data. A novel approach for selecting a forecasting method for univariate time series based on measurable data characteristics is presented that combines elements of data mining, meta-learning, clustering, classification and statistical measurement. We conducted a large-scale empirical study of over 300 time series using four of the most popular forecasting methods. To provide a rich portrait of the global characteristics of univariate time series, we extracted measures from a comprehensive set of features such as trend, seasonality, periodicity, serial correlation, skewness, kurtosis, nonlinearity, self-similarity, and chaos. Both supervised and unsupervised learning methods are used to learn the relationship between the characteristics of the time series and the forecasting method suitability, providing both recommendation rules, as well as visualizations in the feature space. A derived weighting schema based on the rule induction is also used to improve forecasting accuracy based on combined forecasting models.

In this paper, the variable wind power is incorporated into the dynamic model for long-term stability analysis. A theory-based method is proposed for power systems with wind power to conduct long-term stability analysis, which is able to provide accurate stability assessments with fast simulation speed. Particularly, the theoretical foundation for the proposed approximation approach is presented. The accuracy and efficiency of the method are illustrated by several numerical examples., Comment: The paper has been submitted to IEEE Transactions on Sustainable Energy

Sustained oscillations observed in power systems can damage equipment, degrade the power quality and increase the risks of cascading blackouts. There are several mechanisms that can give rise to oscillations, each requiring different countermeasure to suppress or eliminate the oscillation. This work develops mathematical framework for analysis of sustained oscillations and identifies statistical signatures of each mechanism, based on which a novel oscillation diagnosis method is developed via real-time processing of phasor measurement units (PMUs) data. Case studies show that the proposed method can accurately identify the exact mechanism for sustained oscillation, and meanwhile provide insightful information to locate the oscillation sources., Comment: The paper has been accepted by IEEE Transactions on Power Systems

With the growing importance of time series clustering research, particularly for similarity searches amongst long time series such as those arising in medicine or finance, it is critical for us to find a way to resolve the outstanding problems that make most clustering methods impractical under certain circumstances. When the time series is very long, some clustering algorithms may fail because the very notation of similarity is dubious in high dimension space; many methods cannot handle missing data when the clustering is based on a distance metric. This paper proposes a method for clustering of time series based on their structural characteristics. Unlike other alternatives, this method does not cluster point values using a distance metric, rather it clusters based on global features extracted from the time series. The feature measures are obtained from each individual series and can be fed into arbitrary clustering algorithms, including an unsupervised neural network algorithm, self-organizing map, or hierarchal clustering algorithm. Global measures describing the time series are obtained by applying statistical operations that best capture the underlying characteristics: trend, seasonality, periodicity, serial correlation, skewness, kurtosis, chaos, nonlinearity, and self-similarity. Since the method clusters using extracted global measures, it reduces the dimensionality of the time series and is much less sensitive to missing or noisy data. We further provide a search mechanism to find the best selection from the feature set that should be used as the clustering inputs. The proposed technique has been tested using benchmark time series datasets previously reported for time series clustering and a set of time series datasets with known characteristics. The empirical results show that our approach is able to yield meaningful clusters. The resulting clusters are similar to those produced by other methods, but with some promising and interesting variations that can be intuitively explained with knowledge of the global characteristics of the time series.

Heat shock transcription factor 1 (HSF1) monitors the structural integrity of intracellular proteins and its regulation is essential for the health and longevity of eukaryotic organisms. HSF1 also plays a role in the acute inflammatory response in the negative regulation of cytokine gene transcription. Here we show, for the first time, that HSF1 is regulated by the proinflammatory protein kinase MAPKAP kinase 2 (MK2). We have shown that MK2 directly phosphorylates HSF1 and inhibits activity by decreasing its ability to bind the heat shock elements (HSE) found in the promoters of target genes encoding the HSP molecular chaperones and cytokine genes. We show that activation of HSF1 to bind HSE in hsp promoters is inhibited through the phosphorylation of a specific residue, serine 121 by MK2. A potential mechanism for MK2-induced HSF1 inactivation is suggested by the findings that phosphorylation of serine 121 enhances HSF1 binding to HSP90, a major repressor of HSF1. Dephosphorylation of serine 121 in cells exposed to non-steroidal anti-inflammatory drugs leads to HSP90 dissociation from HSF1, which then forms active DNA binding trimers. These experiments indicate a novel mechanism for the regulation of HSF1 by proinflammatory signaling and may permit HSF1 to respond rapidly to extracellular events, permitting optimal physiological regulation.

Fanconi anemia (FA) is a rare autosomal recessive disease characterized by chromosome instability and cancer predisposition. At least 11 complementation groups for FA have been identified, and eight FA genes have been cloned. Interestingly, the eight known FA proteins cooperate in a common pathway leading to the interaction of monoubiquitinated FANCD2 and BRCA2 in damaged chromatin. Disruption of this pathway results in the clinical and cellular abnormalities common to all FA subtypes. This review will examine the interaction of the cloned FA proteins with each other and with other DNA damage response proteins (i.e., ATM, ATR, and NBS1). Also, somatic (acquired) disruption of the FA pathway in human tumors appears to account for their chromosome instability and crosslinker hypersensitivity.

Fanconi anemia (FA) is an autosomal recessive cancer susceptibility syndrome with at least 11 complementation groups (A, B, C, D1, D2, E, F, G, I, J, and L), and eight FA genes have been cloned. The FANCD1 gene is identical to the breast cancer susceptibility gene, BRCA2. The FA proteins cooperate in a common pathway, but the function of BRCA2/FANCD1 in this pathway remains unknown. Here we show that monoubiquitination of FANCD2, which is activated by DNA damage, is required for targeting of FANCD2 to chromatin, where it interacts with BRCA2. FANCD2-Ub then promotes BRCA2 loading into a chromatin complex. FANCD2(-/-) cells are deficient in the assembly of DNA damage-inducible BRCA2 foci and in chromatin loading of BRCA2. Functional complementation with the FANCD2 cDNA restores BRCA2 foci and its chromatin loading following DNA damage. BRCA2(-/-) cells expressing a carboxy-terminal truncated BRCA2 protein form IR-inducible BRCA2 and FANCD2 foci, but these foci fail to colocalize. Functional complementation of these cells with wild-type BRCA2 restores the interaction of BRCA2 and FANCD2. The C terminus of BRCA2 is therefore required for the functional interaction of BRCA2 and FANCD2 in chromatin. Taken together, our results demonstrate that monoubiquitination of FANCD2, which is regulated by the FA pathway, promotes BRCA2 loading into chromatin complexes. These complexes appear to be required for normal homology-directed DNA repair.

Introduction In this study, we evaluated root and alveolar bone development in unilateral osseous impacted immature maxillary central incisors by cone-beam computed tomography before and after closed-eruption treatment, in comparison with naturally erupted contralateral immature maxillary central incisors. Methods and Results The study included 30 patients, 20 boys and 10 girls, with a mean age of 8.44 ± 1.20 years (range, 6.5-11.2 years). After treatment, the root lengths of both the impacted maxillary central incisors (10.66 ± 2.10 mm) and the contralateral maxillary central incisors (11.04 ± 1.76 mm) were significantly greater than their pretreatment values (6.67 ± 1.94 and 9.02 ± 2.13 mm, respectively). The root canal widths of the incisors decreased significantly after treatment. From the posttreatment cone-beam computed tomography images, the ratio of exposed root length to total root length and the thickness of the alveolar bone at 1 mm under the alveolar crest and at the apex were calculated to evaluate alveolar bone development. Impacted immature maxillary central incisors differed significantly from contralateral immature maxillary central incisors in labial exposed root length, labial ratio to total root length, and lingual alveolar crest. Clinical crown height was higher (statistically but not clinically) for the impacted incisors (9.87 mm) than for the contralateral incisors (9.37 mm). Conclusions Impacted immature incisors grew to the same stage as did erupted contralateral incisors after closed-eruption treatment. Both incisor types had some alveolar bone loss, and thin alveolar bone surrounded the roots.

In this paper, pseudo-transient continuation method has been modified and implemented in power system long-term stability analysis. This method is a middle ground between integration and steady state calculation, thus is a good compromise between accuracy and efficiency. Pseudo-transient continuation method can be applied in the long-term stability model directly to accelerate simulation speed and can also be implemented in the QSS model to overcome numerical difficulties. Numerical examples show that pseudo-transient continuation method can provide correct approximations for the long-term stability model in terms of trajectories and stability assessment., This paper has been accepted by IEEE PES general meeting, 2014

In this paper, a theoretical foundation for the Quasi Steady-State (QSS) model in power system long-term stability analysis is developed. Sufficient conditions under which the QSS model gives accurate approximations of the long-term stability model in terms of trajectory and !-limit set are derived. These sufficient conditions provide some physical insights regarding the reason for the failure of the QSS model. Additionally, several numerical examples are presented to illustrate the analytical results derived., Comment: This paper has been accepted by IEEE Transactions on Circuits and Systems I

Consider of influences of noise in sampling signals comprehensively, a method of fault diagnosis which combines matching pursuit (MP) and biomimetic pattern recognition (BPR) is put forward in this paper. Firstly, the matching pursuit (MP) algorithm is used to select optimum wavelets in different SNR situations from the Laplace wavelet dictionary. Then the feature vector is extracted according to the operation result of waveform MP and super high dimensional detection feature spaces of biomimetic pattern recognition (BPR) are constructed. After that, the real-time detected partial discharge (PD) signals are cut, and the feature for each discharge pulse is extracted respectively, which realized the multiple fault recognition revolutionarily. Simulations show that the robustness and accuracy of fault pattern recognition is improved.

A human face recognition algorithm based on greedy kernel principal component analysis (GKPCA) is presented to meet the requirement of quick face recognition on line. In the algorithm, typical human face are decomposed by fast wavelet transform(FWT), then the greedy algorithm is used to reduce training set and the features of the low frequency sub-images are extracted by kernel principal component analysis(KPCA). Consequently, the features extracted are recognized by support vector machine (SVM). Simulations of the algorithm proposed on the basis of ORL (Olivetti Research Lab) face database and NORL face databases show that the algorithm is capable of reducing training time with high recognition rate.