Pole Clustering Research Articles

Strain Accommodation in the Daliangshan Mountain Area, Southeastern Margin of the Tibetan Plateau

AbstractThe Xianshuihe‐Xiaojiang fault system (XXFS) plays a crucial role in accommodating relative motions of the south China block and the expanding Tibetan Plateau. Compared to the narrow southern and northern segments of the XXFS, the central section (also referred to as the Daliangshan mountain area) is broader and dissected by a network of faults. Geologic studies suggest that three major NNW trending faults may dominate slip partitioning of more recent faults in this region. However, due to accessibility issues, sparse geodetic studies have constrained geodetic fault slip rates with contradictory results. In this study, we use 17 new Global Navigation Satellite System sites combined with existing solutions to investigate slip partitioning of the region. We derive a tectonic velocity solution, develop a block kinematic model constrained by Euler pole clustering technique, and calculate geodetic fault slip rates to compare with geologic fault slip rates. We confirm that the Anninghe‐Daliangshan sliver (ADS) is a distinct, rigid block, and we resolve a new, rigid Daliangshan‐Yingjing‐Mabian block (DYB). Kinematic modeling predicts long‐term slip rates that suggest both the ADS and the DYB partition (~10.0–12.0 mm/year) relative motion between the Tibetan Plateau and south China block in the central XXFS, reconciling geologic observations. We suggest that both the continuum and microplate deformation patterns are present in our study area, of which the local tectonics is better explained by the microplate model, but scale of the kinematics makes its motion more consistent with the continuum model.

Aurora A inhibition limits centrosome clustering and promotes mitotic catastrophe in cells with supernumerary centrosomes.

The presence of supernumerary centrosomes is prevalent in cancer, where they promote the formation of transient multipolar mitotic spindles. Active clustering of supernumerary centrosomes enables the formation of a functional bipolar spindle that is competent to complete a bipolar division. Disruption of spindle pole clustering in cancer cells promotes multipolar division and generation of non-proliferative daughter cells with compromised viability. Hence molecular pathways required for spindle pole clustering in cells with supernumerary centrosomes, but dispensable in normal cells, are promising therapeutic targets. Here we demonstrate that Aurora A kinase activity is required for spindle pole clustering in cells with extra centrosomes. While cells with two centrosomes are ultimately able to build a bipolar spindle and proceed through a normal cell division in the presence of Aurora A inhibition, cells with supernumerary centrosomes form multipolar and disorganized spindles that are not competent for chromosome segregation. Instead, following a prolonged mitosis, these cells experience catastrophic divisions that result in grossly aneuploid, and non-proliferative daughter cells. Aurora A inhibition in a panel of Acute Myeloid Leukemia cancer cells has a similarly disparate impact on cells with supernumerary centrosomes, suggesting that centrosome number and spindle polarity may serve as predictive biomarkers for response to therapeutic approaches that target Aurora A kinase function.

Dominant Pole Based Approximation for Discrete Time System

This paper presents an effective procedure for model order reduction of discrete time control system. The exact model derived from complex dynamic systems proves to be very complicated for analysis, control and design. This necessity brings about using a tool known as model order reduction technique or model simplification. A novel mixed method has been implemented in this paper for reducing the order of the large scale dynamic discrete system. Dominant pole based pole clustering method has been used to derive the coefficients of denominator polynomial while Padé approximation has been applied to obtain the coefficients of numerator polynomial of the reduced order model. The proposed method is quite simple and able to generate a stable reduced order model from high order stable discrete systems. The dominancy of poles has been decided by values of the ratio of residue to its pole. The pole is considered dominant which have larger ratio value. An illustrative example has been considered to show the various reduction steps. The result obtained confirms the effectiveness of the approach.

Abstract 1849: Determining the molecular requirements for paclitaxel-induced spindle multipolarity

Abstract Paclitaxel (TaxolTM) and other microtubule poisons are standard chemotherapy agents used in the treatment of breast cancer. However, only a subset of patients experience positive therapeutic effects from these drugs, underscoring the importance of further mechanistic study. Recently we showed that paclitaxel does not accumulate in primary breast cancers at sufficient concentrations to cause mitotic arrest, as it does at typically used concentrations in cell culture. Instead, paclitaxel therapy induces multipolar spindles in breast cancers. Breast cancer cell lines treated with low nanomolar, clinically relevant doses of paclitaxel exhibit chromosome missegregation due to multipolar divisions. We have now extended this to demonstrate that low nanomolar concentrations of other clinically used microtubule poisons (including ixabepilone, vinorelbine, and eribulin) also induce multipolarity in breast cancer cells, suggesting that multipolar mitotic divisions may be a common mechanism of cytotoxicity for microtubule-targeted drugs. Importantly, supernumerary centrosomes are not required for microtubule poison-induced multipolar spindles. To gain mechanistic insight into the formation of paclitaxel-induced multipolar spindles, a variety of mitotic regulators were tested for their ability to affect spindle multipolarity in breast cancer cells treated with clinically relevant concentrations of paclitaxel. Our results demonstrate that the mitotic kinesin Eg5 and the kinase Plk1 promote both the formation and the maintenance of paclitaxel-induced spindle multipolarity, while the mitotic kinesin CENP-E and the kinase Mps1 do not. Interestingly, we found that decreasing the time to anaphase onset decreases the number of multipolar divisions in paclitaxel, resulting in reduced cytotoxicity. Previous studies have shown that Mps1 inhibition sensitizes cells to paclitaxel, and this combination therapy has entered clinical trials (NCT02138812 and NCT02366949). However, our evidence suggests that Mps1 inhibition reduces the efficacy of paclitaxel as a result of decreased time to anaphase onset and fewer multipolar divisions. Additionally, our results suggest that inhibitors of spindle pole clustering increase the number of paclitaxel-induced multipolar divisions and therefore have the potential to increase its efficacy. Identification of the cellular requirements for multipolarity induced by paclitaxel, and of agents that drive the metaphase-to-anaphase transition in the presence of multipolar spindles, may improve the clinical utility of paclitaxel and other microtubule poisons. Citation Format: Christina Scribano. Determining the molecular requirements for paclitaxel-induced spindle multipolarity [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 1849.

Robust pole placement under structural constraints

A new controller synthesis technique is presented which allows the design of output feedback control systems achieving robust regional pole clustering in the presence of parametric uncertainties as well as satisfying prescribed structural constraints. Such features are rarely jointly present in currently available controller synthesis methods. The central idea in the proposed approach consists in reformulating the original robust pole placement problem into an equivalent robust stabilization problem involving highly structured controller and uncertainty. A numerical application corroborating the applicability of the proposed synthesis technique is also presented.

Improved pole clustering-based LTI system reduction using a factor division algorithm

In this paper, a novel technique is proposed to approximate the high-order linear continuous-time and discrete-time systems. The proposed methodology is based on the improved pole clustering (IPC) and factor division algorithm (FDA). The IPC technique is employed to estimate the coefficients of reduced-order denominator polynomial while FDA computes the coefficients of numerator polynomial. The distinctive feature of the proposed scheme is that, the approximants turn out to be always stable, if the original high-order system (HOS) is stable. The efficacy of the proposed scheme is tested by considering six numerical examples from the existing literature. Further, the superiority of the proposed technique over existing techniques of order reduction is verified through performance indices, time and frequency responses.

Reduction of discrete interval system using clustering of poles with Padé approximation: a computer-aided approach

This paper presents a method for the order reduction of discrete interval systems using pole-clustering method along with Padé approximation technique. For a given discrete high-order interval system (HOIS), the denominator of the reduced-order interval model (ROIM) is obtained by pole-clustering while the numerator is obtained by minimizing errors between first r time moments of the HOIS to those of its ROIM where r is the order of ROIM. Inverse distance measure (IDM) criterion is used for clustering of poles of HOIS whereas the time moments of HOIS (or ROIM) are obtained without inverting the denominator of transfer function and presented in generalized form. The error function which is weighted squared sum of errors between first r time moments of HOIS and those of its ROIM is carried out by the algorithm due to Luus-Jaakola. The distinctive feature of this method is that the numerator coefficients are treated free in the process of minimization. A numerical example is given to illustrate the procedure.Keywords: Discrete interval system; Inverse distance measure; Kharitonov polynomials; Model reduction; Padé approximation; Pole-clustering method

A Novel Method Based on Pole Clustering Technique and Differential Evolution for Model Order Reduction

This paper strives to present a model order reduction (MOR) method for complex high order linear time-invariant (LTI) single input-single output (SISO) systems. The recommended method utilises the benefits of pole clustering method and differential evolution algorithm. In this suggested method, approximated denominator polynomial is obtained by pole clustering method whereas; approximated numerator is obtained using differential evolution algorithm. To indicate the effectiveness of the suggested method over existing MOR techniques, a comparison on the basis of a performance index known as integral square error (ISE) is depicted in this paper by using simulation graphs and in tabular form. Further, the suggested method is extended for MIMO systems also. Numerical examples are solved to give better understanding of the propound technique for SISO and MIMO systems. The recommended method derives and guarantees a stable approximated ROM if the original higher order system (HOS) is stable.

Uncertain Saturated Discrete‐Time Sliding Mode Control for A Wind Turbine Using A Two‐Mass Model

AbstractThe aim of this paper is to propose a new design variable speed wind turbine control by discrete‐time sliding mode approach. The control objective is to obtain a maximum extraction of wind energy, while reducing mechanical loads and rotor speed tracking combined with an electromagnetic torque. For this application, we designed a discrete time sliding mode control using the equivalent discrete time reaching law. Furthermore, a systematic and improved design procedure for uncertainties discrete‐time sliding mode control (SMC) with saturation problem is provided in this paper. The saturation constraint is reported on inputs vector. LMI technique and polytopic models are used in the design of the switching surface. To achieve some performance requirements and good robustness, in the sliding mode, the pole clustering method is investigated. Based on the unit vector control approach, a robust control is developed, then, to direct and maintain the system states onto the sliding manifold in finite time. Finally, a systematic design procedure for DSMC required to achieve a given performance level is provided and its effectiveness is varied by applying it to variable speed wind turbine systems.

Precocious centriole disengagement and centrosome fragmentation induced by mitotic delay

The spindle assembly checkpoint (SAC) delays mitotic progression until all sister chromatid pairs achieve bi-orientation, and while the SAC can maintain mitotic arrest for extended periods, moderate delays in mitotic progression have significant effects on the resulting daughter cells. Here we show that when retinal-pigmented epithelial (RPE1) cells experience mitotic delay, there is a time-dependent increase in centrosome fragmentation and centriole disengagement. While most cells with disengaged centrioles maintain spindle bipolarity, clustering of disengaged centrioles requires the kinesin-14, HSET. Centrosome fragmentation and precocious centriole disengagement depend on separase and anaphase-promoting complex/cyclosome (APC/C) activity, which also triggers the acquisition of distal appendage markers on daughter centrioles and the loss of procentriolar markers. Together, these results suggest that moderate delays in mitotic progression trigger the initiation of centriole licensing through centriole disengagement, at which point the ability to maintain spindle bipolarity becomes a function of HSET-mediated spindle pole clustering.

Multivariable Systems Model Reduction Based on the Dominant Modes and Genetic Algorithm

The aim of this letter is the construction of a new model order reduction algorithm generalized to the multi-input/multi-output systems model order reduction. It is essentially based on the complete order model dominant modes retention. This involves the conversion of the overall significant information contained in the original complete order system into the reduced order approximant, permitting the construction of our approximant denominator. The approximant numerator is computed by means of genetic algorithm (GA) tools and a square error criterion. As a result, an optimal approximant of lower order is derived. To show its performances, to highlight some important of its characteristics, and to conclude to its efficiency, a comparative study is carried out. A numerical example is given, where our approximant model is compared to reduced order models computed from two recent and important techniques based on GA tools, namely, the stability equation and the modified pole clustering techniques.

A New Technique For Reduced-Order Modelling of Linear Time-Invariant System

ABSTRACTIn this paper, a new technique for order reduction of linear time-invariant systems is presented. This technique is intended for both single-input single-output (SISO) and multi-input multi-output (MIMO) systems. Motivated by other reduction techniques, the new proposed reduction technique is based on modified pole clustering and factor division algorithm with the objective of obtaining a stable reduced-order system preserving all essential properties of the original system. The new technique is illustrated by three numerical examples which are considered from the literature. To evaluate the superiority and robustness of the new technique, the results of the proposed technique are compared with other well-known and recently developed order-reduction techniques like Routh approximation and Big Bang-Big Crunch algorithm. The comparison of performance indices shows the efficiency and powerfulness of the new technique.

Automatic design and synthesis of control for a plug and play active vibration control module

In this paper, a technique for automatic robust control design and synthesis for plug and play active vibration control module is presented. Robust control theory offers the uncertainty analysis and graphical manipulation of the frequency response as well as analytical solution approach. The prior knowledge of the plant model imposes limitations on the fast and effective implementation of the robust control. Moreover, the design of the weight functions for the robust control is usually a trial and error process. The plant identification and subsequent control design becomes even more tedious for modular devices with plug and play capability. In the present paper, the plant model is identified by using polyreference least square complex frequency estimator and an innovative automatic pole clustering algorithm. The loop shaping robust control is designed, where the parameters of the weight functions are optimised using genetic algorithm. An experimental evaluation is also presented on a prototype modular structure.

Extended approaches based on Genetic Algorithms (GAs) for MIMO singular systems approximation

In this paper we present an extension of three important model reduction techniques: namely, the stability equation, the modified pole clustering and the dominant modes methods for conventional (regular) systems to reduce complexity relating to high dimensionality of mathematical models representing physical, generalized (also called singular) systems. Combining these methods to Genetic Algorithms’ tools and exploiting a special representation base where a full order singular system is deflating into proper and improper subsystems, different natures of stable, optimal low order models are obtained. To show the effectiveness of the proposed algorithms, a numerical example is given, where six approximants are derived from a multi-input multi-output singular system. By the use of two optimal norms, the MOR errors are quantified and permits to conclude to the quality of the proposed reduced order models.

Association between differential gene expression and body mass index among endometrial cancers from The Cancer Genome Atlas Project

ObjectiveThe Cancer Genome Atlas (TCGA) identified four integrated clusters for endometrial cancer (EC): POLE, MSI, CNL and CNH. We evaluated differences in gene expression profiles of obese and non-obese women with EC and examined the association of body mass index (BMI) within the clusters identified in TCGA. MethodsTCGA RNAseq data was used to identify genes related to increasing BMI among ECs. The POLE, MSI and CNL clusters were composed mostly of endometrioid EC. Patient BMI was compared between these three clusters with one-way ANOVA. Association between gene expression and BMI was also assessed while adjusting for confounding effects of potential confounding factors. p-Values testing the association between gene expression and BMI were adjusted for multiple hypothesis testing over the 20,531 genes considered. ResultsMean BMI was statistically different between the ECs in the CNL (35.8) versus POLE (29.8) cluster (p=0.006) and approached significance for the MSI (33.0) versus CNL (35.8) cluster (p=0.05). 181 genes were significantly up- or down-regulated with increasing BMI in endometrioid EC (q-value<0.01), including LPL, IRS-1, IGFBP4, IGFBP7 and the progesterone receptor. DAVID functional annotation analysis revealed significant enrichment in “cell cycle” (adjusted p-value=1.5E−5) and “DNA metabolic processes” (adjusted p-value=1E−3) for the identified genes. ConclusionsObesity related genes were found to be upregulated with increasing BMI among endometrioid ECs. Patients with POLE tumors have the lowest median BMI when compared to MSI and CNL. Given the heterogeneity among endometrioid EC, consideration should be given to abandoning the Type I and II classification of EC tumors.

Optimal approximants for MIMO model reduction systems using genetic algorithms

Several analytical models reduction techniques have been proposed in literature to reduce complexity relating to high dimensionality of mathematical models representing physical systems. Genetic algorithm (GA) has proved to be an excellent optimisation tool in the past few years. Throughout this work, we built three different algorithms namely stability equation, Mihailov criterion, and the modified pole clustering techniques, which solve the multivariable model reduction problems and permit to obtain globally optimised nominal models. The aim of this paper is to highlight the efficiency and the performance of these tools over the existing conventional computing techniques.

Erroneous Silencing of the Mitotic Checkpoint by Aberrant Spindle Pole-Kinetochore Coordination

To segregate chromosomes during cell division, microtubules that form the bipolar spindle attach to and pull on paired chromosome kinetochores. The spindle assembly checkpoint (SAC) is activated at unattached and misattached kinetochores to prevent further mitotic progression. The SAC is silenced after all the kinetochores establish proper and stable attachment to the spindle. Robust timing of SAC silencing after the last kinetochore-spindle attachment herein dictates the fidelity of chromosome segregation. Chromosome missegregation is rare in typical somatic cell mitosis, but frequent in cancer cell mitosis and in meiosis I of mammalian oocytes. In the latter cases, SAC is normally activated in response to disruptions of kinetochore-spindle attachments, suggesting that frequent chromosome missegregation ensues from faulty SAC silencing. In-depth understanding of how SAC silencing malfunctions in these cases is yet missing, but is believed to hold promise for treatment of cancer and prevention of human miscarriage and birth defects. We previously established a spatiotemporal model that, to the best of our knowledge, explained the robustness of SAC silencing in normal mitosis for the first time. In this article, we take advantage of the whole-cell perspective of the spatiotemporal model to identify possible causes of chromosome missegregation out of the distinct features of spindle assembly exhibited by cancer cells and mammalian oocytes. The model results explain why multipolar spindle could inhibit SAC silencing and spindle pole clustering could promote it—albeit accompanied by more kinetochore attachment errors. The model also eliminates geometric factors as the cause for nonrobust SAC silencing in oocyte meiosis, and instead, suggests atypical kinetochore-spindle attachment in meiosis as a potential culprit. Overall, the model shows that abnormal spindle-pole formation and its aberrant coordination with atypical kinetochore-spindle attachments could compromise the robustness of SAC silencing. Our model highlights systems-level coupling between kinetochore-spindle attachment and spindle-pole formation in SAC silencing.

Biased reduction method by combining improved modified pole clustering and improved Pade approximations

This paper presents a mixed method to reduce the order of the linear high order dynamic systems by combining improved modified pole clustering technique and improved Pade approximations. The denominator of the reduced order model is computed by improved modified pole clustering while the numerator coefficients are obtained by improved Pade approximations. The proposed method is competent in generating ‘k’ number of reduced order models from the original high order system. The superiority of the proposed method is concluded through numerical examples taken from literature and compared with existing well- known order reduction methods.

Robust eigenstructure clustering by non-smooth optimisation

We extend classical eigenstructure assignment to more realistic problems, where additional performance and robustness specifications arise. Our aim is to combine time-domain constraints, as reflected by pole location and eigenvector structure, with frequency-domain objectives such as the H2, H∞ or Hankel norms. Using pole clustering, we allow poles to move in polydisks of prescribed size around their nominal values, driven by optimisation. Eigenelements, that is poles and eigenvectors, are allowed to move simultaneously and serve as decision variables in a specialised non-smooth optimisation technique. Two aerospace applications illustrate the power of the new method.

System Reduction Using Modified Pole Clustering and Modified Cauer Continued Fraction

System Reduction Using Modified Pole Clustering and Modified Cauer Continued Fraction