Dynamic Cascade Research Articles

Dynamic Constraint Analytical Target Cascading Optimization of Steering and Suspension Integrated System

Steering system and suspension system are two important subsystem of automobile chassis, and they both influence the vehicle ride comfort, safety and maneuverability. In order to improve the performance of automobile chassis system, it is of great significance to optimize the integrated system of steering and suspension. In this paper, the dynamic model of steering and suspension integrated system is established, and the evaluation indexes of the integrated system are proposed and deduced, which mainly involves steering energy consumption, steering road feel, steering sensitivity, ride comfort and steering stability. Based on the traditional multi-discipline optimization method of analytical target cascading (ATC), a system-level dynamic constraint analytical target cascading (DCATC) optimization method is proposed and applied to optimize the integrated system. DCATC enhances the information exchange between the subsystem and the total system, and improves the convergence and computation speed of the optimization. The simulation results show that the proposed DCATC can improve the comprehensive performance of the steering and suspension integrated system. Steering energy consumption and steering sensitivity are reduced, the steering road feel and ride comfort are improved effectively.

Computational Causal Modeling of the Dynamic Biomarker Cascade in Alzheimer's Disease.

Background Alzheimer's disease (AD) is a major public health concern, and there is an urgent need to better understand its complex biology and develop effective therapies. AD progression can be tracked in patients through validated imaging and spinal fluid biomarkers of pathology and neuronal loss. We still, however, lack a coherent quantitative model that explains how these biomarkers interact and evolve over time. Such a model could potentially help identify the major drivers of disease in individual patients and simulate response to therapy prior to entry in clinical trials. A current theory of AD biomarker progression, known as the dynamic biomarker cascade model, hypothesizes AD biomarkers evolve in a sequential but temporally overlapping manner. A computational model incorporating assumptions about the underlying biology of this theory and its variations would be useful to test and refine its accuracy with longitudinal biomarker data from clinical trials. Methods We implemented a causal model to simulate time-dependent biomarker data under the descriptive assumptions of the dynamic biomarker cascade theory. We modeled pathologic biomarkers (beta-amyloid and tau), neuronal loss biomarkers, and cognitive impairment as nonlinear first-order ordinary differential equations (ODEs) to include amyloid-dependent and nondependent neurodegenerative cascades. We tested the feasibility of the model by adjusting its parameters to simulate three specific natural history scenarios in early-onset autosomal dominant AD and late-onset AD and determine whether computed biomarker trajectories agreed with current assumptions of AD biomarker progression. We also simulated the effects of antiamyloid therapy in late-onset AD. Results The computational model of early-onset AD demonstrated the initial appearance of amyloid, followed by biomarkers of tau and neurodegeneration and the onset of cognitive decline based on cognitive reserve, as predicted by the prior literature. Similarly, the late-onset AD computational models demonstrated the first appearance of amyloid or nonamyloid-related tauopathy, depending on the magnitude of comorbid pathology, and also closely matched the biomarker cascades predicted by the prior literature. Forward simulation of antiamyloid therapy in symptomatic late-onset AD failed to demonstrate any slowing in progression of cognitive decline, consistent with prior failed clinical trials in symptomatic patients. Conclusions We have developed and computationally implemented a mathematical causal model of the dynamic biomarker cascade theory in AD. We demonstrate the feasibility of this model by simulating biomarker evolution and cognitive decline in early- and late-onset natural history scenarios, as well as in a treatment scenario targeted at core AD pathology. Models resulting from this causal approach can be further developed and refined using patient data from longitudinal biomarker studies and may in the future play a key role in personalizing approaches to treatment.

Resilience or robustness: identifying topological vulnerabilities in rail networks.

Many critical infrastructure systems have network structures and are under stress. Despite their national importance, the complexity of large-scale transport networks means that we do not fully understand their vulnerabilities to cascade failures. The research conducted through this paper examines the interdependent rail networks in Greater London and surrounding commuter area. We focus on the morning commuter hours, where the system is under the most demand stress. There is increasing evidence that the topological shape of the network plays an important role in dynamic cascades. Here, we examine whether the different topological measures of resilience (stability) or robustness (failure) are more appropriate for understanding poor railway performance. The results show that resilience, not robustness, has a strong correlation with the consumer experience statistics. Our results are a way of describing the complexity of cascade dynamics on networks without the involvement of detailed agent-based models, showing that cascade effects are more responsible for poor performance than failures. The network science analysis hints at pathways towards making the network structure more resilient by reducing feedback loops.

Identifying gene expression from single cells to single genes.

Gene regulatory networks reveal how transcription factors contribute to a dynamic cascade of cellular information processing. Recent advances in technologies have enhanced the toolkit for testing GRN mechanisms and connections. Here we emphasize three approaches that we have found important for interrogating transcriptional mechanisms in echinoderms: single cell mRNA sequencing (drop-seq), nascent RNA detection and identification, and chromatin immunoprecipitation (ChIP). We present these applications in order since it is a logical experimental protocol. With preliminary information from bulk mRNA transcriptome analysis and differential gene expression studies (DE-seq), one may need to test in what specific cells important genes may be expressed and to use single cell sequencing to define such links. Nascent RNA analysis with the Click-iT chemistry allows the investigator to deduce when the RNA was transcribed, not just identify its presence, and ChIP allows the investigator to study direct interactions of putative transcriptional regulators with the gene promoter of interest. This flow of thinking, and the technologies to support it, is presented here for echinoderms. While many of the procedures are general and applicable to many organisms and cell types, we emphasize unique aspects of the protocols for consideration in using echinoderm embryos, larvae, and adult tissues.

Formation of synergetic effects in integrated circuits operational amplifiers based on the current dynamic cascades

In this paper, the existing IC developments (Bazes operational amplifier with an automatic set of modal current, current feedback operational amplifier (CFA), a two-channel operational amplifier with an accurate combination of AFC areas of low and high-frequency channels) from the standpoint of synergistic effect is analyzed. New architectural, structural and circuit solutions to improve the accuracy, speed and radiation characteristics with the help of directional formation of synergistic effect (SE) proposed by the authors are also considered. A number of criteria to ensure future product improvement compared to commercially available are presented.

Cascade2vec: Learning Dynamic Cascade Representation by Recurrent Graph Neural Networks

An information dissemination network (i.e., a cascade) with a dynamic graph structure is formed when a novel idea or message spreads from person to person. Predicting the growth of cascades is one of the fundamental problems in social network analysis. Existing deep learning models for cascade prediction are primarily based on recurrent neural networks and representation on random walks or propagation paths. However, these models are not sufficient for learning the deep spatial and temporal features of an entire cascade. Therefore, a new model, called Cascade2vec, is proposed to learn the dynamic graph representation of cascades based on graph recurrent neural networks. To learn more effective graph-level representation of cascades, the current graph neural networks are improved by designing a graph residual block, which shares attention weights between nodes, and by transforming features through perception layers. Furthermore, the proposed graph neural network is integrated into a recurrent neural network to learn the temporal features between graphs. With this method, both the spatial and temporal characteristics of cascades are learned in Cascade2vec. The experimental results show that our method significantly reduces the mean squared logarithmic error and median squared logarithmic error by 16.1% and 12%, respectively, in the cascade prediction at one hour in the Microblog network dataset compared with strong baselines.

Synchronization of oscillations for coupled Van der Pol oscillators by output

A number of automatic control tasks, in particular, the synchronization of trajectories, the tracking task, control by a reference system are associated with the synthesis of control algorithms for dynamic cascade systems, which are a set of interconnected active subsystems. In this paper, the oscillation synchronization problem is considered for two Van der Pol coupled oscillators. It is assumed that the driven subsystem depends on the external control action, in addition, the phase vector is not fully known. On the first step the solution of the problem of synchronization in the form of state feedback is written. The aim of the work is to find the synchronizing control in the form of feedback on the state estimation. Such a formulation is relevant, since for many practical applications of control theory, a typical situation is when the complete state vector of the system is unknown and only some of the functions of the state variables - the outputs of the system are accessible to measurement. One can try to use the control law obtained from feedback by replacing the state with its estimate obtained by observer - a special dynamical system whose state eventually approaches (asymptotically or exponentially) to the state of the original system. In this case a question arises whether such control will be solving the synchronization problem. In mathematical control theory, in particular for the stabilization problem of dynamical systems, similar questions constitute the content of the known principle of separation. For the observation problem solving the apparatus of the method of synthesis of auxiliary invariant relations for constructing a nonlinear observer was used. In accordance with this approach a nonlinear observer is constructed for the system under consideration, which ensures the exponential estimates of the phase vector. It is further shown that the use in the control law instead of the state of the system of its evaluation under simultaneously solving the problems of observation and synchronization leads to the local solution of the problem under consideration.

Simulating the service lifetimes and storage phases of consumer electronics in Europe with a cascade stock and flow model

Durability of products and lifetime extension through improvements in product design, repair, reuse and refurbishment are integral components of the circular economy concept. Extended service lifetimes reduce the demand for new resource intensive products and hence contribute to an increase in resource efficiency and resource conservation. However, the use of a consumer product does not only depend on its functioning. Particularly consumer electronics such as smartphones are often only used for comparatively short periods of time before they are replaced by new devices. Even though these used electronics may still have significant value and could be resold as secondhand products, they often remain in households (compartments, shelves, cellars) for long periods of time. When analyzing the effects of technical improvements for lifetime extensions of consumer products, these storage times, which are also referred to as hibernation, need to be taken into account. Hibernation counteracts efforts towards increasing the service lifetimes of consumer electronics and should therefore more strongly be addressed in the discussion about lifetime extension and higher durability. In this paper, we present a dynamic cascade stock and flow model to simulate different use and storage phases of consumer electronics in Europe. This model enables a detailed analysis of modifications in service lifetimes and storage phases. The simulation results emphasize the significant proportion of unused but functioning electronic devices when regarding overall product stocks in society. Based on the simulation results, we show that particularly for small consumer electronics such as smartphones, the systematic implementation of a cascade use system avoiding storage phases could significantly reduce total product demand and the associated resource use. Subsequently, we discuss potential measures that could lead to a reduction of storage time of unused consumer electronics in typical households.

Molecular Modeling of Cathepsin B protein in different Leishmania strains

Cathepsin B like cysteine proteases representing a major component of the lysosomal proteolytic repertoire plays an important role in intracellular protein degradation. Comparative models of cathepsin B (CatB) protein of six different Leishmania strains were developed using MODELLER. The modeled three-dimensional (3-D) structure has the correct stereochemistry as gauged from the Ramachandran plot and good 3-D structure compatibility as assessed by PROCHECK and the DOPE score (DS2.1, Accelrys). The modeled proteins were energy minimized and validated using standard dynamic cascade protocol (DS 2.1). Seven different disulfide bonding sites are predicted in CatB protein of Leishmania. Two domains were identified and different motifs are present in catB protein of Leishmania like aspargine glycosylation site, protein kinase phosphorylation site, Protein kinase C activation site, N-myristoylation site. Considering that cathepsin B is essential for survival of Leishmania, including for virulence to the mammalian host, it may be viewed as an attractive drug target.
 Keyword: Molecular Modelling, Leishmania, Discover Studio, Protein Binding.

연체채권 재고와 회수 콜 비용 간의 동태적 체인구조 연구

연체채권 재고와 회수 콜 비용 간의 동태적 체인구조 연구

Formula omitted]Hop approach for dynamic influence maximization problem

As many forms of social interactions are transient in nature, that is to say, the social network structure formed by relations between individuals evolves constantly. In practice, exploring influence maximization for product publicity and advertisement marking in dynamic networks is more challenging. Therefore, the research of dynamic influence maximization problem (DIMP) is indispensable. In this paper, we propose an efficient algorithm, T×oneHop approach, to solve the DIMP only by updating the nodes affected by the new adding seed node under the dynamic independent cascade model. It means that the seed set and pseudo-seed set always activate their neighbors within one hop in a signal snapshot graph whose total number is T. Consequently, the transfer process is similar to T×one hops of propagation in the static network. The experiment evaluations with three real social networks show that our method outperforms forward algorithm and weighted degree algorithm. In addition, our method precedes forward algorithm on running time. Finally, we find out that the common seed nodes number increases with the similarity of two consecutive snapshot graphs.

Peer contagion processes for problematic internet use among Chinese college students: A process model involving peer pressure and maladaptive cognition

Although affiliation with deviant peers is among the most salient risk factors for college students' maladjustment, few studies have examined the possible peer contagion processes for problematic Internet use and the underlying mechanisms explaining how peer factors may shape the development of problematic Internet use. Based on data from 2516 Chinese undergraduate students (mean age = 18.56 years, SD = 0.84; 38.16% female) across their four college years and utilizing lagged latent difference score analyses, this study tested a process model to elucidate how peer Internet overuse and peer pressure on Internet use may contribute to Chinese undergraduates' problematic Internet use via shaping their maladaptive cognitions over Internet use. Results indicated that peer Internet overuse predicted subsequent increases in students' peer pressure on Internet use, which in turn, were linked with students' elevated problematic Internet use via maladaptive cognitions over Internet use. This dynamic cascade process was robust even after considering a series of potential confounding variables and the prospective associations among the key variables of interest. Such findings shed light on the peer contagion processes for the development of Chinese college students' problematic Internet use, and provide empirical evidence supporting the call for integrating peer factors into prevention and intervention programs aimed at reducing the incidence and severity of college students’ problematic Internet use.

Cellular dynamics during maturation-related decline of adventitious root formation in forest tree species.

Adventitious root formation is a process in which roots are induced, from determined or differentiated cells that have not been specified to develop a root, at positions where they do not normally occur during development. In forest tree species, a decline in the capacity to form adventitious roots from similar cell types in stem cuttings is associated with tree age and maturity. This decline limits the success of vegetative propagation of selected adult trees. The joint action of local signals and a dynamic cascade of regulatory changes in gene expression, resulting in stereotypical cell division patterns, regulate cell fate changes that enable a somatic differentiated cell to reactivate meristem programs toward the induction of an adventitious root meristem.

Troglitazone and Rosiglitazone profiles in Tox21/Toxcast: Verifying results with toxicology data from published in vivo data and human pharmacovigilance data using an evidence-based approach

Dose-response curves are fundamental tools of in vitro toxicology, extensively employed in toxicant or drug screening. They are expressed by a variety of end-points and assays, measured at different time-points in a choice of cell-lines, but are typically quantified only using the mean concentration for 50% response (e.g. drug efficacy or pathway inhibition) as an indicator of overall effect. However, the response is the result of a complex and dynamic cascade of events which occur between the initial exposure and the measured end-point, and the characteristic rates of the contributing stages govern the dose response and ultimately the measured characteristic concentration. A better understanding of the effects and interdependencies of these can help in interpreting the response curves. The system can be modelled according to a phenomenological rate equation approach, in which each stage of the process is characterised by a rate constant, and causal relationships between different processes are incorporated. The current study utilises such an approach to simulate some common response cascades of cell populations to exogenous agents and explores the dependences of the dose dependent response on, for example, number of steps in a cascade, time-point, and scenarios such as additive, synergistic and antagonistic response of multiple exogenous agents.

Early detection of dynamic harmful cascades in large-scale networks

Quickly detecting harmful cascades in networks can allow us to analyze the causes and prevent further spreading of destructive influence. Since it is often impossible to observe the state of all nodes in a network, a common method is to detect harmful cascades from sparsely placed sensors. However, the harmful cascades are usually dynamic (e.g., the cascade initiators and diffusion trajectories can change over the time), which can severely destroy the robustness of selected sensors. Meanwhile the large scale of current networks greatly increases the time complexity of sensor selection. Motivated by the observation, in this paper we investigate the scalable sensor selection problem for early detection of dynamic harmful cascades in networks. Specifically, we first put forward a dynamic susceptible-infected model to describe harmful cascades, and formally define a detection time minimization (DTM) problem which focuses on effective sensors placement for early detection of dynamic cascades. We prove that it is #P-hard to calculate the objective function exactly and propose two Monte-Carlo methods to estimate it efficiently. We prove the NP-hardness of DTM problem and design a corresponding greedy algorithm. Based on that, we propose an efficient upper bound based greedy (UBG) algorithm with the theoretical performance guarantee reserved. To further meet different types of large-scale networks, we propose two accelerations of UBG: Quickest-Path-UBG for sparse networks and Local-Reduction-UBG for dense networks to improve the time complexity. The experimental results on synthetic and real-world social networks demonstrate the practicality of our approaches.

Linking Language and Cognition in Infancy.

Human language, a signature of our species, derives its power from its links to human cognition. For centuries, scholars have been captivated by this link between language and cognition. In this article, we shift this focus. Adopting a developmental lens, we review recent evidence that sheds light on the origin and developmental unfolding of the link between language and cognition in the first year of life. This evidence, which reveals the joint contributions of infants' innate capacities and their sensitivity to experience, highlights how a precocious link between language and cognition advances infants beyond their initial perceptual and conceptual capacities. The evidence also identifies the conceptual advantages this link brings to human infants. By tracing the emergence of a language-cognition link in infancy, this article reveals a dynamic developmental cascade in infants' first year, with each developmental advance providing a foundation for subsequent advances.

Multiple stages of decay in two-dimensional turbulence

We report measurements of the free decay of turbulence in a quasi-two-dimensional laboratory flow. We observe three clearly distinguished stages of decay, each characterized by an exponential decrease of the kinetic energy with time, but with different decay constants. Using filtering techniques, we identify the physics that controls each stage of decay. The first and most rapid stage is not due to the merger of like-sign vortices as has often been suggested but rather to the rapid relaxation of downscale spectral energy leakage. The second stage is a manifestation of dynamical inverse energy cascade processes and lasts until the separation of scales becomes small. The final stage of decay appears to be dominated by the vertical stratification in our experiment. Our results clarify the dynamical processes at work in decaying two-dimensional turbulence.

ATOH1 Promotes Leptomeningeal Dissemination and Metastasis of Sonic Hedgehog Subgroup Medulloblastomas.

Medulloblastoma arising from the cerebellum is the most common pediatric brain malignancy, with leptomeningeal metastases often present at diagnosis and recurrence associated with poor clinical outcome. In this study, we used mouse medulloblastoma models to explore the relationship of tumor pathophysiology and dysregulated expression of the NOTCH pathway transcription factor ATOH1, which is present in aggressive medulloblastoma subtypes driven by aberrant Sonic Hedgehog/Patched (SHH/PTCH) signaling. In experiments with conditional ATOH1 mouse mutants crossed to Ptch1+/- mice, which develop SHH-driven medulloblastoma, animals with Atoh1 transgene expression developed highly penetrant medulloblastoma at a young age with extensive leptomeningeal disease and metastasis to the spinal cord and brain, resembling xenografts of human SHH medulloblastoma. Metastatic tumors retained abnormal SHH signaling like tumor xenografts. Conversely, ATOH1 expression was detected consistently in recurrent and metastatic SHH medulloblastoma. Chromatin immunoprecipitation sequencing and gene expression profiling identified candidate ATOH1 targets in tumor cells involved in development and tumorigenesis. Among these targets specific to metastatic tumors, there was an enrichment in those implicated in extracellular matrix remodeling activity, cytoskeletal network and interaction with microenvironment, indicating a shift in transcriptomic and epigenomic landscapes during metastasis. Treatment with bone morphogenetic protein or SHH pathway inhibitors decreased tumor cell proliferation and suppressed metastatic tumor growth, respectively. Our work reveals a dynamic ATOH1-driven molecular cascade underlying medulloblastoma metastasis that offers possible therapeutic opportunities. Cancer Res; 77(14); 3766-77. ©2017 AACR.

Dose-dependent T-cell Dynamics and Cytokine Cascade Following rVSV-ZEBOV Immunization

BackgroundThe recent West African Ebola epidemic led to accelerated efforts to test Ebola vaccine candidates. As part of the World Health Organisation-led VSV Ebola Consortium (VEBCON), we performed a phase I clinical trial investigating rVSV-ZEBOV (a recombinant vesicular stomatitis virus-vectored Ebola vaccine), which has recently demonstrated protection from Ebola virus disease (EVD) in phase III clinical trials and is currently in advanced stages of licensing. So far, correlates of immune protection are incompletely understood and the role of cell-mediated immune responses has not been comprehensively investigated to date.Methods: We recruited 30 healthy subjects aged 18–55 into an open-label, dose-escalation phase I trial testing three doses of rVSV-ZEBOV (3×105 plaque-forming units (PFU), 3×106 PFU, 2×107 PFU) (ClinicalTrials.gov; NCT02283099). Main study objectives were safety and immunogenicity, while exploratory objectives included lymphocyte dynamics, cell-mediated immunity and cytokine networks, which were assessed using flow cytometry, ELISpot and LUMINEX assay.Findings: Immunization with rVSV-ZEBOV was well tolerated without serious vaccine-related adverse events. Ebola virus-specific neutralizing antibodies were induced in nearly all individuals. Additionally, vaccinees, particularly within the highest dose cohort, generated Ebola glycoprotein (GP)-specific T cells and initiated a cascade of signaling molecules following stimulation of peripheral blood mononuclear cells with Ebola GP peptides.Interpretation: In addition to a benign safety and robust humoral immunogenicity profile, subjects immunized with 2×107 PFU elicited higher cellular immune responses and stronger interlocked cytokine networks compared to lower dose groups. To our knowledge these data represent the first detailed cell-mediated immuneprofile of a clinical trial testing rVSV-ZEBOV, which is of particular interest in light of its potential upcoming licensure as the first Ebola vaccine.VEBCON trial Hamburg, Germany (NCT02283099).

Dynamic lipopolysaccharide transfer cascade to TLR4/MD2 complex via LBP and CD14

Toll-like receptor 4 (TLR4) together with MD2, one of the key pattern recognition receptors for a pathogen-associated molecular pattern, activates innate immunity by recognizing lipopolysaccharide (LPS) of Gram-negative bacteria. Although LBP and CD14 catalyze LPS transfer to the TLR4/MD2 complex, the detail mechanisms underlying this dynamic LPS transfer remain elusive. Using negative-stain electron microscopy, we visualized the dynamic intermediate complexes during LPS transfer—LBP/LPS micelles and ternary CD14/LBP/LPS micelle complexes. We also reconstituted the entire cascade of LPS transfer to TLR4/MD2 in a total internal reflection fluorescence (TIRF) microscope for a single molecule fluorescence analysis. These analyses reveal longitudinal LBP binding to the surface of LPS micelles and multi-round binding/unbinding of CD14 to single LBP/LPS micelles via key charged residues on LBP and CD14. Finally, we reveal that a single LPS molecule bound to CD14 is transferred to TLR4/MD2 in a TLR4-dependent manner. These discoveries, which clarify the molecular mechanism of dynamic LPS transfer to TLR4/MD2 via LBP and CD14, provide novel insights into the initiation of innate immune responses.