McSNAC:A software to approximate first-order signaling networks from mass cytometry data

Background:Mass cytometry(CyTOF)gives unprecedented opportunity to simultaneously measure up to 40 proteins in single cells,with a theoretical potential to reach 100 proteins.This high-dimensional single-cell information can be very useful in dissecting mechanisms of cellular activity.In particular,measuring abundances of signaling proteins like phospho-proteins can provide detailed information on the dynamics of single-cell signaling processes.However,computational analysis is required to reconstruct such networks with a mechanistic model.Methods:We propose our Mass cytometry Signaling Network Analysis Code(McSNAC),a new software capable of reconstructing signaling networks and estimating their kinetic parameters from CyTOF data.McSNAC approximates signaling networks as a network of first-order reactions between proteins.This assumption often breaks down as signaling reactions can involve binding and unbinding,enzymatic reactions,and other nonlinear constructions.Furthermore,McSNAC may be limited to approximating indirect interactions between protein species,as cytometry experiments are only able to assay a small fraction of protein species involved in signaling.Results:We carry out a series of in silico experiments here to show(1)McSNAC is capable of accurately estimating the ground-truth model in a scalable manner when given data originating from a first-order system;(2)McSNAC is capable of qualitatively predicting outcomes to perturbations of species abundances in simple second-order reaction models and in a complex in silico nonlinear signaling network in which some proteins are unmeasured.Conclusions:These findings demonstrate that McSNAC can be a valuable screening tool for generating models of signaling networks from time-stamped CyTOF data.

Interleukin-7 Receptor Signaling Network:An Integrated Systems Perspective

Interleukin-7 （IL-7） is an essential cytokine for the development and homeostatic maintenance of T and B lymphocytes. Binding of IL-7 to its cognate receptor, the IL-7 receptor （IL-7R）, activates multiple pathways that regulate lymphocyte survival, glucose uptake, proliferation and differentiation. There has been much interest in understanding how IL-7 receptor signaling is modulated at multiple interconnected network levels. This review examines how the strength of the signal through the IL-7 receptor is modulated in T and B cells, including the use of shared receptor components, signaling crosstalk, shared interaction domains, feedback loops, integrated gene regulation, multimerization and ligand competition. We discuss how these network control mechanisms could integrate to govern the properties of IL-7R signaling in lymphocytes in health and disease. Analysis of IL-7 receptor signaling at a network level in a systematic manner will allow for a comprehensive approach to understanding the impact of multiple signaling pathways on lymphocyte biology. Cellular ＆ Molecular Immunology.

Molecular Mechanisms Regulating Rapid Stress Signaling Networks in Arabidopsis

As sessile organisms plants must ronmental conditions. To survive cope with ever changing enviplants have evolved elaborate mechanisms to perceive and rapidly respond to a diverse range of abiotic and biotic stresses. Central to this response is the ability to modulate gene expression at both the transcriptional and posttranscriptional levels. This review will focus on recent progress that has been made towards understanding the rapid reprogramming of the transcriptome that occurs in response to stress as well as emerging mechanisms underpinning the reprogramming of gene expression in response to stress,

Attractor landscape analysis of the cardiac signaling network reveals mechanism-based therapeutic strategies for heart failure

Apoptosis and hypertrophy of cardiomyocytes are the primary causes of heart failure （HF）, a global leading cause of death, and are regulated through the complicated intracellular signaling network, limiting the development of effective treatments due to its complexity. To identify effective therapeutic strategies for HF at a system level, we develop a large-scale comprehensive mathematical model of the cardiac signaling network by integrating all available experimental evidence. Attractor landscape analysis of the network model identifies distinct sets of control nodes that effectively suppress apoptosis and hypertrophy of cardiomyocytes under ischemic or pressure overload-induced HF, the two major types of HF. Intriguingly, our system-level analysis suggests that intervention of these control nodes may increase the efficacy of clinical drugs for HF and, of most importance, different combinations of control nodes are suggested as potentially effective candidate drug targets depending on the types of HF. Our study provides a systematic way of developing mechanism-based therapeutic strategies for HF.

Targeting an oncogenic kinase/phosphatase signaling network for cancer therapy

Protein kinases and phosphatases signal by phosphorylation and dephosphorylation to precisely control the activities of their individual and common substrates for a coordinated cellular outcome. In many situations, a kinase/phosphatase complex signals dynamically in time and space through their reciprocal regulations and their cooperative actions on a substrate. This complex may be essential for malignant transformation and progression and can therefore be considered as a target for therapeutic intervention. p38γ is a unique MAPK family member that contains a PDZ motif at its C-terminus and interacts with a PDZ domain-containing protein tyrosine phosphatase PTPH1. This PDZcoupled binding is required for both PTPH1 dephosphorylation and inactivation of p38γ and for p38γ phosphorylation and activation of PTPH1. Moreover, the p38γ/PTPH1 complex can further regulate their substrates phosphorylation and dephosphorylation, which impacts Ras transformation, malignant growth and progression, and therapeutic response. This review will use the p38γ/PTPH1 signaling network as an example to discuss the potential of targeting the kinase/phosphatase signaling complex for development of novel targeted cancer therapy.

Optimization of Global Signal Networks for Island-Style FPGAs

We present a staggered buffer connection method that provides flexibility for buffer insertion while designing global signal networks using the tile-based FPGA design methodology. An exhaustive algorithm is used to analyze the trade-off between area and speed of the global signal networks for this staggered buffer insertion scheme, and the criterion for determining the design parameters is presented. The comparative analytic result shows that the methods in this paper are proven to be more efficient for FPGAs with a large array size.

Network Sorting Algorithm of Multi-Frequency Signal with Adaptive SNR

An signal noise ratio（ SNR） adaptive sorting algorithm using the time-frequency（ TF）sparsity of frequency-hopping（ FH） signal is proposed in this paper. Firstly,the Gabor transformation is used as TF transformation in the system and a sorting model is established under undetermined condition; then the SNR adaptive pivot threshold setting method is used to find the TF single source. The mixed matrix is estimated according to the TF matrix of single source. Lastly,signal sorting is realized through improved subspace projection combined with relative power deviation of source. Theoretical analysis and simulation results showthat this algorithm has good effectiveness and performance.

Robust signal recognition algorithm based on machine learning in heterogeneous networks

There are various heterogeneous networks for terminals to deliver a better quality of service. Signal system recognition and classification contribute a lot to the process. However, in low signal to noise ratio（SNR） circumstances or under time-varying multipath channels, the majority of the existing algorithms for signal recognition are already facing limitations. In this series, we present a robust signal recognition method based upon the original and latest updated version of the extreme learning machine（ELM） to help users to switch between networks. The ELM utilizes signal characteristics to distinguish systems. The superiority of this algorithm lies in the random choices of hidden nodes and in the fact that it determines the output weights analytically, which result in lower complexity. Theoretically, the algorithm tends to offer a good generalization performance at an extremely fast speed of learning. Moreover, we implement the GSM/WCDMA/LTE models in the Matlab environment by using the Simulink tools. The simulations reveal that the signals can be recognized successfully to achieve a 95% accuracy in a low SNR（0 dB） environment in the time-varying multipath Rayleigh fading channel.

AUTOSIM:Automated Urban Traffic Operation Simulation via Meta-Learning

Online traffic simulation that feeds from online information to simulate vehicle movement in real-time has recently seen substantial advancement in the development of intelligent transportation systems and urban traffic management.It has been a challenging problem due to three aspects:1)The diversity of traffic patterns due to heterogeneous layouts of urban intersections;2)The nature of complex spatiotemporal correlations;3)The requirement of dynamically adjusting the parameters of traffic models in a real-time system.To cater to these challenges,this paper proposes an online traffic simulation framework called automated urban traffic operation simulation via meta-learning(AUTOSIM).In particular,simulation models with various intersection layouts are automatically generated using an open-source simulation tool based on static traffic geometry attributes.Through a meta-learning technique,AUTOSIM enables an automated learning process for dynamic model settings of traffic scenarios featured with different spatiotemporal correlations.Besides,AUTOSIM is capable of adapting traffic model parameters according to dynamic traffic information in real-time by using a meta-learner.Through computational experiments,we demonstrate the effectiveness of the meta-learningbased framework that is capable of providing reliable supports to real-time traffic simulation and dynamic traffic operations.

Differential signaling regulatory networks governing hormone refractory prostate cancers

To understand the organization of the biological networks that might potentially govern the pathogenesis of hormone refractory prostate cancer （HRPC）, we investigated the transcriptional circuitry and signaling in androgen-dependent 22Rvl and MDA PCa 2b cells, androgen- and estrogen-dependent LNCaP cells, and androgen-independent DU 145 and PC-3 prostate cancer （PCa） cell lines. We used microarray analyses, quantitative real-time PCR, pathway prediction analyses, and determination of Transcription Factor Binding Site （TFBS） signatures to dissect HRPC regulatory networks. We generated graphical representations of global topology and local network motifs that might be important in prostate carcinogenesis. Many important putative biomarker ＇target hubs＇ were identified in the current study including AP-1, NF-KB, EGFR, ERK1/2, JNK, p38 MAPK, TGF beta, VEGF, PDGF, CD44, Akt, PI3K, NOTCH1, CASP1, MMP2 and AR. Our results suggest that complex cellular events including autoregulation, feedback loops and cross-talk might govern progression from early lesion to clinically diagnosed PCa, as well as metastatic potential of pre-existent high-grade prostate intraepithelial neoplasia （HG-PIN） and/or advancement to HRPC. The identification of TFBS signatures for TCF/LEF, SOX9 and ELK1 in the regulatory elements suggests additional biomarkers for the potential development of chemopreventive/therapeutic strategies against PCa. Taken together, in this study, we have identified putative biomarker ＇target hubs＇ in the architecture of PCa signaling networks, and investigated TFBS signatures that might enhance our understanding of key regulatory nodes in the progression and pathogenesis of HRPC.

Cell type specificity of signaling:view from membrane receptors distribution and their downstream transduction networks

Studies on cell signaling pay more attention to spatial dynamics and how such diverse organization can relate to high order of cellular capabilities.To overview the specificity of cell signaling,we integrated human receptome data with proteome spatial expression profiles to systematically investigate the specificity of receptors and receptor-triggered transduction networks across 62 normal cell types and 14 cancer types.Six percent receptors showed cell-type-specific expression,and 4% signaling networks presented enriched cell-specific proteins induced by the receptors.We introduced a concept of“response context”to annotate the cell-type dependent signaling networks.We found that most cells respond similarly to the same stimulus,as the“response contexts”presented high functional similarity.Despite this,the subtle spatial diversity can be observed from the difference in network architectures.The architecture of the signaling networks in nerve cells displayed less completeness than that in glandular cells,which indicated cellular-context dependent signaling patterns are elaborately spatially organized.Likewise,in cancer cells most signaling networks were generally dysfunctional and less complete than that in normal cells.However,glioma emerged hyper-activated transduction mechanism in malignant state.Receptor ATP6AP2 and TNFRSF21 induced rennin-angiotensin and apoptosis signaling were found likely to explain the glioma-specific mechanism.This work represents an effort to decipher context-specific signaling network from spatial dimension.Our results indicated that although a majority of cells engage general signaling response with subtle differences,the spatial dynamics of cell signaling can not only deepen our insights into different signaling mechanisms,but also help understand cell signaling in disease.

Decomposition of dynamic transcriptomic responses during effector-triggered immunity reveals conserved responses in two distinct plant cell populations

Rapid plant immune responses in the appropriate cells are needed for effective defense against pathogens.Although transcriptome analysis is often used to describe overall immune responses,collection of transcriptome data with sufficient resolution in both space and time is challenging.We reanalyzed public Arabidopsis time-course transcriptome data obtained after low-dose inoculation with a Pseudomonas syringae strain expressing the effector AvrRpt2,which induces effector-triggered immunity in Arabidopsis.Double-peak time-course patterns are prevalent among thousands of upregulated genes.We implemented a multicompartment modeling approach to decompose the double-peak pattern into two single-peak patterns for each gene.The decomposed peaks reveal an“echoing”pattern:the peak times of the first and second peaks correlate well across most upregulated genes.We demonstrated that the two peaks likely represent responses of two distinct cell populations that respond either cell autonomously or indirectly to AvrRpt2.Thus,the peak decomposition has extracted spatial information from the time-course data.The echoing pattern also indicates a conserved transcriptome response with different initiation times between the two cell populations despite different elicitor types.A gene set highly overlapping with the conserved gene set is also upregulated with similar kinetics during pattern-triggered immunity.Activation of a WRKY network via different entry-point WRKYs can explain the similar but not identical transcriptome responses elicited by different elicitor types.We discuss potential benefits of the properties of the WRKY activation network as an immune signaling network in light of pressure from rapidly evolving pathogens.

Online estimation and characteristic analysis of double nugget diameters during aluminum/steel resistance spot welding process

Online estimation of the double nugget diameters was performed by means of a back propagation neural network.The double nugget diameters were obtained using actual welding experiment and numerical simulation,according to different characteristics of aluminum nugget and steel nugget.The input of the neural network was some key characteristic parameters extracted from dynamic power signal,which were peak point,knee point and their variation rate over time,as well as heat energy delivered into the welding system.The architecture of the neural network was confirmed by confirming the number of neurons in hidden layer through a series of calculations.The key parameters of the neural network were obtained by means of training 81 arrays of data set.Then,the neural network was used to test the remaining 20 arrays of verifying data set,and the results showed that both of the mean errors for the two nugget diameters were below 3%.In addition,corresponding analyses showed that the accuracy of two nugget diameters was higher than that of tensile-shear strength.

Network-based identification of feedback modules that control RhoA activity and cell migration

Cancer cell migration enables metastatic spread causing most cancer deaths.Rho-family GTPases control cell migration,but being embedded in a highly interconnected feedback network,the control of their dynamical behavior during cell migration remains elusive.To address this question,wereconstructed the Rho-family GTPases signaling network involved in cell migration,and developed a Boolean network model to analyze the different states and emergent rewiring of the Rho-family GTPases signaling network at protrusions and during extracellular matrix-dependent cell migration.Extensive simulations and experimental validations revealed that the bursts of RhoA activity induced at protrusions by EGFare regulated by a negative-feedback module composed of Src,FAK,and CSK.Interestingly,perturbing this module interfered with cyclic Rho activation and extracellular matrix-dependent migration,suggesting that CSK inhibition can be a novel and effective intervention strategy for blocking extracellular matrix-dependent cancer cell migration,while Src inhibition might fail,depending on the genetic background of cells.Thus,this study provides new insights into the mechanisms that regulate the intricate activation states of Rho-family GTPases during extracellular matrix-dependent migration,revealing potential new targets for interfering with extracellular matrix-dependent cancer cell migration.

Plant Rho GTPase signaling promotes autophagy

The roles of Rho family guanosine triphosphatases(GTPases)of plants(ROPs)in modulating plant growth and development have been well characterized.However,little is known about the roles of ROP signaling pathways in regulating plant autophagy and autophagosome formation.In this study,we identify a unique ROP signaling mechanism,which mediates developmental to autophagic transition under stress conditions in the model plant Arabidopsis.Loss-of-function mutants of ROP8 showed stress-induced hypersensitive phenotypes and compromised autophagic flux.Similar to other ROPs in the ROP/RAC family,ROP8 exhibits both plasma membrane and cytosolic punctate localization patterns.Upon autophagic induction,active ROP8 puncta colocalize with autophagosomal markers and are degraded inside the vacuole.In human cells,RalB,an RAS subfamily GTPase,engages its effector Exo84 for autophagosome assembly.However,a RalB counterpart is missing in the plant lineage.Intriguingly,we discovered that plant ROP8 promotes autophagy via its downstream effector Sec5.Live-cell super-resolution imaging showed that ROP8 and Sec5 reside on phagophores for autophagosome formation.Taken together,our findings highlight a previously unappreciated role of an ROP8-Sec5 signaling axis in autophagy promotion,providing new insights into how plants utilize versatile ROP signaling networks to coordinate developmental and autophagic responses depending on environmental changes.

Molecular signal networks and regulating mechanisms of the unfolded protein response

Within the cell, several mechanisms exist to maintain homeostasis of the endoplasmic reticulum （ER）. One of the primary mechanisms is the unfolded protein response （UPR）. In this review, we primarily focus on the latest signal webs and regulation mechanisms of the UPR. The relationships among ER stress, apoptosis, and cancer are also discussed. Under the normal state, binding immunoglobulin protein （BiP） interacts with the three sensors （protein kinase RNA-like ER kinase （PERK）, activating transcription factor 6 （ATF6）, and inositol-requiring enzyme la （IREla）） Under ER stress, misfolded proteins interact with BiP, resulting in the release of BiP from the sensors. Subsequently, the three sensors dimerize and autophosphorylate to promote the signal cascades of ER stress. ER stress includes a series of positive and negative feedback signals, such as those regulating the stabilization of the sensors/BiP complex, activating and inactivating the sensors by autophosphorylation and dephosphorylation, activating specific transcription factors to enable selective transcription, and augmenting the ability to refold and export. Apart from the three basic pathways, vascular endothelial growth factor （VEGF）-VEGF receptor （VEGFR）-phospholipase C-~ （PLCy）-mammalian target of rapamycin complex 1 （mTORC1） pathway, induced only in solid tumors, can also activate ATF6 and PERK signal cascades, and IREla also can be activated by activated RAC-alpha serine/threonine-protein kinase （AKT）. A moderate UPR functions as a pro-survival signal to return the cell to its state of homeostasis. However, persistent ER stress will induce cells to undergo apoptosis in response to increasing reactive oxygen species （ROS）, Ca2＋ in the cytoplasmic matrix, and other apoptosis signal cascades, such as c-Jun N-terminal kinase （JNK）, signal transducer and activator of transcription 3 （STAT3）, and P38, when cellular damage exceeds the capacity of this adaptive response.

Comfortable Lifestyle-induced Imbalance of Neuro-endocrine-immunity Network:A Possible Mechanism of Vascular Endothelial Dysfunction

Objective： To observe the changes of vascular endothelial functions and general neuroendocrine-immunity （NEI） network under the state of qi-deficiency syndrome induced by excessive idleness and to approach their internal relevance and illuminate initially the pathophysiological mechanism of vascular lesion induced by excessive idleness. Methods： A total of 100 male Wistar rats were randomly divided into the control group and the qi-deficiency syndrome model group, 50 rats in each group. The qi-deficiency syndrome model was established by feeding the animals with hyper-alimentation diet in combination with restricting movement for 10 weeks. Changes of common chemical signal molecules related to NEI and vascular endothelial functions were measured by the end of the experiment. Furthermore, their internal relevance was analyzed by the method of canonical correlation analysis. Results： The vascular endothelial structure and function were obviously injured in the model group. Compared with the control group, the chemical signal molecules, such as 5-hydroxytryptamine （5-HT）, corticosterone （CORT）, triiodothyronine （T3）, tetraiodothyronine （T4）, angiotensin Ⅱ （Ang Ⅱ）, interleukin-1 （IL-1）, and tumor necrosis factor-α （TNF-α） in peripheral blood of the model group （n=43） were changed significantly （P〈0.05 or P〈0.01）. Canonical correlation analysis showed that vascular endothelial dysfunction was correlated to the changes of these signal molecules in the NEI network. Conclusions： Comfortbased lifestyle induced not only vascular endothelial dysfunction but also an imbalance of the NEI network. Vascular endothelial dysfunction and the imbalanced NEI network interacted with each other, and an imbalance of the NEI network may be the pathophysiologic basis for the genesis and development of vascular endothelial dysfunction, even diseases of the blood vessel.

Multipacket Reception in Wireless Ad Hoc Networks Based on CDMA and Polynomial Phase-modulating Sequences

Based on the polynomial phase-modulating sequences algorithm, this paper presents two schemes for the application of CDMA with polynomial phase signals to improve the signal separation performance. Simulation results illustrate the proposed approach have 1～3?dB improvement about signal-to-interference and noise ratio in most environment, compared with the PPS algorithm.

Genetic improvement toward nitrogen-use efficiency in rice: Lessons and perspectives

The indispensable role of nitrogen fertilizer in ensuring world food security together with the severe threats it poses to the ecosystem makes the usage of nitrogen fertilizer a major challenge for sustainable agriculture.Genetic improvement of crops with high nitrogen-use efficiency(NUE)is one of the most feasible solutions for tackling this challenge.In the last two decades,extensive efforts toward dissecting the variation of NUE-related traits and the underlying genetic basis in different germplasms have been made,and a series of achievements have been obtained in crops,especially in rice.Here,we summarize the approaches used for genetic dissection of NUE and the functions of the causal genes in modulating NUE as well as their applications in NUE improvement in rice.Strategies for exploring the variants controlling NUE and breeding future crops with“less-input-more-output”for sustainable agriculture are also proposed.

The advantages of crosstalk during the evolution of the BZR1–ARF6–PIF4 (BAP) module∞

The BAP module,comprising BRASSINAZOLE RESISTANT 1(BZR1),AUXIN RESPONSE FACTOR 6(ARF6),and PHYTOCHROME‐INTERACTING FACTOR 4(PIF4),functions as a molecular hub to orchestrate plant growth and development.In Arabidopsis thaliana,components of the BAP module physically interact to form a complex system that integrates light,brassinosteroid(BR),and auxin signals.Little is known about the origin and evolution of the BAP module.Here,we conducted comparative genomic and transcriptomic analyses to investigate the evolution and functional diversification of the BAP module.Our results suggest that the BAP module originated in land plants and that theζ,ε,andγwhole‐genome duplication/triplication events contributed to the expansion of BAP module components in seed plants.Comparative transcriptomic analysis suggested that the prototype BAP module arose in Marchantia polymorpha,experienced stepwise evolution,and became established as a mature regulatory system in seed plants.We developed a formula to calculate the signal transduction productivity of the BAP module and demonstrate that more crosstalk among components enables higher signal transduction efficiency.Our results reveal the evolutionary history of the BAP module and provide insights into the evolution of plant signaling networks and the strategies employed by plants to integrate environmental and endogenous signals.