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aims: To elucidate the roles of PRC2 and HDACs in cardiomyocyte hypertrophy. background: Postnatal cardiomyocytes respond to stress signals by hypertrophic growth and fetal gene reprogramming, which involves epigenetic remodeling mediated by histone methyltransferase polycomb repressive complex 2 (PRC2) and histone deacetylases (HDACs). However, it remains unclear to what extent these histone modifiers contribute to the development of cardiomyocyte hypertrophy. objective: To compare the dose-dependent effects of GSK126 and TSA, inhibitors of PRC2 and HDACs, respectively, on cardiomyocyte hypertrophy. method: Neonatal rat ventricular myocytes (NRVMs) were stimulated by phenylephrine (PE; 50μM) to induce hypertrophy in the presence or absence of the PRC2 inhibitor GSK126 or the HDACs inhibitor Trichostatin A (TSA). Histone methylation and acetylation were measured by Western blot. Cell size was determined by wheat germ agglutinin (WGA) staining. Cardiac hypertrophy markers were quantified by quantitative reverse transcription polymerase chain reaction (qRT-PCR). result: PE treatment induced the expression of cardiac hypertrophy markers, including natriuretic peptide A (Nppa), natriuretic peptide B (Nppb), and myosin heavy chain 7 (Myh7), in a time-dependent manner in NRVMs. Histone modifications, including H3K27me3, H3K9ac, and H3K27ac, were dynamically altered after PE treatment. Treatment with TSA and GSK126 dose-dependently repressed histone acetylation and methylation, respectively. Whereas TSA reversed the PE-induced cell size enlargement in a wide range of concentrations, cardiomyocyte hypertrophy was only inhibited by GSK126 at a higher dose (1μM). Consistently, TSA dose-dependently suppressed the induction of Nppa, Nppb, and Myh7/Myh6 ratio, while these indexes were only inhibited by GSK126 at 1μM. However, TSA, but not GSK126, caused pro-hypertrophic expression of pathological genes at the basal level. conclusion: Our data demonstrate diversified effects of TSA and GSK126 on PE-induced cardiomyocyte hypertrophy, and shed a light on the epigenetic reprogramming in the pathogenesis of cardiac hypertrophy. other: Our data systematically compared the effects of TSA and GSK126 on PE-induced cardiomyocyte hypertrophy, and demonstrate the concentration thresholds for their protective function.

This Letter solves steering vector estimation under mismatch for adaptive beamforming. The proposed beamformer implements a stepwise estimation of steering vector, and zone orthogonal constraint is added first based on adaptive constraint framework from Khabbazibasmenj [IEEE Trans. Signal Process. 60(6), 2974–2987 (2012)], which ensures that the estimated steering vector does not converge to an interference steering vector outside the predefined sector, even if the sector deviates from the central observation area of arrays. Then uncertainty set error constraint is used to improve the estimation accuracy. The numerical simulation and experimental results verify the effectiveness of the proposed method.

Supported catalysts, consisting of PMo12 immobilized on silver nanomaterials at different recombination time and the silver nanomaterials with different template sodium citrate amount characterized by FT-IR, XRD, SEM, UV-vis and other test methods. The results show that the AgNPs are relatively uniformed with sizes between 100–300 nm when the sodium citrate addition amount is 9.0 mL. As the reaction time of PMo12/AgNPs increases, the adhesion of AgNPs on the surface of PMo12 becomes more complete. Using PMo12 and PMo12/AgNPs composite materials as catalysts, methylene blue (MB) is photocatalytically degraded under simulated visible light conditions. The results show that PMo12 can catalyze MB effectively, and the decolorization rate reached 98.6% when the catalyst content is 2 g/L, the solution pH is 3 and the MB concentration is 5 mg/L. Under the same experimental conditions, photocatalytic performance of the PMo12/AgNPs system is better than that of the PMo12 further improved the photocatalytic degradation effect of the MB solution with a decolorization rate of 100%. The composite still keeps good photocatalytic activity and stability after three cycles of use. Finally, the catalytic mechanism of the POMs composite material is preliminarily discussed.

A novel type of photocatalyst SiW12/α-Fe2O3 created by combining SiW12 NPs and α-Fe2O3 nanorings was synthesized through a “step by step” method. The structure, composition, morphology and the interaction between SiW12 and α-Fe2O3 of the as-prepared composites are characterized by single crystal X-ray diffraction, FT-IR and UV spectroscopy, powder XRD, and SEM and XPS analyses. The results show that the SiW12 unit exhibits a 3D zeolite-like open-framework structure with inter-crossing channels, and the α-Fe2O3 nanorings with an average size of about 150 nm are well dispersed on SiW12 NPs. The UV-vis, photoluminescence and electrochemical impedance results suggest that SiW12/3%α-Fe2O3 nanohybrid exhibits broad spectral absorption and excellent electron transfer capability. Fe2O3 and POM in the composition system can promptly transfer electrons, avoiding the recombination of electron–hole pairs and supplying more reaction active sites. Photocatalytic tests manifested that the composites exhibited excellent and stable activities towards the degradation of methylene blue (MB) under visible-light irradiation, and the SiW12/3%α-Fe2O3 nanohybrid exhibited the best photocatalytic performance, which could reach 97.80% at pH = 6. The radical and hole trapping test results indicate that the hydroxyl radicals were identified as the main active species during the photodegradation process. Meanwhile, it is proved that the concentration of Cl− in the solution has important influence on the photocatalytic performance of the composites. Also, the migration step of ˙O2− radicals to ˙OH radicals and the influence mechanism of chloride ions during the photocatalytic process have been studied. Finally, the probable photocatalytic mechanism was investigated systematically.

RNase III DROSHA is upregulated in multiple cancers and contributes to tumor progression by hitherto unclear mechanisms. Here, we demonstrate that DROSHA interacts with β-Catenin to transactivate STC1 in an RNA cleavage-independent manner, contributing to breast cancer stem-like cell (BCSC) properties. DROSHA mRNA stability is enhanced by N(6)-methyladenosine (m(6)A) modification which is activated by AURKA in BCSCs. AURKA stabilizes METTL14 by inhibiting its ubiquitylation and degradation to promote DROSHA mRNA methylation. Moreover, binding of AURKA to DROSHA transcript further strengthens the binding of the m(6)A reader IGF2BP2 to stabilize m(6)A-modified DROSHA. In addition, wild-type DROSHA, but not an m(6)A methylation-deficient mutant, enhances BCSC stemness maintenance, while inhibition of DROSHA m(6)A modification attenuates BCSC traits. Our study unveils the AURKA-induced oncogenic m(6)A modification as a key regulator of DROSHA in breast cancer and identifies a novel DROSHA transcriptional function in promoting the BCSC phenotype.

Infrastructure code supports embedded scripting languages such as Shell and PowerShell to manage the infrastructure resources and conduct life-cycle operations. Risky patterns in the embedded scripts have widespread of negative impacts across the whole infrastructure, causing disastrous consequences. In this paper, we propose an analysis framework, which can automatically extract and compose the embedded scripts from infrastructure code before detecting their risky code patterns with correlated severity levels and negative impacts. We implement SecureCode based on the proposed framework to check infrastructure code supported by Ansible, i.e., Ansible playbooks. We integrate SecureCode with the DevOp pipeline deployed in IBM cloud and test Secure-Code on 45 IBM Services community repositories. Our evaluation shows that SecureCode can efficiently and effectively identify 3419 true issues with 116 false positives in minutes. Among the 3419 true issues, 1691 have high severity levels.

Due to the effect of complex environment and other interference, sonar images are always filled up with contamination and noise. For that reason, accurate and fast detection and segmentation of targets in sonar images are vital for underwater target recognition. In this paper an algorithm based on depth-first search is proposed for target detection and segmentation in forward looking multi-beam sonar images. The algorithm is made up with one-dimensional peak search and two-dimensional depth-first search segmentation. It can rapidly detect and segment the parts of targets in sonar images. The algorithm is tested with data from an actual multi-beam sonar. It is also compared with other segmentation method such as Otsu, C-means and Markov Random Fields. The result shows that the algorithm can extract the target with high efficiency while automatically avoiding reverberation and noise, making it suitable for real-time process of sonar images with isolated targets.

Studies show that virtual machines (VMs) in cloud are easily forgotten with non-productive status. This incurs unnecessary cost for cloud tenants and resource waste for cloud providers. As a solution to this problem, we present our Cloud Server Idleness Identification (CSI2) system. The CSI2 system collects data from the servers in cloud, performs analytics against the dataset to identify the idle servers, then provides suggestions to the owners of the idle servers. Once the confirmation from the owners are received, the idle servers are deleted or archived. We not only design and implement the CSI2 system, but also bring it alive into production environment.

Software patching is routinely employed for enterprise online applications to guard against ever-increasing security risks and to keep up with customer requirements. However, in a hybrid cloud setting, where an application deployment can span across diverse cloud environments, patching becomes challenging, especially since application components may be deployed as containers or VMs or bare-metal machines. Further, application tiers may have dependencies, which need to be respected. Worse, to minimize application downtime, selected patches need to be applied in a finite time period. This paper presents an automated patching strategy for hybrid-cloud—deployed applications that leverages a greedy algorithm design to optimally patch applications. Our implementation and evaluation results highlight the efficacy of our strategy and its superiority over alternative patching strategies.

Business interest in blockchain technology is increasing due the potential for change that it offers. Large scale adoption in enterprise IT environments brings about a need for service management and DevOps processes for blockchain deployments. Nodes in the network may fail or be taken down for maintenance requiring visibility across hybrid, traditional, cloud enabled and cloud native deployments. Service management is necessary to ease deployment woes for those looking to install code within their own data centers on-premises or on dedicated cloud servers and further to handle the life cycle of the deployments that includes security patches, critical updates, upgrades, change management and restarting services. In this paper, we provide a mechanism to optimally perform such operations with no disruption to the consensus and transactions for such emerging blockchain applications.

The rapidly increasing complexity and scale of hybrid cloud environments requires improved service management capabilities in orchestration and automation. Current methods focus on provisioning infrastructure but lack functionality for consistently enabling and performing operational activities on managed services. We propose a data-driven approach to dynamically generate Orchestration Engine plugins from service descriptor metadata. Our approach extends Orchestration Engines by representing managed services as code within reusable blueprints in order to accelerate service deployments and ease management activities. In our work, we provide a data model and system architecture to allow service providers to easily author and publish resource definitions for a wide range of public and private services. These definitions may be combined into solution blueprints, forming a declarative and reusable representation of a managed workload. After provisioning a workload, administrators can view service instance data and invoke operational activities. For evaluation, we describe the authoring and orchestration of a hybrid cloud workload and discuss the strengths of our solution versus current methods.

Underwater counter-rotation propeller non-cavitation noise has an obvious modulation characteristic which is due to the interaction of flow and blade, the modulation characteristic is critical for underwater target identification. Based on generalized acoustic analogy theory, a semi-empirical modulation model has been presented in this paper to describe its non-cavitation noise characteristics. Theoretical analysis has been verified by numerical simulation and cavitation tunnel experiment results. The numerical simulation has shown the directivity characteristic of typical modulated line-spectra. The experiment results have agreed with theoretical values. The modulation model of counter-rotation propeller is beneficial to the prediction modulation characteristics and identification of underwater high-speed vehicles.

Both the densification of small base stations and the diversity of user activities bring huge challenges for today’s heterogeneous networks, either heavy burdens on base stations or serious energy waste. In order to ensure coverage of the network while reducing the total energy consumption, we adopt a green mobile cyberphysical system (MCPS) to handle this problem. In this paper, we propose a feature extraction method using sliding window to extract the distribution feature of mobile user equipment (UE), and a case study is presented to demonstrate that the method is efficacious in reserving the clustering distribution feature. Furthermore, we present traffic clustering analysis to categorize collected traffic distribution samples into a limited set of traffic patterns, where the patterns and corresponding optimized control strategies are used to similar traffic distributions for the rapid control of base station state. Experimental results show that the sliding window is more superior in enabling higher UE coverage over the grid method. Besides, the optimized control strategy obtained from the traffic pattern is capable of achieving a high coverage that can well serve over 98% of all mobile UE for similar traffic distributions.

In this paper, we present IBM's cognitive strategy for service delivery transformation to a services integration approach for IT service management. This transformation is fueled by three fundamental technological paradigms: cognitive, cloud, and data and digital content. At the foundation of this approach is the IBM Services Platform with Watson—an IBM Cloud and Watson-based platform that uses machine learning, natural language, discovery, and various Watson application programming interfaces to design superior client solutions, achieve an exceptional level of autonomic service management, and facilitate a healthy "always-on " environment. It continuously learns and optimizes information technology performance to enable enhanced client business outcomes.

Some of the most significant challenges in automated CAD–FEA integration are information and model transformations between CAD and FEA tools. These are especially labor-intensive and time-consuming in a newly characterized class of problems termed highly coupled variable topology multi-body (HCVTMB) problems. This paper addresses these challenges with a knowledge-based FEA modeling method called ZAP that consists of three stepping-stone information models and the mapping processes between these models. The information and knowledge of a typical FEA modeling process are explicitly captured in semantically rich information models to achieve benefits including knowledge sharing, system extension, and model modification. ZAP mapping processes automatically transform abstract analytical concepts into tool-specific commands and functions that accomplish HCVTMB model generation and solution management. This method enhances flexibility and reusability in FEA modeling and enables CAD–FEA integration at the knowledge level. To demonstrate the efficacy of ZAP, we overview a sample HCVTMB problem—an electronic chip package plastic ball grid array (PBGA) thermal analysis case study. Experience indicates that ZAP increases knowledge capture and decreases modeling time from days/hours to hours/minutes compared to conventional methods, thus providing a key enabler toward design optimization.

Manipulating geometry to prepare a CAD design model for mesh generation is an important step in the finite element analysis (FEA) process. However, complex problems such as electronic chip packages often consist of hundreds or even thousands of sub-features with varying materials, complex geometrical shapes and changeable connectivity configurations. We define a subclass of such problems, termed highly coupled variable topology multi-body (HCVTMB) problems, where configuration and relative geometric sizes cause meshing changes in one body to propagate throughout much of the model. Today creating FEA models for HCVTMB problems typically requires manual geometrical manipulation which is labour-intensive and error-prone. In this paper we introduce an automated geometric transformation method-GeoTran-HC-within the context of a knowledge-based modeling method for CAD-FEA integration. Through five steps-geometry extraction, cell decomposition, associativity maintenance, feature recognition and associativity recovery-this transformation method automatically decomposes an HCVTMB analytical model into a meshable model. It also manages mesh quality and attribution of non-geometrical information such as material properties and boundary conditions. A plastic ball grid array chip package thermo-mechanical case study demonstrates the efficacy of GeoTran-HC within the context of our knowledge-based FEA modelling method. This and similar studies show reductions in total FEA modeling time from days/hours (using traditional methods) to hours/minutes (using the presented method), as well as enhanced modularity and reusability.

Efficient and flexible server provisioning is one of the most important criteria in measuring quality of cloud service. In order to save time and mitigate risk, images are set up as templates. And to provision a server, the cloud service provider replicates a selected image and then customize it to fulfill the server request. Therefore image set-up is vital to the efficiency and flexibility of cloud service. In this paper, we propose an optimization model of setting up images and mapping server request to correct image in server provisioning, with the purpose of minimizing total cost of setting up images, maintaining images and provisioning servers. After that, we compare the proposed approach with the vanilla image approach in server provisioning with numerical experiment.

This paper analyzes the mechanism of non-cavitation tone noise line-spectrum frequency of counter-rotation propeller. The mechanism is interference effect and circumferential harmonics field effect. According to the generalized acoustic analogy equation, the lift and drag which are caused by the mechanism are the source of noise,the far field sound pressure expression is presented. The line-spectrum frequency and the directionality of sound pressure have been analyzed. The results show: the predication line-spectrum frequency could be written as f=sAPF+mBPF1+nBPF2; sound pressure radiation is "8"-shaped distribution and the Bessel function's peak condition is a necessary condition for line-spectrum. The counter-rotation propeller's non-cavitation condition is numerically simulated using RNG k-e turbulent model、 sliding mesh model and kFW-H equation. The results shows that RNG k-e Turbulent model has the better accuracy than Realizable k-e turbulent model, and the counter-rotation propeller non-cavitation line-spectrum frequency and the directionality of sound pressure are got by this method which agree well with the result of Academic prediction.

The complexity of modern product realization processes requires collaborative work of engineering teams from different disciplines. We apply principles from game theory to model the relationships between engineering teams and facilitate collaborative decision making. In order to maintain design freedom in the early stages of product realization so that engineering teams in the later stages can adjust their decisions while still maintaining feasibility, we postulate the use of design capability indices to facilitate the teams making a ranged set of decisions, instead of specific ones. The effect of game theoretic principles and design capability indices on design freedom and therefore on the design solutions is also investigated. An electronic package design and analysis scenario is used to demonstrate the efficacy of the proposed method.

Automobile, aerospace, and other industrial manufacturers have evolved over the years to use a multitude of computer-aided design (CAD) and product data management (PDM) systems and have long depended on single-vendor solutions to support their enterprise-wide engineering activities. Increased product complexity, distributed authoring environments, and the need for tighter team integration with partners and suppliers have created challenges and new opportunities for information technology (IT) vendors to be able to integrate systems from multiple independent software vendors (ISVs) to form a coherent Enterprise PDM system. This paper will describe, in detail, a case study and solution of an IBM Research project called Hedwig. Hedwig investigates creating robust solutions for Product Life-Cycle Management (PLM). We focus on several research issues, including information federation, data mapping, synchronization, and web services connections. We describe a working system that allows access to several heterogeneous PDM systems that are used in the automotive and aerospace industries.

Endpoint management, including patching, health checking, configuration etc., is a key function for data center and cloud management. Managing multiple nodes through automation tools or scripts significantly increases efficiency. However, the risks of adverse impact due to excessive privilege or human error may propagate to a large pool of endpoints and lead to massive service disruptions and SLA (Service Level Agreement) violations. In this paper, we present a system that proactively and systematically manages the risk throughout the lifecycle phases of automation. We present a prototype implementation consisting of an authorization mechanism that guarantees the right level of eligibility and privilege of accessing the automation content (during the deployment stage), and an execution validator that controls the risk of human error which may cause massive damage to the infrastructure (during execution of the automation content). Our current implementation has been deployed to more than a dozen customer environments and achieved an efficiency gain of 58% with high execution accuracy.