DeepScaler: Holistic Autoscaling for Microservices Based on Spatiotemporal GNN with Adaptive Graph Learning

Autoscaling functions provide the foundation for achieving elasticity in the modern cloud computing paradigm. It enables dynamic provisioning or de-provisioning resources for cloud software services and applications without human intervention to adapt to workload fluctuations. However, autoscaling microservice is challenging due to various factors. In particular, complex, time-varying service dependencies are difficult to quantify accurately and can lead to cascading effects when allocating resources. This paper presents DeepScaler, a deep learning-based holistic autoscaling approach for microservices that focus on coping with service dependencies to optimize service-level agreements (SLA) assurance and cost efficiency. DeepScaler employs (i) an expectation-maximization-based learning method to adaptively generate affinity matrices revealing service dependencies and (ii) an attention-based graph convolutional network to extract spatio-temporal features of microservices by aggregating neighbors' information of graph-structural data. Thus DeepScaler can capture more potential service dependencies and accurately estimate the resource requirements of all services under dynamic workloads. It allows DeepScaler to reconfigure the resources of the interacting services simultaneously in one resource provisioning operation, avoiding the cascading effect caused by service dependencies. Experimental results demonstrate that our method implements a more effective autoscaling mechanism for microservice that not only allocates resources accurately but also adapts to dependencies changes, significantly reducing SLA violations by an average of 41% at lower costs.Comment: To be published in the 38th IEEE/ACM International Conference on Automated Software Engineering (ASE 2023

GNNEvaluator: Evaluating GNN Performance On Unseen Graphs Without Labels

Evaluating the performance of graph neural networks (GNNs) is an essential task for practical GNN model deployment and serving, as deployed GNNs face significant performance uncertainty when inferring on unseen and unlabeled test graphs, due to mismatched training-test graph distributions. In this paper, we study a new problem, GNN model evaluation, that aims to assess the performance of a specific GNN model trained on labeled and observed graphs, by precisely estimating its performance (e.g., node classification accuracy) on unseen graphs without labels. Concretely, we propose a two-stage GNN model evaluation framework, including (1) DiscGraph set construction and (2) GNNEvaluator training and inference. The DiscGraph set captures wide-range and diverse graph data distribution discrepancies through a discrepancy measurement function, which exploits the outputs of GNNs related to latent node embeddings and node class predictions. Under the effective training supervision from the DiscGraph set, GNNEvaluator learns to precisely estimate node classification accuracy of the to-be-evaluated GNN model and makes an accurate inference for evaluating GNN model performance. Extensive experiments on real-world unseen and unlabeled test graphs demonstrate the effectiveness of our proposed method for GNN model evaluation.Comment: Accepted by NeurIPS 202

Angiotensin II diminishes the effect of SGK1 on the WNK4-mediated inhibition of ROMK1 channels

ROMK1 channels are located in the apical membrane of the connecting tubule and cortical collecting duct and mediate the potassium secretion during normal dietary intake. We used a perforated whole-cell patch clamp to explore the effect of angiotensin II on these channels in HEK293 cells transfected with green fluorescent protein (GFP)-ROMK1. Angiotensin II inhibited ROMK1 channels in a dose-dependent manner, an effect abolished by losartan or by inhibition of protein kinase C. Furthermore, angiotensin II stimulated a protein kinase C-sensitive phosphorylation of tyrosine 416 within c-Src. Inhibition of protein tyrosine kinase attenuated the effect of angiotensin II. Western blot studies suggested that angiotensin II inhibited ROMK1 channels by enhancing its tyrosine phosphorylation, a notion supported by angiotensin II's failure to inhibit potassium channels in cells transfected with the ROMK1 tyrosine mutant (R1Y337A). However, angiotensin II restored the with-no-lysine kinase-4 (WNK4)-induced inhibition of R1Y337A in the presence of serum–glucocorticoids-induced kinase 1 (SGK1), which reversed the inhibitory effect of WNK4 on ROMK1. Moreover, protein tyrosine kinase inhibition abolished the angiotensin II-induced restoration of WNK4-mediated inhibition of ROMK1. Angiotensin II inhibited ROMK channels in the cortical collecting duct of rats on a low sodium diet, an effect blocked by protein tyrosine kinase inhibition. Thus, angiotensin II inhibits ROMK channels by two mechanisms: increasing tyrosine phosphorylation of the channel and synergizing the WNK4-induced inhibition. Hence, angiotensin II may have an important role in suppressing potassium secretion during volume depletion

Structure-free Graph Condensation: From Large-scale Graphs to Condensed Graph-free Data

Graph condensation, which reduces the size of a large-scale graph by synthesizing a small-scale condensed graph as its substitution, has immediate benefits for various graph learning tasks. However, existing graph condensation methods rely on the joint optimization of nodes and structures in the condensed graph, and overlook critical issues in effectiveness and generalization ability. In this paper, we advocate a new Structure-Free Graph Condensation paradigm, named SFGC, to distill a large-scale graph into a small-scale graph node set without explicit graph structures, i.e., graph-free data. Our idea is to implicitly encode topology structure information into the node attributes in the synthesized graph-free data, whose topology is reduced to an identity matrix. Specifically, SFGC contains two collaborative components: (1) a training trajectory meta-matching scheme for effectively synthesizing small-scale graph-free data; (2) a graph neural feature score metric for dynamically evaluating the quality of the condensed data. Through training trajectory meta-matching, SFGC aligns the long-term GNN learning behaviors between the large-scale graph and the condensed small-scale graph-free data, ensuring comprehensive and compact transfer of informative knowledge to the graph-free data. Afterward, the underlying condensed graph-free data would be dynamically evaluated with the graph neural feature score, which is a closed-form metric for ensuring the excellent expressiveness of the condensed graph-free data. Extensive experiments verify the superiority of SFGC across different condensation ratios.Comment: Accepted by NeurIPS 202

Model-free adaptive iterative learning control of melt pool width in wire arc additive manufacturing

© 2020, Springer-Verlag London Ltd., part of Springer Nature. Wire arc additive manufacturing (WAAM) is a Direct Energy Deposition (DED) technology, which utilize electrical arc as heat source to deposit metal material bead by bead to make up the final component. However, issues like the lack of assurance in accuracy, repeatability and stability hinder the further application in industry. Therefore, a Model Free Adaptive Iterative Learning Control (MFAILC) algorithm was developed to be applied in WAAM process in this study. The dynamic process of WAAM is modelled by adaptive neuro fuzzy inference system (ANFIS). Based on this ANFIS model, simulations are performed to demonstrate the effectiveness of MFAILC algorithm. Furthermore, experiments are conducted to investigate the tracking performance and robustness of the MFAILC controller. This work will help to improve the forming accuracy and automatic level of WAAM

Protective Mechanism of Adipose-Derived Stem Cells in Remodelling of the Skin Stem Cell Niche During Photoaging

Background/Aims: Skin photoaging is primarily caused by the functional attrition of skin stem cells. The skin stem cell niche plays an important role in maintaining stem cell survival and behaviour. In our study, we hypothesized that UVB irradiation induces skin photoaging by changing skin stem cell niches and that transferred adipose-derived stem cells (ADSCs) can remodel the niches by affecting the BMP signalling pathway and transdifferentiating into skin stem cells. Methods: Sixty-four C57BL/6J mice were divided into the following groups: a control group, the UVB group and the UVB+ADSCs group. Western blot assays, immunofluorescence analysis and real-time PCR were used to measure differences in the expression of niche components among the three groups. Furthermore, we tested whether transplanted ADSCs express skin stem cell markers, such as p63, α6-integrin and CD34. Results: The expression levels of Bmp4, its downstream factors Smad1 and MAPK1 and a regulatory factor of the niche, i.e., NFATc1, were lower in the UVB group than were those in the control group (P< 0.05) but higher in the UVB+ADSCs group than were those in the UVB group (P< 0.05). Compared with Bmp4, Nanog (a downstream factor of Bmp4), and MMP13 (a regulatory factor of the niche), ICAM-1 (a proinflammatory gene), p63 (a basal transcription factor), β1-integrin, Mtnr1a and Tyr (melanogenesis-related factors) showed the opposite expression trends (P< 0.05). Bmp2 and Collagen IV levels did not significantly change among the three groups (P> 0.05). Skin stem cell markers, such as p63, α6-integrin and CD34, were coexpressed in the ADSCs, which suggested the ADSCs may transdifferentiate into skin stem cells. Conclusion: We found that UVB irradiation results in typical photoaging signs by altering skin stem cell niches and that Bmp4 was a key factor in BMP signalling in hair follicles. ADSCs reversed these typical photoaging signs by remodelling skin stem cell niches through BMP4 pathway modulation and transdifferentiation into skin stem cells

Bisleuconothine A potentiates the effect of hyperbaric oxygen therapy against traumatic brain injury by enhancing P2X4 protein expressions

Purpose: To investigate the effect of bisleuconothine A (BA), alone and in combination with hyperbaric oxygen (HO), on traumatic brain injury (TBI) in rats. Methods: Traumatic brain injury (TBI) was induced by dropping a 200-g weight of steel on the left anterior frontal areas of Sprague-Dawley rats. The synergistic effect of BA and HO was determined by assessing neurological score, as well as parameters of oxidative stress and inflammation, expressions of P2X4 protein and other proteins, and levels of reactive oxygen species (ROS) in the brain tissues of TBI rats. Results: Neurological function score, levels of inflammatory mediators and oxidative stress parameters were significantly reduced in rats treated with BA alone, and in those treated with a combination of BA and HO, when compared with untreated TBI rats (p < 0.01). Moreover, treatment with BA alone, and BA-HO combination attenuated the altered protein expressions of P2X4, Akt, PI3K and TLR-4 in the TBI rats, and also upregulated the mRNA expression of P2X4 in the brain tissue, when compared with untreated TBI rats (p < 0.01). Conclusion: These results suggest that BA, when used alone or in combination with HO, reduces neuronal injury through upregulation of the protein expression of P2X4 in rats with traumatic brain injury. Thus, BA may be used clinically with HO therapy for the management of traumatic injury. Keywords: Bisleuconothine A, Hyperbaric oxygen, Neuronal injury, Oxidative stress, Inflammatory mediator

Primase-based whole genome amplification

In vitro DNA amplification methods, such as polymerase chain reaction (PCR), rely on synthetic oligonucleotide primers for initiation of the reaction. In vivo, primers are synthesized on-template by DNA primase. The bacteriophage T7 gene 4 protein (gp4) has both primase and helicase activities. In this study, we report the development of a primase-based Whole Genome Amplification (pWGA) method, which utilizes gp4 primase to synthesize primers, eliminating the requirement of adding synthetic primers. Typical yield of pWGA from 1 ng to 10 ng of human genomic DNA input is in the microgram range, reaching over a thousand-fold amplification after 1 h of incubation at 37°C. The amplification bias on human genomic DNA is 6.3-fold among 20 loci on different chromosomes. In addition to amplifying total genomic DNA, pWGA can also be used for detection and quantification of contaminant DNA in a sample when combined with a fluorescent reporter dye. When circular DNA is used as template in pWGA, 108-fold of amplification is observed from as low as 100 copies of input. The high efficiency of pWGA in amplifying circular DNA makes it a potential tool in diagnosis and genotyping of circular human DNA viruses such as human papillomavirus (HPV)