Gear: Enable Efficient Container Storage and Deployment with a New Image Format

International audienceContainers have been widely used in various cloud platforms as they enable agile and elastic application deployment through their process-based virtualization and layered image system. However, different layers of a container image may contain substantial duplicate and unnecessary data, which slows down its deployment due to long image downloading time and increased burden on the image registry. To accelerate the deployment and reduce the size of the registry, we propose a new image format, named Gear image, that consists of two parts: a Gear index describing the structure of the image’s file system and a set of files that are required when running an application. The Gear index is represented as a single-layer image compatible with the existing deployment framework. Containers can be launched by pulling a Gear index and on demand retrieving files pointed to by the index. Furthermore, the Gear image enables a file-level sharing mechanism, which helps remove duplicate data in the registry and avoid repeated downloading of identical files by a client. We implement a prototype of the container framework, named Gear, supporting the new image format. Evaluation shows that Gear saves 54% storage capacity in the registry, speeds up container startup by up to 5X, and reduces 84% bandwidth demands

Gear: Enable Efficient Container Storage and Deployment with a New Image Format

International audienceContainers have been widely used in various cloud platforms as they enable agile and elastic application deployment through their process-based virtualization and layered image system. However, different layers of a container image may contain substantial duplicate and unnecessary data, which slows down its deployment due to long image downloading time and increased burden on the image registry. To accelerate the deployment and reduce the size of the registry, we propose a new image format, named Gear image, that consists of two parts: a Gear index describing the structure of the image’s file system and a set of files that are required when running an application. The Gear index is represented as a single-layer image compatible with the existing deployment framework. Containers can be launched by pulling a Gear index and on demand retrieving files pointed to by the index. Furthermore, the Gear image enables a file-level sharing mechanism, which helps remove duplicate data in the registry and avoid repeated downloading of identical files by a client. We implement a prototype of the container framework, named Gear, supporting the new image format. Evaluation shows that Gear saves 54% storage capacity in the registry, speeds up container startup by up to 5X, and reduces 84% bandwidth demands

Predictive model of acute kidney injury in critically ill patients with acute pancreatitis: a machine learning approach using the MIMIC-IV database

AbstractBackground Acute kidney injury (AKI) is a common and serious complication in severe acute pancreatitis (AP), associated with high mortality rate. Early detection of AKI is crucial for prompt intervention and better outcomes. This study aims to develop and validate predictive models using machine learning (ML) to identify the onset of AKI in patients with AP.Methods Patients with AP were extracted from the MIMIC-IV database. We performed feature selection using the random forest method. Model construction involved an ensemble of ML, including random forest (RF), support vector machine (SVM), k-nearest neighbors (KNN), naive Bayes (NB), neural network (NNET), generalized linear model (GLM), and gradient boosting machine (GBM). The best-performing model was fine-tuned and evaluated through split-set validation.Results We analyzed 1,235 critically ill patients with AP, of which 667 cases (54%) experienced AKI during hospitalization. We used 49 variables to construct models, including GBM, GLM, KNN, NB, NNET, RF, and SVM. The AUC for these models was 0.814 (95% CI, 0.763 to 0.865), 0.812 (95% CI, 0.769 to 0.854), 0.671 (95% CI, 0.622 to 0.719), 0.812 (95% CI, 0.780 to 0.864), 0.688 (95% CI, 0.624 to 0.752), 0.809 (95% CI, 0.766 to 0.851), and 0.810 (95% CI, 0.763 to 0.856) respectively. In the test set, the GBM’s performance was consistent, with an area of 0.867 (95% CI, 0.831 to 0.903).Conclusions The GBM model’s precision is crucial, aiding clinicians in identifying high-risk patients and enabling timely interventions to reduce mortality rates in critical care

HIF1α-Induced Glycolysis in Macrophage Is Essential for the Protective Effect of Ouabain during Endotoxemia

Ouabain, a steroid binding to the Na+/K+-ATPase, has several pharmacological effects. In addition to the recognized effects of blood pressure, there is more convincing evidence suggesting that ouabain is involved in immunologic functions and inflammation. Hypoxia-inducible factor 1α (HIF-1α) is a metabolic regulator which plays a considerable role in immune responses. Previous studies had shown that HIF-1α-induced glycolysis results in functional reshaping in macrophages. In this study, we investigated the role of glycolytic pathway activation in the anti-inflammatory effect of ouabain. We found that ouabain is involved in anti-inflammatory effects both in vivo and in vitro. Additionally, ouabain can inhibit LPS-induced upregulation of GLUT1 and HK2 at the transcriptional level. GM-CSF pretreatment almost completely reversed the inhibitory effect of ouabain on LPS-induced release of proinflammatory cytokines. Alterations in glycolytic pathway activation were required for the anti-inflammatory effect of ouabain. Ouabain can significantly inhibit the upregulation of HIF-1α at the protein level. Our results also revealed that the overexpression of HIF-1α can reverse the anti-inflammatory effect of ouabain. Thus, we conclude that the HIF-1α-dependent glycolytic pathway is essential for the anti-inflammatory effect of ouabain

High Stability LED-Pumped Nd:YVO₄ Laser with a Cr:YAG for Passive Q-Switching

With improvements in light-emitting diode (LED) performance and a sharp decline in price, a light source with the irradiance of a laser and the cost of an LED is worthy of further study. We demonstrated a LED-pumped Nd:YVO4 laser in quasi-continuous-wave (QCW) and passively Q-switched (PQS) regime. With an incident pump energy of 6.28 mJ (150 μs pulses at 1 Hz), the Nd:YVO4 laser has an energy of 206 μJ at 1064 nm in the QCW regime. The optical conversion efficiency of the system is 4.1%, and the slope efficiency is 9.0%. A pulsed energy of 2.5 μJ was obtained with a duration of 897 ns (FWHM) in the PQS regime, which means the peak power is 2.79 W. The output energy stability is 97.54%