Preoperative Radiographic Simulation for Partial Uncinate Process Resection during Anterior Cervical Discectomy and Fusion to Achieve Adequate Foraminal Decompression and Prevention of Vertebral Artery Injury

Study Design Retrospective radiographic study. Purpose This study aims to demonstrate the proper resection trajectory of a partial posterior uncinate process resection combined with anterior cervical discectomy and fusion (ACDF) and evaluate whether foraminal stenosis or uncinate process degeneration increases the risk of vertebral artery (VA) injury. Overview of Literature Appropriate resection trajectory that could result in sufficient decompression and avoid vertebral artery injury is yet unknown. Methods We retrospectively reviewed patients who underwent cervical magnetic resonance imaging and computed tomography angiography for preoperative ACDF evaluation. The segments were classified according to the presence of foraminal stenosis. The height, thickness, anteroposterior length, horizontal distance from the uncinate process to the VA, and vertical distance from the uncinate process baseline to the VA of the uncinate process were measured. The distance between the uncinate anterior margin and the resection trajectory (UAM-to-RT) was measured. Results There were no VA injuries or root injuries among the 101 patients who underwent ACDF (163 segments, mean age of 56.3±12.2). Uncinate anteroposterior length was considerably longer in foramens with foraminal stenosis, whereas uncinate process height, thickness, and distance between the uncinate process and VA were not significantly associated with foraminal stenosis. There were no significant differences in radiographic parameters based on uncinate degeneration. The UAM-to-RT distances for adequate decompression were 1.6±1.4 mm (range, 0–4.8 mm), 3.4±1.7 mm (range, 0–7.1 mm), 4.0±1.7 mm (range, 0–9.0 mm), and 4.5±1.2 mm (range, 2.5–7.5 mm) for C3–C4, C4–C5, C5–C6, and C6–C7, respectively. Conclusions More than half of the uncinate process in the anteroposterior plane should be removed for adequate neural foramen decompression. Foraminal stenosis or uncinate degeneration did not alter the relative anatomy of the uncinate process and the VA and did not impact VA injury risk

Anterior Decompression and Fusion for the Treatment of Cervical Myelopathy Caused by Ossification of the Posterior Longitudinal Ligament: A Narrative Review

Occasionally, ossification of the posterior longitudinal ligament (OPLL) causes cord compression, resulting in cervical myelopathy. OPLL differs from other causes of cervical spondylotic myelopathy in several ways, and the surgical strategy should be chosen with OPLL’s characteristics in mind. Although both the anterior and posterior approaches are effective surgical methods for the treatment of OPLL cervical myelopathy, they each have their own set of benefits and drawbacks. Anterior decompression and fusion (ADF) may improve neurological recovery, restore lordosis, and prevent OPLL mass progression. The benefits can be seen in patients with a high canal occupying ratio or kyphotic alignment. We discussed the benefits, limitations, indications, and surgical techniques of ADF for the treatment of OPLL-induced cervical myelopathy in this narrative

Anomaly Detection of Operating Equipment in Livestock Farms Using Deep Learning Techniques

In order to establish a smart farm, many kinds of equipment are built and operated inside and outside of a pig house. Thus, the environment for livestock (limited to pigs in this paper) in the barn is properly maintained for its growth conditions. However, due to poor environments such as closed pig houses, lack of stable power supply, inexperienced livestock management, and power outages, the failure of these environment equipment is high. Thus, there are difficulties in detecting its malfunctions during equipment operation. In this paper, based on deep learning, we provide a mechanism to quickly detect anomalies of multiple equipment (environmental sensors and controllers, etc.) in each pig house at the same time. In particular, environmental factors (temperature, humidity, CO2, ventilation, radiator temperature, external temperature, etc.) to be used for learning were extracted through the analysis of data accumulated for the generation of predictive models of each equipment. In addition, the optimal recurrent neural network (RNN) environment was derived by analyzing the characteristics of the learning RNN. In this way, the accuracy of the prediction model can be improved. In this paper, the real-time input data (only in the case of temperature) was intentionally induced above the threshold, and 93% of the abnormalities were detected to determine whether the equipment was abnormal

Deep Learning-Based Approaches for Classifying Foraminal Stenosis Using Cervical Spine Radiographs

Various disease detection models, based on deep learning algorithms using medical radiograph images (MRI, CT, and X-ray), have been actively explored in relation to medicine and computer vision. For diseases related to the spine, primarily MRI-based or CT-based studies have been conducted, but most studies were associated with the lumbar spine, not the cervical spine. Foraminal stenosis offers important clues in diagnosing cervical radiculopathy, which is usually detected based on MRI data because it is difficult even for experts to diagnose using only an X-ray examination. However, MRI examinations are expensive, placing a potential burden on patients. Therefore, this paper proposes a novel model for diagnosing foraminal stenosis using only X-ray images. In addition, we propose methods suitable for cervical spine X-ray images to improve the performance of the proposed classification model. First, the proposed model adopts data preprocessing and augmentation methods, including Histogram Equalization, Flip, and Spatial Transformer Networks. Second, we apply fine-tuned transfer learning using a pre-trained ResNet50 with cervical spine X-ray images. Compared to the basic ResNet50 model, the proposed method improves the performance of foraminal stenosis diagnosis by approximately 5.3–6.9%, 5.2–6.5%, 5.4–9.2%, and 0.8–4.3% in Accuracy, F1 score, specificity, and sensitivity, respectively. We expect that the proposed model can contribute towards reducing the cost of expensive examinations by detecting foraminal stenosis using X-ray images only

Node-Based Horizontal Pod Autoscaler in KubeEdge-Based Edge Computing Infrastructure

KubeEdge (KE) is a container orchestration platform for deploying and managing containerized IoT applications in an edge computing environment based on Kubernetes. It is intended to be hosted at the edge and provides seamless cloud-edge coordination as well as an offline mode that allows the edge to function independently of the cloud. However, there are unreliable communication links between edge nodes in edge computing environments, implying that load balancing in an edge computing environment is not guaranteed while using KE. Furthermore, KE lacks Horizontal Pod Autoscaling (HPA), implying that KE cannot dynamically deploy new resources to efficiently handle increasing requests. Both of the aforementioned issues have a significant impact on the performance of the KE-based edge computing system, particularly when traffic volumes vary over time and geographical location. In this study, a node-based horizontal pod autoscaler (NHPA) is proposed to provide dynamical adjustment for the number of pods of individual nodes independently from each other in an edge computing environment where the traffic volume fluctuates over time and location, and the communication links between edge nodes are not stable. The proposed NHPA can dynamically adjust the number of pods depending on the incoming traffic at each node, which will improve the overall performance of the KubeEdge-based edge computing environment. In the KubeEdge-based edge computing environment, the experimental findings reveal that NHPA outperforms KE in terms of throughput and response time by a factor of about 3 and 25, respectively

Horizontal Pod Autoscaling in Kubernetes for Elastic Container Orchestration

Kubernetes, an open-source container orchestration platform, enables high availability and scalability through diverse autoscaling mechanisms such as Horizontal Pod Autoscaler (HPA), Vertical Pod Autoscaler and Cluster Autoscaler. Amongst them, HPA helps provide seamless service by dynamically scaling up and down the number of resource units, called pods, without having to restart the whole system. Kubernetes monitors default Resource Metrics including CPU and memory usage of host machines and their pods. On the other hand, Custom Metrics, provided by external software such as Prometheus, are customizable to monitor a wide collection of metrics. In this paper, we investigate HPA through diverse experiments to provide critical knowledge on its operational behaviors. We also discuss the essential difference between Kubernetes Resource Metrics (KRM) and Prometheus Custom Metrics (PCM) and how they affect HPA’s performance. Lastly, we provide deeper insights and lessons on how to optimize the performance of HPA for researchers, developers, and system administrators working with Kubernetes in the future

Non-Cryogenic Structure and Dynamics of HIV-1 Integrase Catalytic Core Domain by X-ray Free-Electron Lasers

HIV-1 integrase (HIV-1 IN) is an enzyme produced by the HIV-1 virus that integrates genetic material of the virus into the DNA of infected human cells. HIV-1 IN acts as a key component of the Retroviral Pre-Integration Complex (PIC). Protein dynamics could play an important role during the catalysis of HIV-1 IN; however, this process has not yet been fully elucidated. X-ray free electron laser (XFEL) together with nuclear magnetic resonance (NMR) could provide information regarding the dynamics during this catalysis reaction. Here, we report the non-cryogenic crystal structure of HIV-1 IN catalytic core domain at 2.5 Å using microcrystals in XFELs. Compared to the cryogenic structure at 2.1 Å using conventional synchrotron crystallography, there was a good agreement between the two structures, except for a catalytic triad formed by Asp64, Asp116, and Glu152 (DDE) and the lens epithelium-derived growth factor binding sites. The helix III region of the 140–153 residues near the active site and the DDE triad show a higher dynamic profile in the non-cryogenic structure, which is comparable to dynamics data obtained from NMR spectroscopy in solution state