Pembrolizumab-induced psoriatic arthritis treated with disease-modifying anti-rheumatic drugs in a patient with gastric cancer: A case report.

BACKGROUND: Immune checkpoint inhibitor (ICI)-induced rheumatic immune-related adverse events (irAEs) have been infrequently reported, and the treatment of severe or refractory arthritis as irAEs has not been established yet. CASE SUMMARY: The patient was a 67-year-old man with a history of well-controlled foot psoriasis who presented with polyarthralgia. He had received pembrolizumab for metastatic gastric adenocarcinoma 2 mo previously. Physical examination revealed erythematous swelling in the distal interphalangeal joints, left shoulder, and both knees. He had plaque psoriasis with psoriatic nail dystrophy and dactylitis in the distal joints of the fingers and toes. Inflammatory markers including C-reactive protein and erythrocyte sedimentation rate were elevated but rheumatoid factor and anticyclic citrullinated peptide antibody were negative. The patient was diagnosed with psoriatic arthritis (PsA) and started on methylprednisolone 1 mg/kg/day after pembrolizumab discontinuation. However, despite 1 wk of methylprednisolone treatment, PsA worsened; hence, leflunomide and methotrexate were started. After 4 wk of steroid treatment, PsA worsened and improved repeatedly with steroid tapering. Therefore, the therapy was intensified to include etanercept, a tumor necrosis factor inhibitor, which ultimately resulted in adequate PsA control. CONCLUSION: This is the first report of ICI-induced PsA in a gastric cancer patient. Some rheumatic irAEs with refractory severe arthritis may require disease-modifying anti-rheumatic drugs and long-term management.

Applicability evaluation of a demand-controlled ventilation system in livestock.

The distribution of agricultural and livestock products has been limited owing to the recent rapid population growth and the COVID-19 pandemic; this has led to an increase in the demand for food security. The livestock industry is interested in increasing the growth performance of livestock that has resulted in the need for a mechanical ventilation system that can create a comfortable indoor environment. In this study, the applicability of demand-controlled ventilation (DCV) to energy-efficient mechanical ventilation control in a pigsty was analyzed. To this end, an indoor temperature and CO2 concentration prediction model was developed, and the indoor environment and energy consumption behavior based on the application of DCV control were analyzed. As a result, when DCV control was applied, the energy consumption was smaller than that of the existing control method; however, when it was controlled in an hourly time step, the increase in indoor temperature was large, and several sections exceeded the maximum temperature. In addition, when it was controlled in 15-min time steps, the increase in indoor temperature and energy consumption decreased; however, it was not energy efficient on days with high-outdoor temperature and pig heat.

Estimation of Number of Pigs Taking in Feed Using Posture Filtration.

Pork production is hugely impacted by the health and breeding of pigs. Analyzing the eating pattern of pigs helps in optimizing the supply chain management with a healthy breeding environment. Monitoring the feed intake of pigs in a barn provides information about their eating habits, behavioral patterns, and surrounding environment, which can be used for further analysis to monitor growth in pigs and eventually contribute to the quality and quantity of meat production. In this paper, we present a novel method to estimate the number of pigs taking in feed by considering the pig's posture. In order to solve problems arising from using the pig's posture, we propose an algorithm to match the pig's head and the corresponding pig's body using the major-and-minor axis of the pig detection box. In our experiment, we present the detection performance of the YOLOv5 model according to the anchor box, and then we demonstrate that the proposed method outperforms previous methods. We therefore measure the number of pigs taking in feed over a period of 24 h and the number of times pigs consume feed in a day over a period of 30 days, and observe the pig's feed intake pattern.

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.

IoT-Based Strawberry Disease Prediction System for Smart Farming.

Crop diseases cannot be accurately predicted by merely analyzing individual disease causes. Only through construction of a comprehensive analysis system can users be provided with predictions of highly probable diseases. In this study, cloud-based technology capable of handling the collection, analysis, and prediction of agricultural environment information in one common platform was developed. The proposed Farm as a Service (FaaS) integrated system supports high-level application services by operating and monitoring farms as well as managing associated devices, data, and models. This system registers, connects, and manages Internet of Things (IoT) devices and analyzes environmental and growth information. In addition, the IoT-Hub network model was constructed in this study. This model supports efficient data transfer for each IoT device as well as communication for non-standard products, and exhibits high communication reliability even in poor communication environments. Thus, IoT-Hub ensures the stability of technology specialized for agricultural environments. The integrated agriculture-specialized FaaS system implements specific systems at different levels. The proposed system was verified through design and analysis of a strawberry infection prediction system, which was compared with other infection models.