Automatic mandibular canal detection using a deep convolutional neural network

The practicability of deep learning techniques has been demonstrated by their successful implementation in varied fields, including diagnostic imaging for clinicians. In accordance with the increasing demands in the healthcare industry, techniques for automatic prediction and detection are being widely researched. Particularly in dentistry, for various reasons, automated mandibular canal detection has become highly desirable. The positioning of the inferior alveolar nerve (IAN), which is one of the major structures in the mandible, is crucial to prevent nerve injury during surgical procedures. However, automatic segmentation using Cone beam computed tomography (CBCT) poses certain difficulties, such as the complex appearance of the human skull, limited number of datasets, unclear edges, and noisy images. Using work-in-progress automation software, experiments were conducted with models based on 2D SegNet, 2D and 3D U-Nets as preliminary research for a dental segmentation automation tool. The 2D U-Net with adjacent images demonstrates higher global accuracy of 0.82 than naïve U-Net variants. The 2D SegNet showed the second highest global accuracy of 0.96, and the 3D U-Net showed the best global accuracy of 0.99. The automated canal detection system through deep learning will contribute significantly to efficient treatment planning and to reducing patients’ discomfort by a dentist. This study will be a preliminary report and an opportunity to explore the application of deep learning to other dental fields.Peer reviewe

Pivotal Role of Language Modeling in Recommender Systems: Enriching Task-specific and Task-agnostic Representation Learning

Recent studies have proposed unified user modeling frameworks that leverage user behavior data from various applications. Many of them benefit from utilizing users' behavior sequences as plain texts, representing rich information in any domain or system without losing generality. Hence, a question arises: Can language modeling for user history corpus help improve recommender systems? While its versatile usability has been widely investigated in many domains, its applications to recommender systems still remain underexplored. We show that language modeling applied directly to task-specific user histories achieves excellent results on diverse recommendation tasks. Also, leveraging additional task-agnostic user histories delivers significant performance benefits. We further demonstrate that our approach can provide promising transfer learning capabilities for a broad spectrum of real-world recommender systems, even on unseen domains and services.Comment: 14 pages, 5 figures, 9 table

The limited immunomodulatory effects of escharectomy on the kinetics of endotoxin, cytokines, and adhesion molecules in major burns.

Escharectomy has been shown to improve the survival rates and the outcomes in burns. This observational study was conducted to assess the role of escharectomy on the inflammatory mediators in major burns. Seventeen ASA physical status II or status III adult surviving major burn patients were recruited. When the escharectomy was scheduled, a series of blood samples was obtained at -3 and -1 days preoperation, and +1 and +3 postoperation. The changing levels of endotoxin, cytokines, and adhesion molecules were measured with a quantitative sandwich immunoassay. Extensive escharectomy did not appear to have any significant impact on the levels of tumor necrosis factor alpha, interleukin-10, soluble intracellular adhesion molecule-1 and soluble vascular adhesion molecule-1. Meanwhile, endotoxin and E-selectin were significantly decreased after escharectomy. Escharectomy appeared to have a limited immunomodulatory effect on the inflammatory mediators in systemic inflammatory responses induced by major burns. This is probably related to the timing and extent of surgery, and the complex nature of burn-related inflammation

Scaling Law for Recommendation Models: Towards General-purpose User Representations

Recent advancement of large-scale pretrained models such as BERT, GPT-3, CLIP, and Gopher, has shown astonishing achievements across various task domains. Unlike vision recognition and language models, studies on general-purpose user representation at scale still remain underexplored. Here we explore the possibility of general-purpose user representation learning by training a universal user encoder at large scales. We demonstrate that the scaling law is present in user representation learning areas, where the training error scales as a power-law with the amount of computation. Our Contrastive Learning User Encoder (CLUE), optimizes task-agnostic objectives, and the resulting user embeddings stretch our expectation of what is possible to do in various downstream tasks. CLUE also shows great transferability to other domains and companies, as performances on an online experiment shows significant improvements in Click-Through-Rate (CTR). Furthermore, we also investigate how the model performance is influenced by the scale factors, such as training data size, model capacity, sequence length, and batch size. Finally, we discuss the broader impacts of CLUE in general.Comment: Accepted at AAAI 2023. This version includes the technical appendi

Genomic characterization of Nocardia seriolae strains isolated from diseased fish

Members of the genus Nocardia are widespread in diverse environments; a wide range of Nocardia species are known to cause nocardiosis in several animals, including cat, dog, fish, and humans. Of the pathogenic Nocardia species, N. seriolae is known to cause disease in cultured fish, resulting in major economic loss. We isolated two N. seriolae strains, CK‐14008 and EM15050, from diseased fish and sequenced their genomes using the PacBio sequencing platform. To identify their genomic features, we compared their genomes with those of other Nocardia species. Phylogenetic analysis showed that N. seriolae shares a common ancestor with a putative human pathogenic Nocardia species. Moreover, N. seriolae strains were phylogenetically divided into four clusters according to host fish families. Through genome comparison, we observed that the putative pathogenic Nocardia strains had additional genes for iron acquisition. Dozens of antibiotic resistance genes were detected in the genomes of N. seriolae strains; most of the antibiotics were involved in the inhibition of the biosynthesis of proteins or cell walls. Our results demonstrated the virulence features and antibiotic resistance of fish pathogenic N. seriolae strains at the genomic level. These results may be useful to develop strategies for the prevention of fish nocardiosis.

Severe mitral regurgitation in a young female with pansinusitis and bronchiectasis

SummaryPrimary ciliary dyskinesia (PCD) is a disease characterized by symptoms of upper and lower respiratory tract infections due to abnormal structure and function of cilia.Cardiac involvement is characterized by situs inversus (Kartagener's syndrome in PCD) and other congenital cardiovascular abnormalities. We describe a 34-year-old female with a history of recurrent sinusitis and bronchiectasis but without situs inversus or other congenital cardiac anomalies in whom an association between mitral regurgitation secondary to myxoid degeneration and primary ciliary dyskinesia was suggested