Craniofacial Phenotype in Obstructive Sleep Apnea and Its Impact on Positive Airway Pressure (PAP) Adherence.

Positive airway pressure (PAP) is an important treatment tool for patients with moderate and severe obstructive sleep apnea (OSA), and adherence to PAP significantly affects treatment outcomes. Disease severity, adverse effects, and psychosocial factors are known to predict medication adherence. Cephalometric parameters have been reported to positively correlate with upper airway collapse. However, research on the correlation between these cephalometric parameters and PAP adherence remains insufficient. This study aimed to identify this relationship. This study included 185 patients with OSA who were prescribed PAP. Polysomnography (PSG) was performed to diagnose OSA, and paranasal sinus computed tomography (PNS CT) was performed to check for comorbidities of the upper airway. In addition, cephalometric parameters such as the hyoid-posterior nasal spine (H-PNS), posterior nasal spine-mandibular plane (PNS-MP), and hyoid-mandibular plane (H-MP) were measured in the midsagittal and axial CT views. Adherence was evaluated 3-12 months after the PAP prescription. A total of 136 patients were PAP-adherent, and 49 were nonadherent. There were more males in the adherent group and a higher average height in the adherent group. The PSG results showed that the apnea-hypopnea index (AHI), respiratory disturbance index (RDI), oxygen desaturation index (ODI), arousal index (AI), rapid eye movement (REM) AHI, and supine AHI were significantly higher, and the lowest oxygen saturation was lower in the adherent group. In the analysis of covariance (ANCOVA) model adjusted for sex and height, among the cephalometric parameters, H-MP was significantly longer in the adherent group (p = 0.027), and H-PNS showed a longer tendency (p = 0.074). In the logistic regression analysis model, the odds ratio (OR) and 95% confidence intervals (95% CI) of adherence and severe OSA in the third tertile compared to the first tertile of H-MP were 2.93 (1.25-6.86) and 4.00 (1.87-8.56). In the case of H-PNS, they were 2.58 (1.14-5.81) and 4.86 (2.24-10.54), respectively. This study concluded that an inferiorly placed hyoid bone in adult patients is associated with PAP adherence and disease severity.

A Semantic Data-Based Distributed Computing Framework to Accelerate Digital Twin Services for Large-Scale Disasters.

As natural disasters become extensive, due to various environmental problems, such as the global warming, it is difficult for the disaster management systems to rapidly provide disaster prediction services, due to complex natural phenomena. Digital twins can effectively provide the services using high-fidelity disaster models and real-time observational data with distributed computing schemes. However, the previous schemes take little account of the correlations between environmental data of disasters, such as landscapes and weather. This causes inaccurate computing load predictions resulting in unbalanced load partitioning, which increases the prediction service times of the disaster management agencies. In this paper, we propose a novel distributed computing framework to accelerate the prediction services through semantic analyses of correlations between the environmental data. The framework combines the data into disaster semantic data to represent the initial disaster states, such as the sizes of wildfire burn scars and fuel models. With the semantic data, the framework predicts computing loads using the convolutional neural network-based algorithm, partitions the simulation model into balanced sub-models, and allocates the sub-models into distributed computing nodes. As a result, the proposal shows up to 38.5% of the prediction time decreases, compared to the previous schemes.

A Novel Digital Twin Architecture with Similarity-Based Hybrid Modeling for Supporting Dependable Disaster Management Systems.

Disaster management systems require accurate disaster monitoring and prediction services to reduce damages caused by natural disasters. Digital twins of natural environments can provide the services for the systems with physics-based and data-driven disaster models. However, the digital twins might generate erroneous disaster prediction due to the impracticability of defining high-fidelity physics-based models for complex natural disaster behavior and the dependency of data-driven models on the training dataset. This causes disaster management systems to inappropriately use disaster response resources, including medical personnel, rescue equipment and relief supplies, to ensure that it may increase the damages from the natural disasters. This study proposes a digital twin architecture to provide accurate disaster prediction services with a similarity-based hybrid modeling scheme. The hybrid modeling scheme creates a hybrid disaster model that compensates for the errors of physics-based prediction results with a data-driven error correction model to enhance the prediction accuracy. The similarity-based hybrid modeling scheme reduces errors from the data dependency of the hybrid model by constructing a training dataset using similarity assessments between the target disaster and the historical disasters. Evaluations in wildfire scenarios show that the digital twin decreases prediction errors by approximately 50% compared with those of the existing schemes.

Proteomic analysis of aqueous humor in canine primary angle-closure glaucoma in American Cocker Spaniel dogs.

OBJECTIVE: To analyze proteomic profiles of the aqueous humor (AH) of canines with primary angle-closure glaucoma (PACG) and identify associated protein alterations. ANIMALS STUDIED: Six American Cocker Spaniels with PACG and six American Cocker Spaniels without ocular diseases. METHODS: Aqueous humor samples were collected from six American Cocker Spaniels with PACG at Seoul National University, VMTH, and six healthy Cocker Spaniels without ocular disease at Irion Animal Hospital. For the PACG group, AH samples were obtained by anterior chamber paracentesis prior to glaucoma treatment. For the AH control group, AH samples were collected from patients anesthetized for other reasons. Total AH protein concentration was determined by the bicinchoninic acid (BCA) assay. AH protein samples were quantified by liquid chromatography-mass spectrometry (LC-MS/MS). Raw MS spectra were processed using MaxQuant software 30, and the Gene Ontology (GO) enrichment analysis was performed using ClueGO. RESULTS: The AH protein concentration in the PACG group (10.49 ± 17.98 µg/µl) was significantly higher than that of the control group (0.45 ± 0.11 µg/µl; p < .05). A total of 758 proteins were identified in the AH. Several proteins both significantly increased (n = 69) and decreased (n = 252) in the PACG group compared to those in the control group. GO enrichment analysis showed that the "response to wounding," "negative regulation of endopeptidase activity," and "cell growth" pathways were the most enriched terms in the PACG group compared to the control group. The top 5 proteins that were significantly increased in the AH of the PACG group were secreted phosphoprotein 1 (SPP1), peptidoglycan recognition proteins 2 (PGLYRP2), tyrosine 3-monooxygenase (YWHAE), maltase-glucoamylase (MGAM), and vimentin (VIM). CONCLUSIONS: Gene Ontology enrichment analysis using the proteomic data showed that proteins and pathways related to inflammation were significantly upregulated in the various stage of PACG. Proteomic analysis of the AH from the PACG may provide valuable insights into PACG pathogenesis.

Dependable Fire Detection System with Multifunctional Artificial Intelligence Framework.

A fire detection system requires accurate and fast mechanisms to make the right decision in a fire situation. Since most commercial fire detection systems use a simple sensor, their fire recognition accuracy is deficient because of the limitations of the detection capability of the sensor. Existing proposals, which use rule-based algorithms or image-based machine learning can hardly adapt to the changes in the environment because of their static features. Since the legacy fire detection systems and network services do not guarantee data transfer latency, the required need for promptness is unmet. In this paper, we propose a new fire detection system with a multifunctional artificial intelligence framework and a data transfer delay minimization mechanism for the safety of smart cities. The framework includes a set of multiple machine learning algorithms and an adaptive fuzzy algorithm. In addition, Direct-MQTT based on SDN is introduced to solve the traffic concentration problems of the traditional MQTT. We verify the performance of the proposed system in terms of accuracy and delay time and found a fire detection accuracy of over 95%. The end-to-end delay, which comprises the transfer and decision delays, is reduced by an average of 72%.

A case of unusual persistent pupillary membrane with total anterior capsular pigmentation.

A 5-year-old castrated male poodle presented with blindness. Ophthalmic examinations including slit-lamp biomicroscopy, tonometry, ultrasonography, and electroretinography were performed. The anterior lens capsule of the right eye (OD) was totally pigmented, with persistent pupillary membranes (PPMs). Ultrasonography of the same eye showed severe lens atrophy and retinal detachment. Electroretinography revealed flat a- and b-waves in OD, but normal amplitudes in the left eye (OS). No ocular defects were detected in OS except mature cataract. In this case, it was determined that hypermature cataract with PPMs caused both lens-induced-uveitis and total anterior lens capsule pigmentation. This condition needs to be differentiated from absent pupil. Notably, PPMs with total anterior lens capsular pigmentation are extremely rare in dogs.