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Abstract Whole-heart coronary calcium Agatston score is a well-established predictor of major adverse cardiovascular events (MACE), but it does not account for individual calcification features related to the pathophysiology of the disease (e.g., multiple-vessel disease, spread of the disease along the vessel, stable calcifications, numbers of lesions, and density). We used novel, hand-crafted calcification features (calcium-omics); Cox time-to-event modeling; elastic net; and up and down synthetic sampling methods for imbalanced data, to assess MACE risk. We used 2457 CT calcium score (CTCS) images enriched for MACE events from our large no-cost CLARIFY program (ClinicalTrials.gov Identifier: NCT04075162). Among calcium-omics features, numbers of calcifications, LAD mass, and diffusivity (a measure of spatial distribution) were especially important determinants of increased risk, with dense calcification (> 1000HU, stable calcifications) associated with reduced risk Our calcium-omics model with (training/testing, 80/20) gave C-index (80.5%/71.6%) and 2-year AUC (82.4%/74.8%). Although the C-index is notoriously impervious to model improvements, calcium-omics compared favorably to Agatston and gave a significant difference (P

Abstract Thin-cap fibroatheroma (TCFA) is a prominent risk factor for plaque rupture. Intravascular optical coherence tomography (IVOCT) enables identification of fibrous cap (FC), measurement of FC thicknesses, and assessment of plaque vulnerability. We developed a fully-automated deep learning method for FC segmentation. This study included 32,531 images across 227 pullbacks from two registries (TRANSFORM-OCT and UHCMC). Images were semi-automatically labeled using our OCTOPUS with expert editing using established guidelines. We employed preprocessing including guidewire shadow detection, lumen segmentation, pixel-shifting, and Gaussian filtering on raw IVOCT (r,θ) images. Data were augmented in a natural way by changing θ in spiral acquisitions and by changing intensity and noise values. We used a modified SegResNet and comparison networks to segment FCs. We employed transfer learning from our existing much larger, fully-labeled calcification IVOCT dataset to reduce deep-learning training. Postprocessing with a morphological operation enhanced segmentation performance. Overall, our method consistently delivered better FC segmentation results (Dice: 0.837 ± 0.012) than other deep-learning methods. Transfer learning reduced training time by 84% and reduced the need for more training samples. Our method showed a high level of generalizability, evidenced by highly-consistent segmentations across five-fold cross-validation (sensitivity: 85.0 ± 0.3%, Dice: 0.846 ± 0.011) and the held-out test (sensitivity: 84.9%, Dice: 0.816) sets. In addition, we found excellent agreement of FC thickness with ground truth (2.95 ± 20.73 µm), giving clinically insignificant bias. There was excellent reproducibility in pre- and post-stenting pullbacks (average FC angle: 200.9 ± 128.0°/202.0 ± 121.1°). Our fully automated, deep-learning FC segmentation method demonstrated excellent performance, generalizability, and reproducibility on multi-center datasets. It will be useful for multiple research purposes and potentially for planning stent deployments that avoid placing a stent edge over an FC.

Abstract X-linked inhibitor of apoptosis protein (XIAP) deficiency causes refractory inflammatory bowel disease. The XIAP protein plays a pivotal role in the pro-inflammatory response through the nucleotide-binding oligomerization domain-containing signaling pathway that is important in mucosal homeostasis. We analyzed the molecular mechanism of non-synonymous pathogenic variants (PVs) of XIAP BIR2 domain. We generated N-terminally green fluorescent protein-tagged XIAP constructs of representative non-synonymous PVs. Co-immunoprecipitation and fluorescence cross-correlation spectroscopy showed that wild-type XIAP and RIP2 preferentially interacted in live cells, whereas all non-synonymous PV XIAPs failed to interact properly with RIP2. Structural analysis showed that various structural changes by mutations, such as hydrophobic core collapse, Zn-finger loss, and spatial rearrangement, destabilized the two loop structures (174–182 and 205–215) that critically interact with RIP2. Subsequently, it caused a failure of RIP2 ubiquitination and loss of protein deficiency by the auto-ubiquitination of all XIAP mutants. These findings could enhance our understanding of the role of XIAP mutations in XIAP-deficient inflammatory bowel disease and may benefit future therapeutic strategies.

Abstract It can be difficult/impossible to fully expand a coronary artery stent in a heavily calcified coronary artery lesion. Under-expanded stents are linked to later complications. Here we used machine/deep learning to analyze calcifications in pre-stent intravascular optical coherence tomography (IVOCT) images and predicted the success of vessel expansion. Pre- and post-stent IVOCT image data were obtained from 110 coronary lesions. Lumen and calcifications in pre-stent images were segmented using deep learning, and lesion features were extracted. We analyzed stent expansion along the lesion, enabling frame, segmental, and whole-lesion analyses. We trained regression models to predict the post-stent lumen area and then computed the stent expansion index (SEI). Best performance (root-mean-square-error = 0.04 ± 0.02 mm2, r = 0.94 ± 0.04, p 80% were classified as “well-expanded;” others were “under-expanded.” Under-expansion classification results (e.g., AUC = 0.85 ± 0.02) were significantly improved over a previous, simple calculation, as well as other machine learning solutions. Promising results suggest that such methods can identify lesions at risk of under-expansion that would be candidates for intervention lesion preparation (e.g., atherectomy).

Abstract In this work, stenting in non-calcified and heavily calcified coronary arteries was quantified in terms of diameter-pressure relationships and load transfer from the balloon to the artery. The efficacy of post-dilation in non-calcified and heavily calcified coronary arteries was also characterized in terms of load sharing and the changes in tissue mechanics. Our results have shown that stent expansion exhibits a cylindrical shape in non-calcified lesions, while it exhibits a dog bone shape in heavily calcified lesions. Load-sharing analysis has shown that only a small portion of the pressure load (1.4 N, 0.8% of total pressure load) was transferred to the non-calcified lesion, while a large amount of the pressure load (19 N, 12%) was transferred to the heavily calcified lesion. In addition, the increasing inflation pressure (from 10 to 20 atm) can effectively increase the minimal lumen diameter (from 1.48 to 2.82 mm) of the heavily calcified lesion, the stress (from 1.5 to 8.4 MPa) and the strain energy in the calcification (1.77 mJ to 26.5 mJ), which are associated with the potential of calcification fracture. Results indicated that increasing inflation pressure can be an effective way to improve the stent expansion if a dog bone shape of the stenting profile is observed. Considering the risk of a balloon burst, our results support the design and application of the high-pressure balloon for post-dilation. This work also sheds some light on the stent design and choice of stent materials for improving the stent expansion at the dog bone region and mitigating stresses on arterial tissues.

This study aimed to investigate whether plaque characteristics derived from intravascular optical coherence tomography (IVOCT) could predict a long-term cardiovascular (CV) death. This study was a single-center, retrospective study on 104 patients who had undergone IVOCT-guided percutaneous coronary intervention. Plaque characterization was performed using Optical Coherence TOmography PlaqUe and Stent (OCTOPUS) software developed by our group. A total of 31 plaque features, including lesion length, lumen, calcium, fibrous cap (FC), and vulnerable plaque features (e.g., microchannel), were computed from the baseline IVOCT images. The discriminatory power for predicting CV death was determined using univariate/multivariate logistic regressions. Of 104 patients, CV death was identified in 24 patients (23.1%). Univariate logistic regression revealed that lesion length, calcium angle, calcium thickness, FC angle, FC area, and FC surface area were significantly associated with CV death (p < 0.05). In the multivariate logistic analysis, only the FC surface area (OR 2.38, CI 0.98–5.83, p < 0.05) was identified as a significant determinant for CV death, highlighting the importance of the 3D lesion analysis. The AUC of FC surface area for predicting CV death was 0.851 (95% CI 0.800–0.927, p < 0.05). Patients with CV death had distinct plaque characteristics (i.e., large FC surface area) in IVOCT. Studies such as this one might someday lead to recommendations for pharmaceutical and interventional approaches.

This study investigates the properties of various adhesives and assesses the effects of the coating and drying conditions of aluminum surface treatment agents on adhesion strength and chemical resistance. The adhesion between aluminum and the polymer film is improved through the application of a surface treatment agent to the aluminum surface. This study examines the initial adhesive strength of a manufactured pouch film with respect to the drying temperature and time and evaluates its adhesive strength in the presence of moisture. The results indicate that the residual moisture on the aluminum surface weakens the adhesive strength and significantly affects electrolyte resistance. A noticeable reduction in strength was observed after water spraying, when the drying temperature and time were relatively low during the initial strength measurement. Among the adhesives used for aluminum and CPP lamination, olefin adhesives exhibit less susceptibility to electrolyte effects and have higher adhesive strengths compared to urethane and ester adhesives. Leveraging these characteristics, flexible pouch cells were manufactured and their stability was evaluated. The results confirm that the flexible cells demonstrate excellent stability, exhibiting potential for application in wearable devices.

If we visit famous and iconic landmarks, we may want to take a photo of them. However, such sites are usually crowded, and taking photos with only landmarks without people could be challenging. This paper aims to automatically remove people in a picture and produce a natural image of the landmark alone. To this end, it presents Thanos, a system to generate authentic human-removed images in crowded places. It is designed to produce high-quality images with reasonable computation cost using short video clips of a few seconds. For this purpose, a multi-frame-based recovery region minimization method is proposed. The key idea is to aggregate information partially available from multiple image frames to minimize the area to be restored. The evaluation result presents that the proposed method outperforms alternatives; it shows lower Fréchet Inception Distance (FID) scores with comparable processing latency. It is also shown that the images by Thanos achieve a lower FID score than those of existing applications; Thanos’s score is 242.8, while those by Retouch-photos and Samsung object eraser are 249.4 and 271.2, respectively.

Machine learning (ML) has become more prevalent as a tool used for biogeochemical analysis in agricultural management. However, a common drawback of ML models is the lack of interpretability, as they are black boxes that provide little insight into agricultural management. To overcome this limitation, we compared three tree-based models (decision tree, random forest, and gradient boosting) to explain soil organic matter content through Shapley additive explanations (SHAP). Here, we used nationwide data on field crops, soil, terrain, and climate across South Korea (n = 9584). Using the SHAP method, we identified common primary controls of the models, for example, regions with precipitation levels above 1400 mm and exchangeable potassium levels exceeding 1 cmol+ kg−1, which favor enhanced organic matter in the soil. Different models identified different impacts of macronutrients on the organic matter content in the soil. The SHAP method is practical for assessing whether different ML models yield consistent findings in addressing these inquiries. Increasing the explainability of these models means determining essential variables related to soil organic matter management and understanding their associations for specific instances.

Abstract Thin-cap fibroatheroma (TCFA) and plaque rupture have been recognized as the most frequent risk factor for thrombosis and acute coronary syndrome. Intravascular optical coherence tomography (IVOCT) can identify TCFA and assess cap thickness, which provides an opportunity to assess plaque vulnerability. We developed an automated method that can detect lipidous plaque and assess fibrous cap thickness in IVOCT images. This study analyzed a total of 4360 IVOCT image frames of 77 lesions among 41 patients. Expert cardiologists manually labeled lipidous plaque based on established criteria. To improve segmentation performance, preprocessing included lumen segmentation, pixel-shifting, and noise filtering on the raw polar (r, θ) IVOCT images. We used the DeepLab-v3 plus deep learning model to classify lipidous plaque pixels. After lipid detection, we automatically detected the outer border of the fibrous cap using a special dynamic programming algorithm and assessed the cap thickness. Our method provided excellent discriminability of lipid plaque with a sensitivity of 85.8% and A-line Dice coefficient of 0.837. By comparing lipid angle measurements between two analysts following editing of our automated software, we found good agreement by Bland–Altman analysis (difference 6.7° ± 17°; mean ~ 196°). Our method accurately detected the fibrous cap from the detected lipid plaque. Automated analysis required a significant modification for only 5.5% frames. Furthermore, our method showed a good agreement of fibrous cap thickness between two analysts with Bland–Altman analysis (4.2 ± 14.6 µm; mean ~ 175 µm), indicating little bias between users and good reproducibility of the measurement. We developed a fully automated method for fibrous cap quantification in IVOCT images, resulting in good agreement with determinations by analysts. The method has great potential to enable highly automated, repeatable, and comprehensive evaluations of TCFAs.

Background and objective: Compared with other imaging modalities, intravascular optical coherence tomography (IVOCT) has significant advantages for guiding percutaneous coronary interventions, assessing their outcomes, and characterizing plaque components. To aid IVOCT research studies, we developed the Optical Coherence TOmography PlaqUe and Stent (OCTOPUS) analysis software, which provides highly automated, comprehensive analysis of coronary plaques and stents in IVOCT images. Methods: User specifications for OCTOPUS were obtained from detailed, iterative discussions with IVOCT analysts in the Cardiovascular Imaging Core Laboratory at University Hospitals Cleveland Medical Center, a leading laboratory for IVOCT image analysis. To automate image analysis results, the software includes several important algorithmic steps: pre-processing, deep learning plaque segmentation, machine learning identification of stent struts, and registration of pullbacks for sequential comparisons. Intuitive, interactive visualization and manual editing of segmentations were included in the software. Quantifications include stent deployment characteristics (e.g., stent area and stent strut malapposition), strut level analysis, calcium angle, and calcium thickness measurements. Interactive visualizations include (x,y) anatomical, en face, and longitudinal views with optional overlays (e.g., segmented calcifications). To compare images over time, linked visualizations were enabled to display up to four registered vessel segments at a time. Results: OCTOPUS has been deployed for nearly 1 year and is currently being used in multiple IVOCT studies. Underlying plaque segmentation algorithm yielded excellent pixel-wise results (86.2% sensitivity and 0.781 F1 score). Using OCTOPUS on 34 new pullbacks, we determined that following automated segmentation, only 13% and 23% of frames needed any manual touch up for detailed lumen and calcification labeling, respectively. Only up to 3.8% of plaque pixels were modified, leading to an average editing time of only 7.5 s/frame, an approximately 80% reduction compared to manual analysis. Regarding stent analysis, sensitivity and precision were both greater than 90%, and each strut was successfully classified as either covered or uncovered with high sensitivity (94%) and specificity (90%). We demonstrated use cases for sequential analysis. To analyze plaque progression, we loaded multiple pullbacks acquired at different points (e.g., pre-stent, 3-month follow-up, and 18-month follow-up) and evaluated frame-level development of in-stent neo-atherosclerosis. In ex vivo cadaver experiments, the OCTOPUS software enabled visualization and quantitative evaluation of irregular stent deployment in the presence of calcifications identified in pre-stent images. Conclusions: We introduced and evaluated the clinical application of a highly automated software package, OCTOPUS, for quantitative plaque and stent analysis in IVOCT images. The software is currently used as an offline tool for research purposes; however, the software's embedded algorithms may also be useful for real-time treatment planning.

Abstract Epicardial adipose tissue volume (EAT) has been linked to coronary artery disease and the risk of major adverse cardiac events. As manual quantification of EAT is time-consuming, requires specialized training, and is prone to human error, we developed a deep learning method (DeepFat) for the automatic assessment of EAT on non-contrast low-dose CT calcium score images. Our DeepFat intuitively segmented the tissue enclosed by the pericardial sac on axial slices, using two preprocessing steps. First, we applied a HU-attention-window with a window/level 350/40-HU to draw attention to the sac and reduce numerical errors. Second, we applied a novel look ahead slab-of-slices with bisection (“bisect”) in which we split the heart into halves and sequenced the lower half from bottom-to-middle and the upper half from top-to-middle, thereby presenting an always increasing curvature of the sac to the network. EAT volume was obtained by thresholding voxels within the sac in the fat window (− 190/− 30-HU). Compared to manual segmentation, our algorithm gave excellent results with volume Dice = 88.52% ± 3.3, slice Dice = 87.70% ± 7.5, EAT error = 0.5% ± 8.1, and R = 98.52% (p

IntroductionIn-stent neoatherosclerosis has emerged as a crucial factor in post-stent complications including late in-stent restenosis and very late stent thrombosis. In this study, we investigated the ability of quantitative plaque characteristics from intravascular optical coherence tomography (IVOCT) images taken just prior to stent implantation to predict neoatherosclerosis after implantation.MethodsThis was a sub-study of the TRiple Assessment of Neointima Stent FOrmation to Reabsorbable polyMer with Optical Coherence Tomography (TRANSFORM-OCT) trial. Images were obtained before and 18 months after stent implantation. Final analysis included images of 180 lesions from 90 patients; each patient had images of two lesions in different coronary arteries. A total of 17 IVOCT plaque features, including lesion length, lumen (e.g., area and diameter); calcium (e.g., angle and thickness); and fibrous cap (FC) features (e.g., thickness, surface area, and burden), were automatically extracted from the baseline IVOCT images before stenting using dedicated software developed by our group (OCTOPUS). The predictive value of baseline IVOCT plaque features for neoatherosclerosis development after stent implantation was assessed using univariate/multivariate logistic regression and receiver operating characteristic (ROC) analyses.ResultsFollow-up IVOCT identified stents with (n = 19) and without (n = 161) neoatherosclerosis. Greater lesion length and maximum calcium angle and features related to FC were associated with a higher prevalence of neoatherosclerosis after stent implantation (p < 0.05). Hierarchical clustering identified six clusters with the best prediction p-values. In univariate logistic regression analysis, maximum calcium angle, minimum calcium thickness, maximum FC angle, maximum FC area, FC surface area, and FC burden were significant predictors of neoatherosclerosis. Lesion length and features related to the lumen were not significantly different between the two groups. In multivariate logistic regression analysis, only larger FC surface area was strongly associated with neoatherosclerosis (odds ratio 1.38, 95% confidence interval [CI] 1.05–1.80, p < 0.05). The area under the ROC curve was 0.901 (95% CI 0.859–0.946, p < 0.05) for FC surface area.ConclusionPost-stent neoatherosclerosis can be predicted by quantitative IVOCT imaging of plaque characteristics prior to stent implantation. Our findings highlight the additional clinical benefits of utilizing IVOCT imaging in the catheterization laboratory to inform treatment decision-making and improve outcomes.

Abstract Background Tuberous sclerosis complex (TSC) is an autosomal dominant disorder characterized by hamartomas in multiple organs associated with germline mutations in TSC1 and TSC2, including exonic, intronic, or mosaic mutations. Gastrointestinal (GI) tract Lymphangioleiomyomatosis (LAM) is an extremely rare manifestation of TSC, with few reported cases. Herein, we aimed to determine the driver mutation, pathogenesis, and relationship of germline and somatic mutations of LAM through whole-genome sequencing (WGS) of the tumor and blood samples and whole transcriptome sequencing (WTS) analysis. Case presentation A nine-year-old girl with a full-blown TSC presented with abdominal masses detected during a routine check-up. Resected intestinal masses were diagnosed as LAM by thorough pathological examination. Interestingly, the LAM presented a somatic TSC2 gene mutation in exon 24 (p.R905W, c.C2713T), and the patient had intron retention by a novel germline mutation in the intron region of TSC2 (chr16:2126489, C > G). Conclusion Our case suggests that intron retention by a single nucleotide intronic mutation of TSC2 is sufficient to develop severe manifestations of TSC, but the development of LAM requires an additional somatic oncogenic mutation of TSC2.

Leaf temperature represents the mixed effects of solar radiation, temperature, and CO2 enrichment on the potential production of greenhouse vegetables. However, smart farm applications that monitor and account for changes in leaf temperature are limited. This study developed new hardware and software components for leaf temperature sensing integrated into a conventional smart farm system. We demonstrated a new system to monitor leaf temperatures and improve crop yield at two greenhouse tomato and strawberry farms in South Korea. We observed a rapid decline in leaf temperature at both farms when the indoor air temperature decreased. This pattern often corresponds to stagnant CO2 assimilation. The results suggest that leaf temperature sensing is practical for slow aeration and heating that is required to optimize photosynthetic efficiency, especially in the morning and when leaf temperatures become high (over 25 °C). Specifically, smart farm implementation with leaf temperature sensing increased the yield of tomatoes by 28–43%. Furthermore, our study highlights the need to develop leaf temperature models for smart greenhouse farming that interact with nutrient and water supplies.

Deep learning based methods are routinely used to segment various structures of interest in varied medical imaging modalities. Acquiring annotations for a large number of images requires a skilled analyst, and the process is both time consuming and challenging. Our approach to reduce effort is to reduce the number of images needing detailed annotation. For intravascular optical coherence tomography (IVOCT) image pullbacks, we tested 10% to 100% of training images derived from two schemes: equally-spaced image subsampling and deep-learning- based image clustering. The first strategy involves selecting images at equally spaced intervals from the volume, accounting for the high spatial correlation between neighboring images. In clustering, we used an autoencoder to create a deep feature space representation, performed k-medoids clustering, and then used the cluster medians for training. For coronary calcifications, a baseline U-net model was trained on all images from volumes of interest (VOIs) and compared with fewer images from the sub-sampling strategies. For a given sampling ratio, the clustering based method performed better or similar as compared to the equally spaced sampling approach (e.g., with 10% of images, mean F1 score for calcific class increased from 0.52 to 0.63, with equal spacing and with clustering, respectively). Additionally, for a fixed number of training images, sampling images from more VOIs performed better than otherwise. In conclusion, we recommend the clustering based approach to annotate a small fraction of images, creating a baseline model, which potentially can be improved further by annotating images selected using methods described in active learning research.

Pericoronary adipose tissue (PCAT) features on Computed Tomography (CT) have been shown to reflect local inflammation and increased cardiovascular risk. Our goal was to determine whether PCAT radiomics extracted from coronary CT angiography (CCTA) images are associated with intravascular optical coherence tomography (IVOCT)-identified vulnerable-plaque characteristics (e.g., microchannels (MC) and thin-cap fibroatheroma (TCFA)). The CCTA and IVOCT images of 30 lesions from 25 patients were registered. The vessels with vulnerable plaques were identified from the registered IVOCT images. The PCAT-radiomics features were extracted from the CCTA images for the lesion region of interest (PCAT-LOI) and the entire vessel (PCAT-Vessel). We extracted 1356 radiomic features, including intensity (first-order), shape, and texture features. The features were reduced using standard approaches (e.g., high feature correlation). Using stratified three-fold cross-validation with 1000 repeats, we determined the ability of PCAT-radiomics features from CCTA to predict IVOCT vulnerable-plaque characteristics. In the identification of TCFA lesions, the PCAT-LOI and PCAT-Vessel radiomics models performed comparably (Area Under the Curve (AUC) ± standard deviation 0.78 ± 0.13, 0.77 ± 0.14). For the identification of MC lesions, the PCAT-Vessel radiomics model (0.89 ± 0.09) was moderately better associated than the PCAT-LOI model (0.83 ± 0.12). In addition, both the PCAT-LOI and the PCAT-Vessel radiomics model identified coronary vessels thought to be highly vulnerable to a similar standard (i.e., both TCFA and MC; 0.88 ± 0.10, 0.91 ± 0.09). The most favorable radiomic features tended to be those describing the texture and size of the PCAT. The application of PCAT radiomics can identify coronary vessels with TCFA or MC, consistent with IVOCT. Furthermore, the use of CCTA radiomics may improve risk stratification by noninvasively detecting vulnerable-plaque characteristics that are only visible with IVOCT.

Though flexible microwave integrated circuits (MICs) are desirable for the construction of functional microwave amplifier circuits, realizing low cost III-V-based MMICs remains a challenge. Here, the authors report a heterogeneous integration strategy for the fabrication of flexible low-cost MICs.

We developed a fully automated, two-step deep learning approach for characterizing coronary calcified plaque in intravascular optical coherence tomography (IVOCT) images. First, major calcification lesions were detected from an entire pullback using a 3D convolutional neural network (CNN). Second, a SegNet deep learning model with the Tversky loss function was used to segment calcified plaques in the major calcification lesions. The fully connected conditional random field and the frame interpolation of the missing calcification frames were used to reduce classification errors. We trained/tested the networks on a large dataset comprising 8,231 clinical images from 68 patients with 68 vessels and 4,320 ex vivo cadaveric images from 4 hearts with 4 vessels. The 3D CNN model detected major calcifications with high sensitivity (97.7%), specificity (87.7%), and F1 score (0.922). Compared to the standard one-step approach, our two-step deep learning approach significantly improved sensitivity (from 77.5% to 86.2%), precision (from 73.5% to 75.8%), and F1 score (from 0.749 to 0.781). We investigated segmentation performance for varying numbers of training samples; at least 3,900 images were required to obtain stable segmentation results. We also found very small differences in calcification attributes (e.g., angle, thickness, and depth) and identical calcium scores on repetitive pullbacks, indicating excellent reproducibility. Applied to new clinical pullbacks, our method has implications for real-time treatment planning and imaging research.

Abstract Background IVUS is widely used to quantitatively assess coronary artery disease. The purpose of this study was to automatically characterize dense calcium (DC) tissue in the gray scale intravascular ultrasound (IVUS) images using the image textural features. Methods A total of 316 Gy-scale IVUS and corresponding virtual histology images from 26 patients with acute coronary syndrome who underwent IVUS along with X-ray angiography between October 2009 to September 2014 were retrospectively acquired and analyzed. One expert performed all procedures and assessed their IVUS scans. After image acquisition, the DC candidate and corresponding acoustic shadow regions were automatically determined. Then, nine image-base feature groups were extracted from the DC candidates. In order to reduce the dimensionalities, principal component analysis (PCA) was performed, and selected feature sets were utilized as an input for a deep belief network. Classification results were validated using 10-fold cross validation. Results The dimensionality of the feature map was efficiently reduced by 50% (from 66 to 33) without any performance decrease using PCA method. Sensitivity, specificity, and accuracy of the proposed method were 92.8 ± 0.1%, 85.1 ± 0.1%, and 88.4 ± 0.1%, respectively (p

Microvessels in vascular plaque are associated with plaque progression and are found in plaque rupture and intra-plaque hemorrhage. To analyze this characteristic of vulnerability, we developed an automated deep learning method for detecting microvessels in intravascular optical coherence tomography (IVOCT) images. A total of 8403 IVOCT image frames from 85 lesions and 37 normal segments were analyzed. Manual annotation was performed using a dedicated software (OCTOPUS) previously developed by our group. Data augmentation in the polar (r,θ) domain was applied to raw IVOCT images to ensure that microvessels appear at all possible angles. Pre-processing methods included guidewire/shadow detection, lumen segmentation, pixel shifting, and noise reduction. DeepLab v3+ was used to segment microvessel candidates. A bounding box on each candidate was classified as either microvessel or non-microvessel using a shallow convolutional neural network. For better classification, we used data augmentation (i.e., angle rotation) on bounding boxes with a microvessel during network training. Data augmentation and pre-processing steps improved microvessel segmentation performance significantly, yielding a method with Dice of 0.71 ± 0.10 and pixel-wise sensitivity/specificity of 87.7 ± 6.6%/99.8 ± 0.1%. The network for classifying microvessels from candidates performed exceptionally well, with sensitivity of 99.5 ± 0.3%, specificity of 98.8 ± 1.0%, and accuracy of 99.1 ± 0.5%. The classification step eliminated the majority of residual false positives and the Dice coefficient increased from 0.71 to 0.73. In addition, our method produced 698 image frames with microvessels present, compared with 730 from manual analysis, representing a 4.4% difference. When compared with the manual method, the automated method improved microvessel continuity, implying improved segmentation performance. The method will be useful for research purposes as well as potential future treatment planning.

Background and Objectives: Different types of anesthetics affect thermoregulatory mechanisms, such as the redistribution of body temperature, loss of skin heat, or inhibition of thermoregulatory vasoconstriction. Therefore, we compared remimazolam with propofol in terms of core body temperature in patients undergoing robotic-assisted and laparoscopic radical prostatectomy. Materials and methods: Ninety patients were randomly assigned to either the propofol–remifentanil (PR) group or the remimazolam–remifentanil (RR) group. The PR group (n = 45) received effect-site concentrations of 6.0 μg/mL of propofol and 4 ng/mL of remifentanil, followed by 0.9 mg/kg of 1% rocuronium and maintenance with effect-site concentrations of 2–4 μg/mL of propofol and 3 ng/mL of remifentanil. The RR group (n = 45) received remimazolam 6 mg/kg/h by continuous intravenous infusion and the effect-site concentration of 4 ng/mL of remifentanil, followed by 0.9 mg/kg of 1% rocuronium, remimazolam 1–3 mg/kg/h, and remifentanil 3 ng/mL. The primary outcome was core body temperature, and secondary outcomes included vasoconstriction threshold (°C) and time to onset of vasoconstriction (min). Results: The core body temperature in the RR group was significantly higher at 60, 80, 100, 120, 140, 160, and 180 min after induction than in the PR group (p < 0.01). The vasoconstriction threshold was significantly higher in the RR group (35.2 ± 0.4) than in the PR group (34.8 ± 0.3) (p < 0.01). The time to onset of vasoconstriction was significantly less in the RR group (150.5 ± 10.2) than in the PR group (158.5 ± 8.4) (p < 0.01). However, the incidence of intraoperative hypothermia was not significant between two groups. Conclusions: Remimazolam appears to reduce vasoconstriction threshold less than and had a faster onset of vasoconstriction, resulting in superior thermoregulatory control.

The computational fluid dynamic method has been widely used to quantify the hemodynamic alterations in a diseased artery and investigate surgery outcomes. The artery model reconstructed based on optical coherence tomography (OCT) images generally does not include the side branches. However, the side branches may significantly affect the hemodynamic assessment in a clinical setting, i.e., the fractional flow reserve (FFR), defined as the ratio of mean distal coronary pressure to mean aortic pressure. In this work, the effect of the side branches on FFR estimation was inspected with both idealized and optical coherence tomography (OCT)-reconstructed coronary artery models. The electrical analogy of blood flow was further used to understand the impact of the side branches (diameter and location) on FFR estimation. Results have shown that the side branches decrease the total resistance of the vessel tree, resulting in a higher inlet flowrate. The side branches located at the downstream of the stenosis led to a lower FFR value, while the ones at the upstream had a minimal impact on the FFR estimation. Side branches with a diameter larger than one third of the main vessel diameter are suggested to be considered for a proper FFR estimation. The findings in this study could be extended to other coronary artery imaging modalities and facilitate treatment planning.

Understanding the gating mechanism of ion channel proteins is key to understanding the regulation of cell signaling through these channels. Channel opening and closing are regulated by diverse environmental factors that include temperature, electrical voltage across the channel, and proton concentration. Low permeability in voltage-gated potassium ion channels (Kv) is intimately correlated with the prolonged action potential duration observed in many acidosis diseases. The Kv channels consist of voltage-sensing domains (S1-S4 helices) and central pore domains (S5-S6 helices) that include a selectivity filter and water-filled cavity. The voltage-sensing domain is responsible for the voltage-gating of Kv channels. While the low permeability of Kv channels to potassium ion is highly correlated with the cellular proton concentration, it is unclear how an intracellular acidic condition drives their closure, which may indicate an additional pH-dependent gating mechanism of the Kv family. Here, we show that two residues E327 and H418 in the proximity of the water cavity of Kv1.2 play crucial roles as a pH switch. In addition, we present a structural and molecular concept of the pH-dependent gating of Kv1.2 in atomic detail, showing that the protonation of E327 and H418 disrupts the electrostatic balance around the S6 helices, which leads to a straightening transition in the shape of their axes and causes dewetting of the water-filled cavity and closure of the channel. Our work offers a conceptual advancement to the regulation of the pH-dependent gating of various voltage-gated ion channels and their related biological functions.

Purpose. Temporal summation of pain, which is defined as the perception of greater pain evoked by repetitive painful stimuli, varies among individuals. This study aimed at determining the impact of the timing of rocuronium after induction with propofol on the temporal summation of pain. Methods. One hundred patients aged 19–60 years underwent gynecologic laparoscopic surgery. Patients were randomly assigned to one of the two groups: group PRi received immediate injections of rocuronium after propofol administration and group PRd received rocuronium injections when the bispectral index score (BIS) decreased to

Background and Objectives: Female reproductive hormones may affect core body temperature. This study aimed to investigate the effects of female reproductive hormones on inadvertent intraoperative hypothermia in patients who underwent laparoscopic gynecologic surgery under general anesthesia. Materials and Methods: This retrospective study included 660 menstruating and menopausal female patients aged 19–65 years. The patients were divided into two groups according to the occurrence of inadvertent intraoperative hypothermia: non-hypothermia group (N = 472) and hypothermia group (N = 188). After propensity score matching, 312 patients (N = 156 in each group) were analyzed to investigate the association between intraoperative hypothermia and female reproductive hormones. As potential predictors of inadvertent hypothermia, the levels of female reproductive hormones were analyzed using binary logistic regression. Results: The association of estradiol (r = −0.218, p = 0.000) and progesterone (r = −0.235, p = 0.000) levels with inadvertent intraoperative hypothermia was significant but weakly negative before matching; however, it was significant and moderately negative after matching (r = −0.326, p = 0.000 and r = −0.485, p = 0.000, respectively). In a binary logistic analysis, the odds ratio for estradiol was 0.995 (p = 0.014, 0.993 < 95% confidence interval [CI] < 0.998) before matching and 0.993 (p = 0.000, 0.862 < 95% CI < 0.930) after matching, and that for progesterone was 0.895 (p = 0.000, 0.862 < 95% CI < 0.930) before matching and 0.833 (p = 0.014, 0.990 < 95% CI < 0.996) after matching. Conclusions: Estradiol and progesterone levels were associated with inadvertent intraoperative hypothermia. However, the odds ratio for female reproductive hormone levels was close to 1. Therefore, female reproductive hormones may not be a risk factor for hypothermia during gynecologic surgery under general anesthesia. However, a small sample size in this study limits the generalizability of the results.

A number of Augmented Reality (AR) frameworks are now available and used to support the development of mobile AR applications. In this paper, we measure and compare the recognition performance of the commercial AR frameworks and identify potential issues that can occur in the real application environment. For experiments, we assume a situation in which a consumer purchases food products in a grocery store and consider an application scenario in which AR content related to the products is displayed on a smartphone screen by recognizing such products. We use four performance metrics to compare the performance of the selected AR frameworks, Vuforia, ARCore, and MAXST. Experimental results show that Vuforia is relatively superior to the others. The limitation of the AR frameworks is also identified when they are used in a real grocery store environment.

This paper proposes a novel and accurate method for estimating the flight coefficient of a flying disc typically operating at a high rotation rate. In particular, the proposed method introduces a new algorithm that takes advantage of magnetic data measured by a miniaturized sensor module onboard a conventional disc. Since the geomagnetic field measured by the magnetic sensor mounted on the rotating body yields a general sinusoidal waveform, a frequency domain analysis is employed in computing the rotational rate. Furthermore, on the basis of the estimated rate during a whole flight period, a yaw damping derivative coefficient is derived, which enables an accurate prediction of the disc’s flight trajectory. For performance verification, both a reference rotation table test and a real flight test are performed, for which a miniaturized embedded sensor module is designed and manufactured for an onboard flight test. A reference rotation test validates the performance of the proposed method. Subsequently, a flight test, in which a simulator-based trajectory is compared with the true reference trajectory, verifies that the proposed method better predicts the flight trajectory by incorporating the estimated coefficient.

This study was performed to evaluate effects of no-till (NT) and standard tillage (ST) on POM in two 15-ha neighboring fields from 2003 to 2004. We also evaluated the effects of minimum tillage (MT) on POM after both NT and ST fields were converted to MT in the summer of 2005. We quantified C and N stocks of three size fractions (53–250, 250–1000, and 1000–2000 μm) of POM (0–0.15 m depth). The POM-C 53–250 μm and 250–1000 μm fractions decreased by 25% and 36% after six months under ST, whereas relatively little change occurred under NT, suggesting significant tillage effects over the period 2003-2004. Only small changes in POM content then occurred under MT on both fields. Changes in POM-N were similar to POM-C changes upon tillage conversions. This suggests that reduced tillage did not lead to soil C increase compared to ST but may help maintain the level of soil C for a typical California farming system. Short-term, field level variability of POM was primarily affected by tillage and was further influenced by clay content, bulk density, and scale of observation.