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Purpose: Radical surgery is the primary treatment for early-stage resectable lung cancer, yet recurrence after curative surgery is not uncommon. Identifying patients at high risk of recurrence using preoperative computed tomography (CT) images could enable more aggressive surgical approaches, shorter surveillance intervals, and intensified adjuvant treatments. This study aims to analyze lung cancer sites in CT images to predict potential recurrences in high-risk individuals. Methods: We retrieved anonymized imaging and clinical data from an institutional database, focusing on patients who underwent curative pulmonary resections for non-small cell lung cancers. Our study used a deep learning model, the Mask Region-based Convolutional Neural Network (MRCNN), to predict cancer locations and assign recurrence classification scores. To find optimized trained weighted values in the model, we developed preprocessing python codes, adjusted dynamic learning rate, and modifying hyper parameter in the model. Results: The model training completed; we performed classifications using the validation dataset. The results, including the confusion matrix, demonstrated performance metrics: bounding box (0.390), classification (0.034), mask (0.266), Region Proposal Network (RPN) bounding box (0.341), and RPN classification (0.054). The model successfully identified lung cancer recurrence sites, which were then accurately mapped onto chest CT images to highlight areas of primary concern. Conclusion: The trained model allows clinicians to focus on lung regions where cancer recurrence is more likely, acting as a significant aid in the detection and diagnosis of lung cancer. Serving as a clinical decision support system, it offers substantial support in managing lung cancer patients. [ABSTRACT FROM AUTHOR]

Since 2010, vacuum insulation panels (VIPs) have steadily been used as a main insulator in refrigerators, but interest in the proper recycling of VIPs is lacking. Thus, the purpose of this study was to develop a VIP detection system to determine whether VIPs could be identified in end-of-life refrigerators at the recycling stage. We developed experiments to measure changes in the pressure and thickness of the perforating sidewalls of refrigerators. We conducted a pressure measurement experiment using 30 refrigerators and recorded pressure changes of 3.2 ± 1.1 kPa in the VIP refrigerator group and 0.5 ± 0.8 kPa in the non-VIP group. The mean thickness change for the 14 VIP refrigerators was 10.606 ± 4.128 mm. We conclude that the success rate of the VIP detection system developed in this study in determining VIPs present in refrigerators reached 96.7%. This study provides a basis for developing an infrastructure and environment to recycle end-of-life VIP refrigerators. [ABSTRACT FROM AUTHOR]

Tumor progression and metastasis are regulated by endothelial cells undergoing endothelial–mesenchymal transition (EndoMT), a cellular differentiation process in which endothelial cells lose their properties and differentiate into mesenchymal cells. The cells undergoing EndoMT differentiate through a spectrum of intermediate phases, suggesting that some cells remain in a partial EndoMT state and exhibit an endothelial/mesenchymal phenotype. However, detailed analysis of partial EndoMT has been hampered by the lack of specific markers. Transforming growth factor‐β (TGF‐β) plays a central role in the induction of EndoMT. Here, we showed that inhibition of TGF‐β signaling suppressed EndoMT in a human oral cancer cell xenograft mouse model. By using genetic labeling of endothelial cell lineage, we also established a novel EndoMT reporter cell system, the EndoMT reporter endothelial cells (EMRECs), which allow visualization of sequential changes during TGF‐β‐induced EndoMT. Using EMRECs, we characterized the gene profiles of multiple EndoMT stages and identified CD40 as a novel partial EndoMT‐specific marker. CD40 expression was upregulated in the cells undergoing partial EndoMT, but decreased in the full EndoMT cells. Furthermore, single‐cell RNA sequencing analysis of human tumors revealed that CD40 expression was enriched in the population of cells expressing both endothelial and mesenchymal cell markers. Moreover, decreased expression of CD40 in EMRECs enhanced TGF‐β‐induced EndoMT, suggesting that CD40 expressed during partial EndoMT inhibits transition to full EndoMT. The present findings provide a better understanding of the mechanisms underlying TGF‐β‐induced EndoMT and will facilitate the development of novel therapeutic strategies targeting EndoMT‐driven cancer progression and metastasis. [ABSTRACT FROM AUTHOR]

For avoiding knock occurrence in SI engines, spark timing is retarded whenever the knock has occurred which leads to a loss of thermal efficiency. Therefore, the knock occurrence needs to be properly controlled. For doing that, knock should preemptively be predicted and controlled. Prerequisite data for knock prediction modelling is a knock onset position, which can be figured out by finding the starting point of the oscillation on pressure data. A deep learning knock onset determination model was developed in a previous study, and showed the highest accuracy among the comparable methods, the model showed weak robustness on knock cycles obtained in different engine experiments. Meanwhile, the 1D CNN model has been widely used in signal processing fields with its advantage of having a feature extraction layer, and the model is introduced in this study for determining the knock onset. Dataset from four different engine types were used for verifying the model accuracy and robustness. The dataset was augmented by calculation windows for producing various data with limited data sources. Hyperparameters of the model were optimized with random search. The accuracy standard deviation following engine types in terms of RMSE was improved by 77.4 % from 0.827 CA to 0.187 CA. [ABSTRACT FROM AUTHOR]

Glutathione (GSH) is an essential molecule that plays a pivotal role in maintaining intracellular redox homeostasis, as well as other physiological processes. However, the chemical mechanisms underlying the GSH-induced processes remain insufficiently understood due to the lack of appropriate detection tools. Fluorescence GSH imaging can serve as a useful principle for the rapid, convenient, and non-destructive detection of GSH in living organisms. In this study, we developed a fluorescent GSH probe based on a linear, homoleptic Au(I) complex with two 1,3-diphenylbenzimidazolium carbene ligands. The Au(I) complex produced a fluorescence turn-on response to GSH. Fluorescence GSH signaling was characterized with a short response time of a few seconds. The rapid response was attributed to the displacement of the carbene ligand with GSH, which involved a labile inner-sphere coordination interaction. Finally, we demonstrated the biological utility of our GSH probe by unambiguously discriminating between different GSH levels in normal and senescent preadipocytes. [ABSTRACT FROM AUTHOR]

Spatial transcriptomics is a newly emerging field that enables high‐throughput investigation of the spatial localization of transcripts and related analyses in various applications for biological systems. By transitioning from conventional biological studies to "in situ" biology, spatial transcriptomics can provide transcriptome‐scale spatial information. Currently, the ability to simultaneously characterize gene expression profiles of cells and relevant cellular environment is a paradigm shift for biological studies. In this review, recent progress in spatial transcriptomics and its applications in neuroscience and cancer studies are highlighted. Technical aspects of existing technologies and future directions of new developments (as of March 2023), computational analysis of spatial transcriptome data, application notes in neuroscience and cancer studies, and discussions regarding future directions of spatial multi‐omics and their expanding roles in biomedical applications are emphasized. [ABSTRACT FROM AUTHOR]

The prevalence of genital human papillomavirus (HPV) in women with endometriosis has never been reported in a national representative survey. We aimed to investigate the association of endometriosis with the prevalence of HPV. We analyzed the data on 1768 women (representing 43,824,157 women) in the United States aged 20–54 years from the National Health and Nutrition Examination Survey in the prevaccination era (2003–2006). The diagnosis of endometriosis was based on a self-report. The prevalence of any HPV in women with endometriosis did not differ from that in women without endometriosis after controlling for potential confounders such as age, ethnicity, family income, marital status, and the number of deliveries (adjusted prevalence ratio (aPR) 0.84, 95% confidence interval (CI) 0.61–1.15). No significant association was found between the prevalence of high-risk HPV and the diagnosis of endometriosis (aPR 0.71, 95% CI 0.44–1.14). If the participants were not covered by health insurance, the prevalence of any HPV infection in women with endometriosis was higher than in those without endometriosis (aPR 1.44, 95% CI 0.94–2.20). In contrast, in a subgroup who had health insurance, a lower prevalence of any HPV infection was observed in women with endometriosis (aPR 0.71, 95% CI 0.50–1.03), and P for interaction was statistically significant (P = 0.01). There was no association between endometriosis and HPV infection in this study of HPV vaccine-naïve women of reproductive age. The association was not different by the type of HPV. However, access to healthcare may modify the association between endometriosis and HPV infection. [ABSTRACT FROM AUTHOR]

In the previous research, we had developed a Nitrogen Oxides emissions (NOx) prediction model using deep learning methodology. However, it is necessary to develop a lightweight model for practical application. We developed a machine learning model for predicting NOx using Random Forest method to reduce the negligible input features. For comparison, a Base model was developed with all features. The Shapley Additive Explanations method, which can show the influence of the input features, and the Pearson Correlation Coefficient method, which can show the proportional relationship between the output and the input values, were used to choose the dominant features. The final model was determined using the Shapley Additive Explanations method. The final model shows similar prediction performance to the base model, though it has only 11 features (30 % of the Base model). The final model shows 15.76 for the root mean square error and 0.965 for R2 with the test data. By extracting the dominant features influencing NOx prediction, we could develop a lightweight and accurate NOx prediction model. [ABSTRACT FROM AUTHOR]

Objectives: Big data-based multicenter medical research is expected to bring significant advances to cancer treatment worldwide. However, there are concerns related to data sharing among multicenter networks. Clinical data can be shielded by firewalls using distributed research networks (DRNs). We attempted to develop DRNs for multicenter research that can be easily installed and used by any institution. Patients and Methods: We propose a DRN for multicenter cancer research called the cancer research line (CAREL) and present a data catalog based on a common data model (CDM). CAREL was validated using 1723 patients with prostate cancer and 14 990 patients with lung cancer in a retrospective study. We used the attribute-value pairs and array data type JavaScript object notation (JSON) format to interface third-party security solutions such as blockchain. Results: We developed visualized data catalogs of prostate and lung cancer based on the observational medical outcomes partnership (OMOP) CDM, from which researchers can easily browse and select relevant data. We made the CAREL source code readily available for download and application for relevant purposes. In addition, it is possible to realize a multicenter research network using CAREL development sources. Conclusion: CAREL source can enable medical institutions to participate in multicenter cancer research. Our technology is open source, so small institutions that cannot afford to spend high costs can use it to develop a platform for multicenter research. [ABSTRACT FROM AUTHOR]

Aims Components of bone morphogenetic protein (BMP) signalling have been implicated in both pathogenesis of pulmonary arterial hypertension (PAH) and endothelial-mesenchymal transition (EndoMT). In particular, the importance of BMP type 2 receptor in these processes has been extensively analysed. However, the contribution of BMP type 1 receptors (BMPR1s) to the onset of PAH and EndoMT remains poorly understood. BMPR1A, one of BMPR1s, was recently implicated in the pathogenesis of PAH, and was found to be down-regulated in the lungs of PAH patients, neither the downstream mechanism nor its contribution to EndoMT has been described. Therefore, we aim to delineate the role of endothelial BMPR1A in modulating EndoMT and pathogenesis of PAH. Methods and results We find that BMPR1A knockdown in endothelial cells (ECs) induces hallmarks of EndoMT, and deletion of endothelial Bmpr1a in adult mice (Bmpr1aiECKO) leads to development of PAH-like symptoms due to excessive EndoMT. By lineage tracing, we show that endothelial-derived smooth muscle cells are increased in endothelial Bmpr1a -deleted mice. Mechanistically, we identify ZEB1 as a primary target for BMPR1A in this setting; upon BMPR1A activation, ID2 physically interacts and sequesters ZEB1 to attenuate transcription of Tgfbr2 , which in turn lowers the responses of ECs towards transforming growth factor beta (TGFβ) stimulation and prevents excessive EndoMT. In Bmpr1aiECKO mice, administering endothelial targeting lipid nanoparticles containing siRNA against Tgfbr2 effectively ameliorate PAH, reiterating the importance of BMPR1A-ID2/ZEB1-TGFBR2 axis in modulating progression of EndoMT and pathogenesis of PAH. Conclusions We demonstrate that BMPR1A is key to maintain endothelial identity and to prevent excessive EndoMT. We identify BMPR1A-induced interaction between ID2 and ZEB1 is the key regulatory step for onset of EndoMT and pathogenesis of PAH. Our findings indicate that BMPR1A-ID2/ZEB1-TGFBR2 signalling axis could serve as a potential novel therapeutic target for PAH and other EndoMT-related vascular disorders. [ABSTRACT FROM AUTHOR]

This paper comparatively analyzes the static random-access memory (SRAM) cell designs for fault tolerance. Since SRAM cells are sensitive to radiation-induced single event upsets, various circuit-level approaches have been applied. Compared to the conventional SRAM cell circuits, one possibility is adding redundant storage nodes by means of additional transistors. The strength and weakness of the SRAM cells in terms of various performance aspects—speed, area, power, stability, fault tolerance, etc.—according to the design approaches are compared analytically and discussed. The discussion concludes that, in the future, it is paramount to develop an SRAM cell design with a mitigated trade-off between read/write performance and SEU tolerance. [ABSTRACT FROM AUTHOR]

The detection of Epstein–Barr virus (EBV) in gastric cancer patients is crucial for clinical decision making, as it is related with specific treatment responses and prognoses. Despite its importance, the limited medical resources preclude universal EBV testing. Herein, we propose a deep learning-based EBV prediction method from H&E-stained whole-slide images (WSI). Our model was developed using 319 H&E stained WSI (26 EBV positive; TCGA dataset) from the Cancer Genome Atlas, and 108 WSI (8 EBV positive; ISH dataset) from an independent institution. Our deep learning model, EBVNet consists of two sequential components: a tumor classifier and an EBV classifier. We visualized the learned representation by the classifiers using UMAP. We externally validated the model using 60 additional WSI (7 being EBV positive; HGH dataset). We compared the model's performance with those of four pathologists. EBVNet achieved an AUPRC of 0.65, whereas the four pathologists yielded a mean AUPRC of 0.41. Moreover, EBVNet achieved an negative predictive value, sensitivity, specificity, precision, and F1-score of 0.98, 0.86, 0.92, 0.60, and 0.71, respectively. Our proposed model is expected to contribute to prescreen patients for confirmatory testing, potentially to save test-related cost and labor. [ABSTRACT FROM AUTHOR]

The advent of single-cell RNA sequencing (scRNA-seq) has provided insight into the tumour immune microenvironment (TIME). This review focuses on the application of scRNA-seq in investigation of the TIME. Over time, scRNA-seq methods have evolved, and components of the TIME have been deciphered with high resolution. In this review, we first introduced the principle of scRNA-seq and compared different sequencing approaches. Novel cell types in the TIME, a continuous transitional state, and mutual intercommunication among TIME components present potential targets for prognosis prediction and treatment in cancer. Thus, we concluded novel cell clusters of cancer-associated fibroblasts (CAFs), T cells, tumour-associated macrophages (TAMs) and dendritic cells (DCs) discovered after the application of scRNA-seq in TIME. We also proposed the development of TAMs and exhausted T cells, as well as the possible targets to interrupt the process. In addition, the therapeutic interventions based on cellular interactions in TIME were also summarized. For decades, quantification of the TIME components has been adopted in clinical practice to predict patient survival and response to therapy and is expected to play an important role in the precise treatment of cancer. Summarizing the current findings, we believe that advances in technology and wide application of single-cell analysis can lead to the discovery of novel perspectives on cancer therapy, which can subsequently be implemented in the clinic. Finally, we propose some future directions in the field of TIME studies that can be aided by scRNA-seq technology. [ABSTRACT FROM AUTHOR]

Since dust and flammable gas are generated during the waste recycling process, there is always a risk of a fire accident. However, research on disaster management at recycling facilities deals only with the problem of processing systems from a technical standpoint and does not suggest concrete alternatives from a management aspect. Therefore, this study analyzed the influence of the disaster response network of a Waste of Electrical and Electronic Equipment (WEEE) recycling center at the organizational level based on the concept of the cognitive accuracy of a network considering administrative aspects. Accordingly, a survey was conducted using a structured questionnaire targeting 47 employees at the WEEE recycling center in South Korea and by applying the two-mode network analysis method using UCINET software, the centrality of the actor and the density of the network were quantitatively analyzed. Through this analysis, we confirmed that factors affecting the influence of the network exist, such that the entire network and the networks of different levels of position are different. We suggest that this can be improved by deploying safety and health management managers who perform formal tasks at the center of the network so that everyone can agree on the political approach and by empowering the safety and health management manager to conduct active education and training. Furthermore, we suggest that the network structure should be reorganized, centering on the person in charge of safety and health management to have a network system that matches each position. [ABSTRACT FROM AUTHOR]

Given the vehicle speed during actual driving, it is possible to apply an advanced energy management strategy for achieving better efficiency and less emission. We conducted a study to predict the future speed while driving of city buses, where only a few bus driving data and bus stop IDs are used without external complex traffic information. The speed prediction models were developed based on long time short memory (LSTM) and a gated recurrent unit (GRU), and a deep neural network (DNN) is also adopted for the bus stop ID processing. The performances of the models were analyzed and compared such that we found the LSTM-based model presents remarkable and practical prediction ability in accuracy and time spent. Adopting the proposed speed prediction model would make it a reality sooner, application of the optimal energy control strategy in the real world. [ABSTRACT FROM AUTHOR]

The role of alcohol consumption in the risk of vasomotor symptoms (VMS), the most cardinal climacteric symptoms, is not well established. We examined their relationship with early-onset VMS among premenopausal women. Moderately-to-severely bothersome VMS, the primary outcome, was assessed using the Korean version of the Menopause-Specific Quality of Life questionnaire. The alcohol consumption categories included lifetime abstainer, former drinker, or current drinker, categorized as light, moderate, heavy, and very heavy. Compared with the lifetime-abstinence (reference), the multivariable-adjusted odds ratio (95% CIs) for prevalent VMS in alcohol consumption of <10, 10–19, 20–39, and ≥40 g/day were 1.42 (1.02–1.99), 1.99 (1.27–3.12), 2.06 (1.19–3.57), and 3.52 (1.72–7.20), respectively (p trend <0.01). Compared with the lifetime-abstinence, the multivariable-adjusted hazard ratios (95% CIs) for incident bothersome VMS among average alcohol consumption of <10, 10–19, 20–39, and ≥40 g/day were 1.10 (0.85–1.41), 1.03 (0.70–1.51), 1.72 (1.06–2.78), and 2.22 (1.16–4.23), respectively (p trend = 0.02). Increased alcohol consumption positively and consistently showed a relationship with increased risk of both prevalent and incident early-onset VMS. Refraining from alcohol consumption may help prevent bothersome VMS in premenopausal women. [ABSTRACT FROM AUTHOR]

We conducted a retrospective cohort study to evaluate the temporal pattern of incidence of chronic conditions after developing breast cancer using a population-based national registry. We selected 84,969 women with newly diagnosed breast cancer between 2002 and 2016 and a 1:10 sample of age-matched non-breast cancer controls (N = 1,057,674). The main study exposure was incident breast cancer, considered as a time-varying exposure. The outcomes were incident cases of leukemia, endometrial cancer, myeloma, cardiomyopathy, osteoporosis, end stage renal disease (ESRD), pulmonary fibrosis, hypothyroidism, type 2 diabetes, hypertension and hyperlipidemia. The development of breast cancer was associated with a significantly increased risk of all outcomes analyzed except for ESRD and hypertension. The fully-adjusted risks of leukemia (HR 3.09; 95% CI 2.11–4.51), cardiomyopathy (HR 2.65; 95% CI 1.90–3.68), endometrial cancer (HR 3.53; 95% CI 2.76–4.53), hypothyroidism (HR 1.29; 95% CI 1.19–1.40), pulmonary fibrosis (HR 1.84; 95% CI 1.12–3.02), and hyperlipidemia (HR 1.24; 95% CI 1.20–1.28) remained significantly elevated after more than 5 years since diagnosis. Optimal care for breast cancer survivors requires close collaboration between oncologists and allied health care professionals to identify and manage the long-term morbidity and mortality associated with these chronic conditions. [ABSTRACT FROM AUTHOR]

The development of autoimmune diseases following SARS-CoV-2 infection, including multisystem inflammatory syndrome, has been reported, and several mechanisms have been suggested, including molecular mimicry. We developed a scalable, comparative immunoinformatics pipeline called cross-reactive-epitope-search-using-structural-properties-of-proteins (CRESSP) to identify cross-reactive epitopes between a collection of SARS-CoV-2 proteomes and the human proteome using the structural properties of the proteins. Overall, by searching 4 911 245 proteins from 196 352 SARS-CoV-2 genomes, we identified 133 and 648 human proteins harboring potential cross-reactive B-cell and CD8+ T-cell epitopes, respectively. To demonstrate the robustness of our pipeline, we predicted the cross-reactive epitopes of coronavirus spike proteins, which were recognized by known cross-neutralizing antibodies. Using single-cell expression data, we identified PARP14 as a potential target of intermolecular epitope spreading between the virus and human proteins. Finally, we developed a web application (https://ahs2202.github.io/3M/) to interactively visualize our results. We also made our pipeline available as an open-source CRESSP package (https://pypi.org/project/cressp/), which can analyze any two proteomes of interest to identify potentially cross-reactive epitopes between the proteomes. Overall, our immunoinformatic resources provide a foundation for the investigation of molecular mimicry in the pathogenesis of autoimmune and chronic inflammatory diseases following COVID-19. [ABSTRACT FROM AUTHOR]

This article proposes a reinforced soft gripper with a mechanically strengthened electroadhesion pad and a multilayered dielectric elastomer (DE) actuator, for practical robotic application. The reinforcement of the electroadhesion pad is achieved by a metallic electrode pattern printed on a flexible polyimide film, which has a higher elastic modulus than typical soft materials. Moreover, the multilayered DE actuator is used to increase the bending force of the soft gripper. We maximize the gripper performance by optimizing design factors of the electroadhesion and multilayered DE through experimental and simulation analysis. In this study, we demonstrate dynamic picking up and placing tasks with the designed gripper assembled to a robotic manipulator. The gripper can lift and move various shaped objects 100 times heavier than the gripper's mass of 6.2 g. Moreover, the soft gripper with a large area can firmly hold 16.8 kg, with optimized specifications. [ABSTRACT FROM AUTHOR]

Featured Application: This study demonstrates an artificial intelligence-based tool that could identify the real-time condition of dairy cows by using underutilized data generated from the Internet of Things equipment in dairy farms. One of the main challenges in the adoption of artificial intelligence-based tools, such as integrated decision support systems, is the complexities of their application. This study aimed to define the relevant parameters that can be used as indicators for real-time detection of heat stress and subclinical mastitis in dairy cows. Moreover, this study aimed to demonstrate the use of a developed data-mining hub as an artificial intelligence-based tool that integrates the defined relevant information (parameters or traits) in accurately identifying the condition of the cow. A comprehensive theoretical framework of the data-mining hub is demonstrated, the selection of the parameters that were used for the data-mining hub is listed, and the relevance of the traits is discussed. The practical application of the data-mining hub has shown that using 21 parameters instead of 13 and 8 parameters resulted in a high overall accuracy of detecting heat stress and subclinical mastitis in dairy cows with a high precision effect reflecting a low percentage of misclassifying the conditions of the dairy cows. This study has developed an innovative approach in which combined information from different independent data was used to accurately detect the health and wellness status of the dairy cows. It can also be implied that an artificial intelligence-based tool such as the proposed theoretical data-mining hub of dairy cows could maximize the use of continuously generated and underutilized data in farms, thus ultimately simplifying repetitive and difficult decision-making tasks in dairy farming. [ABSTRACT FROM AUTHOR]

Deep-learning models were developed and evaluated for predicting the engine-out emission of NOx—one of the main pollutants emitted from diesel engines—under transient conditions, that is, the Worldwide Harmonized Light Vehicles Test Procedure (WLTP). Phenomena of transient conditions are difficult to predict accurately via the conventional modeling approaches. Two algorithms—the deep neural network (DNN) and long short-term memory (LSTM)—were evaluated regarding the accuracy and calculation time. Training was performed using measured data, and the results indicated that the LSTM model (R 2 = 0.9777, RMSE = 20.6 ppm) was more accurate than the DNN model (R 2 = 0.9671, RMSE = 25.5 ppm). However, the DNN model had a significantly higher computation speed (0.36 s) than the LSTM model (1381.0 s). Data preprocessing was performed to insert time-related information into the data; the DNN model trained with the measured data lacked this feature. Data were preprocessed by the calculation of the weighted average of previous timestep data to the current-timestep data. The weighted average was calculated according to various ratios, for example, 7:3, 6:4, 5:5, 4:6, and 3:7. By applying a 7:3 weighted average for training the DNN model, the accuracy of the DNN model was achieved to an R 2 value of 0.9741, and RMSE 22.8 ppm (only R 2 value 0.0036 smaller and RMSE 2.2 ppm larger than the LSTM model) without sacrificing the calculation speed. The results of this study suggest that data preprocessing of the DNN model is an effective method for achieving accuracy as high as that of the LSTM model. The developed DNN model for the NOx emission prediction can be used as a virtual sensor for real-time prediction owing to its accuracy and computation time. [ABSTRACT FROM AUTHOR]

Digital pathology systems (DPSs) have been globally implemented, and computer-assisted diagnosis (CAD) software has been actively developed in recent years. This study aimed to investigate perceptions of digital pathology and the demand for CAD. An online survey involving members of the Korean Society of Pathologists was conducted, and a demonstration clip of the diagnostic assistant software for a prostate needle biopsy was shown to them to provide a simple experience with CAD. One hundred sixty-four Korean pathologists (13.6% of 1210 Korean pathologists) participated. The majority (77.4%) answered affirmatively regarding the necessity of implementing a DPS, and 26.8% had plans to implement or increase the use of DPSs in the following 2–3 years at their medical institutions. Pathologists felt that multidisciplinary care or conference accessibility (56.7%), remote consultation (49.4%), and big data building (32.9%) were useful parts of DPSs. Most pathologists (81.7%) responded that CAD software would assist with the diagnostic process. In a prostate needle biopsy, pathologists used the software to improve the measurement of tumor volume and/or length and core length but not to suggest a diagnostic name or Gleason grade. Korean pathologists who participated in the survey had highly positive perceptions of digital pathology and maintained a positive attitude toward the use of CAD software. [ABSTRACT FROM AUTHOR]

Objectives: To develop a model to predict biochemical recurrence (BCR) after radical prostatectomy (RP), using artificial intelligence (AI) techniques. Patients and Methods: This study collected data from 7,128 patients with prostate cancer (PCa) who received RP at 3 tertiary hospitals. After preprocessing, we used the data of 6,755 cases to generate the BCR prediction model. There were 16 input variables with BCR as the outcome variable. We used a random forest to develop the model. Several sampling techniques were used to address class imbalances. Results: We achieved good performance using a random forest with synthetic minority oversampling technique (SMOTE) using Tomek links, edited nearest neighbors (ENN), and random oversampling: accuracy = 96.59%, recall = 95.49%, precision = 97.66%, F1 score = 96.59%, and ROC AUC = 98.83%. Conclusion: We developed a BCR prediction model for RP. The Dr. Answer AI project, which was developed based on our BCR prediction model, helps physicians and patients to make treatment decisions in the clinical follow-up process as a clinical decision support system. [ABSTRACT FROM AUTHOR]

Xanthomonas oryzae pv. oryzae (Xoo) is a plant pathogen responsible for causing bacterial blight in rice. The immediate alterations in Xoo upon initial contact with rice are essential for pathogenesis. We studied time-resolved genome-wide gene expression in pathogenicity-activated Xoo cells at the transcriptome and proteome levels. The early response genes of Xoo include genes related to cell motility, inorganic ion transport, and effectors. The alteration of gene expression is initiated as early as few minutes after the initial interaction and changes with time. The time-resolved comparison of the transcriptome and proteome shows the differences between transcriptional and translational expression peaks in many genes, although the overall expression pattern of mRNAs and proteins is conserved. The discrepancy suggests an important role of translational regulation in Xoo at the early stages of pathogenesis. The gene expression analysis using time-resolved transcriptome and proteome provides unprecedented valuable information regarding Xoo pathogenesis. [ABSTRACT FROM AUTHOR]

The process used by engine manufacturers for the development of a new engine includes the planning and conceptual design phases, followed by the detailed design phase, in which the design target specifications are met. In the conceptual design phase, a rough specification of the target engine is presented to facilitate a detailed design and the additional cost of modification is reduced exponentially. In the conceptual design phase, however, not only is there no real engine. but there are also no 1D and 3D models present, so it is impossible to test and simulate them. Therefore, at this stage, a model that can predict emission and performance only according to the specifications and operating conditions of the engine would be very useful. Previous studies developed an EGR prediction model that can be used in the 0-D NOx prediction using a deep learning method. In this study, a NOx prediction model with high accuracy using only the operating conditions as input variables, without ECU data, was developed using deep neural networks. The developed model has high accuracy with an R-square of 0.988. The feature of this model is that all the input parameters for the deep neural network come from the operating conditions of the engine. Therefore, this model can be used in the early stages of the development of new engines when testing and simulation cannot be performed because they do not exist. The designer can set the range of the operating conditions such that they do not exceed the NOx limits at the specific operating point (specific rpm and BMEP). This variable operating design methodology is expected to be useful in the development of new engines for automobile manufacturers with various engine data. [ABSTRACT FROM AUTHOR]

A glycosidase and kidney disease: Genetic variants on chromosome 4 have been linked to kidney disease by previous genome-wide association studies. Gu et al. now show these loci alter beta mannosidase (MANBA) expression and modulate of chronic kidney disease (CKD) risk. Genetic and phenotypic analyses showed that MANBA loss of function associated with CKD risk and severity in humans. Mouse models with heterozygous and homozygous loss of MANBA functionally validated the role of Manba in kidney disease susceptibility. Mechanistically, reducing Manba expression in kidney tubule cells affected lysosomal structure and function and induced inflammation and fibrosis, suggesting that lysosomal dysfunction plays a role in CKD. More than 800 million people in the world suffer from chronic kidney disease (CKD). Genome-wide association studies (GWAS) have identified hundreds of loci where genetic variants are associated with kidney function; however, causal genes and pathways for CKD remain unknown. Here, we performed integration of kidney function GWAS and human kidney–specific expression quantitative trait analysis and identified that the expression of beta-mannosidase (MANBA) was lower in kidneys of subjects with CKD risk genotype. We also show an increased incidence of renal failure in subjects with rare heterozygous loss-of-function coding variants in MANBA using phenome-wide association analysis of 40,963 subjects with exome sequencing data. MANBA is a lysosomal gene highly expressed in kidney tubule cells. Deep phenotyping revealed structural and functional lysosomal alterations in human kidneys from subjects with CKD risk alleles and mice with genetic deletion of Manba. Manba heterozygous and knockout mice developed more severe kidney fibrosis when subjected to toxic injury induced by cisplatin or folic acid. Manba loss altered multiple pathways, including endocytosis and autophagy. In the absence of Manba, toxic acute tubule injury induced inflammasome activation and fibrosis. Together, these results illustrate the convergence of common noncoding and rare coding variants in MANBA in kidney disease development and demonstrate the role of the endolysosomal system in kidney disease development. [ABSTRACT FROM AUTHOR]

Recently, deep learning has played an important role in the rise of artificial intelligence, and its accuracy has gained recognition in various research fields. Although engine phenomena are very complicated, they can be predicted with high accuracy using deep learning because they are based on the fundamentals of physics and chemistry. In this research, models were built with deep neural networks for gasoline engine prediction. The model consists of two sub-models. The first predicts the knock occurrence, and the second predicts performance, combustion, and emissions. This includes maximum cylinder pressure, crank angle at maximum cylinder pressure, maximum pressure rise rate, and brake mean effective pressure, brake-specific fuel consumption, brake-specific nitrogen oxides, and brake-specific carbon oxide, which are representative results of the engine (for normal combustion cases without knock). Model input parameters were selected considering engine operating conditions, and physically measurable sensor values. For test cases, the accuracy of the first model for knock classification is 99.0%, and the coefficient of determination (R2) values for the second model are all above 0.99. Test times of both models were approximately 2 ms. The robustness of all the models was verified using K-fold cross-validation. A sensitivity study of accuracy, according to the amount of training utilized, was also conducted to determine how many data points are required to effectively train the deep learning model. Accordingly, a deep learning approach was applied to predict the steady-state conditions of a gasoline engine. Achieved model accuracies and robustness proved deep learning to be an effective modeling approach, and test time was recognized to be able to apply for the real-time prediction. The sensitivity analysis can be applied for the preliminary study to define the number of experimental points for the deep learning model. [ABSTRACT FROM AUTHOR]

Introduction: Five decades ago, Fredrickson, Levy, and Lees (FLL) qualitatively characterized clinical dyslipidemias with specific implications for cardiovascular and non-cardiovascular morbidity and mortality. They separated disorders of elevated cholesterol and triglycerides into five phenotypes (types I-V) based on their lipoprotein profile. Although clinicians generally consider them rare entities, modern FLL prevalence may be greater than previously reported.Material and Methods: We performed a cross-sectional analysis in 5,272 participants from the 2011-2014 National Health and Nutrition Examination Survey and 128,506 participants from the Very Large Database of Lipids study with complete, fasting lipid profiles. We used a validated algorithm to define FLL phenotypes employing apolipoprotein B, total cholesterol, and triglycerides.Results: Overall prevalence of FLL phenotypes was 33.9%. FLL prevalence in the general population versus clinical lipid database was: type I (0.05 vs. 0.02%), type IIa (3.2 vs. 3.9%), type IIb (8.0 vs. 10.3%), type III (2.0 vs. 1.7%), type IV (20.5 vs. 24.1%), and type V (0.15 vs. 0.13%). Those aged 40-74 years had a higher overall prevalence compared to other age groups (p < 0.001) and men had overall higher prevalence than women (p < 0.001). Those with diabetes (51.6%) or obese BMI (49.0%) had higher prevalence of FLL phenotypes compared to those without diabetes (31.3%; p < 0.001) and normal BMI (18.3%; p < 0.001).Conclusions: FLL phenotypes are likely far more prevalent than appreciated in clinical practice, in part due to diabetes and obesity epidemics. Given the prognostic and therapeutic importance of these phenotypes, their identification becomes increasingly important in the era of precision medicine. [ABSTRACT FROM AUTHOR]

This brief presents a 9Gb/s transmitter for intra-panel interfaces, with dual-loop calibration and a 2D binary-segmented driver. The dual-loop calibration during the training period compensates the transmitter output for the variations in operating conditions such as supply voltage and reference current. The 2D binary-segmented driver provides wide range and high resolution output characteristics, and independent adjustments for VOD, VCM and FFE strength while maintaining signal integrity. The transmitter reduces power consumption by optimizing the output for the channel. A prototype chip, fabricated in a 55nm CMOS process, occupies 0.057mm2. It provides FFE strength from 0dB to 26.4dB, common-mode voltage from 260mV to 690mV, and differential output voltage from 100mVppd to 1200mVppd, which varies by less than 4% across supply voltage and reference current variations. It also consumes 36mW at a data-rate of 9Gb/s with a 1.2V supply. [ABSTRACT FROM AUTHOR]

Introduction: Dysbetalipoproteinaemia (HLP3) is a disorder characterized by excess cholesterol-enriched, triglyceride-rich lipoprotein remnants in genetically predisposed individuals that powerfully promote premature cardiovascular disease if untreated. The current prevalence of HLP3 is largely unknown.Material and Methods: We performed cross-sectional analysis of 128,485 U.S. adults from the Very Large Database of Lipids (VLDbL), using four algorithms to diagnose HLP3 employing three Vertical Auto Profile ultracentrifugation (UC) criteria and a previously described apolipoprotein B (apoB) method. We evaluated 4,926 participants from the 2011-2014 National Health and Nutrition Examination Survey (NHANES) with the apoB method. We examined demographic and lipid characteristics stratified by presence of HLP3 and evaluated lipid characteristics in those with HLP3 phenotype discordance and concordance as determined by apoB and originally defined UC criteria 1.Results: In U.S. adults in VLDbL and NHANES, a 1.7-2.0% prevalence is observed for HLP3 with the novel apoB method as compared to 0.2-0.8% prevalence in VLDbL via UC criteria 1-3. Participants who were both apoB and UC criteria HLP3 positive had higher remnant particles as well as more elevated triglyceride/apoB and total cholesterol/apoB ratios (all p < 0.001) than those who were apoB method positive and UC criteria 1 negative.Conclusions: HLP3 may be more prevalent than historically and clinically appreciated. The apoB method increases HLP3 identification via inclusion of milder phenotypes. Further work should evaluate the clinical implications of HLP3 diagnosis at various lipid algorithm cut-points to evaluate the ideal standard in the modern era. [ABSTRACT FROM AUTHOR]

Objectives: The importance of clinical outcome prediction models using artificial intelligence (AI) is being emphasized owing to the increasing necessity of developing a clinical decision support system (CDSS) employing AI. Therefore, in this study, we proposed a "Dr. Answer" AI software based on the clinical outcome prediction model for prostate cancer treated with radical prostatectomy. Methods: The Dr. Answer AI was developed based on a clinical outcome prediction model, with a user-friendly interface. We used 7,128 clinical data of prostate cancer treated with radical prostatectomy from three hospitals. An outcome prediction model was developed to calculate the probability of occurrence of 1) tumor, node, and metastasis (TNM) staging, 2) extracapsular extension, 3) seminal vesicle invasion, and 4) lymph node metastasis. Random forest and k-nearest neighbors algorithms were used, and the proposed system was compared with previous algorithms. Results: Random forest exhibited good performance for TNM staging (recall value: 76.98%), while k-nearest neighbors exhibited good performance for extracapsular extension, seminal vesicle invasion, and lymph node metastasis (80.24%, 98.67%, and 95.45%, respectively). The Dr. Answer AI software consisted of three primary service structures: 1) patient information, 2) clinical outcome prediction, and outcomes according to the National Comprehensive Cancer Network guideline. Conclusion: The proposed clinical outcome prediction model could function as an effective CDSS, supporting the decisions of the physicians, while enabling the patients to understand their treatment outcomes. The Dr. Answer AI software for prostate cancer helps the doctors to explain the treatment outcomes to the patients, allowing the patients to be more confident about their treatment plans. [ABSTRACT FROM AUTHOR]

We describe how the off-state current (Ioff) property of amorphous InGaZnO (a-IGZO) thin-film transistors is caused by trap-assisted tunneling (TAT) by using a two-dimensional device simulation software application (Atlas 2 D, Silvaco). We found that Ioff can be increased by controlling the bandgap energy (EG) and the effective mass of electron (me) of a-IGZO transistors. When me was increased from 0.32 to 0.38 mo (mass of a free electron), the point at which Ioff started to increase in the region of negative gate voltage (VGS) shifted from −4.7 to −7.4 V. In addition, when EG was changed from 3.05 to 3.2 eV, the average value of Ioff changed from 3.13 × 10−13 to 2.4 × 10−14 A. This implies that EG and me influence the increase in Ioff in a-IGZO TFTs because of the difficulty associated with TAT. [ABSTRACT FROM AUTHOR]

Most of the parameters needed to predict Nitrogen Oxides (NOx) emissions, for example, combustion temperature, oxygen concentration and in-cylinder composition ratio, can be predicted by phenomenological 0-D prediction model if accurate EGR rates are provided. However, it is difficult to predict the EGR rate itself accurately by the model, so the EGR rate is predicted by the temperature measurement method. Although this method predicts EGR rates very accurately and quickly, there are some problems such as thermocouple failures and the difficulty in applying to mass production engines, so it is necessary to predict EGR rates by another method. The deep learning method follows an inductive methodology that extracts common characteristics of data based on a lot of data themselves. Therefore, although it requires a lot of experimental data, it has an advantage of high accuracy that can be obtained without any feature engineering. In this study, the EGR rate, which was difficult to predict in the past, was predicted by making various models using the deep learning method. Finally, EGR rate was predicted with a high accuracy of R-square 0.9994 and root mean squared error 0.0692 using a deep learning method at 1500 rpm and bmep 4, 6 and 8 bar. This study can be used as a basic study to predict EGR rates in transient and RDE conditions. [ABSTRACT FROM AUTHOR]

We fabricate high-performance solution-processed SnO2 thin-film transistors (TFTs) exhibiting improved carrier transport features by exposing the ultraviolet/ozone (UV/O3) on the SnO2 film during the pre-annealing stage. The SnO2 layer is treated with different UV/O3-exposure times from 0 to 60 minutes before the post-annealing step. As UV/O3-exposure time increases from 0 to 30 minutes, the M-O-M (M, metal; and O, oxygen) network, mass density, and oxygen vacancies of films are enhanced. In contrast, the M-O-M network and mass density decrease, while the oxygen vacancies rather increase when the UV/O3-exposure time reaches 60 minutes beyond 30 minutes. The SnO2 (Sn4+) phase, thickness, and surface morphology of SnO2 films are not considerably changed regardless of UV/O3-exposure time. When the UV/O3-exposure time is 30 minutes, devices demonstrate superior field-effect mobility (10.1 cm2 V−1 s−1) at approximately two times higher than the TFT without UV/O3-exposure. Furthermore, the SnO2 TFT with UV/O3-exposure time for 30 minutes shows improved subthreshold-swing characteristics and a high on/off current ratio. These devices are adequate for use in high-resolution active-matrix LCDs or OLED displays that demand a high field-effect mobility (>10 cm2 V−1 s−1) and on/off ratio (>106). [ABSTRACT FROM AUTHOR]

It is particularly desirable to predict castration-resistant prostate cancer (CRPC) in prostate cancer (PCa) patients, and this study aims to predict patients' likely outcomes to support physicians' decision-making. Serial data is collected from 1592 PCa patients, and a phased long short-term memory (phased-LSTM) model with a special module called a "time-gate" is used to process the irregularly sampled data sets. A synthetic minority oversampling technique is used to overcome the data imbalance between two patient groups: those with and without CRPC treatment. The phased-LSTM model is able to predict the CRPC outcome with an accuracy of 88.6% (precision-recall: 91.6%) using 120 days of data or 94.8% (precision-recall: 96.9%) using 360 days of data. The validation loss converged slowly with 120 days of data and quickly with 360 days of data. In both cases, the prediction model takes four epochs to build. The overall CPRC outcome prediction model using irregularly sampled serial medical data is accurate and can be used to support physicians' decision-making, which saves time compared to cumbersome serial data reviews. This study can be extended to realize clinically meaningful prediction models. [ABSTRACT FROM AUTHOR]

There are many medical demands that still need to be resolved for prostate cancer (PCa), including better diagnosis and predictive medicine. For this to be accomplished, diverse medical data need to be integrated with the development of intelligent software (SW) based on various types of medical data. Various types of information technology have been used to address these medical demands of PCa. We initiated the PROstate Medical Intelligence System Enterprise-Clinical, Imaging, and Pathology (PROMISE CLIP) and a multicenter, big data study to develop PCa SW for patients with PCa and clinicians. We integrated the clinical data of 7257 patients, 610 patients' imaging data, and 39,000 cores of pathology digital scanning data from four tertiary hospitals in South Korea. We developed the PROMISE CLIP registry based on integrated clinical, imaging, and pathology data. Related intelligent SW has been developed for helping patients and clinicians decide on the best treatment option. The PROMISE CLIP study directs guidelines for intelligent SW development to solve medical demands for PCa. The PROMISE CLIP registry plays an important role in advancing PCa research and care. [ABSTRACT FROM AUTHOR]

A quadrature clock corrector uses relaxation oscillators to detect duty-cycle and quadrature phase errors by transforming them into pairs of frequencies, which are then digitized and compared. It achieves good detection accuracy and can detect a wide range of duty-cycle and quadrature phase errors. The prototype is implemented in a 55-nm CMOS process with a supply voltage of 1.2 V and occupies an area of 0.003 mm2. The experimental results show that the operation range is from 1 to 3 GHz, the power efficiency is 0.79 mW/GHz, the maximum duty-cycle error is 0.8% at 3 GHz, and the maximum quadrature phase error is 1.1° at 3 GHz. [ABSTRACT FROM AUTHOR]

This brief presents a 12.8-Gb/s quarter-rate transmitter for DDR interfaces. The 4:1 serialization required in a quarter-rate transmitter is performed by an overlapped multiplexing driver containing four unit drivers. Two of four unit drivers output two identical 1UI full-rate DQ signals simultaneously and these signals are merged while they perform final serialization. This reduces the I/O capacitance. Correct timing of this serialization process is maintained by adaptive alignment of the four phases of the clock signal. Implemented in a 55-nm CMOS technology, single-ended output swing of this transmitter is 400~600 mVpp, and it has an energy efficiency of 1.8 pJ/bit. [ABSTRACT FROM AUTHOR]

Extensive cell-to-cell variation exists even among putatively identical cells, and there is great interest in understanding how the properties of transcription relate to this heterogeneity. Differential expression from the two gene copies in diploid cells could potentially contribute, yet our ability to measure from which gene copy individual RNAs originated remains limited, particularly in the context of tissues. Here, we demonstrate quantitative, single molecule allele-specific RNA FISH adapted for use on tissue sections, allowing us to determine the chromosome of origin of individual RNA molecules in formaldehyde-fixed tissues. We used this method to visualize the allele-specific expression of Xist and multiple autosomal genes in mouse kidney. By combining these data with mathematical modeling, we evaluated models for allele-specific heterogeneity, in particular demonstrating that apparent expression from only one of the alleles in single cells can arise as a consequence of low-level mRNA abundance and transcriptional bursting. [ABSTRACT FROM AUTHOR]

Statistics showed that approximately 1,195,000 refrigerator units (116,094 metric tons) were recycled annually in Korea’s formal sector. Although the recycling level for these was satisfactory, collecting and processing polyurethane at recycling centers (RCs) posed several problems, including the risk of fire from the incinerator, the generation of fugitive dust, and the high cost of disposal. The objective of our study is to provide an overview of refrigerator recycling and to introduce polyurethane solid refuse fuel (SRF) manufacturing facilities. These facilities, installed at four regional RCs in Korea, have a capacity of 800 kg/h, a motor of 476 kWh, and cost $664,300 (USD) to construct (criteria two facilities). According to our examination of the physico-chemical properties of SRF product, all results in categories have been satisfied with the quality standards. In terms of finances, RCs can reduce the total disposal cost from $160-175 to $60 by installing facilities, which results in savings of (on average) $180,546 annually. The Metropolitan Electronics Recycling Center, for example, has reduced its costs by $180,880 each year. Their return on investment was calculated to be about 3 years and 8 months (assuming no operating expenses) or 7 years and 4 months (including operating expenses). [ABSTRACT FROM AUTHOR]

Phase and amplitude emphasis are combined in a 2.1-Gb/s 12-channel transmitter for ultra-high definition (UHD) intra-panel interface. The transmitter performs phase emphasis within the final 2:1 stage of its 20:1 serializer. This reduces data-dependent jitter (DDJ) without increasing IO capacitance by making the timing of bit transitions depend on the previous data. We have also proved the compensation effect of this emphasis by a mathematical analysis. The low-voltage differential signaling (LVDS) driver in the transmitter can accommodate a 300-mV variation in common-mode voltage and swings of 300 mV–1.2 V in the output signal, so as to match the variations between channel characteristics which occur in large displays. A prototype was fabricated in 28-nm CMOS, and occupies 1.35 mm2; it was tested on a 55-in $3840 \times 2160$ thin-film transistor liquid-crystal display (TFT-LCD) intra-panel interface, where it compensated for channel losses exceeding 10 dB, while reducing eye jitter by 38%: phase emphasis is responsible for about half of this reduction. [ABSTRACT FROM AUTHOR]

While Notch signaling has been proposed to play a key role in fibrosis, the direct molecular pathways targeted by Notch signaling and the precise ligand and receptor pair that are responsible for kidney disease remain poorly defined. In this study, we found that JAG1 and NOTCH2 showed the strongest correlation with the degree of interstitial fibrosis in a genome-wide expression analysis of a large cohort of human kidney samples. Transcript analysis of mouse kidney disease models, including folic-acid (FA)–induced nephropathy, unilateral ureteral obstruction (UUO), or apolipoprotein L1 (APOL1)-associated kidney disease, indicated that Jag1 and Notch2 levels were higher in all analyzed kidney fibrosis models. Mice with tubule-specific deletion of Jag1 or Notch2 (Kspcre/Jag1flox / flox and Kspcre/Notch2flox / flox) had no kidney-specific alterations at baseline but showed protection from FA-induced kidney fibrosis. Tubule-specific genetic deletion of Notch1 and global knockout of Notch3 had no effect on fibrosis. In vitro chromatin immunoprecipitation experiments and genome-wide expression studies identified the mitochondrial transcription factor A (Tfam) as a direct Notch target. Re-expression of Tfam in tubule cells prevented Notch-induced metabolic and profibrotic reprogramming. Tubule–specific deletion of Tfam resulted in fibrosis. In summary, Jag1 and Notch2 play a key role in kidney fibrosis development by regulating Tfam expression and metabolic reprogramming. [ABSTRACT FROM AUTHOR]

(1) Background: Prostate cancer risk classifiers have been used for predicting surgical and radiation therapy outcomes; however, a classifier for predicting biochemical recurrence (BCR) in patients undergoing stereotactic body radiation therapy (SBRT) is not available. We attempted to develop a model that creates a risk classifier to predict BCR in patients considering SBRT. (2) Methods: We studied the outcomes of 809 patients treated with SBRT between August 2007 and November 2016. We used Cox regression analysis with time to BCR as the outcome to develop a model that calculates a prostate clinical outlook (PCO) score based on age at diagnosis, clinical-radiological staging, and a modified risk level. We then created the PCO classifier application, which uses the model we created to categorize patients into risk groups based on multiple factors. We assessed the concordance index (c-index) to determine the accuracy of the PCO classifier application and compared the results to the D'Amico and Kattan nomogram classifications. (3) Results: The calculated PCO scores ranged from 0 to 156 points. The PCO classifier application categorized patients into three risk-groups, with 5-year BCR-free survival rates of 98.3% for low risk (n = 137), 95.4% for intermediate risk (n = 570), and 86.4% for high risk (n = 102). We demonstrated the improved prognostic power of the PCO classifier application, with a c-index of 0.75 (training set) and 0.67 (validation set); the c-index of the Kattan nomogram was 0.62 and 0.63, respectively, and that of the D'Amico classifier was 0.64 and 0.64, respectively. (4) Conclusions: The PCO classifier application is a predictive tool for employing readily available clinical parameters to stratify prostate cancer patients and to predict the probability of BCR after SBRT. [ABSTRACT FROM AUTHOR]