Characterization of perivascular alveolar epithelial stem cells and their niche in lung homeostasis and cancer.

Lung alveolar structure and function are maintained by subsets of alveolar type II stem cells (AT2s), but there is a need for characterization of these subsets and their associated niches. Here, we report a CD44high subpopulation of AT2s characterized by increased expression of genes that regulate immune signaling even during steady-state homeostasis. Disruption of one of these immune regulatory transcription factor STAT1 impaired the stem cell function of AT2s. CD44high cells were preferentially located near macro- blood vessels and a supportive niche constituted by LYVE1+ endothelial cells, adventitial fibroblasts, and accumulated hyaluronan. In this microenvironment, CD44high AT2 cells were more responsive to transformation by KRAS than general AT2 cells. Moreover, after bacterial lung injury, there was a significant increase of CD44high AT2s and niche components distributed throughout the lung parenchyma. Taken together, CD44high AT2 cells and their perivascular niche regulate tissue homeostasis and tumor formation.

Pan-cancer discovery of somatic mutations from RNA sequencing data.

Identification of somatic mutations (SMs) is essential for characterizing cancer genomes. While DNA-seq is the prevalent method for identifying SMs, RNA-seq provides an alternative strategy to discover tumor mutations in the transcribed genome. Here, we have developed a machine learning based pipeline to discover SMs based on RNA-seq data (designated as RNA-SMs). Subsequently, we have conducted a pan-cancer analysis to systematically identify RNA-SMs from over 8,000 tumors in The Cancer Genome Atlas (TCGA). In this way, we have identified over 105,000 novel SMs that had not been reported in previous TCGA studies. These novel SMs have significant clinical implications in designing targeted therapy for improved patient outcomes. Further, we have combined the SMs identified by both RNA-seq and DNA-seq analyses to depict an updated mutational landscape across 32 cancer types. This new online SM atlas, OncoDB ( https://oncodb.org ), offers a more complete view of gene mutations that underline the development and progression of various cancers.

Effective methods for monitoring internal contamination among workers handling radioactive materials in various fields.

This study aimed to develop effective methods for monitoring internal contamination among workers handling radioactive materials in various fields. A total of 160 workers from nuclear power plants, medical institutions, military units, and educational/research institutions were included in the study. The monitoring methods included urinalysis and whole-body counting (WBC) using a mobile radio bioassay laboratory (MRL). Gamma-emitting radionuclides were monitored using the MRL WBC system, and a separate pretreatment procedure was used for tritium measurement in urine samples. Gross beta-screening was performed using a liquid scintillation counting system. The results were evaluated on the basis of the established screening criteria and compared with the dose limits. Additionally, tritium concentrations in the bodies of workers in the vicinity of a heavy-water reactor was analysed to assess the association between tritium concentration and occupation. The results showed a wide distribution of tritium concentrations. Workers involved in fuel and maintenance tasks demonstrated the maximum exposure. Workers in medical facilities showed low levels of internal contamination, which was primarily related to tasks involving radioactive isotopes. Military personnel involved in equipment repair showed significant tritium contamination due to damage during repairs. Workers in educational and research institutions in general had low levels of internal contamination.

Convolutional Hough Matching Networks for Robust and Efficient Visual Correspondence.

Despite advances in feature representation, leveraging geometric relations is crucial for establishing reliable visual correspondences under large variations of images. In this work we introduce a Hough transform perspective on convolutional matching and propose an effective geometric matching algorithm, dubbed Convolutional Hough Matching (CHM). The method distributes similarities of candidate matches over a geometric transformation space and evaluates them in a convolutional manner. We cast it into a trainable neural layer with a semi-isotropic high-dimensional kernel, which learns non-rigid matching with a small number of interpretable parameters. To further improve the efficiency of high-dimensional voting, we also propose to use an efficient kernel decomposition with center-pivot neighbors, which significantly sparsifies the proposed semi-isotropic kernels without performance degradation. To validate the proposed techniques, we develop the neural network with CHM layers that perform convolutional matching in the space of translation and scaling. Our method sets a new state of the art on standard benchmarks for semantic visual correspondence, proving its strong robustness to challenging intra-class variations.

Exploring the potential of OMOP common data model for process mining in healthcare.

BACKGROUND AND OBJECTIVE: Recently, Electronic Health Records (EHR) are increasingly being converted to Common Data Models (CDMs), a database schema designed to provide standardized vocabularies to facilitate collaborative observational research. To date, however, rare attempts exist to leverage CDM data for healthcare process mining, a technique to derive process-related knowledge (e.g., process model) from event logs. This paper presents a method to extract, construct, and analyze event logs from the Observational Medical Outcomes Partnership (OMOP) CDM for process mining and demonstrates CDM-based healthcare process mining with several real-life study cases while answering frequently posed questions in process mining, in the CDM environment. METHODS: We propose a method to extract, construct, and analyze event logs from the OMOP CDM for process types including inpatient, outpatient, emergency room processes, and patient journey. Using the proposed method, we extract the retrospective data of several surgical procedure cases (i.e., Total Laparoscopic Hysterectomy (TLH), Total Hip Replacement (THR), Coronary Bypass (CB), Transcatheter Aortic Valve Implantation (TAVI), Pancreaticoduodenectomy (PD)) from the CDM of a Korean tertiary hospital. Patient data are extracted for each of the operations and analyzed using several process mining techniques. RESULTS: Using process mining, the clinical pathways, outpatient process models, emergency room process models, and patient journeys are demonstrated using the extracted logs. The result shows CDM's usability as a novel and valuable data source for healthcare process analysis, yet with a few considerations. We found that CDM should be complemented by different internal and external data sources to address the administrative and operational aspects of healthcare processes, particularly for outpatient and ER process analyses. CONCLUSION: To the best of our knowledge, we are the first to exploit CDM for healthcare process mining. Specifically, we provide a step-by-step guidance by demonstrating process analysis from locating relevant CDM tables to visualizing results using process mining tools. The proposed method can be widely applicable across different institutions. This work can contribute to bringing a process mining perspective to the existing CDM users in the changing Hospital Information Systems (HIS) environment and also to facilitating CDM-based studies in the process mining research community.

Miz1 promotes KRAS-driven lung tumorigenesis by repressing the protocadherin Pcdh10.

Targeting KRAS-mutated non-small-cell lung cancer (NSCLC) remains clinically challenging. Here we show that loss of function of Miz1 inhibits lung tumorigenesis in a mouse model of oncogenic KRAS-driven lung cancer. In vitro, knockout or silencing of Miz1 decreases cell proliferation, clonogenicity, migration, invasion, or anchorage-independent growth in mutant (MT) KRAS murine or human NSCLC cells but has unremarkable impact on non-tumorigenic cells or wild-type (WT) KRAS human NSCLC cells. RNA-sequencing reveals Protocadherin-10 (Pcdh10) as the top upregulated gene by Miz1 knockout in MT KRAS murine lung tumor cells. Chromatin immunoprecipitation shows Miz1 binding on the Pcdh10 promoter in MT KRAS lung tumor cells but not non-tumorigenic cells. Importantly, silencing of Pcdh10 rescues cell proliferation and clonogenicity in Miz1 knockout/knockdown MT KRAS murine or human tumor cells, and rescues allograft tumor growth of Miz1 knockout tumor cells in vivo. Miz1 is upregulated in MT KRAS lung tumor tissues compared with adjacent non-involved tissues in mice. Consistent with this, Miz1 is upregulated while Pcdh10 is downregulated in human lung adenocarcinomas (LUAD) compared with normal tissues, and high Miz1 levels or low Pcdh10 levels are associated with poor survival in lung cancer patients. Furthermore, the Miz1 signature is associated with worse survival in MT but not WT KRAS LUAD, and Pcdh10 is downregulated in MT compared to WT KRAS LUAD. Taken together, our studies implicate the Miz1/Pcdh10 axis in oncogenic KRAS-driven lung tumorigenesis.

The design of an inkjet drive waveform using machine learning.

A drive waveform, which needs to be optimized with ink's fluid properties, is critical to reliable inkjet printing. A generally adopted rule of thumb for its design is mostly dependent on time-consuming and repetitive manual manipulation of its parameters. This work presents a closed-loop machine learning approach to designing an optimal drive waveform for satellite-free inkjet printing at a target velocity. Each of the representative 11 model inks with different fluid properties was ink-jetted with 1100 distinct waveform designs. The high-speed images of their jetting behaviors were acquired and the big datasets of the resulting drop formation and velocity were extracted from the jetting images. Five machine learning models were examined and compared to predict the characteristics of jetting behavior. Among a variety of machine learning models, Multi-layer Perceptron affords the highest prediction accuracy. A closed-loop prediction algorithm that determined the optimal set of waveform parameters for satellite-free drop formation at a target velocity and employed the most superior learning model was established. The proposed method was confirmed through the printing of an unknown model ink with a recommended waveform.

Analysis for DC and RF Characteristics Recessed-Gate GaN MOSFET Using Stacked TiO2/Si3N4 Dual-Layer Insulator.

The self-heating effects (SHEs) on the electrical characteristics of the GaN MOSFETs with a stacked TiO2/Si3N4 dual-layer insulator are investigated by using rigorous TCAD simulations. To accurately analyze them, the GaN MOSFETs with Si3N4 single-layer insulator are conducted to the simulation works together. The stacked TiO2/Si3N4 GaN MOSFET has a maximum on-state current of 743.8 mA/mm, which is the improved value due to the larger oxide capacitance of TiO2/Si3N4 than that of a Si3N4 single-layer insulator. However, the electrical field and current density increased by the stacked TiO2/Si3N4 layers make the device's temperature higher. That results in the degradation of the device's performance. We simulated and analyzed the operation mechanisms of the GaN MOSFETs modulated by the SHEs in view of high-power and high-frequency characteristics. The maximum temperature inside the device was increased to 409.89 K by the SHEs. In this case, the stacked TiO2/Si3N4-based GaN MOSFETs had 25%-lower values for both the maximum on-state current and the maximum transconductance compared with the device where SHEs did not occur; Ron increased from 1.41 mΩ·cm2 to 2.56 mΩ·cm2, and the cut-off frequency was reduced by 26% from 5.45 GHz. Although the performance of the stacked TiO2/Si3N4-based GaN MOSFET is degraded by SHEs, it shows superior electrical performance than GaN MOSFETs with Si3N4 single-layer insulator.

OncoDB: an interactive online database for analysis of gene expression and viral infection in cancer.

Large-scale multi-omics datasets, most prominently from the TCGA consortium, have been made available to the public for systematic characterization of human cancers. However, to date, there is a lack of corresponding online resources to utilize these valuable data to study gene expression dysregulation and viral infection, two major causes for cancer development and progression. To address these unmet needs, we established OncoDB, an online database resource to explore abnormal patterns in gene expression as well as viral infection that are correlated to clinical features in cancer. Specifically, OncoDB integrated RNA-seq, DNA methylation, and related clinical data from over 10 000 cancer patients in the TCGA study as well as from normal tissues in the GTEx study. Another unique aspect of OncoDB is its focus on oncoviruses. By mining TCGA RNA-seq data, we have identified six major oncoviruses across cancer types and further correlated viral infection to changes in host gene expression and clinical outcomes. All the analysis results are integratively presented in OncoDB with a flexible web interface to search for data related to RNA expression, DNA methylation, viral infection, and clinical features of the cancer patients. OncoDB is freely accessible at http://oncodb.org.

Process Mining-Supported Emergency Room Process Performance Indicators.

Emergency room processes are often exposed to the risk of unexpected factors, and process management based on performance measurements is required due to its connectivity to the quality of care. Regarding this, there have been several attempts to propose a method to analyze the emergency room processes. This paper proposes a framework for process performance indicators utilized in emergency rooms. Based on the devil's quadrangle, i.e., time, cost, quality, and flexibility, the paper suggests multiple process performance indicators that can be analyzed using clinical event logs and verify them with a thorough discussion with clinical experts in the emergency department. A case study is conducted with the real-life clinical data collected from a tertiary hospital in Korea tovalidate the proposed method. The case study demonstrated that the proposed indicators are well applied using the clinical data, and the framework is capable of understanding emergency room processes' performance.

Uncertainty quantification of bioassay functions for the internal dosimetry of radioiodine.

Bioassay functions, which are provided by the International Commission on Radiological Protection, are used to estimate the intake activity of radionuclides; however, they include considerable uncertainties in terms of the internal dosimetry for a particular individual. During a practical internal dose assessment, the uncertainty in the bioassay function is generally not introduced because of the difficulty in quantification. Therefore, to clarify the existence of uncertainty in the bioassay function and provide dosimetrists with an insight into this uncertainty, this study attempted to quantify the uncertainty in the thyroid retention function used for radioiodine exposure. The uncertainty was quantified using a probabilistic estimation of the thyroid retention function through the propagation of the distribution of biokinetic parameters by the Monte Carlo simulation technique. The uncertainties in the thyroid retention function, expressed in terms of the scattering factor, were in the ranges of 1.55-1.60 and 1.40-1.50 for within 24 h and after 24 h, respectively. In addition, the thyroid retention function within 24 h was compared with actual measurement data to confirm the uncertainty due to the use of first-order kinetics in the biokinetic model calculation. Significantly higher thyroid uptakes (by a factor of 1.9) were observed in the actual measurements. This study indicates that consideration of the uncertainty in the thyroid retention function can avoid a significant over- and under-estimation of the internal dose, particularly when a high dose is predicted.

Developing data-driven clinical pathways using electronic health records: The cases of total laparoscopic hysterectomy and rotator cuff tears.

OBJECTIVE: A clinical pathway is one of the tools used to support clinical decision making that provides a standardized care process in a specific context. The objective of this research was to develop a method for building data-driven clinical pathways using electronic health record data. MATERIALS AND METHODS: We proposed a matching rate-based clinical pathway mining algorithm that produces the optimal set of clinical orders for each clinical stage by employing matching rates. To validate the approach, we utilized two different datasets of deidentified inpatient records directly related to total laparoscopic hysterectomy (TLH) and rotator cuff tears (RCTs) from a hospital in South Korea. The derived data-driven clinical pathways were evaluated with knowledge-based models by health professionals using a delta analysis. RESULTS: Two different data-driven clinical pathways, i.e., TLH and RCTs, were produced by applying the matching rate-based clinical pathway mining algorithm. We identified that there were significant differences in clinical orders between the data-driven and knowledge-based models. Additionally, the data-driven clinical pathways based on our algorithm outperformed the models by clinical experts, with average matching rates of 82.02% and 79.66%, respectively. CONCLUSION: The proposed algorithm will be helpful for supporting clinical decisions and directly applicable in medical practices.

Internal Dose Assessment after 131I-Iodide Misadministration in a Patient With Incompletely Blocked Thyroid Uptake: Personalized Internal Dose Assessment by Estimating Individual-Specific Biokinetics.

In July 2017, a medical accident occurred in South Korea, in which I-iodide solution was misadministered to the wrong patient. Although the International Commission on Radiological Protection provided internal dose coefficients for iodine for blocked thyroid, they were not reliable enough for determining the dose to the patient (whose thyroid uptake was incompletely blocked) due to a discrepancy in biokinetics. Therefore, a personalized dose assessment was performed to derive the individual-specific dose coefficients for the patient. Initially, the thyroid biokinetics of the patient were statistically clarified by fitting bioassay monitoring results and the corresponding predicted bioassay values, which were calculated repeatedly for varying iodine transfer rates in an iodine biokinetic model. After determining the transfer rate for the patient, the individual-specific dose coefficients were then calculated in accordance with latest recommendations of the International Commission on Radiological Protection. According to the individual-specific biokinetics, the 24 h thyroid uptake fraction of iodine was estimated as 0.52%. The thyroid absorbed dose of the patient was evaluated as 21.2 Gy, which differed greatly (by about 9 Gy) from the dose evaluated simply using the reference data for blocked thyroid uptake. The personalized dose assessment carried out for the patient not only reduced considerable uncertainties in the internal dose calculation, but also improved the reliability of the calculated internal dose by adopting the latest dosimetric data, including specific absorbed fraction values based on voxel phantoms. Through the dose assessment of the patient, the methodology of personalized dose assessment considering individual-specific biokinetics was developed.

SUSTAINABLE MEDICAL PREPAREDNESS AND RESPONSE SYSTEM FOR RADIATION EMERGENCIES IN THE REPUBLIC OF KOREA.

This paper provides information about the medical preparedness and response system for radiation emergencies in Republic of Korea. The National Radiation Emergency Medical Center (NREMC) oversees medical affairs in the Korean radiological disaster prevention system since its establishment in 2002. It has dedicated itself to set up a nationwide radiation emergency medicine network with 24 designated radiation emergency hospitals. NREMC, as one division under the Korea Institute of Radiological And Medical Sciences (KIRAMS), provides efficient medical care to patients suspected with radiation exposure by collaborating with professional medical staffs in Korea Cancer Center Hospital (KCCH). For prompt response to radiation accidents, NREMC has offered specialized trainings for medical staffs and first responders. It has also operated the 24-h on-call system to consult public concerns of radiation exposure, which can be switched into an emergency mode upon receiving accident reports. In addition, NREMC has conducted dose assessments of radiation exposure with high level of accuracy and implemented R&D programs for radiation injury therapeutics and low-dose radiation risks evaluation in daily life. NREMC supports global initiatives for strengthening medical preparedness and response for radiation emergencies with international organizations.

The Sec1/Munc18 (SM) protein Vps45 is involved in iron uptake, mitochondrial function and virulence in the pathogenic fungus Cryptococcus neoformans.

The battle for iron between invading microorganisms and mammalian hosts is a pivotal determinant of the outcome of infection. The pathogenic fungus, Cryptococcus neoformans, employs multiple mechanisms to compete for iron during cryptococcosis, a disease primarily of immunocompromised hosts. In this study, we examined the role of endocytic trafficking in iron uptake by characterizing a mutant defective in the Sec1/Munc18 (SM) protein Vps45. This protein is known to regulate the machinery for vesicle trafficking and fusion via interactions with SNARE proteins. As expected, a vps45 deletion mutant was impaired in endocytosis and showed sensitivity to trafficking inhibitors. The mutant also showed poor growth on iron-limited media and a defect in transporting the Cfo1 ferroxidase of the high-affinity iron uptake system from the plasma membrane to the vacuole. Remarkably, we made the novel observation that Vps45 also contributes to mitochondrial function in that a Vps45-Gfp fusion protein associated with mitotracker, and a vps45 mutant showed enhanced sensitivity to inhibitors of electron transport complexes as well as changes in mitochondrial membrane potential. Consistent with mitochondrial function, the vps45 mutant was impaired in calcium homeostasis. To assess the relevance of these defects for virulence, we examined cell surface properties of the vps45 mutant and found increased sensitivity to agents that challenge cell wall integrity and to antifungal drugs. A change in cell wall properties was consistent with our observation of altered capsule polysaccharide attachment, and with attenuated virulence in a mouse model of cryptococcosis. Overall, our studies reveal a novel role for Vps45-mediated trafficking for iron uptake, mitochondrial function and virulence.

Analysis of length of hospital stay using electronic health records: A statistical and data mining approach.

BACKGROUND: The length of stay (LOS) is an important indicator of the efficiency of hospital management. Reduction in the number of inpatient days results in decreased risk of infection and medication side effects, improvement in the quality of treatment, and increased hospital profit with more efficient bed management. The purpose of this study was to determine which factors are associated with length of hospital stay, based on electronic health records, in order to manage hospital stay more efficiently. MATERIALS AND METHODS: Research subjects were retrieved from a database of patients admitted to a tertiary general university hospital in South Korea between January and December 2013. Patients were analyzed according to the following three categories: descriptive and exploratory analysis, process pattern analysis using process mining techniques, and statistical analysis and prediction of LOS. RESULTS: Overall, 55% (25,228) of inpatients were discharged within 4 days. The department of rehabilitation medicine (RH) had the highest average LOS at 15.9 days. Of all the conditions diagnosed over 250 times, diagnoses of I63.8 (cerebral infarction, middle cerebral artery), I63.9 (infarction of middle cerebral artery territory) and I21.9 (myocardial infarction) were associated with the longest average hospital stay and high standard deviation. Patients with these conditions were also more likely to be transferred to the RH department for rehabilitation. A range of variables, such as transfer, discharge delay time, operation frequency, frequency of diagnosis, severity, bed grade, and insurance type was significantly correlated with the LOS. CONCLUSIONS: Accurate understanding of the factors associating with the LOS and progressive improvements in processing and monitoring may allow more efficient management of the LOS of inpatients.

Vacuolar zinc transporter Zrc1 is required for detoxification of excess intracellular zinc in the human fungal pathogen Cryptococcus neoformans.

Zinc is an important transition metal in all living organisms and is required for numerous biological processes. However, excess zinc can also be toxic to cells and cause cellular stress. In the model fungus Saccharomyces cerevisiae, a vacuolar zinc transporter, Zrc1, plays important roles in the storage and detoxification of excess intracellular zinc to protect the cell. In this study, we identified an ortholog of the S. cerevisiae ZRC1 gene in the human fungal pathogen Cryptococcus neoformans. Zrc1 was localized in the vacuolar membrane in C. neoformans, and a mutant lacking ZRC1 showed significant growth defects under high-zinc conditions. These results suggested a role for Zrc1 in zinc detoxification. However, contrary to our expectation, the expression of Zrc1 was induced in cells grown in zinc-limited conditions and decreased upon the addition of zinc. These expression patterns were similar to those of Zip1, the high-affinity zinc transporter in the plasma membrane of C. neoformans. Furthermore, we used the zrc1 mutant in a murine model of cryptococcosis to examine whether a mammalian host could inhibit the survival of C. neoformans using zinc toxicity. We found that the mutant showed no difference in virulence compared with the wildtype strain. This result suggests that Zrc1-mediated zinc detoxification is not required for the virulence of C. neoformans, and imply that zinc toxicity may not be an important aspect of the host immune response to the fungus.

Robust Guided Image Filtering Using Nonconvex Potentials.

Filtering images using a guidance signal, a process called guided or joint image filtering, has been used in various tasks in computer vision and computational photography, particularly for noise reduction and joint upsampling. This uses an additional guidance signal as a structure prior, and transfers the structure of the guidance signal to an input image, restoring noisy or altered image structure. The main drawbacks of such a data-dependent framework are that it does not consider structural differences between guidance and input images, and that it is not robust to outliers. We propose a novel SD (for static/dynamic) filter to address these problems in a unified framework, and jointly leverage structural information from guidance and input images. Guided image filtering is formulated as a nonconvex optimization problem, which is solved by the majorize-minimization algorithm. The proposed algorithm converges quickly while guaranteeing a local minimum. The SD filter effectively controls the underlying image structure at different scales, and can handle a variety of types of data from different sensors. It is robust to outliers and other artifacts such as gradient reversal and global intensity shift, and has good edge-preserving smoothing properties. We demonstrate the flexibility and effectiveness of the proposed SD filter in a variety of applications, including depth upsampling, scale-space filtering, texture removal, flash/non-flash denoising, and RGB/NIR denoising.

Proposal Flow: Semantic Correspondences from Object Proposals.

Finding image correspondences remains a challenging problem in the presence of intra-class variations and large changes in scene layout. Semantic flow methods are designed to handle images depicting different instances of the same object or scene category. We introduce a novel approach to semantic flow, dubbed proposal flow, that establishes reliable correspondences using object proposals. Unlike prevailing semantic flow approaches that operate on pixels or regularly sampled local regions, proposal flow benefits from the characteristics of modern object proposals, that exhibit high repeatability at multiple scales, and can take advantage of both local and geometric consistency constraints among proposals. We also show that the corresponding sparse proposal flow can effectively be transformed into a conventional dense flow field. We introduce two new challenging datasets that can be used to evaluate both general semantic flow techniques and region-based approaches such as proposal flow. We use these benchmarks to compare different matching algorithms, object proposals, and region features within proposal flow, to the state of the art in semantic flow. This comparison, along with experiments on standard datasets, demonstrates that proposal flow significantly outperforms existing semantic flow methods in various settings.