Missed a live match? Determinants of League of Legends Champions Korea highlights viewership.

This research aims to explore the determinants of the League of Legends Champions Korea (LCK) highlight views and comment counts. The data of 629 game highlight views and comment counts for seven tournaments were collected from YouTube. The highlight views and comment counts were regressed on a series of before-the-game factors (outcome uncertainty and game quality), after-the-game factors (sum and difference of kills, assists, multiple kills, and upset results), and match-related characteristics (game duration, evening game, and clip recentness). A multi-level least square dummy variable regression was conducted to test the model. Among the before-the-game factors, outcome uncertainty and game quality were significantly associated with highlight views and comment counts. This indicated that fans liked watching games with uncertain outcomes and those involving high-quality teams. Among the after-the-game factors, an upset result was a significant determinant of esports highlight views and comment counts. Thus, fans enjoy watching underdogs win. Finally, the sum of kills and assists only affected view counts, which indicated that fans prefer watching offensive games with more kills and a solo performance rather than teamwork.

Feature Alignment by Uncertainty and Self-Training for Source-Free Unsupervised Domain Adaptation.

Most unsupervised domain adaptation (UDA) methods assume that labeled source images are available during model adaptation. However, this assumption is often infeasible owing to confidentiality issues or memory constraints on mobile devices. Some recently developed approaches do not require source images during adaptation, but they show limited performance on perturbed images. To address these problems, we propose a novel source-free UDA method that uses only a pre-trained source model and unlabeled target images. Our method captures the aleatoric uncertainty by incorporating data augmentation and trains the feature generator with two consistency objectives. The feature generator is encouraged to learn consistent visual features away from the decision boundaries of the head classifier. Thus, the adapted model becomes more robust to image perturbations. Inspired by self-supervised learning, our method promotes inter-space alignment between the prediction space and the feature space while incorporating intra-space consistency within the feature space to reduce the domain gap between the source and target domains. We also consider epistemic uncertainty to boost the model adaptation performance. Extensive experiments on popular UDA benchmark datasets demonstrate that the proposed source-free method is comparable or even superior to vanilla UDA methods. Moreover, the adapted models show more robust results when input images are perturbed.

p38 Stabilizes Snail by Suppressing DYRK2-Mediated Phosphorylation That Is Required for GSK3ß-ßTrCP-Induced Snail Degradation.

Snail is a key regulator of epithelial-mesenchymal transition (EMT), which is a major step in tumor metastasis. Although the induction of Snail transcription precedes EMT, posttranslational regulation, especially phosphorylation of Snail, is critical for determining Snail protein levels or stability, subcellular localization, and the ability to induce EMT. To date, several kinases are known that enhance the stability of Snail by preventing its ubiquitination; however, the molecular mechanism(s) underlying this are still unclear. Here, we identified p38 MAPK as a crucial posttranslational regulator that enhances the stability of Snail. p38 directly phosphorylated Snail at Ser107, and this effectively suppressed DYRK2-mediated Ser104 phosphorylation, which is critical for GSK3ß-dependent Snail phosphorylation and ßTrCP-mediated Snail ubiquitination and degradation. Importantly, functional studies and analysis of clinical samples established a crucial role for the p38-Snail axis in regulating ovarian cancer EMT and metastasis. These results indicate the potential therapeutic value of targeting the p38-Snail axis in ovarian cancer. SIGNIFICANCE: These findings identify p38 MAPK as a novel regulator of Snail protein stability and potential therapeutic target in ovarian cancer.

Compositional data analysis and geochemical modeling of CO2-water-rock interactions in three provinces of Korea.

The CO2-rich spring water (CSW) occurring naturally in three provinces, Kangwon (KW), Chungbuk (CB), and Gyeongbuk (GB) of South Korea was classified based on its hydrochemical properties using compositional data analysis. Additionally, the geochemical evolution pathways of various CSW were simulated via equilibrium phase modeling (EPM) incorporated in the PHREEQC code. Most of the CSW in the study areas grouped into the Ca-HCO3 water type, but some samples from the KW area were classified as Na-HCO3 water. Interaction with anorthite is likely to be more important than interaction with carbonate minerals for the hydrochemical properties of the CSW in the three areas, indicating that the CSW originated from interactions among magmatic CO2, deep groundwater, and bedrock-forming minerals. Based on the simulation results of PHREEQC EPM, the formation temperatures of the CSW within each area were estimated as 77.8 and 150 °C for the Ca-HCO3 and Na-HCO3 types of CSW, respectively, in the KW area; 138.9 °C for the CB CSW; and 93.0 °C for the GB CSW. Additionally, the mixing ratios between simulated carbonate water and shallow groundwater were adjusted to 1:9-9:1 for the CSW of the GB area and the Ca-HCO3-type CSW of the KW area, indicating that these CSWs were more affected by carbonate water than by shallow groundwater. On the other hand, mixing ratios of 1:9-5:5 and 1:9-3:7 were found for the Na-HCO3-type CSW of the KW area and for the CSW of the CB area, respectively, suggesting a relatively small contribution of carbonate water to these CSWs. This study proposes a systematic, but relatively simple, methodology to simulate the formation of carbonate water in deep environments and the geochemical evolution of CSW. Moreover, the proposed methodology could be applied to predict the behavior of CO2 after its geological storage and to estimate the stability and security of geologically stored CO2.

A Monte Carlo simulation study for the gamma-ray/neutron dual-particle imager using rotational modulation collimator (RMC).

The aim of this work is to develop a gamma-ray/neutron dual-particle imager, based on rotational modulation collimators (RMCs) and pulse shape discrimination (PSD)-capable scintillators, for possible applications for radioactivity monitoring as well as nuclear security and safeguards. A Monte Carlo simulation study was performed to design an RMC system for the dual-particle imaging, and modulation patterns were obtained for gamma-ray and neutron sources in various configurations. We applied an image reconstruction algorithm utilizing the maximum-likelihood expectation-maximization method based on the analytical modeling of source-detector configurations, to the Monte Carlo simulation results. Both gamma-ray and neutron source distributions were reconstructed and evaluated in terms of signal-to-noise ratio, showing the viability of developing an RMC-based gamma-ray/neutron dual-particle imager using PSD-capable scintillators.

Predicting complications of percutaneous coronary intervention using a novel support vector method.

OBJECTIVE: To explore the feasibility of a novel approach using an augmented one-class learning algorithm to model in-laboratory complications of percutaneous coronary intervention (PCI). MATERIALS AND METHODS: Data from the Blue Cross Blue Shield of Michigan Cardiovascular Consortium (BMC2) multicenter registry for the years 2007 and 2008 (n=41 016) were used to train models to predict 13 different in-laboratory PCI complications using a novel one-plus-class support vector machine (OP-SVM) algorithm. The performance of these models in terms of discrimination and calibration was compared to the performance of models trained using the following classification algorithms on BMC2 data from 2009 (n=20 289): logistic regression (LR), one-class support vector machine classification (OC-SVM), and two-class support vector machine classification (TC-SVM). For the OP-SVM and TC-SVM approaches, variants of the algorithms with cost-sensitive weighting were also considered. RESULTS: The OP-SVM algorithm and its cost-sensitive variant achieved the highest area under the receiver operating characteristic curve for the majority of the PCI complications studied (eight cases). Similar improvements were observed for the Hosmer-Lemeshow χ(2) value (seven cases) and the mean cross-entropy error (eight cases). CONCLUSIONS: The OP-SVM algorithm based on an augmented one-class learning problem improved discrimination and calibration across different PCI complications relative to LR and traditional support vector machine classification. Such an approach may have value in a broader range of clinical domains.

Influences of four different light-emitting diode lights on flowering and polyphenol variations in the leaves of chrysanthemum (Chrysanthemum morifolium).

Light-emitting diodes (LEDs) are an efficient alternative to traditional lamps for plant growth. To investigate the influence of LEDs on flowering and polyphenol biosynthesis in the leaves of chrysanthemum, the plants were grown under supplemental blue, green, red, and white LEDs. Flower budding was formed even after a longer photoperiod than a critical day length of 13.5 h per day under blue light illumination. The weights of leaves and stems were highest under the white light illumination growth condition, whereas the weight of roots appeared to be independent of light quality. Among nine polyphenols characterized by high-performance liquid chromatography-tandem mass spectroscopy, three polyphenols were identified for the first time in chrysanthemum. A quantitation and principal component analysis biplot demonstrated that luteolin-7-O-glucoside (2), luteolin-7-O-glucuronide (3), and quercetagetin-trimethyl ether (8) were the highest polyphenols yielded under green light, and dicaffeoylquinic acid isomer (4), dicaffeoylquinic acid isomer (5), naringenin (7), and apigenin-7-O-glucuronide (6) were greatest under red light. Chlorogenic acid (1) and 1,2,6-trihydroxy-7,8-dimethoxy-3-methylanthraquinone (9) were produced in similar concentrations under both light types. The white and blue light appeared inefficient for polyphenol production. Taken together, our results suggest that the chrysanthemum flowering and polyphenol production are influenced by light quality composition.

Improving surgical models through one/two class learning.

Only a minority of patients undergoing in-patient surgical procedures experience complications. However, the large number of in-patient surgeries (over 48 million procedures each year in the U.S.) results in substantial overall mortality and morbidity due to these complications. This burden can be decreased through improvements in the ability to evaluate patients by the bedside, and to assess surgical quality and out-comes across hospitals. Unfortunately, the process of developing clinical models for surgical complications is made challenging by the availability of generally small datasets for model training, and by class imbalance due to the diminished prevalence of many important complications. In this paper, we address this issue and explore the idea of jointly leveraging the benefits of both supervised and unsupervised learning to model surgical complications that occur infrequently. In particular, we study an approach where the problems of supervised and unsupervised model development are treated as tasks that can be transferred. Focussing this work on support vector machine (SVM) classification, we describe a transfer learning algorithm that improves performance relative to both supervised (i.e., binary or 2-class SVM) and unsupervised (i.e., 1-class SVM) methods, as well as the use of cost-sensitive weighting techniques, for predicting different surgical complications within the American College of Surgeons National Surgical Quality Improvement Program registry.

Statistical file matching of flow cytometry data.

Flow cytometry is a technology that rapidly measures antigen-based markers associated to cells in a cell population. Although analysis of flow cytometry data has traditionally considered one or two markers at a time, there has been increasing interest in multidimensional analysis. However, flow cytometers are limited in the number of markers they can jointly observe, which is typically a fraction of the number of markers of interest. For this reason, practitioners often perform multiple assays based on different, overlapping combinations of markers. In this paper, we address the challenge of imputing the high-dimensional jointly distributed values of marker attributes based on overlapping marginal observations. We show that simple nearest neighbor based imputation can lead to spurious subpopulations in the imputed data and introduce an alternative approach based on nearest neighbor imputation restricted to a cell's subpopulation. This requires us to perform clustering with missing data, which we address with a mixture model approach and novel EM algorithm. Since mixture model fitting may be ill-posed in this context, we also develop techniques to initialize the EM algorithm using domain knowledge. We demonstrate our approach on real flow cytometry data.