Identification of MYC as an antinecroptotic protein that stifles RIPK1-RIPK3 complex formation

The underlying mechanism of necroptosis in relation to cancer is still unclear. Here, MYC, a potent oncogene, is an antinecroptotic factor that directly suppresses the formation of the RIPK1-RIPK3 complex. Gene set enrichment analyses reveal that the MYC pathway is the most prominently down-regulated signaling pathway during necroptosis. Depletion or deletion of MYC promotes the RIPK1-RIPK3 interaction, thereby stabilizing the RIPK1 and RIPK3 proteins and facilitating necroptosis. Interestingly, MYC binds to RIPK3 in the cytoplasm and inhibits the interaction between RIPK1 and RIPK3 in vitro. Furthermore, MYC-nick, a truncated form that is mainly localized in the cytoplasm, prevented TNF-induced necroptosis. Finally, down-regulation of MYC enhances necroptosis in leukemia cells and suppresses tumor growth in a xenograft model upon treatment with birinapant and emricasan. MYC-mediated suppression of necroptosis is a mechanism of necroptosis resistance in cancer, and approaches targeting MYC to induce necroptosis represent an attractive therapeutic strategy for cancer

Geotechnical Design Based on CPT and PMT

This research presents the correlation of undrained shear strength based on the cone resistance from the cone penetration test (CPT) for clayey soils in Indiana. It utilized the field cone penetration test program including the CPT, the index test, the one dimensional test and the triaxial test. The cone factor, which is essential to reliable estimation of undrained shear strength from cone resistance, has been evaluated considering the plasticity index of soils. The cone factor is influenced by the penetration rate during the CPT and test methods that are used for obtaining the undrained shear strength. The rate effect of the CPT has been examined to ensure undrained penetration, and the isotropic consolidated undrained compression test (CIUC) for shear strength assessment has been used to effectively reflect in-situ strength. Following the field cone penetration test program, clayey soils from 4 sites in Indiana have been investigated. Based on the results from the test program, the cone factor ranges from 8.0 at to 12.1 at for over-consolidated clays. This result parallels the increasing trend of the cone factor as the plasticity index increases, which was reported by Aas et al. (1986), while Lunne et al. (1976) and Baligh et al. (1980) showed decreasing trends. The equation is suggested for estimating the cone factor in geotechnical design. 7.9 p I 20.0 p I 0.285 7.636 k p N

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Precipitation is one of the main factors that affect water and energy cycles, and its estimation plays a very important role in securing water resources and timely responding to water disasters. Satellitebased quantitative precipitation estimation (QPE) has the advantage of covering large areas at high spatiotemporal resolution. In this study, machine learning-based rainfall intensity models were developed using Himawari-8 Advanced Himawari Imager (AHI) water vapor channel (6.7 ??m), infrared channel (10.8 ??m), and weather radar Column Max (CMAX) composite data based on random forest (RF). The target variables were weather radar reflectivity (dBZ) and rainfall intensity (mm/hr) converted by the ZR relationship. The results showed that the model which learned CMAX reflectivity produced the Critical Success Index (CSI) of 0.34 and the Mean-Absolute-Error (MAE) of 4.82 mm/hr. When compared to the GeoKompsat-2 and Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks (PERSIANN)-Cloud Classification System (CCS) rainfall intensity products, the accuracies improved by 21.73% and 10.81% for CSI, and 31.33% and 23.49% for MAE, respectively. The spatial distribution of the estimated rainfall intensity was much more similar to the radar data than the existing products

Development of a Portable Respiratory Gas Analyzer for Measuring Indirect Resting Energy Expenditure (REE)

Objective. A rapidly growing home healthcare market has resulted in the development of many portable or wearable products. Most of these products measure, estimate, or calculate physiologic signals or parameters, such as step counts, blood pressure, or electrocardiogram. One of the most important applications in home healthcare is monitoring one’s metabolic state since the change of metabolic state could reveal minor or major changes in one’s health condition. A simple and noninvasive way to measure metabolism is through breath monitoring. With breath monitoring by breath gas analysis, two important indicators like the respiratory quotient (RQ) and resting energy exposure (REE) can be calculated. Therefore, we developed a portable respiratory gas analyzer for breath monitoring to monitor metabolic state, and the performance of the developed device was tested in a clinical trial. Approach. The subjects consisted of 40 healthy men and women. Subjects begin to measure exhalation gas using Vmax 29 for 15 minutes. After that, subjects begin to measure exhalation gas via the developed respiratory gas analyzer. Finally, the recorded data on the volume of oxygen (VO2), volume of carbon dioxide (VCO2), RQ, and REE were used to validate correlations between Vmax 29 and the developed respiratory gas analyzer. Results. The results showed that the root-mean-square errors (RMSE) values of VCO2, VO2, RQ, and REE are 0.0315, 0.0417, 0.504, and 0.127. Bland-Altman plots showed that most of the VCO2, VO2, RQ, and REE values are within 95% of the significance level. Conclusions. We have successfully developed and tested a portable respiratory gas analyzer for home healthcare. However, there are limitations of the clinical trial; the number of subjects is small in size, and the age and race of subjects are confined. The developed portable respiratory gas analyzer is a cost-efficient method for measuring metabolic state and a new application of home healthcare

Precipitation nowcasting using ground radar data and simpler yet better video prediction deep learning

Skillful quantitative precipitation nowcasting (QPN) is important for predicting precipitation in the upcoming few hours and thus avoiding significant socioeconomic damage. Recent QPN studies have actively adopted deep learning (DL) to generate precipitation maps using sequences of ground radar data. Although high skill scores in forecasting precipitation areas of weak intensity (similar to 1 mm/h) have been achieved, the horizontal movement of precipitation areas could not be accurately simulated, exhibiting poor forecasting skills for stronger intensities. For lead times up to 120 min, this study suggests using an improved radar-based QPN model that utilizes a state-of-the-art DL model termed simpler yet better video prediction (SimVP). An independent evaluation using ground radar data in South Korea from June to September 2022 demonstrated that the proposed model outperformed the existing DL models in terms of critical score index (CSI) with a lead time of 120 min (0.46, 0.23, and 0.09 for 1, 5, and 10 mm/h thresholds, respectively). Three case analyses were conducted to reflect various precipitation conditions: heavy rainfall, typhoons, and fast-moving narrow convection events. The proposed SimVP-based QPN model yielded robust performance for all cases, producing a comparable or highest CSI at the lead time of 120 min with a 1 mm/h threshold (0.49, 0.69, and 0.29 for heavy rainfall, typhoon, and narrow convection, respectively). Qualitative evaluation of the model indicated better results in terms of displacement movement and reduced underestimation than other models under the high variability of precipitation patterns of the three cases. A comparison of model complexity among DL-QPN models was conducted, taking into consideration operational applications across various study areas and environments. The proposed approach is expected to provide a new baseline for DL-based QPN, and the improved prediction using the proposed model can lead to reduced socioeconomic damage incurred as a result of short-term intense precipitation

Different Spectral Domain Transformation for Land Cover Classification Using Convolutional Neural Networks with Multi-Temporal Satellite Imagery

This study compares some different types of spectral domain transformations for convolutional neural network (CNN)-based land cover classification. A novel approach was proposed, which transforms one-dimensional (1-D) spectral vectors into two-dimensional (2-D) features: Polygon graph images (CNN-Polygon) and 2-D matrices (CNN-Matrix). The motivations of this study are that (1) the shape of the converted 2-D images is more intuitive for human eyes to interpret when compared to 1-D spectral input; and (2) CNNs are highly specialized and may be able to similarly utilize this information for land cover classification. Four seasonal Landsat 8 images over three study areas-Lake Tapps, Washington, Concord, New Hampshire, USA, and Gwangju, Korea-were used to evaluate the proposed approach for nine land cover classes compared to several other methods: Random forest (RF), support vector machine (SVM), 1-D CNN, and patch-based CNN. Oversampling and undersampling approaches were conducted to examine the effect of the sample size on the model performance. The CNN-Polygon had better performance than the other methods, with overall accuracies of about 93%-95 % for both Concord and Lake Tapps and 80%-84% for Gwangju. The CNN-Polygon particularly performed well when the training sample size was small, less than 200 per class, while the CNN-Matrix resulted in similar or higher performance as sample sizes became larger. The contributing input variables to the models were carefully analyzed through sensitivity analysis based on occlusion maps and accuracy decreases. Our result showed that a more visually intuitive representation of input features for CNN-based classification models yielded higher performance, especially when the training sample size was small. This implies that the proposed graph-based CNNs would be useful for land cover classification where reference data are limited

CHIP controls necroptosis through ubiquitylation- and lysosome-dependent degradation of RIPK3

Receptor-interacting protein kinase 3 (RIPK3) functions as a key regulator of necroptosis. Here, we report that the RIPK3 expression level is negatively regulated by CHIP (carboxyl terminus of Hsp70-interacting protein; also known as STUB1) E3 ligase-mediated ubiquitylation. Chip(-/-) mouse embryonic fibroblasts and CHIP-depleted L929 and HT-29 cells exhibited higher levels of RIPK3 expression, resulting in increased sensitivity to necroptosis induced by TNF (also known as TNF alpha). These phenomena are due to the CHIP-mediated ubiquitylation of RIPK3, which leads to its lysosomal degradation. Interestingly, RIPK1 expression is also negatively regulated by CHIP-mediated ubiquitylation, validating the major role of CHIP in necrosome formation and sensitivity to TNF-mediated necroptosis. Chip(-/-) mice (C57BL/6) exhibit inflammation in the thymus and massive cell death and disintegration in the small intestinal tract, and die within a few weeks after birth. These phenotypes are rescued by crossing with Ripk3(-/-) mice. These results imply that CHIP is a bona fide negative regulator of the RIPK1-RIPK3 necrosome formation leading to desensitization of TNF-mediated necroptosis