Abstract The FDA-approved oral multi-kinase inhibitor, sorafenib (BAY 43-9006, Nexavar), is the first approved systemic therapy for patients with unresectable hepatocellular carcinoma (HCC). Although it has been shown to significantly improve the overall survival of patients with HCC, drug resistance limits the response rate to this therapeutic. Here, we report that acquired sorafenib resistance is associated with overexpression of the deacetylase, SIRT7, and a high level of ERK phosphorylation. Further, we identify that the hyperactivation of ERK is controlled by SIRT7-mediated deacetylation of DDX3X. The inhibition of SIRT7 combined with sorafenib resulted in a marked reduction of cell viability in vitro and of tumor growth in vivo. It seems plausible that SIRT7 is responsible for the acquired sorafenib resistance and its inhibition is most likely beneficial together in conjunction with sorafenib by suppressing ERK signaling. Highlights Sorafenib resistance in HCC is associated with SIRT7 and ERK hyperactivation. Suppression of SIRT7 combined with sorafenib restores sensitivity to sorafenib. SIRT7 controls sorafenib resistance through ERK activation by mediating DDX3X deacetylation.
In this paper, a new learning method to quantify data uncertainty without suffering from performance degradation in Single Image Super Resolution (SISR) is proposed. Our work is motivated by the fact that the idea of loss design for capturing uncertainty and that for solving SISR are contradictory. As to capturing data uncertainty, we often model the output of a network as a Euclidian distance divided by a predictive variance, negative log-likelihood (NLL) for the Gaussian distribution, so that images with high variance have less impact on training. On the other hand, in the SISR domain, recent works give more weights to the loss of challenging images to improve the performance by using attention models. Nonetheless, the conflict should be handled to make neural networks capable of predicting the uncertainty of a super-resolved image, without suffering from performance degradation. Therefore, we propose a method called Gradient Rescaling Attention Model (GRAM) that combines both attempts effectively. Since variance may reflect the difficulty of an image, we rescale the gradient of NLL by the degree of variance. Hence, the neural network can focus on the challenging images, similarly to attention models. We conduct performance evaluation using standard SISR benchmarks in terms of peak signal-noise ratio (PSNR) and structural similarity (SSIM). The experimental results show that the proposed gradient rescaling method generates negligible performance degradation compared to SISR outputs with the Euclidian loss, whereas NLL without attention degrades the SR quality.
A self-assembled monolayer (SAM) of l-cysteine [HSCH2CH(NH2)COOH] was prepared on a Au(111) surface by vapor deposition in ultrahigh vacuum and was characterized by techniques of temperature-programmed desorption (TPD), Cs+ reactive ion scattering (Cs+ RIS), and low-energy secondary ion mass spectrometry (LESIMS). Analysis of the amino acid functional groups of SAM indicated that l-cysteine molecules exist in the zwitterionic form. Upon physisorption of the D2O overlayer on the SAM, the −NH3+ functional group of cysteine readily exchanges their H atoms with D2O in the temperature range 125−230 K. The H/D exchange of the −NH3+ group sequentially occurs with D2O molecules that are directly hydrogen-bonded to the SAM, and the long-range proton transfer to the upper layer water molecules does not occur. Temperature-programmed reaction study and kinetic analysis yielded an activation energy of 13 ± 1 kJ mol-1 for the H/D exchange reaction, which suggests proton tunneling as a mechanism.
본 연구에서는 화재 해석 프로그램인 FDS와 Pathfinder를 이용하여 협소 거주공간의 사고재현을 위해 테스트베드의 화재를 시뮬레이션하였다. 해석 결과, 내부 구조 형태가 H형태인 경우가 피난대피 시간이 285초로 가장 빠르게 나타났다. 또한 실험 조건 중 출입구가 닫히고 스프링클러가 작동하는 경우가 온도의 분포가 가장 낮게 나타났으며, 이는 가시도와 연기농도에도 큰 영향을 끼치는 것으로 나타났다. In this study, the fire analysis program FDS and Pathfinder was used to analysis a simulated accidental fire of a narrow dwelling space as a test bed. The results showed that the evacuation time of the H form internal building structure was the fastest at 285 seconds. In addition, when the automatic sprinkler system functioned with the entrances closed, the temperature distribution was lower and the visible smoke density was reduced.
Winding is one of the major processes in roll-to-roll systems. Taper tension profile in a winding determines the distribution of stress in the radial direction, i.e., the radial stress in the wound rolls. Maximum radial stress is major cause of material defect, and this study has been actively proceeded. Traditional models of radial stress model were focused on flexible and light substrate. In this study, we developed an advanced radial stress model including effects of both these parameters(weight and stiffness) on the radial stress. The accuracy of the developed model was verified through FEM(Finite Element Method) analysis. FEM result of maximum radial stress value corresponds to 99 % in comparison to result with the model. From this study, the material defects does not occur when the steel winding. And steel industry can be applied to improve the winding process.
Abstract Currently, the identification of stroke patients with an increased suicide risk is mainly based on self‐report questionnaires, and this method suffers from a lack of objectivity. This study developed and validated a suicide ideation (SI) prediction model using clinical data and identified SI predictors. Significant variables were selected through traditional statistical analysis based on retrospective data of 385 stroke patients; the data were collected from October 2012 to March 2014. The data were then applied to three boosting models (Xgboost, CatBoost, and LGBM) to identify the comparative and best performing models. Demographic variables that showed significant differences between the two groups were age, onset, type, socioeconomic, and education level. Additionally, functional variables also showed a significant difference with regard to ADL and emotion (p < 0.05). The CatBoost model (0.900) showed higher performance than the other two models; and depression, anxiety, self-efficacy, and rehabilitation motivation were found to have high importance. Negative emotions such as depression and anxiety showed a positive relationship with SI and rehabilitation motivation and self-efficacy displayed an inverse relationship with SI. Machine learning-based SI models could augment SI prevention by helping rehabilitation and medical professionals identify high-risk stroke patients in need of SI prevention intervention.
Background: The separation of pharmaceutical prescription and dispensing law was implemented in July 1st of 2000. This law was initiated by government without a through consensus among related stakeholders in the process of policy decision, eventually raising contention about decision making process rather than the performance of the policy. Methods: Therefore, this study tries to identify the accomplishment of the policy goals; based on the last decade's research we assessed inhibition of unnecessary prescription, drug misuse and overuse prevention, prevention of drug-related sentinel events, reducing unnecessary drug utilization, and reducing nation's medical cost. Results: Assessment results represent that government-suggested goal of the policy lacks sufficient evidence to evaluate accomplishment. Conclusion: Unlike other studies that evaluate problems regarding drug dispensing policy in the policy decision process, this study is meaningful in that it evaluated the policy goal based on the last ten years of related study results.
In this paper, we propose a confidence-calibration method for predicting the winner of a famous multiplayer online battle arena (MOBA) game, League of Legends. In MOBA games, the dataset may contain a large amount of input-dependent noise; not all of such noise is observable. Hence, it is desirable to attempt a confidence-calibrated prediction. Unfortunately, most existing confidence calibration methods are pertaining to image and document classification tasks where consideration on uncertainty is not crucial. In this paper, we propose a novel calibration method that takes data uncertainty into consideration. The proposed method achieves an outstanding expected calibration error (ECE) (0.57%) mainly owing to data uncertainty consideration, compared to a conventional temperature scaling method of which ECE value is 1.11%.
항공 LiDAR 측량으로 토석류 발생 전·후의 지형자료를 취득하는 경우 토석류로 인하여 유출 된 토사량을 알 수 있다. 그러나 토석류 발생지를 미리 예측하여 촬영하기가 힘들고, 토석류 발생지역의 과거 항공 LiDAR 자료는 존재가능성이 낮아 토석류 발생이전 지형자료를 이용하는 것은 어렵다. 따라서 본 연구에서는 토석류 발생지역의 토사량 추정을 위해 발생전 지형을 복원하고, 토사유출의 공간적 범위를 파악할 수 있는 지형복원기법을 개발하였다. 지형복원기법은 토석류 발생지역에서 추출한 선형 및 비선형 횡단면을 가우시안혼합모델로 수식화하고 중심점 추정방법과 근사정확도로 근사결과를 평가하여 토석류 발생이전의 지형을 복원한다. 지형복원기법은 토석류 발생 전·후의 항공 LiDAR 자료를 이용하여 두 가지 방법으로 검증하였다. 먼저 토석류 발생구간에서 추출한 각 횡단면을 지형복원하여 발생전 항공 LiDAR 자료와 비교하였다. 또한 토석류 발생지역에 지형복원기법을 적용한 뒤 지형자료를 제작하여 토석류 발생전 항공 LiDAR DEM과 비교하여 검증하였다. 지형복원기법의 검증한 결과 전반적으로 근사정확도가 0.5m에 가까운 높은 정확도를 나타냈다.
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