A Deep Ranking Model for Spatio-Temporal Highlight Detection from a 360 Video

We address the problem of highlight detection from a 360 degree video by summarizing it both spatially and temporally. Given a long 360 degree video, we spatially select pleasantly-looking normal field-of-view (NFOV) segments from unlimited field of views (FOV) of the 360 degree video, and temporally summarize it into a concise and informative highlight as a selected subset of subshots. We propose a novel deep ranking model named as Composition View Score (CVS) model, which produces a spherical score map of composition per video segment, and determines which view is suitable for highlight via a sliding window kernel at inference. To evaluate the proposed framework, we perform experiments on the Pano2Vid benchmark dataset and our newly collected 360 degree video highlight dataset from YouTube and Vimeo. Through evaluation using both quantitative summarization metrics and user studies via Amazon Mechanical Turk, we demonstrate that our approach outperforms several state-of-the-art highlight detection methods. We also show that our model is 16 times faster at inference than AutoCam, which is one of the first summarization algorithms of 360 degree videosComment: In AAAI 2018, 9 page

MET gene alterations predict poor survival following chemotherapy in patients with advanced cancer

Background: To aid in oncology drug development, we investigated MET proto-oncogene receptor tyrosine kinase gene aberrations in 2,239 oncology patients who underwent next-generation sequencing (NGS) in clinical practice.Materials and methods: From November 2019 to January 2021, 2,239 patientswith advanced solid tumors who visited oncology clinics underwent NGS. The NGS panel included >500 comprehensive NGS tests using archival tissue specimens. Programmed death-ligand 1(PD-L1) 22C3 assay results and clinical records regarding initial chemotherapy were available for 1,137 (50.8%) and 1,761 (78.7%) patients, respectively for overall survival (OS) analysis.Results: The 2,239 patients represented 37 types of cancer. The NGS panel included >500 genes, microsatellite instability status, tumor mutational burden, and fusions. The most common cancer types were colorectal (N = 702), gastric (N = 481), and sarcoma (N = 180). MET aberrations were detected in 212 patients. All MET-amplified tumors had microsatellite stable status, and 8 had a high tumor mutational burden. Of 46 patients with MET-amplified cancers, 8 had MET-positive protein expression by immunohistochemistry (2+ and 3+). MET fusion was detected in 10 patients. Partner genes of MET fusion included ST7, TFEC, LRRD1, CFTR, CAV1, PCM1, HLA-DRB1, and CAPZA2. In survival analysis, patients with amplification of MET gene fusion had shorter OS and progression-free survival (PFS) than those without. Thus, MET aberration was determined to be a factor of response to chemotherapy.Conclusion: Approximately 2.1% and 0.4% of patients with advanced solid tumors demonstrated MET gene amplification and fusion, respectively, and displayed a worse response to chemotherapy and significantly shorter OS and PFS than those without MET gene amplification or fusion

Intelligent Ensemble Deep Learning System for Blood Glucose Prediction Using Genetic Algorithms

Forecasting blood glucose (BG) values for patients can help prevent hypoglycemia and hyperglycemia events in advance. To this end, this study proposes an intelligent ensemble deep learning system to predict BG values in 15, 30, and 60 min prediction horizons (PHs) based on historical BG values collected via continuous glucose monitoring devices as an endogenous factor and carbohydrate intake and insulin administration information (times) as exogenous factors. Although there are numerous deep learning algorithms available, this study applied five algorithms, namely, recurrent neural network (RNN), which is optimized for sequence data (e.g., time-series), and RNN-based algorithms (e.g., long short-term memory (LSTM), stacked LSTM, bidirectional LSTM, and gated recurrent unit). Then, a genetic algorithm (GA) was applied to the five prediction models to optimize their weights through ensemble techniques and to yield (output) the final predicted BG values. The performance of the proposed model was compared to that of the autoregressive integrated moving average (ARIMA) model as a baseline. The results show that the proposed model significantly outperforms the baseline in terms of the root mean square error (RMSE) and continuous glucose error grid analysis. For the valid 29 diabetic patients for the multivariate models, the RMSE was 11.08 (±3.19), 19.25 (±5.28), and 31.30 (±8.81) mg/DL for 15, 30, and 60 min PH, respectively. When the same data were applied to univariate models, the RMSE was 11.28 (±3.34), 19.99 (±5.59), and 33.13 (±9.27) mg/DL for 15, 30, and 60 min PH, respectively. Both the univariate and multivariate models showed a statistically significant difference compared with the baseline at a 5% statistical significance level. Instead of using a model with a single algorithm, applying a GA based on each output of a model with multiple algorithms was found to play a significant role in improving model performance

Impact of programmed death‐ligand 1 (PD‐L1) positivity on clinical and molecular features of patients with metastatic gastric cancer

Abstract Background Programmed death‐ligand 1 (PD‐L1) is an important screening biomarker to select patients with gastric cancer (GC) for optimized treatment, including immune checkpoint inhibitors (ICI). Methods In this single‐institution retrospective cohort study, patients with metastatic GC with available PD‐L1 results between October 2019 and September 2021 were identified by reviewing their electronic medical records. Genomic data were obtained from the Samsung Medical Center Clinical Sequencing Platform. Results Among the 399 patients, 276 (69%) had a PD‐L1 combined positive score (CPS) ≥1, 155 (39%) had a CPS between 1 and 5, and 121 (30%) had a CPS ≥5. Of the 121 patients with CPS ≥5, 28 (23%) had a known etiology for “inflamed tumor,” with Epstein–Barr virus (EBV) positivity (N = 11) or high tumor mutational burden (TMB) (N = 17), which included microsatellite instability (MSI) (N = 9). PD‐L1 CPS ≥5 was observed in 11/11 (100%) patients with EBV positivity, 9/12 (75%) patients with MSI, and 17/33 (52%) patients with high TMB. For the 108 patients who received ICI therapy, CPS ≥5 was the only predictor significantly associated with survival in multivariable analyses, including TMB, MSI, or EBV. Objective response rate (ORR) was 49% in patients with CPS ≥5, 30% in patients with 1 ≤ CPS <5, and 19% in patients with CPS <1. Among the 31 responders to ICI therapy, 27 (87%) had a CPS of ≥1. Mutations in TET2, IRS2, DOT1L, PTPRT, and LRP1B were associated with a higher ORR (63%–100%), whereas MDC1 mutations were associated with a low ORR (22%). Conclusions PD‐L1 expression is an independent and sensitive biomarker for ICI therapy. Considering its significant association with several gene alterations, including PIK3CA mutations and MET amplification, combining ICI therapy with other targeted agents may be a promising therapeutic strategy for GC

Developing an Individual Glucose Prediction Model Using Recurrent Neural Network

In this study, we propose a personalized glucose prediction model using deep learning for hospitalized patients who experience Type-2 diabetes. We aim for our model to assist the medical personnel who check the blood glucose and control the amount of insulin doses. Herein, we employed a deep learning algorithm, especially a recurrent neural network (RNN), that consists of a sequence processing layer and a classification layer for the glucose prediction. We tested a simple RNN, gated recurrent unit (GRU), and long-short term memory (LSTM) and varied the architectures to determine the one with the best performance. For that, we collected data for a week using a continuous glucose monitoring device. Type-2 inpatients are usually experiencing bad health conditions and have a high variability of glucose level. However, there are few studies on the Type-2 glucose prediction model while many studies performed on Type-1 glucose prediction. This work has a contribution in that the proposed model exhibits a comparative performance to previous works on Type-1 patients. For 20 in-hospital patients, we achieved an average root mean squared error (RMSE) of 21.5 and an Mean absolute percentage error (MAPE) of 11.1%. The GRU with a single RNN layer and two dense layers was found to be sufficient to predict the glucose level. Moreover, to build a personalized model, at most, 50% of data are required for training

Image1_MET gene alterations predict poor survival following chemotherapy in patients with advanced cancer.pdf

Background: To aid in oncology drug development, we investigated MET proto-oncogene receptor tyrosine kinase gene aberrations in 2,239 oncology patients who underwent next-generation sequencing (NGS) in clinical practice.Materials and methods: From November 2019 to January 2021, 2,239 patientswith advanced solid tumors who visited oncology clinics underwent NGS. The NGS panel included >500 comprehensive NGS tests using archival tissue specimens. Programmed death-ligand 1(PD-L1) 22C3 assay results and clinical records regarding initial chemotherapy were available for 1,137 (50.8%) and 1,761 (78.7%) patients, respectively for overall survival (OS) analysis.Results: The 2,239 patients represented 37 types of cancer. The NGS panel included >500 genes, microsatellite instability status, tumor mutational burden, and fusions. The most common cancer types were colorectal (N = 702), gastric (N = 481), and sarcoma (N = 180). MET aberrations were detected in 212 patients. All MET-amplified tumors had microsatellite stable status, and 8 had a high tumor mutational burden. Of 46 patients with MET-amplified cancers, 8 had MET-positive protein expression by immunohistochemistry (2+ and 3+). MET fusion was detected in 10 patients. Partner genes of MET fusion included ST7, TFEC, LRRD1, CFTR, CAV1, PCM1, HLA-DRB1, and CAPZA2. In survival analysis, patients with amplification of MET gene fusion had shorter OS and progression-free survival (PFS) than those without. Thus, MET aberration was determined to be a factor of response to chemotherapy.Conclusion: Approximately 2.1% and 0.4% of patients with advanced solid tumors demonstrated MET gene amplification and fusion, respectively, and displayed a worse response to chemotherapy and significantly shorter OS and PFS than those without MET gene amplification or fusion.</p

Magnetic surface-enhanced Raman spectroscopic (M-SERS) dots for the identification of bronchioalveolar stem cells in normal and lung cancer mice

Bronchioalveolar stem cells (BASCs) play an important role in the development of cancer. To study the characterization of BASCs, their isolation and purification are important. However, the cells are very rare in tissues and the available methods of isolating them are limited. The current study was performed to isolate BASCs in the murine lung using magnetic nanoparticle-based surface-enhanced Raman spectroscopic dots (M-SERS Dots). We used K-ras(LA1) mice, a laboratory animal model of non-small cell lung cancer of human, and C57BL/6 mice having the same age as a control. We compared the BASCs between 2 species by FACS analysis with 4 markers of BASCs, CCSP, SP-C, CD34, and Sca-1. We found that BASCs were more abundant in the K-ras(LA1) mice than in the C57BL/6 mice. Also, the M-SERS Dot-mediated positive selection of the CD34(pos) cells enabled the BASCs to be enriched to an approximately 4- to 5-fold higher level than that in the case without pre-separation. In summary, our study demonstrates the potential of using M-SERS Dots as a sorting system with very effective isolation of BASCs and multiplex targeting probe, showing that they may play an effective role in the study of BASCs in the future. (C) 2009 Elsevier Ltd. All rights reserved.