DataSheet_1_Causes of death and conditional survival estimates of long-term lung cancer survivors.docx

IntroductionLung cancer ranks the leading cause of cancer-related death worldwide. This retrospective cohort study was designed to determine time-dependent death hazards of diverse causes and conditional survival of lung cancer.MethodsWe collected 816,436 lung cancer cases during 2000-2015 in the SEER database, after exclusion, 612,100 cases were enrolled for data analyses. Cancer-specific survival, overall survival and dynamic death hazard were assessed in this study. Additionally, based on the FDA approval time of Nivolumab in 2015, we evaluated the effect of immunotherapy on metastatic patients’ survival by comparing cases in 2016-2018 (immunotherapy era, n=7135) and those in 2013-2016 (non-immunotherapy era, n=42061).ResultsOf the 612,100 patients, 285,705 were women, the mean (SD) age was 68.3 (11.0) years old. 252,558 patients were characterized as lung adenocarcinoma, 133,302 cases were lung squamous cell carcinoma, and only 78,700 cases were small cell lung carcinomas. TNM stage was I in 140,518 cases, II in 38,225 cases, III in 159,095 cases, and IV in 274,262 patients. 164,394 cases underwent surgical intervention. The 5-y overall survival and cancer-specific survival were 54.2% and 73.8%, respectively. The 5-y conditional survival rate of cancer-specific survival is improved in a time-dependent pattern, while conditional overall survival tends to be steady after 5-y follow-up. Except from age, hazard disparities of other risk factors (such as stage and surgery) diminished over time according to the conditional survival curves. After 8 years since diagnosis, mortality hazard from other causes became higher than that from lung cancer. This critical time point was earlier in elder patients while was postponed in patients with advanced stages. Moreover, both cancer-specific survival and overall survival of metastatic patients in immunotherapy era were significantly better than those in non-immunotherapy era (PConclusionsOur findings expand on previous studies by demonstrating that non-lung-cancer related death risk becomes more and more predominant over the course of follow-up, and we establish a personalized web-based calculator to determine this critical time point for long-term survivors. We also confirmed the survival benefit of advanced lung cancer patients in immunotherapy era.</p

Self-Aligned Nanotube–Nanowire Phase Change Memory

A central issue of nanoelectronics concerns their fundamental scaling limits, that is, the smallest and most energy-efficient devices that can function reliably. Unlike charge-based electronics that are prone to leakage at nanoscale dimensions, memory devices based on phase change materials (PCMs) are more scalable, storing digital information as the crystalline or amorphous state of a material. Here, we describe a novel approach to self-align PCM nanowires with individual carbon nanotube (CNT) electrodes for the first time. The highly scaled and spatially confined memory devices approach the ultimate scaling limits of PCM technology, achieving ultralow programming currents (∼0.1 μA set, ∼1.6 μA reset), outstanding on/off ratios (∼10³), and improved endurance and stability at few-nanometer bit dimensions. In addition, the powerful yet simple nanofabrication approach described here can enable confining and probing many other nanoscale and molecular devices self-aligned with CNT electrodes

Video_1_Adaptive optimal output regulation for wheel-legged robot Ollie: A data-driven approach.MP4

The dynamics of a robot may vary during operation due to both internal and external factors, such as non-ideal motor characteristics and unmodeled loads, which would lead to control performance deterioration and even instability. In this paper, the adaptive optimal output regulation (AOOR)-based controller is designed for the wheel-legged robot Ollie to deal with the possible model uncertainties and disturbances in a data-driven approach. We test the AOOR-based controller by forcing the robot to stand still, which is a conventional index to judge the balance controller for two-wheel robots. By online training with small data, the resultant AOOR achieves the optimality of the control performance and stabilizes the robot within a small displacement in rich experiments with different working conditions. Finally, the robot further balances a rolling cylindrical bottle on its top with the balance control using the AOOR, but it fails with the initial controller. Experimental results demonstrate that the AOOR-based controller shows the effectiveness and high robustness with model uncertainties and external disturbances.</p