Time-varying effect in the competing risks based on restricted mean time lost

Patients with breast cancer tend to die from other diseases, so for studies that focus on breast cancer, a competing risks model is more appropriate. Considering subdistribution hazard ratio, which is used often, limited to model assumptions and clinical interpretation, we aimed to quantify the effects of prognostic factors by an absolute indicator, the difference in restricted mean time lost (RMTL), which is more intuitive. Additionally, prognostic factors may have dynamic effects (time-varying effects) in long-term follow-up. However, existing competing risks regression models only provide a static view of covariate effects, leading to a distorted assessment of the prognostic factor. To address this issue, we proposed a dynamic effect RMTL regression that can explore the between-group cumulative difference in mean life lost over a period of time and obtain the real-time effect by the speed of accumulation, as well as personalized predictions on a time scale. Through Monte Carlo simulation, we validated the dynamic effects estimated by the proposed regression having low bias and a coverage rate of around 95%. Applying this model to an elderly early-stage breast cancer cohort, we found that most factors had different patterns of dynamic effects, revealing meaningful physiological mechanisms underlying diseases. Moreover, from the perspective of prediction, the mean C-index in external validation reached 0.78. Dynamic effect RMTL regression can analyze both dynamic cumulative effects and real-time effects of covariates, providing a more comprehensive prognosis and better prediction when competing risks exist

Potential of Core-Collapse Supernova Neutrino Detection at JUNO

JUNO is an underground neutrino observatory under construction in Jiangmen, China. It uses 20kton liquid scintillator as target, which enables it to detect supernova burst neutrinos of a large statistics for the next galactic core-collapse supernova (CCSN) and also pre-supernova neutrinos from the nearby CCSN progenitors. All flavors of supernova burst neutrinos can be detected by JUNO via several interaction channels, including inverse beta decay, elastic scattering on electron and proton, interactions on C12 nuclei, etc. This retains the possibility for JUNO to reconstruct the energy spectra of supernova burst neutrinos of all flavors. The real time monitoring systems based on FPGA and DAQ are under development in JUNO, which allow prompt alert and trigger-less data acquisition of CCSN events. The alert performances of both monitoring systems have been thoroughly studied using simulations. Moreover, once a CCSN is tagged, the system can give fast characterizations, such as directionality and light curve

Detection of the Diffuse Supernova Neutrino Background with JUNO

As an underground multi-purpose neutrino detector with 20 kton liquid scintillator, Jiangmen Underground Neutrino Observatory (JUNO) is competitive with and complementary to the water-Cherenkov detectors on the search for the diffuse supernova neutrino background (DSNB). Typical supernova models predict 2-4 events per year within the optimal observation window in the JUNO detector. The dominant background is from the neutral-current (NC) interaction of atmospheric neutrinos with 12C nuclei, which surpasses the DSNB by more than one order of magnitude. We evaluated the systematic uncertainty of NC background from the spread of a variety of data-driven models and further developed a method to determine NC background within 15\% with {\it{in}} {\it{situ}} measurements after ten years of running. Besides, the NC-like backgrounds can be effectively suppressed by the intrinsic pulse-shape discrimination (PSD) capabilities of liquid scintillators. In this talk, I will present in detail the improvements on NC background uncertainty evaluation, PSD discriminator development, and finally, the potential of DSNB sensitivity in JUNO

Real-time Monitoring for the Next Core-Collapse Supernova in JUNO

Core-collapse supernova (CCSN) is one of the most energetic astrophysical events in the Universe. The early and prompt detection of neutrinos before (pre-SN) and during the SN burst is a unique opportunity to realize the multi-messenger observation of the CCSN events. In this work, we describe the monitoring concept and present the sensitivity of the system to the pre-SN and SN neutrinos at the Jiangmen Underground Neutrino Observatory (JUNO), which is a 20 kton liquid scintillator detector under construction in South China. The real-time monitoring system is designed with both the prompt monitors on the electronic board and online monitors at the data acquisition stage, in order to ensure both the alert speed and alert coverage of progenitor stars. By assuming a false alert rate of 1 per year, this monitoring system can be sensitive to the pre-SN neutrinos up to the distance of about 1.6 (0.9) kpc and SN neutrinos up to about 370 (360) kpc for a progenitor mass of 30$M_{\odot}$ for the case of normal (inverted) mass ordering. The pointing ability of the CCSN is evaluated by using the accumulated event anisotropy of the inverse beta decay interactions from pre-SN or SN neutrinos, which, along with the early alert, can play important roles for the followup multi-messenger observations of the next Galactic or nearby extragalactic CCSN.Comment: 24 pages, 9 figure

Design of Real Network Hardware In-Loop Simulation Test Platform for Internet of Vehicles Testing

Based on the function and performance testing requirements of Internet of Vehicles, a design scheme for automatic simulation test and verification platform for the Internet of Vehicles based on cellular networks and real network in-loop is proposed. First, the overall structure of the simulation test platform and the specific function of each component are presented. The main and functional components of the real cellular network simulation subsystem are then discussed. Finally, two typical application test scenarios are designed and presented; namely, vehicle remote-startup delay test based on a cloud platform and Internet of Vehicles local performance test of different frequency cell switching in a dynamic scenario. Experimental results verify that the simulation test platform can meet the test verification, performance evaluation, and troubleshooting requirements of the Internet of Vehicles function. It is important to improve the application experience of intelligent connected vehicles and the Internet of Vehicles

Natural Gas Hydrate CT Image Threshold Segmentation Based on Time Evolution

Micro-scale X-ray computed tomography (CT) has been widely used to study the occurrence forms of gas hydrate-bearing sediments. However, the similarity between the X-ray attenuation coefficient of hydrate and that of water leads to a strong non-uniqueness in their phase differentiation in CT images. To improve threshold segmentation accuracy between hydrate and water in CT images, this study proposes a CT image and histogram normalized method by analyzing the histogram characteristics of CT images at different times during the growth process of natural gas hydrate. First, the peak gray value baseline of methane gas and quartz sand was selected. Then, a Gaussian function was used to fit the curves corresponding to methane gas and quartz sand in the current CT image histogram to obtain the peak gray values. In addition, the peak gray values of methane gas and quartz sand in the current CT image histogram were normalized to the chosen peak gray baseline. Subsequently, the normalized histogram was used to normalize the corresponding CT images. Finally, according to the changing trend of normalized gray histogram curves, the increasing gray ranges of hydrate and decreasing gray ranges of gas-water in CT images were obtained quantitatively, which guided threshold segmentation of CT images. Experimental results show that the proposed threshold segmentation method can provide a basis for phase differentiation between hydrate and water in CT images, improving the threshold segmentation accuracy

Assessing the Economic Energy Level of the Chengdu–Chongqing Economic Circle: An Integrative Perspective of “Field Source” and “Field”

As a densely overlapping area under the national overarching development strategy, the Chengdu–Chongqing Economic Circle (CCEC) possesses a significant strategic location. However, compared with the other three growth pillars, the economic energy of the CCEC is still at a low level and in urgent need of improvement, which has to be implemented step by step in a systematic manner. At present, the focus remains on the two central cities—Chengdu and Chongqing. In contrast to the traditional evaluation of the regional economic energy level (EEL) solely from the “internal comprehensive development level”, this paper takes an angle on the interdependence and co-existence of “field source” and “field” to construct a preliminary index system which accounts for the “external economic connection level” as well. We then calibrate and validate the proposed model from both statistical and empirical angles. Finally, by optimizing the model, this paper evaluates the EELs of the Chengdu–Chongqing twin cities by fuzzy integrals of comprehensive weights. The results show the following: (1) From the perspective of overall indicators, the EELs of Chengdu and Chongqing have been rising from 2000 to 2018. In 2019, due to deglobalization and the Sino-US trade war, both cities appeared to reach an inflection point. (2) In terms of horizontal comparison, the EELs of the two cities basically coincide with each other, in line with the positioning of Chengdu–Chongqing as the two leading cities in Western China. However, their EELs have been lagging behind those of Beijing, indicating more room for further improvement. (3) From the point of view of sub-indexes, Chongqing has the advantage in the “external economic connection level” while Chengdu has the advantage in the “internal comprehensive development level”. The dislocation and complementarity of Chongqing and Chengdu has become an opportunity to break away from the stiff competition and jointly improve their EELs. (4) By comparing our evaluation with the traditional assessment, we note that the EEL tends to be misestimated if comprehensive factors regarding the “external economic connection level” are not taken into account

Prognostic Value, Immune Signature, and Molecular Mechanisms of the PHLDA Family in Pancreatic Adenocarcinoma

Background: Increasing evidence supports the belief that the pleckstrin homology domain family A (PHLDA) family is associated with the development of a variety of cancers. However, the function of the PHLDA family members in PAAD is still unclear. Methods: Comprehensive bioinformatic analyses using R (version 3.6.3), Cytoscape (version 3.9.1), UALCAN, etc., were performed to study the clinicopathological characteristics, prognostic value, immune features, and functional mechanisms of the PHLDA family members in PAAD. Results: The PHLDA family members showed significantly elevated expression in PAAD compared with paracancerous or normal tissues. Their high expression or amplification were significantly correlated with worse clinicopathological characteristics and prognosis in PAAD patients. In addition, the role of the PHLDA family members in the immune regulation is diverse and complex. Mechanistically, TP53 mutations were significantly associated with the promoter methylation and expression levels of the PHLDA family members, which were activated in multiple oncogenic pathways, including the EMT, RAS/MAPK, and TSC/mTOR pathways. Moreover, we found that their expression levels were significantly correlated with the sensitivity of multiple traditional chemotherapeutic drugs and novel targeted MEK1/2 inhibitors. Conclusion: The PHLDA family members play an oncogenic role in the development of PAAD and might serve as new biomarkers or therapeutic targets

Restricted mean survival time regression model with time-dependent covariates

In clinical or epidemiological follow-up studies, methods based on time scale indicators such as the restricted mean survival time (RMST) have been developed to some extent. Compared with traditional hazard rate indicator system methods, the RMST is easier to interpret and does not require the proportional hazard assumption. To date, regression models based on the RMST are indirect or direct models of the RMST and baseline covariates. However, time-dependent covariates are becoming increasingly common in follow-up studies. Based on the inverse probability of censoring weighting (IPCW) method, we developed a regression model of the RMST and time-dependent covariates. Through Monte Carlo simulation, we verified the estimation performance of the regression parameters of the proposed model. Compared with the time-dependent Cox model and the fixed (baseline) covariate RMST model, the time-dependent RMST model has a better prediction ability. Finally, an example of heart transplantation was used to verify the above conclusions.Comment: 24 pages, 4 table