Background When developing a clinical predicGon model using Gme-to-event data (i.e., with censoring and different lengths of follow-up), previous research focuses on the sample size needed to minimise overfifng and pr...
详细信息
Background When developing a clinical predicGon model using Gme-to-event data (i.e., with censoring and different lengths of follow-up), previous research focuses on the sample size needed to minimise overfifng and precisely esGmaGng the overall risk. However, instability of individual-level risk esGmates may sGll be large. Methods We propose using a decomposiGon of Fisher’s informaGon matrix to help examine and calculate the sample size required for developing a model that aims for precise and fair risk esGmates. We propose a six-step process which can be used before data collecGon or when an exisGng dataset is available. Steps (1) to (5) require researchers to specify the overall risk in the target populaGon at a key Gme-point of interest;an assumed pragmaGc ‘core model’ in the form of an exponenGal regression model;the (anGcipated) joint distribuGon of core predictors included in that model;and the distribuGon of any censoring. The ‘core model’ can be specified directly or based on a specified C-index and relaGve effects of (standardised) predictors. The joint distribuGon of predictors may be available directly in an exisGng dataset, in a pilot study, or in a syntheGc dataset provided by other researchers. Results We derive closed-form soluGons that decompose the variance of an individual’s esGmated event rate into Fisher’s unit informaGon matrix, predictor values and total sample size;this allows researchers to calculate and examine uncertainty distribuGons around individual risk esGmates and misclassificaGon probabiliGes for specified sample sizes. We provide an illustraGve example in breast cancer and emphasise the importance of clinical context, including any risk thresholds for decision making, and examine fairness concerns for pre- and post-menopausal women. Lastly, in two empirical evaluaGons, we provide reassurance that uncertainty interval widths based on our exponenGal approach are close to using more flexible parametric models. Conclusions Our approach
The Coupled Model Inter-comparison Project Phase 5 (CMIP5) is the output of many coupled atmosphere-ocean of global climate models (GCMs) and widely used for climate research, especially for driving regional climate m...
详细信息
The Internet of Things (IoT) paradigm aims to bring continuous interaction between small embedded devices and humans. The IoT has the potential to affect our daily lives and bring many benefits to society. Low-Power W...
ISBN:
(数字)9781728139494
ISBN:
(纸本)9781728139500
The Internet of Things (IoT) paradigm aims to bring continuous interaction between small embedded devices and humans. The IoT has the potential to affect our daily lives and bring many benefits to society. Low-Power Wide-Area Networks (LPWAN) is a new IoT technology that offers long distance connectivity for a massive number of devices. LPWAN is a promising solution to enable complex IoT scenarios, such as smart cities and smart healthcare. LoRa is currently one of the leading LPWAN solutions available for public use. Due to the great number of connected devices and, in some cases, sensitive data transmitted in IoT networks, security is one of the main concerns in LPWAN. In this paper, we focus on the issues of key management in LoRaWAN. We propose a secure architecture for key management based on private blockchain and smart contracts in order to increase the levels of security and availability in LoRaWAN environments. In order to show the feasibility of the proposed architecture, a working prototype was implemented using open-source tools and commodity hardware.
The developmental process of embryos follows a monotonic order. An embryo can progressively cleave from one cell to multiple cells and finally transform to morula and blastocyst. For time-lapse videos of embryos, most...
详细信息
In the current landscape of deep learning research, there is a predominant emphasis on achieving high predictive accuracy in supervised tasks involving large image and language datasets. However, a broader perspective...
详细信息
In the current landscape of deep learning research, there is a predominant emphasis on achieving high predictive accuracy in supervised tasks involving large image and language datasets. However, a broader perspective reveals a multitude of overlooked metrics, tasks, and data types, such as uncertainty, active and continual learning, and scientific data, that demand attention. Bayesian deep learning (BDL) constitutes a promising avenue, offering advantages across these diverse settings. This paper posits that BDL can elevate the capabilities of deep learning. It revisits the strengths of BDL, acknowledges existing challenges, and highlights some exciting research avenues aimed at addressing these obstacles. Looking ahead, the discussion focuses on possible ways to combine large-scale foundation models with BDL to unlock their full potential. Copyright 2024 by the author(s)
Stimulator of interferon genes (STING) has emerged as a critical cancer immunotherapy target. However, no STING agonist has advanced beyond phase I/II clinical trials, as obstacles center around applying STING agonism...
详细信息
Stimulator of interferon genes (STING) has emerged as a critical cancer immunotherapy target. However, no STING agonist has advanced beyond phase I/II clinical trials, as obstacles center around applying STING agonism to the appropriate clinical context, retaining it in the tumor microenvironment (TME), and limiting its T cell toxicity. Using triple-negative breast cancer (TNBC), we identify defective STING turnover as a cancer state promoting hypersensitivity to STING agonism. We also repurpose a US Food and Drug Administration (FDA)-approved polyethylene glycol (PEG) biopsy marker to deliver STING agonists in a controlled release fashion into the TME. However, STING agonist-induced T cell toxicity limits robust endogenous clonal T cell response, which can be overcome by sequential co-delivery of the STING agonists with CAR T cell therapy using the same PEG marker, eradicating orthotopic TNBC in mouse models while also controlling distant disease. These findings identify a highly translatable platform to combine STING agonists with CAR T cell therapy locally for TNBC and potentially other solid cancers.
Within the causal dynamical triangulations approach to the quantization of gravity, striking evidence has emerged for the dynamical reduction of spacetime dimension on sufficiently small scales. Specifically, the spec...
详细信息
Within the causal dynamical triangulations approach to the quantization of gravity, striking evidence has emerged for the dynamical reduction of spacetime dimension on sufficiently small scales. Specifically, the spectral dimension decreases from the topological value of 4 toward a value near 2 as the scale being probed decreases. The physical scales over which this dimensional reduction occurs have not previously been ascertained. We present and implement a method to determine these scales in units of either the Planck length or the quantum spacetime geometry’s effective de Sitter length. We find that dynamical reduction of the spectral dimension occurs over physical scales of the order of 10 Planck lengths, which, for the numerical simulation considered below, corresponds to the order of 10−1 de Sitter lengths.
暂无评论