Extrapolating Survival from Randomized Trials Using External Data: A Review of Methods.

This article describes methods used to estimate parameters governing long-term survival, or times to other events, for health economic models. Specifically, the focus is on methods that combine shorter-term individual-level survival data from randomized trials with longer-term external data, thus using the longer-term data to aid extrapolation of the short-term data. This requires assumptions about how trends in survival for each treatment arm will continue after the follow-up period of the trial. Furthermore, using external data requires assumptions about how survival differs between the populations represented by the trial and external data. Study reports from a national health technology assessment program in the United Kingdom were searched, and the findings were combined with "pearl-growing" searches of the academic literature. We categorized the methods that have been used according to the assumptions they made about how the hazards of death vary between the external and internal data and through time, and we discuss the appropriateness of the assumptions in different circumstances. Modeling choices, parameter estimation, and characterization of uncertainty are discussed, and some suggestions for future research priorities in this area are given

Thermodynamic entropy as an indicator for urban sustainability?

As foci of economic activity, resource consumption, and the production of material waste and pollution, cities represent both a major hurdle and yet also a source of great potential for achieving the goal of sustainability. Motivated by the desire to better understand and measure sustainability in quantitative terms we explore the applicability of thermodynamic entropy to urban systems as a tool for evaluating sustainability. Having comprehensively reviewed the application of thermodynamic entropy to urban systems we argue that the role it can hope to play in characterising sustainability is limited. We show that thermodynamic entropy may be considered as a measure of energy efficiency, but must be complimented by other indices to form part of a broader measure of urban sustainability

Microscopy Image Segmentation via Point and Shape Regularized Data Synthesis

Current deep learning-based approaches for the segmentation of microscopy images heavily rely on large amount of training data with dense annotation, which is highly costly and laborious in practice. Compared to full annotation where the complete contour of objects is depicted, point annotations, specifically object centroids, are much easier to acquire and still provide crucial information about the objects for subsequent segmentation. In this paper, we assume access to point annotations only during training and develop a unified pipeline for microscopy image segmentation using synthetically generated training data. Our framework includes three stages: (1) it takes point annotations and samples a pseudo dense segmentation mask constrained with shape priors; (2) with an image generative model trained in an unpaired manner, it translates the mask to a realistic microscopy image regularized by object level consistency; (3) the pseudo masks along with the synthetic images then constitute a pairwise dataset for training an ad-hoc segmentation model. On the public MoNuSeg dataset, our synthesis pipeline produces more diverse and realistic images than baseline models while maintaining high coherence between input masks and generated images. When using the identical segmentation backbones, the models trained on our synthetic dataset significantly outperform those trained with pseudo-labels or baseline-generated images. Moreover, our framework achieves comparable results to models trained on authentic microscopy images with dense labels, demonstrating its potential as a reliable and highly efficient alternative to labor-intensive manual pixel-wise annotations in microscopy image segmentation. The code is available.Comment: Accepted by The 3rd MICCAI Workshop on Data Augmentation, Labeling, and Imperfection

Vedolizumab for the Treatment of Adults with Moderate-to-Severe Active Ulcerative Colitis: An Evidence Review Group Perspective of a NICE Single Technology Appraisal.

As part of its single technology appraisal (STA) process, the National Institute for Health and Care Excellence (NICE) invited the manufacturer of vedolizumab (Takeda UK) to submit evidence of the clinical effectiveness and cost effectiveness of vedolizumab for the treatment of patients with moderate-to-severe active ulcerative colitis (UC). The Evidence Review Group (ERG) produced a critical review of the evidence for the clinical effectiveness and cost effectiveness of the technology, based upon the company's submission to NICE. The evidence was derived mainly from GEMINI 1, a Phase 3, multicentre, randomised, double-blinded, placebo-controlled study of the induction and maintenance of clinical response and remission by vedolizumab (MLN0002) in patients with moderate-to-severe active UC with an inadequate response to, loss of response to or intolerance of conventional therapy or anti-tumour necrosis factor (TNF)-α. The clinical evidence showed that vedolizumab performed significantly better than placebo in both the induction and maintenance phases. In the post hoc subgroup analyses in patients with or without prior anti-TNF-α therapy, vedolizumab performed better then placebo (p value not reported). In addition, a greater improvement in health-related quality of life was observed in patients treated with vedolizumab, and the frequency and types of adverse events were similar in the vedolizumab and placebo groups, but the evidence was limited to short-term follow-up. There were a number of limitations and uncertainties in the clinical evidence base, which warrants caution in its interpretation-in particular, the post hoc subgroup analyses and high dropout rates in the maintenance phase of GEMINI 1. The company also presented a network meta-analysis of vedolizumab versus other biologic therapies indicated for moderate-to-severe UC. However, the ERG considered that the results presented may have underestimated the uncertainty in treatment effects, since fixed-effects models were used, despite clear evidence of heterogeneity among the trials included in the network. Results from the company's economic evaluation (which included price reductions to reflect the proposed patient access scheme for vedolizumab) suggested that vedolizumab is the most effective option compared with surgery and conventional therapy in the following three populations: (1) a mixed intention-to-treat population, including patients who have previously received anti-TNF-α therapy and those who are anti-TNF-α naïve; (2) patients who are anti-TNF-α naïve only; and (3) patients who have previously failed anti-TNF-α therapy only. The ERG concluded that the results of the company's economic evaluation could not be considered robust, because of errors in model implementation, omission of relevant comparators, deviations from the NICE reference case and questionable model assumptions. The ERG amended the company's model and demonstrated that vedolizumab is expected to be dominated by surgery in all three populations

Modelling and Optimizing an Open-Pit Truck Scheduling Problem

This paper addresses a special truck scheduling problem in the open-pit mine with different transport revenue consideration. A mixed integer programming model is formulated to define the problem clearly and a few valid inequalities are deduced to strengthen the model. Some properties and two upper bounds of the problem are proposed. Based on these inequalities, properties, and upper bounds, a heuristic solution approach with two improvement strategies is proposed to resolve the problem and the numerical experiment demonstrates that the proposed solution approach is effective and efficient

Clinical and Epidemiological Investigation of TCM Syndromes of Patients with Coronary Heart Disease in China

To compare the regional differences in TCM syndromes of patients with coronary heart disease (CHD) between North and South China. A total of 624 patients with a diagnosis of CHD, confirmed by coronary angiography, were included in the comparative analysis to determine the occurrence pattern, characteristics of TCM syndrome distribution, and differences in syndrome combinations and major syndrome types (deficiency or excess) between North and South China. The incidence of CHD tended to be higher in North China (54.6%) compared with that in South China (45.4%). The proportions of patients with a qi-deficiency syndrome (83.7%), turbid phlegm syndrome (68.9%), or blood stasis syndrome (91.5%) were generally higher in the South group, while the proportion of patients with a cold congelation syndrome (7.9%) was identified to be obviously higher in the North group (P < 0.01). Moreover, compared with that in the South group, the overall frequency of syndrome combinations tended to be lower in the North group (P < 0.01); and the most common types of TCM syndrome were excess syndrome (193, 56.6%) and primary deficiency and secondary excess syndrome (244, 86.2%) in the North and South groups, respectively (P < 0.01). A regional difference does exist in the TCM syndromes of patients with CHD between North and South China, indicating that the prevention and treatment of CHD in South China should not only focus on promoting blood circulation and removing blood stasis, but also include supplementing qi and eliminating phleg

Ultra-high-resolution detection of Pb2+ ions using a black phosphorus functionalized microfiber coil resonator

A black phosphorus (BP) functionalized optical fiber sensor based on a microfiber coil resonator (MCR) for Pb2+ ion detection in an aquatic environment is presented and experimentally demonstrated. The MCR-BP sensor is manufactured by winding a tapered microfiber on a hollow rod composed of a low-refractive-index polycarbonate (PC) resin with the BP deposited on the internal wall of the rod. Based on the propagation properties of the MCR, the chemical interaction between the Pb2+ ions and the BP alters the refractive index of the ambient environment and thus results in a detectable shift in the transmission spectrum. The resonance wavelength moves towards longer wavelengths with an increasing concentration of Pb2+ ions, and the sensor has an ultra-high detection resolution of 0.0285 ppb (parts per billion). The temperature dependence is 106.95 pm/°C due to the strong thermo-optic and thermal-expansion effect of the low-refractive-index PC resin. In addition, the sensor shows good stability over a period of 15 days. The local pH also influences the sensor, with the resonance wavelength shift increasing as pH approaches a value of 7 but then decreasing as the pH value increases further due to the effect of the BP layer by H+ and OH− ions. The sensor shows the potential for high-resolution detection of Pb2+ ions in a liquid environment with the particular advantages of having a simple structure, ease of fabrication, low cost, low loss, and simple interrogation