Should intermittent pneumatic compression devices be standard therapy for the prevention of venous thromboembolic events in major surgery? Protocol for a randomised clinical trial (IMPOSTERS).

INTRODUCTION: Venous thromboembolism (VTE) is a recognised postsurgical risk. Current prevention methods involve low molecular weight heparin (LMWH), graduated compression stockings (GCS), and intermittent pneumatic compression devices (IPCDs). Australian guidelines, commonly adopted by surgeons, recommend LMWH with GCS and/or IPCDs. IPCDs pose clinical risks, increase care burden, are poorly tolerated, and are costly single-use plastic items. Utilising only LMWH and GCS, without IPCDs, could be more practical, patient-friendly, and cost-effective, with added environmental benefits. METHODS: This is a multicentre, prospective, two-arm randomised controlled non-inferiority trial at five New South Wales (NSW) hospitals, in Australia. We propose to randomise 4130 participants in a 1:1 ratio between arm A: LMWH+GCS+IPCDs (n=2065) or arm B: LMWH+GCS (n=2065). The primary outcome of interest is symptomatic VTE (deep vein thrombosis/pulmonary embolism) identified at the day 30 phone follow-up (FU), confirmed by ultrasound or imaging. Radiologists interpreting the lower-extremity ultrasonography will be blinded to intervention allocation. Secondary outcomes are quality of life at baseline, days 30 and 90 FU using the 5-level European Quality of Life Score, compliance and adverse events with IPCDs, GCS, and LMWH, as well as healthcare costs (from the perspective of the patient and the hospital), and all-cause mortality. The trial has 90% power to detect a 2% non-inferiority margin to detect a reduction rate of VTE from 4% to 2%. ETHICS AND DISSEMINATION: This study has been approved by the Hunter New England Human Research Ethics Committee (2022/ETH02276) protocol V.10, 13 July 2023. Study findings will be presented at local and national conferences and in scientific research journals. TRIAL REGISTRATION NUMBER: ANZCTR12622001527752.

The future burden of kidney and bladder cancers preventable by behavior modification in Australia: A pooled cohort study.

Substantial changes in the prevalence of the principal kidney and bladder cancer risk factors, smoking (both cancers) and body fatness (kidney cancer), have occurred but the contemporary cancer burden attributable to these factors has not been evaluated. We quantified the kidney and bladder cancer burden attributable to individual and joint exposures and assessed whether these burdens differ between population subgroups. We linked pooled data from seven Australian cohorts (N = 367,058) to national cancer and death registries and estimated the strength of the associations between exposures and cancer using adjusted proportional hazards models. We estimated exposure prevalence from representative contemporaneous health surveys. We combined these estimates to calculate population attributable fractions (PAFs) with 95% confidence intervals (CIs), accounting for competing risk of death, and compared PAFs for population subgroups. During the first 10-year follow-up, 550 kidney and 530 bladder cancers were diagnosed and over 21,000 people died from any cause. Current levels of overweight and obesity explain 28.8% (CI = 17.3-38.7%), current or past smoking 15.5% (CI = 6.0-24.1%) and these exposures jointly 39.6% (CI = 27.5-49.7%) of the kidney cancer burden. Current or past smoking explains 44.4% (CI = 35.4-52.1%) of the bladder cancer burden, with 24.4% attributable to current smoking. Ever smoking explains more than half (53.4%) of the bladder cancer burden in men, and the burden potentially preventable by quitting smoking is highest in men (30.4%), those aged <65 years (28.0%) and those consuming >2 standard alcoholic drinks/day (41.2%). In conclusion, large fractions of kidney and bladder cancers in Australia are preventable by behavior change.

Estimating individual cone fundamentals from their color-matching functions.

Estimation of individual spectral cone fundamentals from color-matching functions is a classical and longstanding problem in color science. In this paper we propose a novel method to carry out this estimation based on a linear optimization technique, employing an assumption of a priori knowledge of the retinal absorptance functions. The result is an estimation of the combined lenticular and macular filtration for an individual, along with the nine coefficients in the linear combination that relates their color-matching functions to their estimated spectral-cone fundamentals. We test the method on the individual Stiles and Burch color-matching functions and derive cone-fundamental estimations for different viewing fields and matching experiment repetition. We obtain cone-fundamental estimations that are remarkably similar to those available in the literature. This suggests that the method yields results that are close to the true fundamentals.

The use of thermographic imaging to evaluate therapeutic response in human tumour xenograft models.

Non-invasive methods to monitor tumour growth are an important goal in cancer drug development. Thermographic imaging systems offer potential in this area, since a change in temperature is known to be induced due to changes within the tumour microenvironment. This study demonstrates that this imaging modality can be applied to a broad range of tumour xenografts and also, for the first time, the methodology's suitability to assess anti-cancer agent efficacy. Mice bearing subcutaneously implanted H460 lung cancer xenografts were treated with a novel vascular disrupting agent, ICT-2552, and the cytotoxin doxorubicin. The effects on tumour temperature were assessed using thermographic imaging over the first 6 hours post-administration and subsequently a further 7 days. For ICT-2552 a significant initial temperature drop was observed, whilst for both agents a significant temperature drop was seen compared to controls over the longer time period. Thus thermographic imaging can detect functional differences (manifesting as temperature reductions) in the tumour response to these anti-cancer agents compared to controls. Importantly, these effects can be detected in the first few hours following treatment and therefore the tumour is observable non-invasively. As discussed, this technique will have considerable 3Rs benefits in terms of reduction and refinement of animal use.

Improved method for skin reflectance reconstruction from camera images.

A improved spectral reflectance reconstruction method is developed to transform camera RGB to spectral reflectance for skin images. Rather than using conventional direct or two-step processes, we transform camera RGB to skin reflectance directly using a principal component analysis (PCA) approach. The novelty in our direct method (RGB to spectra) is the use of a skin-specific colour characterisation chart with spectra closer to human skin spectra, and a new database of skin reflectances to derive the PCA bases. The experimental results using the facial images of 17 subjects demonstrate that our new direct method gives a significantly better performance than conventional, two-step methods and direct methods with traditional characterization charts. This new spectral reconstruction algorithm is sufficiently precise to reconstruct spectral properites relating to chromophores and its performance is within the acceptable range for maxillofacial soft tissue prostheses (error < 3 ΔE*ab units).

Weighted Constrained Hue-Plane Preserving Camera Characterization.

Color correction relates device dependent sensor responses (RGB) to device independent color values (XYZ). Here we present a new approach to Hue-plane Preserving Color Correction (HPPCC) using weighted constrained 3 × 3 matrices. Hue-plane preservation was introduced in [1] in conjunction with an HPPCC method. That method maps using a finite number of local white point preserving 3 × 3 matrices, each of which operates in a hue-angle delimited subregion of device space defined by the white and two adjacent chromatic training set colors. However, that formulation does not leave room for optimization or continuity beyond C0 in the transitions between the subregions. To remedy that our new method uses hue-angle specific weighted matrixing: given a device RGB from which a device hue-angle is derived, a corresponding transformation matrix is found as the normalized weighted sum of all precalculated constrained white point and training color preserving matrices. Each weight is calculated as a power function of the minimum difference between the device and the training color hue-angle. The weighting function provides local influence to the matrices that are in close hue-angle proximity to the device color. The power of the function is optimized for global accuracy. We call this Hue-plane Preserving Color Correction by Weighted Constrained Matrixing HPPCC-WCM 1 1. Experiments performed using different input spectra show that our method consistently improves on both stability and accuracy compared to state of the art methods.

Spectral edge: gradient-preserving spectral mapping for image fusion.

This paper describes a novel approach to image fusion for color display. Our goal is to generate an output image whose gradient matches that of the input as closely as possible. We achieve this using a constrained contrast mapping paradigm in the gradient domain, where the structure tensor of a high-dimensional gradient representation is mapped exactly to that of a low-dimensional gradient field which is then reintegrated to form an output. Constraints on output colors are provided by an initial RGB rendering. Initially, we motivate our solution with a simple "ansatz" (educated guess) for projecting higher-D contrast onto color gradients, which we expand to a more rigorous theorem to incorporate color constraints. The solution to these constrained optimizations is closed-form, allowing for simple and hence fast and efficient algorithms. The approach can map any N-D image data to any M-D output and can be used in a variety of applications using the same basic algorithm. In this paper, we focus on the problem of mapping N-D inputs to 3D color outputs. We present results in five applications: hyperspectral remote sensing, fusion of color and near-infrared or clear-filter images, multilighting imaging, dark flash, and color visualization of magnetic resonance imaging diffusion-tensor imaging.

Perceptual color characterization of cameras.

Color camera characterization, mapping outputs from the camera sensors to an independent color space, such as XYZ, is an important step in the camera processing pipeline. Until now, this procedure has been primarily solved by using a 3 × 3 matrix obtained via a least-squares optimization. In this paper, we propose to use the spherical sampling method, recently published by Finlayson et al., to perform a perceptual color characterization. In particular, we search for the 3 × 3 matrix that minimizes three different perceptual errors, one pixel based and two spatially based. For the pixel-based case, we minimize the CIE ΔE error, while for the spatial-based case, we minimize both the S-CIELAB error and the CID error measure. Our results demonstrate an improvement of approximately 3% for the ΔE error, 7% for the S-CIELAB error and 13% for the CID error measures.

Lookup-table-based gradient field reconstruction.

In computer vision, there are many applications, where it is advantageous to process an image in the gradient domain and then reintegrate the gradient field: important examples include shadow removal, lightness calculation, and data fusion. A serious problem with this approach is that the reconstruction step often introduces artefacts-commonly, smoothed and smeared edges-to the recovered image. This is a result of the inherent ill-posedness of reintegrating a nonintegrable field. Artefacts can be diminished but not removed, by using complex to highly complex reintegration techniques. Here, we present a remarkably simple (and on the face of it naive) algorithm for reconstructing gradient fields. Suppose we start with a multichannel original, and from it derive a (possibly one of many) 1-D gradient field; for many applications, the derived gradient field will be nonintegrable. Here, we propose a lookup-table-based map relating the multichannel original to a reconstructed scalar output image, whose gradient best matches the target gradient field. The idea, at base, is that if we learn how to map the gradients of the multichannel original onto the desired output gradient, and then using the lookup table (LUT) constraint, we effectively derive the mapping from the multichannel input to the desired, reintegrated, image output. While this map could take a variety of forms, here we derive the best map from the multichannel gradient as a (nonlinear) function of the input to each of the target scalar gradients. In this framework, reconstruction is a simple equation-solving exercise of low dimensionality. One obvious application of our method is to the image-fusion problem, e.g., the problem of converting a color or higher-D image into grayscale. We will show, through extensive experiments and complementary theoretical arguments, that our straightforward method preserves the target contrast as well as do complex previous reintegration methods, but without artefacts, and with a substantially cheaper computational cost. Finally, we demonstrate the generality of the method by applying it to gradient field reconstruction in an additional area, the shading recovery problem.

Optimal bases for convex color mixture.

Partitive color mixing is the process by which the human eye integrates different neighboring colors to result in a single uniform surface. This process is convex: The perceived color is the weighted average of a small set of basis colors, and given that the weights represent the relative area of each color, they must sum to one. We present an efficient algorithm that generates a small number of new, natural bases such that a large set of spectra can be adequately expressed as a convex combination of these bases. Our results show that 9-11 bases are sufficient to represent a set of 1269 Munsell surfaces within the convex model.

Characterization of trichromatic color cameras by using a new multispectral imaging technique.

We investigate methods for the recovery of reflectance spectra from the responses of trichromatic camera systems and the application of these methods to the problem of camera characterization. The recovery of reflectance from colorimetric data is an ill-posed problem, and a unique solution requires additional constraints. We introduce a novel method for reflectance recovery that finds the smoothest spectrum consistent with both the colorimetric data and a linear model of reflectance. Four multispectral methods were tested using data from a real trichromatic camera system. The new method gave the lowest maximum colorimetric error in terms of camera characterization with test data that were independent of the training data. However, the average colorimetric performances of the four multispectral methods were statistically indistinguishable from each other but were significantly worse than conventional methods for camera characterization such as polynomial transforms.