On model selection criteria for climate change impact studies

Climate change impact studies inform policymakers on the estimated damages of future climate change on economic, health and other outcomes. In most studies, an annual outcome variable is observed, e.g. annual mortality rate, along with higher-frequency regressors, e.g. daily temperature and precipitation. Practitioners use summaries of the higher-frequency regressors in fixed effects panel models. The choice over summary statistics amounts to model selection. Some practitioners use Monte Carlo cross-validation (MCCV) to justify a particular specification. However, conventional implementation of MCCV with fixed testing-to-full sample ratios tends to select over-fit models. This paper presents conditions under which MCCV, and also information criteria, can deliver consistent model selection. Previous work has established that the Bayesian information criterion (BIC) can be inconsistent for non-nested selection. We illustrate that the BIC can also be inconsistent in our framework, when all candidate models are misspecified. Our results have practical implications for empirical conventions in climate change impact studies. Specifically, they highlight the importance of a priori information provided by the scientific literature to guide the models considered for selection. We emphasize caution in interpreting model selection results in settings where the scientific literature does not specify the relationship between the outcome and the weather variables.Comment: Additional simulation results available from authors by reques

Consistency of a hybrid block bootstrap for distribution and variance estimation for sample quantiles of weakly dependent sequences

Consistency and optimality of block bootstrap schemes for distribution and variance estimation of smooth functionals of dependent data have been thoroughly investigated by Hall, Horowitz & Jing (1995), among others. However, for nonsmooth functionals, such as quantiles, much less is known. Existing results, due to Sun & Lahiri (2006), regarding strong consistency for distribution and variance estimation via the moving block bootstrap (MBB) require that b→∞, where b=⌊n/ℓ⌋ is the number of resampled blocks to be pasted together to form the bootstrap data series, n is the available sample size, and ℓ is the block length. Here we show that, in fact, weak consistency holds for any b such that 1≤b=O(n/ℓ). In other words we show that a hybrid between the subsampling bootstrap (b=1) and MBB is consistent. Empirical results illustrate the performance of hybrid block bootstrap estimators for varying numbers of blocks

Block bootstrap optimality and empirical block selection for sample quantiles with dependent data

We establish a general theory of optimality for block bootstrap distribution estimation for sample quantiles under mild strong mixing conditions. In contrast to existing results, we study the block bootstrap for varying numbers of blocks. This corresponds to a hybrid between the sub- sampling bootstrap and the moving block bootstrap, in which the number of blocks is between 1 and the ratio of sample size to block length. The hybrid block bootstrap is shown to give theoretical benefits, and startling improvements in accuracy in distribution estimation in important practical settings. The conclusion that bootstrap samples should be of smaller size than the original sample has significant implications for computational efficiency and scalability of bootstrap methodologies with dependent data. Our main theorem determines the optimal number of blocks and block length to achieve the best possible convergence rate for the block bootstrap distribution estimator for sample quantiles. We propose an intuitive method for empirical selection of the optimal number and length of blocks, and demonstrate its value in a nontrivial example

Human COL2A1-directed SV40 T antigen expression in transgenic and chimeric mice results in abnormal skeletal development

The ability of SV40 T antigen to cause abnormalities in cartilage development in transgenic mice and chimeras has been tested. The cis- regulatory elements of the COL2A1 gene were used to target expression of SV40 T antigen to differentiating chondrocytes in transgenic mice and chimeras derived from embryonal stem (ES) cells bearing the same transgene. The major phenotypic consequences of transgenic (pAL21) expression are malformed skeleton, disproportionate dwarfism, and perinatal/neonatal death. Expression of T antigen was tissue specific and in the main characteristic of the mouse α1(II) collagen gene. Chondrocyte densities and levels of α1(II) collagen mRNAs were reduced in the transgenic mice. Islands of cells which express cartilage characteristic genes such as type IIB procollagen, long form α1(IX) collagen, α2(XI) collagen, and aggrecan were found in the articular and growth cartilages of pAL21 chimeric fetuses and neonates. But these cells, which were expressing T antigen, were not properly organized into columns of proliferating chondrocytes. Levels of α1(II) collagen mRNA were reduced in these chondrocytes. In addition, these cells did not express type X collagen, a marker for hypertrophic chondrocytes. The skeletal abnormality in pAL21 mice may therefore be due to a retardation of chondrocyte maturation or an impaired ability of chondrocytes to complete terminal differentiation and an associated paucity of some cartilage matrix components.published_or_final_versio

Refining Humane Endpoints in Mouse Models of Disease by Systematic Review and Machine Learning-Based Endpoint Definition

Ideally, humane endpoints allow for early termination of experiments by minimizing an animal’s discomfort, distress and pain, while ensuring that scientific objectives are reached. Yet, lack of commonly agreed methodology and heterogeneity of cut-off values published in the literature remain a challenge to the accurate determination and application of humane endpoints. With the aim to synthesize and appraise existing humane endpoint definitions for commonly used physiological parameters, we conducted a systematic review of mouse studies of acute and chronic disease models, which used body weight, temperature and/or sickness scores for endpoint definition. In the second part of the study, we used previously published and unpublished data on weight, temperature and sickness scores from mouse models of sepsis and stroke and applied machine learning algorithms to assess the usefulness of this method for parameter selection and endpoint definition across models. Studies were searched for in two electronic databases (MEDLINE/Pubmed and Embase). Out of 110 retrieved full-text manuscripts, 34 studies were included. We found large intra- and inter-model variance in humane endpoint determination and application due to varying animal models, lack of standardized experimental protocols and heterogeneity of performance metrics (part 1). Machine learning models trained with physiological data and sickness severity score or modified DeSimoni neuroscore identified animals with a high risk of death at an early time point in both mouse models of stroke (male: 93.2% at 72h post-treatment; female: 93.0% at 48h post-treatment) and sepsis (96.2% at 24h post-treatment), thus demonstrating generalizability in endpoint determination across models (part 2)

Macrophage Inhibitory Cytokine 1 (MIC-1/GDF15) Decreases Food Intake, Body Weight and Improves Glucose Tolerance in Mice on Normal & Obesogenic Diets

Food intake and body weight are controlled by a variety of central and peripheral factors, but the exact mechanisms behind these processes are still not fully understood. Here we show that that macrophage inhibitory cytokine-1 (MIC-1/GDF15), known to have anorexigenic effects particularly in cancer, provides protection against the development of obesity. Both under a normal chow diet and an obesogenic diet, the transgenic overexpression of MIC-1/GDF15 in mice leads to decreased body weight and fat mass. This lean phenotype was associated with decreased spontaneous but not fasting-induced food intake, on a background of unaltered energy expenditure and reduced physical activity. Importantly, the overexpression of MIC-1/GDF15 improved glucose tolerance, both under normal and high fat-fed conditions. Altogether, this work shows that the molecule MIC-1/GDF15 might be beneficial for the treatment of obesity as well as perturbations in glucose homeostasis

Metameric Inpainting for Image Warping

Image-warping , a per-pixel deformation of one image into another, is an essential component in immersive visual experiences such as virtual reality or augmented reality. The primary issue with image warping is disocclusions, where occluded (and hence unknown) parts of the input image would be required to compose the output image. We introduce a new image warping method, Metameric image inpainting - an approach for hole-filling in real-time with foundations in human visual perception. Our method estimates image feature statistics of disoccluded regions from their neighbours. These statistics are inpainted and used to synthesise visuals in real-time that are less noticeable to study participants, particularly in peripheral vision. Our method offers speed improvements over the standard structured image inpainting methods while improving realism over colour-based inpainting such as push-pull. Hence, our work paves the way towards future applications such as depth image-based rendering, 6-DoF 360 rendering, and remote render-streaming

The effect of acetaminophen (four grams a day for three consecutive days) on hepatic tests in alcoholic patients – a multicenter randomized study

Background: Hepatic failure has been associated with reported therapeutic use of acetaminophen by alcoholic patients. The highest risk period for alcoholic patients is immediately after discontinuation of alcohol intake. This period exhibits the largest increase in CYP2E1 induction and lowest glutathione levels. Our hypothesis was that common liver tests would be unaffected by administration of the maximum recommended daily dosage of acetaminophen for 3 consecutive days to newly-abstinent alcoholic subjects. Methods: Adult alcoholic subjects entering two alcohol detoxification centers were enrolled in a prospective double-blind, randomized, placebo-controlled trial. Subjects were randomized to acetaminophen, 4 g/day, or placebo for 3 consecutive days. The study had 95% probability of detecting a 15 IU/L difference in serum ALT. Results: A total of 443 subjects were enrolled: 308 (258 completed) received acetaminophen and 135 subjects (114 completed) received placebo. Study groups did not differ in demographics, alcohol consumption, nutritional status or baseline laboratory assessments. The peak mean ALT activity was 57 [plus or minus] 45 IU/L and 55 [plus or minus] 48 IU/L in the acetaminophen and placebo groups, respectively. Subgroup analyses for subjects presenting with an elevated ALT, subjects fulfilling a diagnosis of alcoholic hepatitis and subjects attaining a peak ALT greater than 200 IU/L showed no statistical difference between the acetaminophen and control groups. The one participant developing an increased international normalized ratio was in the placebo group. Conclusion: Alcoholic patients treated with the maximum recommended daily dose of acetaminophen for 3 consecutive days did not develop increases in serum transaminase or other measures of liver injury. Treatment of pain or fever for 3 days with acetaminophen appears safe in newly-abstinent alcoholic patients, such as those presenting for acute medical care.Funding for this study was provided by McNeil Consumer Healthcare to the Denver Health Authority, Denver, Colorado