On Measuring Consumer Welfare Effects of Trade Reform

We develop a measure of consumer welfare by approximating Hicksian compensating variation as a function of all commodity prices and compensated price elasticities. The unique feature of this approach is that all direct- and cross-commodity effects of a demand system are incorporated into the welfare measurement. This approach is useful for developing an instrumental model to evaluate the consumer welfare effects of trade reform. For illustration, the proposed procedure is applied to Taiwan's meat industry, and various scenarios are considered to show the effects of eliminating meat tariff rates on the quantities of meat demanded and on the savings of meat expenditures.Consumer/Household Economics, International Relations/Trade,

Do Americans Change Toward Healthy Diets?

American’s nutritional and health status appear to be trending toward healthier diets, as measured by a reduction in cholesterol intake and an increase in calcium intake. The levels of food energy and total fats, however, increased substantially.Changes in American diet, nutrient economic responses, Health Economics and Policy, Poster 3601001,

How Increased Food and Energy Prices Affect Consumer Welfare

We analyze the consumer welfare effects of increased food and energy prices and find that the own-price elasticities of both food and energy are relatively inelastic, which explain well the dynamics of the recent soaring food and energy prices. The estimated demand elasticities are then used to analyze the consumer welfare effects of price changes in food and energy. The results indicate that an increase of food and energy prices would incur a substantial consumer welfare loss, which is a heavy burden for low income households.Demand elasticity, compensating variation, consumer welfare, Demand and Price Analysis,

Leveraging Program Analysis to Reduce User-Perceived Latency in Mobile Applications

Reducing network latency in mobile applications is an effective way of improving the mobile user experience and has tangible economic benefits. This paper presents PALOMA, a novel client-centric technique for reducing the network latency by prefetching HTTP requests in Android apps. Our work leverages string analysis and callback control-flow analysis to automatically instrument apps using PALOMA's rigorous formulation of scenarios that address "what" and "when" to prefetch. PALOMA has been shown to incur significant runtime savings (several hundred milliseconds per prefetchable HTTP request), both when applied on a reusable evaluation benchmark we have developed and on real applicationsComment: ICSE 201

Prediction of protein allosteric signalling pathways and functional residues through paths of optimised propensity

Allostery commonly refers to the mechanism that regulates protein activity through the binding of a molecule at a different, usually distal, site from the orthosteric site. The omnipresence of allosteric regulation in nature and its potential for drug design and screening render the study of allostery invaluable. Nevertheless, challenges remain as few computational methods are available to effectively predict allosteric sites, identify signalling pathways involved in allostery, or to aid with the design of suitable molecules targeting such sites. Recently, bond-to-bond propensity analysis has been shown successful at identifying allosteric sites for a large and diverse group of proteins from knowledge of the orthosteric sites and its ligands alone by using network analysis applied to energy-weighted atomistic protein graphs. To address the identification of signalling pathways, we propose here a method to compute and score paths of optimised propensity that link the orthosteric site with the identified allosteric sites, and identifies crucial residues that contribute to those paths. We showcase the approach with three well-studied allosteric proteins: h-Ras, caspase-1, and 3-phosphoinositide-dependent kinase-1 (PDK1). Key residues in both orthosteric and allosteric sites were identified and showed agreement with experimental results, and pivotal signalling residues along the pathway were also revealed, thus providing alternative targets for drug design. By using the computed path scores, we were also able to differentiate the activity of different allosteric modulators

Incorporating Prior Knowledge in Deep Learning Models via Pathway Activity Autoencoders

Motivation: Despite advances in the computational analysis of high-throughput molecular profiling assays (e.g. transcriptomics), a dichotomy exists between methods that are simple and interpretable, and ones that are complex but with lower degree of interpretability. Furthermore, very few methods deal with trying to translate interpretability in biologically relevant terms, such as known pathway cascades. Biological pathways reflecting signalling events or metabolic conversions are Small improvements or modifications of existing algorithms will generally not be suitable, unless novel biological results have been predicted and verified. Determining which pathways are implicated in disease and incorporating such pathway data as prior knowledge may enhance predictive modelling and personalised strategies for diagnosis, treatment and prevention of disease. Results: We propose a novel prior-knowledge-based deep auto-encoding framework, PAAE, together with its accompanying generative variant, PAVAE, for RNA-seq data in cancer. Through comprehensive comparisons among various learning models, we show that, despite having access to a smaller set of features, our PAAE and PAVAE models achieve better out-of-set reconstruction results compared to common methodologies. Furthermore, we compare our model with equivalent baselines on a classification task and show that they achieve better results than models which have access to the full input gene set. Another result is that using vanilla variational frameworks might negatively impact both reconstruction outputs as well as classification performance. Finally, our work directly contributes by providing comprehensive interpretability analyses on our models on top of improving prognostication for translational medicine

CD24 Expression and differential resistance to chemotherapy in triple-negative breast cancer.

Breast cancer (BC) is a leading cause of cancer-related death in women. Adjuvant systemic chemotherapies are effective in reducing risks of recurrence and have contributed to reduced BC mortality. Although targeted adjuvant treatments determined by biomarkers for endocrine and HER2-directed therapies are largely successful, predicting clinical benefit from chemotherapy is more challenging. Drug resistance is a major reason for treatment failures. Efforts are ongoing to find biomarkers to select patients most likely to benefit from chemotherapy. Importantly, cell surface biomarkers CD44+/CD24- are linked to drug resistance in some reports, yet underlying mechanisms are largely unknown. This study focused on the potential role of CD24 expression in resistance to either docetaxel or doxorubicin in part by the use of triple-negative BC (TNBC) tissue microarrays. In vitro assays were also done to assess changes in CD24 expression and differential drug susceptibility after chemotherapy. Further, mouse tumor xenograft studies were done to confirm in vitro findings. Overall, the results show that patients with CD24-positive TNBC had significantly worse overall survival and disease-free survival after taxane-based treatment. Also, in vitro cell studies show that CD44+/CD24+/high cells are more resistant to docetaxel, while CD44+/CD24-/low cells are resistant to doxorubicin. Both in vitro and in vivo studies show that cells with CD24-knockdown are more sensitive to docetaxel, while CD24-overexpressing cells are more sensitive to doxorubicin. Further, mechanistic studies indicate that Bcl-2 and TGF-βR1 signaling via ATM-NDRG2 pathways regulate CD24. Hence, CD24 may be a biomarker to select chemotherapeutics and a target to overcome TNBC drug resistance

Resampling methods to reduce the selection bias in genetic effect estimation in genome-wide scans

Using the simulated data of Problem 2 for Genetic Analysis Workshop 14 (GAW14), we investigated the ability of three bootstrap-based resampling estimators (a shrinkage, an out-of-sample, and a weighted estimator) to reduce the selection bias for genetic effect estimation in genome-wide linkage scans. For the given marker density in the preliminary genome scans (7 cM for microsatellite and 3 cM for SNP), we found that the two sets of markers produce comparable results in terms of power to detect linkage, localization accuracy, and magnitude of test statistic at the peak location. At the locations detected in the scan, application of the three bootstrap-based estimators substantially reduced the upward selection bias in genetic effect estimation for both true and false positives. The relative effectiveness of the estimators depended on the true genetic effect size and the inherent power to detect it. The shrinkage estimator is recommended when the power to detect the disease locus is low. Otherwise, the weighted estimator is recommended

GNOM v1.0: an optimized steady-state model of the modern marine neodymium cycle

Spatially distant sources of neodymium (Nd) to the ocean that carry different isotopic signatures (εNd) have been shown to trace out major water masses and have thus been extensively used to study large-scale features of the ocean circulation both past and current. While the global marine Nd cycle is qualitatively well understood, a complete quantitative determination of all its components and mechanisms, such as the magnitude of its sources and the paradoxical conservative behavior of εNd, remains elusive. To make sense of the increasing collection of observational Nd and εNd data, in this model description paper we present and describe the Global Neodymium Ocean Model (GNOM) v1.0, the first inverse model of the global marine biogeochemical cycle of Nd. The GNOM is embedded in a data-constrained steady-state circulation that affords spectacular computational efficiency, which we leverage to perform systematic objective optimization, allowing us to make preliminary estimates of biogeochemical parameters. Owing to its matrix representation, the GNOM model is additionally amenable to novel diagnostics that allow us to investigate open questions about the Nd cycle with unprecedented accuracy. This model is open-source and freely accessible, is written in Julia, and its code is easily understandable and modifiable for further community developments, refinements, and experiments.</p