Killing by lung cancer or by diabetes? The trade-off between smoking and obesity

As the prevalence of smoking has decreased to below 20%, health practitioners interest has shifted towards the prevalence of obesity, and reducing it is one of the major health challenges in decades to come. In this paper we study the impact that the final product of the anti-smoking campaign, that is, smokers quitting the habit, had on average weight in the population. To these ends, we use data from the Behavioral Risk Factors Surveillance System, a large series of independent representative cross-sectional surveys. We construct a synthetic panel that allows us to control for unobserved heterogeneity and we exploit the exogenous changes in taxes and regulations to instrument the endogenous decision to give up the habit of smoking. Our estimates, are very close to estimates issued in the ’90s by the US Department of Health, and indicate that a 10% decrease in the incidence of smoking leads to an average weight increase of 2.2 to 3 pounds, depending on choice of specification. In addition, we find evidence that the effect overshoots in the short run, although a significant part remains even after two years. However, when we split the sample between men and women, we only find a significant effect for men. Finally, the implicit elasticity of quitting smoking to the probability of becoming obese is calculated at 0.58. This implies that the net benefit from reducing the incidence of smoking by 1% is positive even though the cost to society is $0.6 billions.

Data analysis in chemistry and bio-medical sciences

EditorialMinisterio de Economía y Competitividad; CTQ2013-41229-PMinisterio de Economía y Competitividad; CTQ2013-41229-P/BQUMinisterio de Economía y Competitividad; CTQ2016-74881-PPaís Vasco. Gobierno; IT1045-1

Scalable Similarity Search for Molecular Descriptors

Similarity search over chemical compound databases is a fundamental task in the discovery and design of novel drug-like molecules. Such databases often encode molecules as non-negative integer vectors, called molecular descriptors, which represent rich information on various molecular properties. While there exist efficient indexing structures for searching databases of binary vectors, solutions for more general integer vectors are in their infancy. In this paper we present a time- and space- efficient index for the problem that we call the succinct intervals-splitting tree algorithm for molecular descriptors (SITAd). Our approach extends efficient methods for binary-vector databases, and uses ideas from succinct data structures. Our experiments, on a large database of over 40 million compounds, show SITAd significantly outperforms alternative approaches in practice.Comment: To be appeared in the Proceedings of SISAP'1

A new dataset of global irrigation areas from 2001 to 2015

About 40% of global crop production takes place on irrigated land, which accounts for approximately 20% of the global farmland. The great majority of freshwater consumption by human societies is associated with irrigation, which contributes to a major modification of the global water cycle by enhancing evapotranspiration and reducing surface and groundwater runoff. In many regions of the world irrigation contributes to streamflow and groundwater depletion, soil salinization, cooler microclimate conditions, and altered land-atmosphere interactions. Despite the important role played by irrigation in food security, water cycle, soil productivity, and near-surface atmospheric conditions, its global extent remains poorly quantified. To date global maps of irrigated land are often based on estimates from circa year 2000. Here we apply artificial intelligence methods based on machine learning algorithms to satellite remote sensing and monthly climate data to map the spatial extent of irrigated areas between 2001 and 2015. We provide global annual maps of irrigated land at ≈9km resolution for the 2001-2015 and we make this dataset available online

Human leukocyte antigen (Hla) haplotype does not influence the inflammatory pattern of duodenal lymphocytosis linked to irritable bowel syndrome

Background and objectives: Duodenal lymphocytosis (DL) is a condition characterized by enhanced infiltration of intraepithelial lymphocytes (IELs) in the duodenal mucosa, and it can be linked to both gluten-and non-gluten-related diseases, such as irritable bowel syndrome (IBS). Materials and methods: We retrospectively selected patients with DL linked to IBS. Formalin-embedded biopsy samples of the duodenum were collected. CD3 lymphocyte immunohistochemistry was used for IELs. The real-time polymerase chain reaction was used to quantify the amount of mRNA coding for tissue transglutaminase 2 (tTG2), interferon-gamma (IFNγ), toll-like receptor 2 (TLR2), and myeloid differentiation primary response 88 (MyD88). All subjects underwent DQ2-8 haplotype analysis. Controls were represented by subjects with IBS without DL. Results: Thirty-two patients with IBS-DL were retrospectively recruited. Fourteen subjects (43.8%) had a DQ2-8 haplotype. DQ2-8 positive subjects had similar levels compared to negative ones for tTG2, IFNγ, TLR2, and MyD88. Cigarette smoke did not influence molecular expression in our study. Smokers had a statistically higher IELs count than non-smokers (54.2 ± 7.7 vs. 36.0 ± 8.8, p < 0.001). A significant, direct correlation between IELs and duodenal expression of IFNγ was found (r = 0.36, p = 0.04). Conclusions: IBS with DL showed higher expression of inflammatory markers than controls, but DQ2-8 haplotype did not seem to affect their expression. Smoking might increase IELs infiltration

Combining noisy well data and expert knowledge in a Bayesian calibration of a flow model under uncertainties: an application to solute transport in the Ticino basin

Groundwater flow modeling is commonly used to calculate groundwater heads, estimate groundwater flow paths and travel times, and provide insights into solute transport processes within an aquifer. However, the values of input parameters that drive groundwater flow models are often highly uncertain due to subsurface heterogeneity and geologic complexity in combination with lack of measurements/unreliable measurements. This uncertainty affects the accuracy and reliability of model outputs. Therefore, parameters' uncertainty must be quantified before adopting the model as an engineering tool. In this study, we model the uncertain parameters as random variables and use a Bayesian inversion approach to obtain a posterior,data-informed, probability density function (pdf) for them: in particular, the likelihood function we consider takes into account both well measurements and our prior knowledge about the extent of the springs in the domain under study. To keep the modelistic and computational complexities under control, we assume Gaussianity of the posterior pdf of the parameters. To corroborate this assumption, we run an identifiability analysis of the model: we apply the inversion procedure to several sets of synthetic data polluted by increasing levels of noise, and we determine at which levels of noise we can effectively recover the "true value" of the parameters. We then move to real well data (coming from the Ticino River basin, in northern Italy, and spanning a month in summer 2014), and use the posterior pdf of the parameters as a starting point to perform an Uncertainty Quantification analysis on groundwater travel-time distributions.Comment: First submissio

Process Control Parameters Evaluation Using Discrete Event Simulation for Business Process Optimization

The quest for manufacturing process improvement and higher levels of customer satisfaction mandates that organizations must be equipped with advanced tools and techniques in order to respond towards ever changing internal and external customer demands by maintaining the optimal process performance, lower cost and higher profit levels. A manufacturing process can be defined as a collection of activities designed to produce a specific output for a particular customer or market. To achieve internal and external objectives, significant process parameters must be identified and evaluated to optimize the process performance. This even becomes more important to deal with fierce competition and ever changing customer demands. This paper illustrates an integrated approach using design of experiments techniques and discrete event simulation (Simul8) to understand and optimize the system dynamic based on operational control parameter evaluation and their boundary conditions. Further, the proposed model is validated using a real world manufacturing process case study to optimize the manufacturing process performance. Discrete event simulation tool is used to mimic the real world scenario, which provides a flexible and powerful way to comprehensively understand the manufacturing process variations and allows controlled 'What-If´ analysis based on design of experiments approach. Finally, this paper discusses the potential applications of the proposed methodology in the cable industry in order to optimize the cable manufacturing process by regulating the operational control parameters such as dealing with various product configurations with different equipment settings, different product flows and work in process (WIP) space limitations

Combining the Morris Method and Multiple Error Metrics to Assess Aquifer Characteristics and Recharge in the Lower Ticino Basin, in Italy

Groundwater flow model accuracy is often limited by the uncertainty in model parameters that characterize aquifer properties and aquifer recharge. Aquifer properties such as hydraulic conductivity can have an uncertainty spanning orders of magnitude. Meanwhile, parameters used to configure model boundary conditions can introduce additional uncertainty. In this study, the Morris Method sensitivity analysis is performed on multiple quantities of interest to assess the sensitivity of a steady-state groundwater flow model to uncertain input parameters. The Morris Method determines which of these parameters are less influential on model outputs. Uninfluential parameters can be set constant during subsequent parameter optimization to reduce computational expense. Combining multiple quantities of interest (e.g., RMSE, groundwater fluxes) when performing both the Morris Method and parameter optimization offers a more complete assessment of groundwater models, providing a more reliable and physically consistent estimate of uncertain parameters. The parameter optimization procedure also provides us an estimate of the residual uncertainty in the parameter values, resulting in a more complete estimate of the remaining uncertainty. By employing such techniques, the current study was able to estimate the aquifer hydraulic conductivity and recharge rate due to rice field irrigation in a groundwater basin in Northern Italy, revealing that a significant proportion of surficial aquifer recharge (approximately 81-94%) during the later summer is due to the flood irrigation practices applied to these fields.Comment: second submission after minor revision

Hidden genetic variation in LCA9-associated congenital blindness explained by 5′UTR mutations and copy-number variations of NMNAT1

Leber congenital amaurosis (LCA) is a severe autosomal-recessive retinal dystrophy leading to congenital blindness. A recently identified LCA gene is NMNAT1, located in the LCA9 locus. Although most mutations in blindness genes are coding variations, there is accumulating evidence for hidden noncoding defects or structural variations (SVs). The starting point of this study was an LCA9-associated consanguineous family in which no coding mutations were found in the LCA9 region. Exploring the untranslated regions of NMNAT1 revealed a novel homozygous 5'UTR variant, c.-70A>T. Moreover, an adjacent 5'UTR variant, c.-69C>T, was identified in a second consanguineous family displaying a similar phenotype. Both 5'UTR variants resulted in decreased NMNAT1 mRNA abundance in patients' lymphocytes, and caused decreased luciferase activity in human retinal pigment epithelial RPE-1 cells. Second, we unraveled pseudohomozygosity of a coding NMNAT1 mutation in two unrelated LCA patients by the identification of two distinct heterozygous partial NMNAT1 deletions. Molecular characterization of the breakpoint junctions revealed a complex Alu-rich genomic architecture. Our study uncovered hidden genetic variation in NMNAT1-associated LCA and emphasized a shift from coding to noncoding regulatory mutations and repeat-mediated SVs in the molecular pathogenesis of heterogeneous recessive disorders such as hereditary blindness