Learned color taste associations in a repeated brief exposure paradigm

Individuals can learn to associate tastes with odors through repeated exposure. We adapted this paradigm, testing whether individuals can learn to associate tastes with colors, and whether these learned color-taste associations generalize to unconditioned, but qualitatively similar stimuli. Experiment 1 tested if individuals could learn color-taste associations for prototypical tastes, while Experiment 2 tested if individuals could learn to discriminate specific bitter compounds using color-taste associations. Conditioned stimuli in Experiment 1 consisted of solutions representing four different prototypical taste qualities. Conditioned stimuli in Experiment 2 consisted of three different bitter stimuli and a sucrose control. Both experiments consisted of six laboratory visits. On day 1, participants rated intensities of conditioned and unconditioned stimuli. On days 2–5 participants completed triangle tests, where a color was systematically paired with high/low intensity combinations of the conditioned stimuli. On day 6, participants matched colorless solutions containing the conditioned and unconditioned stimuli to one of four colors in a forced choice task. In Experiment 1, group performance was significantly above chance (25%) when matching the conditioned taste stimulus to the previously paired color. Also, learning generalized to unconditioned stimuli with a similar taste quality. In Experiment 2, group performance versus chance (25%) indicated participants learned associated sweetness with the previously paired color, both for sucrose (conditioned stimulus) and aspartame (unconditioned stimulus). However, matching performance did not exceed chance for the three different bitterants. These data suggest that particular color-taste associations can be learned with repeated exposure, even when the exposure period is relatively brief.     

Incomplete analytic hierarchy process with minimum weighted ordinal violations

ABSTRACT

Incomplete pairwise comparison matrices offer a natural way of expressing preferences in decision-making processes. Although ordinal information is crucial, there is a bias in the literature: cardinal models dominate. Ordinal models usually yield nonunique solutions; therefore, an approach blending ordinal and cardinal information is needed. In this work, we consider two cascading problems: first, we compute ordinal preferences, maximizing an index that combines ordinal and cardinal information; then, we obtain a cardinal ranking by enforcing ordinal constraints. Notably, we provide a sufficient condition (that is likely to be satisfied in practical cases) for the first problem to admit a unique solution and we develop a provably polynomial-time algorithm to compute it. The effectiveness of the proposed method is analyzed and compared with respect to other approaches and criteria at the state of the art.     

How to perform and report an impactful analysis using partial least squares: Guidelines for confirmatory and explanatory IS research

Partial least squares path modeling (PLS-PM) is an estimator that has found widespread application for causal information systems (IS) research. Recently, the method has been subject to many improvements, such as consistent PLS (PLSc) for latent variable models, a bootstrap-based test for overall model fit, and the heterotrait-to-monotrait ratio of correlations for assessing discriminant validity. Scholars who would like to rigorously apply PLS-PM need updated guidelines for its use. This paper explains how to perform and report empirical analyses using PLS-PM including the latest enhancements, and illustrates its application with a fictive example on business value of social media.     

Crude Oil Production

Current data on crude oil, field/lease condensate, and natural gas liquids produced from NGL plants. Updated on a monthly basis.     

Translational aspects of the modern genetics in head and neck cancers

Oral Cancer (OC) is one of the most recurrent cancers in the head and neck squamous cancer (SCCHN) category. Recently, the genome-wide association studies (GWAS) have gained growing interest in the scientific community. GWAS have identified several pathways involved in the interactions among general risk factors and genomic variants affecting SCCHN. This systematic overview aims to critically evaluate the latest data reported within the scientific literature. The aim was to investigate the impact of genetic aspects on SCCHN onset and prognosis, involving other clinical and systemic co-factors. PubMed, Google Scholar, and Cancer Genetics Web databases have been systematically investigated for original articles published in the last two years, reporting studies on the main queries addressed in this work. This review also comparatively describes the impact of environmental and pathological co-factors in different types of cancers, clarifying and updating the role of genetic factors in SCCHN onset and development. The main outcomes reported may be helpful to drive clinicians towards their clinical evaluations for the most appropriate therapeutic approach in SCCHN.     

MDA-TOEPGA: A novel method to identify miRNA-disease association based on two-objective evolutionary programming genetic algorithm

The association between miRNA and disease has attracted more and more attention. Until now, existing methods for identifying miRNA related disease mainly rely on top-ranked association model, which may not provide a full landscape of association between miRNA and disease. Hence there is strong need of new computational method to identify the associations from miRNA group view. In this paper, we proposed a framework, MDA-TOEPGA, to identify miRNAdisease association based on two-objective evolutionary programming genetic algorithm, which identifies latent miRNAdisease associations from the view of functional module. To understand the miRNA functional module in diseases, the case study is presented. We have been compared MDA-TOEPGA with several state-of-the-art functional module algorithm. Experimental results showed that our method cannot only outperform classical algorithms, such as K-means, IK-means, MCODE, HC-PIN, and ClusterONE, but can also achieve an ideal overall performance in terms of a composite score consisting of f1, Sensitivity, and Accuracy. Altogether, our study showed that MDA-TOEPGA is a promising method to investigate miRNA-disease association from the landscapes of functional module.     

Commercial farmers’ strategies to control water resources in South Africa: an empirical view of reform

This article shows how large-scale commercial farmers, individually and collectively, are responding to land and water reform processes in the Thukela River basin, KwaZulu-Natal, South Africa. With a high degree of innovative agency, commercial farmers have effectively executed four strategies, enabling them to adapt and use their access to resources to neutralize multiple water reform efforts that once promised to be catalysts for inclusive change in the post-apartheid era. It is likely that policy alone will not facilitate the envisioned transformation, if local practices are not sufficiently understood and anticipated by the governmental officials charged with the implementation of water reform processes.     

Polynomial filtering for nonlinear stochastic systems with state‐ and disturbance‐dependent noises

This article is concerned with the polynomial filtering problem for a class of nonlinear stochastic systems governed by the Itô differential equation. The system under investigation involves polynomial nonlinearities, unknown‐but‐bounded disturbances, and state‐ and disturbance‐dependent noises ((x,d)‐dependent noises for short). By expanding the polynomial nonlinear functions in Taylor series around the state estimate, a new polynomial filter design method is developed with hope to reduce the conservatism of the existing results. In virtue of stochastic analysis and inequality technique, sufficient conditions in terms of parameter‐dependent linear matrix inequalities (PDLMIs) are derived to guarantee that the estimation error system is input‐to‐state stable in probability. Moreover, the desired polynomial matrix can be obtained by solving the PDLMIs via the sum‐of‐squares approach. The effectiveness and applicability of the proposed method are illustrated by two numerical examples with one concerning the permanent magnet synchronous motor.     

Genome-wide association studies (GWAS) and post-GWAS analyses for technological traits in Assaf and Churra dairy breeds

This study aimed to perform a GWAS to identify genomic regions associated with milk and cheese-making traits in Assaf and Churra dairy sheep breeds; second, it aimed to identify possible positional and functional candidate genes and their interactions through post-GWAS studies. For 2,020 dairy ewes from 2 breeds (1,039 Spanish Assaf and 981 Churra), milk samples were collected and analyzed to determine 6 milk production and composition traits and 6 traits related to milk coagulation properties and cheese yield. The genetic profiles of the ewes were obtained using a genotyping chip array that included 50,934 SNP markers. For both milk and cheese-making traits, separate single-breed GWAS were performed using GCTA software. The set of positional candidate genes identified via GWAS was subjected to guilt-by-association-based prioritization analysis with ToppGene software. Totals of 84 and 139 chromosome-wise significant associations for the 6 milk traits and the 6 cheese-making traits were identified in this study. No significant SNPs were found in common between the 2 studied breeds, possibly due to their genetic heterogeneity of the phenotypes under study. Additionally, 63 and 176 positional candidate genes were located in the genomic intervals defined as confidence regions in relation to the significant SNPs identified for the analyzed traits for Assaf and Churra breeds. After the functional prioritization analysis, 71 genes were identified as promising positional and functional candidate genes and proposed as targets of future research to identify putative causative variants in relation to the traits under examination. In addition, this multitrait study allowed us to identify variants that have a pleiotropic effect on both milk production and cheese-related traits. The incorporation of variants among the proposed functional and positional candidate genes into genomic selection strategies represent an interesting approach for achieving rapid genetic gains, specifically for those traits difficult to measure, such as cheese-making traits.     

A Systematic Review and Meta-Analysis of Pharmacogenetic Studies in Patients with Chronic Kidney Disease

Chronic kidney disease (CKD) is an important global public health problem due to its high prevalence and morbidity. Although the treatment of nephrology patients has changed considerably, ineffectiveness and side effects of medications represent a major issue. In an effort to elucidate the contribution of genetic variants located in several genes in the response to treatment of patients with CKD, we performed a systematic review and meta-analysis of all available pharmacogenetics studies. The association between genotype distribution and response to medication was examined using the dominant, recessive, and additive inheritance models. Subgroup analysis based on ethnicity was also performed. In total, 29 studies were included in the meta-analysis, which examined the association of 11 genes (16 polymorphisms) with the response to treatment regarding CKD. Among the 29 studies, 18 studies included patients with renal transplantation, 8 involved patients with nephrotic syndrome, and 3 studies included patients with lupus nephritis. The present meta-analysis provides strong evidence for the contribution of variants harbored in the ABCB1, IL-10, ITPA, MIF, and TNF genes that creates some genetic predisposition that reduces effectiveness or is associated with adverse events of medications used in CKD.