ObjectiveA physical effort such as lifting up a weight affects our perception and cognition. A previous study reported in two experiments that weight lifting improves visual acuity. In the previous study, participants’ visual acuity was higher while lifting weights than while resting. Moreover, via a case study, that study further showed that the heavier the weight, the better the visual acuity. These experiments, although interesting, lacked methodological details and thorough statistical analyses. We thus conducted experiments similar to these two previous ones that mitigated these issues.ResultsAlthough our results of Experiment 1 echoed those of the previous study, the results of Experiment 2 did not support the latter case report. Thus, our results suggest that the bodily experience of weights improves visual acuity, but a gradual increase in weight does not seem to lead to a gradual increase in visual acuity.Electronic supplementary materialThe online version of this article (doi:10.1186/s13104-017-2699-1) contains supplementary material, which is available to authorized users.

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Pleiotropy is a phenomenon that a single gene inflicts multiple correlated phenotypic effects, often characterized as traits, involving multiple biological systems. We propose a two-stage method to identify pleiotropic effects on multiple longitudinal traits from a family-based data set. The first stage analyzes each longitudinal trait via a three-level mixed-effects model. Random effects at the subject-level and at the family-level measure the subject-specific genetic effects and between-subjects intraclass correlations within families, respectively. The second stage performs a simultaneous association test between a single nucleotide polymorphism and all subject-specific effects for multiple longitudinal traits. This is performed using a quasi-likelihood scoring method in which the correlation structure among related subjects is adjusted. Two simulation studies for the proposed method are undertaken to assess both the type I error control and the power. Furthermore, we demonstrate the utility of the two-stage method in identifying pleiotropic genes or loci by analyzing the Genetic Analysis Workshop 16 Problem 2 cohort data drawn from the Framingham Heart Study and illustrate an example of the kind of complexity in data that can be handled by the proposed approach. We establish that our two-stage method can identify pleiotropic effects whilst accommodating varying data types in the model.

When distinguishing stop consonants, Korean listeners use the fundamental frequency of the following vowel (F0) as well as the voice onset time (VOT). However, it is unclear whether the F0 and VOT of natural speech sounds play an effective role in hearing nonnative stops. In the present study, we examined whether Korean listeners perceive Japanese voiced stops, rather than Japanese voiceless stops, as being similar to Korean lenis stops depending on F0 and VOT. Native Korean listeners matched a speech sound starting with either a Japanese voiced or voiceless stop to a target. The targets were meaningful visual words starting with a lenis stop in Experiment 1 and meaningless speech sounds starting with a lenis stop in Experiment 2. Native Japanese listeners, who primarily use VOT for stop distinction, also participated in Experiment 2. The results revealed that the extent to which Korean listeners chose voiced stops was influenced consistently by F0, and the pattern of results differed between Korean and Japanese listeners. These findings suggest that even when hearing natural speech sounds, Korean listeners use F0 when judging the similarity between lenis and nonnative stops.

IntroductionThe Canadian Partnership Against Cancer reports on pan-Canadian system performance across the cancer control continuum, including how sociodemographic disparities create barriers in access and utilization of cancer control services. This has been done using mostly ecological data, which does not contain the individual level information required to identify the extent of disparities. Objectives and ApproachThe Partnership collaborated with Statistics Canada (STC) to build individual level datasets that will allow researchers to investigate the relationship between sociodemographic factors, cancer outcomes and treatment patterns in Canada.The record linkage was conducted at STC within the Social Data Linkage Environment. Data from the Canadian Cancer Registry were linked to the Discharge Abstract Database, the National Ambulatory Care Reporting System and the Canadian Vital Statistics Death Database to obtain treatment information and deathand death outcomes. To obtain sociodemographic information the following datasets are also being linked: T1 Personal Master File (income), Immigrant Landing File and the Census Long Form (education and geography). ResultsLinkage of all datasets is expected to complete by the end of January 2019. For the first time in Canada, record-level linkage of national cancer registry data with key datasets containing sociodemographic information will be available for exploratory analysis. The challenges with linkage and data limitations will be discussed, as well as the application of these linked databases to answer current disparities-related research questions. Conclusion/ImplicationsThis initiative illustrates the value of collaboration between data custodians and health researchers as well as how linkage of existing datasets can leverage the full potential of available data, and broaden cancer research in supporting efforts to create a more equitable cancer control system.