70 research outputs found
An Exploratory Analysis of Pharmaceutical Price Disparities and Their Implications Among Six Developed Nations
In our study of 43 drugs, prescription drug prices in several wealthy nations (Australia, Canada, France, Germany, and the U.K.) were much lower than in the U.S. on average, well below relative per capita GDP. There was relatively little difference among the five foreign nations. All this is consistent with previous research. After separating less-unique from more unique drugs, however, important new findings emerged. Relative prices for less-unique drugs, which are subject to strong competition, were at about half the U.S. level. We suggest that this reflects the exercise of monopsony power that does not exist in the U.S., where buyers as well as sellers compete. On the other hand, relative prices for highly unique drugs tended to be approximately proportional to per capita GDP or higher. Remarkably, biotech drugs were priced at or above U.S. levels in Canada and France
The relationship between creativity and psychosocial development among college honors students and non-honors students
The purpose of this study was to determine if there was a difference in measures
of creativity and psychosocial development in college Honors and Non-Honors students
and also to determine interaction effects of demographic and academic background data.
Additionally, another purpose was to establish any relationship between measures of
creativity and psychosocial development. Of the 284 college students participating, 120
were honors students and 164 were non-honors students. Participants were administered
the Torrance Tests of Creative Thinking (TTCT) Verbal Form B, Activities 4 and 5 and
the Student Development Task and Lifestyle Assessment (SDTLA). The TTCT included
scales of fluency, flexibility, originality, and average standard creativity score. The
SDTLA includes the measurement of three developmental tasks, ten subtasks, and two
scales. The participants were volunteers and were tested in four regularly scheduled
classes during the 2006 spring and summer semesters.
Two-tailed independent t-tests performed on the dependent variables of the
TTCT indicated that the Non-Honors studentâs scores were statistically significantly higher on fluency, originality, and the average standard creativity measures. On the
average standard score, which is considered the best overall gauge of creative power,
neither Non-Honors nor Honors student groups TTCT scores were considered higher
than weak (0-16%) (Torrance, 1990). The results of the two-tailed independent t-tests
performed on the dependent variables of the SDTLA resulted in the statistically
significant higher development outcome scores in the Honors students. The mean
SDTLA scores of both the Honors and Non-Honors scores were not outside of norm
group average scores. The MANOVA data produced moderately statistically significant
interaction effects between classification level and fluency. However, the post hoc tests
did not confirm the difference in classification and fluency. Additional MANOVA data
indicated a significant interaction effect between ethnicity and Lifestyle Planning (LP),
and post hoc analysis confirmed the interaction with significant differences in Caucasian
and âOtherâ students. Classification level significantly interacted with eight of the
fourteen development outcomes, nevertheless the post hoc tests showed inconsistent
differences between classification groups within the developmental outcomes.
Correlations between the TTCT and SDTLA did not yield statistically significant
relationships between the creativity and psychosocial development variables
Beyond advertising: New infrastructures for publishing integrated research objects
ABSTRACT: Moving beyond static text and illustrations is a central challenge for scientific publishing in the 21st century. As early as 1995, Donoho and Buckheit paraphrased John Claerbout that âan article about [a] computational result is advertising, not scholarship. The actual scholarship is the full software environment, code and data, that produced the resultâ [1]. Awareness of this problem has only grown over the last 25 years; nonetheless, scientific publishing infrastructures remain remarkably resistant to change [2]. Even as these infrastructures have largely stagnated, the internet has ushered in a transition âfrom the wet lab to the web labâ [3]. New expectations have emerged in this shift, but these expectations must play against the reality of currently available infrastructures and associated sociological pressures. Here, we compare current scientific publishing norms against those associated with online content more broadly, and we argue that meeting the âClaerbout challengeâ of providing the full software environment, code, and data supporting a scientific result will require open infrastructure development to create environments for authoring, reviewing, and accessing interactive research objects
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BRAIN Initiative: Cutting-Edge Tools and Resources for the Community.
The overarching goal of the NIH BRAIN (Brain Research through Advancing Innovative Neurotechnologies) Initiative is to advance the understanding of healthy and diseased brain circuit function through technological innovation. Core principles for this goal include the validation and dissemination of the myriad innovative technologies, tools, methods, and resources emerging from BRAIN-funded research. Innovators, BRAIN funding agencies, and non-Federal partners are working together to develop strategies for making these products usable, available, and accessible to the scientific community. Here, we describe several early strategies for supporting the dissemination of BRAIN technologies. We aim to invigorate a dialogue with the neuroscience research and funding community, interdisciplinary collaborators, and trainees about the existing and future opportunities for cultivating groundbreaking research products into mature, integrated, and adaptable research systems. Along with the accompanying Society for Neuroscience 2019 Mini-Symposium, "BRAIN Initiative: Cutting-Edge Tools and Resources for the Community," we spotlight the work of several BRAIN investigator teams who are making progress toward providing tools, technologies, and services for the neuroscience community. These tools access neural circuits at multiple levels of analysis, from subcellular composition to brain-wide network connectivity, including the following: integrated systems for EM- and florescence-based connectomics, advances in immunolabeling capabilities, and resources for recording and analyzing functional connectivity. Investigators describe how the resources they provide to the community will contribute to achieving the goals of the NIH BRAIN Initiative. Finally, in addition to celebrating the contributions of these BRAIN-funded investigators, the Mini-Symposium will illustrate the broader diversity of BRAIN Initiative investments in cutting-edge technologies and resources
Nilearn for new use cases: Scaling up computational and community efforts
Introduction
Nilearn (https://nilearn.github.io) is a well-established Python package that provides statistical
and machine learning tools for fast and easy analysis of brain images with instructive
documentation and a friendly community. This focus has led to its current position as a crucial
part of the neuroimaging communityâs open-source software ecosystem, supporting efficient and
reproducible science [1]. It has been continuously developed over the past 10 years, currently
with 900 stars, 500 forks, and 176 contributors on GitHub. Nilearn leverages and builds upon
other central Python machine learning packages, such as Scikit-Learn [2], that are extensively
used, tested, and optimized by a large scientific and industrial community.
In recent years, efforts in Nilearn have been focused on meeting evolving community needs by
increasing General Linear Model (GLM) support, interfacing with initiatives like fMRIPrep and
BIDS, and improving the user documentation. Here we report on progress regarding our current
priorities.
Methods
Nilearn is developed to be accessible and easy-to-use for researchers and the open-source
community. It features user-focused documentation that includes a user guide and an example
gallery as well as comprehensive contribution guidelines. Nilearn is also presented in tutorials
and workshops throughout the year including the Montreal Artificial Intelligence and
Neuroscience (MAIN) Educational Workshop, the OHBM Brainhack event, and for the Chinese
Open Science Network.
The community is encouraged to ask questions, report bugs, make suggestions for
improvements or new features, and make direct contributions to the source code. We use the
platforms Neurostars, GitHub, and Discord to interact with contributors and users on a daily
basis.
Nilearn adheres to best practices in software development including using version control, unit
testing, and requiring multiple reviews of contributions. We also have a continuous integration
infrastructure set up to automate many aspects of our development process and make sure our
code is continuously tested and up-to-date.
Results
Nilearn supports methods such as image manipulation and processing, decoding, functional
connectivity analysis, GLM, multivariate pattern analysis, along with plotting volumetric and
surface data.
In the latest release, cluster-level and TFCE-based family-wise error rate (FWER) control have
been added to support the mass univariate and GLM analysis modules, expanding from the
already implemented voxel-level correction method (see Fig1). Optimizing Nilearnâs maskers is
also underway such as the recently added classes for handling multi-subject 4D image data.
These also provide the option to use parallelization to speed up computation.
In addition, Nilearn has introduced a new theme to update the documentation making the
website more readable and accessible (https://nilearn.github.io/). This change also sets the
stage for further improvement and modernisation of several aspects of the documentation, like
the user guide.
Development on Nilearnâs interfaces module added a new function to write BIDS-compatible
model results to disk. This and further development of the BIDS interface will facilitate
interaction with other relevant community tools such as FitLins [3]. Finally, several surface
plotting enhancements are in progress including improving the API for background maps (see
Fig2).
Conclusion
Nilearn is extensively used by researchers of the neuroimaging community due to its
implementations of well-founded methods and visualization tools which are often essential in
brain imaging research for quality control and communicating results. Recent work has
highlighted areas where more active work is needed to scale the project both technically and
socially, including: working with large datasets, better supporting analyses on the cortical
surface, and advancing standard practice in neuroimaging statistics through active community
outreach.
References
[1] Poldrack, R., Gorgolewski, K., Varoquaux, G. (2019). Computational and Informatic
Advances for Reproducible Data Analysis in Neuroimaging Annual Review of Biomedical Data
Science 2(1), 119-138. https://dx.doi.org/10.1146/annurev-biodatasci-072018-021237
[2] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M.,
Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher,
M., Perrot, M., Duchesnay, E. (2011). Scikit-learn: Machine Learning in Python, Journal of
Machine Learning Research, 12, 2825-2830.
[3] Markiewicz, C. J., De La Vega, A., Wagner, A., Halchenko, Y. O., Finc, K., Ciric, R.,
Goncalves, M., Nielson, D. M., Kent, J. D., Lee, J. A., Bansal, S., Poldrack, R. A., Gorgolewski,
K. J. (2022). poldracklab/fitlins: 0.11.0 (0.11.0). Zenodo. https://doi.org/10.5281/zenodo.7217447This poster was presented at OHBM 2023
The past, present, and future of the brain imaging data structure (BIDS)
The Brain Imaging Data Structure (BIDS) is a community-driven standard for the organization of data and metadata from a growing range of neuroscience modalities. This paper is meant as a history of how the standard has developed and grown over time. We outline the principles behind the project, the mechanisms by which it has been extended, and some of the challenges being addressed as it evolves. We also discuss the lessons learned through the project, with the aim of enabling researchers in other domains to learn from the success of BIDS
Widening global variability in grassland biomass since the 1980s
Global change is associated with variable shifts in the annual production of aboveground plant biomass, suggesting localized sensitivities with unclear causal origins. Combining remotely sensed normalized difference vegetation index data since the 1980s with contemporary field data from 84 grasslands on 6 continents, we show a widening divergence in site-level biomass ranging from +51% to â34% globally. Biomass generally increased in warmer, wetter and species-rich sites with longer growing seasons and declined in species-poor arid areas. Phenological changes were widespread, revealing substantive transitions in grassland seasonal cycling. Grazing, nitrogen deposition and plant invasion were prevalent in some regions but did not predict overall trends. Grasslands are undergoing sizable changes in production, with implications for food security, biodiversity and carbon storage especially in arid regions where declines are accelerating
Compositional variation in grassland plant communities
Human activities are altering ecological communities around the globe. Understanding the implications of these changes requires that we consider the composition of those communities. However, composition can be summarized by many metrics which in turn are influenced by different ecological processes. For example, incidence-based metrics strongly reflect species gains or losses, while abundance-based metrics are minimally affected by changes in the abundance of small or uncommon species. Furthermore, metrics might be correlated with different predictors. We used a globally distributed experiment to examine variation in species composition within 60 grasslands on six continents. Each site had an identical experimental and sampling design: 24 plotsâĂâ4âyears. We expressed compositional variation within each siteânot across sitesâusing abundance- and incidence-based metrics of the magnitude of dissimilarity (BrayâCurtis and Sorensen, respectively), abundance- and incidence-based measures of the relative importance of replacement (balanced variation and species turnover, respectively), and species richness at two scales (per plot-year [alpha] and per site [gamma]). Average compositional variation among all plot-years at a site was high and similar to spatial variation among plots in the pretreatment year, but lower among years in untreated plots. For both types of metrics, most variation was due to replacement rather than nestedness. Differences among sites in overall within-site compositional variation were related to several predictors. Environmental heterogeneity (expressed as the CV of total aboveground plant biomass in unfertilized plots of the site) was an important predictor for most metrics. Biomass production was a predictor of species turnover and of alpha diversity but not of other metrics. Continentality (measured as annual temperature range) was a strong predictor of Sorensen dissimilarity. Metrics of compositional variation are moderately correlated: knowing the magnitude of dissimilarity at a site provides little insight into whether the variation is driven by replacement processes. Overall, our understanding of compositional variation at a site is enhanced by considering multiple metrics simultaneously. Monitoring programs that explicitly incorporate these implications, both when designing sampling strategies and analyzing data, will have a stronger ability to understand the compositional variation of systems and to quantify the impacts of human activities
The past, present, and future of the Brain Imaging Data Structure (BIDS)
The Brain Imaging Data Structure (BIDS) is a community-driven standard for the organization of
data and metadata from a growing range of neuroscience modalities. This paper is meant as a
history of how the standard has developed and grown over time. We outline the principles
behind the project, the mechanisms by which it has been extended, and some of the challenges
being addressed as it evolves. We also discuss the lessons learned through the project, with the
aim of enabling researchers in other domains to learn from the success of BIDS
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