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 student’s 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 “Other” 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

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

An empirical evaluation of functional alignment using inter-subject decoding

International audienceInter-individual variability in the functional organization of the brain presents a major obstacle to identifying generalizable neural coding principles. Functional alignment-a class of methods that matches subjects' neural signals based on their functional similarity-is a promising strategy for addressing this variability. At present, however, a range of functional alignment methods have been proposed and their relative performance is still unclear. In this work, we benchmark five functional alignment methods for inter-subject decoding on four publicly available datasets. Specifically, we consider piecewise Procrustes, searchlight Procrustes, piecewise Optimal Transport, Shared Response Modelling (SRM), and intra-subject alignment; as well as associated methodological choices such as ROI definition. We find that functional alignment generally improves inter-subject decoding accuracy though the best performing method depends on the research context. Specifically, SRM performs best within a region-of-interest while piecewise Optimal Transport performs best at a whole-brain scale. We also benchmark the computational e ciency of each of the surveyed methods, providing insight into their usability and scalability. Taking inter-subject decoding accuracy as a quantification of inter-subject similarity, our results support the use of functional alignment to improve inter-subject comparisons in the face of variable structure-function organization. We provide open implementations of the methods used

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 “an article about [a] computational result is advertising, not scholarship. The actual scholarship is the full software environment, code and data, that produced the result” [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 “from the wet lab to the web lab” [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 “Claerbout challenge” 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

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 community’s 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 Nilearn’s 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 Nilearn’s 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

Frequent failure of nutrients to increase plant biomass supports the need for precision fertilization in agriculture

Abstract Implementing precision fertilization to maximize crop yield while minimizing economic and environmental impacts has become critical for agriculture. Variability in biomass response to fertilization within fields, among regions, and over time creates simultaneous risks of under-yielding and overfertilization. We quantify factors determining fertilization responsiveness (i.e., biomass increases with fertilization) up to 15 years in 61 unfertilized rangelands on six continents. We demonstrate widespread multi-year variability in responsiveness, with fertilization increasing average yield by 43% but failing to improve biomass 26% of the time. All sites were responsive at least once, but only four of 61 responded in all plots and years. Modelled management scenarios highlighted that fertilizer cessation is likely to generate sizable economic savings but always reduces yield because of the difficulty in predicting when and where biomass will be unresponsive. This work reveals substantial scale-dependent variability in fertilization responsiveness globally, while clarifying the prospects and pitfalls of managing more spatially and temporally precise nutrient application.Abstract Implementing precision fertilization to maximize crop yield while minimizing economic and environmental impacts has become critical for agriculture. Variability in biomass response to fertilization within fields, among regions, and over time creates simultaneous risks of under-yielding and overfertilization. We quantify factors determining fertilization responsiveness (i.e., biomass increases with fertilization) up to 15 years in 61 unfertilized rangelands on six continents. We demonstrate widespread multi-year variability in responsiveness, with fertilization increasing average yield by 43% but failing to improve biomass 26% of the time. All sites were responsive at least once, but only four of 61 responded in all plots and years. Modelled management scenarios highlighted that fertilizer cessation is likely to generate sizable economic savings but always reduces yield because of the difficulty in predicting when and where biomass will be unresponsive. This work reveals substantial scale-dependent variability in fertilization responsiveness globally, while clarifying the prospects and pitfalls of managing more spatially and temporally precise nutrient application

Widening global variability in grassland biomass since the 1980s

Abstract 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 −34% 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.Abstract 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 −34% 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

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

Interactions among nutrients govern the global grassland biomass-precipitation relationship

Ecosystems are experiencing changing global patterns of mean annual precipitation (MAP) and enrichment with multiple nutrients that potentially colimit plant biomass production. In grasslands, mean aboveground plant biomass is closely related to MAP, but how this relationship changes after enrichment with multiple nutrients remains unclear. We hypothesized the global biomass–MAP relationship becomes steeper with an increasing number of added nutrients, with increases in steepness corresponding to the form of interaction among added nutrients and with increased mediation by changes in plant community diversity. We measured aboveground plant biomass production and species diversity in 71 grasslands on six continents representing the global span of grassland MAP, diversity, management, and soils. We fertilized all sites with nitrogen, phosphorus, and potassium with micronutrients in all combinations to identify which nutrients limited biomass at each site. As hypothesized, fertilizing with one, two, or three nutrients progressively steepened the global biomass–MAP relationship. The magnitude of the increase in steepness corresponded to whether sites were not limited by nitrogen or phosphorus, were limited by either one, or were colimited by both in additive, or synergistic forms. Unexpectedly, we found only weak evidence for mediation of biomass–MAP relationships by plant community diversity because relationships of species richness, evenness, and beta diversity to MAP and to biomass were weak or opposing. Site-level properties including baseline biomass production, soils, and management explained little variation in biomass–MAP relationships. These findings reveal multiple nutrient colimitation as a defining feature of the global grassland biomass–MAP relationship