Meta-analysis of gender performance gaps in undergraduate natural science courses

To investigate patterns of gender-based performance gaps, we conducted a meta-analysis of published studies and unpublished data collected across 169 undergraduate biology and chemistry courses. While we did not detect an overall gender gap in performance, heterogeneity analyses suggested further analysis was warranted, so we investigated whether attributes of the learning environment impacted performance disparities on the basis of gender. Several factors moderated performance differences, including class size, assessment type, and pedagogy. Specifically, we found evidence that larger classes, reliance on exams, and undisrupted, traditional lecture were associated with lower grades for women. We discuss our results in the context of natural science courses and conclude by making recommendations for instructional practices and future research to promote gender equity

The genetic architecture of the human cerebral cortex

The cerebral cortex underlies our complex cognitive capabilities, yet little is known about the specific genetic loci that influence human cortical structure. To identify genetic variants that affect cortical structure, we conducted a genome-wide association meta-analysis of brain magnetic resonance imaging data from 51,665 individuals. We analyzed the surface area and average thickness of the whole cortex and 34 regions with known functional specializations. We identified 199 significant loci and found significant enrichment for loci influencing total surface area within regulatory elements that are active during prenatal cortical development, supporting the radial unit hypothesis. Loci that affect regional surface area cluster near genes in Wnt signaling pathways, which influence progenitor expansion and areal identity. Variation in cortical structure is genetically correlated with cognitive function, Parkinson's disease, insomnia, depression, neuroticism, and attention deficit hyperactivity disorder

Brain multiplexes reveal morphological connectional biomarkers fingerprinting late brain dementia states

Accurate diagnosis of mild cognitive impairment (MCI) before conversion to Alzheimer’s disease (AD) is invaluable for patient treatment. Many works showed that MCI and AD affect functional and structural connections between brain regions as well as the shape of cortical regions. However, ‘shape connections’ between brain regions are rarely investigated -e.g., how morphological attributes such as cortical thickness and sulcal depth of a specific brain region change in relation to morphological attributes in other regions. To fill this gap, we unprecedentedly design morphological brain multiplexes for late MCI/AD classification. Specifically, we use structural T1-w MRI to define morphological brain networks, each quantifying similarity in morphology between different cortical regions for a specific cortical attribute. Then, we define a brain multiplex where each intra-layer represents the morphological connectivity network of a specific cortical attribute, and each inter-layer encodes the similarity between two consecutive intra-layers. A significant performance gain is achieved when using the multiplex architecture in comparison to other conventional network analysis architectures. We also leverage this architecture to discover morphological connectional biomarkers fingerprinting the difference between late MCI and AD stages, which included the right entorhinal cortex and right caudal middle frontal gyrus

Multimodal and Multiscale Deep Neural Networks for the Early Diagnosis of Alzheimer’s Disease using structural MR and FDG-PET images

Alzheimer’s Disease (AD) is a progressive neurodegenerative disease where biomarkers for disease based on pathophysiology may be able to provide objective measures for disease diagnosis and staging. Neuroimaging scans acquired from MRI and metabolism images obtained by FDG-PET provide in-vivo measurements of structure and function (glucose metabolism) in a living brain. It is hypothesized that combining multiple different image modalities providing complementary information could help improve early diagnosis of AD. In this paper, we propose a novel deep-learning-based framework to discriminate individuals with AD utilizing a multimodal and multiscale deep neural network. Our method delivers 82.4% accuracy in identifying the individuals with mild cognitive impairment (MCI) who will convert to AD at 3 years prior to conversion (86.4% combined accuracy for conversion within 1–3 years), a 94.23% sensitivity in classifying individuals with clinical diagnosis of probable AD, and a 86.3% specificity in classifying non-demented controls improving upon results in published literature

The impact of PICALM genetic variations on reserve capacity of posterior cingulate in AD continuum

Phosphatidylinositolbinding clathrin assembly protein (PICALM) gene is one novel genetic player associated with late-onset Alzheimer’s disease (LOAD), based on recent genome wide association studies (GWAS). However, how it affects AD occurrence is still unknown. Brain reserve hypothesis highlights the tolerant capacities of brain as a passive means to fight against neurodegenerations. Here, we took the baseline volume and/or thickness of LOAD-associated brain regions as proxies of brain reserve capacities and investigated whether PICALM genetic variations can influence the baseline reserve capacities and the longitudinal atrophy rate of these specific regions using data from Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset. In mixed population, we found that brain region significantly affected by PICALM genetic variations was majorly restricted to posterior cingulate. In sub-population analysis, we found that one PICALM variation (C allele of rs642949) was associated with larger baseline thickness of posterior cingulate in health. We found seven variations in health and two variations (rs543293 and rs592297) in individuals with mild cognitive impairment were associated with slower atrophy rate of posterior cingulate. Our study provided preliminary evidences supporting that PICALM variations render protections by facilitating reserve capacities of posterior cingulate in non-demented elderly

The zirconium isotope composition of the mantle and upper continental crust through time

International audienc

Zirconium isotopic composition of the upper continental crust through time

International audienceThe stable isotopic composition of insoluble, refractory elements such as titanium (Ti) or zirconium (Zr), which are modified by magmatic differentiation but, a priori, are poorly affected by weathering or diagenesis, serve as powerful potential proxies to reconstruct the compositional evolution of the continental crust. Here we present the evolution of the Zr stable isotopic compositions δ94/90 ZrIPGP-Zr, per mille deviation of 94Zr/90Zr from IPGP-Zr standard) of the continental crust through time, using 38 sedimentary samples from the upper continental crust (UCC), including 12 Holocene loesses from the Chinese Loess Plateau and Xinjiang, three oceanic sediments from the sea floor outboard of the Lesser Antilles island arc and 23 glacial diamictite composites with depositional ages ranging from ∼ 2.9 Ga to 0.3 Ga from South Africa, South America, Canada, USA and China. The samples show limited Zr isotopic variations with δ94/90 ZrIPGP-Zr values of 0.043‰ - 0.109‰ for loess; 0.069‰ - 0.083‰ for oceanic sediments and 0.031‰ - 0.118‰ for glacial diamictites; their Zr-weighted average values are, 0.081 ± 0.044‰ (2SD, n = 12), 0.073 ± 0.015‰ (2SD, n = 3) and 0.078 ± 0.047‰ (2SD, n = 23), respectively. The isotopic similarity among loess, oceanic sediments and glacial diamictites, suggests that zircon enrichment effects previously documented in some sedimentary samples have not biased the Zr isotope compositions of these sedimentary rocks from their source rocks. Two groups with or without Zr enrichment have similar average ZrIPGP-Zr values (0.075 ± 0.040‰ and 0.080 ± 0.046‰). There is no correlation between Zr isotope compositions and any proxy of chemical weathering (e.g., Al2O3/SiO2, Fe2O3/SiO2, CIA, K2O/Al2O3 and δ7 Li ). The δ94/90 ZrIPGP-Zr values are quite constant for these sedimentary samples regardless of their depositional ages and locations. Therefore, the UCC appears to have had a constant Zr isotopic composition between 3 Ga and present, and is homogeneous at a large scale. Combining data for sedimentary reference materials from the literature and the sedimentary rocks in this study, we suggest a Zr-weighted δ94/90 ZrIPGP-Zr value of 0.077 ± 0.058‰ (2SD, n = 44) for the UCC, which is statistically distinct (t test, p value = 2.88 × 10−10) and higher than that of the mantle (0.040 ± 0.044‰, n = 72). Combining the δ94/90 ZrIPGP-Zr values of different terrestrial reservoirs, the δ94/90 ZrIPGP-Zr of the BSE and bulk Earth is constrained to be 0.041 ± 0.041

Evaluating the Representation of Community Colleges in Biology Education Research Publications following a Call to Action

Interest in biology education research (BER) has been growing over the last two decades, yet few BER publications focus on community colleges, which serve a large percentage of the undergraduate student population and a majority of those students who identify with historically underserved groups. In this paper, we define community college biology education research (CC BER) as publications with a community college faculty member as an author, publications with a community college study context or a focus on community college biology teaching and learning, and publications that use community college students as a source of data. We conducted a literature review to quantify how CC BER has progressed since initial calls for broadening participation by recording the number of CC BER publications in seven prominent journals between 2016 and 2020. Our formal analysis of peer-reviewed BER literature indicates that there has been a statistically significant increase in CC BER publications from 3.2% to 5.9% of total BER publications since the last analysis in 2017. We conclude with a discussion of strategies for further broadening of participation in CC BER.publishedVersio

Studying the birth of exoplanetary systems with the Planet Formation Imager (PFI)

International audienceDespite recent advancements, many fundamental questions still surround the processes that are involved in planetary birth: Where in the protoplanetary disk do the planets form and how do they grow? What factors determine the final architecture of planetary systems? How are water and other volatiles delivered to the protoplanets and how does this affect the potential habitability of these worlds?As part of the "Planet Formation Imager" (PFI) project we develop the roadmap for a future infrared high-angular resolution imaging facility that aims to answer these questions by witnessing the planetary formation processes on the natural scales where the material is assembled, which is the Hill sphere of the forming planets. PFI will detect giant protoplanets on all stellocentric radii, image their interaction with the ambient disk material, and trace their dynamical evolution during the first 100 million years, thereby reveal the processes that determine the architecture of planetary systems.In this contribution we give an overview about the work of the PFI science and technical working group and present radiation-hydrodynamics simulations from which we derive preliminary specifications that guide the design of the facility. We will present a baseline PFI architecture that can achieve these goals, point at remaining technical challenges, and suggest activities today that will help make the Planet Formation Imager facility a reality