Biology subjects

Meyer, P.

Publications and source records attributed to Meyer, P..

28 records · Page 2Linked to original sources

bioRxiv2021

The effect of mentee and mentor gender on scientific productivity of applicants for NIH training fellowships

Several studies have suggested that women in science are less productive than men, and that this gap contributes to their under-representation in the ranks of senior researchers. However, few studies have examined the role of mentoring, and in particular mentor gender, on the productivity of female scientists early in their careers. Such efforts are limited by the difficulties of unambiguously linking mentees to their mentors and measuring the research productivity resulting from those relationships. Here we use our novel author disambiguation solution to investigate the role of self-identified gender in mentorship of 12,932 trainees who either successfully or unsuccessfully applied to the National Institutes of Health for research fellowships between fiscal years 2011 and 2017, applying a multi-dimensional framework to assess productivity. We found that, after normalizing for the funding level of mentors, the productivity of female and male mentees is indistinguishable; it is also independent of the gender of the mentor, other than in measures of clinical impact, where women mentored by women outperform other mentee-mentor dyads.

scientific communication and education↗

bioRxiv2020

European Primary Forest Database (EPFD) v2.0

Primary forests are scarce in Europe and continue to disappear at an alarming rate. Despite these losses, we know little about where such forests still occur. Here, we present an updated geodatabase and map of Europes known primary forests. Our geodatabase harmonizes 48 different datasets of primary forests, and contains 18,411 individual patches (41.1 Mha) spread across 33 countries. When available, we provide information on each patch (name, location, naturalness, extent and dominant tree species) and the surrounding landscape (biogeographical regions, protection status, potential natural vegetation, current forest extent). To assess the robustness of our geodatabase, we checked each patch for forest disturbance events using Landsat satellite-image time series (1985-2018). We estimate that 94% of the patches in our database did not experience significant disturbances that would alter their primary forest status in the last 30 year. Our database is the most comprehensive dataset on primary forests in Europe, and will be useful for biogeographic and ecological studies, and conservation planning to safeguard these unique forests.

ecology↗

bioRxiv2020

High feature overlap reveals the importance of anterior and medial temporal lobe structures for learning by means of fast mapping

Contrary to traditional theories of declarative memory, it has recently been shown that novel, arbitrary associations can be rapidly and directly integrated into cortical memory networks by means of a learning procedure called fast mapping (FM), possibly bypassing time-consuming hippocampal-neocortical consolidation processes. In the typical FM paradigm, a picture of a previously unknown item is presented next to a picture of a previously known item and participants answer a question referring to an unfamiliar label. It is assumed that they thereby incidentally create associations between the unknown item and the label. However, contradictory findings have been reported and factors moderating rapid cortical integration through FM yet need to be identified. In the context of previous behavioral results showing rapid semantic integration through FM especially if the unknown and the known item shared many features, we propose that due to its computational mechanisms during the processing of complex and particularly highly similar objects, the perirhinal cortex might be especially qualified to support the rapid incorporation of these associations into cortical memory networks within the FM paradigm. We therefore expected that a high degree of feature overlap between the unknown and the known item would trigger strong engagement of the perirhinal cortex at encoding, which in turn might enhance rapid cortical integration of the novel picture-label associations. Within an fMRI experiment, we observed stronger activation for subsequent hits than misses during encoding in the perirhinal cortex and an associated anterior temporal network if the items shared many features than if they shared few features, indicating that the perirhinal cortex indeed contributes to the acquisition of novel associations by means of FM if feature overlap is high.

neuroscience↗

bioRxiv2019

Predicting cellular position in the Drosophila embryo from Single-Cell Transcriptomics data

Single-cell RNA-seq technologies are rapidly evolving but while very informative, in standard scRNAseq experiments the spatial organization of the cells in the tissue of origin is lost. Conversely, spatial RNA-seq technologies designed to keep the localization of the cells have limited throughput and gene coverage. Mapping scRNAseq to genes with spatial information increases coverage while providing spatial location. However, methods to perform such mapping have not yet been benchmarked. To bridge the gap, we organized the DREAM Single-Cell Transcriptomics challenge focused on the spatial reconstruction of cells from the Drosophila embryo from scRNAseq data, leveraging as gold standard genes with in situ hybridization data from the Berkeley Drosophila Transcription Network Project reference atlas. The 34 participating teams used diverse algorithms for gene selection and location prediction, while being able to correctly localize rare subpopulations of cells. Selection of predictor genes was essential for this task and such genes showed a relatively high expression entropy, high spatial clustering and the presence of prominent developmental genes such as gap and pair-ruled genes and tissue defining markers.

systems biology↗

bioRxiv2019

Usefulness of a familiarity signal during recognition depends on test format: Neurocognitive evidence for a core assumption of the CLS framework

Familiarity-based discrimination between studied target items and similar foils in yes/no recognition memory tests is relatively poor. According to the complementary learning systems (CLS) framework this is due do a relatively small difference in familiarity strength between these two item classes. The model, however, also predicts that when targets and corresponding similar foils are presented next to each other in a forced-choice corresponding (FCC) test format, familiarity values for targets and foils can be directly compared because in each trial, targets are reliably more familiar than their corresponding foils. In contrast, when forced-choice displays contain non-corresponding foils (FCNC) which are similar to other studied items (but not the target), familiarity should not be diagnostic because familiarity values are not directly comparable (as in yes/no-tasks). We compared ERP old/new effects (ERPs of targets vs. foils) when participants were tested with FCC vs. FCNC displays after having intentionally encoded pictures of objects. As predicted, the mid-frontal old/new effect which is associated with familiarity was significantly larger in FCC compared to FCNC displays. Moreover, the target-foil amplitude difference predicted the accuracy of the recognition judgment in a given trial. This is one of the very few studies which support the assumption of the CLS framework that the test format can influence the diagnosticity of familiarity. Moreover, it implies that the mid-frontal old/new effect does not reflect the mean difference in the familiarity signal itself between studied and non-studied items but reflects the task-adequate assessment of the familiarity signal.

neuroscience↗

bioRxiv2019

Feature overlap modulates rapid semantic but not lexical integration of novel associations by means of fast mapping

There is evidence that rapid integration of novel associations into cortical networks is possible if associations are acquired through a learning procedure called fast mapping (FM). FM requires precise visual discrimination of sometimes highly similar pictures of a previously unknown and a known item, and linking an unfamiliar label to the unknown item. In order to shed light on the mechanisms underlying learning through FM, we manipulated feature overlap between the two items as potential modulating factor. In Experiment 1, we found that labels of the unknown items generally evoked instantaneous lexical competition when encoded through FM, indicating rapid integration into lexical networks. In Experiment 2, we observed semantic priming immediately after FM encoding but only if the items shared many features. This indicates that whereas feature overlap leaves item-level lexical integration unaffected, it might mediate semantic integration of arbitrary picture-label associations, which could explain contradictory findings in the literature.\n\nHighlightsO_LIWe examined cortical integration of associations using implicit memory measures.\nC_LIO_LIFast mapping enables immediate integration of associations into cortical networks.\nC_LIO_LISemantic integration requires the discrimination between items sharing many features.\nC_LIO_LIItem-level lexical integration is unaffected by feature overlap.\nC_LI

neuroscience↗

bioRxiv2018

Genome wide association study of body weight, body mass index, adiposity, and fasting glucose in 3,173 outbred rats

Objective Obesity is influenced by genetic and environmental factors. Despite success of human genome wide association studies (GWAS), the specific genes that confer obesity remain largely unknown. The objective of this study was to use outbred rats to identify genetic loci underlying obesity and related morphometric and metabolic traits.Methods We measured obesity-relevant traits including body weight, body length, body mass index, fasting glucose, and retroperitoneal, epididymal, and parametrial fat pad weight in 3,173 male and female adult N/NIH heterogeneous stock (HS) rats across three institutions, providing data for the largest rat GWAS to date. Genetic loci were identified using a linear mixed model that accounted for the complex family relationships of the HS and covariate to account for differences among the three phenotyping centers.Results We identified 32 independent loci, several of which contained only a single gene (e.g. Epha5, Nrg1 and Klhl14) or obvious candidate genes (Adcy3, Prlhr). There were strong phenotypic and genetic correlations among obesity-related traits, and extensive pleiotropy at individual loci.Conclusions These studies demonstrate utility of HS rats for investigating the genetics of obesity related traits across institutions and identify several candidate genes for future functional testing.Competing Interest StatementThe authors have declared no competing interest.View Full Text

genetics↗

bioRxiv2018

Predicting natural language descriptions of smells

There has been recent progress in predicting whether common verbal descriptors such as \"fishy\", \"floral\" or \"fruity\" apply to the smell of odorous molecules. However, the number of descriptors for which such a prediction is possible to date is very small compared to the large number of descriptors that have been suggested for the profiling of smells. We show here that the use of natural language semantic representations on a small set of general olfactory perceptual descriptors allows for the accurate inference of perceptual ratings for mono-molecular odorants over a large and potentially arbitrary set of descriptors. This is a noteworthy approach given that the prevailing view is that humans capacity to identify or characterize odors by name is poor [1, 2, 3, 4, 5]. Our methods, when combined with a molecule-to-ratings model using chemoinformatic features, also allow for the zero-shot learning inference [6, 7] of perceptual ratings for arbitrary molecules. We successfully applied our semantics-based approach to predict perceptual ratings with an accuracy higher than 0.5 for up to 70 olfactory perceptual descriptors in a well-known dataset, a ten-fold increase in the number of descriptors from previous attempts. Moreover we accurately predict paradigm odors of four common families of molecules with an AUC of up to 0.75. Our approach solves the need for the consuming task of handcrafting domain specific sets of descriptors in olfaction and collecting ratings for large numbers of descriptors and odorants [8, 9, 10, 11] while establishing that the semantic distance between descriptors defines the equivalent of an odorwheel.

neuroscience↗

bioRxiv2017

Origins of fractional control in regulated cell death

Individual cells in clonal populations often respond differently to environmental changes; for binary phenotypes, such as cell death, this can be measured as a fractional response. These types of responses have been attributed to cell-intrinsic stochastic processes and variable abundances of biochemical constituents, such as proteins, but the influence of organelles has yet to be determined. We use the response to TNF-related apoptosis inducing ligand (TRAIL) and a new statistical framework for determining parameter influence on cell-to-cell variability through the inference of variance explained, DEPICTIVE, to demonstrate that variable mitochondria abundance correlates with cell survival and determines the fractional cell death response. By quantitative data analysis and modeling we attribute this effect to variable effective concentrations at the mitochondria surface of the pro-apoptotic protein Bax. Further, we demonstrate that inhibitors of antiapoptotic Bcl-2 family proteins, used in cancer treatment, may increase the diversity of cellular responses, enhancing resistance to treatment.

systems biology↗

bioRxiv2016

Reverse-engineering human olfactory perception from chemical features of odor molecules

Despite 25 years of progress in understanding the molecular mechanisms of olfaction, it is still not possible to predict whether a given molecule will have a perceived odor, or what olfactory percept it will produce. To address this stimulus-percept problem for olfaction, we organized the crowd-sourced DREAM Olfaction Prediction Challenge. Working from a large olfactory psychophysical dataset, teams developed machine learning algorithms to predict sensory attributes of molecules based on their chemoinformatic features. The resulting models predicted odor intensity and pleasantness with high accuracy, and also successfully predicted eight semantic descriptors (\"garlic\", \"fish\", \"sweet\", \"fruit\", \"burnt\", \"spices\", \"flower\", \"sour\"). Regularized linear models performed nearly as well as random-forest-based approaches, with a predictive accuracy that closely approaches a key theoretical limit. The models presented here make it possible to predict the perceptual qualities of virtually any molecule with an impressive degree of accuracy to reverse-engineer the smell of a molecule.\n\nOne Sentence SummaryResults of a crowdsourcing competition show that it is possible to accurately predict and reverse-engineer the smell of a molecule.

neuroscience↗