Biology subjects

Fanelli, D.

Publications and source records attributed to Fanelli, D..

4 recordsLinked to original sources

bioRxiv2022

BRAin-wide Neuron quantification Tool reveals strong sexual dimorphism in the evolution of fear memory

Fear responses are functionally adaptive behaviors that are strengthened as memories. Indeed, a detailed knowledge of the neural circuitry modulating fear and fear memory could be the turning point for the comprehension of this emotion and its pathological states. A comprehensive understading of the neural circuits mediating memory encoding, consolidation, and retrieval over time presents the fundamental technological challenge of analyzing activity in the entire brain with single-neuron resolution. In this context, we developed the BRAin-wide Neuron quantification Toolkit (BRANT) for mapping whole-brain neuronal activation at micron-scale resolution, combining tissue clearing, high-resolution light-sheet microscopy, and automated 3D image analysis. The robustness and scalability of this method allowed us to quantify the evolution of neuronal activation patterns across multiple phases of memory in mice. This approach highlighted a strong sexual dimorphism in the circuits recruited during memory recall, which had no counterpart in the behaviour. The methodology presented here paves the way for a comprehensive functional characterization of the evolution of fear memory.

neuroscience↗

bioRxiv2020

Deep learning and alignment of spatially-resolved whole transcriptomes of single cells in the mouse brain with Tangram

Charting a biological atlas of an organ, such as the brain, requires us to spatially-resolve whole transcriptomes of single cells, and to relate such cellular features to the histological and anatomical scales. Single-cell and single-nucleus RNA-Seq (sc/snRNA-seq) can map cells comprehensively5,6, but relating those to their histological and anatomical positions in the context of an organs common coordinate framework remains a major challenge and barrier to the construction of a cell atlas7-10. Conversely, Spatial Transcriptomics allows for in-situ measurements11-13 at the histological level, but at lower spatial resolution and with limited sensitivity. Targeted in situ technologies1-3 solve both issues, but are limited in gene throughput which impedes profiling of the entire transcriptome. Finally, as samples are collected for profiling, their registration to anatomical atlases often require human supervision, which is a major obstacle to build pipelines at scale. Here, we demonstrate spatial mapping of cells, histology, and anatomy in the somatomotor area and the visual area of the healthy adult mouse brain. We devise Tangram, a method that aligns snRNA-seq data to various forms of spatial data collected from the same brain region, including MERFISH1, STARmap2, smFISH3, and Spatial Transcriptomics4 (Visium), as well as histological images and public atlases. Tangram can map any type of sc/snRNA-seq data, including multi-modal data such as SHARE-seq data5, which we used to reveal spatial patterns of chromatin accessibility. We equipped Tangram with a deep learning computer vision pipeline, which allows for automatic identification of anatomical annotations on histological images of mouse brain. By doing so, Tangram reconstructs a genome-wide, anatomically-integrated, spatial map of the visual and somatomotor area with [~]30,000 genes at single-cell resolution, revealing spatial gene expression and chromatin accessibility patterning beyond current limitation of in-situ technologies.

genomics↗

bioRxiv2019

What difference do retractions make? An estimate of the epistemic impact of retractions on recent meta-analyses.

Every year, several hundred publications are retracted due to fabrication and falsification of data or plagiarism and other breeches of research integrity and ethics. Despite considerable research on this phenomenon, the extent to which a retraction requires revising previous scientific estimates and beliefs - which we define as the epistemic impact - is unknown. We collected a representative sample of recently retracted studies that had been included in recent meta-analyses, and compared the summary effect size of these meta analyses with and without the refracted studies. On average, the retractions had occurred about six years prior to the publication of the corresponding meta-analyses.\n\nOur results suggest that retractions have varying impacts depending on their causes. In particular, removing from an analysis a study retracted because of issues with data, methods or results, led to a statistically significant reduction of the estimated effect size. Assuming that the results of these retracted studies are completely false, then the meta-analyses that had included them had overestimated the summary effect sizes by, averaging across effect size metrics, 30% (median, 13%). However, retractions due to plagiarism or other issues not related to data, methods or results had no impact on the conclusions of meta-analyses.\n\nSince retractions due to plagiarism or other non-data related issues typically constitute over 75% of total retractions, our results suggest that the epistemic impact of most retractions is likely to be null. However, our results also suggest that retractions due to issues with data, methods or results should be accompanied by a revision of relevant meta-analyses, and by extension a downwards revision of prior scientific beliefs.

scientific communication and education↗

bioRxiv2017

Why Do Scientists Fabricate And Falsify Data? A Matched-Control Analysis Of Papers Containing Problematic Image Duplications

It is commonly hypothesized that scientists are more likely to engage in data falsification and fabrication when they are subject to pressures to publish, when they are not restrained by forms of social control, when they work in countries lacking policies to tackle scientific misconduct, and when they are male. Evidence to test these hypotheses, however, is inconclusive due to the difficulties of obtaining unbiased data.\n\nHere we report a pre-registered test of these four hypotheses, conducted on papers that were identified in a previous study as containing problematic image duplications through a systematic screening of the journal PLoS ONE. Image duplications were classified into three categories based on their complexity, with category 1 being most likely to reflect unintentional error and category 3 being most likely to reflect intentional fabrication. Multiple parameters connected to the hypotheses above were tested with a matched-control paradigm, by collecting two controls for each paper containing duplications.\n\nCategory 1 duplications were mostly not associated with any of the parameters tested, in accordance with the assumption that these duplications were mostly not due to misconduct. Category 2 and 3, however, exhibited numerous statistically significant associations. Results of univariable and multivariable analyses support the hypotheses that academic culture, peer control, cash-based publication incentives and national misconduct policies might affect scientific integrity. Significant correlations between the risk of image duplication and individual publication rates or gender, however, were only observed in secondary and exploratory analyses.\n\nCountry-level parameters generally exhibited effects of larger magnitude than individual-level parameters, because a subset of countries was significantly more likely to produce problematic image duplications. Promoting good research practices in all countries should be a priority for the international research integrity agenda.

scientific communication and education↗