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Este trabajo analiza el acto de dibujar como una experiencia corporal situada de percepción, comprensión y comunicación del espacio vivido. Es sabido que las experiencias son la base para renovar el examen, la interpretación y la teoría arquitectónica. Numerosos estudios han señalado que dibujar juega un papel central en la naturaleza de la cognición y conceptualización arquitectónica. Así, los dibujos son herramientas no solo para proyectar, sino también para generar nuevos conocimientos. Se sostiene aquí que dibujar, como un acto de consciencia corporal, encarna lo percibido por el dibujante y se presenta como relato de experiencia. Las conclusiones sugieren que dibujar in situ favorece una serie de encuentros y, como investigación del espacio vivido, es una forma peculiar de cognición corporal que se externaliza para comunicar una experiencia singular.

Abstract Background In recent years, there has been a rapid increase in the number of microbial genomes reconstructed through shotgun sequencing, and obtained by newly developed approaches including metagenomic binning and single-cell sequencing. However, our ability to functionally characterize these genomes by experimental assays is orders of magnitude less efficient. Consequently, there is a pressing need for the development of swift and automated strategies for the functional classification of microbial genomes. Results The present work leverages a suite of supervised machine learning algorithms to establish a range of 86 metabolic and other ecological functions, such as methanotrophy and plastic degradation, starting from widely obtainable microbial genome annotations. Tests performed on independent datasets demonstrated robust performance across complete, fragmented, and incomplete genomes above a 70% completeness level for most of the considered functions. Application of the algorithms to the Biogas Microbiome database yielded predictions broadly consistent with current biological knowledge and correctly detecting functionally-related nuances of archaeal genomes. Finally, a case study focused on acetoclastic methanogenesis demonstrated how the developed machine learning models can be refined or expanded with models describing novel functions of interest. Conclusions The resulting tool, MICROPHERRET, incorporates a total of 86 models, one for each tested functional class, and can be applied to high-quality microbial genomes as well as to low-quality genomes derived from metagenomics and single-cell sequencing. MICROPHERRET can thus aid in understanding the functional role of newly generated genomes within their micro-ecological context.

Bioactive peptides are short amino acid chains possessing biological activity and exerting physiological effects relevant to human health. Despite their therapeutic value, their identification remains a major problem, as it mainly relies on time-consuming in vitro tests. While bioinformatic tools for the identification of bioactive peptides are available, they are focused on specific functional classes and have not been systematically tested on realistic settings. To tackle this problem, bioactive peptide sequences and functions were here gathered from a variety of databases to generate a unified collection of bioactive peptides from microbial fermentation. This collection was organized into nine functional classes including some previously studied and some unexplored such as immunomodulatory, opioid and cardiovascular peptides. Upon assessing their sequence properties, four alternative encoding methods were tested in combination with a multitude of machine learning algorithms, from basic classifiers like logistic regression to advanced algorithms like BERT. Tests on a total of 171 models showed that, while some functions are intrinsically easier to detect, no single combination of classifiers and encoders worked universally well for all classes. For this reason, we unified all the best individual models for each class and generated CICERON (Classification of bIoaCtive pEptides fRom micrObial fermeNtation), a classification tool for the functional classification of peptides. State-of-the-art classifiers were found to underperform on our realistic benchmark dataset compared to the models included in CICERON. Altogether, our work provides a tool for real-world peptide classification and can serve as a benchmark for future model development.

Abstract Background Microbial biopolymers such as poly-3-hydroxybutyrate (PHB) are emerging as promising alternatives for sustainable production of biodegradable bioplastics. Their promise is heightened by the potential utilisation of photosynthetic organisms, thus exploiting sunlight and carbon dioxide as source of energy and carbon, respectively. The cyanobacterium Synechocystis sp. B12 is an attractive candidate for its superior ability to accumulate high amounts of PHB as well as for its high-light tolerance, which makes it extremely suitable for large-scale cultivation. Beyond its practical applications, B12 serves as an intriguing model for unravelling the molecular mechanisms behind PHB accumulation. Results Through a multifaceted approach, integrating physiological, genomic and transcriptomic analyses, this work identified genes involved in the upregulation of chlorophyll biosynthesis and phycobilisome degradation as the possible candidates providing Synechocystis sp. B12 an advantage in growth under high-light conditions. Gene expression differences in pentose phosphate pathway and acetyl-CoA metabolism were instead recognised as mainly responsible for the increased Synechocystis sp. B12 PHB production during nitrogen starvation. In both response to strong illumination and PHB accumulation, Synechocystis sp. B12 showed a metabolic modulation similar but more pronounced than the reference strain, yielding in better performances. Conclusions Our findings shed light on the molecular mechanisms of PHB biosynthesis, providing valuable insights for optimising the use of Synechocystis in economically viable and sustainable PHB production. In addition, this work supplies crucial knowledge about the metabolic processes involved in production and accumulation of these molecules, which can be seminal for the application to other microorganisms as well.

Prostate cancer is a highly heterogeneous disease; therefore, estimating patient prognosis accurately is challenging due to the lack of biomarkers with sufficient specificity and sensitivity. One of the current challenges lies in integrating genomic and transcriptomic data with clinico-pathological features and in incorporating their application in everyday clinical practice. Therefore, we aimed to model a risk score and nomogram containing long non-coding RNA (lncRNA) expression and clinico-pathological data to better predict the probability of prostate cancer progression. We performed bioinformatics analyses to identify lncRNAs differentially expressed across various prostate cancer stages and associated with progression-free survival. This information was further integrated into a prognostic risk score and nomogram containing transcriptomic and clinico-pathological features to estimate the risk of disease progression. We used RNA-seq data from 5 datasets from public repositories (total n = 178) comprising different stages of prostate cancer: pre-treatment primary prostate adenocarcinomas, post-treatment tumors and metastatic castration resistant prostate cancer. We found 30 lncRNAs with consistent differential expression in all comparisons made using two R-based packages. Multivariate progression-free survival analysis including the ISUP group as covariate, revealed that 7/30 lncRNAs were significantly associated with time-to-progression. Next, we combined the expression of these 7 lncRNAs into a multi-lncRNA score and dichotomized the patients into low- or high-score. Patients with a high-score showed a 4-fold risk of disease progression (HR = 4.30, 95 %CI = 2.66–6.97, p = 3.1e-9). Furthermore, we modelled a combined risk-score containing information on the multi-lncRNA score and ISUP group. We found that patients with a high-risk score had nearly 8-fold risk of progression (HR = 7.65, 95 %CI = 4.05–14.44, p = 3.4e-10). Finally, we created and validated a nomogram to help uro-oncologists to better predict patient's risk of progression at 3- and 5-years post-diagnosis. In conclusion, the integration of lncRNA expression data and clinico-pathological features of prostate tumors into predictive models might aid in tailored disease risk assessment and treatment for patients with prostate cancer.

Background: Introducing variability in tidal volume, ventilatory frequency, or both is beneficial during mechanical ventilation in acute respiratory distress syndrome (ARDS). We investigated whether applying cycle-by-cycle variability in the positive end-expiratory pressure (PEEP) exerts beneficial effect on lung function in a model of ARDS. Methods: Rabbits with lung injury were randomly allocated to receive mechanical ventilation for 6 h by applying a pressure-controlled mode with constant PEEP of 7 cm H2O (PC group: n=6) or variable PEEP (VEEP) with a coefficient of variation of 21.4%, range 4–10 cm H2O (PC-VEEP group; n=6). Lung oxygenation index (Pao2/FiO2) after 6 h of ventilation (H6) was the primary outcome and respiratory mechanics, lung volume, intrapulmonary shunt, and lung inflammatory markers were secondary outcomes. Results: After lung injury, both groups presented moderate-to-severe ARDS (Pao2/FiO2

BackgroundThe time-varying reproduction number R is a critical variable for situational awareness during infectious disease outbreaks; however, delays between infection and reporting of cases hinder its accurate estimation in real-time. A number of nowcasting methods, leveraging available information on data consolidation delays, have been proposed to mitigate this problem.MethodsIn this work, we retrospectively validate the use of a nowcasting algorithm during 18 months of the COVID-19 pandemic in Italy by quantitatively assessing its performance against standard methods for the estimation of R.ResultsNowcasting significantly reduced the median lag in the estimation of R from 13 to 8 days, while concurrently enhancing accuracy. Furthermore, it allowed the detection of periods of epidemic growth with a lead of between 6 and 23 days.ConclusionsNowcasting augments epidemic awareness, empowering better informed public health responses.

Abstract Background Mutations in the β-glucocerebrosidase (GBA1) gene do cause the lysosomal storage Gaucher disease (GD) and are among the most frequent genetic risk factors for Parkinson’s disease (PD). So far, studies on both neuronopathic GD and PD primarily focused on neuronal manifestations, besides the evaluation of microglial and astrocyte implication. White matter alterations were described in the central nervous system of paediatric type 1 GD patients and were suggested to sustain or even play a role in the PD process, although the contribution of oligodendrocytes has been so far scarcely investigated. Methods We exploited a system to study the induction of central myelination in vitro, consisting of Oli-neu cells treated with dibutyryl-cAMP, in order to evaluate the expression levels and function of β-glucocerebrosidase during oligodendrocyte differentiation. Conduritol-B-epoxide, a β-glucocerebrosidase irreversible inhibitor was used to dissect the impact of β-glucocerebrosidase inactivation in the process of myelination, lysosomal degradation and α-synuclein accumulation in vitro. Moreover, to study the role of β-glucocerebrosidase in the white matter in vivo, we developed a novel mouse transgenic line in which β-glucocerebrosidase function is abolished in myelinating glia, by crossing the Cnp1-cre mouse line with a line bearing loxP sequences flanking Gba1 exons 9–11, encoding for β-glucocerebrosidase catalytic domain. Immunofluorescence, western blot and lipidomic analyses were performed in brain samples from wild-type and knockout animals in order to assess the impact of genetic inactivation of β-glucocerebrosidase on myelination and on the onset of early neurodegenerative hallmarks, together with differentiation analysis in primary oligodendrocyte cultures. Results Here we show that β-glucocerebrosidase inactivation in oligodendrocytes induces lysosomal dysfunction and inhibits myelination in vitro. Moreover, oligodendrocyte-specific β-glucocerebrosidase loss-of-function was sufficient to induce in vivo demyelination and early neurodegenerative hallmarks, including axonal degeneration, α-synuclein accumulation and astrogliosis, together with brain lipid dyshomeostasis and functional impairment. Conclusions Our study sheds light on the contribution of oligodendrocytes in GBA1-related diseases and supports the need for better characterizing oligodendrocytes as actors playing a role in neurodegenerative diseases, also pointing at them as potential novel targets to set a brake to disease progression.

Gut microbiota is responsible for essential functions in human health. Several communication axes between gut microbiota and other organs via neural, endocrine, and immune pathways have been described, and perturbation of gut microbiota composition has been implicated in the onset and progression of an emerging number of diseases. Here, we analyzed peripheral nerves, dorsal root ganglia (DRG), and skeletal muscles of neonatal and young adult mice with the following gut microbiota status: a) germ-free (GF), b) gnotobiotic, selectively colonized with 12 specific gut bacterial strains (Oligo-Mouse-Microbiota, OMM12), or c) natural complex gut microbiota (CGM). Stereological and morphometric analyses revealed that the absence of gut microbiota impairs the development of somatic median nerves, resulting in smaller diameter and hypermyelinated axons, as well as in smaller unmyelinated fibers. Accordingly, DRG and sciatic nerve transcriptomic analyses highlighted a panel of differentially expressed developmental and myelination genes. Interestingly, the type III isoform of Neuregulin1 (NRG1), known to be a neuronal signal essential for Schwann cell myelination, was overexpressed in young adult GF mice, with consequent overexpression of the transcription factor Early Growth Response 2 (Egr2), a fundamental gene expressed by Schwann cells at the onset of myelination. Finally, GF status resulted in histologically atrophic skeletal muscles, impaired formation of neuromuscular junctions, and deregulated expression of related genes. In conclusion, we demonstrate for the first time a gut microbiota regulatory impact on proper development of the somatic peripheral nervous system and its functional connection to skeletal muscles, thus suggesting the existence of a novel ‘Gut Microbiota-Peripheral Nervous System-axis.’

Background: We aimed to evaluate the impact of two different phytotherapeutic agents on decision making regarding prostate biopsy for patients with higher-than-normal prostate-specific antigen (PSA) levels. Methods: From June 2022 to May 2023, all patients attending two urological institutions due to higher-than-normal PSA levels were randomized to receive either oral capsules of Curcuma Longa, Boswellia, Pinus pinaster and Urtica dioica (Group A) or Serenoa Repens 320 mg (Group B) for 3 months. At the follow-up visit after 3 months, all patients underwent PSA tests and multiparametric magnetic resonance imaging (mpMRI). Results: In the per-protocol analysis, data from 66 patients in Group A and 76 in Group B were analyzed. Fifty patients in Group A (75.7%) showed a significant reduction in total PSA compared to forty-nine in Group B (64.4%) (p < 0.001). Twenty-eight patients had PI-RADS III or higher in mpMRI: twelve in Group A and fourteen in Group B. Twenty-three patients (34.8%) in Group A and fifty-nine (77.6%) in Group B (p < 0.001) underwent prostate biopsy based on the mpMRI findings and PSA values. Three patients in Group A showed a significant reduction in total PSA values while having positive mpMRI findings (6%) compared with nine in Group B (19.5%) (p < 0.001). On the contrary, 7 patients in Group A did not show significant reduction in total PSA values and had negative mpMRI findings (43%) compared to 22 in Group B (81.4%) (p < 0.001). Conclusions: In conclusion, a three-month course of a combination of Curcuma Longa, Boswellia, Pinus pinaster and Urtica dioica seems to be an interesting tool to avoid unnecessary prostate biopsies among men with higher-than-normal PSA levels.

Galectins have the potential to interact with transmembrane glycoproteins to modulate their functions. Since galectin-1 interacts with PDGF-Rβ, we analyzed the effect of galectin-1 on PDGF-BB-mediated AKT signaling in primary human retinal pigment epithelial (RPE) cells and galectin-1-deficient immortalized human RPE cells (LGALS1−/−/ARPE-19) following incubation with PDGF-BB and galectin-1. Expression and localization of galectin-1, PDGF-Rβ and pAKT were investigated using western blot analysis and immunohistochemical staining. Cell proliferation of RPE cells was analyzed using BrdU ELISA. Following treatment of human RPE cells with human recombinant (hr)-galectin-1 and PDGF-BB, an intense clustering of PDGF-Rβ and colocalization with galectin-1 were detected. By Western blot analysis and immunocytochemistry of human RPE cells, an enhanced PDGF-BB-mediated expression of pAKT was observed, which was substantially reduced by additional incubation with hr-galectin-1. Vice versa, in LGALS1−/−/ARPE-19 cells, the PDGF-BB-induced pAKT signal was enhanced compared to wild-type cells. Furthermore, a decreased expression of PDGF-Rβ in human RPE cells was observed after treatment with PDGF-BB and hr-galectin-1, while in untreated LGALS1−/−/ARPE-19 cells, its constitutive expression was increased. In addition, after treatment of RPE cells with hr-galectin-1, the PDGF-BB-induced proliferation was markedly reduced. In summary, galectin-1 has the distinct potential to reduce PDGF-mediated pAKT signaling and proliferation in human RPE cells—an effect that is most likely facilitated via a decreased expression of PDGF-Rβ.

We analyzed 131 human brains (44 neurotypical, 19 with Tourette syndrome, 9 with schizophrenia, and 59 with autism) for somatic mutations after whole genome sequencing to a depth of more than 200×. Typically, brains had 20 to 60 detectable single-nucleotide mutations, but ~6% of brains harbored hundreds of somatic mutations. Hypermutability was associated with age and damaging mutations in genes implicated in cancers and, in some brains, reflected in vivo clonal expansions. Somatic duplications, likely arising during development, were found in ~5% of normal and diseased brains, reflecting background mutagenesis. Brains with autism were associated with mutations creating putative transcription factor binding motifs in enhancer-like regions in the developing brain. The top-ranked affected motifs corresponded to MEIS (myeloid ectopic viral integration site) transcription factors, suggesting a potential link between their involvement in gene regulation and autism.

Abstract Background The proliferation of health misinformation on social media is a growing public health concern. Online communities for mental health (OCMHs) are also considered an outlet for exposure to misinformation. This study explored the impact of the self-reported volume of exposure to mental health misinformation in misinformation agreement and the moderating effects of depression literacy and type of OCMHs participation (expert vs. peer-led). Methods Participants (n = 403) were recruited in Italian-speaking OCMHs on Facebook. We conducted regression analyses using PROCESS macro (moderated moderation, Model 3). Measures included: the Depression Literacy Questionnaire (Griffiths et al., 2004), the self-reported misinformation exposure in the OCMHs (3 items), and misinformation agreement with the exposure items (3 items). Whether participants were members of expert or peer-led OCMHs was also investigated. Results The final model explained the 12% variance in the agreement. There was a positive and significant relationship between misinformation exposure and misinformation agreement (β = 0.3221, p

BackgroundPost-zygotic mutations incurred during DNA replication, DNA repair, and other cellular processes lead to somatic mosaicism. Somatic mosaicism is an established cause of various diseases, including cancers. However, detecting mosaic variants in DNA from non-cancerous somatic tissues poses significant challenges, particularly if the variants only are present in a small fraction of cells.ResultsHere, the Brain Somatic Mosaicism Network conducts a coordinated, multi-institutional study to examine the ability of existing methods to detect simulated somatic single-nucleotide variants (SNVs) in DNA mixing experiments, generate multiple replicates of whole-genome sequencing data from the dorsolateral prefrontal cortex, other brain regions, dura mater, and dural fibroblasts of a single neurotypical individual, devise strategies to discover somatic SNVs, and apply various approaches to validate somatic SNVs. These efforts lead to the identification of 43 bona fide somatic SNVs that range in variant allele fractions from ~ 0.005 to ~ 0.28. Guided by these results, we devise best practices for calling mosaic SNVs from 250× whole-genome sequencing data in the accessible portion of the human genome that achieve 90% specificity and sensitivity. Finally, we demonstrate that analysis of multiple bulk DNA samples from a single individual allows the reconstruction of early developmental cell lineage trees.ConclusionsThis study provides a unified set of best practices to detect somatic SNVs in non-cancerous tissues. The data and methods are freely available to the scientific community and should serve as a guide to assess the contributions of somatic SNVs to neuropsychiatric diseases.

Mixed DNA SAMs labeled with a fluorophore (either AlexaFluor488 or AlexaFluor647) were prepared on a single crystal gold bead electrode using potential-assisted thiol exchange and studied using Förster resonance energy transfer (FRET). A measure of the local environment of the DNA SAM (e.g., crowding) was possible using FRET imaging on these surfaces since electrodes prepared this way have a range of surface densities (ΓDNA). The FRET signal was strongly dependent on ΓDNA and on the ratio of AlexaFluor488 to AlexaFluor647 used to make the DNA SAM, which were consistent with a model of FRET in 2D systems. FRET was shown to provide a direct measure of the local DNA SAM arrangement on each crystallographic region of interest providing a direct assessment of the probe environment and its influence on the rate of hybridization. The kinetics of duplex formation for these DNA SAMs was also studied using FRET imaging over a range of coverages and DNA SAM compositions. Hybridization of the surface-bound DNA increased the average distance between the fluorophore label and the gold electrode surface and decreased the distance between the donor (D) and acceptor (A), both of which result in an increase in FRET intensity. This increase in FRET was modeled using a second order Langmuir adsorption rate equation, reflecting the fact that both D and A labeled DNA are required to become hybridized to observe a FRET signal. The self-consistent analysis of the hybridization rates on low and high coverage regions on the same electrode showed that the low coverage regions achieved full hybridization 5× faster than the higher coverage regions, approaching rates typically found in solution. The relative increase in FRET intensity from each region of interest was controlled by manipulating the donor to acceptor composition of the DNA SAM without changing the rate of hybridization. The FRET response can be optimized by controlling the coverage and the composition of the DNA SAM sensor surface and could be further improved with the use of a FRET pair with a larger (e.g., > 5 nm) Förster radius.

The production of biopeptides from food waste through microbial fermentation faces challenges arising from the diverse proteolytic abilities of microorganisms and substrate variability, impacting both the quality and yield of generated biopeptides. To address these challenges, preliminary in-silico bioinformatics analyses play a crucial role in evaluating suitable substrates and proteases for the fermentation process. However, existing tools lack comprehensive predictive capabilities for relevant proteases, substrate performance assessment, and final biopeptide family characterization. To overcome these limitations, we developed FEEDS (Food wastE biopEptiDe claSsifier), a novel biopeptide prediction and classification tool. FEEDS predicts biopeptide families based on microbial genome protease profiles and substrate composition during proteolysis. The tool also employs a machine learning approach for functional biopeptide classification. Results from testing on 1000 microbial genomes demonstrate the effectiveness of biopeptide classification, particularly in categorizing peptides derived from substrates like Hordeum vulgare and Vitis vinifera seed storage proteins. In addition to biopeptide classification, our study delves into the distinctive protease profiles of bacteria and yeast genomes. Bacterial genomes exhibited 60 to 100 proteases across 40–55 families. Contrastingly, yeast genomes displayed a more evenly distributed pattern with 150 to 160 protease-encoding genes across 60 to 67 families, surpassing bacterial counts. Remarkably, a substantial portion of yeast proteases (∼66 %) was secreted. Moreover, our integration of a machine learning methodology within the FEEDS pipeline proved highly effective, achieving over 80 % accuracy in predicting the function of peptides derived from seed storage proteins. Notably, longer peptide sequences exceeding 20 amino acids consistently displayed a higher probability of correct assignment compared to shorter counterparts.

Although cell lineage information is fundamental to understanding organismal development, very little direct information is available for humans. We performed high-depth (250×) whole-genome sequencing of multiple tissues from three individuals to identify hundreds of somatic single-nucleotide variants (sSNVs). Using these variants as "endogenous barcodes" in single cells, we reconstructed early embryonic cell divisions. Targeted sequencing of clonal sSNVs in different organs (about 25,000×) and in more than 1000 cortical single cells, as well as single-nucleus RNA sequencing and single-nucleus assay for transposase-accessible chromatin sequencing of ~100,000 cortical single cells, demonstrated asymmetric contributions of early progenitors to extraembryonic tissues, distinct germ layers, and organs. Our data suggest onset of gastrulation at an effective progenitor pool of about 170 cells and about 50 to 100 founders for the forebrain. Thus, mosaic mutations provide a permanent record of human embryonic development at very high resolution.

Abstract The legalisation of hemp has led to wide commercial availability of cannabidiol (CBD)-containing products. Here we show that the CBD-hydroxyquinone (HU-331), a readily formed oxidation product and common impurity in CBD isolates, undergoes a previously unknown photo-isomerisation to produce a highly reactive intermediate in solution. Studies supported by calculations indicate that this intermediate rapidly reacts with oxygen to form a multitude of cannabinoid products. The purple colour observed in light-aged CBD-containing solutions is largely due to the anions of these by-products and is not significantly due to the HU-331 anion. Our findings suggest that these uncharacterized cannabinoid derivatives can be present in CBD-containing e-liquids and solutions that have been stored under ambient light conditions, calling for quality control processes that manage HU-331 contamination.

Abstract Background Obesogenic environment is important in driving obesity epidemic. Children spend large amount of their time in schools. School neighborhood environment, as well as its interaction with socioeconomic status (SES) needs to be explored to provide evidence for children obesity prevention policies. Methods Objective anthropometric measurement, a household structured questionnaire, and school geospatial analyses were carried out on 3670 children (aged 9–12 years) of 26 schools in northeast China. Interaction between SES inter-categorical intersectionality group and school neighborhood environment was tested for the effect on children’s body mass index z scores (z-BMI) and waist–hip ratio z scores (z-WHR), following formulation of SES inter-categorical intersectionality group based on household wealth, parental education, and parental occupation. Results SES groups formed by household wealth, parental education and parental occupation was associated with z-BMI and z-WHR for girls. Those from moderate wealth & self-employed (M&S) families had the highest adjusted z-BMI and z-WHR among all SES groups. School neighborhood environment factors interacted with SES groups in association with WHR for girls. Number of school neighborhood supermarkets and residential sites were negatively associated with z-WHR for girls from M&S families (β= -0.45 (95%CI: -0.76, -0.15) for supermarkets; β= -0.01 (95%CI: -0.03, 0.00) for residential sites). Number of school neighborhood convenience stores and public transport stops were positively associated with z-WHR for girls from M&S families (β = 0.02 (95%CI: 0.00, 0.03) for convenience stores; β = 0.23 (95%CI: 0.15, 0.31) for public transport stops). While non-significant association was found for number of vegetable stores. Conclusion Girls from moderate wealth & self-employed families may be the group susceptible to school neighborhood environment. Local policies targeted at improving the school neighborhood environment may be one avenue for reducing socioeconomic disparities in obesity especially for girls.

Numerous studies suggest that by elementary school, children have implicit and explicit gender stereotypes about the toys, activities, roles, and abilities associated with boys vs. girls. Furthermore, these stereotypes have been shown to affect children's goals and behaviors, leading them to pursue activities that are associated with their own gender and avoid those that are not. The majority of previous research examining the development of children's implicit gender stereotypes has used the Implicit Association Test, which measures two associations simultaneously. Thus, it is often unclear which association is driving children's gender stereotypes, which hampers the ability to effectively target harmful associations for bias change. The current research uses the Preschool Auditory Stroop, an adaptation of the Auditory Stroop, to measure distinct implicit gender stereotypes in three to seven-year-old children. Across two studies, the first using human voices and the second using computer-generated voices, children were faster to respond when female-stereotypical words were paired with female voices and male-stereotypical words were paired with male voices. These results indicate that children have implicit gender stereotypes as early as age three. Furthermore, results suggested that the magnitude of these implicit gender stereotypes was comparable across our age range. This study indicates that implicit gender stereotypes are present in children as young as three, and results suggest that this methodology can be used in future research to chart the trajectory of distinct implicit gender stereotypes across development.