Measurement matters: An individual differences examination of family socioeconomic factors, latent dimensions of children\u27s experiences, and resting state functional brain connectivity in the ABCD sample

The variation in experiences between high and low-socioeconomic status contexts are posited to play a crucial role in shaping the developing brain and may explain differences in child outcomes. Yet, examinations of SES and brain development have largely been limited to distal proxies of these experiences (e.g., income comparisons). The current study sought to disentangle the effects of multiple socioeconomic indices and dimensions of more proximal experiences on resting-state functional connectivity (rsFC) in a sample of 7834 youth (aged 9-10 years) from the Adolescent Brain Cognitive Development (ABCD) study. We applied moderated nonlinear factor analysis (MNLFA) to establish measurement invariance among three latent environmental dimensions of experience (material/economic deprivation, caregiver social support, and psychosocial threat). Results revealed measurement biases as a function of child age, sex, racial group, family income, and parental education, which were statistically adjusted in the final MNLFA scores. Mixed-effects models demonstrated that socioeconomic indices and psychosocial threat differentially predicted variation in frontolimbic networks, and threat statistically moderated the association between income and connectivity between the dorsal and ventral attention networks. Findings illuminate the importance of reducing measurement biases to gain a more socioculturally-valid understanding of the complex and nuanced links between socioeconomic context, children\u27s experiences, and neurodevelopment

Mastering the game of Go without human knowledge

A long-standing goal of artificial intelligence is an algorithm that learns, tabula rasa, superhuman proficiency in challenging domains. Recently, AlphaGo became the first program to defeat a world champion in the game of Go. The tree search in AlphaGo evaluated positions and selected moves using deep neural networks. These neural networks were trained by supervised learning from human expert moves, and by reinforcement learning from self-play. Here we introduce an algorithm based solely on reinforcement learning, without human data, guidance or domain knowledge beyond game rules. AlphaGo becomes its own teacher: a neural network is trained to predict AlphaGo’s own move selections and also the winner of AlphaGo’s games. This neural network improves the strength of the tree search, resulting in higher quality move selection and stronger self-play in the next iteration. Starting tabula rasa, our new program AlphaGo Zero achieved superhuman performance, winning 100–0 against the previously published, champion-defeating AlphaGo

Protein structure prediction using multiple deep neural networks in the 13th Critical Assessment of Protein Structure Prediction (CASP13)

We describe AlphaFold, the protein structure prediction system that was entered by the group A7D in CASP13 Submissions were made by three free-modelling methods which combine the predictions of three neural networks. All three systems were guided by predictions of distances between pairs of residues produced by a neural network. Two systems assembled fragments produced by a generative neural network, one using scores from a network trained to regress GDT_TS. The third system shows that simple gradient descent on a properly constructed potential is able to perform on-par with more expensive traditional search techniques and without requiring domain segmentation. In the CASP13 free-modelling assessors' ranking by summed z-scores, this system scored highest with 68.3 vs 48.2 for the next closest group. (An average GDT_TS of 61.4.) The system produced high-accuracy structures (with GDT_TS scores of 70 or higher) for 11 out of 43 free-modelling domains. Despite not explicitly using template information, the results in the template category were comparable to the best performing template-based methods

Improved protein structure prediction using potentials from deep learning

Protein structure prediction can be used to determine the three-dimensional shape of a protein from its amino acid sequence1. This problem is of fundamental importance as the structure of a protein largely determines its function2; however, protein structures can be difficult to determine experimentally. Considerable progress has recently been made by leveraging genetic information. It is possible to infer which amino acid residues are in contact by analysing covariation in homologous sequences, which aids in the prediction of protein structures3. Here we show that we can train a neural network to make accurate predictions of the distances between pairs of residues, which convey more information about the structure than contact predictions. Using this information, we construct a potential of mean force4 that can accurately describe the shape of a protein. We find that the resulting potential can be optimized by a simple gradient descent algorithm to generate structures without complex sampling procedures. The resulting system, named AlphaFold, achieves high accuracy, even for sequences with fewer homologous sequences. In the recent Critical Assessment of Protein Structure Prediction5 (CASP13)—a blind assessment of the state of the field—AlphaFold created high-accuracy structures (with template modelling (TM) scores6 of 0.7 or higher) for 24 out of 43 free modelling domains, whereas the next best method, which used sampling and contact information, achieved such accuracy for only 14 out of 43 domains. AlphaFold represents a considerable advance in protein-structure prediction. We expect this increased accuracy to enable insights into the function and malfunction of proteins, especially in cases for which no structures for homologous proteins have been experimentally determined7

The quijote simulations

The Quijote simulations are a set of 44,100 full N-body simulations spanning more than 7000 cosmological models in the hyperplane. At a single redshift, the simulations contain more than 8.5 trillion particles over a combined volume of 44,100 each simulation follows the evolution of 2563, 5123, or 10243 particles in a box of 1 h -1 Gpc length. Billions of dark matter halos and cosmic voids have been identified in the simulations, whose runs required more than 35 million core hours. The Quijote simulations have been designed for two main purposes: (1) to quantify the information content on cosmological observables and (2) to provide enough data to train machine-learning algorithms. In this paper, we describe the simulations and show a few of their applications. We also release the petabyte of data generated, comprising hundreds of thousands of simulation snapshots at multiple redshifts; halo and void catalogs; and millions of summary statistics, such as power spectra, bispectra, correlation functions, marked power spectra, and estimated probability density functions

Symptom-related screening programme for early detection of chronic thromboembolic pulmonary hypertension after acute pulmonary embolism: the SYSPPE study

Background Chronic thromboembolic pulmonary hypertension (CTEPH) is the most severe long-term complication of acute pulmonary embolism (PE). We aimed to evaluate the impact of a symptom screening programme to detect CTEPH in PE survivors.Methods This was a multicentre cohort study of patients diagnosed with acute symptomatic PE between January 2017 and December 2018 in 16 centres in Spain. Patients were contacted by phone 2 years after the index PE diagnosis. Those with dyspnoea corresponding to a New York Heart Association (NYHA)/WHO scale≥II, visited the outpatient clinic for echocardiography and further diagnostic tests including right heart catheterisation (RHC). The primary outcome was the new diagnosis of CTEPH confirmed by RHC.Results Out of 1077 patients with acute PE, 646 were included in the symptom screening. At 2 years, 21.8% (n=141) reported dyspnoea NYHA/WHO scale≥II. Before symptom screening protocol, five patients were diagnosed with CTEPH following routine care. In patients with NYHA/WHO scale≥II, after symptom screening protocol, the echocardiographic probability of pulmonary hypertension (PH) was low, intermediate and high in 76.6% (n=95), 21.8% (n=27) and 1.6% (n=2), respectively. After performing additional diagnostic test in the latter 2 groups, 12 additional CTEPH cases were confirmed.Conclusions The implementation of this simple strategy based on symptom evaluation by phone diagnosed more than doubled the number of CTEPH cases. Dedicated follow-up algorithms for PE survivors help diagnosing CTEPH earlier.Thrombosis and Hemostasi

Unraveling static olivine grain growth properties in the Earth's upper mantle

International audienceGrain size in the Earth's upper mantle is a fundamental parameter that has crucial implications on large-scale processes, such as the permeability and the rheology of rocks. However, grain size is constantly evolving with time, where static grain growth implies an increase of the average grain size whereas dynamic recrystallization contributes to its decrease. Static grain growth is most dominant in grain size-sensitive deformation regimes and is classically defined by a grain growth law of the form:rfn - rin = k twith rf and ri, the final and initial grain radii, n the grain size exponent, t the duration, k the grain growth rate. These growth parameters are highly dependent on the value of n, which has considerable implications when extrapolating from laboratory to geological length and time scales. Here, we will show that there is no clear n value that can be extracted from grain growth experiments and that this value must be fixed based on the appropriate theoretical background. We have therefore investigated static grain growth of olivine aggregates where the intergranular medium is dry, wet or contains melt. Grain growth experiments were performed and modeled by considering different growth mechanisms (i.e. diffusion-limited and interface reaction-limited). We have established the dry grain growth law from previously published experiments at 1-atm and high-temperature conditions. Grain growth rates for these samples are limited by Si diffusion at grain boundaries (GB), implying n = 2. On the contrary, experiments on melt- and H2O-bearing aggregates indicate faster growth rates than for dry samples, regardless of the liquid fraction (i.e. >0%). We propose a general grain growth law, which takes into account dry GB as well as wetted grain-grain interfaces, by using the wetting properties of the liquid phase as shown by our high-resolution images. We show that our unified grain growth law considerably deviates from the classical grain growth law, with critical differences at geological time scales. We expect that our law will help unravel physical properties that are dependent on processes happening at the GB scale, such as rheology, diffusion or permeability

Unraveling static olivine grain growth properties in the Earth's upper mantle

International audienceGrain size in the Earth's upper mantle is a fundamental parameter that has crucial implications on large-scale processes, such as the permeability and the rheology of rocks. However, grain size is constantly evolving with time, where static grain growth implies an increase of the average grain size whereas dynamic recrystallization contributes to its decrease. Static grain growth is most dominant in grain size-sensitive deformation regimes and is classically defined by a grain growth law of the form:rfn - rin = k twith rf and ri, the final and initial grain radii, n the grain size exponent, t the duration, k the grain growth rate. These growth parameters are highly dependent on the value of n, which has considerable implications when extrapolating from laboratory to geological length and time scales. Here, we will show that there is no clear n value that can be extracted from grain growth experiments and that this value must be fixed based on the appropriate theoretical background. We have therefore investigated static grain growth of olivine aggregates where the intergranular medium is dry, wet or contains melt. Grain growth experiments were performed and modeled by considering different growth mechanisms (i.e. diffusion-limited and interface reaction-limited). We have established the dry grain growth law from previously published experiments at 1-atm and high-temperature conditions. Grain growth rates for these samples are limited by Si diffusion at grain boundaries (GB), implying n = 2. On the contrary, experiments on melt- and H2O-bearing aggregates indicate faster growth rates than for dry samples, regardless of the liquid fraction (i.e. >0%). We propose a general grain growth law, which takes into account dry GB as well as wetted grain-grain interfaces, by using the wetting properties of the liquid phase as shown by our high-resolution images. We show that our unified grain growth law considerably deviates from the classical grain growth law, with critical differences at geological time scales. We expect that our law will help unravel physical properties that are dependent on processes happening at the GB scale, such as rheology, diffusion or permeability

Unraveling static olivine grain growth properties in the Earth's upper mantle

International audienceGrain size in the Earth's upper mantle is a fundamental parameter that has crucial implications on large-scale processes, such as the permeability and the rheology of rocks. However, grain size is constantly evolving with time, where static grain growth implies an increase of the average grain size whereas dynamic recrystallization contributes to its decrease. Static grain growth is most dominant in grain size-sensitive deformation regimes and is classically defined by a grain growth law of the form:rfn - rin = k twith rf and ri, the final and initial grain radii, n the grain size exponent, t the duration, k the grain growth rate. These growth parameters are highly dependent on the value of n, which has considerable implications when extrapolating from laboratory to geological length and time scales. Here, we will show that there is no clear n value that can be extracted from grain growth experiments and that this value must be fixed based on the appropriate theoretical background. We have therefore investigated static grain growth of olivine aggregates where the intergranular medium is dry, wet or contains melt. Grain growth experiments were performed and modeled by considering different growth mechanisms (i.e. diffusion-limited and interface reaction-limited). We have established the dry grain growth law from previously published experiments at 1-atm and high-temperature conditions. Grain growth rates for these samples are limited by Si diffusion at grain boundaries (GB), implying n = 2. On the contrary, experiments on melt- and H2O-bearing aggregates indicate faster growth rates than for dry samples, regardless of the liquid fraction (i.e. >0%). We propose a general grain growth law, which takes into account dry GB as well as wetted grain-grain interfaces, by using the wetting properties of the liquid phase as shown by our high-resolution images. We show that our unified grain growth law considerably deviates from the classical grain growth law, with critical differences at geological time scales. We expect that our law will help unravel physical properties that are dependent on processes happening at the GB scale, such as rheology, diffusion or permeability

Unraveling static olivine grain growth properties in the Earth's upper mantle

International audienceGrain size in the Earth's upper mantle is a fundamental parameter that has crucial implications on large-scale processes, such as the permeability and the rheology of rocks. However, grain size is constantly evolving with time, where static grain growth implies an increase of the average grain size whereas dynamic recrystallization contributes to its decrease. Static grain growth is most dominant in grain size-sensitive deformation regimes and is classically defined by a grain growth law of the form:rfn - rin = k twith rf and ri, the final and initial grain radii, n the grain size exponent, t the duration, k the grain growth rate. These growth parameters are highly dependent on the value of n, which has considerable implications when extrapolating from laboratory to geological length and time scales. Here, we will show that there is no clear n value that can be extracted from grain growth experiments and that this value must be fixed based on the appropriate theoretical background. We have therefore investigated static grain growth of olivine aggregates where the intergranular medium is dry, wet or contains melt. Grain growth experiments were performed and modeled by considering different growth mechanisms (i.e. diffusion-limited and interface reaction-limited). We have established the dry grain growth law from previously published experiments at 1-atm and high-temperature conditions. Grain growth rates for these samples are limited by Si diffusion at grain boundaries (GB), implying n = 2. On the contrary, experiments on melt- and H2O-bearing aggregates indicate faster growth rates than for dry samples, regardless of the liquid fraction (i.e. >0%). We propose a general grain growth law, which takes into account dry GB as well as wetted grain-grain interfaces, by using the wetting properties of the liquid phase as shown by our high-resolution images. We show that our unified grain growth law considerably deviates from the classical grain growth law, with critical differences at geological time scales. We expect that our law will help unravel physical properties that are dependent on processes happening at the GB scale, such as rheology, diffusion or permeability