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• Dispersal is a key process driving local-scale community assembly and global-scale biogeography of plant symbiotic arbuscular mycorrhizal (AM) fungal communities. A trait-based approach could improve predictions regarding how AM fungal aerial dispersal varies by species. • We conducted month-long collections of aerial AM fungi for 12 consecutive months in an urban mesic environment at heights of 20 m. We measured morphological functional traits of collected spores and assessed aerial AM fungal community structure both morphologically and with high-throughput sequencing. • Large numbers of AM fungal spores were present in the air over the course of 1 yr, and these spores exhibited traits that facilitate aerial dispersal. Measured aerial spores were smaller than average for Glomeromycotinan fungi. Trait-based predictions indicate that nearly one third of described species from diverse genera demonstrate the potential for aerial dispersal. Diversity of aerial AM fungi was relatively high (20 spore species and 17 virtual taxa), and both spore abundance and community structure shifted temporally. • The prevalence of aerial dispersal in AM fungi is perhaps greater than previously indicated, and a hypothesized model of AM fungal aerial dispersal mechanisms is presented. Anthropogenic soil impacts may liberate AM fungal propagules initiating the dispersal of ruderal species.

Cognition and behavior emerge from brain network interactions, such that investigating causal interactions should be central to the study of brain function. Approaches that characterize statistical associations among neural time series—functional connectivity (FC) methods—are likely a good starting point for estimating brain network interactions. Yet only a subset of FC methods (‘effective connectivity’) is explicitly designed to infer causal interactions from statistical associations. Here we incorporate best practices from diverse areas of FC research to illustrate how FC methods can be refined to improve inferences about neural mechanisms, with properties of causal neural interactions as a common ontology to facilitate cumulative progress across FC approaches. We further demonstrate how the most common FC measures (correlation and coherence) reduce the set of likely causal models, facilitating causal inferences despite major limitations. Alternative FC measures are suggested to immediately start improving causal inferences beyond these common FC measures.

Which reference is appropriate for the scalp ERP and EEG studies? This unsettled problem still inspires unceasing debate. The ideal reference should be the one with zero or constant potential but unfortunately it is well known that no point on the body fulfills this condition. Consequently, more than ten references are used in the present EEG-ERP studies. This diversity seriously undermines the reproducibility and comparability of results across laboratories. A comprehensive review accompanied by a brief communication with rigorous derivations and notable properties (Hu et al. Brain Topogr, 2019. https://doi.org/10.1007/s10548-019-00706-y) is thus necessary to provide application-oriented principled recommendations. In this paper current popular references are classified into two categories: (1) unipolar references that construct a neutral reference, including both online unipolar references and offline re-references. Examples of unipolar references are the reference electrode standardization technique (REST), average reference (AR), and linked-mastoids/ears reference (LM); (2) non-unipolar references that include the bipolar reference and the Laplacian reference. We show that each reference is derived with a different assumption and serves different aims. We also note from (Hu et al. 2019) that there is a general form for the reference problem, the ‘no memory’ property of the unipolar references, and a unified estimator for the potentials at infinity termed as the regularized REST (rREST) which has more advantageous statistical evidence than AR. A thorough discussion of the advantages and limitations of references is provided with recommendations in the hope to clarify the role of each reference in the ERP and EEG practice.

The Organization for Human Brain Mapping (OHBM) has been active in advocating for the instantiation of best practices in neuroimaging data acquisition, analysis, reporting and sharing of both data and analysis code to deal with issues in science related to reproducibility and replicability. Here we summarize recommendations for such practices in magnetoencephalographic (MEG) and electroencephalographic (EEG) research, recently developed by the OHBM neuroimaging community known by the abbreviated name of COBIDAS MEEG. We discuss the rationale for the guidelines and their general content, which encompass many topics under active discussion in the field. We highlight future opportunities and challenges to maximizing the sharing and exploitation of MEG and EEG data, and we also discuss how this ‘living’ set of guidelines will evolve to continually address new developments in neurophysiological assessment methods and multimodal integration of neurophysiological data with other data types.The Organization for Human Brain Mapping presents its best practices report for reproducible EEG and MEG research, highlighting issues and main recommendations in this Perspective.

•Malnutrition is a burden that occurs worldwide•Malnutrition affects the development of children that may last a lifetime•Majority of studies were done on indirect measures of brain development•Affordable neuroimaging is key in combatting malnutrition in developing countries Approximately one in five children worldwide suffers from childhood malnutrition and its complications, including increased susceptibility to inflammation and infectious diseases. Due to improved early interventions, most of these children now survive early malnutrition, even in low-resource settings (LRS). However, many continue to exhibit neurodevelopmental deficits, including low IQ, poor school performance, and behavioral problems over their lifetimes. Most studies have relied on neuropsychological tests, school performance, and mental health and behavioral measures. Few studies, in contrast, have assessed brain structure and function, and to date, these have mainly relied on low-cost techniques, including electroencephalography (EEG) and evoked potentials (ERP). The use of more advanced methods of neuroimaging, including magnetic resonance imaging (MRI) and functional near-infrared spectroscopy (fNIRS), has been limited by cost factors and lack of availability of these technologies in developing countries, where malnutrition is nearly ubiquitous. This report summarizes the current state of knowledge and evidence gaps regarding childhood malnutrition and the study of its impact on neurodevelopment. It may help to inform the development of new strategies to improve the identification, classification, and treatment of neurodevelopmental disabilities in underserved populations at the highest risk for childhood malnutrition.

Obtaining “hard” and “crack-resistant” glasses have always been of great important in glass science and glass technology. However, in most commercial glasses both properties are not compatible. In this work, colorless and transparent x Al 2 O 3 –(100– x )SiO 2 glasses (30 ≤  x  ≤ 60) were fabricated by the aerodynamic levitation technique. The elastic moduli and Vickers hardness monotonically increased with an increase in the atomic packing density as the Al 2 O 3 content increased. Although a higher atomic packing density generally enhances crack formation in conventional oxide glasses, the indentation cracking resistance increased by approximately seven times with an increase in atomic packing density in binary Al 2 O 3 –SiO 2 glasses. In particular, the composition of 60Al 2 O 3 •40SiO 2 glass, which is identical to that of mullite, has extraordinary high cracking resistance with high elastic moduli and Vickers hardness. The results indicate that there exist aluminosilicate compositions that can produce hard and damage-tolerant glasses.

A lot of methane is emitted from oxygenated seawater, where its production should be inhibited. Seawater incubations and organic matter characterizations reveal that bacteria aerobically produce methane from phosphonates in organic matter. Biogenic methane is widely thought to be a product of archaeal methanogenesis, an anaerobic process that is inhibited or outcompeted by the presence of oxygen and sulfate 1 , 2 , 3 . Yet a large fraction of marine methane delivered to the atmosphere is produced in high-sulfate, fully oxygenated surface waters that have methane concentrations above atmospheric equilibrium values, an unexplained phenomenon referred to as the marine methane paradox 4 , 5 . Here we use nuclear magnetic resonance spectroscopy to show that polysaccharide esters of three phosphonic acids are important constituents of dissolved organic matter in seawater from the North Pacific. In seawater and pure culture incubations, bacterial degradation of these dissolved organic matter phosphonates in the presence of oxygen releases methane, ethylene and propylene gas. Moreover, we found that in mutants of a methane-producing marine bacterium, Pseudomonas stutzeri , disrupted in the C–P lyase phosphonate degradation pathway, methanogenesis was also disabled, indicating that the C–P lyase pathway can catalyse methane production from marine dissolved organic matter. Finally, the carbon stable isotope ratio of methane emitted during our incubations agrees well with anomalous isotopic characteristics of seawater methane. We estimate that daily cycling of only about 0.25% of the organic matter phosphonate inventory would support the entire atmospheric methane flux at our study site. We conclude that aerobic bacterial degradation of phosphonate esters in dissolved organic matter may explain the marine methane paradox.

This is the final paper in a Comments and Controversies series dedicated to “The identification of interacting networks in the brain using fMRI: Model selection, causality and deconvolution”. We argue that discovering effective connectivity depends critically on state-space models with biophysically informed observation and state equations. These models have to be endowed with priors on unknown parameters and afford checks for model Identifiability. We consider the similarities and differences among Dynamic Causal Modeling, Granger Causal Modeling and other approaches. We establish links between past and current statistical causal modeling, in terms of Bayesian dependency graphs and Wiener–Akaike–Granger–Schweder influence measures. We show that some of the challenges faced in this field have promising solutions and speculate on future developments.

Computer‐assisted online interaction (CAOI) has become predominant in daily life and is increasingly supplanting offline verbal interaction (FVI). Previous research has shown that face‐to‐face verbal interaction (VI) exhibits significant differences in interpersonal neural synchronization (INS) compared to computer‐assisted online VI. However, the differences between various forms of FVI and CAOI remain unclear. In this work, we designed different forms of naturalistic VI tasks between dual persons and adopted electroencephalography (EEG) hyperscanning to simultaneously record neural activities from both participants. The experiment included three conditions: online versus offline, with versus without feedback, with versus without visual information or eye contact. Thirty‐one pairs of labmates with ordinary levels of intimacy were recruited as subjects. To analyze the impacts of these VI forms on INS, we calculated intersubject correlation at both scalp and cortex levels and constructed brain‐to‐brain networks based on intersubject functional connectivity using the phase lag index at the scalp level and the phase locking value at the cortex level. We found that interactions with feedback exhibit higher synchronization than interactions without feedback. VIs with visual information or eye contact are more effective in eliciting stronger INS. Additionally, compared to FVI, CAOI exhibits weakened neural synchronization. Intriguingly, online text‐based interaction also results in high neural coupling. Our study reveals significant differences in various CAOIs and FVIs concerning typical factors, providing crucial insights into the mechanisms of INS during online interactions. This study systematically investigates neural synchronization in various forms of offline and online verbal interactions. It demonstrates that feedback and visual information similarly affect neural synchronization in online settings and reveals varying degrees of weakened neural synchronization in different online interactions compared to offline counterparts.

We identified the main changes in serum metabolites associated with severe (n = 46) and mild (n = 19) COVID-19 patients by gas chromatography coupled to mass spectrometry. The modified metabolic profiles were associated to an altered amino acid catabolism in hypoxic conditions. Noteworthy, three α-hydroxyl acids of amino acid origin increased with disease severity and correlated with altered oxygen saturation levels and clinical markers of lung damage. We hypothesize that the enzymatic conversion of α-keto-acids to α- hydroxyl-acids helps to maintain NAD recycling in patients with altered oxygen levels, highlighting the potential relevance of amino acid supplementation during SARS-CoV-2 infection.