Connections between the gut and brain monitor the intestinal tissue and its microbial and dietary content
, regulating both physiological intestinal functions such as nutrient absorption and motility
..., and brain-wired feeding behaviour
. It is therefore plausible that circuits exist to detect gut microorganisms and relay this information to areas of the central nervous system that, in turn, regulate gut physiology
. Here we characterize the influence of the microbiota on enteric-associated neurons by combining gnotobiotic mouse models with transcriptomics, circuit-tracing methods and functional manipulations. We find that the gut microbiome modulates gut-extrinsic sympathetic neurons: microbiota depletion leads to increased expression of the neuronal transcription factor cFos, and colonization of germ-free mice with bacteria that produce short-chain fatty acids suppresses cFos expression in the gut sympathetic ganglia. Chemogenetic manipulations, translational profiling and anterograde tracing identify a subset of distal intestine-projecting vagal neurons that are positioned to have an afferent role in microbiota-mediated modulation of gut sympathetic neurons. Retrograde polysynaptic neuronal tracing from the intestinal wall identifies brainstem sensory nuclei that are activated during microbial depletion, as well as efferent sympathetic premotor glutamatergic neurons that regulate gastrointestinal transit. These results reveal microbiota-dependent control of gut-extrinsic sympathetic activation through a gut-brain circuit.
Despite evidence linking the human microbiome to health and disease, how the microbiota affects human physiology remains largely unknown. Microbiota-encoded metabolites are expected to play an ...integral role in human health. Therefore, assigning function to these metabolites is critical to understanding these complex interactions and developing microbiota-inspired therapies. Here, we use large-scale functional screening of molecules produced by individual members of a simplified human microbiota to identify bacterial metabolites that agonize G-protein-coupled receptors (GPCRs). Multiple metabolites, including phenylpropanoic acid, cadaverine, 9-10-methylenehexadecanoic acid, and 12-methyltetradecanoic acid, were found to interact with GPCRs associated with diverse functions within the nervous and immune systems, among others. Collectively, these metabolite-receptor pairs indicate that diverse aspects of human health are potentially modulated by structurally simple metabolites arising from primary bacterial metabolism.
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•Metabolite library from human microbiota screened for direct agonism of 241 GPCRs•Taxa-specific primary metabolites agonize individual GPCRs or broad GPCR families•Bacteria agonize receptors linked to metabolism, neurotransmission, and immunity•Simple bacterial metabolites may play a role in modulating host pathways
Colosimo et al. use functional screening of small molecules produced by individual members of a simplified human microbiota to identify bacterial metabolites that agonize G protein-coupled receptors (GPCRs). These results indicate that diverse aspects of human health are potentially modulated by structurally simple metabolites arising from primary bacterial metabolism.
Linear sweep stripping voltammetry (LSSV) is demonstrated as a sensitive, rapid, and cost-efficient analytical technique for the quantification of silver nanoparticle (AgNP) dissolution rates in ...simulated sweat. LSSV does not require the extensive sample preparation (e.g., ultrafiltration or centrifugation) needed by more commonly employed techniques, such as atomic spectroscopy. The limit of detection (LOD) of Ag(I)(aq) was 14 ± 6 μg L–1, and measured dissolution rate constants, k dissolution, varied from 0.0168–0.1524 h–1, depending on solution conditions. These values are comparable and agree well with those determined by others in the literature using atomic spectroscopy. Importantly, LSSV had the necessary sensitivity to distinguish the effects of SSW solution conditions on AgNP dissolution rates. Specifically, enhanced dissolution rates were observed with decreased pH and with increased NaCl concentration. The colloidal stability of AgNPs in SSW solutions was also characterized using dynamic light scattering (DLS), ζ potential, and quantitative UV–vis spectroscopy measurements. An increase in AgNP aggregation rate was observed with increased NaCl concentration in SSW, suggesting that the enhancement in AgNP dissolution is driven by the large Cl/Ag ratio, even as the AgNPs undergo significant aggregation.
Studies of the relationship between the gastrointestinal microbiota and outcomes in allogeneic hematopoietic stem cell transplantation (allo-HCT) have thus far largely focused on early complications, ...predominantly infection and acute graft-versus-host disease (GVHD). We examined the potential relationship of the microbiome with chronic GVHD (cGVHD) by analyzing stool and plasma samples collected late after allo-HCT using a case-control study design. We found lower circulating concentrations of the microbe-derived short-chain fatty acids (SCFAs) propionate and butyrate in day 100 plasma samples from patients who developed cGVHD, compared with those who remained free of this complication, in the initial case-control cohort of transplant patients and in a further cross-sectional cohort from an independent transplant center. An additional cross-sectional patient cohort from a third transplant center was analyzed; however, serum (rather than plasma) was available, and the differences in SCFAs observed in the plasma samples were not recapitulated. In sum, our findings from the primary case-control cohort and 1 of 2 cross-sectional cohorts explored suggest that the gastrointestinal microbiome may exert immunomodulatory effects in allo-HCT patients at least in part due to control of systemic concentrations of microbe-derived SCFAs.
•The microbe-derived SCFAs butyrate and propionate in the systemic circulation are associated with protection from cGVHD.•cGVHD is associated with gastrointestinal dysbiosis late after HCT.
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Gut-brain connections monitor the intestinal tissue and its microbial and dietary content
1
, regulating both intestinal physiological functions such as nutrient absorption and motility
2
,
3
, and ...brain–wired feeding behaviour
2
. It is therefore plausible that circuits exist to detect gut microbes and relay this information to central nervous system (CNS) areas that, in turn, regulate gut physiology
4
. We characterized the influence of the microbiota on enteric–associated neurons (EAN) by combining gnotobiotic mouse models with transcriptomics, circuit–tracing methods, and functional manipulations. We found that the gut microbiome modulates gut–extrinsic sympathetic neurons; while microbiota depletion led to increased cFos expression, colonization of germ-free mice with short-chain fatty acid–producing bacteria suppressed cFos expression in the gut sympathetic ganglia. Chemogenetic manipulations, translational profiling, and anterograde tracing identified a subset of distal intestine-projecting vagal neurons positioned to play an afferent role in microbiota–mediated modulation of gut sympathetic neurons. Retrograde polysynaptic neuronal tracing from the intestinal wall identified brainstem sensory nuclei activated during microbial depletion, as well as efferent sympathetic premotor glutamatergic neurons that regulate gastrointestinal transit. These results reveal microbiota–dependent control of gut extrinsic sympathetic activation through a gut-brain circuit.
Graph neural network (GNN) is a variant of deep neural networks (DNNs) operating on graphs. However, GNNs are more complex compared with DNNs as they simultaneously exhibit attributes of both DNN and ...graph computations. In this work, we propose a ReRAM-based 3-D manycore processing-in-memory architecture called ReMaGN, tailored for on-chip training of GNNs. ReMaGN implements GNN training using reduced-precision representation to make the computation faster and reduce the load on the communication backbone. However, reduced precision can potentially compromise the accuracy of training. Hence, we undertake a study of performance and accuracy tradeoffs in such architectures. We demonstrate that ReMaGN outperforms conventional GPUs by up to <inline-formula> <tex-math notation="LaTeX">9.5\times </tex-math></inline-formula> (on average <inline-formula> <tex-math notation="LaTeX">7.1\times </tex-math></inline-formula>) in terms of execution time, while being up to <inline-formula> <tex-math notation="LaTeX">42\times </tex-math></inline-formula> (on average <inline-formula> <tex-math notation="LaTeX">33.5\times </tex-math></inline-formula>) more energy efficient without sacrificing accuracy.
ReRAM-based manycore architectures enable acceleration of Graph Neural Network (GNN) inference and training. GNNs exhibit characteristics of both DNNs and graph analytics. Hence, GNN ...training/inferencing on ReRAM-based manycore architectures gives rise to both computation and on-chip communication challenges. In this work, we leverage model pruning and efficient graph storage to reduce the computation and communication bottlenecks associated with GNN training on ReRAM-based manycore accelerators. However, traditional pruning techniques are either targeted for inferencing only, or they are not crossbar-aware. In this work, we propose a GNN pruning technique called DietGNN. DietGNN is a crossbar-aware pruning technique that achieves high accuracy training and enables energy, area, and storage efficient computing on ReRAMbased manycore platforms. The DietGNN pruned model can be trained from scratch without any noticeable accuracy loss. Our experimental results show that when mapped on to a ReRAMbased manycore architecture, DietGNN can reduce the number of crossbars by over 90% and accelerate GNN training by 2.7× compared to its unpruned counterpart. In addition, DietGNN reduces energy consumption by more than 3.5× compared to the unpruned counterpart.
•Generative Adversarial Network for inverting Moho architecture from observed gravity anomalies.•Spherical prism based forward gravity modelling for high accuracy.•Near realistic Moho topography ...generation using FFT filtering technique for training the network.•Fast and accurate estimation of high-resolution Moho without prior knowledge of inversion algorithms.
Accurate estimation of Moho topography plays a crucial role in understanding Earth’s structure, geodynamic processes, and resource exploration. This study presents a novel approach that utilizes conditional Generative Adversarial Networks (cGAN) to reveal Moho topography based on observed gravity anomalies. Synthetic training datasets of Moho topography were generated using the FFT filtering method due to the scarcity of true datasets. Spherical prism-based forward gravity modeling was employed to evaluate the resulting gravity anomalies. We compared the performance of our developed deep learning algorithm cGAN (conditional Generative Adversarial Networks) with a traditional inversion technique using various synthetic datasets, and a real case study in southern peninsular India, a geologically diverse region comprising ancient continental tectonic blocks. Bott’s inversion scheme was employed as a verification method for the Moho surface estimation using the presented deep learning model. Using spherical prism-based forward gravity modeling, observed gravity anomalies were corrected for multiple factors such as topography, bathymetry, sediments, crustal heterogeneities, and mantle heterogeneities. By removing these effects, we isolated the gravity contribution solely related to pure Moho undulation. The mean Moho depth and density contrast between the crust and mantle were derived from seismic constraints for improving estimation accuracy. The findings demonstrate the potential of the cGAN and spherical prism-based gravity modeling approach in accurately estimating the Moho topography, offering insights into Earth’s subsurface structures and enhancing our understanding of geodynamic processes and resource exploration efforts.
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