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One major challenge in analyzing spatial transcriptomic datasets is to simultaneously incorporate the cell transcriptome similarity and their spatial locations. Here, we introduce SpaceFlow, which generates spatially-consistent low-dimensional embeddings by incorporating both expression similarity and spatial information using spatially regularized deep graph networks. Based on the embedding, we introduce a pseudo-Spatiotemporal Map that integrates the pseudotime concept with spatial locations of the cells to unravel spatiotemporal patterns of cells. By comparing with multiple existing methods on several spatial transcriptomic datasets at both spot and single-cell resolutions, SpaceFlow is shown to produce a robust domain segmentation and identify biologically meaningful spatiotemporal patterns. Applications of SpaceFlow reveal evolving lineage in heart developmental data and tumor-immune interactions in human breast cancer data. Our study provides a flexible deep learning framework to incorporate spatiotemporal information in analyzing spatial transcriptomic data. A critical task in spatial transcriptomics analysis is to understand inherently spatial relationships among cells. Here, the authors present a deep learning framework to integrate spatial and transcriptional information, spatially extending pseudotime and revealing spatiotemporal organization of cells.

Neural communication networks form the fundamental basis for brain function. These communication networks are enabled by emitted ligands such as neurotransmitters, which activate receptor complexes to facilitate communication. Thus, neural communication is fundamentally dependent on the transcriptome. Here we develop NeuronChat, a method and package for the inference, visualization and analysis of neural-specific communication networks among pre-defined cell groups using single-cell expression data. We incorporate a manually curated molecular interaction database of neural signaling for both human and mouse, and benchmark NeuronChat on several published datasets to validate its ability in predicting neural connectivity. Then, we apply NeuronChat to three different neural tissue datasets to illustrate its functionalities in identifying interneural communication networks, revealing conserved or context-specific interactions across different biological contexts, and predicting communication pattern changes in diseased brains with autism spectrum disorder. Finally, we demonstrate NeuronChat can utilize spatial transcriptomics data to infer and visualize neural-specific cell-cell communication. Neurons communicate differently from non-neuronal cells. Here, authors present a method, NeuronChat, that utilizes scRNA-seq data and/or spatial transcriptomics to infer, visualize and analyze neural-specific cell-cell communication.

For heat transfer outside borehole heat exchanger (BHE) arrays in aquifers, existing analytical models mostly adopt Neumann or Robin boundary conditions, whereas constant-temperature (Dirichlet) boundaries are more practical and convenient for monitoring in engineering applications. Considering the coupled effects of heat advection and conduction induced by groundwater seepage, and based on the engineering reality that vertical heat flow is much smaller than horizontal heat flow, this study idealized the BHE array as a constant-temperature boundary and established a one-dimensional simplified model. The advection term of the governing equation was removed through the exponential transformation of the dependent variable, and an analytical solution was derived using Fourier transformation. A three-dimensional coupled hydro-thermal numerical model was established in FEFLOW for validation. The results indicate that relative errors between analytical and numerical solutions remain below 3% outside the BHE array; however, the analytical method is inapplicable inside the array due to significant thermal interference, and independent field validation is precluded by prior thermal disturbances. The proposed solution features fast computation and clear physical interpretation, providing a simple and efficient tool for rapid estimation of temperature variations during preliminary feasibility studies of ground-source heat-pump projects.

Background Cerebral ischemia–reperfusion (I/R) injury is a major cause of early complications and unfavorable outcomes after endovascular thrombectomy (EVT) therapy in patients with acute ischemic stroke (AIS). Recent studies indicate that modulating microglia/macrophage polarization and subsequent inflammatory response may be a potential adjunct therapy to recanalization. Annexin A1 (ANXA1) exerts potent anti-inflammatory and pro-resolving properties in models of cerebral I/R injury. However, whether ANXA1 modulates post-I/R-induced microglia/macrophage polarization has not yet been fully elucidated. Methods We retrospectively collected blood samples from AIS patients who underwent successful recanalization by EVT and analyzed ANXA1 levels longitudinally before and after EVT and correlation between ANXA1 levels and 3-month clinical outcomes. We also established a C57BL/6J mouse model of transient middle cerebral artery occlusion/reperfusion (tMCAO/R) and an in vitro model of oxygen–glucose deprivation and reoxygenation (OGD/R) in BV2 microglia and HT22 neurons to explore the role of Ac2-26, a pharmacophore N-terminal peptide of ANXA1, in regulating the I/R-induced microglia/macrophage activation and polarization. Results The baseline levels of ANXA1 pre-EVT were significantly lower in 23 AIS patients, as compared with those of healthy controls. They were significantly increased to the levels found in controls 2–3 days post-EVT. The increased post-EVT levels of ANXA1 were positively correlated with 3-month clinical outcomes. In the mouse model, we then found that Ac2-26 administered at the start of reperfusion shifted microglia/macrophage polarization toward anti-inflammatory M2-phenotype in ischemic penumbra, thus alleviating blood–brain barrier leakage and neuronal apoptosis and improving outcomes at 3 days post-tMCAO/R. The protection was abrogated when mice received Ac2-26 together with WRW4, which is a specific antagonist of formyl peptide receptor type 2/lipoxin A4 receptor (FPR2/ALX). Furthermore, the interaction between Ac2-26 and FPR2/ALX receptor activated the 5’ adenosine monophosphate-activated protein kinase (AMPK) and inhibited the downstream mammalian target of rapamycin (mTOR). These in vivo findings were validated through in vitro experiments. Conclusions Ac2-26 modulates microglial/macrophage polarization and alleviates subsequent cerebral inflammation by regulating the FPR2/ALX-dependent AMPK-mTOR pathway. It may be investigated as an adjunct strategy for clinical prevention and treatment of cerebral I/R injury after recanalization. Plasma ANXA1 may be a potential biomarker for outcomes of AIS patients receiving EVT.

Key messageGenetic resources contributes to the sustainable protein production in soybean.Soybean is an important crop for food, oil, and forage and is the main source of edible vegetable oil and vegetable protein. It plays an important role in maintaining balanced dietary nutrients for human health. The soybean protein content is a quantitative trait mainly controlled by gene additive effects and is usually negatively correlated with agronomic traits such as the oil content and yield. The selection of soybean varieties with high protein content and high yield to secure sustainable protein production is one of the difficulties in soybean breeding. The abundant genetic variation of soybean germplasm resources is the basis for overcoming the obstacles in breeding for soybean varieties with high yield and high protein content. Soybean has been cultivated for more than 5000 years and has spread from China to other parts of the world. The rich genetic resources play an important role in promoting the sustainable production of soybean protein worldwide. In this paper, the origin and spread of soybean and the current status of soybean production are reviewed; the genetic characteristics of soybean protein and the distribution of resources are expounded based on phenotypes; the discovery of soybean seed protein-related genes as well as transcriptomic, metabolomic, and proteomic studies in soybean are elaborated; the creation and utilization of high-protein germplasm resources are introduced; and the prospect of high-protein soybean breeding is described.

The rapid development of spatial transcriptomics (ST) techniques has allowed the measurement of transcriptional levels across many genes together with the spatial positions of cells. This has led to an explosion of interest in computational methods and techniques for harnessing both spatial and transcriptional information in analysis of ST datasets. The wide diversity of approaches in aim, methodology and technology for ST provides great challenges in dissecting cellular functions in spatial contexts. Here, we synthesize and review the key problems in analysis of ST data and methods that are currently applied, while also expanding on open questions and areas of future development. Walker et al. review methods used to analyse spatial transcriptomics data and discuss open questions and areas of future development.

The relationship between cerebral glucose metabolism and glucose transporter expression after intracerebral hemorrhage (ICH) is unclear. Few studies have used positron emission tomography (PET) to explore cerebral glucose metabolism after ICH in rodents. In this study, we produced ICH in mice with an intrastriatal injection of collagenase to investigate whether glucose metabolic changes in 18 F-fluoro-2-deoxy-D-glucose (FDG)-PET images are associated with expression of glucose transporters (GLUTs) over time. On days 1 and 3 after ICH, the ipsilateral striatum exhibited significant hypometabolism. However, by days 7 and 14, glucose metabolism was significantly higher in the ipsilateral striatum than in the contralateral striatum. The contralateral hemisphere did not show hypermetabolism at any time after ICH. Qualitative immunofluorescence and Western blotting indicated that the expression of GLUT1 in ipsilateral striatum decreased on days 1 and 3 after ICH and gradually returned to baseline by day 21. The 18 F-FDG uptake after ICH was associated with expression of GLUT1 but not GLUT3 or GLUT5. Our data suggest that ipsilateral cerebral glucose metabolism decreases in the early stage after ICH and increases progressively in the late stage. Changes in 18 F-FDG uptake on PET imaging are associated with the expression of GLUT1 in the ipsilateral striatum.

Drought stress, which is increasing with climate change, is a serious threat to agricultural sustainability worldwide. Seed germination is an essential growth phase that ensures the successful establishment and productivity of soybean, which can lose substantial productivity in soils with water deficits. However, only limited genetic information is available about how germinating soybean seeds may exert drought tolerance. In this study, we examined the germinating seed drought-tolerance phenotypes and genotypes of a panel of 259 released Chinese soybean cultivars panel. Based on 4616 Single-Nucleotide Polymorphisms (SNPs), we conducted a mixed-linear model GWAS that identified a total of 15 SNPs associated with at least one drought-tolerance index. Notably, three of these SNPs were commonly associated with two drought-tolerance indices. Two of these SNPs are positioned upstream of genes, and 11 of them are located in or near regions where QTLs have been previously mapped by linkage analysis, five of which are drought-related. The SNPs detected in this study can both drive hypothesis-driven research to deepen our understanding of genetic basis of soybean drought tolerance at the germination stage and provide useful genetic resources that can facilitate the selection of drought stress traits via genomic-assisted selection.

Salt is recognized as one of the most major factors that limits soybean yield in acidic soils. Soil enzyme activity and bacterial community have a critical function in improving the tolerance to soybean. Our aim was to assess the activities of soil enzyme, the structure of bacteria and their potential functions for salt resistance between Salt-tolerant (Salt-T) and -sensitive (Salt-S) soybean genotypes when subject to salt stress. Plant biomass, soil physicochemical properties, soil catalase, urease, sucrase, amylase, and acid phosphatase activities, and rhizosphere microbial characteristics were investigated in Salt-T and Salt-S soybean genotypes under salt stress with a pot experiment. Salt stress significantly decreased the soil enzyme activities and changed the rhizosphere microbial structure in a genotype-dependent manner. In addition, 46 ASVs which were enriched in the Salt-T geotype under the salt stress, such as ASV19 ( Alicyclobacillus ), ASV132 ( Tumebacillus ), ASV1760 ( Mycobacterium ) and ASV1357 ( Bacillus ), which may enhance the tolerance to soybean under salt stress. Moreover, the network structure of Salt-T soybean was simplified by salt stress, which may result in soil bacterial communities being susceptible to external factors. Salt stress altered the strength of soil enzyme activities and the assembly of microbial structure in Salt-T and Salt-S soybean genotypes. Na + , NO 3 − –N, NH 4 + –N and Olsen-P were the most important driving factors in the structure of bacterial community in both genotypes. Salt-T genotypes enriched several microorganisms that contributed to enhance salt tolerance in soybeans, such as Alicyclobacillus, Tumebacillus , and Bacillus. Nevertheless, the simplified network structure of salt-T genotype due to salt stress may render its bacterial community structure unstable and susceptible.

When considering the adsorption effect, traditional experimental methods have faced significant challenges in obtaining the solute transport parameters for composite pollutants. Based on the adsorption test data of three types of composite pollutants collected from the Web of Science and China National Knowledge Infrastructure databases from 2014 to 2024, this study employed four commonly used machine learning models, that is, Random Forest (RF), Support Vector Machine (SVM), Back Propagation Neural Network (BPNN), and Decision Tree (DT) models, to establish adsorption isotherms of pollutants with liquid-phase equilibrium concentration as the horizontal coordinate and solid-phase adsorption capacity as the vertical coordinate, and systematically investigated the adsorption characteristics of combined pollutants in the porous aquifer. Subsequently, the Mean Square Errors (MSEs) and coefficients of determination, two commonly used evaluation metrics for regression models in machine learning, were chosen to estimate the prediction effect of datasets. Combined with the convection–diffusion equation, the adsorption kinetic parameters under the mutual interference of composite pollutants, namely, the retardation factor, were solved. The results show that for the adsorption isotherms of heavy metal composite pollutants, organic composite pollutants, and heavy metal and organic combined composite pollutants, SVM, BPNN, and RF models have the best prediction effect, respectively, and their MSEs are 0.032, 0.001, and 0.018. The adsorption isotherm fitting results indicate that the heavy metal composite pollutants and organic composite pollutants conform to the Freundlich model. The retardation factor of organic composite pollutants is significantly higher than that of heavy metal composite pollutants.