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Previous works have demonstrated that inverting magnetotelluric (MT) data jointly with gravity data can synergize the high lateral resolution of gravity and the vertical resolution of MT. However, the existing joint stabilizers usually work for structured grids instead of unstructured ones that are more powerful for characterizing complex geology. Here, we utilize the local Pearson correlation coefficient (LPCC) for the joint inversion of gravity and MT data based on unstructured grids. We first establish a background mesh by discretizing the research area into virtual rectangular grids and then enhance the structured similarity between density and resistivity via the LPCC. Compared to existing joint constraints, our method is more flexible in solving multiscale joint inversions thanks to the adjustable subdomain size. The synthetic experiments show that the joint stabilizer can recover the subsurface targets at a higher resolution, especially for gravity data, than the standalone inversions. This method is further applied to the joint inversion of gravity and MT data from the Yellowstone area and the inverted density and resistivity models are structurally consistent. Then, based on the inverted subsurface structures and incorporating the existing research, we infer that the two inverted zones with low density and low resistivity correspond to the partially molten rhyolitic and basaltic, respectively. The proposed joint constraint can be further extended to the inversion of other geophysical data.

Satellite magnetic data contain significant information about the Earth’s interior electrical structure. However, the altitudes of satellites vary over time and latitude. Theoretically, the signals from the external magnetosphere and ionosphere, along with the induced magnetic field from the Earth, exhibit considerable variation at different altitudes. The specific effects of altitude variations on the global electromagnetic induction signal, and their subsequent impact on the transfer function, are not yet fully understood. Given that the orbital altitude of the Challenging Minisatellite Payload (CHAMP) satellite changes continuously, we have calculated the Q-response and the C-response at different altitudes through numerical simulations and CHAMP satellite data, respectively. The Q-response represents the ratio of coefficients of the internal and external fields, while the C-response, derived from the Q-response, is altitude-dependent. The findings indicate that the amplitudes of the induced (internal) magnetic field decrease with increasing altitude, with discrepancies exceeding 10 nT, particularly in mid-latitude regions. Conversely, the inducing (external) fields increase with altitude. The Q-responses exhibit distinct behaviors across different degrees of Gaussian spherical harmonics (SHs). Specifically, the real part of the orbital C-response increases with satellite altitude, although higher degrees tend to yield lower magnitudes. This study offers insights into the design of satellite orbits in magnetic field measurements.

At present, the mainstream technology for leachate detection in landfills is electrical resistivity tomography (ERT), known for its efficiency and nondestructive nature. However, the conventional ERT data interpretation primarily uses inversion based on structured grids, which cannot accurately simulate the complex and thin impermeable layers of landfills, leading to unreliable results. To address this issue, we propose a novel ERT observation system and a new 3-D inversion technology. In our observation system, all measuring electrodes are placed around the landfill at once, and only a limited number of transmitting sources are needed to sequentially inject current, which effectively reduces the time for data acquisition. For 3-D inversions, we employ an unstructured tetrahedral grid for fine discretization of structures at various scales. The node-based finite-element method is used for high-precision forward and adjoint forward calculations, while the gradient filtering method in combination with limited-memory quasi-Newtonian (L-BFGS) algorithm is used to update the inversion model. Numerical experiments indicate that the proposed method can mitigate the influence of thin impermeable landfill layers and provide more accurate imaging results compared to the conventional methods. In addition, we also test the effects of water-bearing layers, faults, and near-surface interferences on the leakage inversion results. The results show that the proposed method can achieve reliable high-resolution imaging under various conditions, making it an effective technique for landfill leachate detection.

The choice of data misfit measure has a great impact on the convergence of electromagnetic (EM) inversion. The conventional measure based on the $l_{2}$ -norm tends to excessively amplify the weights of a larger misfit, inadvertently neglecting data with a smaller misfit during the inversion process, thereby diminishing the resolution to a certain degree. To solve this problem, we propose a robust inversion strategy based on $l_{1}$ -norm data misfit and adaptive moment estimation (Adam). In this scheme, we use the Ekblom-type $l_{1}$ -norm to simplify the derivative computation of the absolute value function. The Adam algorithm is further applied to optimize this type of non-smooth objective function, which incorporates momentum terms and adaptive steps, allowing it to better adapt to the irregularities in gradient changes. The inversion results obtained from both synthetic models and field measurements demonstrate that the Adam method performs considerably better than the Broyden-Fletcher-Goldfarb-Shanno (BFGS) method for optimizing the $l_{1}$ - $l_{2}$ norm of the objective function. Compared with the conventional ${l} _{2}$ -norm data misfit, the $l_{1}$ -norm data misfit can effectively avoid excessive optimization of data with large misfits and achieve high-resolution inversion results.

Semi-airborne transient electromagnetic (SATEM) method is an effective exploration tool in complex environments due to its utilization of unmanned aerial platforms for data collection. With the escalating demands of detection accuracy, the conventional 1-D inversion is no longer sufficient. Over the last two decades, there has been rapid development in 3-D electromagnetic (EM) inversions. SATEM method usually has a substantial number of receiver stations. To obtain accurate 3-D solutions, one has to refine the grids near the receiver points, which causes a huge number of grids and reduces computational efficiency. Thus, computational complexity is one of the primary factors restricting the practicality of 3-D inversions. In this article, we have developed an approach using decoupled meshes. This method uses a series of meshes to parallelly calculate the forward modeling and Jacobian information, with one mesh containing only a subset of receiver points. This scheme aims to decrease the number of grids and improve the efficiency. After that, we map the results to an overarching inversion mesh using the node cloud technique and renew the model parameters. We check our algorithm via both synthetic and field data. Numerical experiments show that the decoupled mesh inversion method can effectively recover the location and resistivities of the anomalies under the complex topography. When compared with the conventional inversion method, the decoupled mesh inversion method can significantly reduce the calculation time.

The conventional geo-electromagnetic data inversions are mostly based on gradient optimization methods. However, this type of method can only provide a single “optimal” inverse model under specific prior conditions, which cannot effectively evaluate the reliability and uncertainty of the inversion results. The widely used uncertainty quantification (UQ) methods are based on the theory of Bayesian inference. Although they have achieved success in many applications, they suffer from the curse of dimensionality and low efficiency. To overcome these problems, we propose a novel UQ strategy for geo-electromagnetic inversions based on Bayesian processes and surrogate modeling with adaptive deep neural network (DNN). In this method, an embedded DNN is used for forward modeling in the Bayesian inference to improve computational efficiency. The training of the DNN is divided into two stages. First, a predesigned small training set is used and the resulting DNN only gives a low-accuracy result. Second, this DNN is fine-tuned dynamically during the Metropolis-Hastings (M-H) sampling process, in which the training set is adaptively supplemented according to the modeling errors. Compared to the conventional data-driven approach, this dynamically adaptive constructing method of the training set can greatly reduce the training set and constantly maintain high accuracy in forward modeling. We demonstrate the effectiveness and practicality of our surrogate modeling Bayesian and analyze the effects of different sampling numbers, noise levels, prior distributions, and sampling radius. Compared with Occam’s inversion and conventional Bayesian inversions, our method shows good robustness and high accuracy, making it an effective Bayesian inversion technique.

Tumor-infiltrating T cells offer a promising avenue for cancer treatment, yet their states remain to be fully characterized. Here we present a single-cell atlas of T cells from 308,048 transcriptomes across 16 cancer types, uncovering previously undescribed T cell states and heterogeneous subpopulations of follicular helper, regulatory and proliferative T cells. We identified a unique stress response state, TSTR, characterized by heat shock gene expression. TSTRcells are detectable in situ in the tumor microenvironment across various cancer types, mostly within lymphocyte aggregates or potential tertiary lymphoid structures in tumor beds or surrounding tumor edges. T cell states/compositions correlated with genomic, pathological and clinical features in 375 patients from 23 cohorts, including 171 patients who received immune checkpoint blockade therapy. We also found significantly upregulated heat shock gene expression in intratumoral CD4/CD8+cells following immune checkpoint blockade treatment, particularly in nonresponsive tumors, suggesting a potential role of TSTRcells in immunotherapy resistance. Our well-annotated T cell reference maps, web portal and automatic alignment/annotation tool could provide valuable resources for T cell therapy optimization and biomarker discovery.

Single-cell transcriptomics has broadened our understanding of cellular diversity and gene expression dynamics in healthy and diseased tissues. Recently, spatial transcriptomics has emerged as a tool to contextualize single cells in multicellular neighbourhoods and to identify spatially recurrent phenotypes, or ecotypes. These technologies have generated vast datasets with targeted-transcriptome and whole-transcriptome profiles of hundreds to millions of cells. Such data have provided new insights into developmental hierarchies, cellular plasticity and diverse tissue microenvironments, and spurred a burst of innovation in computational methods for single-cell analysis. In this Review, we discuss recent advancements, ongoing challenges and prospects in identifying and characterizing cell states and multicellular neighbourhoods. We discuss recent progress in sample processing, data integration, identification of subtle cell states, trajectory modelling, deconvolution and spatial analysis. Furthermore, we discuss the increasing application of deep learning, including foundation models, in analysing single-cell and spatial transcriptomics data. Finally, we discuss recent applications of these tools in the fields of stem cell biology, immunology, and tumour biology, and the future of single-cell and spatial transcriptomics in biological research and its translation to the clinic.