How to build a [2Fe–2S] cluster
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Constructing multicomponent cluster expansions with machine-learning and chemical embedding
Cluster expansions are commonly employed as surrogate models to link the electronic structure of an alloy to its finite-temperature properties. Using cluster expansions to model materials with several alloying elements is challenging due to a rapid increase in the number of fitting parameters and training set size. We introduce the embedded cluster expansion (eCE) formalism that enables the parameterization of accurate on-lattice surrogate models for alloys containing several chemical species. The eCE model simultaneously learns a low dimensional embedding of site basis functions along with the weights of an energy model. A prototypical senary alloy comprised of elements in groups 5 and 6 of the periodic table is used to demonstrate that eCE models can accurately reproduce ordering energetics of complex alloys without a significant increase in model complexity. Further, eCE models can leverage similarities between chemical elements to efficiently extrapolate into compositional spaces that are not explicitly included in the training dataset. The eCE formalism presented in this study unlocks the possibility of employing cluster expansion models to study multicomponent alloys containing several alloying elements.
Integrated proteogenomic characterization of ampullary adenocarcinoma
Ampullary adenocarcinoma (AMPAC) is a rare and heterogeneous malignancy. Here we performed a comprehensive proteogenomic analysis of 198 samples from Chinese AMPAC patients and duodenum patients. Genomic data illustrate that 4q loss causes fatty acid accumulation and cell proliferation. Proteomic analysis has revealed three distinct clusters (C-FAM, C-AD, C-CC), among which the most aggressive cluster, C-AD, is associated with the poorest prognosis and is characterized by focal adhesion. Immune clustering identifies three immune clusters and reveals that immune cluster M1 (macrophage infiltration cluster) and M3 (DC cell infiltration cluster), which exhibit a higher immune score compared to cluster M2 (CD4+ T-cell infiltration cluster), are associated with a poor prognosis due to the potential secretion of IL-6 by tumor cells and its consequential influence. This study provides a comprehensive proteogenomic analysis for seeking for better understanding and potential treatment of AMPAC.
A deep learning pipeline for three-dimensional brain-wide mapping of local neuronal ensembles in teravoxel light-sheet microscopy
Teravoxel-scale, cellular-resolution images of cleared rodent brains acquired with light-sheet fluorescence microscopy have transformed the way we study the brain. Realizing the potential of this technology requires computational pipelines that generalize across experimental protocols and map neuronal activity at the laminar and subpopulation-specific levels, beyond atlas-defined regions. Here, we present artficial intelligence-based cartography of ensembles (ACE), an end-to-end pipeline that employs three-dimensional deep learning segmentation models and advanced cluster-wise statistical algorithms, to enable unbiased mapping of local neuronal activity and connectivity. Validation against state-of-the-art segmentation and detection methods on unseen datasets demonstrated ACE’s high generalizability and performance. Applying ACE in two distinct neurobiological contexts, we discovered subregional effects missed by existing atlas-based analyses and showcase ACE’s ability to reveal localized or laminar neuronal activity brain-wide. Our open-source pipeline enables whole-brain mapping of neuronal ensembles at a high level of precision across a wide range of neuroscientific applications.
Astrocyte heterogeneity reveals region-specific astrogenesis in the white matter
Astrocyte heterogeneity has been well explored, but our understanding of white matter (WM) astrocytes and their distinctions from gray matter (GM) astrocytes remains limited. Here, we compared astrocytes from cortical GM and WM/corpus callosum (WM/CC) using single-cell RNA sequencing and spatial transcriptomics of the murine forebrain. The comparison revealed similarities but also significant differences between WM and GM astrocytes, including cytoskeletal and metabolic hallmarks specific to WM astrocytes with molecular properties also shared with human WM astrocytes. When we compared murine astrocytes from two different WM regions, the cortex and cerebellum, we found that they exhibited distinct, region-specific molecular properties, with the cerebellum lacking, for example, a specific cluster of WM astrocytes expressing progenitor and proliferation genes. Functional experiments confirmed astrocyte proliferation in the WM/CC, but not in the cerebellar WM, suggesting that the WM/CC may be a source of continued astrogenesis.
Colloidal clusters as models for circular microswimmers
Circular swimmers, particles that propel in circular trajectories, are gaining traction due to their potential for novel collective behaviors. However, synthetic active particles capable of controlled circular propulsion remain scarce. We present a facile experimental strategy to fabricate synthetic swimmers using chemically cross-linked Janus colloid clusters, driven by induced charge electrophoresis. By quantifying the propulsion dynamics of active clusters, we demonstrate that cluster geometry dictates orbit diameter, angular velocity, and chirality. Through statistical analysis of clusters, we identify compact clusters as promising candidates for tunable circular propulsion. To scale up fabrication, we employ capillary-assisted assembly for achieving monodisperse clusters. Our validation of the kinetic model for active trimers and tetramers suggests that clustering as a strategy for circular propulsion extends to Janus colloids propelled by different mechanisms. Our findings establish Janus clusters as versatile systems for controlled circular propulsion, enabling new experimental studies on the collective behavior of circular microswimmers.
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