Optimize Electron Beam Energy toward In Situ Imaging of Thick Frozen Bio-Samples with Nanometer Resolution Using MeV-STEM.

To optimize electron energy for in situ imaging of large biological samples up to 10 µm in thickness with nanoscale resolutions, we implemented an analytical model based on elastic and inelastic characteristic angles. This model has been benchmarked by Monte Carlo simulations and can be used to predict the transverse beam size broadening as a function of electron energy while the probe beam traverses through the sample. As a result, the optimal choice of the electron beam energy can be realized. In addition, the impact of the dose-limited resolution was analysed. While the sample thickness is less than 10 µm, there exists an optimal electron beam energy below 10 MeV regarding a specific sample thickness. However, for samples thicker than 10 µm, the optimal beam energy is 10 MeV or higher depending on the sample thickness, and the ultimate resolution could become worse with the increase in the sample thickness. Moreover, a MeV-STEM column based on a two-stage lens system can be applied to reduce the beam size from one micron at aperture to one nanometre at the sample with the energy tuning range from 3 to 10 MeV. In conjunction with the state-of-the-art ultralow emittance electron source that we recently implemented, the maximum size of an electron beam when it traverses through an up to 10 µm thick bio-sample can be kept less than 10 nm. This is a critical step toward the in situ imaging of large, thick biological samples with nanometer resolution.

Self-Healing Polyurethane Elastomers with Superior Tensile Strength and Elastic Recovery Based on Dynamic Oxime-Carbamate and Hydrogen Bond Interactions.

The preparation of self-healing polyurethane elastomers (PUEs) incorporating dynamic bonds is of considerable practical significance. However, developing a PUE with outstanding mechanical properties and high self-healing efficiency poses a significant challenge. Herein, this work has successfully developed a series of self-healing PUEs with various outstanding properties through rational molecular design. These PUEs incorporate m-xylylene diisocyanate and reversible dimethylglyoxime as hard segment, along with polytetramethylene ether glycol as soft segment. A significant amount of dynamic oxime-carbamate and hydrogen bonds are formed in hard segment. The microphase separated structure of the PUEs enables them to be colorless with a transparency of >90%. Owing to the chemical composition and multiple dynamic interactions, the PUEs are endowed with ultra-high tensile strength of 34.5 MPa, satisfactory toughness of 53.9 MJ m-3, and great elastic recovery both at low and high strains. The movement of polymer molecular chains and the dynamic reversible interactions render a self-healing efficiency of 101% at 70 °C. In addition, this self-healing polyurethane could still maintain high mechanical properties after recycling. This study provides a design strategy for the preparation of a comprehensive polyurethane with superior overall performance, which holds wide application prospects in the fields of flexible displays and solar cells.

Cryo2StructData: A Large Labeled Cryo-EM Density Map Dataset for AI-based Modeling of Protein Structures.

The advent of single-particle cryo-electron microscopy (cryo-EM) has brought forth a new era of structural biology, enabling the routine determination of large biological molecules and their complexes at atomic resolution. The high-resolution structures of biological macromolecules and their complexes significantly expedite biomedical research and drug discovery. However, automatically and accurately building atomic models from high-resolution cryo-EM density maps is still time-consuming and challenging when template-based models are unavailable. Artificial intelligence (AI) methods such as deep learning trained on limited amount of labeled cryo-EM density maps generate inaccurate atomic models. To address this issue, we created a dataset called Cryo2StructData consisting of 7,600 preprocessed cryo-EM density maps whose voxels are labelled according to their corresponding known atomic structures for training and testing AI methods to build atomic models from cryo-EM density maps. Cryo2StructData is larger than existing, publicly available datasets for training AI methods to build atomic protein structures from cryo-EM density maps. We trained and tested deep learning models on Cryo2StructData to validate its quality showing that it is ready for being used to train and test AI methods for building atomic models.

Study on the Effect of Citric Acid-Modified Chitosan on the Mechanical Properties, Shrinkage Properties, and Durability of Concrete.

As an environmentally friendly natural polymer, citric acid-modified chitosan (CAMC) can effectively regulate the hydration and exothermic processes of cement-based materials. However, the influence of CAMC on the macroscopic properties of concrete and the optimal dosage are still unclear. This work systematically investigates the effects of CAMC on the mixing performance, mechanical properties, shrinkage performance, and durability of concrete. The results indicated that CAMC has a thickening effect and prolongs the setting time of concrete. CAMC has a negative impact on the early strength of concrete, but it is beneficial for the development of the subsequent strength of concrete. With the increase in CAMC content, the self-shrinkage rate of concrete samples decreased from 86.82 to 14.52 µÎµ. However, the CAMC-0.6% sample eventually expanded, with an expansion value of 78.49 µÎµ. Moreover, the long-term drying shrinkage rate was decreased from 551.46 to 401.94 µÎµ. Furthermore, low-dose CAMC can significantly reduce the diffusion coefficient of chloride ions, improve the impermeability and density of concrete, and thereby enhance the freeze-thaw cycle resistance of concrete.

Degradation-Based Protein Profiling: A Case Study of Celastrol.

Natural products, while valuable for drug discovery, encounter limitations like uncertainty in targets and toxicity. As an important active ingredient in traditional Chinese medicine, celastrol exhibits a wide range of biological activities, yet its mechanism remains unclear. In this study, they introduced an innovative "Degradation-based protein profiling (DBPP)" strategy, which combined PROteolysis TArgeting Chimeras (PROTAC) technology with quantitative proteomics and Immunoprecipitation-Mass Spectrometry (IP-MS) techniques, to identify multiple targets of natural products using a toolbox of degraders. Taking celastrol as an example, they successfully identified its known targets, including inhibitor of nuclear factor kappa B kinase subunit beta (IKKß), phosphatidylinositol-4,5-bisphosphate 3-kinase catalytic subunit alpha (PI3Kα), and cellular inhibitor of PP2A (CIP2A), as well as potential new targets such as checkpoint kinase 1 (CHK1), O-GlcNAcase (OGA), and DNA excision repair protein ERCC-6-like (ERCC6L). Furthermore, the first glycosidase degrader is developed in this work. Finally, by employing a mixed PROTAC toolbox in quantitative proteomics, they also achieved multi-target identification of celastrol, significantly reducing costs while improving efficiency. Taken together, they believe that the DBPP strategy can complement existing target identification strategies, thereby facilitating the rapid advancement of the pharmaceutical field.

Promiscuous G-protein activation by the calcium-sensing receptor.

The human calcium-sensing receptor (CaSR) detects fluctuations in the extracellular Ca2+ concentration and maintains Ca2+ homeostasis1,2. It also mediates diverse cellular processes not associated with Ca2+ balance3-5. The functional pleiotropy of CaSR arises in part from its ability to signal through several G-protein subtypes6. We determined structures of CaSR in complex with G proteins from three different subfamilies: Gq, Gi and Gs. We found that the homodimeric CaSR of each complex couples to a single G protein through a common mode. This involves the C-terminal helix of each Gα subunit binding to a shallow pocket that is formed in one CaSR subunit by all three intracellular loops (ICL1-ICL3), an extended transmembrane helix 3 and an ordered C-terminal region. G-protein binding expands the transmembrane dimer interface, which is further stabilized by phospholipid. The restraint imposed by the receptor dimer, in combination with ICL2, enables G-protein activation by facilitating conformational transition of Gα. We identified a single Gα residue that determines Gq and Gs versus Gi selectivity. The length and flexibility of ICL2 allows CaSR to bind all three Gα subtypes, thereby conferring capacity for promiscuous G-protein coupling.

Correlative single-cell hard X-ray computed tomography and X-ray fluorescence imaging.

X-ray computed tomography (XCT) and X-ray fluorescence (XRF) imaging are two non-invasive imaging techniques to study cellular structures and chemical element distributions, respectively. However, correlative X-ray computed tomography and fluorescence imaging for the same cell have yet to be routinely realized due to challenges in sample preparation and X-ray radiation damage. Here we report an integrated experimental and computational workflow for achieving correlative multi-modality X-ray imaging of a single cell. The method consists of the preparation of radiation-resistant single-cell samples using live-cell imaging-assisted chemical fixation and freeze-drying procedures, targeting and labeling cells for correlative XCT and XRF measurement, and computational reconstruction of the correlative and multi-modality images. With XCT, cellular structures including the overall structure and intracellular organelles are visualized, while XRF imaging reveals the distribution of multiple chemical elements within the same cell. Our correlative method demonstrates the feasibility and broad applicability of using X-rays to understand cellular structures and the roles of chemical elements and related proteins in signaling and other biological processes.

Cryo-EM sample preparation for high-resolution structure studies.

High-resolution structures of biomolecules can be obtained using single-particle cryo-electron microscopy (SPA cryo-EM), and the rapidly growing number of structures solved by this method is encouraging more researchers to utilize this technique. As with other structural biology methods, sample preparation for an SPA cryo-EM data collection requires some expertise and an understanding of the strengths and limitations of the technique in order to make sensible decisions in the sample-preparation process. In this article, common strategies and pitfalls are described and practical advice is given to increase the chances of success when starting an SPA cryo-EM project.

CryoTransformer: a transformer model for picking protein particles from cryo-EM micrographs.

MOTIVATION: Cryo-electron microscopy (cryo-EM) is a powerful technique for determining the structures of large protein complexes. Picking single protein particles from cryo-EM micrographs (images) is a crucial step in reconstructing protein structures from them. However, the widely used template-based particle picking process requires some manual particle picking and is labor-intensive and time-consuming. Though machine learning and artificial intelligence (AI) can potentially automate particle picking, the current AI methods pick particles with low precision or low recall. The erroneously picked particles can severely reduce the quality of reconstructed protein structures, especially for the micrographs with low signal-to-noise ratio. RESULTS: To address these shortcomings, we devised CryoTransformer based on transformers, residual networks, and image processing techniques to accurately pick protein particles from cryo-EM micrographs. CryoTransformer was trained and tested on the largest labeled cryo-EM protein particle dataset-CryoPPP. It outperforms the current state-of-the-art machine learning methods of particle picking in terms of the resolution of 3D density maps reconstructed from the picked particles as well as F1-score, and is poised to facilitate the automation of the cryo-EM protein particle picking. AVAILABILITY AND IMPLEMENTATION: The source code and data for CryoTransformer are openly available at: https://github.com/jianlin-cheng/CryoTransformer.

A novel iterative detection method based on a lattice reduction-aided algorithm for MIMO OFDM systems.

The lattice reduction-aided algorithm has received broad attention from researchers since it operates as a maximum likelihood receiver with better system performance for multiple-input multiple-output orthogonal frequency division multiplexing systems and contains a full diversity. A novel iterative detection algorithm canceling parallel iterations that employ the lattice reduction-aided approach is proposed. Soft information is exchanged through the detector itself. Its iteration occurs inside the detector, which reduces much of the exchange cost between the multiple-input multiple-output orthogonal frequency division multiplexing detector and the turbo decoder. Since the parallel interference cancellation algorithm is constrained by the accuracy of the initial value of the detection, it is easy to form error propagation after several iterations. Due to the lattice reduction-aided algorithm, its performance is approximated with the maximum likelihood algorithm. Therefore, the lattice reduction-aided algorithm is introduced into the parallel interference cancellation algorithm to make its detection algorithm more accurate and overcome the effect of error propagation in the manuscript. Simulation results indicate that the proposed algorithm leads to an improvement of 0.8-2 dB when the bit error rate is set to 10-4 when compared to other algorithms.

TAX1BP1 downregulation by STAT3 in cardiac fibroblasts contributes to diabetes-induced heart failure with preserved ejection fraction.

Heart failure (HF) with preserved ejection fraction (HFpEF) is now the most common form of HF and has been reported to be closely related to diabetes. Accumulating evidence suggests that HFpEF patients exhibit cardiac fibrosis. This study investigates whether direct targeted inhibition of the activation of cardiac fibroblasts (CFs), the main effector cells in cardiac fibrosis, improves diabetes-induced HFpEF and elucidates the underlying mechanisms. Twenty-week-old db/db mice exhibited HFpEF, as confirmed by echocardiography and hemodynamic measurements. Proteomics was performed on CFs isolated from the hearts of 20-week-old C57BL/6 and db/db mice. Bioinformatic prediction was used to identify target proteins. Experimental validation was performed in both high glucose (HG)-treated neonatal mouse CFs (NMCFs) and diabetic hearts. TAX1 binding protein 1 (TAX1BP1) was identified as the most significantly differentially expressed protein between 20-week-old C57BL/6 and db/db mice. TAX1BP1 mRNA and protein were markedly downregulated in CFs from diabetic hearts and HG-cultured NMCFs. Overexpression of TAX1BP1 profoundly inhibited HG/diabetes-induced NF-κB nuclear translocation and collagen synthesis in CFs, improved cardiac fibrosis, hypertrophy, inflammation and HFpEF in diabetic mice. Mechanistically, signal transducer and activator of transcription 3 (STAT3), which is phosphorylated and translocated from the cytoplasm into the nucleus under hyperglycemic conditions, bound to TAX1BP1 promoter and blocked TAX1BP1 transcriptional activity, consequently promoting NF-κB nuclear translocation and collagen synthesis in CFs, aggravating cardiac fibrosis, hypertrophy and inflammation, leading to HFpEF in db/db mice. Taken together, our findings demonstrate that targeting regulation of STAT3-TAX1BP1-NF-κB signaling in CFs may be a promising therapeutic approach for diabetes-induced HFpEF.

Cryo2StructData: A Large Labeled Cryo-EM Density Map Dataset for AI-based Modeling of Protein Structures.

The advent of single-particle cryo-electron microscopy (cryo-EM) has brought forth a new era of structural biology, enabling the routine determination of large biological molecules and their complexes at atomic resolution. The high-resolution structures of biological macromolecules and their complexes significantly expedite biomedical research and drug discovery. However, automatically and accurately building atomic models from high-resolution cryo-EM density maps is still time-consuming and challenging when template-based models are unavailable. Artificial intelligence (AI) methods such as deep learning trained on limited amount of labeled cryo-EM density maps generate inaccurate atomic models. To address this issue, we created a dataset called Cryo2StructData consisting of 7,600 preprocessed cryo-EM density maps whose voxels are labelled according to their corresponding known atomic structures for training and testing AI methods to build atomic models from cryo-EM density maps. It is larger and of higher quality than any existing, publicly available dataset. We trained and tested deep learning models on Cryo2StructData to make sure it is ready for the large-scale development of AI methods for building atomic models from cryo-EM density maps.

SETD2 loss in renal epithelial cells drives epithelial-to-mesenchymal transition in a TGF-ß-independent manner.

Histone-lysine N-methyltransferase SETD2 (SETD2), the sole histone methyltransferase that catalyzes trimethylation of lysine 36 on histone H3 (H3K36me3), is often mutated in clear cell renal cell carcinoma (ccRCC). SETD2 mutation and/or loss of H3K36me3 is linked to metastasis and poor outcome in ccRCC patients. Epithelial-to-mesenchymal transition (EMT) is a major pathway that drives invasion and metastasis in various cancer types. Here, using novel kidney epithelial cell lines isogenic for SETD2, we discovered that SETD2 inactivation drives EMT and promotes migration, invasion, and stemness in a transforming growth factor-beta-independent manner. This newly identified EMT program is triggered in part through secreted factors, including cytokines and growth factors, and through transcriptional reprogramming. RNA-seq and assay for transposase-accessible chromatin sequencing uncovered key transcription factors upregulated upon SETD2 loss, including SOX2, POU2F2 (OCT2), and PRRX1, that could individually drive EMT and stemness phenotypes in SETD2 wild-type (WT) cells. Public expression data from SETD2 WT/mutant ccRCC support the EMT transcriptional signatures derived from cell line models. In summary, our studies reveal that SETD2 is a key regulator of EMT phenotypes through cell-intrinsic and cell-extrinsic mechanisms that help explain the association between SETD2 loss and ccRCC metastasis.

Accurate cryo-EM protein particle picking by integrating the foundational AI image segmentation model and specialized U-Net.

Picking protein particles in cryo-electron microscopy (cryo-EM) micrographs is a crucial step in the cryo-EM-based structure determination. However, existing methods trained on a limited amount of cryo-EM data still cannot accurately pick protein particles from noisy cryo-EM images. The general foundational artificial intelligence (AI)-based image segmentation model such as Meta's Segment Anything Model (SAM) cannot segment protein particles well because their training data do not include cryo-EM images. Here, we present a novel approach (CryoSegNet) of integrating an attention-gated U-shape network (U-Net) specially designed and trained for cryo-EM particle picking and the SAM. The U-Net is first trained on a large cryo-EM image dataset and then used to generate input from original cryo-EM images for SAM to make particle pickings. CryoSegNet shows both high precision and recall in segmenting protein particles from cryo-EM micrographs, irrespective of protein type, shape, and size. On several independent datasets of various protein types, CryoSegNet outperforms two top machine learning particle pickers crYOLO and Topaz as well as SAM itself. The average resolution of density maps reconstructed from the particles picked by CryoSegNet is 3.32 Å, 7% better than 3.57 Å of Topaz and 14% better than 3.85 Å of crYOLO.

Current Status of Research on the Coal and Gas Outburst Control Technology of Hydration and Anhydrous.

China is one of the countries with the most frequent coal and gas outburst accidents in the world. In the early years of the founding of New China, coal and gas outburst accidents occurred frequently, causing serious casualties, equipment damage, and economic losses. In recent years, some scholars have tried to simulate the coal and gas outburst phenomenon using physical models to study the mechanisms of its occurrence. However, due to the complexity and nonreproducibility of coal and gas outburst disasters, the study of coal and gas outburst mechanisms is still in the hypothesis stage. In order to effectively reduce the risk of coal and gas outburst accidents, coal and gas outburst prevention and control technology have emerged and achieved remarkable results, and the probability of coal and gas outburst accidents has been greatly reduced. Among coal and gas protrusion outburst and control technologies, hydraulic and anhydrous prevention and control technologies are widely used. The purpose of this paper is to briefly explain the mainstream hypothesis of coal and gas outburst, analyze the action principle of hydration and anhydrous control technologies, discuss the current status of research on hydration and anhydrous control technologies, and put forward the problems of current technologies and future development trends.

CryoTransformer: A Transformer Model for Picking Protein Particles from Cryo-EM Micrographs.

Cryo-electron microscopy (cryo-EM) is a powerful technique for determining the structures of large protein complexes. Picking single protein particles from cryo-EM micrographs (images) is a crucial step in reconstructing protein structures from them. However, the widely used template-based particle picking process requires some manual particle picking and is labor-intensive and time-consuming. Though machine learning and artificial intelligence (AI) can potentially automate particle picking, the current AI methods pick particles with low precision or low recall. The erroneously picked particles can severely reduce the quality of reconstructed protein structures, especially for the micrographs with low signal-to-noise (SNR) ratios. To address these shortcomings, we devised CryoTransformer based on transformers, residual networks, and image processing techniques to accurately pick protein particles from cryo-EM micrographs. CryoTransformer was trained and tested on the largest labelled cryo-EM protein particle dataset - CryoPPP. It outperforms the current state-of-the-art machine learning methods of particle picking in terms of the resolution of 3D density maps reconstructed from the picked particles as well as F1-score and is poised to facilitate the automation of the cryo-EM protein particle picking.

Senescent and disease-associated microglia are modifiable features of aged brain white matter.

Brain white matter tracts undergo structural and functional changes linked to late-life cognitive decline, but the cellular and molecular contributions to their selective vulnerability are not well defined. In naturally aged mice, we demonstrate that senescent and disease-associated microglia (DAM) phenotypes converge in hippocampus-adjacent white matter. Through gold-standard gene expression and immunolabeling combined with high-dimensional spatial mapping, we identified microglial cell fates in aged white matter characterized by aberrant morphology, microenvironment reorganization, and expression of senescence and DAM markers, including galectin 3 (GAL3/Lgals3), B-cell lymphoma 2 (Bcl2), and cyclin dependent kinase inhibitors, including Cdkn2a/p16ink4a. Pharmacogenetic or pharmacological targeting of p16ink4a or BCL2 reduced white matter GAL3+ DAM abundance and rejuvenated microglial fimbria organization. Our results demonstrate dynamic changes in microglial identity in aged white matter that can be reverted by senotherapeutic intervention to promote homeostatic maintenance in the aged brain.

Discovery of small molecule degraders for modulating cell cycle.

The cell cycle is a complex process that involves DNA replication, protein expression, and cell division. Dysregulation of the cell cycle is associated with various diseases. Cyclin-dependent kinases (CDKs) and their corresponding cyclins are major proteins that regulate the cell cycle. In contrast to inhibition, a new approach called proteolysis-targeting chimeras (PROTACs) and molecular glues can eliminate both enzymatic and scaffold functions of CDKs and cyclins, achieving targeted degradation. The field of PROTACs and molecular glues has developed rapidly in recent years. In this article, we aim to summarize the latest developments of CDKs and cyclin protein degraders. The selectivity, application, validation and the current state of each CDK degrader will be overviewed. Additionally, possible methods are discussed for the development of degraders for CDK members that still lack them. Overall, this article provides a comprehensive summary of the latest advancements in CDK and cyclin protein degraders, which will be helpful for researchers working on this topic.

Lung and bone metastases patterns in limb osteosarcoma: Surgical treatment of primary site improves overall survival.

Osteosarcoma (OS) is one of the most prevalent malignant bone tumors. The proportion of patients with limb OS was relatively high. Lung metastasis (LM) and bone metastasis are the first and second most common metastatic types of OS, respectively. A total of 270 new cases of LM, 55 new cases of bone metastases (BM), and 36 new cases of lung and BM were diagnosed in the surveillance, epidemiology and end results database from 2010 to 2019. Univariate and multivariate logistic regression analyses were used to identify the risk factors for lung and/or BM, and Cox regression analyses were performed to identify the prognostic factors for lung and/or BM. Kaplan-Meier curves and log-rank tests were used to analyze the overall survival of limb OS patients with lung and/or BM. Female sex, telangiectatic OS type, central OS type, T3 stage, N1 stage, BM, surgical treatments, radiotherapy and chemotherapy were significantly correlated with LM. T3 stage, LM, liver metastases, and radiotherapy significantly correlated with BM. The small cell OS type, T2 stage, T3 stage, N1 stage, liver metastases, and radiotherapy were significantly correlated with lung and BM. Among limb OS patients with LM, the mean survival months of older age, black race, N1 stage, BM, brain metastases, no surgery, and no chemotherapy were lower than those of the control group. In limb OS patients with LM and BM, the mean survival months in the no surgery group was lower than in the surgery group. T stage and radiotherapy significantly influence the occurrence of limb OS with lung and/or BM. Surgery at the primary site has been shown to be effective in improving the survival rate of patients with lung and/or BM.

CEGAT: A CNN and enhanced-GAT based on key sample selection strategy for hyperspectral image classification.

In recent years, the application of convolutional neural networks (CNNs) and graph convolutional networks (GCNs) in hyperspectral image classification (HSIC) has achieved remarkable results. However, the limited label samples are still a major challenge when using CNN and GCN to classify hyperspectral images. In order to alleviate this problem, a double branch fusion network of CNN and enhanced graph attention network (CEGAT) based on key sample selection strategy is proposed. First, a linear discrimination of spectral inter-class slices (LD_SICS) module is designed to eliminate spectral redundancy of HSIs. Then, a spatial spectral correlation attention (SSCA) module is proposed, which can extract and assign attention weight to the spatial and spectral correlation features. On the graph attention (GAT) branch, the HSI is segmented into some super pixels as input to reduce the amount of network parameters. In addition, an enhanced graph attention (EGAT) module is constructed to enhance the relationship between nodes. Finally, a key sample selection (KSS) strategy is proposed to enable the network to achieve better classification performance with few labeled samples. Compared with other state-of-the-art methods, CEGAT has better classification performance under limited label samples.