The primordial differentiation of tumor-specific memory CD8+ T cells as bona fide responders to PD-1/PD-L1 blockade in draining lymph nodes.

Blocking PD-1/PD-L1 signaling transforms cancer therapy and is assumed to unleash exhausted tumor-reactive CD8+ T cells in the tumor microenvironment (TME). However, recent studies have also indicated that the systemic tumor-reactive CD8+ T cells may respond to PD-1/PD-L1 immunotherapy. These discrepancies highlight the importance of further defining tumor-specific CD8+ T cell responders to PD-1/PD-L1 blockade. Here, using multiple preclinical tumor models, we revealed that a subset of tumor-specific CD8+ cells in the tumor draining lymph nodes (TdLNs) was not functionally exhausted but exhibited canonical memory characteristics. TdLN-derived tumor-specific memory (TTSM) cells established memory-associated epigenetic program early during tumorigenesis. More importantly, TdLN-TTSM cells exhibited superior anti-tumor therapeutic efficacy after adoptive transfer and were characterized as bona fide responders to PD-1/PD-L1 blockade. These findings highlight that TdLN-TTSM cells could be harnessed to potentiate anti-tumor immunotherapy.

Modulation of Allostery with Multiple Mechanisms by Hotspot Mutations in TetR.

Modulating allosteric coupling offers unique opportunities for biomedical applications. Such efforts can benefit from efficient prediction and evaluation of allostery hotspot residues that dictate the degree of cooperativity between distant sites. We demonstrate that effects of allostery hotspot mutations can be evaluated qualitatively and semiquantitatively by molecular dynamics simulations in a bacterial tetracycline repressor (TetR). The simulations recapitulate the effects of these mutations on abolishing the induction function of TetR and provide a rationale for the different rescuabilities observed to restore allosteric coupling of the hotspot mutations. We demonstrate that the same noninducible phenotype could be the result of perturbations in distinct structural and energetic properties of TetR. Our work underscores the value of explicitly computing the functional free energy landscapes to effectively evaluate and rank hotspot mutations despite the prevalence of compensatory interactions and therefore provides quantitative guidance to allostery modulation for therapeutic and engineering applications.

Spatial transcriptomics prediction from histology jointly through Transformer and graph neural networks.

The rapid development of spatial transcriptomics allows the measurement of RNA abundance at a high spatial resolution, making it possible to simultaneously profile gene expression, spatial locations of cells or spots, and the corresponding hematoxylin and eosin-stained histology images. It turns promising to predict gene expression from histology images that are relatively easy and cheap to obtain. For this purpose, several methods are devised, but they have not fully captured the internal relations of the 2D vision features or spatial dependency between spots. Here, we developed Hist2ST, a deep learning-based model to predict RNA-seq expression from histology images. Around each sequenced spot, the corresponding histology image is cropped into an image patch and fed into a convolutional module to extract 2D vision features. Meanwhile, the spatial relations with the whole image and neighbored patches are captured through Transformer and graph neural network modules, respectively. These learned features are then used to predict the gene expression by following the zero-inflated negative binomial distribution. To alleviate the impact by the small spatial transcriptomics data, a self-distillation mechanism is employed for efficient learning of the model. By comprehensive tests on cancer and normal datasets, Hist2ST was shown to outperform existing methods in terms of both gene expression prediction and spatial region identification. Further pathway analyses indicated that our model could reserve biological information. Thus, Hist2ST enables generating spatial transcriptomics data from histology images for elucidating molecular signatures of tissues.

Functional plasticity and evolutionary adaptation of allosteric regulation.

Allostery is a fundamental regulatory mechanism of protein function. Despite notable advances, understanding the molecular determinants of allostery remains an elusive goal. Our current knowledge of allostery is principally shaped by a structure-centric view, which makes it difficult to understand the decentralized character of allostery. We present a function-centric approach using deep mutational scanning to elucidate the molecular basis and underlying functional landscape of allostery. We show that allosteric signaling exhibits a high degree of functional plasticity and redundancy through myriad mutational pathways. Residues critical for allosteric signaling are surprisingly poorly conserved while those required for structural integrity are highly conserved, suggesting evolutionary pressure to preserve fold over function. Our results suggest multiple solutions to the thermodynamic conditions of cooperativity, in contrast to the common view of a finely tuned allosteric residue network maintained under selection.

Molecular Dynamics Simulations Establish the Molecular Basis for the Broad Allostery Hotspot Distributions in the Tetracycline Repressor.

It is imperative to identify the network of residues essential to the allosteric coupling for the purpose of rationally engineering allostery in proteins. Deep mutational scanning analysis has emerged as a function-centric approach for identifying such allostery hotspots in a comprehensive and unbiased fashion, leading to observations that challenge our understanding of allostery at the molecular level. Specifically, a recent deep mutational scanning study of the tetracycline repressor (TetR) revealed an unexpectedly broad distribution of allostery hotspots throughout the protein structure. Using extensive molecular dynamics simulations (up to 50 µs) and free energy computations, we establish the molecular and energetic basis for the strong anticooperativity between the ligand and DNA binding sites. The computed free energy landscapes in different ligation states illustrate that allostery in TetR is well described by a conformational selection model, in which the apo state samples a broad set of conformations, and specific ones are selectively stabilized by either ligand or DNA binding. By examining a range of structural and dynamic properties of residues at both local and global scales, we observe that various analyses capture different subsets of experimentally identified hotspots, suggesting that these residues modulate allostery in distinct ways. These results motivate the development of a thermodynamic model that qualitatively explains the broad distribution of hotspot residues and their distinct features in molecular dynamics simulations. The multifaceted strategy that we establish here for hotspot evaluations and our insights into their mechanistic contributions are useful for modulating protein allostery in mechanistic and engineering studies.

Making Ultra-Tough Nanoceramics by Columnar Submicrocrystals with Three-Level Micro-Nano Structures.

The low fracture toughness of equiaxed nanocrystalline ceramics is the main bottleneck of its wide range of commercial applications. Here, the authors report a method to overcome this limitation for preparing ultra-tough nanoceramics from using amorphous and supersaturated Al2 O3 /ZrO2 solid solution micro-powders, which is fabricated by Al-O2 ultrahigh-temperature combustion synthesis assisted rapid water cooling. The Al2 O3 /ZrO2 micro-powders containing amorphous and metastable dendritic solid solutions can induce the three-level micro-nano structure (submicro/nano/supra-nano) of the high-content (up to 70-90%) columnar submicro-crystals accompanied with high-density nanoprecipitation after sintering or annealing, which makes the fracture toughness of Al2 O3 /ZrO2 ceramics with a unique combination of high-strength and high-hardness increased by 50-100%. This method is beneficial to microstructural design of high-performance ceramics and can be widely applied to various ceramic systems, coupled with simplicity, low-cost, and high-efficiency, making it suitable to industrially produce large-sized nanoceramics with specific grain geometry in large quantities.

[Risk Factors of Bleeding-related Perioperative Cardiac Arrest].

Objective To reveal the incidence,mortality,and risk factors of bleeding-related perioperative cardiac arrest(POCA). Methods We carried out a single-center retrospective case-control study which enrolled all the POCA cases reported from January 2010 to September 2020 in the patient safety incident reporting system of Peking Union Medical College Hospital.For the screening of risk factors,the patients were respectively assigned into the POCA group and the control group at a ratio of 1∶3 according to the same sex,age,American Society of Anesthesiologists(ASA)physical status,and type of surgery in the same month.Potential risk factors for POCA were first selected by univariate analysis.The significant risk factors were then checked based on the clinical experience and further included in the multivariate Logistic regression model. Results Totally 16 bleeding-related POCA cases were collected from the patient safety incident reporting system among the study period,with an overall incidence of 0.36/10 000.The blood loss volume of POCA group and control group was(7 037.50±5 477.70)ml and(375.63±675.14)ml,respectively(P<0.001),and 14(87.5%)patients suffering from bleeding-related POCA died within three days after anesthesia.According to the univariate analysis,patients' body mass index[(21.79±3.57)kg/m2 vs.(24.26±3.91)kg/m2,P=0.043],hemoglobin level[(113.44±31.08)g/L vs.(131.75±19.70)g/L,P=0.039],and alanine aminotransferase level[(17.31±7.73)U/L vs.(26.91±24.73)U/L,P=0.022]were significantly lower in the POCA group than in the control group.Further Logistic regression analysis showed that smaller body mass index and lower preoperative hemoglobin level were independently associated with the occurrence of bleeding-related POCA. Conclusions Bleeding-related POCA rarely occurred but had high mortality.Adequate precautions should be taken for the patients who are to receive surgeries with high risk of intraoperative massive bleeding.Elevating preoperative hemoglobin level might decrease the incidence of bleeding-related POCA.

Perioperative Allogeneic Red Blood Cell Transfusion and Wound Infections: An Observational Study.

BACKGROUND: It remains unclear whether the benefits of performing perioperative allogeneic red blood cell (RBC) transfusion outweigh the risks of postoperative wound infection. The aim of this study was to assess the impact of perioperative RBC transfusion as well as dose-response relationship on wound infections in surgical patients in a large cohort. METHODS: As a retrospective observational study, the national Hospital Quality Monitoring System database was used to retrieve information about in-hospital surgical patients without limitations on surgical types in the People's Republic of China between 2013 and 2018. Patients were divided into the perioperative RBC transfusion and non-RBC transfusion groups, and wound infection rates (the primary end point) were compared. Secondary end points included in-hospital mortality, nosocomial infections, and length of hospital stay. Furthermore, patients who underwent RBC transfusion were subdivided into 6 groups based on the volume of transfused RBCs to investigate the dose-response relationship between RBC transfusions and wound infections. The association between RBC transfusion and patient outcomes were analyzed using multivariable logistic regression models adjusted for potential confounders. RESULTS: A total of 1,896,584 patients from 29 provinces were included, among whom 76,078 (4.0%) underwent RBC transfusions; the overall wound infection rate was 0.7%. After adjusting for confounding factors, perioperative RBC transfusion was associated with higher odds of wound infection (odds ratio [OR] = 2.24, 95% confidence interval [CI], 2.09-2.40; P < .001). As the volume of transfused RBCs increased, so did the odds of wound infection with a clear dose-response relationship (OR of >0 and ≤1 U, >1 and ≤2 U, >2 and ≤4 U, >4 and ≤8 U, >8 U transfusion compared with no RBC transfusion were 1.20, 95% CI, 0.76-1.91; 1.27, 95% CI, 1.10-1.47; 1.70, 95% CI, 1.49-1.93; 2.12, 95% CI, 1.83-2.45 and 3.65, 95% CI, 3.13-4.25, respectively). RBC transfusion was also found to be associated with higher odds of in-hospital mortality, nosocomial infection, and longer hospital stay. CONCLUSIONS: RBC transfusion was associated with an increased odd of postoperative wound infection in surgical patients, and a significant dose-related relationship was also observed. While there are still essential confounders not adjusted for and the results do not necessarily indicate a causal relationship, we still recommend to lessen perioperative blood loss and optimize blood conservation strategies.

Cancer type prediction based on copy number aberration and chromatin 3D structure with convolutional neural networks.

BACKGROUND: With the developments of DNA sequencing technology, large amounts of sequencing data have been produced that provides unprecedented opportunities for advanced association studies between somatic mutations and cancer types/subtypes which further contributes to more accurate somatic mutation based cancer typing (SMCT). In existing SMCT methods however, the absence of high-level feature extraction is a major obstacle in improving the classification performance. RESULTS: We propose DeepCNA, an advanced convolutional neural network (CNN) based classifier, which utilizes copy number aberrations (CNAs) and HiC data, to address this issue. DeepCNA first pre-process the CNA data by clipping, zero padding and reshaping. Then, the processed data is fed into a CNN classifier, which extracts high-level features for accurate classification. Experimental results on the COSMIC CNA dataset indicate that 2D CNN with both cell lines of HiC data lead to the best performance. We further compare DeepCNA with three widely adopted classifiers, and demonstrate that DeepCNA has at least 78% improvement of performance. CONCLUSIONS: This paper demonstrates the advantages and potential of the proposed DeepCNA model for processing of somatic point mutation based gene data, and proposes that its usage may be extended to other complex genotype-phenotype association studies.

DeepGene: an advanced cancer type classifier based on deep learning and somatic point mutations.

BACKGROUND: With the developments of DNA sequencing technology, large amounts of sequencing data have become available in recent years and provide unprecedented opportunities for advanced association studies between somatic point mutations and cancer types/subtypes, which may contribute to more accurate somatic point mutation based cancer classification (SMCC). However in existing SMCC methods, issues like high data sparsity, small volume of sample size, and the application of simple linear classifiers, are major obstacles in improving the classification performance. RESULTS: To address the obstacles in existing SMCC studies, we propose DeepGene, an advanced deep neural network (DNN) based classifier, that consists of three steps: firstly, the clustered gene filtering (CGF) concentrates the gene data by mutation occurrence frequency, filtering out the majority of irrelevant genes; secondly, the indexed sparsity reduction (ISR) converts the gene data into indexes of its non-zero elements, thereby significantly suppressing the impact of data sparsity; finally, the data after CGF and ISR is fed into a DNN classifier, which extracts high-level features for accurate classification. Experimental results on our curated TCGA-DeepGene dataset, which is a reformulated subset of the TCGA dataset containing 12 selected types of cancer, show that CGF, ISR and DNN all contribute in improving the overall classification performance. We further compare DeepGene with three widely adopted classifiers and demonstrate that DeepGene has at least 24% performance improvement in terms of testing accuracy. CONCLUSIONS: Based on deep learning and somatic point mutation data, we devise DeepGene, an advanced cancer type classifier, which addresses the obstacles in existing SMCC studies. Experiments indicate that DeepGene outperforms three widely adopted existing classifiers, which is mainly attributed to its deep learning module that is able to extract the high level features between combinatorial somatic point mutations and cancer types.

Effective Semi-Supervised Medical Image Segmentation with Probabilistic Representations and Prototype Learning.

Label scarcity, class imbalance and data uncertainty are three primary challenges that are commonly encountered in the semi-supervised medical image segmentation. In this work, we focus on the data uncertainty issue that is overlooked by previous literature. To address this issue, we propose a probabilistic prototype-based classifier that introduces uncertainty estimation into the entire pixel classification process, including probabilistic representation formulation, probabilistic pixel-prototype proximity matching, and distribution prototype update, leveraging principles from probability theory. By explicitly modeling data uncertainty at the pixel level, model robustness of our proposed framework to tricky pixels, such as ambiguous boundaries and noises, is greatly enhanced when compared to its deterministic counterpart and other uncertainty-aware strategy. Empirical evaluations on three publicly available datasets that exhibit severe boundary ambiguity show the superiority of our method over several competitors. Moreover, our method also demonstrates a stronger model robustness to simulated noisy data. Code is available at https://github.com/IsYuchenYuan/PPC.

Intraoperative nitrous oxide inhalation to prevent chronic postsurgical pain in video-assisted thoracoscopic surgery: a prospective observational cohort study.

Background: Chronic postsurgical pain (CPSP) is a significant detriment to postsurgical recovery. Previous studies have shown that nitrous oxide (N2O) may produce long-term analgesia and may benefit the prevention of CPSP in Chinese patients. We tested the hypothesis that N2O is a protective factor against chronic pain after video-assisted thoracoscopic surgery (VATS). Methods: Two groups of patients with and without N2O inhalation during VATS in Peking Union Medical College Hospital were recruited. Perioperative information was documented, and postsurgical pain was followed up by telephone. The primary outcome was the presence of CPSP at 6 months postoperatively. Odds ratios (ORs) and their 95% confidence intervals (CIs) were estimated using a multivariate logistic regression model adjusted for relevant confounding factors. Results: A total of 833 patients were eligible, among whom 33.6% were male and 66.4% were female, with an average age of 56.3±11.1 years. A total of 387 (46.5%) patients reported incision-related pain at 6 months after surgery, and 160 (40.0%) out of 400 patients with N2O inhalation during surgery and 227 (52.4%) out of 433 patients without N2O inhalation during surgery developed CPSP. After adjusting for confounding factors, N2O inhalation during surgery was associated with lower odds of CPSP (OR =0.654; 95% CI: 0.480-0.890; P=0.007). Conclusions: N2O inhalation during surgery was associated with lower odds of CPSP in VATS patients, and N2O may benefit the prevention of chronic pain after thoracoscopic surgery.

Thermal-Assisted Multiscale Patterning of Nonplanar Colloidal Nanostructures for Multi-Modal Anti-Counterfeiting.

Nanotransfer printing of colloidal nanoparticles is a promising technique for the fabrication of functional materials and devices. However, patterning nonplanar nanostructures pose a challenge due to weak adhesion from the extremely small nanostructure-substrate contact area. Here, the study proposes a thermal-assisted nonplanar nanostructure transfer printing (NP-NTP) strategy for multiscale patterning of polystyrene (PS) nanospheres. The printing efficiency is significantly improved from ≈3.1% at low temperatures to ≈97.2% under the glass transition temperature of PS. Additionally, the arrangement of PS nanospheres transitioned from disorder to long-range order. The mechanism of printing efficiency enhancement is the drastic drop of Young's modulus of nanospheres, giving rise to an increased contact area, self-adhesive effect, and inter-particle necking. To demonstrate the versatility of the NP-NTP strategy, it is combined with the intaglio transfer printing technique, and multiple patterns are created at both micro and macro scales at a 4-inch scale with a resolution of ≈2757 pixels per inch (PPI). Furthermore, a multi-modal anti-counterfeiting concept based on structural patterns at hierarchical length scales is proposed, providing a new paradigm of imparting multiscale nanostructure patterning into macroscale functional devices.

PROST: quantitative identification of spatially variable genes and domain detection in spatial transcriptomics.

Computational methods have been proposed to leverage spatially resolved transcriptomic data, pinpointing genes with spatial expression patterns and delineating tissue domains. However, existing approaches fall short in uniformly quantifying spatially variable genes (SVGs). Moreover, from a methodological viewpoint, while SVGs are naturally associated with depicting spatial domains, they are technically dissociated in most methods. Here, we present a framework (PROST) for the quantitative recognition of spatial transcriptomic patterns, consisting of (i) quantitatively characterizing spatial variations in gene expression patterns through the PROST Index; and (ii) unsupervised clustering of spatial domains via a self-attention mechanism. We demonstrate that PROST performs superior SVG identification and domain segmentation with various spatial resolutions, from multicellular to cellular levels. Importantly, PROST Index can be applied to prioritize spatial expression variations, facilitating the exploration of biological insights. Together, our study provides a flexible and robust framework for analyzing diverse spatial transcriptomic data.

A multi-omic single-cell landscape of cellular diversification in the developing human cerebral cortex.

The vast neuronal diversity in the human neocortex is vital for high-order brain functions, necessitating elucidation of the regulatory mechanisms underlying such unparalleled diversity. However, recent studies have yet to comprehensively reveal the diversity of neurons and the molecular logic of neocortical origin in humans at single-cell resolution through profiling transcriptomic or epigenomic landscapes, owing to the application of unimodal data alone to depict exceedingly heterogeneous populations of neurons. In this study, we generated a comprehensive compendium of the developing human neocortex by simultaneously profiling gene expression and open chromatin from the same cell. We computationally reconstructed the differentiation trajectories of excitatory projection neurons of cortical origin and inferred the regulatory logic governing lineage bifurcation decisions for neuronal diversification. We demonstrated that neuronal diversity arises from progenitor cell lineage specificity and postmitotic differentiation at distinct stages. Our data paves the way for understanding the primarily coordinated regulatory logic for neuronal diversification in the neocortex.

Accurate and Efficient Multilevel Free Energy Simulations with Neural Network-Assisted Enhanced Sampling.

Free energy differences (ΔF) are essential to quantitative characterization and understanding of chemical and biological processes. Their direct estimation with an accurate quantum mechanical potential is of great interest and yet impractical due to high computational cost and incompatibility with typical alchemical free energy protocols. One promising solution is the multilevel free energy simulation in which the estimate of ΔF at an inexpensive low level of theory is combined with the correction toward a higher level of theory. The poor configurational overlap generally expected between the two levels of theory, however, presents a major challenge. We overcome this challenge by using a deep neural network model and enhanced sampling simulations. An adversarial autoencoder is used to identify a low-dimensional (latent) space that compactly represents the degrees of freedom that encode the distinct distributions at the two levels of theory. Enhanced sampling in this latent space is then used to drive the sampling of configurations that predominantly contribute to the free energy correction. Results for both gas phase and condensed phase systems demonstrate that this data-driven approach offers high accuracy and efficiency with great potential for scalability to complex systems.

Modulation of Allostery with Multiple Mechanisms by Hotspot Mutations in TetR.

Modulating allosteric coupling offers unique opportunities for biomedical applications. Such efforts can benefit from efficient prediction and evaluation of allostery hotspot residues that dictate the degree of co-operativity between distant sites. We demonstrate that effects of allostery hotspot mutations can be evaluated qualitatively and semi-quantitatively by molecular dynamics simulations in a bacterial tetracycline repressor (TetR). The simulations recapitulate the effects of these mutations on abolishing the induction function of TetR and provide a rationale for the different degrees of rescuability observed to restore allosteric coupling of the hotspot mutations. We demonstrate that the same non-inducible phenotype could be the result of perturbations in distinct structural and energetic properties of TetR. Our work underscore the value of explicitly computing the functional free energy landscapes to effectively evaluate and rank hotspot mutations despite the prevalence of compensatory interactions, and therefore provide quantitative guidance to allostery modulation for therapeutic and engineering applications.

MemBridge: Video-Language Pre-Training With Memory-Augmented Inter-Modality Bridge.

Video-language pre-training has attracted considerable attention recently for its promising performance on various downstream tasks. Most existing methods utilize the modality-specific or modality-joint representation architectures for the cross-modality pre-training. Different from previous methods, this paper presents a novel architecture named Memory-augmented Inter-Modality Bridge (MemBridge), which uses the learnable intermediate modality representations as the bridge for the interaction between videos and language. Specifically, in the transformer-based cross-modality encoder, we introduce the learnable bridge tokens as the interaction approach, which means the video and language tokens can only perceive information from bridge tokens and themselves. Moreover, a memory bank is proposed to store abundant modality interaction information for adaptively generating bridge tokens according to different cases, enhancing the capacity and robustness of the inter-modality bridge. Through pre-training, MemBridge explicitly models the representations for more sufficient inter-modality interaction. Comprehensive experiments show that our approach achieves competitive performance with previous methods on various downstream tasks including video-text retrieval, video captioning, and video question answering on multiple datasets, demonstrating the effectiveness of the proposed method. The code has been available at https://github.com/jahhaoyang/MemBridge.

Multi-Level Attention Network for Retinal Vessel Segmentation.

Automatic vessel segmentation in the fundus images plays an important role in the screening, diagnosis, treatment, and evaluation of various cardiovascular and ophthalmologic diseases. However, due to the limited well-annotated data, varying size of vessels, and intricate vessel structures, retinal vessel segmentation has become a long-standing challenge. In this paper, a novel deep learning model called AACA-MLA-D-UNet is proposed to fully utilize the low-level detailed information and the complementary information encoded in different layers to accurately distinguish the vessels from the background with low model complexity. The architecture of the proposed model is based on U-Net, and the dropout dense block is proposed to preserve maximum vessel information between convolution layers and mitigate the over-fitting problem. The adaptive atrous channel attention module is embedded in the contracting path to sort the importance of each feature channel automatically. After that, the multi-level attention module is proposed to integrate the multi-level features extracted from the expanding path, and use them to refine the features at each individual layer via attention mechanism. The proposed method has been validated on the three publicly available databases, i.e. the DRIVE, STARE, and CHASE _ DB1. The experimental results demonstrate that the proposed method can achieve better or comparable performance on retinal vessel segmentation with lower model complexity. Furthermore, the proposed method can also deal with some challenging cases and has strong generalization ability.