Nanoparticle-Patch System for Localized, Effective, and Sustained miRNA Administration into Infarcted Myocardium to Alleviate Myocardial Ischemia-Reperfusion Injury.

Timely blood reperfusion after myocardial infarction (MI) paradoxically triggers ischemia-reperfusion injury (I/RI), which currently has not been conquered by clinical treatments. Among innovative repair strategies for myocardial I/RI, microRNAs (miRNAs) are expected as genetic tools to rescue damaged myocardium. Our previous study identified that miR-30d can provide protection against myocardial apoptosis and fibrosis to alleviate myocardial injury. Although common methods such as liposomes and viral vectors have been used for miRNA transfection, their therapeutic efficiencies have struggled with inefficient in vivo delivery, susceptible inactivation, and immunogenicity. Here, we establish a nanoparticle-patch system for miR-30d delivery in a murine myocardial I/RI model, which contains ZIF-8 nanoparticles and a conductive microneedle patch. Loaded with miR-30d, ZIF-8 nanoparticles leveraging the proton sponge effect enable miR-30d to escape the endocytic pathway, thus avoiding premature degradation in lysosomes. Meanwhile, the conductive microneedle patch offers a distinct advantage by intramyocardial administration for localized, effective, and sustained miR-30d delivery, and it simultaneously releases Au nanoparticles to reconstruct electrical impulses within the infarcted myocardium. Consequently, the nanoparticle-patch system supports the consistent and robust expression of miR-30d in cardiomyocytes. Results from echocardiography and electrocardiogram (ECG) revealed improved heart functions and standard ECG wave patterns in myocardial I/RI mice after implantation of a nanoparticle-patch system for 3 and 6 weeks. In summary, our work incorporated conductive microneedle patch and miR-30d nanodelivery systems to synergistically transcend the limitations of common RNA transfection methods, thus mitigating myocardial I/RI.

Collaborative CRISPR-Cas System-Enabled Detection of Circulating Circular RNA for Reliable Monitoring of Acute Myocardial Infarction.

Acute myocardial infarction (AMI) is one of the major causes of death worldwide, posing significant global health challenges. Circular RNA (circRNA) has recently emerged as a potential diagnostic biomarker for AMI, providing valuable information for timely medical care. In this work, a new electrochemical method for circRNA detection by engineering a collaborative CRISPR-Cas system is developed. This system integrates the unique circRNA-targeting ability with cascade trans-cleavage activities of Cas effectors, using an isothermal primer exchange reaction as the bridge. Using cZNF292, a circulating circRNA biomarker for AMI is identified by this group; as a model, the collaborative CRISPR-Cas system-based method exhibits excellent accuracy and sensitivity with a low detection limit of 2.13 × 10-15 m. Moreover, the method demonstrates a good diagnostic performance for AMI when analyzing whole blood samples. Therefore, the method may provide new insight into the detection of circRNA biomarkers and is expected to have great potential in AMI diagnosis in the future.

Complete spatially resolved gene expression is not necessary for identifying spatial domains.

Spatially resolved transcriptomics (SRT) technologies have revolutionized the study of tissue organization. We introduce a graph convolutional network with an attention and positive emphasis mechanism, termed BINARY, relying exclusively on binarized SRT data to accurately delineate spatial domains. BINARY outperforms existing methods across various SRT data types while using significantly less input information. Our study suggests that precise gene expression quantification may not always be essential, inspiring further exploration of the broader applications of spatially resolved binarized gene expression data.

Structural and chemical study of complex silver patterns additively manufactured by multi-photon reduction.

Multi-photon reduction (MPR) based on femtosecond laser makes rapid prototyping and molding in micro-nano scale feasible, but is limited in material selectivity due to lack of the understanding of the reaction mechanism in MPR process. In this paper, additively manufacturing of complex silver-based patterns through MPR is demonstrated. The effects of laser parameters, including laser pulse energies and scanning speeds, on the structural and chemical characteristics of the printed structures are systematically investigated. The results show that the geometric size of printed cubes deviates from the designed size further by increasing laser pulse energy or decreasing scanning speed. The reaction mechanism of MPR is revealed by studying the elemental composition and chemical structures of printed cubes. The evolution of Raman spectra upon the laser processing parameters suggests that the MPR process mainly includes two processes: reduction and decomposition. In the MPR process, silver ions are reduced and grow into particles by accepting the electrons from ethonal molecules; meanwhile carboxyl groups in polyvinylpyrrolidone are decomposed and form amorphous carbon that is attached on the surface of silver particles. The conductivity of silver wires fabricated by MPR reaches 2 × 105S m-1and stays relatively constant as varying their cross section area, suggesting excellent electrical conduction. The understanding of the MPR process would accelerate the development of MPR technology and the implementation of MPR in micro-electromechanical systems could therefore be envisioned.

Inhibition of Hmbox1 Promotes Cardiomyocyte Survival and Glucose Metabolism Through Gck Activation in Ischemia/Reperfusion Injury.

BACKGROUND: Exercise-induced physiological cardiac growth regulators may protect the heart from ischemia/reperfusion (I/R) injury. Homeobox-containing 1 (Hmbox1), a homeobox family member, has been identified as a putative transcriptional repressor and is downregulated in the exercised heart. However, its roles in exercise-induced physiological cardiac growth and its potential protective effects against cardiac I/R injury remain largely unexplored. METHODS: We studied the function of Hmbox1 in exercise-induced physiological cardiac growth in mice after 4 weeks of swimming exercise. Hmbox1 expression was then evaluated in human heart samples from deceased patients with myocardial infarction and in the animal cardiac I/R injury model. Its role in cardiac I/R injury was examined in mice with adeno-associated virus 9 (AAV9) vector-mediated Hmbox1 knockdown and in those with cardiac myocyte-specific Hmbox1 ablation. We performed RNA sequencing, promoter prediction, and binding assays and identified glucokinase (Gck) as a downstream effector of Hmbox1. The effects of Hmbox1 together with Gck were examined in cardiomyocytes to evaluate their cell size, proliferation, apoptosis, mitochondrial respiration, and glycolysis. The function of upstream regulator of Hmbox1, ETS1, was investigated through ETS1 overexpression in cardiac I/R mice in vivo. RESULTS: We demonstrated that Hmbox1 downregulation was required for exercise-induced physiological cardiac growth. Inhibition of Hmbox1 increased cardiomyocyte size in isolated neonatal rat cardiomyocytes and human embryonic stem cell-derived cardiomyocytes but did not affect cardiomyocyte proliferation. Under pathological conditions, Hmbox1 was upregulated in both human and animal postinfarct cardiac tissues. Furthermore, both cardiac myocyte-specific Hmbox1 knockout and AAV9-mediated Hmbox1 knockdown protected against cardiac I/R injury and heart failure. Therapeutic effects were observed when sh-Hmbox1 AAV9 was administered after I/R injury. Inhibition of Hmbox1 activated the Akt/mTOR/P70S6K pathway and transcriptionally upregulated Gck, leading to reduced apoptosis and improved mitochondrial respiration and glycolysis in cardiomyocytes. ETS1 functioned as an upstream negative regulator of Hmbox1 transcription, and its overexpression was protective against cardiac I/R injury. CONCLUSIONS: Our studies unravel a new role for the transcriptional repressor Hmbox1 in exercise-induced physiological cardiac growth. They also highlight the therapeutic potential of targeting Hmbox1 to improve myocardial survival and glucose metabolism after I/R injury.

Learning With Noisy Labels Over Imbalanced Subpopulations.

Learning with noisy labels (LNL) has attracted significant attention from the research community. Many recent LNL methods rely on the assumption that clean samples tend to have a "small loss." However, this assumption often fails to generalize to some real-world cases with imbalanced subpopulations, that is, training subpopulations that vary in sample size or recognition difficulty. Therefore, recent LNL methods face the risk of misclassifying those "informative" samples (e.g., hard samples or samples in the tail subpopulations) into noisy samples, leading to poor generalization performance. To address this issue, we propose a novel LNL method to deal with noisy labels and imbalanced subpopulations simultaneously. It first leverages sample correlation to estimate samples' clean probabilities for label correction and then utilizes corrected labels for distributionally robust optimization (DRO) to further improve the robustness. Specifically, in contrast to previous works using classification loss as the selection criterion, we introduce a feature-based metric that takes the sample correlation into account for estimating samples' clean probabilities. Then, we refurbish the noisy labels using the estimated clean probabilities and the pseudo-labels from the model's predictions. With refurbished labels, we use DRO to train the model to be robust to subpopulation imbalance. Extensive experiments on a wide range of benchmarks demonstrate that our technique can consistently improve state-of-the-art (SOTA) robust learning paradigms against noisy labels, especially when encountering imbalanced subpopulations. We provide our code in https://github.com/chenmc1996/LNL-IS.

Integrative analysis of genetics, epigenetics and RNA expression data reveal three susceptibility loci for smoking behavior in Chinese Han population.

Despite numerous studies demonstrate that genetics and epigenetics factors play important roles on smoking behavior, our understanding of their functional relevance and coordinated regulation remains largely unknown. Here we present a multiomics study on smoking behavior for Chinese smoker population with the goal of not only identifying smoking-associated functional variants but also deciphering the pathogenesis and mechanism underlying smoking behavior in this under-studied ethnic population. After whole-genome sequencing analysis of 1329 Chinese Han male samples in discovery phase and OpenArray analysis of 3744 samples in replication phase, we discovered that three novel variants located near FOXP1 (rs7635815), and between DGCR6 and PRODH (rs796774020), and in ARVCF (rs148582811) were significantly associated with smoking behavior. Subsequently cis-mQTL and cis-eQTL analysis indicated that these variants correlated significantly with the differential methylation regions (DMRs) or differential expressed genes (DEGs) located in the regions where these variants present. Finally, our in silico multiomics analysis revealed several hub genes, like DRD2, PTPRD, FOXP1, COMT, CTNNAP2, to be synergistic regulated each other in the etiology of smoking.

Study on the Effect of Electrolytes on Processing Efficiency and Accuracy of Titanium Alloy Utilizing Laser and Shaped Tube Electrochemical Machining.

Electrochemical machining (ECM) has become more prevalent in titanium alloy processing. However, the presence of the passivation layer on the titanium alloys significantly impacts the performance of ECM. In an attempt to overcome the passivation effects, a high-temperature electrolyte or the addition of halogen ions to the electrolyte has been used. Still, it often results in compromised machining accuracy and surface roughness. This study applied laser and shaped tube electrolytic machining (Laser-STEM) for titanium alloy drilling, where the laser was guided to the machining zone via total internal reflection. The performance of Laser-STEM using different types of electrolytes was compared. Further, the effects of laser power and pulse voltage on the machining side gap, material removal rate (MRR), and surface roughness were experimentally studied while drilling small holes in titanium alloy. The results indicated that the use of passivating electrolytes improved the machining precision, while the MRR decreased with an increase in laser power during Laser-STEM. The MRR showed an increase while using aggressive electrolytes; however, at the same time, the machining precision deteriorated with the increase in laser power. Particularly, the maximum feeding rate of 6.0 mm/min for the tool electrode was achieved using NaCl solution as the electrolyte during Laser-STEM, marking a 100% increase compared to the rate without the use of a laser. Moreover, the model and equivalent circuits were also established to illustrate the material removal mechanisms of Laser-STEM in different electrolytes. Lastly, the processing of deep small holes with a diameter of 1.5 mm, a depth of 38 mm, and a surface roughness of Ra 2 µm was achieved via Laser-STEM without the presence of a recast layer and heat-affected zones. In addition, the cross-inner flow channels in the titanium alloys were effectively processed.

Synergistic Effect of CO2 in Accelerating the Galvanic Corrosion of Lithium/Sodium Anodes in Alkali Metal-Carbon Dioxide Batteries.

Rechargeable alkali metal-CO2 batteries, which combine high theoretical energy density and environmentally friendly CO2 fixation ability, have attracted worldwide attention. Unfortunately, their electrochemical performances are usually inferior for practical applications. Aiming to reveal the underlying causes, a combinatorial usage of advanced nondestructive and postmortem characterization tools is used to intensively study the failure mechanisms of Li/Na-CO2 batteries. It is found that a porous interphase layer is formed between the separator and the Li/Na anode during the overvoltage rising and battery performance decaying process. A series of control experiments are designed to identify the underlying mechanisms dictating the observed morphological evolution of Li/Na anodes, and it is found that the CO2 synergist facilitates Li/Na chemical corrosion, the process of which is further promoted by the unwanted galvanic corrosion and the electrochemical cycling conditions. A detailed compositional analysis reveals that the as-formed interphase layers under different conditions are similar in species, with the main differences being their inconsistent quantity. Theoretical calculation results not only suggest an inherent intermolecular affinity between the CO2 and the electrolyte solvent but also provide the most thermodynamically favored CO2 reaction pathways. Based on these results, important implications for the further development of rechargeable alkali metal-CO2 batteries are discussed. The current discoveries not only fundamentally enrich our knowledge of the failure mechanisms of rechargeable alkali metal-CO2 batteries but also provide mechanistic directions for protecting metal anodes to build high-reversible alkali metal-CO2 batteries.

Alcohol drinking, DNA methylation and psychiatric disorders: A multi-omics Mendelian randomization study to investigate causal pathways.

BACKGROUND AND AIMS: Whether alcohol-related DNA methylation has a causal effect on psychiatric disorders has not been investigated. Furthermore, a comprehensive investigation into the causal relationship and underlying mechanisms linking alcohol consumption and psychiatric disorders has been lacking. This study aimed to evaluate the causal effect of general alcohol intake and pathological drinking behaviors on psychiatric disorders, alcohol-associated DNA methylation on gene expression and psychiatric disorders, and gene expression on psychiatric disorders. DESIGN: Two-sample design Mendelian randomization (MR) analysis. Various sensitivity and validation analyses, including colocalization analysis, were conducted to test the robustness of the results. SETTING: Genome-wide association study (GWAS) data mainly from GWAS and Sequencing Consortium of Alcohol and Nicotine use (GSCAN), Genetics of DNA Methylation Consortium (GoDMC) and Psychiatric Genomics Consortium (PGC) with European ancestry. PARTICIPANTS: The GWAS summary data on general alcohol intake (drinks per week, n = 941 280), pathological drinking behaviors (including alcohol use disorder [AUD, n = 313 959] and problematic alcohol use [PAU, n = 435 563]) and psychiatric disorders (including schizophrenia, major depressive disorder and bipolar disorder, n = 51 710-500 199) were included. Alcohol-related DNA methylation CpG sites (n = 9643) and mQTL data from blood (n = 27 750) and brain (n = 1160), BrainMeta v2 and GTEx V8 eQTL summary data (n = 73-2865) were also included. MEASUREMENTS: Genetic variants were selected as instrumental variables for exposures, including drinks per week, AUD, PAU, alcohol-related DNA methylation CpG sites (mQTL) and genes selected (eQTL). FINDINGS: Pathological drinking behaviors were associated with an increased risk of psychiatric disorders after removing outliers or controlling for alcohol consumption. MR analysis identified 10 alcohol-related CpG sites with colocalization evidence that were causally associated with psychiatric disorders (P = 1.65 × 10-4-7.52 × 10-22). Furthermore, the expression of genes (RERE, PTK6, GATAD2B, COG8, PDF and GAS5) mapped to these CpG sites in the brain, led by the cortex, were significantly associated with psychiatric disorders (P = 1.19 × 10-2-3.51 × 10-7). CONCLUSIONS: Pathological drinking behavior and alcohol-related DNA methylation appear to have a causal effect on psychiatric disorders. The expression of genes regulated by the alcohol-related DNA methylation sites may underpin this association.

A real-time augmented reality system integrated with artificial intelligence for skin tumor surgery: experimental study and case series.

BACKGROUND: Skin tumors affect many people worldwide, and surgery is the first treatment choice. Achieving precise preoperative planning and navigation of intraoperative sampling remains a problem and is excessively reliant on the experience of surgeons, especially for Mohs surgery for malignant tumors. MATERIALS AND METHODS: To achieve precise preoperative planning and navigation of intraoperative sampling, we developed a real-time augmented reality (AR) surgical system integrated with artificial intelligence (AI) to enhance three functions: AI-assisted tumor boundary segmentation, surgical margin design, and navigation in intraoperative tissue sampling. Non-randomized controlled trials were conducted on manikin, tumor-simulated rabbits, and human volunteers in Hunan Engineering Research Center of Skin Health and Disease Laboratory to evaluate the surgical system. RESULTS: The results showed that the accuracy of the benign and malignant tumor segmentation was 0.9556 and 0.9548, respectively, and the average AR navigation mapping error was 0.644 mm. The proposed surgical system was applied in 106 skin tumor surgeries, including intraoperative navigation of sampling in 16 Mohs surgery cases. Surgeons who have used this system highly recognize it. CONCLUSIONS: The surgical system highlighted the potential to achieve accurate treatment of skin tumors and to fill the gap in global research on skin tumor surgery systems.

CTEC: a cross-tabulation ensemble clustering approach for single-cell RNA sequencing data analysis.

MOTIVATION: Cell-type clustering is a crucial first step for single-cell RNA-seq data analysis. However, existing clustering methods often provide different results on cluster assignments with respect to their own data pre-processing, choice of distance metrics, and strategies of feature extraction, thereby limiting their practical applications. RESULTS: We propose Cross-Tabulation Ensemble Clustering (CTEC) method that formulates two re-clustering strategies (distribution- and outlier-based) via cross-tabulation. Benchmarking experiments on five scRNA-Seq datasets illustrate that the proposed CTEC method offers significant improvements over the individual clustering methods. Moreover, CTEC-DB outperforms the state-of-the-art ensemble methods for single-cell data clustering, with 45.4% and 17.1% improvement over the single-cell aggregated from ensemble clustering method (SAFE) and the single-cell aggregated clustering via Mixture model ensemble method (SAME), respectively, on the two-method ensemble test. AVAILABILITY AND IMPLEMENTATION: The source code of the benchmark in this work is available at the GitHub repository https://github.com/LWCHN/CTEC.git.

Deep Spatio-Temporal Network for Low SNR Cryo-EM Movie Frame Enhancement.

Cryo-EM in single particle analysis is known to have low SNR and requires to utilize several frames of the same particle sample to restore one high-quality image for visualizing that particle. However, the low SNR of cryo-EM movie and motion caused by beam striking make the task very challenging. Video enhancement algorithms in computer vision shed new light on tackling such tasks by utilizing deep neural networks. However, they are designed for natural images with high SNR. Meanwhile, the lack of ground truth in cryo-EM movie seems to be one major limiting factor of the progress. Hence, we present a synthetic cryo-EM movie generation pipeline, which can produce realistic diverse cryo-EM movie datasets with low-SNR movie frames and multiple ground truth values. Then we propose a deep spatio-temporal network (DST-Net) for cryo-EM movie frame enhancement trained on our synthetic data. Spatial and temporal features are first extracted from each frame. Spatio-temporal fusion and high-resolution re-constructor are designed to obtain the enhanced output. For evaluation, we train our model on seven synthetic cryo-EM movie datasets and infer on real cryo-EM data. The experimental results show that DST-Net can achieve better enhancement performance both quantitatively and qualitatively compared with others.

Benchmarking spatial clustering methods with spatially resolved transcriptomics data.

Spatial clustering, which shares an analogy with single-cell clustering, has expanded the scope of tissue physiology studies from cell-centroid to structure-centroid with spatially resolved transcriptomics (SRT) data. Computational methods have undergone remarkable development in recent years, but a comprehensive benchmark study is still lacking. Here we present a benchmark study of 13 computational methods on 34 SRT data (7 datasets). The performance was evaluated on the basis of accuracy, spatial continuity, marker genes detection, scalability, and robustness. We found existing methods were complementary in terms of their performance and functionality, and we provide guidance for selecting appropriate methods for given scenarios. On testing additional 22 challenging datasets, we identified challenges in identifying noncontinuous spatial domains and limitations of existing methods, highlighting their inadequacies in handling recent large-scale tasks. Furthermore, with 145 simulated data, we examined the robustness of these methods against four different factors, and assessed the impact of pre- and postprocessing approaches. Our study offers a comprehensive evaluation of existing spatial clustering methods with SRT data, paving the way for future advancements in this rapidly evolving field.

scPROTEIN: a versatile deep graph contrastive learning framework for single-cell proteomics embedding.

Single-cell proteomics sequencing technology sheds light on protein-protein interactions, posttranslational modifications and proteoform dynamics in the cell. However, the uncertainty estimation for peptide quantification, data missingness, batch effects and high noise hinder the analysis of single-cell proteomic data. It is important to solve this set of tangled problems together, but the existing methods tailored for single-cell transcriptomes cannot fully address this task. Here we propose a versatile framework designed for single-cell proteomics data analysis called scPROTEIN, which consists of peptide uncertainty estimation based on a multitask heteroscedastic regression model and cell embedding generation based on graph contrastive learning. scPROTEIN can estimate the uncertainty of peptide quantification, denoise protein data, remove batch effects and encode single-cell proteomic-specific embeddings in a unified framework. We demonstrate that scPROTEIN is efficient for cell clustering, batch correction, cell type annotation, clinical analysis and spatially resolved proteomic data exploration.

Study on Microstructure and High Temperature Stability of WTaVTiZrx Refractory High Entropy Alloy Prepared by Laser Cladding.

The extremely harsh environment of the high temperature plasma imposes strict requirements on the construction materials of the first wall in a fusion reactor. In this work, a refractory alloy system, WTaVTiZrx, with low activation and high entropy, was theoretically designed based on semi-empirical formula and produced using a laser cladding method. The effects of Zr proportions on the metallographic microstructure, phase composition, and alloy chemistry of a high-entropy alloy cladding layer were investigated using a metallographic microscope, XRD (X-ray diffraction), SEM (scanning electron microscope), and EDS (energy dispersive spectrometer), respectively. The high-entropy alloys have a single-phase BCC structure, and the cladding layers exhibit a typical dendritic microstructure feature. The evolution of microstructure and mechanical properties of the high-entropy alloys, with respect to annealing temperature, was studied to reveal the performance stability of the alloy at a high temperature. The microstructure of the annealed samples at 900 °C for 5-10 h did not show significant changes compared to the as-cast samples, and the microhardness increased to 988.52 HV, which was higher than that of the as-cast samples (725.08 HV). When annealed at 1100 °C for 5 h, the microstructure remained unchanged, and the microhardness increased. However, after annealing for 10 h, black substances appeared in the microstructure, and the microhardness decreased, but it was still higher than the matrix. When annealed at 1200 °C for 5-10 h, the microhardness did not increase significantly compared to the as-cast samples, and after annealing for 10 h, the microhardness was even lower than that of the as-cast samples. The phase of the high entropy alloy did not change significantly after high-temperature annealing, indicating good phase stability at high temperatures. After annealing for 10 h, the microhardness was lower than that of the as-cast samples. The phase of the high entropy alloy remained unchanged after high-temperature annealing, demonstrating good phase stability at high temperatures.

SPDB: a comprehensive resource and knowledgebase for proteomic data at the single-cell resolution.

The single-cell proteomics enables the direct quantification of protein abundance at the single-cell resolution, providing valuable insights into cellular phenotypes beyond what can be inferred from transcriptome analysis alone. However, insufficient large-scale integrated databases hinder researchers from accessing and exploring single-cell proteomics, impeding the advancement of this field. To fill this deficiency, we present a comprehensive database, namely Single-cell Proteomic DataBase (SPDB, https://scproteomicsdb.com/), for general single-cell proteomic data, including antibody-based or mass spectrometry-based single-cell proteomics. Equipped with standardized data process and a user-friendly web interface, SPDB provides unified data formats for convenient interaction with downstream analysis, and offers not only dataset-level but also protein-level data search and exploration capabilities. To enable detailed exhibition of single-cell proteomic data, SPDB also provides a module for visualizing data from the perspectives of cell metadata or protein features. The current version of SPDB encompasses 133 antibody-based single-cell proteomic datasets involving more than 300 million cells and over 800 marker/surface proteins, and 10 mass spectrometry-based single-cell proteomic datasets involving more than 4000 cells and over 7000 proteins. Overall, SPDB is envisioned to be explored as a useful resource that will facilitate the wider research communities by providing detailed insights into proteomics from the single-cell perspective.

Streamlining spatial omics data analysis with Pysodb.

Advances in spatial omics technologies have improved the understanding of cellular organization in tissues, leading to the generation of complex and heterogeneous data and prompting the development of specialized tools for managing, loading and visualizing spatial omics data. The Spatial Omics Database (SODB) was established to offer a unified format for data storage and interactive visualization modules. Here we detail the use of Pysodb, a Python-based tool designed to enable the efficient exploration and loading of spatial datasets from SODB within a Python environment. We present seven case studies using Pysodb, detailing the interaction with various computational methods, ensuring reproducibility of experimental data and facilitating the integration of new data and alternative applications in SODB. The approach offers a reference for method developers by outlining label and metadata availability in representative spatial data that can be loaded by Pysodb. The tool is supplemented by a website ( https://protocols-pysodb.readthedocs.io/ ) with detailed information for benchmarking analysis, and allows method developers to focus on computational models by facilitating data processing. This protocol is designed for researchers with limited experience in computational biology. Depending on the dataset complexity, the protocol typically requires ~12 h to complete.

scMHNN: a novel hypergraph neural network for integrative analysis of single-cell epigenomic, transcriptomic and proteomic data.

Technological advances have now made it possible to simultaneously profile the changes of epigenomic, transcriptomic and proteomic at the single cell level, allowing a more unified view of cellular phenotypes and heterogeneities. However, current computational tools for single-cell multi-omics data integration are mainly tailored for bi-modality data, so new tools are urgently needed to integrate tri-modality data with complex associations. To this end, we develop scMHNN to integrate single-cell multi-omics data based on hypergraph neural network. After modeling the complex data associations among various modalities, scMHNN performs message passing process on the multi-omics hypergraph, which can capture the high-order data relationships and integrate the multiple heterogeneous features. Followingly, scMHNN learns discriminative cell representation via a dual-contrastive loss in self-supervised manner. Based on the pretrained hypergraph encoder, we further introduce the pre-training and fine-tuning paradigm, which allows more accurate cell-type annotation with only a small number of labeled cells as reference. Benchmarking results on real and simulated single-cell tri-modality datasets indicate that scMHNN outperforms other competing methods on both cell clustering and cell-type annotation tasks. In addition, we also demonstrate scMHNN facilitates various downstream tasks, such as cell marker detection and enrichment analysis.

A Systematic Study on the Processing Strategy in Femtosecond Laser Scribing via a Two-Temperature Model.

Balancing quality and productivity, especially deciding on the optimal matching strategy for multiple process parameters, is challenging in ultrashort laser processing. In this paper, an economical and new processing strategy was studied based on the laser scribing case. To reveal the temperature evolution under the combination of multiple process parameters in the laser scribing process, a two-temperature model involving a moving laser source was developed. The results indicated that the peak thermal equilibrium temperature between the electron and lattice increased with the increase in the laser fluence, and the temperature evolution at the initial position, influenced by subsequent pulses, was strongly associated with the overlap ratio. The thermal ablation effect was strongly enhanced with the increase in laser fluence. The groove morphology was controllable by selecting the overlap ratio at the same laser fluence. The removal volume per joule (i.e., energy utilization efficiency) and the removal volume per second (i.e., ablation efficiency) were introduced to analyze the ablation characteristics influenced by multiple process parameters. The law derived from statistical analysis is as follows; at the same laser fluence with the same overlap ratio, the energy utilization efficiency is insensitive to changes in the repetition rate, and the ablation efficiency increases as the repetition rate increases. As a result, a decision-making strategy for balancing quality and productivity was created.