High-density generation of spatial transcriptomics with STAGE.

Spatial transcriptome technologies have enabled the measurement of gene expression while maintaining spatial location information for deciphering the spatial heterogeneity of biological tissues. However, they were heavily limited by the sparse spatial resolution and low data quality. To this end, we develop a spatial location-supervised auto-encoder generator STAGE for generating high-density spatial transcriptomics (ST). STAGE takes advantage of the customized supervised auto-encoder to learn continuous patterns of gene expression in space and generate high-resolution expressions for given spatial coordinates. STAGE can improve the low quality of spatial transcriptome data and smooth the generated manifold of gene expression through the de-noising function on the latent codes of the auto-encoder. Applications to four ST datasets, STAGE has shown better recovery performance for down-sampled data than existing methods, revealed significant tissue structure specificity, and enabled robust identification of spatially informative genes and patterns. In addition, STAGE can be extended to three-dimensional (3D) stacked ST data for generating gene expression at any position between consecutive sections for shaping high-density 3D ST configuration.

CdSe Quantum Dots Enable High Thermoelectric Performance in Solution-Processed Polycrystalline SnSe.

Here, a high peak ZT of ≈2.0 is reported in solution-processed polycrystalline Ge and Cd codoped SnSe. Microstructural characterization reveals that CdSe quantum dots are successfully introduced by solution process method. Ultraviolet photoelectron spectroscopy evinces that CdSe quantum dots enhance the density of states in the electronic structure of SnSe, which leads to a large Seebeck coefficient. It is found that Ge and Cd codoping simultaneously optimizes carrier concentration and improves electrical conductivity. The enhanced Seebeck coefficient and optimization of carrier concentration lead to marked increase in power factor. CdSe quantum dots combined with strong lattice strain give rise to strong phonon scattering, leading to an ultralow lattice thermal conductivity. Consequently, high thermoelectric performance is realized in solution-processed polycrystalline SnSe by designing quantum dot structures and introducing lattice strain. This work provides a new route for designing prospective thermoelectric materials by microstructural manipulation in solution chemistry.

LncRNA 51325 Alleviates Bone Cancer Induced Hyperalgesia Through Inhibition of Pum2.

Background: Bone cancer pain (BCP) represents one of the most challenging comorbidities associated with cancer metastasis. Long non-coding RNAs (lncRNAs) have garnered attention as potential therapeutic agents in managing neuropathic pain. However, their role in the regulation of nociceptive information processing remains poorly understood. In this study, we observed a significant down-regulation of the spinal lncRNA ENSRNOG00000051325 (lncRNA51325) in a rat model of bone cancer pain. Our study sought to elucidate the potential involvement of lncRNA51325 in the development of BCP by modulating the expression of molecules associated with pain modulation. Methods: We established the BCP model by injecting Walker 256 cells into the tibial plateau of rats. We conducted tests on the pain behaviors and anxiety-like responses of rats through von-Frey test, Gait analysis, and Open Field Test. Spinal lumbar expansion was harvested for molecular biology experiments to explore the relationship between lncRNA51325 and Pumilio RNA binding family member 2 (Pum2). Results: Notably, the overexpression of lncRNA51325 effectively attenuated mechanical allodynia in rats afflicted with BCP, whereas the knockdown of lncRNA51325 induced pain behaviors and anxiety-like responses in naïve rats. Additionally, we observed a time-dependent increase in the expression of Pum2 in BCP-afflicted rats, and intrathecal injection of Pum2-siRNA alleviated hyperalgesia. Furthermore, our investigations revealed that lncRNA51325 exerts a negative modulatory effect on Pum2 expression. The overexpression of lncRNA51325 significantly suppressed Pum2 expression in BCP rats, while the knockdown of lncRNA51325 led to elevated Pum2 protein levels in the spinal cord of naïve rats. Subsequent treatment with Pum2-siRNA mitigated the downregulation of lncRNA51325-induced mechanical allodynia in naïve rats. Conclusion: Our findings indicate that lncRNA51325 plays a role in regulating bone cancer pain by inhibiting Pum2 expression, offering a promising avenue for novel treatments targeting nociceptive hypersensitivity induced by bone metastatic cancer.

Tessellating the Latent Space for Non-Adversarial Generative Auto-Encoders.

Non-adversarial generative models are relatively easy to train and have less mode collapse than adversarial models. However, they are not very accurate in approximating the target distribution in latent space because they don't have a discriminator. To this end, we develop a novel divide-and-conquer model called Tessellated Wasserstein Auto-Encoders (TWAE) which has less statistical error in approximating the target distribution. TWAE tessellates the support of the target distribution into a given number of regions using the centroidal Voronoi tessellation (CVT) technique and designs data batches according to the tessellation instead of random shuffling for accurate computation of discrepancy. Theoretically, we demonstrate that the error in estimating the discrepancy decreases as the number of samples n and the regions m of the tessellation increase at rates of [Formula: see text] and [Formula: see text], respectively. TWAE is very flexible to different non-adversarial metrics and can significantly enhance their generative performance in terms of Fréchet inception distance (FID) compared to existing ones. Furthermore, numerical results demonstrate that TWAE is competitive to the adversarial model and shows powerful generative ability.

ConvMatch: Rethinking Network Design for Two-View Correspondence Learning.

Multilayer perceptron (MLP) has become the de facto backbone in two-view correspondence learning, for it can extract effective deep features from unordered correspondences individually. However, the problem of natively lacking context information limits its performance although many context-capturing modules are appended in the follow-up studies. In this paper, from a novel perspective, we design a correspondence learning network called ConvMatch that for the first time can leverage a convolutional neural network (CNN) as the backbone, inherently capable of context aggregation. Specifically, with the observation that sparse motion vectors and a dense motion field can be converted into each other with interpolating and sampling, we regularize the putative motion vectors by estimating the dense motion field implicitly, then rectify the errors caused by outliers in local areas with CNN, and finally obtain correct motion vectors from the rectified motion field. Moreover, we propose global information injection and bilateral convolution, to fit the overall spatial transformation better and accommodate the discontinuities of the motion field in case of large scene disparity. Extensive experiments reveal that ConvMatch consistently outperforms state-of-the-arts for relative pose estimation, homography estimation, and visual localization.

Effects of adding steel slag on humification and characteristics of bacterial community during phosphate-amended composting of municipal sludge.

This study aimed to investigate the effects of different proportions (0%, 5%, 7.5%, and 10%) of steel slag (SS) on humification and bacterial community characteristics during phosphate-amended composting of municipal sludge. Compared with adding KH2PO4 alone, co-adding SS significantly promoted the temperature, pH, nitrification, and critical enzyme activities (polyphenol oxidase, cellulase, laccase); especially organic matter (OM) degradation rate (25.5%) and humification degree (1.8) were highest in the 5%-SS treatment. Excitation-emission matrix-parallel factor confirmed that co-adding SS could promote the conversion of protein-like substances or microbial by-products into humic-like substances. Furthermore, adding 5%-SS significantly improved the relative abundances of Actinobacteria, Firmicutes and the genes related to carbohydrate and amino acid metabolism, and enhanced the interactions of bacterial community in stability and complexity. The partial least squares path model indicated that OM was the primary factor affecting humification. These results provided a promising strategy to optimize composting of municipal sludge via SS.

The involvement of the gut microbiota in postoperative cognitive dysfunction based on integrated metagenomic and metabolomics analysis.

IMPORTANCE: As the population ages and medical technology advances, anesthesia procedures for elderly patients are becoming more common, leading to an increased prevalence of postoperative cognitive dysfunction. However, the etiology and correlation between the gut microbiota and cognitive dysfunction are poorly understood, and research in this area is limited. In this study, mice with postoperative cognitive dysfunction were found to have reduced levels of fatty acid production and anti-inflammatory flora in the gut, and Bacteroides was associated with increased depression, leading to cognitive dysfunction and depression. Furthermore, more specific microbial species were identified in the disease model, suggesting that modulation of host metabolism through gut microbes may be a potential avenue for preventing postoperative cognitive dysfunction.

A Python Package itca for Information-Theoretic Classification Accuracy: A Criterion That Guides Data-Driven Combination of Ambiguous Outcome Labels in Multiclass Classification.

The itca Python package offers an information-theoretic criterion to assist practitioners in combining ambiguous outcome labels by balancing the tradeoff between prediction accuracy and classification resolution. This article provides instructions for installing the itca Python package, demonstrates how to evaluate the criterion, and showcases its application in real-world scenarios for guiding the combination of ambiguous outcome labels.

Effects of Fmr1 Gene Mutations on Sex Differences in Autism-Like Behavior and Dendritic Spine Development in Mice and Transcriptomic Studies.

Fragile X syndrome (FXS) is the most common single gene disorder contributing to autism spectrum disorder (ASD). Although significant sex differences are observed in FXS, few studies have focused on the phenotypic characteristics as well as the differences in brain pathological changes and gene expression in FXS by sex. Therefore, we analyzed sex differences in autism-like behavior and dendritic spine development in two-month-old male and female Fmr1 KO and C57 mice and evaluated the mechanisms at transcriptome level. Results suggest that Fmr1 KO mice display sex differences in autism-like behavior and dendritic spine density. Compared to females, male had more severe effects on anxiety, repetitive stereotype-like behaviors, and socializing, with higher dendritic spine density. Furthermore, two male-biased and five female-biased expressed genes were screened based on KEGG pathway enrichment and protein-protein interaction (PPI) analyses. In conclusion, our findings show mutations in the Fmr1 gene lead to aberrant expression of related genes and affect the sex-differentiated behavioral phenotypes of Fmr1 KO mice by affecting brain development and functional architecture, and suggest future studies should focus on including female subjects to comprehensively reflect the differentiation of FXS in both sexes and develop more precise and effective therapeutic strategies.

STAMarker: determining spatial domain-specific variable genes with saliency maps in deep learning.

Spatial transcriptomics characterizes gene expression profiles while retaining the information of the spatial context, providing an unprecedented opportunity to understand cellular systems. One of the essential tasks in such data analysis is to determine spatially variable genes (SVGs), which demonstrate spatial expression patterns. Existing methods only consider genes individually and fail to model the inter-dependence of genes. To this end, we present an analytic tool STAMarker for robustly determining spatial domain-specific SVGs with saliency maps in deep learning. STAMarker is a three-stage ensemble framework consisting of graph-attention autoencoders, multilayer perceptron (MLP) classifiers, and saliency map computation by the backpropagated gradient. We illustrate the effectiveness of STAMarker and compare it with serveral commonly used competing methods on various spatial transcriptomic data generated by different platforms. STAMarker considers all genes at once and is more robust when the dataset is very sparse. STAMarker could identify spatial domain-specific SVGs for characterizing spatial domains and enable in-depth analysis of the region of interest in the tissue section.

Single-cell RNA-sequencing data clustering using variational graph attention auto-encoder with self-supervised leaning.

The emergence of single-cell RNA-seq (scRNA-seq) technology makes it possible to capture their differences at the cellular level, which contributes to studying cell heterogeneity. By extracting, amplifying and sequencing the genome at the individual cell level, scRNA-seq can be used to identify unknown or rare cell types as well as genes differentially expressed in specific cell types under different conditions using clustering for downstream analysis of scRNA-seq. Many clustering algorithms have been developed with much progress. However, scRNA-seq often appears with characteristics of high dimensions, sparsity and even the case of dropout events', which make the performance of scRNA-seq data clustering unsatisfactory. To circumvent the problem, a new deep learning framework, termed variational graph attention auto-encoder (VGAAE), is constructed for scRNA-seq data clustering. In the proposed VGAAE, a multi-head attention mechanism is introduced to learn more robust low-dimensional representations for the original scRNA-seq data and then self-supervised learning is also recommended to refine the clusters, whose number can be automatically determined using Jaccard index. Experiments have been conducted on different datasets and results show that VGAAE outperforms some other state-of-the-art clustering methods.

Exercise improved bone health in aging mice: a role of SIRT1 in regulating autophagy and osteogenic differentiation of BMSCs.

Introduction: This study was designed to investigate the effect of running exercise on improving bone health in aging mice and explore the role of the SIRT1 in regulating autophagy and osteogenic differentiation of Bone marrow Mesenchymal Stem Cells (BMSCs). Methods: Twelve-month-old male C57BL/6J mice were used in this study as the aging model and were assigned to treadmill running exercise for eight weeks. Non-exercise male C57BL/6J mice of the same old were used as aging control and five-month-old mice were used as young controls. BMSCs were isolated from mice and subjected to mechanical stretching stimulation in vitro. Results: The results showed that aging mice had lower bone mass, bone mineral density (BMD), and autophagy than young mice, while running exercise improved BMD and bone mass as well as upregulated autophagy in bone cells. Mechanical loading increased osteogenic differentiation and autophagy in BMSCs, and knockdown of SIRT1 in BMSCs demonstrated that SIRT1-regulated autophagy involved the mechanical loading activation of osteogenic differentiation. Conclusion: Taken together, this study revealed that exercise improved bone health during aging by activating bone formation, which can be attributed to osteogenic differentiation of BMSCs through the activation of SIRT1-mediated autophagy. The mechanisms underlying this effect may involve mechanical loading.

Exercise improves subchondral bone microenvironment through regulating bone-cartilage crosstalk.

Articular cartilage degeneration has been proved to cause a variety of joint diseases, among which osteoarthritis is the most typical. Osteoarthritis is characterized by articular cartilage degeneration and persistent pain, which affects the quality of life of patients as well as brings a heavy burden to society. The occurrence and development of osteoarthritis is related to the disorder of the subchondral bone microenvironment. Appropriate exercise can improve the subchondral bone microenvironment, thus playing an essential role in preventing and treating osteoarthritis. However, the exact mechanism whereby exercise improves the subchondral bone microenvironment remains unclear. There is biomechanical interaction as well as biochemical crosstalk between bone and cartilage. And the crosstalk between bone and cartilage is the key to bone-cartilage homeostasis maintenance. From the perspective of biomechanical and biochemical crosstalk between bone and cartilage, this paper reviews the effects of exercise-mediated bone-cartilage crosstalk on the subchondral bone microenvironment, aiming to provide a theoretical basis for the prevention and treatment of degenerative bone diseases.

Spatiotemporal transcriptomic atlas reveals the dynamic characteristics and key regulators of planarian regeneration.

Whole-body regeneration of planarians is a natural wonder but how it occurs remains elusive. It requires coordinated responses from each cell in the remaining tissue with spatial awareness to regenerate new cells and missing body parts. While previous studies identified new genes essential to regeneration, a more efficient screening approach that can identify regeneration-associated genes in the spatial context is needed. Here, we present a comprehensive three-dimensional spatiotemporal transcriptomic landscape of planarian regeneration. We describe a pluripotent neoblast subtype, and show that depletion of its marker gene makes planarians more susceptible to sub-lethal radiation. Furthermore, we identified spatial gene expression modules essential for tissue development. Functional analysis of hub genes in spatial modules, such as plk1, shows their important roles in regeneration. Our three-dimensional transcriptomic atlas provides a powerful tool for deciphering regeneration and identifying homeostasis-related genes, and provides a publicly available online spatiotemporal analysis resource for planarian regeneration research.

[Exercise regulates bone metabolism via microRNAs].

It has been well documented that exercise can improve bone metabolism, promote bone growth and development, and alleviate bone loss. MicroRNAs (miRNAs) are widely involved in the proliferation and differentiation of bone marrow mesenchymal stem cells, osteoblasts, osteoclasts and other bone tissue cells, and regulation of balance between bone formation and bone resorption by targeting osteogenic factors or bone resorption factors. Thus miRNAs play an important role in the regulation of bone metabolism. Recently, regulation of miRNAs are shown to be one of the ways by which exercise or mechanical stress promotes the positive balance of bone metabolism. Exercise induces changes of miRNAs expression in bone tissue and regulates the expression of related osteogenic factors or bone resorption factors, to further strengthen the osteogenic effect of exercise. This review summarizes relevant studies on the mechanism whereby exercise regulates bone metabolism via miRNAs, providing a theoretical basis for osteoporosis prevention and treatment with exercise.

Blood typing and transfusion therapy in a patient with A2 subtype acute myeloid leukemia M2: A case report.

BACKGROUND: Acute myeloid leukemia (AML) is one of the most common types of leukemia in adults. However, AML is relatively rare in the population overall, accounting for only about 1 percent of all cancers. Treatment for AML can be very effective for some patients, yet it leaves others with serious and even life-threatening side effects. Chemotherapy is still the primary treatment for most AML, but over time, leukemia cells become resistant to chemotherapy drugs. In addition, stem cell transplantation, targeted therapy, and immunotherapy are currently available. At the same time, with the progression of the disease, the patient may have corresponding complications, such as coagulation dysfunction, anemia, granulocytopenia, and repeated infection, so transfusion supportive therapy will be involved in the overall treatment regime. To date, few articles have reported on blood transfusion treatment options for patients with ABO subtypes AML-M2. Blood transfusion therapy is an important supportive treatment for AML-M2, and accurate determination of patients' blood type is one of the most important steps in the treatment process. In this study, we explored blood typing and supportive treatment strategies for a patient with A2 subtype AML-M2 to provide the basis for treatment for all patients. CASE SUMMARY: In order to determine the blood type of the patient, serological and molecular biological methods were used for reference tests, and the genetic background was studied to determine the patient's final blood type and select the appropriate blood products for infusion treatment. According to the results obtained by serological and molecular biological methods, the blood type of the patient was A2 subtype; the genotype was A02/001; the irregular antibody screening was negative, and anti-A1 was found in the plasma. According to the overall treatment plan, active anti-infection, elevated cells, component blood transfusion support, and other rescue and supportive treatments were given, and the patient successfully passed the stage of myelosuppression after chemotherapy. Re-examination of bone marrow smears showed that AL was in complete remission of bone marrow signs, and minimal residual leukemia lesions suggested no cells with obvious abnormal immunophenotype (residual leukemia cells < 10-4). CONCLUSION: The infusion of patients with A2 subtype AML-M2 with A irradiated platelets and O washing red blood cells can meet the needs of clinical treatment.

SmartGate is a spatial metabolomics tool for resolving tissue structures.

Imaging mass spectrometry (IMS) is one of the powerful tools in spatial metabolomics for obtaining metabolite data and probing the internal microenvironment of organisms. It has dramatically advanced the understanding of the structure of biological tissues and the drug treatment of diseases. However, the complexity of IMS data hinders the further acquisition of biomarkers and the study of certain specific activities of organisms. To this end, we introduce an artificial intelligence tool, SmartGate, to enable automatic peak selection and spatial structure identification in an iterative manner. SmartGate selects discriminative m/z features from the previous iteration by differential analysis and employs a graph attention autoencoder model to perform spatial clustering for tissue segmentation using the selected features. We applied SmartGate to diverse IMS data at multicellular or subcellular spatial resolutions and compared it with four competing methods to demonstrate its effectiveness. SmartGate can significantly improve the accuracy of spatial segmentation and identify biomarker metabolites based on tissue structure-guided differential analysis. For multiple consecutive IMS data, SmartGate can effectively identify structures with spatial heterogeneity by introducing three-dimensional spatial neighbor information.

Defining the separation landscape of topological domains for decoding consensus domain organization of the 3D genome.

Topologically associating domains (TADs) have emerged as basic structural and functional units of genome organization and have been determined by many computational methods from Hi-C contact maps. However, the TADs obtained by different methods vary greatly, which makes the accurate determination of TADs a challenging issue and hinders subsequent biological analyses about their organization and functions. Obvious inconsistencies among the TADs identified by different methods indeed make the statistical and biological properties of TADs overly depend on the chosen method rather than on the data. To this end, we use the consensus structural information captured by these methods to define the TAD separation landscape for decoding the consensus domain organization of the 3D genome. We show that the TAD separation landscape could be used to compare domain boundaries across multiple cell types for discovering conserved and divergent topological structures, decipher three types of boundary regions with diverse biological features, and identify consensus TADs (ConsTADs). We illustrate that these analyses could deepen our understanding of the relationships between the topological domains and chromatin states, gene expression, and DNA replication timing.

Evolution process and failure mechanism of a large expressway roadside landslide.

Site investigation, deformation monitoring, laboratory test, and theoretical calculations were used to analyze the evolution details of a large expressway roadside landslide during the start-up sliding process. The monitoring results show that the initial deformation and failure occurred on the protective wall at the slope toe, then gradually developed to the upper part of the slope, and finally led to tensile cracks at the slope trailing edge. Accelerated deformation of the slope support structures, such as the protective wall at the slope toe, the anti-slide pile, and the anchor cable, were observed during the continuous extreme rainfall. The infiltrated rainwater can change the weight, the osmotic pressure, the anti-sliding force, the sliding force of the sliding mass, and further soften the fully weathered tuff soil and reduce its strength, resulting in the landslide occurrence. Block the slope surface runoff is an effective measure to reduce the landslide risk. The current analysis will be helpful to the prevention, control, and emergency disposal of similar landslides.

ncR2Met (lncR2metasta v2.0): An updated database for experimentally supported ncRNAs during cancer metastatic events.

Non-coding RNAs (ncRNAs) are widely involved in cancer metastatic events (CMEs, e.g., cancer cell invasion, intravasation, extravasation, proliferation), which collaboratively accelerate tumor spread and cause high patient mortality. In early 2020, we developed a manually curated database named 'lncR2metasta' to provide a comprehensive repository for long ncRNA (lncRNA) regulation during CMEs. We updated this database by supplementing other two important ncRNA types, microRNAs (miRNAs) and circular RNAs (circRNAs), for their involvement during CMEs after a thorough manual curation from published studies. ncR2metasta documents 1565 lncRNA-associated, 882 miRNA-associated, and 628 circRNA-associated entries for ncRNA-CME associations during 50 CMEs across 63 human cancer subtypes. ncR2Met has a concise web interface for researchers to easily browse, search and download as well as to submit novel ncRNA-CME associations. We anticipated that it could be a valuable resource, which will significantly improve our understanding of ncRNA functions in metastasis. It is freely available at http://ncr2met.wchoda.com.