ImputeHiFI: An Imputation Method for Multiplexed DNA FISH Data by Utilizing Single-Cell Hi-C and RNA FISH Data.

Although multiplexed DNA fluorescence in situ hybridization (FISH) enables tracking the spatial localization of thousands of genomic loci using probes within individual cells, the high rates of undetected probes impede the depiction of 3D chromosome structures. Current data imputation methods neither utilize single-cell Hi-C data, which elucidate 3D genome architectures using sequencing nor leverage multimodal RNA FISH data that reflect cell-type information, limiting the effectiveness of these methods in complex tissues such as the mouse brain. To this end, a novel multiplexed DNA FISH imputation method named ImputeHiFI is proposed, which fully utilizes the complementary structural information from single-cell Hi-C data and the cell type signature from RNA FISH data to obtain a high-fidelity and complete spatial location of chromatin loci. ImputeHiFI enhances cell clustering, compartment identification, and cell subtype detection at the single-cell level in the mouse brain. ImputeHiFI improves the recognition of cell-type-specific loops in three high-resolution datasets. In short, ImputeHiFI is a powerful tool capable of imputing multiplexed DNA FISH data from various resolutions and imaging protocols, facilitating studies of 3D genome structures and functions.

U-Match: Exploring Hierarchy-aware Local Context for Two-view Correspondence Learning.

Rejecting outlier correspondences is one of the critical steps for successful feature-based two-view geometry estimation, and contingent heavily upon local context exploration. Recent advances focus on devising elaborate local context extractors whereas typically adopting explicit neighborhood relationship modeling at a specific scale, which is intrinsically flawed and inflexible, because 1) severe outliers often populated in putative correspondences and 2) the uncertainty in the distribution of inliers and outliers make the network incapable of capturing adequate and reliable local context from such neighborhoods, therefore resulting in the failure of pose estimation. This prospective study proposes a novel network called U-Match that has the flexibility to enable implicit local context awareness at multiple levels, naturally circumventing the aforementioned issues that plague most existing studies. Specifically, to aggregate multi-level local context implicitly, a hierarchy-aware graph representation module is designed to flexibly encode and decode hierarchical features. Moreover, considering that global context always works collaboratively with local context, an orthogonal local-and-global information fusion module is presented to integrate complementary local and global context in a redundancy-free manner, thus yielding compact feature representations to facilitate correspondence learning. Thorough experimentation across relative pose estimation, homography estimation, visual localization, and point cloud registration affirms U-Match's remarkable capabilities. Our code is publicly available at https://github.com/ZizhuoLi/U-Match.

Whole brain alignment of spatial transcriptomics between humans and mice with BrainAlign.

The increasing utilization of mouse models in human neuroscience research places higher demands on computational methods to translate findings from the mouse brain to the human one. In this study, we develop BrainAlign, a self-supervised learning approach, for the whole brain alignment of spatial transcriptomics (ST) between humans and mice. BrainAlign encodes spots and genes simultaneously in two separated shared embedding spaces by a heterogeneous graph neural network. We demonstrate that BrainAlign could integrate cross-species spots into the embedding space and reveal the conserved brain regions supported by ST information, which facilitates the detection of homologous regions between humans and mice. Genomic analysis further presents gene expression connections between humans and mice and reveals similar expression patterns for marker genes. Moreover, BrainAlign can accurately map spatially similar homologous regions or clusters onto a unified spatial structural domain while preserving their relative positions.

Relationship between Vitamin D Concentration and Lipid Concentration in Patients with NAFLD in the Hulunbuir Region of China.

BACKGROUND: We aimed to characterize the relationship between the serum 25-hydroxyvitamin D concentration and the circulating lipid concentrations of patients with NAFLD in the Hulunbuir region of China. METHODS: One hundred fifty-six patients, who were diagnosed with NAFLD in the Physical Examination Department of the Second Clinical College of Inner Mongolia University for the Nationalities between January 2021 and March 2023, were recruited as NAFLD group, and 160 healthy people were recruited as a control group during the same period. The serum 25(OH)VitD, TBIL, TG, TC, LDL-C, HDL-C, AST, ALT, GGT, and FPG activities of the participants were measured, and hepatic ultrasonography was performed. RESULTS: The BMI of the NAFLD group was higher than of the control group (p < 0.05). The serum 25(OH)VitD3 (p < 0.05) and the HDL-C concentrations of the NAFLD group were lower than those of the normal control group. However, the AST (p < 0.05), ALT (p < 0.05), and GGT (p < 0.05) activities, and the serum TG (p < 0.05), TC (p < 0.05), LDL-C (p < 0.05), and the fasting glucose (p < 0.05) concentrations of the NAFLD group were higher than those of the normal control group. The serum 25(OH)VitD3 concentrations of the NAFLD group significantly cor-related negatively with BMI (r = -0.302, p < 0.01), TG (r = -0.221, p < 0.05), and fasting glucose (r = -0.236, p < 0.05). The BMI, TG, and fasting glucose of vitamin D-deficient participants were higher than of the participants with adequate or insufficient levels of vitamin D (p < 0.05). Finally, the BMI of vitamin D-deficient participants was higher than of those with an adequate vitamin D status (p < 0.05). CONCLUSIONS: A deficiency of 25(OH)VitD is more common in people from the Hulunbuir region of China than elsewhere. In addition, the vitamin D status is significantly associated with NAFLD; as the serum vitamin D concentration decreases, patients with NAFLD show greater dyslipidemia and hyperglycemia and a higher BMI.

A competitive-type photoelectrochemical aptasensor for 17 beta-estradiol detection in microfluidic devices based on a novel Au@Cd:SnO2/SnS2 nanocomposite.

A competitive-type photoelectrochemical (PEC) aptasensor coupled with a novel Au@Cd:SnO2/SnS2 nanocomposite was designed for the detection of 17ß-estradiol (E2) in microfluidic devices. The designed Au@Cd:SnO2/SnS2 nanocomposites exhibit high photoelectrochemical activity owing to the good matching of cascade band-edge and the efficient separation of photo-generated e-/h+ pairs derived from the Cd-doped defects in the energy level. The Au@Cd:SnO2/SnS2 nanocomposites were loaded into carbon paste electrodes (CPEs) to immobilize complementary DNA (cDNA) and estradiol aptamer probe DNA (E2-Apt), forming a double-strand DNA structure on the CPE surface. As the target E2 interacts with the double-strand DNA, E2-Apt is sensitively released from the CPE, subsequently increasing the photocurrent intensity due to the reduced steric hindrance of the electrode surface. The competitive-type sensing mechanism, combined with high PEC activity of the Au@Cd:SnO2/SnS2 nanocomposites, contributed to the rapid and sensitive detection of E2 in a "signal on" manner. Under the optimized conditions, the PEC aptasensor exhibited a linear range from 1.0 × 10-13 mol L-1 to 3.2 × 10-6 mol L-1 and a detection limit of 1.2 × 10-14 mol L-1 (S/N = 3). Moreover, the integration of microfluidic device with smartphone controlled portable electrochemical workstation enables the on-site detection of E2. The small sample volume (10 µL) and short analysis time (40 min) demonstrated the great potential of this strategy for E2 detection in rat serum and river water. With these advantages, the PEC aptasensor can be utilized for point-of-care testing (POCT) in both clinical and environmental applications.

Divacancy and resonance level enables high thermoelectric performance in n-type SnSe polycrystals.

N-type polycrystalline SnSe is considered as a highly promising candidates for thermoelectric applications due to facile processing, machinability, and scalability. However, existing efforts do not enable a peak ZT value exceeding 2.0 in n-type polycrystalline SnSe. Here, we realized a significant ZT enhancement by leveraging the synergistic effects of divacancy defect and introducing resonance level into the conduction band. The resonance level and increased density of states resulting from tungsten boost the Seebeck coefficient. The combination of the enhanced electrical conductivity (achieved by increasing carrier concentration through WCl6 doping and Se vacancies) and large Seebeck coefficient lead to a high power factor. Microstructural analyses reveal that the co-existence of divacancy defects (Se vacancies and Sn vacancies) and endotaxial W- and Cl-rich nanoprecipitates scatter phonons effectively, resulting in ultralow lattice conductivity. Ultimately, a record-high peak ZT of 2.2 at 773 K is achieved in n-type SnSe0.92 + 0.03WCl6.

Characteristics of humification, functional enzymes and bacterial community metabolism during manganese dioxide-added composting of municipal sludge.

The aim of this study was to assess effects of MnO2 addition (CK-0%, T1-2% and T2-5%) on humification and bacterial community during municipal sludge (MS) composting. The results suggested that MnO2 addition inhibited the growth of Nitrospira but stimulated Nonomuraea, Actinomadura, Streptomyces and Thermopolyspora, facilitating the lignocellulose degradation and humification with the increase in organic matter degradation by 13.8%-19.2% and humic acid content by 10.9%-20.6%. Compared to CK, the abundances of exoglucanase (EC:3.2.1.91), endo-1,4-beta-xylanase (EC:3.2.1.136) and endomannanase (EC:3.2.1.78) increased by 88-99, 52-66 and 4-15 folds, respectively. However, 5%-MnO2 induced the enrichment of Mizugakiibacter that harms the environment of agricultural production. The addition of 2%-MnO2 was recommended for MS composting. Furthermore, metabolic function analysis indicated that MnO2 addition altered amino acid and carbohydrate metabolism, especially enhancing propanoate metabolism and butanoate metabolism but inhibiting citrate cycle. Structural equation modeling revealed that Nonomuraea and Actinomadura were the main drivers for lignocellulose degradation. This study provided theoretical guidance in regulating humification via MnO2 for MS composting.

Exploring the promoting effect of nitrilotriacetic acid on hydroxyl radical and humification during magnetite-amended composting of sewage sludge.

The OH production by adding magnetite (MGT) alone has been reported in composting. However, the potential of nitrilotriacetic acid (NTA) addition for magnetite-amended sludge composting remained unclear. Three treatments with different addition [control check (CK); T1: 5 % MGT; T2: 5 % MGT + 5 % NTA] were investigated to characterize hydroxyl radical, humification and bacterial community response. The NTA addition manifested the best performance, with the peak OH content increase by 52 % through facilitating the cycle of Fe(Ⅱ)/Fe(Ⅲ). It led to the highest organic matters degradation (22.3 %) and humic acids content (36.1 g/kg). Furthermore, NTA addition altered bacterial community response, promoting relative abundances of iron-redox related genera, and amino acid metabolism but decreasing carbohydrate metabolism. Structural equation model indicated that temperature and Streptomyces were the primary factors affecting OH content. The study suggests that utilizing chelators is a promising strategy to strengthen humification in sewage sludge composting with adding iron-containing minerals.

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.

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.

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.

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.