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Complex biological tissues consist of numerous cells in a highly coordinated manner and carry out various biological functions. Therefore, segmenting a tissue into spatial and functional domains is critically important for understanding and controlling the biological functions. The emerging spatial transcriptomics technologies allow simultaneous measurements of thousands of genes with precise spatial information, providing an unprecedented opportunity for dissecting biological tissues. However, how to utilize such noisy, sparse, and high dimensional data for tissue segmentation remains a major challenge. Here, we develop a deep learning-based method, named SCAN-IT by transforming the spatial domain identification problem into an image segmentation problem, with cells mimicking pixels and expression values of genes within a cell representing the color channels. Specifically, SCAN-IT relies on geometric modeling, graph neural networks, and an informatics approach, DeepGraphInfomax. We demonstrate that SCAN-IT can handle datasets from a wide range of spatial transcriptomics techniques, including the ones with high spatial resolution but low gene coverage as well as those with low spatial resolution but high gene coverage. We show that SCAN-IT outperforms state-of-the-art methods using a benchmark dataset with ground truth domain annotations.

Phase transition at the initial solidification stage notably affects subsequent phase structure of peritectic alloys, however, detailed dynamics between entangled three-phase region remains ambiguous in the far-from-equilibrium condition during laser additive manufacturing. Here, in-situ peritectic phase transition during single-layer laser printing of an exemplary 304L stainless steel is directly investigated and quantified at high temporospatial resolution by high-speed synchrotron X-ray diffraction and imaging. These quantitative observations clarify the mechanism of coupled peritectic growth and local remelting at three-phase region. The results revealed provide in-depth understanding of phase transformation dynamics and delicate design of phase structure for metal additive manufacturing.

Duck is often used in meat fraud as a substitute for more expensive meats. Rapid detection of duck ingredient in meat products is of great significance for combating meat fraud and safeguarding the interests of consumers. Therefore, we aim to develop duck-specific recombinase polymerase amplification (RPA)-based assays for the rapid detection of duck ingredient in animal-derived foods. Using Cytb gene as target, the real-time RPA and RPA combined with lateral flow strips (LFS RPA) were developed successfully for the rapid detection of ducks in 20 min at 39 °C and 40 °C, respectively. The assays did not show cross-reactions with 6 other livestock and poultry. The developed RPA assays could detect 10 pg duck genomic DNA per reaction and 0.1 % (w/w) duck ingredient in duck and mutton mixed powder within 30 min, including a rapid nucleic acid extraction. Furthermore, duck ingredient could be detected in 30 different actual foods including heat-processed meats and blood products. Therefore, duck-specific real-time RPA and LFS RPA assays were successfully developed with good specificity and sensitivity, which could enable rapid detection of duck ingredient in the field and provide technical support for combating the meat fraud.

To clarify the influence of geometric parameters of parallel plate on rheological properties of polyurea elastomer (PUA)-modified asphalt, nano-PUA powder was prepared, and nano-PUA powder modifier was modified by using high-speed shearing apparatus. The apparent viscosity of modified asphalt was evaluated by Brookfield viscosity. The rheological parameters of PUA-modified asphalt were determined by comparing the rheological test results of temperature scanning, frequency scanning, and multiple stress creep recover test using 8 and 25 mm parallel plates. Results indicated that the higher the content of nano-PUA modifier was, the better the high-temperature performance of asphalt would be. When using the 8 mm parallel plate, the high-temperature performance of modified asphalt was worse than that of matrix asphalt, and the PUA modifier would lead to a negative effect on the rheological property of asphalt. Regarding the 25 mm parallel plate, the high-temperature performance of modified asphalt was better than that of matrix asphalt, which was contrary to the results of 8 mm parallel plate. The rheological test results using 25 mm parallel plate were consistent with the results of Brookfield viscosity, indicating that 25 mm parallel plate was more suitable for evaluating the rheological performance of PUA-modified asphalt.

Hepatitis E virus (HEV) is a zoonotic pathogen that causes global hepatitis E. Outbreaks of hepatitis E are directly linked to the consumption of pork liver products. Herein reverse transcription recombinase polymerase amplification assays targeting the ORF2 gene were developed for the rapid detection of HEV by integrating the fluorescence detection platform (qRT-RPA) and the visible lateral flow biosensor by naked eyes (LFB RT-RPA). The qRT-RPA assay effectively detected HEV RNA with a limit of detection (LOD) of 154 copies/μl (95%CI: 126–333 copies/µl) in Genie III at 41°C for 20 min. Besides this, the LFB RT-RPA detected the HEV RNA with a LOD of 24 copies/μl (95%CI: 20–57 copies/µl) in an incubator block at 41°C for 20 min. The developed RT-RPA assays also showed good specificity for HEV, with no cross-reactions with any of the other important swine pathogens examined in this work. The performance of the developed RT-RPA assays was validated on 14 HEV RNA-positive and 66 HEV RNA-negative raw pork liver samples identified by a previously described qRT-PCR. Consequently, 11 and 12 samples were HEV RNA-positive as detected by the qRT-RPA and the LFB RT-RPA, respectively. Compared to qRT-PCR, the qRT-RPA and LFB RT- RPA assays revealed a coincidence rate of 96.3 and 97.5% as well as a Kappa value of 0.858 and 0.908, respectively. These results ascertain that the developed RT-RPA assays are effective diagnostic tools for the point-of-care detection of HEV in resource-limited settings.

Identifying scaling laws in metal 3D printing is key to process optimization and materials development. Here the authors report scaling laws to quantify correlation between process parameters, keyhole stability and pore formation by high-speed synchrotron X-ray imaging and multiphysics modeling.

To improve the fracture toughness, and reduce density and cost of Nb–Si alloy, the influence of the substitution of Zr for Hf in Nb–16Si–16Ti–xZr–yHf (x + y = 4) alloys is investigated in this study, and the microstructural evolution and mechanical properties, including fracture toughness and compressive properties are analyzed. The results show that the substitution of Zr for Hf in Nb–16Si–16Ti–xZr–yHf (x + y = 4) alloys can refine the microstructure, and promote the formation of (Nb, X)5Si3/Nbss eutectic. The fracture toughness KQ value increases with the increase of Zr content, since the high Zr content alloys exhibits the low c/a ratio and fine microstructure. Moreover, the addition of 1~4 at.% Hf in Nb–16Si–16Ti–4Zr alloy has less influence on the mechanical properties. The substitution of Zr for Hf in Nb–16Si–16Ti–xZr–yHf (x + y = 4) alloys can improve the fracture toughness of alloys, and the Zr element exhibits the low density and cost compared with Hf element, which indicates that Zr is a promising alloying element.

3D printing pore-free complex metal parts remains a challenge. Here, the authors combine in-situ imaging and simulations to show thermocapillary force can eliminate pores from the melt pool during a laser powder bed fusion process.

Objective To investigate the effects of serum from healthy individuals obtained following moxibustion at the “Jiudianfeng” point on melanocytes in vitro . Methods Ten healthy adults (five male and five female) were treated by moxibustion at the “Jiudianfeng” point for 30 minutes once daily for 3 months. The effects of treatment with serum obtained following moxibustion on melanocyte proliferation, melanin content, tyrosinase activity, cell cycle progression, and c-kit mRNA and protein expression were assessed in vitro before and after moxibustion for 1, 2, and 3 months. Results Exposure to sera from healthy adults following moxibustion therapy promoted melanocyte proliferation, melanin synthesis, tyrosinase activity, and c-kit mRNA and protein expression in vitro . Melanin synthesis and tyrosinase activity increased in the first 2 months following moxibustion and a synchronous decline was observed during the third month. Serum also promoted melanocyte entry into the G1 phase of the cell cycle. Conclusions Serum treatment following moxibustion at the “Jiudianfeng” point promoted melanocyte proliferation and melanin synthesis. Further exploration of this intriguing phenomenon is essential.

Aims. To develop a simple hypoglycemic prediction model to evaluate the risk of hypoglycemia during hospitalization in patients with type 2 diabetes treated with intensive insulin therapy. Methods. We performed a cross-sectional chart review study utilizing the electronic database of the Third Affiliated Hospital of Sun Yat-sen University, and included 257 patients with type 2 diabetes undergoing intensive insulin therapy in the Department of Endocrinology and Metabolism. Logistic regression analysis was used to derive the clinical prediction rule with hypoglycemia (blood glucose≤3.9 mmol/L) as the main result, and internal verification was performed. Results. In the derivation cohort, the incidence of hypoglycemia was 51%. The final model selected included three variables: fasting insulin, fasting blood glucose, and total treatment time. The area under the curve (AUC) of this model was 0.666 (95% CI: 0.594–0.738, P

Abstract Powder spreading is a key step in the powder-bed-based additive manufacturing process, which determines the quality of the powder bed and, consequently, affects the quality of the manufactured part. However, powder spreading behavior under additive manufacturing condition is still not clear, largely because of the lack of particle-scale experimental study. Here, we studied particle-scale powder dynamics during the powder spreading process by using in-situ high-speed high-energy x-ray imaging. Evolution of the repose angle, slope surface speed, slope surface roughness, and the dynamics of powder clusters at the powder front were revealed and quantified. Interactions of the individual metal powders, with boundaries (substrate and container wall), were characterized, and coefficients of friction between the powders and boundaries were calculated. The effects of particle size on powder flow dynamics were revealed. The particle-scale powder spreading dynamics, reported here, are important for a thorough understanding of powder spreading behavior in the powder-bed-based additive manufacturing process, and are critical to the development and validation of models that can more accurately predict powder spreading behavior.

Abstract We employ the high-speed synchrotron hard X-ray imaging and diffraction techniques to monitor the laser powder bed fusion (LPBF) process of Ti-6Al-4V in situ and in real time. We demonstrate that many scientifically and technologically significant phenomena in LPBF, including melt pool dynamics, powder ejection, rapid solidification, and phase transformation, can be probed with unprecedented spatial and temporal resolutions. In particular, the keyhole pore formation is experimentally revealed with high spatial and temporal resolutions. The solidification rate is quantitatively measured, and the slowly decrease in solidification rate during the relatively steady state could be a manifestation of the recalescence phenomenon. The high-speed diffraction enables a reasonable estimation of the cooling rate and phase transformation rate, and the diffusionless transformation from β to α ’ phase is evident. The data present here will facilitate the understanding of dynamics and kinetics in metal LPBF process, and the experiment platform established will undoubtedly become a new paradigm for future research and development of metal additive manufacturing.

The goal of the paper is to study the charge transfer and reactions at the columbite-(Fe) (FeNb2O6) mineral surface during the HF leaching process. In this paper, X-ray photoelectron spectroscopy (XPS), leaching experiments, and density functional theory (DFT) calculations were used to study the surface element adsorption, charge distribution, chemical state, and energy changes of the mineral surface during the process of leaching columbite–(Fe) with different concentrations of hydrofluoric acid. The results showed that as the concentration of F atoms was increased during the acid leaching process, the Nb–O bond was more likely to be broken than the Fe–O bond; the amount of charge transferred from Nb atom to F atom (0.78 e–0.94 e/atom) was greater than that from Fe atom to the F atom (0.25 e–0.28 e/atom), so it was determined that compared to Fe atoms, it was easier for the Nb atoms to bind to F. The results of XPS analysis showed that the electron binding energies of Nb5+–O, Fe3+–O, and Fe2+–O bonds on the mineral surface increased sequentially, and the M–O bond broke during the acid leaching process, forming more stable M–F bonds. Therefore, the Nb5+–F bonds were easier to form a stable structure. Combined with the ICP results, it was found that in the filtrate after 5M HF and 10M HF acid leaching minerals, c(Nb)/c(Fe) were 2.69 and 2.95, respectively, and the concentration ratio of Nb to Fe element in the mineral was 2 which was lower than 2.69 and 2.95, confirming the result of DFT calculation and illustrating that Nb atoms in columbite-(Fe) mineral were more soluble than Fe atoms.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Spattering has been a problem in metal processing involving high-power lasers, like laser welding, machining, and recently, additive manufacturing. Limited by the capabilities of in situ diagnostic techniques, typically imaging with visible light or laboratory x-ray sources, a comprehensive understanding of the laser-spattering phenomenon, particularly the extremely fast spatters, has not been achieved yet. Here, using MHz single-pulse synchrotron-x-ray imaging, we probe the spattering behavior of Ti-6Al-4V with micrometer spatial resolution and subnanosecond temporal resolution. Combining direct experimental observations, quantitative image analysis, as well as numerical simulations, our study unravels a novel mechanism of laser spattering: The bulk explosion of a tonguelike protrusion forming on the front keyhole wall leads to the ligamentation of molten metal at the keyhole rims and the subsequent spattering. Our study confirms the critical role of melt and vapor flow in the laser-spattering process and opens a door to manufacturing spatter- and defect-free metal parts via precise control of keyhole dynamics.

C118P, a phosphate prodrug of C118, which is a novel microtubule protein inhibitor, is currently under Phase I clinical development in China for treating ovarian cancer and lung cancer. The preclinical pharmacokinetics of prodrug C118P and its metabolite C118 were extensively characterized in vivo in mice, rats, and dogs and in vitro to support the further development of C118P. The preclinical tissue distribution and excretion were investigated in rats. Plasma protein binding in mice, rat, and human, and hepatic microsomal metabolic stability in mice, rat, dog, monkey, and human, were also evaluated. The (AUC0-inf) and C30s of C118P at 50 mg/kg in rats and 6 mg/kg in dogs, and the C2min of C118 at 6 mg/kg in dogs increased less than the dosage increase, suggested nonlinear pharmacokinetic occurred at high dose. As a prodrug, C118P can be quickly hydrolyzed into C118 after an intravenous administration. The unbound C118 in plasma is slightly higher than C118P. C118P can hardly penetrate the tissue, while C118 can distribute widely into tissues. In tumor-bearing nude mice, the concentration of C118 is high in lung, ovary, and tumor, with an extended half-life in tumor. C118P is a promising candidate prodrug for further clinical development.

In the title molecule, C26H25FN2O5, the fluoromethoxy-, methoxy- and trimethoxy-substituted benzene rings form dihedral angles of 12.65 (2), 84.15 (2) and 55.67 (2)°, respectively, with the imidazole ring. The crystal structure is stabilized weak intermolecular C—H...F and C—H...O hydrogen bonds.

LMNA -related dilated cardiomyopathy (DCM) is an autosomal-dominant genetic condition with cardiomyocyte and conduction system dysfunction often resulting in heart failure or sudden death. The condition is caused by mutation in the Lamin A/C ( LMNA ) gene encoding Type-A nuclear lamin proteins involved in nuclear integrity, epigenetic regulation of gene expression, and differentiation. The molecular mechanisms of the disease are not completely understood, and there are no definitive treatments to reverse progression or prevent mortality. We investigated possible mechanisms of LMNA -related DCM using induced pluripotent stem cells derived from a family with a heterozygous LMNA c.357-2A>G splice-site mutation. We differentiated one LMNA -mutant iPSC line derived from an affected female (Patient) and two non-mutant iPSC lines derived from her unaffected sister (Control) and conducted single-cell RNA sequencing for 12 samples (four from Patients and eight from Controls) across seven time points: Day 0, 2, 4, 9, 16, 19, and 30. Our bioinformatics workflow identified 125,554 cells in raw data and 110,521 (88%) high-quality cells in sequentially processed data. Unsupervised clustering, cell annotation, and trajectory inference found complex heterogeneity: ten main cell types; many possible subtypes; and lineage bifurcation for cardiac progenitors to cardiomyocytes (CMs) and epicardium-derived cells (EPDCs). Data integration and comparative analyses of Patient and Control cells found cell type and lineage-specific differentially expressed genes (DEGs) with enrichment, supporting pathway dysregulation. Top DEGs and enriched pathways included 10 ZNF genes and RNA polymerase II transcription in pluripotent cells (PP); BMP4 and TGF Beta/BMP signaling, sarcomere gene subsets and cardiogenesis, CDH2 and EMT in CMs; LMNA and epigenetic regulation, as well as DDIT4 and mTORC1 signaling in EPDCs. Top DEGs also included XIST and other X-linked genes, six imprinted genes ( SNRPN , PWAR6 , NDN , PEG10 , MEG3 , MEG8 ), and enriched gene sets related to metabolism, proliferation, and homeostasis. We confirmed Lamin A/C haploinsufficiency by allelic expression and Western blot. Our complex Patient-derived iPSC model for Lamin A/C haploinsufficiency in PP, CM, and EPDC provided support for dysregulation of genes and pathways, many previously associated with Lamin A/C defects, such as epigenetic gene expression, signaling, and differentiation. Our findings support disruption of epigenomic developmental programs, as proposed in other LMNA disease models. We recognized other factors influencing epigenetics and differentiation; thus, our approach needs improvement to further investigate this mechanism in an iPSC-derived model.

Electrostatics is of paramount importance to chemistry, physics, biology, and medicine. The Poisson-Boltzmann (PB) theory is a primary model for electrostatic analysis. However, it is highly challenging to compute accurate PB electrostatic solvation free energies for macromolecules due to the nonlinearity, dielectric jumps, charge singularity, and geometric complexity associated with the PB equation. The present work introduces a PB-based machine learning (PBML) model for biomolecular electrostatic analysis. Trained with the second-order accurate MIBPB solver, the proposed PBML model is found to be more accurate and faster than several eminent PB solvers in electrostatic analysis. The proposed PBML model can provide highly accurate PB electrostatic solvation free energy of new biomolecules or new conformations generated by molecular dynamics with much reduced computational cost., Competing Interests: Declaration of interests The authors declare no competing interests., (Copyright © 2024 Biophysical Society. Published by Elsevier Inc. All rights reserved.)

Accurate calculation of flow discharge for sluice gates is essential in irrigation, water supply, and structure safety. The measurement of discharge with the requirement of distinguishing flow regimes is not conducive to application. In this study, a novel approach that considers both free and submerged flow was proposed. The energy-momentum method was employed to derive the coefficient of discharge. Subsequently, the discharge coefficient was determined through the experiment which was performed on the physical model of a vertical sluice gate with a broad-crested weir. Feature engineering, incorporating dimensional analysis, feature construction, and correlation-based selection were performed. The best subset regression method was employed to develop regression equations of the discharge coefficient with the generated features. The derived formula was applied to compute the discharge coefficient in the vertical sluice gate and determine the flow discharge. The accuracy of adopted method was assessed by comparing it with recent studies on submerged flow, and the results demonstrate that the developed approach achieves a high level of accuracy in calculating flow discharge. The coefficient of determination for the calculated flow rate is 0.993, and the root mean square percentage error is 5.04%., Competing Interests: The authors declare there is no conflict., (© 2024 The Authors This is an Open Access article distributed under the terms of the Creative Commons Attribution Licence (CC BY 4.0), which permits copying, adaptation and redistribution, provided the original work is properly cited (http://creativecommons.org/licenses/by/4.0/).)

Single-cell RNA sequencing (scRNA-seq) enables dissecting cellular heterogeneity in tissues, resulting in numerous biological discoveries. Various computational methods have been devised to delineate cell types by clustering scRNA-seq data, where clusters are often annotated using prior knowledge of marker genes. In addition to identifying pure cell types, several methods have been developed to identify cells undergoing state transitions, which often rely on prior clustering results. The present computational approaches predominantly investigate the local and first-order structures of scRNA-seq data using graph representations, while scRNA-seq data frequently display complex high-dimensional structures. Here, we introduce scGeom, a tool that exploits the multiscale and multidimensional structures in scRNA-seq data by analyzing the geometry and topology through curvature and persistent homology of both cell and gene networks. We demonstrate the utility of these structural features to reflect biological properties and functions in several applications, where we show that curvatures and topological signatures of cell and gene networks can help indicate transition cells and the differentiation potential of cells. We also illustrate that structural characteristics can improve the classification of cell types., (© The Author(s) 2024. Published by Oxford University Press.)

Skin soft tissue expansion is the process of obtaining excess skin mixed with skin development, wound healing, and mechanical stretching. Previous studies have reported that tissue expansion significantly induces epidermal proliferation throughout the skin. However, the mechanisms underlying epidermal regeneration during skin soft tissue expansion are yet to be clarified. Hair follicle stem cells (HFSCs) have been recognized as a promising approach for epidermal regeneration. This study examines HFSC-related epidermal regeneration mechanisms under expanded condition and proposes a potential method for its cellular and molecular regulation., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Zhang, Cui, Cang, Pei, Zhang, Song, Fan, Ma and Li.)

The utilization of steel slag for CO 2 sequestration is an effective way to reduce carbon emissions. The reactivity of steel slag in CO 2 sequestration depends mainly on material and process parameters. However, there are many puzzles in regard to practical applications due to the different evaluations of process parameters and the lack of investigation of material parameters. In this study, 318 samples were collected to investigate the interactive influence of 12 factors on the carbonation reactivity of steel slag by machine learning with SHapley Additive exPlanations (SHAP). Multilayer perceptron (MLP), random forest, and support vector regression models were built to predict the slurry-phase CO 2 sequestration of steel slag. The MLP model performed well in terms of prediction ability and generalization with comprehensive interpretability. The SHAP results showed that the impact of the process parameters was greater than that of the material parameters. Interestingly, the iron ore phase of steel slag was revealed to have a positive effect on steel slag carbonation by SHAP analysis. Combined with previous literature, the carbonation mechanism of steel slag was proposed. Quantitative analysis based on SHAP indicated that steel slag had good carbonation reactivity when the mass fractions of "CaO + MgO", "SiO 2 + Al 2 O 3 ", "Fe 2 O 3 ", and "MnO" varied from 50-55%, 10-15%, 30-35%, and <5%, respectively.

Bandpass microwave photonic filter (MPF) can be achieved based on the well-known phase to intensity conversion method by using phase modulation and single micro-ring resonator (MRR) notch filter. Since MRR could introduce residual phase in handling one optical sideband, the out-of-band radio frequency (RF) rejection ratio and the shape factor of the bandpass MPF are very limited. Here, by introducing another MRR to handle the other optical sideband, the residual phase can be greatly suppressed, thus the filter's performance can be greatly improved. The proposed bandpass MPF was both verified theoretically and experimentally. Compared with the single MRR, the out-of-band RF rejection ratio and the shape factor were improved by 20 dB and 1.67, respectively. Furthermore, the bandpass MPF's bandwidth is reconfigurable by adjusting the optical carrier's frequency or the two MRRs' amplitude coupling coefficients. The bandpass MPF's center frequency is also tunable by changing the resonant wavelengths of two MRRs in the opposite direction simultaneously. Experimentally, bandwidth reconfiguration from 0.38 GHz to 15.74 GHz, the shape factor optimization from 2 to 1.23, and frequency tuning from 4 GHz to 21.5 GHz were achieved. We believe that the proposed bandpass MPF has great potential for microwave photonic signal processing.

Purpose: This systematic review aimed to analyze the characteristics of different diagnostic techniques for micrognathia, summarize the consistent diagnostic criteria of each technique, and provide a simple and convenient prenatal diagnosis strategy for micrognathia., Methods: In accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, the search was undertaken in three international databases (PubMed, Scopus, and Web of Science). The three reviewers assessed all papers and extracted the following variables: author's name and year of publication, country, study design, number of participants, gestational age, equipment for prenatal examination, biometric parameters related to micrognathia, main results., Results: A total of 25 articles included in the analysis. Nineteen articles described cross-sectional studies (76 percent), 4 (16 percent) were case-control studies, and 2 (8 percent) were cohort studies. Fifteen studies (60 percent) had a prospective design, 9 (36 percent) had a retrospective design, and one (4 percent) had both prospective and retrospective design. Thirty-two percent of the studies ( n  = 8) were performed in USA, and the remaining studies were performed in China ( n  = 4), Israel ( n  = 3), Netherlands ( n  = 3), UK ( n  = 1), France ( n  = 1), Italy ( n  = 1), Belgium( n  = 1), Germany ( n  = 1), Spain ( n  = 1), and Austria ( n  = 1). The prenatal diagnosis of micrognathia can be performed as early as possible in the first trimester, while the second and third trimester of pregnancy were the main prenatal diagnosis period. The articles that were included in the qualitative synthesis describe 30 biometric parameters related to the mandible., Conclusion: Of the 30 biometric parameters related to the mandible, 15 can obtain the simple and convenient diagnostic criteria or warning value for micrognathia. Based on these diagnostic criteria or warning value, clinicians can quickly make a preliminary judgment on facial deformities, to carry out cytologic examination to further clarify the diagnosis of micrognathia., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (© 2023 Cang, Cui, Pei, Wang, Du, Mu, Dou, Fan, Zhang and Li.)

Spatial transcriptomic technologies and spatially annotated single-cell RNA sequencing datasets provide unprecedented opportunities to dissect cell-cell communication (CCC). However, incorporation of the spatial information and complex biochemical processes required in the reconstruction of CCC remains a major challenge. Here, we present COMMOT (COMMunication analysis by Optimal Transport) to infer CCC in spatial transcriptomics, which accounts for the competition between different ligand and receptor species as well as spatial distances between cells. A collective optimal transport method is developed to handle complex molecular interactions and spatial constraints. Furthermore, we introduce downstream analysis tools to infer spatial signaling directionality and genes regulated by signaling using machine learning models. We apply COMMOT to simulation data and eight spatial datasets acquired with five different technologies to show its effectiveness and robustness in identifying spatial CCC in data with varying spatial resolutions and gene coverages. Finally, COMMOT identifies new CCCs during skin morphogenesis in a case study of human epidermal development., (© 2023. The Author(s).)

One major challenge in analyzing spatial transcriptomic datasets is to simultaneously incorporate the cell transcriptome similarity and their spatial locations. Here, we introduce SpaceFlow, which generates spatially-consistent low-dimensional embeddings by incorporating both expression similarity and spatial information using spatially regularized deep graph networks. Based on the embedding, we introduce a pseudo-Spatiotemporal Map that integrates the pseudotime concept with spatial locations of the cells to unravel spatiotemporal patterns of cells. By comparing with multiple existing methods on several spatial transcriptomic datasets at both spot and single-cell resolutions, SpaceFlow is shown to produce a robust domain segmentation and identify biologically meaningful spatiotemporal patterns. Applications of SpaceFlow reveal evolving lineage in heart developmental data and tumor-immune interactions in human breast cancer data. Our study provides a flexible deep learning framework to incorporate spatiotemporal information in analyzing spatial transcriptomic data., (© 2022. The Author(s).)

The rapid development of spatial transcriptomics (ST) techniques has allowed the measurement of transcriptional levels across many genes together with the spatial positions of cells. This has led to an explosion of interest in computational methods and techniques for harnessing both spatial and transcriptional information in analysis of ST datasets. The wide diversity of approaches in aim, methodology and technology for ST provides great challenges in dissecting cellular functions in spatial contexts. Here, we synthesize and review the key problems in analysis of ST data and methods that are currently applied, while also expanding on open questions and areas of future development., (© 2022. The Author(s).)

Circular RNAs play essential roles in the development of various human diseases. However, how circRNAs are involved in diabetic nephropathy (DN) are not fully understood. Our study aimed to investigate the effects of circRNA circEIF4G2 on DN. Experiments were performed in the db/db mouse model of type 2 diabetes and NRK-52E cells. We found that circEIF4G2 was significantly up-regulated in the kidneys of db/db mice and NRK-52E cells stimulated by high glucose. circEIF4G2 knockdown inhibited the expressions of TGF-β1, Collagen I and Fibronectin in high glucose-stimulated NRK-52E cells, which could be rescued by miR-218 inhibitor. Knockdown of SERBP1 reduced the expression of TGF-β1, Collagen I and Fibronectin in HG-stimulated NRK-52E cells. In summary, our findings suggested that circEIF4G2 promotes renal tubular epithelial cell fibrosis via the miR-218/SERBP1 pathway, presenting a novel insight for DN treatment., (© 2020 The Authors. Journal of Cellular and Molecular Medicine published by Foundation for Cellular and Molecular Medicine and John Wiley & Sons Ltd.)

Neural crest (NC) cells migrate throughout vertebrate embryos to give rise to a huge variety of cell types, but when and where lineages emerge and their regulation remain unclear. We have performed single-cell RNA sequencing (RNA-seq) of cranial NC cells from the first pharyngeal arch in zebrafish over several stages during migration. Computational analysis combining pseudotime and real-time data reveals that these NC cells first adopt a transitional state, becoming specified mid-migration, with the first lineage decisions being skeletal and pigment, followed by neural and glial progenitors. In addition, by computationally integrating these data with RNA-seq data from a transgenic Wnt reporter line, we identify gene cohorts with similar temporal responses to Wnts during migration and show that one, Atp6ap2, is required for melanocyte differentiation. Together, our results show that cranial NC cell lineages arise progressively and uncover a series of spatially restricted cell interactions likely to regulate such cell-fate decisions., Competing Interests: Declaration of interests The authors declare no competing interests., (Copyright © 2021 The Authors. Published by Elsevier Inc. All rights reserved.)

Genetic mutations to the Lamin A/C gene (LMNA) can cause heart disease, but the mechanisms making cardiac tissues uniquely vulnerable to the mutations remain largely unknown. Further, patients with LMNA mutations have highly variable presentation of heart disease progression and type. In vitro patient-specific experiments could provide a powerful platform for studying this phenomenon, but the use of induced pluripotent stem cell-derived cardiomyocytes (iPSC-CM) introduces heterogeneity in maturity and function thus complicating the interpretation of the results of any single experiment. We hypothesized that integrating single cell RNA sequencing (scRNA-seq) with analysis of the tissue architecture and contractile function would elucidate some of the probable mechanisms. To test this, we investigated five iPSC-CM lines, three controls and two patients with a (c.357-2A>G) mutation. The patient iPSC-CM tissues had significantly weaker stress generation potential than control iPSC-CM tissues demonstrating the viability of our in vitro approach. Through scRNA-seq, differentially expressed genes between control and patient lines were identified. Some of these genes, linked to quantitative structural and functional changes, were cardiac specific, explaining the targeted nature of the disease progression seen in patients. The results of this work demonstrate the utility of combining in vitro tools in exploring heart disease mechanics., (© 2021. The Author(s).)

Complex biological tissues consist of numerous cells in a highly coordinated manner and carry out various biological functions. Therefore, segmenting a tissue into spatial and functional domains is critically important for understanding and controlling the biological functions. The emerging spatial transcriptomics technologies allow simultaneous measurements of thousands of genes with precise spatial information, providing an unprecedented opportunity for dissecting biological tissues. However, how to utilize such noisy, sparse, and high dimensional data for tissue segmentation remains a major challenge. Here, we develop a deep learning-based method, named SCAN-IT by transforming the spatial domain identification problem into an image segmentation problem, with cells mimicking pixels and expression values of genes within a cell representing the color channels. Specifically, SCAN-IT relies on geometric modeling, graph neural networks, and an informatics approach, DeepGraphInfomax. We demonstrate that SCAN-IT can handle datasets from a wide range of spatial transcriptomics techniques, including the ones with high spatial resolution but low gene coverage as well as those with low spatial resolution but high gene coverage. We show that SCAN-IT outperforms state-of-the-art methods using a benchmark dataset with ground truth domain annotations.

Scorpion α-toxins bind at the pharmacologically-defined site-3 on the sodium channel and inhibit channel inactivation by preventing the outward movement of the voltage sensor in domain IV (IVS4), whereas scorpion β-toxins bind at site-4 on the sodium channel and enhance channel activation by trapping the voltage sensor of domain II (IIS4) in its outward position. However, limited information is available on the role of the voltage-sensing modules (VSM, comprising S1-S4) of domains I and III in toxin actions. We have previously shown that charge reversing substitutions of the innermost positively-charged residues in IIIS4 (R4E, R5E) increase the activity of an insect-selective site-4 scorpion toxin, Lqh-dprIT 3- c, on BgNa v 1-1a, a cockroach sodium channel. Here we show that substitutions R4E and R5E in IIIS4 also increase the activity of two site-3 toxins, LqhαIT from Leiurusquinquestriatus hebraeus and insect-selective Av3 from Anemonia viridis. Furthermore, charge reversal of either of two conserved negatively-charged residues, D1K and E2K, in IIIS2 also increase the action of the site-3 and site-4 toxins. Homology modeling suggests that S2-D1 and S2-E2 interact with S4-R4 and S4-R5 in the VSM of domain III (III-VSM), respectively, in the activated state of the channel. However, charge swapping between S2-D1 and S4-R4 had no compensatory effects on gating or toxin actions, suggesting that charged residue interactions are complex. Collectively, our results highlight the involvement of III-VSM in the actions of both site 3 and site 4 toxins, suggesting that charge reversing substitutions in III-VSM allosterically facilitate IIS4 or IVS4 voltage sensor trapping by these toxins., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

Inflammatory pathway is the significant marker of neuro-inflammation and plays a significant role in the expansion of cerebral ischemia/reperfusion injury. Umbelliferone (UF), 7-hydroxy coumarin, has been already proved for its anti-inflammatory and anti-oxidative effects against ischemic brain injury in the rodent model, but its underlying pharmacological mechanism for neuro-protection remain unclear. In this study, we try to explore the neuro-protective effect of umbelliferone against ischemia/Reperfusion induced neurological deficits in rats and explore the underlying mechanism. Inserting thread into the middle cerebral artery was used to induce the ischemic stroke model. The rats were treated with the umbelliferone (5, 10 and 20 mg/kg) for 14 days prior to the ischemic stroke. At the end of the experimental study, brain infarction volume, neurological score, brain edema, pro-inflammatory cytokines, inflammatory mediator were estimated in the region of brain and serum. The mRNA expression of Toll-like receptor-4 (TLR4), myeloid differentiation factor 88 (MyD88), Fas and FasL were also estimated at the end of the study. Dose dependently treatment of umbelliferone down-regulated the neurological score, brain infarction, inflammatory mediator (TNF-α, IL-1β, IL-6, COX-2, NF-kB and PGE 2 ) in the serum and brain tissue as compared to I/R induced control group rats. Umbelliferone also reduced the expression of TRL4, MyD88, Fas and FasL as compared to I/R control group rats. Umbelliferone also decreased the level of nuclear factor kappa B (NF-kB) compared to MACO control group rats. Collectively, the obtained result showed that the umbelliferone protected the brain against the ischemic injury in the rats through the inhibition of inflammatory pathway.Communicated by Ramaswamy H. Sarma.

Cell-cell communication is a fundamental process that shapes biological tissue. Historically, studies of cell-cell communication have been feasible for one or two cell types and a few genes. With the emergence of single-cell transcriptomics, we are now able to examine the genetic profiles of individual cells at unprecedented scale and depth. The availability of such data presents an exciting opportunity to construct a more comprehensive description of cell-cell communication. This review discusses the recent explosion of methods that have been developed to infer cell-cell communication from non-spatial and spatial single-cell transcriptomics, two promising technologies which have complementary strengths and limitations. We propose several avenues to propel this rapidly expanding field forward in meaningful ways., Competing Interests: Declaration of interests The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

As an emerging contaminant in water, antibiotic resistant bacteria are threatening the public health gravely. In this study, sulfidated ZVI was used to activate persulfate, for antibiotic resistant E. coli and antibiotic resistant genes removal. Impressively, 7 log of antibiotic resistant E. coli was inactivated within 30 min, in sulfidated ZVI activated persulfate system (S/Fe = 0.05). Electron paramagnetic resonance and free radical quenching experiments suggested that sulfidation treatment did not change the specie of radicals. SO 4 •- and HO• were the main reactive oxygen species for the removal of antibiotic resistant E. coli and genes. Investigation on the activation mechanism of persulfate indicated that persulfate decomposition was mainly attributed to heterogeneous activation. More importantly, in-situ characterization (ATR-FTIR) indicated that the main charge transfer complex was formed on the surface of sulfidated ZVI, which would predominantly mediate the generation of SO 4 •- and HO•. Finally, the proposed system was evaluated in modeling water and secondary effluent. Results revealed that only 2.86 log and 0.84 log of antibiotic resistant E. coli were inactivated in the presence of NOM ( 10 mg/L) and HCO 3 - (84 mg/L), respectively. Besides, sulfidated ZVI activated persulfate system could be pH-dependent in actual wastewater treatment., (Copyright © 2020 Elsevier B.V. All rights reserved.)

Single-cell RNA sequencing (scRNA-seq) data provides unprecedented information on cell fate decisions; however, the spatial arrangement of cells is often lost. Several recent computational methods have been developed to impute spatial information onto a scRNA-seq dataset through analyzing known spatial expression patterns of a small subset of genes known as a reference atlas. However, there is a lack of comprehensive analysis of the accuracy, precision, and robustness of the mappings, along with the generalizability of these methods, which are often designed for specific systems. We present a system-adaptive deep learning-based method (DEEPsc) to impute spatial information onto a scRNA-seq dataset from a given spatial reference atlas. By introducing a comprehensive set of metrics that evaluate the spatial mapping methods, we compare DEEPsc with four existing methods on four biological systems. We find that while DEEPsc has comparable accuracy to other methods, an improved balance between precision and robustness is achieved. DEEPsc provides a data-adaptive tool to connect scRNA-seq datasets and spatial imaging datasets to analyze cell fate decisions. Our implementation with a uniform API can serve as a portal with access to all the methods investigated in this work for spatial exploration of cell fate decisions in scRNA-seq data. All methods evaluated in this work are implemented as an open-source software with a uniform interface., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Maseda, Cang and Nie.)

During early mammalian embryo development, a small number of cells make robust fate decisions at particular spatial locations in a tight time window to form inner cell mass (ICM), and later epiblast (Epi) and primitive endoderm (PE). While recent single-cell transcriptomics data allows scrutinization of heterogeneity of individual cells, consistent spatial and temporal mechanisms the early embryo utilize to robustly form the Epi/PE layers from ICM remain elusive. Here we build a multiscale three-dimensional model for mammalian embryo to recapitulate the observed patterning process from zygote to late blastocyst. By integrating the spatiotemporal information reconstructed from multiple single-cell transcriptomic datasets, the data-informed modeling analysis suggests two major processes critical to the formation of Epi/PE layers: a selective cell-cell adhesion mechanism (via EphA4/EphrinB2) for fate-location coordination and a temporal attenuation mechanism of cell signaling (via Fgf). Spatial imaging data and distinct subsets of single-cell gene expression data are then used to validate the predictions. Together, our study provides a multiscale framework that incorporates single-cell gene expression datasets to analyze gene regulations, cell-cell communications, and physical interactions among cells in complex geometries at single-cell resolution, with direct application to late-stage development of embryogenesis., Competing Interests: The authors have declared that no competing interests exist.

Circular RNAs (circRNAs) play vital roles in the development of diabetic nephropathy (DN). In this study, we investigated the function of circ&#95;0037128 and molecular mechanism via which it regulates diabetic nephropathy development. It was found that expression of circ&#95;0037128 was significantly increased in mouse DN model and high glucose treated mesangial cells (MCs), and circ&#95;0037128 loss-of-function led to reduced cell proliferation and fibrosis in vitro. Moreover, miR-17-3p acts as competitive endogenous RNA (ceRNA) that directly interacts with circ&#95;0037128 through its miRNA response elements (MREs). Consistently, expression of miR-17-3p was remarkably down-regulated in DN model, and negatively regulated cell proliferation and fibrosis. Further investigations revealed that AKT3 was the putative target of miR-17-3p, whose expression was elevated in DN model. In conclusion, we have characterized the function of a novel circ&#95;0037128 and illustrated the significance of circ&#95;0037128-miR-17-3p-AKT3 axis in DN pathogenesis., (Copyright © 2020 Elsevier B.V. All rights reserved.)

While the spatial topological persistence is naturally constructed from a radius-based filtration, it has hardly been derived from a temporal filtration. Most topological models are designed for the global topology of a given object as a whole. There is no method reported in the literature for the topology of an individual component in an object to the best of our knowledge. For many problems in science and engineering, the topology of an individual component is important for describing its properties. We propose evolutionary homology (EH) constructed via a time evolution-based filtration and topological persistence. Our approach couples a set of dynamical systems or chaotic oscillators by the interactions of a physical system, such as a macromolecule. The interactions are approximated by weighted graph Laplacians. Simplices, simplicial complexes, algebraic groups and topological persistence are defined on the coupled trajectories of the chaotic oscillators. The resulting EH gives rise to time-dependent topological invariants or evolutionary barcodes for an individual component of the physical system, revealing its topology-function relationship. In conjunction with Wasserstein metrics, the proposed EH is applied to protein flexibility analysis, an important problem in computational biophysics. Numerical results for the B-factor prediction of a benchmark set of 364 proteins indicate that the proposed EH outperforms all the other state-of-the-art methods in the field., Competing Interests: Conflict of interest On behalf of all authors, the corresponding author states that there is no conflict of interest.

Objective: To evaluate the efficacy and safety of flesh-moistening paste for treating psoriasis vulgaris in patients with blood stasis pattern in terms of Traditional Chinese Medicine (TCM)., Methods: Eudipleural rashes on both the left and right side of the same patients with psoriasis vulgaris, diagnosed via TCM as blood stasis pattern, were selected as the targeted skin lesions. A randomized, double-blind, parallel-controlled, multicenter controlled trial was conducted. The targeted skin lesions were categorized into either the treatment or control group. The treatment group used the flesh-moistening paste; the control group used a placebo. Both the paste and the placebo were topically applied twice daily for eight weeks. The patients were examined biweekly to evaluate the effects. The two groups were compared in terms of the psoriasis area and severity index (PASI) of the targeted skin lesions, which is scored according to erythema, desquamation, infiltration, area, pruritus, and improvement of skin barrier function. Adverse events were recorded during the study period. SPSS 21.0 was used to analyze the data., Results: Fifty-six patients were enrolled between February 2016 and October 2017. Two were complicated by cardio-cerebrovascular disease and were excluded; thus, 54 outpatients were finally enrolled in the study. Four dropped out during the study period: three failed to complete their follow-up visits for unknown reasons, and one exited due to an adverse event. The final trial comprised 50 of the 56 originally selected patients, with a 92.6% completion rate. After 8 weeks of treatment, the targeted skin lesion scores differed significantly (P < 0.05). The PASI scores of the targeted skin lesions differed significantly beginning at week 6 (P < 0.05). The treatment group presented better results than those of the control group. Only one patient had an adverse reaction associated with the treatment. Improvements in skin barrier function differed significantly (P < 0.05)., Conclusion: The flesh-moistening paste demonstrated a reliable curative effect and safety for treating psoriasis vulgaris in patients with blood stasis patterns. The topical paste improved the barrier function of the skin lesions.

Diabetic retinopathy (DR) is complication resulted from Type 2 diabetes mellitus. Accumulating evidence has proved the functions of long noncoding RNAs (lncRNAs) in the progression of DR. Recent reports exert the numerous regulatory functions of lncRNA nuclear-enriched abundant transcript 1 (NEAT1) in various diseases. However, its implications in DR remain barely known. Therefore, this study was carried out to explore the role of NEAT1 in high-glucose (HG)-triggered injury of human retinal endothelial cells (hRECs). Here, we found the NEAT1 level was significantly elevated in patients with DR, in the retina of diabetic rats and mice. Meanwhile, hRECs under HG stimuli also exhibited an increase of NEAT1. Moreover, the loss of NEAT1 enhanced hRECs proliferation and repressed HG-induced apoptosis, which was accompanied by an upregulation of Bcl-2 and a downregulation of Bax. Subsequently, the knockdown of NEAT1 obviously reduced HG-triggered oxidative stress injury in hRECs. It was reflected that intracellular reactive oxygen species and malondialdehyde level induced by HG were repressed by NEAT1 downregulation, while superoxide dismutase activity was increased. In addition, decreased NEAT1 repressed the inflammatory processes effectively as indicated by the inactivation of inflammatory cytokines Cox-2, interleukin-6, and tumor necrosis factor-α. Furthermore, vascular endothelial growth factor A (VEGF) and transforming growth factor-β1 (TGF-β1) expression in patients with DR, DR rats, and HG-incubated hRECs was obviously increased. The silence of NEAT1 could reduce the enhanced expression of VEGF and TGF-β1 induced by HG. Hence, we concluded NEAT1 might contribute to the development of DR through activating TGF-β1 and VEGF., (© 2020 Wiley Periodicals, Inc.)

Diabetic nephropathy (DN) as one of the most frequent microvascular complications of diabetic patients causes chronic renal failure and end-stage renal disease. Noncoding RNAs including circular RNAs (circRNAs) and micro RNAs (miRNAs) were widely reported to play a critical role in numerous human diseases including DN. This research was designed to investigate the role of circ&#95;0000064 in diabetic nephropathy progression. The results showed that circ&#95;0000064 significantly promoted mesangial cells proliferation and aggravated fibrosis in DN. In the subsequent mechanism investigation, we found that circ&#95;0000064 was involved in this process by targeting micro RNA miR-143. The inhibition of miR-143 significantly reverses the effect of circ&#95;0000064 silencing on DN. In conclusion, we demonstrated the regulatory role of circ&#95;0000064 in DN and clarified that circ&#95;0000064 play a role in DN via targeting miR-143. Circ&#95;0000064 and miR-143 also showed the potential as a biomarker for DN., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2020 Elsevier B.V. All rights reserved.)

Packing interaction is a critical driving force in the folding of helical membrane proteins. Despite the importance, packing defects (i.e., cavities including voids, pockets, and pores) are prevalent in membrane-integral enzymes, channels, transporters, and receptors, playing essential roles in function. Then, a question arises regarding how the two competing requirements, packing for stability vs. cavities for function, are reconciled in membrane protein structures. Here, using the intramembrane protease GlpG of Escherichia coli as a model and cavity-filling mutation as a probe, we tested the impacts of native cavities on the thermodynamic stability and function of a membrane protein. We find several stabilizing mutations which induce substantial activity reduction without distorting the active site. Notably, these mutations are all mapped onto the regions of conformational flexibility and functional importance, indicating that the cavities facilitate functional movement of GlpG while compromising the stability. Experiment and molecular dynamics simulation suggest that the stabilization is induced by the coupling between enhanced protein packing and weakly unfavorable lipid desolvation, or solely by favorable lipid solvation on the cavities. Our result suggests that, stabilized by the relatively weak interactions with lipids, cavities are accommodated in membrane proteins without severe energetic cost, which, in turn, serve as a platform to fine-tune the balance between stability and flexibility for optimal activity., Competing Interests: The authors declare no competing interest.

Single-cell RNA sequencing (scRNA-seq) provides details for individual cells; however, crucial spatial information is often lost. We present SpaOTsc, a method relying on structured optimal transport to recover spatial properties of scRNA-seq data by utilizing spatial measurements of a relatively small number of genes. A spatial metric for individual cells in scRNA-seq data is first established based on a map connecting it with the spatial measurements. The cell-cell communications are then obtained by "optimally transporting" signal senders to target signal receivers in space. Using partial information decomposition, we next compute the intercellular gene-gene information flow to estimate the spatial regulations between genes across cells. Four datasets are employed for cross-validation of spatial gene expression prediction and comparison to known cell-cell communications. SpaOTsc has broader applications, both in integrating non-spatial single-cell measurements with spatial data, and directly in spatial single-cell transcriptomics data to reconstruct spatial cellular dynamics in tissues.

Our knowledge of transcriptional heterogeneities in epithelial stem and progenitor cell compartments is limited. Epidermal basal cells sustain cutaneous tissue maintenance and drive wound healing. Previous studies have probed basal cell heterogeneity in stem and progenitor potential, but a comprehensive dissection of basal cell dynamics during differentiation is lacking. Using single-cell RNA sequencing coupled with RNAScope and fluorescence lifetime imaging, we identify three non-proliferative and one proliferative basal cell state in homeostatic skin that differ in metabolic preference and become spatially partitioned during wound re-epithelialization. Pseudotemporal trajectory and RNA velocity analyses predict a quasi-linear differentiation hierarchy where basal cells progress from Col17a1 Hi /Trp63 Hi state to early-response state, proliferate at the juncture of these two states, or become growth arrested before differentiating into spinous cells. Wound healing induces plasticity manifested by dynamic basal-spinous interconversions at multiple basal transcriptional states. Our study provides a systematic view of epidermal cellular dynamics, supporting a revised "hierarchical-lineage" model of homeostasis., Competing Interests: Declaration of Interests The authors declare no competing interests., (Copyright © 2020 The Author(s). Published by Elsevier Inc. All rights reserved.)

Recently, machine learning (ML) has established itself in various worldwide benchmarking competitions in computational biology, including Critical Assessment of Structure Prediction (CASP) and Drug Design Data Resource (D3R) Grand Challenges. However, the intricate structural complexity and high ML dimensionality of biomolecular datasets obstruct the efficient application of ML algorithms in the field. In addition to data and algorithm, an efficient ML machinery for biomolecular predictions must include structural representation as an indispensable component. Mathematical representations that simplify the biomolecular structural complexity and reduce ML dimensionality have emerged as a prime winner in D3R Grand Challenges. This review is devoted to the recent advances in developing low-dimensional and scalable mathematical representations of biomolecules in our laboratory. We discuss three classes of mathematical approaches, including algebraic topology, differential geometry, and graph theory. We elucidate how the physical and biological challenges have guided the evolution and development of these mathematical apparatuses for massive and diverse biomolecular data. We focus the performance analysis on protein-ligand binding predictions in this review although these methods have had tremendous success in many other applications, such as protein classification, virtual screening, and the predictions of solubility, solvation free energies, toxicity, partition coefficients, protein folding stability changes upon mutation, etc.