ESICCC as a systematic computational framework for evaluation, selection, and integration of cell-cell communication inference methods.

Cell-cell communication (CCC) is critical for determining cell fates and functions in multicellular organisms. With the advent of single-cell RNA-sequencing (scRNA-seq) and spatial transcriptomics (ST), an increasing number of CCC inference methods have been developed. Nevertheless, a thorough comparison of their performances is yet to be conducted. To fill this gap, we developed a systematic benchmark framework called ESICCC to evaluate 18 ligand-receptor (LR) inference methods and five ligand/receptor-target inference methods using a total of 116 data sets, including 15 ST data sets, 15 sets of cell line perturbation data, two sets of cell type-specific expression/proteomics data, and 84 sets of sampled or unsampled scRNA-seq data. We evaluated and compared the agreement, accuracy, robustness, and usability of these methods. Regarding accuracy evaluation, RNAMagnet, CellChat, and scSeqComm emerge as the three best-performing methods for intercellular ligand-receptor inference based on scRNA-seq data, whereas stMLnet and HoloNet are the best methods for predicting ligand/receptor-target regulation using ST data. To facilitate the practical applications, we provide a decision-tree-style guideline for users to easily choose best tools for their specific research concerns in CCC inference, and develop an ensemble pipeline CCCbank that enables versatile combinations of methods and databases. Moreover, our comparative results also uncover several critical influential factors for CCC inference, such as prior interaction information, ligand-receptor scoring algorithm, intracellular signaling complexity, and spatial relationship, which may be considered in the future studies to advance the development of new methodologies.

scHyper: reconstructing cell-cell communication through hypergraph neural networks.

Cell-cell communications is crucial for the regulation of cellular life and the establishment of cellular relationships. Most approaches of inferring intercellular communications from single-cell RNA sequencing (scRNA-seq) data lack a comprehensive global network view of multilayered communications. In this context, we propose scHyper, a new method that can infer intercellular communications from a global network perspective and identify the potential impact of all cells, ligand, and receptor expression on the communication score. scHyper designed a new way to represent tripartite relationships, by extracting a heterogeneous hypergraph that includes the source (ligand expression), the target (receptor expression), and the relevant ligand-receptor (L-R) pairs. scHyper is based on hypergraph representation learning, which measures the degree of match between the intrinsic attributes (static embeddings) of nodes and their observed behaviors (dynamic embeddings) in the context (hyperedges), quantifies the probability of forming hyperedges, and thus reconstructs the cell-cell communication score. Additionally, to effectively mine the key mechanisms of signal transmission, we collect a rich dataset of multisubunit complex L-R pairs and propose a nonparametric test to determine significant intercellular communications. Comparing with other tools indicates that scHyper exhibits superior performance and functionality. Experimental results on the human tumor microenvironment and immune cells demonstrate that scHyper offers reliable and unique capabilities for analyzing intercellular communication networks. Therefore, we introduced an effective strategy that can build high-order interaction patterns, surpassing the limitations of most methods that can only handle low-order interactions, thus more accurately interpreting the complexity of intercellular communications.

Benchmarking and integration of methods for deconvoluting spatial transcriptomic data.

MOTIVATION: The rapid development of spatial transcriptomics (ST) approaches has provided new insights into understanding tissue architecture and function. However, the gene expressions measured at a spot may contain contributions from multiple cells due to the low-resolution of current ST technologies. Although many computational methods have been developed to disentangle discrete cell types from spatial mixtures, the community lacks a thorough evaluation of the performance of those deconvolution methods. RESULTS: Here, we present a comprehensive benchmarking of 14 deconvolution methods on four datasets. Furthermore, we investigate the robustness of different methods to sequencing depth, spot size and the choice of normalization. Moreover, we propose a new ensemble learning-based deconvolution method (EnDecon) by integrating multiple individual methods for more accurate deconvolution. The major new findings include: (i) cell2loction, RCTD and spatialDWLS are more accurate than other ST deconvolution methods, based on the evaluation of three metrics: RMSE, PCC and JSD; (ii) cell2location and spatialDWLS are more robust to the variation of sequencing depth than RCTD; (iii) the accuracy of the existing methods tends to decrease as the spot size becomes smaller; (iv) most deconvolution methods perform best when they normalize ST data using the method described in their original papers; and (v) the integrative method, EnDecon, could achieve more accurate ST deconvolution. Our study provides valuable information and guideline for practically applying ST deconvolution tools and developing new and more effective methods. AVAILABILITY AND IMPLEMENTATION: The benchmarking pipeline is available at https://github.com/SunXQlab/ST-deconvoulution. An R package for EnDecon is available at https://github.com/SunXQlab/EnDecon. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Multilayer stacked crystalline silicon switch with nanosecond-order switching time.

To realize compact and denser photonic integrated circuits, three-dimensional integration has been widely accepted and researched. In this article, we demonstrate the operation of a 3D integrated silicon photonic platform fabricated through wafer bonding. Benefiting from the wafer bonding process, the material of all layers is c-Si, which ensures that the mobility is high enough to achieve a nanosecond response via the p-i-n diode shifter. Optical components, including multimode interferences (MMIs), waveguide crossing, and Mach-Zehnder interferometer (MZI)-based switch, are fabricated in different layers and exhibit great performance. The interlayer coupler and crossing achieve a 0.98 dB coupling loss and <-43.58 dB cross talk, while the crossing fabricated in the same layer shows <-36.00 dB cross talk. A nanosecond-order switch response is measured in different layers.

Supramolecular Sequential Light-Harvesting Systems for Constructing White LED Device and Latent Fingerprint Imaging.

The fabrication of supramolecular light-harvesting systems (LHS) with sequential energy transfer is of significance in utilizing light energy. In this study, we report the non-covalent self-assembly of a sequential LHS by pillar[5]arene-based host-guest interaction in water and its applications in white light-emitting diode (LED) device and latent fingerprint imaging. The host-guest complex WP5 ⊃ ${ \supset }$ G self-assembles into nanoparticles in water and shows enhanced aggregation-induced emission (AIE) effect. The nanoparticles can be further used to construct sequential LHS with fluorescent dyes 4,7-di(2-thienyl)-benzo[2,1,3]thiadiazole (DBT) and sulforhodamine 101 (SR101). Impressively, the system shows white-light emission when the molar ratio of WP5 ⊃ ${ \supset }$ G/DBT/SR101 is 1100/2/16. The material can be coated on a LED bulb to achieve white-light emission. In addition, the sequential LHS exhibit multicolor fluorescence including red emission, which have been successfully applied to high-resolution imaging of latent fingerprints. Therefore, we demonstrated a general strategy for the construction of sequential LHS in water based on macrocyclic host-guest interaction and explored its multi-functional applications in white-light LED device and imaging of latent fingerprints, which will promote future development and application of supramolecular LHSs.

Research and development of microenvironment's influence on stem cells from the apical papilla - construction of novel research microdevices: tooth-on-a-chip.

Stem cells are crucial in tissue engineering, and their microenvironment greatly influences their behavior. Among the various dental stem cell types, stem cells from the apical papilla (SCAPs) have shown great potential for regenerating the pulp-dentin complex. Microenvironmental cues that affect SCAPs include physical and biochemical factors. To research optimal pulp-dentin complex regeneration, researchers have developed several models of controlled biomimetic microenvironments, ranging from in vivo animal models to in vitro models, including two-dimensional cultures and three-dimensional devices. Among these models, the most powerful tool is a microfluidic microdevice, a tooth-on-a-chip with high spatial resolution of microstructures and precise microenvironment control. In this review, we start with the SCAP microenvironment in the regeneration of pulp-dentin complexes and discuss research models and studies related to the biological process.

Safety and feasibility of early drinking water after general anesthesia recovery in patients undergoing daytime surgery.

BACKGROUND: Patients who are recovering from general anesthesia commonly exhibit symptoms such as dry lips, throat irritation, and thirst, prompting a desire to drink water in the post-anesthesia care unit (PACU). In this study, we aimed to evaluate the therapeutic effects and any potential complications of administering varying quantities of water to such patients. The primary objectives are to assess the safety and feasibility of early water intake after general anesthesia, specifically in the context of daytime surgery. METHODS: A total of 200 nongastrointestinal patients who underwent outpatient surgery were randomly assigned to four groups: Group A (drinking < 1 ml/kg), Group B (drinking 1-2 ml/kg), Group C (drinking > 2 ml/kg), and Group D (no water intake). We monitored changes in the assessment parameters before and after water consumption, as well as the incidence of post-drinking nausea and vomiting, and compared these outcomes among the four groups. RESULTS: Water intake led to a significant reduction in thirst, oropharyngeal discomfort, and pain scores and a notable increase in the gastric antrum motility index (MI), exhibiting statistical significance compared to the values before drinking (p < 0.05). Remarkably, higher water consumption correlated with enhanced gastrointestinal peristalsis. There was a significant difference in the antral MI among groups B, C, and A (p < 0.05). The occurrence of nausea and vomiting did not significantly differ among groups A, B, C, and D (p > 0.05). Early water consumption enhanced patient satisfaction with medical care, significantly varying from Group D (p < 0.05). CONCLUSION: Non-gastrointestinal surgical patients who passed pre-drinking water assessments post GA(general anesthesia)recovery could safely ingest moderate amounts of water in the PACU. Early water intake is both safe and feasible, effectively fostering swift postoperative recovery.

The amygdala volume moderates the relationship between childhood maltreatment and callous-unemotional traits in adolescents with conduct disorder.

CU traits, characterized by shallow affect, lack of fear, and absence of remorse, have been moderately associated with childhood maltreatment in a recent meta-analysis. However, the potential impact of brain structures remains undetermined. This paper examines the relationship between callous-unemotional (CU) traits, childhood maltreatment, and amygdala volumes. In this study, we used a region-of-interest (ROI) analysis to explore the interaction between the volumes of the amygdala, childhood maltreatment, and the manifestation of CU traits in adolescents diagnosed with conduct disorder (CD, N = 67), along with a comparison group of healthy-control youths (HCs, N = 89). The ROI analysis revealed no significant group differences in the bilateral amygdalar volumes. Significant positive correlation was discovered between all forms of child maltreatment (except for physical neglect) and CU traits across subjects. But the interaction of physical abuse and amygdala volumes was only significant within CD patients. Notably, a sensitivity analysis suggested that gender significantly influences these findings. These results contribute critical insights into the etiology of CU traits, emphasizing the need for customized clinical assessment tools and intervention strategies.

Investigation of the Zero-Frequency Component of Nonlinear Lamb Waves in a Symmetrical Undulated Plate.

When an ultrasonic pulse propagates in a thin plate, nonlinear Lamb waves with higher harmonics and a zero-frequency component (ZFC) will be generated because of the nonlinearity of materials. The ZFC, also known as the static displacement or static component, has its unique application on the evaluation of early-stage damages in the elastic symmetrical undulated plate. In this study, analysis of the excitation mechanism of the ZFC and the second harmonic component (SHC) was theoretically and numerically investigated, and the material early-stage damage of a symmetrical undulated was characterized by studying the propagation of nonlinear Lamb waves. Both the ZFC and SHC can be effectively employed in monitoring the material damages of the undulated plate in its early stage. However, several factors must be considered for the propagation of the SHC in an undulated plate because of the geometric curvature and interference between the second harmonics during propagation, preventing efficient application of this technique. If the fundamental wave can propagate in the plate regardless of the plate boundary conditions, an accumulative effect always exists for the ZFC in a thin plate, indicating that the ZFC is independent of the structural geometry. This study reveals that the ZFC-based inspection technique is more efficient and powerful in characterizing the damages of a symmetrical undulated plate in the early stage of service compared to the second harmonic method.

Identification of Key Molecular Pathways and Associated Genes as Targets to Overcome Radiotherapy Resistance Using a Combination of Radiotherapy and Immunotherapy in Glioma Patients.

Recent mechanistic studies have indicated that combinations of radiotherapy (RT) plus immunotherapy (via CSF-1R inhibition) can serve as a strategy to overcome RT resistance and improve the survival of glioma mice. Given the high mortality rate for glioma, including low-grade glioma (LGG) patients, it is of critical importance to investigate the mechanism of the combination of RT and immunotherapy and further translate the mechanism from mouse studies to improve survival of RT-treated human glioma patients. Using the RNA-seq data from a glioma mouse study, 874 differentially expressed genes (DEGs) between the group of RT-treated mice at glioma recurrence and the group of mice with combination treatment (RT plus CSF-1R inhibition) were translated to the human genome to identify significant molecular pathways using the KEGG enrichment analysis. The enrichment analysis yields statistically significant signaling pathways, including the phosphoinositide 3-kinase (PI3K)/AKT pathway, Hippo pathway, and Notch pathway. Within each pathway, a candidate gene set was selected by Cox regression models as genetic biomarkers for resistance to RT and response to the combination of RT plus immunotherapies. Each Cox model is trained using a cohort of 295 RT-treated LGG patients from The Cancer Genome Atlas (TCGA) database and validated using a cohort of 127 RT-treated LGG patients from the Chinese Glioma Genome Atlas (CGGA) database. A four-DEG signature (ITGB8, COL9A3, TGFB2, JAG1) was identified from the significant genes within the three pathways and yielded the area under time-dependent ROC curve AUC = 0.86 for 5-year survival in the validation set, which indicates that the selected DEGs have strong prognostic value and are potential intervention targets for combination therapies. These findings may facilitate future trial designs for developing combination therapies for glioma patients.

Effect of intravenous different drugs on the prevention of restlessness during recovery period of pediatric laparoscopic surgery: a randomized control trial.

PURPOSE: To explored the impact of dexmedetomidine and esketamine in mitigating restlessness during the postoperative recovery phase following laparoscopic surgery in children. METHODS: 102 individuals aged 1 to 7 years experiencing laparoscopic surgery were randomly allocated into three groups, each accepting 1 µg/kg of dexmedetomidine, 0.3 mg/kg of esketamine, or saline immediately at the end of carbon dioxide pneumoperitoneum. Emergence agitation (EA) occurrence was assessed by PAED scale and 5-point agitation scale. Pain was judged using Face, Legs, Activity, Cry, and Consolability (FLACC) scale. The recovery time, extubation time, and post-anesthesia care unit (PACU) stay time were recorded for all three groups. RESULTS: Patients administered 1 µg/kg of dexmedetomidine (8.8%) and individuals given 0.3 mg/kg of esketamine (11.8%) showed lower incidences of emergence agitation compared to those receiving saline (35.5%; P = 0.009). There was no statistically significant difference in the time to discharge from the PACU among the three groups of patients (P > 0.05). The recovery time and extubation time were notably extended in the dexmedetomidine group (40.88 ± 12.95 min, 42.50 ± 13.38 min) when compared to the saline group (32.56 ± 13.05 min, 33.29 ± 11.30 min; P = 0.009, P = 0.010). CONCLUSION: Following CO2 pneumoperitoneum in pediatric laparoscopic surgeries, the intravenous administration of 1 µg/kg dexmedetomidine or 0.3 mg/kg esketamine effectively lowers EA occurrence without extending PACU time.

Inferring microenvironmental regulation of gene expression from single-cell RNA sequencing data using scMLnet with an application to COVID-19.

Inferring how gene expression in a cell is influenced by cellular microenvironment is of great importance yet challenging. In this study, we present a single-cell RNA-sequencing data based multilayer network method (scMLnet) that models not only functional intercellular communications but also intracellular gene regulatory networks (https://github.com/SunXQlab/scMLnet). scMLnet was applied to a scRNA-seq dataset of COVID-19 patients to decipher the microenvironmental regulation of expression of SARS-CoV-2 receptor ACE2 that has been reported to be correlated with inflammatory cytokines and COVID-19 severity. The predicted elevation of ACE2 by extracellular cytokines EGF, IFN-γ or TNF-α were experimentally validated in human lung cells and the related signaling pathway were verified to be significantly activated during SARS-COV-2 infection. Our study provided a new approach to uncover inter-/intra-cellular signaling mechanisms of gene expression and revealed microenvironmental regulators of ACE2 expression, which may facilitate designing anti-cytokine therapies or targeted therapies for controlling COVID-19 infection. In addition, we summarized and compared different methods of scRNA-seq based inter-/intra-cellular signaling network inference for facilitating new methodology development and applications.

Temperature-insensitive and fabrication-tolerant coarse wavelength division (de)multiplexing on a silica platform using an angled multimode interferometer.

Wavelength division (de)multiplexing (WDM) device is a crucial component for optical transmission networks. In this paper, we demonstrate a 4 channel WDM device with a 20 nm wavelength spacing on silica based planar lightwave circuits (PLC) platform. The device is designed using an angled multimode interferometer (AMMI) structure. Since there are fewer bending waveguides than other WDMs, the device footprint is smaller, at 21 mm × 0.4 mm. Owing to the low thermo-optic coefficient (TOC) of silica, a low temperature sensitivity of 10 pm/°C is achieved. The fabricated device exhibits high performance of an insertion loss (IL) lower than 1.6 dB, a polarization dependent loss (PDL) lower than 0.34 dB, and the crosstalk between adjacent channels lower than -19 dB. The 3 dB bandwidth is 12.3∼13.5 nm. Moreover, the device shows a high tolerance with a sensitivity of central wavelength to the width of multimode interferometer < 43.75 pm/nm.

Fluorescent Nanoassemblies in Water Exhibiting Tunable LCST Behavior and Responsive Light Harvesting Ability.

Responsive fluorescent nanomaterials have been received considerable attention in recent years. In this work, a bola-type amphiphilic molecule, CSO, was synthesized which contains a hydrophobic cyanostilbene core and hydrophilic oligo(ethylene glycol) (OEG) coils at both sides. The cyanostilbene group is aggregation-induced emission (AIE) active, while the OEG coils are thermo-responsive. As a result, the CSO molecules can self-assemble into blue-fluorescent nanoassemblies with lower critical solution temperature (LCST) behavior in aqueous media. It is noteworthy that the LCST behavior can be reversibly regulated with changes in concentration and the introduction of K+ . Intriguingly, fluorescence of CSO assembly shows a blue-shift upon heating. Finally, by employing CSO as a light capturing antenna and energy donor, an artificial light harvesting system with tunable emission and thermo-responsive characteristics was fabricated. This study not only demonstrates an integrated approach to create responsive fluorescent nanomaterials, but also shows great potential for producing luminescent materials and mimicking photosynthesis in nature.

Selective C(sp3)-S Bond Cleavage of Thioethers to Build Up Unsymmetrical Disulfides.

The selective C(sp3)-S bond cleavage of thioethers was first developed to prepare unsymmetrical disulfides by using electrophilic halogenation reagents. In this strategy, NBS (N-bromosuccinimide) achieves selective furfuryl C(sp3)-S bond cleavage of furfuryl alkylthioethers at room temperature. Meanwhile, NFSI (N-fluorobenzenesulfonimide) enables selective methyl C(sp3)-S bond cleavage of aryl and alkyl methylthioethers at an elevated temperature. Notably, the substrate scope investigation indicates that the order of selectivity of the C-S bond cleavage is furfuryl C(sp3)-S > benzyl C(sp3)-S > alkyl C(sp3)-S > C(sp2)-S bond. Moreover, this practical and operationally simple strategy also provides an important complementary way to access various unsymmetrical disulfides with excellent functional group tolerances and moderate to good yields.

Structural abnormalities in adolescents with conduct disorder and high versus low callous unemotional traits.

There may be distinct conduct disorder (CD) etiologies and neural morphologies in adolescents with high callous unemotional (CU) traits versus low CU traits. Here, we employed surface-based morphometry methods to investigate morphological differences in adolescents diagnosed with CD [42 with high CU traits (CD-HCU) and 40 with low CU traits (CD-LCU)] and healthy controls (HCs, N = 115) in China. Whole-brain analyses revealed significantly increased cortical surface area (SA) in the left inferior temporal cortex and the right precuneus, but decreased SA in the left superior temporal cortex in the CD-LCU group, compared with the HC group. There were no significant cortical SA differences between the CD-HCU and the HC groups. Compared to the CD-HCU group, the CD-LCU group had a greater cortical thickness (CT) in the left rostral middle frontal cortex. Region-of-interest analyses revealed significant group differences in the right hippocampus, with CD-HCU group having lower right hippocampal volumes than HCs. We did not detect significant group differences in the amygdalar volume, however, the right amygdalar volume was found to be a significant moderator of the correlation between CU traits and the proactive aggression in CD patients. The present results suggested that the manifestations of CD differ between those with high CU traits versus low CU traits, and underscore the importance of sample characteristics in understanding the neural substrates of CD.

Vehicle State Estimation Combining Physics-Informed Neural Network and Unscented Kalman Filtering on Manifolds.

This paper proposes a novel vehicle state estimation (VSE) method that combines a physics-informed neural network (PINN) and an unscented Kalman filter on manifolds (UKF-M). This VSE aimed to achieve inertial measurement unit (IMU) calibration and provide comprehensive information on the vehicle's dynamic state. The proposed method leverages a PINN to eliminate IMU drift by constraining the loss function with ordinary differential equations (ODEs). Then, the UKF-M is used to estimate the 3D attitude, velocity, and position of the vehicle more accurately using a six-degrees-of-freedom vehicle model. Experimental results demonstrate that the proposed PINN method can learn from multiple sensors and reduce the impact of sensor biases by constraining the ODEs without affecting the sensor characteristics. Compared to the UKF-M algorithm alone, our VSE can better estimate vehicle states. The proposed method has the potential to automatically reduce the impact of sensor drift during vehicle operation, making it more suitable for real-world applications.

Neural Network-Based Adaptive Height Tracking Control of Active Air Suspension System with Magnetorheological Fluid Damper Subject to Uncertain Mass and Input Delay.

In this paper, we present a novel robust adaptive neural network-based control framework to address the ride height tracking control problem of active air suspension systems with magnetorheological fluid damper (MRD-AAS) subject to uncertain mass and time-varying input delay. First, a radial basis function neural network (RBFNN) approximator is designed to compensate for unmodeled dynamics of the MRD. Then, a projector-based estimator is developed to estimate uncertain parameter variation (sprung mass). Additionally, to deal with the effect of input delay, a time-delay compensator is integrated in the adaptive control law to enhance the transient response of MRD-AAS system. By introducing a Lyapunov-Krasovskii (LK) functional, both ride height tracking and estimator errors can robustly converge towards the neighborhood of the desired values, achieving uniform ultimate boundness. Finally, comparative simulation results based on a dynamic co-simulator built in AMESim 2021.2 and Matlab/Simulink 2019(b) are given to illustrate the validity of the proposed control framework, showing its effectiveness to operate ride height regulation with MRD-AAS systems accurately and reliably under random road excitations.

Single-cell transcriptome-based multilayer network biomarker for predicting prognosis and therapeutic response of gliomas.

Occurrence and development of cancers are governed by complex networks of interacting intercellular and intracellular signals. The technology of single-cell RNA sequencing (scRNA-seq) provides an unprecedented opportunity for dissecting the interplay between the cancer cells and the associated microenvironment. Here we combined scRNA-seq data with clinical bulk gene expression data to develop a computational pipeline for identifying the prognostic and predictive signature that connects cancer cells and microenvironmental cells. The pipeline was applied to glioma scRNA-seq data and revealed a tumor-associated microglia/macrophage-mediated EGFR/ERBB2 feedback-crosstalk signaling module, which was defined as a multilayer network biomarker (MNB) to predict survival outcome and therapeutic response of glioma patients. We used publicly available clinical data sets from large cohorts of glioma patients to examine the prognostic significance and predictive accuracy of the MNB, which outperformed conventional gene biomarkers and other methods. Additionally, the MNB was found to be predictive of the sensitivity or resistance of glioma patients to molecularly targeted therapeutics. Moreover, the MNB was an independent and the strongest prognostic factor when adjusted for clinicopathologic risk factors and other existing gene signatures. The robustness of the MNB was further tested on additional data sets. Our study presents a promising scRNA-seq transcriptome-based multilayer network approach to elucidate the interactions between tumor cell and tumor-associated microenvironment and to identify prognostic and predictive signatures of cancer patients. The proposed MNB method may facilitate the design of more effective biomarkers for predicting prognosis and therapeutic resistance of cancer patients.

Inferring latent temporal progression and regulatory networks from cross-sectional transcriptomic data of cancer samples.

Unraveling molecular regulatory networks underlying disease progression is critically important for understanding disease mechanisms and identifying drug targets. The existing methods for inferring gene regulatory networks (GRNs) rely mainly on time-course gene expression data. However, most available omics data from cross-sectional studies of cancer patients often lack sufficient temporal information, leading to a key challenge for GRN inference. Through quantifying the latent progression using random walks-based manifold distance, we propose a latent-temporal progression-based Bayesian method, PROB, for inferring GRNs from the cross-sectional transcriptomic data of tumor samples. The robustness of PROB to the measurement variabilities in the data is mathematically proved and numerically verified. Performance evaluation on real data indicates that PROB outperforms other methods in both pseudotime inference and GRN inference. Applications to bladder cancer and breast cancer demonstrate that our method is effective to identify key regulators of cancer progression or drug targets. The identified ACSS1 is experimentally validated to promote epithelial-to-mesenchymal transition of bladder cancer cells, and the predicted FOXM1-targets interactions are verified and are predictive of relapse in breast cancer. Our study suggests new effective ways to clinical transcriptomic data modeling for characterizing cancer progression and facilitates the translation of regulatory network-based approaches into precision medicine.