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Developers often need to decide which APIs to use for the functions being implemented. With the ever-growing number of APIs and libraries, it becomes increasingly difficult for developers to find appropriate APIs, indicating the necessity of automatic API usage recommendation. Previous studies adopt statistical models or collaborative filtering methods to mine the implicit API usage patterns for recommendation. However, they rely on the occurrence frequencies of APIs for mining usage patterns, thus prone to fail for the low-frequency APIs. Besides, prior studies generally regard the API call interaction graph as homogeneous graph, ignoring the rich information (e.g., edge types) in the structure graph. In this work, we propose a novel method named MEGA for improving the recommendation accuracy especially for the low-frequency APIs. Specifically, besides call interaction graph, MEGA considers another two new heterogeneous graphs: global API co-occurrence graph enriched with the API frequency information and hierarchical structure graph enriched with the project component information. With the three multi-view heterogeneous graphs, MEGA can capture the API usage patterns more accurately. Experiments on three Java benchmark datasets demonstrate that MEGA significantly outperforms the baseline models by at least 19% with respect to the Success Rate@1 metric. Especially, for the low-frequency APIs, MEGA also increases the baselines by at least 55% regarding the Success Rate@1.

ObjectiveRevealing the single-cell immune ecosystems in true versus de novo hepatocellular carcinoma (HCC) recurrences could help the optimal development of immunotherapies.DesignWe performed 5’and VDJ single-cell RNA-sequencing on 34 samples from 20 recurrent HCC patients. Bulk RNA-sequencing, flow cytometry, multiplexed immunofluorescence, and in vitro functional analyses were performed on samples from two validation cohorts.ResultsAnalyses of mutational profiles and evolutionary trajectories in paired primary and recurrent HCC samples using whole-exome sequencing identified de novo versus true recurrences, some of which occurred before clinical diagnosis. The tumour immune microenvironment (TIME) of truly recurrent HCCs was characterised by an increased abundance in KLRB1+CD8+T cells with memory phenotype and low cytotoxicity. In contrast, we found an enrichment in cytotoxic and exhausted CD8+T cells in the TIME of de novo recurrent HCCs. Transcriptomic and interaction analyses showed elevated GDF15 expression on HCC cells in proximity to dendritic cells, which may have dampened antigen presentation and inhibited antitumour immunity in truly recurrent lesions. In contrast, myeloid cells’ cross talk with T cells-mediated T cell exhaustion and immunosuppression in the TIME ofde novorecurrent HCCs. Consistent with these findings, a phase 2 trial of neoadjuvant anti-PD-1 immunotherapy showed more responses in de novo recurrent HCC patients.ConclusionTrue and de novo HCC recurrences occur early, have distinct TIME and may require different immunotherapy strategies. Our study provides a source for genomic diagnosis and immune profiling for guiding immunotherapy based on the type of HCC recurrence and the specific TIME.

Purpose:Targeted therapy and immunotherapy are transforming the treatment approach for intrahepatic cholangiocarcinoma (ICC). However, little is known about the intertumor heterogeneity (ITH) of multifocal ICC and its impacts on patient response to these treatments. We aimed to characterize the immunogenomic and epigenomic heterogeneity of multifocal ICC to guide treatment decision making.Experimental Design:We obtained 66 tumor samples from 16 patients with multifocal ICC and characterized the tumor and immune heterogeneity using whole-exome sequencing, bulk and single-cell RNA sequencing, methylation microarray, and multiplex immunostaining. Patients were divided into high- or low-ITH groups according to the median ITH index. Two independent cohorts were used to validate findings. Responses to anti-PD-1 therapy were assessed.Results:Multifocal ICC presented considerable intertumor genomic, transcriptional, and epigenomic heterogeneity within a patient in high ITH group. The immune profile among multiple tumors within a patient was relatively less heterogeneous in high- or low-ITH group, and consistent responses of multiple tumors to anti-PD-1 immunotherapy were observed. Unsupervised clustering of immune markers identified one low and one high immune subtype, with higher immune cell infiltration, closer tumor–immune cell interactions, and upregulated IFN-signature expression in high-immune subtype. Determining expression levels of CD8B and ICOS facilitated this immune classification and prediction of patient prognosis. Finally, promoter DNA methylation contributed to different immune profiles of two subtypes by regulating immune-gene expression.Conclusions:There is comprehensive heterogeneity in the genome, transcriptome, and epigenome of multifocal ICC. On the basis of the less heterogeneous immune profile of ICC, we suggest an immune classification that stratifies patients' prognosis and may support personalized immunotherapy.

Supplementary Data from Multiomic Analysis Reveals Comprehensive Tumor Heterogeneity and Distinct Immune Subtypes in Multifocal Intrahepatic Cholangiocarcinoma

Cancer organoids, a three-dimensional (3D) culture system of cancer cells derived from tumor tissues, recapitulate physiological structure of the parental tumor. Different tumor organoids have been established for a variety of tumor types, such as colorectal, liver, stomach, pancreatic and brain tumors. Some tumor organoid biobanks are built to screen and discover novel antitumor drug targets. Moreover, patients-derived tumor organoids (PDOs) could predict treatment response to chemoradiotherapy, targeted therapy and immunotherapy to provide guidance for personalized cancer therapy. In this review, we provide an updated overview of tumor organoid development, summarize general approach to establish tumor organoids, and discuss the application of anti-cancer drug screening based on tumor organoid and its application in personalized therapy. We also outline the opportunities and challenges for organoids to guide precision medicine.

SummaryRevealing differential tumor immune microenvironment (TIME) characteristics between true versus de novo hepatocellular carcinoma (HCC) recurrence could help optimal development and use of immunotherapies. Here, we studied the TIME of recurrent HBV-related HCCs by 5’and VDJ single-cell and bulk RNA-sequencing, flow cytometry, and multiplexed immunofluorescence. Analyses of mutational profiles, evolutionary trajectories, and clonal architecture using whole-exome sequencing identified de novo versus true recurrences, some of which occurred before clinical diagnosis. The TIME of truly recurrent HCCs was characterized by an increased abundance in KLRB1+CD8+ T cells with memory phenotype and low cytotoxicity. In contrast, we found an enrichment in cytotoxic and exhausted CD8+ T cells in the TIME of de novo recurrent HCCs. Transcriptomic and interaction analyses showed an upregulated GDF15 expression level on HCC cells in proximity to dendritic cells, which may have dampened antigen presentation and inhibited anti-tumor immunity in the TIME of truly recurrent lesions. In contrast, we found that myeloid cells’ crosstalk with T cells mediated T cell exhaustion and immunosuppression in the TIME of de novo recurrent HCC. In conclusion, our results support genomic diagnosis and immune profiling for guiding immunotherapy implementation based on the type of HCC recurrence and TIME.HighlightsTruly recurrent lesions are seeded before primary tumor diagnosis, and that de novo cancer can occur earlier than the clinically used 2-year limit.ScRNA-seq unravels distinct immune ecosystems in true versus de novo HCC recurrences, highlighting the need for different immunotherapy strategies for two types of HCC recurrence.CD8+ T cells in de novo recurrence displayed cytotoxic and exhausted phenotypes while those in truly recurrent lesions showed a memory phenotype with weak cytotoxicity.HCC cells expressing the inhibitory molecule GDF15 were in the proximity of DCs only in truly recurrent lesions.High GDF15 expression level was associated with truly recurrent HCC and worse prognosis.Graphical abstract

The 7075 aluminum alloy was an important structural material in the field of aeronautics and astronautics. With the development of this field, the requirements for its comprehensive properties were increasing. A novel casting method, the impact jet solid-liquid compound casting, was adopted to fabricate the 3A21/7075 aluminum alloy cladding material. Retrogression and reaging (RRA) treatment was further applied to the 3A21/7075 aluminum alloy cladding material in order to investigate the influence of RRA on microstructure and microhardness of the 3A21/7075 aluminum alloy cladding material. Then, the electron backscattered diffraction (EBSD) technique and the transmission electron microscope (TEM) together with the energy dispersive spectrometer (EDS) and the hardness test were utilized to analyze the influence of RRA treatment on microstructure and hardness of the 3A21/7075 aluminum alloy cladding material. The research results showed the 3A21/7075 aluminum alloy cladding material had better deformability. The amount of fine and dispersed phases precipitated in grains was in favor of improving the comprehensive mechanical properties of the 3A21/7075 aluminum alloy cladding material. This research might provide data and a theoretical approach for the engineering application of the 3A21/7075 aluminum alloy cladding material.

This paper presents one method and one hybrid genetic algorithm for multi-depot open vehicle routing problem with fuzzy time windows (MDOVRPFTW) without maximum time windows. For the method, the degree of customers’ willingness to accept goods (DCWAG) is firstly proposed, it’s one fuzzy vague and determines maximum time windows. Referring to methods to determine fuzzy membership function, the function between DCWAG and the starting service time is constructed. By setting an threshold for DCWAG, the starting service time that the threshold corresponds can be treated as the maximum time window, which meets the actual situation. The goal of the model is to minimize the total cost. For the algorithm, MDOVRPFTW without maximum time windows is an extension of the NP-hard problem, the hybrid genetic algorithm was designed, which is combination of genetic algorithm and Hungarian algorithm. When the hybrid genetic algorithm applied to one pharmaceutical logistics company in Beijing City, China, one optimal scheme is determined. Then the rationality and the stability of solutions by the hybrid genetic algorithm are proved. Finally, sensitivity analyses are performed to investigate the impact of someone factor on DCWAG and some suggestions are proposed.

Aiming at the dynamic multi-depot multi-compartment refrigerated vehicle routing problem with multi-path based on real-time traffic information, based on the idea of pre-optimization followed by real-time adjustment, a two stages optimization model with the goal of minimizing total cost is established. To solve this problem, this paper designed a hybrid chaotic genetic algorithm with variable neighbourhood search (HCGAVNS) to generate the initial routes. In the real-time adjustment phase, this paper proposed a path selection strategy to update the selected paths. Multiple experiments are constructed to verify the validity of the model and the algorithm. This research has important theoretical and practical significance.

The progress in efficiency improvements of blade-coated two-dimensional perovskite solar cells (PSCs) lags behind that of spin-coated ones. Pinholes and voids are two important structural defects hiding in blade-coated large-area perovskite films to deteriorate device efficiency and stability. However, the formation of pinholes and voids is still difficult to predict. On substrates with moderate wettability, Dion–Jacobson perovskite films can be blade-coated with good coverage, but we highlight that the formation of nanoscale voids buried at the bottom side of perovskite films can occur frequently, depending critically on the wettability of substrates. It is revealed that super hydrophilic NiOx substrates with contact angle θ < 5° possesses strong bubble repellent property, which thermal dynamically prevents the retraction of precursor solution and plays an important role to suppress the attachment and growth of bubbles on its surface. The probabilities of forming both pinholes and buried voids in perovskite films on super hydrophilic NiOx substrates are one order of magnitude lower than that on hydrophilic PTAA and poly-TPD substrates. The blade-coated (BDA)MA4Pb5I16 films on NiOx substrate offer PSCs with excellent interfacial hole extraction and reduced leakage current, contributing a significant power conversion efficiency improvement from ∼10% to 16.26%.

Background SARS-CoV-2 has caused the COVID-19 pandemic, 14-53% with COVID-19 developed hepatic dysfunction. More data is required to ascertain the pattern of liver injury in patients with COVID-19. Here we report the use of human liver and biliary organoids as a tool to investigate the SARSCoV- 2 infection and virus-induced tissue damage ex vivo at the cellular and molecular levels. Methods Immunofluorescence staining and RT-qPCR was performed to examine the susceptibility and replication of human liver and biliary organoids to SARS-CoV-2. Transmission electron microscopy was performed to investigate the ultrastructure of SARS-CoV-2 infected biliary organoid. The mRNA sequence analysis was performed to determine gene expression changes induced by SARS-CoV-2-infection of biliary organoids. Results Immunofluorescence staining showed that SARS-CoV-2 spike (S) glycoprotein protein was readily detected in patchy areas of the human liver and biliary organoids, whereas no signal was found in uninfected control. RT-qPCR showed that the SARS-CoV-2 genomic RNAs revealed a dramatic increase of viral load in organoids at 24 h post-infection (MOI=1) and it can remain stable 96 hours in the liver organoids and 48 hours in biliary organoids. Unsupervised transmission electron microscopy (TEM) showed that viral particles occurred in the lumen of the organoid, at the basolateral and apical side of the organoid, even in membrane-bound vesicles. Additionally, RNA-seq revealed early cell response to virus infection, including the well-known upregulation of chemokines and inflammatory cytokines. Conclusions Therefore, the hepatocytes and bile duct cells have effectively been infected by COVID-19, and human liver and biliary organoids can serve as a pathophysiological model to investigate the underlying mechanism of SARS-CoV-2 infection.

Technical debt is a metaphor to reflect the tradeoff software engineers make between short-term benefits and long-term stability. Self-admitted technical debt (SATD), a variant of technical debt, has been proposed to identify debt that is intentionally introduced during software development, e.g., temporary fixes and workarounds. Previous studies have leveraged human-summarized patterns (which represent n-gram phrases that can be used to identify SATD) or text-mining techniques to detect SATD in source code comments. However, several characteristics of SATD features in code comments, such as vocabulary diversity, project uniqueness, length, and semantic variations, pose a big challenge to the accuracy of pattern or traditional text-mining-based SATD detection, especially for cross-project deployment. Furthermore, although traditional text-mining-based method outperforms pattern-based method in prediction accuracy, the text features it uses are less intuitive than human-summarized patterns, which makes the prediction results hard to explain. To improve the accuracy of SATD prediction, especially for cross-project prediction, we propose a Convolutional Neural Network-- (CNN) based approach for classifying code comments as SATD or non-SATD. To improve the explainability of our model’s prediction results, we exploit the computational structure of CNNs to identify key phrases and patterns in code comments that are most relevant to SATD. We have conducted an extensive set of experiments with 62,566 code comments from 10 open-source projects and a user study with 150 comments of another three projects. Our evaluation confirms the effectiveness of different aspects of our approach and its superior performance, generalizability, adaptability, and explainability over current state-of-the-art traditional text-mining-based methods for SATD classification.

All-inorganic halide perovskites have attracted significant attention because of their excellent photoelectric performance. However, several remaining challenges, including large-scale synthesis, strict limitations on the experimental conditions, and material stability, limit their practical application. In this study, we propose a simple ligand-assisted supersaturated recrystallization (LASR) procedure in order to prepare perovskite-related plum-pudding-like Cs4PbBr6/CsPbBr3 microcrystals (MCs) on a large scale and at room temperature. The LASR approach was proved to rely on the selection of an appropriate antisolvent. The synthesized Cs4PbBr6/CsPbBr3 MCs exhibit strong green emission at 520 nm with a narrow full-width at half-maximum (FWHM) value of 20 nm originating from the CsPbBr3 nanocrystals (NCs) embedded in the Cs4PbBr6 MCs. Additionally, the environmental stability experiment showed ∼20% PL intensity loss after storage under ambient conditions with 50% relative humidity (RH) for 2 months.

Polyetheretherketone (PEEK) has been widely used as orthopedic and dental materials due to excellent mechanical and physicochemical tolerance. However, its biological inertness, poor osteoinduction, and weak antibacterial activity make the clinical applications in a dilemma. Inspired by the mussel adhesion mechanism, here we reported a biomimetic surface strategy for rational integration and optimization of anti-infectivity and osteo-inductivity onto PEEK surfaces using a mussel foot proteins (Mfps)-mimic peptide with clickable azido terminal. The peptide enables mussel-like adhesion on PEEK biomaterial surfaces, leaving azido groups for the further steps of biofunctionalizations. In this study, antimicrobial peptide (AMP) and osteogenic growth peptide (OGP) were bioorthogonally clicked on the azido-modified PEEK biomaterials to obtain a dual-effect of host defense and tissue repair. Since bioorthogonal clicking allows precise collocation between AMP and OGP through changing their feeding molar ratios, an optimal PEEK surface was finally obtained in this research, which could long-term inhibit bacterial growth, stabilize bone homeostasis and facilitate interfacial bone regeneration. In a word, this upgraded mussel surface strategy proposed in this study is promising for the surface bioengineering of inert medical implants, in particular, achieving rational integration of multiple biofunctions to match clinical requirements., Graphical abstract A dual-effect of host defense and tissue repair can be rationally constructed by bioorthogonal conjugations of both anti-infective and osteo-inductive peptides on PEEK osteoimplants via an interlayer of mussel adhesive peptide with bioclickable properties. This study may provide a simple, efficient, and multi-functional surface strategy for improving the clinical outcomes of bio-inert medical implants.Image 1, Highlights • An upgraded mussel-inspired surface strategy were developed. • A typical AMP and the active sequence of OGP were co-clicked on PEEK osteoimplants. • The coating can defend against bacterial invasion, facilitate osteogenesis. • The optimal dual-functional coating improves osseointegration.

API developers have been working hard to evolve APIs to provide more simple, powerful, and robust API libraries. Although API evolution has been studied for multiple domains, such as Web and Android development, API evolution for deep learning frameworks has not yet been studied. It is not very clear how and why APIs evolve in deep learning frameworks, and yet these are being more and more heavily used in industry. To fill this gap, we conduct a large-scale and in-depth study on the API evolution of Tensorflow 2, which is currently the most popular deep learning framework. We first extract 6,329 API changes by mining API documentation of Tensorflow 2 across multiple versions and mapping API changes into functional categories on the Tensorflow 2 framework to analyze their API evolution trends. We then investigate the key reasons for API changes by referring to multiple information sources, e.g., API documentation, commits and StackOverflow. Finally, we compare API evolution in non-deep learning projects to that of Tensorflow 2, and identify some key implications for users, researchers, and API developers.

Aiming at the multi-depot heterogeneous vehicle routing problem under the time-dependent road network and soft time window, considering vehicle fixed cost, time window penalty cost and vehicle transportation cost, an optimization model of time-dependent multi-depot heterogeneous vehicle routing problem is established with the objective of minimizing distribution cost. According to the characteristics of the problem, a hybrid genetic algorithm with variable neighborhood search considering the temporal–spatial distance is designed. Customers are clustered according to the temporal–spatial distance to generate initial solutions, which improves the quality of the algorithm. The depth search capability of the variable neighborhood search algorithm is applied to the local search strategy of the genetic algorithm to enhance the local search capability of the algorithm. An adaptive neighborhood search number strategy and a new acceptance mechanism of simulated annealing are proposed to balance the breadth and depth required for population evolution. The validity of the model and algorithm is verified by several sets of examples of different scales. The research results not only deepen and expand the relevant research on vehicle routing problem, but also provide theoretical basis for logistics enterprises to optimize distribution scheme.

Purpose: Targeted therapy and immunotherapy are transforming the treatment approach for intrahepatic cholangiocarcinoma (ICC). However, little is known about the intertumor heterogeneity (ITH) of multifocal ICC and its impacts on patient response to these treatments. We aimed to characterize the immunogenomic and epigenomic heterogeneity of multifocal ICC to guide treatment decision making. Experimental Design: We obtained 66 tumor samples from 16 patients with multifocal ICC and characterized the tumor and immune heterogeneity using whole-exome sequencing, bulk and single-cell RNA sequencing, methylation microarray, and multiplex immunostaining. Patients were divided into high- or low-ITH groups according to the median ITH index. Two independent cohorts were used to validate findings. Responses to anti-PD-1 therapy were assessed. Results: Multifocal ICC presented considerable intertumor genomic, transcriptional, and epigenomic heterogeneity within a patient in high ITH group. The immune profile among multiple tumors within a patient was relatively less heterogeneous in high- or low-ITH group, and consistent responses of multiple tumors to anti-PD-1 immunotherapy were observed. Unsupervised clustering of immune markers identified one low and one high immune subtype, with higher immune cell infiltration, closer tumor–immune cell interactions, and upregulated IFN-signature expression in high-immune subtype. Determining expression levels of CD8B and ICOS facilitated this immune classification and prediction of patient prognosis. Finally, promoter DNA methylation contributed to different immune profiles of two subtypes by regulating immune-gene expression. Conclusions: There is comprehensive heterogeneity in the genome, transcriptome, and epigenome of multifocal ICC. On the basis of the less heterogeneous immune profile of ICC, we suggest an immune classification that stratifies patients' prognosis and may support personalized immunotherapy.

Cancer cells selectively promote translation of specific oncogenic transcripts to facilitate cancer survival and progression, but the underlying mechanisms are poorly understood. Here, we find that N

API misuses cause significant problem in software development. Existing methods detect API misuses against frequent API usage patterns mined from codebase. They make a naive assumption that API usage that deviates from the most-frequent API usage is a misuse. However, there is a big knowledge gap between API usage patterns and API usage caveats in terms of comprehensiveness, explainability and best practices. In this work, we propose a novel approach that detects API misuses directly against the API caveat knowledge, rather than API usage patterns. We develop open information extraction methods to construct a novel API-constraint knowledge graph from API reference documentation. This knowledge graph explicitly models two types of API-constraint relations (call-order and condition-checking) and enriches return and throw relations with return conditions and exception triggers. It empowers the detection of three types of frequent API misuses - missing calls, missing condition checking and missing exception handling, while existing detectors mostly focus on only missing calls. As a proof-of-concept, we apply our approach to Java SDK API Specification. Our evaluation confirms the high accuracy of the extracted API-constraint relations. Our knowledge-driven API misuse detector achieves 0.60 (68/113) precision and 0.28 (68/239) recall for detecting Java API misuses in the API misuse benchmark MuBench. This performance is significantly higher than that of existing pattern-based API misused detectors. A pilot user study with 12 developers shows that our knowledge-driven API misuse detection is very promising in helping developers avoid API misuses and debug the bugs caused by API misuses.

API usage directives in official API documentation describe the contracts, constraints and guidelines for using APIs in natural language. Through the investigation of API misuse scenarios on Stack Overflow, we identify three barriers that hinder the understanding of the API usage directives, i.e., lack of specific usage context, indirect relationships to cooperative APIs, and confusing APIs with subtle differences. To overcome these barriers, we develop a text mining approach to discover the crowdsourced API misuse scenarios on Stack Overflow and extract from these scenarios erroneous code examples and patches, as well as related API and confusing APIs to construct demystification reports to help developers understand the official API usage directives described in natural language. We apply our approach to API usage directives in official Android API documentation and android-tagged discussion threads on Stack Overflow. We extract 159,116 API misuse scenarios for 23,969 API usage directives of 3138 classes and 7471 methods, from which we generate the demystification reports. Our manual examination confirms that the extracted information in the generated demystification reports are of high accuracy. By a user study of 14 developers on 8 API-misuse related error scenarios, we show that our demystification reports help developer understand and debug API-misuse related program errors faster and more accurately, compared with reading only plain API usage-directive sentences.

A stable and highly efficient Ag–Ag2S hetero-dendrites (HDs) photocatalyst has been obtained via an extremely facile electrodeposition and subsequently in situ sulfuration route. The SEM and TEM show well-defined uniform dendrites morphology with an average diameter about 30 nm. In addition, the absorption spectra of Ag–Ag2S HDs exhibit strong absorption in both visible and NIR regions. The EIS indicates that the low charge transfer resistance of Ag–Ag2S HDs enhances the photocatalytic activity. The prepared HDs-4 photocatalyst with moderate degree of vulcanization shows excellent activity for degradation the methylene blue (MB) under the sunlight irradiation owing to the suitable ratios of Ag–Ag2S. The enhanced photocatalytic activity could be attributed to the favorable synergistic effects between Ag and Ag2S materials, such as huge extended absorption of solar light, low charge transfer resistance and high electron–hole separation efficiency. Moreover, radical scavenger experiments confirmed that superoxide radicals and holes were the main reactive species for MB degradation. Meanwhile, the Ag–Ag2S HDs displays good photocatalytic stability after being recycled five times.

Summary Cancer cells selectively promote translation of specific oncogenic transcripts to facilitate cancer survival and progression, but the underlying mechanisms are poorly understood. Here, we find that N7-methylguanosine (m7G) tRNA modification and its methyltransferase complex components, METTL1 and WDR4, are significantly upregulated in intrahepatic cholangiocarcinoma (ICC) and associated with poor prognosis. We further reveal the critical role of METTL1/WDR4 in promoting ICC cell survival and progression using loss- and gain-of-function assays in vitro and in vivo. Mechanistically, m7G tRNA modification selectively regulates the translation of oncogenic transcripts, including cell-cycle and epidermal growth factor receptor (EGFR) pathway genes, in m7G-tRNA-decoded codon-frequency-dependent mechanisms. Moreover, using overexpression and knockout mouse models, we demonstrate the crucial oncogenic function of Mettl1-mediated m7G tRNA modification in promoting ICC tumorigenesis and progression in vivo. Our study uncovers the important physiological function and mechanism of METTL1-mediated m7G tRNA modification in the regulation of oncogenic mRNA translation and cancer progression.

The immune responses are involved in every stage after implantation but the reported immune-regulated materials only work at the beginning without fully considering the different phases of bone healing. Here, poly(aryl-ether-ether-ketone) (PEEK) is coated with a programmed surface, which rapidly releases interleukin-10 (IL-10) in the first week and slowly delivers dexamethasone (DEX) up to 4 weeks. Owing to the synergistic effects of IL-10 and DEX, an aptly weak inflammation is triggered within the first week, followed by significant M2 polarization of macrophages and upregulation of the autophagy-related factors. The suitable immunomodulatory activities pave the way for osteogenesis and the steady release of DEX facilitates bone regeneration thereafter. The sequential immune-mediated process is also validated by an 8-week implementation on a rat model. This is the first attempt to construct implants by taking advantage of both immune-mediated modulation and sequential regulation spanning all bone regeneration phases, which provides insights into the fabrication of advanced biomaterials for tissue engineering and immunological therapeutics., Graphical abstract, Public summary • A programed surface is designed and fabricated for immune-mediated osteogenesis • The degradation of PTMC coating enables a sequential release of IL-10 and DEX • Initially, osteoimmunomodulation is achieved by IL-10 and a small amount of DEX • Afterwards, sustained release of DEX fosters the peri-implant bone regeneration

The characteristics of the three-point powertrain mounting system are studied by multi-body d6ynamics method. The 6-DOF model of the powertrain mounting system is established in ADAMS software. The natural frequency and the decoupling rate of modal energy are obtained through the simulation analysis. The 13-DOF model of the complete vehicle is established based on 6-DOF model, and the dynamic simulation analysis is carried out. The accuracy of the vehicle model is verified by comparing with the vehicle test data. The model is optimized by taking the stiffness and position parameters as the optimization variables, taking the transmission force and modal coupling rate as the optimization objectives. The decoupling rate of each mode is greatly improved, and the transmission force of the mounting system is effectively reduced after optimization.