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Abstract Malignant mesothelioma is a rare tumour caused by asbestos exposure that originates mainly from the pleural lining or the peritoneum. Treatment options are limited, and the prognosis is dismal. Although immune checkpoint blockade (ICB) can improve survival outcomes, the determinants of responsiveness remain elusive. Here, we report the outcomes of a multi-centre phase II clinical trial (MiST4, NCT03654833) evaluating atezolizumab and bevacizumab (AtzBev) in patients with relapsed mesothelioma. We also use tumour tissue and gut microbiome sequencing, as well as tumour spatial immunophenotyping to identify factors associated with treatment response. MIST4 met its primary endpoint with 50% 12-week disease control, and the treatment was tolerable. Aneuploidy, notably uniparental disomy (UPD), homologous recombination deficiency (HRD), epithelial-mesenchymal transition and inflammation with CD68+ monocytes were identified as tumour-intrinsic resistance factors. The log-ratio of gut-resident microbial genera positively correlated with radiological response to AtzBev and CD8+ T cell infiltration, but was inversely correlated with UPD, HRD and tumour infiltration by CD68+ monocytes. In summary, a model is proposed in which both intrinsic and extrinsic determinants in mesothelioma cooperate to modify the tumour microenvironment and confer clinical sensitivity to AtzBev. Gut microbiota represent a potentially modifiable factor with potential to improve immunotherapy outcomes for individuals with this cancer of unmet need.

Over the past few decades, there has been a remarkable advancement in the field of transplantation. But the shortage of donors is still an urgent problem that requires immediate attention. As with xenotransplantation, bioengineered organs are promising solutions to the current shortage situation. And decellularization is a unique technology in organ-bioengineering. However, at present, there is no unified decellularization method for different tissues, and there is no gold-standard for evaluating decellularization efficiency. Meanwhile, recellularization, re-endothelialization and modification are needed to form transplantable organs. With this mind, we can start with decellularization and re-endothelialization or modification of small blood vessels, which would serve to address the shortage of small-diameter vessels while simultaneously gathering the requisite data and inspiration for further recellularization of the whole organ-scale vascular network. In this review, we collect the related experiments of decellularization and post-decellularization approaches of small vessels in recent years. Subsequently, we summarize the experience in relation to the decellularization and post-decellularization combinations, and put forward obstacle we face and possible solutions.

Carbon dioxide (CO2) is one of the main greenhouse gases and has become a major concern as its concentration has been growing in recent years. Satellite remote sensing is an efficient way to monitor CO2 in the atmosphere, and several satellites are already used for CO2 monitoring. It is imperative to investigate the spatial coverage and spatio-temporal trends of satellite products, as well as identify the satellites with higher levels of accuracy. Additionally, examining the disparities between the older and new generations of satellites would be meaningful. Therefore, this paper provides a comprehensive evaluation and inter-comparison for the commonly used satellite column-averaged dry-air mole fraction of CO2 (XCO2) products. Specifically, the temporal trends and monthly coverage of the Greenhouse Gases Observing SATellite (GOSAT), Greenhouse Gases Observing SATellite-2 (GOSAT-2), Orbiting Carbon Observatory-2 (OCO-2), Orbiting Carbon Observatory-3 (OCO-3), and SCanning Imaging Absorption spectroMeter for Atmospheric CartograpHY (SCIAMACHY) are investigated. The accuracy of these satellite products is evaluated and analyzed based on Total Carbon Column Observing Network (TCCON) data. The results indicate that the XCO2 of all the satellite products show a year-by-year increase, with seasonal periodicity. In terms of overall accuracy, the OCO series satellites exhibit a slightly higher level of accuracy compared to the GOSAT series. The products of the new generation of satellites are less stable than those of the older generation, probably due to the impacts of the inversion algorithm and platforms.

IntroductionKidney transplant recipients (KTRs) are at a higher risk of severe coronavirus disease (COVID-19) because of their immunocompromised status. However, the effect of allograft function on the prognosis of severe COVID-19 in KTRs is unclear. In this study, we aimed to analyze the correlation between pre-infection allograft function and the prognosis of severe COVID-19 in KTRs.MethodsThis retrospective cohort study included 82 patients who underwent kidney transplantation at the Sichuan Provincial Peoples Hospital between October 1, 2014 and December 1, 2022 and were diagnosed with severe COVID-19. The patients were divided into decreased eGFR and normal eGFR groups based on the allograft function before COVID-19 diagnosis (n=32 [decreased eGFR group], mean age: 43.00 years; n=50 [normal eGFR group, mean age: 41.88 years). We performed logistic regression analysis to identify risk factors for death in patients with severe COVID-19. The nomogram was used to visualize the logistic regression model results.ResultsThe mortality rate of KTRs with pre-infection allograft function insufficiency in the decreased eGFR group was significantly higher than that of KTRs in the normal eGFR group (31.25% [10/32] vs. 8.00% [4/50], P=0.006). Pre-infection allograft function insufficiency (OR=6.96, 95% CI: 1.4633.18, P=0.015) and maintenance of a mycophenolic acid dose >1500 mg/day before infection (OR=7.59, 95% CI: 1.0853.20, P=0.041) were independent risk factors, and the use of nirmatrelvir/ritonavir before severe COVID-19 (OR=0.15, 95% CI: 0.030.72, P=0.018) was a protective factor against death in severe COVID-19.ConclusionsPre-infection allograft function is a good predictor of death in patients with severe COVID-19. Allograft function was improved after treatment for severe COVID-19, which was not observed in patients with non-severe COVID-19.

BackgroundGenerative Artificial Intelligence (AI) technology, for instance Chat Generative Pre-trained Transformer (ChatGPT), is continuously evolving, and its userbase is growing. These technologies are now being experimented by the businesses to leverage their potential and minimise their risks in business operations. The continuous adoption of the emerging Generative AI technologies will help startups gain more and more experience with adoptions, helping them to leverage continuously evolving technological innovation landscape. However, there is a dearth of prior research on ChatGPT adoption in the startup context, especially from Entrepreneur perspective, highlights the urgent need for a thorough investigation to identify the variables influencing this technological adoption. The primary objective of this study is to ascertain the factors that impact the uptake of ChatGPT technology by startups, anticipate their influence on the triumph of companies, and offer pragmatic suggestions for various stakeholders, including entrepreneurs, and policymakers.Method and analysisThis study attempts to explore the variables impacting startups' adoption of ChatGPT technology, with an emphasis on comprehending entrepreneurs' attitudes and perspectives. To identify and then empirically validate the Generative AI technology adoption framework, the study uses a two-stage methodology that includes experience-based research, and survey research. The research method design is descriptive and Correlational design. Stage one of the research study is descriptive and involves adding practical insights, and real-world context to the model by drawing from the professional consulting experiences of the researchers with the SMEs. The outcome of this stage is the adoption model (also called as research framework), building Upon Technology Adoption Model (TAM), that highlight the technology adoption factors (also called as latent variables) connected with subset of each other and finally to the technology adoption factor (or otherwise). Further, the latent variables and their relationships with other latent variables as graphically highlighted by the adoption model will be translated into the structured questionnaire. Stage two involves survey based research. In this stage, structured questionnaire is tested with small group of entrepreneurs (who has provided informed consent) and finally to be distributed among startup founders to further validate the relationships between these factors and the level of influence individual factors have on overall technology adoption. Partial Least Squares Structural Equation Modeling (PLS-SEM) will be used to analyze the gathered data. This multifaceted approach allows for a comprehensive analysis of the adoption process, with an emphasis on understanding, describing, and correlating the key elements at play.DiscussionThis is the first study to investigate the factors that impact the adoption of Generative AI, for instance ChatGPT technology by startups from the Entrepreneurs perspectives. The study's findings will give Entrepreneurs, Policymakers, technology providers, researchers, and Institutions offering support for entrepreneurs like Academia, Incubators and Accelerators, University libraries, public libraries, chambers of commerce, and foreign embassies important new information that will help them better understand the factors that encourage and hinder ChatGPT adoption. This will allow them to make well-informed strategic decisions about how to apply and use this technology in startup settings thereby improving their services for businesses.

In this comprehensive meta-analysis, our objective was to evaluate the diagnostic utility of graft-derived cell-free DNA (GcfDNA) in kidney allograft rejection and explore associated factors. We conducted a thorough search of PubMed, Embase, and the Cochrane Library databases, spanning from their inception to September 2022. Statistical analysis was executed utilizing Stata 15, Meta-DiSc 1.4, and Review Manager 5.4 software. The combined pooled sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratio (DOR), and the area under the summary receiver operating characteristics (SROC) curve from the synthesis of findings across ten studies were as follows: 0.75 (0.67–0.81), 0.78 (0.72–0.83), 3.36 (2.89–4.35), 0.32 (0.24–0.44), 8.77 (4.34–17.74), and 0.83 (0.80–0.86), respectively. Among the ten studies primarily focused on GcfDNA’s diagnostic potential for antibody-mediated rejection (ABMR), the optimal cut-off threshold demonstrated substantial diagnostic efficacy, with pooled sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, DOR, and area under the summary receiver operating characteristics curve values of 0.83 (0.74–0.89), 0.75 (0.70–0.80), 3.37 (2.64–4.30), 0.23 (0.15–0.36), 14.65 (7.94–27.03), and 0.85 (0.82–0.88), respectively. These results underscore the high diagnostic accuracy of GcfDNA in detecting rejection. Furthermore, the optimal cut-off threshold proves effective in diagnosing ABMR, while a 1% threshold remains a robust diagnostic criterion for rejection. Notably, for ABMR diagnosis, droplet digital PCR digital droplet polymerase chain reaction emerges as a superior method in terms of accuracy when compared to other techniques. Nonetheless, further research is warranted to substantiate these findings.

AbstractBackground The kidney transplant recipients (KTRs) were diagnosed with Chronic Kidney Disease after transplantation (CKD-T). CKD-T can be affected by the microbial composition and metabolites. The present study integrates the analysis of gut microbiome and metabolites to further identify the characteristics of CKD-T.Methods We collected 100 fecal samples of KTRs and divided them into two groups according to the stage progression of CKD-T. Among them, 55 samples were analyzed by Hiseq sequencing, and 100 samples were used for non-targeted metabolomics analysis. The gut microbiome and metabolomics of KTRs were comprehensively characterized.Results As well as significant differences in gut microbiome diversity between the CKD G1-2T group and CKD G3T group. Eight flora including Akkermansia were found to be enriched in CKD G3T group. As compared with CKD G1-2T group, the relative abundance of some amino acid metabolism, glycerophospholipid metabolism, amino acid biosynthesis, carbohydrate metabolism and purine metabolism in CKD G3T group were differential expressed significantly. In addition, fecal metabolome analysis indicated that CKD G3T group had a unique metabolite distribution characteristic. Two differentially expressed metabolites, N-acetylornithine and 5-deoxy-5'-(Methylthio) Adenosine, were highly correlated with serum creatinine, eGFR and cystatin C. The enrichment of gut microbial function in CKD-T is correlated with the expression of gut metabolites.Conclusion Gut microbiome and metabolites in the progression of CKD-T display some unique distribution and expression characteristics. The composition of the gut microbiome and their metabolites appears to be different between patients with CKD G3T and those with CKD G1-2T.

Traffic flow prediction is considered to be one of the fundamental technologies in intelligent transportation systems (ITSs) with a tremendous application prospect. Unlike traditional time series analysis tasks, the key challenge in traffic flow prediction lies in effectively modelling the highly complex and dynamic spatiotemporal dependencies within the traffic data. In recent years, researchers have proposed various methods to enhance the accuracy of traffic flow prediction, but certain issues still persist. For instance, some methods rely on specific static assumptions, failing to adequately simulate the dynamic changes in the data, thus limiting their modelling capacity. On the other hand, some approaches inadequately capture the spatiotemporal dependencies, resulting in the omission of crucial information and leading to unsatisfactory prediction outcomes. To address these challenges, this paper proposes a model called the Dynamic Spatial–Temporal Self-Attention Network (DSTSAN). Firstly, this research enhances the interaction between different dimension features in the traffic data through a feature augmentation module, thereby improving the model’s representational capacity. Subsequently, the current investigation introduces two masking matrices: one captures local spatial dependencies and the other captures global spatial dependencies, based on the spatial self-attention module. Finally, the methodology employs a temporal self-attention module to capture and integrate the dynamic temporal dependencies of traffic data. We designed experiments using historical data from the previous hour to predict traffic flow conditions in the hour ahead, and the experiments were extensively compared to the DSTSAN model, with 11 baseline methods using four real-world datasets. The results demonstrate the effectiveness and superiority of the proposed approach.

Enabling a flexible and natural human-robot interaction (HRI) for industrial robots is a critical yet challenging task that can be facilitated by the use of conversational artificial intelligence (AI). Prior research has concentrated on strengthening interactions through the deployment of social robots, while disregarding the capabilities required to boost the flexibility and user experience associated with human-robot collaboration (HRC) on manufacturing tasks. One of the main challenges is the lack of publicly available industrial-oriented dialogue datasets for the training of conversational AI. In this work, we present an Industrial Robot Wizard-of-Oz Dialoguing Dataset (IRWoZ) focused on enabling HRC in manufacturing tasks. The dataset covers four domains: assembly, transportation, position, and relocation. It is created using the Wizard-of-Oz technique to be less noisy. We manually constructed, annotated and validated dialogue segments (e.g., intentions, slots, annotations), as well as the responses. Building upon the proposed dataset, we benchmark it on the state-of-the-art (SoTA) language models, generative pre-trained (GPT-2) models, on dialogue state tracking and response generation tasks. We expect that the IRWoZ dataset will facilitate exciting ongoing dialogue research and we provide it freely accessible at https://github.com/lcroy/ToD4IR/tree/main/dataset.

The introduction of a constraint mechanism to the resource description framework schema (RDFS) poses challenges for keeping RDFS ontologies consistent, particularly in the presence of reasoning rules (RRs). This study presents a method to detect inconsistencies in a given constraint enhanced RDFS, termed RDFS $_{\mathrm {(c)}}$ , under a given set of RRs. This method can be used to efficiently infer the type qualifications and constraints that may be violated when the RRs are applied to the graph instances of the schema. First, the concept of an internal schema is developed, and the conversion of an RDFS $_{\mathrm {(c)}}$ to an internal schema is examined. An internal schema is more versatile than the corresponding original schema language. Second, the concept of key instances of an internal schema is established as a basis for detecting schema inconsistencies. The number of key instances is considerably smaller than that of original instances, resulting in a significantly higher detection efficiency. Moreover, a rigorous formal proof is given to ensure that the schema generated based on the key instances is semantically equivalent to the original schema. Finally, an algorithm is provided to detect schema inconsistencies in the key instances of the corresponding internal schema. This algorithm extends the key instances by query rewriting and constructs potential instances that violate the constraints. The minimality of rewriting ensures that a minimum number of instances are constructed, thereby further improving the efficiency for inferring constraint violations. Test results show that key instances and query rewriting can be used to significantly enhance the efficiency of detecting inconsistencies and that the proposed method is superior to relevant methods in terms of time performance.

Carbon dioxide (CO2) is a crucial greenhouse gas with substantial effects on climate change. Satellite-based remote sensing is a commonly used approach to detect CO2 with high precision but often suffers from extensive spatial gaps. Thus, the limited availability of data makes global carbon stocktaking challenging. In this paper, a global gap-free column-averaged dry-air mole fraction of CO2 (XCO2) dataset with a high spatial resolution of 0.1° from 2014 to 2020 is generated by the deep learning-based multisource data fusion, including satellite and reanalyzed XCO2 products, satellite vegetation index data, and meteorological data. Results indicate a high accuracy for 10-fold cross-validation (R2 = 0.959 and RMSE = 1.068 ppm) and ground-based validation (R2 = 0.964 and RMSE = 1.010 ppm). Our dataset has the advantages of high accuracy and fine spatial resolution compared with the XCO2 reanalysis data as well as that generated from other studies. Based on the dataset, our analysis reveals interesting findings regarding the spatiotemporal pattern of CO2 over the globe and the national-level growth rates of CO2. This gap-free and fine-scale dataset has the potential to provide support for understanding the global carbon cycle and making carbon reduction policy, and it can be freely accessed at https://doi.org/10.5281/zenodo.7721945.

How to translate basic research on surveillances for post organs transplantation facilitates the development of new laboratory tools for clinical application. Here, we provide the pertinent guide on how to analyze laboratory data in order to help clinicians to accurately determine the status of allograft in the recipient. We begin by discussing the molecular and cellular events that lead to allograft rejection across innate and adaptive immune responses. We focus on the allograft injury events that occur around the time of transplantation as well as molecular activities that ensue long before observable microscopic abnormalities are evident in biopsied samples. Then, we introduce and comment on new technologies involved in rejection detection from a practical point of view. This review outlines the current diagnostic development, describes unmet needs and challenges, and proposes new approaches for future directions.

Despite the fact that task-oriented conversation systems have received much attention from the dialogue research community, only a handful of them have been studied in a real-world manufacturing context using industrial robots. One stumbling block is the lack of a domain-specific discourse corpus for training these systems. Another difficulty is that earlier attempts to integrate natural language interfaces (such as chatbots) into the industrial sector have primarily focused on task completion rates. When designing a dialogue system for social robots, the user experience is prioritized above industrial robots. To overcome these challenges, we provide the Industrial Robots Domain Wizard-of-Oz dataset (IRWoZ), a fully-labeled discourse dataset covering four robotics domains. It delivers simulated discussions between shop floor workers and industrial robots, with over 401 dialogues, to promote language-assisted Human-Robot Interaction (HRI) in industrial settings. Small talk concepts and human-to-human conversation strategies are provided to support human-like answer generation and give a more natural and adaptable dialogue environment to increase user experience and engagement. Finally, we propose and evaluate an end-to-end Task-oriented Dialogue for Industrial Robots (ToD4IR) using two types of pre-trained backbone models: GPT-2 and GPT-Neo, on the IRWoZ dataset. We performed a series of trials to validate ToD4IR’s performance in a real manufacturing context. Our experiments demonstrate that ToD4IR outperforms three downstream task-oriented dialogue tasks, i.e., dialogue state tracking, dialogue act generation, and response generation, on the IRWoZ dataset. Our source code of ToD4IR and the IRWoZ dataset is accessible at https://github.com/lcroy/ToD4IR for reproducible research.

Graft-derived cell-free DNA (GcfDNA) is a promising non-invasive biomarker for detecting allograft injury. In this study, we aimed to evaluate the efficacy of programmed monitoring of GcfDNA for identifying BK polyomavirus-associated nephropathy (BKPyVAN) in kidney transplant recipients. We recruited 158 kidney transplant recipients between November 2020 and December 2021. Plasma GcfDNA was collected on the tenth day, first month, third month, and sixth month for programmed monitoring and one day before biopsy. ΔGcfDNA (cp/mL) was obtained by subtracting the baseline GcfDNA (cp/mL) from GcfDNA (cp/mL) of the latest programmed monitoring before biopsy. The receiver operating characteristic curve showed the diagnostic performance of GcfDNA (cp/mL) at biopsy time and an optimal area under the curve (AUC) of 0.68 in distinguishing pathologically proven BKPyVAN from pathologically unconfirmed BKPyVAN. In contrast, ΔGcfDNA (cp/mL) had a sensitivity and specificity of 80% and 84.6%, respectively, and an AUC of 0.83. When distinguishing clinically diagnosed BKPyVAN from clinical excluded BKPyVAN, the AUC of GcfDNA (cp/mL) was 0.59 at biopsy time, and ΔGcfDNA (cp/mL) had a sensitivity and specificity of 81.0% and 76.5%, respectively, and an AUC of 0.81. Plasma ΔGcfDNA (cp/mL) was not significantly different between TCMR [0.15 (0.08, 0.24) cp/mL] and pathologically proven BKPyVAN[0.34 (0.20, 0.49) cp/mL]. In conclusion, we recommend programmed monitoring of plasma GcfDNA levels after a kidney transplant. Based on our findings from the programmed monitoring, we have developed a novel algorithm that shows promising results in identifying and predicting BKPyVAN.

Abstract Background WD repeat domain 3 (WDR3) is involved in a variety of cellular processes including gene regulation, cell cycle progression, signal transduction and apoptosis. However, the biological role of WDR3 in pancreatic cancer and the associated mechanism remains unclear. We seek to explore the immune-independent functions and relevant mechanism for WDR3 in pancreatic cancer. Methods The GEPIA web tool was searched, and IHC assays were conducted to determine the mRNA and protein expression levels of WDR3 in pancreatic cancer patients. MTS, colony formation, and transwell assays were conducted to determine the biological role of WDR3 in human cancer. Western blot analysis, RT-qPCR, and immunohistochemistry were used to detect the expression of specific genes. An immunoprecipitation assay was used to explore protein-protein interactions. Results Our study proved that overexpressed WDR3 was correlated with poor survival in pancreatic cancer and that WDR3 silencing significantly inhibited the proliferation, invasion, and tumor growth of pancreatic cancer. Furthermore, WDR3 activated the Hippo signaling pathway by inducing yes association protein 1 (YAP1) expression, and the combination of WDR3 silencing and administration of the YAP1 inhibitor TED-347 had a synergistic inhibitory effect on the progression of pancreatic cancer. Finally, the upregulation of YAP1 expression induced by WDR3 was dependent on an interaction with GATA binding protein 4 (GATA4), the transcription factor of YAP1, which interaction induced the nuclear translocation of GATA4 in pancreatic cancer cells. Conclusions We identified a novel mechanism by which WDR3 plays a critical role in promoting pancreatic cancer progression by activating the Hippo signaling pathway through the interaction with GATA4. Therefore, WDR3 is potentially a therapeutic target for pancreatic cancer treatment.

The impact of intratumour heterogeneity on immune surveillance and clinical outcomes has not been adequately explored in malignant pleural mesothelioma (MPM). Here the authors analyse the influence of evolution on the survival and immune landscape of MPM patients using multi-region sequencing data.

Reasoning on large ontologies has been identified as an important challenge. Concept satisfiability is one of the core reasoning tasks in ontology engineering. To improve the efficiency of concept satisfiability checking for OWL RL ontologies, we propose an approach for extracting modules from the OWL RL ontology. Our approach is based on the idea that for a given concept being checked, not all of the ontology elements are related to the checking process. By transforming the OWL RL axioms into first-order constraints and analyzing the dependencies among the constraints, we give a strategy for deleting the irrelevant elements; thus, a simplified ontology that preserves the original checking result can be obtained. The experimental results obtained for a set of real-world ontologies show that our method can effectively reduce the reasoning time, and the efficiency is increased by 1.15 times to 9.08 times.

Some airports construct several buildings in city centre to offer check in and other services, denoted as city air terminals, which help passengers to check in and drop off luggage closer to their residences. Multi-objective location optimization methods play an important role in planning the locations of city air terminals. The objectives are to improve passenger experience and enhance the competitiveness of air transportation. A mathematical model of this problem is introduced. The model takes three factors into accounted as the optimization objectives, including the average path length from passengers to city air terminals, the maximum tolerable distance of passengers, and the service capacity of a station. Secondly, an efficient hybrid evolutionary method is presented for efficiently optimizing the locations of city air terminals, which includes an improved ripple-spreading algorithm for solving the many-to-many path optimization problem and a genetic algorithm for optimizing the facility location problem. Finally, a case study based on a large city in China is performed to test the proposed method for locating city air terminals in urban area and to show its effectiveness and efficiency.

Choreography-driven microservice composition has provided a better way to integrate components in the Cyber-physical-Social System (CPSS). Choreography is a global contract that specifies interactions among microservices participating in a composite service. After modeling a choreography, a problem arises here is whether the choreography specification at design time can be implemented correctly by generated microservices that interact with each other via exchanging messages. In this paper, we propose a novel approach for choreography analysis. Specifically, a choreography is specified using a Labeled Transition Systems (LTSs); then, the microservices participating in a composite service can be generated from the given choreography via projection and ε-remove; finally, the analysis of the choreography can be checked for both synchronous and asynchronous compositions using refinement checking. Our approach is completely automated under the support of our developed tool and the Process Analysis Toolkit (PAT) tool.

Admittedly, the design requirements of compactness, low frequency, and broadband seem to constitute an impossible trinity, hindering the further development of elastic metamaterials (EMMs) in wave shielding engineering. To break through these constraints, we propose theoretical combinations of effective parameters for wave isolation based on the propagation properties of Lamb waves in the EMM layer. Accordingly, we design compact EMMs with a novel ultralow-frequency bandgap, and the role of auxeticity in the dissociation between the dipole mode and the toroidal dipole mode is clearly revealed. Finally, under the guidance of the improved gradient design, we integrate multiple bandgaps to assemble metamaterial barriers (MMBs) for broadband wave isolation. In particular, the original configuration is further optimized and its ultralow-frequency and broadband performance are proven by transmission tests. It is foreseeable that our work will provide a meaningful reference for the application of the new EMMs in disaster prevention and protection engineering.

Efforts at finding potential biomarkers of tolerance after kidney transplantation have been hindered by limited sample size, as well as the complicated mechanisms underlying tolerance and the potential risk of rejection after immunosuppressant withdrawal. In this work, three different publicly available genome-wide expression data sets of peripheral blood lymphocyte (PBL) from 63 tolerant patients were used to compare 14 different machine learning models for their ability to predict spontaneous kidney graft tolerance. We found that the Best Subset Selection (BSS) regression approach was the most powerful with a sensitivity of 91.7% and a specificity of 93.8% in the test group, and a specificity of 86.1% and a sensitivity of 80% in the validation group. A feature set with five genes (HLA-DOA, TCL1A, EBF1, CD79B, and PNOC) was identified using the BSS model. EBF1 downregulation was also an independent factor predictive of graft rejection and graft loss. An AUC value of 84.4% was achieved using the two-gene signature (EBF1 and HLA-DOA) as an input to our classifier. Overall, our systematic machine learning exploration suggests novel biological targets that might affect tolerance to renal allografts, and provides clinical insights that can potentially guide patient selection for immunosuppressant withdrawal.

Immune checkpoint inhibitors(ICIs) that activate tumor-specific immune responses bring new hope for the treatment of hepatocellular carcinoma(HCC). However, there are still some problems, such as uncertain curative effects and low objective response rates, which limit the curative effect of immunotherapy. Therefore, it is an urgent problem to guide the use of ICIs in HCC based on molecular typing. We downloaded the The Cancer Genome Atlas-Liver hepatocellular carcinoma(TCGA-LIHC) and Mongolian-LIHC cohort. Unsupervised clustering was applied to the highly variable data regarding expression of DNA damage repair(DDR). The CIBERSORT was used to evaluate the proportions of immune cells. The connectivity map(CMap) and pRRophetic algorithms were used to predict the drug sensitivity. There were significant differences in DDR molecular subclasses in HCC(DDR1 and DDR2), and DDR1 patients had low expression of DDR-related genes, while DDR2 patients had high expression of DDR-related genes. Of the patients who received traditional treatment, DDR2 patients had significantly worse overall survival(OS) than DDR1 patients. In contrast, of the patients who received ICIs, DDR2 patients had significantly prolonged OS compared with DDR1 patients. Of the patients who received traditional treatment, patients with high DDR scores had worse OS than those with low DDR scores. However, the survival of patients with high DDR scores after receiving ICIs was significantly higher than that of patients with low DDR scores. The DDR scores of patients in the DDR2 group were significantly higher than those of patients in the DDR1 group. The tumor microenvironment(TME) of DDR2 patients was highly infiltrated by activated immune cells, immune checkpoint molecules and proinflammatory molecules and antigen presentation-related molecules. In this study, HCC patients were divided into the DDR1 and DDR2 group. Moreover, DDR status may serve as a potential biomarker to predict opposite clinical prognosis immunotherapy and non-immunotherapy in HCC.

Background: Modern surgical techniques and scientific advancements have made liver transplant (LT) in infants feasible. However, there are only a small number of studies examining the short- as well as long-term outcomes of LT in this vulnerable subset of children.Methods: Comprehensive searches were done systematically through the PubMed, Scopus, and Google scholar databases. Studies that were retrospective record based or adopted a cohort approach and reported either patient survival rates or graft survival rates or complications of LT in infants were included in the meta-analysis. Statistical analysis was done using STATA version 13.0.Results: A total of 22 studies were included in the meta-analysis. The overall pooled patient survival rate at 1 year, >1–5 years, and >5 years post-transplantation was 85% (95% CI: 78-−92%), 71% (95% CI: 59–83%), and 80% (95% CI: 69–91%), respectively. The overall pooled graft survival rate at 1 year, >1–5 years, and >5 years post-transplantation was 72% (95% CI: 68–76%), 62% (95% CI: 46–78%), and 71% (95% CI: 56–86%), respectively. The overall pooled rate for vascular complications, need for re-transplantation, biliary complications, and infection/sepsis was 12% (95% CI: 10–15%), 16% (95% CI: 12–20%), 15% (95% CI: 9–21%), and 50% (95% CI: 38–61%), respectively.Conclusion: The current meta-analysis showed modest patient and graft survival rates for infant liver transplantation. However, the complication rates related to infection/sepsis were high. More comprehensive evidence is required from studies with larger sample sizes and a longer duration of follow-up.

Software verification can ensure the software quality by inspecting the properties of program. A key issue for software verification is to check whether the software can meet user requirements especially when the requirements change frequently. To tackle this problem, we propose an approach to verify the program by inspecting the internal relations with the user requirements. In the approach, the constraints in the requirements are represented by a concern-based model defined in our previous work by Liu et al. and the internal relations of program are extracted based on static analysis methods; then, a framework of verification system is defined to inspect whether the program can satisfy the constraints for discovering the errors with their locations. The main contribution of this paper includes: 1) kinds of internal relations of program are defined and their calculation methods are given to transform the source codes to a formalized model, which is taken as the object to be verified and 2) formal description of verification system framework is given to support the automation of verification process. Since the verification tasks can be set freely based on the requirements in the system, the proposed approach can help developers to cope with the change of requirements better.

The determination of structural dynamic characteristics can be challenging, especially for complex cases. This can be a major impediment for dynamic load identification in many engineering applications. Hence, avoiding the need to find numerous solutions for structural dynamic characteristics can significantly simplify dynamic load identification. To achieve this, we rely on machine learning. The recent developments in machine learning have fundamentally changed the way we approach problems in numerous fields. Machine learning models can be more easily established to solve inverse problems compared to standard approaches. Here, we propose a novel method for dynamic load identification, exploiting deep learning. The proposed algorithm is a time-domain solution for beam structures based on the recurrent neural network theory and the long short-term memory. A deep learning model, which contains one bidirectional long short-term memory layer, one long short-term memory layer and two full connection layers, is constructed to identify the typical dynamic loads of a simply supported beam. The dynamic inverse model based on the proposed algorithm is then used to identify a sinusoidal, an impulsive and a random excitation. The accuracy, the robustness and the adaptability of the model are analyzed. Moreover, the effects of different architectures and hyperparameters on the identification results are evaluated. We show that the model can identify multi-points excitations well. Ultimately, the impact of the number and the position of the measuring points is discussed, and it is confirmed that the identification errors are not sensitive to the layout of the measuring points. All the presented results indicate the advantages of the proposed method, which can be beneficial for many applications.

Glucagon-like peptide-1 (GLP-1) is a gastrointestinal (GI) peptide hormone that stimulates insulin secretion, gene expression and β-cell proliferation, representing a potentially novel and promising therapeutic agent for the treatment of T2DM. DPP-IV-resistant, long-acting GLP-1 analogs have already been approved by FDA as injectable drugs for treating patients with T2DM. Oral delivery of therapeutic peptides and proteins would be preferred owing to advantages of lower cost, ease of administration and greater patient adherence. However, oral delivery of proteins can be affected by rapid enzymatic degradation in the GI tract and poor penetration across the intestinal membrane, which may require amounts that exceed practical consideration. Various production strategies have been explored to overcome challenges associated with the oral delivery of therapeutic peptides and proteins. The goal of this review is to provide an overview of the current state of progress made towards the oral delivery of GLP-1 and its analogs in the treatment of T2DM, with special emphasis on the development of plant and food-grade bacterial delivery systems. Recently, genetically engineered plants and food-grade bacteria have been increasingly explored as novel carrier systems for the oral delivery of peptide and protein drugs. These have a largely unexplored potential to serve both as an expression system and as a delivery vehicle for clinically relevant, cost effective therapeutics. As such, they hold great promise for human biopharmaceuticals and novel therapies against various diseases.

Post-transplant (post-Tx) kidney cancer has become the second-highest cause of death in kidney recipients. Late diagnosis and treatment are the main reasons for high mortality. Further research into early diagnosis and potential treatment is therefore required. In this current study, through genome-wide RNA-Seq profile analysis of post-Tx malignant blood samples and post-Tx non-malignant control blood samples (CTRL-Tx), we found Rap GTPase Interactor (RADIL) and Aprataxin (APTX) to be the most meaningful markers for cancer diagnosis. Receiver operating characteristic (ROC) curve analysis showed that the area under the curve (AUC) of the RADIL-APTX signature model was 0.92 (P < 0.0001). Similarly, the AUC of RADIL alone was 0.91 (P < 0.0001) and that of APTX was 0.81 (P = 0.001). Additionally, using a semi-supervised method, we found that RADIL alone could better predict malignancies in kidney transplantation recipients than APTX alone. Kaplan-Meier analysis indicated that RADIL was expressed significantly higher in the early stages (I and II) of kidney, liver, stomach, and pancreatic cancer, suggesting the potential use of RADIL in early diagnosis. Multivariable Cox regression analysis found that RADIL together with other factors (including age, stage III, stage IV and CD8+ T cells) play a key role in kidney cancer development. Among those factors, RADIL could promote kidney cancer development (HR > 1, P < 0.05) while CD8+ T cells could inhibit kidney cancer development (HR < 1, P < 0.05). RADIL may be a new immunotherapy target for kidney cancer post kidney transplantation.

Human creativity needs improvement in contemporary society. Due to the fast development and pervasive utilisation, computing has been a good servant to support creativity. It is not only utilised to facilitate creativity research, but also leveraged to assist creative activities in everyday life. Up to now, people have never stopped exploring the great potentials of computing to facilitate human creativity. Various new concepts of computing are currently emerging, especially creative computing that inspires a novel approach to improving human creativity. This paper presents a comprehensive review of Creative Computing, including its origin, scope, research and applications.