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Abstract Analysis of the adsorptive behaviour of kaolinite to sodium dodecyl benzene sulphonate (SDBS) at different concentrations can provides a basis for selecting the best concentration. The adsorptive capacity and adsorptive behaviour of kaolinite to SDBS at different concentrations were studied using ultraviolet spectrophotometer, pseudo-first-order adsorption kinetics model, and pseudo-second-order adsorption kinetics model. Scanning electron microscopy with energy dispersive spectrometry (SEM–EDS), X-ray diffraction (XRD), and infrared spectroscopy (FTIR) were used to study the variation characteristics of surface structure, crystallinity indices, and main functional groups on kaolinite before, and after, adsorption. The results show that as the SDBS concentration increase, the adsorptive capacity of kaolinite to SDBS increase. The adsorption process can be accurately fitted by the pseudo-secondary adsorption kinetic model, which means the adsorptive behaviour was mainly chemical in origin. The adsorption of SDBS by kaolinite mainly occurs on the surface. The solidification, lamellar aggregation, and crystallinity index of kaolinite are more obvious after the adsorption of SDBS, but the interlayer spacing of kaolinite did not change to any significant. After the adsorption of SDBS, the intensity ratio of 1000–1008 cm−1 bands changed significantly, indicating the change of the chemical environment, and the adsorptive behaviour was chemical.

The platooning technique has shown potential to bring pronounced benefits to road traffic, including higher safety, better fuel economy, and greater comfort. However, an overlong platoon may jeopardize the overall traffic efficiency because of the unintentional obstruction to other traffic participants. Hence, this paper proposes an advance adjustment strategy for the truck platoon in the off-ramp scenarios to reduce the impact on the predictable lane changes of other vehicles. We derive the appropriate platoon length with an analytic method and verify the effectiveness of conclusions via simulations. Both theoretical calculation and simulation experiment results prove that controlling the platoon length within a reasonable range can significantly improve traffic efficiency near a ramp. This study highlights the importance of truck platoon length management on traffic dynamics and provides a valuable reference for future researchers.

ObjectiveThis study aims to identify clinically relevant diagnostic biomarkers in chronic obstructive pulmonary disease (COPD) while exploring how immune cell infiltration contributes towards COPD pathogenesis.MethodsThe GEO database provided two human COPD gene expression datasets (GSE38974 and GSE76925; n=134) along with the relevant controls (n=49) for differentially expressed gene (DEG) analyses. Candidate biomarkers were identified using the support vector machine recursive feature elimination (SVM-RFE) analysis and the LASSO regression model. The discriminatory ability was determined using the area under the receiver operating characteristic curve (AUC) values. These candidate biomarkers were characterized in the GSE106986 dataset (14 COPD patients and 5 controls) in terms of their respective diagnostic values and expression levels. The CIBERSORT program was used to estimate patterns of tissue infiltration of 22 types of immune cells. Furthermore, the in vivo and in vitro model of COPD was established using cigarette smoke extract (CSE) to validated the bioinformatics results.Results80 genes were identified via DEG analysis that were primarily involved in cellular amino acid and metabolic processes, regulation of telomerase activity and phagocytosis, antigen processing and MHC class I-mediated peptide antigen presentation, and other biological processes. LASSO and SVM-RFE were used to further characterize the candidate diagnostic markers for COPD, SLC27A3, and STAU1. SLC27A3 and STAU1 were found to be diagnostic markers of COPD in the metadata cohort (AUC=0.734, AUC=0.745). Their relevance in COPD were validated in the GSE106986 dataset (AUC=0.900 AUC=0.971). Subsequent analysis of immune cell infiltration discovered an association between SLC27A3 and STAU1 with resting NK cells, plasma cells, eosinophils, activated mast cells, memory B cells, CD8+, CD4+, and helper follicular T-cells. The expressions of SLC27A3 and STAU1 were upregulated in COPD models both in vivo and in vitro. Immune infiltration activation was observed in COPD models, accompanied by the enhanced expression of SLC27A3 and STAU1. Whereas, the knockdown of SLC27A3 or STAU1 attenuated the effect of CSE on BEAS-2B cells.ConclusionSTUA1 and SLC27A3 are valuable diagnostic biomarkers of COPD. COPD pathogenesis is heavily influenced by patterns of immune cell infiltration. This study provides a molecular biology insight into COPD occurrence and in exploring new therapeutic means useful in COPD.

Abstract Background Tooth morphology within theropod dinosaurs has been extensively investigated and shows high disparity throughout the Cretaceous. Changes or diversification in feeding ecology, i.e., adoption of an herbivorous diet (e.g., granivorous), is proposed as a major driver of tooth evolution in Paraves (e.g., Microraptor, troodontids and avialans). Here, we studied the microscopic features of paravian non-avian theropod and avialan teeth using high-spatial-resolution synchrotron transmission X-ray microscopy and scanning electron microscopy. Results We show that avialan teeth are characterized by the presence of simple enamel structures and a lack of porous mantle dentin between the enamel and orthodentin. Reduced internal structures of teeth took place independently in Early Cretaceous birds and a Microraptor specimen, implying that shifts in diet in avialans from that of closely related dinosaurs may correlate with a shift in feeding ecology during the transition from non-avian dinosaurs to birds. Conclusion Different lines of evidence all suggest a large reduction in biting force affecting the evolution of teeth in the dinosaur-bird transition. Changes in teeth microstructure and associated dietary shift may have contributed to the early evolutionary success of stemward birds in the shadow of other non-avian theropods.

Abstract Background Both intra- and inter-sentential semantic relations in biomedical texts provide valuable information for biomedical research. However, most existing methods either focus on extracting intra-sentential relations and ignore inter-sentential ones or fail to extract inter-sentential relations accurately and regard the instances containing entity relations as being independent, which neglects the interactions between relations. We propose a novel sequence labeling-based biomedical relation extraction method named Bio-Seq. In the method, sequence labeling framework is extended by multiple specified feature extractors so as to facilitate the feature extractions at different levels, especially at the inter-sentential level. Besides, the sequence labeling framework enables Bio-Seq to take advantage of the interactions between relations, and thus, further improves the precision of document-level relation extraction. Results Our proposed method obtained an F1-score of 63.5% on BioCreative V chemical disease relation corpus, and an F1-score of 54.4% on inter-sentential relations, which was 10.5% better than the document-level classification baseline. Also, our method achieved an F1-score of 85.1% on n2c2-ADE sub-dataset. Conclusion Sequence labeling method can be successfully used to extract document-level relations, especially for boosting the performance on inter-sentential relation extraction. Our work can facilitate the research on document-level biomedical text mining.

In this paper, a method of multi UAV cluster control based on improved artificial potential field (APF) is proposed. The k-means method is used to integrate and optimize the attractive force between UAVs, and the concept of virtual core is introduced to realize the cluster control and adaptive formation flight of multiple UAVs. The attractive disturbance component of the target point is introduced and the backtracking-filling method is proposed to solve the local minimum problem in the APF. The repulsion force in the APF can realize obstacle avoidance and collision avoidance, and the virtual core can control the UAV cluster to fly to the target point under the attractive force of potential field, so as to realize the track planning and multi aircraft cooperative task. In the process of cluster flight when the UAV fails, merges or dispatches, the method can realize cluster reconfiguration and the cluster control effect and task execution success rate can be improved. The simulation experiments in virtual APF and urban environment APF show the effectiveness of this method.

Background. AML (acute myeloid leukemia) is a common hematological malignancy in children with poor treatment effects and poor prognosis. Recent studies have shown that as a novel BRD4 (bromodomain containing 4) PROTACs (proteolysis targeting chimeras) degrader, GNE-987 can slow down the growth of various tumors and increase apoptosis, with promising clinical prospects. However, the function and molecular mechanism of GNE-987 in AML remain unclear. This study is aimed at investigating the therapeutic effect of GNE-987 on AML and its underlying mechanism. Methods. The association between BRD4 and AML was assessed by studying public databases. After GNE-987 was added to AML cells, cell proliferation slowed down, the cycle was disturbed, and apoptosis increased. Western blotting was used to detect BRD2 (bromodomain containing 2), BRD3 (bromodomain containing 3), BRD4, and PARP (poly ADP-ribose polymerase) proteins. The effect of GNE-987 on AML cells was analyzed in vivo. RNA-seq (RNA sequencing) and ChIP-seq (chromatin immunoprecipitation sequencing) validated the function and molecular pathways of GNE-987 in processing AML. Results. BRD4 expression was significantly elevated in pediatric AML samples compared with healthy donors. GNE-987 inhibited AML cell proliferation by inhibiting the cell cycle and inducing apoptosis. BRD2, BRD3, and BRD4 were consistent with decreased VHL (Von Hippel Lindau) expression in AML cells. In an AML xenograft model, GNE-987 significantly reduced the hepatosplenic infiltration of leukemia cells and increased the mouse survival time. Based on analysis of RNA-seq and ChIP-seq analyses, GNE-987 could target multiple SE- (super-enhancer-) related genes, including LYL1 (lymphoblastic leukemia 1), to inhibit AML. Conclusions. GNE-987 had strong antitumor activity in AML. GNE-987 could effectively inhibit the expression of SE-related oncogenes including LYL1 in AML. Our results suggested that GNE-987 had broad prospects in the treatment of AML.

Abstract Background To detect attributes of medical concepts in clinical text, a traditional method often consists of two steps: named entity recognition of attributes and then relation classification between medical concepts and attributes. Here we present a novel solution, in which attribute detection of given concepts is converted into a sequence labeling problem, thus attribute entity recognition and relation classification are done simultaneously within one step. Methods A neural architecture combining bidirectional Long Short-Term Memory networks and Conditional Random fields (Bi-LSTMs-CRF) was adopted to detect various medical concept-attribute pairs in an efficient way. We then compared our deep learning-based sequence labeling approach with traditional two-step systems for three different attribute detection tasks: disease-modifier, medication-signature, and lab test-value. Results Our results show that the proposed method achieved higher accuracy than the traditional methods for all three medical concept-attribute detection tasks. Conclusions This study demonstrates the efficacy of our sequence labeling approach using Bi-LSTM-CRFs on the attribute detection task, indicating its potential to speed up practical clinical NLP applications.

ObjectiveSuppression of bromodomain and extra terminal (BET) proteins has a bright prospect to treat MYC-driven tumors. Bromodomain containing 4 (BRD4) is one of the BET proteins. ARV-825, consisting of a BRD4 inhibitor conjugated with a cereblon ligand using proteolysis-targeting chimera (PROTAC) technology, was proven to decrease the tumor growth effectively and continuously. Nevertheless, the efficacy and mechanisms of ARV-825 in gastric cancer are still poorly understood.MethodsCell counting kit 8 assay, lentivirus infection, Western blotting analysis, Annexin V/propidium iodide (PI) staining, RNA sequencing, a xenograft model, and immunohistochemistry were used to assess the efficacy of ARV-825 in cell level and animal model.ResultsThe messenger RNA (mRNA) expression of BRD4 in gastric cancer raised significantly than those in normal tissues, which suggested poor outcome of patients with gastric cancer. ARV-825 displayed higher anticancer efficiency in gastric cancer cells than OTX015 and JQ1. ARV-825 could inhibit cell growth, inducing cell cycle block and apoptosis in vitro. ARV-825 induced degradation of BRD4, BRD2, BRD3, c-MYC, and polo-like kinase 1 (PLK1) proteins in four gastric cancer cell lines. In addition, cleavage of caspase 3 and poly-ADP-ribose polymerase (PARP) was elevated. Knockdown or overexpression CRBN could increase or decrease, respectively, the ARV-825 IC50 of gastric cancer cells. ARV-825 reduced MYC and PLK1 expression in gastric cancer cells. ARV-825 treatment significantly reduced tumor growth without toxic side effects and downregulated the expression of BRD4 in vivo.ConclusionsHigh mRNA expression of BRD4 in gastric cancer indicated poor prognosis. ARV-825, a BRD4 inhibitor, could effectively suppress the growth and elevate the apoptosis of gastric cancer cells via transcription downregulation of c-MYC and PLK1. These results implied that ARV-825 could be a good therapeutic strategy to treat gastric cancer.

Summary: The sterile inflammation caused by damage-associated molecular patterns (DAMPs) worsens the prognosis following primary injury such as ischemic stroke. However, there are no effective treatments to regulate DAMPs. Here, we report that AIM (or CD5L) protein reduces sterile inflammation by attenuating DAMPs and that AIM administration ameliorates the deleterious effects of ischemic stroke. AIM binds to DAMPs via charge-based interactions and disulfide bond formation. This AIM association promotes the phagocytic removal of DAMPs and neutralizes DAMPs by impeding their binding to inflammatory receptors. In experimental stroke, AIM-deficient mice exhibit severe neurological damage and higher mortality with greater levels of DAMPs and associated inflammation in the brain than wild-type mice, in which brain AIM levels increase following stroke onset. Recombinant AIM administration reduces sterile inflammation in the infarcted region, leading to a profound reduction of animal mortality. Our findings provide a basis for the therapies targeting DAMPs to improve ischemic stroke.

Sequential pattern mining (SPM) is a major class of data mining topics with a wide range of applications. The continuity and uncertain nature of trajectory data make it distinctively different from typical transactional data, which requires additional data transformation to prepare for SPM. However, little research focuses on comparing the performance of SPM algorithms and their applications in the context of trajectory data. This study selected some representative sequential pattern mining algorithms and evaluated them with various parameters to understand the effect of the involved parameters on their performances. We studied the resultant sequential patterns, runtime, and RAM consumption in the context of the taxi trajectory dataset, the T-drive dataset. It was demonstrated in this work that a method to discretize trajectory data and different SPM algorithms were performed on trajectory databases. The results were visualized on actual Beijing road maps, reflecting traffic congestion conditions. Results demonstrated contiguous constraint-based algorithms could provide a concise representation of output sequences and functions at low min_sup with balanced RAM consumption and execution time. This study can be used as a guide for academics and professionals when determining the most suitable SPM algorithm for applications that involve trajectory data.

Thus far, few studies have been conducted on fine-grained classification tasks for the latest convolutional neural network ConvNeXt, and no effective optimization method has been made available. To achieve more accurate fine-grained classification, this paper proposes two attention embedding methods based on ConvNeXt network and designs a new bilinear CBAM; simultaneously, a multiscale, multi-perspective and all-around attention framework is proposed, which is then applied in ConvNeXt. Experimental verification shows that the accuracy rate of the improved ConvNeXt for fine-grained image classification reaches 87.8%, 91.2%, and 93.2% on fine-grained classification datasets CUB-200-2011, Stanford Cars, and FGVC Aircraft, respectively, showing increases of 2.7%, 0.3% and 0.4%, respectively, compared to those of the original network without optimization, and increases of 3.7%, 8.0% and 2.0%, respectively, compared to those of the traditional BCNN. In addition, ablation experiments are set up to verify the effectiveness of the proposed attention framework.

BackgroundA detailed means of prognostic stratification in patients with non-small cell lung cancer (NSCLC) is urgently needed to support individualized treatment plans. Recently, microRNAs (miRNAs) have been used as biomarkers due to their previously reported prognostic roles in cancer. This study aimed to construct an immune-related miRNA signature that effectively predicts NSCLC patient prognosis.MethodsThe miRNAs and mRNA expression and mutation data of NSCLC was obtained from The Cancer Genome Atlas (TCGA). Immune-associated miRNAs were identified using immune scores calculated by the ESTIMATE algorithm. LASSO-penalized multivariate survival models were using for development of a tumor immune-related miRNA signature (TIM-Sig), which was evaluated in several public cohorts from the Gene Expression Omnibus (GEO) and the CellMiner database. The miRTarBase was used for constructing the miRNA-target interactions.ResultsThe TIM-Sig, including 10 immune-related miRNAs, was constructed and successfully predicted overall survival (OS) in the validation cohorts. TIM-Sig score negatively correlated with CD8+ T cell infiltration, IFN-γ expression, CYT activity, and tumor mutation burden. The correlation between TIM-Sig score and genomic mutation and cancer chemotherapeutics was also evaluated. A miRNA-target network of 10 miRNAs in TIM-Sig was constructed. Further analysis revealed that these target genes showed prognostic value in both lung squamous cell carcinoma and adenocarcinoma.ConclusionsWe concluded that the immune-related miRNAs demonstrated a potential value in clinical prognosis.

Abstract Background Protein–protein interaction (PPI) information extraction from biomedical literature helps unveil the molecular mechanisms of biological processes. Especially, the PPIs associated with human malignant neoplasms can unveil the biology behind these neoplasms. However, such PPI database is not currently available. Results In this work, a database of protein–protein interactions associated with 171 kinds of human malignant neoplasms named HMNPPID is constructed. In addition, a visualization program, named VisualPPI, is provided to facilitate the analysis of the PPI network for a specific neoplasm. Conclusions HMNPPID can hopefully become an important resource for the research on PPIs of human malignant neoplasms since it provides readily available data for healthcare professionals. Thus, they do not need to dig into a large amount of biomedical literatures any more, which may accelerate the researches on the PPIs of malignant neoplasms.

Abstract Background Learning distributional representation of clinical concepts (e.g., diseases, drugs, and labs) is an important research area of deep learning in the medical domain. However, many existing relevant methods do not consider temporal dependencies along the longitudinal sequence of a patient’s records, which may lead to incorrect selection of contexts. Methods To address this issue, we extended three popular concept embedding learning methods: word2vec, positive pointwise mutual information (PPMI) and FastText, to consider time-sensitive information. We then trained them on a large electronic health records (EHR) database containing about 50 million patients to generate concept embeddings and evaluated them for both intrinsic evaluations focusing on concept similarity measure and an extrinsic evaluation to assess the use of generated concept embeddings in the task of predicting disease onset. Results Our experiments show that embeddings learned from information within one visit (time window zero) improve performance on the concept similarity measure and the FastText algorithm usually had better performance than the other two algorithms. For the predictive modeling task, the optimal result was achieved by word2vec embeddings with a 30-day sliding window. Conclusions Considering time constraints are important in training clinical concept embeddings. We expect they can benefit a series of downstream applications.

The fossil record of the reproductive traits of early birds is limited. Here, Bailleul and colleagues describe the Cretaceous enantiornithine bird Avimaia schweitzerae, which preserves an unlaid egg in the abdominal cavity and putative medullary bone.

The purpose of this paper is to study the short-term demand prediction for online car-hailing services problem. Prediction of short-term network car demand can provide many benefits such as an increase in the income of network car drivers. In addition, demand prediction is an important resource for recommendation systems, carpooling systems, and network car scheduling; and therefore, predicting the demand for network cars has great significance. In the last few decades, scholars have studied various problems related to short-term demand prediction for online car-hailing services based on clustering algorithms and regression algorithms. However, these studies are still problematic because the accuracy of demand prediction is not high enough. Therefore, this paper studies a method of improving the accuracy and the efficiency of demand prediction. Due to the high prediction accuracy and the fast training efficiency of least squares support vector machine (LS-SVM), a short-term demand prediction method for online car-hailing services based on LS-SVM is proposed. The modeling process involves selecting the dependent and independent variables, the basic principles of the LS-SVM, the kernel function and superparameter, model training, and prediction. In a numerical experiment, we use network car order data as the network car demand data to test the model. We show that the experimental results with the LS-SVM method implemented in this paper and then compare the model with lasso linear regression, nearest neighbor regression, decision tree regression, and neural network. The experimental results show that the short-term demand prediction model for online car-hailing services based on LS-SVM performs better than the other methods.

In recent years, intelligent fault diagnosis technology with the deep learning algorithm has been widely used in the manufacturing industry for substituting time-consuming human analysis method to enhance the efficiency of fault diagnosis. The rolling bearing as the connection between the rotor and support is the crucial component in rotating equipment. However, the working condition of the rolling bearing is under changing with complex operation demand, which will significantly degrade the performance of the intelligent fault diagnosis method. In this paper, a new deep transfer model based on Wasserstein distance guided multi-adversarial networks (WDMAN) is proposed to address this problem. The WDMAN model exploits complex feature space structures to enable the transfer of different data distributions based on multiple domain critic networks. The essence of our method is learning the shared feature representation by minimizing the Wasserstein distance between the source domain and target domain distribution in an adversarial training way. The experiment results demonstrate that our model outperforms the state-of-the-art methods on rolling bearing fault diagnosis under different working conditions. The t-distributed stochastic neighbor embedding (t-SNE) technology is used to visualize the learned domain invariant feature and investigate the transferability behind the great performance of our proposed model.

This paper considers the control problem of automated vehicles with delays. We develop a comprehensive control method within the framework of model predictive control (MPC) with compensation for the input delay and the state delay for automated vehicles on the multi-lane road, where the road boundaries and lane markings are modeled as potential functions. The original vehicle dynamics model is modified to formulate the augmented prediction model to compensate for delays. We also present a parallel strategy for the executions of the perception system, the controller, and the actuator, which can directly cut delays in the loop. The simulations show that this method can greatly reduce the impact of delays on the performance of the controller and, therefore, enhance its capabilities.

The long short-term memory (LSTM) model is one of the most commonly used vehicle trajectory predicting models. In this paper, we study two problems of the existing LSTM models for long-term trajectory prediction in dense traffic. First, the existing LSTM models cannot simultaneously describe the spatial interactions between different vehicles and the temporal relations between the trajectory time series. Thus, the existing models cannot accurately estimate the influence of the interactions in dense traffic. Second, the basic LSTM models often suffer from vanishing gradient problem and are, thus, hard to train for long time series. These two problems sometimes lead to large prediction errors in vehicle trajectory predicting. In this paper, we proposed a spatio-temporal LSTM-based trajectory prediction model (ST-LSTM) which includes two modifications. We embed spatial interactions into LSTM models to implicitly measure the interactions between neighboring vehicles. We also introduce shortcut connections between the inputs and the outputs of two consecutive LSTM layers to handle gradient vanishment. The proposed new model is evaluated on the I-80 and US-101 datasets. Results show that our new model has a higher trajectory predicting accuracy than one state-of-the-art model [maneuver-LSTM (M-LSTM)].

Abstract Background Extracting relations between important clinical entities is critical but very challenging for natural language processing (NLP) in the medical domain. Researchers have applied deep learning-based approaches to clinical relation extraction; but most of them consider sentence sequence only, without modeling syntactic structures. The aim of this study was to utilize a deep neural network to capture the syntactic features and further improve the performances of relation extraction in clinical notes. Methods We propose a novel neural approach to model shortest dependency path (SDP) between target entities together with the sentence sequence for clinical relation extraction. Our neural network architecture consists of three modules: (1) sentence sequence representation module using bidirectional long short-term memory network (Bi-LSTM) to capture the features in the sentence sequence; (2) SDP representation module implementing the convolutional neural network (CNN) and Bi-LSTM network to capture the syntactic context for target entities using SDP information; and (3) classification module utilizing a fully-connected layer with Softmax function to classify the relation type between target entities. Results Using the 2010 i2b2/VA relation extraction dataset, we compared our approach with other baseline methods. Our experimental results show that the proposed approach achieved significant improvements over comparable existing methods, demonstrating the effectiveness of utilizing syntactic structures in deep learning-based relation extraction. The F-measure of our method reaches 74.34% which is 2.5% higher than the method without using syntactic features. Conclusions We propose a new neural network architecture by modeling SDP along with sentence sequence to extract multi-relations from clinical text. Our experimental results show that the proposed approach significantly improve the performances on clinical notes, demonstrating the effectiveness of syntactic structures in deep learning-based relation extraction.

As key components of the tetrapod pectoral girdle, the scapula and coracoid have played a significant role in the evolution of forelimb locomotion among terrestrial vertebrates. The transition from a rigid fused scapulocoracoid in ancestral non-avian theropods to a presumably more flexible separated scapula-coracoid in early birds is considered to be one of the key morphological transitions related to the rapid refinement of flight. In most Mesozoic birds (e.g., Enantiornithes and Ornithuromorpha) and crown birds the scapula and coracoid are separate (unfused), with few exceptions (e.g., flightless paleognaths). In contrast, in Confuciusornis, a basal pygostylian from the Early Cretaceous Jehol Biota known from thousands of specimens, the scapula and coracoid remain plesiomorphically fused. This raises questions regarding the influence of shoulder girdle architecture on the early evolution and refinement of avian flight. The paravian scapula-coracoid joint has never previously been investigated using histology, and thus joint morphology has only been inferred superficially. In order to better understand the evolution of this joint in Mesozoic birds, we make the first histological study of the scapulocoracoid glenoid joint in Confuciusornis. The results demonstrate that the scapula and coracoid both consist of cancellous and compact bone, with both fibrolamellar and parallel-fibered structure. A thin layer of calcified cartilage is present on the glenoid fossa surface, representing remnants of the articular surface for the humerus. Both histology and computed tomography reveal that the scapulocoracoid of Confuciusornis is fully fused, forming a synostosis. Humeral histology suggests the studied individual was nearing completion of its first year of growth, suggesting the Confuciusornis scapulocoracoid fused before skeletal maturity was achieved, as in flightless paleognaths, whereas in the plesiomorphic condition fusion occurs late in ontogeny. We hypothesize the fused scapulocoracoid of Confuciusornis is secondarily evolved and suggest the primary factor responsible for this morphology may have been a decrease in mechanical stimulation at the glenoid of Confuciusornis relative to other volant birds, linked to the unique flight style of this taxon. Further investigation into the histology of the glenoid joint in other Mesozoic paravians and extant birds will help to clarify the morphological transition of the scapula-coracoid joint in early avian evolution.

BackgroundAberrant expression of CD123 (IL-3Rα) was observed in various hematological malignancies including acute lymphoblastic leukemia (ALL), which is the most common malignancy in childhood. Although widely used for minimal residual disease (MRD) monitoring, the prognostic value of CD123 has not been fully characterized in pediatric B-ALL. This retrospective study aims to evaluate the association between the CD123 expression of leukemic blasts and the outcomes of the pediatric B-ALL patients.MethodsA total of 976 pediatric B-ALL, including 328 treated with CCLG-ALL-2008 protocol and 648 treated with CCCG-ALL-2015 protocol, were recruited in this retrospective study. CD123 expression was evaluated by flow cytometry. Patients with >50, 20–50, or

Gastroliths, where preserved, can provide indirect evidence regarding diet in extinct avian and non-avian dinosaurs. Masses of gastroliths consistent with the presence of a gastric mill are preserved in many Early Cretaceous Jehol birds mostly belonging to the Ornithuromorpha. Gastroliths are also present in basal birds Sapeornis and Jeholornis in which herbivory is supported by direct evidence these taxa consumed seeds in the form of crop or stomach contents. Although gastroliths have been correlated with herbivory in non-avian dinosaurs, the presence of gastroliths and bone together in Ambopteryx calls this association in to question. Despite being known from greater numbers of specimens than other avian lineages, no unequivocal direct or indirect evidence of diet has been recovered from Jehol deposits for the Enantiornithes. A referred specimen of Bohaiornis guoi IVPP V17963 was described as preserving a small number of gastroliths interpreted as rangle, gastroliths whose function is cleaning the stomach in extant raptorial birds. However, based on comparison with gastroliths in other Jehol birds, it has alternatively been suggested that the identified structures are not ingested stones at all but some unusual mineral precipitate. Considering the limited evidence regarding diet in Enantiornithes and the importance of accurately identifying the traces in Bohaiornis in order to understand the enantiornithine digestive system, we extracted two samples of these purported gastroliths and explored these traces using computerized laminography scanning, scanning electron microscopy, energy dispersive x-ray spectroscopy, ground sections, and body size to gastral mass regressions. Similar analyses were conducted on gastroliths extracted from undisputed gastral masses of two Jehol ornithuromorphs and the non-avian pennaraptoran Caudipteryx. The combined results contradict the hypothesis that these traces are gastroliths and supports the interpretation they are mineral precipitate, most likely authigenic quartz (chalcedony). Although authigenesis is commonly responsible for the preservation of soft tissues, it is unclear if these traces record part of the tissues of this Bohaiornis. This study highlights the importance of a multidisciplinary approach in understanding unusual traces in the fossil record and reveal a previously unidentified taphonomic phenomenon in fossils from Jehol deposits.

The prognostics and health management (PHM) of electric vehicles is an important guarantee for their safety and long-term development. At present, there are few studies researching about life cycle PHM system of electric vehicles. In this paper, we first summarize the research progress and key methods of PHM. Then, we propose a three-level PHM system with a hierarchy fusion architecture for electric vehicles based on the structure, data source of them. In the PHM system, we introduce a database consisting of the factory data, real-time data, and detection data. The electric vehicle's factory parameters are used for determining the life curve of the electric vehicle and its components, the real-time data are used for predicting the remaining useful lifetime (RUL) of the electric vehicle and its components, and the detection data are used for fault diagnosis. This health management database is established to help make condition-based maintenance decisions for electric vehicles. In this way, a complete electric vehicle PHM system is formed, which can realize the whole-life-cycle life prediction and fault diagnosis of electric vehicles.

This paper studies a dynamic capacitated arc routing problem for battery replacement in an e-bike sharing system, where workers replace batteries for underpowered e-bikes along street segments dynamically. The objective is to replace as many batteries as possible and minimize pickup failures. The temporal dependency of the routing decisions, the conflict of the workers’ operations, and the stochastic and dynamic nature of user demands all make this a difficult problem. To cope with these difficulties, a “Partition-First, Route-Second” bi-level solution framework is adopted to describe the problem in two different time scales. On the large time scale, a spatiotemporal partitioning method, which divides the road network into nonoverlapping subzones, is proposed to decompose the problem. On the small time scale, this paper models the routing decision process of individual worker as a Markov Decision Process. We adopt a lookahead policy that simulates future information and decisions over some horizons to evaluate the long-term influence of current feasible decisions. A Monte Carlo Tree Search algorithm is also used to improve the simulation efficiency. By performing numerical computation experiments on a test case study and comparing with some benchmarking policies, we demonstrate the effectiveness and efficiency of the suggested method.

Neuroblastoma (NB) is one of the most common solid tumors in childhood. To date, targeting MYCN, a well-established driver gene in high-risk neuroblastoma, is still challenging. In recent years, inhibition of bromodomain and extra terminal (BET) proteins shows great potential in multiple of Myc-driven tumors. ARV-825 is a novel BET inhibitor using proteolysis-targeting chimera (PROTAC) technology which degrades target proteins by the proteasome. In this study, we investigated the effect of ARV-825 in neuroblastoma in vitro and in vivo. Our results showed that ARV-825 treatment robustly induced proliferative suppression, cell cycle arrest, and apoptosis in NB cells. Moreover, ARV-825 efficiently depleted BET protein expression, subsequently repressing the expression of MYCN or c-Myc. In the NB xenograft model, ARV-825 profoundly reduced tumor growth and led to the downregulation of BRD4 and MYCN expression in mice. Taken together, these findings provide evidence that PROTAC BET inhibitor is an efficient way to achieve MYCN/c-Myc manipulation, and ARV-825 can be used as a potential therapeutic strategy for the treatment of neuroblastoma.

The partial skeleton of a new extinct taxon, Linxiavis inaquosus, from the Liushu Formation (6–9 Ma) at the edge of the Tibetan Plateau in Gansu Province, China is the most substantial known fossil record of sandgrouse (Pteroclidae). While adding to the rapidly growing known Liushu avian fauna of vultures, falcons, pheasants, and ostrich, this new fossil is likely the oldest record of crown Pteroclidae (as a potential molecular clock calibration point), the oldest record of the group in Asia (from a probable African origin), and derives from a significant temporal gap in their Neogene history. The fossil specimen includes articulated and associated elements of the wings, shoulder girdle, vertebrae, and hind limb, exhibiting apomorphies of Columbiformes and Pteroclidae such as a notarium, and a short coracoid shaft. As part of the diverse Hipparion fauna, Linxiavis inaquosus reinforces the interpretation of the late Miocene Linxia Basin habitat as an arid savannah, associated with the uplift of the Tibetan Plateau. The holotype suggests that the arid high elevation Tibetan area habitats may have been continuously occupied since the late Miocene by sandgrouse carrying water in their modified breast feathers to their young.

The pterosaur is the first known vertebrate clade to achieve powered flight. Its hyoid apparatus shows a simplification similar to that of birds, although samples of the apparatus are rare, limiting the ability to make an accurate determination. In this study we reveal a new pterosaur specimen, including the first definite basihyal. Through the comparison of pterosaur hyoids, a trend has been discovered for the shortened hyoid relative to the length of the skull, indicating a diminished role of lingual retraction during the evolution of the pterosaur. The new material, possibly from a gallodactylid Gladocephaloideus, represents one of the least effective lingual retractions in all pterosaurs. Based on the structure of an elongated ceratobranchial and retroarticular process on mandibles, the function of the Y-shaped istiodactylid tongue bone is similar to those of scavenger crows rather than chameleons, which is consistent with the interpretation of the scavenging behavior of this taxon. More fossil samples are needed for further study on the function of other pterosaur hyoids.

Though a tradeoff between growth rate and locomotor performance has been proposed, empirical data on this relationship are still limited. Here we statistically analyze the associations of growth rate and flight ability in birds by assessing how growth rate is correlated with three wing parameters of birds: flight muscle ratio, wing aspect ratio, and wing loading. We find that fast-growing birds tended to have higher flight muscle ratios and higher wing loadings than slow-growing birds, which suggests that fast-growing birds may have better takeoff performance, but lower efficiency in maneuvering flight. Accordingly, our findings suggest that the relationship between growth rate and flight ability is more complex than a simple tradeoff. Since the hindlimbs also contribute greatly to the locomotion of birds, future investigations on the relationship between growth rate and hindlimb performance will provide more insights into the evolution of birds.

As an important component of vehicle design and energy conservation, electric vehicle dynamics simulation is essential, especially under complicated testing conditions. The current commercial vehicle simulation software is mostly used for fuel vehicle dynamics simulation, which lacks accurate electric powertrain parts and open sources. To address this problem, this paper proposes an open-source and flexible vehicle dynamics simulation platform that includes 27 degrees of freedom (DOFs) based on Simulink, which can compatibly support both traditional vehicle dynamics simulations and electric vehicle dynamics simulations. In addition, the platform can support module customization, which is convenient for researchers. Although this platform still needs some iterations to reach industrial and commercial standards, it can already achieve parameter consistency under the stability demands in general scenarios. We believe this work should receive research attention and participation to provide lower thresholds and more references to the dynamic simulation of electric vehicles to reduce vehicle energy consumption.

Multivariate time series forecasting has long been a research hotspot because of its wide range of application scenarios. However, the dynamics and multiple patterns of spatiotemporal dependencies make this problem challenging. Most existing methods suffer from two major shortcomings: (1) They ignore the local context semantics when modeling temporal dependencies. (2) They lack the ability to capture the spatial dependencies of multiple patterns. To tackle such issues, we propose a novel Transformer-based model for multivariate time series forecasting, called the spatial–temporal convolutional Transformer network (STCTN). STCTN mainly consists of two novel attention mechanisms to respectively model temporal and spatial dependencies. Local-range convolutional attention mechanism is proposed in STCTN to simultaneously focus on both global and local context temporal dependencies at the sequence level, which addresses the first shortcoming. Group-range convolutional attention mechanism is designed to model multiple spatial dependency patterns at graph level, as well as reduce the computation and memory complexity, which addresses the second shortcoming. Continuous positional encoding is proposed to link the historical observations and predicted future values in positional encoding, which also improves the forecasting performance. Extensive experiments on six real-world datasets show that the proposed STCTN outperforms the start-of-the-art methods and is more robust to nonsmooth time series data.

Birds have a more crouched posture compared to their theropod dinosaur ancestors. Here, Jiang and colleagues describe a lower hindlimb of the Early Cretaceous birdConfuciusorniswith soft tissues apparently preserved even as molecules, indicating a somewhat more modern posture in ancient birds.

The adsorption characteristics of sodium dodecylbenzene sulfonate (SDBS) on the surface of montmorillonite can lay a foundation for obtaining the optimum concentration of the anionic surfactant. The best absorption wavelength of SDBS was determined using an ultraviolet spectrophotometer. The standard curves of concentration and absorbance of SDBS were established. The amount of SDBS adsorbed on the surface of montmorillonite at various concentrations was calculated by stirring adsorption method. Scanning electron microscopy–energy dispersive X-ray spectrometry (SEM-EDS), X-ray diffraction (XRD), zeta potentiometer, and Fourier transform infrared (FTIR) spectroscopy were used to observe the changes of the structure, main ions, interlayer spacing, potential, and main functional groups on the montmorillonite surface before, and after, adsorption. The test results of SEM with EDS (SEM–EDS) showed that the surface of the montmorillonite after SDBS adsorption was rougher, and the adsorption capacity of the surface was enhanced as the SDBS concentration increased. The XRD results indicated that SDBS adsorbed on the interlayer of montmorillonite repulsed interlayer water and reduced the interlayer water content. With the increase of SDBS concentration, the interlayer spacing of the montmorillonite available for adsorbing SDBS decreased further. Additionally, interlayer adsorption and surface adsorption exist simultaneously in montmorillonite in SDBS solution. The distribution of total adsorption capacity of SDBS in the layers and on the surface of montmorillonite accords with the adsorption result simulated by a pseudo-second-order kinetic model. The increase in concentration of SDBS adsorbed by montmorillonite is the main reason for the decreased initial adsorption rate. The zeta potential test showed that the addition of H+ to the SDBS solution could reduce electrostatic repulsion and promote the adsorption of SDBS on montmorillonite. The results of this study provide an experimental basis for the study of the mechanism of SDBS adsorption on montmorillonite.

The tongue, with fleshy, muscular, and bony components, is an innovation of the earliest land-dwelling vertebrates with key functions in both feeding and respiration. Here, we bring together evidence from preserved hyoid elements from dinosaurs and outgroup archosaurs, including pterosaurs, with enhanced contrast x-ray computed tomography data from extant taxa. Midline ossification is a key component of the origin of an avian hyoid. The elaboration of the avian tongue includes the evolution of multiple novel midline hyoid bones and a larynx suspended caudal to these midline elements. While variable in dentition and skull shape, most bird-line archosaurs show a simple hyoid structure. Bony, or well-mineralized, hyoid structures in dinosaurs show limited modification in response to dietary shifts and across significant changes in body-size. In Dinosauria, at least one such narrow, midline element is variably mineralized in some basal paravian theropods. Only in derived ornithischians, pterosaurs and birds is further significant hyoid elaboration recorded. Furthermore, only in the latter two taxa does the bony tongue structure include elongation of paired hyobranchial elements that have been associated in functional studies with hyolingual mobility. Pterosaurs and enantiornithine birds achieve similar elongation and inferred mobility via elongation of ceratobranchial elements while within ornithurine birds, including living Aves, ossified and separate paired epibranchial elements (caudal to the ceratobranchials) confer an increase in hyobranchial length. The mobile tongues seen in living birds may be present in other flighted archosaurs showing a similar elongation. Shifts from hypercarnivory to more diverse feeding ecologies and diets, with the evolution of novel locomotor strategies like flight, may explain the evolution of more complex tongue function.

The interplay between the pectoral module (the pectoral girdle and limbs) and the pelvic module (the pelvic girdle and limbs) plays a key role in shaping avian evolution, but prior empirical studies on trait covariation between the two modules are limited. Here we empirically test whether (size-corrected) sternal keel length and ilium length are correlated during avian evolution using phylogenetic comparative methods. Our analyses on extant birds and Mesozoic birds both recover a significantly positive correlation. The results provide new evidence regarding the integration between the pelvic and pectoral modules. The correlated evolution of sternal keel length and ilium length may serve as a mechanism to cope with the effect on performance caused by a tradeoff in muscle mass between the pectoral and pelvic modules, via changing moment arms of muscles that function in flight and in terrestrial locomotion.

Interleukin-33 (IL-33) is associated with multiple diseases, including asthma, rheumatoid arthritis, tissue injuries and infections. Although IL-33 has been indicated to be involved in Staphylococcus aureus (S. aureus) wound infection, little is known about how IL-33 is regulated as a mechanism to increase host defense against skin bacterial infections. To explore the underlying intricate mechanism we first evaluated the expression of IL-33 in skin from S. aureus-infected human patients. Compared to normal controls, IL-33 was abundantly increased in skin of S. aureus-infected patients. We next developed a S. aureus cutaneous infection mouse model and found that IL-33 was significantly increased in dermal macrophages of infected mouse skin. The expression of IL-33 by macrophages was induced by staphylococcal peptidoglycan (PGN) and lipoteichoic acid (LTA) via activation of toll-like receptor 2(TLR2)-mitogen-activated protein kinase (MAPK)-AKT-signal transducer and activator of transcription 3(STAT3) signaling pathway as PGN and LTA failed to induce IL-33 in Tlr2-deficient peritoneal macrophages, and MAPK,AKT, STAT3 inhibitors significantly decreased PGN- or LTA-induced IL-33. IL-33, in turn, acted on macrophages to induce microbicidal nitric oxygen (NO) release. This induction was dependent on inducible nitric oxide synthase (iNOS) activation, as treatment of macrophages with an inhibitor of iNOS, aminoguanidine, significantly decreased IL-33-induced NO release. Moreover, aminoguanidine significantly blocked the capacity of IL-33 to inhibit the growth of S. aureus, and IL-33 silencing in macrophages significantly increased the survival of S. aureus in macrophages. Furthermore, the administration of IL-33-neutralizing antibody into mouse skin decreased iNOS production but increased the survival of S. aureus in skin. These findings reveal that IL-33 can promote antimicrobial capacity of dermal macrophages, thus enhancing antimicrobial defense against skin bacterial infections.

Staphylococcus epidermidis (S.epidermidis) plays important protective roles by directly producing or by stimulating hosts to produce antimicrobial peptides (AMPs) against pathogenic infections. Although several AMPs from S.epidermidis have been identified, molecules that stimulate hosts to produce AMPs remain largly unknown. Here we demonstrate that a new lipopeptide (named LP01) purified from S.epidermidis culture media has a unique structure with heneicosanoic acid (21 carbons) binding to lysine(11) of a peptide chain. In vitro LP01 increased the expression of β-defensin 2(hBD2) and hBD3 in neonatal human epidermal keratinocytes(NHEK), leading to increased capacity of cell lysates to inhibit the growth of S.aureus. In vivo LP01 induced the expression of mouse β-defensin 4(mBD4) to decrease the survival of local S.aureus in skin and systemic S.aureus survival in liver. The induction of beta-defensins by LP01 was dependent on TLR2 as Tlr2-deficient mice had decreased mBD4. Furthermore, knockdown of CD36 decreased the expression of hBD2 and hBD3, and p38 MAPK inhibitor significantly inhibited the expression of hBDs induced by LP01.Taken together, these findings demonstrate that lipopeptide LP01 from normal commensal S.epidermidis increases antimicrobial peptide hBD2 and hBD3 expression via the activation of TLR2/CD36-p38 MAPK, thus enhancing antimicrobial defense against pathogenic infections.