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The role of programmed death ligand 1 (PD-L1) has been extensively investigated in adaptive immune system. However, increasing data show that innate immune responses are also affected by the immune checkpoint molecule. It has been demonstrated that regulation of PD-L1 signaling in macrophages may be a potential therapeutic method for acute respiratory distress syndrome (ARDS). However, the PD-L1 expression pattern in local macrophages and whole lung tissues remains mysterious, hindering optimization of the potential treatment program. Therefore, we aim to determine the PD-L1 expression pattern during ARDS. Our findings show that PD-L1 levels are markedly increased in lipopolysaccharide (LPS)-stimulated lung tissues, which might be attributable to an increase in the gene expression by immune cells, including macrophages and neutrophils. In vitro experiments are performed to explore the mechanism involved in LPS-induced PD-L1 production. We find that PD-L1 generation is controlled by transcription factors early growth response 1 (Egr-1) and CCAAT/enhancer binding protein delta (C/EBPδ). Strikingly, PD-L1 production is enhanced by phosphoinositide-3 kinase (PI3K)–protein kinase B (AKT) signaling pathway via up-regulation of Egr-1 and C/EBPδ expressions. Additionally, we observe that expressions of Egr-1 and C/EBPδ mutually reinforce each other. Moreover, we observe that PD-L1 is protective for ARDS due to its regulatory role in macrophage-associated inflammatory response. In summary, during LPS-induced ARDS, PD-L1 expression, which is beneficial for the disease, is increased via the PI3K-AKT1-Egr-1/C/EBPδ signaling pathway, providing theoretical basis for application of methods controlling PD-L1 signaling in macrophages for ARDS treatment in clinic. [ABSTRACT FROM AUTHOR]

To improve the diagnostic accuracy of adenoid hypertrophy (AH) in children and prevent further complications in time, it is important to study and quantify the effects of different degrees of AH on pediatric upper airway (UA) aerodynamics. In this study, based on computed tomography (CT) scans of a child with AH, UA models with different degrees of obstruction (adenoidal-nasopharyngeal (AN) ratio of 0.9, 0.8, 0.7, and 0.6) and no obstruction (AN ratio of 0.5) were constructed through virtual surgery to quantitatively analyze the aerodynamic characteristics of UA with different degrees of obstruction in terms of the peak velocity, pressure drop (△P), and maximum wall shear stress (WSS). We found that two obvious whirlpools are formed in the anterior upper part of the pediatric nasal cavity and in the oropharynx, which is caused by the sudden increase in the nasal cross-section area, resulting in local flow separation and counterflow. In addition, when the AN ratio was ≥ 0.7, the airflow velocity peaked at the protruding area in the nasopharynx, with an increase 1.1–2.7 times greater than that in the nasal valve area; the △P in the nasopharynx was significantly increased, with an increase 1.1–6.8 times greater than that in the nasal cavity; and the maximum WSS of the posterior wall of the nasopharynx was 1.1–4.4 times larger than that of the nasal cavity. The results showed that the size of the adenoid plays an important role in the patency of the pediatric UA. [ABSTRACT FROM AUTHOR]

Periodontitis has a known association with pathological calcification in the cardiovascular system. Considering the close anatomic and circulatory association between dental pulp and the periodontium, this study aimed to evaluate the prevalence of pulp calcification (PC) under different periodontal conditions, as well as the associations of PC with the degree of periodontal damage, via cone beam computed tomography (CBCT) examination. In this study, 55 patients were categorized into three groups according to periodontal condition: group 1 (healthy controls), group 2 (periodontitis stage I–II), and group 3 (periodontitis stage III–IV). PC and radiographic bone loss (RBL) was assessed by CBCT in sagittal, axial, and coronal views, and statistical analyses were conducted. PC was identified in 378 of 1170 teeth (32.3%). The prevalence significantly differed among the three groups (P < 0.001). Group 2 had a 2.43-fold (P < 0.001, 95% confidence interval [CI] 1.64–3.61) higher risk of PC than group 1; and the risk of PC was 3.04-fold (P < 0.001, 95% CI 2.06–4.48) higher in group 3 than group 1. Teeth with more severe RBL exhibited a higher prevalence of PC (P < 0.001). Molar teeth had a higher risk of PC than incisors and premolars. In conclusion, the occurrence of PC is related to the periodontal state, and the prevalence of PC is higher in teeth with periodontitis; tooth type and periodontitis status are important risk factors for PC. [ABSTRACT FROM AUTHOR]

Previously, we isolated a novel Phocaeicola strain, Phocaeicola faecalis FXJYN30E22, from the feces of a healthy human from China. Metagenomic analysis revealed that the distribution of FXJYN30E22 differed in the intestinal tract of different hosts. We aimed to determine whether FXJYN30E22 protects against ulcerative colitis by employing a mouse model. In this study, dextran sulfate sodium was used to construct the UC model. The disease activity index, colon length, body weight changes, and histological scores were used as the pathological indicators to assess the anti-inflammatory effect of P. faecalis FXJYN30E22. Further, cytokine levels, tight junction mRNA expression levels, and short-chain fatty acid (SCFA) concentrations were also analyzed. Phocaeicola faecalis FXJYN30E22 could reduce the DSS-induced increase in DAI score, and enhance the colon length and body weight. Phocaeicola faecalis FXJYN30E22 could enhance TJ protein concentration and modulate the level of cytokines to reach levels close to those of the control group. FXJYN30E22 could also upregulate the concentrations of SCFA, which include acetate and butyrate. Based on the correlation analysis, four factors, including interleukin (IL)-6, IL-10, IL-1β levels, and propionate concentration, were related to the protective roles of FXJYN30E22 in UC mice to different degrees. According to an analysis of the genomic information, the potential protective effects of strain FXJYN30E22 may be associated with the secretion of SCFA by specific genes. These findings suggest that oral P. faecalis FXJYN30E22 could help maintain the epithelial barrier by regulating cytokine levels and secreting SCFA. [ABSTRACT FROM AUTHOR]

Nowadays, human activity recognition is becoming a more and more significant topic, and there is also a wide range of applications for it in real world scenarios. Sensor data is an important data source in engineering and application. At present, some studies have been carried out in the field of human activity recognition based on sensor data in a macroscopic perspective. However, many studies in this perspective face some limitations. One pivotal limitation is uncontrollable data segment length of different kinds of activities. Suitable feature and data form are also influencing factors. This paper carries out the study creatively on a microscopic perspective with an emphasis on the logic and relevance between data segments, attempting to apply the idea of natural language processing and the method of data symbolization to the study of human activity recognition and try to solve the problem above. In this paper, several activity-element definitions and three algorithms are proposed, including the algorithm of dictionary building, the algorithm of corpus building, and activity recognition algorithm improved from a natural language analysis method, TF-IDF. Numerous experiments on different aspects of this model are taken. The experiments are carried out on six complex and representative single-level sensor datasets, namely UCI Sports and Daily dataset, Skoda dataset, WISDM Phoneacc dataset, WISDM Watchacc dataset, Healthy Older People dataset and HAPT dataset, which prove that this model can be applied to different datasets and achieve a satisfactory recognition result. [ABSTRACT FROM AUTHOR]

Mars Surface Composition Detector (MarSCoDe) is a scientific payload onboard the Mars rover of China's Tianwen-1 mission. With the capability of 1.6∼7 m remote detection and analysis, MarSCoDe instrument suite consists of a two dimensional pointing mirror and an optical head outside the rover cabin, a Laser Induced Breakdown Spectroscopy (LIBS) spectrometer and a Short Wave Infrared (SWIR) spectrometer collectively covering 240∼2400 nm and a master controller unit inside the rover body, calibration target sets, optical fibers and power cables connecting the internal and external units. Combining the techniques of active LIBS, passive SWIR and micro-imaging, MarSCoDe provides functions including elemental composition discrimination and quantitative determination, classification of rock and soil characteristics, sample texture imaging and characterization of plasma-excited area. This paper introduces MarSCoDe mainly in terms of scientific objectives, design requirements, assembly and implementation, spectral and radiation calibration, and performance verification. The LIBS laser irradiance on the target can soundly exceed 10 MW/mm2, and the performance of the LIBS module operated at different temperatures has been tested. The field of view of the SWIR spectrometer is 36.5 mrad. The micro-imager can extract the central pixel area of 320 × 320 and 1024 × 1024, and the former is binned into 64 × 64. The 2D pointing mirror has a wide forward detection range and a narrow backward calibration range, with the pointing pitch accuracy better than 0.133°. [ABSTRACT FROM AUTHOR]

The studies on the TDN (time-dependent network), in which the travel time of the same road segment varies depending on the time of the day, have attracted much attention of researchers, but there is little work focusing on the travel time functions prediction problem. Though traditional methods for travel time or travel speed prediction problem can be used to generate the travel time functions, they have some limitations due to the need of less breakpoints, fine granularity, and long-term prediction. In this paper, we study the travel time functions prediction problem for TDN based on taxi trajectory data. In order to maintain a high degree of accuracy in fine-grained and long-predicted situations, we take into account not only the traffic incidents but also the data sparsity. Specifically, a traffic incident detection method is proposed based on k-means algorithm and a downstream-based strategy is proposed to estimate the speeds of segments considering the data sparsity. To make the breakpoints of function not so much, a prediction algorithm based on classification using ELM (extreme learning machine) is proposed, which predicts the speed classes taking both the weather and the adjacent segment conditions into account. In addition, a transformation method is presented to convert the discrete travel speeds into piecewise linear functions satisfying FIFO (First-In-First-Out) property. The experimental results show that ELM outperforms SVM (support vector machine) with regard to both the training time and prediction accuracy. Moreover, it also can be seen that both the weather conditions and the adjacent segment conditions have impact on the prediction accuracy. [ABSTRACT FROM AUTHOR]

In this era of big data, more and more models need to be trained to mine useful knowledge from large scale data. It has become a challenging problem to train multiple models accurately and efficiently so as to make full use of limited computing resources. As one of ELM variants, online sequential extreme learning machine (OS-ELM) provides a method to learn from incremental data. MapReduce, which provides a simple, scalable and fault-tolerant framework, can be utilized for large scale learning. In this paper, we propose an efficient parallel method for batched online sequential extreme learning machine (BPOS-ELM) training using MapReduce. Map execution time is estimated with historical statistics, where regression method and inverse distance weighted interpolation method are used. Reduce execution time is estimated based on complexity analysis and regression method. Based on the estimations, BPOS-ELM generates a Map execution plan and a Reduce execution plan. Finally, BPOS-ELM launches one MapReduce job to train multiple OS-ELM models according to the generated execution plan, and collects execution information to further improve estimation accuracy. Our proposal is evaluated with real and synthetic data. The experimental results show that the accuracy of BPOS-ELM is at the same level as those of OS-ELM and parallel OS-ELM (POS-ELM) with higher training efficiencies. [ABSTRACT FROM AUTHOR]

Bisphenol A allyl compound-bismaleimide (MBAE) composite modified by SCE-SiO and polyethersulfone (PES) resin has been prepared and researched. SCE-SiO was modified by super-critical ethanol and PES thermoplastic resin used as modifiers. The composite was prepared via the hot melting method. The FT-IR measurements indicated that ethanol molecular had adsorbed on the nano-SiO surface. SEM images showed that the composite had a multiphase structure, PES and SCE-SiO existed as a dispersed phase, and the interaction of the three phases affected each other, such that the bending fracture behavior transformed from brittle fracture to ductile fracture, and the modifiers of SCE-SiO and PES resin could improve the mechanical properties. The impact and the bending strength of the composite was 16.5 kJ/mm and 150.4 MPa, improved by 68.3% and 56.7% compared with those of the MBAE matrix, respectively, when the content of SCE-SiO was 2 wt.% and PES 5 wt.%. The dielectric constant ( ε) of the composites was less than 3.9 and decreased with increasing frequency, and the dielectric loss was less than 9 × 10 for frequencies between 10 Hz and 10 Hz. These properties could meet the requirement of insulating material. [ABSTRACT FROM AUTHOR]

We investigate the disorder effect on coherent fraction and the quantum phase transition of ultracold dilute Bose gases trapped in disordered optical lattices. Within the framework of Bogoliubov theory, an analytical expression for the particle density is derived and the dependence of coherent fraction on disorder strength as well as on lattice depth is discussed. In weak disorder regime, we find a decreased sensitivity of coherent fraction to disorder with the increase of on-site interaction strength. For strong disorder, the quantum phase boundary between superfluid phase and Bose glass phase in the disordered Bose-Hubbard system in weak interaction regime is discussed qualitatively. The obtained phase diagram is in agreement with the empirical square-root law. The dependence of the corresponding critical value of the disorder strength on optical lattice depth is presented as well, and may serve as a reference object for possible experimental investigation. Graphical abstract: [ABSTRACT FROM AUTHOR]

The number of cycle matchings increases exponentially with the number of subscriptions and the maximum length of cycle matchings, which needs a large amount of space to store intermediate results. Approximate cycle matching aims to store only a small part of intermediate results and find cycle matchings as many as possible. The existing solution prunes the intermediate results by a threshold of probability of a subscription to be matched, where the discrete degree of probabilities is neglected. In this paper, we propose an approximate dynamic cycle matching algorithm based on intermediate results classification using extreme learning machine. We first introduce a method of incorporating probability information into feature vector, and then propose the approximate cycle algorithm. Further, we propose a dynamic classification strategy considering that the data distribution of subscriptions may change as time goes on. The proposed approximate cycle matching algorithm and the dynamic classification strategy are evaluated in a simulated environment. The results show that compared with the approximate cycle matching based on probability threshold, the approximate cycle matching based on ELM classification is faster, and the dynamic classification strategy is more efficient and convenient. ELM is more suitable for approximate dynamic cycle matching than SVM with regards to response time. [ABSTRACT FROM AUTHOR]

Targeting the problem of available water conservation in sand fixation, the sand-fixing and grass-planting materials were prepared with clay modified by emulsifying vegetable waxes and octylphenol polyoxyethylene ether (OP4). The water retention property was studied in simulating desertification environmental climate and the materials were characterized by means of UV-Vis, SEM, FTIR, XRD and TGA measurements. The experimental result showed that the materials had excellent water retention properties, due to that vegetable waxes adhered evenly to clay particle surfaces, made the clay pores changing from hydrophilic to hydrophobic and so inhibited the water evaporation. Grass-planting experiment showed that, with reasonable mass ratio of clay, vegetable waxes and surfactant, the materials not only inhibited water evaporation but also maintained sound air permeability so shat the germination rate and survival rate of grass were significantly improved. [ABSTRACT FROM AUTHOR]

Performing mobile k nearest neighbor (MkNN) queries whilst also being mobile is a challenging problem. All the mobile objects issuing queries and/or being queried aremobile. The performance of this kind of query relies heavily on the maintenance of the current locations of the objects. The index used for mobile objects must support efficient update operations and efficient query handling. This study aims to improve the performance of the MkNN queries while reducing update costs. Our approach is based on an observation that the frequency of one region changing between being occupied or not by mobile objects is much lower than the frequency of the position changes reported by the mobile objects. We first propose an virtual grid quadtree with Voronoi diagram(VGQ-Vor), which is a two-layer index structure that indexes regions occupied by mobile objects in a quadtree and builds a Voronoi diagram of the regions. Then we propose a moving k nearest neighbor (kNN) query algorithm on the VGQ-Vor and prove the correctness of the algorithm. The experimental results show that the VGQ-Vor outperforms the existing techniques (Bx-tree, Bdual-tree) by one to three orders of magnitude in most cases. [ABSTRACT FROM AUTHOR]

Mobile object index should support efficient update operations besides efficient query operations. In this paper, we consider the issue of the efficient updating of mobile object index. Based on a model for the mobile data, we introduce a method of incorporating statistical information of the regions covered by the mobile objects into feature vectors. We then propose a novel architecture of mobile object index, where R-tree is used to index the occupied regions instead of the mobile objects themselves and extreme learning machine (ELM) is used to classify the regions. Further, we describe several related algorithms and the update strategy based on the classification of the regions. The proposed strategy and algorithms are evaluated in a simulated environment. The experiments demonstrate that the proposed update strategy based on region classification using ELM can achieve higher performance with respect to I/O operations. Compared to the strategy without region classification, the proposed method can reduce the number of I/O operations more than 80%. [ABSTRACT FROM AUTHOR]