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With the rapid development of port construction and the shipping industry, port water quality issues are of great concern. This is always a challenging task due to the frequent human activities and dynamic processes involved. A parallel intelligent water quality monitoring system is therefore proposed to ensure the effective monitoring and intelligent control of water pollutants. The real monitoring system and the artificial monitoring system of port water quality are established by applying artificial systems, computational experiments and parallel execution (ACP method). Both systems interact with each other and execute in parallel. The artificial monitoring system simulates complex scenarios, while the real monitoring system feeds the artificial monitoring system with actual monitoring data. By means of data-driven and model-driven approaches, the two systems can compute, observe and evaluate to control, manage and train models. Through the continuous optimization between the two systems, the efficiency and accuracy of the water quality monitoring system could be improved. Technical support can be further provided for the planning of water quality monitoring sites, implementation of monitoring tasks, allocation of emergency resources, etc. As in-situ monitoring data are obtained, computational experiments and parallel executions could be conducted to achieve the ultimate goal of port water quality management.

As an important mode of transportation for the global trade, waterborne transportation has become a priority option for import and export trade due to its large load capacity and relatively low cost. Meanwhile, shipping safety has been highly valued. By collecting technological water traffic accident data from the EM-DAT database, the spatiotemporal distribution and evolution characteristics were investigated in Asia since 2000. The methods of gravity center and standard deviation ellipse analysis were utilized to determine the spatial and data-related characteristics of water traffic accidents. Temporally, the results indicated that accidents occurred most frequently during the seasons of autumn and winter, leading to a significant number of casualties. Spatially, both South-eastern Asia and Southern Asia emerged as regions with a high frequency of water traffic accidents, particularly along the borders of Singapore, Malaysia, Indonesia, and the Bay of Bengal region. In addition, the Daniel trend test and R/S analysis were conducted to demonstrate the evolution trend of accidents across various regions and seasons. The present study provides guidance for improving marine shipping safety, emergency resource management, and relevant policy formulation.

Marine microorganisms often produce exopolysaccharides with novel structures and diverse biological activities due to their specific marine environment. The novel active exopolysaccharides from marine microorganisms have become an important research area in new drug discovery, and show enormous development prospects. In the present study, a homogeneous exopolysaccharide from the fermented broth of the mangrove endophytic fungus Penicillium janthinellum N29, designated as PJ1-1, was obtained. The results of chemical and spectroscopic analyses showed that PJ1-1 was a novel galactomannan with a molecular weight of about 10.24 kDa. The backbone of PJ1-1 was composed of →2)-α-d-Manp-(1→, →4)-α-d-Manp-(1→, →3)-β-d-Galf-(1→ and →2)-β-d-Galf-(1→ units with partial glycosylation at C-3 of →2)-β-d-Galf-(1→ unit. PJ1-1 had a strong hypoglycemic activity in vitro, evaluated using the assay of α-glucosidase inhibition. The anti-diabetic effect of PJ1-1 in vivo was further investigated using mice with type 2 diabetes mellitus induced by a high-fat diet and streptozotocin. The results indicated that PJ1-1 markedly reduced blood glucose level and improved glucose tolerance. Notably, PJ1-1 increased insulin sensitivity and ameliorated insulin resistance. Moreover, PJ1-1 significantly decreased the levels of serum total cholesterol, triglyceride and low-density lipoprotein cholesterol, enhanced the level of serum high-density lipoprotein cholesterol and alleviated dyslipidemia. These results revealed that PJ1-1 could be a potential source of anti-diabetic agent.

Introduction: Brain is a major site of metastasis for lung cancer, and effective therapy for developed brain metastasis (BM) is limited. Prophylactic cranial irradiation (PCI) has been shown to reduce BM rate and improve survival in small cell lung cancer, but this result was not replicated in unselected non-small cell lung cancer (NSCLC) and had the risk of inducing neurocognitive dysfunctions. We aimed to develop a radiomics BM prediction model for BM risk stratification in NSCLC patients. Methods: 256 NSCLC patients with no BM at baseline brain magnetic resonance imaging (MRI) were selected; 128 patients developed BM within three years after diagnosis and 128 remained BM-free. For radiomics analysis, both the BM and non-BM groups were randomly distributed into training and testing datasets at an 70%:30% ratio. Both brain MRI (representing the soil) and chest computed tomography (CT, representing the seed) radiomic features were extracted to develop the BM prediction models. We first developed the radiomic models using the training dataset (89 non-BM and 90 BM cases) and subsequently validated the models in the testing dataset (39 non-BM and 38 BM cases). A radiomics BM score (RadBM score) was generated, and BM-free survival were compared between RadBM score-high and RadBM score-low groups. Results: The radiomics model developed from baseline brain MRI features alone can predict BM development in NSCLC patients. A fusion model integrating brain MRI features with primary tumor CT features (seed-and-soil model) provided synergetic effect and was more efficient in predicting BM (areas under the receiver operating characteristic curve 0.84 (95% confidence interval: 0.80–0.89) and 0.80 (95% confidence interval: 0.71–0.88) in the training and testing datasets, respectively). BM-free survival was significantly shorter in the RadBM score-high group versus the RadBM score-low group (Log-rank, p < 0.001). Hazard ratios for BM were 1.056 (95% confidence interval: 1.044–1.068) per 0.01 increment in RadBM score. Cumulative BM rates at three years were 75.8% and 24.2% for the RadBM score-high and RadBM score-low groups, respectively. Only 1.2% (7/565) of the BM lesions were located within the hippocampal avoidance region. Conclusion: The results demonstrated that intrinsic features of a non-metastatic brain exert a significant impact on BM development, which is first-in-class in metastasis prediction studies. A radiomics BM prediction model utilizing both primary tumor and pre-metastatic brain features might provide a useful tool for individualized PCI administration in NSCLC patients more prone to develop BM.

CP symmetry violation is one of the fundamental requirements for having more matter than antimatter in our universe. However, the observed CP violation is not enough to explain the matter dominance. Searching for the new CP violation in the hyperon sector is therefore attracting people’s interests. In this article, we review the theoretical description of the spin one-half hyperon nonleptonic decay. In order to test the CP conservation, the asymmetry decay parameters might be compared between hyperon and antihyperon. The fixed-target experiments achieved the relevant measurements of the hyperon parameters but in the absence of the antihyperon results. The BESIII experiment with e+e− collision provided an entanglement environment to measure Σ+ and Σ¯− simultaneously. The important role of experimental inputs in CP violation evaluation is discussed in this paper, underlying the great potential of the future collider, which may allow us to reach the precision of the Standard Model predictions.

Purpose: To explore the clinical characteristics, management, and survival outcomes of advanced NSCLC patients treated with PD-1/PD-L1 inhibitors who presented with an atypical response (AR). Methods: A total of 926 PD-1/PD-L1-inhibitor-treated patients with metastatic NSCLC from three academic centers were retrospectively reviewed. All measurable lesions were evaluated by RECIST version 1.1. Results: Fifty-six (6.1%) patients developed AR. The median time to the occurrence of AR was 2.0 months. Patients with no fewer than 3 metastatic organs at baseline were more prone to develop AR in advanced NSCLC (p = 0.038). The common sites of progressive lesions were lymph nodes (33.8%) and lungs (29.7%). The majority (78.2%) of patients with AR had only 1–2 progressive tumor lesions, and most (89.1%) of the progressive lesions developed from originally existing tumor sites. There was no significance in terms of survival between patients with AR and those with typical response (TR). Local therapy was an independent predictor for PFS of patients with AR (p = 0.025). Conclusions: AR was not an uncommon event in patients with metastatic NSCLC treated with PD-1/PD-L1 inhibitors, and it had a comparable prognosis to those with TR. Proper local therapy targeting progressive lesions without discontinuing original PD-1/PD-L1 inhibitors may improve patient survival.

In recent years, global automotive industries are going through a significant revolution from traditional internal combustion engine vehicles (ICEVs) to electric vehicles (EVs) for CO2 emission reduction. Very similarly, the aviation industry is developing towards more electric aircraft (MEA) in response to the reduction in global CO2 emission. To promote this technology revolution and performance advancement, plenty of electronic devices with high heat flux are implemented on board automobiles and aircraft. To cope with the thermal challenges of electronics, in addition to developing wide bandgap (WBG) semiconductors with satisfactory electric and thermal performance, providing proper thermal management solutions may be a much more cost-effective way at present. This paper provides an overview of the thermal management technologies for electronics used in automobiles and aircraft. Meanwhile, the active methods include forced air cooling, indirect contact cold plate cooling, direct contact baseplate cooling, jet impingement, spray cooling, and so on. The passive methods include the use of various heat pipes and PCMs. The features, thermal performance, and development tendency of these active and passive thermal management technologies are reviewed in detail. Moreover, the environmental influences introduced by vibrations, shock, acceleration, and so on, on the thermal performance and reliability of the TMS are specially emphasized and discussed in detail, which are usually neglected in normal operating conditions. Eventually, the possible future directions are discussed, aiming to serve as a reference guide for engineers and promote the advancement of the next-generation electronics TMS in automobile and aircraft applications.

Algae accumulate large amounts of polysaccharides in their cell walls or intercellular regions. Polysaccharides from algae possess high potential as promising candidates for marine drug development. In this study, a sulfated polysaccharide, UCP, from the green alga Ulva conglobata Kjellman was obtained by water extraction, anion-exchange, and size-exclusion chromatography purification, and its structure was characterized by a combination of chemical and spectroscopic methods. UCP mainly consisted of →4)-α/β-l-Rhap-(1→, →4)-β-d-Xylp-(1→ and →4)-β-d-GlcAp-(1→ residues. Sulfate ester groups were substituted mainly at C-3 of →4)-l-Rhap-(1→ and C-2 of →4)-β-d-Xylp-(1→. Partial glycosylation was at C-2 of →4)-α-l-Rhap-(1→ residues. UCP possessed a potent immunomodulatory effect in vitro, evaluated by the assays of lymphocyte proliferation and macrophage phagocytosis. The immunomodulatory activity of UCP in vivo was further investigated using immunosuppressive mice induced by cyclophosphamide. The results showed that UCP markedly increased the spleen and thymus indexes and ameliorated the cyclophosphamide-induced damage to the spleen and thymus. UCP could increase the levels of white blood cells, lymphocytes, and platelets, and improve the hematopoietic inhibition caused by cyclophosphamide. Moreover, UCP significantly promoted the secretions of the immunoglobulin (Ig)G, IgE, and IgM. The data demonstrated that UCP is a novel sulfated polysaccharide and may be a promising immunomodulatory agent.

Performing clustering analysis on a large amount of historical trajectory data can obtain information such as frequent flight patterns of aircraft and air traffic flow distribution, which can provide a reference for the revision of standard flight procedures and the optimization of the division of airspace sectors. At present, most trajectory clustering uses a single clustering algorithm. When other processing remains unchanged, it is difficult to improve the clustering effect by using a single clustering method. Therefore, this paper proposes a trajectory clustering ensemble method based on a similarity matrix. Firstly, a stacked autoencoder is used to learn a small number of features that are sufficiently representative of the trajectory and used as the input to the subsequent clustering algorithm. Secondly, each basis cluster is used to cluster the data set, and then a consistent similarity matrix is obtained by using the clustering results of each basis cluster. On this basis, using the deformation of the matrix as the distance matrix between trajectories, the agglomerative hierarchical clustering algorithm is used to ensemble the results of each basis cluster. Taking the Nanjing Lukou Airport terminal area as an example, the experimental results show that integrating multiple basis clusters eliminates the inherent randomness of a single clustering algorithm, and the trajectory clustering results are more robust.

Aircraft four dimensional (4D, including longitude, latitude, altitude and time) trajectory prediction is a key technology for existing automation systems and the basis for future trajectory-based operations. This paper firstly summarizes the background and significance of the trajectory prediction problems and then introduces the definition and basic process of trajectory prediction, including four modules: preparation, prediction, update, and output. In addition, the trajectory prediction methods are summarized into three types: the state estimation model, the Kinetic model, and the machine learning model, and in-depth analysis of various models is carried out. Further, the relevant databases required for the study are introduced, including the aircraft performance database, aircraft monitoring database, and meteorological database. Finally, challenges and future development directions of the current trajectory prediction problem are summarized.

The aircraft trajectory clustering analysis in the terminal airspace is conducive to determining the representative route structure of the arrival and departure trajectory and extracting their typical patterns, which is important for air traffic management such as airspace structure optimization, trajectory planning, and trajectory prediction. However, the current clustering methods perform poorly due to the large flight traffic, high density, and complex airspace structure in the terminal airspace. In recent years, the continuous development of Deep Learning has demonstrated its powerful ability to extract internal potential features of large dataset. Therefore, this paper mainly tries a deep trajectory clustering method based on deep autoencoder (DAE). To this end, this paper proposes a trajectory clustering method based on deep autoencoder (DAE) and Gaussian mixture model (GMM) to mine the prevailing traffic flow patterns in the terminal airspace. The DAE is trained to extract feature representations from historical high-dimensional trajectory data. Subsequently, the output of DAE is input into GMM for clustering. This paper takes the terminal airspace of Guangzhou Baiyun International Airport in China as a case to verify the proposed method. Through the direct visualization and dimensionality reduction visualization of the clustering results, it is found that the traffic flow patterns identified by the clustering method in this paper are intuitive and separable.

Aircraft trajectory prediction is the basis of approach and departure sequencing, conflict detection and resolution and other air traffic management technologies. Accurate trajectory prediction can help increase the airspace capacity and ensure the safe and orderly operation of aircraft. Current research focuses on single aircraft trajectory prediction without considering the interaction between aircraft. Therefore, this paper proposes a model based on the Social Long Short-Term Memory (S-LSTM) network to realize the multi-aircraft trajectory collaborative prediction. This model establishes an LSTM network for each aircraft and a pooling layer to integrate the hidden states of the associated aircraft, which can effectively capture the interaction between them. This paper takes the aircraft trajectories in the Northern California terminal area as the experimental data. The results show that, compared with the mainstream trajectory prediction models, the S-LSTM model in this paper has smaller prediction errors, which proves the superiority of the model’s performance. Additionally, another comparative experiment is conducted on airspace scenes with aircraft interactions, and it is found that S-LSTM has a better prediction effect than LSTM, which proves the effectiveness of the former considering aircraft interaction.

Rosmarinic acid (RA), a polyphenolic phytochemical, is a natural prolyl oligopeptidase inhibitor. In the present study, we found that RA exerted potent anti-inflammatory effects in in vivo models of acute lung injury (ALI) induced by lipopolysaccharide (LPS). Mice were pretreated with RA one hour before challenge with a dose of 0.5 mg/kg LPS. Twenty-four hours after LPS was given, bronchoalveolar lavage fluid (BALF) was obtained to measure pro-inflammatory mediator and total cell counts. RA significantly decreased the production of LPS-induced TNF-a, IL-6, and IL-1β compare with the LPS group. When pretreated with RA (5, 10, or 20 mg/kg) the lung wet-to-dry weight (W/D) ratio of the lung tissue and the number of total cells, neutrophils and macrophages in the BALF were decreased significantly. Furthermore, RA may enhance oxidase dimutase (SOD) activity during the inflammatory response to LPS-induced ALI. And we further demonstrated that RA exerts anti-inflammation effect in vivo models of ALI through suppresses ERK/MAPK signaling in a dose dependent manner. These studies have important implications for RA administration as a potential treatment for ALI.

Bio-oil from fast pyrolysis of biomass is a low-grade liquid fuel, which can be upgraded through the emulsification with diesel oil. In this study, we prepared two rice husk bio-oil samples with different solid char contents and three bio-oil/diesel-oil emulsion samples with the bio-oil content of 10 wt%, 30 wt% and 50 wt%, respectively. The lubrication properties of these oil samples were evaluated by a four-ball tester. The morphologies of the worn ball surface were observed by scanning electron microscope (SEM). The chemical states of the elements on the worn surface and non-worn surface were analyzed by X-ray photoelectron spectroscope (XPS). The results showed that the bio-oil possessed better extreme-pressure, anti-wear and friction-reducing properties than the commercial diesel oil (number zero). The solid char particles in the bio-oil could improve its lubrication performance. Moreover, the lubrication ability of the emulsions would be enhanced with the increasing of the bio-oil content in the emulsions.

Ginkgolide B is an anti-inflammatory extract of Ginkgo biloba and has been used therapeutically. It is a known inhibitor of platelet activating factor (PAF), which is important in the pathogenesis of asthma. Here, a non-infectious mouse model of asthma is used to evaluate the anti-inflammatory capacity of ginkgolide B (GKB) and characterize the interaction of GKB with the mitogen activated protein kinase (MAPK) pathway. BALB/c mice that were sensitized and challenged to ovalbumin (OVA) were treated with GKB (40 mg/kg) one hour before they were challenged with OVA. Our study demonstrated that GKB may effectively inhibit the increase of T-helper 2 cytokines, such as interleukin (IL)-5 and IL-13 in bronchoalveolar lavage fluid (BALF). Furthermore, the eosinophil count in BALF significantly decreased after treatment of GKB when compared with the OVA-challenged group. Histological studies demonstrated that GKB substantially inhibited OVA-induced eosinophilia in lung tissue and mucus hyper-secretion by goblet cells in the airway. These results suggest that ginkgolide B may be useful for the treatment of asthma and its efficacy is related to suppression of extracellular regulating kinase/MAPK pathway.

Rosmarinic acid (RA) has numerous pharmacologic effects, including anti-oxidant, anti-inflammatory, and analgesic effects. This study aimed to evaluate the preventive activity of RA in a murine model of asthma and to investigate its possible molecular mechanisms. Female BALB/c mice sensitized and challenged with ovalbumin (Ova) were pretreated with RA (5, 10 or 20 mg/kg) at 1 h before Ova challenge. The results demonstrated that RA markedly inhibited increases in inflammatory cells and Th2 cytokines in the bronchoalveolar lavage fluid (BALF), significantly reduced the total IgE and Ova-specific IgE concentrations, and greatly ameliorated airway hyperresponsiveness (AHR) compared with the control Ova-induced mice. Histological analyses showed that RA substantially decreased the number of inflammatory cells and mucus hypersecretion in the airway. In addition, our results suggested that the protective effects of RA might be mediated by the suppression of ERK, JNK and p38 phosphorylation and activation of nuclear factor-κB (NF-κB). Furthermore, RA pretreatment resulted in a noticeable reduction in AMCase, CCL11, CCR3, Ym2 and E-selectin mRNA expression in lung tissues. These findings suggest that RA may effectively delay the progression of airway inflammation.

Two new asteltoxins named asteltoxin E (2) and F (3), and a new chromone (4), together with four known compounds were isolated from a marine sponge–derived fungus, Aspergillus sp. SCSIO XWS02F40. The structures of the compounds (1–7) were determined by the extensive 1D- and 2D-NMR spectra, and HRESIMS spectrometry. All the compounds were tested for their antiviral (H1N1 and H3N2) activity. Compounds 2 and 3 showed significant activity against H3N2 with the prominent IC50 values of 6.2 ± 0.08 and 8.9 ± 0.3 μM, respectively. In addition, compound 2 also exhibited inhibitory activity against H1N1 with an IC50 value of 3.5 ± 1.3 μM.