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Abstract Camellia osmantha is a new species of the genus Camellia and is an economically important ornamental plant. Its activity and ingredients are less studied than other Camellia plants. This study investigated the antithrombotic effect and chemical components of C. osmantha fruit cores using platelet aggregation assays and coagulation function tests. The cores of C. osmantha fruits were extracted with ethanol to obtain a crude extract. The extract was dissolved in water and further eluted with different concentrations of methanol on an MCI resin column to obtain three fractions. These samples were used for antithrombotic activity tests and phytochemical analysis. The results showed that the extract and its fractions of C. osmantha have strong antithrombotic activity, significantly reducing the platelet aggregation rate and prolonging the thrombin time (TT). The total saponins, flavonoids, and polyphenols in the active fractions may be responsible for the antithrombotic activity. The chemical constituents were analyzed by ultra‐performance liquid chromatography‐quadrupole time‐of‐flight mass spectrometry (UPLC‐QTOF/MS). Twenty‐three compounds were identified rapidly and accurately. Among them, ellagic acid, naringenin, and quercetin 3‐O‐glucuronide may be important antithrombotic constituents. Furthermore, interactions between these compounds and the P2Y1 receptor were investigated via molecular modeling, because the P2Y1 receptor is a key drug target of antiplatelet aggregative activity. The molecular docking results suggested that these compounds could combine tightly with the P2Y1R protein. Our results showed that C. osmantha fruit cores are rich in polyphenols, flavonoids, and saponins, which can be developed into a promising antithrombotic functional beverage for the prevention and treatment of cardiovascular and cerebrovascular diseases.

Abstract Aim The aim of this study is to explore the use of the Questionnaire Appraisal System with a combination of qualitative and quantitative methods in cognitive interviewing for cross‐cultural adaptation. Design This is a descriptive methodological study. Methods Using the Mandarin version of the Post‐Stroke Checklist as an example, cognitive interviews were conducted with 27 stroke survivors in Guangzhou between November 2020 and February 2021. The Questionnaire Appraisal System was applied as a codebook in focus group discussions to perform quantitative data collections and quantitative content analysis. Results Thirty‐eight problems were proposed in focus group discussions and identified all but four of the 30 questions that emerged in the cognitive interviews. A new item was added to the Questionnaire Appraisal System for better categorization. Four categories and six subcategories of problems in the checklist were revealed.

Glial heterotopia is a rare benign congenital tumor, which refers to the growth of nerve tissue outside the central nervous system. We report two cases of glial heterotopia at different sites, and describe their clinical symptoms, imaging findings, and treatment.

Background: The expression of noncoding RNAs (ncRNAs) is closely related to breast cancer progression. However, there is no systemic analysis of ncRNAs in breast cancer diagnosis of the blood circulatory system. Herein, we aimed to collect all the evidence to test the potential role of ncRNAs as novel biomarkers in human breast cancers. Methods: We used a comprehensive search strategy to search relevant literature in the Web of Science, PubMed, and Embase databases from January 2012 to April 2021. The correlation of ncRNAs expression in serum, plasma, or blood circulatory system and the diagnostic accuracy of breast cancer (BC) markers were analyzed. We assessed the methodological quality of each study using the Quality Assessment of Diagnostic Accuracy Studies-2. Statistical analysis was performed using the STATA (version 12.0), Meta-Disc (version 1.4), and Review Manager (version 5.3) software. Results: This study analyzes the ncRNAs expression data of BC patients and healthy specimens? blood from 2,120 patients in 29 publications (40 studies). The pooled sensitivity, specificity, and area under the curve (AUC) values were 0.81, 0.83, and 0.89, respectively. Subgroup analyses showed that the expression of ncRNA (micro RNA [miRNA], circular RNAs [circRNA], and long noncoding RNAs [lncRNA]) in the blood circulatory system (blood, plasma, and serum) of BC was more prone to be detected in miRNA group and subtype of BC (human epidermal growth factor receptor 2 [HER2]) group, with a high value of the sensitivity and AUC. Conclusions: The blood circulatory system (blood, plasma, and serum) of miRNAs, circRNAs, and lncRNAs detection may be useful for diagnosing breast cancer, particularly in the miRNA group and HER2 of breast cancer. Further studies are needed to identify the value of these ncRNAs as novel markers in clinical.

Ambient concentrations of volatile organic compounds (VOCs) vary with emission rates, meteorology, and chemistry. Conventional positive matrix factorization (PMF) loses information because of dilution variations and chemical losses. Multiply improved PMF incorporates the ventilation coefficient, and total solar radiation or oxidants to reduce the effects of dispersion and chemical loss. These methods were applied to hourly speciated VOC data from November 2019 to March 2020 including during the COVID-19 shutdown. Various comparisons were made to assess the influences of these fluctuation drivers by time of day. Dispersion normalized PMF (DN-PMF) reduced the dispersion variations. Dispersion-radiation normalized PMF (DRN-PMF) reduced the impact of chemical loss, especially at night, which was better than Dispersion-Ox normalized PMF (DON-PMF). The conditional bivariate probability function (CBPF) plots of DRN-PMF results were consist with actual source locations. The DN-PMF, DRN-PMF, and DON-PMF results were consistent between 10:00 and 15:00, suggesting dispersion was significantly more influential than photochemical reactions during these times. The DRN-PMF results indicated that the highest VOC contributors during the COVID-19 shutdown were liquefied petroleum gas (LPG) (28.8%), natural gas (25.2%), and pulverized coal boilers emissions (19.6%). Except for petrochemical-related enterprises and LPG, the contribution concentrations of all other sources decreased substantially during the COVID-19 shutdown, by 94.7%, 90.6%, and 86.8% for vehicle emissions, gasoline evaporation, and the mixed source of diesel evaporation and solvent use, respectively. Controlling the use of motor vehicles and related volatilization of diesel fuel and gasoline can be effective in controlling VOCs in the future.

Abstract Online sex has become a fast-growing business in both developing and developed network, with advertisements of (not necessarily unique) individuals numbering in the hundreds of millions across different Web portals. One such major hub of sex advertisement activity, before it was shut down by US federal agencies, was backpage.com. The backpage.com website was a classifieds-advertising portal that had become the largest marketplace for buying and selling sex by the time that federal law enforcement agencies seized it in April 2018. Since then, investigations have been actively underway. However, the data (which has recently been made available to us for research on UK Backpage) also offers valuable insights into the nature of the online sex business, including complex properties that can be best studied using network science. One of the challenges, however, is a rigorous modeling of the data as a network, since the primary data are web advertisements and metadata (backend database) on accounts that posted that ad. In this article, we conduct an empirical study of an important sample of the online sex marketplace using UK backpage, including presenting a methodology for constructing simple ‘activity networks’ that define some notion of real-world collaboration or connection between two entities (in our case, at the level of ad-posting accounts) and then studying the properties of these networks. We gather a set of insights into a domain that has not been studied at scale, let alone a national level, but that is continuing to be a growing social problem for many countries.

Moving target indication (MTI) based on space–time adaptive processing (STAP) has been widely used in airborne radar due to its ability for clutter suppression performance. However, the existing MTI methods suffer from the problems of insufficient training samples and low detection probability in a non-homogeneous clutter environment. To address these issues, this paper proposes a novel deep learning framework to improve target indication capability. First, combined with the problems of target indication caused by the non-homogeneous clutter, the clutter-plus-target training dataset was modeled by simulation, where various non-ideal factors, such as aircraft crabbing, array errors and internal clutter motion (ICM), were considered. The dataset considers various realistic situations, making the proposed method more robust. Then, a five-layer two-dimensional convolutional neural network (D2CNN) was designed and applied to learn the clutter and target characteristics distribution. The proposed D2CNN can predict the target with a high resolution to implement an end-to-end moving target indication (ETE-MTI) with a higher detection accuracy. In this D2CNN, the input was obtained by the clutter-plus-target angle-Doppler spectrum with a low-resolution estimated only by a few samples. The label was given by the target angle-Doppler spectrum with a high-resolution obtained by the target’s exact angle and Doppler. Thirdly, the proposed method used a few samples to improve the target indication and detection probability, which solved the problem of insufficient samples in the non-homogeneous clutter environments. To elaborate, the proposed method directly implements ETE-MTI without the support of the conventional STAP algorithm to suppress the clutter. The results verify the validity and the robustness of the proposed ETE-MTI with a few samples in the non-homogeneous and low signal-to-clutter ratio (SCR) environments.

The purpose of this study was to investigate the effects of dietary Selenohomolanthionine (SeHLan) on antioxidant status and immune response in canine parvovirus (CPV) vaccinated puppies. In this study, 30 weaned puppies were randomly divided into six groups: control group (–Se/–Vacc), immunization group (–Se/+Vacc), supplementation of sodium selenite group (SS/+Vacc, 0.35 mg/kg DM), low-dose SeHLan group (SeHLan-L/+Vacc, 0.35 mg/kg DM), mid-dose SeHLan group (SeHLan-M/+Vacc, 1.0 mg/kg DM), and high-dose SeHLan group (SeHLan-H/+Vacc, 2.0 mg/kg DM). The puppies were fed for 42 days and vaccinated with Vanguard Plus 5 on day 0 and day 21. Blood samples were collected on 7, 14, 21, 28, 35, 42 days post-immunization (PI) for determination of antioxidant indicators, lymphocyte proliferation index, serum cytokine concentration (IL-2, IL-4), canine polymorphonuclear neutrophils (PMN) phagocytic function, and the level of CPV antibody titers. The results showed that SeHLan supplementation raised the serum Se concentration and glutathione peroxidase (GSH-Px) activity in a dose-dependent manner (P < 0.05). It also increased the activity of serum superoxide dismutase (SOD) and decreased serum malondialdehyde (MDA) content, especially in SeHLan-M/+Vacc group (1.0 mg/kg DM) (P < 0.01). SeHLan supplementation significantly increased lymphocyte proliferation, IL-2, and IL-4 levels in canine serum, and enhanced phagocytosis of PMN in vaccinated puppies (P < 0.05). Moreover, SeHLan supplementation shortened the CPV antibody production time and increased the CPV antibody titers (P < 0.05). Of note, the beneficial effects of SeHLan were superior to those of SS. In conclusion, dietary SeHLan supplementation improved antioxidant activity, increased CPV antibody titers, and enhanced immune function in puppies after weaning. An appropriate dosage of SeHLan (1~2 mg/kg DM) may confer nutritional benefits in puppies.

The Twain-Hu basin (THB), covering the lower plain of Hubei and Hunan provinces in Central China, has experienced severe air pollution in recent years. However, the terrain effects of such sub-basin on air quality over the THB have been incomprehensibly understood. A heavy PM2.5 pollution event occurred over the THB during 4–10 January 2019. By using the observations and WRF-Chem simulations, we investigated the underlying mechanisms of sub-basin effects on the air pollution with several sensitivity experiments. Observationally, air pollution in the western THB urban area with an average PM2.5 concentration of 189.8 μg m−3, which was more serious than the eastern urban area with the average PM2.5 concentration of 106.3 μg m−3, reflecting a different influence of topography on air pollution over the THB. Simulation results revealed that the terrain effect can contribute 12.0% to increasing the PM2.5 concentrations in the western THB, but slightly mitigate the pollution extent in the eastern THB with the contribution of −4.6% to PM2.5 during the heavy pollution episode. In particular, the sub-basin terrain was conducive to the accumulation of PM2.5 by regional transport with the contribution of 39.1 %, and contrarily lowered its local pollution by −57.0% via the enhanced atmospheric boundary layer height and ventilation coefficients. Given a heavy air pollution episode occurring over the THB, such inverse contribution of terrain effects reflected a unique importance of sub-basin topography in regional transport of air pollutants for air pollution in central China.

Due to instant availability of data on social media platforms like Twitter, and advances in machine learning and data management technology, real-time crisis informatics has emerged as a prolific research area in the last decade. Although several benchmarks are now available, especially on portals like CrisisLex, an important, practical problem that has not been addressed thus far is the rapid acquisition, benchmarking and visual exploration of data from free, publicly available streams like the Twitter API in the immediate aftermath of a crisis. In this paper, we present such a pipeline for facilitating immediate post-crisis data collection, curation and relevance filtering from the Twitter API. The pipeline is minimally supervised, alleviating the need for feature engineering by including a judicious mix of data preprocessing and fast text embeddings, along with an active learning framework. We illustrate the utility of the pipeline by describing a recent case study wherein it was used to collect and analyze millions of tweets in the immediate aftermath of the Las Vegas shootings in 2017.

A series of double metal cyanide (DMC) complex catalysts were prepared in two different methods by using ß-cyclodextrin, PEG-1000 and Tween-60 as an additional complex ligands respectively. It was showed that a mixture of crystalline and amorphous DMC was synthesized by using traditional method in which the additional complex ligand was added after the precipitation of DMC. Amorphous and dispersed DMC with higher activity could be obtained when the additional complex ligand was added in the reactant solution before reaction. The effect of additional complex ligand and preparation method on the crystalline state and catalytic property of DMC were also investigated.

Abstract Current-driven dynamics of topological spin textures, such as skyrmions and antiskyrmions, have garnered considerable attention in condensed matter physics and spintronics. As compared with skyrmions, the current-driven dynamics of their antiparticles – antiskyrmions − remain less explored due to the increased complexity of antiskyrmions. Here, we design and employ fabricated microdevices of a prototypical antiskyrmion host, (Fe0.63Ni0.3Pd0.07)3P, to allow in situ current application with Lorentz transmission electron microscopy observations. The experimental results and related micromagnetic simulations demonstrate current-driven antiskyrmion dynamics confined within stripe domains. Under nanosecond-long current pulses, antiskyrmions exhibit directional motion along the stripe regardless of the current direction, while the antiskyrmion velocity is linearly proportional to the current density. Significantly, the antiskyrmion mobility could be enhanced when the current flow is perpendicular to the stripe direction. Our findings provide novel and reliable insights on dynamical antiskyrmions and their potential implications on spintronics.

Abstract In photonic crystal systems, topologically protected edge states and corner states can be achieved by breaking spatial inversion symmetry, which is expected to be applied to topologically protected lasers, optical communication and integrated photonics. However, designing ultrabroadband topological photonic crystals is still a challenge. In this work, we propose a valley photonic crystal composed of dendritic structures, which can realize valley transmission with a relative bandwidth up to 59.65%. Compared with the previously reported two-dimensional broadband photonic crystals with 32.02% bandwidth, the relative bandwidth of the proposed valley transmission is increased by almost 100%. Theoretical analysis, numerical simulation and experimental measurement all confirm flexible manipulation of electromagnetic wave propagation paths. Ultrabroadband topological waveguides with the zigzag and armchair interface are demonstrated, which can achieve experimentally 58.71% and 36.78% relative bandwidth, respectively. In addition, several topological channel intersections are designed. Finally, two types of corner states with valley switchability and selectivity are demonstrated.

Traumatic fractures and stress fractures are common orthopedic diseases, and there is great potential in researching bone turnover, repair, and promotion of fracture healing. Basic medical experiments often use animal models of long bone fractures in limbs to study the mechanisms of various interventions on fracture healing. Fracture healing is a complex process influenced by multiple factors and involves multiple molecules and pathways. Therefore, to explore the mechanisms more deeply, accelerate the translation of results, and improve the clinical efficacy, it is particularly important to choose the appropriate animal fracture modeling methods in experimental research. Based on this, this paper conducts a literature review of animal species and modeling methods commonly used for long bone fracture models in experimental research. It summarizes five methods: bone defect method, physical impact method, mechanical bending method, open osteotomy method, and drilling method. A side-by-side comparison of their advantages, disadvantages, and scope of application is made, aiming to provide suitable fracture models for studying the mechanisms of fracture healing interventions.

This article reviewed the research progress of micro-chemical technology, especially micro-reaction technology, in the field of water treatment chemical synthesis. The focus was on the synthesis of polycarboxylic acid scale inhibitors and dispersants such as polyacrylic acid and polymaleic anhydride, benzotriazole and organophosphine corrosion inhibitors, organic sulfur and peroxide fungicides, as well as water clearing agents, defoamers and other products by using this technology. As a new green synthesis technology, micro-chemical technology was still mainly explored in the synthesis of water treatment chemicals. In terms of cost, it still lacked advantages, and it was still far from practical industrial application. However, its advantages of being green, safe, and efficient were in line with the trends of carbon reduction, environmental protection, safety, production integration and automation. With the advancement of micro-reactor technology, further reduction of costs, and the needs of social development, micro-chemical technology would have great application potential in the synthesis of water treatment chemicals and might rapidly promote its technological progress.

Aiming at the high cost and poor effect of catalytic materials for cathodic oxygen reduction reaction(ORR) in electrochemical deoxygenation, NiCoFe ternary alloy was efficiently prepared by electrodeposition under different current densities, and the morphology and structure of the alloy material were characterized and analyzed, which showed that NiCoFe alloy had a fluffy cauliflower-like surface morphology and alloy crystalline structure. The ORR performance of the NiCoFe alloy was tested in a three-electrode system at different current densities. When the electrodeposition current density was 1.0 A/cm2, the alloy was obtained with a half-wave potential of 0.782 V, an onset potential of 0.936 V, and a limiting current density of 2.9 mA/cm2. The rotating ring disk electrode(RRDE) test showed that the number of transferred electrons of the alloy was 3.52, which was close to that of the four-electron transfer of commercial Pt/C catalysts. An electrochemical deoxidation test equipment was designed to investigate the removal effect of NiCoFe alloy as a cathode electrode on dissolved oxygen in simulated industrial water. The results showed that the deoxidation rate of NiCoFe cathode was over 99%, making it an excellent ORR electrocatalyst material.