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Abstract We retrospectively investigated the relationship between cardiopulmonary exercise testing (CPET) parameters and coronary microvascular dysfunction (CMD) using a novel angiography-based index of microcirculatory resistance (AccuIMR) in patients with ST-elevation myocardial infarction (STEMI) who underwent primary percutaneous coronary intervention (PCI) with complete revascularization. In 418 patients, the culprit vessel AccuIMR was calculated after successful primary PCI. CPET was conducted 44.04 ± 19.28 days after primary PCI. Overall, 157 patients (37.6%) showed elevated AccuIMR (> 40 U) in the culprit vessels. The LVEF was significantly lower in the CMD group than in the Non-CMD group. The CMD group showed worse results in VO2peak, peak O2-pulse, and VE/VCO2 slope than the Non-CMD group. Spearman correlation analysis suggested that VO2peak (r = -0.354), peak O2-pulse (r = -0.385) and VE/VCO2 slope (r = 0.294) had significant linear correlations with AccuIMR (P

Species of the spider genus Belisana Thorell, 1898 exhibit high diversity in Guizhou, southwestern China. Previously, only eight species of Belisana were recorded in Guizhou. In this study, nine new species are described from karst caves: Belisana bijie Wang, S. Li & Yao, sp. nov., B. liupanshui Wang, S. Li & Yao, sp. nov., B. majiang Wang, S. Li & Yao, sp. nov., B. nayong Wang, S. Li & Yao, sp. nov., B. qixingguan Wang, S. Li & Yao, sp. nov., B. xiuwen Wang, S. Li & Yao, sp. nov., B. yongcong Wang, S. Li & Yao, sp. nov., B. zhouxi Wang, S. Li & Yao, sp. nov., and B. zunyi Wang, S. Li & Yao, sp. nov. Belisana zhangi Tong & Li, 2007 is reported from Guizhou for the first time. In addition, a list of all species of Belisana from Guizhou is provided.

Abstract Photo-thermal-coupling ammonia decomposition presents a promising strategy for utilizing the full-spectrum to address the H2 storage and transportation issues. Herein, we exhibit a photo-thermal-catalytic architecture by assembling gallium nitride nanowires-supported ruthenium nanoparticles on a silicon for extracting hydrogen from ammonia aqueous solution in a batch reactor with only sunlight input. The photoexcited charge carriers make a predomination contribution on H2 activity with the assistance of the photothermal effect. Upon concentrated light illumination, the architecture significantly reduces the activation energy barrier from 1.08 to 0.22 eV. As a result, a high turnover number of 3,400,750 is reported during 400 h of continuous light illumination, and the H2 activity per hour is nearly 1000 times higher than that under the pure thermo-catalytic conditions. The reaction mechanism is extensively studied by coordinating experiments, spectroscopic characterizations, and density functional theory calculation. Outdoor tests validate the viability of such a multifunctional architecture for ammonia decomposition toward H2 under natural sunlight.

Abstract Hydrogen (H2) has emerged as a clean and versatile energy carrier to power a carbon‐neutral economy for the post‐fossil era. Hydrogen generation from low‐cost and renewable biomass by virtually inexhaustible solar energy presents an innovative strategy to process organic solid waste, combat the energy crisis, and achieve carbon neutrality. Herein, the progress and breakthroughs in solar‐powered H2 production from biomass are reviewed. The basic principles of solar‐driven H2 generation from biomass are first introduced for a better understanding of the reaction mechanism. Next, the merits and shortcomings of various semiconductors and cocatalysts are summarized, and the strategies for addressing the related issues are also elaborated. Then, various bio‐based feedstocks for solar‐driven H2 production are reviewed with an emphasis on the effect of photocatalysts and catalytic systems on performance. Of note, the concurrent generation of value‐added chemicals from biomass reforming is emphasized as well. Meanwhile, the emerging photo‐thermal coupling strategy that shows a grand prospect for maximally utilizing the entire solar energy spectrum is also discussed. Further, the direct utilization of hydrogen from biomass as a green reductant for producing value‐added chemicals via organic reactions is also highlighted. Finally, the challenges and perspectives of photoreforming biomass toward hydrogen are envisioned.

Context: Ultrasound (US) is the most economical and widely used method for detecting lesions in parathyroid regions. Identifying typically parathyroid adenomas as hypoechoic nodules with clear margins. However, 10 % of lesions exhibit atypical features, such as the dual concentric sign, and the cognition of them still needs to be improved. Objective: To promote understanding of clinical and histopathological features for parathyroid lesions with the dual concentric echo sign and to investigate its pathogenesis and methods for distinguishing from cervical lymph nodes to improve US diagnostic accuracy. Methods: Retrospectively, patients were categorized into three groups: Group 1, with 36 patients showing parathyroid lesions with dual concentric echo signs; Group 2, with 40 patients displaying classic hypoechoic parathyroid lesions; and Group 3, comprising 36 patients with identified lymph nodes, which were all examined from January 2018 to December 2019. The clinical data on demographics, clinical symptoms, serum levels, histopathologic findings, and US image characteristics were thoroughly reviewed. Results: According to the clinical data, no significant differences in demographics or lesion sizes were observed in Group 1 and Group 2 (p > 0.05). No significant variances were noted in biochemical markers, including PTH, T-25OHD, and ALP. However, a notable difference was identified in adjusted serum calcium levels, which were significantly lower in Group 1 compared to Group 2 (p

Abstract The anonymous and tamper‐proof nature of the blockchain poses significant challenges in auditing and regulating the behaviour and data on the chain. Criminal activities and anomalies are frequently changing, and fraudsters are devising new ways to evade detection. Moreover, the high volume and complexity of transactions and asymmetric errors make data classification more challenging. Also, class imbalances and high labelling costs are hindering the development of effective algorithms. In response to these issues, the authors present BlockDetective, a novel framework based on GCN that utilizes student–teacher architecture to detect fraudulent cryptocurrency transactions that are related to money laundering. The authors’ method leverages pre‐training and fine‐tuning, allowing the pre‐trained model (teacher) to adapt better to the new data distribution and enhance the prediction performance while teaching a new, light‐weight model (student) that provides abstract and top‐level information. The authors’ experimental results show that BlockDetective outperforms state‐of‐the‐art research methods by achieving top‐notch performance in detecting fraudulent transactions on the blockchain. This framework can assist regulators and auditors in detecting and preventing fraudulent activities on the blockchain, thereby promoting a more secure and transparent financial system.

Summary: Background: The escalating resistance of Mycoplasma pneumoniae to macrolides has become a significant global health concern, particularly in low-income and middle-income countries (LMICs). Although tetracyclines and quinolones have been proposed as alternative therapeutic options, concerns regarding age-specific safety issues and the lack of consensus in recommendations across various national guidelines prevail. Thus, the primary objective of this study is to ascertain the most efficacious interventions for second-line treatment of M. pneumoniae infection while considering the age-specific safety issues associated with these interventions. Methods: In this systematic review and network meta-analysis we searched PubMed, Embase, CNKI, and WanFang Data, from inception up to November 11th, 2023. Studies of quinolones or tetracyclines for the treatment of people with M. pneumoniae infection were collected and screened by reading published reports, with any type of study included, and no individual patient-level data requested. A systematic review and direct meta-analysis compared the efficacy of tetracyclines and quinolones regarding time to defervescence (TTD) and the rates of fever disappearance within 24 h and 48 h of antibiotic administration, for managing M. pneumoniae infection. Bayesian network meta-analysis (NMA) was employed to indirectly assess the relative effectiveness of different interventions in people with M. pneumoniae infection and the safety profile of medication in paediatric patients. This study is registered with PROSPERO, CRD42023478383. Findings: The systematic review and direct meta-analysis included a total of 4 articles involving 246 patients, while the NMA encompassed 85 articles involving a substantial cohort of 7095 patients. The NMA measured the effectiveness across all ages and included 7043 patients, with a mean age of 37.80 ± 3.91 years. Of the 85 included studies, 14 (16.5%) were at low risk of bias, 71 (83.5%) were at moderate risk, and no studies were rated as having a high risk of bias. In the direct meta-analysis, no statistically significant differences were found between tetracyclines and quinolones concerning TTD (mean difference: −0.40, 95% CI: −1.43 to 0.63; I2 = 0%), fever disappearance rate within 24 h of antibiotic administration (OR: 0.37, 95% CI: 0.08–1.79; I2 = 58%), and fever disappearance rate within 48 h of antibiotic administration (OR: 1.10, 95% CI: 0.30–3.98; I2 = 59%). However, the comprehensive NMA analysis of clinical response (in 70 studies; n = 6143 patients), shortening of TTD (in 52 studies; n = 4363 patients), shortening length of cough relief or disappearance (in 39 studies; n = 3235 patients), fever disappearance rate at 48 h (in four studies; n = 418 patients) revealed that minocycline exhibited the most favourable outcomes across these various parameters, and the analysis of fever disappearance rate at 24 h (in three studies; n = 145 patients) revealed that levofloxacin may be the most effective, as indicated by the rank probabilities and surface under the cumulative ranking area (SUCRA) value. Moxifloxacin ranked second in clinical response and in shortening the length of cough relief or disappearance, and third in shortening TTD. Notably, when evaluating the occurrence of adverse reactions in paediatric patients (in four studies; n = 239 children), levofloxacin was associated with the highest SUCRA value rankings for the rate of adverse events. Interpretation: Our findings suggest that tetracyclines and quinolones may be equally effective. Based on the age of participants in the included studies, minocycline may be the most effective intervention for children over eight years of age when all preventive measures are considered, whereas moxifloxacin may benefit people under eight years of age. However, these results should be interpreted with caution, given the limited number of studies and patients included, and the heterogeneity between included studies. Based on a limited number of studies in children, levofloxacin is likely to have one of the highest rates of adverse reactions. The majority of the studies included in the NMA were from the Asian region, and more randomised controlled trials comparing different therapeutic strategies in patients with M. pneumoniae are warranted. This comparative study provides clinical pharmacists and clinicians with important information to enable them to make informed decisions about treatment options, considering drug efficacy and safety. Funding: The Natural Science Foundation of Fujian Province, China.

Arsenic, a ubiquitous environmental toxicant with various forms and complex food matrix interactions, can reportedly exert differential effects on the liver compared to drinking water exposure. To examine its specific liver-related harms, we targeted the liver in C57BL/6 J mice (n=48, 8-week-old) fed with arsenic-contaminated food (30 mg/kg) for 60 days, mimicking the rice arsenic composition observed in real-world scenarios (iAsV: 7.3%, iAsIII: 72.7%, MMA: 1.0%, DMA: 19.0%). We then comprehensively evaluated liver histopathology, metabolic changes, and the potential role of the gut-liver axis using human hepatocellular carcinoma cells (HepG2) and microbiota/metabolite analyses. Rice arsenic exposure significantly altered hepatic lipid (fatty acids, glycerol lipids, phospholipids, sphingolipids) and metabolite (glutathione, thioneine, spermidine, inosine, indole-derivatives, etc.) profiles, disrupting 33 metabolic pathways (bile secretion, unsaturated fatty acid biosynthesis, glutathione metabolism, ferroptosis, etc.). Pathological examination revealed liver cell necrosis/apoptosis, further confirmed by ferroptosis induction in HepG2 cells. Gut microbiome analysis showed enrichment of pathogenic bacteria linked to liver diseases and depletion of beneficial strains. Fecal primary and secondary bile acids, short-chain fatty acids, and branched-chain amino acids were also elevated. Importantly, mediation analysis revealed significant correlations between gut microbiota, fecal metabolites, and liver metabolic alterations, suggesting fecal metabolites may mediate the impact of gut microbiota and liver metabolic disorders. Gut microbiota and its metabolites may play significant roles in arsenic-induced gut-liver injuries. Overall, our findings demonstrate that rice arsenic exposure triggers oxidative stress, disrupts liver metabolism, and induces ferroptosis.

Pattern recognition (PR)-based myoelectric control systems can naturally provide multifunctional and intuitive control of upper limb prostheses and restore lost limb function, but understanding their robustness remains an open scientific question. This study investigates how limb positions and electrode shifts—two factors that have been suggested to cause classification deterioration—affect classifiers’ performance by quantifying changes in the class distribution using each factor as a class and computing the repeatability and modified separability indices. Ten intact-limb participants took part in the study. Linear discriminant analysis (LDA) was used as the classifier. The results confirmed previous studies that limb positions and electrode shifts deteriorate classification performance (14–21% decrease) with no difference between factors (p > 0.05). When considering limb positions and electrode shifts as classes, we could classify them with an accuracy of 96.13 ± 1.44% and 65.40 ± 8.23% for single and all motions, respectively. Testing on five amputees corroborated the above findings. We have demonstrated that each factor introduces changes in the feature space that are statistically new class instances. Thus, the feature space contains two statistically classifiable clusters when the same motion is collected in two different limb positions or electrode shifts. Our results are a step forward in understanding PR schemes’ challenges for myoelectric control of prostheses and further validation needs be conducted on more amputee-related datasets.

Currently, there is an absence of an exhaustive elucidation regarding the long-term spatiotemporal characteristics of meteorological droughts in China. Previous studies have predominantly fixated on the lower-dimensional (temporal or spatial) facets of drought events, overlooking critical spatiotemporal details. This paper proposes an integrated processing framework that employs a three-dimensional clustering algorithm to discern drought events and quantifies the spatiotemporal patterns and dynamic attributes of China’s meteorological droughts during 1950–2021 from a three-dimensional perspective encompassing longitude, latitude, and time, capturing comprehensive spatiotemporal information. Additionally, the cross wavelet transform is harnessed to elucidate the impact of teleconnection factors on meteorological drought. The findings underscore that prolonged and extensive meteorological drought events, characterized by heightened severity, primarily manifest during the 1950 s, 1960 s, and 2010 s in temporal, predominantly within the central and western China spatially. From 1950 onwards, the severity, duration, and affected area of meteorological droughts in China exhibit a discernible pattern of initial decline followed by escalation. Into the 2010 s, meteorological droughts became more severe and active. China's meteorological drought displays two relatively prevailing spatial migration patterns: an overarching eastward and westward migration. In the former, the most frequent migration occurs to the east and northeast during its monthly evolution, while the latter involves migration primarily towards the west, southwest, and northwest. The impact of teleconnection factors on meteorological drought shows a spatiotemporal heterogeneity, with ENSO exhibiting a relatively substantial and intricate influencer. This research unearths the latest long-term spatiotemporal dynamics of meteorological drought in China, which contributes to an improved comprehension of drought patterns and behaviors.

BackgroundMetabolic diseases during pregnancy result in negative consequences for mothers. Pre-pregnancy body mass index (BMI) and late-pregnancy glycated-hemoglobin (HbA1c) are most important factors independently affecting the risk of gestational diabetes mellitus (GDM). However how both affect the combined risk of other metabolic diseases in women with GDM is unclear. The study aims to investigate the influence of pre-pregnancy BMI and pregnancy glycemic levels on other gestational metabolic diseases in women with GDM.MethodsPregnancies with GDM from January 2015 to December 2018 in the Xi’an longitudinal mother-child cohort study (XAMC) were retrospectively enrolled. Those without other metabolic diseases by the time of oral glucose tolerance test (OGTT) detection were finally recruited and divided into four groups by pre-pregnancy BMI (Underweight

Abstract With the distribution of the large‐scale emergence of wind power connected to the power system, the stability problem of small disturbance has become increasingly prominent, especially the low‐frequency oscillation phenomenon caused by the weak or the negative damping. Aiming at the problem of probabilistic stability of small disturbance in power system connected with wind power, this paper proposes a method of probabilistic stability based on a variational Bayes and Lyapunov theory using PMU data. This method aims to minimize the computational efficiency when the number of wide‐area time series PMU data is large. Firstly, PMU data are classified by the Random Forest Gini classification index. Secondly, a variational Bayesian framework is introduced to improve the computational efficiency. And then, to obtain the probabilistic stability criterion, this framework is combined with the direct Lyapunov theory. Finally, simulation results show that the method proposed in this paper greatly improves the system probabilistic stability of small disturbance. The comparison between the traditional Monte Carlo based on the Markov chain method and the method in this paper has been achieved, which could provide the theoretical support for the study of the low‐frequency oscillation significantly.

Thermocatalytic hydrogenation holds great promise for commercial utilization of carbon dioxide, but the process is energy-intense with high temperature and pressure requirements. Here, the authors engineer a GaN1- x O x rhodium nanoparticle catalyst for CO2 to CO hydrogenation that functions at temperatures as low as 170 °C.

To investigate the mechanism of aquatic pathogens in quorum sensing (QS) and decode the signal transmission of aquatic Gram-negative pathogens, this paper proposes a novel method for the intelligent matching identification of eight quorum signaling molecules (N-acyl-homoserine lactones, AHLs) with similar molecular structures, using terahertz (THz) spectroscopy combined with molecular dynamics simulation and spectral similarity calculation. The THz fingerprint absorption spectral peaks of the eight AHLs were identified, attributed, and resolved using the density functional theory (DFT) for molecular dynamics simulation. To reduce the computational complexity of matching recognition, spectra with high peak matching values with the target were preliminarily selected, based on the peak position features of AHL samples. A comprehensive similarity calculation (CSC) method using a weighted improved Jaccard similarity algorithm (IJS) and discrete Fréchet distance algorithm (DFD) is proposed to calculate the similarity between the selected spectra and the targets, as well as to return the matching result with the highest accuracy. The results show that all AHL molecular types can be correctly identified, and the average quantization accuracy of CSC is 98.48%. This study provides a theoretical and data-supported foundation for the identification of AHLs, based on THz spectroscopy, and offers a new method for the high-throughput and automatic identification of AHLs.

Pancreatic cancer is a malignancy that affects the digestive tract and has a low 5-year survival rate of lower than 15%. Owing to its genetic mutation and metabolic complexity, pancreatic cancer is difficult to treat with surgical resection, radiotherapy, and chemotherapy. The predominant modality of pancreatic cancer is pancreatic ductal adenocarcinoma (PDAC), primarily attributed to mutations in KRAS gene. Ferroptosis, an iron-mediated reactive oxygen species (ROS)-elevated nonapoptotic cell death caused by lipid peroxidation, is distinct from any other known type of cell death. Ferroptosis is closely related to the occurrence and progression of different types of cancers, including PDAC. Previous research has demonstrated that ferroptosis not only triggers cell death in PDAC and hampers tumor growth but also enhances the effectiveness of antitumor medications. In our review, we mainly focus on the core mechanism of ferroptosis, reveal its interrelationship with PDAC, and illustrate the progress of ferroptosis in different treatment methods of PDAC.

Abstract Zebrafish (Danio rerio) have attracted much attention over the past decade as a reliable model for gut microbiome research. Owing to their low cost, strong genetic and development coherence, efficient preparation of germ‐free (GF) larvae, availability in high‐throughput chemical screening, and fitness for intravital imaging in vivo, zebrafish have been extensively used to investigate microbiome‐host interactions and evaluate the toxicity of environmental pollutants. In this review, the advantages and disadvantages of zebrafish for studying the role of the gut microbiome compared with warm‐blooded animal models are first summarized. Then, the roles of zebrafish gut microbiome on host development, metabolic pathways, gut‐brain axis, and immune disorders and responses are addressed. Furthermore, their applications for the toxicological assessment of aquatic environmental pollutants and exploration of the molecular mechanism of pathogen infections are reviewed. We highlight the great potential of the zebrafish model for developing probiotics for xenobiotic detoxification, resistance against bacterial infection, and disease prevention and cure. Overall, the zebrafish model promises a brighter future for gut microbiome research.

With the continuous emergence of new applications such as holographic multimedia, integration of space, air and ground, metaverse and social Internet of things, the development of business applications moved towards digitalization, integration, bandwidth and intelligence, which correspondingly puts forward new demands for the transformation of the network.Through the research and analysis of future network characteristics, combined with the characteristics of the cloud native network, the cloud native advantage can be used to meet the needs of current hot business applications.The general architecture of the cloud native network was proposed.The capabilities of data plane, control plane and management plane were defined.Finally, the typical and hot application cases of the cloud native network were given.

In recent years, the prevalence of metabolic-associated fatty liver disease (MAFLD) has reached pandemic proportions as a leading cause of liver fibrosis worldwide. However, the stage of liver fibrosis is associated with an increased risk of severe liver-related and cardiovascular events and is the strongest predictor of mortality in MAFLD patients. More and more people believe that MAFLD is a multifactorial disease with multiple pathways are involved in promoting the progression of liver fibrosis. Numerous drug targets and drugs have been explored for various anti-fibrosis pathways. The treatment of single medicines is brutal to obtain satisfactory results, so the strategies of multi-drug combination therapies have attracted increasing attention. In this review, we discuss the mechanism of MAFLD-related liver fibrosis and its regression, summarize the current intervention and treatment methods for this disease, and focus on the analysis of drug combination strategies for MAFLD and its subsequent liver fibrosis in recent years to explore safer and more effective multi-drug combination therapy strategies.

IntroductionPhysical activity among college students since the COVID-19 pandemic was well studied; however, subjective exercise experience and the emotional response toward physical exercise received less attention.MethodsThe present study used latent profile analysis (LPA) to explore the latent class of subjective exercise experience among physically vulnerable college students who scored 59 points or less in tests under the National Student Physical Health Standard. Three non-duplicated samples at different stages of the COVID-19 pandemic were collected in March 2020 (N = 127), March 2021 (N = 118), and November 2021 (N = 206) respectively. Psychometrically validated scales, namely, Subjective Exercise Experiences Scale (SEES), Generalized Anxiety Disorder (GAD-7), and Patient Health Questionnaire (PHQ-9) were used to measure subjective exercise experience, anxiety symptoms, and depressive symptoms.Results and discussionLPA revealed a 3-class solution for the subjective exercise experience of physically unfit students, namely, the “negative experience group” (30.82%), the “fatigue group” (41.91%), and the “positive experience group” (27.27%). Multinomial regression showed that probable anxiety [odds ratio (OR) = 0.12] was associated with the overall negative exercise experience while probable depression (OR = 0.19) was associated with psychological fatigue. Women (OR = 0.496) were more likely to experience overall negative exercise experience, and the outbreak of the COVID- 19 (OR = 2.14) pandemic influenced the psychological distress of the subjective exercise experience compared with the other two phases in the post-COVID- 19 era. Our findings provided significant implications for physical education targeting university students that interventions should be tailored differently for three profiles of the subjective exercise experience.

Geographical traceability is crucial to the quality and safety control of gelatin. However, currently, methods for gelatin traceability have not been established anywhere in the world. This study aimed to investigate the possibility of differentiating the geographical origins of gelatin from different regions in China using stable isotope technology. To achieve this objective, 47 bovine stick bone samples from three different regions (Inner Mongolia, Shandong, and Guangxi, respectively) in China were collected, and gelatin was extracted from these bones using the enzymatic method. The fingerprint characteristics of stable isotopes of δ13C, δ15N, and δ2H of gelatin from different regions in China were studied. Moreover, isotopic changes from the bone to gelatin during the processing were examined to evaluate the effectiveness of these factors as origin indicators. The results of the one-way analysis of variance (ANOVA) showed that the δ13C, δ15N, and δ2H of gelatin from different regions display significant differences, and using the linear discriminant analysis (LDA), the correct differentiation of origin reached 97.9%. Certain differences in stable isotope ratios were observed during the processing of bone to gelatin samples. Nonetheless, the fractionation effect caused by the processing of bone to gelatin samples was not sufficient to influence the identification of gelatin from different origins, which proves that δ13C, δ15N, and δ2H are effective origin indicators of gelatin. In conclusion, the stable isotope ratio analysis combined with the chemometric analysis can be used as a reliable tool for identifying gelatin traceability.

An NAD+-dependent xylitol dehydrogenase from A. flavus (AfXDH) was cloned and successfully expressed in Escherichia coli. AfXDH gene sequence revealed an open reading frame of 1,110 bp, encoding a polypeptide of 369 amino acids with a calculated molecular mass of 38,893 Da. Among various polyols, sorbitol and xylitol were preferred substrates of AfXDH with Km values of 16.2 and 16.9 mM, respectively. AfXDH showed the highest activity in Tris-glycine-NaOH buffer (pH 9.5) at 50°C; it required Zn2+ or Mn2+ for enzyme activity. The half-life at 40°C and half denaturation temperature (T1/2) was 200 min and 45°C, respectively. Bioinformatic analyses along with biochemical properties confirmed that AfXDH belonged to the medium-chain dehydrogenase/reductase family. AfXDH exhibits higher thermostability and kcat values than those of other XDHs. The feasibility of using AfXDH in l-xylulose production was demonstrated. AfXDH, when coupled with Streptococcus pyogenes NADH oxidase, efficiently converted xylitol to l-xylulose with 97% yield, suggesting its usefulness for the industrial l-xylulose production from xylitol.

Wireless energy transfer technology (WET)-enabled mobile charging provides an innovative strategy for energy replenishment in wireless rechargeable sensor networks (WRSNs), where the mobile charger (MC) can charge the sensors sequentially by WET according to the mobile charging scheduling scheme. Although there have been fruitful studies, they usually assume that all sensors will be charged fully once scheduled or charged to a fixed percentage determined by a charging upper threshold, resulting in low charging performance as they cannot adjust the charging operation on each sensor adaptively according to the real-time charging demands. To tackle this challenge, we first formulate the mobile charging scheduling as a joint mobile charging sequence scheduling and charging upper threshold control problem (JSSTC), where the charging upper threshold of each sensor can adjust adaptively. Then, we propose a novel multi-discrete action space deep Q-network approach for JSSTC (MDDRL-JSSTC), where MC is regarded as an agent exploring the environment. The state information observed by MC at each time step is encoded to construct a high-dimensional vector. Furthermore, a two-dimensional action is mapped to the charging destination of MC and the corresponding charging upper threshold at the next time step, using bidirectional gated recurrent units (Bi-GRU). Finally, we conduct a series of experiments to verify the superior performance of the proposed approach in prolonging the lifetime compared with the state-of-the-art approaches.

The Sendai Framework for Disaster Risk Reduction 2015–2030 (SFDRR) proposed seven targets comprising 38 quantified indicators and various sub-indicators to monitor the progress of disaster risk and loss reduction efforts. However, challenges persist regarding the availability of disaster-related data and the required resources to address data gaps. A promising way to address this issue is the utilization of Earth observation (EO). In this study, we proposed an EO-based disaster evaluation framework in service of the SFDRR and applied it to the context of tropical cyclones (TCs). We first investigated the potential of EO in supporting the SFDRR indicators, and we then decoupled those EO-supported indicators into essential variables (EVs) based on regional disaster system theory (RDST) and the TC disaster chain. We established a mapping relationship between the measurement requirements of EVs and the capabilities of EO on Google Earth Engine (GEE). An end-to-end framework that utilizes EO to evaluate the SFDRR indicators was finally established. The results showed that the SFDRR contains 75 indicators, among which 18.7% and 20.0% of those indicators can be directly and indirectly supported by EO, respectively, indicating the significant role of EO for the SFDRR. We provided four EV classes with nine EVs derived from the EO-supported indicators in the proposed framework, along with available EO data and methods. Our proposed framework demonstrates that EO has an important contribution to supporting the implementation of the SFDRR, and that it provides effective evaluation solutions.

Abstract Background To evaluate the root anatomy, root canal morphology and the anatomical relationship between the roots and maxillary sinus of maxillary second premolars by CBCT in a western Chinese population. Methods A total of 1118 CBCT scans of the maxillary second premolars were collected from West China Hospital of Stomatology, Sichuan University. Information below were measured on axial, coronal and sagittal sections, recorded and evaluated properly: the number of roots and canals, the morphology of canal system classified by Vertucci standard, the inter-orifice distance of canal orifices, the curvature of each canal and the distance from root tip to maxillary sinus floor. Results Among the 1118 teeth, 94.2% (1053) are single-rooted and 55.1% (616) have one canal. Type I (55.1%) is the commonest root canal morphology followed by Type II (31.9%). The mean inter-orifice distance (IOD) for multi-canal teeth ranging from 2.72 ± 0.32 to 3.41 ± 0.11 mm. Of 1622 canals, 38.8% (630) curvature are mesiodistal and 30.9% (501) are straight canals. The distance from root tip to maxillary sinus floor increased with age and the mean distance of single-rooted ones is 2.47 ± 3.45 mm. Conclusions All kinds of canal morphology category can be detected in maxillary second premolars. The IOD might be a predictable factor for root canal morphology. Roots of maxillary second premolars are related to maxillary sinus which should be treated carefully.

Abstract Background This study aimed to investigate the anesthetic effect of butorphanol with different doses in patients undergoing gastroscopy and colonoscopy. Methods 480 patients undergoing gastroscopy and colonoscopy were recruited and randomly divided into four groups to receive different doses of butorphanol (Group A = 2.5 μg/kg, Group B = 5 μg/kg, Group C = 7.5 μg/kg and Group D = 10 μg/kg). Butorphanol was administered 5 min before propofol infusion. The primary outcome was the incidence of body movement. Secondary outcomes were postoperative recovery time, length of stay in the Post-Anesthesia Care Unit (PACU), the total dose of propofol, and the incidence of intraoperative hypoxemia, propofol injection pain, cough, postoperative nausea and vomiting, drowsiness, and dizziness. Results The incidence of body movement and the dose of propofol in Group C and D were lower than those in Group A and B (P

In China, due to labor shortages and increasing labor costs, manual topping is gradually being replaced by chemical capping with mepiquat chloride (DPC). External chemicals can adjust plant growth by affecting endogenous hormones. Based on the hormone changes combined with the development of the main stem of cotton plants, a comparative experiment was conducted in 2019 and 2020 to determine the regulatory mechanism of the growth of the cotton main stem after chemical capping. In the experiment, two treatment times (T1: 12 July, T2: 18 July) and two treatment agents (CA [chemical capping agent] and DPC) were set, the hormone (auxin IAA, abscisic acid ABA, Gibberellin GA3 and Zeatin ZR) concentrations at the top of main stem (0–5 and 5–10 cm) were continuously measured and the main stem development situation was observed and recorded. The results showed that after chemical capping, the IAA concentration decreased firstly and increased later, lower than that of CK. ABA concentration increased significantly and GA3 concentration decreased significantly compared with CK. ZR concentrations fluctuated obviously at T1 and gently at T2. In terms of main stem growth, the plant height, number of fruit branches and average length of upper internode (fifth and above) were decreased compared with CK, while the CA treatment was inhibited more strongly than the DPC treatment. To conclude, chemical capping operation affected the hormone concentration at the plant apex significantly and effectively regulated plant development. In comparison with DPC treatment, CA regulated hormones effectively, which is favorable for conducive reasonable plant shaping.

Abstract Background The aim of this study was to investigate the prevalence of the middle mesial canal (MMC) and radix entomolaris (RE) in mandibular first permanent molars in a western Chinese population using cone-beam computed tomography (CBCT). Methods A total of 1174 CBCT images of the mandibular first molars were collected from West China Hospital of Stomatology, Sichuan University. The following information was recorded and evaluated: the detection rate and location of the MMC and RE, the curvature of the RE, the canal configuration and bilateral symmetry. Results The detection rates of the MMC and RE were 3.41 and 25.04%, respectively, as calculated by individuals, and 1.79 and 22.15%, respectively, as calculated by total teeth. The average curvature in the buccolingual (BL) orientation (40.63 ± 14.39°) was significantly larger than that in the mesiodistal (MD) orientation (17.64 ± 7.82°) (p

There is a complex network structure of space-ground integrated network.And this kind of network has heterogeneous network,dynamic topology,intermittently connected node height exposure,and so on.There are many limitations in traditional SDN implementation.A method of SDN implement based on end side computing and a space-ground integrated network based on SDN were presented,which separated space network control and data,using the computing power of the communication terminal (mobile terminal and fixed ground backbone network),unloading the calculation of SDN controller in space-based plane,thus simplifying the data forwarding plane.A practical and effective verification method was also proposed.

Abstract Background Cholangiocarcinoma (CCA) is a mortal cancer with high mortality, whereas the function and mechanism of occurrence and progression of CCA are still mysterious. Long non-coding RNAs (lncRNAs) could function as important regulators in carcinogenesis and cancer progression. Growing evidences have indicated that the novel lncRNA linc00473 plays an important role in cancer progression and metastasis. However, its function and molecular mechanism in CCA remain unknown. Methods The linc00473 expression in CCA tissues and cell lines was analyzed using qRT-PCR. Gain- and loss-of-function experiments were conducted to investigate the biological functions of linc00473 both in vitro and in vivo. Insights into the underlying mechanisms of competitive endogenous RNAs (ceRNAs) were determined by bioinformatics analysis, dual-luciferase reporter assays, qRT-PCR arrays, RNA immunoprecipitation (RIP) and rescue experiments. Results Linc00473 was highly expressed in CCA tissues and cell lines. Linc00473 knockdown inhibited CCA growth and metastasis. Furthermore, linc00473 acted as miR-506 sponge and regulated its target gene DDX5 expression. Rescue assays verified that linc00473 modulated the tumorigenesis of CCA by regulating miR-506. Conclusions The data indicated that linc00473 played an oncogenic role in CCA growth and metastasis, and could serve as a novel molecular target for treating CCA.

The rapid development of new types of services places greater demands on support ability of the personalized service in mobile communication systems.Traditional intelligent pipe carriers only consider limited factors such as current traffic status of the network and number of loading binding quantity,indicating that they don’t take account of the demands and features of wireless customers.Meanwhile,the rule-based method is overwhelmed due to numerous metrics of the wireless network,the complicated correlation among them and heterogeneous requirements of metrics from various users.Therefore,a user-centric intelligent network operation service was proposed,which utilized machine learning and deep learning method.The service had the ability to learn the correlation between user QoS guarantee requirements and wireless network metric status to construct a joint support architecture based on user requirements and wireless network status.The validity of the method on real data was verified.

Due to the increase in wind power accessing power system in recent years, the low-frequency oscillation of power system is more complicated. Therefore, a new adaptive switching control method combined with a nonlinear system is proposed for the complex operating conditions of the grid-connected power system. Firstly, this method decomposes the low-frequency oscillation control problem of the power system into two parts, which are the linear system control and the nonlinear system control. Then, in the linear system control part, the iterative identification method is used to control this part. In the nonlinear system control part, the nonlinear term is decomposed into a combination of the previous beating measurable and the nonlinear estimation increment. Secondly, the nonlinear system is divided into three different operating conditions by the nonlinear estimation increment. Thus, four different adaptive controllers are obtained. Then, the designed controller is switched according to the controlled object with four controllers by the switching function, which not only ensures the stability of the closed-loop system, but also improves the control performance of the controller under different operating conditions. Finally, the feasibility and effectiveness of this method are verified, and the controller can fit the control efficiency of the real power system well.

In wireless rechargeable sensor networks (WRSNs), a novel approach to energy replenishment is offered by the utilization of mobile chargers (MCs), which charge nodes via wireless energy transfer technology. However, previous research on mobile charging schemes has commonly prioritized charging efficiency as a performance index, neglecting the importance of quality of sensing coverage (QSC). As the network scale increases, the MC’s charging power becomes unable to meet the energy needs of all nodes, leading to a decline in network QSC when nodes’ energy is depleted. To solve this problem, we study the problem of mobile charging sequence scheduling for optimal network QSC (MSSQ) and propose an improved quantum-behaved particle swarm optimization (IQPSO) algorithm. With the attraction of potential energy in quantum space, this algorithm will adaptively adjust the contraction expansion coefficient iteratively, leading to a global optimal solution for the mobile charging sequence. Extensive simulation results demonstrate the superiority of IQPSO over the widely used QPSO and Greedy algorithms in terms of network QSC, especially in large-scale networks.

To explore the contributions of chemical components in the particulate matter in Lhasa, the size distribution characteristics of the chemical components, such as carbonaceous species (organic carbon, OC; elemental carbon, EC), water-soluble ions (NH4+, Cl−, SO42−, and NO3−) and trace elements (Na, Ca, Al, V, Cr, Mn, Fe, Co, Ni, Cu, Zn, K, Mg, and Pb), were investigated from August 2018 to May 2019. Principal component analysis (PCA) was also used to simulate the source of the particulate matter. The chemical components showed bimodal size distributions, except for Cu. The highest mass concentration of OC appeared at < 0.49 μm, and the second highest one existed at 1.5–3.0 μm in winter or at 3.0–7.2 μm in other seasons. The maximum concentrations of NH4+, Cl−, SO42−, and NO3− were at < 0.49 μm and peaked at 0.95–1.5 μm or > 3.0 μm. For seasonality, the concentrations of NO3− and SO42− were considerably higher in summer and autumn; trace elements (except for Cu, Zn, V, and Ni), OC, and EC presented higher values during late autumn and winter. The ratio between OC and EC (4.15–33.80) indicated the existence of secondary pollution in Lhasa. The [NO3−]–[SO42−] ratios during summer and autumn exceeding 1 suggested that the currently predominant vehicle exhaust made a greater contribution to the aerosols. According to the results of a PCA, the main pollution sources of particulate matter in Lhasa were suspended dust, biomass burning, fossil fuel combustion, secondary pollution, and vehicular emissions.

Aquaculture plays a key role in achieving Sustainable Development Goals (SDGs), while it is difficult to accurately extract single-object aquaculture ponds (SOAPs) from medium-resolution remote sensing images (Mr-RSIs). Due to the limited spatial resolutions of Mr-RSIs, most studies have aimed to obtain aquaculture areas rather than SOAPs. This study proposed an object-oriented method for extracting SOAPs. We developed an iterative algorithm combining grayscale morphology and edge detection to segment water bodies and proposed a segmentation degree detection approach to select and edit potential SOAPs. Then a classification decision tree combining aquaculture knowledge about morphological, spectral, and spatial characteristics of SOAPs was constructed for object filter. We selected a 707.26 km2 study region in Sri Lanka and realized our method on Google Earth Engine (GEE). A 25.11 km2 plot was chosen for verification, where 433 SOAPs were manually labeled from 0.5 m high-resolution RSIs. The results showed that our method could extract SOAPs with high accuracy. The relative error of total areas between extracted result and the labeled dataset was 1.13%. The MIoU of the proposed method was 0.6965, representing an improvement of between 0.1925 and 0.3268 over the comparative segmentation algorithms provided by GEE. The proposed method provides an available solution for extracting SOAPs over a large region and shows high spatiotemporal transferability and potential for identifying other objects.

Background: Fine particle pollution, specifically pollution by fine particulate matter (PM2.5), remains a significant concern in developing countries and plays an important role in the development and progression of respiratory diseases. Increasing evidences have demonstrated that long non-coding RNAs (lncRNAs) may act as vital molecules by binding to specific RNA-binding protein (RBP); however, their relationship with PM2.5 pollution is largely unexplored. Objective: We investigated the association between lncRNA and respiratory system inflammation caused by PM2.5. Methods: PM2.5 components were detected by gas chromatography-mass spectrometry (GC-MS), inductively coupled plasma-mass spectrometry (ICP-MS), and ionic chromatography. We established an inflammation model of PM2.5-induced toxicity in vivo (male and female SD rats, 0, 25, 50 and 100 mg/k PM2.5, 1, 7 and 14 days, single non-invasive tracheal instillation) and in vitro (rat alveolar macrophage cell line (NR8383), 0, 50, 100, 200, 400 μM PM2.5 for 24, 48, and 72 h). lncRNA high-throughput sequencing (lncRNA-seq) was used to investigate lncRNA profiles in PM2.5-treated NR8383 cells, and RNA interference (RNAi) was applied to explore the function of the target lncRNA. The mechanisms associated with specific lncRNAs were explored using comprehensive identification of RNA-binding proteins by mass spectrometry (ChIRP-MS) and western blot. Results: PM2.5-treated NR8383 cells and SD rats exhibited respiratory inflammation. lncRNA AABR07005593.1 was a pro-inflammatory factor that regulated IL-6 levels. Mechanistically, ChIRP-MS and western blot analyses revealed that highly expressed lncRNA AABR07005593.1 interacted with MCCC1 to involve in the activation of NF-κB pathway, and ultimately promoted the expression of IL-6. Conclusion: This study demonstrated that PM2.5 induced inflammation in vivo and in vitro. Furthermore, lncRNA AABR07005593.1 bound to MCCC1 to potentiated IL-6 expression. Therefore, lncRNA AABR07005593.1 may act as a potential biomarker for PM2.5 inflammation.

Cirsium japonicum (C. japonicum) is a traditional Chinese medicine belonging to the family Asteraceae. The previous studies have indicated that the chemical compound content of C. japonicum from different places was different. To distinguish C. japonicum from different geographies, the chloroplast genome of C. japonicum from China was sequenced and compared with that from Korea. The total length of this genome is 152,602 bp, similar to that of Korea (152,606 bp). It has a conservative quartile structure which is composed of a large single-copy (LSC) region, a small single-copy (SSC) region and a pair of inverted repeats (IRs) regions, with lengths of 83,487 bp, 18,721 bp, and 25,197 bp, respectively. It encodes 79 protein-coding, 27 transfer RNAs, and 4 ribosomal RNA genes. The overall GC content of the genome is 37.70%. A total of 20 single nucleotide polymorphisms and 6 insertions and deletions were identified between the chloroplast genome of C. japonicum from China and Korea. These results can be applied to develop molecular markers to distinguish C. japonicum from different geographical origins.

Motion adaptation has been attributed in flying insects a pivotal functional role in spatial vision based on optic flow. Ongoing motion enhances in the visual pathway the representation of spatial discontinuities, which manifest themselves as velocity discontinuities in the retinal optic flow pattern during translational locomotion. There is evidence for different spatial scales of motion adaptation at the different visual processing stages. Motion adaptation is supposed to take place, on the one hand, on a retinotopic basis at the level of local motion detecting neurons and, on the other hand, at the level of wide-field neurons pooling the output of many of these local motion detectors. So far, local and wide-field adaptation could not be analyzed separately, since conventional motion stimuli jointly affect both adaptive processes. Therefore, we designed a novel stimulus paradigm based on two types of motion stimuli that had the same overall strength but differed in that one led to local motion adaptation while the other did not. We recorded intracellularly the activity of a particular wide-field motion-sensitive neuron, the horizontal system equatorial cell (HSE) in blowflies. The experimental data were interpreted based on a computational model of the visual motion pathway, which included the spatially pooling HSE-cell. By comparing the difference between the recorded and modeled HSE-cell responses induced by the two types of motion adaptation, the major characteristics of local and wide-field adaptation could be pinpointed. Wide-field adaptation could be shown to strongly depend on the activation level of the cell and, thus, on the direction of motion. In contrast, the response gain is reduced by local motion adaptation to a similar extent independent of the direction of motion. This direction-independent adaptation differs fundamentally from the well-known adaptive adjustment of response gain according to the prevailing overall stimulus level that is considered essential for an efficient signal representation by neurons with a limited operating range. Direction-independent adaptation is discussed to result from the joint activity of local motion-sensitive neurons of different preferred directions and to lead to a representation of the local motion direction that is independent of the overall direction of global motion.

The breakthrough of wireless energy transmission (WET) technology has greatly promoted the wireless rechargeable sensor networks (WRSNs). A promising method to overcome the energy constraint problem in WRSNs is mobile charging by employing a mobile charger to charge sensors via WET. Recently, more and more studies have been conducted for mobile charging scheduling under dynamic charging environments, ignoring the consideration of the joint charging sequence scheduling and charging ratio control (JSSRC) optimal design. This paper will propose a novel attention-shared multi-agent actor–critic-based deep reinforcement learning approach for JSSRC (AMADRL-JSSRC). In AMADRL-JSSRC, we employ two heterogeneous agents named charging sequence scheduler and charging ratio controller with an independent actor network and critic network. Meanwhile, we design the reward function for them, respectively, by considering the tour length and the number of dead sensors. The AMADRL-JSSRC trains decentralized policies in multi-agent environments, using a centralized computing critic network to share an attention mechanism, and it selects relevant policy information for each agent at every charging decision. Simulation results demonstrate that the proposed AMADRL-JSSRC can efficiently prolong the lifetime of the network and reduce the number of death sensors compared with the baseline algorithms.

The protective effect of Cannabidiol on Parkinson’s disease (PD) has been found in recent study. However, the specific mechanism of the protective effect of Cannabidiol on PD nerve damage require further exploration. This study aims to investigate effect of Cannabidiol on MMP-induced Neural Cells (SH-SY5Y) mitochondrial dysfunction. MMP+ and Cannabidiol were used to treat SH-SY5Y cells, the cells viability was measured by MTT assay. The expression of Tyrosine hydroxylase (TH) in cells was measured by western blotting and Immunofluorescence staining. The relationship among Cannabidiol, Silent mating type information regulation 2 homolog-1 (SIRT1) and NOTCH signaling, NF-κB signaling was examined by western blotting. The effect of Cannabidiol on MMP+-induced mitochondrial dysfunction of SH-SY5Y cells was measured by western blotting. Cannabidiol alleviated loss of TH expression and cytotoxicity in the MPP+-induced SH-SY5Y cells. Further mechanistic investigation showed that Cannabidiol induced SH-SY5Y cells autophagy to protects cells from mitochondrial dysfunction by upregulating SIRT1 to Inhibits NF-κB and NOTCH Pathways. Taken together, Cannabidiol acts as a protector in PD.

Energy storage is an important adjustment method to improve the economy and reliability of a power system. Due to the complexity of the coupling relationship of elements such as the power source, load, and energy storage in the microgrid, there are problems of insufficient performance in terms of economic operation and efficient dispatching. In view of this, this paper proposes an energy storage configuration optimization model based on reinforcement learning and battery state of health assessment. Firstly, a quantitative assessment of battery health life loss based on deep learning was performed. Secondly, on the basis of considering comprehensive energy complementarity, a two-layer optimal configuration model was designed to optimize the capacity configuration and dispatch operation. Finally, the feasibility of the proposed method in microgrid energy storage planning and operation was verified by experimentation. By integrating reinforcement learning and traditional optimization methods, the proposed method did not rely on the accurate prediction of the power supply and load and can make decisions based only on the real-time information of the microgrid. In this paper, the advantages and disadvantages of the proposed method and existing methods were analyzed, and the results show that the proposed method can effectively improve the performance of dynamic planning for energy storage in microgrids.

Hepatoid adenocarcinoma of lung (HAL) is a rare and aggressive tumor. The current study reported a new HAL case in the right lower lung with high serum α-fetoprotein (AFP) level in a 71-year-old male patient. After the confirmation of morphology and immunohistochemistry, the patient was diagnosed clinically with HAL and treated with radio-frequency ablation. However, the patient whose disease progressed eventually died 4 months after diagnosis. Whole genome sequencing analysis identified a driver gene mutation in the FAT atypical cadherin 1 gene (FAT1) and the copy number loss. The tumor was microsatellite-stable and tumor mutation burden (TMB) was 1.69 mutations/Mb. PD-L1 expression was negative by IHC. Our finding provide further clues for the molecular basis of HAL and the efficacy of immunotherapy needs to be explored.

For planar closed-loop structures with clearances, the angular and positional error uncertainties are studied. By using the vector translation method and geometric method, the boundaries of the errors are analyzed. The joint clearance is considered as being distributed uniformly in a circle area. A virtual link projection method is proposed to deal with the clearance affected length error probability density function (PDF) for open-loop links. The error relationship between open loop and closed loop is established. The open-loop length PDF and the closed-loop angular error PDF both approach being Gaussian distribution if there are many clearances. The angular propagation error of multi-loop structures is also investigated by using convolution. The positional errors of single and multiple loops are both discussed as joint distribution functions. Monte Carlo simulations are conducted to verify the proposed methods.

Battery energy storage technology is an important part of the industrial parks to ensure the stable power supply, and its rough charging and discharging mode is difficult to meet the application requirements of energy saving, emission reduction, cost reduction, and efficiency increase. As a classic method of deep reinforcement learning, the deep Q-network is widely used to solve the problem of user-side battery energy storage charging and discharging. In some scenarios, its performance has reached the level of human expert. However, the updating of storage priority in experience memory often lags behind updating of Q-network parameters. In response to the need for lean management of battery charging and discharging, this paper proposes an improved deep Q-network to update the priority of sequence samples and the training performance of deep neural network, which reduces the cost of charging and discharging action and energy consumption in the park. The proposed method considers factors such as real-time electricity price, battery status, and time. The energy consumption state, charging and discharging behavior, reward function, and neural network structure are designed to meet the flexible scheduling of charging and discharging strategies, and can finally realize the optimization of battery energy storage benefits. The proposed method can solve the problem of priority update lag, and improve the utilization efficiency and learning performance of the experience pool samples. The paper selects electricity price data from the United States and some regions of China for simulation experiments. Experimental results show that compared with the traditional algorithm, the proposed approach can achieve better performance in both electricity price systems, thereby greatly reducing the cost of battery energy storage and providing a stronger guarantee for the safe and stable operation of battery energy storage systems in industrial parks.

Background/Aims: Cancer stem cells (CSCs) are considered to be responsible for tumor relapse and metastasis, which serve as a potential therapeutic target for cancer. Aspirin has been shown to reduce cancer risk and mortality, particularly in colorectal cancer. However, the CSCs-suppressing effect of aspirin and its relevant mechanisms in colorectal cancer remain unclear. Methods: CCK8 assay was employed to detect the cell viability. Sphere formation assay, colony formation assay, and ALDH1 assay were performed to identify the effects of aspirin on CSC properties. Western blotting was performed to detect the expression of the stemness factors. Xenograft model was employed to identify the anti-cancer effects of aspirin in vivo. Unpaired Student t test, ANOVA test and Kruskal-Wallis test were used for the statistical comparisons. Results: Aspirin attenuated colonosphere formation and decreased the ALDH1 positive cell population of colorectal cancer cells. Aspirin inhibited xenograft tumor growth and reduced tumor cells stemness in nude mice. Consistently, aspirin decreased the protein expression of stemness-related transcription factors, including c-Myc, OCT4 and NANOG. Suppression of NANOG blocked the effect of aspirin on sphere formation. Conversely, ectopic expression of NANOG rescued the aspirin-repressed sphere formation, suggesting that NANOG is a key downstream target. Moreover, we found that aspirin repressed NANOG expression in protein level by decreasing its stability. Conclusion: We have provided new evidence that aspirin attenuates CSC properties through down-regulation of NANOG, suggesting aspirin as a promising therapeutic agent for colorectal cancer treatment.

Neuronal representation and extraction of spatial information are essential for behavioral control. For flying insects, a plausible way to gain spatial information is to exploit distance-dependent optic flow that is generated during translational self-motion. Optic flow is computed by arrays of local motion detectors retinotopically arranged in the second neuropile layer of the insect visual system. These motion detectors have adaptive response characteristics, i.e. their responses to motion with a constant or only slowly changing velocity decrease, while their sensitivity to rapid velocity changes is maintained or even increases. We analyzed by a modeling approach how motion adaptation affects signal representation at the output of arrays of motion detectors during simulated flight in artificial and natural 3D environments. We focused on translational flight, because spatial information is only contained in the optic flow induced by translational locomotion. Indeed, flies, bees and other insects segregate their flight into relatively long intersaccadic translational flight sections interspersed with brief and rapid saccadic turns, presumably to maximize periods of translation (80% of the flight). With a novel adaptive model of the insect visual motion pathway we could show that the motion detector responses to background structures of cluttered environments are largely attenuated as a consequence of motion adaptation, while responses to foreground objects stay constant or even increase. This conclusion even holds under the dynamic flight conditions of insects.

Mobile edge computing supports the connected cars to ensure real-time, interactive, secured, and distributed services for customers. Connected car-sharing systems, as the promising appliance of connected cars, provide a convenient transportation mode for citizens’ intra-urban commutes. Determining the locations of depots is the primary job in connected car-sharing systems. Existing methods mainly use qualitative method and do not consider spatial–temporal dynamic travel demands. This article proposes a mobile edge computing–based connected car framework which uses normal taxis as connected cars to describe their Global Positioning System trajectory and perform the computing tasks in each mobile edge computing server independently. A spatial–temporal demand coverage approach is developed to optimize the location of depots. This article proposes a deep learning method to predict car-sharing demand constructed by a stacked auto-encoder model and a logistic regression layer. The stacked auto-encoder model is employed for learning the latent spatial and temporal correlation features of demand. A graph-based resource relocation model is proposed to minimize the cost of relocation considering spatio-temporal variation of car-sharing demand. Experiments performed on the large-scale real-world data sets illustrate that our proposed model has superior performance than existing methods.

[This corrects the article DOI: 10.1371/journal.pone.0175845.].

Accumulation of metal contaminants in soil as a result of various industrial and anthropogenic activities has reduced soil fertility significantly. Phytoextraction of metal contaminants can improve soil fertility and provide inexpensive feedstock for biorefineries. We investigated the hyperaccumulation capacity of sunflower (Helianthus annuus) biomass by cultivating these plants in various concentrations of metal contaminants. Sunflowers were grown in soils contaminated with various levels of heavy metals (10-2,000 mg/kg dry soil). The degree of metal uptake by different parts of the biomass and the residual concentration in the soil were estimated through inductively coupled plasma mass spectrometry. An almost 2.5-fold hyperaccumulation of Zn2+ was observed in the leaf and flower biomass compared with the concentration in the soil. For the subsequent saccharification of biomass with hyperaccumulated contaminants, a fungal lignocellulosic consortium was used. The fungal consortium cocktail retained more than 95% filter paper activity with 100 mM Ni2+ ions even after 36 h. The highest saccharification yield (SY, 87.4%) was observed with Ni2+ as the contaminant (10 mg/kg dry wt), whereas Pb2+ (251.9 mg/kg dry wt) was the strongest inhibitor of biomass hydrolysis, resulting in only a 30% SY. Importantly, the enzyme cocktail produced by the fungal consortium resulted in almost the same SY (%) as that obtained from a combination of commercial cellulase and β-glucosidase. Significant sugar conversion (61.7%) from H. annuus biomass hydrolysate occurred, resulting in the production of 11.4 g/L of bioethanol. This is the first study to assess the suitability of phytoremediated sunflower biomass for bioethanol production.

Flying insects, such as flies or bees, rely on consistent information regarding the depth structure of the environment when performing their flight maneuvers in cluttered natural environments. These behaviors include avoiding collisions, approaching targets or spatial navigation. Insects are thought to obtain depth information visually from the retinal image displacements (`optic flow') during translational ego-motion. Optic flow in the insect visual system is processed by a mechanism that can be modeled by correlation-type elementary motion detectors (EMDs). However, it is still an open question how spatial information can be extracted reliably from the responses of the highly contrast- and pattern-dependent EMD responses, especially if the vast range of light intensities encountered in natural environments is taken into account. This question will be addressed here by systematically modeling the peripheral visual system of flies, including various adaptive mechanisms. Different model variants of the peripheral visual system were stimulated with image sequences that mimic the panoramic visual input during translational ego-motion in various natural environments, and the resulting peripheral signals were fed into an array of EMDs. We characterized the influence of each peripheral computational unit on the representation of spatial information in the EMD responses. Our model simulations reveal that information about the overall light level needs to be eliminated from the EMD input as is accomplished under light-adapted conditions in the insect peripheral visual system. The response characteristics of large monopolar cells resemble that of a band-pass filter, which reduces the contrast dependency of EMDs strongly, effectively enhancing the representation of the nearness of objects and, especially, of their contours. We furthermore show that local brightness adaptation of photoreceptors allows for spatial vision under a wide range of dynamic light conditions.

6BaO·xCaO·2Al2O3 (x = 0.8, 1.2, 1.6, 2, and 2.2) aluminates were synthesized via a liquid phase co-precipitation method. Effects of the molar amount of CaO on the phase of aluminates before and after melting and their hygroscopic phase, melting properties, environmental stability, evaporation, and emission properties were systematically studied. The results show that with the increase of the molar amount of CaO, the aluminates change from a mixture phase to a single phase of Ba3CaAl2O7, and the diffraction peak shifts to a higher angle. The melted phase of the aluminates changed from a single phase to a mixed phase of Ba5CaAl4O12 and Ba3CaAl2O7. Meanwhile, the comprehensive properties of the aluminates are improved. The weight gain of 6BaO·2CaO·2Al2O3 aluminates is only 10.88% after exposure to air for 48 h; the pulse emission current density of barium tungsten cathodes impregnated with 6BaO·2CaO·2Al2O3 aluminates in the porous tungsten matrix can reach 28.60 A/cm2 at 1050 °C, and the evaporation rate is 2.52 × 10−10 g/(cm2·s).