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Based on the cooperative principle brought up by American philosopher of language named Grice, with exemplification and explanation of the violation of four maxims, this paper also combines the cooperative principle with hedges, analyzing the translation of hedges so as to better manage implicit and explicit information in the translation. Meanwhile, analyzing how conversational maxims operate in a text can contribute to the characterization of style, “the perceived distinctive manner of expression in writing or speaking” (Wales, 2014, p. 397), helping the translator in maintaining a close stylistic link with the original text (Boase-Beier, 2014, p. 394). Analyzing how meaning is constructed or generated via maxims in a fictional text assists the translator in both hearing and recreating the literary style or narrative voice that guides the linguistic choices made by the author (Munday, 2008, p. 19). Take children literature translation as example, this paper not only offers a peek into the causes of explicitation and implicitation in children literature translation, but makes an explanatory study of it. Since the translation of children’s literature should comply with children’s cognition and characteristics so as to reconcile the relation between readability and educational functions, this paper will again analyze how meaning is constructed or generated via maxims in the text. Such analysis will help translators in maintaining the style of the original, which is conducive to managing implicit and explicit information in the translation.

Background The placenta, as a unique exchange organ between mother and fetus, is essential for successful human pregnancy and fetal health. Preeclampsia (PE) caused by placental dysfunction contributes to both maternal and infant morbidity and mortality. Accurate identification of PE patients plays a vital role in the formulation of treatment plans. However, the traditional clinical methods of PE have a high misdiagnosis rate. Results Here, we first designed a computational biology method that used single-cell transcriptome (scRNA-seq) of healthy pregnancy (38 wk) and early-onset PE (28–32 wk) to identify pathological cell subpopulations and predict PE risk. Based on machine learning methods and feature selection techniques, we observed that the Tuning ReliefF (TURF) score hybrid with XGBoost (TURF_XGB) achieved optimal performance, with 92.61% accuracy and 92.46% recall for classifying nine cell subpopulations of healthy placentas. Biological landscapes of placenta heterogeneity could be mapped by the 110 marker genes screened by TURF_XGB, which revealed the superiority of the TURF feature mining. Moreover, we processed the PE dataset with LASSO to obtain 497 biomarkers. Integration analysis of the above two gene sets revealed that dendritic cells were closely associated with early-onset PE, and C1QB and C1QC might drive preeclampsia by mediating inflammation. In addition, an ensemble model-based risk stratification card was developed to classify preeclampsia patients, and its area under the receiver operating characteristic curve (AUC) could reach 0.99. For broader accessibility, we designed an accessible online web server (http://bioinfor.imu.edu.cn/placenta). Conclusion Single-cell transcriptome-based preeclampsia risk assessment using an ensemble machine learning framework is a valuable asset for clinical decision-making. C1QB and C1QC may be involved in the development and progression of early-onset PE by affecting the complement and coagulation cascades pathway that mediate inflammation, which has important implications for better understanding the pathogenesis of PE.

Carbohydrate can be converted into fat by de novo lipogenesis, a process upregulated in fatty liver disease. Chemically, de novo lipogenesis involves polymerization and reduction of acetyl-CoA, using NADPH as the electron donor. The feedstocks used to generate acetyl-CoA and NADPH in lipogenic tissues remain, however, unclear. Here we show using stable isotope tracing in mice that de novo lipogenesis in adipose is supported by glucose and its catabolism via the pentose phosphate pathway to make NADPH. The liver, in contrast, derives acetyl-CoA for lipogenesis from acetate and lactate, and NADPH from folate-mediated serine catabolism. Such NADPH generation involves the cytosolic serine pathway in liver running in the opposite direction to that observed in most tissues and tumours, with NADPH made by the SHMT1-MTHFD1-ALDH1L1 reaction sequence. SHMT inhibition decreases hepatic lipogenesis. Thus, liver folate metabolism is distinctively wired to support cytosolic NADPH production and lipogenesis. More generally, while the same enzymes are involved in fat synthesis in liver and adipose, different substrates are used, opening the door to tissue-specific pharmacological interventions.

BACKGROUND: In recent years, immunotherapy has made great progress, and the regulatory role of epigenetics has been verified. However, the role of 5-methylcytosine (m(5)C) in the tumor microenvironment (TME) and immunotherapy response remains unclear. METHODS: Based on 11 m(5)C regulators, we evaluated the m(5)C modification patterns of 572 lung adenocarcinoma (LUAD) patients. The m(5)C score was constructed by principal component analysis (PCA) algorithms in order to quantify the m(5)C modification pattern of individual LUAD patients. RESULTS: Two m(5)C methylation modification patterns were identified according to 11 m(5)C regulators. The two patterns had a remarkably distinct TME immune cell infiltration characterization. Next, 226 differentially expressed genes (DEGs) related to the m(5)C phenotype were screened. Patients were divided into three different gene cluster subtypes based on these genes, which had different TME immune cell infiltration and prognosis characteristics. The m(5)C score was constructed to quantify the m(5)C modification pattern of individual LUAD patients. We found that the high m(5)C score group had a better prognosis. The role of the m(5)C score in predicting prognosis was also verified in the dataset GSE31210. CONCLUSIONS: Our study revealed that m(5)C modification played a significant role in TME regulation of LUAD. Investigation of the m(5)C regulation mode may have some implications for tumor immunotherapy in the future.

This study presents a multi-objective optimization model to solve aircraft landing problem within a predetermined time window. Minimizing total fuel cost, minimizing total delays, maximizing throughput, and maximizing fairness are considered to construct the multi-objective optimization model. According to the different weights of objectives, the first-come-first-serve and simulated annealing are used to solve the multi-objective optimization problem. The algorithms’ feasibility is analysed and a suggestion about how to get satisfactory solutions for all stakeholders is given through the expert evaluation method. The results show that the simulated annealing can get better solution than the first-come-first-serve when the number of flights exceeds 10.

In this paper, the chaotic characteristics of air traffic flow are studied, ADS-B data easily available to ground aviation users are selected as the basic data of traffic flow, and a high-dimensional prediction model of air traffic flow time series based on the non-iterative PSR-ELM algorithm is established. The prediction results of the proposed algorithm are then compared with those of the SVR algorithm, which requires iteration. Moreover, airspace operation data before and after the outbreak of the COVID-19 epidemic are selected as the experimental scene, and the prediction effects of time series with different degrees of chaos are comparatively analyzed. The experimental results reveal that the PSR-ELM algorithm achieves fast and accurate results, and, when the traffic flow state is sparse, the degree of chaos is reduced and the prediction effect is improved. The findings of this research provide a reference for air traffic flow theory.

Objective: To compare the survival benefit between neoadjuvant and adjuvant radiotherapy for non-inflammatory non-metastatic breast cancer patients.Method: We conducted a retrospective cohort study using surveillance, epidemiology and end results databases. Our study recruited patients who had been diagnosed with stage I-III breast cancer and underwent surgery and radiotherapy. The overall survival was calculated by Kaplan Meier method. Cox risk model was used to determine the impact of radiotherapy according to stage, molecular subtype and other risk factors. Propensity score matching was used to balance measurable confounding factors. Results: Of all the 411483 enrolled patients varying from 1975 to 2016, 1712 patients received neoadjuvant radiotherapy, and 409567 patients received adjuvant radiotherapy. Compared with the adjuvant radiotherapy group, the neoadjuvant radiotherapy group showed significantly higher risks of overall mortality and breast cancer-specific mortality. Survival differences in treatment sequences were correlated with stage, molecular subtypes and other risk factors.Conclusion: According to the results of this study, neoadjuvant radiotherapy did not show a survival advantage, and adjuvant radiotherapy is still the primary treatment. However, neoadjuvant radiotherapy also has some theoretical advantages, such as phase reduction and recurrence reduction. Therefore, it is still worthy of further exploration.

To compare the survival benefit between preoperative and postoperative radiotherapy for stage I–III non-inflammatory breast cancer patients, we conducted a retrospective cohort study using surveillance, epidemiology and end results databases. Our study recruited patients who had been diagnosed with stage I–III breast cancer and underwent surgery and radiotherapy. The overall survival was calculated by Kaplan–Meier method. Cox risk model was used to determine the impact of radiotherapy according to stage, molecular subtype and other risk factors. Propensity score matching was used to balance measurable confounding factors. Of all the 411,279 enrolled patients varying from 1975 to 2016, 1712 patients received preoperative radiotherapy, and 409,567 patients received postoperative radiotherapy. Compared with the postoperative radiotherapy group, the preoperative radiotherapy group showed significantly higher risks of overall mortality and breast cancer-specific mortality. Survival differences in treatment sequences were correlated with stage, molecular subtypes and other risk factors. According to the results of this study, preoperative radiotherapy did not show a survival advantage, and postoperative radiotherapy is still the primary treatment. However, preoperative radiotherapy also has some theoretical advantages, such as phase reduction and recurrence reduction. Therefore, it is still worthy of further exploration.

Understanding the chaos of air traffic flow is significant to the achievement of advanced air traffic management, and trajectory data are the basic material for studying the chaotic characteristics. However, at present, there are two main obstacles to this task, namely, large amounts of noise in the measured data and the tedium of existing data processing methods. This paper improves the incorrect trajectory processing method based on ADS-B trajectory data and proposes a method by which to quickly extract the traffic flow through a certain waypoint. Currently, the commonly used theoretical analysis tools for nonlinear complex systems include the classical nonlinear dynamics analysis method and the newly developed complex network-based analysis method. The latter is currently in an exploratory stage because it has just been introduced into the study of air traffic flow. From these two perspectives, the chaotic characteristics of air traffic flow are studied in the present work. From the perspective of nonlinear dynamics, the improved C-C method is used to calculate the reliability parameters, namely, the time delay τ and embedding dimension m, of phase-space reconstruction, and the maximum Lyapunov index is calculated by using the small data volume method to prove the existence of chaos in the system. From the perspective of complex networks, the construction of a visibility graph and horizontal visibility graph is used to prove the existence of chaos in the system, and the goodness-of-fit parameters of the degree distributions of two fitting methods under different time scales are evaluated, which provides support for the air traffic flow theory.

Per capita fructose consumption has increased 100-fold over the last century1. Epidemiological studies suggest that excessive fructose consumption, and especially consumption of sweet drinks, is associated with hyperlipidaemia, non-alcoholic fatty liver disease, obesity and diabetes2–7. Fructose metabolism begins with its phosphorylation by the enzyme ketohexokinase (KHK), which exists in two alternatively spliced forms8. The more active isozyme, KHK-C, is expressed most strongly in the liver, but also substantially in the small intestine9,10 where it drives dietary fructose absorption and conversion into other metabolites before fructose reaches the liver11–13. It is unclear whether intestinal fructose metabolism prevents or contributes to fructose-induced lipogenesis and liver pathology. Here we show that intestinal fructose catabolism mitigates fructose-induced hepatic lipogenesis. In mice, intestine-specific KHK-C deletion increases dietary fructose transit to the liver and gut microbiota and sensitizes mice to fructose’s hyperlipidaemic effects and hepatic steatosis. In contrast, intestine-specific KHK-C overexpression promotes intestinal fructose clearance and decreases fructose-induced lipogenesis. Thus, intestinal fructose clearance capacity controls the rate at which fructose can be safely ingested. Consistent with this, we show that the same amount of fructose is more strongly lipogenic when drunk than eaten, or when administered as a single gavage, as opposed to multiple doses spread over 45 min. Collectively, these data demonstrate that fructose induces lipogenesis when its dietary intake rate exceeds the intestinal clearance capacity. In the modern context of ready food availability, the resulting fructose spillover drives metabolic syndrome. Slower fructose intake, tailored to intestinal capacity, can mitigate these consequences. Modulating the capacity of the intestinal epithelium to catabolize fructose is shown to alter fructose-induced lipogenesis in the mouse liver, suggesting that fructose clearance in the small intestine protects from steatosis.

The development of civil aviation has led to flight operations generating massive datasets. Derived from Automatic Dependent Surveillance-Broadcast technology, an eigengap-based automatic hierarchical clustering algorithm is proposed, aiming to overcome the human intervention requirement of hierarchical clustering to determine the number of track clusters in the terminal area. First, the trajectory pair similarity matrix is calculated based on the Euclidean distance and an adaptive scale parameter. Second, the Laplace transformation is applied on the similarity matrix and the eigenvalues are obtained in order to determine the number of clusters. Finally, the hierarchical clustering algorithm divides the terminal area trajectory into several sub-categories. Taking a terminal approach trajectory as an example, simulation analysis is performed, with results revealing that the algorithm divides 404 north-south-oriented aircraft flight trajectories into 2 and 3 categories. Furthermore, the new-index is used to evaluate the clustering results, demonstrating its effectiveness is better than the automatic k-means algorithm. Our study provides support for reliability analysis of air traffic network in the terminal area.

Background: The optimal therapeutic strategy in locally advanced esophageal squamous cell carcinoma (ESCC) primarily treated by surgery remains unknown. This study was designed to evaluate the impact of postoperative chemoradiotherapy and postoperative sequential chemoradiotherapy on survival in this population. Methods: The study included a total of 228 consecutive patients who underwent radical esophagectomy and were confirmed to have stage pT3–4 or pN+ ESCC from September 2011 to September 2017 at our institution. All patients received postoperative radiotherapy with or without concurrent or sequential chemotherapy after esophagectomy. Univariate and multivariate analyses were used to compare the survival of patients with postoperative radiotherapy, postoperative concurrent chemoradiotherapy, and postoperative sequential chemoradiotherapy. Results: After a median follow-up of 52 months, the 3- and 5-year overall survival (OS) rates were 70.2% [95% confidence interval (CI), 63.7–76.7%] and 62.2% (95% CI, 54.6–69.8%), respectively. The disease-free survival (DFS) rates at 3 and 5 years were 65.2% (95% CI, 58.7–71.7%) and 55.2% (95% CI, 47.6–62.8%), respectively. The 3- and 5-year locoregional recurrence-free survival (LRFS) rates were 65.1% (95% CI, 58.4–71.8%) and 55.5% (95% CI, 47.7–63.3%). Of the 228 patients, 38 (16.7%) had distant metastases. Subgroup analysis showed that being male and having a higher T stage were independent poor prognostic factors for OS and DFS in patients with pN+ or stage III + IVA ESCC. The results also showed that in patients with stage III + IVA ESCC, the DFS of the patients in the concurrent chemotherapy (CCT) group was improved compared with that in the no CCT group [hazard ratio (HR), 0.551; 95% CI, 0.323–0.938; P=0.028]. Multivariate analysis showed that sequential chemoradiotherapy was associated with poor LRFS (HR, 2.312; 95% CI, 1.078–4.959; P=0.031), especially in stage T3–4 patients, and it was also related to the poor DFS (HR, 1.781; 95% CI, 1.086–2.921; P=0.022) in patients with stage T3–4 ESCC. Conclusions: In patients with locally advanced ESCC, those who underwent sequential chemoradiotherapy had a worse LRFS. Postoperative concurrent chemoradiotherapy was the most effective adjuvant therapy for resected stage III–IVA ESCC. In addition, being male, having a higher T stage, and being node-positive were independent poor prognostic factors for OS and DFS.

Fractal theory is a new scientific method and theory that can describe the complexity and irregularity of nature. Aiming at the problem that the frequency modulation slope obtained by the traditional linear frequency modulation (LFM) signal parameter estimation algorithm has high complexity with poor real-time performance and small SNR adaptation range, the LFM signal frequency modulation slope estimation method based on the fractal-box dimension is proposed. The proposed method is utilized to evaluate the frequency modulation slope of the LFM signal, and the affect of signal amplitude and phase on the fractal-box dimension of the signal is taken into account. The estimation error at different SNRs is analyzed, and the relationship graph of the pulse width, FM bandwidth and fractal-box dimension is drawn. The simulation results demonstrate that the proposed method can accurately estimate the parameters of LFM signals under varying SNR environment. Compared with the traditional linear frequency modulation (LFM) signal parameter estimation algorithms, the anti-noise performance of the proposed algorithm is stronger, and the proposed algorithm is relatively simple and has good application value.

Carbohydrate can be converted into fat by de novo lipogenesis, a process upregulated in fatty liver disease. Chemically, de novo lipogenesis involves polymerization and reduction of acetyl-CoA, using NADPH as the electron donor. The feedstocks used to generate acetyl-CoA and NADPH in lipogenic tissues remain, however, unclear. Here we show using stable isotope tracing in mice that de novo lipogenesis in adipose is supported by glucose and its catabolism via the pentose phosphate pathway to make NADPH. The liver, in contrast, derives acetyl-CoA for lipogenesis from acetate and lactate, and NADPH from folate-mediated serine catabolism. Such NADPH generation involves the cytosolic serine pathway in liver running in the opposite direction to that observed in most tissues and tumours, with NADPH made by the SHMT1-MTHFD1-ALDH1L1 reaction sequence. SHMT inhibition decreases hepatic lipogenesis. Thus, liver folate metabolism is distinctively wired to support cytosolic NADPH production and lipogenesis. More generally, while the same enzymes are involved in fat synthesis in liver and adipose, different substrates are used, opening the door to tissue-specific pharmacological interventions.

Contralateral axillary lymph node metastasis (CAM) in breast cancer is rare, and the reason is unclear. CAM may be found at the time of primary breast cancer diagnosis or following primary tumor treatment. CAM staging and treatment methods are controversial. Hence, we summarized the features of CAM patients and explored the therapeutic options. We report the case of an 82-year-old woman with right breast cancer accompanied by CAM. The records of breast cancer patients with CAM in PubMed (January 2000-May 2020) were also reviewed. After undergoing comprehensive treatments (neoadjuvant chemotherapy, surgery, radiotherapy, endocrine therapy, and targeted therapy), no signs of recurrence and metastasis were noted during the 25-month follow-up. Overall, 17 studies (36 patients) were selected for the review, of which 15 had synchronous CAM and 21 had metachronous CAM. Nineteen of 22 patients had histologic grade 3. Forty percent (12/30) had ≥4 positive lymph nodes. Among these 36 patients, 9 had triple-negative breast cancers, and 9 were human epidermal growth factor receptor 2 positive. Among the 30 patients staged, 6 (20%) were in stage I; 7 (23.33%), stage II; and 17 (56.67%), stage III. Two patients received systemic therapy, while one received an unknown treatment. The remaining 33 patients all received local treatment (surgery or radiotherapy). The survival time of synchronous and metachronous CAM patients was 12-72 months with the death rate of 1/7 and 12-144 months with that of 3/12, respectively. CAM may be a regional manifestation rather than a distant metastasis. Comprehensive treatment, including surgery and radiotherapy, may provide better control.

To achieve flight risk identification and assessment in the new field of UAVs, two index systems of risk prediction and safety evaluation are constructed in this work by categorizing the set of UAV flight risks, and the identification subset is set according to the UAV characteristics and use. Risk identification elements are set for the identification subset, and the qualitative attributes of risk identification elements are quantified by set-valued statistics. A general quantitative model for the flight risk identification of UAVs is established. According to the different flight risk factors, UAV flight risks are categorized, the fuzzy comprehensive evaluation method is adopted, and case data are used to quantitatively evaluate different types of risks. Finally, the application of the techno-economic evaluation method to the feasibility evaluation of the UAV flight risk prevention scheme is proposed to provide support for UAV flight risk management.

Background To determine the diagnostic accuracy of preoperative T/N stage with magnetic resonance imaging (MRI) in lower and middle rectal cancer patients and the impacts on clinical decision‐making. Patients and methods A total of 211 patients were recruited from October 2015 to March 2017 in this retrospective study. High‐resolution MRI was performed within 2 weeks before surgery. Histopathologic results were evaluated for the postoperative T/N stage and the diagnostic accuracy of MRI was assessed according to the postoperative histopathologic results. The accuracy, sensitivity, specificity, positive predictive value and negative predictive value were evaluated for T/N staging and κ values were used to evaluate MRI consistent analysis compared with postoperative histopathologic staging. Results The overall MRI diagnostic accuracy was 79.62% for T1‐4 staging and 54.50% for N0‐2 staging. The κ values were 0.619 and 0.255 for T1‐4 and N0‐2 staging, respectively. The diagnostic accuracy of MRI for treatment decision‐making was 80.57%. Conclusion MRI allows a highly accurate preoperative assessment of T stage but only a fairly accurate preoperative assessment of N stage for rectal cancer. The diagnostic accuracy of MRI for treatment decision‐making is promising, but additional studies are needed to validate these findings in a larger sample size from multiple centers.

Background: The management of rectal carcinoma has substantially evolved over the past two decades, so as AJCC staging and NCCN guidelines. The inherent relationships of pathologic factors warrant further study. The present study aimed to assess the associations of clinical and pathological factors in rectal cancer patients undergoing radical surgery.Methods: From October 2015 to February 2019, all rectal cancer patients treated with radical surgery without neoadjuvant therapy were identified. The analysis was performed with data obtained from the prospectively collected database. Predictive factors for lymph node metastasis were analysed.Results: In total, 692 patients with a median age of 61.64 years (range: 22-89) were included. There was no significant difference in onset age between male and female patients (61.75±11.10 vs 61.43±11.92, P=0.723).Tumour location (P=0.004), perineural invasion (PNI) (P=0.000), lymphovascular invasion (LVI) (P=0.000), tumour deposit (TD) (P=0.000), and differentiation grade (P=0.000) were significantly related to pathologic T stage in univariate analysis, while sex was not (p=0.192).Compared to patients with T1 disease, there was a significantly higher proportion of positive LVI in patients with stage T3 disease (P=0.011, OR=3.404, 95% CI: 1.319-8.787) but not in those with T2 (P=0.686, OR=0.804, 95% CI: 0.280-2.310) and T4 (P=0.063, OR=3.200, 95% CI: 0.941-10.886) disease. Compared to patients with T2 disease, there was a significantly higher proportion of perineural invasion in patients with stage T3 (P=0.000, OR=6.2376, 95% CI: 3.371-11.685) but not T4 (P=0.172, OR=2.309, 95% CI: 0.694-7.676) disease. Compared to patients with T1 disease, a significantly higher proportion of TDs occurred in patients with stage T3 (P=0.013, OR=6.106, 95% CI: 1.455-25.631) and stage T4 (P=0.019, OR=7.146, 95% CI: 1.378-37.044) but not stage T2 (P=0.435, OR=0.503, 95% CI: 0.089-2.824) disease. The overall incidence of lymph node metastasis was 44.9% (19.6% for T1, 23.6% for T2, 56.7% for T3, and 67.8% for T4). Patient age, sex, and tumour location did not significantly affect lymph node metastasis (LNM). The presence of LVI (OR=3.882, 95% CI=2.338-6.440, P=0.000), TD (OR=27.645, 95% CI=9.805-77.947, P=0.000), higher T stage (OR=1.969, 95% CI=1.471-2.635, P=0.000), and poorly differentiated histology (OR=2.255, 95% CI=1.544-3.293, P=0.000) were associated with a higher incidence of LNM on multivariate analysis. Perineural invasion (P=0.000) significantly affected LNM in univariate but not multivariate analysis (OR=1.213, 95% CI=0.734-2.003, P=0.452).Conclusion: There was no significant difference between male and female patients in onset age. Tumour location, PNI, LVI, TD, and differentiation grade were significantly related to pathologic T stage. Patients with the presence of LVI and TD, higher T stage, and poorly differentiated histology have a significantly higher chance of LNM.

NADH provides electrons for aerobic ATP production. In cells deprived of oxygen or with impaired electron transport chain activity, NADH accumulation can be toxic. To minimize such toxicity, elevated NADH inhibits the classical NADH producing pathways: glucose, glutamine, and fat oxidation. Here, through deuterium tracing studies in cultured cells and mice, we show that folate-dependent serine catabolism also produces substantial NADH. Strikingly, when respiration is impaired, serine catabolism through methylene tetrahydrofolate dehydrogenase (MTHFD2) becomes a major NADH source. In cells whose respiration is slowed by hypoxia, metformin, or genetic lesions, mitochondrial serine catabolism inhibition partially normalizes NADH levels and facilitates cell growth. In mice with engineered mitochondrial complex I deficiency (NDUSF4-/-), serine’s contribution to NADH is elevated and progression of spasticity is modestly slowed by pharmacological blockade of serine degradation. Thus, when respiration is impaired, serine catabolism contributes to toxic NADH accumulation.

Flight operation data provides a record of air traffic behaviour in detail, and has become an important resource for studying air traffic patterns. In this paper, the degree characteristic of air traffic networks is studied using flight operation data in order to explain air traffic behaviour. The degree indexes were founded from the perspective of network structure and network function based on complex network theory and traffic flow theory. The connectivity degree, traffic degree and delay degree were established, and the characteristics of these three degrees were studied based on American flight data from different days in 2015 over different time periods of each day. Research shows that at all scales the number degrees, volume degrees and duration degrees are all of power-law distribution, and the power exponents are different depending on the scale, date and index. The power exponents fluctuate from 0.5 to 1.5. In general, the connectivity degree exponent is greater than that of the traffic degree and the delay degree. The duration degree exponent is greater than the traffic degree. The exponents are greater on holidays than they are on non-holidays, as well as during non-working hours than during working hours of the day. Compared with non-holidays, the average connectivity degree and delay degree are larger on holidays, and the average traffic degree is smaller. This reveals that most airports only provide flights to one airport, and the airports’ numbers and flight volumes both decrease on holidays. The power indexes of the delay degree and traffic degree are basically the same. The main mode of traffic (arrivals or departures) during each time period is also studied.

iWith the rapid development and the increasing security threats of D2D communication, the D2D communication security has become more and more important. Device recognition is the foundation of communication security. Traditional device authentication is the cryptographic mechanism and the security protocol that are not enough for the new security threat. RF fingerprint of wireless device has physical characteristics, which is difficult to clone. Therefore, it can be used for D2D device recognition. In this paper, a recognition algorithm of D2D device based on RF fingerprint is proposed. First, Hilbert transform and principal component analysis is used to generate the RF fingerprint of D2D device. Second, support vector machine (SVM) is introduced and CV-SVM is used as the classifier. Finally, simulation results show that the recognition rate is more than 90% under SNR = 5 dB, which proves that the proposed method is effective for D2D device recognition. Therefore, it can be used as the trust management of D2D communication.

Automated modulation classification plays a very important part in cognitive radio networks. Deep learning is also a powerful tool that we could not overlook its potential in addressing signal modulation recognition problem. In our last work, we propose a new data conversion algorithm in order to gain a better classification accuracy of communication signal modulation, but we still believe that the convolution neural network (CNN) can work better. However, its application to signal modulation recognition is often hampered by insufficient data and overfitting. Here, we propose a smart approach to programmatic data augmentation method by using the auxiliary classifier generative adversarial networks (ACGANs). The famous CNN model, AlexNet, has been utilized to be the classifier and ACGAN to be the generator, which will enlarge our data set. In order to alleviate the common issues in the traditional generative adversarial nets training, such as discriminator overfitting, generator disconverge, and mode collapse, we apply several training tricks in our training. With the result on original data set as our baseline, we will evaluate our result on enlarged data set to validate the ACGAN’s performance. The result shows that we can gain 0.1~6% increase in the classification accuracy in the ACGAN-based data set.

Chemical de-caging has emerged as an attractive strategy for gain-of-function study of proteins via small-molecule reagents. The previously reported chemical de-caging reactions have been largely centered on liberating the side chain of lysine on a given protein. Herein, we developed an allene-based caging moiety and the corresponding palladium de-caging reagents for chemical rescue of tyrosine (Tyr) activity on intracellular proteins. This bioorthogonal de-caging pair has been successfully applied to unmask enzymatic Tyr sites (e.g., Y671 on Taq polymerase and Y728 on Anthrax lethal factor) as well as the post-translational Tyr modification site (Y416 on Src kinase) in vitro and in living cells. Our strategy provides a general platform for chemical rescue of Tyr-dependent protein activity inside cells.

The growing need of responsiveness for enterprises facing market volatility raises a strong demand for flexibility in their human resource management. This paper presents a multi-objective model for Multi-skill Project Scheduling Problem. We propose a new wage distribution method in which different perform efficiencies of human resources are taken into account. The model aims at minimizing project duration and project costs concurrently. An improved NSGA-II algorithm is designed to solve the model. The algorithm introduces a multi-dimensional chromosome coding scheme to identify the priorities and staff allocation of each activity. Special chromosome crossover and mutation operation are employed to address resource conflicts and constraint violations. Eventually, A case study is presented to verify the efficiency of the proposed approach.

In this paper, analysis is conducted on the risk assessment regarding the vertical collision of CSPR (Closely Spaced Parallel Runways) paired-approach, to ensure flight safety. A vertical kinematics equation is established with analysis of CSPR paired approach and starting from the preconditions that the proceeding aircraft altitude is lower than that of the following aircraft during paired approach: the time consuming of passing initial safety separation by the proceeding aircraft decelerated less or greater than that of the proceeding aircraft with uniform speed. Based on the two conditions, its corresponding risk-evaluation model is established, proceeding from the aircraft ADS-B data and the analysis on the relation between aircraft position error and altitude maintain ability, relevant model parameters specified. Conclusion has been achieved on risk assessment that implementing vertical collision risk of paired approach has little to do with aircraft type and initial longitudinal separation, but has more correlation with initial vertical interval and aircraft altitude maintain ability; rules of at least 180-m vertical interval and altitude error not exceeding 40.77 m (within 95% flight time) must be obeyed when paired approach applied.

This paper aims to provide an insight into the roles of the different types of airports in China by improved K-means clustering algorithm. The first part of the work analyzed the characteristics of Chinese airline network and pointed out that the key to construct hub-and-spoke airline network is determining the function of each airport. The index system of airport function orientation was established from airport operation index, airport hinterland index and airport growth index. The airports in China were classified into four classes by the K-means clustering algorithm. In order to improve reliability of clustering algorithm, a formula was used to normalize the value of each index, and the airports were clustered by improved K-means clustering algorithm. The algorithm was simulated by the MATLAB and the clustered results show the airports have obvious hierarchy.

To overcome the traditional disadvantages of single source points detection methods in underdetermined blind source separation problem, this paper proposes a novel algorithm to detect single source points for the linear instantaneous mixed model. First, the algorithm utilizes a certain relationship between the time-frequency coefficients and the complex conjugate factors of the observation signal to realize single source points detection. Then, the algorithm finds more time-frequency points that meets the requirements automatically and cluster them by utilizing a clustering algorithm based on the improved potential function. Finally, the estimation of the mixed matrix is achieved by clustering the re-selected single source points. Simulation experiments on linear mixture model demonstrates the efficiency and feasibility for estimating the mixing matrix.

1090ES ADS-B is a new technology in civil aviation, mainly used to monitor dynamic condition of aircrafts and share the flight information. However, the ADS-B signals are often interfered with other overlapped signals during the signal transmission, causing signal overlap and bringing difficulties to signal processing afterwards. This article applies Blind Source Separation (BSS) into the separation process of ADS-B overlapped signals and constructs the ADS-B overlapped signal separation model using Infomax algorithm, in order to separate ADS-B overlapped signals into single-way ADS-B signals and improve signal decoding rate.

With the popularization of self-driving cars and the rapid development of intelligent transportation, pedestrian detection shows more and more extensive application scenarios in daily life, which have higher and higher application values. It also raises more and more interest from academic community. Pedestrian detection is fundamental in many human-oriented tasks, including trajectory tracking of people, recognition of pedestrian gait, and autopilot recognition of pedestrians to take appropriate response measures. In this context, this paper studies the design and implementation of a pedestrian detection system. The pedestrian detection system of this article is mainly composed of two parts. The first part is a pedestrian detector based on deep learning, and the second part is a graphical interface that interacts with the user. The former part mainly uses the Faster R-RCNN learning model, which can use convolutional neural networks to learn features from the data and extract the features of the image. It can also search the image through RPN network for areas where the target is located and then classify them. In this paper, a complete pedestrian detection system is implemented on the basis of deep learning framework Caffe. Experiments show that the system has high recognition rate and fast recognition speed in real world.

Background To determine the diagnostic accuracy of preoperative T/N stage using MRI in lower and middle rectal cancer patients and the impacts on clinical decision-making. Patients and methods There were 354 patients recruited from May 2017 to February 2019. MRI was performed within 2 weeks before surgery. Histopathologic results were evaluated for the postoperative T/N stage and MRI diagnostic accuracy was assessed based on the postoperative histopathologic results. Accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and Kappa values were used to evaluate MRI diagnostic accuracy and analysis consistency compared with postoperative histopathologic staging. Results Overall MRI diagnostic accuracy was 78.2% and 56.8% for T1–4 and N0–2 staging. The Kappa values were 0.625 and 0.323 for T1–4 and N0–2 staging, respectively. After combination, MRI diagnostic accuracy was 85% and 69.5% for T and N staging. The Kappa values were 0.693 and 0.4 for T and N staging. The diagnostic accuracy of MRI for treatment decision-making was 79.1%. Conclusion MRI enables a highly accurate preoperative assessment of T stage but only a fairly accurate preoperative assessment of the N stage for rectal cancer with surgery. The diagnostic accuracy of MRI for treatment decision-making is promising.

Insights into the temporal-spatial characteristics of air traffic flow is the prerequisite of air traffic flow modeling, prediction and optimization. The intrinsic dynamics of air traffic flow were analyzed according to radar track data by classical nonlinear analysis and complex network methods. The nonlinear dynamic characteristics of air traffic flow were revealed from multiple perspectives by means of phase space reconstruction, recurrence plot, Lyapunov exponent, correlation dimension and Hurst exponent. The experimental results show that the example traffic flow became the most chaotic and the state persistence was poorest at time scale of 30 min. Besides, this paper explored the application of a complex network to analyze the nonlinearity of air traffic flow, and verified the chaotic and fractal characteristics by analyzing the degree distribution of the converted network. The degree distribution of the network derived from the traffic flow time series was approximately fitted to the exponential function and featured a strength level of chaos that was positively correlated with the size of network degree distribution power exponent. The linear relationship between network degree distribution power exponent and Hurst exponent was revealed, and the nonlinearity of air route traffic flow was systematically characterized. Particularly, the complex network-based analysis method helped examine air traffic flow from a new perspective, enriching the research means. These novel findings will provide theoretical basis for aggregated air traffic flow modeling, decision support system design and tactical flow management.

In order to study the applicability of data mining in the study of ATC operational safety, take the six typical factors that may affect the safety of ATC as the former, and the level of unsafe incidents in ATC as the next term, use correlation analysis and Apriori algorithm, And set a reasonable degree of confidence in the rules, the degree of support for the rules, analysis of air traffic insecurity incidents. Taking the general ATC operational safety incident as an example, the results show that the data mining has applicability in the problem of ATC operational safety, and each of the influencing factors has a certain relevance; Each of the preceding factors has an impact on the safety of ATC operations, but the degree of impact is different. Among them, the factors that have a greater impact are mainly control load, airspace environment and control equipment.

Expression map of the 11 prognostic lncRNAs across four UCEC subtypes. Kruskal-Wallis test was used to compare expression levels for each lncRNAs across four UCEC subtypes. (DOC 765 kb)

lncRNAs significantly associated with overall survival in univariate Cox regression analyses. (DOC 38 kb)

Recent studies have demonstrated the utility and superiority of long non-coding RNAs (lncRNAs) as novel biomarkers for cancer diagnosis, prognosis, and therapy. In the present study, the prognostic value of lncRNAs in glioblastoma multiforme was systematically investigated by performing a genome-wide analysis of lncRNA expression profiles in 419 glioblastoma patients from The Cancer Genome Atlas (TCGA) project. Using survival analysis and Cox regression model, we identified a set of six lncRNAs (AC005013.5, UBE2R2-AS1, ENTPD1-AS1, RP11-89C21.2, AC073115.6, and XLOC_004803) demonstrating an ability to stratify patients into high- and low-risk groups with significantly different survival (median 0.899 vs. 1.611 years, p = 3.87e−09, log-rank test) in the training cohort. The six-lncRNA signature was successfully validated on independent test cohort of 219 patients with glioblastoma, and it revealed superior performance for risk stratification with respect to existing lncRNA-related signatures. Multivariate Cox and stratification analysis indicated that the six-lncRNA signature was an independent prognostic factor after adjusting for other clinical covariates. Further in silico functional analysis suggested that the six-lncRNA signature may be involved in the immune-related biological processes and pathways which are very well known in the context of glioblastoma tumorigenesis. The identified lncRNA signature had important clinical implication for improving outcome prediction and guiding the tailored therapy for glioblastoma patients with further prospective validation.

Aim This meta-analysis was designed to fully assess the curative effects of radiotherapy-based therapies for human epidermal growth factor receptor 2-positive (HER2+) breast cancer (BC). Methods English articles were retrieved through searching Cochrane library, PubMed, and Embase databases updated to February 2017. Studies were selected based on the inclusion and exclusion criteria. The curative effects of radiotherapy-based therapies forHER2+ BC patients were assessed using hazard rates (HRs) or odds ratios (ORs), as well as their 95% confidence intervals (CIs). In addition, Egger test was used to assess publication bias, followed by sensitivity analysis. All statistic methods were conducted using R 3.12 software. Results A total of 9 eligible studies were included into this meta-analysis, which involved 2236 HER2+ BC patients. Egger test showed that the eligible studies had no publication bias (t = 2.198, P = .05918). Sensitivity analysis demonstrated that the results were stable. HER2+ BC patients in radiotherapy group had lower locoregional recurrences than those in other groups. Moreover, meta-analysis showed that no significant difference was found between HER2+ BC patients in radiotherapy group and other groups on the 1-year overall survival (P = 0.5263, I = 65.4%), 3-year overall survival (P = 0.4591, I = 0), and 5-year overall survival (P = 0.06277, I = 0). Conclusion Radiotherapy-based therapies might have certain advantages in treating HER2+ BC patients.

We herein report a chemical decaging strategy for the in situ generation of neuramic acid (Neu), a unique type of sialic acid, on live cells by the use of a palladium-mediated bioorthogonal elimination reaction. Palladium nanoparticles (Pd NPs) were found to be a highly efficient and biocompatible depropargylation catalyst for the direct conversion of metabolically incorporated N-(propargyloxycarbonyl)neuramic acid (Neu5Proc) into Neu on cell-surface glycans. This conversion chemically mimics the enzymatic de-N-acetylation of N-acetylneuramic acid (Neu5Ac), a proposed mechanism for the natural occurrence of Neu on cell-surface glycans. The bioorthogonal elimination was also exploited for the manipulation of cell-surface charge by unmasking the free amine at C5 to neutralize the negatively charged carboxyl group at C1 of sialic acids.

The INK4b-ARF-INK4a gene cluster encodes three tumor suppressors: p15(INK4b), p14(ARF), and p16(INK4a). Antisense non-coding RNA in the INK4 locus (ANRIL) is transcribed in the opposite direction from this gene cluster. Recent studies suggest that ANRIL represses the expression of p15(INK4b), p14(ARF), and p16(INK4a); however, the underlying mechanism is unclear. In this study, the expressions of ANRIL in human esophageal squamous cell carcinoma (ESCC) tissues and matched adjacent non-tumor tissues were examined by quantitative real-time polymerase chain reaction. Compared with matched adjacent non-tumor tissues, the expression levels of ANRIL in ESCC tissues were significantly increased. Furthermore, inhibition of ANRIL was found to increase the expression of p15(INK4b) and transforming growth factor β1 (TGFβ1) and depletion of ANRIL in ESCC cell lines may inhibit cellular proliferation. Thus, our findings suggest a significant role of ANRIL in the occurrence and development of ESCC through TGFβ1 signaling pathways.