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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.

SUMMARYMammalian organs are nourished by nutrients carried by the blood circulation. These nutrients originate from diet and internal stores, and can undergo various interconversions before their eventual use as tissue fuel. Here we develop isotope tracing, mass spectrometry, and mathematical analysis methods to determine the direct sources of circulating nutrients, their interconversion rates, and eventual tissue-specific contributions to TCA cycle metabolism. Experiments with fifteen nutrient tracers enabled extensive accounting both for circulatory metabolic cycles and tissue TCA inputs, across fed and fasted mice on either high carbohydrate or ketogenic diet. We find that a majority of circulating carbon flux is carried by two major cycles: glucose-lactate and triglyceride-glycerol-fatty acid. Futile cycling through these pathways is prominent when dietary content of the associated nutrients is low, rendering internal metabolic activity robust to food choice. The presented in vivo flux quantification methods are broadly applicable to different physiological and disease states.

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.

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.

NADPH donates high-energy electrons for antioxidant defence and reductive biosynthesis. Cytosolic NADP is recycled to NADPH by the oxidative pentose-phosphate pathway (oxPPP), malic enzyme 1 (ME1) and isocitrate dehydrogenase 1 (IDH1). Here we show that any one of these routes can support cell growth, but the oxPPP is uniquely required to maintain a normal NADPH/NADP ratio, mammalian dihydrofolate reductase (DHFR) activity and folate metabolism. These findings are based on CRISPR deletions of glucose-6-phosphate dehydrogenase (G6PD, the committed oxPPP enzyme), ME1, IDH1 and combinations thereof in HCT116 colon cancer cells. Loss of G6PD results in high NADP, which induces compensatory increases in ME1 and IDH1 flux. But the high NADP inhibits DHFR, resulting in impaired folate-mediated biosynthesis, which is reversed by recombinant expression of Escherichia coli DHFR. Across different cancer cell lines, G6PD deletion produced consistent changes in folate-related metabolites, suggesting a general requirement for the oxPPP to support folate metabolism. The oxidative pentose-phosphate pathway (oxPPP) is a major NADPH producer. Here the authors show that malic enzyme or isocitrate dehydrogenase can support the growth of cells lacking the oxPPP, but the oxPPP is necessary to maintain a normal NADPH/NADP ratio, DHFR activity and folate metabolism.

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.

In this paper, information entropy and ensemble learning based signal recognition theory and algorithms have been proposed. We have extracted 16 kinds of entropy features out of 9 types of modulated signals. The types of information entropy used are numerous, including Renyi entropy and energy entropy based on S Transform and Generalized S Transform. We have used three feature selection algorithms, including sequence forward selection (SFS), sequence forward floating selection (SFFS) and RELIEF-F to select the optimal feature subset from 16 entropy features. We use five classifiers, including k-nearest neighbor (KNN), support vector machine (SVM), Adaboost, Gradient Boosting Decision Tree (GBDT) and eXtreme Gradient Boosting (XGBoost) to classify the original feature set and the feature subsets selected by different feature selection algorithms. The simulation results show that the feature subsets selected by SFS and SFFS algorithms are the best, with a 48% increase in recognition rate over the original feature set when using KNN classifier and a 34% increase when using SVM classifier. For the other three classifiers, the original feature set can achieve the best recognition performance. The XGBoost classifier has the best recognition performance, the overall recognition rate is 97.74% and the recognition rate can reach 82% when the signal to noise ratio (SNR) is −10 dB.

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)

Much understanding of metabolism is based on monitoring chemical reactions in cells with isotope tracers. For this purpose, 13C is well suited due to its stable incorporation into bio-molecules and minimal kinetic isotope effect. For redox reactions, however, deuterium tracing can provide valuable additional information. To date, studies examining NADPH production with deuterated carbon sources have failed to account for roughly half of NADPH’s redox active hydrogen. Here we show that the missing hydrogen is the result of enzyme-catalyzed H-D exchange between water and NADPH. While isolated NADPH does not undergo H-D exchange with water, such exchange is catalyzed by Flavin enzymes and occurs rapidly in cells. Correction for H-D exchange is required for accurate assessment of the biological sources of NADPH’s high energy electrons. Deuterated water (D2O) is frequently used to monitor fat synthesis in vivo, but the chemical pathway of the deuteron into fat remains unclear. We show that D2O labels fatty acids primarily via NADPH. Knowledge of this labeling route enables calculation, without any fitting parameters, of the mass isotope distributions of fatty acids from cells grown in D2O. Thus, knowledge of enzyme-catalyzed H-D exchange between water and NADPH enables chemically accurate interpretation of deuterium tracing studies of redox cofactor and fatty acid metabolism.

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.

Deregulation of long non-coding RNAs (lncRNAs) expression has been proven to be involved in the development and progression of cancer. However, expression pattern and prognostic value of lncRNAs in breast cancer recurrence remain unclear. Here, we analyzed lncRNA expression profiles of breast cancer patients who did or did not develop recurrence by repurposing existing microarray datasets from the Gene Expression Omnibus database, and identified 12 differentially expressed lncRNAs that were closely associated with tumor recurrence of breast cancer patients. We constructed a lncRNA-focus molecular signature by the risk scoring method based on the expression levels of 12 relapse-related lncRNAs from the discovery cohort, which classified patients into high-risk and low-risk groups with significantly different recurrence-free survival (HR = 2.72, 95% confidence interval 2.07–3.57; p = 4.8e-13). The 12-lncRNA signature also represented similar prognostic value in two out of three independent validation cohorts. Furthermore, the prognostic power of the 12-lncRNA signature was independent of known clinical prognostic factors in at least two cohorts. Functional analysis suggested that the predicted relapse-related lncRNAs may be involved in known breast cancer-related biological processes and pathways. Our results highlighted the potential of lncRNAs as novel candidate biomarkers to identify breast cancer patients at high risk of tumor recurrence.

Long non-coding RNAs (lncRNAs) represent an emerging layer of cancer biology and have been implicated in the development and progression of cancers. However, the prognostic significance of lncRNAs in diffuse large-B-cell lymphoma (DLBCL) remains unclear and needs to be systematically investigated. In this study, we obtained and analyzed lncRNA expression profiles in three cohorts of 1043 DLBCL patients by repurposing the publicly available microarray datasets from the Gene Expression Omnibus (GEO) database. In the discovery series of 207 patients, we identified a set of six lncRNAs that was significantly associated with patients’ overall survival (OS) using univariate Cox regression analysis. The six prognostic lncRNAs were combined to form an expression-based six-lncRNA signature which classified patients of the discovery series into the high-risk group and low-risk group with significantly different survival outcome (HR = 2.31, 95% CI = 1.8 to 2.965, p

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.

Glucose is catabolized by two fundamental pathways, glycolysis to make ATP and the oxidative pentose phosphate pathway to make reduced nicotinamide adenine dinucleotide phosphate (NADPH). The first step of the oxidative pentose phosphate pathway is catalyzed by the enzyme glucose-6-phosphate dehydrogenase (G6PD). Here we develop metabolite reporter and deuterium tracer assays to monitor cellular G6PD activity. Using these, we show that the most widely cited G6PD antagonist, dehydroepiandosterone, does not robustly inhibit G6PD in cells. We then identify a small molecule (G6PDi-1) that more effectively inhibits G6PD. Across a range of cultured cells, G6PDi-1 depletes NADPH most strongly in lymphocytes. In T cells but not macrophages, G6PDi-1 markedly decreases inflammatory cytokine production. In neutrophils, it suppresses respiratory burst. Thus, we provide a cell-active small molecule tool for oxidative pentose phosphate pathway inhibition, and use it to identify G6PD as a pharmacological target for modulating immune response.