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Industrial Time-Sensitive Networking (TSN) provides deterministic mechanisms for real-time and reliable flow transmission. Increasing attention has been paid to efficient scheduling for time-sensitive flows with stringent requirements such as ultra-low latency and jitter. In TSN, the fine-grained traffic shaping protocol, cyclic queuing and forwarding (CQF), eliminates uncertain delay and frame loss by cyclic traffic forwarding and queuing. However, it inevitably causes high scheduling complexity. Moreover, complexity is quite sensitive to flow attributes and network scale. The problem stems in part from the lack of an attribute mining mechanism in existing frame-based scheduling. For time-critical industrial networks with large-scale complex flows, a so-called hyper-flow graph based scheduling scheme is proposed to improve the scheduling scalability in terms of schedulability, scheduling efficiency and latency & jitter. The hyper-flow graph is built by aggregating similar flow sets as hyper-flow nodes and designing a hierarchical scheduling framework. The flow attribute-sensitive scheduling information is embedded into the condensed maximal cliques, and reverse maps them precisely to congestion flow portions for re-scheduling. Its parallel scheduling reduces network scale induced complexity. Further, this scheme is designed in its entirety as a comprehensive scheduling algorithm GH^2. It improves the three criteria of scalability along a Pareto front. Extensive simulation studies demonstrate its superiority. Notably, GH^2 is verified its scheduling stability with a runtime of less than 100 ms for 1000 flows and near 1/430 of the SOTA FITS method for 2000 flows.

Zhengyang Hao, Yanzhou Zhang Department of Cardiology, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, People’s Republic of ChinaCorrespondence: Yanzhou Zhang, Department of Cardiology, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, People’s Republic of China, Email zhangyanzhou666@outlook.comObjective: Dapagliflozin 10 mg and empagliflozin 10 mg have been recommended to treat heart failure with reduced ejection fraction (HFrEF), and the purpose of this study was to compare the efficacy and safety of them in HFrEF.Methods: Two hundred and thirty-three patients with HFrEF admitted to a tertiary hospital of Zhengzhou and commenced to take dapagliflozin 10 mg/d or empagliflozin 10 mg/d were retrospectively included and separated into the dapagliflozin group (n=105) and the empagliflozin group (n=128). Their cardiac function indices before and after therapy were compared, together with the ratios of adverse events during therapy.Results: After 6 months of therapy, left ventricular ejection fraction was higher, and the ratio of New York Heart Association functional class III or IV, left ventricular end-diastolic diameter, and N-terminal pro-B-type natriuretic peptide were lower in the empagliflozin group than the dapagliflozin group (P< 0.05). During 6 months of therapy, there were no statistically significant differences for the ratios of hypotension, deteriorating kidney function, and genitourinary infections between the dapagliflozin and empagliflozin groups (P> 0.05).Conclusion: Despite its many limitations, this study suggested that different SGLT2 inhibitors might have differences regarding efficacy in HFrEF. We look forward to future studies to verify our conjectures.Keywords: dapagliflozin, empagliflozin, heart failure with reduced ejection fraction

BACKGROUND AND OBJECTIVES: In hepatocellular carcinoma (HCC) patients, microvascular invasion (MVI) is associated with worse outcomes regardless of treatment. No single reliable preoperative factor exists to predict MVI. The aim of the work described here was to develop a new MVI- based mRNA biomarker to differentiate between high and low risk patients. METHODS: Using The Cancer Genome Atlas (TCGA) database, we collected data from 315 HCC patients, including mRNA expression and complete clinical data. We generated a seven-mRNA signature to predict patient outcomes. The mRNA signature was validated using the GSE36376 cohort. Finally, we tested the formula in our own 53 HCC patients using qPCR for the seven mRNAs and analyzing the computed tomography (CT) features. RESULTS: This seven-mRNA signature significantly correlated with length of recurrence-free survival (RFS) and overall survival (OS) for both the training and validation groups. RFS and OS were briefer in high risk versus low risk patients. A Kaplan-Meier analysis also indicated that survival time was significantly shortened in the high risk group versus the low risk group. Time-dependent receiver operating characteristic analysis demonstrated good predictive performance for the seven-mRNA signature. The mRNA signature also acts as an independent factor according to a Multivariate analysis. Our results are consistent with the seven-mRNA formula risk score. CONCLUSION: Our research showed a novel seven-mRNA biomarker based on MVI predicting RFS and OS in HCC patients. This mRNA signature can stratify patients into subgroups based on their risk of recurrence to help guide individualized treatment and precision management in HCC.

Multiple myeloma is an incurable hematological malignancy that relies on drug combinations for first and secondary lines of treatment. The inclusion of proteasome inhibitors, such as bortezomib, into these combination regimens has improved median survival. Resistance to bortezomib, however, is a common occurrence that ultimately contributes to treatment failure, and there remains a need to identify improved drug combinations. We developed the quadratic phenotypic optimization platform (QPOP) to optimize treatment combinations selected from a candidate pool of 114 approved drugs. QPOP uses quadratic surfaces to model the biological effects of drug combinations to identify effective drug combinations without reference to molecular mechanisms or predetermined drug synergy data. Applying QPOP to bortezomib-resistant multiple myeloma cell lines determined the drug combinations that collectively optimized treatment efficacy. We found that these combinations acted by reversing the DNA methylation and tumor suppressor silencing that often occur after acquired bortezomib resistance in multiple myeloma. Successive application of QPOP on a xenograft mouse model further optimized the dosages of each drug within a given combination while minimizing overall toxicity in vivo, and application of QPOP to ex vivo multiple myeloma patient samples optimized drug combinations in patient-specific contexts.

Multiple myeloma is an incurable hematological malignancy that relies on drug combinations for first and secondary lines of treatment. The inclusion of proteasome inhibitors, such as bortezomib, into these combination regimens has improved median survival. Resistance to bortezomib, however, is a common occurrence that ultimately contributes to treatment failure, and there remains a need to identify improved drug combinations. We developed the quadratic phenotypic optimization platform (QPOP) to optimize treatment combinations selected from a candidate pool of 114 approved drugs. QPOP uses quadratic surfaces to model the biological effects of drug combinations to identify effective drug combinations without reference to molecular mechanisms or predetermined drug synergy data. Applying QPOP to bortezomib-resistant multiple myeloma cell lines determined the drug combinations that collectively optimized treatment efficacy. We found that these combinations acted by reversing the DNA methylation and tumor suppressor silencing that often occur after acquired bortezomib resistance in multiple myeloma. Successive application of QPOP on a xenograft mouse model further optimized the dosages of each drug within a given combination while minimizing overall toxicity in vivo, and application of QPOP to ex vivo multiple myeloma patient samples optimized drug combinations in patient-specific contexts.

Jun Rong,1,* Qian Wang,2,* Yanzhou Zhang,1 Daolong Zhu,1 Handong Sun,2 Weiwei Tang,3 Rongping Wang,1 Weihong Shi,3 Xiu-feng Cao1,2 1Department of Thoracic surgery, Taikang Xianlin Drum Tower Hospital, School of Medicine, Nanjing University, 2Department of Oncology Surgery, Nanjing First Hospital, Nanjing Medical University, Nanjing, China; 3Department of Clinical Medicine, Jiangsu Vocational College of Medicine, Yancheng, China *These authors contributed equally to this work Background: Circular RNAs (circRNAs) are a class of noncoding RNAs with closed loop structures. There has been growing evidence showing that circRNAs are involved in the pathogenesis of human diseases including various carcinomas. Our study is aimed to investigate the association between a new circRNA named circ-DLG1 (hsa_circ_0007203) and esophageal squamous cell carcinoma (ESCC) carcinogenesis. Methods: The circ-DLG1 expression levels were detected by real-time quantitative reverse transcription-polymerase chain reaction in cells, tissues, and plasmas. The correlation between circ-DLG1 expression and clinicopathologic features was then analyzed. The effect of circ-DLG1 expression on cell proliferation was evaluated in vitro by CCK8 assay and clone formation experiment. Finally, a network of circ-DLG1 with its targeted miRNA interactions and corresponding mRNAs was constructed. Results: circ-DLG1was found to be significantly upregulated in ESCC cells, tissues, and plasmas compared to normal cases. Furthermore, in vitro assays, TE10 and KYSE180, of the ESCC cell lines demonstrated that knockdown of circ-DLG1 reduced cell proliferation significantly. Prediction and annotation revealed that circ-DLG1 was able to sponge to 20 miRNAs and 60 corresponding target mRNAs. Conclusion: Our study indicated that upregulation of circ-DLG1 promoted esophageal cell proliferation ability and might serve as a novel biomarker for ESCC. Keywords: ESCC, spong, upregulation, biomarker, miRNA Erratum&nbs

Rong J, Wang Q, Zhang Y, et al. Onco Targets Ther. 2018;11:6723–6730. On page 6723, Xiu-feng Cao correspondence details were incorrectly listed as Correspondence: Xiu-feng CaoDepartment of Oncology Surgery, Nanjing First Hospital, 68 Changle Road, Nanjing 210006, ChinaEmail cxf551101@sina.com The correct details are Correspondence: Xiu-feng CaoDepartment of Thoracic Surgery, Taikang Xianlin Drum Tower Hospital, School of Medicine, Nanjing University, Nanjing 210023, ChinaEmail cxf551101@sina.com  Read the original article&nbsp

Background A well-informed choice of genetic locus is central to the efficacy of DNA barcoding. Current DNA barcoding in animals involves the use of the 5' half of the mitochondrial cytochrome oxidase 1 gene (CO1) to diagnose and delimit species. However, there is no compelling a priori reason for the exclusive focus on this region, and it has been shown that it performs poorly for certain animal groups. To explore alternative mitochondrial barcoding regions, we compared the efficacy of the universal CO1 barcoding region with the other mitochondrial protein-coding genes in eutherian mammals. Four criteria were used for this comparison: the number of recovered species, sequence variability within and between species, resolution to taxonomic levels above that of species, and the degree of mutational saturation. Results Based on 1,179 mitochondrial genomes of eutherians, we found that the universal CO1 barcoding region is a good representative of mitochondrial genes as a whole because the high species-recovery rate (> 90%) was similar to that of other mitochondrial genes, and there were no significant differences in intra- or interspecific variability among genes. However, an overlap between intra- and interspecific variability was still problematic for all mitochondrial genes. Our results also demonstrated that any choice of mitochondrial gene for DNA barcoding failed to offer significant resolution at higher taxonomic levels. Conclusions We suggest that the CO1 barcoding region, the universal DNA barcode, is preferred among the mitochondrial protein-coding genes as a molecular diagnostic at least for eutherian species identification. Nevertheless, DNA barcoding with this marker may still be problematic for certain eutherian taxa and our approach can be used to test potential barcoding loci for such groups.

BACKGROUND Explicit evolutionary models are required in maximum-likelihood and Bayesian inference, the two methods that are overwhelmingly used in phylogenetic studies of DNA sequence data. Appropriate selection of nucleotide substitution models is important because the use of incorrect models can mislead phylogenetic inference. To better understand the performance of different model-selection criteria, we used 33,600 simulated data sets to analyse the accuracy, precision, dissimilarity, and biases of the hierarchical likelihood-ratio test, Akaike information criterion, Bayesian information criterion, and decision theory. RESULTS We demonstrate that the Bayesian information criterion and decision theory are the most appropriate model-selection criteria because of their high accuracy and precision. Our results also indicate that in some situations different models are selected by different criteria for the same dataset. Such dissimilarity was the highest between the hierarchical likelihood-ratio test and Akaike information criterion, and lowest between the Bayesian information criterion and decision theory. The hierarchical likelihood-ratio test performed poorly when the true model included a proportion of invariable sites, while the Bayesian information criterion and decision theory generally exhibited similar performance to each other. CONCLUSIONS Our results indicate that the Bayesian information criterion and decision theory should be preferred for model selection. Together with model-adequacy tests, accurate model selection will serve to improve the reliability of phylogenetic inference and related analyses.