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Camrelizumab, an anti-PD-1 antibody, has shown moderate efficacy in oesophageal squamous cell carcinoma. Apatinib, a selective inhibitor of VEGFR2, has a synergistic effect with immunotherapy. We aimed to assess the combination of camrelizumab and apatinib as second-line treatment for advanced oesophageal squamous cell carcinoma.This single-arm, open-label, phase 2 study was conducted at eight centres in China. Eligible patients were aged 18-75 years, with an Eastern Cooperative Oncology Group performance status of 0 or 1, who had unresectable locally advanced, locally recurrent, or metastatic oesophageal squamous cell carcinoma, and had progressed after or were intolerant to first-line chemotherapy. Patients received intravenous camrelizumab 200 mg once every 2 weeks plus oral apatinib 250 mg once daily for a 28-day cycle until disease progression, unacceptable adverse events, or withdrawal of consent. The primary endpoint was investigator-assessed confirmed objective response rate. Efficacy was analysed in patients who had received at least one dose of study drug, and safety was analysed in patients who received the study drug and had at least one post-baseline safety assessment. The study of this cohort is complete and this trial is registered with ClinicalTrials.gov, number NCT03736863.Between Dec 5, 2019, and Feb 10, 2021, 52 patients were enrolled and included in analyses. At data cutoff (June 20, 2021), median follow-up was 7·5 months (IQR 4·0-11·2). 18 (34·6%, [95% CI 22·0-49·1]) of 52 patients had a confirmed objective response. 23 (44%) of 52 patients had grade 3 or worse treatment-related adverse events. The most common grade 3 or worse treatment-related adverse events were increased aspartate aminotransferase (10 [19%]), increased gamma-glutamyltransferase (10 [19%]), and increased alanine aminotransferase (five [10%]). No treatment-related deaths occurred.Camrelizumab combined with apatinib showed promising activity and manageable toxicity, and might be a potential second-line treatment option for patients with advanced oesophageal squamous cell carcinoma. Another cohort of this study, enrolling patients previously treated with first-line immunotherapy, is ongoing.Jiangsu Hengrui Pharmaceuticals.

Aim Janagliflozin is an orally selective SGLT2 inhibitor. To predict human pharmacokinetics/pharmacodynamics (PK/PD) characteristics of janagliflozin. To design optimal starting dose and effective dose for janagliflozin first-in-human (FIH) study. Methods Animal PK/PD properties of janagliflozin were obtained from preclinical in vivo and in vitro study. Pharmacologically effective level of same class SGLT2 inhibitors were assessed through preclinical and clinical efficacy data of dapagliflozin, empagliflozin and canagliflozin. Human PK parameters and profiles of janagliflozin were predicted by various methods such as allometric scaling (AS), dedrick and PK/PD modeling analysis. Mechanistic PK/PD model was developed to describe janagliflozin-mediated impact on urinary glucose excretion (UGE). Human IC50 was scaled from rat model-estimated IC50 by correcting interspecies difference of in vitro IC50 and plasma fu of rat and human. The quantitative PK/PD prediction of janagliflozin was evaluated via observed PK/PD profiles of healthy subjects. Predicted PK/PD characteristics of janagliflozin were applied in FIH dose design. Optimal starting dose was suggested by considering preclinical PD and toxicity data of janagliflozin. Effective dose was suggested by considering pharmacologically effective level of same class drugs. Results PK/PD characteristics of janagliflozin in preclinical species were summarized. Pharmacologically effective level for SGLT2 inhibitors was defined as 25~30% ΔUGE ( Δ UGE = − − ( PG * GFR ) within 24 h ) based on efficacy data of three same class drugs. Human predicted CL, Vss and F were 1.04 L/h, 77.5 L and 0.80. Predicted AUC and Cmax of janagliflozin of 10 and 50 mg were within 0.47~2.08 fold of observed values. Predicted human UGE0-24 h and UGE0-144 h of 10 and 50 mg dose range were within 0.66~1.41 fold of observed values. Optimal starting dose and pharmacologically active dose (PAD) were suggested as 10 mg and 50 mg. Dose range for FIH study was designed as 10–450 mg. Conclusions This study predicted human PK/PD characteristics of janagliflozin based on preclinical data and provide optimal dose design for janagliflozin FIH study based on pharmacologically effective level of same class drugs.

Cross-company defect prediction (CCDP) is a practical way that trains a prediction model by exploiting one or multiple projects of a source company and then applies the model to a target company. Unfortunately, larger irrelevant cross-company (CC) data usually make it difficult to build a prediction model with high performance. On the other hand, brute force leveraging of CC data poorly related to within-company data may decrease the prediction model performance. To address such issues, we aim to provide an effective solution for CCDP. First, we propose a novel semi-supervised clustering-based data filtering method (i.e., SSDBSCAN filter) to filter out irrelevant CC data. Second, based on the filtered CC data, we for the first time introduce multi-source TrAdaBoost algorithm, an effective transfer learning method, into CCDP to import knowledge not from one but from multiple sources to avoid negative transfer. Experiments on 15 public datasets indicate that: (1) our proposed SSDBSCAN filter achieves better overall performance than compared data filtering methods; (2) our proposed CCDP approach achieves the best overall performance among all tested CCDP approaches; and (3) our proposed CCDP approach performs significantly better than with-company defect prediction models.

Bug triaging refers to the process of assigning a bug to the most appropriate fixer. As the scale and complexity of software increases, bug triaging becomes a tedious and time-consuming work. Existing bug triaging approaches typically treat it as a problem of optimizing recommendation accuracy. However, the time that different fixers may spend also varies. Thus, we take time cost as another optimizing objective aside from accuracy and use modern portfolio theory to strike a balance between them. In addition, for fixers with little fixing records, we need more data to build profiles about their expertise. To address these problems, we propose a bug triaging approach with awareness of accuracy and time cost, and we use bug reports from other projects to enrich the bug fixing history of fixers. We evaluate our approach with experiments on data collected from Bugzilla. The experiment results validate the effectiveness of our approach.

Epithelial-mesenchymal transition (EMT) plays a pivotal role in the development of cancer invasion and metastasis. Many studies have significantly enhanced the knowledge on EMT through the characterization of microRNAs (miRNAs) influencing the signaling pathways and downstream events that define EMT on a molecular level. In this study, we found that miR-143 suppressed EMT. Up-regulating miR-143 enhanced E-cadherin-mediated cell-cell adhesion ability, reduced mesenchymal markers, and decreased cell proliferation, migration, and invasion in vitro. In vivo, the xenograft mouse model also unveiled the suppressive effects of miR-143 on tumor growth. Additionally, we demonstrated that up-regulating extracellular signal regulated kinase 5 (ERK5) was associated with poor prognosis of breast cancer patients. Moreover, we observed an inverse correlation between miR-143 and ERK5 in breast cancer tissues. miR-143 directly targeted seed sequences in the 3'-untranslated regions of ERK5. Furthermore, we revealed that the downstream molecules of glycogen synthase kinase 3 beta (GSK-3β)/Snail signaling were involved in EMT and modulated by ERK5. In summary, our findings demonstrated that miR-143 down-regulated its target ERK5, leading to the suppression of EMT induced by GSK-3β/Snail signaling of breast cancer. © 2015 Wiley Periodicals, Inc.

Pharmacogenetics or pharmacogenomics approaches are important for addressing the individual variabilities of drug efficacy especially in the era of precision medicine. One particular interesting gene to investigate is APOA5 which has been repeatedly linked with the inter-individual variations of serum triglycerides. Here, we explored APOA5-statin interactions in 195 Chinese subjects randomized to rosuvastatin (5-10 mg/day), atorvastatin (10-20 mg/day), or simvastatin (40 mg/day) for 12 weeks by performing a targeted genotyping analysis of the APOA5 promoter SNP rs662799 (-1131T>C). There were no significant differences between the treatment arms for any of the statin-induced changes in clinical biomarkers. Reductions in LDL cholesterol were influenced by the APOA5 genotype in all three treatment groups. By contrast, changes in HDL cholesterol and triglycerides were only affected by the APOA5 genotype in the atorvastatin and simvastatin groups and not in the rosuvastatin group. Our results support earlier findings indicating that rosuvastatin is a better treatment option and that future studies should consider stratifying subjects not only by genetic background but also by statin type.AbbreviationsApoA5apolipoprotein A5BMIbody mass indexFFAfree fatty acidsHDLchigh-density lipoprotein cholesterolLDLclow-density lipoprotein cholesterolLp(a)lipoprotein(a)SNPsingle nucleotide polymorphismT2Dtype 2 diabetesTctotal cholesterolTgtriglycerides.

The invention relates to a mixed feature selection method for software defect prediction. The method includes the steps of firstly, selecting m most relevant features from an original feature set, and discarding irrelevant features; secondly, clustering the m features according to the correlation between features, wherein the features with high redundancy between them are clustered into the same cluster; finally, by using ideas from wrapped feature selection, deleting one of the most irrelevant feature in each cluster from a current feature subset to form a new feature subset, and using the evaluation function of accuracy to evaluate the feature subset. Meanwhile, by deleting one of the most irrelevant feature in different clusters to form the new feature subset, the number of searched feature subsets can be effectively reduced.

In the era of big data, the rapid development of mobile participatory sensing devices brings the explosive expansion of data, making information overload a serious problem. In this case, a personalized recommendation system on mobile social media appears. Collaborative filtering is the most widely used approach in a recommendation system. Nevertheless, there still exist many problems, such as the serious data sparsity problem and the cold start problem. Existing approaches cannot effectively solve these problems. Most of the existing recommendation approaches are based on single information source and cannot effectively solve the cold start and data sparsity problems. In addition, some approaches proposed to solve data sparsity fail to consider the effects of users’ influences and prediction order on recommendation accuracy. Accordingly, from the perspective of increasing the categories of information, the similarity propagation approach based on a heterogeneous network is proposed to ease the cold start problems by improving the similarity calculation method. In addition, to ease the data sparsity problems, we propose a hybrid collaborative filtering approach based on a score prediction graph to finish the user-item score matrix in order. Finally, we conduct validation experiments on the MovieLens dataset. Compared with five state-of-the-art approaches, our approach outperforms them in terms of the performances of mean absolute error, root-mean-square error, recall, and diversity.

Cross-company defect prediction (CCDP) is a practical way that trains a prediction model by exploiting one or multiple projects of a source company and then applies the model to target company. Unfortunately, larger irrelevant crosscompany (CC) data usually makes it difficult to build a prediction model with high performance. On the other hand, brute force leveraging of CC data poorly related to withincompany (WC) data may decrease the prediction model performance. To address such issues, this paper introduces Multi-Source TrAdaBoost algorithm, an effective transfer learning approach to perform CCDP. The core idea of our approach is that: 1) employ limited amount of labeled WC data to weaken the impact of irrelevant CC data; 2) import knowledge not from one but from multiple sources to avoid negative transfer. The experimental results indicate that: 1) our proposed approach achieves the best overall performance among all tested CCDP approaches; 2) only 10% labeled WC data is enough to achieve good performance of CCDP by using our proposed approach. Keywords—software defect prediction;cross-company defect prediction; transfer learning; Multi-Source TrAdaBoost

MiRNAs are short single stranded RNAs that are associated with gene regulation at the transcriptional and translational level. Changes in their expression were found in a variety of human cancers. In the present study, we aimed to investigate expression profiles of miRNA (miRNA) in the clear cell subtype of kidney cancer and to develop further understanding of the molecular mechanisms involved in the pathogenesis of renal cell carcinoma. We analyzed the miRNA expression profiles in 30 pairs of renal cell carcinoma and adjacent nontumorous tissue (NT), using a mammalian miRNA microarray containing whole human mature and precursor miRNA sequences. Real-time quantitative PCR was performed to confirm the array results. The miRNA microarray chip analysis identified 86 miRNAs differentially expressed in renal cell carcinoma tissues and a total of 38 miRNAs exhibited higher expression in the renal cell carcinoma samples than that in the NT samples, while 48 miRNA demonstrated lower expression in the renal cell carcinoma samples than that in the NT samples. Quantitative real-time PCR analysis confirmed microarray data. The report supports that many miRNA expressions were altered in renal carcinoma, whose expression profiling may provide a useful clue for the pathophysiology research. However, further longer-term researches are required to investigate the relationship between miRNA and renal carcinoma as well as their role in carcinogenesis.

Epithelial-mesenchymal transition (EMT) plays a pivotal role in the development of cancer invasion and metastasis. Many studies have significantly enhanced the knowledge on EMT through the characterization of microRNAs (miRNAs) influencing the signaling pathways and downstream events that define EMT on a molecular level. In this study, we found that miR-143 suppressed EMT. Up-regulating miR-143 enhanced E-cadherin-mediated cell-cell adhesion ability, reduced mesenchymal markers, and decreased cell proliferation, migration, and invasion in vitro. In vivo, the xenograft mouse model also unveiled the suppressive effects of miR-143 on tumor growth. Additionally, we demonstrated that up-regulating extracellular signal regulated kinase 5 (ERK5) was associated with poor prognosis of breast cancer patients. Moreover, we observed an inverse correlation between miR-143 and ERK5 in breast cancer tissues. miR-143 directly targeted seed sequences in the 3'-untranslated regions of ERK5. Furthermore, we revealed that the downstream molecules of glycogen synthase kinase 3 beta (GSK-3β)/Snail signaling were involved in EMT and modulated by ERK5. In summary, our findings demonstrated that miR-143 down-regulated its target ERK5, leading to the suppression of EMT induced by GSK-3β/Snail signaling of breast cancer. © 2015 Wiley Periodicals, Inc.

A mobile ad hoc network (MANET) operates much depending on node's willingness to cooperate in the network. A node's misbehavior can have severe impact in overall network performance. One of the different kinds of misbehavior a node may exhibit is malicious packet dropping. In this paper, we focus on detecting and excluding nodes that misbehave by dropping some or all packets forwarded through them. We propose a reputation-based scheme for efficiently solving the problem. The proposed scheme use both self-observation and second-hand information to establish comprehensive reputation of a node. Nodes with bad comprehensive reputation will be excluded from the network. History reputation is also taken into account to providing indeliberate faults tolerance. Furthermore, we use fuzzy analysis to deal with the imprecision based on the fact that the cause of packet dropping can be heterogeneous and therefore improve the performance without imposing a high computational burden.

Robustness is the important issue in watermarking, robustness at the same time with blind watermark recovering algorithm remains especially challenging. This paper presents a combined DWT and DCT still image blind watermarking algorithm. The two-level DWT are performed on the original image, the low-frequency sub-band is divided into blocks, the DCT is performed on the every block, the DCT coefficients of every block are sorted using Zig-Zag order, the DCT low-frequency coefficient is selected as embedding watermarking. The watermarking signals are embedded into the selected embedding points using the modular arithmetic. The watermark recovering is the inverse process of the watermark embedding, according to the answer of the modular arithmetic, we can estimate the value of embedded the watermark. The algorithm is compared with a pure DWT-based scheme Experiment results shown that the proposed algorithm is robust to many attacks such as JPEG compression, addition noise, cropping, JPEG compression, median filter, rotation, and resize etc. Proposed algorithm is shown to provide good results in term of image imperceptibility, too.

We present MinMax, a new algorithm for mining maximal frequent itemsets(MFI) from a transaction database. It is based on depth-first traversal and iterative. It combines a vertical tidset representation of the database with effective pruning mechanisms. MinMax removes all the non-maximal frequent itemsets to get the exact set of MFI directly, needless to enumerate all the frequent itemsets from smaller ones step by step. It backtracks to the proper ancestor directly, needless level by level . We found MinMax to be more effective than GenMax, a state-of-the-art algorithm for finding maximal frequent item-sets, to prune the search space to get the exact set of MFI.

We propose a new watermarking algorithm for relational database which is based on spread spectrum techniques in this paper. We assign different owners for different identification key to generate the special watermarking signal and insert it into appropriate tuples. At the watermarking detection step, we use the technique of even parity check and majority voting for the watermark accuracy. It is to establish that a false positive judgment is highly unlikely by evaluating the similarity of watermarks between the original and the one gotten from the detection step. Experimental results are also presented to support that it is robust in the present of the various attacks and positive updating such as adding, deleting and modifying.

Mining sequential patterns in large database is an important problem in data mining research. Enormous sizes of available datasets and possibly large number of mined patterns demand efficient and scalable algorithms. In this paper, we present a new dynamic load algorithm based HPSPM (hash-based parallel algorithm for mining sequential patterns) on shared-nothing environment. Experiments on Dawning 300 cluster system show that this algorithm achieves good speedup and is substantially improved compared to HPSPM.