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1
Parthiban, Gunasingham, Ramachandren Dushanan, Samantha Weerasinghe, Dhammike Dissanayake, and Rajendram Senthilnithy. "Exploration of Novel Mono Hydroxamic Acid Derivatives as Inhibitors for Histone Deacetylase Like Protein (HDLP) by Molecular Dynamics Studies." Indonesian Journal of Chemistry 22, no. 6 (November 25, 2022): 1534. http://dx.doi.org/10.22146/ijc.74167.
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The acetylation modification process of histone has an essential role in the epigenetic regulation of gene expression. This process is controlled by the balance between histone deacetylases (HDAC) and histone acetyltransferases (HAT). HDACs are thought to be vital for cell function. Particularly, higher HDAC expression is frequent in various cancers, resulting in the dysregulation of several target genes involved in cell proliferation, differentiation, and survival. In this study, the inhibitory feasibility of several HDAC inhibitors was investigated, including vorinostat (SAHA), N-hydroxy-3-phenylprop-2-enamide (CPD1), N-hydroxy-3-(pyridine-4-yl)prop-2-enamide (CPD2), N-hydroxy-3-(pyridine-2-yl)prop-2-enamide (CPD3), 4-(diphenylamino)-N-(5-(hydroxyamino)-5-oxopentyl)benzamide (CPD4), 2-(6-(((6-fluoronaphthalen-2-yl)methyl)amino)-3-azabicyclo[3.1.0]hex-3-yl)-N-hydroxypirimidine-5-carboxamide (CPD5), and N-(3-aminopropyl)-N-hydroxy-2-((naphthalene-1-yloxy)methyl)oct-2-enediamide (CPD6). By examining the stability of the enzyme, positional stability of the individual amino acids, and binding energies of HDLP-inhibitor complexes, the inhibitory feasibility was assessed. The complexes of the HDLP enzyme with SAHA, CPD4, CPD5, and CPD6 had higher stability than the other studied complexes, according to the results of trajectory analysis and the Ramachandran plot. Based on the calculated MM-PBSA binding free energies, the stability of the HDLP enzyme followed this order CPD4 > CPD5 > SAHA > CPD6 > CPD2 > CPD3 > CPD1. The drugability values followed the same trend as the previous ones. Based on the obtained in silico results, CPD4, CPD5, and CPD6 were discovered to be possible lead compounds as reference inhibitors of SAHA.
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Zhong, Yiwen, Kun Song, ShengKai Lv, and Peng He. "An Empirical Study of Software Metrics Diversity for Cross-Project Defect Prediction." Mathematical Problems in Engineering 2021 (November 28, 2021): 1–11. http://dx.doi.org/10.1155/2021/3135702.
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Cross-project defect prediction (CPDP) is a mainstream method estimating the most defect-prone components of software with limited historical data. Several studies investigate how software metrics are used and how modeling techniques influence prediction performance. However, the software’s metrics diversity impact on the predictor remains unclear. Thus, this paper aims to assess the impact of various metric sets on CPDP and investigate the feasibility of CPDP with hybrid metrics. Based on four software metrics types, we investigate the impact of various metric sets on CPDP in terms of F-measure and statistical methods. Then, we validate the dominant performance of CPDP with hybrid metrics. Finally, we further verify the CPDP-OSS feasibility built with three types of metrics (orient-object, semantic, and structural metrics) and challenge them against two current models. The experimental results suggest that the impact of different metric sets on the performance of CPDP is significantly distinct, with semantic and structural metrics performing better. Additionally, trials indicate that it is helpful for CPDP to increase the software’s metrics diversity appropriately, as the CPDP-OSS improvement is up to 53.8%. Finally, compared with two baseline methods, TCA+ and TDSelector, the optimized CPDP model is viable in practice, and the improvement rate is up to 50.6% and 25.7%, respectively.
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Toshchakov, Vladimir Y., and Artur Javmen. "Targeting the TLR signalosome with TIR domain-derived cell-permeable decoy peptides: the current state and perspectives." Innate Immunity 26, no. 1 (January 2020): 35–47. http://dx.doi.org/10.1177/1753425919844310.
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The ability to engineer pharmaceuticals that target the signal-dependent interactions of signaling proteins should revolutionize drug development. One approach to the rational design of protein interaction inhibitors uses decoy peptides, i.e. segments of protein primary sequence, which are derived from interfaces that mediate functional protein interactions. Decoy peptides often retain the ability of the full-length prototype to bind the docking site of the folded protein and thereby block the signal transduction. This review summarizes advances made in the last decade in the development of cell-permeable decoy peptide (CPDP) inhibitors to target the Toll/IL-1R resistance (TIR) domain-mediated protein interactions in TLR signaling, in connection with the recent progress in understanding of the TLR signalosome assembly mechanisms. We present a large collection of currently available, TIR-targeting CPDPs and propose their classification based on the types of TIR–TIR interactions they target. The binding behavior of different CPDP-TIR pairs, studied in cell-based assays and in binary in vitro systems using recombinant TIR domains, is also reviewed. The available affinity data provide benchmarks for rapid preliminary evaluation of future inhibitors. We review literature that evaluates the in vivo potency of select CPDPs and attempt to outline the areas of forthcoming progress, towards the development of CPDP-based TLR inhibitors of pharmaceutical grade.
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You, Guoan, Feng Wang, and Yutao Ma. "An Empirical Study of Ranking-Oriented Cross-Project Software Defect Prediction." International Journal of Software Engineering and Knowledge Engineering 26, no. 09n10 (November 2016): 1511–38. http://dx.doi.org/10.1142/s0218194016400155.
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Cross-project defect prediction (CPDP) has recently become very popular in the field of software defect prediction. It was generally treated as a binary classification problem or a regression problem in most of previous studies. However, these existing CPDP methods may be not suitable for those software projects that have limited manpower and budget. To address the issue of priority estimation for buggy software entities, in this paper CPDP is formulated as a ranking problem. Inspired by the idea of the pointwise approach to learning to rank, we propose a ranking-oriented CPDP approach called ROCPDP. A case study conducted on the datasets collected from AEEEM and PROMISE shows that ROCPDP outperforms the eight baseline methods in two CPDP scenarios, namely one-to-one and many-to-one. Besides, in the many-to-one scenario ROCPDP is, by and large, comparable to the best baseline method performed in a specific within-project defect prediction scenario.
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Qiu, Shaojian, Hao Xu, Jiehan Deng, Siyu Jiang, and Lu Lu. "Transfer Convolutional Neural Network for Cross-Project Defect Prediction." Applied Sciences 9, no. 13 (June 29, 2019): 2660. http://dx.doi.org/10.3390/app9132660.
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Cross-project defect prediction (CPDP) is a practical solution that allows software defect prediction (SDP) to be used earlier in the software lifecycle. With the CPDP technique, the software defect predictor trained by labeled data of mature projects can be applied for the prediction task of a new project. Most previous CPDP approaches ignored the semantic information in the source code, and existing semantic-feature-based SDP methods do not take into account the data distribution divergence between projects. These limitations may weaken defect prediction performance. To solve these problems, we propose a novel approach, the transfer convolutional neural network (TCNN), to mine the transferable semantic (deep-learning (DL)-generated) features for CPDP tasks. Specifically, our approach first parses the source file into integer vectors as the network inputs. Next, to obtain the TCNN model, a matching layer is added into convolutional neural network where the hidden representations of the source and target project-specific data are embedded into a reproducing kernel Hilbert space for distribution matching. By simultaneously minimizing classification error and distribution divergence between projects, the constructed TCNN could extract the transferable DL-generated features. Finally, without losing the information contained in handcrafted features, we combine them with transferable DL-generated features to form the joint features for CPDP performing. Experiments based on 10 benchmark projects (with 90 pairs of CPDP tasks) showed that the proposed TCNN method is superior to the reference methods.
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Luo, Shajie, Fajian Ren, Jiangang Dai, Yan Chen, and Zhongzhu Yang. "Novel poly(arylene ether nitrile) containing pendant aliphtatic ring in the chain: Synthesis and properties." High Performance Polymers 34, no. 2 (November 1, 2021): 221–31. http://dx.doi.org/10.1177/09540083211042267.
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Two kinds of novel poly(arylene ether nitrile)s (CPDP-DCBN and CHDP-DCBN) containing pendant aliphtatic ring were synthesized by 4,4′-cyclopentane-1,1′-diyldiphenol (CPDP) or 4,4′-cyclohexane-1,1′-diyldiphenol (CHDP) and 2,6-dichlorobenzonitrile (DCBN) in this work. The inherent viscosities of poly(arylene ether nitrile)s (PENs) were in the range of 0.701–0.806 dL g−1. The polymers showed high glass transition temperatures ( T g) of 185.4–196.4°C and weight-loss temperatures ( T5%) of 447.8–454.3°C. The obtained CPDP-DCBN and CHDP-DCBN could be hot pressed into the films, which showed the tensile strengths of 82.6 MPa and 86.8 MPa, respectively. And the storage modulus of CPDP-DCBN and CHDP-DCBN were about 1.0 GPa and 1.5 GPa at 150°C, respectively. Additionally, the PENs could be dissolved in many solutions at room temperature, such as NMP and concentrated H2SO4, indicating that they had good solubility; they can be processed by the solution method. Meanwhile, the optical transmittance of CPDP-DCBN was 78.1% at 450 nm; it has potential to be applied to the heat-resistant optical film.
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Xing, Ying, Wanting Lin, Xueyan Lin, Bin Yang, and Zhou Tan. "Cross-Project Defect Prediction Based on Two-Phase Feature Importance Amplification." Computational Intelligence and Neuroscience 2022 (April 18, 2022): 1–14. http://dx.doi.org/10.1155/2022/2320447.
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As the typical application of computational intelligence in software engineering, cross-project defect prediction (CPDP) uses labeled data from other projects (source projects) for building models to predict the defects in the current projects (target projects), helping testers quickly locate the defective modules. But class imbalance and different data distribution among projects make CPDP a challenging topic. To address the above two problems, we propose a two-phase feature importance amplification (TFIA) CPDP model in this paper which can solve these two problems from domain adaptation phase and classification phase. In the domain adaptation phase, the differences in data distribution among projects are reduced by filtering both source and target projects, and the correlation-based feature selection with greedy best-first search amplifies the importance of features with strong feature-class correlation. In the classification phase, Random Forest works as the classifier to further amplify the importance of highly correlated features and establish a model which is sensitive to highly correlated features. We conducted both ablation experiments and comparison experiments on the widely used AEEEM database. Experimental results show that TFIA can yield significant improvement on CPDP. And the performance of TFIA CPDP model in all experiments is stable and efficient, which lays a solid foundation for its further application in practical engineering.
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Norouz Dizaji, Araz, Matin Yazdani Kohneshahri, Sena Gafil, Muhammed Tilahun Muhammed, Tulin Ozkan, Ilyas Inci, Cengiz Uzun, and Esin Aki Yalcin. "Fluorescence labelled XT5 modified nano-capsules enable highly sensitive myeloma cells detection." Nanotechnology 33, no. 26 (April 8, 2022): 265101. http://dx.doi.org/10.1088/1361-6528/ac60dc.
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Abstract Accurate diagnosis of cancer cells in early stages plays an important role in reliable therapeutic strategies. In this study, we aimed to develop fluorescence-conjugated polymer carrying nanocapsules (NCs) which is highly selective for myeloma cancer cells. To gain specific targeting properties, NCs, XT5 molecules (a benzamide derivative) which shows high affinity properties against protease-activated receptor-1 (PAR1), that overexpressed in myeloma cancer cells, was used. For this purpose, 1,2-distearoyl-sn-glycero-3-phosphoethanolamine-N-[carboxy(polyethylene glycol)-2000]-carboxylic acid (DSPE-PEG2000-COOH) molecules, as a main encapsulation material, was conjugated to XT5 molecules due to esterification reaction using N,N′-dicyclohexylcarbodiimide as a coupling agent. The synthesized DSPE-PEG2000-COO-XT5 was characterized by using FT-IR and 1H NMR spectroscopies and results indicated that XT5 molecules were successfully conjugated to DSPE-PEG2000-COOH. Poly(fluorene-alt-benzothiadiazole) (PFBT) conjugated polymer (CP) was encapsulated with DSPE-PEG2000-COO-XT5 due to dissolving in tetrahydrofuran and ultra-sonication in an aqueous solution, respectively. The morphological properties, UV–vis absorbance, and emission properties of obtained CP encapsulated DSPE-PEG2000−COO-XT5 (CPDP-XT5) NCs was determined by utilizing scanning electron microscopy, UV–vis spectroscopy, and fluorescent spectroscopy, respectively. Cytotoxicity properties of CPDP-XT5 was evaluated by performing MTT assay on RPMI 8226 myeloma cell lines. Cell viability results confirmed that XT5 molecules were successfully conjugated to DSPE-PEG2000-COOH. Specific targeting properties of CPDP-XT5 NCs and XT5-free NCs (CPDP NCs) were investigated on RPMI 8226 myeloma cell lines by utilizing fluorescent microscopy and results indicated that CPDP-XT5 NCs shows significantly high affinity in comparison to CPDP NCs against the cells. Homology modeling and molecular docking properties of XT5 molecules were evaluated and simulation results confirmed our results.
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Qiu, Shaojian, Lu Lu, Siyu Jiang, and Yang Guo. "An Investigation of Imbalanced Ensemble Learning Methods for Cross-Project Defect Prediction." International Journal of Pattern Recognition and Artificial Intelligence 33, no. 12 (November 2019): 1959037. http://dx.doi.org/10.1142/s0218001419590377.
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Machine-learning-based software defect prediction (SDP) methods are receiving great attention from the researchers of intelligent software engineering. Most existing SDP methods are performed under a within-project setting. However, there usually is little to no within-project training data to learn an available supervised prediction model for a new SDP task. Therefore, cross-project defect prediction (CPDP), which uses labeled data of source projects to learn a defect predictor for a target project, was proposed as a practical SDP solution. In real CPDP tasks, the class imbalance problem is ubiquitous and has a great impact on performance of the CPDP models. Unlike previous studies that focus on subsampling and individual methods, this study investigated 15 imbalanced learning methods for CPDP tasks, especially for assessing the effectiveness of imbalanced ensemble learning (IEL) methods. We evaluated the 15 methods by extensive experiments on 31 open-source projects derived from five datasets. Through analyzing a total of 37504 results, we found that in most cases, the IEL method that combined under-sampling and bagging approaches will be more effective than the other investigated methods.
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Noreen, Sundas, Rizwan Bin Faiz, Sultan Alyahya, and Mohamed Maddeh. "Performance Evaluation of Convolutional Neural Network for Multi-Class in Cross Project Defect Prediction." Applied Sciences 12, no. 23 (November 30, 2022): 12269. http://dx.doi.org/10.3390/app122312269.
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Cross-project defect prediction (CPDP) is a practical approach for finding software defects in projects which have incomplete or fewer data. Improvements to the defect prediction accuracy of CPDP—such as the PROMISE repository, the correct classification of the source data, removing the noise, reducing the distribution gap, and balancing the output classes—are an ongoing challenge, as is the selection of an optimal feature set. This research paper aims to achieve a higher defect prediction accuracy for multi-class CPDP by selecting an optimal feature set through XGBoost combined with an automatic feature extraction using a convolutional neural network (CNN). This research type is explanatory, and this research method is controlled experimentation, for which the independent variable prediction accuracy was dependent upon two variables, XGBoost and CNN. The Softmax layer was added to the output layers of the CNN classifier to classify the output into multiple classes. In our experimentation with CPDP, we selected all 28 versions of the multi-class, in which 11 versions were selected as the source projects, against which we predicted 28 target versions with an average AUC of 75.57%. We validated this research paper’s results through the Wilcoxon test. Therefore, after removing the noise, class imbalances, and the data distribution gap, and treating the PROMISE dataset as multi-class, the optimal features selected through XGBoost and classified through the CNN can substantially increase the prediction accuracy in CPDP as evident from our exploratory data analysis (EDA).
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Székely, Iván. "CPDP - Computers, Privacy and Data Protection, tizedszer." Információs Társadalom 17, no. 1 (July 11, 2017): 87. http://dx.doi.org/10.22503/inftars.xvii.2017.1.6.
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2017 januárjában tízéves jubileumához érkezett a CPDP, a számítógépek, a magánélet és az adatvédelem kapcsolatának talán legnagyobb és legjelentősebb, évente megrendezett nemzetközi konferenciája, amely magát „multistakeholder platform”-nak, vagyis olyan rendezvénynek határozza meg, ahol a témában bármilyen oldalról érdekelt, esetenként ellentétes érdekeket és álláspontokat képviselő szereplők találkozhatnak egymással, megismerhetik egymás nézeteit és nyíltan kifejthetik álláspontjukat. E beszámoló szerzőjének alkalma volt előadóként részt venni a legelső konferencián, majd számos azt követőn is, az utóbbi években pedig közreműködni a rendezvénysorozat szakmai irányító testületében és több programjában, ezért ez a beszámoló nemcsak a legutóbbi konferencia szerteágazó programjáról kíván rövid áttekintést adni, hanem igyekszik felvázolni azt a kontextust is, amely a CPDP-t kiemeli a hasonló tárgyú szakmai rendezvények sorából.
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Conference report: The 10th edition of CPDP – Computers, Privacy and Data Protection
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Vashisht, Rohit, and Syed Afzal Murtaza Rizvi. "An Empirical Study of Heterogeneous Cross-Project Defect Prediction Using Various Statistical Techniques." International Journal of e-Collaboration 17, no. 2 (April 2021): 55–71. http://dx.doi.org/10.4018/ijec.2021040104.
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Cross-project defect prediction (CPDP) forecasts flaws in a target project through defect prediction models (DPM) trained by defect data of another project. However, CPDP has a prevalent problem (i.e., distinct projects must have identical features to describe themselves). This article emphasizes on heterogeneous CPDP (HCPDP) modeling that does not require same metric set between two applications and builds DPM based on metrics showing comparable distribution in their values for a given pair of datasets. This paper evaluates empirically and theoretically HCPDP modeling, which comprises of three main phases: feature ranking and feature selection, metric matching, and finally, predicting defects in the target application. The research work has been experimented on 13 benchmarked datasets of three open source projects. Results show that performance of HCPDP is very much comparable to baseline within project defect prediction (WPDP) and XG boosting classification model gives best results when used in conjunction with Kendall's method of correlation as compared to other set of classifiers.
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Qian, Xiao Ming, and Dun Bing Tang. "Research on DSM for Simulation of Concurrent Product Development Process." Advanced Materials Research 44-46 (June 2008): 595–600. http://dx.doi.org/10.4028/www.scientific.net/amr.44-46.595.
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In this paper a simulation algorithm for concurrent product development process (CPDP) is presented based on Design Structure Matrix (DSM). An aggregate DSM is used to model the CPDP. To simulate the influence on the process of the time limit and the resource competition, the schedule and resource model are established. A method is also advanced to handle task delay. At last a case is used to validate the simulation algorithm and to show the influence on the process of task duration and resource.
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Ren, Shengbing, Wanying Zhang, Hafiz Shahbaz Munir, and Lei Xia. "Dissimilarity Space Based Multi-Source Cross-Project Defect Prediction." Algorithms 12, no. 1 (January 2, 2019): 13. http://dx.doi.org/10.3390/a12010013.
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Software defect prediction is an important means to guarantee software quality. Because there are no sufficient historical data within a project to train the classifier, cross-project defect prediction (CPDP) has been recognized as a fundamental approach. However, traditional defect prediction methods use feature attributes to represent samples, which cannot avoid negative transferring, may result in poor performance model in CPDP. This paper proposes a multi-source cross-project defect prediction method based on dissimilarity space (DM-CPDP). This method not only retains the original information, but also obtains the relationship with other objects. So it can enhances the discriminant ability of the sample attributes to the class label. This method firstly uses the density-based clustering method to construct the prototype set with the cluster center of samples in the target set. Then, the arc-cosine kernel is used to calculate the sample dissimilarities between the prototype set and the source domain or the target set to form the dissimilarity space. In this space, the training set is obtained with the earth mover’s distance (EMD) method. For the unlabeled samples converted from the target set, the k-Nearest Neighbor (KNN) algorithm is used to label those samples. Finally, the model is learned from training data based on TrAdaBoost method and used to predict new potential defects. The experimental results show that this approach has better performance than other traditional CPDP methods.
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Nevendra, Meetesh, and Pradeep Singh. "Cross-Project Defect Prediction with Metrics Selection and Balancing Approach." Applied Computer Systems 27, no. 2 (December 1, 2022): 137–48. http://dx.doi.org/10.2478/acss-2022-0015.
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Abstract In software development, defects influence the quality and cost in an undesirable way. Software defect prediction (SDP) is one of the techniques which improves the software quality and testing efficiency by early identification of defects(bug/fault/error). Thus, several experiments have been suggested for defect prediction (DP) techniques. Mainly DP method utilises historical project data for constructing prediction models. SDP performs well within projects until there is an adequate amount of data accessible to train the models. However, if the data are inadequate or limited for the same project, the researchers mainly use Cross-Project Defect Prediction (CPDP). CPDP is a possible alternative option that refers to anticipating defects using prediction models built on historical data from other projects. CPDP is challenging due to its data distribution and domain difference problem. The proposed framework is an effective two-stage approach for CPDP, i.e., model generation and prediction process. In model generation phase, the conglomeration of different pre-processing, including feature selection and class reweights technique, is used to improve the initial data quality. Finally, a fine-tuned efficient bagging and boosting based hybrid ensemble model is developed, which avoids model over -fitting/under-fitting and helps enhance the prediction performance. In the prediction process phase, the generated model predicts the historical data from other projects, which has defects or clean. The framework is evaluated using25 software projects obtained from public repositories. The result analysis shows that the proposed model has achieved a 0.71±0.03 f1-score, which significantly improves the state-of-the-art approaches by 23 % to 60 %.
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Goel, Lipika, Neha Nandal, and Sonam Gupta. "An optimized approach for class imbalance problem in heterogeneous cross project defect prediction." F1000Research 11 (September 16, 2022): 1060. http://dx.doi.org/10.12688/f1000research.123616.1.
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Background: In recent studies, Cross Project Defect Prediction (CPDP) has proven to be feasible in software defect prediction. When both the source as well as the target projects have the same metric sets, it is termed as a homogeneous CPDP. Current CPDP strategies are difficult to implement through projects with a variety of different metric sets. Aside from that, training data often has a problem with class imbalance. The number of defective/bug-ridden and non-defective/clean instances of the source class is usually unbalanced. To address this issue, we propose a heterogeneous cross-project defect prediction framework that can predict defects across projects with different metric sets. Methods: To construct a prediction framework between projects with heterogeneous metric sets, our heterogeneous cross project defect prediction approach uses metric selection, metric matching, class imbalance (CIB) learning followed by ensemble modelling. For our study, we have considered six open-source object-oriented projects. Results: The proposed model resolved the class imbalance issue and records the highest recall value of 7.5 with f-score value as 7.4 in comparison with other baseline models. The highest AUC (area under curve) value of 0.86 has also been recorded. K fold cross validation was performed to evaluate the training accuracy of the model. The proposed optimized model was validated using the Wilcoxon signed rank test (WSR) with a significance level of 5% (i.e., P-value=0.05). Conclusions: Our empirical research on these six projects shows that predictions based on our methodology outperform or are statistically comparable to Within-Project Defect Prediction (WPDP) and other heterogeneous CPDP baseline models.
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李勇, 李勇, Ming Wen Yong Li, Zhandong Liu Ming Wen, and Haijun Zhang Zhandong Liu. "Using Cost-cognitive Bagging Ensemble to Improve Cross-project Defects Prediction." 網際網路技術學刊 23, no. 4 (July 2022): 779–89. http://dx.doi.org/10.53106/160792642022072304013.
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<p>Cross-project defect prediction (CPDP) is a field of study that allows predicting defects in software projects for which the availability of data is limited and produces generalizable prediction models. Due to the heterogeneity of cross projects, CPDP is particularly challenging and several methods have been employed to address this problem. Nevertheless, the class-imbalanced characteristic of the cross-project defect data also increases the learning difficulty of such a task but has not been investigated in depth. This paper proposed a novel, cost-cognitive ensemble method for CPDP, which includes four phases: bagging balanced resampling phase, base classifiers learning phase, cost value cognitive phase, and base classifiers ensemble phase. These phases create a composition of classifiers that are used for predicting defects. Results of an empirical evaluation on 10 datasets from the PROMISE repository indicated that our method achieves the best overall performance with respect to conventional methods. Moreover, our method could cognize the cost value automatically during the model training, it is shown to be more effective and practical.</p> <p> </p>
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Tang, Shiqi, Song Huang, ErHu Liu, YongMing Yao, KaiShun Wu, and Haijin Ji. "Tsbagging: A Novel Cross-Project Software Defect Prediction Algorithm Based on Semisupervised Clustering." Scientific Programming 2022 (September 28, 2022): 1–28. http://dx.doi.org/10.1155/2022/6339684.
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Software defect prediction (SDP) is an important technology which is widely applied to improve software quality and reduce development costs. It is difficult to train the SDP model when software to be test only has limited historical data. Cross-project defect prediction (CPDP) has been proposed to solve this problem by using source project data to train the defect prediction model. Most of CPDP methods build defect prediction models based on the similarity of feature space or data distance between different projects. However, when the target project has a small amount of label data, these methods usually do not consider this part of data information. Therefore, when the distribution between source project and target project is quite different, these methods are difficult to achieve good prediction performance. To solve this problem, this paper proposes a CPDP method based on a semisupervised clustering (namely, Tsbagging). Tsbagging has two stages; in the first stage, we cluster to the source project data based on the limited labeled data in the target project and assign different weights to these source project data according to the clustering results. In the second stage, we use bagging method to train the prediction model based on the weight assigned in the first stage. The experimental results show that the performance achieved by Tsbagging is better than other existing SDP methods.
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Bhat, Nayeem Ahmad, and Sheikh Umar Farooq. "Local modeling approach for cross-project defect prediction." Intelligent Decision Technologies 15, no. 4 (January 10, 2022): 623–37. http://dx.doi.org/10.3233/idt-210130.
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Prediction approaches used for cross-project defect prediction (CPDP) are usually impractical because of high false alarms, or low detection rate. Instance based data filter techniques that improve the CPDP performance are time-consuming and each time a new test set arrives for prediction the entire filter procedure is repeated. We propose to use local modeling approach for the utilization of ever-increasing cross-project data for CPDP. We cluster the cross-project data, train per cluster prediction models and predict the target test instances using corresponding cluster models. Over 7 NASA Data sets performance comparison using statistical methods between within-project, cross-project, and our local modeling approach were performed. Compared to within-project prediction the cross-project prediction increased the probability of detection (PD) associated with an increase in the probability of false alarm (PF) and decreased overall performance Balance. The application of local modeling decreased the (PF) associated with a decrease in (PD) and an overall performance improvement in terms of Balance. Moreover, compared to one state of the art filter technique – Burak filter, our approach is simple, fast, performance comparable, and opens a new perspective for the utilization of ever-increasing cross-project data for defect prediction. Therefore, when insufficient within-project data is available we recommend training local cluster models than training a single global model on cross-project datasets.
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Faiz, Rizwan bin, Saman Shaheen, Mohamed Sharaf, and Hafiz Tayyab Rauf. "Optimal Feature Selection through Search-Based Optimizer in Cross Project." Electronics 12, no. 3 (January 19, 2023): 514. http://dx.doi.org/10.3390/electronics12030514.
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Cross project defect prediction (CPDP) is a key method for estimating defect-prone modules of software products. CPDP is a tempting approach since it provides information about predicted defects for those projects in which data are insufficient. Recent studies specifically include instructions on how to pick training data from large datasets using feature selection (FS) process which contributes the most in the end results. The classifier helps classify the picked-up dataset in specified classes in order to predict the defective and non-defective classes. The aim of our research is to select the optimal set of features from multi-class data through a search-based optimizer for CPDP. We used the explanatory research type and quantitative approach for our experimentation. We have F1 measure as our dependent variable while as independent variables we have KNN filter, ANN filter, random forest ensemble (RFE) model, genetic algorithm (GA), and classifiers as manipulative independent variables. Our experiment follows 1 factor 1 treatment (1F1T) for RQ1 whereas for RQ2, RQ3, and RQ4, there are 1 factor 2 treatments (1F2T) design. We first carried out the explanatory data analysis (EDA) to know the nature of our dataset. Then we pre-processed our data by removing and solving the issues identified. During data preprocessing, we analyze that we have multi-class data; therefore, we first rank features and select multiple feature sets using the info gain algorithm to get maximum variation in features for multi-class dataset. To remove noise, we use ANN-filter and get significant results more than 40% to 60% compared to NN filter with base paper (all, ckloc, IG). Then we applied search-based optimizer i.e., random forest ensemble (RFE) to get the best features set for a software prediction model and we get 30% to 50% significant results compared with genetic instance selection (GIS). Then we used a classifier to predict defects for CPDP. We compare the results of the classifier with base paper classifier using F1-measure and we get almost 35% more than base paper. We validate the experiment using Wilcoxon and Cohen’s d test.
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Sinaga, Benyamin Langgu, Sabrina Ahmad, Zuraida Abal Abas, and Intan Ermahani A. Jalil. "A recommendation system of training data selection method for cross-project defect prediction." Indonesian Journal of Electrical Engineering and Computer Science 27, no. 2 (August 1, 2022): 990. http://dx.doi.org/10.11591/ijeecs.v27.i2.pp990-1006.
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Cross-project <span lang="EN-US">defect prediction (CPDP) has been a popular approach to address the limited historical dataset when building a defect prediction model. Directly applying cross-project datasets to learn the prediction model produces an unsatisfactory predictive model. Therefore, the selection of training data is essential. Many studies have examined the effectiveness of training data selection methods, and the best-performing method varied across datasets. While no method consistently outperformed the others across all datasets, predicting the best method for a specific dataset is essential. This study proposed a recommendation system to select the most suitable training data selection method in the CPDP setting. We evaluated the proposed system using 44 datasets, 13 training data selection methods, and six classification algorithms. The findings concluded that the recommendation system effectively recommends the best method to select training data.</span>
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He, Peng, Yao He, Lvjun Yu, and Bing Li. "An Improved Method for Cross-Project Defect Prediction by Simplifying Training Data." Mathematical Problems in Engineering 2018 (June 7, 2018): 1–18. http://dx.doi.org/10.1155/2018/2650415.
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Cross-project defect prediction (CPDP) on projects with limited historical data has attracted much attention. To the best of our knowledge, however, the performance of existing approaches is usually poor, because of low quality cross-project training data. The objective of this study is to propose an improved method for CPDP by simplifying training data, labeled as TDSelector, which considers both the similarity and the number of defects that each training instance has (denoted by defects), and to demonstrate the effectiveness of the proposed method. Our work consists of three main steps. First, we constructed TDSelector in terms of a linear weighted function of instances’ similarity and defects. Second, the basic defect predictor used in our experiments was built by using the Logistic Regression classification algorithm. Third, we analyzed the impacts of different combinations of similarity and the normalization of defects on prediction performance and then compared with two existing methods. We evaluated our method on 14 projects collected from two public repositories. The results suggest that the proposed TDSelector method performs, on average, better than both baseline methods, and the AUC values are increased by up to 10.6% and 4.3%, respectively. That is, the inclusion of defects is indeed helpful to select high quality training instances for CPDP. On the other hand, the combination of Euclidean distance and linear normalization is the preferred way for TDSelector. An additional experiment also shows that selecting those instances with more bugs directly as training data can further improve the performance of the bug predictor trained by our method.
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R.Kolte, Roshan, Rahul Deshmukh, and Niraj V. Telrandhe. "CPDP Scheme to Provide Data Integrity in Multicloud." International Journal of Computer Applications 83, no. 10 (December 18, 2013): 7–10. http://dx.doi.org/10.5120/14482-2790.
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Bouchard, Corinne, Alexe Grenier, Sébastien Cardinal, Sarah Bélanger, Normand Voyer, and Roxane Pouliot. "Antipsoriatic Potential of Quebecol and Its Derivatives." Pharmaceutics 14, no. 6 (May 26, 2022): 1129. http://dx.doi.org/10.3390/pharmaceutics14061129.
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Psoriasis is a chronic inflammatory skin disease mainly characterized by the hyperproliferation and abnormal differentiation of the epidermal keratinocytes. An interesting phenolic compound, namely quebecol (2,3,3-tri-(3-methoxy-4-hydroxyphenyl)-1-propanol) (compound 1, CPD1), was isolated from maple syrup in 2011 and was recently synthesized. Quebecol and its derivatives ethyl 2,3,3-tris(3-hydroxy-4-methoxyphenyl)propenoate (compound 2, CPD2) and bis(4-hydroxy-3-methoxyphenyl)methane (compound 3, CPD3) have shown antiproliferative and anti-inflammatory potential, making them promising candidates for the treatment of psoriasis. This study aimed to evaluate the antipsoriatic potential of quebecol and its derivatives on psoriatic skin substitutes produced according to the self-assembly method. A sulforhodamine B (SRB) assay determining the concentration that inhibits 20% of cell growth (IC20) was performed for CPD1, CPD2 and CPD3, and their IC20 values were 400, 150 and 350 μM, respectively. At these concentrations, cell viability was 97%, 94% and 97%, respectively. The comparative control methotrexate (MTX) had a cell viability of 85% at a concentration of 734 μM. Histological analyses of psoriatic skin substitutes treated with CPD1, CPD2 and CPD3 exhibited significantly reduced epidermal thickness compared with untreated psoriatic substitutes, which agreed with a decrease in keratinocyte proliferation as shown by Ki67 immunofluorescence staining. The immunofluorescence staining of differentiation markers (keratin 14, involucrin and loricrin) showed improved epidermal differentiation. Taken together, these results highlight the promising potential of quebecol and its derivatives for the treatment of psoriasis.
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Darwish, Mohamed, Fedaa Ali, and Rabi Mohtar. "Prospect of Using Nuclear CPDP in Qatar and other GCCC." QScience Proceedings 2012, no. 2 (March 11, 2012): 16. http://dx.doi.org/10.5339/qproc.2012.gccenergy.2.16.
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Strubbe, Stefan, and Arjan van der Schaft. "STOCHASTIC EQUIVALENCE OF CPDP-AUTOMATA AND PIECEWISE DETERMINISTIC MARKOV PROCESSES." IFAC Proceedings Volumes 38, no. 1 (2005): 25–30. http://dx.doi.org/10.3182/20050703-6-cz-1902.00289.
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Maksimov, Sergey A., Natalia S. Karamnova, Svetlana A. Shalnova, Galina A. Muromtseva, Anna V. Kapustina, and Oksana M. Drapkina. "Regional Living Conditions and Individual Dietary Characteristics of the Russian Population." Nutrients 15, no. 2 (January 12, 2023): 396. http://dx.doi.org/10.3390/nu15020396.
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The goal of our study was to examine the effects of the regional characteristics of the living environment on individual a priori and a posteriori dietary patterns of the Russian population. For the analysis, we used cross-sectional data from the Epidemiology of Cardiovascular Diseases in the Regions of the Russian Federation study from 2013–2014. The sample included 18,054 men and women 25–64 years of age from 12 regions. Based on the frequency of consumption of basic foods, four a posteriori empirical dietary patterns (EDPs), along with an a priori cardioprotective dietary pattern (CPDP), were identified. To describe the regional living environment, five regional indices were used. Adherence to the meat-based EDP was directly associated with deterioration of social living conditions and a more northerly location for the region of residence. The probability of a CPDP increased with greater deterioration of social living conditions, aggravation of demographic crises, and higher industrial development in the region, as well as with declines in the economic development of the region, income, and economic inequality among the population. We detected several gender-dependent differences in the associations established. The patterns revealed reflect the national dietary preferences of Russians, and the regional indices characterize the effect of the living environment.
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Andrews, Christopher, Debra Burleson, Kristi Dunks, Kimberly Elmore, Carie S. Lambert, Brett Oppegaard, Elizabeth E. Pohland, et al. "A New Method in User-Centered Design: Collaborative Prototype Design Process (CPDP)." Journal of Technical Writing and Communication 42, no. 2 (April 2012): 123–42. http://dx.doi.org/10.2190/tw.42.2.c.
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Bertolotto, Ana María, Maria Adelaida Córdoba, Yaris Anzully Vargas Vaca, Paula Carolina Guzmán Cruz, and Angélica Natalia Álvarez. "Caracterización de los pacientes, tratamiento y complicaciones más frecuentes de los recién nacidos con gastrosquisis y onfalocele manejados en la Unidad de Recién Nacidos del Hospital Universitario San Ignacio. Experiencia de 10 años." Universitas Médica 57, no. 3 (February 6, 2017): 323–31. http://dx.doi.org/10.11144/javeriana.umed57-3.cpdp.
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Introducción: Los defectos de la pared abdominal son infrecuentes; por ello se debe optimizar su diagnóstico y manejo, identificando las características clínicas más frecuentes. Objetivo: Caracterizar la población de pacientes con gastrosquisis y onfalocele atendidos en el Hospital Universitario San Ignacio (HUSI) en los últimos 10 años. Métodos: Se revisaron todas las historias de pacientes con defectos de pared abdominal atendidos en la Unidad de ReciénNacidos (URN) del HUSI entre el 2004 y el2014. Se estimaron las frecuencias de los hallazgosmás relevantes. Resultados: Se evaluaron 29pacientes, 18 pacientes con gastrosquisis (62 %)y 11 con onfalocele (38 %). Se encontró asociacióncon otras malformaciones en 20 pacientes(68,9 %) y en 5 casos de pacientes con onfalocelese evidenció cromosomopatías (17,2 %). Todoslos pacientes con gastrosquisis, y el 37 % de losniños con onfalocele recibieron manejo quirúrgico;el 55 %, manejo médico, y el 8 %, manejocombinado. El tiempo promedio de ayuno fue de11,8 días y con nutrición parenteral fue de 19,8días. No se encontró asociación con el consumomaterno de sustancias psicoactivas. Conclusión:Los defectos de pared son una condición rara querequirió un manejo especializado en la URN delHUSI durante los años 2004-2014. Fueron atendidos29 pacientes con características y evoluciónclínica similar a la reportada en la literaturalatinoamericana, aunque con tiempo de ayuno yde nutrición parenteral menor. No se encontróasociación con el consumo materno de sustanciaspsicoactivas.
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Prodanov, Goran. "A PROPOSED METHODOLOGY FOR CONDUCTING DATA PROTECTION IMPACT ASSESSMENT AND RISK ASSESSMENT IN AN ORGANIZATION." Journal Scientific and Applied Research 18, no. 1 (March 3, 2020): 94–100. http://dx.doi.org/10.46687/jsar.v18i1.284.
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Two years after the enforcement of the General Data Protection Regulation (GDPR) many organizations in Bulgaria are still experiencing problems with its implementation. Part of the reason is that there is a lack of methodological guidelines provided by the Bulgarian data protection authority (CPDP) on how to assess and manage the risk associated with the processing of personal data. Here is a basic structure of such methodology which can be used by organizations in the public and private sector alike. It is heavily influenced by the principles adopted by the French data protection authority (CNIL) which was the first to introduce such guidelines. The methodology can be implemented as is, or expanded according to the specific organizational needs.
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Darwish, M. A., Fatimah M. Al Awadhi, and Anwar Bin Amer. "The impact of cogeneration power and desalting plants (CPDP) on the environment in Kuwait." Desalination and Water Treatment 12, no. 1-3 (December 2009): 185–95. http://dx.doi.org/10.5004/dwt.2009.1060.
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Jalil, Abeer, Rizwan Bin Faiz, Sultan Alyahya, and Mohamed Maddeh. "Impact of Optimal Feature Selection Using Hybrid Method for a Multiclass Problem in Cross Project Defect Prediction." Applied Sciences 12, no. 23 (November 28, 2022): 12167. http://dx.doi.org/10.3390/app122312167.
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The objective of cross-project defect prediction (CPDP) is to develop a model that trains bugs on current source projects and predicts defects of target projects. Due to the complexity of projects, CPDP is a challenging task, and the precision estimated is not always trustworthy. Our goal is to predict the bugs in the new projects by training our model on the current projects for cross-projects to save time, cost, and effort. We used experimental research and the type of research is explanatory. Our research method is controlled experimentation, for which our independent variable is prediction accuracy and dependent variables are hyper-parameters which include learning rate, epochs, and dense layers of neural networks. Our research approach is quantitative as the dataset is quantitative. The design of our research is 1F1T (1 factor and 1 treatment). To obtain the results, we first carried out exploratory data analysis (EDA). Using EDA, we found that the dataset is multi-class. The dataset contains 11 different projects consisting of 28 different versions of all the projects in total. We also found that the dataset has significant issues of noise, class imbalance, and distribution gaps between different projects. We pre-processed the dataset for experimentation by resolving all these issues. To resolve the issue of noise, we removed duplication from the dataset by removing redundant rows. We then covered the data distribution gap between different sources and target projects using the min-max normalization technique. After covering the data distribution gap, we generated synthetic data using a CTGANsynthesizer to solve class imbalance issues. We solved the class imbalance issue by generating an equal number of instances, as well as an equal number of output classes. After carrying out all of these steps, we obtained normalized data. We applied the hybrid feature selection technique on the pre-processed data to optimize the feature set. We obtained significant results of an average AUC of 75.98%. From the empirical study, it was demonstrated that feature selection and hyper-parameter tuning have a significant impact on defect prediction accuracy in cross-projects.
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Vashisht, Rohit, and Syed Afzal Murtaza Rizvi. "Class Imbalance Learning to Heterogeneous Cross-Software Projects Defect Prediction." International Journal of Software Innovation 10, no. 1 (January 2022): 1–18. http://dx.doi.org/10.4018/ijsi.292021.
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Heterogeneous CPDP (HCPDP) attempts to forecast defects in a software application having insufficient previous defect data. Nonetheless, with a Class Imbalance Problem (CIP) perspective, one should have a clear view of data distribution in the training dataset otherwise the trained model would lead to biased classification results. Class Imbalance Learning (CIL) is the method of achieving an equilibrium ratio between two classes in imbalanced datasets. There are a range of effective solutions to manage CIP such as resampling techniques like Over-Sampling (OS) & Under-Sampling (US) methods. The proposed research work employs Synthetic Minority Oversampling TEchnique (SMOTE) and Random Under Sampling (RUS) technique to handle CIP. In addition to this, the paper proposes a novel four-phase HCPDP model and contrasts the efficiency of basic HCPDP model with CIP and after handling CIP using SMOTE & RUS with three prediction pairs. Results show that training performance with SMOTE is substantially improved but RUS displays variations in relation to HCPDP for all three prediction pairs.
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Vashisht, Rohit, and Syed Afzal Murtaza Rizvi. "Addressing Noise and Class Imbalance Problems in Heterogeneous Cross-Project Defect Prediction." International Journal of e-Collaboration 19, no. 1 (January 6, 2023): 1–27. http://dx.doi.org/10.4018/ijec.315777.
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When a software project either lacks adequate historical data to build a defect prediction (DP) model or is in the initial phases of development, the DP model based on related source project's defect data might be used. This kind of SDP is categorized as heterogeneous cross-project defect prediction (HCPDP). According to a comprehensive literature review, no research has been done in the field of CPDP to deal with noise and class imbalance problem (CIP) at the same time. In this paper, the impact of noise and imbalanced data on the efficiency of the HCPDP and with-in project defect prediction (WPDP) model is examined empirically and conceptually using four different classification algorithms. In addition, CIP is handled using a novel technique known as chunk balancing algorithm (CBA). Ten prediction combinations from three open-source projects are used in the experimental investigation. The findings show that noise in an imbalanced dataset has a significant impact on defect prediction accuracy.
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Bach-Ségura, Pascale. "Les fentes faciales diagnostiquées avant la naissance : du dépistage échographique systématique au centre pluridisciplinaire de diagnostic prénatal (CPDP)." Revue d'Orthopédie Dento-Faciale 46, no. 3 (July 2012): 265–74. http://dx.doi.org/10.1051/odf/2012302.
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Dunlap, P. V., and S. M. Callahan. "Characterization of a periplasmic 3':5'-cyclic nucleotide phosphodiesterase gene, cpdP, from the marine symbiotic bacterium Vibrio fischeri." Journal of Bacteriology 175, no. 15 (1993): 4615–24. http://dx.doi.org/10.1128/jb.175.15.4615-4624.1993.
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Chen, Siao, Yi He, Yajiao Geng, Zhi Wang, Lu Han, and Weiwei Han. "Molecular Dynamic Simulations of Bromodomain and Extra-Terminal Protein 4 Bonded to Potent Inhibitors." Molecules 27, no. 1 (December 26, 2021): 118. http://dx.doi.org/10.3390/molecules27010118.
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Bromodomain and extra-terminal domain (BET) subfamily is the most studied subfamily of bromodomain-containing proteins (BCPs) family which can modulate acetylation signal transduction and produce diverse physiological functions. Thus, the BET family can be treated as an alternative strategy for targeting androgen-receptor (AR)-driven cancers. In order to explore the effect of inhibitors binding to BRD4 (the most studied member of BET family), four 150 ns molecular dynamic simulations were performed (free BRD4, Cpd4-BRD4, Cpd9-BRD4 and Cpd19-BRD4). Docking studies showed that Cpd9 and Cpd19 were located at the active pocket, as well as Cpd4. Molecular dynamics (MD) simulations indicated that only Cpd19 binding to BRD4 can induce residue Trp81-Ala89 partly become α-helix during MD simulations. MM-GBSA calculations suggested that Cpd19 had the best binding effect with BRD4 followed by Cpd4 and Cpd9. Computational alanine scanning results indicated that mutations in Phe83 made the greatest effects in Cpd9-BRD4 and Cpd19-BRD4 complexes, showing that Phe83 may play crucial roles in Cpd9 and Cpd19 binding to BRD4. Our results can provide some useful clues for further BCPs family search.
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Volatier, Thomas, Björn Schumacher, Berbang Meshko, Karina Hadrian, Claus Cursiefen, and Maria Notara. "Short-Term UVB Irradiation Leads to Persistent DNA Damage in Limbal Epithelial Stem Cells, Partially Reversed by DNA Repairing Enzymes." Biology 12, no. 2 (February 7, 2023): 265. http://dx.doi.org/10.3390/biology12020265.
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The cornea is frequently exposed to ultraviolet (UV) radiation and absorbs a portion of this radiation. UVB in particular is absorbed by the cornea and will principally damage the topmost layer of the cornea, the epithelium. Epidemiological research shows that the UV damage of DNA is a contributing factor to corneal diseases such as pterygium. There are two main DNA photolesions of UV: cyclobutane pyrimidine dimers (CPDs) and pyrimidine-pyrimidone (6–4) photoproducts (6-4PPs). Both involve the abnormal linking of adjacent pyrimide bases. In particular, CPD lesions, which account for the vast majority of UV-induced lesions, are inefficiently repaired by nucleotide excision repair (NER) and are thus mutagenic and linked to cancer development in humans. Here, we apply two exogenous enzymes: CPD photolyase (CPDPL) and T4 endonuclease V (T4N5). The efficacy of these enzymes was assayed by the proteomic and immunofluorescence measurements of UVB-induced CPDs before and after treatment. The results showed that CPDs can be rapidly repaired by T4N5 in cell cultures. The usage of CPDPL and T4N5 in ex vivo eyes revealed that CPD lesions persist in the corneal limbus. The proteomic analysis of the T4N5-treated cells shows increases in the components of the angiogenic and inflammatory systems. We conclude that T4N5 and CPDPL show great promise in the treatment of CPD lesions, but the complete clearance of CPDs from the limbus remains a challenge.
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Koff, Harlan, Mariana Villada Canela, Carmen Maganda, Octavio Pérez-Maqueo, Ma Xóchitl Molina González, Jesús Arturo González Herrera, Diego Porras, et al. "Promoting participative policy coherence for sustainable development." Regions and Cohesion 12, no. 1 (March 1, 2022): 1–24. http://dx.doi.org/10.3167/reco.2022.120102.
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English Abstract: Coherence for Sustainable Development (PCSD) has promoted sustainability through policy coordination, but to what extent does it respond to the needs of local communities? Scholars of PCSD have acknowledged how it has been considered as an end in itself rather than a means to achieve normative impact. A major limit of PCSD has been its institutionalized nature, as mechanisms for social participation have not been highlighted in implementation strategies. This article addresses this issue. It proposes “pull-push-match” as a methodology for the establishment of participative PCSD. The article, co-authored by a team of researchers and practitioners from Mexico, addresses PCSD in relation to Indigenous communities.Spanish Abstract: La Coherencia de Políticas para el Desarrollo Sostenible (CPDS) ha promovido la sostenibilidad mediante la coordinación de políticas, pero ¿en qué medida responde a las necesidades de las comunidades locales? Los estudiosos de la CPDS han reconocido que ésta se ha considerado un fin en sí misma más que un medio para lograr un impacto normativo. Uno de los principales límites del CPDS ha sido su carácter institucionalizado, ya que los mecanismos de participación social no se han destacado en las estrategias de implementación. Este artículo aborda esta cuestión. Propone el “pull-push-match” como metodología para establecer un CPDS participativo. El artículo, elaborado por un equipo de investigadores y profesionales de México, aborda la CPDS en relación con las comunidades indígenas.French Abstract: La cohérence des politiques de développement durable (CPDD) favorise la durabilité par la coordination des politiques, mais dans quelle mesure répond-elle aux besoins des communautés locales? Les chercheurs dans ce domaine ont montré que la CPDD est considérée comme une fin en soi plutôt que comme un moyen d’avoir un impact normatif. L’une des principales limites de la CPDD a été sa nature institutionnalisée, car les mécanismes de participation sociale n’ont pas été mis en évidence dans les stratégies de mise en oeuvre. Cet article aborde la question et propose la méthode “pull-push-match” pour l’établissement d’une CPDD participative. Il est co-écrit par une équipe de chercheurs et de praticiens au Mexique et traite de la CPDD en relation avec les communautés indigènes.
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Kwon, Hansol, Youngjin Park, Uk Hee Nam, Eunkyung Lee, and Eungsun Byon. "Comparative Research on Corrosion Resistant Non-Skid Al and Al-3%Ti Coating Fabricated by Twin Wire arc Spraying." Korean Journal of Metals and Materials 61, no. 4 (April 5, 2023): 242–51. http://dx.doi.org/10.3365/kjmm.2023.61.4.242.
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To ensure the lifetime of marine constructions and the safety of workers and pedestrians, corrosion protective non-skid coating is an effective solution. However, the conventional polymer-based coating has some limitations. In this study, newly-suggested Al and Al-3%Ti coatings were deposited on high strength low alloyed steel substrate using twin wire arc spraying (TWAS). The static and dynamic friction coefficients of the Al-based coatings under dry and wet conditions were measured using portable friction testers. To evaluate the corrosion behavior under sea water conditions, a cyclic potentiodynamic polarization test (CPDP) and salt solution immersion test (SSIT) were performed with a 3.5% NaCl solution. To confirm the coating degradation, mechanical properties (Vickers hardness and adhesion strength) were compared before and after SSIT. The results showed that the TWAS Al-based coatings were well fabricated on HSLA steel and had the general microstructure of a thermal spray. The coatings provided excellent corrosion protection for the steel substrate and greatly increased the friction coefficient of the surface. The Vickers hardness slightly increased and adhesion strength decreased after SSIT. The microstructure observation revealed that the TWAS coatings had a bimodal structure induced by non-uniform droplet generation at the TWAS tips. After SSIT, some oxides formed on the surface and porous regions of the coatings. This indicated that the TWAS coating successfully provided corrosion protection and non-skid properties.
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Yao, Jingxiu, Bin Liu, Yumei Wu, and Zhibo Li. "Multi-Source Heterogeneous Kernel Mapping in Software Defect Prediction." Applied Sciences 13, no. 9 (April 28, 2023): 5526. http://dx.doi.org/10.3390/app13095526.
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Heterogeneous defect prediction (HDP) is a significant research topic in cross-project defect prediction (CPDP), due to the inconsistency of metrics used between source and target projects. While most HDP methods aim to improve the performance of models trained on data from one source project, few studies have investigated how the number of source projects affects predictive performance. In this paper, we propose a new multi-source heterogeneous kernel mapping (MSHKM) algorithm to analyze the effects of different numbers of source projects on prediction results. First, we introduce two strategies based on MSHKM for multi-source HDP. To determine the impact of the number of source projects on the predictive performance of the model, we regularly vary the number of source projects in each strategy. Then, we compare the proposed MSHKM with state-of-the-art HDP methods and within-project defect prediction (WPDP) methods, in terms of three common performance measures, using 28 data sets from five widely used projects. Our results demonstrate that, (1) in the multi-source HDP scenario, strategy 2 outperforms strategy 1; (2) for MSHKM, a lower number of source projects leads to better results and performance under strategy 1, while n = 4 is the optimal number under strategy 2; (3) MSHKM performs better than related state-of-the-art HDP methods; and (4) MSHKM outperforms WPDP. In summary, our proposed MSHKM algorithm provides a promising solution for heterogeneous cross-project defect prediction, and our findings suggest that the number of source projects should be carefully selected to achieve optimal predictive performance.
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Shao, Yanli, Jingru Zhao, Xingqi Wang, Weiwei Wu, and Jinglong Fang. "Research on Cross-Company Defect Prediction Method to Improve Software Security." Security and Communication Networks 2021 (August 24, 2021): 1–19. http://dx.doi.org/10.1155/2021/5558561.
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As the scale and complexity of software increase, software security issues have become the focus of society. Software defect prediction (SDP) is an important means to assist developers in discovering and repairing potential defects that may endanger software security in advance and improving software security and reliability. Currently, cross-project defect prediction (CPDP) and cross-company defect prediction (CCDP) are widely studied to improve the defect prediction performance, but there are still problems such as inconsistent metrics and large differences in data distribution between source and target projects. Therefore, a new CCDP method based on metric matching and sample weight setting is proposed in this study. First, a clustering-based metric matching method is proposed. The multigranularity metric feature vector is extracted to unify the metric dimension while maximally retaining the information contained in the metrics. Then use metric clustering to eliminate metric redundancy and extract representative metrics through principal component analysis (PCA) to support one-to-one metric matching. This strategy not only solves the metric inconsistent and redundancy problem but also transforms the cross-company heterogeneous defect prediction problem into a homogeneous problem. Second, a sample weight setting method is proposed to transform the source data distribution. Wherein the statistical source sample frequency information is set as an impact factor to increase the weight of source samples that are more similar to the target samples, which improves the data distribution similarity between the source and target projects, thereby building a more accurate prediction model. Finally, after the above two-step processing, some classical machine learning methods are applied to build the prediction model, and 12 project datasets in NASA and PROMISE are used for performance comparison. Experimental results prove that the proposed method has superior prediction performance over other mainstream CCDP methods.
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China, M. A., N. J. Deedam, and P. N. Olumati. "Effect of fluted pumpkin seeds flour on the proximate and sensory properties of cooking banana flour biscuits and queens cake for household consumption." Research Journal of Food Science and Nutrition 5, no. 2 (April 30, 2020): 30–34. http://dx.doi.org/10.31248/rjfsn2019.082.
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Biscuits and queens cakes were produced from the blends of cooking banana/fluted pumpkin seeds flour at a substitution levels of 100% wheat flour (control), 90% cooking banana, 10% fluted pumpkin seeds flour, 80% cooking banana, 20% fluted pumpkin seeds flour and 70% cooking banana, 30% fluted pumpkin seeds for households consumption. Proximate and sensory properties of the food products (biscuits and queens cakes) was analyzed. Results for proximate composition showed that samples CPDB 70% cooking banana, 30% fluted pumpkin seeds flour and CPDQ 70% cooking banana, 30% fluted pumpkin seeds flour had the highest value for protein while sample WHAB 100% wheat flour biscuits and WHAQ 100% wheat flour queens cake (control) had the highest value for carbohydrate content. Result for Sensory properties: colour, texture, taste, flavour and general acceptability) revealed that 100% wheat flour products were most preferred compared to all the samples. However, sample CPBB and CPBQ 90% cooking banana with 10% fluted pumpkin seeds flour substitution were comparable to the control in all the attributes evaluated. Sample CPDB and CPDQ had the lowest value and were least preferred. Protein content of biscuits and queen cakes samples improved progressively with increased substitution levels of enrichment with fluted pumpkin seeds flour. This confirms that the developed biscuits and queens cake have a better nutritional value compared to the control sample and could be used to stem the tide of protein energy – malnutrition in the family.
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Barbosa, Maria D. F. S., Gaoyun Yang, Jie Fang, Michael G. Kurilla, and David L. Pompliano. "Development of a Whole-Cell Assay for Peptidoglycan Biosynthesis Inhibitors." Antimicrobial Agents and Chemotherapy 46, no. 4 (April 2002): 943–46. http://dx.doi.org/10.1128/aac.46.4.943-946.2002.
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ABSTRACT Osmotically stabilized Escherichia coli cells subjected to freezing and thawing were utilized as the source of enzymes for a peptidoglycan pathway assay that can be used to simultaneously test all targets of the committed steps of cell wall biosynthesis. The use of 14C-labeled UDP-N-acetylglucosamine (UDP-GlcNAc) as a substrate allows the direct detection of cross-linked peptidoglycan formed. The assay was validated with known antibiotics. Fosfomycin was the strongest inhibitor of the pathway assay, with a 50% inhibitory concentration of 1 μM. Flavomycin, bacitracin, vancomycin, d-cycloserine, penicillin G, and ampicillin also inhibited formation of radiolabeled peptidoglycan by the E. coli cells. Screening of compounds identified two inhibitors of the pathway, Cpd1 and Cpd2. Subsequent tests with a biochemical assay utilizing purified enzyme implicated UDP-GlcNAc enolpyruvyl transferase (MurA) as the target of Cpd1. This compound inhibits the first enzyme of the pathway in a time-dependent manner. Moreover, enzyme inactivation is dependent on preincubation in the presence of UDP-GlcNAc, which forms a complex with MurA, exposing its active site. Cpd1 also displayed antimicrobial activity against a panel of microorganisms. The pathway assay used in conjunction with assays for individual enzymes provides an efficient means of detecting and characterizing novel antimicrobial agents.
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Alagumuthu, Manikandan, Vanshika Srivastava, Manisha Shah, Sivakumar Arumugam, Mohandoss Sonaimuthu, and Napoleon Ayyakannu Arumugam. "Pro- and Anti-Inflammatory Cytokine Expression Levels in Macrophages; An Approach to Develop Indazolpyridin-methanones as Novel Inflammation Medication." Anti-Inflammatory & Anti-Allergy Agents in Medicinal Chemistry 19, no. 4 (October 15, 2020): 425–35. http://dx.doi.org/10.2174/1871523019666191226104724.
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Background: Macrophages play a serious part in the instigation, upkeep, and resolution of inflammation. They are activated or deactivated during inflammation progression. Activation signals include cytokines (IF-γ, granulocyte-monocyte colonystimulating factor (GM-CSF), and TNF-α), extracellular matrix proteins, and other chemical mediators. Activated macrophages are deactivated by anti-inflammatory cytokines (IL- 10 and TGF-β (transforming growth factor-beta) and cytokine antagonists that are mainly produced by macrophages. Based on this, the present study aimed to develop novel (E)- Benzylidene-indazolpyridin methanones (Cpd-1-10) as effective anti-inflammatory agents by analyzing pro- and anti-inflammatory cytokine levels in macrophages. Objectives: To determine the anti-inflammatory effect of indazolpyridin-methanones by examining pro- and anti-inflammatory interleukin levels in J77A.1 macrophages. Methods: Expression of cytokines such as TNF-α, IL-1β, IL-6 and IL-10 serum levels measured by ELISA method. Anti-cancer and cytotoxicity studies were carried out by MTT assay. COX-2 seems to be associated with cancers and atypical developments in the duodenal tract. So, a competitive ELISA based COX-2 inhibition assay was done. To validate the inhibitory potentials and to get more insight into the interaction of COX-2 with Cpd1-10, molecular docking was performed. Results: Briefly, the COX-2 inhibitory relative activity was found to be in between the range of 80-92% (Diclofenac showed 84%, IC50 0.95 μM). Conclusion: Cytotoxicity effect of the compounds against breast cancer cell lines found excellent and an extended anticancer study ensured that these compounds are also alternative therapeutic agents against breast cancer. Among all the tested cancer cell lines, the anti- cancer effect on breast cancer was exceptional for the most active compounds Cpd5 and Cpd9.
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Di Stefano, Miriana, Salvatore Galati, Gabriella Ortore, Isabella Caligiuri, Flavio Rizzolio, Costanza Ceni, Simone Bertini, et al. "Machine Learning-Based Virtual Screening for the Identification of Cdk5 Inhibitors." International Journal of Molecular Sciences 23, no. 18 (September 13, 2022): 10653. http://dx.doi.org/10.3390/ijms231810653.
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Cyclin-dependent kinase 5 (Cdk5) is an atypical proline-directed serine/threonine protein kinase well-characterized for its role in the central nervous system rather than in the cell cycle. Indeed, its dysregulation has been strongly implicated in the progression of synaptic dysfunction and neurodegenerative diseases, such as Alzheimer’s disease (AD) and Parkinson’s disease (PD), and also in the development and progression of a variety of cancers. For this reason, Cdk5 is considered as a promising target for drug design, and the discovery of novel small-molecule Cdk5 inhibitors is of great interest in the medicinal chemistry field. In this context, we employed a machine learning-based virtual screening protocol with subsequent molecular docking, molecular dynamics simulations and binding free energy evaluations. Our virtual screening studies resulted in the identification of two novel Cdk5 inhibitors, highlighting an experimental hit rate of 50% and thus validating the reliability of the in silico workflow. Both identified ligands, compounds CPD1 and CPD4, showed a promising enzyme inhibitory activity and CPD1 also demonstrated a remarkable antiproliferative activity in ovarian and colon cancer cells. These ligands represent a valuable starting point for structure-based hit-optimization studies aimed at identifying new potent Cdk5 inhibitors.
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Sakurada, Mieko, Hideo Inaba, Jiro Sato, Haruo Uchida, Tetsuro Ohwada, and Tadanobu Mizuguchi. "Fluctuating CPAP (F-CPAP) versus conventional CPAP (C-CPAP) in dogs with blood aspiration." Journal of Anesthesia 5, no. 1 (January 1991): 36–42. http://dx.doi.org/10.1007/s0054010050036.
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Grassetti, D. R. "CPDS." Drugs of the Future 11, no. 7 (1986): 559. http://dx.doi.org/10.1358/dof.1986.011.07.53119.
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Hamada, Eriko, Motoo Yamauchi, Yukio Fujita, and Shigeo Muro. "0461 CPAP adherence in commercial bus drivers in a public transportation company." SLEEP 46, Supplement_1 (May 1, 2023): A205. http://dx.doi.org/10.1093/sleep/zsad077.0461.
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Abstract Introduction We have been serving a sleep health promotion in a local public transportation company. One of the projects is OSA screening for all commercial bus drivers. In addition, since April 2018, we have made provision for the CPAP adherence, that is, all of the bus drivers have been monitored adherence, and if CPAP adherence was poor for two consecutive times, an interview with us is mandated. In case of the drivers whose adherence is not improved after the interview, they must be away from driving until CPAP adherence is improved. Thus, we hypothesized such a provision makes CPAP adherence better as compared with real-world data of CPAP adherence in Japan (45.6%) which was our presentation at the SLEEP 2022. Methods Pulse oximetry-based OSA screening is performed for all of the commercial bus drivers in the public transportation company. In case of 3% oxygen desaturation index ≥15, In-lab diagnostic PSG is performed. The drivers who are prescribed CPAP must submit CPAP adherence data sheet basically every month. Using the data of current employed bus drivers, we calculated the prevalence of OSA and recent CPAP adherence for a total of more than 90 days. CPAP adherence was defined as CMS criteria. Results The total number of commercial bus drivers in the company was 1237, of which 131 were diagnosed with OSA, 123 were CPAP users. The prevalence of OSA was 10.6%. Among 123 of CPAP users, 7 drivers lost the CPAP adherence data sheet, 1 driver was absent for a long time due to a malignant disease, and 2 drivers have recently started CPAP therapy, so CPAP adherence for 90 days could not be obtained, Eventually, CPAP adherence was assessed with 113 CPAP users. Eighty-seven drivers achieved CMS criteria, thus CPAP adherence was 77.0% which is comparable to the recent cloud-based CPAP adherence data in the US (72.6%). Conclusion CPAP adherence in this study was higher than in the real-world data in Japan. Our sleep health promotion of CPAP therapy in this company successfully increased CPAP adherence. Support (if any)
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Brill, Anne-Kathrin, Thomas Horvath, Andrea Seiler, Millene Camilo, Alan G. Haynes, Sebastian R. Ott, Matthias Egger, and Claudio L. Bassetti. "CPAP as treatment of sleep apnea after stroke." Neurology 90, no. 14 (March 9, 2018): e1222-e1230. http://dx.doi.org/10.1212/wnl.0000000000005262.
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ObjectiveTo perform a systematic review and meta-analysis of randomized controlled trials (RCTs) examining the effectiveness of continuous positive airway pressure (CPAP) in stroke patients with sleep disordered breathing (SDB).MethodsIn a systematic literature search of electronic databases (MEDLINE, Embase, and the Cochrane Library) from 1980 to November 2016, we identified RCTs that assessed CPAP compared to standard care or sham CPAP in adult patients with stroke or TIA with SDB. Mean CPAP use, odds ratios (ORs), and standardized mean differences (SMDs) were calculated. The prespecified outcomes were adherence to CPAP, neurologic improvement, adverse events, new vascular events, and death.ResultsTen RCTs (564 participants) with CPAP as intervention were included. Two studies compared CPAP with sham CPAP; 8 compared CPAP with usual care. Mean CPAP use across the trials was 4.53 hours per night (95% confidence interval [CI] 3.97–5.08). The OR of dropping out with CPAP was 1.83 (95% CI 1.05–3.21, p = 0.033). The combined analysis of the neurofunctional scales (NIH Stroke Scale and Canadian Neurological Scale) showed an overall neurofunctional improvement with CPAP (SMD 0.5406, 95% CI 0.0263–1.0548) but with a considerable heterogeneity (I2 = 78.9%, p = 0.0394) across the studies. Long-term survival was improved with CPAP in 1 trial.ConclusionCPAP use after stroke is acceptable once the treatment is tolerated. The data indicate that CPAP might be beneficial for neurologic recovery, which justifies larger RCTs.