Kloning Gen Coat Protein (CP) Carnation Mottle Virus (CarMV) pada Vektor Ekspresi [Cloning of Carnation Mottle Virus (CarMV) Coat Protein Gene into Expression Vector]

<p>Carnation mottle virus (CarMV) termasuk anggota genus Carmovirus dalam famili Tombusviridae. Virus ini banyak ditemukan menginfeksi tanaman anyelir di Jawa Barat dan menyebabkan gejala mottle. Sebagai langkah awal untuk memproduksi antiserum melalui teknik ekspresi gen CP perlu diklon pada vektor yang sesuai. Penelitian ini bertujuan mendapatkan klon CarMV yang berfungsi melalui kloning dan subkloning gen CP CarMV ke dalam vektor ekspresi yang sesuai. Penelitian dilakukan dalam beberapa tahap, yaitu ekstraksi RNA total dan amplifikasi cDNA CarMV dengan RT-PCR, menggunakan primer spesifik CarMVF dan CarMVR yang mengandung situs enzim restriksi XhoI dan BamHI, kloning dan subkloning DNA sisipan, serta konfirmasi transforman. Rekombinan gen sisipan CP CarMV dalam bakteri dikonfirmasi dengan koloni PCR. Gen CP CarMV berhasil dikloning ke dalam TA vektor pTZ57R/T dan disubkloning ke vektor ekspresi pET28a. Sekuen rekombinan CP CarMV berhasil dikonfirmasi melalui perunutan DNA. Penelitian lebih lanjut diperlukan untuk mendapatkan produksi antigen rekombinan yang melimpah pada bakteri ekspresi dan kondisi yang sesuai.</p><p><strong>Keywords</strong></p><p>Dianthus caryophillus L.; Carmovirus; Kloning; Subkloning; Bakteri ekspresi</p><p><strong>Abstract</strong></p><p>Carnation mottle virus (CarMV) is a type member of Carmovirus genus in family of Tombusvirus. The virus infects carnation plants in the centre area production of West Java and it cause mottle symptoms. The research aimed to obtain functional clone(s) of CarMV CP gene in suitable expression kloning vector. The research was carried out through several steps, namely total RNA extraction and amplification of cDNA of CP CarMV by RT-PCR using specific primer pairs CarMVF and CarMVR containing restriction enzyme sites XhoI and BamHI, respectively, TA cloning, and subcloning into expression vector pET28a and confirmation of recombinant plasmids by colony PCR. CarMV CP gen was successfully cloned into TA cloning vector pTZ57R/T and subcloned into vector pET28a, alsowere confirmed by DNA sequencing. Future experiment is necessary to be conducted to obtain abundance recombinant antigen production of CarMV CP in suitable expression condition and bacterial host.</p>

A Novel Method of Clone Detection by Neural Networks

Code clone is that type of engine that helps to find duplicate code patterns find within the whole code. Programmers usually adopt code reusability task from previous few years, so that time consumption can be reduces. Code reusability can be done via replication or by just copy-paste. Code reusability leads to not writing code from scratch, just copy paste the useful part of the code. In finding of duplicated code fragment or text, plagiarism detection also work pretty well but it is not applicable to the large system in finding functional clone and also it is more time consuming even at small scale which make the detection method inappropriate. In this paper, we proposed a pattern similarity conditions on the basis of textual similarity for finding the code or text clones in the large content on the basis of SVM, Neural Network using Java coding, Neural Network and Sim Cad. This approach detects code or text clones from original one. The resultant simulation is taken place in the MATLAB environment, and it has shown that it is providing better results. The proposed algorithm performance is measured using parameters i.e. FRR, FAR and Accuracy.

Positive and Unlabeled Learning for Detecting Software Functional Clones with Adversarial Training

Software clone detection is an important problem for software maintenance and evolution and it has attracted lots of attentions. However, existing approaches ignore a fact that people would label the pairs of code fragments as \emph{clone} only if they happen to discover the clones while a huge number of undiscovered clone pairs and non-clone pairs are left unlabeled. In this paper, we argue that the clone detection task in the real-world should be formalized as a Positive-Unlabeled (PU) learning problem, and address this problem by proposing a novel positive and unlabeled learning approach, namely CDPU, to effectively detect software functional clones, i.e., pieces of codes with similar functionality but differing in both syntactical and lexical level, where adversarial training is employed to improve the robustness of the learned model to those non-clone pairs that look extremely similar but behave differently. Experiments on software clone detection benchmarks indicate that the proposed approach together with adversarial training outperforms the state-of-the-art approaches for software functional clone detection.

Supervised Deep Features for Software Functional Clone Detection by Exploiting Lexical and Syntactical Information in Source Code

Software clone detection, aiming at identifying out code fragments with similar functionalities, has played an important role in software maintenance and evolution. Many clone detection approaches have been proposed. However, most of them represent source codes with hand-crafted features using lexical or syntactical information, or unsupervised deep features, which makes it difficult to detect the functional clone pairs, i.e., pieces of codes with similar functionality but differing in both syntactical and lexical level. In this paper, we address the software functional clone detection problem by learning supervised deep features. We formulate the clone detection as a supervised learning to hash problem and propose an end-to-end deep feature learning framework called CDLH for functional clone detection. Such framework learns hash codes by exploiting the lexical and syntactical information for fast computation of functional similarity between code fragments. Experiments on software clone detection benchmarks indicate that the CDLH approach is effective and outperforms the state-of-the-art approaches in software functional clone detection.