Judul: Penentuan daya inhibisi ekstrak air dan etanol temu putih (Curcuma zedoaria) terhadap aktivitas tirosin kinase secara in vitro
Abstrak: Tirosin kinase memainkan peranan penting dalam perkembangan sel kanker. Suatu zat yang dapat menginhibisi aktivitas tirosin kinase diharapkan mampu menghambat perkembangan sel kanker. Temu putih merupakan tanaman yang memiliki potensi sebagai tanaman obat kanker. Pada penelitian ini, hendak ditentukan daya inhibisi ekstrak air dan etanol temu putih terhadap aktivitas tirosin kinase. Ekstrak hasil maserasi rimpang temu putih kering (kadar air 10,42%) dalam tiga pelarut, yaitu etanol 70%, air demineralisasi (akuadem), dan air panas diperoleh dengan rendemen berturut-turut sebesar 24,51%; 9,65%; dan 12,22%. Penentuan daya inhibisi terhadap aktivitas tirosin kinase dari ketiga ekstrak dilakukan dengan menggunakan metode ELISA (enzyme linked immunosorbent assay) dan dibandingkan hasilnya dengan genistein sebagai kontrol positif. Konsentrasi sebesar 300 dan 700 ppm, keduanya di bawah nilai konsentrasi letal dari semua ekstrak terhadap Artemia salina Leach, digunakan untuk pengukuran daya inhibisi tersebut.
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Judul: Uji daya inhibisi ekstrak kasar flavonoid smabiloto (Andrographis paniculata [Burm, F] Ness) dan temu putih (Curcuma zedoaria Roscoe) terhadap aktivitas tirosin kinase secara in vitro
Abstrak: Tirosin kinase mempunyai peran penting dalam perkembangan sel kanker. Senyawa yang dapat menjadi inhibitor spesifik tirosin kinase merupakan salah satu metode yang digunakan untuk mencari obat antikanker. Senyawa flavonoid berpotensi menghambat aktivitas tirosin kinase. Berdasarkan uji fitokimia, tanaman sambiloto (A. paniculata [Burm. F] Ness) dan rimpang temu putih (C. zedoaria Roscoe) mengandung senyawa flavonoid. Tanaman sambiloto dan rimpang temu putih diekstraksi dengan metode maserasi. Jenis pelarut dan nisbah perbandingan mengikuti metode maserasi Markham (1988). Ekstraksi menggunakan pelarut metanol-air dengan nisbah 9:1 dan 1:1. Setelah itu, dipartisi dengan pelarut heksana dan kloroform. Rendemen ekstrak kasar flavonoid sambiloto dan rimpang temu putih masing-masing sebesar 16,90% dan 19,81%. Selanjutnya, uji toksisitas larva udang dan penentuan total fenol dilakukan. Daya inhibisi ekstrak kasar sambiloto konsentrasi 300 ppm terhadap tirosin kinase adalah sebesar 67,19% atau lebih besar dari kontrol positif (genistein), yaitu sebesar 6,71%. Daya inhibisi ekstrak kasar flavonoid rimpang temu putih terhadap tirosin kinase konsentrasi 300 ppm dan 700 ppm masingmasing sebesar 2,83% dan 27,49% atau lebih tinggi dibandingkan genistein. Daya hambat ekstrak kasar sambiloto terhadap tirosin kinase cukup tinggi sehingga dapat berpotensi sebagai obat antikanker, sedangkan daya hambat ekstrak kasar flavonoid temu putih lebih rendah.
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Judul: The Construction of Greedy SVP LLL Algorithm
Abstrak: A lattice is a set of all integer linear combination of a set of linearly independent vectors in ℝ𝑛���. The independent vectors are called bases of lattice. Any lattice can be generated from many bases, and these bases have the same cardinality. In the lattice, the most fundamental and renowned problem is the Shortest Vector Problem (SVP). SVP is a tracking problem of the shortest nonzero vector in a lattice with equivalent bases. In two dimensions, SVP problem has resolved exactly by Gauss’ algorithm. When the lattice dimension is higher than two, A.K. Lenstra, H.W. Lenstra and L. Lovasz gave a LLL algorithm to compute approximation of nonzero shortest vector in the lattice. Reduced bases solution obtained from LLL algorithm still be an approximation and has polynomial running time of arbitrary dimension which large enough. In 1994, Schnoor and Euchner modified this LLL algorithm as a new variant which later was named deep insertion LLL algorithm. This algorithm is a modified version of the LLL algorithm to the terms of the exchange step to improve the accuracy of output of LLL reduced bases and applying it to the subset sum problem. The purpose of this research are: (1) Reconstruct the LLL algorithm and deep insertion LLL algorithm using a geometric approach. (2) Construct a greedy SVP LLL algorithm. (3) Implement the three algorithms in a symbolic programming language, and evaluate them experimentally. A bases ℬ=[𝐛���1,𝐛���2,…,𝐛���𝑛���] in ℝ𝑚��� is called reduced LLL with parameter 𝛿��� if satisfies: (a) |𝜇���𝑗���𝑖���|≤12, for every integer 𝑖���,𝑗��� with 1≤𝑖���<𝑗���<𝑛���. (b)𝛿���‖𝜋���𝑗���(𝐛���𝑗���)‖2≤‖𝜋���𝑗���(𝐛���𝑗���+1)‖2, for 𝑗���=1,2,…,𝑛���−1,where 𝛿��� is a reduced parameter of real numbers with 14<𝛿���<1. The first requirement is the reduced basis 𝛿��� must “nearly orthogonal” and in its computation case, this requirement can be reached out by using the Gram-Schmidt’s orthogonalization. While the second requirement is called exchange step, or used to called as Lovasz condition. By using both of these requirements, then they establish the LLL algorithm. Then, Schnoor and Euchner modify the LLL algorithm based on the requirements of the exchange step called the deep insertion LLL algorithm, where the exchange step is based on the comparison of the projected vector in orthogonal complement [𝐛���1,𝐛���2,…,𝐛���𝑘���−1]⊥ after j-th reduction. To construct greedy SVP LLL algorithm, the requirements of the exchange step is based on purely by comparing norm of lattice 𝐛���𝑗��� with norm lattice 𝐛���𝑖��� for 𝑖���=1,2,3…,𝑗���−1. The process of insertion reduced vector after the exchange step also has done greedily. Then, we calculate the number of arithmetic operation in greedy SVP LLL algorithm for further analysis. The result of our exprimentation for three algorithms on the certain matrix size shows that the running time of the three algorithms increases propotional to the size of the matrix. The result also shows that by using 𝛿���=34 for the LLL algorithm and the deep insertion LLL algorithm, and the greedy SVP LLL algorithm which is a new variant made by using no parameter of 𝛿���, outperform of the other of two previous algorithm in terms of speed with the same output.
Keyword: LLL algorithm, LLL deep insertion algorithm, greedy SVP LLL algorithm