Judul: Radioprotective Effect of Rosella Extract (Hibiscus sabdariffa L.) against Recurrent Radiodiagnostic Ionizing Radiation: Study of Leukocytes Differentiation in Mice (Mus musculus)
Abstrak: Tujuan penelitian ini adalah untuk mengetahui efek radioprotektif ekstrak tanaman rosela (Hibiscus sabdariffa L.) terhadap radiasi ionisasi radiodiagnostik berulang melalui studi differensiasi sel leukosit darah perifer pada mencit (Mus musculus). Mencit betina sebanyak 48 ekor dibagi kedalam 4 kelompok yaitu: K- (diberi NaCl fisiologis tanpa radiasi), K+ (diberi NaCl fisiologis dengan radiasi), R- (diberi Ekstrak rosela tanpa radiasi), R+ (diberi Ekstrak rosela dengan radiasi). Mencit diberi ekstrak rosela dengan dosis 50 mg/kg berat badan dan NaCl fisiologis dengan volume 0.2 ml/ekor/2 hari sebelum pemaparan radiasi. Radiasi ionisasi dilakukan dengan dosis 0,2 mSv/paparan/2 hari selama 4 & 8 minggu pada berkas sinar utama. Pengambilan darah perifer dilakukan pada setiap kelompok pada minggu ke-0 (sebelum perlakuan), 2, 4, 6 & 8. Pengambilan data pemulihan selama 30 hari setelah minggu 4 dan 8 tanpa paparan radiasi. Hasil penelitian menunjukkan paparan radiasi dapat meningkatkan persentase netrofil band meningkat, netrofil adult, migrasi limfoblas, dan mieloblas serta menurunkan persentase monosit dan limfosit pada jaringan darah perifer. Pemberian ekstrak rosela dapat meningkatkan persentase limfosit dan monosit serta menurunkan persentase netrofil band, netrofil adult, limfoblas, dan mieloblas. Dari hasil penelitian didapatkan bahwa ekstrak tanaman rosela (Hibibscus sabdariffa L.) memiliki efek radioprotektif terhadap radiodiagnostik berulang pada sel leukosit darah perifer mencit (Mus musculus)
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Judul: Efek Radioprotektif Ekstrak Rosela (Hibiscus sabdariffa L.) terhadap Radiasi Ionisasi Radiodiagnostik Berulang: Studi Gambaran Eritrosit Darah Perifer Mencit (Mus musculus).
Abstrak: The aim of this study was to determine the radioprotective effect of ethanolic roselle extract (Hibiscus sabdariffa L.) in recurrent radiodiagnostic ionizing radiation on peripheral red blood cells of mice (Mus musculus). Fourty eight male mice were used in this study and divided into 4 group; K-(physiological saline without radiation), K+ (physiological saline with radiation), R- (roselle extract without radiation), R+ (roselle extract with radiation). The mice were treated with 50 mg per kg body weight ethanolic roselle extract prior to radiation exposure. Following these treatment, the mice were exposed with ionizing radiation at dose 0.2 mSv every two days for 8 weeks. Bloods sample were collected of each group at week 0 before treatment, 2, 4, 6, and 8 week after treatment. The blood samples were taken also at 4 and 8 week, which previously without radiation exposure for 30 days as recovery phase. The result showed that radiation exposure causes an increasing the number of Mean Corpuscular Volume (MCV), Mean Corpuscular Hemoglobin (MCH), Mean Corpuscular Hemoglobin Concentration (MCHC) and Howell Jolly Bodies in the peripheral blood tissue. However ethanolic roselle extract causes a decreasing number of the parameters. This result indicated that ethanolic roselle extract has radioprotective effect on of red blood cells repair process from ionizing radiation destruction (p<0.05).
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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