Judul: Fungsi Hati Mencit pada Uji Toksisitas Subkronis Ekstrak Air Kulit Batang Mahoni (Swietenia macrophylla King)
Abstrak: Mahogany’s bark is industrial waste that can potentially be developed as herbal medicines. This research was performed to study the extract’s subchronic toxicity in the liver. Fourty mice consist of 20 males and 20 females were divided into 4 groups: normal, dosage 1 (D1), dosage 2 (D2), and dosage 3 (D3), each consisting of 5 males and 5 females. Normal group was administered with aquadest, whereas D1, D2, and D3 groups were orally gavaged with mahogany’s bark aqueous extract at concentration of 21, 200, and 400 mg/Kg BW for 56 days. Biochemical and histopathological parameters were observed at the end of experimental period. There were no difference in body weight, food intake, and liver enzymes. Range of ALT and AST activities were consecutively between 54.20±15.09 to 83.00±44.98 U/L and 83.80±21.73 to 164.33±99.28 U/L, and these values were normal. There was no toxic effect of mahogany’s bark aqueous extract to the liver, moreover according to histopathological examination the extract tended to protect female hepatocytes from nonspesific degeneration.
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Judul: Peroksidasi Lipid pada Tikus Hiperkolesterolemia selama Pemberian Ekstrak Kulit Batang Mahoni (Swietenia macrophylla)
Abstrak: Mahogany’s bark is known as wood industrial waste that contains potential secondary metabolites. However, its potency as an antioxidant, especially in rats which are subjected to hypercholesterolemic treatment, has not been carried out. The aim of this study was to test the effect of mahogany’s water extract consumption on the concentration of lipid peroxides in hypercholesterolemic rats. Thirty five male rats were divided into 5 groups (n=7) namely normal group fed with standard rat chow, hypercholesterolemic group (HK) fed with cholesterol chow (1,5%) and PTU (0,5 mg/KgBW), lovastatin group (HK+lovastatin 0,2857 mg/KgBW), E1 group (HK+ mahogany’s extract 4,2 mg/KgBW), and E2 group (HK+mahogany’s extract 21 mg/KgBW). Experiments were carried out for 8 consecutive weeks. The concentration of lipid peroxides in blood were measured with thiobarbituric acid assay at week 0, 2, 4, 6, and 8 using spectrophotometer. Cholesterol chow (0.86%) and PTU increased the lipid peroxides concentration of HK group by 20.92% compared to the normal group (3.380±0.436 nmol/mL). However, it is not statistically significant (p=0.239). The lipid peroxides concentration in E1 and E2 weren’t increased and relatively similar to normal group at concentration of 2.353±0.196 nmol/mL, 2.786±0.256 nmol/mL, and 2.673±0.279 nmol/mL, respectively. Although it is not statistically significant, both doses of extracts tend to prevent the lipid peroxidation in the rat’s blood.
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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