Kelimpahan Dan Keanekaragaman Plankton Di Perairan Laguna Desa Tolongano Kecamatan Banawa Selatan

Penelitian bertujuan untuk mengetahui kelimpahan dan keanekaragaman plankton yang ada di Perairan Laguna, Desa Tolongano, Kecamatan Banawa Selatan. Penelitian dilaksanakan pada bulan Juni – Juli 2009. Pengambilan sampel plankton bertempat di Perairan Laguna, Desa Tolongano, Kecamatan Banawa Selatan, Kabupaten Donggala. Identifikasi sampel dilakukan di Laboratorium Budidaya Perairan, Fakultas Pertanian, Universitas Tadulako. Metode penelitian yang digunakan adalah purpossive sampling method (penempatan titik sampel dengan sengaja). Stasiun pengambilan sampel terdiri atas 5 stasiun, dilakukan sebanyak 3 kali yaitu pada pukul 07.00, 12.00, dan 17.00 WITA. Hasil penelitian menunjukkan, bahwa kelimpahan fitoplankton dari kelas Bacillariophyceae berkisar antara 8.925 – 16.135 ind/l dan kelimpahan zooplankton dari kelas Crustacea berkisar antara 35 – 70 ind/l, indeks keanekaragaman fitoplankton dari kelas Bacillariophyceae berkisar antara 2,010 – 2,504 dan indeks keanekaragaman zooplankton dari kelas Crustacea berkisar antara 0 – 0,6931, indeks dominansi dari kelas Bacillariophyceae berkisar antara 1,1995 – 1,2326 menunjukkan ada jenis plankton yang mendominasi, yaitu Nitzchia sp

Titanium Oxide Nanotube Surface Topography and MicroRNA-488 Contribute to Modulating Osteogenesis

Understanding the biocomplexity of cell behavior in relation to the topographical characteristics of implants is essential for successful osseointegration with good longevity and minimum failure. Here, we investigated whether culture on titanium oxide (TiO2) nanotubes of various diameters could affect the behavior and differentiation of MC3T3-E1 cells. Among the tested nanotubes, those of 50 nm in diameter were found to trigger the expression of the osteoblast-specific transcription factors, sp7 and Dlx5, and upregulate the expression of alkaline phosphatase (ALP). Here, we report that miR-488 was significantly induced in osteoblasts cultured on 50 nm nanotubes and continued to increase with the progression of osteoblast differentiation. Furthermore, downregulation of miR-488 suppressed the expression levels of ALP and matrix metalloprotease-2 (MMP-2). This suppression of ALP transcription was overcome by treatment with the MMP-2 activator, bafilomycin A1. Collectively, these results suggest that 50 nm is the optimum TiO2 nanotube diameter for implants, and that modulation of miR-488 can change the differentiation activity of cells on TiO2 nanotubes. This emphasizes that we must fully understand the physicochemical properties of TiO2 nanotubes and the endogenous biomolecules that interact with such surfaces, in order to fully support their clinical application

A Multi-Sample Based Method for Identifying Common CNVs in Normal Human Genomic Structure Using High-Resolution aCGH Data

BACKGROUND: It is difficult to identify copy number variations (CNV) in normal human genomic data due to noise and non-linear relationships between different genomic regions and signal intensity. A high-resolution array comparative genomic hybridization (aCGH) containing 42 million probes, which is very large compared to previous arrays, was recently published. Most existing CNV detection algorithms do not work well because of noise associated with the large amount of input data and because most of the current methods were not designed to analyze normal human samples. Normal human genome analysis often requires a joint approach across multiple samples. However, the majority of existing methods can only identify CNVs from a single sample. METHODOLOGY AND PRINCIPAL FINDINGS: We developed a multi-sample-based genomic variations detector (MGVD) that uses segmentation to identify common breakpoints across multiple samples and a k-means-based clustering strategy. Unlike previous methods, MGVD simultaneously considers multiple samples with different genomic intensities and identifies CNVs and CNV zones (CNVZs); CNVZ is a more precise measure of the location of a genomic variant than the CNV region (CNVR). CONCLUSIONS AND SIGNIFICANCE: We designed a specialized algorithm to detect common CNVs from extremely high-resolution multi-sample aCGH data. MGVD showed high sensitivity and a low false discovery rate for a simulated data set, and outperformed most current methods when real, high-resolution HapMap datasets were analyzed. MGVD also had the fastest runtime compared to the other algorithms evaluated when actual, high-resolution aCGH data were analyzed. The CNVZs identified by MGVD can be used in association studies for revealing relationships between phenotypes and genomic aberrations. Our algorithm was developed with standard C++ and is available in Linux and MS Windows format in the STL library. It is freely available at: http://embio.yonsei.ac.kr/~Park/mgvd.php

Predicting performance comparison of the proposed method with four existing methods using PPI data to identify informative genes.

<p>For each experiment, the optimal combination of two thresholds was obtained using the approach mentioned above and was applied to an independent test using unlabeled samples. Bold font indicates the superior performer.</p><p>TSVM: <i>P</i> (the ratio of two class labels).</p><p>SVM: PolyKernel –C 250007–E 1.0, The complexity parameter C (1.0), epsilon (1.0E−12), filterType (Normalized training data).</p><p>Naïve Bayesian: No parameters.</p><p>Random Forest: numTrees (10), seed (1).</p

Datasets used throughout the manuscript.

<p>−1: non-recurrence, +1: recurrence.</p

Integrative Gene Network Construction to Analyze Cancer Recurrence Using Semi-Supervised Learning

<div><p>Background</p><p>The prognosis of cancer recurrence is an important research area in bioinformatics and is challenging due to the small sample sizes compared to the vast number of genes. There have been several attempts to predict cancer recurrence. Most studies employed a supervised approach, which uses only a few labeled samples. Semi-supervised learning can be a great alternative to solve this problem. There have been few attempts based on manifold assumptions to reveal the detailed roles of identified cancer genes in recurrence.</p><p>Results</p><p>In order to predict cancer recurrence, we proposed a novel semi-supervised learning algorithm based on a graph regularization approach. We transformed the gene expression data into a graph structure for semi-supervised learning and integrated protein interaction data with the gene expression data to select functionally-related gene pairs. Then, we predicted the recurrence of cancer by applying a regularization approach to the constructed graph containing both labeled and unlabeled nodes.</p><p>Conclusions</p><p>The average improvement rate of accuracy for three different cancer datasets was 24.9% compared to existing supervised and semi-supervised methods. We performed functional enrichment on the gene networks used for learning. We identified that those gene networks are significantly associated with cancer-recurrence-related biological functions. Our algorithm was developed with standard C++ and is available in Linux and MS Windows formats in the STL library. The executable program is freely available at: <a href="http://embio.yonsei.ac.kr/~Park/ssl.php" target="_blank">http://embio.yonsei.ac.kr/~Park/ssl.php</a>.</p></div

Experimental results of parameter testing.

<p>We performed 100 different experiments while changing two threshold values and obtained 100 average accuracies for each dataset using 10-fold cross validation. We found the maximum, minimum, and average accuracies for each dataset in two cases. (1) We carried out 10-fold cross validation over 100 times, varying the two thresholds of the original samples as shown in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0086309#pone-0086309-t001" target="_blank">Table 1</a>. (2) We also carried out 10-fold cross validation over 100 times, varying the two thresholds after balancing the number of samples in the two classes. We randomly removed samples 27, 73, and 83 from the non-recurrence groups GSE2990, GSE17536, and GSE17538, respectively.</p

Experimental results of AUC comparison of the proposed method with three existing methods.

<p>We compared AUC values of the proposed method and other supervised learning algorithms.</p

Detailed workflow to determine the optimal parameter set.

<p>First, we construct a graph for regularization with only labeled samples by varying two parameters. In this phase, we use <i>k</i>-fold cross validation to determine the optimal parameter set. We then apply semi-supervised learning with the obtained optimal parameter set and predict the labels of the unknown samples. The proposed method uses unlabeled sample information to build a classifier by iterating the procedure.</p