Kombinasi Steganografi Bit Plane Complexity Segmentation (Bpcs) dan Kriptografi Data Encryption Standard (Des) untuk Penyisipan Pesan Teks pada Citra Bitmap Grayscale 8 Bit

Bit Plane Complexity Segmentation (BPCS) is steganography method that using uncapability of human\u27s vision in interpreting difficult biner form. Data Encryption Standard (DES) is cryptography algorhytm that is chiper block and changing data become blocks 64 bit and then using encryption key amount 56 bit. By combining steganography algorhytm and cryptography will increase quality of data security. In this research combination of BPCS and DES done by inserting text message into bitmap image, the increating text message restricted maximum 248 characteristic with the long of the key must 16 characteristic in hexsadecimal format. The result obtained by this system testing with image test about 30 images is the inserting text can be read again with the provision of using the same key for inserting process and reading text. This image of insertion result can\u27t stand to adding contrast operation (25%) and rotation (90 to the right, 90 to the left, 180) and cutting operation on the upper side dan left image, but if cutting on the lower side and right (image resolution > 100 piksel) the inserting text can be read again correctly. In image inserting result, will be found noise of the upper left side from image because these region is the initial region is inserted

Peningkatan Motivasi Dan Hasil Belajar Siswa Kelas IX SMPN 5 Kupang Pada Materi Fungsi Kuadrat Melalui Penerapan Pendekatan Saintifik Berbantuan GeoGebra

Topik fungsi kuadrat dimulai dari menggambar grafik, menganalisis bentuk grafik serta penentuan sumbu simetri dan nilai optimum. Ketiga materi ini dalam praktiknya memiliki masalah terkait pemahaman dan motivasi siswa. Penelitian ini bertujuan menerapkan pendekatan saintifik dengan GeoGebra untuk meningkatkan motivasi dan hasil belajar siswa. Jenis penelitian adalah penelitian tindakan kelas, dengan subjek penelitian sebanyak 32 siswa pada kelas IX C SMPN 5 Kupang. Instrumen penelitian yang digunakan yakni soal tes hasil belajar tiap siklus, dan lembar observasi motivasi siswa. Analisis data menggunakan statistik deskriptif. Indikator keberhasilan dalam penelitian ini adalah jika secara individu siswa telah mencapai nilai KKM 70 dan secara klasikal 75% siswa mencapai nilai KKM tersebut. Hasil penelitian menunjukkan pada siklus I, 73,73% siswa memiliki motivasi yang baik dalam mengikuti pembelajaran dan meningkat menjadi 80,93% pada siklus II. Lebih lanjut, hasil belajar pada siklus I dikategorikan memenuhi indikator ketuntasan karena lebih dari 70% siswa tuntas secara klasikal. Presentasi ini meningkat menjadi 78,125% pada siklus II. Peningkatan ini menunjukan penerapan pendekatan saintifik dengan media GeoGebra memberi dampak yang postif terhadap hasil belajar siswa. Berdasarkan hasil penelitian, peneliti merekomendasikan kegiatan pembelajaran dengan pendekatan saintifik dengan GeoGebra dapat diterapkan dalam pembelajaran fungsi kuadra

Comparing the vibration of effects due to model, data pre-processing and sampling uncertainty on a large data set in personality psychology

Researchers have great flexibility in the analysis of observational data. If combined with selective reporting and pressure to publish, this flexibility can have devastating consequences on the validity of research findings. We extend the recently proposed vibration of effects approach to provide a framework comparing three main sources of uncertainty which lead to instability in observational associations, namely data pre-processing, model and sampling uncertainty. We analyze their behavior for varying sample sizes for two associations in personality psychology. While all types of vibration show a decrease for increasing sample sizes, data pre-processing and model vibration remain non-negligible, even for a sample of over 80000 participants. The increasing availability of large data sets that are not initially recorded for research purposes can make data pre-processing and model choices very influential. We therefore recommend the framework as a tool for the transparent reporting of the stability of research findings

Integrated pathway modules using time-course metabolic profiles and EST data from Milnesium tardigradum

<p>Abstract</p> <p>Background</p> <p>Tardigrades are multicellular organisms, resistant to extreme environmental changes such as heat, drought, radiation and freezing. They outlast these conditions in an inactive form (tun) to escape damage to cellular structures and cell death. Tardigrades are apparently able to prevent or repair such damage and are therefore a crucial model organism for stress tolerance. Cultures of the tardigrade <it>Milnesium tardigradum</it> were dehydrated by removing the surrounding water to induce tun formation. During this process and the subsequent rehydration, metabolites were measured in a time series by GC-MS. Additionally expressed sequence tags are available, especially libraries generated from the active and inactive state. The aim of this integrated analysis is to trace changes in tardigrade metabolism and identify pathways responsible for their extreme resistance against physical stress.</p> <p>Results</p> <p>In this study we propose a novel integrative approach for the analysis of metabolic networks to identify modules of joint shifts on the transcriptomic and metabolic levels. We derive a tardigrade-specific metabolic network represented as an undirected graph with 3,658 nodes (metabolites) and 4,378 edges (reactions). Time course metabolite profiles are used to score the network nodes showing a significant change over time. The edges are scored according to information on enzymes from the EST data. Using this combined information, we identify a key subnetwork (functional module) of concerted changes in metabolic pathways, specific for de- and rehydration. The module is enriched in reactions showing significant changes in metabolite levels and enzyme abundance during the transition. It resembles the cessation of a measurable metabolism (e.g. glycolysis and amino acid anabolism) during the tun formation, the production of storage metabolites and bioprotectants, such as DNA stabilizers, and the generation of amino acids and cellular components from monosaccharides as carbon and energy source during rehydration.</p> <p>Conclusions</p> <p>The functional module identifies relationships among changed metabolites (e.g. spermidine) and reactions and provides first insights into important altered metabolic pathways. With sparse and diverse data available, the presented integrated metabolite network approach is suitable to integrate all existing data and analyse it in a combined manner.</p

Algorithm engineering for optimal alignment of protein structure distance matrices

Protein structural alignment is an important problem in computational biology. In this paper, we present first successes on provably optimal pairwise alignment of protein inter-residue distance matrices, using the popular Dali scoring function. We introduce the structural alignment problem formally, which enables us to express a variety of scoring functions used in previous work as special cases in a unified framework. Further, we propose the first mathematical model for computing optimal structural alignments based on dense inter-residue distance matrices. We therefore reformulate the problem as a special graph problem and give a tight integer linear programming model. We then present algorithm engineering techniques to handle the huge integer linear programs of real-life distance matrix alignment problems. Applying these techniques, we can compute provably optimal Dali alignments for the very first time

An Exact Algorithm for Side-Chain Placement in Protein Design

Computational protein design aims at constructing novel or improved functions on the structure of a given protein backbone and has important applications in the pharmaceutical and biotechnical industry. The underlying combinatorial side-chain placement problem consists of choosing a side-chain placement for each residue position such that the resulting overall energy is minimum. The choice of the side-chain then also determines the amino acid for this position. Many algorithms for this NP-hard problem have been proposed in the context of homology modeling, which, however, reach their limits when faced with large protein design instances. In this paper, we propose a new exact method for the side-chain placement problem that works well even for large instance sizes as they appear in protein design. Our main contribution is a dedicated branch-and-bound algorithm that combines tight upper and lower bounds resulting from a novel Lagrangian relaxation approach for side-chain placement. Our experimental results show that our method outperforms alternative state-of-the art exact approaches and makes it possible to optimally solve large protein design instances routinely

A critical evaluation of network and pathway based classifiers for outcome prediction in breast cancer

Recently, several classifiers that combine primary tumor data, like gene expression data, and secondary data sources, such as protein-protein interaction networks, have been proposed for predicting outcome in breast cancer. In these approaches, new composite features are typically constructed by aggregating the expression levels of several genes. The secondary data sources are employed to guide this aggregation. Although many studies claim that these approaches improve classification performance over single gene classifiers, the gain in performance is difficult to assess. This stems mainly from the fact that different breast cancer data sets and validation procedures are employed to assess the performance. Here we address these issues by employing a large cohort of six breast cancer data sets as benchmark set and by performing an unbiased evaluation of the classification accuracies of the different approaches. Contrary to previous claims, we find that composite feature classifiers do not outperform simple single gene classifiers. We investigate the effect of (1) the number of selected features; (2) the specific gene set from which features are selected; (3) the size of the training set and (4) the heterogeneity of the data set on the performance of composite feature and single gene classifiers. Strikingly, we find that randomization of secondary data sources, which destroys all biological information in these sources, does not result in a deterioration in performance of composite feature classifiers. Finally, we show that when a proper correction for gene set size is performed, the stability of single gene sets is similar to the stability of composite feature sets. Based on these results there is currently no reason to prefer prognostic classifiers based on composite features over single gene classifiers for predicting outcome in breast cancer