Judul: Isolasi Kapang Endofit dari Daun Bakau (Rhizophora mucronata dan Sonneratia alba) Serta Penapisan Aktivitas Antibakteri.
Abstrak: Kapang endofit merupakan penghasil senyawa aktif dan telah dibuktikan memiliki banyak metabolit sekunder aktif yang unik secara struktur. Penelitian ini bertujuan untuk mendapatkan isolat dan karakterisasi kapang endofit daun bakau (Rhizophora mucronata dan Sonneratia alba) serta menentukan aktivitas antibakteri isolat kapang endofit terhadap bakteri Pseudomonas aeruginosa dan Staphylococcus aureus. Isolat kapang yang berhasil diisolasi pada penelitian ini diisolasi menggunakan media PDA dan media PDA dengan penambahan NaCl. Isolat RN1, RN2, dan RN3 diisolasi dari sampel Rhizophora mucronata. Isolat SN dan S1 diisolasi dari sampel Sonneratia alba. Isolat yang didapatkan diduga termasuk kedalam jenis kapang Aspergillus sp., Nigrospora sp., dan Fusarium sp.. Isolat RN2 memiliki aktivitas antibakteri terhadap bakteri Pseudomonas aeruginosa. Tidak ada isolat kapang endofit yang memiliki aktivitas antibakteri terhadap bakteri Staphylococcus aureus.
Keyword: aktivitas antibakteri, daun bakau, kapang endofit

Judul: Isolasi dan Identifikasi Kapang Endofit Mangrove Penghasil Metabolit Sekunder dan Kajian Metabolitnya sebagai Antimikroba Alami pada Pangan
Abstrak: Mangrove dikenal memiliki beberpa manfaat dalam pengobatan tradisional. Kapang Endofit mangrove berpotensi menghasilkan metabolit sekunder yang bisa dimanfaatkan seperti antibakteri. Antibakteri merupakan senyawa yang banyak diaplikasikan industri sebagai bahan baku produk dalam sektor makanan ataupun kesehatan. Tujuh belas isolat kapang endofit berhasil diisolasi dari 7 spesies mangrove yang berasal dari Berau, Kalimantan Timur, Indonesia. Tiga isolat yang diekstrak dari isolat kapang OJ-B, OJ-Bu, dan 6Xg-Bh memiliki aktivitas antibakteri tertinggi dengan zona hambat 9.63-25.4 mm dan dengan nilai MIC (minimum inhibitory concentration) dan MBC (minimum bactericidal concentration) sekitar 0.625-5 mg/mL dan 2.5-10 mg/mL. Identifikasi kimia dengan LC-MS (liquid chromatography-mass spectrometry) menunjukkan beberapa senyawa yang berpotensi sebagai antibakteri yaitu beauvericin, betaine, proline, adenosin, L-phenylalanine, quinoline, cyclo(phenylalanyl-prolyl), dan borrelidin. Sedangkan identifikasi spesies kapang dengan metode molecular berhasil mengidentifikasi tiga spesies yaitu Diaporthe phaseolorum, Fusarium proliferatum dan Phomopsis sp. Hasil penelitian ini menunjukkan bahwa kapang endofit dari beberapa spesies mangrove di Berau Kalimantan Timur mampu menghasilkan senyawa bioaktif sebagai antibakteri.
Keyword: Acanthus ilicifolius, Antimikroba, Isolasi, Kapang Endofit, Mangrove

Judul: Mixed effect models for high –dimensional longitudinal data with latent variables
Abstrak: A set of data that consist of repeated measurements on a large number of outcomes and covariates are known as high-dimensional longitudinal data. The high-dimensional longitudinal data may consist of several effects of interest that cannot be directly measured, known as latent factors or latent variables. During the last decade, the multivariate responses or high-dimensional in longitudinal data have been a big issue. The analysis of high-dimensional longitudinal data is complicated due to its complex correlation structures between outcomes. To understand changes over time of outcome variables, having correlations in individuals with explanatory variables is not enough. Hence, complex correlation structures between outcomes need to be considered. One approach that is commonly used to overcome high-dimensional longitudinal data is simultaneous equation modeling. A well-known simultaneous equation modeling method is the structural equation model (SEM). This approach has several appealing modeling abilities and can be used for high-dimensional longitudinal data. Under the SEM framework, the continuous time SEM is developed to avoid some issues associated with autoregressive and cross-lagged problems in SEM. Another simultaneous equation modeling method is by combining factor analysis and multivariate analysis methods to overcome high dimensional longitudinal data, namely latent factor linear mixed model (LFLMM). The factor analysis is used to reduce the high-dimensional outcomes, and the multivariate linear mixed model is used to study the longitudinal trends of several latent factors. One example of high-dimensional longitudinal data is the General Election Study. This study is carried out repeatedly to observe tendencies towards political attitudes and behavior over time in Belgium. The data contain political information, knowledge, perceptions, and preferences of a political party and the level of participation in politics. One of the most interesting things to study from the data is to analyze the change of political attitudes and behavior of respondents over time. Also, the relationship of changes in these outcomes is important to analyze. The General Election Study in Belgium was designed to include a representative sample of the target population under the Belgian electorate, so accurate estimates about the population could be made. This sampling design was created by the Institute for Social and Political Opinion (ISPO) and the Inter-university Center for Political Opinion Research (PIOP). The Belgian data set contained three subsamples, the Flemish (Dutch-speaking), the Walloon (French-speaking), and the Brussels Capital Region (Dutch and French-speaking). Several studies conducted on the Belgium data are carried out to understand the relationships between the latent variables Individualism (I), Ethnocentrism (E), and Authoritarianism (A) in Flanders. Cross-sectional or a longitudinal studies have also been carried out. In such cases, it is critical to capture the trend of the latent variables over time and, more importantly, whether there is any association or relationships between the development of nationalism (N), ethnocentrism (E), individualism (I), and authoritarianism (A) in Belgium. An empirical analysis of CT-SEM has been done to present the interdependencies among the four latent variables mentioned above on the basis of the General Election Studies for Belgium in 1991, 1995, and 1999 (Interuniversitair steunpunt politieke-opinieonderzoek, 1991, 1995, 1999). Although the four variables have been the subject of
Keyword: Belgium, CT-SEM, high-dimensional longitudinal data, LFLMM, Supplemented EM Algorithm