Judul: Penapisan Bakteri Filosfer Penghasil Senyawa Bioaktif Anti Pyricularia oryzae dan Deteksi Gen Penyandinya.
Abstrak: Pyricularia oryzae adalah cendawan penyebab penyakit blas pada padi yang dapat mengakibatkan penurunan produksi beras di berbagai negara, termasuk Indonesia. Penggunaan agens biokontrol berbasis mikrob dapat digunakan sebagai pengganti fungisida. Sumber agens biokontrol P. oryzae dapat berasal dari area permukaan daun (filosfer). Penelitian ini bertujuan menapis dan mengkarakterisasi bakteri filosfer padi penghasil senyawa bioaktif anti P. oryzae penyebab penyakit blas pada padi. Metode dalam penelitian ini meliputi uji antagonisme, ekstraksi DNA genom bakteri, amplifikasi dan sekuensing gen parsial dari 16S rRNA, gen penyandi domain A dari peptida non-ribosomal sintetase (NRPS), serta gen penyandi domain KS dari poliketida sintase (PKS). Berdasarkan uji antagonisme, sebanyak lima isolat bakteri filosfer padi diketahui mampu menghambat P. oryzae. Identifikasi molekuler berdasarkan gen parsial dari 16S rRNA pada lima isolat menunjukkan isolat BFR 69E dan BFR 99 sebagai genus Bacillus, BFR 202B sebagai genus Pseudomonas, sedangkan isolat BFR 88 dan BFR 95 sebagai genus Acinetobacter. Kelima isolat tersebut memiliki gen penyandi domain A dari NRPS dan isolat BFR 88 memiliki gen penyandi domain KS dari PKS.
Keyword: 16S rRNA, domain A, domain KS, NRPS, PKS

Judul: Penapisan Bakteri Filosfer Padi Penghasil Senyawa Bioaktif Anti-Xanthomonas Oryzae Pv. Oryzae Dan Deteksi Sekuen Gen Penyandi Peptida Non-Ribosomal Sintetase
Abstrak: Xanthomonas oryzae pv. oryzae (Xoo) adalah bakteri penyebab penyakit hawar daun yang dapat menyebabkan penurunan produksi padi. Bakteri filosfer mampu menghasilkan senyawa bioaktif anti-Xoo yang dapat dijadikan sebagai alternatif agens biokontrol. Penelitian ini bertujuan menyeleksi isolat bakteri filosfer padi penghasil senyawa bioaktif penghambat pertumbuhan Xoo dan mendeteksi keberadaan gen penyandi peptida non-ribosomal sintetasenya. Sepuluh isolat bakteri filosfer padi yang diuji mampu menghasilkan senyawa bioaktif anti-Xoo yang ditunjukkan dengan terbentuknya zona bening di sekitar koloni. Analisa sekuen gen penyandi 16S rRNA menunjukkan bahwa enam isolat (BFR 35, BFR 64, BFR 69, BFR 153, BFR 162, dan BFR 266) termasuk anggota genus Bacillus, tiga isolat (BFR 183, BFR 217, BFR 238) termasuk anggota genus Pseudomonas, dan satu isolat (BFR 203) diidentifikasi sebagai Staphylococcus sciuri. BFR 64, BFR 69, BFR 153, dan BFR 183 terdeteksi memiliki gen penyandi domain A dari peptida non-ribosomal sintetase dengan homologi 91%, 89%, 98%, dan 76% berturut-turut untuk keempat isolat tersebut. Bacillus aerius BFR 153, Pseudomonas cremoricolorata BFR 183, Staphylococcus sciuri BFR 203, dan Pseudomonas parafulva BFR 217 bersifat non-hemolitik sehingga potensial dan aman dikembangkan sebagai agens biokontrol untuk mengendalikan Xoo.
Keyword: Bacillus, domain A, gen 16S rRNA, hemolisis darah

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