Judul: Potensi Rizobakteri dalam Penghambatan Penyebab Blas Padi (Pyricularia oryzae Cav.)
Abstrak: Pyricularia oryzae Cav. adalah penyebab penyakit blas di padi gogo. Akhir 1980, P.oryzae juga menyerang padi sawah dan menyebabkan gagal panen 10%-30% di tingkat dunia. Pengendalian hayati menggunakan bakteri sudah mulai berkembang. Rizobakteri adalah bakteri yang hidup di sekitar perakaran tumbuhan dan memiliki hubungan simbiotik dengan tumbuhan tersebut. Rizobakteri yang menguntungkan tanaman dapat mengurangi jumlah inokulum patogen. Penelitian ini bertujuan mengeksplorasi rizobakteri yang berpotensi menghambat P.oryzae. Eksplorasi rizobakteri dilakukan pada padi sawah di 4 desa sekitar Kampus IPB Dramaga, yaitu Babakan Lebak (Blk), Balumbang Jaya (Blb), Cikarawang (Ckr), dan Situgede (Stgd). Rizobakteri yang berhasil diisolasi sebanyak 33 isolat. Uji hipersensitif pada daun tembakau menunjukkan bahwa 17 diantaranya adalah bakteri non patogenik terhadap tanaman. Uji Gram pada isolat rizobakteri yang tidak patogenik menunjukkan bahwa 15 diantaranya merupakan bakteri Gram positif dan 2 isolat merupakan bakteri Gram negatif. Uji antagonisme menunjukkan bahwa rizobakteri yang dapat menghambat pertumbuhan P.oryzae secara efektif adalah Blk 3.6, Blk 5.3, Blb 3.11, dan Ckr 3.2 berturut-turut sebesar 91.98%, 66%, 63.56%, dan 57.11% penghambatan.
Keyword: padi sawah, Pyricularia oryzae, rizobakteri, uji antagonisme.

Judul: Eksplorasi Khamir dan Bakteri sebagai Agens Antagonis terhadap Penyebab Penyakit Blas pada Padi (Pyricularia oryzae).
Abstrak: Padi merupakan bahan baku makanan pokok bagi penduduk Indonesia. Produksi padi di Indonesia belum optimal karena adanya organisme pengganggu tanaman (OPT) yang dapat menurunkan kuantitas dan kualitas panen. Saat ini, salah satu penyakit penting pada padi adalah penyakit blas yang disebabkan oleh cendawan Pyricularia oryzae. Pengendalian penyakit blas dapat dilakukan dengan penanaman varietas tahan. Akan tetapi, penggunaan varietas tahan blas menjadi tidak efektif karena patogen ini sudah memiliki banyak ras dan mampu membentuk ras baru dengan cepat. Kemapuan dalam membentuk ras baru dari patogen akan terpicu dengan penggunaan pestisida yang tidak bijaksana. Penelitian ini bertujuan mendapatkan isolat khamir dan atau bakteri yang berpotensi efektif untuk mengendalikan penyakit blas. Penelitian dimulai pada bulan Oktober 2016 hingga Februari 2017. Hasil ekplorasi menunjukkan 5 dari 10 isolat khamir dan bakteri filoplan berpotensi sebagai agens antagonis P. oryzae. Tiga isolat khamir, yaitu Cryptococcus, Rhodotorula, dan Candida, serta dua isolat bakteri, yaitu Bacillus sp. dan isolat PPY1 tidak berpotensi sebagai patogen bagi tanaman maupun manusia. Cryptococcus sp. memiliki keefektifan tertinggi dalam menekan P. oryzae dengan mekanisme penghambatan sintesis melanin, menghasilkan senyawa volatil, hiperparasitisme, dan menghasilkan enzim kitinase. Bacillus sp. cukup efektif dalam menekan P. oryzae pada uji kultur ganda dan produksi senyawa volatil, serta mampu melisis hifa P. oryzae. Candida sp. dan Rhodotorula sp. mampu menekan P. oryzae dengan penetrasi ke hifa, menghambat sintesis melanin, serta dapat menghasilkan enzim kitinase dan senyawa volatil.
Keyword: bakteri antagonis, khamir antagonis, padi, penyakit blas, Pyricularia oryzae

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