Judul: Uji Kemampuan Infeksi Cendawan Penyebab Penyakit Blas (Pyricularia grisea Ras 173) pada Akar Padi
Abstrak: Penyakit blas merupakan salah satu penyakit yang menyerang padi. Penyakit ini disebabkan oleh cendawan Pyricularia grisea Sacc. Secara umum, P. grisea menyerang padi pada bagian batang, daun, dan leher malai. Informasi terbaru menyatakan bahwa P. grisea juga mampu menyerang padi melalui akar. Penelitian ini bertujuan melihat kemampuan cendawan P. grisea dalam menginfeksi padi melalui akar dan kolonisasinya. Penelitian dilakukan dengan menggunakan padi (varietas Kencana bali, Cisokan, dan IR 64) yang ditanam di kultur cair menggunakan larutan hara Yoshida dan P. grisea ras 173. Akar padi berumur 21 hari direndam dalam suspensi spora dengan konsentrasi 105 spora/ ml selama 24 jam dan diinkubasi pada ruang gelap. Selanjutnya tanaman padi dipindah ke ruang inkubasi terang dan dilakukan pengamatan pada 21 hari setelah infeksi. Pengamatan kolonisasi dilakukan dengan dua metode, yaitu pewarnaan akar dan blok parafin menggunakan pewarna trypan blue 0.05%. Hasil pengamatan menggunakan pewarnaan akar menunjukkan ada hifa yang menempel pada permukaan akar. Sedangkan pengamatan dengan blok parafin menunjukkan bahwa pada jaringan akar tidak ada kolonisasi hifa baik pada korteks, endodermis, xilem, maupun floem. Ini menunjukkan bahwa P. grisea ras 173 tidak dapat menginfeksi tanaman padi melalui akar.
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Judul: Uji Pergantian antar Spesies Inang Pyricularia grisea Asal Rumput Digitaria ciliaris
Abstrak: Penyakit blas merupakan salah satu faktor biotik yang menyebabkan penurunan produksi padi. Beberapa jenis rumput di sekitar pertanaman padi yang terserang blas juga terserang penyakit blas. Penyakit ini disebabkan oleh Pyricularia grisea. Penelitian ini bertujuan mengetahui kemampuan isolat P. grisea asal rumput Digitaria ciliaris yang diambil dari Jasinga Bogor (R14J1) dalam menginfeksi spesies inang yang berbeda, dan mendeteksi perubahan genotipenya menggunakan penanda molekuler SCAR yaitu CUT1 dan PWL2. Hasil penelitian menunjukkan bahwa isolat R14J1 mampu menginfeksi padi varietas Kencana bali, Cisokan dan rumput Panicum repens. Isolat 173 asal padi yang digunakan sebagai pembanding, mampu menginfeksi padi varietas Kencana bali dan Cisokan. Bercak pada pada Kencana bali, Cisokan dan Panicum repens yang diinfeksi isolat R14J1kemudian direisolasi. Isolat R14J1 awal memiliki ukuran spora yang berbeda dengan spora hasil reisolasi isolat R14J1 dari padi varietas Kencana bali, Cisokan dan P. repens. Spora isolat R14J1 awal memiliki ukuran yang lebih kecil dibanding spora hasil reisolasi isolat R14J1 dari Kencana bali, Cisokan dan P. repens. Amplifikasi isolat R14J1 awal dengan primer CUT1 menghasilkan fragmen DNA, namun tidak menghasilkan fragmen DNA yang diamplifikasi dengan primer PWL2. Sebaliknya, amplifikasi isolat R14J1 yang direisolasi dari Kencana bali dan Cisokan hanya memiliki fragmen DNA hasil amplifikasi dengan PWL2. Sedangkan isolat R14J1 hasil reisolasi dari P. repens memiliki penanda yang sama dengan isolat R14J1 awal. Berdasarkan hasil penelitian dapat disimpulkan bahwa isolat R14J1 mampu menginfeksi padi varietas Kencana bali, Cisokan dan rumput Panicum repens.
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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