Judul: Antibiogram Staphylococcus Aureus Yang Diisolasi Dari Air Susu Kasus Mastitis
Abstrak: Staphylococcalmastitis yang disebabkan oleh S.auereus yaitu bakteri Gram yang seharusnya tepat bila diobati dengan antibiotika Penicillin, namun keadaannya tidak demikian karena bakteri ini sebagian ada yang resisten terhadap Penicillin. Timbulnya resistensi ini karena Staphylococcus auereus menghasilkan enzym penicillinase yang dapat merusak Penicillin sehingga kehilangan daya antibodinya (Sulistia, 1981; Yeoman, 1982).
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Judul: Karakterisasi dan Uji Kepekaan terhadap Antibiotik Isolat Bakteri Staphylococcus aureus diisolasi dari Sapi Mastitis Subklinis
Abstrak: Mastitis subklinis merupakan infeksi pada ambing yang tidak disertai dengan gejala klinis. Staphylococcus aureus merupakan salah satu bakteri penyebab mastitis. Identifikasi bakteri Staphylococcus aureus penyebab mastitis subklinis biasanya dilakukan secara fenotipik. Namun, belum mampu mengidentifikasi S.aureus hingga tingkat spesies. Bakteri S.aureus memiliki gen nuc mengkodekan produksi Tnase (Thermonuclease) yang memiliki sekuen spesifik dan amplifikasi gen nuc memiliki potensi yang cepat untuk mendeteksi infeksi S.aureus menggunakan metode PCR, sehingga bakteri S.aureus dapat teridentifikasi hingga tingkat spesies dibandingkan dengan identifikasi secara fenotipik. Identifikasi secara tepat pada S.aureus berhubungan dengan terapi antibiotik yang diberikan pada sapi mastitis subklinis. Namun penggunaan antibiotik β-laktam terkendala oleh resistansi antibiotik β-laktam yang dikenal dengan MRSA (Methicillin Resistant of Staphylococcus aureus) dan regulasinya dikendalikan oleh gen mecA. Penelitian ini dilakukan untuk mengidentifikasi secara fenotipik dan genotipik terhadap bakteri penyebab infeksi mastitis subklinis, melakukan uji kepekaan antibiotik terhadap bakteri S.aureus penyebab mastitis subklinis, serta mendeteksi adanya gen mecA yang menyandikan MRSA sebagai indikator penyebab resistansi antibiotik β-laktam. Sampel susu sapi mastitis subklinis diambil di kawasan usaha peternak (KUNAK) Cibungbulang Bogor, sebanyak 102 sampel susu sapi. Uji CMT (California Mastitis Test) dilakukan sebagai identifikasi dini adanya penyakit mastitis subklinis. Sampel susu yang diambil, dikultur dalam media agar darah dan diidentifikasi secara fenotipik melalui pewarnaan Gram, uji katalase, dan uji oksidase. Identifikasi secara genotipik dilakukan dengan PCR sebagai konfirmasi adanya gen nuc. Isolat yang positif ditemukan gen nuc dinyatakan sebagai bakteri S.aureus dan selanjutnya dilakukan uji kepekaan antibiotik. Antibiotik yang digunakan diantaranya tetrasiklin, oksasilin, gentamisin, eritromisin, kloramfenikol, sefoksitin, trimetrtoprim-sulfametoksazol. Isolat yang resistan terhadap antibiotik oksasilin dan sefoksitin dilakukan konfirmasi dengan PCR untuk mengetahui adanya gen mecA. Hasil identifikasi secara fenotipik melalui pewarnaan Gram dan uji biokimia, didapatkan 72% yang memiliki karakteristik Gram positif, koloni menyerupai anggur, berbentuk bulat, tidak berspora, katalase positif, dan oksidase negatif. Kemudian dilanjutkan dengan identifikasi secara genotipik melalui gen nuc didapatkan 48% yang dinyatakan positif bakteri S.aureus. Semua isolat bakteri S.aureus penyebab mastitis subklinis sensitif terhadap antibiotik tetrasiklin, gentamisin, kloramfenikol, eritromisin, dan trimetoprim-sulfametoksazol, sedangkan 22.5% dinyatakan resistan terhadap β-laktam (sefoksitin dan oksasilin) dan ditemukan adanya gen mecA.
Keyword: S.aureus, nuc, MRSA, mecA, antibiotik

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