Judul: Sitotoksisitas, Aktivitas Antibakteri, dan Antibiofilm Tanaman Biduri (Calotropis gigantea)
Abstrak: Beberapa jenis bakteri dapat membentuk biofilm yang dapat menyebabkan bakteri resisten terhadap antibiotik. Biduri dipercaya dapat mengobati sakit gigi yang disebabkan bakteri, namun potensi bioaktivitas biduri belum banyak diketahui. Penelitian ini bertujuan menentukan nilai Letality Concentration 50 (LC₅₀), konsentrasi optimum getah dan ekstrak daun biduri sebagai antibakteri dan antibiofilm. Daun biduri diekstrak dengan pelarut air, etanol 96 %, dan n-heksana. Nilai rendemen ekstrak air lebih besar dibandingkan dengan ekstrak etanol 96 % dan n-heksana yaitu 19.2 %. Hasil analisis fitokimia diketahui bahwa senyawa yang terkandung dalam getah dan ekstrak daun biduri: flavonoid, saponin, steroid, dan triterpenoid. Nilai LC₅₀ getah biduri sebesar 1690 μg/mL, ekstrak air 570 μg/mL, ekstrak etanol 96 % 5970 μg/mL, dan ekstrak n-heksana sebesar 760 μg/mL. Nilai LC₅₀ menunjukkan getah biduri dan ekstrak etanol 96% tidak memiliki potensi bioaktivitas. Ekstrak air dan n-heksana memiliki potensi bioaktivitas relatif kecil. Getah dan ekstrak daun biduri tidak menunjukkan aktivitas antibakteri dan antibiofilm.
Keyword: Calotropis gigantea, Antibakteri, Antibiofilm, Sitotoksisitas

Judul: Aktivitas antibakteri Daun Senggugu (Clerodendron serratum [L.] Spr.)
Abstrak: Salah satu tanaman obat yang sering digunakan masyarakat untuk mengobati penyakit adalah senggugu. Seluruh bagian tanaman senggugu hampir semuanya digunakan untuk mengobati penyakit. Penelitian ini mempelajari aktivitas antibakteri dari daun senggugu tehadap bakteri uji (Staphylococcus aureus, Bacillus subtilis, Escherichia coli, Pseudomonas aeruginosa), menentukan Konsentrasi Hambat Tumbuh Minimumnya (KHTM), serta melakukan uji kualitatif fitokimia. Daun senggugu dimaserasi dengan menggunakan pelarut metanol, aseton, dan heksana. Konsentrasi yang digunakan untuk menentukan KHTM bervariasi dari 500 mg/mL sampai 1 mg/mL. Ampisilin 0.4 mg/mL digunakan sebagai pembanding. Uji kualitatif fitokimia dilakukan untuk mengetahui golongan senyawa aktif yang diharapkan berpotensi sebagai antibakteri. Senyawa aktif yang diduga berpotensi sebagai antibakteri yang dimiliki oleh daun senggugu adalah alkaloid dan steroid. Ekstrak aseton memiliki aktivitas antibakteri paling besar. Konsentrasi terkecil ekstrak aseton daun senggugu yang masih bisa menghambat bakteri P. aeruginosa adalah 3 mg/mL, sedangkan pada S. aureus, B. subtilis, E. coli adalah 2 mg/mL. Hubungan konsentrasi dengan zona hambat pada semua bakteri uji berbanding lurus, yaitu semakin tinggi konsentrasi ekstrak daun senggugu maka semakin tinggi zona hambat yang dihasilkan. Potensi antibakteri yang dimiliki oleh ekstrak daun senggugu adalah lemah.
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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