ASUPAN ZAT GIZI, HEMOGLOBIN, ALBUMIN DAN BERAT BADAN ANAK BALITA GIZI KURANG YANG DIBERI COOKIES KELOR

Around the world, including Indonesia, the malnutrition cases are still high. One of the malnutrition symptoms is underweight children. In trying to ease this problem, the children were supplied by supplementary food, in this case, Moringa cookies. The purpose of this research was to measure the effects of protein and iron intake, hemoglobin levels, and albumin levels of those children. This research was conducted in the rural of Petumbukan, the jurisdiction of a local government health facility. This type of research is a quasi-experimental, with a sample of 29 malnourished children under five years old. Data on sample identity and respondent identity were collected by interview. Data on nutrient intake, hemoglobin levels, albumin levels, and body weight were collected before and after the intervention. Data on nutrient intake were collected by interview of 24-hour food recall for two consecutive intermittent days. Blood samples were taken by analysts at the office and determined the hemoglobin and albumin levels. Weights were measured using a digital scale. Data were analyzed univariately and bivariately. The hypotheses were tested statistically. There were differences in protein intakes and weights of those children. Iron intake, albumin, and hemoglobin levels were not different. The 21 days of Moringa cookies supply has increased the protein intake and weight of those children but failed to increase the iron intake, albumin, and hemoglobin levels.This research gives some information to increase nutrient intake and weight of malnourished underweight children. Further research is needed with a longer duration of the Moringa cookies supply

Multiplexing droplet-based single cell RNA-sequencing using natural genetic barcodes

Droplet-based single-cell RNA-sequencing (dscRNA-seq) has enabled rapid, massively parallel profiling of transcriptomes from tens of thousands of cells. Multiplexing samples for single cell capture and library preparation in dscRNA-seq would enable cost-effective designs of differential expression and genetic studies while avoiding technical batch effects, but its implementation remains challenging. Here, we introduce an in-silico algorithm demuxlet that harnesses natural genetic variation to discover the sample identity of each cell and identify droplets containing two cells. These capabilities enable multiplexed dscRNA-seq experiments where cells from unrelated individuals are pooled and captured at higher throughput than standard workflows. To demonstrate the performance of demuxlet, we sequenced 3 pools of peripheral blood mononuclear cells (PBMCs) from 8 lupus patients. Given genotyping data for each individual, demuxlet correctly recovered the sample identity of > 99% of singlets, and identified doublets at rates consistent with previous estimates. In PBMCs, we demonstrate the utility of multiplexed dscRNA-seq in two applications: characterizing cell type specificity and inter-individual variability of cytokine response from 8 lupus patients and mapping genetic variants associated with cell type specific gene expression from 23 donors. Demuxlet is fast, accurate, scalable and could be extended to other single cell datasets that incorporate natural or synthetic DNA barcodes.

Characterization of Polycyclic Aromatic Hydrocarbon Profiles by Multivariate Statistical Analysis

In the present study, statistical methods based on multivariate analyses such as the Descriptive Discriminant Analysis (DDA) and Principal Component Analysis (PCA) were applied to determine relationships between particle sizes and the composition of the associated semi-volatile compounds, in addition to evaluating these observations in relation to the emission sources, study areas, sampling campaigns and season. Results from the DDA showed that the PAHs distributions give the best discrimination capacity within the data set, whereas the PAH distribution in intermediate particle fractions incorporates noise in the statistical analysis. The PCA was useful in identifying the main emission sources in each study area. It showed that in the city of La Plata the most important pollution sources are traffic emissions and the industrial activity associated with oil and petrochemical plants. In Leipzig, the main sources are those associated with traffic and also a power plant. The combined PCA and DDA methods applied to PAH distributions is a valuable tool in characterizing types of emissions burdens and also in obtaining a differentiation of sample identity according to study areas and sampling times.

Characterization of Polycyclic Aromatic Hydrocarbon Profiles by Multivariate Statistical Analysis

In the present study, statistical methods based on multivariate analyses such as the Descriptive Discriminant Analysis (DDA) and Principal Component Analysis (PCA) were applied to determine relationships between particle sizes and the composition of the associated semi-volatile compounds, in addition to evaluating these observations in relation to the emission sources, study areas, sampling campaigns and season. Results from the DDA showed that the PAHs distributions give the best discrimination capacity within the data set, whereas the PAH distribution in intermediate particle fractions incorporates noise in the statistical analysis. The PCA was useful in identifying the main emission sources in each study area. It showed that in the city of La Plata the most important pollution sources are traffic emissions and the industrial activity associated with oil and petrochemical plants. In Leipzig, the main sources are those associated with traffic and also a power plant. The combined PCA and DDA methods applied to PAH distributions is a valuable tool in characterizing types of emissions burdens and also in obtaining a differentiation of sample identity according to study areas and sampling times.