Ekstrak Bawang Putih Bubuk Dengan Menggunakan Proses Spray Drying

Bawang putih banyak digunakan sebagai bumbu utama pada berbagai masakan karena aromanya yang khas. Aroma khas tersebut karena adanya komponen aktif (Allicin) pada bawang putih. Allicin juga berguna untuk daya anti kolesterol yang dapat mencegah penyakit jantung, tekanan darah tinggi dan lain sebagainya. Komponen Allicin bersifat volatil sehingga bila penanganannya salah maka dapat menyebabkan kerusakan. Untuk mengawetkan bawang putih yaitu dengan cara pengeringan. Salah satu proses yang dapat digunakan adalah spray drying karena proses ini membutuhkan waktu yang singkat. Proses spray drying adalah proses pengeringan dengan cara menyemprotkan fase cair menjadi butiran-butiran kecil kemudian mengontakkannya dengan udara panas sehingga menjadi bubuk. Umpan yang akan dikeringkan dapat berupa larutan ataupun suspensi dengan viskositas tertentu. Penelitian ini dilakukan percobaan pembuatan ekstrak bawang putih bubuk dengan variasi perbandingan massa bawang putih dengan pelarut air tertentu yang dimulai dari perbandingan 1:1, variasi konsentrasi maltodekstrin 0%, 10%, 20%, 30%, 40% dan 50%, serta variasi suhu udara masuk 160 oC, 170 oC, 180 oC dan 190 oC. Hal yang diamati adalah pengaruh konsentrasi maltodekstrin dan suhu udara masuk terhadap karakteristik ekstrak bawang putih bubuk yang dihasilkan. Karakteristik bubuk yang dianalisa meliputi kadar air, bulk density, wettability, solubility dan organoleptik. Dari hasil analisa diketahui bahwa dengan meningkatnya suhu udara inlet menyebabkan terjadinya penurunan kadar air. Begitu juga dengan meningkatnya suhu udara masuk menyebabkan terjadinya peningkatan bulk density, wettability dan solubility

An <i>in vitro</i> model of HIV gene expression exhibits a distribution of integration-site-dependent phenotypes, including noise-driven Switching phenotypes.

<p>(A) Schematic of the full-length HIV lentiviral model of the Tat-mediated positive feedback loop (sLTR-Tat-GFP). Viral proteins other than Tat were inactivated and Nef was replaced with GFP. (B–C) Flow cytometry histogram of Jurkat cells infected with a single HIV WT virus for (B) a bulk population with mixed integration positions and (C) sample Jurkat clonal populations, each containing a single (different) genomic integration of the WT HIV provirus. Representative Dim and Bright clonal histograms were chosen to span the range of fluorescence means. For Switching phenotypes, representative clonal histograms were chosen from the distribution clusters that were used to define a quantitative Switching criterion. GFP axis range is the same for all histograms. (D) Quantification of the WT Switching fraction based on a stratified sample of clones from the full range of GFP expression (“Full”), and based on a sub-sample of clones sorted from only the Mid region of the bulk fluorescence range (“Mid”). Error bars mark 95% confidence intervals, estimated by a bootstrap method.</p

Computational models exploring Switching fraction modulation by the Sp1 mutation.

<p>(A) Model phase diagrams varying basal transcriptional parameters at fixed values of Tat feedback parameters. Drawn boundaries separate parameter combinations leading to distinct phenotypes (as in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003135#pcbi-1003135-g002" target="_blank">Figure 2C</a>). Superimposed color map estimates the probability density with which the virus samples basal transcription parameters over genomic integrations for the WT promoter (left) and Sp1 mutant promoter (right). Tat feedback parameters that result in a WT Switching-fraction estimate of 12% specify the solid phenotypic boundaries (base). Decreasing the fold-amplification of Tat feedback (reduced feedback, short dashed lines) shifts phenotypic boundaries to the right, while impaired reinitiation (long dashed lines) has little effect on phenotypic boundaries. (B) Estimated Switching fractions for the sets of Tat feedback parameters used in (A), normalized by the predicted WT Switching fraction for the base set of parameters (solid line). (C) Sample Switching (grey) and Bright (black) distributions for the base set of Tat feedback parameters (solid) and for impaired reinitiation parameters (dashed). The degree of transcriptional reinitiation impairment was chosen to produce a comparable shift in Bright phenotype as the parameters for reduced feedback (A–B). The model extension to include transcriptional reinitiation was implemented by a simple rescaling of model parameters according to: (rescaled basal transcription rate); (rescaled amplification factor for transactivated transcription rate); (rescaled feedback saturation parameter). Details may be found in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003135#pcbi.1003135.s008" target="_blank">Text S1</a>.</p

A dynamic forward genetic screen selects for LTR promoter sites that increase the frequency of delayed gene expression activation and deactivation.

<p>(A) Schematic of the genetic screen. (B–G) Jurkat cells were infected with the HIV lentiviral vector containing the WT promoter, the unselected library of promoters, or promoter libraries from each round of selection for delayed activation or deactivation. (B) Fraction of cells that showed delayed activation 5 days after sorting from the Dim gate. (C) Fraction of cells that showed delayed deactivation 5 days after sorting from the Bright gate. (D,E) Median GFP expression of the bright peak for promoter libraries selected from the (D) activation screen or (E) deactivation screen. All bar graphs are presented as the mean ± standard deviation of 3 replicates, and are representative of duplicate experiments. (F,G) Flow cytometry histograms comparing the WT initial bulk, multi-integration expression profile to the profile following four rounds of selection for (F) delayed activation or (G) delayed deactivation.</p

A computational model of LTR transcription with Tat feedback demonstrates noise-driven Switching phenotypes with delayed activation/deactivation (A) Model schematic: The viral LTR promoter probabilistically switches between a transcriptionally inactive state and a transcriptionally active state, with rates and . In the active state, transcripts are produced with rate , and degraded at rate .

<p>Protein translation occurs from each transcript independently at rate , and each protein is degraded with rate . As a model of basal transcription, all rates are assumed constant, and transcript is produced in bursts when and is of order 1 or greater <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003135#pcbi.1003135-Skupsky1" target="_blank">[22]</a>. For the transactivation circuit, the translated protein is Tat (plus GFP), and we include a Michaelis-Menten-like dependence on Tat for the promoter activation and the transcription rates (highlighted in red in the model schematic): , , . The parameters and specify fold-amplification at saturated Tat binding, and specifies the saturation concentration. The model output is the predicted steady-state distribution of protein (GFP and Tat) count across a clonal population of cells, which is then converted to cytometer RFU based on previous calibration <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003135#pcbi.1003135-Skupsky1" target="_blank">[22]</a>. (B) Simulated protein distributions were evolved over time from a Dim initialization (left) for representative parameter values that lead to Dim, Switching, and Bright steady-state phenotypes (right, blue curves). Simulated steady-state basal expression distributions for the same parameter values without Tat feedback are given for comparison (i.e. ; green curves). Simulated histograms are normalized and plotted on the same fluorescence axis as the cytometer data in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003135#pcbi-1003135-g001" target="_blank">Figure 1</a>. (C) A phase diagram summarizes the expression phenotypes predicted by the Tat feedback model as basal transcription parameters ( and ) are varied over the observed experimental range of values while remaining model parameters are fixed. Drawn boundaries separate parameter combinations leading to distinct expression phenotypes. Model-predicted equilibration times (i.e., the time after which half of a Dim-initialized population crosses an intermediate expression threshold between Dim and Bright) are represented on a color scale, with longer times predicted for parameter combinations that specify Switching phenotypes. Parameter combinations used in (B) are marked with an asterisk.</p

Selected mutations result in small but significant differences in basal gene expression.

<p>(A) Flow cytometry bulk-infection histograms for Jurkat cell populations. Each cell contains a single (different) integration of the Tat-null vector (sLTR-GFP-TatKO) with a WT LTR promoter (black), or an LTR with an Sp1 site III mutation (red). Uninfected Jurkat histogram is displayed for reference (gray). (B–D) Distribution noise (defined as CV²) versus mean GFP for Sp1 mutant clones sorted and expanded from the bulk populations in (A). (C–D) Clonal histograms were fit with the stochastic gene-expression model in the absence of feedback (<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003135#pcbi-1003135-g002" target="_blank">Figure 2A</a>), and best-fit parameters were calculated for (C) transcriptional burst size and (D) transcriptional burst frequency. Each point in B–D represents a single-integration clone from a WT (gray) or Sp1 mutant (red) infection.</p

Selected mutations in Sp1 site III and the TATA box increase the Switching fraction.

<p>Jurkat cells were infected with the HIV lentiviral vector containing the WT promoter, with a single point mutation in Sp1 site III (position 4), or with a single point mutation in the TATA box (position 2). (A) Relative fraction of cells that activated 5 days after sorting from the Off gate. (B) Relative fraction of cells that deactivated 5 days after sorting from the Bright gate. (C) Flow cytometry histograms comparing the WT bulk-infection profile (gray) to the profile for TATAmutP2 (left) and Sp1mutIII (right). Note the reduced weight and position of the Bright (Tat-transactivated) peak and the increased weight of the mid region. (D) Switching fractions for WT and selected mutants. Approximately 80 clones were sorted from the mid region for each infected population, and the Switching fraction was estimated as described in the main text. Error bars indicate 95% CIs, estimated by a bootstrap method. Significant differences from WT (<i>p<0.01</i>) indicated by (*).</p

Genetic screen selects for mutations in the core LTR promoter.

<p>(A) Approximately 90 clones were sequenced per library of promoters. (Top) Sequenced clones from the activation and deactivation screens were combined and the distribution of mutations in functional regions of the LTR was compared to the distribution of mutations throughout the entire LTR. (Bottom) The frequency of mutations was plotted for each position of the LTR for the delayed activation screen (red), the delayed inactivation screen (blue), and the unselected library (black). (B) Frequency of mutations within the core promoter region for the delayed activation screen (red) and the delayed inactivation screen (blue). Arrows indicate the top two mutations that were selected in both screens. (C) Bar graph displaying the fraction of selected LTR sequences that have mutations in Sp1 site III or the TATA box for the activation screen (red) and the deactivation screen (blue).</p

Long-term integration of the transplanted neurons.

<p>Confocal microscopy images extracted from xyz-tile acquisitions showing GFP+ neuron implantation throughout the hippocampus 24 weeks post-transplantation. <b>a</b>) shows beads at the injection site carrying GFP+ neurons which are projecting their processes in the host hippocampus, <b>b</b>) shows neurons in Or -oriens layer of the hippocampus sending out processes through the radiatum layer, and <b>c</b>) shows cells in the stratum lucidum of the CA3. Brain slices were stained with CD11b a marker for microglia cells (<b>d</b>), and CD68 a marker for macrophages (<b>e</b>). Confocal microscopy images 4 xy frames extracted from xyz-tile acquisitions showing glass bead cluster were projected in z. Increase in microglia cells and macrophages was associated with the presence of GFP+ cells without processes (arrows). Beads without cells were free of microglia and macrophages, suggesting that these cells were there to clear non-integrated GFP+ neurons. All scale bars  =  100 µm.</p

Development and manipulation of neurons supported on silica beads.

<p>Confocal microscopy z series are projected on the xy scanning plane. E18 hippocampal neurons cultured seeded at 4k cells/cm² on 125 µm (<b>a-c</b>), and on 45 µm (<b>d-f</b>) PLL coated beads shown at DIV 4. Cells were fixed and stained with a neuron specific alphãtubulin antibody (green), and with an axon specific smi-312 antibody (red). Neurons were polarized in both preparations independently of bead radius of curvature. The number of neurons per bead is proportional to bead surface area, as 45 µm beads carried on average one cell, and 125 µm beads carried about 10 cells. (<b>g</b>) Bright field image of neurons seeded at 100k cells/cm² on 45 µm beads at DIV 4. (<b>h</b>) Cells were fixed and stained with a neuron specific Tuj-1 antibody (red), and the nuclear marker DAPI (blue). Twenty-one of the twenty-five cells on this bead are Tuj-1 positive. At this high density, cells in direct contact with the bead surface wrap their processes around the beads (highlighted in red) while the others sit on this layer (highlighted in blue) as illustrated in the color-coded picture (<b>i</b>). All Scale bars  =  50 µm.</p