Karakteristik Nanoemulsi Minyak Sawit Merah Yang Diperkaya Beta Karoten

Red palm oil (RPO) and β-carotene are insoluble in water. It makescan be used to improve RPO and βThis research is aimed to produce stable RPO nanoemulsion enriched withβ-carotene. The research was conducted in the SEAFAST CENTERLaboratory, Bogor Agriculture University from January to Septemberfollowing steps, i.e. enrichment of RPO with βusing a high pressure homogenizer at a pressure of 34.5 MPa in 10 cycles.The ratio of RPO and water in the mixture were 5 : 95; 7.5 : 92.5; and 10 :10% (w/w) of the total emulsions. In the second stage, nanoemulsionswere prepared on various RPO percentage of 2, 4, and 6% (w/w) andhad a droplet size from 115.1 to 145.2 nm and stable. Nanoemulsions wereresulting from the second stage had droplet size from 94.9 to 125.5 nm,and β-carotene content were 47.6 to 130.9 mg/l. Droplet size ofnanoemulsions is less than 125 nm. It can be produced with RPO an

Pembuatan Ethanol Dari Jerami Padi Dengan Proses Hidrolisis Dan Fermentasi

Jerami Padi banyak mengandung Pati, Selulosa dan Glukosa yang cukup tinggi. Alkohol dapat dihasilkan dari tanaman yang banyak mengandung senyawa selulosa dengan menggunakan bantuan aktivitas mikroba salah satu jenis tanamannya adalah jerami padi Tujuan penelitian ini yaitu untuk mendapatkan kadar ethanol yang terbaik pada jerami padi dengan menggunakan proses hidrolisis dan fermentasi. Kondisi yang ditetapkan larutan Hidrolisis sebanyak 2500 ml, pH hidrolisis 3, waktu hidrolisis 2 hari, dan pH fermentasi sebesar 4,5, sedangkan peubah yang dijalankan adalah waktu fermentasi (2,3,4,5,6,7 (hari)), berat jerami padi (40,50,60 (gram)), dan volume stater yang ditambahkan (8%, 10%, 12%, kali volume cairan fermentasi). Hasil penelitian menunjukkan bahwa kondisi terbaik pada berat jerami 50 gram dengan volume stater yang ditambahkan sebanyak 12% volume cairan fermentasi yang difermentasi selama 7 hari yang menghasilkan kadar ethanol sebesar 12,89%. Jerami padi dapat digunakan sebagai bahan baku alternatif pembuatan bioethanol

Encoding time-dependent stimuli in the population activity.

<p>(<b>A</b>) Population activity responses (middle panel; PSTH from 25,000 repeated simulations in blue, quasi-renewal theory in black) to the time-dependent stimuli shown in the bottom panel (black). The difference between direct simulation and theory is shown in the top panel. The stimulus is an Ornstein-Uhlenbeck process with correlation time constant of 300 ms, a STD increasing every 2 seconds (20,40,60 pA) and a mean of 10 pA. (<b>B</b>) Correlation coefficients between direct simulation and QR for various STDs and mean (in pA) of the input current.</p

Supplementary Text from Hebbian plasticity requires compensatory processes on multiple timescales

Supplementary notes on the notion of a timescale for synaptic plasticit

Encoding and Decoding with neuronal populations.

<p>What is the function that relates an arbitrary stimulus to the population activity of adapting neurons? We focus on the problem of relating the filtered input to the activity . The population activity is the fraction of active neurons (red) in the population of neurons (right). All neurons are identical and receive the same stimulus. One possible stimulus is a step current (left).</p

Approximative theories.

<p>(<b>A</b>–<b>C</b>) Population activity responses (top panels; PSTH from 25,000 repeated simulations in blue, renewal theory in black, first order moment expansion (EME1) in red, second order (EME2) in green) to the step current input (bottom panels; black). (<b>D</b>) Activity at the steady state vs. input current as calculated from the direct simulation of 25,000 model neurons (blue squares, error bars show one standard error of the mean), prediction from renewal theory (black), and 1st order moment-expansion (red, <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002711#pcbi.1002711.e394" target="_blank">Eq. 51</a>).</p

Decoding the stimulus from the population activity.

<p>(<b>A</b>–<b>C</b>) The original (bottom panels, black line) and decoded stimulus (bottom panels, red line; arbitrary units) recovered from the PSTH of 25,000 independent SRM neurons (top panels; blue line) with the QR decoder (<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002711#pcbi.1002711.e340" target="_blank">Eq. 45</a>). (<b>D</b>) Same as before but for time-dependent input. The decoded waveform of negative input is occasionally undefined and corresponds to input outside the dynamic range. The difference between direct simulation and theory is shown in the bottom panel. (<b>E</b>) Correlation coefficient between original and decoded input as a function of input STD, shown for three distinct mean input ( pA, pA, and pA). Decoding based on quasi-renewal theory (<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002711#s4" target="_blank">Methods</a>).</p

Diversity of receptive field size, position and orientation.

<p>(<b>a</b>) The optimization value of localized oriented receptive fields, within a 16x16 pixel patch of sensors, as a function of size (see <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005070#sec014" target="_blank">Methods</a>), for five nonlinearities (colors as in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005070#pcbi.1005070.g002" target="_blank">Fig 2a</a>). Optimal size is a receptive field of width around 3 to 4 pixels (filled triangles). (<b>b</b>) The optimization value as a function of position of the receptive field center, for a receptive field width of 4 pixels, indicates invariance to position within the 16x16 patch, except near the borders. (<b>c</b>) The optimization value as a function of orientation shows preference toward horizontal and vertical directions, for all five nonlinearities. (<b>d</b>) Receptive field position, orientation and length (colored bars) learned for 50 single-neuron trials. The color code indicates different orientations. (<b>e</b>) Receptive field positions and orientations learned in a 50 neuron network reveal diversification of positions, except at the borders. (<b>f</b>) With 1000 neurons, positions and orientations cover the full range of combinations (top). Selecting 50 randomly chosen receptive fields highlights the diversification of position, orientation and size (bottom). Receptive fields were learned through the quadratic rectifier nonlinearity (<i>θ</i>₁ = 1., <i>θ</i>₂ = 2.).</p

Optimal receptive field shapes in model networks induce diversity.

<p>(<b>a-f</b>) Gray level indicates the optimization value for different lengths and widths (see inset in <b>a</b>) of oriented receptive fields for natural images, for the quadratic rectifier (left, see <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005070#pcbi.1005070.g002" target="_blank">Fig 2a</a>), linear rectifier (center) and <i>L</i>₀ sparse coding (right). Optima marked with a black cross. (<b>a-c</b>) Colored circles indicate the receptive fields of different shapes developed in a network of 50 neurons with lateral inhibitory connections. Insets on the right show example receptive fields developed during simulation. (<b>d-f</b>) Same for a network of 1000 neurons.</p

Summary of SpikeSuM-C parameters with stochasticity parameter <i>K</i> = 4.

The volatility parameter H = 1/1000 is used in the main text (middle column). Further results with H = 1/500 and H = 1/2000 can be found in Table B of S1 Text.</p