Diffusion in sparse networks: linear to semi-linear crossover

We consider random networks whose dynamics is described by a rate equation, with transition rates $w_{nm}$ that form a symmetric matrix. The long time evolution of the system is characterized by a diffusion coefficient $D$. In one dimension it is well known that $D$ can display an abrupt percolation-like transition from diffusion ($D>0$) to sub-diffusion (D=0). A question arises whether such a transition happens in higher dimensions. Numerically $D$ can be evaluated using a resistor network calculation, or optionally it can be deduced from the spectral properties of the system. Contrary to a recent expectation that is based on a renormalization-group analysis, we deduce that $D$ is finite; suggest an "effective-range-hopping" procedure to evaluate it; and contrast the results with the linear estimate. The same approach is useful for the analysis of networks that are described by quasi-one-dimensional sparse banded matrices.Comment: 13 pages, 4 figures, proofed as publishe

CEL-Seq2: sensitive highly-multiplexed single-cell RNA-Seq

Single-cell transcriptomics requires a method that is sensitive, accurate, and reproducible. Here, we present CEL-Seq2, a modified version of our CEL-Seq method, with threefold higher sensitivity, lower costs, and less hands-on time. We implemented CEL-Seq2 on Fluidigm’s C1 system, providing its first single-cell, on-chip barcoding method, and we detected gene expression changes accompanying the progression through the cell cycle in mouse fibroblast cells. We also compare with Smart-Seq to demonstrate CEL-Seq2’s increased sensitivity relative to other available methods. Collectively, the improvements make CEL-Seq2 uniquely suited to single-cell RNA-Seq analysis in terms of economics, resolution, and ease of use.Seventh Framework Programme (European Commission)Israel Science Foundatio

CEL-Seq-pipeline: CEL-Seq2 pipeline version 1.0

<p>CEL-Seq2 pipeline version 1.0</p

Additional file 6: of CEL-Seq2: sensitive highly-multiplexed single-cell RNA-Seq

Supplementary file 2. CEL-Seq in C1. The file includes the complete information for performing CEL-Seq on the Fluidigm C1 instrument. (DOCX 17 kb

Additional file 1: Figure S1. of CEL-Seq2: sensitive highly-multiplexed single-cell RNA-Seq

Optimization of the CEL-Seq protocol. A Number of genes obtained from ten replicates of 100 pg RNA performed with each type of primer: the original primer, the original primer with the inclusion of UMI, and the shortened UMI primer. B Number of transcripts identified for the two primers containing a UMI. C Estimating the efficiency of CEL-Seq using UMIs and ERCC spike-ins. The efficiency is computed as the y-intercept. D Side-by-side comparison of column clean-up, bead clean-up, and two RTs relative to CEL-Seq with a UMI primer. E Side-by-side comparison of different second-strand synthesis enzymes. The MessageAmp II enzyme was the one used originally. (PDF 519 kb

Central dopaminergic neurons in tilapia: Effects of gonadectomy and hypothalamic lesion

10.1016/0168-0102(94)90161-9Neuroscience Research184255-266NERA