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During EAE, surfen increases lymph node cell count, but reduces proliferation in extracted CD4 positive T cells. Superficial cervical, axillary, brachial and inguinal lymph nodes were pooled from individual mice (n shown). Total cells were counted (a) and then isolated T cells were stimulated in vivo with anti-CD3, anti CD-28 T cell expander beads for 24 h, and proliferation assessed by Oregon Green staining (b). Spleens were also homogenized (n indicates number of mice, with one spleen per mouse) and cells stained with antibodies directed against surface markers and then analyzed by flow cytometry. The percentage of CD4 positive T cells among the extracts is shown (c) along with the gating strategy (d). Data compare EAE treated with vehicle (EAE-V) or surfen (EAE-S), and is shown as mean ± SEM. Significance compares surfen with vehicle (* = P

The development of a cost structure for energy storage systems (ESS) has received limited attention. In this study, we developed data-intensive techno-economic models to assess the economic feasibility of ESS. The ESS here includes pump hydro storage (PHS) and compressed air energy storage (CAES). The costs were developed using data-intensive bottom-up models. Scale factors were developed for each component of the storage systems. The life cycle costs of energy storage were estimated for capacity ranges of 98-491 MW, 81-404 MW, and 60-298 MW for PHS, conventional CAES (C-CAES), and adiabatic CAES (A-CAES), respectively, to ensure a market-driven price can be achieved. For CAES systems, costs were developed for storage in salt caverns hard rock caverns, and porous formations. The results show that the annual life cycle storage cost is $220-400 for PHS, $215-265 for C-CAES, and $375-480 per kW-year for A-CAES. The levelised cost of electricity is $69-121 for PHS, $58-70 for C-CAES, and $96-121 per MWh for A-CAES. C-CAES is economically attractive at all capacities, PHS is economically attractive at higher capacities, and A-CAES is not attractive at all. The developed information is helpful in making investment decision related to large energy storage systems.

Optimal clustering selection of bacterial data. The number of optimal clustering of all data was determined using the Calinski-Harabasz (CH) index. Optimal number of clusters did not identify the classical three enterotypes but rather favored a two cluster partitioning. (TIFF 11074Â kb)

Allele frequency distribution for the 6 populations that experienced bottlenecks. Histograms were created based on 10 microsatellite loci. The Y-axis represents the number of alleles per class of frequency. (TIF 6077 kb)

Figure showing overview of spectrophotometry set-up. Set-up used for the spectrophotometric measurements of the ONT eyeshine in the studied species. 1) spectroradiometer; 2) endoscope, attached via a C-Mount adapter; 3) tripod; 4) cold light source, using the inbuilt cyan filter; 5) optic cable; 6) platform that allowed for controlled vertical movements; 7) cylindrical acrylic glass tank that could be rotated and displaced horizontally on the platform; 8) rubber foam; 9) diffuse white reflectance standard made of foamed PTFE; 10) fish, euthanized and immobilised with pins; 11) laptop running SpectraWinÂŽ, version 2.3.7, for data collection. (PNG 282Â kb)

Figure S2. Migration, invasion and proliferation in MCF7 cells after CPEB2KD. (A) Migration and (B) invasion measured in transwells respectively at 24 and 48 h reveal significant increases in CPEB2KD cells (p

Additional file 1: Supplementary Figure S1. Heatmap representing the bacterial community composition in each sample determined using 16S rRNA gene (D), 16S rRNA (R) and 16S rRNA reads recovered from metagenomic data (M). Results of the two duplicate samples are shown for 16S rRNA gene and 16s rRNA. Only lineages with relative abundance >1% in at least one sample are shown. The right part of the graph represents the bacterial 16S rRNA gene quantification in the duplicate samples (blue bars), as well as the relative proportion between bacteria and archaea determined by 16S rRNA gene qPCR quantification (D; red bars) and in the metagenomic dataset (M; purple bar).

Immunoblots assessing antibody reactivity for (A) TNFRSF12A, (B) PTCH1, (C) CDC25A and (D) IL8. Only antibodies yielding a single band of expected size were used in subsequent immunohistochemical assays. (TIFF 69 kb)

The correlation analysis between cis-acting elements and stress-induced expression of BdPP2C genes. (A) The correlation analysis between ABRE and ABA-induced expression of BdPP2C genes. The samples were treated with ABA for 3 h. (B) The correlation analysis between ABRE and ABA-induced expression of BdPP2C genes. The samples were treated with ABA for 6 h. (C) The correlation analysis between ABRE and NaCl-induced expression of BdPP2C genes. The samples were treated with NaCl for 3 h. (D) The correlation analysis between ABRE and NaCl-induced expression of BdPP2C genes. The samples were treated with NaCl for 6 h. (E) The correlation analysis between ABRE and PEG-induced expression of BdPP2C genes. The samples were treated with PEG for 3 h. (F) The correlation analysis between ABRE and PEG-induced expression of BdPP2C genes. The samples were treated with PEG for 6 h. (TIF 6349 kb)