Systematic identification of cell cycle regulated transcription factors from microarray time series data-6

Espectively. The Fisher's G-tests are applied to each of the 203 TF AC score profiles and expression profiles. Distributions of the resulting p-values are shown in (C) and (D), respectively.<p><b>Copyright information:</b></p><p>Taken from "Systematic identification of cell cycle regulated transcription factors from microarray time series data"</p><p>http://www.biomedcentral.com/1471-2164/9/116</p><p>BMC Genomics 2008;9():116-116.</p><p>Published online 3 Mar 2008</p><p>PMCID:PMC2315658.</p><p></p

AC score profiles of four TFs which may be perturbed in activity by -factor synchronizing treatment

<p><b>Copyright information:</b></p><p>Taken from "Systematic identification of cell cycle regulated transcription factors from microarray time series data"</p><p>http://www.biomedcentral.com/1471-2164/9/116</p><p>BMC Genomics 2008;9():116-116.</p><p>Published online 3 Mar 2008</p><p>PMCID:PMC2315658.</p><p></p

Inferred AC score profiles (A) and mRNA expression profiles (B) of 4 known CCRTFs

<p><b>Copyright information:</b></p><p>Taken from "Systematic identification of cell cycle regulated transcription factors from microarray time series data"</p><p>http://www.biomedcentral.com/1471-2164/9/116</p><p>BMC Genomics 2008;9():116-116.</p><p>Published online 3 Mar 2008</p><p>PMCID:PMC2315658.</p><p></p

– plots of spike-in data after SUB-SUB normalization: () trimming fraction is 0

<p><b>Copyright information:</b></p><p>Taken from "Sub-array normalization subject to differentiation"</p><p>Nucleic Acids Research 2005;33(17):5565-5573.</p><p>Published online 4 Oct 2005</p><p>PMCID:PMC1243797.</p><p>© The Author 2005. Published by Oxford University Press. All rights reserved</p>4; () trimming fraction is 0

Systematic identification of cell cycle regulated transcription factors from microarray time series data-0

Espectively. The Fisher's G-tests are applied to each of the 203 TF AC score profiles and expression profiles. Distributions of the resulting p-values are shown in (C) and (D), respectively.<p><b>Copyright information:</b></p><p>Taken from "Systematic identification of cell cycle regulated transcription factors from microarray time series data"</p><p>http://www.biomedcentral.com/1471-2164/9/116</p><p>BMC Genomics 2008;9():116-116.</p><p>Published online 3 Mar 2008</p><p>PMCID:PMC2315658.</p><p></p

(–)– plots of perturbed spike-in dataset after SUB-SUB normalization

<p><b>Copyright information:</b></p><p>Taken from "Sub-array normalization subject to differentiation"</p><p>Nucleic Acids Research 2005;33(17):5565-5573.</p><p>Published online 4 Oct 2005</p><p>PMCID:PMC1243797.</p><p>© The Author 2005. Published by Oxford University Press. All rights reserved</p> The -axis is the average of log-intensities from two arrays. The -axis is their difference after normalization. In the top four subplots, 20% randomly selected genes have been artificially up-regulated by 2.5-fold in Array Q, R, S and T. The differentiated genes are marked red, and undifferentiated genes are marked black. The trimming fractions in the subplots are: A, 50%; B, 30%; C, 20%; D, 10%. In the two subplots at the bottom, 20% randomly selected genes have been artificially up-regulated by 1.5-fold in Array Q, R, S and T. The trimming fractions are: E, 50%; F, 0%

Table_1_Effects of whole maize high-grain diet feeding on colonic fermentation and bacterial community in weaned lambs.DOCX

High-grain diet is commonly used in intensive production to boost yield in short term, which may cause adverse effects such as rumen and colonic acidosis in ruminants. Maize is one of the key components of high-grain diet, and different processing methods of maize affect the digestive absorption and gastrointestinal development of ruminants. To investigate the effects of maize form in high-grain diets on colonic fermentation and bacterial community of weaned lambs, twenty-two 2.5-month-old healthy Hu lambs were fed separately a maize meal low-grain diet (19.2% grain; CON), a maize meal high-grain diet (50.4% grain; CM), and a whole maize high-grain diet (50.4% grain; CG). After 7 weeks of feeding, the total volatile fatty acid concentration (P = 0.035) were significantly higher in lambs from CM than that from CON. The sequencing results of colonic content microbial composition revealed that the relative abundance of genera Parasutterella (P = 0.028), Comamonas (P = 0.031), Butyricicoccus (P = 0.049), and Olsenella (P = 0.010) were higher in CM than those in CON; compared with CM, the CG diet had the higher relative abundance of genera Bacteroides (P = 0.024) and Angelakisella (P = 0.020), while the lower relative abundance of genera Olsenella (P = 0.031) and Paraprevotella (P = 0.006). For colonic mucosal microbiota, the relative abundance of genera Duncaniella (P = 0.024), Succiniclasticum (P = 0.044), and Comamonas (P = 0.012) were significantly higher in CM than those in CON. In comparison, the relative abundance of genera Alistipes (P = 0.020) and Campylobacter (P = 0.017) were significantly lower. And the relative abundance of genera Colidextribacter (P = 0.005), Duncaniella (P = 0.032), Christensenella (P = 0.042), and Lawsonibacter (P = 0.018) were increased in the CG than those in the CM. Furthermore, the CG downregulated the relative abundance of genes encoding infectious-disease-parasitic (P = 0.049), cancer-specific-types (P = 0.049), and neurodegenerative-disease (P = 0.037) in colonic microbiota than those in the CM. Overall, these results indicated that maize with different grain sizes might influence the colonic health of weaned lambs by altering the composition of the colonic bacterial community.</p

Solubility and Preferential Solvation of Carbazochrome in Solvent Mixtures of <i>N</i>,<i>N</i>‑Dimethylformamide Plus Methanol/Ethanol/<i>n</i>‑Propanol and Dimethyl Sulfoxide Plus Water

The carbazochrome (3) solubility in solvent mixtures of DMF (<i>N</i>,<i>N</i>-dimethylformamide, 1) + methanol (2), DMF (1) + ethanol (2), DMF (1) + <i>n</i>-propanol (2), and dimethyl sulfoxide (DMSO, 1) + water (2) was measured by the static method within the temperature range from (278.15 to 318.15) K under atmospheric pressure, <i>p</i> = 101.0 kPa. The solubility of carbazochrome increased with rising mass fraction of DMF or DMSO and temperature. The Jouyban–Acree, van’t Hoff–Jouyban–Acree, and Apelblat–Jouyban–Acree models were used to correlate the obtained solubility, and the Apelblat–Jouyban–Acree model provided better correlation results. The parameters of preferential solvation (<i>δx</i>_1,3) were acquired from the mixture properties with the method of inverse Kirkwood–Buff integrals. The values of <i>δx</i>_1,3 changed nonlinearly with the DMF/DMSO (1) proportion in the studied mixed solvents. The carbazochrome was solvated preferentially by alcohol or water in alcohol or water-rich solutions and preferentially solvated by DMF/DMSO in DMF/DMSO-rich mixtures. It could be speculated that in DMF/DMSO-rich mixtures the interaction by acidic hydrogen bonding with the basic sites of carbazochrome played a significant role in carbazochrome solvation

A probe-treatment-reference (PTR) model for the analysis of oligonucleotide expression microarrays-2

Ent of non-spike-in genes. The PTR method gives the smallest variation for all three normalization algorithms.<p><b>Copyright information:</b></p><p>Taken from "A probe-treatment-reference (PTR) model for the analysis of oligonucleotide expression microarrays"</p><p>http://www.biomedcentral.com/1471-2105/9/194</p><p>BMC Bioinformatics 2008;9():194-194.</p><p>Published online 14 Apr 2008</p><p>PMCID:PMC2375129.</p><p></p

A probe-treatment-reference (PTR) model for the analysis of oligonucleotide expression microarrays-5

Ference" across all the probe-sets. It is computed from the residual assessment after the PTR method with the invariant-set normalization on the data set "Expt-3-4".<p><b>Copyright information:</b></p><p>Taken from "A probe-treatment-reference (PTR) model for the analysis of oligonucleotide expression microarrays"</p><p>http://www.biomedcentral.com/1471-2105/9/194</p><p>BMC Bioinformatics 2008;9():194-194.</p><p>Published online 14 Apr 2008</p><p>PMCID:PMC2375129.</p><p></p