Effect of inhibitors on model parameters as inferred by SBI. by Johannes C. J. Heyn (21567894)

“…For each trajectory in a given population we sampled 1000 different points in parameter space. …”

Rank-ordered distributions in biological systems. by Gustavo Martínez-Mekler (209559)

“…(B) Local field potential measurements of cat cerebral cortex taken every 4 ms in an awake state, total of 8192 data points plotted in decreasing order <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0004791#pone.0004791-Destexhe1" target="_blank">[18]</a> (a,b,R²) = (0.08,0.25,0.98).…”

Instrument characterization. by Tony Hoang (5105216)

“…<p>a) Data from different cell concentrations (SIMS) show decreased signal at lower concentrations. …”

White matter structural connectivity revealed by HARDI. by Corinna M. Bauer (3854026)

“…<p>A) Exploratory analysis (uncorrected; p<0.05) of ROI-pairs revealed trends for increased as well as decreased white matter connectivity (as indexed by fiber number) in early blind compared to sighted control individuals. …”

Transcripts whose abundance directly responds to TcUBP1 expression levels. by Karina B. Sabalette (18576394)

“…The pointed line marks a difference of ± 2X.</p>…”

Regulatory effects of MLR-Tregs in micro-CML assays of responding purified CD8⁺ cells. by Yuming Yu (212650)

“…Similar to the data in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0022450#pone-0022450-g004" target="_blank">Figure 4A</a>, the lysis of target cells was decreasingly inhibited by decreasing concentrations of MLR-Tregs (** = p<.01; n = 6). …”

Effect of Trk receptor inhibitor on <i>in vivo</i> tumor growth of MES-SA/Dx5 xenografts in athymic nude mice. by Kenichi Makino (149077)

“…Tumor volume on the first day of treatment was expressed as a relative tumor volume of 1. <i>Points</i>, mean; <i>bars</i>, SE. *, <i>P</i><0.05 vs. control. …”

Auditory and visual feedback. by Andrey Eliseyev (438089)

“…Each time the motion intention was detected, the size of the bar was increased by 0.1 until reaching of 1. Otherwise, it was decreased by 0.1, until reaching 0. Motion intention periods were indicated with a green arrow pointing up and a red hourglass was pictured when no motion intention patterns were detected. …”

Low-Photon Regime. by Bryan Kaye (3621782)

“…The sample standard deviation decreases approximately as . Power law fit to <i>a</i> × <i>x</i>^<i>b</i> for all but the four lowest values of <i>n</i>_{<i>photon</i>} shown in gray, with <i>a</i> = 0.04 ± 0.01 and <i>b</i> = −0.48 ± 0.04 (95% confidence interval).…”

Hyperthyroidism, but not hypertension, impairs PITX2 expression leading to Wnt-microRNA-ion channel remodeling - Fig 8 by Estefanía Lozano-Velasco (4637050)

“…Observe that most ion channel are significantly decreased in both experimental conditions at both time points (A) whereas microRNAs are all significantly decreased after Pitx2 over-expression and increased following Pitx2c silencing at 12h and 24h after H₂0₂ administration. …”

Bistability and hysteresis in mitochondrial fatty acid oxidation. by Fentaw Abegaz (11211853)

“…A. Steady state uptake flux of palmitoyl-CoA for increasing (red curve) and decreasing (blue curve) palmitoyl-CoA concentrations. …”

Inhibition of proliferation of Jurkat, Hut78 and EL4 cells by 13-MTD treatment. by Qingqing Cai (317710)

“…(<b>B</b>) Cell viability of 13-MTD-treated Jurkat cells decreased in a time-dependent manner at different incubation time points (24, 48, 72 h).…”

Mitochondrionopathy Phenotype in Doxorubicin-Treated Wistar Rats Depends on Treatment Protocol and Is Cardiac-Specific by Gonçalo C. Pereira (156673)

“…In the acute treatment model, ADP-stimulated respiration was increased in liver and decreased in kidney mitochondria. Aconitase activity, a marker of oxidative stress, was decreased in renal mitochondria in the acute and in heart in the sub-chronic model. …”

Table_3_Identifying Temporally Regulated Root Nodulation Biomarkers Using Time Series Gene Co-Expression Network Analysis.xlsx by William L. Poehlman (7598351)

“…Following gene expression quantification, we identified 1,758 differentially expressed genes at various time points. We constructed a gene co-expression network (GCN) from the same data and identified link community modules (LCMs) that were comprised entirely of differentially expressed genes at specific time points post-inoculation. …”

Image_2_Identifying Temporally Regulated Root Nodulation Biomarkers Using Time Series Gene Co-Expression Network Analysis.tif by William L. Poehlman (7598351)

“…Following gene expression quantification, we identified 1,758 differentially expressed genes at various time points. We constructed a gene co-expression network (GCN) from the same data and identified link community modules (LCMs) that were comprised entirely of differentially expressed genes at specific time points post-inoculation. …”

Table_1_Identifying Temporally Regulated Root Nodulation Biomarkers Using Time Series Gene Co-Expression Network Analysis.xlsx by William L. Poehlman (7598351)

“…Following gene expression quantification, we identified 1,758 differentially expressed genes at various time points. We constructed a gene co-expression network (GCN) from the same data and identified link community modules (LCMs) that were comprised entirely of differentially expressed genes at specific time points post-inoculation. …”

Table_4_Identifying Temporally Regulated Root Nodulation Biomarkers Using Time Series Gene Co-Expression Network Analysis.xlsx by William L. Poehlman (7598351)

“…Following gene expression quantification, we identified 1,758 differentially expressed genes at various time points. We constructed a gene co-expression network (GCN) from the same data and identified link community modules (LCMs) that were comprised entirely of differentially expressed genes at specific time points post-inoculation. …”

Image_1_Identifying Temporally Regulated Root Nodulation Biomarkers Using Time Series Gene Co-Expression Network Analysis.tif by William L. Poehlman (7598351)

“…Following gene expression quantification, we identified 1,758 differentially expressed genes at various time points. We constructed a gene co-expression network (GCN) from the same data and identified link community modules (LCMs) that were comprised entirely of differentially expressed genes at specific time points post-inoculation. …”

Table_5_Identifying Temporally Regulated Root Nodulation Biomarkers Using Time Series Gene Co-Expression Network Analysis.xlsx by William L. Poehlman (7598351)

“…Following gene expression quantification, we identified 1,758 differentially expressed genes at various time points. We constructed a gene co-expression network (GCN) from the same data and identified link community modules (LCMs) that were comprised entirely of differentially expressed genes at specific time points post-inoculation. …”

Table_2_Identifying Temporally Regulated Root Nodulation Biomarkers Using Time Series Gene Co-Expression Network Analysis.xlsx by William L. Poehlman (7598351)

“…Following gene expression quantification, we identified 1,758 differentially expressed genes at various time points. We constructed a gene co-expression network (GCN) from the same data and identified link community modules (LCMs) that were comprised entirely of differentially expressed genes at specific time points post-inoculation. …”