DataSheet1_Artificial intelligence analysis of the impact of fibrosis in arrhythmogenesis and drug response.DOCX by Ana María Sánchez de la Nava (13945119)

“…Four different AI algorithms (Quadratic Support Vector Machine, QSVM, Cubic Support Vector Machine, CSVM, decision trees, DT, and K-Nearest Neighbors, KNN) were evaluated in predicting conduction of a stimulated electrical impulse.</p><p>Results: Overall, fibrosis implementation lowered conduction velocity (CV) for the conducting profiles (0% fibrosis: 67.52 ± 7.3 cm/s; 5%: 58.81 ± 14.04 cm/s; 10%: 57.56 ± 14.78 cm/s; p < 0.001) in combination with a reduced 90% action potential duration (0% fibrosis: 187.77 ± 37.62 ms; 5%: 93.29 ± 82.69 ms; 10%: 106.37 ± 85.15 ms; p < 0.001) and peak membrane potential (0% fibrosis: 89.16 ± 16.01 mV; 5%: 70.06 ± 17.08 mV; 10%: 82.21 ± 19.90 mV; p < 0.001). …”

DataSheet2_Artificial intelligence analysis of the impact of fibrosis in arrhythmogenesis and drug response.DOCX by Ana María Sánchez de la Nava (13945119)

“…Four different AI algorithms (Quadratic Support Vector Machine, QSVM, Cubic Support Vector Machine, CSVM, decision trees, DT, and K-Nearest Neighbors, KNN) were evaluated in predicting conduction of a stimulated electrical impulse.</p><p>Results: Overall, fibrosis implementation lowered conduction velocity (CV) for the conducting profiles (0% fibrosis: 67.52 ± 7.3 cm/s; 5%: 58.81 ± 14.04 cm/s; 10%: 57.56 ± 14.78 cm/s; p < 0.001) in combination with a reduced 90% action potential duration (0% fibrosis: 187.77 ± 37.62 ms; 5%: 93.29 ± 82.69 ms; 10%: 106.37 ± 85.15 ms; p < 0.001) and peak membrane potential (0% fibrosis: 89.16 ± 16.01 mV; 5%: 70.06 ± 17.08 mV; 10%: 82.21 ± 19.90 mV; p < 0.001). …”

Table2_Artificial intelligence analysis of the impact of fibrosis in arrhythmogenesis and drug response.docx by Ana María Sánchez de la Nava (13945119)

“…Four different AI algorithms (Quadratic Support Vector Machine, QSVM, Cubic Support Vector Machine, CSVM, decision trees, DT, and K-Nearest Neighbors, KNN) were evaluated in predicting conduction of a stimulated electrical impulse.</p><p>Results: Overall, fibrosis implementation lowered conduction velocity (CV) for the conducting profiles (0% fibrosis: 67.52 ± 7.3 cm/s; 5%: 58.81 ± 14.04 cm/s; 10%: 57.56 ± 14.78 cm/s; p < 0.001) in combination with a reduced 90% action potential duration (0% fibrosis: 187.77 ± 37.62 ms; 5%: 93.29 ± 82.69 ms; 10%: 106.37 ± 85.15 ms; p < 0.001) and peak membrane potential (0% fibrosis: 89.16 ± 16.01 mV; 5%: 70.06 ± 17.08 mV; 10%: 82.21 ± 19.90 mV; p < 0.001). …”

Table1_Artificial intelligence analysis of the impact of fibrosis in arrhythmogenesis and drug response.docx by Ana María Sánchez de la Nava (13945119)

“…Four different AI algorithms (Quadratic Support Vector Machine, QSVM, Cubic Support Vector Machine, CSVM, decision trees, DT, and K-Nearest Neighbors, KNN) were evaluated in predicting conduction of a stimulated electrical impulse.</p><p>Results: Overall, fibrosis implementation lowered conduction velocity (CV) for the conducting profiles (0% fibrosis: 67.52 ± 7.3 cm/s; 5%: 58.81 ± 14.04 cm/s; 10%: 57.56 ± 14.78 cm/s; p < 0.001) in combination with a reduced 90% action potential duration (0% fibrosis: 187.77 ± 37.62 ms; 5%: 93.29 ± 82.69 ms; 10%: 106.37 ± 85.15 ms; p < 0.001) and peak membrane potential (0% fibrosis: 89.16 ± 16.01 mV; 5%: 70.06 ± 17.08 mV; 10%: 82.21 ± 19.90 mV; p < 0.001). …”

Image1_Artificial intelligence analysis of the impact of fibrosis in arrhythmogenesis and drug response.png by Ana María Sánchez de la Nava (13945119)

“…Four different AI algorithms (Quadratic Support Vector Machine, QSVM, Cubic Support Vector Machine, CSVM, decision trees, DT, and K-Nearest Neighbors, KNN) were evaluated in predicting conduction of a stimulated electrical impulse.</p><p>Results: Overall, fibrosis implementation lowered conduction velocity (CV) for the conducting profiles (0% fibrosis: 67.52 ± 7.3 cm/s; 5%: 58.81 ± 14.04 cm/s; 10%: 57.56 ± 14.78 cm/s; p < 0.001) in combination with a reduced 90% action potential duration (0% fibrosis: 187.77 ± 37.62 ms; 5%: 93.29 ± 82.69 ms; 10%: 106.37 ± 85.15 ms; p < 0.001) and peak membrane potential (0% fibrosis: 89.16 ± 16.01 mV; 5%: 70.06 ± 17.08 mV; 10%: 82.21 ± 19.90 mV; p < 0.001). …”

Data_Sheet_1_Anaemia among women of reproductive age in selected sub-Saharan African countries: multivariate decomposition analyses of the demographic and health surveys data 2008–... by Mohammed Gazali Salifu (11888625)

“…</p>Results<p>Ghana and Sierra Leone experienced 17.07% [95% CI: 14.76–19.37, p < 0.001] and 1% [95% CI: 1.0–2.9, p > 0.05] of anaemia decrease from period 1 to period 2, respectively, while Mali and Benin experienced 11% [95% CI: 9.14–12.90, p < 0.001] and 16.7% [95% CI: 14.99–18.5, p < 0.001] of increase in anaemia prevalence from period 1 to period 2, respectively. …”

TL plots of the Derbyshire data following data manipulation. by Quentin S. Hanley (637406)

“…Panels c and d model the effects of applying a threshold to the reports. Panel a shows data subjected to an incentive to reach 10 reports a month operating above 5 reports. …”

The selective erbB2 antagonist AG825 dampens the NRG1α-induced MRVA. by Christopher G. Kanakry (83122)

“…<p>Administration of AG825 in DMSO vehicle (n = 5) caused a robust, dose-dependent decrease in the MRVA induced by NRG1α. …”

Activity responses to cerebroventricular (ICV) administration of leptin. by Ana C. Ribeiro (210764)

“…<p>(<i>A</i>) ICV leptin (5 ng/h) increased HCA during weeks 1 (*p≤0.05) and 2 in <i>obob</i> mice. …”

Kinetic signatures of acylcarnitines. by Marc Breit (789355)

“…An early response pattern is shown for valerylcarnitine (C5) with a decrease in relative concentration of approx. 16%. …”

Fig 8 - by Antoine H. P. Morice (5278511)

“…., keep velocity constant, * symbols) and push actions on collective stick (i.e., decrease velocity, ▼ symbols) as a function of current value at their onset. …”

Variance parameters for person-caused fires. by Devan G. Becker (13272469)

“…<p>Again, there is a slight decrease in the estimates for region 5 and <i>σ</i>_<i>b</i> with an increase in the AR(1) parameter.…”

CD45⁺ cells, including T cells, infiltrate skeletal muscle after CTX injury. by Alessandra Castiglioni (748889)

“…(C) The percentage of T cells (red) among muscle-infiltrating CD45⁺ cells increase with time, passing from a few cells present at day 1 (12 ± 3%, mean ± SD) to 25 ± 7% (mean ± SD) at day 3, and 37 ± 7% and 34 ± 7% (mean ± SD) at day 5 and 7 respectively (n ≥ 4 mice analyzed per time point). …”

Anthrax toxin-mediated translocation of NADase. by Onkar Sharma (2552671)

“…<p>(A) Kinetics of occlusion of PA pores in planar bilayers by LFn-NADase, LFn-190NADase, LFnNADase G330D, and LFn-190NADase G330D. …”

pRNA expression in HeLa cells. by Muzaffer Ahmad Kassab (750048)

“…Analysis was done with Real Time PCR using pRNA specific primers. S1 and S5 transfection showed significant (p < 0.01) decrease in pRNA expression. …”

Identification of genes down-regulated by androgen deprivation and AR knockdown using cDNA microarray by Tzu-Huey Li (59012)

“…</p><p>© 2007 The Author(s)</p> () Treatment scheme for microarray experiment. () The number of genes with at least 1.5-fold decrease in expression in response to androgen deprivation, AR knockdown or both. () Fold change distribution of genes that showed decreased expression (>1.5 fold) in response to androgen deprivation or AR knockdown. …”

Plasma HIV-1 RNA levels, coreceptor usage and sequence coverage for four subjects failing VVC therapy. by Athe M. N. Tsibris (263584)

“…Protocol-defined virologic failure was defined as <1 log₁₀ viral load decrease from baseline at or after week 16. Coreceptor usage was determined phenotypically by the Trofile assay; R5, CCR5-using virus only detected; DM, both CCR5- and CXCR4-using viruses detected. …”

Quality scores are of limited use in predicting accuracy of unknown sequences by Susan M Huse (45350)

Identification of Mrr1p Target Genes by DNA Microarray Analysis by Joachim Morschhäuser (81347)

“…<div><p>(A) Genes that were upregulated by the P683S mutation in the SC5314 genetic background (<i>MRR1</i>^F5 versus <i>MRR1</i>^F2–1). …”

Table_1_The Influence of Aging, Hearing, and Tinnitus on the Morphology of Cortical Gray Matter, Amygdala, and Hippocampus.DOCX by Oliver Profant (697314)

“…<p>Age related hearing loss (presbycusis) is a natural process represented by elevated auditory thresholds and decreased speech intelligibility, especially in noisy conditions. …”