1,635 research outputs found
Interval mapping of quantitative trait loci with selective DNA pooling data
Selective DNA pooling is an efficient method to identify chromosomal regions that harbor quantitative trait loci (QTL) by comparing marker allele frequencies in pooled DNA from phenotypically extreme individuals. Currently used single marker analysis methods can detect linkage of markers to a QTL but do not provide separate estimates of QTL position and effect, nor do they utilize the joint information from multiple markers. In this study, two interval mapping methods for analysis of selective DNA pooling data were developed and evaluated. One was based on least squares regression (LS-pool) and the other on approximate maximum likelihood (ML-pool). Both methods simultaneously utilize information from multiple markers and multiple families and can be applied to different family structures (half-sib, F2 cross and backcross). The results from these two interval mapping methods were compared with results from single marker analysis by simulation. The results indicate that both LS-pool and ML-pool provided greater power to detect the QTL than single marker analysis. They also provide separate estimates of QTL location and effect. With large family sizes, both LS-pool and ML-pool provided similar power and estimates of QTL location and effect as selective genotyping. With small family sizes, however, the LS-pool method resulted in severely biased estimates of QTL location for distal QTL but this bias was reduced with the ML-pool
Extension of non-minimal derivative coupling theory and Hawking radiation in black-hole spacetime
We study the greybody factor and Hawking radiation with a non-minimal
derivative coupling between the scalar field and the curvature in the
background of the slowly rotating Kerr-Newman black hole.
Our results show that both the absorption probability and luminosity of
Hawking radiation of the scalar field increase with the coupling.
Moreover, we also find that for the weak coupling , the
absorption probability and luminosity of Hawking radiation decrease when the
black hole's Hawking temperature decreases; while for stronger coupling
, the absorption probability and luminosity of Hawking radiation
increase on the contrary when the black hole's Hawking temperature decreases.
This feature is similar to the Hawking radiation in a -dimensional static
spherically-symmetric black hole surrounded by quintessence \cite{chensong}.Comment: 17 pages, 6 figures, 1 table, Title changed, Appendix changed,
accepted by JHE
Gene and protein expression of glucose transporter 1 and glucose transporter 3 in human laryngeal cancer—the relationship with regulatory hypoxia-inducible factor-1α expression, tumor invasiveness, and patient prognosis
Increased glucose uptake mediated by glucose
transporters and reliance on glycolysis are common features
of malignant cells. Hypoxia-inducible factor-1α supports the
adaptation of hypoxic cells by inducing genes related to
glucose metabolism. The contribution of glucose transporter
(GLUT) and hypoxia-inducible factor-1α (HIF-1α) activity to
tumor behavior and their prognostic value in head and neck
cancers remains unclear. The aim of this study was to examine
the predictive value of GLUT1, GLUT3, and HIF-1α messenger
RNA (mRNA)/protein expression as markers of tumor
aggressiveness and prognosis in laryngeal cancer. The level of
hypoxia/metabolic marker genes was determined in 106 squamous
cell laryngeal cancer (SCC) and 73 noncancerous
matched mucosa (NCM) controls using quantitative realtime
PCR. The related protein levels were analyzed by
Western blot. Positive expression of SLC2A1, SLC2A3, and
HIF-1α genes was noted in 83.9, 82.1, and 71.7 % of SCC
specimens and in 34.4, 59.4, and 62.5 % of laryngeal cancer
samples. Higher levels of mRNA/protein for GLUT1 and
HIF-1α were noted in SCC compared to NCM (p<0.05).
SLC2A1 was found to have a positive relationship with grade,
tumor front grading (TFG) score, and depth and mode of
invasion (p<0.05). SLC2A3 was related to grade and invasion
type (p<0.05). There were also relationships of HIF-1α with
pTNM, TFG scale, invasion depth and mode, tumor recurrences,
and overall survival (p<0.05). In addition, more advanced
tumors were found to be more likely to demonstrate
positive expression of these proteins. In conclusion, the
hypoxia/metabolic markers studied could be used as molecular
markers of tumor invasiveness in laryngeal cancer.This work was supported, in part, by the statutory
fund of the Department of Cytobiochemistry, University of Łódź, Poland
(506/811), and by grant fromtheNational Science Council, Poland (N403
043 32/2326)
Measuring our universe from galaxy redshift surveys
Galaxy redshift surveys have achieved significant progress over the last
couple of decades. Those surveys tell us in the most straightforward way what
our local universe looks like. While the galaxy distribution traces the bright
side of the universe, detailed quantitative analyses of the data have even
revealed the dark side of the universe dominated by non-baryonic dark matter as
well as more mysterious dark energy (or Einstein's cosmological constant). We
describe several methodologies of using galaxy redshift surveys as cosmological
probes, and then summarize the recent results from the existing surveys.
Finally we present our views on the future of redshift surveys in the era of
Precision Cosmology.Comment: 82 pages, 31 figures, invited review article published in Living
Reviews in Relativity, http://www.livingreviews.org/lrr-2004-
Fragmentation patterns and personalized sequencing of cell-free DNA in urine and plasma of glioma patients.
Glioma-derived cell-free DNA (cfDNA) is challenging to detect using liquid biopsy because quantities in body fluids are low. We determined the glioma-derived DNA fraction in cerebrospinal fluid (CSF), plasma, and urine samples from patients using sequencing of personalized capture panels guided by analysis of matched tumor biopsies. By sequencing cfDNA across thousands of mutations, identified individually in each patient's tumor, we detected tumor-derived DNA in the majority of CSF (7/8), plasma (10/12), and urine samples (10/16), with a median tumor fraction of 6.4 × 10-3 , 3.1 × 10-5 , and 4.7 × 10-5 , respectively. We identified a shift in the size distribution of tumor-derived cfDNA fragments in these body fluids. We further analyzed cfDNA fragment sizes using whole-genome sequencing, in urine samples from 35 glioma patients, 27 individuals with non-malignant brain disorders, and 26 healthy individuals. cfDNA in urine of glioma patients was significantly more fragmented compared to urine from patients with non-malignant brain disorders (P = 1.7 × 10-2 ) and healthy individuals (P = 5.2 × 10-9 ). Machine learning models integrating fragment length could differentiate urine samples from glioma patients (AUC = 0.80-0.91) suggesting possibilities for truly non-invasive cancer detection
Pre-ejection period by radial artery tonometry supplements echo doppler findings during biventricular pacemaker optimization
<p>Abstract</p> <p>Background</p> <p>Biventricular (Biv) pacemaker echo optimization has been shown to improve cardiac output however is not routinely used due to its complexity. We investigated the role of a simple method involving computerized pre-ejection time (PEP) assessment by radial artery tonometry in guiding Biv pacemaker optimization.</p> <p>Methods</p> <p>Blinded echo and radial artery tonometry were performed simultaneously in 37 patients, age 69.1 ± 12.8 years, left ventricular (LV) ejection fraction (EF) 33 ± 10%, during Biv pacemaker optimization. Effect of optimization on echo derived velocity time integral (VTI), ejection time (ET), myocardial performance index (MPI), radial artery tonometry derived PEP and echo-radial artery tonometry derived PEP/VTI and PEP/ET indices was evaluated.</p> <p>Results</p> <p>Significant improvement post optimization was achieved in LV ET (286.9 ± 37.3 to 299 ± 34.6 ms, p < 0.001), LV VTI (15.9 ± 4.8 cm to 18.4 ± 5.1 cm, p < 0.001) and MPI (0.57 ± 0.2 to 0.45 ± 0.13, p < 0.001) and in PEP (246.7 ± 36.1 ms to 234.7 ± 35.5 ms, p = 0.003), PEP/ET (0.88 ± 0.21 to 0.79 ± 0.17, p < 0.001), and PEP/VTI (17.3 ± 7 to 13.78 ± 4.7, p < 0.001). The correlation between comprehensive echo Doppler and radial artery tonometry-PEP guided optimal atrioventricular delay (AVD) and optimal interventricular delay (VVD) was 0.75 (p < 0.001) and 0.69 (p < 0.001) respectively. In 29 patients with follow up assessment, New York Heart Association (NYHA) class reduced from 2.5 ± 0.8 to 2.0 ± 0.9 (p = 0.004) at 1.8 ± 1.4 months.</p> <p>Conclusion</p> <p>An acute shortening of PEP by radial artery tonometry occurs post Biv pacemaker optimization and correlates with improvement in hemodynamics by echo Doppler and may provide a cost-efficient approach to assist with Biv pacemaker echo optimization.</p
Comparison of hospital charge prediction models for gastric cancer patients: neural network vs. decision tree models
<p>Abstract</p> <p>Background</p> <p>In recent years, artificial neural network is advocated in modeling complex multivariable relationships due to its ability of fault tolerance; while decision tree of data mining technique was recommended because of its richness of classification arithmetic rules and appeal of visibility. The aim of our research was to compare the performance of ANN and decision tree models in predicting hospital charges on gastric cancer patients.</p> <p>Methods</p> <p>Data about hospital charges on 1008 gastric cancer patients and related demographic information were collected from the First Affiliated Hospital of Anhui Medical University from 2005 to 2007 and preprocessed firstly to select pertinent input variables. Then artificial neural network (ANN) and decision tree models, using same hospital charge output variable and same input variables, were applied to compare the predictive abilities in terms of mean absolute errors and linear correlation coefficients for the training and test datasets. The transfer function in ANN model was sigmoid with 1 hidden layer and three hidden nodes.</p> <p>Results</p> <p>After preprocess of the data, 12 variables were selected and used as input variables in two types of models. For both the training dataset and the test dataset, mean absolute errors of ANN model were lower than those of decision tree model (1819.197 vs. 2782.423, 1162.279 vs. 3424.608) and linear correlation coefficients of the former model were higher than those of the latter (0.955 vs. 0.866, 0.987 vs. 0.806). The predictive ability and adaptive capacity of ANN model were better than those of decision tree model.</p> <p>Conclusion</p> <p>ANN model performed better in predicting hospital charges of gastric cancer patients of China than did decision tree model.</p
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