Preparation and structure characterization of soluble bone collagen peptide chelating calcium

In this study, G-25 gel chromatography, X-diffraction, scanning electron microscopy (SEM), UV and Fourier transform infrared spectroscopy (FTIR) were used to analyze soluble collagen peptides chelating calcium. Collagen peptide hydrolysis can be divided into four components using G-25 gel chromatography. Each component of calcium binding capacity was different and the components whose molecular weight was less than 5000 Da had a relatively high calcium binding capacity. In the infrared spectra experimental certification, after the collagen peptides had combined with calcium, amide I, II wave number was displaced, which indicated that amino nitrogen atoms and oxygen atoms on the carboxyl groups were involved in chelation. In the UV scan spectra, the characteristic absorption peak of the collagen peptide’s carbonyl and the peptide bond was clearly shifted, indicating that collagen peptides have reacted with calcium. In SEM spectra, a lot of white grains were seen to be "embedded" clearly in the surface of the collagen peptide, indicating that besides the reaction of coordination between collagen peptides and calcium, there was a certain degree of adsorption. After combination with calcium, the X-ray diffraction spectra showed that the no rules non-crystal structure collagen peptides turned into rules crystal structure. According to the structure analysis which showed that collagen peptide chelated calcium is a five-membered ring structure, calcium is in the center and was combined strongly with both the amino- and carboxyl-group.Key words: Bone, calcium binding, molecular weight, collagen peptide

Prognostic value of PDCD-1 and CTLA-4 in ovarian cancer patients

Therapeutic effectiveness of treatments for ovarian cancer is not optimal. PDCD-1 and CTLA-4 offers the potential as a prognostic marker in addition to being a target for therapy. To assess the prognostic roles of PDCD-1 and CTLA-4 Gene in ovarian cancer, we utilized the Kaplan Meier plotter, a biomarker assessment tool with large quantities of data. The relationship between PDCD-1 and overall survival (OS) as well as CTLA-4 and OS were presented using Hazard Ratio, 95% CI and logrank P value. Then gene expression level was compared using H-Test and U test. The results were as follows: PDCD-1 and CTLA-4 gene expressions among 1582 ovarian cancer patients were shown with median gene expression value as the cut-off. Expression of PDCD-1 and CTLA-4 did not differ with regard to stages and P53 gene mutation. But the expression of CTLA-4 was higher in endometrioid than in serous cancer patients. Different grades of both PDCD-1 and CTLA-4 had different mean values. Higher expression of the PDCD-1 was not significantly correlated with better OS with HR 0.88 (95% CI: 0.77-1.01, P=0.061) but higher CTLA-4 was associated with better survival with HR 0.84 (95% CI: 0.73-0.96, P=0.0099) on the transcriptome level. In conclusion, lower expression of CTLA-4, but not PDCD-1 predicts worse survival

Validation of nonlinear PCA

Linear principal component analysis (PCA) can be extended to a nonlinear PCA by using artificial neural networks. But the benefit of curved components requires a careful control of the model complexity. Moreover, standard techniques for model selection, including cross-validation and more generally the use of an independent test set, fail when applied to nonlinear PCA because of its inherent unsupervised characteristics. This paper presents a new approach for validating the complexity of nonlinear PCA models by using the error in missing data estimation as a criterion for model selection. It is motivated by the idea that only the model of optimal complexity is able to predict missing values with the highest accuracy. While standard test set validation usually favours over-fitted nonlinear PCA models, the proposed model validation approach correctly selects the optimal model complexity.Comment: 12 pages, 5 figure

Probabilistic segmentation of volume data for visualization using SOM-PNN classifier

We present a new probabilistic classifier, called SOM-PNN classifier, for volume data classification and visualization. The new classifier produces probabilistic classification with Bayesian confidence measure which is highly desirable in volume rendering. Based on the SOM map trained with a large training data set, our SOM-PNN classifier performs the probabilistic classification using the PNN algorithm. This combined use of SOM and PNN overcomes the shortcomings of the parametric methods, the nonparametric methods, and the SOM method. The proposed SOM-PNN classifier has been used to segment the CT sloth data and the 20 human MRI brain volumes resulting in much more informative 3D rendering with more details and less artifacts than other methods. Numerical comparisons demonstrate that the SOM-PNN classifier is a fast, accurate and probabilistic classifier for volume rendering.published_or_final_versio

Neuroimaging and biomarker evidence of neurodegeneration in asthma

Background: Epidemiological studies have shown that Alzheimer’s disease and related dementias (ADRD) are seen more frequently with asthma, especially with greater asthma severity or exacerbation frequency. // Objective: To examine the changes in brain structure that may underlie this phenomenon, we examined diffusion-weighted magnetic resonance imaging (dMRI) and blood-based biomarkers of AD (p-Tau181), neurodegeneration (NfL) and glial activation (GFAP). // Methods: dMRI data were obtained in 111 individuals with asthma, ranging in disease severity from mild to severe, and 135 healthy controls. Regression analyses were used to test the relationships between asthma severity and neuroimaging measures, as well as AD pathology, neurodegeneration and glial activation, indexed by plasma p-Tau181, NfL and GFAP respectively. Additional relationships were tested with cognitive function. // Results: Asthma participants had widespread and large magnitude differences in several dMRI metrics, which were indicative of neuroinflammation and neurodegeneration, and robustly associated with GFAP and to a lesser extent, with NfL. The AD biomarker p-Tau181 was only minimally associated with neuroimaging outcomes. Further, asthma severity was associated with deleterious changes in neuroimaging outcomes, which in turn, were associated with slower processing speed, a test of cognitive performance. // Conclusion: These data suggest that asthma, particularly when severe, is associated with characteristics of neuroinflammation and neurodegeneration and may be a potential risk factor for neural injury and cognitive dysfunction. The results suggest a need to determine how asthma may affect brain health and whether treatment directed toward characteristics of asthma associated with these risks can mitigate these effects

Formative evaluation of electricity distribution utilities using data envelopment analysis

The use of Data Envelopment Analysis (DEA) in the electricity distribution sector has been prolific in the number of papers published in research journals. However, while numerous studies have been documented, they have mostly been summative. Their aim has been predominantly descriptive and classificatory. This paper argues that evaluations of a formative nature are more effective than summative studies in promoting a better understanding of the structures and processes of electricity distribution utilities and, consequently, are more appropriate to contribute to performance improvement. To illustrate the use of DEA for formative evaluation, and highlight some of the difficulties of using DEA in practice, this paper compares the cost-efficiency of the Portuguese electricity distribution companies from 2002 to 2006. A dynamic analysis using Malmquist Indices is also conducted in order to evaluate the changes in productivity over this period. Our analysis shows that the application of DEA for formative purposes meets some difficulties. In particular it shows that while the modelling of productivity/efficiency scores using DEA is relatively straightforward, it is comparatively more difficult to develop models that are economically valid and that produce results with face validity. On the basis of the insights derived from this analysis, the paper provides some recommendations regarding the successful application of DEA for performance improvement

Adherens junctions remain dynamic

One of the four principal categories of cell-cell junctions that hold together and shape distinct tissues and organs in vertebrates, adherens junctions (AJs) form cell-cell contacts that connect transmembrane proteins with cytoskeletal actin filaments to provide architectural strength, aid in morphogenesis, and help to maintain proper tissue homeostasis. The classical organization of AJs, consisting of transmembrane cadherins and cytoplasmically attached β-catenins and α-catenins assembled together into a multiprotein complex, was once thought obligatory to craft a robust and stable connection to actin-based cytoskeletal elements, but this architecture has since been challenged and questioned to exist. In a stimulating paper published in a recent issue of BMC Biology, Millán et al. provide convincing evidence that in confluent vascular endothelial cells a novel dynamic vascular endothelial (VE)-cadherin-based AJ type exists that interacts with and physically connects prominent bundles of tension-mediating actin filaments, stress fibers, between neighboring cells. Stress fibers were known previously to link to integrin-based focal adhesion complexes but not to cell-cell adhesion mediating AJs. These new findings, together with previous results support the concept that different AJ subtypes, sharing the same transmembrane cadherin types, can assemble in various configurations to either increase barrier function and promote physical cell-cell adhesion, or to lessen cell-cell adhesion and promote cell separation and migration

Musical Ratios in Sounds from the Human Cochlea

The physiological roots of music perception are a matter of long-lasting debate. Recently light on this problem has been shed by the study of otoacoustic emissions (OAEs), which are weak sounds generated by the inner ear following acoustic stimulation and, sometimes, even spontaneously. In the present study, a high-resolution time–frequency method called matching pursuit was applied to the OAEs recorded from the ears of 45 normal volunteers so that the component frequencies, amplitudes, latencies, and time-spans could be accurately determined. The method allowed us to find that, for each ear, the OAEs consisted of characteristic frequency patterns that we call resonant modes. Here we demonstrate that, on average, the frequency ratios of the resonant modes from all the cochleas studied possessed small integer ratios. The ratios are the same as those found by Pythagoras as being most musically pleasant and which form the basis of the Just tuning system. The statistical significance of the results was verified against a random distribution of ratios. As an explanatory model, there are attractive features in a recent theory that represents the cochlea as a surface acoustic wave resonator; in this situation the spacing between the rows of hearing receptors can create resonant cavities of defined lengths. By adjusting the geometry and the lengths of the resonant cavities, it is possible to generate the preferred frequency ratios we have found here. We conclude that musical perception might be related to specific geometrical and physiological properties of the cochlea

Does Increased Arterial Stiffness Increase the Risk for Postural Hypotension?

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/75516/1/j.1076-7460.2005.04503.x.pd