Age-dependent changes in rat lacrimal gland anti-oxidant and vesicular related protein expression profiles

Purpose: Anti-oxidation and exocytosis are important for maintaining exocrine tissue homeostasis. During aging, functional and structural alterations occur in the lacrimal gland (LG), including oxidative damage to proteins, lipids, and DNA. The aims of the present study were to determine in the aging LG: a) the effects of aging on LG structure and secretory activity and b) changes in the expression of oxidative stress markers. Methods: To address these goals, tear secretion composition and corneal impression cytology were compared between male Wistar rats of 2 (control) and 24 (aged) months. LG morphology and the expression levels of vitamin E and malonaldehyde (MDA) were evaluated to determine the anti-oxidant activity and lipid peroxidation, respectively. RT-PCR and western blot analysis were used for the analysis of Ras related in brain GTPase protein (Rab) and soluble N-ethylmaleimide-sensitive factor attachment protein receptor (SNARE) proteins of the secretory machinery (i.e.; Rab 3d, Rab 27, vesicle-associated membrane protein-2 (Vamp-2), and syntaxin). Results: Histological analysis of aged rats revealed a higher frequency of corneal epithelia metaplasia. In the acinar cells, organelles underwent degeneration, and lipofucsin-like material accumulated in the cytoplasm along with declines in the anti-oxidant marker vitamin E. Rab3d and Rab27b mRNA levels fell along with Rab3d protein expression, whereas syntaxin levels increased. Conclusions: These findings indicate that exocytotic and anti-oxidant mechanisms become impaired with age in the rat LG. In parallel with these structural alterations, functional declines may contribute to the pathophysiology caused by tear film modification in dry eye disease.

Decreasing the number of false positives in sequence classification

Abstract Background A large number of probabilistic models used in sequence analysis assign non-zero probability values to most input sequences. To decide when a given probability is sufficient the most common way is bayesian binary classification, where the probability of the model characterizing the sequence family of interest is compared to that of an alternative probability model. We can use as alternative model a null model. This is the scoring technique used by sequence analysis tools such as HMMER, SAM and INFERNAL. The most prevalent null models are position-independent residue distributions that include: the uniform distribution, genomic distribution, family-specific distribution and the target sequence distribution. This paper presents a study to evaluate the impact of the choice of a null model in the final result of classifications. In particular, we are interested in minimizing the number of false predictions in a classification. This is a crucial issue to reduce costs of biological validation. Results For all the tests, the target null model presented the lowest number of false positives, when using random sequences as a test. The study was performed in DNA sequences using GC content as the measure of content bias, but the results should be valid also for protein sequences. To broaden the application of the results, the study was performed using randomly generated sequences. Previous studies were performed on aminoacid sequences, using only one probabilistic model (HMM) and on a specific benchmark, and lack more general conclusions about the performance of null models. Finally, a benchmark test with P. falciparum confirmed these results. Conclusions Of the evaluated models the best suited for classification are the uniform model and the target model. However, the use of the uniform model presents a GC bias that can cause more false positives for candidate sequences with extreme compositional bias, a characteristic not described in previous studies. In these cases the target model is more dependable for biological validation due to its higher specificity.

Effects of RegCM3 parameterizations on simulated rainy season over South America

The impacts of change in the Grell convective scheme and biosphere-atmosphere transfer scheme (BATS) in RegCM3 are described. Three numerical experiments (RegZhang, RegClaris and RegArain) are conducted to reduce the RegCM3-Grell rainfall underestimation over tropical South America. The simulation referred to as RegZhang follows modifications made by Zhang et al. (2008) in the BATS. The RegClaris combines the RegZhang BATS parameters with a reduction of water drainage at the bottom of the subsoil layer in the regions covered by the tropical rain forest and a shorter convective time period for the Grell scheme. The RegArain considers this same modification in the Grell scheme, but uses a deeper total soil column and a deeper root system in the BATS. After the first year of simulation, the soil water content in RegZhang is progressively drained out of the soil column resulting in a deficit of rainfall in the Amazon. The RegClaris and RegArain, on the other hand, simulate a similar rainfall annual cycle in the Amazon, showing substantial improvement not only in phase but also in intensity. This improvement is partially related to an increase in evapotranspiration due to a larger availability of water in the soil column. A remote effect is also noted over the La Plata Basin region, where the larger summer rainfall rate may be related to the increase in moisture transport from the Amazon. Wind- and rainfall-based indices are applied to identify South American monsoon (SAM) timing. The RegClaris rainfall rates are adequate to identify the onset and the demise of SAM according to the observed data, whereas the rainfall deficit in RegZhang is associated with a delay in the onset and an early demise of the SAM.

Bone tissue, cellular, and molecular responses to titanium implants treated by anodic spark deposition

A myriad of titanium (Ti) surface modifications has been proposed to hasten the osseointegration. In this context, the aim of this study was to perform histomorphometric, cellular, and molecular analyses of the bone tissue grown in close contact with Ti implants treated by anodic spark deposition (ASD-AK). Acid-etched (AE) Ti implants either untreated or submitted to ASD-AK were placed into dog mandibles and retrieved at 3 and 8 weeks. It was noticed that both implants, AE and ASD-AK, were osseointegrated at 3 and 8 weeks. Histomorphometric analysis showed differences between treatments only for bone-to-implant contact, being higher on AE implants. Although not backed by histomorphometric results, gene expression of key bone markers was higher for bone grown in close contact with ASD-AK and for cells harvested from these fragments and cultured until subconfluence. Cell proliferation at days 7 and 10 and alkaline phosphatase activity at day 10 was higher on AE surfaces. No statistical significant difference was noticed for extracellular matrix mineralization at 17 days. Our results have shown that the Ti fixtures treated by ASD-AK allowed in vivo osseointegration and induced higher expression of key markers of osteoblast phenotype, suggesting that this surface treatment could be considered to produce implants for clinical applications. (c) 2012 Wiley Periodicals, Inc. J Biomed Mater Res Part A: 100A:30923098, 2012.

Nuclear-modification factor for open-heavy-flavor production at forward rapidity in Cu plus Cu collisions at root s(NN)=200 GeV

Background: Heavy-flavor production in p + p collisions is a good test of perturbative-quantum-chromodynamics (pQCD) calculations. Modification of heavy-flavor production in heavy-ion collisions relative to binary-collision scaling from p + p results, quantified with the nuclear-modification factor (R-AA), provides information on both cold-and hot-nuclear-matter effects. Midrapidity heavy-flavor R-AA measurements at the Relativistic Heavy Ion Collider have challenged parton-energy-loss models and resulted in upper limits on the viscosity-entropy ratio that are near the quantum lower bound. Such measurements have not been made in the forward-rapidity region. Purpose: Determine transverse-momentum (p(T)) spectra and the corresponding R-AA for muons from heavy-flavor meson decay in p + p and Cu + Cu collisions at root s(NN) = 200 GeV and y = 1.65. Method: Results are obtained using the semileptonic decay of heavy-flavor mesons into negative muons. The PHENIX muon-arm spectrometers measure the p(T) spectra of inclusive muon candidates. Backgrounds, primarily due to light hadrons, are determined with a Monte Carlo calculation using a set of input hadron distributions tuned to match measured-hadron distributions in the same detector and statistically subtracted. Results: The charm-production cross section in p + p collisions at root s = 200 GeV, integrated over p(T) and in the rapidity range 1.4 < y < 1.9, is found to be d(sigma e (e) over bar)/dy = 0.139 +/- 0.029 (stat)(-0.058)(+0.051) (syst) mb. This result is consistent with a perturbative fixed-order-plus-next-to-leading-log calculation within scale uncertainties and is also consistent with expectations based on the corresponding midrapidity charm-production cross section measured by PHENIX. The R-AA for heavy-flavor muons in Cu + Cu collisions is measured in three centrality bins for 1 < p(T) < 4 GeV/c. Suppression relative to binary-collision scaling (R-AA < 1) increases with centrality. Conclusions: Within experimental and theoretical uncertainties, the measured charm yield in p + p collisions is consistent with state-of-the-art pQCD calculations. Suppression in central Cu + Cu collisions suggests the presence of significant cold-nuclear-matter effects and final-state energy loss.

Surface modification by metal ion implantation forming metallic nanoparticles in insulating matrix.

There is special interest in the incorporation of metallic nanoparticles in a surrounding dielectric matrix for obtaining composites with desirable characteristics such as for surface plasmon resonance, which can be used in photonics and sensing, and controlled surface electrical conductivity. We investigated nanocomposites produced through metallic ion implantation in insulating substrate, where the implanted metal self-assembles into nanoparticles. During the implantation, the excess of metal atom concentration above the solubility limit leads to nucleation and growth of metal nanoparticles, driven by the temperature and temperature gradients within the implanted sample including the beam-induced thermal characteristics. The nanoparticles nucleate near the maximum of the implantation depth profile (projected range), that can be estimated by computer simulation using the TRIDYN. This is a Monte Carlo simulation program based on the TRIM (Transport and Range of Ions in Matter) code that takes into account compositional changes in the substrate due to two factors: previously implanted dopant atoms, and sputtering of the substrate surface. Our study suggests that the nanoparticles form a bidimentional array buried few nanometers below the substrate surface. More specifically we have studied Au/PMMA (polymethylmethacrylate), Pt/PMMA, Ti/alumina and Au/alumina systems. Transmission electron microscopy of the implanted samples showed the metallic nanoparticles formed in the insulating matrix. The nanocomposites were characterized by measuring the resistivity of the composite layer as function of the dose implanted. These experimental results were compared with a model based on percolation theory, in which electron transport through the composite is explained by conduction through a random resistor network formed by the metallic nanoparticles. Excellent agreement was found between the experimental results and the predictions of the theory. It was possible to conclude, in all cases, that the conductivity process is due only to percolation (when the conducting elements are in geometric contact) and that the contribution from tunneling conduction is negligible.