Dual Phases of Respiration Chain Defect-Augmented mROS-Mediated mCa 2+

Mitochondrial respiratory chain (RC) deficits, resulting in augmented mitochondrial ROS (mROS) generation, underlie pathogenesis of astrocytes. However, mtDNA-depleted cells (ρ0) lacking RC have been reported to be either sensitive or resistant to apoptosis. In this study, we sought to determine the effects of RC-enhanced mitochondrial stress following oxidative insult. Using noninvasive fluorescence probe-coupled laser scanning imaging microscopy, the ability to resist oxidative stress and levels of mROS formation and mitochondrial calcium (mCa2+) were compared between two different astrocyte cell lines, control and ρ0 astrocytes, over time upon oxidative stress. Our results showed that the cytoplasmic membrane becomes permeated with YO-PRO-1 dye at 150 and 130 minutes in RBA-1 and ρ0 astrocytes, respectively. In contrast to RBA-1, 30 minutes after 20 mM H2O2 exposure, ρ0 astrocytes formed marked plasma membrane blebs, lost the ability to retain Mito-R, and showed condensation of nuclei. Importantly, H2O2-induced ROS and accompanied mCa2+ elevation in control showed higher levels than ρ0 at early time point but vice versa at late time point. Our findings underscore dual phase of RC-defective cells harboring less mitochondrial stress due to low RC activity during short-term oxidative stress but augmented mROS-mediated mCa2+ stress during severe oxidative insult

The Impact of Customer Profile and Customer Participation on Customer Relationship Management Performance

There are two main sources of knowledge about customers: customer profile and customer participation. The companies use information technologies to analyze the customer profiles and extract tacit knowledge about customer via customer participation. The result of this experiment demonstrates that the use of customer profile improves customers’ perception on goods quality and increase the effectiveness of Customer Relationship Management (CRM). In addition, customer participation can improve customers’ perception on goods quality and enhance performance of CRM through perceived participation. The result indicates that the customer profiles and customer participation are two crucial factors for companies to maintain customer relationship

Percutaneous Endoscopic Gastrostomy in the Enteral Feeding of the Elderly

SummaryToday we are faced with an aging society that may develop malnutrition because of dysphagia related to dementia, stroke, and malignancy seen often in the elderly. The preferred form of nutritional supplementation for this group is enteral nutrition, and the most appropriate long-term method is by use of a gastrostomy. Percutaneous endoscopic gastrostomy (PEG) was first introduced in 1980 as an alternative to the traditional operative procedure and rapidly became the preferred procedure. In geriatric patients, the principal indications are neurological dysphagia and malnutrition, related to an underlying disease or anorexia-cachexia in very elderly. PEG is contraindicated in the presence of respiratory distress, previous gastric resection, total esophageal obstruction, coagulation disorders and sepsis in the elderly. Common complications include wound infection, leakage, hemorrhage, and fistula in the general population, but aspiration pneumonia is the major case of death in this group. Risks and complications of PEG must be discussed with patients and their families; and the decision for percutaneous endoscopic gastrostomy insertion should only be made after careful consideration and discussion between managing physicians, allied health professionals, and the patient and/or family. Four ethical principles may help make feeding decisions: beneficence, non-maleficence, autonomy and justice. Attentive long-term care after tube replacement is mandatory. Acceptance of percutaneous endoscopic gastrostomy placement by patients and their families tends to increase once favorable outcomes are offered

LrrA, a novel leucine-rich repeat protein involved in cytoskeleton remodeling, is required for multicellular morphogenesis in Dictyostelium discoideum

AbstractCell sorting by differential cell adhesion and movement is a fundamental process in multicellular morphogenesis. We have identified a Dictyostelium discoideum gene encoding a novel protein, LrrA, which composes almost entirely leucine-rich repeats (LRRs) including a putative leucine zipper motif. Transcription of lrrA appeared to be developmentally regulated with robust expression during vegetative growth and early development. lrrA null cells generated by homologous recombination aggregated to form loose mounds, but subsequent morphogenesis was blocked without formation of the apical tip. The cells adhered poorly to a substratum and did not form tight cell–cell agglomerates in suspension; in addition, they were unable to polarize and exhibit chemotactic movement in the submerged aggregation and Dunn chamber chemotaxis assays. Fluorescence-conjugated phalloidin staining revealed that both vegetative and aggregation competent lrrA− cells contained numerous F-actin-enriched microspikes around the periphery of cells. Quantitative analysis of the fluorescence-stained F-actin showed that lrrA− cells exhibited a dramatically increase in F-actin as compared to the wild-type cells. When developed together with wild-type cells, lrrA− cells were unable to move to the apical tip and sorted preferentially to the rear and lower cup regions. These results indicate that LrrA involves in cytoskeleton remodeling, which is needed for normal chemotactic aggregation and efficient cell sorting during multicellular morphogenesis, particularly in the formation of apical tip

Parametric modeling of cellular state transitions as measured with flow cytometry

<p>Abstract</p> <p>Background</p> <p>Gradual or sudden transitions among different states as exhibited by cell populations in a biological sample under particular conditions or stimuli can be detected and profiled by flow cytometric time course data. Often such temporal profiles contain features due to transient states that present unique modeling challenges. These could range from asymmetric non-Gaussian distributions to outliers and tail subpopulations, which need to be modeled with precision and rigor.</p> <p>Results</p> <p>To ensure precision and rigor, we propose a parametric modeling framework StateProfiler based on finite mixtures of skew <it>t</it>-Normal distributions that are robust against non-Gaussian features caused by asymmetry and outliers in data. Further, we present in StateProfiler a new greedy EM algorithm for fast and optimal model selection. The parsimonious approach of our greedy algorithm allows us to detect the genuine dynamic variation in the key features as and when they appear in time course data. We also present a procedure to construct a well-fitted profile by merging any redundant model components in a way that minimizes change in entropy of the resulting model. This allows precise profiling of unusually shaped distributions and less well-separated features that may appear due to cellular heterogeneity even within clonal populations.</p> <p>Conclusions</p> <p>By modeling flow cytometric data measured over time course and marker space with StateProfiler, specific parametric characteristics of cellular states can be identified. The parameters are then tested statistically for learning global and local patterns of spatio-temporal change. We applied StateProfiler to identify the temporal features of yeast cell cycle progression based on knockout of S-phase triggering cyclins Clb5 and Clb6, and then compared the S-phase delay phenotypes due to differential regulation of the two cyclins. We also used StateProfiler to construct the temporal profile of clonal divergence underlying lineage selection in mammalian hematopoietic progenitor cells.</p

Andreev bound states and π\pi -junction transition in a superconductor / quantum-dot / superconductor system

We study Andreev bound states and $\pi$-junction transition in a superconductor / quantum-dot / superconductor (S-QD-S) system by Green function method. We derive an equation to describe the Andreev bound states in S-QD-S system, and provide a unified understanding of the $\pi$-junction transition caused by three different mechanisms: (1) {\it Zeeman splitting.} For QD with two spin levels $E_{\uparrow}$ and $E_{\downarrow}$, we find that the surface of the Josephson current $I(\phi =\frac \pi 2)$ vs the configuration of $(E_{\uparrow},E_{\downarrow})$ exhibits interesting profile: a sharp peak around $E_{\uparrow}=E_{\downarrow}=0$; a positive ridge in the region of $E_{\uparrow}\cdot E_{\downarrow}>0$; and a {\em % negative}, flat, shallow plain in the region of $E_{\uparrow}\cdot E_{\downarrow}<0$. (2){\it \ Intra-dot interaction.} We deal with the intra-dot Coulomb interaction by Hartree-Fock approximation, and find that the system behaves as a $\pi$-junction when QD becomes a magnetic dot due to the interaction. The conditions for $\pi$-junction transition are also discussed. (3) {\it \ Non-equilibrium distribution.} We replace the Fermi distribution $f(\omega)$ by a non-equilibrium one $\frac 12[ f(\omega -V_c)+f(\omega +V_c)]$, and allow Zeeman splitting in QD where $$ The curves of $I(\phi =\frac \pi 2)$ vs $$ show the novel effect of interplay of non-equilibrium distribution with magnetization in QD.Comment: 18 pages, 8 figures, Late

Extending mixtures of factor models using the restricted multivariate skew-normal distribution

The mixture of factor analyzers (MFA) model provides a powerful tool for analyzing high-dimensional data as it can reduce the number of free parameters through its factor-analytic representation of the component covariance matrices. This paper extends the MFA model to incorporate a restricted version of the multivariate skew-normal distribution for the latent component factors, called mixtures of skew-normal factor analyzers (MSNFA). The proposed MSNFA model allows us to relax the need of the normality assumption for the latent factors in order to accommodate skewness in the observed data. The MSNFA model thus provides an approach to model-based density estimation and clustering of high-dimensional data exhibiting asymmetric characteristics. A computationally feasible Expectation Conditional Maximization (ECM) algorithm is developed for computing the maximum likelihood estimates of model parameters. The potential of the proposed methodology is exemplified using both real and simulated data. (C) 2015 Elsevier Inc. All rights reserved