Serum cytokine profiles in healthy young and elderly population assessed using multiplexed bead-based immunoassays

<p>Abstract</p> <p>Background</p> <p>Lipid metabolites and cytokines, including chemokines and growth factors, are the key regulators of immune cell function and differentiation, and thus, dysregulation of these regulators is associated with various human diseases. However, previous studies demonstrating a positive correlation of cytokine levels with aging may have been influenced by various environmental factors and underlying diseases. Also, data regarding cytokine profiling in the elderly are limited to a small subset of cytokines.</p> <p>Methods</p> <p>We compared the profiles of 22 cytokines, including chemokines and growth factors, in a case-controlled study group of a gender-matched, healthy cohort of 55 patients over the age of 65 and 55 patients under the age of 45. Assessment of serum cytokine concentrations was performed using commercially-available multiplex bead-based sandwich immunoassays.</p> <p>Results</p> <p>Soluble CD40 ligand (sCD40L) and transforming growth factor alpha (TGF-α) levels were significantly higher in the elderly patients, whereas granulocyte colony-stimulating factor (G-CSF), granulocyte-monocyte colony-stimulating factor (GM-CSF), and monocyte chemoattractant protein-1 (MCP-1) levels were significantly lower in the elderly patients. The partial correlation analysis demonstrating the correlation between cytokine levels when controlled for gender, systolic blood pressure, total cholesterol, HDL cholesterol, triglyceride, and serum creatinine levels further demonstrated that G-CSF, GM-CSF, and MCP-1 had significant negative correlations with age, whereas sCD40L and TGF-α had significant positive correlations.</p> <p>Conclusions</p> <p>Future studies will focus on examining the significance of these age-related changes in circulating cytokines and other biological markers and their potential contribution to the development of different age-associated diseases.</p

Rapid exploration with multi-rotors: A frontier selection method for high speed flight

Exploring and mapping previously unknown environments while avoiding collisions with obstacles is a fundamental task for autonomous robots. In scenarios where this needs to be done rapidly, multi-rotors are a good choice for the task, as they can cover ground at potentially very high velocities. Flying at high velocities, however, implies the ability to rapidly plan trajectories and to react to new information quickly. In this paper, we propose an extension to classical frontier -based exploration that facilitates exploration at high speeds. The extension consists of a reactive mode in which the multi-rotor rapidly selects a goal frontier from its field of view. The goal frontier is selected in a way that minimizes the change in velocity necessary to reach it. While this approach can increase the total path length, it significantly reduces the exploration time, since the multi-rotor can fly at consistently higher speeds

Experimental demonstration of decoherence suppression via quantum measurement reversal

Taming decoherence is essential in realizing quantum computation and quantum communication. Here we experimentally demonstrate that decoherence due to amplitude damping can be suppressed by exploiting quantum measurement reversal in which a weak measurement and the reversing measurement are introduced before and after the decoherence channel, respectively. We have also investigated the trade-off relation between the degree of decoherence suppression and the channel transmittance

Fault Log Recovery Using an Incomplete-data-trained FDA Classifier for Failure Diagnosis of Engineered Systems

In the 2015 PHM Data Challenge Competition, the goal of the competition problem was to diagnose failure of industrial plant systems using incomplete data. The available data consisted of sensor measurements, control reference signals, and fault logs. A detailed description of the plant system of interest was not revealed, and partial fault logs were eliminated from the dataset. This paper presents a fault log recovery method using a machine-learning-based fault classification approach for failure diagnosis. For optimal performance, it was critical to be able to utilize a set of incomplete data and to select relevant features. First, physical interpretation of the given data was performed to select proper features for a fault classifier. Second, Fisher discriminant analysis (FDA) was employed to minimize the effect of outliers in the incomplete data sets. Finally, the type of the missing fault logs and the duration of the corresponding faults were recovered. The proposed approach, based on the use of an incomplete-data-trained FDA classifier, led to the second-highest score in the 2015 PHM Data Challenge Competition