Heterogeneous Off-Target Effects of Ultra-Low Dose Dimethyl Sulfoxide (DMSO) on Targetable Signaling Events in Lung Cancer In Vitro Models

Targetable alterations in cancer offer novel opportunities to the drug discovery process. However, pre-clinical testing often requires solubilization of these drugs in cosolvents like dimethyl sulfoxide (DMSO). Using a panel of cell lines commonly used for in vitro drug screening and pre-clinical testing, we explored the DMSO off-target effects on functional signaling networks, drug targets, and downstream substrates. Eight Non-Small Cell Lung Cancer (NSCLC) cell lines were incubated with three concentrations of DMSO (0.0008%, 0.002%, and 0.004% v/v) over time. Expression and activation levels of 187 proteins, of which 137 were kinases and downstream substrates, were captured using the Reverse Phase Protein Array (RPPA). The DMSO effect was heterogeneous across cell lines and varied based on concentration, exposure time, and cell line. Of the 187 proteins measured, all were statistically different in at least one comparison at the highest DMSO concentration, followed by 99.5% and 98.9% at lower concentrations. Only 46% of the proteins were found to be statistically different in more than 5 cell lines, indicating heterogeneous response across models. These cell line specific alterations modulate response to in vitro drug screening. Ultra-low DMSO concentrations have broad and heterogeneous effects on targetable signaling proteins. Off-target effects need to be carefully evaluated in pre-clinical drug screening and testing.     

A Memory Reliability Enhancement Technique for Multi Bit Upsets

Technological advances allow the production of increasingly complex electronic systems. Nevertheless, technology and voltage scaling increased dramatically the susceptibility of new devices not only to Single Bit Upsets (SBU), but also to Multiple Bit Upsets (MBU). In safety critical applications, it is mandatory to provide fault-tolerant systems, providing high reliability while meeting applications requirements. The problem of reliability is particularly expressed within the memory which represents more than 80 % of systems on chips. To tackle this problem we propose a new memory reliability techniques referred to as DPSR: Double Parity Single Redundancy. DPSR is designed to enhance computing systems resilience to SBU and MBU. Based on a thorough fault injection experiments, DPSR shows promising results; It detects and corrects more than 99.6 % of encountered MBU and has an average time overhead of less than 3 %.

     

Multivariate Data Analysis of Fatty Acid Content in the Classification of Olive Oils Developed Through Controlled Crossbreeding

The fatty acid (FA) composition of 540 Tunisian virgin olive oil hybrids (VOO) were classified by principal component analysis (PCA). Pearson correlation between FA variables revealed an inverse association between C18:1 and C18:2; C18:1 and C16:0, while C16:0 and C16:1 were positively correlated. PCA yielded five significant PCs, which together account for 79.95% of the total variance; with PC1 contributing 36.84% of the total. Eigenvalue analysis revealed that PC1 was mainly attributed to C18:1, monounsaturated fatty acids (MUFA) and the ratios oleic/linoleic (O/L) and monounsaturated fatty acids/polyunsaturated fatty acids (MUFA/PUFA); PC2, by C16:0, saturated fatty acids (SFA) and the palmitic/linoleic ratio (P/L); PC3 by C18:2 and C22:0, PC4 by C18:0 and PC5, by C17:1. Then, PCA analysis indicated that in addition to C16:0, C18:0, C18:1, C17:1, and C22:0, MUFA, SFA and the ratios O/L, P/L and MUFA/PUFA were determined to be the main factors responsible for the olive oil hybrids discrimination.     

A blind multichannel identification algorithm robust to orderoverestimation

Active research in blind single input multiple output (SIMO) channel identification has led to a variety of second-order statistics-based algorithms, particularly the subspace (SS) and the linear prediction (LP) approaches. The SS algorithm shows good performance when the channel output is corrupted by noise and available for a finite time duration. However, its performance is subject to exact knowledge of the channel order, which is not guaranteed by current order detection techniques. On the other hand, the linear prediction algorithm is sensitive to observation noise, whereas its robustness to channel order overestimation is not always verified when the channel statistics are estimated. We propose a new second-order statistics-based blind channel identification algorithm that is truly robust to channel order overestimation, i.e., it is able to accurately estimate the channel impulse response from a finite number of noisy channel measurements when the assumed order is arbitrarily greater than the exact channel order. Another interesting feature is that the identification performance can be enhanced by increasing a certain smoothing factor. Moreover, the proposed algorithm proves to clearly outperform the LP algorithm. These facts are justified theoretically and verified through simulations     

A numerical study of the scalar field in turbulent round jet with co-flowing stream

The influence of co-flow on a turbulent binary gas mixing round jet is numerically studied using a first and a second order turbulence closure models. The objective of the study is to obtain a better understanding of the flow structure and mixing process in turbulent variable-density jets. Comparisons between recently published experimental results and mean mixture fraction, the scalar turbulent fluctuation, and the jet spreading rate, feature reasonably good agreements. It is mainly shown that the co-flow reduces the jet spreading rate, but on the otherhand increases the mixing efficiency.An erratum to this article can be found at