Expression Pattern of Purinergic Signaling Components in Colorectal Cancer Cells and Differential Cellular Outcomes Induced by Extracellular ATP and Adenosine

The purine nucleotide adenosine triphosphate (ATP) is known for its fundamental role in cellular bioenergetics. However, in the last decades, different works have described emerging functions for ATP, such as that of a danger signaling molecule acting in the extracellular space on both tumor and stromal compartments. Beside its role in immune cell signaling, several studies have shown that high concentrations of extracellular ATP can directly or indirectly act on cancer cells. Accordingly, it has been reported that purinergic receptors are widely expressed in tumor cells. However, their expression pattern is often associated with contradictory cellular outcomes. In this work, we first investigated gene expression profiles through “RNA-Sequencing” (RNA Seq) technology in four colorectal cancer (CRC) cell lines (HT29, LS513, LS174T, HCT116). Our results demonstrate that CRC cells mostly express the A2B, P2X4, P2Y1, P2Y2 and P2Y11 purinergic receptors. Among these, the P2Y1 and P2Y2 coding genes are markedly overexpressed in all CRC cells compared to the HCEC-1CT normal-like colonic cells. We then explored the cellular outcomes induced by extracellular ATP and adenosine. Our results show that in terms of cell death induction extracellular ATP is consistently more active than adenosine against CRC, while neither compound affected normal-like colonic cell survival. Intriguingly, while for the P2Y2 receptor pharmacological inhibition completely abolished the rise in cytoplasmic Ca²⁺ observed after ATP exposure in all CRC cell lines, Ca²⁺ mobilization only impacted the cellular outcome for HT29. In contrast, non-selective phosphodiesterase inhibition completely abolished the effects of extracellular ATP on CRC cells, suggesting that cAMP and/or cGMP levels might determine cellular outcome. Altogether, our study provides novel insights into the characterization of purinergic signaling in CRC.     

Automatic boosting of cross-product coverage using Bayesian networks

Closing the feedback loop from coverage data to the stimuli generator is one of the main challenges in the verification process. Typically, verification engineers with deep domain knowledge manually prepare a set of stimuli generation directives for that purpose. Bayesian networks based CDG (coverage directed generation) systems have been successfully used to assist the process by automatically closing this feedback loop. However, constructing these CDG systems requires manual effort and a certain amount of domain knowledge from a machine learning specialist. We propose a new method that boosts coverage in the early stages of the verification process with minimal effort, namely a fully automatic construction of a CDG system that requires no domain knowledge. Experimental results on a real-life cross-product coverage model demonstrate the efficiency of the proposed method.     

Underwater light polarization and radiance fluctuations induced by surface waves

The underwater light field is an ever-changing environment. Surface waves induce variability in the radiance and the light's polarization. We examined the dependence of the polarization fluctuations associated with diffuse light (not including contribution from direct skylight) on the viewing zenith angle (30 degrees, 70 degrees, and 90 degrees), solar zenith angle (23 degrees -72 degrees), depth of 0.5-3 m, and light wavelength (380-650 nm) while observing within the azimuthal plane in the wind-wave direction. Polarization and radiance fluctuated with time. Light variability (presented by the coefficient of variation calculated over a series of fluctuations in the radiance and percent polarization, and by the standard deviation calculated over a series of fluctuations in the e-vector orientation) was highest at a viewing zenith angle of 70 degrees , depended positively on the solar zenith angle, and decreased with depth at viewing zenith angles of 30 degrees and 70 degrees . Additionally, the variability of the percent polarization was significantly higher than that of the radiance. The temporal light fluctuations offer possibilities, such as enhancing the detection of transparent and reflecting objects; however, they set constraints on the optimal underwater polarization vision by both animals and by the use of instruments.     

并联电池模块用磁性元件优化设计

为了有效地利用有限的占用蓄电池电能,直流变换模块的电能转换效率成为直流模块认证的关键参数之一。直流模块的能量损耗主要集中在功率开关管、磁性元件、整流管三方面。其中磁性元件的设计方式决定了变换器的转换效率,在提高模块效率方面起着关键作用。本文从磁性器件的几个关键设计点进行分析,实现了变换器的最高电能转换效率。     

Modeling the effect of immobilization of microorganisms on the rate of biodegradation of phenol under inhibitory conditions

The main objective of this research is to model the effect of biodegradation process of phenol at high initial concentrations using a well known immobilization technique of the biomass. This work focused on testing the effect of activated carbon and clay while considering the diffusive internal mass transfer limitations. Biodegradation of phenol was performed by using enriched microorganisms from a compost of agricultural wastes. The average phenol biodegradation rate (uptake) of free biomass system was 235.3 mg g⁻¹ h⁻¹ at initial concentration range of 212-260 mg/L. However, the values for the systems of immobilized biomass in alginate and activated carbon (1 mm), alginate, activated carbon (4 mm), alginate, activated carbon and clay (1 mm) and alginate, activated carbon and clay (4 mm) were 64.9, 27.6, 27.5, and 8 mg g⁻¹ h⁻¹ respectively. The effective diffusion factors in different matrix were obtained using an intra-particle diffusion-based mathematical model. Diffusion limitation was observed when the matrix contained clay in addition to activated carbon. The diffusion coefficient was decreased from 1.6 × 10⁻⁸ to 1.2 × 10⁻⁹ cm²/s when clay was added to the matrix of 1 mm of alginate and activated carbon. Also, slight differences between the diffusion factors were observed for larger beads. The combination of clay and AC contributes to better mineralization of phenol at high concentrations. This could be attributed to the synergism of both additives.     

Approaches to AI methodology in reinforced concrete structures: a case study

A system developed earlier for reinforced concrete halls layout is studied and justified from different aspects. Also, updates of both capabilities and limitations, which were recently incorporated in the system, are emphasized. Some of the original aspects of the system were reviewed and modified, to overcome some of the drawbacks in the earlier presentation. The system's organization remains almost the same. Some suggestions for further improvements to the system are presented as well. Finally the function of the system is illustrated by a sample run of the system.     

Online Learning versus Offline Learning

We present an off-line variant of the mistake-bound model of learning. This is an intermediate model between the on-line learning model (Littlestone, 1988, Littlestone, 1989) and the self-directed learning model (Goldman, Rivest & Schapire, 1993, Goldman & Sloan, 1994). Just like in the other two models, a learner in the off-line model has to learn an unknown concept from a sequence of elements of the instance space on which it makes guess and test trials. In all models, the aim of the learner is to make as few mistakes as possible. The difference between the models is that, while in the on-line model only the set of possible elements is known, in the off-line model the sequence of elements (i.e., the identity of the elements as well as the order in which they are to be presented) is known to the learner in advance. On the other hand, the learner is weaker than the self-directed learner, which is allowed to choose adaptively the sequence of elements presented to him.We study some of the fundamental properties of the off-line model. In particular, we compare the number of mistakes made by the off-line learner on certain concept classes to those made by the on-line and self-directed learners. We give bounds on the possible gaps between the various models and show examples that prove that our bounds are tight.Another contribution of this paper is the extension of the combinatorial tool of labeled trees to a unified approach that captures the various mistake bound measures of all the models discussed. We believe that this tool will prove to be useful for further study of models of incremental learning.     

Infinitesimal displacement analysis of a parallel manipulator with circular guide via the differentiation of constraint equations

This paper considers several subproblems associated with solving the forward and inverse position and speed problems and defining singular configurations for a parallel robot with six degrees of freedom and six kinematic chains that move along a circular guide (rotopod). The solution of these subproblems is necessary for analyzing the topological features of the parallel robot and employing the results of such analysis in control algorithms design.     

Novel acrylic pressure-sensitive adhesive (PSA) containing silver particles

Acrylic pressure-sensitive adhesives (PSAs) are generally considered as nonelectrically conductive materials. The electrical conductivity is incorporated into acrylic polymer after blending with electrical conductive additives like silver particles. After the addition of electrically conductive silver filler, the main and typical properties of PSAs such as tack, peel adhesion, and shear strength will decline. This study is the first trial which reveals that the acrylic self-adhesive basis must be synthesized with ameliorated initial performances like high tack and excellent adhesion. Currently, the electrically conductive solvent-borne acrylic PSA containing silver fillers are not commercially available on the market. They are promising materials which can be applied for the manufacturing of diverse technical or medical high performance self-adhesive products, such as broadest line of special electrically conductive sensitive tapes.     

SNAP-Tag-Targeted MRI-Fluorescent Multimodal Probes

Magnetic resonance imaging (MRI) is a powerful imaging modality, widely employed in research and clinical settings. However, MRI images suffer from low signals and a lack of target specificity. We aimed to develop a multimodal imaging probe to detect targeted cells by MRI and fluorescence microscopy. We synthesized a trifunctional imaging probe consisting of a SNAP-tag substrate for irreversible and specific labelling of cells, cyanine dyes for bright fluorescence, and a chelated Gd^III molecule for enhancing MRI contrast. Our probes exhibit specific and efficient labelling of genetically defined cells (expressing SNAP-tag at their membrane), bright fluorescence and MRI signal. Our synthetic approach provides a versatile platform for the production of multimodal imaging probes, particularly for light microscopy and MRI.