Time- and Space-Resolved Flow-Cytometry of Cell Organelles to Quantify Nanoparticle Uptake and Intracellular Trafficking by Cells

The design of targeted nanomedicines requires intracellular space- and time-resolved data of nanoparticle distribution following uptake. Current methods to study intracellular trafficking, such as dynamic colocalization by fluorescence microscopy in live cells, are usually low throughput and require extensive analysis of large datasets to quantify colocalization in several individual cells. Here a method based on flow cytometry to easily detect and characterize the organelles in which nanoparticles are internalized and trafficked over time is proposed. Conventional cell fractionation methods are combined with immunostaining and high-sensitivity organelle flow cytometry to get space-resolved data of nanoparticle intracellular distribution. By extracting the organelles at different times, time-resolved data of nanoparticle intracellular trafficking are obtained. The method is validated by determining how nanoparticle size affects the kinetics of arrival to the lysosomes. The results demonstrate that this method allows high-throughput analysis of nanoparticle uptake and intracellular trafficking by cells, therefore it can be used to determine how nanoparticle design affects their intracellular behavior

PairWise Neighbours database: overlaps and spacers among prokaryote genomes

<p>Abstract</p> <p>Background</p> <p>Although prokaryotes live in a variety of habitats and possess different metabolic and genomic complexity, they have several genomic architectural features in common. The overlapping genes are a common feature of the prokaryote genomes. The overlapping lengths tend to be short because as the overlaps become longer they have more risk of deleterious mutations. The spacers between genes tend to be short too because of the tendency to reduce the non coding DNA among prokaryotes. However they must be long enough to maintain essential regulatory signals such as the Shine-Dalgarno (SD) sequence, which is responsible of an efficient translation.</p> <p>Description</p> <p>PairWise Neighbours is an interactive and intuitive database used for retrieving information about the spacers and overlapping genes among bacterial and archaeal genomes. It contains 1,956,294 gene pairs from 678 fully sequenced prokaryote genomes and is freely available at the URL <url>http://genomes.urv.cat/pwneigh</url>. This database provides information about the overlaps and their conservation across species. Furthermore, it allows the wide analysis of the intergenic regions providing useful information such as the location and strength of the SD sequence.</p> <p>Conclusion</p> <p>There are experiments and bioinformatic analysis that rely on correct annotations of the initiation site. Therefore, a database that studies the overlaps and spacers among prokaryotes appears to be desirable. PairWise Neighbours database permits the reliability analysis of the overlapping structures and the study of the SD presence and location among the adjacent genes, which may help to check the annotation of the initiation sites.</p

3D UWB tomography for medical imaging applications

In this paper, a novel 3D UWB tomographic algorithm has been presented for brain hemorrhagic stroke detection. While 2D reconstructions are unable to distinguish between features at different heights, 3D reconstructions allow to obtain a complete representation of the brain and thus to differentiate them. A human brain model has been successfully reconstructed and the position of a blood vessel detected.Peer ReviewedPostprint (published version

Some issues when using Fourier analysis for the extraction of modal parameters

It is sometimes necessary to determine the manner in which structures deteriorate with respect to time; for instance when quantifying a material's ability to withstand sustained dynamic loads. In such cases, it is well established that loss of structural integrity is reflected by variations in modal characteristics such as stiffness. This paper addresses some practical limitations of Fourier analysis with respect to temporal resolution and the uncertainties associated with extracting variations in modal parameters. The statistical analysis of numerous numerical experiments shows how techniques, such as data overlapping and zero-padding, can be used to improve the sensitivity of modal parameter extraction

Preliminary phantom-based dynamic calibration techniques assessment for microwave colonoscopy systems

Early detection and resection of colon polyp is the best way to reduce colorectal cancer (CRC) mortality. The current method for early detection is colonoscopy, which has a limited field of view, and its efficacy is highly dependant on the endoscopist's experience and colon preparation. This work presents a device for combining microwave imaging with optical colonoscopy. The challenges of this new microwave imaging system are presented, such as the unknown distance to the colon mucosa, which leads to undesired scattered fields and, the antenna size limitations. Four dynamic calibration techniques are proposed to remove the effects of the undefined distance from the imaging region to colon mucosa. These calibration methods are based on averaging the colonoscopy trajectory frames and subtracting the calibration set from the current frame. The phantom preliminary results show that these calibration methods completely delete the undesired scatter.A.G. acknowledges the financial support from DIN2019- 010857, M.G., and W.D acknowledge the financial support from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 960251 and from the European Institute of Innovation and Technology (EIT). J.R. acknowledges the financial support from Agencia Estatal Investigacion PID2019-107885GB-C31/AEI/10.13039/.Peer ReviewedPostprint (author's final draft

OPTIMIZER: a web server for optimizing the codon usage of DNA sequences

OPTIMIZER is an on-line application that optimizes the codon usage of a gene to increase its expression level. Three methods of optimization are available: the ‘one amino acid–one codon’ method, a guided random method based on a Monte Carlo algorithm, and a new method designed to maximize the optimization with the fewest changes in the query sequence. One of the main features of OPTIMIZER is that it makes it possible to optimize a DNA sequence using pre-computed codon usage tables from a predicted group of highly expressed genes from more than 150 prokaryotic species under strong translational selection. These groups of highly expressed genes have been predicted using a new iterative algorithm. In addition, users can use, as a reference set, a pre-computed table containing the mean codon usage of ribosomal protein genes and, as a novelty, the tRNA gene-copy numbers. OPTIMIZER is accessible free of charge at http://genomes.urv.es/OPTIMIZER

Identifying Polymeric Constitutive Equations for Incremental Sheet Forming Modelling

AbstractRecent publications have revealed an increasing interest in forming polymer materials using Incremental Sheet Forming. Therefore, several constitutive material models are being developed in an attempt to predict the physical response of polymeric materials during the process. This paper discuss several material models that could be used to predict experimental data collected on samples of PVC and PC subjected to simple uniaxial test performed at various temperatures and testing speeds. The results have shown that the Marlow and the rule of mixture material models could be used to describe viscoelastic and softening and permanent set effects, respectively, to predict the behaviour of a part formed by Incremental Sheet Forming

On the Use of Machine Learning to Detect Shocks in Road Vehicle Vibration Signals

The characterization of transportation hazards is paramount for protective packaging validation. It is used to estimate and simulate the loads and stresses occurring during transport that are essential to optimize packaging and ensure that products will resist the transportation environment with the minimum amount of protective material. Characterizing road transportation vibrations is rather complex because of the nature of the dynamic motion produced by vehicles. For instance, different levels of vibration are induced to freight depending on the vehicle speed and the road surface; which often results in non-stationary random vibration. Road aberrations (such as cracks, potholes and speed bumps) also produce transient vibrations (shocks) that can damage products. Because shocks and random vibrations cannot be analysed with the same statistical tools, the shocks have to be separated from the underlying vibrations. Both of these dynamic loads have to be characterized separately because they have different damaging effects. This task is challenging because both types of vibration are recorded on a vehicle within the same vibration signal. This paper proposes to use machine learning to identify shocks present in acceleration signals measured on road vehicles. In this paper, a machine learning algorithm is trained to identify shocks buried within road vehicle vibration signals. These signals are artificially generated using non-stationary random vibration and shock impulses that reproduce typical vehicle dynamic behaviour. The results show that the machine learning algorithm is considerably more accurate and reliable in identifying shocks than the more common approaches based on the crest factor