Image analysis for the quantitative comparison of stress fibers and focal adhesions

Actin stress fibers (SFs) detect and transmit forces to the extracellular matrix through focal adhesions (FAs), and molecules in this pathway determine cellular behavior. Here, we designed two different computational tools to quantify actin SFs and the distribution of actin cytoskeletal proteins within a normalized cellular morphology. Moreover, a systematic cell response comparison between the control cells and those with impaired actin cytoskeleton polymerization was performed to demonstrate the reliability of the tools. Indeed, a variety of proteins that were present within the string beginning at the focal adhesions (vinculin) up to the actin SFs contraction (non-muscle myosin II (NMMII)) were analyzed. Finally, the software used allows for the quantification of the SFs based on the relative positions of FAs. Therefore, it provides a better insight into the cell mechanics and broadens the knowledge of the nature of SFs

Offensive Language Detection in Spanish Social Media: Testing From Bag-of-Words to Transformers Models

Social networks allow us to communicate with people around the world. However, some users usually take advantage of anonymity for writing offensive comments to others, which might affect those who receive offensive messages or discourage the use of these networks. However, it is impossible to manually check every message. This has promoted several proposals for automatic detection systems. Current state-of-the-art systems are based on the transformers’ architecture and most of the work has been focused on the English language. However, these systems do not pay too much attention to the unbalanced nature of data, since there are fewer offensive comments than non-offensive in a real environment. Besides, these previous works have not studied the impact on the final results of pre-processing or the corpora used for pre-training the models. In this work, we propose and evaluate a series of automatic methods aimed at detecting offensive language in Spanish texts addressing the unbalanced nature of data. We test different learning models, from those based on classical Machine Learning algorithms using Bag-of-Words as data representation to those based in large language models and neural networks such as transformers, paying more attention to minor classes and the corpora used for pre-training the transformer-based models. We show how transformer-based models continue obtaining the best results, but we improved previous results by a 6,2% by adding new steps of pre-processing and using models pre-trained with Spanish social-media data, setting new state-of-the-art results

Collagen fibre orientation in human bridging veins

Bridging veins (BVs) drain the blood from the cerebral cortex into dural sinuses. BVs have one end attached to the brain and the other to the superior sagittal sinus (SSS), which is attached to the skull. Relative movement between these two structures can cause BV to rupture producing acute subdural haematoma, a head injury with a mortality rate between 30 and 90%. A clear understanding of the BVs microstructure is required to increase the biofidelity of BV models when simulating head impacts. Twelve fresh BV samples draining in the superior sagittal sinus (SSS) from a single human cadaver were cut open along their length and placed on an inverted multiphoton microscope. To ensure that the BVs were aligned with the axial direction an in-house built, uniaxial tension set-up was used. Two scans were performed per sample. Before the first scan, a minor displacement was applied to align the tissue; then, a second scan was taken applying 50% strain. Each BV was scanned for a length of 5 mm starting from the drainage site into the SSS. Imaging was performed on a Zeiss LSM780 microscope with an 25[Formula: see text] water immersion objective (NA 0.8), coupled to a tunable MaiTai DS (Spectraphysics) pulsed laser with the wavelength set at 850 nm. Second harmonic and fluorescence signals were captured in forward and backward direction on binary GaAsP (BiG) detectors and stored as four colour Z-stacks. Prior to the calculation of the local orientations, acquired Z-stacks were denoised and enhanced to highlight fibrillar structures from the background. Then, for each Z-plane of the stack, the ImageJ plugin OrientationJ was used to extract the local 2D orientations of the fibres based on structure tensors. Two kinds of collagen architectures were seen. The most common (8[Formula: see text]12 samples) was single layered and had a uniform distribution of collagen. The less common (4[Formula: see text]12 samples) had 2 layers and 7 to 34 times thicker collagen bundles on the outer layer. Fibre angle analysis showed that collagen was oriented mainly along the axial direction of the vessel. The von Mises fittings showed that in order to describe the fibre distribution 3 components were needed with mean angles [Formula: see text] at [Formula: see text] 0.35, 0.21, [Formula: see text] 0.02 rad or [Formula: see text] 20.2[Formula: see text], 12.1[Formula: see text], [Formula: see text] 1.2[Formula: see text] relative to the vessel's axial direction which was also the horizontal scan direction.status: publishe

3D full-field quantification of cell-induced large deformations in fibrillar biomaterials by combining non-rigid image registration with label-free second harmonic generation

To advance our current understanding of cell-matrix mechanics and its importance for biomaterials development, advanced three-dimensional (3D) measurement techniques are necessary. Cell-induced deformations of the surrounding matrix are commonly derived from the displacement of embedded fiducial markers, as part of traction force microscopy (TFM) procedures. However, these fluorescent markers may alter the mechanical properties of the matrix or can be taken up by the embedded cells, and therefore influence cellular behavior and fate. In addition, the currently developed methods for calculating cell-induced deformations are generally limited to relatively small deformations, with displacement magnitudes and strains typically of the order of a few microns and less than 10% respectively. Yet, large, complex deformation fields can be expected from cells exerting tractions in fibrillar biomaterials, like collagen. To circumvent these hurdles, we present a technique for the 3D full-field quantification of large cell-generated deformations in collagen, without the need of fiducial markers. We applied non-rigid, Free Form Deformation (FFD)-based image registration to compute full-field displacements induced by MRC-5 human lung fibroblasts in a collagen type I hydrogel by solely relying on second harmonic generation (SHG) from the collagen fibrils. By executing comparative experiments, we show that comparable displacement fields can be derived from both fibrils and fluorescent beads. SHG-based fibril imaging can circumvent all described disadvantages of using fiducial markers. This approach allows measuring 3D full-field deformations under large displacement (of the order of 10 μm) and strain regimes (up to 40%). As such, it holds great promise for the study of large cell-induced deformations as an inherent component of cell-biomaterial interactions and cell-mediated biomaterial remodeling.publisher: Elsevier articletitle: 3D full-field quantification of cell-induced large deformations in fibrillar biomaterials by combining non-rigid image registration with label-free second harmonic generation journaltitle: Biomaterials articlelink: http://dx.doi.org/10.1016/j.biomaterials.2017.05.015 content_type: article copyright: © 2017 Elsevier Ltd. All rights reserved.status: publishe