Andres Sanz-Garcia

YAP regulates cell mechanics by controlling focal adhesion assembly.

Hippo effectors YAP/TAZ act as on–off mechanosensing switches by sensing modifications in extracellular matrix (ECM) composition and mechanics. The regulation of their activity has been described by a hierarchical model in which elements of Hippo pathway are under the control of focal adhesions (FAs). Here we unveil the molecular mechanism by which cell spreading and RhoA GTPase activity control FA formation through YAP to stabilize the anchorage of the actin cytoskeleton to the cell membrane. This mechanism requires YAP co-transcriptional function and involves the activation of genes encoding for integrins and FA docking proteins. Tuning YAP transcriptional activity leads to the modification of cell mechanics, force development and adhesion strength, and determines cell shape, migration and differentiation. These results provide new insights into the mechanism of YAP mechanosensing activity and qualify this Hippo effector as the key determinant of cell mechanics in response to ECM cues.

Nanofibrillar cellulose wound dressing in skin graft donor site treatment.

BACKGROUNDnAlthough new therapeutic approaches for burn treatment have made progress, there is still need for better methods to enhance wound healing and recovery especially in severely burned patients. Nanofibrillar cellulose (NFC) has gained attention due to its renewable nature, good biocompatibility and excellent physical properties that are of importance for a range of applications in pharmaceutical and biomedical fields. In the present study, we investigated the potential of a wood based NFC wound dressing in a clinical trial on burn patients. Previously, we have investigated NFC as a topical functionalized wound dressing that contributes to improve wound healing in mice.nnnMETHODSnWood based NFC wound dressing was tested in split-thickness skin graft donor site treatment for nine burn patients in clinical trials at Helsinki Burn Centre. NFC dressing was applied to split thickness skin graft donor sites. The dressing gradually dehydrated and attached to donor site during the first days. During the clinical trials, physical and mechanical properties of NFC wound dressing were optimized by changing its composition. From patient 5 forward, NFC dressing was compared to commercial lactocapromer dressing, Suprathel® (PMI Polymedics, Germany).nnnRESULTSnEpithelialization of the NFC dressing-covered donor site was faster in comparison to Suprathel®. Healthy epithelialized skin was revealed under the detached NFC dressing. NFC dressing self-detached after 11-21days for patients 1-9, while Suprathel® self-detached after 16-28days for patients 5-9. In comparison studies with patients 5-9, NFC dressing self-detached on average 4days earlier compared with Suprathel®. Lower NFC content in the material was evaluated to influence the enhanced pliability of the dressing and attachment to the wound bed. No allergic reaction or inflammatory response to NFC was observed. NFC dressing did not cause more pain for patients than the traditional methods to treat the skin graft donor sites.nnnCONCLUSIONnBased on the preliminary clinical data, NFC dressing seems to be promising for skin graft donor site treatment since it is biocompatible, attaches easily to wound bed, and remains in place until donor site has renewed. It also detaches from the epithelialized skin by itself.

GA-PARSIMONY

Graphical abstractDisplay Omitted HighlightsGA-PARSIMONY combines feature selection and model parameter optimization.Selection of best parsimonious models according to cost and complexity separately.Lower number of features selected in 65% of 20 UCI and Statlib databases tested.GA-PARSIMONY proved useful in SVR control models for a hot dip galvanizing line. This article proposes a new genetic algorithm (GA) methodology to obtain parsimonious support vector regression (SVR) models capable of predicting highly precise setpoints in a continuous annealing furnace (GA-PARSIMONY). The proposal combines feature selection, model tuning, and parsimonious model selection in order to achieve robust SVR models. To this end, a novel GA selection procedure is introduced based on separate cost and complexity evaluations. The best individuals are initially sorted by an error fitness function, and afterwards, models with similar costs are rearranged according to model complexity measurement so as to foster models of lesser complexity. Therefore, the user-supplied penalty parameter, utilized to balance cost and complexity in other fitness functions, is rendered unnecessary. GA-PARSIMONY performed similarly to classical GA on twenty benchmark datasets from public repositories, but used a lower number of features in a striking 65% of models. Moreover, the performance of our proposal also proved useful in a real industrial process for predicting three temperature setpoints for a continuous annealing furnace. The results demonstrated that GA-PARSIMONY was able to generate more robust SVR models with less input features, as compared to classical GA.

Improving Hotel Room Demand Forecasting with a Hybrid GA-SVR Methodology Based on Skewed Data Transformation, Feature Selection and Parsimony Tuning

This paper presents a hybrid methodology, in which a KDD-scheme is optimized to build accurate parsimonious models. The methodology tries to find the best model by using genetic algorithms to optimize a KDD scheme formed with the following stages: feature selection, transformation of the skewed input and output data, parameter tuning, and parsimonious model selection. In this work, experiments demonstrated that optimization of these steps significantly improved the model generalization capabilities in some UCI databases. Finally, this methodology was applied to create room demand parsimonious models using booking databases from a hotel located in a region of Northern Spain. Results proved that the proposed method was useful to create models with higher generalization capacity and lower complexity to those obtained with classical KDD processes.

Estimation of total soluble solids in grape berries using a hand-held NIR spectrometer under field conditions

BACKGROUNDnRecent studies have reported the potential of near infrared (NIR) spectral analysers for monitoring the ripeness of grape berries as an alternative to wet chemistry methods. This study covers various aspects regarding the calibration and implementation of predictive models of total soluble solids (TSS) in grape berries using laboratory and in-field collected NIR spectra.nnnRESULTSnThe performance of the calibration models obtained under laboratory conditions indicated that at least 700 berry samples are required to assure enough prediction accuracy. A statistically significant error reduction (ΔRMSECVu2009=u20090.1°Brix) with Pu2009<u20090.001 was observed when measuring berries without epicuticular wax, which was negligible from a practical point of view. Under field conditions, the prediction errors (RMSEPu2009=u20091.68°Brix, and SEPu2009=u20091.67°Brix) were close to those obtained with the laboratory dataset (RMSEPu2009=u20091.42°Brix, SEPu2009=u20091.40°Brix).nnnCONCLUSIONnThis work clarifies several methodological factors to develop a protocol for in-field assessing TSS in grape berries using an affordable, non-invasive, portable NIR spectral analyser.

Accurate Calibration in Multi-Material 3D Bioprinting for Tissue Engineering

Most of the studies in three-dimensional (3D) bioprinting have been traditionally based on printing a single bioink. Addressing the complexity of organ and tissue engineering, however, will require combining multiple building and sacrificial biomaterials and several cells types in a single biofabrication session. This is a significant challenge, and, to tackle that, we must focus on the complex relationships between the printing parameters and the print resolution. In this paper, we study the influence of the main parameters driven multi-material 3D bioprinting and we present a method to calibrate these systems and control the print resolution accurately. Firstly, poloxamer hydrogels were extruded using a desktop 3D printer modified to incorporate four microextrusion-based bioprinting (MEBB) printheads. The printed hydrogels provided us the particular range of printing parameters (mainly printing pressure, deposition speed, and nozzle z-offset) to assure the correct calibration of the multi-material 3D bioprinter. Using the printheads, we demonstrated the excellent performance of the calibrated system extruding different fluorescent bioinks. Representative multi-material structures were printed in both poloxamer and cell-laden gelatin-alginate bioinks in a single session corroborating the capabilities of our system and the calibration method. Cell viability was not significantly affected by any of the changes proposed. We conclude that our proposal has enormous potential to help with advancing in the creation of complex 3D constructs and vascular networks for tissue engineering.

Methodology based on micro-projects in DIY desktop machines for educational purposes in engineering degrees

The 21 st century university has the big educational challenge of how to encourage “a will to learn” in students living in a world saturated with a huge amount of information and distractions. A needed step to keep students motivated is to update their learning environments. Herein we present a proposal with a methodology based on microprojects in DIY desktop machines (MicroP-DIY-DkM). The main idea is to consolidate students’ theoretical background using motivating microprojects in which foreign entities act as petitioners. The students will also receive a broad view of current state of manufacturing technologies. At the same time, English language and Information and Communication Technologies skills can be promoted by our methodology. We provide information about the implementation of several examples of these microprojects, which were applied in the technical subject ‘Manufacturing Technology’. The use of open source DIY-DkM offers students the possibility to understand essential principles of industrial technologies and processes. According to our surveys, students’ scores and success rate results, the methodology proposed demonstrated its convenience to be applied in technical subjects. Students showed greater motivation level and success rate than previous years using conventional methods. Limitation of the proposal and possible means of improvement are also included.

On-line Soft Sensor Based on Regression Models and Feature Selection Techniques for Predicting Rubber Properties in Mixture Processes

The paper deals with the complexity of rubber mixture process. The main issue is to develop well performing on-line soft sensors to monitoring rheological rubber properties. When mixing all raw materials, continual discards of defective materials with high costs associated can be caused by unexpected process variations and incorrect operating set points. Therefore, accurate on-line rubber properties predictions are crucial to obtain higher quality rubber bands. An on-line soft sensor based on a wrapper scheme is proposed to this end. The wrapper is mainly composed of a regression model and a feature selection routine. This routine is designed to find those optimal process variable subsets (input variables) that explain better the rubber properties (output variables). A backwards selection strategy is the basis of the feature selection routine. After an iterative process, the subset finally selected as inputs for the regression model was the one that predicted better the rubber properties. The proposed approach showed several advantages. First, wider and deeper knowledge of the industrial process was clearly achieved. In addition, the final on-line soft sensor was able to establish clear relations between the independent process variables and some rheological parameters of the rubber. A parsimony model was achieved thanks to a combination of a linear model and a selection feature routine that provided these good results.

Soft Computing Metamodels for the Failure Prediction of T-stub Bolted Connections

In structural and mechanical fields, there is a growing trend to replace expensive numerical simulations with more cost-effective approximations. In this context, the use of metamodels represents an attractive option. Without significant loss of accuracy, metamodelling techniques can drastically reduce the computational burden required by simulations. This paper proposes a method for developing soft computing metamodels to predict the failure of steel bolted connections. The setting parameters of the metamodels are tuned by an optimisation based on genetic algorithms during the training process. The method also includes the selection of the most relevant input features to reduce the models’ complexity. In total, two well-known metamodelling techniques are evaluated to compare their performances on accuracy and parsimony. This case studies the T-stub bolted connection, which allows us to validate the proposed models. The results show soft computing’s metamodelling capacity to accurately predict the T-stub response, while reducing the number of variables and with negligible computation cost.