Divide-and-Conquer Matrisome Protein (DC-MaP) Strategy: An MS-Friendly Approach to Proteomic Matrisome Characterization

Currently, the extracellular matrix (ECM) is considered a pivotal complex meshwork of macromolecules playing a plethora of biomolecular functions in health and disease beyond its commonly known mechanical role. Only by unraveling its composition can we leverage related tissue engineering and pharmacological efforts. Nevertheless, its unbiased proteomic identification still encounters some limitations mainly due to partial ECM enrichment by precipitation, sequential fractionation using unfriendly-mass spectrometry (MS) detergents, and resuspension with harsh reagents that need to be entirely removed prior to analysis. These methods can be technically challenging and labor-intensive, which affects the reproducibility of ECM identification and induces protein loss. Here, we present a simple new method applicable to tissue fragments of 10 mg and more. The technique has been validated on human ovarian tissue and involves a standardized procedure for sample processing with an MS-compatible detergent and combined centrifugation. This two-step protocol eliminates the need for laborious sample clarification and divides our samples into 2 fractions, soluble and insoluble, successively enriched with matrisome-associated (ECM-interacting) and core matrisome (structural ECM) proteins.     

Bibliometric analysis of the evolution of biochar research trends and scientific production

Clean Technologies and Environmental Policy - Biochar has caught great attention over the last decade, yielding a large number of publications in a broad range of disciplines. This scientometric...     

Analysis of e-business models in real estate

We use Canvas Application Framework to analyze e-business models of Realestate.yahoo.com, Yahoo.com, Redfin.com, Realtor.com, Trulia.com, Zillow.com, and Craigslist.com. We investigate the similarities and differences among e-business models and how e-business model differentiation affects company success. We looked at which business model components, such as distribution channels, business partnerships, attracting customers, revenue streams, etc., are important. We further investigate whether the web-advertising, brokerage, virtual value-chain, or the diversified internet business model is used by each company, and which ones are more successful. We discover similarities and differences among e-business models. Differences include the use of revenue streams for generating revenue, the extent of partnerships, and the role they play in a company’s success.     

Polyvinylidene fluoride nanofibers obtained by electrospinning and blowspinning: Electrospinning enhances the piezoelectric β-phase – myth or reality?

Considerable effort has been devoted to improving the properties of PVDF (polyvinylidene fluoride), arguably the most technologically important piezoelectric polymer. Electrospinning has been found to be a particularly effective method of producing PVDF nanofibers with superior piezoelectric properties due to the resulting exceptionally high fraction of the piezoelectrically active crystalline β-phase. It is typically assumed that the high external electric fields applied during electrospinning enhance the formation of this β-phase, with the confused literature offering various unsatisfactory mechanistic explanations. However, by comparing PVDF nanofibers produced by two different processes (electrospinning and blowspinning), we show that the electric field is entirely unnecessary; indeed, the crystallization dynamics are principally driven by the applied mechanical stress, as evidenced by structurally identical 200 nm diameter PVDF fibers produced with and without external electric fields.     

Fusion of thermal infrared and visible spectra for robust moving object detection

The detection of moving objects is a crucial step for many video surveillance applications whether using a visible camera (VIS) or an infrared (IR) one. In order to profit from both types, several fusion methods were proposed in the literature: low-level fusion, medium-level fusion and high-level fusion. The first one is the most used for moving objects’ detection in IR and VIS spectra. In this paper, we present an overview of the different moving object detection methods in IR and VIS spectra and a state of the art of the low-level fusion techniques. Moreover, we propose a new method for moving object detection using low-level fusion of IR and VIS spectra. In order to evaluate quantitatively and qualitatively our proposed method, three series of experiments were carried out using two well-known datasets namely “OSU Color-Thermal Database” and “INO-Database”; the results of these evaluations show promising results and demonstrate the effectiveness of the proposed method.     

Runoff Estimation for an Ungauged Catchment Using Geomorphological Instantaneous Unit Hydrograph (GIUH) and Copulas

A methodology is proposed to apply Geomorphological Instantaneous Unit Hydrograph to ungauged basins using Monte Carlo Simulations and copulas. The effective rainfall, input of GIUH is assumed to be unknown; it is estimated with infiltration index method (?-index). Correlations are detected between this index and the characteristics of rainfall. They are modeled with copulas, and are used to derive effective rainfall hyetographs. The generated hydrographs from GIUH are analyzed and give statistically the same results: dispersion and variability for all studied characteristics (volume, peak discharge, peak time and base time). However, only these hydrographs derived from ? conditioned to maximum intensity distribution allow reconstituting the observed hydrographs. Moreover comparing the series of order statistics of interest output and observed series, leads to decide on the representative hydrograph of the catchment behavior.     

Compositional characteristics and aromatic profile of date palm seeds from seven varieties grown in Tunisia

This paper reports a discrimination study based on the physico‐chemical characteristics, fatty acids and profile of volatile compounds of the seeds from seven date palm varieties (Phoenix dactylifera L.) grown in Tunisia. Date seeds contained 10.49–14.76% moisture; 6.28–11.2% fat (on a dry weight basis); 2.67–12.85% protein; 0.91–6.06% reducing sugar; 0.61–2.98% sucrose and 0.97–1.17% ash. Gas liquid chromatography revealed that the oil fraction of the date palm seeds contained eighteen fatty acids, with oleic acid (30.77–42.50%) and lauric acid (18.51–27.48%) as the main unsaturated and saturated ones. Volatile profile showed differences among varieties. In total, forty‐five compounds were identified, mainly alcohols, aldehydes and unsaturated hydrocarbons ones. This study provides evidence that the seeds of date may be a potential source of valuable nutrients with interesting functionality.     

Simulation-based investigations on the drape behavior of 3D woven fabrics made of commingled yarns

Composites based on 3D woven fabrics offer excellent mechanical properties due to the nearly non-crimp reinforcement fibers. Additionally, a reduced number of process steps results from the 3D nature of the reinforcement. Their in-plane and out-of-plane behaviors are purposefully adjustable to the expected loading conditions. A previously introduced modelling approach is extended to the simulation of 3D woven fabrics. The tensile, shear and bending behaviors of the fabric are considered in a material formulation for large deformations using shell elements. The model is successfully validated and parameter studies show the significant influence of shear and bending parameters on forming results. Furthermore, it is shown that also the forming process parameters have a significant influence on the draping results. It is concluded that the shearing and wrinkling can be reduced to a minimum with adapted material and process parameters.     

Face Recognition Through Different Facial Expressions

Face recognition has become an accessible issue for experts as well as ordinary people as it is a focal non-interfering biometric modality. In this paper, we introduced a new approach to perform face recognition under varying facial expressions. The proposed approach consists of two main steps: facial expression recognition and face recognition. They are two complementary steps to improve face recognition across facial expression variation. In the first step, we selected the most expressive regions responsible for facial expression appearance using the Mutual Information technique. Such a process helps not only improve the facial expression classification accuracy but also reduce the features vector size. In the second step, we used the Principal Component Analysis (PCA) to build EigenFaces for each facial expression class. Then, a face recognition is performed by projecting the face onto the corresponding facial expression Eigenfaces. The PCA technique significantly reduces the dimensionality of the original space since the face recognition is carried out in the reduced Eigenfaces space. An experimental study was conducted to evaluate the performance of the proposed approach in terms of face recognition accuracy and spatial-temporal complexity.