Acute and chronic wounds have varying etiologies and are an economic burden to healthcare systems around the world. The advanced wound care market is expected to exceed $22 billion by 2024. Wound care professionals rely heavily on images and image documentation for proper diagnosis and treatment. Unfortunately lack of expertise can lead to improper diagnosis of wound etiology and inaccurate wound management and documentation. Fully automatic segmentation of wound areas in natural images is an important part of the diagnosis and care protocol since it is crucial to measure the area of the wound and provide quantitative parameters in the treatment.  https://www.selleckchem.com/products/amenamevir.html  Various deep learning models have gained success in image analysis including semantic segmentation. This manuscript proposes a novel convolutional framework based on MobileNetV2 and connected component labelling to segment wound regions from natural images. The advantage of this model is its lightweight and less compute-intensive architecture. The performance is not compromised and is comparable to deeper neural networks. We build an annotated wound image dataset consisting of 1109 foot ulcer images from 889 patients to train and test the deep learning models. We demonstrate the effectiveness and mobility of our method by conducting comprehensive experiments and analyses on various segmentation neural networks. The full implementation is available at https//github.com/uwm-bigdata/wound-segmentation .The climate changes observed over the last decades have been promoting a massive transformation on the energy sector, that is still, in truth highly dependent on fossil fuels. Renewable energies are a plausible alternative, because they have lower emissions of toxic gases in comparison with non-renewable ones. In the group of renewable energies, solar technology has the biggest overall potential, mainly because it is cheap and easy to set. Several solar technologies allow to equip their photovoltaic panels with concentrators, mostly to increase the output power and possibly their efficiency. However, some problems related to the use of concentrators have to be dealt in order to improve the entire photovoltaic system performance. One of these issues is the corrosion of the concentrators, leading to a premature ageing and, consequently an increase in maintenance costs. This problem is going to be analysed in this paper, presenting some simulation from a ray traicing software and also some experimental results, from our own laboratory experiences. The used software allows to trace the solar rays of the concentrator, in order to assess the effect of the defects caused by corrosion due to the ambient circumstances. After it, experimental results will help to analyse this effect and to prove simulation ones.Networked systems emerge and subsequently evolve. Although several models describe the process of network evolution, researchers know far less about the initial process of network emergence. Here, we report temporal survey results of a real-world social network starting from its point of inception. We find that individuals' ties undergo an initial cycle of rapid expansion and contraction. This process helps to explain the eventual interactions and working structure in the network (in this case, scientific collaboration). We propose a stylized concept and model of "churn" to describe the process of network emergence and stabilization. Our empirical and simulation results suggest that these network emergence dynamics may be instrumental for explaining network details, as well as behavioral outcomes at later time periods.Ancient statues are usually fragile and have a tendency to deteriorate over time, developing cracks, corrosion, and losing color. Before any intervention on the object of art, a conservator must map degradation and take measurements. Deterioration mapping is an extremely long process, as the conservator or restorer must locate and digitize the damages manually and collect physical measurements from the artwork. Extracting and measuring the deterioration automatically from images is less expensive and aids the digital documentation process, thus reducing the time cost of manual deterioration mapping. In this paper, we propose an effective approach named Missing Color Area Extraction in order to extract and measure missing color areas from high-resolution imagery statues, using a thresholding technique. The conversion from RGB color space to HSV color space is applied, in addition to morphological operations to remove the dust and small objects.Olfactomedin 4 (OLFM4) is expressed in normal prostate epithelial cells and immortalized normal human prostate epithelial cells (RWPE1), but the identity of OLFM4-expressing cells within these populations and OLFM4's physiological functions in these cells have not been elucidated. Using single-cell RNA sequencing analysis, we found here that OLFM4 was expressed in multiple stem/progenitor-like cell populations in both the normal prostate epithelium and RWPE1 cells and was frequently co-expressed with KRT13 and LY6D in RWPE1 cells. Functionally, OLFM4-knockout RWPE1 cells exhibited enhanced proliferation of the stem/progenitor-like cell population, shifts stem/progenitor-like cell division to favor symmetric division and differentiated into higher levels PSA expression cells in organoid assays compared with OLFM4-wild RWPE1 cells. Bulk-cell RNA sequencing analysis pinpointed that cMYC expression were enhanced in the OLFM4-knockout RWPE1 cells compared with OLFM4-wild cells. Molecular and signaling pathway studies revealed an increase in the WNT/APC/MYC signaling pathway gene signature, as well as that of MYC target genes that regulate multiple biological processes, in OLFM4-knockout RWPE1 cells. These findings indicated that OLFM4 is co-expressed with multiple stem/progenitor cell marker genes in prostate epithelial cells and acts as a novel mediator in prostate stem/progenitor cell proliferation and differentiation.A signalling pathway involving PLEKHG5 (guanine exchange factor) for the Ras superfamily member RAB26 to transcription factor NF-κB was discovered in autophagy. PLEKHG5 was reported in glioblastoma multiforme (GBM) and correlates with patient survival. Thus, the generation of a cellular model for understanding PLEKHG5 signalling is the study purpose. We generated a CRISPR/Cas9-mediated knockout of PLEKHG5 in U251-MG glioblastoma cells and analysed resulting changes. Next, we used a mRFP-GFP-LC3+ reporter for visualisation of autophagic defects and rescued the phenotype of PLEKHG5 wildtype via transduction of a constitutively active RAB26QL-plasmid. Effects of overexpressing RAB26 were investigated and correlated with the O6-methylguanine-DNA methyltransferase (MGMT) and cellular survival. PLEKHG5 knockout showed changes in morphology, loss of filopodia and higher population doubling times. Accumulation of autolysosomes was resulted by decreased LAMP-1 in PLEKHG5-deficient cells. Rescue of PLEKHG5-/- restored the downregulation of RhoA activity, showed faster response to tumour necrosis factor and better cellular fitness.