Furthermore, the opportunity to tune the compositions of HEAs over a large range to optimise particular irradiation responses could be very powerful, even if the design process remains challenging.Even though computer vision has been developing, edge detection is still one of the challenges in that field. It comes from the limitations of the complementary metal oxide semiconductor (CMOS) Image sensor used to collect the image data, and then image signal processor (ISP) is additionally required to understand the information received from each pixel and performs certain processing operations for edge detection. Even with/without ISP, as an output of hardware (camera, ISP), the original image is too raw to proceed edge detection image, because it can include extreme brightness and contrast, which is the key factor of image for edge detection. To reduce the onerousness, we propose a pre-processing method to obtain optimized brightness and contrast for improved edge detection. In the pre-processing, we extract meaningful features from image information and perform machine learning such as k-nearest neighbor (KNN), multilayer perceptron (MLP) and support vector machine (SVM) to obtain enhanced model by adjusting brightness and contrast. The comparison results of F1 score on edgy detection image of non-treated, pre-processed and pre-processed with machine learned are shown. The pre-processed with machine learned F1 result shows an average of 0.822, which is 2.7 times better results than the non-treated one. Eventually, the proposed pre-processing and machine learning method is proved as the essential method of pre-processing image from ISP in order to gain better edge detection image. In addition, if we go through the pre-processing method that we proposed, it is possible to more clearly and easily determine the object required when performing auto white balance (AWB) or auto exposure (AE) in the ISP. It helps to perform faster and more efficiently through the proactive ISP.Red blood cell (RBC) deformability is an essential component of microcirculatory function that appears to be enhanced by physiological shear stress, while being negatively affected by supraphysiological shears and/or free radical exposure. Given that blood contains RBCs with non-uniform physical properties, whether all cells equivalently tolerate mechanical and oxidative stresses remains poorly understood. We thus partitioned blood into old and young RBCs which were exposed to phenazine methosulfate (PMS) that generates intracellular superoxide and/or specific mechanical stress. Selleckchem TAS-120 Measured RBC deformability was lower in old compared to young RBCs. PMS increased total free radicals in both sub-populations, and RBC deformability decreased accordingly. Shear exposure did not affect reactive species in the sub-populations but reduced RBC nitric oxide synthase (NOS) activation and intriguingly increased RBC deformability in old RBCs. The co-application of PMS and shear exposure also improved cellular deformability in older cells previously exposed to reactive oxygen species (ROS), but not in younger cells. Outputs of NO generation appeared dependent on cell age; in general, stressors applied to younger RBCs tended to induce S-nitrosylation of RBC cytoskeletal proteins, while older RBCs tended to reflect markers of nitrosative stress. We thus present novel findings pertaining to the interplay of mechanical stress and redox metabolism in circulating RBC sub-populations.Wood-decaying Basidiomycetes are among the most efficient degraders of plant cell walls, making them key players in forest ecosystems, global carbon cycle, and in bio-based industries. Recent insights from -omics data revealed a high functional diversity of wood-decay strategies, especially among the traditional white-rot and brown-rot dichotomy. We examined the mechanistic bases of wood-decay in the conifer-specialists Armillaria ostoyae and Armillaria cepistipes using transcriptomic and proteomic approaches. Armillaria spp. (Fungi, Basidiomycota) include devastating pathogens of temperate forests and saprotrophs that decay wood. They have been discussed as white-rot species, though their response to wood deviates from typical white-rotters. While we observed an upregulation of a diverse suite of plant cell wall degrading enzymes, unlike white-rotters, they possess and express an atypical wood-decay repertoire in which pectinases and expansins are enriched, whereas lignin-decaying enzymes (LDEs) are generally downregulated. This combination of wood decay genes resembles the soft-rot of Ascomycota and appears widespread among Basidiomycota that produce a superficial white rot-like decay. These observations are consistent with ancestral soft-rot decay machinery conserved across asco- and Basidiomycota, a gain of efficient lignin-degrading ability in white-rot fungi and repeated, complete, or partial losses of LDE encoding gene repertoires in brown- and secondarily soft-rot fungi.In recent years, an increasing diversity of species has been recognized within the family Francisellaceae. Unfortunately, novel isolates are sometimes misnamed in initial publications or multiple sources propose different nomenclature for genetically highly similar isolates. Thus, unstructured and occasionally incorrect information can lead to confusion in the scientific community. Historically, detection of Francisella tularensis in environmental samples has been challenging due to the considerable and unknown genetic diversity within the family, which can result in false positive results. We have assembled a comprehensive collection of genome sequences representing most known Francisellaceae species/strains and restructured them according to a taxonomy that is based on phylogenetic structure. From this structured dataset, we identified a small number of genomic regions unique to F. tularensis that are putatively suitable for specific detection of this pathogen in environmental samples. We designed and validated specific PCR assays based on these genetic regions that can be used for the detection of F. tularensis in environmental samples, such as water and air filters.