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Results obtained suggest that the 34 kDa major allergen of P. oxalicum may be a vacuolar serine proteinase. The 34 and the 32 kDa major allergens of P. notatum may be the alkaline and the vacuolar serine proteinases of P. notatum, respectively. The 30 and 16 kDa IgE-binding components of P. oxalicum and the 28 kDa IgE-binding component of P. notatum may be breakdown products of the 34 and the 32 kDa major vacuolar serine proteinase allergens of P. oxalicum and P. notatum, respectively.

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COVID-19 pandemic has killed millions of people worldwide since its outbreak in Dec 2019. The pandemic is caused by the SARS-CoV-2 virus whose main protease (Mpro) is a promising drug target since it plays a key role in viral proliferation and replication. Currently, designing an effective therapy is an urgent task, which requires accurately estimating ligand-binding free energy to the SARS-CoV-2 Mpro. However, it should be noted that the accuracy of a free energy method probably depends on the protein target. A highly accurate approach for some targets may fail to produce a reasonable correlation with experiment when a novel enzyme is considered as a drug target. Therefore, in this context, the ligand-binding affinity to SARS-CoV-2 Mpro was calculated via various approaches. The Autodock Vina (Vina) and Autodock4 (AD4) packages were manipulated to preliminary investigate the ligand-binding affinity and pose to the SARS-CoV-2 Mpro. The binding free energy was then refined using the fast pulling of ligand (FPL), linear interaction energy (LIE), molecular mechanics-Poission Boltzmann surface area (MM-PBSA), and free energy perturbation (FEP) methods. The benchmark results indicated that for docking calculations, Vina is more accurate than AD4 and for free energy methods, FEP is the most accurate followed by LIE, FPL and MM-PBSA (FEP > LIE > FPL > MM-PBSA). Moreover, the binding mechanism was also revealed by atomistic simulations. The vdW interaction is the dominant factor. The residues <i>Thr25</i>, <i>Thr26</i>, <i>His41</i>, <i>Ser46</i>, <i>Asn142</i>, <i>Gly143</i>, <i>Cys145</i>, <i>Glu166</i>, and <i>Gln189</i> are essential elements affecting on the binding process. Furthermore, the <i>Ser46</i> and related residues probably are important elements affecting the enlarge/dwindle of the SARS-CoV-2 Mpro binding cleft. The benchmark probably guide for further investigations using computational approaches.

Abstract.A markup language for business reporting must satisfy many demanding criteria: readable by novices, extendable by users, minimum payload overheads, and a uniform graph structure to enable validation of document instance with minimal programming effort. To be elegant and robust it must be based on a model that reflects the intricacies of business reporting, and to be efficient in terms of maintenance it must be modular in structure. We suggest the skeleton of a derivative of the XBRL that exhibits most of the criteria stated above which uses the basic semantic structure provided in its specification and the associated C&I taxonomy. Our proposal provides domain-specific tags so that even the source documents are very readable. We provide a proof-of-concept schema for the Balance Sheet (using the XBRL C&I taxonomy) as an instance of a canonical generic labeled graph model for any financial statement. We also provide an algorithm for the validation of such labeled directed graph representation of a financial statement and its implementation in the programming language Java.

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Volume rendering is a valuable and important technique for scientific visualization. One well known application area is the reconstruction and visualization of output from medical scanners like computed tomography (CT). 2D greyscale slices produced by these scanners can be reconstructed and displayed onscreen as a 3D model.Volume visualization of medical images must address two important issues. First, it is difficult to segment medical scans into individual materials based only on intensity values. This can result in volumes that contain large amounts of unimportant or unnecessary material. Second, although greyscale images are the normal method for displaying medical volumes, these types of images are not necessarily appropriate for highlighting regions of interest within the volume. Studies of the human visual system have shown that individual intensity values are difficult to detect in a greyscale image. In these situations colour is a more effective visual feature, since the lowlevel visual system can rapidly and accurately detect the presence or absence of a particular target colour in a multi-coloured image.We addressed both problems during the visualization of CT scans of abdominal aortic aneurysms. We have developed a classification method that empirically segments regions of interest in each of the 2D slices. We use a perceptual colour selection technique to identify each region of interest in both the 2D slices and the 3D reconstructed volumes. The result is a colourized volume that the radiologists are using to rapidly and accurately identify the locations and spatial interactions of different materials from their scans. Our technique is being used in an experimental post-operative environment to help to evaluate the results of surgery designed to prevent the rupture of the aneurysm. In the future, we hope to use the technique during the planning of placement of support grafts prior to the actual operation.

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