Molecular Characterization of Infectious Bronchitis Virus Strain HH06 Isolated in a Poultry Farm in Northeastern China

Spike (S) glycoprotein is an important virulent factor for coronaviruses (CoVs), and variants of CoVs have been characterized based on S gene analysis. We present phylogenetic relationship of an isolated infectious bronchitis virus (IBV) strain with reference to the available genome and protein sequences based on network, multiple sequence, selection pressure, and evolutionary fingerprinting analysis in People's Republic of China. One hundred and elven strains of CoVs i.e., Alphacoronaviruses (Alpha-CoVs; n = 12), Betacoronaviruses (Beta-CoVs; n = 37), Gammacoronaviruses (Gamma-CoVs; n = 46), and Deltacoronaviruses (Delta-CoVs; n = 16) were selected for this purpose. Phylogenetically, SARS-CoV-2 and SARS-CoVs clustered together with Bat-CoVs and MERS-CoV of Beta-CoVs (C). The IBV HH06 of Avian-CoVs was closely related to Duck-CoV and partridge S14, LDT3 (teal and chicken host). Beluga whale-CoV (SW1) and Bottlenose dolphin-CoVs of mammalian origin branched distantly from other animal origin viruses, however, making group with Avian-CoVs altogether into Gamma-CoVs. The motif analysis indicated well-conserved domains on S protein, which were similar within the same phylogenetic class and but variable at different domains of different origins. Recombination network tree indicated SARS-CoV-2, SARS-CoV, and Bat-CoVs, although branched differently, shared common clades. The MERS-CoVs of camel and human origin spread branched into a different clade, however, was closely associated closely with SARS-CoV-2, SARS-CoV, and Bat-CoVs. Whereas, HCoV-OC43 has human origin and branched together with bovine CoVs with but significant distant from other CoVs like SARS CoV-2 and SARS-CoV of human origin. These findings explain that CoVs' constant genetic recombination and evolutionary process that might maintain them as a potential veterinary and human epidemic threat.

Spatial Data Sequence Selection Based on a User-Defined Condition Using GPGPU

The size of spatial data is growing intensively due to the emergence of and the tremendous advances in technology such as sensors and the internet of things. Supporting high-performance queries on this large volume of data becomes essential in several data- and compute-intensive applications. Unfortunately, most of the existing methods and approaches are based on a traditional computing framework (uniprocessors) which makes them not scalable and not adequate to deal with large-scale data. In this work, we present a high-performance query for massive spatio–temporal data. The query consists of selecting fixed size raster subsequences, based on the average of their region of interest, from a spatio–temporal raster sequence satisfying a user threshold condition. In our paper, for the purpose of simplification, we consider that the region of interest is the entire raster and not only a subregion. Our aim is to speed up the execution using parallel primitives and pure CUDA. Furthermore, we propose a new method based on a sorting step to save computations and boost the speed of the query execution. The test results show that the proposed methods are faster and good performance is achieved even with large-scale rasters and data.

Effective Strain Image Sequence Selection by Using Semi-Automated Image Processing Technique

One fourth of the cancer detected in women worldwide is breast cancer which leads this as a major threat for women. There are many methods of detecting cancer among which ultra-sound strain imaging is one of the promising techniques. However, in strain sequence, not all the frames show clear tumor visibility. Consequently, in this paper we tested some well-defined algorithms to find only those frames where the tumor is comparatively clearly visible. We have used Mean Pixel Difference (MPD) and Gray- Level Co-occurrence Matrix (GLCM) to find the frames with better tumor visibility. We have tested our methods in several real-life cases and the results have been examined by a professional doctor. The MPD has an accuracy of 96.2% and the GLCM. Contrast has that of 55.55%. GUB JOURNAL OF SCIENCE AND ENGINEERING, Vol 7, Dec 2020 P 8-13

The Effect of Attentional Direction on Sub-Stages of Preparing for Motor Skill Execution Across Practice

While several empirical studies using dual-task methodology have examined the effect of attentional direction on motor skill execution; few have studied the effect of attentional direction on just the preparation phase of motor practice. In this study, via a keying sequence paradigm, we explored processing stages of preparation for a motor skill and disentangled the effect of attentional direction on various stages across practice. First, participants learned two keying sequences (three versus six keys). Then, they practiced the keying sequences in response to corresponding sequence labels under two block-wise alternating dual-task conditions. To dissect the preparation phase into sequence selection and sequence initiation stages, participants received varying amounts of preparation time (0, 300, 900 ms) before a starting signal instructed them to begin sequence execution. In each trial, a tone was paired with one of the three or six keypresses, and participants indicated either the keypress with which the tone was presented (skill-focused dual task) or the tone’s pitch (extraneous dual task) after the sequence execution. We found that attentional direction affected only the sequence selection stage, not the sequence initiation stage. During early practice, compared to drawing attention away from execution, directing attention toward execution led to faster sequence selection. This advantage decreased with practice and vanished during late blocks of trials. Moreover, for the execution phase, relative to directing attention toward execution, drawing attention away from execution led to better performance of keying sequence execution across practice. Thus, attentional direction alone does not fully explain the difference between performance patterns at different skill levels in the dual-task literature; rather, types of motor skills and dual task difficulty levels may also drive performance differences.

The prognostic value of foreshocks - a statistical reevaluation

<p>The ability to forecast large earthquakes on short time scales is strongly limited by our understanding of the earthquake nucleation process. Foreshocks represent promising seismic signals that may improve earthquake forecasting as they precede many large earthquakes. However, foreshocks can currently only be identified as such after a large earthquake occurred. This inability is because it remains unclear whether foreshocks represent a different physical process than general seismicity (i.e., mainshocks and aftershocks). Several studies compared foreshock occurrence in real and synthetic catalogs, as simulated with a well-established earthquake triggering/forecasting model called Epidemic-Type Aftershock Sequence (ETAS) that does not discriminate between foreshocks, mainshocks, and aftershocks. Some of these studies show that the spatial distribution of foreshocks encodes information about the subsequent mainshock magnitude and that foreshock activity is significantly higher than predicted by the ETAS model. These findings attribute a unique underlying physical process to foreshocks, making them potentially useful for forecasting large earthquakes. We reinvestigate these scientific questions using high-quality earthquake catalogs and study carefully the influence of subjective parameter choices and catalog artifacts on the results. For instance, we use data from different regions, account for the short-term catalog incompleteness and its spatial variability, and explore different criteria for sequence selection and foreshock definition.</p>

Sequence selection properties in Cp (X) with the double ideals

Recently Bukovský, Das and Šupina [Ideal quasi-normal convergence and related notions, Colloq. Math. 146 (2017), 265–281] started the study of sequence selection properties (𝓘, 𝓙-α 1) and (𝓘, 𝓙-α 4) of Cp (X) using the double ideals, where 𝓘 and 𝓙 are the proper admissible ideals of ω, which are motivated by Arkhangeľskii local αi -properties [The frequency spectrum of a topological space and the classification of spaces, Dokl. Akad. Nauk SSSR 13 (1972), 1185–1189]. In this paper, we obtain some characterizations of (𝓘, 𝓙-α 1) and (𝓘, 𝓙-α 4) properties of Cp (X) in the terms of covering properties and selection principles. Under certain conditions on ideals 𝓘 and 𝓙, we identify the minimal cardinalities of a space X for which Cp (X) does not have (𝓘, 𝓙-α 1) and (𝓘, 𝓙-α 4) properties.