Three-dimensional structure of the HSV1 nucleocapsid

The three-dimensional structures of full and empty capsids of HSV1 were determined by computer analysis of low dose cryo-electron images of ice embedded capsids. The full capsid structure is organized into outer, intermediate, and inner structural layers. The empty capsid structure has only one layer which is indistinguishable from the outer layer of the full capsids. This layer is arranged according to T=16 icosahedral symmetry. The intermediate layer of full capsids appears to lie on a T=4 icosahedral lattice. The genomic DNA is located inside the T=4 shell and is the component of the innermost layer of the full capsids. The outer and intermediate layers interact in such a way that the channels along their icosahedral two-fold axis coincide and form a direct pathway between the DNA and the environment outside the capsid

Protein subunit structures in the herpes simplex virus A-capsid determined from 400 kV spot-scan electron cryomicroscopy

The three-dimensional structure of the A-capsid of herpes simplex virus type-1 has been determined to a resolution of approximately 26 A by using 400 kV spot-scan electron cryomicroscopy and computer image reconstruction techniques. The density map of the capsid has revealed several new structural details in the protein subunits of pentons, hexons, and triplexes. Our structural analysis has provided further evidence for the assignment of the four major capsid proteins to these various subunits. VP5, a 150 kDa major capsid protein that makes up both the penton and the bulk of the hexon subunits, has three domains: an upper diamond-shaped domain, a middle stem-like domain, and a lower anchoring domain. Structural differences are noticeable between the VP5 subunits in various quasi-equivalent environments. A horn-shaped mass density present at the distal end of each hexon subunit but missing from the penton subunit has been assigned to VP26, a minor 12 kDa protein. The six types of triplexes have similar, but not identical, features that include two legs and an upper domain that has a tail, which are interpreted to be formed from two copies of VP23 (36 kDa) and one copy of VP19c (57 kDa), respectively. Each triplex has two arms that interact with the adjacent VP5 subunits, and the modes of interaction vary among the quasi-equivalent triplexes. The 25 A-thick floor of the capsid is formed by the close association of the lower domains of subunits from the hexons, pentons, and triplexes. The interior of the capsid is accessible through the trans-capsomeric channels and the holes at the base of each triplex. These openings may play a role in the transport of genomic DNA and scaffolding proteins during capsid morphogenesis

Remote monitoring system using slow-fast deep convolution neural network model for identifying anti-social activities in surveillance applications

Remote monitoring is the process that monitors and observes information from a distance utilizing sensors or electronic types of equipment. Remote monitoring is used in real-time applications like traffic, forest, military, shops, and hospitals to determine abnormal activities. Earlier research has done video processing methods based on computer vision techniques, but the computational complexity regarding time and memory is high. This paper designs and implements a novel Slow-Fast Convolution Neural Network (SF–CNN) to identify, detect, and classify abnormal behaviours from a surveillance video. The proposed CNN architecture learns the video frames automatically, obtains the most appropriate properties about various objects' behaviour from a large set of videos. The learning process of SF-CNN is carried out in two ways, such as slow learning and fast learning. The slow learning process is enabled when the frame rate is less, and the rapid learning process is enabled when the frame rate is high. Both the learning processes learn spatial and temporal information from the input video. Different objects, such as humans, vehicles, and animals, are detected and recognized according to their actions. All the videos have normal and abnormal activities that vary in various contexts. The proposed SF-CNN architecture provides an end-to-end solution to dealing with multiple constraints abnormal movements. The experiment is carried out on several benchmark datasets, and the performance of the SF-CNN architecture is evaluated. The proposed approach obtained 99.6% of accuracy, which is higher than the other existing techniques

Remote monitoring system using slow-fast deep convolution neural network model for identifying anti-social activities in surveillance applications

Remote monitoring is the process that monitors and observes information from a distance utilizing sensors or electronic types of equipment. Remote monitoring is used in real-time applications like traffic, forest, military, shops, and hospitals to determine abnormal activities. Earlier research has done video processing methods based on computer vision techniques, but the computational complexity regarding time and memory is high. This paper designs and implements a novel Slow-Fast Convolution Neural Network (SF–CNN) to identify, detect, and classify abnormal behaviours from a surveillance video. The proposed CNN architecture learns the video frames automatically, obtains the most appropriate properties about various objects' behaviour from a large set of videos. The learning process of SF-CNN is carried out in two ways, such as slow learning and fast learning. The slow learning process is enabled when the frame rate is less, and the rapid learning process is enabled when the frame rate is high. Both the learning processes learn spatial and temporal information from the input video. Different objects, such as humans, vehicles, and animals, are detected and recognized according to their actions. All the videos have normal and abnormal activities that vary in various contexts. The proposed SF-CNN architecture provides an end-to-end solution to dealing with multiple constraints abnormal movements. The experiment is carried out on several benchmark datasets, and the performance of the SF-CNN architecture is evaluated. The proposed approach obtained 99.6% of accuracy, which is higher than the other existing techniques

The VP3 gene of human group C rotavirus

The complete nucleotide sequence of genome segment 4 from the human group C rotavirus (Bristol strain) was determined. Comparison of the nucleotide sequences of the genome termini with the consensus 5' and 3' terminal non-coding sequences of the human group C rotavirus genome revealed characteristic 5' and 3' sequence motifs. Human group C rotavirus genome segment 4 is 2,166bp long and encodes a single open reading frame of 2,082 nucleotides (693 amino acids) starting at nucleotide 55 and terminating at nucleotide 2,136 giving a 3' untranslated region of 30 nucleotides. Alignment with the porcine group C VP3 equivalent gene showed the human gene is one amino acid longer, and that the proteins have 84.1% amino acid sequence identity. A conserved potential nucleotide binding motif shared with the porcine VP3 sequence was identified. Analogy with the group A rotaviruses suggested that the genome segment 4 encodes the group C rotavirus guanylyltransferase