Utilization and Selection of Best SU Act as Relay via Cooperative NOMA (CNOMA)-Based CRNs for Next-Generation (5G) Communications

2021 ◽  
pp. 797-805
Author(s):  
Ashok Kumar ◽  
Mani Shekhar Gupta ◽  
Krishan Kumar ◽  
Sumit Kumar
Keyword(s):  
Next Generation ◽  
Selection Of

scholarly journals Design and selection of DMP 850 and DMP 851: the next generation of cyclic urea HIV protease inhibitors

1998 ◽  
Vol 5 (10) ◽  
pp. 597-608 ◽  
Author(s):  
James D. Rodgers ◽  
Patrick Y.S. Lam ◽  
Barry L. Johnson ◽  
Haisheng Wang ◽  
Soo S. Ko ◽  
...  
Keyword(s):  
Protease Inhibitors ◽  
Hiv Protease ◽  
Hiv Protease Inhibitors ◽  
Next Generation ◽  
Selection Of

scholarly journals The Next Generation of TEMs

1999 ◽  
Vol 7 (3) ◽  
pp. 20-23
Author(s):  
D. B. Williams
Keyword(s):  
Diffraction Pattern ◽  
Exposure Time ◽  
Image Plane ◽  
List Type ◽  
Type Number ◽  
Next Generation ◽  
Optical Instruments ◽  
Selection Of

A selection of the more common questions arising during a typical lab course for materials majors learning to use a TEM is as follows1) Why must I insert the objective aperture, which governs image contrast, in the diffraction plane?2) Why must I insert the selected area diffraction aperture, which governs the diffraction pattern contrast in the image plane?3) Why is the TEM relatively useless if both apertures are in at the same time or both are out?4) Why is the contrast in my images always greatest when the beam is parallel - which makes the screen intensity so weak, I sometimes can't see the contrast anyhow?5) Why, when I need to record a diffraction pattern, do I have to learn to estimate exposure time from the analog screen, rather than just push a button and get a correct exposure, as in most other optical instruments and cameras?

scholarly journals cDNA display coupled with next-generation sequencing for rapid activity-based screening: Comprehensive analysis of transglutaminase substrate preference

2021 ◽  
Author(s):  
Jasmina Damnjanović ◽  
Nana Odake ◽  
Jicheng Fan ◽  
Beixi Jia ◽  
Takaaki Kojima ◽  
...  
Keyword(s):  
Next Generation Sequencing ◽  
Substrate Preference ◽  
Stable Complex ◽  
Next Generation ◽  
Library Size ◽  
Binary Model ◽  
Wide Range ◽  
Generation Sequencing ◽  
Selection Of

AbstractcDNA display is an in vitro display technology based on a covalent linkage between a protein and its corresponding mRNA/cDNA, where a stable complex is formed suitable for a wide range of selection conditions. A great advantage of cDNA display is the ability to handle enormous library size (1012) in a microtube scale, in a matter of days. To harness its benefits, we aimed at developing a platform which combines the advantages of cDNA display with high-throughput and accuracy of next-generation sequencing (NGS) for the selection of preferred substrate peptides of transglutaminase 2 (TG2), a protein cross-linking enzyme. After the optimization of the platform by the repeated screening of binary model libraries consisting of the substrate and non-substrate peptides at different ratios, screening and selection of combinatorial peptide library randomized at positions -1, +1, +2, and +3 from the glutamine residue was carried out. Enriched cDNA complexes were analyzed by NGS and bioinformatics, revealing the comprehensive amino acid preference of the TG2 at targeted positions of the peptide backbone. This is the first report on the cDNA display/NGS screening system to yield comprehensive data on TG substrate preference. Although some issues remain to be solved, this platform can be applied to the selection of other TGs and easily adjusted for the selection of other peptide substrates and even larger biomolecules.

scholarly journals Selection of Laboratory Mice for Improved Reproductive Performance at High Environmental Temperature

1979 ◽  
Vol 32 (1) ◽  
pp. 133 ◽  
Author(s):  
Pamela R Pennycuik
Keyword(s):  
Reproductive Performance ◽  
Reproductive Output ◽  
Environmental Temperature ◽  
Laboratory Mice ◽  
Next Generation ◽  
Hot Environment ◽  
High Environmental Temperature ◽  
Selection Of ◽  
Mouse Lines

When mice are transferred from a 'temperate' to a hot environment their reproductive output is reduced. To test whether this reduction could be countered by selection, three mouse lines, housed permanently as 32�C, were selected for increase in the numbers of young reared to 3 weeks when pairs were allowed to remain together for 12 weeks. In one line (R95) in which all fertile pairs contributed to the next generation, no improvement was observed in 19 generations. In two other lines (W21 and W34) in which only half the pairs in one generation contributed to the next, productivity returned, in about 12 generations, to almost the same levels as those of controls housed at 21�C.

scholarly journals Bioinformatics and Computational Tools for Next-Generation Sequencing Analysis in Clinical Genetics

2020 ◽  
Vol 9 (1) ◽  
pp. 132 ◽  
Author(s):  
Rute Pereira ◽  
Jorge Oliveira ◽  
Mário Sousa
Keyword(s):  
Next Generation Sequencing ◽  
Clinical Genetics ◽  
Sequencing Analysis ◽  
Next Generation ◽  
Process Data ◽  
Next Generation Sequencing Analysis ◽  
Generate Process ◽  
Next Generation Sequencing Ngs ◽  
Generation Sequencing ◽  
Selection Of

Clinical genetics has an important role in the healthcare system to provide a definitive diagnosis for many rare syndromes. It also can have an influence over genetics prevention, disease prognosis and assisting the selection of the best options of care/treatment for patients. Next-generation sequencing (NGS) has transformed clinical genetics making possible to analyze hundreds of genes at an unprecedented speed and at a lower price when comparing to conventional Sanger sequencing. Despite the growing literature concerning NGS in a clinical setting, this review aims to fill the gap that exists among (bio)informaticians, molecular geneticists and clinicians, by presenting a general overview of the NGS technology and workflow. First, we will review the current NGS platforms, focusing on the two main platforms Illumina and Ion Torrent, and discussing the major strong points and weaknesses intrinsic to each platform. Next, the NGS analytical bioinformatic pipelines are dissected, giving some emphasis to the algorithms commonly used to generate process data and to analyze sequence variants. Finally, the main challenges around NGS bioinformatics are placed in perspective for future developments. Even with the huge achievements made in NGS technology and bioinformatics, further improvements in bioinformatic algorithms are still required to deal with complex and genetically heterogeneous disorders.

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