scholarly journals Data‐driven in silico prediction of regulation heterogeneity and ATP demands of Escherichia coli in large‐scale bioreactors

2020 ◽  
Vol 118 (1) ◽  
pp. 265-278 ◽  
Author(s):  
Julia Zieringer ◽  
Moritz Wild ◽  
Ralf Takors
Keyword(s):  
Escherichia Coli ◽  
In Silico ◽  
Large Scale ◽  
Data Driven ◽  
In Silico Prediction

scholarly journals In Silico Prediction and Design of Uropathogenic Escherichia coli Alpha-Hemolysin Generate a Soluble and Hemolytic Recombinant Toxin

2022 ◽  
Vol 10 (1) ◽  
pp. 172
Author(s):  
Bruna De Lucca Caetano ◽  
Marta de Oliveira Domingos ◽  
Miriam Aparecida da Silva ◽  
Jessika Cristina Alves da Silva ◽  
Juliana Moutinho Polatto ◽  
...  
Keyword(s):  
Escherichia Coli ◽  
In Silico ◽  
Diagnostic Tests ◽  
Polyclonal Antibodies ◽  
Uropathogenic Escherichia Coli ◽  
Therapeutic Interventions ◽  
Antigenic Determinants ◽  
Cytotoxic Activities ◽  
In Silico Prediction ◽  
Tract Infections

The secretion of α-hemolysin by uropathogenic Escherichia coli (UPEC) is commonly associated with the severity of urinary tract infections, which makes it a predictor of poor prognosis among patients. Accordingly, this toxin has become a target for diagnostic tests and therapeutic interventions. However, there are several obstacles associated with the process of α-hemolysin purification, therefore limiting its utilization in scientific investigations. In order to overcome the problems associated with α-hemolysin expression, after in silico prediction, a 20.48 kDa soluble α-hemolysin recombinant denoted rHlyA was constructed. This recombinant is composed by a 182 amino acid sequence localized in the aa542–723 region of the toxin molecule. The antigenic determinants of the rHlyA were estimated by bioinformatics analysis taking into consideration the tertiary form of the toxin, epitope analysis tools, and solubility inference. The results indicated that rHlyA has three antigenic domains localized in the aa555–565, aa600–610, and aa674–717 regions. Functional investigation of rHlyA demonstrated that it has hemolytic activity against sheep red cells, but no cytotoxic effect against epithelial bladder cells. In summary, the results obtained in this study indicate that rHlyA is a soluble recombinant protein that can be used as a tool in studies that aim to understand the mechanisms involved in the hemolytic and cytotoxic activities of α-hemolysin produced by UPEC. In addition, rHlyA can be applied to generate monoclonal and/or polyclonal antibodies that can be utilized in the development of diagnostic tests and therapeutic interventions.

Speciation of cultivated temperate and tropical Pleurotus species –an in silico prediction using conserved sequences

2019 ◽  
Vol 28 (1) ◽  
Author(s):  
Anupam Barh ◽  
V P Sharma ◽  
Shwet Kamal ◽  
Mahantesh Shirur ◽  
Sudheer Kumar Annepu ◽  
...  
Keyword(s):  
In Silico ◽  
In Silico Prediction ◽  
Conserved Sequences ◽  
Pleurotus Species

In silico prediction of influenza vaccine effectiveness by sequence analysis

Vaccine ◽  
2021 ◽  
Vol 39 (7) ◽  
pp. 1030-1034
Author(s):  
Lirong Cao ◽  
Jingzhi Lou ◽  
Shi Zhao ◽  
Renee W.Y. Chan ◽  
Martin Chan ◽  
...  
Keyword(s):  
Sequence Analysis ◽  
Influenza Vaccine ◽  
In Silico ◽  
Vaccine Effectiveness ◽  
In Silico Prediction

scholarly journals Accelerating In-Transit Co-Processing for Scientific Simulations Using Region-Based Data-Driven Analysis

Algorithms ◽  
2021 ◽  
Vol 14 (5) ◽  
pp. 154
Author(s):  
Marcus Walldén ◽  
Masao Okita ◽  
Fumihiko Ino ◽  
Dimitris Drikakis ◽  
Ioannis Kokkinakis
Keyword(s):  
Large Scale ◽  
Data Driven ◽  
Data Sets ◽  
Output Constraints ◽  
Data Driven Approach ◽  
Scientific Simulations ◽  
Multiple Metrics ◽  
In Transit ◽  
Multiple Compression ◽  
Large Scale Simulations

Increasing processing capabilities and input/output constraints of supercomputers have increased the use of co-processing approaches, i.e., visualizing and analyzing data sets of simulations on the fly. We present a method that evaluates the importance of different regions of simulation data and a data-driven approach that uses the proposed method to accelerate in-transit co-processing of large-scale simulations. We use the importance metrics to simultaneously employ multiple compression methods on different data regions to accelerate the in-transit co-processing. Our approach strives to adaptively compress data on the fly and uses load balancing to counteract memory imbalances. We demonstrate the method’s efficiency through a fluid mechanics application, a Richtmyer–Meshkov instability simulation, showing how to accelerate the in-transit co-processing of simulations. The results show that the proposed method expeditiously can identify regions of interest, even when using multiple metrics. Our approach achieved a speedup of 1.29× in a lossless scenario. The data decompression time was sped up by 2× compared to using a single compression method uniformly.

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