Tree bark scrape fungus: a potential source of laccase for application in bioremediation of non-textile dyes

Although laccase has been recognized as a wonder molecule and green enzyme, the use of low yielding fungal strains, poor production, purification, and low enzyme kinetics have hampered its large-scale application. Thus,this study aims to select high yielding fungal strains and optimize the production, purification, and kinetics of laccase of Aspergillus sp. HB-RZ4. The results obtained indicated that Aspergillus sp. HB-RZ4 produced a significantly large amount of laccase under meso-acidophilic shaking conditions in a medium containing glucose and yeast extract. A 25 μM CuSO4 was observed to enhance the enzyme yield. The enzyme was best purified on a Sephadex G-100 column. The purified enzyme resembled laccase of A. flavus. The kinetics of the purified enzyme revealed high substrate specificity and good velocity of reaction,using ABTS as a substrate. The enzyme was observed to be stable over various pH values and temperatures. The peptide structure of the purified enzyme was found to resemble laccase of A. kawachii IFO 4308. The fungus was observed to decolorize various dyes independent of the requirement of a laccase mediator system. Aspergillus sp. HB-RZ4 was observed to be a potent natural producer of laccase, and it decolorized the dyes even in the absence of a laccase mediator system. Thus, it can be used for bioremediation of effluent that contains non-textile dyes. © 2020 Sayyed et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited

The Sandia Fracture Challenge: blind round robin predictions of ductile tearing

Existing and emerging methods in computational mechanics are rarely validated against problems with an unknown outcome. For this reason, Sandia National Laboratories, in partnership with US National Science Foundation and Naval Surface Warfare Center Carderock Division, launched a computational challenge in mid-summer, 2012. Researchers and engineers were invited to predict crack initiation and propagation in a simple but novel geometry fabricated from a common off-the-shelf commercial engineering alloy. The goal of this international Sandia Fracture Challenge was to benchmark the capabilities for the prediction of deformation and damage evolution associated with ductile tearing in structural metals, including physics models, computational methods, and numerical implementations currently available in the computational fracture community. Thirteen teams participated, reporting blind predictions for the outcome of the Challenge. The simulations and experiments were performed independently and kept confidential. The methods for fracture prediction taken by the thirteen teams ranged from very simple engineering calculations to complicated multiscale simulations. The wide variation in modeling results showed a striking lack of consistency across research groups in addressing problems of ductile fracture. While some methods were more successful than others, it is clear that the problem of ductile fracture prediction continues to be challenging. Specific areas of deficiency have been identified through this effort. Also, the effort has underscored the need for additional blind prediction-based assessments

Fault and performance management in multi-cloud based NFV using shallow and deep predictive structures

Deployment of network function virtualization (NFV) over multiple clouds accentuates its advantages such as flexibility of virtualization, proximity to customers and lower total cost of operation. However, NFV over multiple clouds has not yet attained the level of performance to be a viable replacement for traditional networks. One of the reasons is the absence of a standard based fault, configuration, accounting, performance and security (FCAPS) framework for the virtual network services. In NFV, faults and performance issues can have complex geneses within virtual resources as well as virtual networks and cannot be effectively handled by traditional rule-based systems. To tackle the above problem, we propose a fault detection and localization model based on a combination of shallow and deep learning structures. Relatively simpler detection has been effectively shown to be handled by shallow machine learning structures such as support vector machine (SVM). Deeper structure, i.e., the stacked autoencoder has been found to be useful for a more complex localization function where a large amount of information needs to be worked through to get to the root cause of the problem. We provide evaluation results using a dataset adapted from fault datasets available on Kaggle and another based on multivariate kernel density estimation and Markov sampling. - 2017, Springer International Publishing AG, part of Springer Nature.This work has been supported under the grant ID NPRP 6 - 901 - 2 - 370 for the project entitled “Middleware Architecture for cloud based services using software defined networking (SDN),” which is funded by the Qatar national research fund (QNRF) and by Huawei Technologies. The statements made herein are solely the responsibility of the authors.Scopu

Correction: Tree bark scrape fungus: A potential source of laccase for application in bioremediation of non-textile dyes.

[This corrects the article DOI: 10.1371/journal.pone.0229968.]

Learning Noise in Web Data Prior to Elimination

This research work explores how noise in web data is currently addressed. We establish that current research works eliminate noise in web data mainly based on the structure and layout of web pages i.e. they consider noise as any data that does not form part of the main web page. However, not all data that form part of the main web page is of a user interest and not every data considered noise is actually noise to a given user. The ability to determine what is useful from noise data taking into account dynamic change of user interests has not been fully addressed by current research works. We aim to justify a claim that it is important to learn noise prior to elimination, not only to decrease levels of noise but also reduce the loss of useful information otherwise eliminated as noise. This is because if the process of eliminating noise in web data is not user-driven, the interestingness of web data available to a user will not reflect their interests given the time of the request

The sandia fracture challenge: Blind round robin predictions of ductile tearing

Existing and emerging methods in computational mechanics are rarely validated against problems with an unknown outcome. For this reason, Sandia National Laboratories, in partnership with US National Science Foundation and Naval Surface Warfare Center Carderock Division, launched a computational challenge in mid-summer, 2012. Researchers and engineers were invited to predict crack initiation and propagation in a simple but novel geometry fabricated from a common off-the-shelf commercial engineering alloy. The goal of this international Sandia Fracture Challenge was to benchmark the capabilities for the prediction of deformation and damage evolution associated with ductile tearing in structural metals, including physics models, computational methods, and numerical implementations currently available in the computational fracture community. Thirteen teams participated, reporting blind predictions for the outcome of the Challenge. The simulations and experiments were performed independently and kept confidential. The methods for fracture prediction taken by the thirteen teams ranged from very simple engineering calculations to complicated multiscale simulations. The wide variation in modeling results showed a striking lack of consistency across research groups in addressing problems of ductile fracture. While some methods were more successful than others, it is clear that the problem of ductile fracture prediction continues to be challenging. Specific areas of deficiency have been identified through this effort. Also, the effort has underscored the need for additional blind prediction-based assessments