scholarly journals FireProtDB: database of manually curated protein stability data

2020 ◽  
Vol 49 (D1) ◽  
pp. D319-D324
Author(s):  
Jan Stourac ◽  
Juraj Dubrava ◽  
Milos Musil ◽  
Jana Horackova ◽  
Jiri Damborsky ◽  
...  
Keyword(s):  
Machine Learning ◽  
Elevated Temperatures ◽  
Recent Literature ◽  
Single Point ◽  
Biotechnological Applications ◽  
Computational Tools ◽  
Mild Conditions ◽  
Naturally Occurring ◽  
Stability Data ◽  
Living Organisms

Abstract The majority of naturally occurring proteins have evolved to function under mild conditions inside the living organisms. One of the critical obstacles for the use of proteins in biotechnological applications is their insufficient stability at elevated temperatures or in the presence of salts. Since experimental screening for stabilizing mutations is typically laborious and expensive, in silico predictors are often used for narrowing down the mutational landscape. The recent advances in machine learning and artificial intelligence further facilitate the development of such computational tools. However, the accuracy of these predictors strongly depends on the quality and amount of data used for training and testing, which have often been reported as the current bottleneck of the approach. To address this problem, we present a novel database of experimental thermostability data for single-point mutants FireProtDB. The database combines the published datasets, data extracted manually from the recent literature, and the data collected in our laboratory. Its user interface is designed to facilitate both types of the expected use: (i) the interactive explorations of individual entries on the level of a protein or mutation and (ii) the construction of highly customized and machine learning-friendly datasets using advanced searching and filtering. The database is freely available at https://loschmidt.chemi.muni.cz/fireprotdb.

scholarly journals Metabolomics-Guided Elucidation of Plant Abiotic Stress Responses in the 4IR Era: An Overview

Metabolites ◽  
2021 ◽  
Vol 11 (7) ◽  
pp. 445
Author(s):  
Morena M. Tinte ◽  
Kekeletso H. Chele ◽  
Justin J. J. van der Hooft ◽  
Fidele Tugizimana
Keyword(s):  
Machine Learning ◽  
Abiotic Stress ◽  
Stress Responses ◽  
Abiotic Stresses ◽  
Industrial Revolution ◽  
Chemical Space ◽  
Big Data Analytics ◽  
Plant Responses ◽  
Next Generation ◽  
Computational Tools

Plants are constantly challenged by changing environmental conditions that include abiotic stresses. These are limiting their development and productivity and are subsequently threatening our food security, especially when considering the pressure of the increasing global population. Thus, there is an urgent need for the next generation of crops with high productivity and resilience to climate change. The dawn of a new era characterized by the emergence of fourth industrial revolution (4IR) technologies has redefined the ideological boundaries of research and applications in plant sciences. Recent technological advances and machine learning (ML)-based computational tools and omics data analysis approaches are allowing scientists to derive comprehensive metabolic descriptions and models for the target plant species under specific conditions. Such accurate metabolic descriptions are imperatively essential for devising a roadmap for the next generation of crops that are resilient to environmental deterioration. By synthesizing the recent literature and collating data on metabolomics studies on plant responses to abiotic stresses, in the context of the 4IR era, we point out the opportunities and challenges offered by omics science, analytical intelligence, computational tools and big data analytics. Specifically, we highlight technological advancements in (plant) metabolomics workflows and the use of machine learning and computational tools to decipher the dynamics in the chemical space that define plant responses to abiotic stress conditions.

scholarly journals Distributed Learning Applications in Power Systems: A Review of Methods, Gaps, and Challenges

Energies ◽  
2021 ◽  
Vol 14 (12) ◽  
pp. 3654
Author(s):  
Nastaran Gholizadeh ◽  
Petr Musilek
Keyword(s):  
Machine Learning ◽  
Power Systems ◽  
Learning Algorithm ◽  
Single Point ◽  
Distributed Learning ◽  
Large Data ◽  
Multi Agent Systems ◽  
Power Quality Monitoring ◽  
Multi Agent ◽  
Learning Frameworks

In recent years, machine learning methods have found numerous applications in power systems for load forecasting, voltage control, power quality monitoring, anomaly detection, etc. Distributed learning is a subfield of machine learning and a descendant of the multi-agent systems field. Distributed learning is a collaboratively decentralized machine learning algorithm designed to handle large data sizes, solve complex learning problems, and increase privacy. Moreover, it can reduce the risk of a single point of failure compared to fully centralized approaches and lower the bandwidth and central storage requirements. This paper introduces three existing distributed learning frameworks and reviews the applications that have been proposed for them in power systems so far. It summarizes the methods, benefits, and challenges of distributed learning frameworks in power systems and identifies the gaps in the literature for future studies.

scholarly journals Societal impact of synthetic biology: responsible research and innovation (RRI)

2016 ◽  
Vol 60 (4) ◽  
pp. 371-379 ◽  
Author(s):  
Daniel Gregorowius ◽  
Anna Deplazes-Zemp
Keyword(s):  
Synthetic Biology ◽  
Technology Assessment ◽  
Ethical Issues ◽  
Responsible Research And Innovation ◽  
Societal Impact ◽  
Biotechnological Applications ◽  
Research And Innovation ◽  
Challenges And Opportunities ◽  
Living Organisms ◽  
Responsible Research

Synthetic biology is an emerging field at the interface between biology and engineering, which has generated many expectations for beneficial biomedical and biotechnological applications. At the same time, however, it has also raised concerns about risks or the aim of producing new forms of living organisms. Researchers from different disciplines as well as policymakers and the general public have expressed the need for a form of technology assessment that not only deals with technical aspects, but also includes societal and ethical issues. A recent and very influential model of technology assessment that tries to implement these aims is known as RRI (Responsible Research and Innovation). In this paper, we introduce this model and its historical precursor strategies. Based on the societal and ethical issues which are presented in the current literature, we discuss challenges and opportunities of applying the RRI model for the assessment of synthetic biology.

scholarly journals Assessing Conformer Energies using Electronic Structure and Machine Learning Methods

2020 ◽  
Author(s):  
Dakota Folmsbee ◽  
Geoffrey Hutchison
Keyword(s):  
Machine Learning ◽  
Electronic Structure ◽  
Density Functional ◽  
Large Scale ◽  
Single Point ◽  
Semiempirical Method ◽  
Coupled Cluster ◽  
Scale Evaluation ◽  
Machine Learning Methods ◽  
Electronic Structure Methods

We have performed a large-scale evaluation of current computational methods, including conventional small-molecule force fields, semiempirical, density functional, ab initio electronic structure methods, and current machine learning (ML) techniques to evaluate relative single-point energies. Using up to 10 local minima geometries across ~700 molecules, each optimized by B3LYP-D3BJ with single-point DLPNO-CCSD(T) triple-zeta energies, we consider over 6,500 single points to compare the correlation between different methods for both relative energies and ordered rankings of minima. We find promise from current ML methods and recommend methods at each tier of the accuracy-time tradeoff, particularly the recent GFN2 semiempirical method, the B97-3c density functional approximation, and RI-MP2 for accurate conformer energies. The ANI family of ML methods shows promise, particularly the ANI-1ccx variant trained in part on coupled-cluster energies. Multiple methods suggest continued improvements should be expected in both performance and accuracy.

scholarly journals Assessing Conformer Energies using Electronic Structure and Machine Learning Methods

2020 ◽  
Author(s):  
Dakota Folmsbee ◽  
Geoffrey Hutchison
Keyword(s):  
Machine Learning ◽  
Electronic Structure ◽  
Density Functional ◽  
Large Scale ◽  
Single Point ◽  
Semiempirical Method ◽  
Coupled Cluster ◽  
Scale Evaluation ◽  
Machine Learning Methods ◽  
Electronic Structure Methods

We have performed a large-scale evaluation of current computational methods, including conventional small-molecule force fields, semiempirical, density functional, ab initio electronic structure methods, and current machine learning (ML) techniques to evaluate relative single-point energies. Using up to 10 local minima geometries across ~700 molecules, each optimized by B3LYP-D3BJ with single-point DLPNO-CCSD(T) triple-zeta energies, we consider over 6,500 single points to compare the correlation between different methods for both relative energies and ordered rankings of minima. We find promise from current ML methods and recommend methods at each tier of the accuracy-time tradeoff, particularly the recent GFN2 semiempirical method, the B97-3c density functional approximation, and RI-MP2 for accurate conformer energies. The ANI family of ML methods shows promise, particularly the ANI-1ccx variant trained in part on coupled-cluster energies. Multiple methods suggest continued improvements should be expected in both performance and accuracy.

Artificial Metabolic Networks: enabling neural computation with metabolic networks

2022 ◽  
Author(s):  
Leon Faure ◽  
Bastien Mollet ◽  
Wolfram Liebermeister ◽  
Jean-Loup Faulon
Keyword(s):  
Machine Learning ◽  
Recurrent Neural Networks ◽  
Metabolic Networks ◽  
Cross Validation ◽  
Regression Coefficient ◽  
Network Models ◽  
Biotechnological Applications ◽  
Neural Network Models ◽  
Surrogate Constraint ◽  
Constraint Based Modeling

Metabolic networks have largely been exploited as mechanistic tools to predict the behavior of microorganisms with a defined genotype in different environments. However, flux predictions by constraint-based modeling approaches are limited in quality unless labor-intensive experiments including the measurement of media intake fluxes, are performed. Using machine learning instead of an optimization of biomass flux - on which most existing constraint-based methods are based - provides ways to improve flux and growth rate predictions. In this paper, we show how Recurrent Neural Networks can surrogate constraint-based modeling and make metabolic networks suitable for backpropagation and consequently be used as an architecture for machine learning. We refer to our hybrid - mechanistic and neural network - models as Artificial Metabolic Networks (AMN). We showcase AMN and illustrate its performance with an experimental dataset of Escherichia coli growth rates in 73 different media compositions. We reach a regression coefficient of R2=0.78 on cross-validation sets. We expect AMNs to provide easier discovery of metabolic insights and prompt new biotechnological applications.

scholarly journals Novel H-Bonded Synthons in Copper Supramolecular Frameworks with Aminoethylpiperazine-Based Ligands. Synthesis, Structure and Catalytic Activity

Materials ◽  
2020 ◽  
Vol 13 (23) ◽  
pp. 5435
Author(s):  
Oksana V. Nesterova ◽  
Armando J. L. Pombeiro ◽  
Dmytro S. Nesterov
Keyword(s):  
Reaction Mechanisms ◽  
Active Catalyst ◽  
Single Point ◽  
Organic Ligand ◽  
Schiff Base Complexes ◽  
X Ray Diffraction ◽  
X Ray ◽  
Mild Conditions ◽  
Butyl Peroxide ◽  
Supramolecular Networks

New Schiff base complexes [Cu2(HL1)(L1)(N3)3]∙2H2O (1) and [Cu2L2(N3)2]∙H2O (2) were synthesized. The crystal structures of 1 and 2 were determined by single-crystal X-ray diffraction analysis. The HL1 ligand results from the condensation of salicylaldehyde and 1-(2-aminoethyl)piperazine, while a new organic ligand, H2L2, was formed by the dimerization of HL1 via a coupling of two piperazine rings of HL1 on a carbon atom coming from DMF solvent. The dinuclear building units in 1 and 2 are linked into complex supramolecular networks through hydrogen and coordination bondings, resulting in 2D and 1D architectures, respectively. Single-point and broken-symmetry DFT calculations disclosed negligible singlet–triplet splittings within the dinuclear copper fragments in 1 and 2. Catalytic studies showed a remarkable activity of 1 and 2 towards cyclohexane oxidation with H2O2 in the presence of nitric acid and pyridine as promoters and under mild conditions (yield of products up to 21%). Coordination compound 1 also acts as an active catalyst in the intermolecular coupling of cyclohexane with benzamide using di-tert-butyl peroxide (tBuOOtBu) as a terminal oxidant. Conversion of benzamide at 55% was observed after 24 h reaction time. By-product patterns and plausible reaction mechanisms are discussed.

scholarly journals Tool wear estimation in turning of Inconel 718 based on wavelet sensor signal analysis and machine learning paradigms

2020 ◽  
Vol 14 (5-6) ◽  
pp. 693-705
Author(s):  
Tiziana Segreto ◽  
Doriana D’Addona ◽  
Roberto Teti
Keyword(s):  
Machine Learning ◽  
Tool Wear ◽  
Inconel 718 ◽  
Signal Analysis ◽  
Elevated Temperatures ◽  
Wavelet Packet ◽  
Research Work ◽  
High Strength ◽  
Signal Features ◽  
Tool Wear Estimation

AbstractIn the last years, hard-to-machine nickel-based alloys have been widely employed in the aerospace industry for their properties of high strength, excellent resistance to corrosion and oxidation, and long creep life at elevated temperatures. As the machinability of these materials is quite low due to high cutting forces, high temperature development and strong work hardening, during machining the cutting tool conditions tend to rapidly deteriorate. Thus, tool health monitoring systems are highly desired to improve tool life and increase productivity. This research work focuses on tool wear estimation during turning of Inconel 718 using wavelet packet transform (WPT) signal analysis and machine learning paradigms. A multiple sensor monitoring system, based on the detection of cutting force, acoustic emission and vibration acceleration signals, was employed during experimental turning trials. The detected sensor signals were subjected to WPT decomposition to extract diverse signal features. The most relevant features were then selected, using correlation measurements, in order to be utilized in artificial neural network based machine learning paradigms for tool wear estimation.

scholarly journals Infection Control in Dental Practice During the COVID-19 Pandemic

2020 ◽  
Vol 17 (13) ◽  
pp. 4769 ◽  
Author(s):  
Alessandra Amato ◽  
Mario Caggiano ◽  
Massimo Amato ◽  
Giuseppina Moccia ◽  
Mario Capunzo ◽  
...  
Keyword(s):  
Recent Literature ◽  
Close Contact ◽  
Severe Form ◽  
Dental Practice ◽  
Interpersonal Distance ◽  
Airborne Infection ◽  
Dental Practitioners ◽  
The World ◽  
Living Organisms ◽  
Respiratory Droplets

COVID-19 is the disease supported by SARS-CoV-2 infection, which causes a severe form of pneumonia. Due to the pathophysiological characteristics of the COVID-19 syndrome, the particular transmissibility of SARS-CoV-2, and the high globalization of our era, the epidemic emergency from China has spread rapidly all over the world. Human-to-human transmission seems to occur mainly through close contact with symptomatic people affected by COVID-19, and the main way of contagion is via the inhalation of respiratory droplets, for example when patients talk, sneeze or cough. The ability of the virus to survive outside living organisms, in aerosol or on fomites has also been recognized. The dental practitioners are particularly exposed to a high risk of SARS-CoV-2 infection because they cannot always respect the interpersonal distance of more than a meter and are exposed to saliva, blood, and other body fluids during surgical procedures. Moreover, many dental surgeries can generate aerosol, and the risk of airborne infection is to be considered higher. The aim of this paper is to provide practical advice for dentists based on the recent literature, which may be useful in reducing the risk of spreading COVID-19 during clinical practice.

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