scholarly journals From Ethnomedicine to Plant Biotechnology and Machine Learning: The Valorization of the Medicinal Plant Bryophyllum sp.

2020 ◽  
Vol 13 (12) ◽  
pp. 444
Author(s):  
Pascual García-Pérez ◽  
Eva Lozano-Milo ◽  
Mariana Landin ◽  
Pedro P. Gallego
Keyword(s):  
Machine Learning ◽  
Bioactive Compounds ◽  
Large Scale ◽  
Plant Biotechnology ◽  
Wild Plant ◽  
Learning Technology ◽  
Plant Resources ◽  
Gynecological Disorders ◽  
Pharmaceutical Industries ◽  
Promising Source

The subgenus Bryophyllum includes about 25 plant species native to Madagascar, and is widely used in traditional medicine worldwide. Different formulations from Bryophyllum have been employed for the treatment of several ailments, including infections, gynecological disorders, and chronic diseases, such as diabetes, neurological and neoplastic diseases. Two major families of secondary metabolites have been reported as responsible for these bioactivities: phenolic compounds and bufadienolides. These compounds are found in limited amounts in plants because they are biosynthesized in response to different biotic and abiotic stresses. Therefore, novel approaches should be undertaken with the aim of achieving the phytochemical valorization of Bryophyllum sp., allowing a sustainable production that prevents from a massive exploitation of wild plant resources. This review focuses on the study of phytoconstituents reported on Bryophyllum sp.; the application of plant tissue culture methodology as a reliable tool for the valorization of bioactive compounds; and the application of machine learning technology to model and optimize the full phytochemical potential of Bryophyllum sp. As a result, Bryophyllum species can be considered as a promising source of plant bioactive compounds, with enormous antioxidant and anticancer potential, which could be used for their large-scale biotechnological exploitation in cosmetic, food, and pharmaceutical industries.

Survey of Machine Learning Techniques in Drug Discovery

2019 ◽  
Vol 20 (3) ◽  
pp. 185-193 ◽  
Author(s):  
Natalie Stephenson ◽  
Emily Shane ◽  
Jessica Chase ◽  
Jason Rowland ◽  
David Ries ◽  
...  
Keyword(s):  
Machine Learning ◽  
Drug Discovery ◽  
Large Scale ◽  
State Of The Art ◽  
New Drugs ◽  
Machine Learning Techniques ◽  
Current Status ◽  
Learning Techniques ◽  
Pharmaceutical Industries ◽  
Current Stage

Background:Drug discovery, which is the process of discovering new candidate medications, is very important for pharmaceutical industries. At its current stage, discovering new drugs is still a very expensive and time-consuming process, requiring Phases I, II and III for clinical trials. Recently, machine learning techniques in Artificial Intelligence (AI), especially the deep learning techniques which allow a computational model to generate multiple layers, have been widely applied and achieved state-of-the-art performance in different fields, such as speech recognition, image classification, bioinformatics, etc. One very important application of these AI techniques is in the field of drug discovery.Methods:We did a large-scale literature search on existing scientific websites (e.g, ScienceDirect, Arxiv) and startup companies to understand current status of machine learning techniques in drug discovery.Results:Our experiments demonstrated that there are different patterns in machine learning fields and drug discovery fields. For example, keywords like prediction, brain, discovery, and treatment are usually in drug discovery fields. Also, the total number of papers published in drug discovery fields with machine learning techniques is increasing every year.Conclusion:The main focus of this survey is to understand the current status of machine learning techniques in the drug discovery field within both academic and industrial settings, and discuss its potential future applications. Several interesting patterns for machine learning techniques in drug discovery fields are discussed in this survey.

scholarly journals Machine Learning Technology Reveals the Concealed Interactions of Phytohormones on Medicinal Plant In Vitro Organogenesis

Biomolecules ◽  
2020 ◽  
Vol 10 (5) ◽  
pp. 746 ◽  
Author(s):  
Pascual García-Pérez ◽  
Eva Lozano-Milo ◽  
Mariana Landín ◽  
Pedro Pablo Gallego
Keyword(s):  
Machine Learning ◽  
Large Scale ◽  
Callus Formation ◽  
In Vitro Regeneration ◽  
Pleiotropic Effect ◽  
Learning Technology ◽  
In Vitro Organogenesis ◽  
Neurofuzzy Logic

Organogenesis constitutes the biological feature driving plant in vitro regeneration, in which the role of plant hormones is crucial. The use of machine learning (ML) technology stands out as a novel approach to characterize the combined role of two phytohormones, the auxin indoleacetic acid (IAA) and the cytokinin 6-benzylaminopurine (BAP), on the in vitro organogenesis of unexploited medicinal plants from the Bryophyllum subgenus. The predictive model generated by neurofuzzy logic, a combination of artificial neural networks (ANNs) and fuzzy logic algorithms, was able to reveal the critical factors affecting such multifactorial process over the experimental dataset collected. The rules obtained along with the model allowed to decipher that BAP had a pleiotropic effect on the Bryophyllum spp., as it caused different organogenetic responses depending on its concentration and the genotype, including direct and indirect shoot organogenesis and callus formation. On the contrary, IAA showed an inhibiting role, restricted to indirect shoot regeneration. In this work, neurofuzzy logic emerged as a cutting-edge method to characterize the mechanism of action of two phytohormones, leading to the optimization of plant tissue culture protocols with high large-scale biotechnological applicability.

scholarly journals Classification of Tennis Video Types Based on Machine Learning Technology

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Xun Gong ◽  
Fucheng Wang
Keyword(s):  
Machine Learning ◽  
Large Scale ◽  
Rapid Development ◽  
Video Data ◽  
Learning Technology ◽  
Video Content ◽  
Video Classification ◽  
The World ◽  
Spatial Detection

With the rapid development of online video data, how to find the required information has become an urgent problem to be solved. This article focuses on sports videos and studies video classification and content-based retrieval techniques. Its purpose is to establish a mark and index of video content and to promote user acquisition through computer processing, analysis, and understanding of video content. Video tennis classification has high research and application value. This article focuses on video tennis based on the selection of the basic frame of each shot and proposes an algorithm for classification of shots based on average grouping. Based on this, we use a color-coded spatial detection method to detect the type of tennis match. Then, it integrates the results of audiovisual analysis to identify and classify exciting events in tennis matches. According to statistics, although the number of people participating in tennis cannot enter the top ten, the number of spectators ranks fourth. Four tennis tournaments, masters, and crown tournaments are held every year around the world. Watching large-scale international tennis matches has become a pillar of leisure and vacation for many people. Tennis matches last from two hours to four hours or more, and there are countless large and small tennis matches around the world every year, so the number of tennis records created is staggering. And artificial intelligence technology is rarely used in tennis in the sports world (5%), but football has reached 50%. Therefore, when dealing with such a large amount of data, we urgently need to find a fast and effective video retrieval classification method to find the required information. The experiment of tennis video classification research based on machine learning technology proves that the accuracy of tennis video classification reaches 98%, so this system has high feasibility.

scholarly journals Indonesia Network Infrastructures and Workforce Adequacy to Implement Machine Learning for Large-Scale Manufacturing

2021 ◽  
Vol 8 (1) ◽  
pp. 1-16
Author(s):  
Steven Anderson ◽  
Ansarullah Lawi
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Large Scale ◽  
Manufacturing Industry ◽  
Industrial Revolution ◽  
Technological Development ◽  
Learning Technology ◽  
Monitoring And Control ◽  
Manufacturing Activity ◽  
Industrial Monitoring

Technological development prior to industrial revolution 4.0 incentivized manufacturing industries to invest into digital industry with the aim of increasing the capability and efficiency in manufacturing activity. Major manufacturing industry has begun implementing cyber-physical system in industrial monitoring and control. The system itself will generate large volumes of data. The ability to process those big data requires algorithm called machine learning because of its ability to read patterns of big data for producing useful information. This study conducted on premises of Indonesia’s current network infrastructure and workforce capability on supporting the implementation of machine learning especially in large-scale manufacture. That will be compared with countries that have a positive stance in implementing machine learning in manufacturing. The conclusions that can be drawn from this research are Indonesia current infrastructure and workforce is still unable to fully support the implementation of machine learning technology in manufacturing industry and improvements are needed.

scholarly journals Machine learning and big scientific data

2020 ◽  
Vol 378 (2166) ◽  
pp. 20190054 ◽  
Author(s):  
Tony Hey ◽  
Keith Butler ◽  
Sam Jackson ◽  
Jeyarajan Thiyagalingam
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Language Processing ◽  
High Performance ◽  
Large Scale ◽  
Materials Science ◽  
Numerical Algorithms ◽  
Scientific Data ◽  
Learning Technology ◽  
Challenges And Opportunities

This paper reviews some of the challenges posed by the huge growth of experimental data generated by the new generation of large-scale experiments at UK national facilities at the Rutherford Appleton Laboratory (RAL) site at Harwell near Oxford. Such ‘Big Scientific Data’ comes from the Diamond Light Source and Electron Microscopy Facilities, the ISIS Neutron and Muon Facility and the UK's Central Laser Facility. Increasingly, scientists are now required to use advanced machine learning and other AI technologies both to automate parts of the data pipeline and to help find new scientific discoveries in the analysis of their data. For commercially important applications, such as object recognition, natural language processing and automatic translation, deep learning has made dramatic breakthroughs. Google's DeepMind has now used the deep learning technology to develop their AlphaFold tool to make predictions for protein folding. Remarkably, it has been able to achieve some spectacular results for this specific scientific problem. Can deep learning be similarly transformative for other scientific problems? After a brief review of some initial applications of machine learning at the RAL, we focus on challenges and opportunities for AI in advancing materials science. Finally, we discuss the importance of developing some realistic machine learning benchmarks using Big Scientific Data coming from several different scientific domains. We conclude with some initial examples of our ‘scientific machine learning’ benchmark suite and of the research challenges these benchmarks will enable. This article is part of a discussion meeting issue ‘Numerical algorithms for high-performance computational science’.

Conscious Mind, Resonant Brain

2021 ◽  
Author(s):  
Stephen Grossberg
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Large Scale ◽  
Neural Circuits ◽  
Physical World ◽  
Brain Regions ◽  
Human Condition ◽  
Learning Technology ◽  
Large Scale Problems ◽  
The World

The book is the culmination of 50 years of intensive research by the author, who is broadly acknowledged to be the most important pioneer and current research leader who models how brains give rise to minds, notably how neural circuits in multiple brain regions interact together to generate psychological functions. The book provides a unified understanding of how, where, and why our brains can consciously see, hear, feel, and know about the world, and effectively plan and act within it. It hereby embodies a revolutionary Principia of Mind that clarifies how autonomous adaptive intelligence is achieved, thereby providing mechanistic explanations of multiple mental disorders, biological bases of morality, religion, and the human condition, as well as solutions to large-scale problems in machine learning, technology, and Artificial Intelligence. Because brains embody a universal developmental code, unifying insights also emerge about all living cellular tissues and about how mental laws reflect laws of the physical world.

scholarly journals The Combination of Untargeted Metabolomics and Machine Learning Predicts the Biosynthesis of Phenolic Compounds in Bryophyllum Medicinal Plants (Genus Kalanchoe)

Plants ◽  
2021 ◽  
Vol 10 (11) ◽  
pp. 2430
Author(s):  
Pascual García-Pérez ◽  
Leilei Zhang ◽  
Begoña Miras-Moreno ◽  
Eva Lozano-Milo ◽  
Mariana Landin ◽  
...  
Keyword(s):  
Machine Learning ◽  
Phenolic Compounds ◽  
Medicinal Plants ◽  
Bioactive Compounds ◽  
Culture Media ◽  
Anthocyanin Biosynthesis ◽  
Critical Role ◽  
Untargeted Metabolomics ◽  
Dependent Manner ◽  
Promising Source

Phenolic compounds constitute an important family of natural bioactive compounds responsible for the medicinal properties attributed to Bryophyllum plants (genus Kalanchoe, Crassulaceae), but their production by these medicinal plants has not been characterized to date. In this work, a combinatorial approach including plant tissue culture, untargeted metabolomics, and machine learning is proposed to unravel the critical factors behind the biosynthesis of phenolic compounds in these species. The untargeted metabolomics revealed 485 annotated compounds that were produced by three Bryophyllum species cultured in vitro in a genotype and organ-dependent manner. Neurofuzzy logic (NFL) predictive models assessed the significant influence of genotypes and organs and identified the key nutrients from culture media formulations involved in phenolic compound biosynthesis. Sulfate played a critical role in tyrosol and lignan biosynthesis, copper in phenolic acid biosynthesis, calcium in stilbene biosynthesis, and magnesium in flavanol biosynthesis. Flavonol and anthocyanin biosynthesis was not significantly affected by mineral components. As a result, a predictive biosynthetic model for all the Bryophyllum genotypes was proposed. The combination of untargeted metabolomics with machine learning provided a robust approach to achieve the phytochemical characterization of the previously unexplored species belonging to the Bryophyllum subgenus, facilitating their biotechnological exploitation as a promising source of bioactive compounds.

scholarly journals Automatic Freezing-Tolerant Rapeseed Material Recognition Using UAV Images and Deep Learning

2021 ◽  
Author(s):  
Lili Li ◽  
Jiangwei Qiao ◽  
Jian Yao ◽  
Jie Li ◽  
Li Li
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Large Scale ◽  
Field Investigation ◽  
Freezing Injury ◽  
Learning Technology ◽  
Learning Networks ◽  
Material Recognition ◽  
Comparison Results ◽  
Uav Images

Abstract Background: Freezing injury is a serious and common damage that occurs to winter rapeseed during the overwintering period. The freezing injury directly reduces the rapeseed yield and causes serious economic loss. Thus, it is an important and urgent task for crop breeders to find the freezing-tolerant rapeseed materials in the process of breeding. Existing large-scale freezing-tolerant rapeseed material recognition methods mainly rely on the field investigation conducted by the agricultural experts using some professional equipment. These methods are time-consuming, inefficient and laborious. In addition, the accuracy of these traditional methods depends heavily on the knowledge and experience of experts. Methods: To solve these problems of existing methods, we propose a low-cost freezing-tolerant rapeseed material recognition approach using deep learning technology and unmanned aerial vehicle (UAV) images captured by a consumer drone. We formulate the problem of freezing-tolerant material recognition as a binary classification problem, which can be solved well using deep learning technology. The proposed method can automatically and efficiently recognize the freezing-tolerant rapeseed materials from a large number of candidates. To train the deep learning network, we first manually construct the real dataset using the UAV images of rapeseed materials collected by the Phantom 4 Pro. Then, five classic deep learning networks (AlexNet, VGGNet16, ResNet18, ResNet50 and GoogLeNet) are selected to perform the freezing-tolerant rapeseed material recognition. Result and Conclusion: The accuracy of the five deep learning networks used in our work is all over 92%. Especially, ResNet50 provides the best accuracy (93.33%) in this task. In addition, we also compare deep learning networks with traditional machine learning methods. The comparison results show that the deep learning-based approach significantly outperforms the traditional machine learning-based methods in our task. The experimental results show that it is feasible to recognize the freezing-tolerant rapeseed using UAV images and deep learning.

Sign in / Sign up

Export Citation Format

Share Document