scholarly journals Using Peptidomics and Machine Learning to Assess Effects of Drying Processes on the Peptide Profile within a Functional Ingredient

Processes ◽  
2021 ◽  
Vol 9 (3) ◽  
pp. 425
Author(s):  
Sweeny Chauhan ◽  
Sean O’Callaghan ◽  
Audrey Wall ◽  
Tomasz Pawlak ◽  
Ben Doyle ◽  
...  
Keyword(s):  
Machine Learning ◽  
Bioactive Peptides ◽  
Physicochemical Characteristics ◽  
Peptide Profile ◽  
Functional Ingredient ◽  
Natural Peptide ◽  
Anti Inflammatory ◽  
Peptide Profiles ◽  
Drying Conditions ◽  
Processing Techniques

Bioactive peptides are known to have many health benefits beyond nutrition; yet the peptide profile of high protein ingredients has been largely overlooked when considering the effects of different processing techniques. Therefore, to investigate whether drying conditions could affect the peptide profile and bioactivity within a functional ingredient, we examined the effects of spray (SD) and freeze (FD) drying on rice natural peptide network (NPN), a characterised functional ingredient sourced from the Oryza sativa proteome, which has previously been shown to effectively modulate circulating cytokines and improve physical performance in humans. In the manufacturing process, rice NPN was either FD or SD. Employing a peptidomic approach, we investigated the physicochemical characteristics of peptides common and unique to FD and SD preparations. We observed similar peptide profiles regarding peptide count, amino acid distribution, weight, charge, and hydrophobicity in each sample. Additionally, to evaluate the effects of drying processes on functionality, using machine learning, we examined constituent peptides with predicted anti-inflammatory activity within both groups and identified that the majority of anti-inflammatory peptides were common to both. Of note, key bioactive peptides validated within rice NPN were recorded in both SD and FD samples. The present study provides an important insight into the overall stability of the peptide profile and the use of machine learning in assessing predicted retention of bioactive peptides contributing to functionality during different types of processing.

scholarly journals A review of infant cry analysis and classification

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Chunyan Ji ◽  
Thosini Bamunu Mudiyanselage ◽  
Yutong Gao ◽  
Yi Pan
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Signal Analysis ◽  
Future Research ◽  
Prosodic Features ◽  
Infant Cry ◽  
Machine Learning Classification ◽  
Machine Learning Classifiers ◽  
Learning Classifiers ◽  
Processing Techniques

AbstractThis paper reviews recent research works in infant cry signal analysis and classification tasks. A broad range of literatures are reviewed mainly from the aspects of data acquisition, cross domain signal processing techniques, and machine learning classification methods. We introduce pre-processing approaches and describe a diversity of features such as MFCC, spectrogram, and fundamental frequency, etc. Both acoustic features and prosodic features extracted from different domains can discriminate frame-based signals from one another and can be used to train machine learning classifiers. Together with traditional machine learning classifiers such as KNN, SVM, and GMM, newly developed neural network architectures such as CNN and RNN are applied in infant cry research. We present some significant experimental results on pathological cry identification, cry reason classification, and cry sound detection with some typical databases. This survey systematically studies the previous research in all relevant areas of infant cry and provides an insight on the current cutting-edge works in infant cry signal analysis and classification. We also propose future research directions in data processing, feature extraction, and neural network classification fields to better understand, interpret, and process infant cry signals.

The Role of Machine Learning in Centralized Authorization Process of Nanomedicines in European Union

2021 ◽  
Vol 21 ◽  
Author(s):  
Ricardo Santana ◽  
Enrique Onieva ◽  
Robin Zuluaga ◽  
Aliuska Duardo-Sánchez ◽  
Piedad Gañán
Keyword(s):  
Machine Learning ◽  
European Union ◽  
Scientific Knowledge ◽  
Predictive Models ◽  
Physicochemical Characteristics ◽  
Success Rates ◽  
European Regulation ◽  
European Public ◽  
European Public Assessment Report

Background: Machine Learning (ML) has experienced an increasing use given the possibilities to expand the scientific knowledge of different disciplines, such as nanotechnology. This has allowed the creation of Cheminformatic models, capable of predicting biological activity and physicochemical characteristics of new components with high success rates in training and test partitions. Given the current gaps of scientific knowledge and the need of efficient application of medicines products law, this paper analyzes the position of regulators for marketing medicinal nanoproducts in European Union and the role of ML in the authorization process. Methods: In terms of methodology, a dogmatic study of the European regulation and the guidances of the European Medicine Agency on the use of predictive models for nanomaterials was carried out. The study has, as the framework of reference, the European Regulation 726/2004 and has focused on the analysis of how ML processes are contemplated in the regulations. Results: As result, we present a discussion of the information that must be provided for every case for simulation methods. The results show a favorable and flexible position for the development of the use of predictive models to complement the applicant's information. Conclusion: It is concluded that Machine Learning has the capacity to help to improve the application of nanotechnology medicine products regulation. Future regulations should promote this kind of information given the advanced state of art in terms of algorithms that are able to build accurate predictive models. This especially applies to methods such as Perturbation Theory Machine Learning (PTML), given that it is aligned with principles promoted by the standards of Organization for Economic Co-operation and Development (OECD), European Union regulations and European Authority Medicine. To our best knowledge this is the first study focused on nanotechnology medicine products and machine learning use to support technical European public assessment report (EPAR) for complementary information.

scholarly journals Enabling Smart Buildings by Indoor Visible Light Communications and Machine Learning

2019 ◽  
Author(s):  
Shuping Dang ◽  
Guoqing Ma ◽  
Basem Shihada ◽  
Mohamed-Slim Alouini
Keyword(s):  
Machine Learning ◽  
Visible Light ◽  
Data Transmission ◽  
Visible Light Communications ◽  
Monitoring And Control ◽  
Smart Building ◽  
Wide Range ◽  
Tremendous Amount ◽  
Processing Techniques ◽  
And Control

<pre>The smart building (SB), a promising solution to the fast-paced and continuous urbanization around the world, is an integration of a wide range of systems and services and involves a construction of multiple layers. The SB is capable of sensing, acquiring and processing a tremendous amount of data as well as performing proper action and adaptation accordingly. With rapid increases in the number of connected nodes and thereby the data transmission demand in SBs, conventional transmission and processing techniques are insufficient to provide satisfactory services. To enhance the intelligence of SBs and achieve efficient monitoring and control, both indoor visible light communications (VLC) and machine learning (ML) shall be applied jointly to construct a reliable data transmission network with powerful data processing and reasoning abilities. In this regard, we envision an SB framework enabled by indoor VLC and ML in this article.</pre>

RESEARCHING METHODS FOR PROCESSING TEXT INFORMATION AND REVIEWING THE STAGES OF AN ARTIFICIAL INTELLIGENCE MODEL CREATION AT PRODUCING CHATBOTS

2021 ◽  
Vol 2021 (2) ◽  
pp. 19-23
Author(s):  
Anastasiya Ivanova ◽  
Aleksandr Kuz'menko ◽  
Rodion Filippov ◽  
Lyudmila Filippova ◽  
Anna Sazonova ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Machine Learning ◽  
Text Processing ◽  
Text Format ◽  
Machine Learning Methods ◽  
Methods And Techniques ◽  
Text Information ◽  
Processing Techniques ◽  
Machine Processing

The task of producing a chatbot based on a neural network supposes machine processing of the text, which in turn involves using various methods and techniques for analyzing phrases and sentences. The article considers the most popular solutions and models for data analysis in the text format: methods of lemmatization, vectorization, as well as machine learning methods. Particular attention is paid to the text processing techniques, after their analyzing the best method was identified and tested.

PENGARUH PROSES M EMBRAN U LTRAFILTRASI DAN ULTRAVIOLET TERHADAP KOMPOSISI GIZI, SIFAT FISIKOKIMIA DAN ORGANOLEPTIK MINUMAN AIR KELAPA / The Effects of Ultrafiltration and Ultraviolet Process on Nutritional Composition, Physicochemical and Organoleptic

2016 ◽  
Vol 22 (1) ◽  
pp. 43 ◽  
Author(s):  
SARI INTAN KAILAKU ◽  
BUDI SETIAWAN ◽  
AHMAD SULAEMAN
Keyword(s):  
Shelf Life ◽  
Food Additives ◽  
Physicochemical Characteristics ◽  
Nutritional Composition ◽  
Coconut Water ◽  
Organoleptic Properties ◽  
Membrane Unit ◽  
Organoleptic Characteristics ◽  
Analysis Laboratory ◽  
Processing Techniques

<p>The obstacle in developing coconut water-based product is its easily altered properties. Ultrafiltration and ultraviolet processing are potential to obtain a longer shelf life for coconut water drink without altering its nutritional values and unique organoleptic properties, unlike other processing techniques e.g. pasteurization and ultra high temperature. Shelf-life estimation experiment showed that ultrafiltration-and- ultraviolet-processed coconut water without any addition of food additives can be stored for 51 days in 00C. The objective of this research was to evaluate the effects of ultrafiltration and ultraviolet treatments on the nutritional, physicochemical and organoleptic properties of coconut water drink. The experiments were carried out at Food Analysis Laboratory, Indonesian Center of Agricultural Postharvest Research and Development, on January-April 2015. Coconut water was flown through the ultrafiltration membrane unit and ultraviolet light unit, samples were collected in three repetitions. Nutritional composition and physicochemical properties of fresh coconut water (FCW) and coconut water drink obtained from ultrafiltration and ultraviolet process (CUU) were evaluated and compared. Organoleptic analysis was done by 20 panelists, observations included quality hedonic (aroma, sweetness, saltiness, sourness and turbidity), and acceptance (preferance and ranking test), comparing FCW and CUU with commercial coconut water drink (CWD). CUU showed indistinguishable nutritional composition and physicochemical characteristics from FCW (p&gt;0,05), except on total sugar (p=0,049), clarity (p=0,001), L* (lightness) (p=0,000) and b* (yellowish) (p=0,002). Panelists gave CUU a statistically equal rank to FCW, and better than CWD. The organoleptic characteristics of CUU were concluded as relatively same in saltiness and aroma as FCW, and less intense in sweetness and turbidity compared to CWD. After 10 days storage, panelists level of liking was higher for CUU compared to CWD in color (p=0,004) and general appearance (p=0,016).</p><p>Keywords: coconut water, nutritional composition, organoleptic properties ultrafiltration, ultraviolet</p>

The Classification of Skateboarding Trick Manoeuvres Through the Integration of Image Processing Techniques and Machine Learning

2020 ◽  
pp. 347-356
Author(s):  
Muhammad Nur Aiman Shapiee ◽  
Muhammad Ar Rahim Ibrahim ◽  
Mohd Azraai Mohd Razman ◽  
Muhammad Amirul Abdullah ◽  
Rabiu Muazu Musa ◽  
...  
Keyword(s):  
Machine Learning ◽  
Image Processing ◽  
Image Processing Techniques ◽  
Processing Techniques
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