scholarly journals Analysis of Membrane Process Model from Black Box to Machine Learning

2022 ◽  
pp. 1-8
Author(s):  
Agnar Alfons Ramel
Keyword(s):  
Machine Learning ◽  
Process Model ◽  
Membrane Computing ◽  
Biological Activities ◽  
Mechanistic Modeling ◽  
Optimum Process ◽  
Membrane Process ◽  
The Past ◽  
Membrane Processing ◽  
Data Driven Modeling

The membrane processes include the complex frameworks, typically integrating various physio-chemical aspects, and the biological activities, based on the systems researched. In that regard, the process modeling is essential to predict and simulate the process and the performance of membranes, to infer concerning the optimum process aspects, meant to analyze fouling developments, and principally, the controls and monitoring of processes. Irrespective of the real terminological dissemination such as Machine Learning (ML), the application of computing instruments to the processes of model membrane was considered in the past are insignificant from the scholarly perspective, not contributing to our knowledge of the aspects included. Irrespective of the controversies, in the past two decades, non-mechanistic and data-driven modeling is applicable to illustrate various membrane process, and in the establishment of novel tracking and modeling approaches. In that regard, this paper concentrates on the provision of a custom aspect regarding the use of Non-Mechanistic Modeling (NMM) in membrane processing, assessing the transformations endorsed by our experience, accomplished as a research segment operational in the membrane process segment. Furthermore, the guidelines are the problems for the application of the state-of-the-art computational instruments Membrane Computing (MC).

Click Reactions in Chemistry of Triterpenes - Advances Towards Development of Potential Therapeutics

2018 ◽  
Vol 25 (5) ◽  
pp. 636-658 ◽  
Author(s):  
Jan Pokorny ◽  
Lucie Borkova ◽  
Milan Urban
Keyword(s):  
Biological Activities ◽  
Synthetic Approach ◽  
Clinical Use ◽  
New Approach ◽  
Click Reactions ◽  
Drug Candidates ◽  
The Past ◽  
Low Toxicity ◽  
New Compounds ◽  
Potential Therapeutics

Triterpenoids are natural compounds with a large variety of biological activities such as anticancer, antiviral, antibacterial, antifungal, antiparazitic, antiinflammatory and others. Despite their low toxicity and simple availability from the natural resources, their clinical use is still severely limited by their higher IC50 and worse pharmacological properties than in the currently used therapeutics. This fact encouraged a number of researchers to develop new terpenic derivatives more suitable for the potential clinical use. This review summarizes a new approach to improve both, the activity and ADME-Tox properties by connecting active terpenes to another modifying molecules using click reactions. Within the past few years, this synthetic approach was well explored yielding a lot of great improvements of the parent compounds along with some less successful attempts. A large quantity of the new compounds presented here are superior in both activity and ADME-Tox properties to their parents. This review should serve the researchers who need to promote their hit triterpenic structures towards their clinical use and it is intended as a guide for the chemical synthesis of better drug candidates.

Structural and Bioactive Studies of Halogenated Constituents from Sponges

2020 ◽  
Vol 27 (14) ◽  
pp. 2335-2360 ◽  
Author(s):  
Chao Li ◽  
Dayong Shi
Keyword(s):  
Microbial Communities ◽  
Organic Compounds ◽  
Biological Activities ◽  
Marine Sponges ◽  
Nitrogen Compounds ◽  
Halogenated Organic Compounds ◽  
Bioactive Natural Products ◽  
The Past ◽  
Tyrosine Derivatives ◽  
Polyunsaturated Lipids

: Marine organisms are abundant sources of bioactive natural products. Among metabolites produced by sponges and their associated microbial communities, halogenated natural compounds accounted for an important part due to their potent biological activities. The present review updates and compiles a total of 258 halogenated organic compounds isolated in the past three decades, especially brominated derivatives derived from 31 genera of marine sponges. These compounds can be classified as the following classes: brominated polyunsaturated lipids, nitrogen compounds, brominated tyrosine derivatives and other halogenated compounds. These substances were listed together with their source organisms, structures and bioactivities. For this purpose, 84 references were consulted.

Single Heterocyclic Compounds as Monoamine Oxidase Inhibitors: From Past to Present

2020 ◽  
Vol 20 (10) ◽  
pp. 908-920 ◽  
Author(s):  
Su-Min Wu ◽  
Xiao-Yang Qiu ◽  
Shu-Juan Liu ◽  
Juan Sun
Keyword(s):  
Monoamine Oxidase ◽  
Heterocyclic Compounds ◽  
Biological Activities ◽  
The Past ◽  
Structure Activity ◽  
Receptor Interactions ◽  
Heterocyclic Derivatives ◽  
Inhibitory Activities ◽  
Leading Compounds ◽  
Heterocyclic Moiety

Inhibitors of monoamine oxidase (MAO) have shown therapeutic values in a variety of neurodegenerative diseases such as depression, Parkinson’s disease and Alzheimer’s disease. Heterocyclic compounds exhibit a broad spectrum of biological activities and vital leading compounds for the development of chemical drugs. Herein, we focus on the synthesis and screening of novel single heterocyclic derivatives with MAO inhibitory activities during the past decade. This review covers recent pharmacological advancements of single heterocyclic moiety along with structure- activity relationship to provide better correlation among different structures and their receptor interactions.

Recent Progress in Machine Learning-based Prediction of Peptide Activity for Drug Discovery

2019 ◽  
Vol 19 (1) ◽  
pp. 4-16 ◽  
Author(s):  
Qihui Wu ◽  
Hanzhong Ke ◽  
Dongli Li ◽  
Qi Wang ◽  
Jiansong Fang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Drug Discovery ◽  
Large Scale ◽  
Recent Progress ◽  
High Specificity ◽  
Learning Approaches ◽  
Anticancer Peptides ◽  
The Past ◽  
Traditional Approaches ◽  
Large Scale Screening

Over the past decades, peptide as a therapeutic candidate has received increasing attention in drug discovery, especially for antimicrobial peptides (AMPs), anticancer peptides (ACPs) and antiinflammatory peptides (AIPs). It is considered that the peptides can regulate various complex diseases which are previously untouchable. In recent years, the critical problem of antimicrobial resistance drives the pharmaceutical industry to look for new therapeutic agents. Compared to organic small drugs, peptide- based therapy exhibits high specificity and minimal toxicity. Thus, peptides are widely recruited in the design and discovery of new potent drugs. Currently, large-scale screening of peptide activity with traditional approaches is costly, time-consuming and labor-intensive. Hence, in silico methods, mainly machine learning approaches, for their accuracy and effectiveness, have been introduced to predict the peptide activity. In this review, we document the recent progress in machine learning-based prediction of peptides which will be of great benefit to the discovery of potential active AMPs, ACPs and AIPs.

Metal and Non-Metal Catalysts in the Synthesis of Five-Membered S-Heterocycles

2019 ◽  
Vol 16 (2) ◽  
pp. 258-275 ◽  
Author(s):  
Navjeet Kaur
Keyword(s):  
Organic Synthesis ◽  
Biological Activities ◽  
Reaction Times ◽  
Research Field ◽  
Environmentally Benign ◽  
Metal Catalyst ◽  
Efficient Synthesis ◽  
Heterocyclic Molecules ◽  
The Past ◽  
Harsh Conditions

Background:A wide variety of biological activities are exhibited by N, O and S containing heterocycles and recently, many reports appeared for the synthesis of these heterocycles. The synthesis of heterocycles with the help of metal and non-metal catalyst has become a highly rewarding and important method in organic synthesis. This review article concentrated on the synthesis of S-heterocylces in the presence of metal and non-metal catalyst. The synthesis of five-membered S-heterocycles is described here.Objective:There is a need for the development of rapid, efficient and versatile strategy for the synthesis of heterocyclic rings. Metal, non-metal and organocatalysis involving methods have gained prominence because traditional conditions have disadvantages such as long reaction times, harsh conditions and limited substrate scope.Conclusion:The metal-, non-metal-, and organocatalyst assisted organic synthesis is a highly dynamic research field. For ßthe chemoselective and efficient synthesis of heterocyclic molecules, this protocol has emerged as a powerful route. Various methodologies in the past few years have been pointed out to pursue more sustainable, efficient and environmentally benign procedures and products. Among these processes, the development of new protocols (catalysis), which avoided the use of toxic reagents, are the focus of intense research.

Δα,β-Butenolides [Furan-2(5H)-ones]: Ring Construction Approaches and Biological Aspects - A Mini-Review

2020 ◽  
Vol 17 (8) ◽  
pp. 922-945
Author(s):  
Andrés-Felipe Villamizar-Mogotocoro ◽  
Andrés-Felipe León-Rojas ◽  
Juan-Manuel Urbina-González
Keyword(s):  
Biological Activities ◽  
Ring Formation ◽  
High Interest ◽  
The Past ◽  
Biological Aspects ◽  
Synthetic Methodologies ◽  
New Procedures

The five-membered oxacyclic system of furan-2(5H)-ones, commonly named as γ- butenolides or appropriately as Δα,β-butenolides, is of high interest since many studies have proven its bioactivity. During the past few years, Δα,β-butenolides have been important synthetic targets, with several reports of new procedures for their construction. A short compendium of the main different synthetic methodologies focused on the Δα,β-butenolide ring formation, along with selected examples of compounds with relevant biological activities of these promising pharmaceutical entities is presented.

scholarly journals Towards CRISP-ML(Q): A Machine Learning Process Model with Quality Assurance Methodology

2021 ◽  
Vol 3 (2) ◽  
pp. 392-413
Author(s):  
Stefan Studer ◽  
Thanh Binh Bui ◽  
Christian Drescher ◽  
Alexander Hanuschkin ◽  
Ludwig Winkler ◽  
...  
Keyword(s):  
Machine Learning ◽  
Quality Assurance ◽  
Process Model ◽  
Practical Experience ◽  
Special Focus ◽  
Close Monitoring ◽  
Machine Learning Applications ◽  
Project Organizations ◽  
Considerable Impact ◽  
Learning Development

Machine learning is an established and frequently used technique in industry and academia, but a standard process model to improve success and efficiency of machine learning applications is still missing. Project organizations and machine learning practitioners face manifold challenges and risks when developing machine learning applications and have a need for guidance to meet business expectations. This paper therefore proposes a process model for the development of machine learning applications, covering six phases from defining the scope to maintaining the deployed machine learning application. Business and data understanding are executed simultaneously in the first phase, as both have considerable impact on the feasibility of the project. The next phases are comprised of data preparation, modeling, evaluation, and deployment. Special focus is applied to the last phase, as a model running in changing real-time environments requires close monitoring and maintenance to reduce the risk of performance degradation over time. With each task of the process, this work proposes quality assurance methodology that is suitable to address challenges in machine learning development that are identified in the form of risks. The methodology is drawn from practical experience and scientific literature, and has proven to be general and stable. The process model expands on CRISP-DM, a data mining process model that enjoys strong industry support, but fails to address machine learning specific tasks. The presented work proposes an industry- and application-neutral process model tailored for machine learning applications with a focus on technical tasks for quality assurance.

scholarly journals Cytokinin N-glucosides: Occurrence, Metabolism and Biological Activities in Plants

Biomolecules ◽  
2020 ◽  
Vol 11 (1) ◽  
pp. 24
Author(s):  
Eva Pokorná ◽  
Tomáš Hluska ◽  
Petr Galuszka ◽  
H. Tucker Hallmark ◽  
Petre I. Dobrev ◽  
...  
Keyword(s):  
Metabolic Pathways ◽  
Growth And Development ◽  
Biological Activities ◽  
Physiological Effects ◽  
Inhibitory Effects ◽  
Plant Growth And Development ◽  
The Past ◽  
Complex Process ◽  
Considerable Impact ◽  
Physiological Impact

Cytokinins (CKs) are a class of phytohormones affecting many aspects of plant growth and development. In the complex process of CK homeostasis in plants, N-glucosylation represents one of the essential metabolic pathways. Its products, CK N7- and N9-glucosides, have been largely overlooked in the past as irreversible and inactive CK products lacking any relevant physiological impact. In this work, we report a widespread distribution of CK N-glucosides across the plant kingdom proceeding from evolutionary older to younger plants with different proportions between N7- and N9-glucosides in the total CK pool. We show dramatic changes in their profiles as well as in expression levels of the UGT76C1 and UGT76C2 genes during Arabidopsis ontogenesis. We also demonstrate specific physiological effects of CK N-glucosides in CK bioassays including their antisenescent activities, inhibitory effects on root development, and activation of the CK signaling pathway visualized by the CK-responsive YFP reporter line, TCSv2::3XVENUS. Last but not least, we present the considerable impact of CK N7- and N9-glucosides on the expression of CK-related genes in maize and their stimulatory effects on CK oxidase/dehydrogenase activity in oats. Our findings revise the apparent irreversibility and inactivity of CK N7- and N9-glucosides and indicate their involvement in CK evolution while suggesting their unique function(s) in plants.

scholarly journals Predicting Fraud Victimization Using Classical Machine Learning

Entropy ◽  
2021 ◽  
Vol 23 (3) ◽  
pp. 300
Author(s):  
Mark Lokanan ◽  
Susan Liu
Keyword(s):  
Machine Learning ◽  
Financial Literacy ◽  
Learning Algorithm ◽  
Demographic Characteristics ◽  
Financial Knowledge ◽  
The Past ◽  
Machine Learning Model ◽  
Long Time ◽  
Regulatory Organization ◽  
Investment Fraud

Protecting financial consumers from investment fraud has been a recurring problem in Canada. The purpose of this paper is to predict the demographic characteristics of investors who are likely to be victims of investment fraud. Data for this paper came from the Investment Industry Regulatory Organization of Canada’s (IIROC) database between January of 2009 and December of 2019. In total, 4575 investors were coded as victims of investment fraud. The study employed a machine-learning algorithm to predict the probability of fraud victimization. The machine learning model deployed in this paper predicted the typical demographic profile of fraud victims as investors who classify as female, have poor financial knowledge, know the advisor from the past, and are retired. Investors who are characterized as having limited financial literacy but a long-time relationship with their advisor have reduced probabilities of being victimized. However, male investors with low or moderate-level investment knowledge were more likely to be preyed upon by their investment advisors. While not statistically significant, older adults, in general, are at greater risk of being victimized. The findings from this paper can be used by Canadian self-regulatory organizations and securities commissions to inform their investors’ protection mandates.

scholarly journals Sphingolipids of Asteroidea and Holothuroidea: Structures and Biological Activities

Marine Drugs ◽  
2021 ◽  
Vol 19 (6) ◽  
pp. 330
Author(s):  
Timofey V. Malyarenko ◽  
Alla A. Kicha ◽  
Valentin A. Stonik ◽  
Natalia V. Ivanchina
Keyword(s):  
Marine Invertebrates ◽  
Biological Activities ◽  
Complete Information ◽  
Structural Features ◽  
Marine Organisms ◽  
Structural Components ◽  
Chemical Structures ◽  
The Past ◽  
Complex Lipids ◽  
Complex Sphingolipids

Sphingolipids are complex lipids widespread in nature as structural components of biomembranes. Commonly, the sphingolipids of marine organisms differ from those of terrestrial animals and plants. The gangliosides are the most complex sphingolipids characteristic of vertebrates that have been found in only the Echinodermata (echinoderms) phylum of invertebrates. Sphingolipids of the representatives of the Asteroidea and Holothuroidea classes are the most studied among all echinoderms. In this review, we have summarized the data on sphingolipids of these two classes of marine invertebrates over the past two decades. Recently established structures, properties, and peculiarities of biogenesis of ceramides, cerebrosides, and gangliosides from starfishes and holothurians are discussed. The purpose of this review is to provide the most complete information on the chemical structures, structural features, and biological activities of sphingolipids of the Asteroidea and Holothuroidea classes.

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