Digital biology approach for macroscale studies of biofilm growth and biocide effects with electron microscopy

Kozlov, Konstantin S.; Boiko, Daniil A.; Detusheva, Elena V.; Detushev, Konstantin V.; Pentsak, Evgeniy O.; Vereshchagin, Anatoly N.; Ananikov, Valentine P.

doi:10.1039/d3dd00048f

Cited by 4 publications

(3 citation statements)

References 103 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Transforming chemistry through Big Data analytics is an ongoing effort, [31][32][33][34][35][36] but desired ambitious goals are difficult to achieve using single-target/single-objective databases alone. Data fragmentation is a limiting factor that has to be overcome to improve the performance.…”

Section: Big Data and Integrated Datamentioning

confidence: 99%

Top 20 Influential AI-Based Technologies in Chemistry

Ananikov

2024

Preprint

View full text Add to dashboard Cite

The beginning and ripening of digital chemistry is analyzed focusing on the role of artificial intelligence (AI) in an expected leap in chemical sciences to bring this area to the next evolutionary level. The analytic description selects and highlights the top 20 AI-based technologies and 7 broader themes that are reshaping the field. It underscores the integration of digital tools such as machine learning, big data, digital twins, the Internet of Things (IoT), robotic platforms, smart control of chemical processes, virtual reality and blockchain, among many others, in enhancing research methods, educational approaches, and industrial practices in chemistry. The significance of this study lies in its focused overview of how these digital innovations foster a more efficient, sustainable, and innovative future in chemical sciences. This article not only illustrates the transformative impact of these technologies but also draws new pathways in chemistry, offering a broad appeal to researchers, educators, and industry professionals to embrace these advancements for addressing contemporary challenges in the field.

show abstract

Section: Big Data and Integrated Datamentioning

confidence: 99%

Top 20 Influential AI-Based Technologies in Chemistry

Ananikov

2024

Preprint

View full text Add to dashboard Cite

show abstract

“…The investigation of biofilm formation under precisely controlled environments, bacteria concentration, and temperature and under various flow regimes was achieved with a versatile microfluidic platform. With the aid of this platform, some features dedicated to biofilm growth were integrated to obtain rapidly defined, reproducible, and localized biofilms with the aim of getting closer to the concept of digital microfluidics for biofilms . With such a tool, traditional static or macroscale flow chamber biofilm studies could be easily transformed, allowing for biofilm culture with lower reagent consumption and higher throughput.…”

Section: Introductionmentioning

confidence: 99%

“…With the aid of this platform, some features dedicated to biofilm growth were integrated to obtain rapidly defined, reproducible, and localized biofilms with the aim of getting closer to the concept of digital microfluidics for biofilms. 42 With such a tool, traditional static or macroscale flow chamber biofilm studies could be easily transformed, allowing for biofilm culture with lower reagent consumption and higher throughput. Moreover, the described method covers some gaps in the existing methods regarding experimental assessment of flows especially regarding the channel’s homogeneity and reproducibility of the biofilm formation.…”

Section: Introductionmentioning

confidence: 99%

Microfluidic Platform with Precisely Controlled Hydrodynamic Parameters and Integrated Features for Generation of Microvortices to Accurately Form and Monitor Biofilms in Flow

Wen,

Gorbushina,

Schwibbert

et al. 2024

ACS Biomater. Sci. Eng.

View full text Add to dashboard Cite

Recognition of Molecular Structure of Phosphonium Salts from the Visual Appearance of Material with Deep Learning Can Reveal Subtle Homologs

Boiko,

Arkhipova,

Ananikov

2024

Small

View full text Add to dashboard Cite

Determining molecular structures is foundational in chemistry and biology. The notion of discerning molecular structures simply from the visual appearance of a material remained almost unthinkable until the advent of machine learning. This paper introduces a pioneering approach bridging the visual appearance of materials (both at the micro‐ and nanostructural levels) with traditional chemical structure analysis methods. Quaternary phosphonium salts are opted as the model compounds, given their significant roles in diverse chemical and medicinal fields and their ability to form homologs with only minute intermolecular variances. This research results in the successful creation of a neural network model capable of recognizing molecular structures from visual electron microscopy images of the material. The performance of the model is evaluated and related to the chemical nature of the studied chemicals. Additionally, unsupervised domain transfer is tested as a method to use the resulting model on optical microscopy images, as well as test models trained on optical images directly. The robustness of the method is further tested using a complex system of phosphonium salt mixtures. To the best of the authors' knowledge, this study offers the first evidence of the feasibility of discerning nearly indistinguishable molecular structures.

show abstract

Digital biology approach for macroscale studies of biofilm growth and biocide effects with electron microscopy

Abstract: Combination of automated scanning electron microscopy and a comprehensive software system that uses deep neural networks to perform an in-depth analysis of biofilms.

Cited by 4 publications

References 103 publications

Top 20 Influential AI-Based Technologies in Chemistry

Top 20 Influential AI-Based Technologies in Chemistry

Microfluidic Platform with Precisely Controlled Hydrodynamic Parameters and Integrated Features for Generation of Microvortices to Accurately Form and Monitor Biofilms in Flow

Recognition of Molecular Structure of Phosphonium Salts from the Visual Appearance of Material with Deep Learning Can Reveal Subtle Homologs

Contact Info

Product

Resources

About