CMOS electrochemical imaging arrays for the detection and classification of microorganisms

AbstractMicroorganisms account for most of the biodiversity on earth. Yet while there are increasingly powerful tools for studying microbial genetic diversity, there are fewer tools for studying microorganisms in their natural environments. In this paper, we present recent advances in CMOS electrochemical imaging arrays for detecting and classifying microorganisms. These microscale sensing platforms can provide non-optical measurements of cell geometries, behaviors, and metabolic markers. We review integrated electronic sensors appropriate for monitoring microbial growth, and present measurements of single-celled algae using a CMOS sensor array with thousands of active pixels. Integrated electrochemical imaging can contribute to improved medical diagnostics and environmental monitoring, as well as discoveries of new microbial populations.

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CMOS electrochemical imaging arrays for the detection and classification of microorganisms

2021 IEEE International Symposium on Circuits and Systems (ISCAS) ◽

10.1109/iscas51556.2021.9401471 ◽

2021 ◽

Author(s):

Christopher E. Arcadia ◽

Kangping Hu ◽

Slava Epstein ◽

Meni Wanunu ◽

Aaron Adler ◽

...

Keyword(s):

Imaging Arrays ◽

Electrochemical Imaging

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Multi-layer Perceptrons for Voxel-Based Classification of Point Clouds from Natural Environments

Advances in Computational Intelligence - Lecture Notes in Computer Science ◽

10.1007/978-3-319-19222-2_21 ◽

2015 ◽

pp. 250-261 ◽

Cited By ~ 2

Author(s):

Victoria Plaza ◽

Jose Antonio Gomez-Ruiz ◽

Anthony Mandow ◽

Alfonso Garcia-Cerezo

Keyword(s):

Point Clouds ◽

Natural Environments

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Landscape Terms of Thai Coast-Dwellers

MANUSYA ◽

10.1163/26659077-01601004 ◽

2013 ◽

Vol 16 (1) ◽

pp. 65-82

Author(s):

Sunee Kamnuansin

Keyword(s):

Data Collection ◽

Cultural Heritage ◽

Knowledge Base ◽

Classification System ◽

Local Ecological Knowledge ◽

Marine Resources ◽

Ecological Knowledge ◽

Natural Environments ◽

Folk Taxonomy

This paper examines terms and classification of landscape among coastdwellers who earn a living by exploiting marine resources. It is based on fieldwork conducted from the end of 2009 to the end of 2010 with a group of local Thai coastal dwellers in Bang Khunsai Subdistrict, Ban Laem District in Phetchaburi Province. Data collection involved interviews and observation, especially during fieldwalking in the area with the locals. An analysis of componential meaning and folk taxonomy is applied for this study. Landscape terms reflect the coast-dwellers’ perceptions and classification system of their natural environments and enable us to understand the local ecological knowledge, a crucial knowledge base for management, utilization, and conservation of marine resources. It is also seen as an important part of their cultural heritage.

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3D reconstruction and classification of natural environments by an autonomous vehicle using multi-baseline stereo

Intelligent Service Robotics ◽

10.1007/s11370-014-0146-x ◽

2014 ◽

Vol 7 (2) ◽

pp. 79-92 ◽

Cited By ~ 16

Author(s):

Annalisa Milella ◽

Giulio Reina

Keyword(s):

3D Reconstruction ◽

Autonomous Vehicle ◽

Natural Environments

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BIFURCATIONS OF AVERAGED IMMUNOFLUORESCENCE DISTRIBUTIONS DUE TO OXIDATIVE ACTIVITY OF DNA IN MEDICAL DIAGNOSTICS

Biophysical Reviews and Letters ◽

10.1142/s1793048010001196 ◽

2010 ◽

Vol 05 (04) ◽

pp. 227-240 ◽

Cited By ~ 8

Author(s):

N. E. GALICH

Keyword(s):

Large Scale ◽

Inflammatory Diseases ◽

Medical Diagnostics ◽

Bronchial Tree ◽

Statistical Distributions ◽

Immunofluorescence Analysis ◽

Healthy Donors ◽

Biochemical Analyses ◽

Large Groups

In this paper, novel nonlinear statistical methods of immunofluorescence analysis are presented. We investigated the experimental data of DNA fluorescence in neutrophils nuclei of peripheral blood. DNA fluorescence is triggered by biochemical reactions of respiratory oxidative burst. Histograms of photon counts statistics are generated by flow cytometry method. The histograms represent distributions of fluorescence flash frequency as functions of fluorescence intensity. Large-scale averaging of initial histograms gives bifurcations of immunofluorescence statistical distributions for healthy and unhealthy donors. The statistical signs of transcritical bifurcation provide the classification of all available immunofluorescence histograms into three large groups. The first group corresponds to oncology and autoimmune diseases; the second group corresponds to inflammatory diseases; the third group corresponds to healthy donors. The fluorescence histograms for pregnant women on the other hand show two groups of health status. The immunofluorescence analysis supplements a well-known biochemical analyses. Some discussions on biophysical and biomedical analogies for the proposed approaches with another physical classifications, such as bronchial tree, pathology of heart rhythms, different bistabilities switching and others, are presented.

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A novel classification of quiescent and transit amplifying adult neural stem cells by surface and metabolic markers permits a defined simultaneous isolation

Stem Cell Research ◽

10.1016/j.scr.2010.05.001 ◽

2010 ◽

Vol 5 (2) ◽

pp. 131-143 ◽

Cited By ~ 24

Author(s):

Franz-Josef Obermair ◽

Roberto Fiorelli ◽

Aileen Schroeter ◽

Sarah Beyeler ◽

Claudia Blatti ◽

...

Keyword(s):

Stem Cells ◽

Neural Stem Cells ◽

Metabolic Markers ◽

Adult Neural Stem Cells

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Metasurface Sensing Platforms for Medical Diagnostics

Proceedings of the 1st ACM International Workshop on Nanoscale Computing, Communication, and Applications ◽

10.1145/3416006.3431273 ◽

2020 ◽

Author(s):

Antoine Durant ◽

Célia Lacoste ◽

Erin Donnelly ◽

Zahra Lalegani ◽

Luigi La Spada

Keyword(s):

Medical Diagnostics ◽

Sensing Platforms

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Machine learning for predictions of cervical cancer identification – preliminary investigation based on refractive index

10.21203/rs.3.rs-948525/v1 ◽

2021 ◽

Author(s):

Michał Kruczkowski ◽

Anna Drabik-Kruczkowska ◽

Anna Marciniak ◽

Martyna Tarczewska ◽

Monika Kosowska ◽

...

Keyword(s):

Machine Learning ◽

Cervical Cancer ◽

Refractive Index ◽

Early Diagnosis ◽

Learning Algorithm ◽

Optical Measurements ◽

Prediction Algorithm ◽

Gradient Boosting ◽

Extreme Gradient Boosting

Abstract Cervical cancer is one of the most commonly appearing cancers, which early diagnosis is of greatest importance. Unfortunately, many diagnoses are based on subjective opinions of doctors – to date, there is no general measurement method with a calibrated standard. The problem can be solved with the measurement system being a fusion of an optoelectronic sensor and machine learning algorithm to provide reliable assistance for doctors in the early diagnosis stage of cervical cancer. We demonstrate the preliminary research on cervical cancer assessment utilizing optical sensor and prediction algorithm. Since each matter is characterized by refractive index, measuring its value and detecting changes give information about the state of the tissue. The optical measurements provided datasets for training and validating the analyzing software. We present data preprocessing, machine learning results utilizing three algorithms (Random Forest, eXtreme Gradient Boosting, Naïve Bayes) and assessment of their performance for classification of tissue as healthy or sick. All of them provided high values (>89%) of the measures describing them. Our solution allows for rapid sample measurement and automatic classification of the results constituting a potential support tool for doctors.

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The Classification of Patient Semantical Records and Medical Images

Journal of Biomedical and Sustainable Healthcare Applications ◽

10.53759/0088/jbsha202101009 ◽

2021 ◽

pp. 67-75

Author(s):

Wang Yue Dong ◽

Wang Na

Keyword(s):

Artificial Intelligence ◽

Data Mining ◽

Decision Making ◽

Treatment Outcomes ◽

Support System ◽

Medical Diagnostics ◽

Abstract Representation ◽

Cancerous Cells ◽

Informative Text

In order to alleviate suffering and pain, clinical diagnosis and therapy are critical. Medical photographs play an important role in diagnosing disorders and tracking treatment outcomes. Images have visual and semantic qualities. Texture are essential parameters, whereas form and spatial connection are geometrical elements. The meaning of a picture in an abstract representation based on phrases or informative text is known as semantic characteristics. Both qualities are used in medical diagnostics to extract properties at the micro- and macro-levels, such as distinguishing cancerous cells from standard ones. Extracting characteristics may be done in a number of ways. Computational and numerical modifications are used in these techniques. Following the extraction of the characteristics, classifications based on expertise and domain norms commence. The normalcy or irregularity of a particular picture might be used to make medical judgments. In this paper, we propose using artificial intelligence and data mining approaches to extract and categorize features for a decision - making support system that includes a comprehensive database of client semantic and syntactic records and photographs.

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