scholarly journals What do machines see? Utilizing artificial intelligence to explore cell biology

2021 ◽  
Author(s):  
Matt De Vries ◽  
Chris Bakal
Keyword(s):  
Artificial Intelligence ◽  
Cell Biology ◽  
Three Dimensional ◽  
Data Set ◽  
Feature Article ◽  
Underlying Distribution ◽  
Essential Step ◽  
Accurate Picture ◽  
Living Organisms ◽  
Microscopy Data

With our ability to take and quantify numerous complex images of cells and cell populations, the ability to paint an accurate picture of the underlying data has never been more valuable. Deferring from the contemporary classics in data visualization to methods that exploit advances in artificial intelligence is an essential step in understanding high-throughput, three-dimensional microscopy data. This feature article discusses how generating or simulating representative cells that may not exist in the data set, yet summarize the underlying distribution, allows researchers to effectively and efficiently analyse cellular morpho-dynamics. Furthermore, learning from these artificial intelligence-based techniques allows us to ‘see what the machine is seeing’ in a step towards unpacking the chaos of cell biology to understand the very fundamentals of living organisms.

Three-dimensional image analysis and visualization in confocal light microscopy

1993 ◽  
Vol 51 ◽  
pp. 158-159
Author(s):  
J. K. Samarabandu ◽  
R. Acharya ◽  
D. R. Pareddy ◽  
P. C. Cheng
Keyword(s):  
Depth Perception ◽  
Computer Graphic ◽  
Three Dimensional ◽  
Cellular Organization ◽  
Data Set ◽  
Computational Burden ◽  
Interactive Operation ◽  
Cell Organization ◽  
Confocal Images ◽  
3D Digitization

In the study of cell organization in a maize meristem, direct viewing of confocal optical sections in 3D (by means of 3D projection of the volumetric data set, Figure 1) becomes very difficult and confusing because of the large number of nucleus involved. Numerical description of the cellular organization (e.g. position, size and orientation of each structure) and computer graphic presentation are some of the solutions to effectively study the structure of such a complex system. An attempt at data-reduction by means of manually contouring cell nucleus in 3D was reported (Summers et al., 1990). Apart from being labour intensive, this 3D digitization technique suffers from the inaccuracies of manual 3D tracing related to the depth perception of the operator. However, it does demonstrate that reducing stack of confocal images to a 3D graphic representation helps to visualize and analyze complex tissues (Figure 2). This procedure also significantly reduce computational burden in an interactive operation.

Exploration of cell wall architecture with the rapid-freeze deep-etch technique

1993 ◽  
Vol 51 ◽  
pp. 128-129
Author(s):  
Béatrice Satiat-Jeunemaitre ◽  
Chris Hawes
Keyword(s):  
Plant Cell ◽  
Cell Walls ◽  
Cell Biology ◽  
Molecular Model ◽  
Three Dimensional ◽  
Secretory Activity ◽  
Wall Structure ◽  
Specimen Preparation ◽  
Molecular Architecture ◽  
Plant Cell Walls

The comprehension of the molecular architecture of plant cell walls is one of the best examples in cell biology which illustrates how developments in microscopy have extended the frontiers of a topic. Indeed from the first electron microscope observation of cell walls it has become apparent that our understanding of wall structure has advanced hand in hand with improvements in the technology of specimen preparation for electron microscopy. Cell walls are sub-cellular compartments outside the peripheral plasma membrane, the construction of which depends on a complex cellular biosynthetic and secretory activity (1). They are composed of interwoven polymers, synthesised independently, which together perform a number of varied functions. Biochemical studies have provided us with much data on the varied molecular composition of plant cell walls. However, the detailed intermolecular relationships and the three dimensional arrangement of the polymers in situ remains a mystery. The difficulty in establishing a general molecular model for plant cell walls is also complicated by the vast diversity in wall composition among plant species.

EMCAT: An automatic image-processing system for electron-microscopic tomography

1994 ◽  
Vol 52 ◽  
pp. 418-419
Author(s):  
Weiping Liu ◽  
John W. Sedat ◽  
David A. Agard
Keyword(s):  
Image Processing ◽  
Structural Information ◽  
Imaging Technique ◽  
Three Dimensional ◽  
Processing System ◽  
Electron Microscopic ◽  
Data Set ◽  
Ordered Structures ◽  
Automatic Image Processing ◽  
Em Tomography

Any real world object is three-dimensional. The principle of tomography, which reconstructs the 3-D structure of an object from its 2-D projections of different view angles has found application in many disciplines. Electron Microscopic (EM) tomography on non-ordered structures (e.g., subcellular structures in biology and non-crystalline structures in material science) has been exercised sporadically in the last twenty years or so. As vital as is the 3-D structural information and with no existing alternative 3-D imaging technique to compete in its high resolution range, the technique to date remains the kingdom of a brave few. Its tedious tasks have been preventing it from being a routine tool. One keyword in promoting its popularity is automation: The data collection has been automated in our lab, which can routinely yield a data set of over 100 projections in the matter of a few hours. Now the image processing part is also automated. Such automations finish the job easier, faster and better.

Automated 3-D cell population analysis in thick tissue sections using laser-scanning confocal-microscopy data

1993 ◽  
Vol 51 ◽  
pp. 562-563
Author(s):  
Hakan Ancin
Keyword(s):  
Laser Scanning ◽  
Population Analysis ◽  
Three Dimensional ◽  
Large Data ◽  
Laser Scanning Confocal Microscopy ◽  
Tissue Slices ◽  
Scanning Confocal Microscopy ◽  
Tissue Sections ◽  
Spatially Adaptive ◽  
Microscopy Data

This paper presents methods for performing detailed quantitative automated three dimensional (3-D) analysis of cell populations in thick tissue sections while preserving the relative 3-D locations of cells. Specifically, the method disambiguates overlapping clusters of cells, and accurately measures the volume, 3-D location, and shape parameters for each cell. Finally, the entire population of cells is analyzed to detect patterns and groupings with respect to various combinations of cell properties. All of the above is accomplished with zero subjective bias.In this method, a laser-scanning confocal light microscope (LSCM) is used to collect optical sections through the entire thickness (100 - 500μm) of fluorescently-labelled tissue slices. The acquired stack of optical slices is first subjected to axial deblurring using the expectation maximization (EM) algorithm. The resulting isotropic 3-D image is segmented using a spatially-adaptive Poisson based image segmentation algorithm with region-dependent smoothing parameters. Extracting the voxels that were labelled as "foreground" into an active voxel data structure results in a large data reduction.

scholarly journals Cyber-Physical Systems Improving Building Energy Management: Digital Twin and Artificial Intelligence

Energies ◽  
2021 ◽  
Vol 14 (8) ◽  
pp. 2338
Author(s):  
Sofia Agostinelli ◽  
Fabrizio Cumo ◽  
Giambattista Guidi ◽  
Claudio Tomazzoli
Keyword(s):  
Artificial Intelligence ◽  
Renewable Energy ◽  
Energy Management ◽  
Three Dimensional ◽  
Cost Effective ◽  
Automation System ◽  
Climate Conditions ◽  
Digital Twin ◽  
Building Energy Management ◽  
Computing Paradigm

The research explores the potential of digital-twin-based methods and approaches aimed at achieving an intelligent optimization and automation system for energy management of a residential district through the use of three-dimensional data model integrated with Internet of Things, artificial intelligence and machine learning. The case study is focused on Rinascimento III in Rome, an area consisting of 16 eight-floor buildings with 216 apartment units powered by 70% of self-renewable energy. The combined use of integrated dynamic analysis algorithms has allowed the evaluation of different scenarios of energy efficiency intervention aimed at achieving a virtuous energy management of the complex, keeping the actual internal comfort and climate conditions. Meanwhile, the objective is also to plan and deploy a cost-effective IT (information technology) infrastructure able to provide reliable data using edge-computing paradigm. Therefore, the developed methodology led to the evaluation of the effectiveness and efficiency of integrative systems for renewable energy production from solar energy necessary to raise the threshold of self-produced energy, meeting the nZEB (near zero energy buildings) requirements.

Research on the university intelligent learning analysis system based on AI

2021 ◽  
pp. 1-10
Author(s):  
Meng Huang ◽  
Shuai Liu ◽  
Yahao Zhang ◽  
Kewei Cui ◽  
Yana Wen
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Academic Performance ◽  
Clustering Algorithm ◽  
Back Propagation ◽  
Three Dimensional ◽  
Training Model ◽  
Future Trend ◽  
Artificial Intelligence Technology ◽  
Visualization Technology

The integration of Artificial Intelligence technology and school education had become a future trend, and became an important driving force for the development of education. With the advent of the era of big data, although the relationship between students’ learning status data was closer to nonlinear relationship, combined with the application analysis of artificial intelligence technology, it could be found that students’ living habits were closely related to their academic performance. In this paper, through the investigation and analysis of the living habits and learning conditions of more than 2000 students in the past 10 grades in Information College of Institute of Disaster Prevention, we used the hierarchical clustering algorithm to classify the nearly 180000 records collected, and used the big data visualization technology of Echarts + iView + GIS and the JavaScript development method to dynamically display the students’ life track and learning information based on the map, then apply Three Dimensional ArcGIS for JS API technology showed the network infrastructure of the campus. Finally, a training model was established based on the historical learning achievements, life trajectory, graduates’ salary, school infrastructure and other information combined with the artificial intelligence Back Propagation neural network algorithm. Through the analysis of the training resulted, it was found that the students’ academic performance was related to the reasonable laboratory study time, dormitory stay time, physical exercise time and social entertainment time. Finally, the system could intelligently predict students’ academic performance and give reasonable suggestions according to the established prediction model. The realization of this project could provide technical support for university educators.

scholarly journals Training and deploying a deep learning model for endoscopic severity grading in ulcerative colitis using multicenter clinical trial data

2021 ◽  
Vol 14 ◽  
pp. 263177452199062
Author(s):  
Benjamin Gutierrez Becker ◽  
Filippo Arcadu ◽  
Andreas Thalhammer ◽  
Citlalli Gamez Serna ◽  
Owen Feehan ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Ulcerative Colitis ◽  
Clinical Trials ◽  
Deep Learning ◽  
Mayo Clinic ◽  
Automated System ◽  
Phase Iii ◽  
Plain Language ◽  
Data Set ◽  
Endoscopic Videos

Introduction: The Mayo Clinic Endoscopic Subscore is a commonly used grading system to assess the severity of ulcerative colitis. Correctly grading colonoscopies using the Mayo Clinic Endoscopic Subscore is a challenging task, with suboptimal rates of interrater and intrarater variability observed even among experienced and sufficiently trained experts. In recent years, several machine learning algorithms have been proposed in an effort to improve the standardization and reproducibility of Mayo Clinic Endoscopic Subscore grading. Methods: Here we propose an end-to-end fully automated system based on deep learning to predict a binary version of the Mayo Clinic Endoscopic Subscore directly from raw colonoscopy videos. Differently from previous studies, the proposed method mimics the assessment done in practice by a gastroenterologist, that is, traversing the whole colonoscopy video, identifying visually informative regions and computing an overall Mayo Clinic Endoscopic Subscore. The proposed deep learning–based system has been trained and deployed on raw colonoscopies using Mayo Clinic Endoscopic Subscore ground truth provided only at the colon section level, without manually selecting frames driving the severity scoring of ulcerative colitis. Results and Conclusion: Our evaluation on 1672 endoscopic videos obtained from a multisite data set obtained from the etrolizumab Phase II Eucalyptus and Phase III Hickory and Laurel clinical trials, show that our proposed methodology can grade endoscopic videos with a high degree of accuracy and robustness (Area Under the Receiver Operating Characteristic Curve = 0.84 for Mayo Clinic Endoscopic Subscore ⩾ 1, 0.85 for Mayo Clinic Endoscopic Subscore ⩾ 2 and 0.85 for Mayo Clinic Endoscopic Subscore ⩾ 3) and reduced amounts of manual annotation. Plain language summary Patient, caregiver and provider thoughts on educational materials about prescribing and medication safety Artificial intelligence can be used to automatically assess full endoscopic videos and estimate the severity of ulcerative colitis. In this work, we present an artificial intelligence algorithm for the automatic grading of ulcerative colitis in full endoscopic videos. Our artificial intelligence models were trained and evaluated on a large and diverse set of colonoscopy videos obtained from concluded clinical trials. We demonstrate not only that artificial intelligence is able to accurately grade full endoscopic videos, but also that using diverse data sets obtained from multiple sites is critical to train robust AI models that could potentially be deployed on real-world data.

scholarly journals Prioritizing and Analyzing the Role of Climate and Urban Parameters in the Confirmed Cases of COVID-19 Based on Artificial Intelligence Applications

2020 ◽  
Vol 17 (10) ◽  
pp. 3730 ◽  
Author(s):  
Sina Shaffiee Haghshenas ◽  
Behrouz Pirouz ◽  
Sami Shaffiee Haghshenas ◽  
Behzad Pirouz ◽  
Patrizia Piro ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Infectious Disease ◽  
Relative Humidity ◽  
Cost Function ◽  
Selection Process ◽  
Environmental Parameters ◽  
Pso Algorithm ◽  
Data Set ◽  
De Algorithm

Nowadays, an infectious disease outbreak is considered one of the most destructive effects in the sustainable development process. The outbreak of new coronavirus (COVID-19) as an infectious disease showed that it has undesirable social, environmental, and economic impacts, and leads to serious challenges and threats. Additionally, investigating the prioritization parameters is of vital importance to reducing the negative impacts of this global crisis. Hence, the main aim of this study is to prioritize and analyze the role of certain environmental parameters. For this purpose, four cities in Italy were selected as a case study and some notable climate parameters—such as daily average temperature, relative humidity, wind speed—and an urban parameter, population density, were considered as input data set, with confirmed cases of COVID-19 being the output dataset. In this paper, two artificial intelligence techniques, including an artificial neural network (ANN) based on particle swarm optimization (PSO) algorithm and differential evolution (DE) algorithm, were used for prioritizing climate and urban parameters. The analysis is based on the feature selection process and then the obtained results from the proposed models compared to select the best one. Finally, the difference in cost function was about 0.0001 between the performances of the two models, hence, the two methods were not different in cost function, however, ANN-PSO was found to be better, because it reached to the desired precision level in lesser iterations than ANN-DE. In addition, the priority of two variables, urban parameter, and relative humidity, were the highest to predict the confirmed cases of COVID-19.

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