scholarly journals Data science in cell imaging

2021 ◽  
Vol 134 (7) ◽  
pp. jcs254292
Author(s):  
Meghan K. Driscoll ◽  
Assaf Zaritsky
Keyword(s):  
Cell Biology ◽  
Data Science ◽  
Cell Imaging ◽  
New Technologies ◽  
Image Data ◽  
Imaging Data ◽  
Current Image ◽  
Cell Image ◽  
Multidimensional Imaging ◽  
New Generation

ABSTRACTCell imaging has entered the ‘Big Data’ era. New technologies in light microscopy and molecular biology have led to an explosion in high-content, dynamic and multidimensional imaging data. Similar to the ‘omics’ fields two decades ago, our current ability to process, visualize, integrate and mine this new generation of cell imaging data is becoming a critical bottleneck in advancing cell biology. Computation, traditionally used to quantitatively test specific hypotheses, must now also enable iterative hypothesis generation and testing by deciphering hidden biologically meaningful patterns in complex, dynamic or high-dimensional cell image data. Data science is uniquely positioned to aid in this process. In this Perspective, we survey the rapidly expanding new field of data science in cell imaging. Specifically, we highlight how data science tools are used within current image analysis pipelines, propose a computation-first approach to derive new hypotheses from cell image data, identify challenges and describe the next frontiers where we believe data science will make an impact. We also outline steps to ensure broad access to these powerful tools – democratizing infrastructure availability, developing sensitive, robust and usable tools, and promoting interdisciplinary training to both familiarize biologists with data science and expose data scientists to cell imaging.

scholarly journals Computational Methods for Single-Cell Imaging and Omics Data Integration

2022 ◽  
Vol 8 ◽  
Author(s):  
Ebony Rose Watson ◽  
Atefeh Taherian Fard ◽  
Jessica Cara Mar
Keyword(s):  
Single Cell ◽  
Biomedical Research ◽  
Cell Imaging ◽  
New Technologies ◽  
Single Cells ◽  
Tissue Level ◽  
Machine Learning Algorithms ◽  
Multidimensional Data ◽  
Imaging Data ◽  
Single Cell Imaging

Integrating single cell omics and single cell imaging allows for a more effective characterisation of the underlying mechanisms that drive a phenotype at the tissue level, creating a comprehensive profile at the cellular level. Although the use of imaging data is well established in biomedical research, its primary application has been to observe phenotypes at the tissue or organ level, often using medical imaging techniques such as MRI, CT, and PET. These imaging technologies complement omics-based data in biomedical research because they are helpful for identifying associations between genotype and phenotype, along with functional changes occurring at the tissue level. Single cell imaging can act as an intermediary between these levels. Meanwhile new technologies continue to arrive that can be used to interrogate the genome of single cells and its related omics datasets. As these two areas, single cell imaging and single cell omics, each advance independently with the development of novel techniques, the opportunity to integrate these data types becomes more and more attractive. This review outlines some of the technologies and methods currently available for generating, processing, and analysing single-cell omics- and imaging data, and how they could be integrated to further our understanding of complex biological phenomena like ageing. We include an emphasis on machine learning algorithms because of their ability to identify complex patterns in large multidimensional data.

scholarly journals Systems thinking for the transition of existing technologies to blockchain technologies

2020 ◽  
Vol 9 ◽  
pp. 105-118
Author(s):  
Nikolaos Zoannos ◽  
Nikitas Assimakopoulos
Keyword(s):  
Systems Thinking ◽  
Data Science ◽  
New Technologies ◽  
Social Systems ◽  
Digital Transformation ◽  
Technical Equipment ◽  
Electronic Money ◽  
Industrial Evolution ◽  
Learning Machine ◽  
New Generation

The 4th Industrial evolution has brought along a lot of technological achievements which can change the form of humanity. Peer-to-peer networks (Distributed networks), network of sensors (Internet of Things), algorithms capable to take decisions (Artificial Intelligence), computers with the ability of self-learning (Machine Learning), more complex queries for analyzing the data, that we are collecting since the birth of internet (Data Science) and new electronic money(cryptocurrencies) are some of the characteristics of those new technologies. But the adoption of those achievements (known as Digital Transformation or Digitization) demands Managers open-minded, well-educated on those technologies and ready to trace the new possible Risks. They must also be capable to use the Systems Thinking, as the Blockchain Technologies have created an Ecosystem (Sociotechnical Systems); the combination of Social Systems (Organizations - Companies), whose behavior is not predictable, and Mechanical Systems (technical equipment) with a predefined way of function. So, this kind of Systems (Sociotechnical) need a more delicate approach using a combination of, not only Systemic methodologies and technics, but also other theories and proper tools. We are going to publish a series of articles in which we are going to specify the proper theories and methodologies in each phase of the digital transformation. Thus, the purpose of this study is to explain to the new generation of Managers how the Systems Thinking, DCSYM Methodology and VSM Model, are applied on those Ecosystems.

scholarly journals An image-based data-driven analysis of cellular architecture in a developing tissue

eLife ◽  
2020 ◽  
Vol 9 ◽  
Author(s):  
Jonas Hartmann ◽  
Mie Wong ◽  
Elisa Gallo ◽  
Darren Gilmour
Keyword(s):  
Data Science ◽  
Image Data ◽  
Data Driven ◽  
Numerical Representation ◽  
Contextual Knowledge ◽  
Imaging Data ◽  
Cellular Architecture ◽  
Posterior Lateral Line ◽  
Data Driven Approach ◽  
The Rich

Quantitative microscopy is becoming increasingly crucial in efforts to disentangle the complexity of organogenesis, yet adoption of the potent new toolbox provided by modern data science has been slow, primarily because it is often not directly applicable to developmental imaging data. We tackle this issue with a newly developed algorithm that uses point cloud-based morphometry to unpack the rich information encoded in 3D image data into a straightforward numerical representation. This enabled us to employ data science tools, including machine learning, to analyze and integrate cell morphology, intracellular organization, gene expression and annotated contextual knowledge. We apply these techniques to construct and explore a quantitative atlas of cellular architecture for the zebrafish posterior lateral line primordium, an experimentally tractable model of complex self-organized organogenesis. In doing so, we are able to retrieve both previously established and novel biologically relevant patterns, demonstrating the potential of our data-driven approach.

scholarly journals ESR white paper: blockchain and medical imaging

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
◽  
Elmar Kotter ◽  
Luis Marti-Bonmati ◽  
Adrian P. Brady ◽  
Nandita M. Desouza
Keyword(s):  
Medical Imaging ◽  
Image Data ◽  
Distributed Database ◽  
Relevant Information ◽  
White Paper ◽  
Imaging Data ◽  
Data Set ◽  
Blockchain Technology ◽  
Potential Applications ◽  
Basic Technology

AbstractBlockchain can be thought of as a distributed database allowing tracing of the origin of data, and who has manipulated a given data set in the past. Medical applications of blockchain technology are emerging. Blockchain has many potential applications in medical imaging, typically making use of the tracking of radiological or clinical data. Clinical applications of blockchain technology include the documentation of the contribution of different “authors” including AI algorithms to multipart reports, the documentation of the use of AI algorithms towards the diagnosis, the possibility to enhance the accessibility of relevant information in electronic medical records, and a better control of users over their personal health records. Applications of blockchain in research include a better traceability of image data within clinical trials, a better traceability of the contributions of image and annotation data for the training of AI algorithms, thus enhancing privacy and fairness, and potentially make imaging data for AI available in larger quantities. Blockchain also allows for dynamic consenting and has the potential to empower patients and giving them a better control who has accessed their health data. There are also many potential applications of blockchain technology for administrative purposes, like keeping track of learning achievements or the surveillance of medical devices. This article gives a brief introduction in the basic technology and terminology of blockchain technology and concentrates on the potential applications of blockchain in medical imaging.

An efficient approach to converting three-dimensional image data into highly accurate computational models

2008 ◽  
Vol 366 (1878) ◽  
pp. 3155-3173 ◽  
Author(s):  
P.G Young ◽  
T.B.H Beresford-West ◽  
S.R.L Coward ◽  
B Notarberardino ◽  
B Walker ◽  
...  
Keyword(s):  
Mesh Generation ◽  
Computational Models ◽  
Three Dimensional ◽  
Image Data ◽  
Model Material ◽  
Imaging Data ◽  
Material Inhomogeneity ◽  
Dimensional Image ◽  
Wide Range ◽  
Impact Modelling

Image-based meshing is opening up exciting new possibilities for the application of computational continuum mechanics methods (finite-element and computational fluid dynamics) to a wide range of biomechanical and biomedical problems that were previously intractable owing to the difficulty in obtaining suitably realistic models. Innovative surface and volume mesh generation techniques have recently been developed, which convert three-dimensional imaging data, as obtained from magnetic resonance imaging, computed tomography, micro-CT and ultrasound, for example, directly into meshes suitable for use in physics-based simulations. These techniques have several key advantages, including the ability to robustly generate meshes for topologies of arbitrary complexity (such as bioscaffolds or composite micro-architectures) and with any number of constituent materials (multi-part modelling), providing meshes in which the geometric accuracy of mesh domains is only dependent on the image accuracy (image-based accuracy) and the ability for certain problems to model material inhomogeneity by assigning the properties based on image signal strength. Commonly used mesh generation techniques will be compared with the proposed enhanced volumetric marching cubes (EVoMaCs) approach and some issues specific to simulations based on three-dimensional image data will be discussed. A number of case studies will be presented to illustrate how these techniques can be used effectively across a wide range of problems from characterization of micro-scaffolds through to head impact modelling.

Image stabilization technology for active bridge-vessel anti-collision monitoring system

2021 ◽  
Author(s):  
Zhufeng Shao ◽  
Haiying Ma ◽  
Ye Xia ◽  
Junjie Wang
Keyword(s):  
New Technologies ◽  
Target Recognition ◽  
Image Data ◽  
Tracking Data ◽  
Collision System ◽  
High Quality ◽  
Image Stabilization ◽  
Site Monitoring ◽  
New Research ◽  
Sea Area

<p>In recent years, the active anti-collision system using new technologies such as image target recognition between ship and bridge becomes a new research hotspot. Due to camera jitter, it is not easy to deeply mine the monitoring image data. This paper puts forward an anti-jitter algorithm to obtain the ship monitoring track in the sea area removing the camera jitter. It uses electronic image stabilization, sea-sky line anti jitter filtering, and other methods to process the on-site monitoring video, then compares the effect of each technique, and finally obtains high-quality ship tracking data. Through this method, a high-quality ship monitoring track in the bridge area can be obtained.</p>

Future directions for the discovery of antibiotics from actinomycete bacteria

2017 ◽  
Vol 1 (1) ◽  
pp. 1-12 ◽  
Author(s):  
Rebecca Devine ◽  
Matthew I. Hutchings ◽  
Neil A. Holmes
Keyword(s):  
New Technologies ◽  
Gene Clusters ◽  
Biosynthetic Gene Clusters ◽  
Future Directions ◽  
Streptomyces Species ◽  
Genus Streptomyces ◽  
New Antibiotics ◽  
The Uk ◽  
Almost All ◽  
New Generation

Antimicrobial resistance (AMR) is a growing societal problem, and without new anti-infective drugs, the UK government-commissioned O'Neil report has predicted that infectious disease will claim the lives of an additional 10 million people a year worldwide by 2050. Almost all the antibiotics currently in clinical use are derived from the secondary metabolites of a group of filamentous soil bacteria called actinomycetes, most notably in the genus Streptomyces. Unfortunately, the discovery of these strains and their natural products (NPs) peaked in the 1950s and was then largely abandoned, partly due to the repeated rediscovery of known strains and compounds. Attention turned instead to rational target-based drug design, but this was largely unsuccessful and few new antibiotics have made it to clinic in the last 60 years. In the early 2000s, however, genome sequencing of the first Streptomyces species reinvigorated interest in NP discovery because it revealed the presence of numerous cryptic NP biosynthetic gene clusters that are not expressed in the laboratory. Here, we describe how the use of new technologies, including improved culture-dependent and -independent techniques, combined with searching underexplored environments, promises to identify a new generation of NP antibiotics from actinomycete bacteria.

scholarly journals IMPACT OF SOCIAL MEDIA ON SALES FUNNELS IN B2C AND B2B SEGMENTS IN THE REPUBLIC OF NORTH MACEDONIA

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Martin Kiselicki ◽  
Saso Josimovski ◽  
Lidija Pulevska Ivanovska ◽  
Mijalce Santa
Keyword(s):  
Social Media ◽  
New Technologies ◽  
Secondary Data ◽  
Primary Data ◽  
World Population ◽  
Social Media Platforms ◽  
The Republic ◽  
New Generation ◽  
Sales Funnel ◽  
Media Data

The research focuses on introducing social media platforms as either a complementary or main channel in the company sales funnel. Internet technologies and Web 2.0 continue to provide innovations in digital marketing, with the latest iteration being lead generation services through social media. Data shows that almost half of the world population is active on social media, with the new Generation Alpha being projected to be entirely online dependent and proficient in the use of new technologies. The paper provides an overview of the digitalization of sales funnels, as well as the benefits that social media platforms can offer if implemented correctly. Secondary data provides the basis for transforming sales funnels with social media, where previous research provides limited data on the effectiveness of these types of efforts. Primary data demonstrates that introducing social media platforms can provide improvements of up to 3 to 4 times in analyzed case studies, as well as the shorter time when deciding about purchase in use case scenarios. Social media advertising can also be utilized to shorten the sales funnel process and serve as a unified point of entrance and exit in the first few stages.

scholarly journals A deep generative model of 3D single-cell organization

2021 ◽  
Author(s):  
Rory Donovan-Maiye ◽  
Jackson Brown ◽  
Caleb Chan ◽  
Liya Ding ◽  
Calysta Yan ◽  
...  
Keyword(s):  
Single Cell ◽  
Cell Morphology ◽  
Image Data ◽  
Subcellular Structure ◽  
Cell Image ◽  
Fluorescent Image ◽  
Trade Offs ◽  
Cell Organization ◽  
Latent Representations ◽  
Target Effects

We introduce a framework for end-to-end integrative modeling of 3D single-cell multi-channel fluorescent image data of diverse subcellular structures. We employ stacked conditional β-variational autoencoders to first learn a latent representation of cell morphology, and then learn a latent representation of subcellular structure localization which is conditioned on the learned cell morphology. Our model is flexible and can be trained on images of arbitrary subcellular structures and at varying degrees of sparsity and reconstruction fidelity. We train our full model on 3D cell image data and explore design trade-offs in the 2D setting. Once trained, our model can be used to impute structures in cells where they were not imaged and to quantify the variation in the location of all subcellular structures by generating plausible instantiations of each structure in arbitrary cell geometries. We apply our trained model to a small drug perturbation screen to demonstrate its applicability to new data. We show how the latent representations of drugged cells differ from unperturbed cells as expected by on-target effects of the drugs.

Sign in / Sign up

Export Citation Format

Share Document