This document discusses the development of an open data exchange ecosystem for cell migration research. It describes CellMissy, an open-source software tool for managing and analyzing cell migration data. CellMissy allows researchers to capture experimental metadata, import diverse data types into a structured database, and perform both collective and single-cell analysis. The document proposes extending CellMissy's data sharing capabilities into a larger repository called MULTIMOT to facilitate global exchange of cell migration data as part of the broader open science movement.
Presentation in the "Whole genome sequencing for clinical microbiology:Translation into routine applications" Symposium , Basel , Switzerland, 2 Sep 2017
The Human Cell Atlas Data Coordination PlatformLaura Clarke
This presentation gives a brief summary of the Human Cell Atlas project and describes the data coordination platform which is being built to support it.
De-centralized but global: Redesigning biodiversity data aggregation for impr...taxonbytes
Biodiversity data pose fundamental challenges for unification-based paradigms of data science. In particular, a hierarchical, backbone-driven approach to aggregating global biodiversity data tends to limit community engagement. Data quality, trust, fitness for use, and impact are similarly reduced. This presentation will outline an alternative, de-centralized design for aggregating biodiversity data globally. The design requires a coordinative approach to representing and reconciling evolving systematic perspectives, and further social but technologically mediated coordination between regionally and taxonomically constrained "communities of practice" (sensu Wenger, 2000, https://doi.org/10.1177/135050840072002). Important next steps in this direction include the development of use cases that quantify the benefits of a de-centralized biodiversity data aggregation - in terms of lowering costs to expert engagement, raising efficiency of curation, validating novel integration services, and improving reproducibility and provenance tracking across heterogenous data structures and portals.
Scott Edmunds slides for class 8 from the HKU Data Curation (module MLIM7350 from the Faculty of Education) course covering science data, medical data and ethics, and the FAIR data principles.
SyMBA (http://symba.sf.net) is a data archive and integrator based on Version 1 of the Functional Genomics Experiment (FuGE, http://fuge.sf.net) Object Model (FuGE-OM), and which archives, stores, and retrieves raw high-throughput data. Until now, few published systems have successfully integrated multiple omics data types and information about experiments in a single database. SyMBA includes a database back-end, expert and standard interfaces, and a Life Science Identifier (LSID) Resolution and Assigning service to identify objects and provide programmatic access to the database. Having a central data repository prevents deletion, loss, or accidental modification of primary data, while giving convenient access to the data for publication and analysis. It also provides a central location for storage of metadata for the high-throughput data sets, and will facilitate subsequent data integration strategies.
We encourage the use, installation and development of SyMBA by other groups. Please let us know if you are interested in using or evaluating SyMBA for use at your own Centre. Contact us: symba-devel at lists.sourceforge.net
Scott Edmunds: GigaScience - a journal or a database? Lessons learned from th...GigaScience, BGI Hong Kong
Scott Edmunds talk at the HUPO congress in Geneva, September 6th 2011 on GigaScience - a journal or a database? Lessons learned from the Genomics Tsunami.
BioCASE web services for germplasm data sets, at FAO, Rome (2006)Dag Endresen
Sharing of biodiversity data with web services - demonstration of the BioCASE software. Food and Agriculture Organization of the United Nations (FAO) 2nd March 2006.
Centralized Model Organism Database (Biocuration 2014 poster)Andrew Su
A Centralized Model Organism Database (CMOD) for the Long Tail of Genomes
Presented at Biocuration 2014 in Toronto http://biocuration2014.events.oicr.on.ca/
See related slides at http://www.slideshare.net/andrewsu/20140116-gmod-short
This presentation was provided by Violeta Ilik of Northwestern University during the NISO Virtual Conference held on Feb 15, 2017, entitled Institutional Repositories: Ensuring Yours is Populated, Useful and Thriving. The DOI for this presentation is http://dx.doi.org/10.18131/G3VP6R
Scott Edmunds slides for class 8 from the HKU Data Curation (module MLIM7350 from the Faculty of Education) course covering open science and data publishing
CellMissy: enabling management and dissemination of cell migration dataPaola Chiara Masuzzo
This talk presents some key features of the CellMissy software for the management and dissemination of cell migration data. The slides were presented during the 'Data management and standardization in cell migration research' workshop.
Distributed Ledger Technologies just left the peek of the Gartner’s Hype Cycle for Emerging technologies of 2017. However the status of the art for Blockchain-based initiatives in healthcare has not yet been reached, mostly due to the lack of knowledge about the need of interoperability amongst blockchain practitioners.
Following the adagio “The nice thing about standards is that you have so many to choose from”, the GrapevineWorld Project brings together DLT technologies in the healthcare context following the rules set by the IHE international standardisation body, whose specifications are the pillars of continental healthcare information exchange.
First, this presentation will introduce the IHE governance model.
Then it will tackle the benefit of DLTs to introduce the Grapevine research ecosystem.
This is a talk I gave at a Northwestern University - Complete Genomics Workshop on April 21, 2011 about using clouds to support research in genomics and related areas.
Presentation in the "Whole genome sequencing for clinical microbiology:Translation into routine applications" Symposium , Basel , Switzerland, 2 Sep 2017
The Human Cell Atlas Data Coordination PlatformLaura Clarke
This presentation gives a brief summary of the Human Cell Atlas project and describes the data coordination platform which is being built to support it.
De-centralized but global: Redesigning biodiversity data aggregation for impr...taxonbytes
Biodiversity data pose fundamental challenges for unification-based paradigms of data science. In particular, a hierarchical, backbone-driven approach to aggregating global biodiversity data tends to limit community engagement. Data quality, trust, fitness for use, and impact are similarly reduced. This presentation will outline an alternative, de-centralized design for aggregating biodiversity data globally. The design requires a coordinative approach to representing and reconciling evolving systematic perspectives, and further social but technologically mediated coordination between regionally and taxonomically constrained "communities of practice" (sensu Wenger, 2000, https://doi.org/10.1177/135050840072002). Important next steps in this direction include the development of use cases that quantify the benefits of a de-centralized biodiversity data aggregation - in terms of lowering costs to expert engagement, raising efficiency of curation, validating novel integration services, and improving reproducibility and provenance tracking across heterogenous data structures and portals.
Scott Edmunds slides for class 8 from the HKU Data Curation (module MLIM7350 from the Faculty of Education) course covering science data, medical data and ethics, and the FAIR data principles.
SyMBA (http://symba.sf.net) is a data archive and integrator based on Version 1 of the Functional Genomics Experiment (FuGE, http://fuge.sf.net) Object Model (FuGE-OM), and which archives, stores, and retrieves raw high-throughput data. Until now, few published systems have successfully integrated multiple omics data types and information about experiments in a single database. SyMBA includes a database back-end, expert and standard interfaces, and a Life Science Identifier (LSID) Resolution and Assigning service to identify objects and provide programmatic access to the database. Having a central data repository prevents deletion, loss, or accidental modification of primary data, while giving convenient access to the data for publication and analysis. It also provides a central location for storage of metadata for the high-throughput data sets, and will facilitate subsequent data integration strategies.
We encourage the use, installation and development of SyMBA by other groups. Please let us know if you are interested in using or evaluating SyMBA for use at your own Centre. Contact us: symba-devel at lists.sourceforge.net
Scott Edmunds: GigaScience - a journal or a database? Lessons learned from th...GigaScience, BGI Hong Kong
Scott Edmunds talk at the HUPO congress in Geneva, September 6th 2011 on GigaScience - a journal or a database? Lessons learned from the Genomics Tsunami.
BioCASE web services for germplasm data sets, at FAO, Rome (2006)Dag Endresen
Sharing of biodiversity data with web services - demonstration of the BioCASE software. Food and Agriculture Organization of the United Nations (FAO) 2nd March 2006.
Centralized Model Organism Database (Biocuration 2014 poster)Andrew Su
A Centralized Model Organism Database (CMOD) for the Long Tail of Genomes
Presented at Biocuration 2014 in Toronto http://biocuration2014.events.oicr.on.ca/
See related slides at http://www.slideshare.net/andrewsu/20140116-gmod-short
This presentation was provided by Violeta Ilik of Northwestern University during the NISO Virtual Conference held on Feb 15, 2017, entitled Institutional Repositories: Ensuring Yours is Populated, Useful and Thriving. The DOI for this presentation is http://dx.doi.org/10.18131/G3VP6R
Scott Edmunds slides for class 8 from the HKU Data Curation (module MLIM7350 from the Faculty of Education) course covering open science and data publishing
CellMissy: enabling management and dissemination of cell migration dataPaola Chiara Masuzzo
This talk presents some key features of the CellMissy software for the management and dissemination of cell migration data. The slides were presented during the 'Data management and standardization in cell migration research' workshop.
Distributed Ledger Technologies just left the peek of the Gartner’s Hype Cycle for Emerging technologies of 2017. However the status of the art for Blockchain-based initiatives in healthcare has not yet been reached, mostly due to the lack of knowledge about the need of interoperability amongst blockchain practitioners.
Following the adagio “The nice thing about standards is that you have so many to choose from”, the GrapevineWorld Project brings together DLT technologies in the healthcare context following the rules set by the IHE international standardisation body, whose specifications are the pillars of continental healthcare information exchange.
First, this presentation will introduce the IHE governance model.
Then it will tackle the benefit of DLTs to introduce the Grapevine research ecosystem.
This is a talk I gave at a Northwestern University - Complete Genomics Workshop on April 21, 2011 about using clouds to support research in genomics and related areas.
Slide 1: Title Slide
Extrachromosomal Inheritance
Slide 2: Introduction to Extrachromosomal Inheritance
Definition: Extrachromosomal inheritance refers to the transmission of genetic material that is not found within the nucleus.
Key Components: Involves genes located in mitochondria, chloroplasts, and plasmids.
Slide 3: Mitochondrial Inheritance
Mitochondria: Organelles responsible for energy production.
Mitochondrial DNA (mtDNA): Circular DNA molecule found in mitochondria.
Inheritance Pattern: Maternally inherited, meaning it is passed from mothers to all their offspring.
Diseases: Examples include Leber’s hereditary optic neuropathy (LHON) and mitochondrial myopathy.
Slide 4: Chloroplast Inheritance
Chloroplasts: Organelles responsible for photosynthesis in plants.
Chloroplast DNA (cpDNA): Circular DNA molecule found in chloroplasts.
Inheritance Pattern: Often maternally inherited in most plants, but can vary in some species.
Examples: Variegation in plants, where leaf color patterns are determined by chloroplast DNA.
Slide 5: Plasmid Inheritance
Plasmids: Small, circular DNA molecules found in bacteria and some eukaryotes.
Features: Can carry antibiotic resistance genes and can be transferred between cells through processes like conjugation.
Significance: Important in biotechnology for gene cloning and genetic engineering.
Slide 6: Mechanisms of Extrachromosomal Inheritance
Non-Mendelian Patterns: Do not follow Mendel’s laws of inheritance.
Cytoplasmic Segregation: During cell division, organelles like mitochondria and chloroplasts are randomly distributed to daughter cells.
Heteroplasmy: Presence of more than one type of organellar genome within a cell, leading to variation in expression.
Slide 7: Examples of Extrachromosomal Inheritance
Four O’clock Plant (Mirabilis jalapa): Shows variegated leaves due to different cpDNA in leaf cells.
Petite Mutants in Yeast: Result from mutations in mitochondrial DNA affecting respiration.
Slide 8: Importance of Extrachromosomal Inheritance
Evolution: Provides insight into the evolution of eukaryotic cells.
Medicine: Understanding mitochondrial inheritance helps in diagnosing and treating mitochondrial diseases.
Agriculture: Chloroplast inheritance can be used in plant breeding and genetic modification.
Slide 9: Recent Research and Advances
Gene Editing: Techniques like CRISPR-Cas9 are being used to edit mitochondrial and chloroplast DNA.
Therapies: Development of mitochondrial replacement therapy (MRT) for preventing mitochondrial diseases.
Slide 10: Conclusion
Summary: Extrachromosomal inheritance involves the transmission of genetic material outside the nucleus and plays a crucial role in genetics, medicine, and biotechnology.
Future Directions: Continued research and technological advancements hold promise for new treatments and applications.
Slide 11: Questions and Discussion
Invite Audience: Open the floor for any questions or further discussion on the topic.
Professional air quality monitoring systems provide immediate, on-site data for analysis, compliance, and decision-making.
Monitor common gases, weather parameters, particulates.
Observation of Io’s Resurfacing via Plume Deposition Using Ground-based Adapt...Sérgio Sacani
Since volcanic activity was first discovered on Io from Voyager images in 1979, changes
on Io’s surface have been monitored from both spacecraft and ground-based telescopes.
Here, we present the highest spatial resolution images of Io ever obtained from a groundbased telescope. These images, acquired by the SHARK-VIS instrument on the Large
Binocular Telescope, show evidence of a major resurfacing event on Io’s trailing hemisphere. When compared to the most recent spacecraft images, the SHARK-VIS images
show that a plume deposit from a powerful eruption at Pillan Patera has covered part
of the long-lived Pele plume deposit. Although this type of resurfacing event may be common on Io, few have been detected due to the rarity of spacecraft visits and the previously low spatial resolution available from Earth-based telescopes. The SHARK-VIS instrument ushers in a new era of high resolution imaging of Io’s surface using adaptive
optics at visible wavelengths.
This pdf is about the Schizophrenia.
For more details visit on YouTube; @SELF-EXPLANATORY;
https://www.youtube.com/channel/UCAiarMZDNhe1A3Rnpr_WkzA/videos
Thanks...!
(May 29th, 2024) Advancements in Intravital Microscopy- Insights for Preclini...Scintica Instrumentation
Intravital microscopy (IVM) is a powerful tool utilized to study cellular behavior over time and space in vivo. Much of our understanding of cell biology has been accomplished using various in vitro and ex vivo methods; however, these studies do not necessarily reflect the natural dynamics of biological processes. Unlike traditional cell culture or fixed tissue imaging, IVM allows for the ultra-fast high-resolution imaging of cellular processes over time and space and were studied in its natural environment. Real-time visualization of biological processes in the context of an intact organism helps maintain physiological relevance and provide insights into the progression of disease, response to treatments or developmental processes.
In this webinar we give an overview of advanced applications of the IVM system in preclinical research. IVIM technology is a provider of all-in-one intravital microscopy systems and solutions optimized for in vivo imaging of live animal models at sub-micron resolution. The system’s unique features and user-friendly software enables researchers to probe fast dynamic biological processes such as immune cell tracking, cell-cell interaction as well as vascularization and tumor metastasis with exceptional detail. This webinar will also give an overview of IVM being utilized in drug development, offering a view into the intricate interaction between drugs/nanoparticles and tissues in vivo and allows for the evaluation of therapeutic intervention in a variety of tissues and organs. This interdisciplinary collaboration continues to drive the advancements of novel therapeutic strategies.
A brief information about the SCOP protein database used in bioinformatics.
The Structural Classification of Proteins (SCOP) database is a comprehensive and authoritative resource for the structural and evolutionary relationships of proteins. It provides a detailed and curated classification of protein structures, grouping them into families, superfamilies, and folds based on their structural and sequence similarities.
Seminar of U.V. Spectroscopy by SAMIR PANDASAMIR PANDA
Spectroscopy is a branch of science dealing the study of interaction of electromagnetic radiation with matter.
Ultraviolet-visible spectroscopy refers to absorption spectroscopy or reflect spectroscopy in the UV-VIS spectral region.
Ultraviolet-visible spectroscopy is an analytical method that can measure the amount of light received by the analyte.
The increased availability of biomedical data, particularly in the public domain, offers the opportunity to better understand human health and to develop effective therapeutics for a wide range of unmet medical needs. However, data scientists remain stymied by the fact that data remain hard to find and to productively reuse because data and their metadata i) are wholly inaccessible, ii) are in non-standard or incompatible representations, iii) do not conform to community standards, and iv) have unclear or highly restricted terms and conditions that preclude legitimate reuse. These limitations require a rethink on data can be made machine and AI-ready - the key motivation behind the FAIR Guiding Principles. Concurrently, while recent efforts have explored the use of deep learning to fuse disparate data into predictive models for a wide range of biomedical applications, these models often fail even when the correct answer is already known, and fail to explain individual predictions in terms that data scientists can appreciate. These limitations suggest that new methods to produce practical artificial intelligence are still needed.
In this talk, I will discuss our work in (1) building an integrative knowledge infrastructure to prepare FAIR and "AI-ready" data and services along with (2) neurosymbolic AI methods to improve the quality of predictions and to generate plausible explanations. Attention is given to standards, platforms, and methods to wrangle knowledge into simple, but effective semantic and latent representations, and to make these available into standards-compliant and discoverable interfaces that can be used in model building, validation, and explanation. Our work, and those of others in the field, creates a baseline for building trustworthy and easy to deploy AI models in biomedicine.
Bio
Dr. Michel Dumontier is the Distinguished Professor of Data Science at Maastricht University, founder and executive director of the Institute of Data Science, and co-founder of the FAIR (Findable, Accessible, Interoperable and Reusable) data principles. His research explores socio-technological approaches for responsible discovery science, which includes collaborative multi-modal knowledge graphs, privacy-preserving distributed data mining, and AI methods for drug discovery and personalized medicine. His work is supported through the Dutch National Research Agenda, the Netherlands Organisation for Scientific Research, Horizon Europe, the European Open Science Cloud, the US National Institutes of Health, and a Marie-Curie Innovative Training Network. He is the editor-in-chief for the journal Data Science and is internationally recognized for his contributions in bioinformatics, biomedical informatics, and semantic technologies including ontologies and linked data.
Earliest Galaxies in the JADES Origins Field: Luminosity Function and Cosmic ...Sérgio Sacani
We characterize the earliest galaxy population in the JADES Origins Field (JOF), the deepest
imaging field observed with JWST. We make use of the ancillary Hubble optical images (5 filters
spanning 0.4−0.9µm) and novel JWST images with 14 filters spanning 0.8−5µm, including 7 mediumband filters, and reaching total exposure times of up to 46 hours per filter. We combine all our data
at > 2.3µm to construct an ultradeep image, reaching as deep as ≈ 31.4 AB mag in the stack and
30.3-31.0 AB mag (5σ, r = 0.1” circular aperture) in individual filters. We measure photometric
redshifts and use robust selection criteria to identify a sample of eight galaxy candidates at redshifts
z = 11.5 − 15. These objects show compact half-light radii of R1/2 ∼ 50 − 200pc, stellar masses of
M⋆ ∼ 107−108M⊙, and star-formation rates of SFR ∼ 0.1−1 M⊙ yr−1
. Our search finds no candidates
at 15 < z < 20, placing upper limits at these redshifts. We develop a forward modeling approach to
infer the properties of the evolving luminosity function without binning in redshift or luminosity that
marginalizes over the photometric redshift uncertainty of our candidate galaxies and incorporates the
impact of non-detections. We find a z = 12 luminosity function in good agreement with prior results,
and that the luminosity function normalization and UV luminosity density decline by a factor of ∼ 2.5
from z = 12 to z = 14. We discuss the possible implications of our results in the context of theoretical
models for evolution of the dark matter halo mass function.
1. AN OPEN DATA EXCHANGE
FOR CELL MIGRATION RESEARCH
paola masuzzo
@pcmasuzzo
paola.masuzzo@vib-ugent.be
computational omics and systems biology group
VIB / Ghent University, Ghent, Belgium
2. CC BY-SA 4.0
MULTIMOT: towards global exchange
of cell migration data
CellMissy: a ‘little but fierce’ software
for cell migration data
Open science: why you should speak it
and how to learn it
3. CC BY-SA 4.0
MULTIMOT: towards global exchange
of cell migration data
CellMissy: a ‘little but fierce’ software
for cell migration data
Open science: why you should speak it
and how to learn it
4. CC BY-SA 4.0
Cell migration is necessary for many
physiological conditions
Masuzzo, PhD thesis, 2016
5. CC BY-SA 4.0
Unfortunately, it is also implicated in many
diseases, such as metastatic cancer
Masuzzo, PhD thesis, 2016
6. CC BY-SA 4.0
A typical experimental workflow for a cell
migration study is composed of diverse steps
Servier Medical Art, CC-BY 3.0; Cell Image Library, CC-BY 3.0
sample
preparation
image
acquisition
image
processing
data
analysis
7. CC BY-SA 4.0
High-throughput experiments
produce complex and rich data sets
sample
preparation
image
acquisition
image
processing
data
analysis
• paper laboratory
notebooks
• electronic
laboratory
notebooks
• spreadsheets
• text files
• protocols
• papers...
• raw files
• XML files
• proprietary
microscope or
acquisition
software files
ND2 for Nikon, LIF
for Leica, OIB or
OIF for Olympus,
LSM or ZVI for
Zeiss
• image files with
pixel values and
metadata
• png, jpeg, tiff, avi
• text files
describing
processing
algorithms
• text files
describing
extracted features
• graphs, plots
• analysis pipelines
• text files
describing
computational
algorithms...
Servier Medical Art, CC-BY 3.0; Cell Image Library, CC-BY 3.0
8. CC BY-SA 4.0
CellMissy is our open-source tool for cell
migration data management and analysis
https://github.com/compomics/cellmissy
Masuzzo, Bioinformatics, 2013
9. CC BY-SA 4.0
CellMissy is our open-source tool for cell
migration data management and analysis
https://github.com/compomics/cellmissy
Masuzzo, Bioinformatics, 2013
11. CC BY-SA 4.0
This experimental setup encloses detailed
metadata annotation
12. CC BY-SA 4.0
CellMissy is our open-source tool for cell
migration data management and analysis
https://github.com/compomics/cellmissy
Masuzzo, Bioinformatics, 2013
14. CC BY-SA 4.0
All the data are then stored in a structured
way in a relational database
15. CC BY-SA 4.0
CellMissy is our open-source tool for cell
migration data management and analysis
https://github.com/compomics/cellmissy
Masuzzo, Bioinformatics, 2013
16. CC BY-SA 4.0
Cell migration can occur in both collective
and individual fashion
Collective
migration
Multicellular
streaming
Mesenchymal
Amoeboid
(blebs)
Amoeboid
(pseudopodia,
filopodia)
INDIVIDUAL
MIGRATION
COLLECTIVE
MIGRATION
Adapted from Friedl, J. Exp. Med., 2010
17. CC BY-SA 4.0
CellMissy enables efficient collective cell
migration data exploration and analysis
time (min)
Area(μm2
)
wound area
cell-covered area
18. CC BY-SA 4.0
A primary focus of data analysis is statistical
comparison of samples
analysis report with graphs,
tables and results
cell sheet velocity (µm²/min)
19. CC BY-SA 4.0
Many informative parameters can be derived
from single-cell trajectories
x
y
single cell
Masuzzo, 2017, under revision
20. CC BY-SA 4.0
Many informative parameters can be derived
from single-cell trajectories
Euclidean
distance
Cumulative
distance
x
y
single cell
Masuzzo, 2017, under revision
21. CC BY-SA 4.0
Many informative parameters can be derived
from single-cell trajectories
Euclidean
distance
Cumulative
distance
x
y
single cell parameter mathematical description
di: instantaneous
displacement of the
cell centroid between
adjacent time points
𝑑𝑖 = 𝑥𝑖+1 − 𝑥𝑖
2 + 𝑦𝑖+1 − 𝑦𝑖
2
si: instantaneous speed
between adjacent time
points
𝑠𝑖 = 𝑑(𝑝𝑖, 𝑝𝑖+1) ∆𝑡
αi: turning angle
between consecutive
steps
𝛼𝑖 = 𝑡𝑎𝑛−1 𝑦𝑖+1 − 𝑦𝑖 𝑥𝑖+1 − 𝑥𝑖
dtot: cumulative
distance, total distance
travelled
𝑑 𝑡𝑜𝑡 =
𝑖=1
𝑁−1
𝑑 𝑝𝑖, 𝑝𝑖+1
dnet: Euclidean distance,
net distance travelled
𝑑 𝑛𝑒𝑡 = 𝑑 𝑝1, 𝑝 𝑁
ep_dr: end-point
directionality ratio
(confinement ratio,
meandering index)
𝑒𝑝_𝑑𝑟 = 𝑑 𝑛𝑒𝑡 𝑑 𝑡𝑜𝑡
MD: median
displacement
𝑀𝐷 = 𝑚𝑒𝑑𝑖𝑎𝑛 𝑑𝑖
MS: median speed 𝑀𝑆 = 𝑚𝑒𝑑𝑖𝑎𝑛 𝑠𝑖
MTA: median turning
angle
𝑀𝑇𝐴 = 𝑚𝑒𝑑𝑖𝑎𝑛 𝛼𝑖
Masuzzo, 2017, under revision
23. CC BY-SA 4.0
Trajectory-centric parameters are instead
computed on each cell and then averaged for the
cell population
...
trajectory 1
trajectory 2
trajectory 3
trajectory 4
trajectory 5
24. CC BY-SA 4.0
The new single-cell module allows for both
these computations to take place
...
trajectory 1
trajectory 2
trajectory 3
trajectory 4
trajectory 5
trajectory-centric parameters
trajectory displacement (µm)
density
step displacement (µm)
density
pool of migration steps
step-centric parameters
Masuzzo, 2017, under revision
25. CC BY-SA 4.0
A flexible two-step filtering criterion is
implemented for data quality control
Masuzzo, 2017, under revision
26. CC BY-SA 4.0
MULTIMOT: towards global exchange
of cell migration data
CellMissy: a ‘little but fierce’ software
for cell migration data
Open science: why you should speak it
and how to learn it
27. CC BY-SA 4.0
Data and metadata exchange options are
already available in CellMissy
lab A
28. CC BY-SA 4.0
Data and metadata exchange options are
already available in CellMissy
lab A lab B
29. CC BY-SA 4.0
Data and metadata exchange options are
already available in CellMissy
lab B
This is one file in CellMissy! (≈10 MB)
lab A
30. CC BY-SA 4.0
But we can easily extend this concept
to a bigger scale
Data
Repository
Local Software
31. CC BY-SA 4.0
MULTIMOT is creating an open cell migration
data exchange ecosystem
https://multimot.org/
Masuzzo, Trends in Cell Biology, 2016
32. CC BY-SA 4.0
MULTIMOT is creating an open cell migration
data exchange ecosystem
https://cmso.science/
Masuzzo, Trends in Cell Biology, 2016
33. CC BY-SA 4.0
MULTIMOT: towards global exchange
of cell migration data
CellMissy: a ‘little but fierce’ software
for cell migration data
Open science: why you should speak it
and how to learn it
34. CC BY-SA 4.0
This open data ecosystem falls into the
broader context of open science
Knoth and Pontika, Open Science Taxonomy, figshare, 2015
35. CC BY-SA 4.0
There are four widely recognized thematic
pillars of open science
Masuzzo, PeerJ Preprints, 2017
https://doi.org/10.7287/peerj.preprints.2689v1
36. CC BY-SA 4.0
There are four widely recognized thematic
pillars of open science
Masuzzo, PeerJ Preprints, 2017
https://doi.org/10.7287/peerj.preprints.2689v1
OPEN
SCIENCE
D
A
T
A
C
O
D
E
P
A
P
E
R
S
R
E
V
I
E
W
> OPEN DATA
37. CC BY-SA 4.0
Open data means freedom to access, use
and re-use for any purpose
http://opendefinition.org/od/
38. CC BY-SA 4.0
Open data means freedom to access, use
and re-use for any purpose
There are many open knowledge definition conformant licenses
CC0 waiver
https://creativecommons.org/publicdomain/zero/1.0/
CC BY (Attribution only)
https://creativecommons.org/licenses/by/4.0/
CC BY-SA (Attribution ShareAlike)
https://creativecommons.org/licenses/by-sa/4.0/
http://opendefinition.org/od/, http://opendefinition.org/licenses
39. CC BY-SA 4.0
A lot of repositories are available to upload
research materials and data
40. CC BY-SA 4.0
A lot of repositories are available to upload
research materials and data
41. CC BY-SA 4.0
You certainly don’t need to know more than
1,500 repositories by heart
https://biosharing.org/databases/
42. CC BY-SA 4.0
There are four widely recognized thematic
pillars of open science
Masuzzo, PeerJ Preprints, 2017
https://doi.org/10.7287/peerj.preprints.2689v1
OPEN
SCIENCE
D
A
T
A
C
O
D
E
P
A
P
E
R
S
R
E
V
I
E
W
> OPEN ACCESS
43. CC BY-SA 4.0
The impacts of open access are very broad
and affect many areas
Tennant, Masuzzo, F1000Research, 2016
Wikimedia Commons, Public Domain
44. CC BY-SA 4.0
Publish your work open access can bring you
enormous benefits
CC-BY Danny Kingsley & Sarah Brown
45. CC BY-SA 4.0
Do you want to engage with Open Science?
When possible, use and cite existing public data
Whenever feasible, share your research data through trusted
repositories. General-purpose repositories and domain-specific ones
are available on the web. Make sure you share metadata as well
If you use software code as part of your research cycle, release the
code. Specify the open source license you intend to use, and link the
readers to a stable repository that hosts the code
Post free copies of your research articles online. Most journals allow
this, sometimes after an embargo period of 6-12 months
Post preprints of your research manuscripts online, ideally at the same
time of official submission to a journal
When possible, choose an open access journal as venue for your
scientific articles. Keep in mind that subscription journals also offer an
open access solution, upon payment of extra fees
Masuzzo, PeerJ Preprints, 2017
https://doi.org/10.7287/peerj.preprints.2689v1
collective: building and remodeling tissues, cancer invasion, wound healing
single: immune processes, but also cancer invasion
2D conditions are together, 3D conditions much slower
statistical comparison of the median cell sheet velocity across biological conditions: from control + drug solvent to different concentrations of latrunculin (important for dose response analysis)
latrunculin -- from 65.2 µM to 750.0 µM
latrunculin -- disruption of the actin filaments of the cytoskeleton
Inhibition of actin polymerization by latrunculin A disrupts actin filament formation, cytoskeletal organization, cell migration, and chemotaxis15,38.
Representation in 2D Euclidean space of single-cell trajectory -- define the parameters in the table.
In particular, the turning angle between two points, the ED, and the CD.
Representation in 2D Euclidean space of single-cell trajectory -- define the parameters in the table.
In particular, the turning angle between two points, the ED, and the CD.
Representation in 2D Euclidean space of single-cell trajectory -- define the parameters in the table.
In particular, the turning angle between two points, the ED, and the CD.
people who use the data must credit whoever has published or generate the data (attribution)
copies or adaptations of the data must be released similarly as open data (share-alike)