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NIH Image to ImageJ: 25 years of image analysis

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For the past 25 years NIH Image and ImageJ software have been pioneers as open tools for the analysis of scientific images. We discuss the origins, challenges and solutions of these two programs, and how their history can serve to advise and inform other software projects.
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Maryland graduate program that would
allow him to pursue his masters degree
in computer science and thus leave the
service early. One day in 1970, in the
commons at the University of Maryland,
College Park, he saw a notice for a part-
time programming position at the NIH in
Bethesda, Maryland, USA, to work on the
laboratory instrument computer (LINC)
created at the Massachusetts Institute
of Technology. Rasband applied for this
position, was hired and worked at the NIH
until he retired in 2010.
NIH Image: image analysis on the Mac
When Rasband began working at the
Research Services Branch at the National
Institute of Mental Health, part of the
intramural campus of the NIH, most
scientific data processing was done on
mainframe computers, and the personal
computer revolution was just beginning.
There was no image-analysis program for
the Macintosh computer, and Rasband
had just obtained one of the first Apple
Macintosh (Mac) II computers. Rasband
realized that it had the appropriate
hardware and low-level software to be
an ideal base for a small, low-cost image-
analysis system; all it needed was some
software for image analysis. Rasband
decided to write that software in support
of the imaging analysis needs he saw at the
time: chiefly, better access in terms both of
ease of adoption and cost.
It was his goal to have a low-cost
image-analysis system that the average
bench scientist could afford and deploy.
Rasband wanted to create a system that
was smaller and more affordable than
his earlier software systems that required
the $150,000 PDP-11 minicomputers
NIH Image to ImageJ: 25 years of image analysis
Caroline A Schneider, Wayne S Rasband & Kevin W Eliceiri
For the past 25 years NIH Image and ImageJ software have been pioneers as open tools for the
analysis of scientific images. We discuss the origins, challenges and solutions of these two programs,
and how their history can serve to advise and inform other software projects.
The last 50 years have seen tremendous
technological advances, few greater
than in the area of scientific computing.
One of the fields in which scientific
computing has made particular inroads
has been the area of biological imaging.
The modern computer coupled to
advances in microscopy technology is
enabling previously inaccessible realms
in biology to be visualized. Although the
roles of optical technologies and methods
have been well documented, the role
of scientific imaging software and its
origins have been seldom discussed in any
historical context. This is due in part to the
relative youth of the field, the wide variety
of imaging software tools available, sheer
diversity of subfields and specialized tools,
and the constant creation and evolution of
new tools.
In this great diversity and change, one
software tool has not only survived but
thrived. The scientific image-analysis pro-
gram, ImageJ
1,2
, known in previous incar-
nations as NIH Image
3
, was an early pio-
neer in image analysis. Twenty-five years
after its introduction the program not only
persists but continues to push and drive
the field. It does so not by continuously
reinventing itself but by sticking to a core
set of design principles that have allowed
it to become a modern image-processing
platform and yet retain an interface that a
user from over 20 years ago would recog-
nize and readily use.
Given the great success and impact
of ImageJ, one would expect that this
application was a software initiative with
official backing and formal planning by a
central funding body. Despite its original
name, NIH Image, and its home at the US
National Institutes of Health (NIH) for
over 30 years in some form, ImageJ is a
product of need, user-driven development
and collaboration—rather than a specific
plan by the NIH to create it at the onset.
ImageJ became what it is through years
of collaborative effort, and NIH nurtured
it by providing the resources to support
the primary programmer, Wayne Rasband,
throughout this period. In this current
age of careful oversight and scrutiny from
administrative bodies, the story of ImageJ
and the independent track that Rasband
had in its development is both interesting
and telling for other projects. To best
understand this, one needs to look at how
ImageJ started.
Rasband created NIH Image, the
predecessor to ImageJ, at the NIH in
1987, but the foundation for this program
was laid even earlier at the beginning
of Rasbands career. Rasband received
his bachelor’s degree in math from the
University of New Mexico, Albuquerque,
in 1965. He was involved early on with
the IBM computer punch card systems
while still in school. He leveraged this
expertise to get a job with the State of
New Mexicos Department of Automated
Processing, where he performed
common business-oriented language
(COBOL) programming and general
systems programming. Shortly thereafter,
Rasband was drafted by the US Army and
assigned to the Pentagon. While there,
Rasband became aware of a University of
Caroline A. Schneider and Kevin W. Eliceiri are
at the Laboratory for Optical and Computational
Instrumentation, University of Wisconsin at Madison,
Madison, Wisconsin, USA. Wayne S. Rasband is at the
Section on Instrumentation, US National Institutes of
Health, Bethesda, Maryland, USA.
e-mail: eliceiri@wisc.edu
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beyond the Mac platform. The late 1990s
was a notable period in Apple history
as the Mac was in a period of decline,
with the PC rapidly gaining ground. In
scientific research, the Mac still had a
loyal following, but this following, too, was
being eroded both owing to technologies
only being available on the PC platform
and the lower hardware cost of the PC.
Rasband faced a major challenge: how to
continue a program for the Mac and yet
support one for the PC. Rasband did not
want to port NIH Image to the PC and
did not want to maintain two programs or
trust a third party to maintain one.
In 1995 Sun Microsystems created
the Java programming language and
runtime environment in a bid to create an
operating system–agnostic programming
platform that would allow programmers to
‘write once, run anywhere, freeing them
from having to choose what operating
system to support. Rasband found this
idea appealing and liked the idea of
maintaining a single code base that could
run in any operating system with the
Java runtime environment installed or
on a Web browser as a Java applet, thus
allowing a single program to be run not
only on the Mac and Windows platforms
but also on the Unix operating system that
was becoming popular among scientists.
Furthermore, after using Pascal for over 20
years, Rasband was ready to try another
programming language.
In the transition of NIH Image to Java,
Rasband wanted to retain the elements of
NIH Image that had made it so successful
but felt the software deserved a new
name; he chose ImageJ to maintain the
connection to NIH Image but with a “J” to
indicate its Java foundation.
The transition from NIH Image to
ImageJ was not without its problems,
however, as the cross-platform
compatibility proved difficult at times. The
first public implementation of Java had
many rough edges. Instead of ‘write once,
run everywhere, Rasband found himself
writing once and debugging everywhere.
As one of the first end-user scientific
programs to widely use Java, there were
many difficulties in getting ImageJ to
work properly on different platforms and
Java environment distributions. As an
early Java adopter, Rasband had to tackle
many software-interface issues—from
talking to native hardware code for data
acquisition to dealing with varying levels
in use at the time. He had developed an
image-analysis program called “Image
for this platform. The program ran an
imaging system that used a rotating drum
film scanner to digitize images and a
512 × 512 pixel frame buffer to display
the digitized images, and it supported a
custom-built joystick that could be used to
outline objects. The PDP-11 systems were
used to analyze gels, autoradiographs,
and computed tomography, magnetic
resonance and positron emission
tomography images.
As a successor to ‘Image, Rasband
set out to build a program that would
provide the same utility but could be used
on the desktop computers that were just
becoming widely available, chief among
them the Mac II. With its relative low cost
of adoption, widespread use, easy graphic
interface and good developer support, the
Mac II was the ideal platform for a new
‘Image’ program. The Mac II had several
key additions over the earlier Mac that
made Rasband’s vision of NIH Image
possible, specifically (i) expansion slots:
the ability to add third-party acquisition
boards, (ii) advanced graphics: the
ability to handle not only color but most
importantly 8-bit 256 gray colors, the
mainstay format of light microscopy, and
(iii) support for the Pascal programming
language to allow third-party developers
to easily develop their own applications.
In the spring of 1987, just a few months
after Rasband had obtained his Mac II,
he handed out copies of the NIH Image
program on floppy disks to anyone who
asked. Rasband also promoted NIH Image
on the Mac forum on the CompuServe
Information Service electronic bulletin
boards and made the program available
on several Mac bulletin board systems.
Rasband wanted to create a general-
purpose extensible image-analysis
program that could be used by anyone
who wanted to capture, display and
enhance images, and he never targeted a
specific biological application or type of
imaging such as microscopy. His goal was
to let the users drive the applications for
NIH Image.
Rasband continued to develop the
program, but—through innovative
concepts such as mailing lists, free
reusable code, plug-ins and macros—he
also encouraged the users to develop
their own code to address their own
application needs. Medical researchers
were some of the first users of the program
because autoradiographs, computed
axial tomography or positron emission
tomography scans and X-rays could be
viewed, analyzed and notated. As NIH
Image became increasingly used in many
fields—biological microscopy being the
largest—the functionality of the program
and application base grew.
The move to other operating systems
As the code could be freely used in any
form, NIH Image was used in a variety
of cases, including spinoffs and related
programs such as Scion Image (Scion
Corporation) for the personal computer
(PC) platform. Scion Image was a notable
effort by the Scion Corporation to address
an unmet need—providing an NIH
Image for the PC (Microsoft Windows
operating system) community. In the
early 1990s the PC had caught up to the
Mac and had the graphics functionality
and extensibility needed to run a program
such as NIH Image, but the NIH Image
program was only available for the Mac.
Scion Corporations products were very
popular with NIH Image users because they
made a frame-grabber board that was the
principal way users collected their images
in NIH Image, whether from a gel imager
or analog microscopy camera. Scion saw
the opportunity to expand its hardware
framegrabber market to the PC by making
a Windows version of NIH Image. On their
own, with no input from Rasband, they
fully ported the Pascal-based NIH Image to
the C programming language and released
the resulting program as Scion Image.
Unfortunately, users found it to be ‘buggy’,
and because the program was closed-
source, there was no way for Rasband and
the community to fix these problems. Scion
Image never achieved a large user base,
and the need for NIH Image for Windows
largely remained unmet.
After NIH Image had been established,
Rasband started thinking about expanding
its capabilities to other operating
systems. He saw increasing interest in
the Scion Image program because it ran
on Microsoft Windows and also saw the
frustration that it did not work as well as
NIH Image did. He also saw the danger
in having a separate Windows program,
both in terms of support and in diluting
the user base and plug-ins.
Yet the climate and timing were such
that he felt he had to have a solution
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cient for a necessary application and users
need to add new functionality. In 1993,
Rasband saw the great utility of plug-ins
being used by Photoshop (Adobe) to add
new functionality to that software and
decided to add these modular software
elements to NIH Image. NIH Image was
one of the first scientific image-processing
tools to have plug-ins and the first with
such a large user base. Example plug-ins
included facilities for three-dimensional
rendering of images and particle analy-
sis. ImageJ has had plug-in support from
its inception, and the number of plug-ins
has increased rapidly, with over 500 (May
2012) plug-ins that cover a wide range of
functions available on the ImageJ website
(Fig. 2). Some of these plug-ins are dis-
tributed with the core ImageJ and most
are available for separate download and
install by the user. Rasband’s philoso-
phy of limiting complexity drove how he
decided what functionality to integrate
into the program directly in the menus,
distribute as core plug-ins that come pre-
packaged with ImageJ or make available as
downloads from the ImageJ website. Many
of the plug-ins built into ImageJ are from
outside contributors, and the decision to
include a plug-in in the base distribution
was based on whether Rasband thought
it would have widespread use. Additional
ImageJ plug-ins are available at third-party
websites with links to these resources from
the ImageJ website.
It is important to note that Rasband
never sought to replace commercial
image-analysis solutions. In part, this is
because a good part of the functionality
of NIH Image or ImageJ was created as
a result of there not being another solu-
tion, commercial or open-source, to do
it. Of course, out of necessity to be a full-
of Java support on different operating
systems. But over time, as the Java runtime
environments improved and coding
problems were solved, porting NIH Image
to Java set the stage for ImageJ to achieve
even greater success.
During Rasband’s many years
developing NIH Image and ImageJ at
NIH, occasionally a concerned lawyer or
administrator would come see him with
questions or concerns about the open
nature of ImageJ and its commercial
potential. Nothing came of these
infrequent meetings, and Rasband was
left unfettered to develop the program as
he wanted.
A driving design criterion of both
NIH Image and ImageJ was to keep the
program simple with no complex user
interfaces. Upon opening ImageJ, just
a single toolbar appears, and it is from
this straightforward interface that all of
the capabilities of ImageJ can be found
and used. The ImageJ toolbar has stayed
essentially the same for 15 years, similar
to how NIH Image has remained largely
the same (Fig. 1). Rasband wanted a stable
program interface that would not change,
but he also needed a way to add new
functionality based on user needs. This
philosophy of limiting complexity also
drove how he decided what functionality
to integrate into the program directly or
distribute as plug-ins.
Plugins and macros
To facilitate community input into NIH
Image and ImageJ, Rasband established
a community-driven development model
with several key elements: (i) user-driven
need and request for Rasband to address;
(ii) user-driven need that another member
of the community can address; (iii) user
developer can create a solution to his or
her own need but then share it with the
community, and (iv) user feedback can
be provided on an existing feature to
either improve functionality or add new
functionality.
A single developer–driven model such
that all code is developed by one person
would have resulted in a monolithic pro-
gram. Although this would provide the
simplicity of having only one way of doing
things, the breadth and depth of the solu-
tions would be greatly attenuated. Rasband
instead chose a more flexible approach
that would allow users to add functionality
on their own, but in a manner that would
allow the functionality to be shared with
others. This was accomplished through
the use of macros and plug-ins.
Macros are simple, custom program-
ming scripts that automate tasks inside a
large piece of software. Because of macros
rather basic programming format, general
users can create macros with no formal
programming experience. Rasband added
a macro language to NIH Image in 1989
after he saw an article titled “Building your
own C interpreter.” He realized he could
use the source code that was included
in the article to create a Pascal language
interpreter for NIH Image.
When Rasband later developed ImageJ,
he based the macro language on the one
in NIH Image. Similar to how the Pascal-
based macro language remained very con-
stant in NIH Image, ImageJ’s macro lan-
guage has remained very stable over the
last 15 years. Many new commands have
been added, but the early commands all
still work. Although macros are used by
programmers, they are especially useful
to the bench biologist, with ~325 macros
currently available on the ImageJ website.
The use of macros requires little or no
programming experience. New features
such as the macro recorder directly facili-
tate this, allowing users to record any
actions they manually do. This recording
is put into a macro syntax that users can
execute for future application of this work-
flow, modify it as necessary and share it
with others. ImageJ has since evolved in its
scripting capabilities and now allows other
scripting environments to be harnessed,
such as JavaScript, or other languages to be
called, such as Python, through an ImageJ
Jython Bridge.
In many cases, linking together exist-
ing functionality using macros is insuffi-
ab
Figure 1 | Appearance of NIH Image and ImageJ. (a,b) Screenshots of NIH Image 1.62, released in
1999 (a), and ImageJ 1.45, released in 2011 (b). Although the look is slightly different, the overall
feature layout and menu structure is basically the same.
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image workflows and take advantage of
algorithm capabilities provided by Matlab.
ImageJ connectivity with other software
programs, such as Imaris, Cell Profiler
5
and Knime
6–8
, has also been established.
Although Rasband did not specifical-
ly envision these collaborations when
designing the program, they have enabled
a variety of new functionality ranging
from automated screening and segmenta-
tion-based measurements to sophisticated
signal processing analysis, thus extending
the utility of ImageJ.
A prominent example of how ImageJ
has been adopted by the community is Fiji
(Fiji Is Just ImageJ) and ImageJ2. The goal
of Fiji
9
was to design a complete instal-
lation that was identical on any platform
and which was easy to download and
unpack. ImageJ2 (http://developer.imagej.
net/), the next generation of ImageJ, is an
NIH-funded collaboration between sev-
eral institutions, groups and individuals,
including Rasband. The ImageJ2 collabo-
ration hopes to create more extensibility,
modularity and interoperability as well
as extend ImageJ community resources.
ImageJ2 retains the interface of ImageJ
but adds new architecture to remove some
of the current limitations of ImageJ, such
as data types, image size and dimensions.
In addition to Fiji and ImageJ2, several
other variants and programs based on
ImageJ are currently available (Tabl e 1).
These variants all developed from target-
ing a specific community need that NIH
Image or ImageJ did not have, organizing
or adding additional tools for convenience
in one bundle or making a custom version
that is very use case–specific.
Rasband more than just tolerated this;
he has encouraged it as another mecha-
nism for addressing the diverse needs
of the ImageJ analysis community. For
example, when NIH Image core devel-
opment ceased in favor of focusing on
ImageJ, this resulted in NIH Image not
being ported to the OSX (Apple) oper-
ating system. There was a population of
electron microscopists that did not want
to change their workflow and ported NIH
Image as a new program, ImageSXM that
runs on OSX with a focus on electron-
microscopy analysis.
Other variants arose because of the
desire to improve access to new users and
provide documentation. MBF_ImageJ was
developed by Tony Collins and colleagues
to provide a comprehensive user manual
A major example of this—and a vast
improvement to ImageJ’s ability to read
and parse proprietary image data—was
the advent of Bio-Formats
4
, a library
from the Open Microscopy Environment
(http://www.openmicroscopy.org/)
for reading proprietary image formats.
Whereas Bio-Formats is a general library
used by many programs, ImageJ is its big-
gest user with the Bio-Formats ImageJ
plug-in used in over 30,000 laboratories.
ImageJ has been far more than just a user
of Bio-Formats; without the community-
driven model of ImageJ and the resulting
vetting and testing process for every new
format, Bio-Formats arguably would not
have the performance and functionality it
currently has. In this way ImageJ contin-
ues to benefit other programs that do not
directly use ImageJ but that take advan-
tage of its framework and plug-ins and
other code such as Bio-Formats.
Integration with other tools
Biologists often need to use a variety
of different software to acquire and
analyze data, and connectivity between
these tools can be crucial. Owing to the
Mac-only support of NIH image and its
pioneering status, there were few early
examples of NIH Image connecting with
external programs. There were several
prominent examples mediated by export
of an open file format, however, such
as the export of a .csv file for statistical
analysis. From the beginning of ImageJ
there was interest in directly connecting
to external toolkits without the need
to export and open files, and early
connections to Matlab (MathWorks) are
a prime example. ImageJ’s third-party tool
connections have allowed it to be used in
featured program, NIH Image and ImageJ
recapitulated many of the features present
in a commercial image-processing pro-
gram such as Adobe Photoshop. Certainly,
many of the NIH Image and ImageJ users
were first attracted to using the software
because they could not afford an expen-
sive seat (per computer) license for spe-
cialized commercial image-analysis pack-
ages. But many users of ImageJ also use
commercial software, so clearly that is not
the only draw. In fact, many imaging-soft-
ware companies also use and recommend
ImageJ. As well, many commercial tools
have emulated the key concepts of ImageJ,
for example, most modern analysis pro-
grams now offer some sort of scripting
functionality.
File format challenges
One of the main challenges of image-
analysis programs is being able to open
any of the myriad image file formats
that have been developed over the years.
Owing to code contributions and add-
ons from various sources through its
community development model, NIH
Image could read multiple image types—a
rare capability among the early image-
analysis programs. Needing support for
proprietary formats from microscopes
and other imaging equipment, users added
support for the majority of formats.
As one of the first programs to widely
support proprietary formats, NIH Image
had the best-supported and functional
readers, which are modular software code
used to read a file format and translate it
into the open formats used by the software.
These readers led to the development of a
reader code used not only in NIH Image
and ImageJ but other programs as well.
Figure 2 | ImageJ plug-ins bundled with each ImageJ release over time. Each data point is labeled with
the version number and letter.
500
2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013
Number of ImageJ plug-ins
Year
400
300
200
1.23y
1.29w
1.33u
1.34s
1.35s
1.36b
1.37v
1.38x
1.39u
1.41c
1.42n
1.43u
1.44o
1.45s
1.46p
100
0
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ACKNOWLEDGMENTS
We thank members of our research groups and
software projects for helpful feedback on the
manuscript, in particular C. Rueden, J. Fong and
J. Schindelin for input on the manuscript and help
with the figures. This work was supported in part
by NIH grant RC2 GM092519 (to K.W.E.), but the
opinions expressed are solely those of the authors.
COMPETING FINANCIAL INTERESTS
The authors declare no competing financial interests.
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with an organized preloaded plug-in
and macro structure for ImageJ so that
users could follow the instructions to do
certain steps such as thresholding and
three-dimensional rendering. ImageJA
was developed to allow for an applet ver-
sion of ImageJ that could be run in any
web browser, and this is now integrated
into Fiji. SalsaJ was a targeted version of
ImageJ with an interface and content for
astronomy users. There have also been
several attempts to extend the functional-
ity and data model of ImageJ, including
ImageJX and ImageJ2X. These are no lon-
ger active initiatives, but ideas from those
projects have been incorporated in cur-
rent ImageJ efforts, including the ImageJ2
project. Other applications are not vari-
ants of ImageJ but use components of
ImageJ, such as the plug-ins; these include
mManager
10
, Icy
11
, CellProfiler
5,12,13
and
Bio7.
As the ImageJ family of programs
moves forward, Rasband continues to play
a large part in maintaining and support-
ing ImageJ. Although he retired in 2010
after 40 years as a programmer at the
Research Services Branch, he now volun-
teers with the Section of Instrumentation
at the NIH and works closely with the
Center for Information Technology at the
NIH, which hosts the ImageJ website and
mailing list. Rasband fixes bugs, adds fea-
tures requested by users, and manages the
website and mailing list.
The continued popularity and growth
of ImageJ throughout the scientific com-
munity has surprised Rasband. The
ImageJ website has ~7,000 visitors a day,
and there are ~1,900 subscribers to the
ImageJ mailing list as of May 2012. A
recent PubMed Central search (May 2012)
of “ImageJ” returned over 20,000 papers.
Furthermore, ImageJ has been used in
teaching, such as in an image-processing
textbook
14
that illustrates imaging pro-
cessing examples using ImageJ. Rasband
hopes to see the continued use and evolu-
tion of ImageJ as a teaching and research
tool as more people recognize and under-
stand its capabilities.
Ten years from now, Rasband expects
to still be working on ImageJ. Although
the program and its variants will continue
to develop, and other programs will be
developed based on ImageJ, he expects the
program and its variants to retain the two
fundamental hallmarks of ImageJ: flex-
ibility and extensibility developed over 25
years ago. He also expects ImageJ to con-
tinue to be used for diverse applications
ranging from materials science and soil
science, astronomy and climate science,
to medical imaging and crystallography.
Table 1 | List of NIH Image and ImageJ variants
Date initiated Description
NIH Image 1987 The predecessor of ImageJ, created by Rasband; made for the Macintosh; no longer under active development
ImageSXM May 1993 A version of NIH Image for OSX extended by Steve Barrett; intended to handle loading, display and analysis of
images from the scanning microscope
ImageJ 1997 The current version of ImageJ developed by Rasband; sometimes called ImageJ1
ImageJ2x Unknown An offshoot of ImageJ; modified to use Swing interface; no longer under active development
ImageJA July 2005 An offshoot of ImageJ developed by Johannes Schindelin; used as the core of Fiji
Fiji December 2007 A ‘batteries included’ distribution of ImageJ popular in the life sciences
ImageJX March 2009 Created by Grant Harris to discuss improvements to ImageJ; formed the basis of an application to NIH that launched
ImageJDev
ImageJ2
(ImageJDev)
December 2009 Under development by the ImageJDev project; a complete rewrite of ImageJ; includes ImageJ1 to allow for old-style
plug-ins and macros
MBF_ImageJ 2005 Bundle developed by Tony Collins for light microscopists; plug-ins from MBF_ImageJ can be installed on Fiji,
combining the programs
SalsaJ Unknown An offshoot of ImageJ intended for astronomy applications; designed for use in classrooms; available in over 30
languages
CellProfiler 2006 Free, open-source software started by Anne Carpenter and Thouis Jones; aids biologists without computer-vision
training to quantitatively measure cell images automatically
Icy 2011 Created by the Quantitative Image Analysis Unit at Institut Pasteur, Icy provides integrated software to bridge the
gap between users and developers through open-source software and a central website
Bio7 Unknown Application used for ecological modeling; integrated development environment; focuses on individual-based
modeling and spatially explicit models
mManager 2005 Open-source microscopy software; controls automated microscopes; comprehensive imaging solution when used
with ImageJ; developed by Arthur Edelstein, Ziah Dean, Henry Pinkard and Nico Stuurman
npg
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... An additional fragment was mounted onto aluminum stubs, coated with 10 nm of gold, and imaged with a FEI Quanta 600 FEG scanning electron microscope (SEM) at the University of Utah Surface Analysis Lab. ImageJ software was used to measure structural features (Schneider et al., 2012). ...
... An additional fragment was mounted onto aluminum stubs, coated with 10 nm of gold, and imaged with a FEI Quanta 600 FEG scanning electron microscope (SEM) at the University of Utah Surface Analysis Lab. ImageJ software was used to measure structural features (Schneider et al., 2012). In addition to the microstructure of the eggshell, egg size can be used to narrow down the mass of the female bird and the number of possible eggs in a clutch . ...
Thesis
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Nests and eggs represent the beginning of life for many vertebrates. Determining the nesting strategies of extant amniotes is crucial in elucidating the evolution and diversification of reproductive traits as nesting materials are poorly preserved in the fossil record. Avian and chelonian nests are particularly rare compared to non-avian dinosaurs. The goal of this dissertation is to investigate and describe the sedimentology, taphonomy, and ecology of two fossil nesting localities and examine modern eggshell porosity via micro-CT images. I characterized two nesting localities, one from the Late Cretaceous Kaiparowits Formation in Grand Staircase-Escalante National Monument outside of Escalante, Utah, the second in the Eocene Bridger Formation east of Lyman, Wyoming, in terms of sedimentology and stratigraphy and taphonomy. Eggshell from the Kaiparowits Formation is identified as testudine and unique characteristics of the shell unit height-to-width ratio, egg size, eggshell thickness, and ornamentation warrant the naming of a new ootaxa, Testudoolithus tuberi. The distribution of eggshell is interpreted as resulting from nest predation. The Bridger Formation eggshell material is similar since the distribution and preservation of half eggs is interpreted as being caused by predation. Both of the nesting localities are imbedded in greenish-gray mudstones overlying and under lithic sandstones, suggesting flood-plain deposition. The avian eggshell is named a new ootaxon as well, (Doolithus bridgerensis), from the number of observable ultrastructural layers. Micro-CT images of modern rigid-shelled amniotes, including birds, crocodylians, a tortoise, and a gecko, reveals a complex network of internal pores that do not connect to the external surface of the eggshell. The functional pores tend to be cylindrical to trumpet-shaped in birds and pear-shaped in the tortoise, but are bowl-shaped in the gecko, conical in the crocodylian genera Crocodylus and Osteolaemus, and globular in Melanosuchus niger. Eggs in open and closed nesting strategies have generally similar functional eggshell porosity ranges of 0.1-0.8% sample volume. However, covered nesters generally have higher total porosities of 2.9-16.1%. The internal porosity arises from the interstitial spaces between mineralized egg units. The functional value of the internal porosity is yet unknown, but may improve properties of shell insulation and retention of water vapor.
... Samples were vitrified using a Leica EM-GP plunge freezer (Leica Microsystems GmbH, Wetzlar, Germany) and imaged using a 200 kV FEI Talos Arctica cryoelectron microscope (FEI Company, Hillsboro, OR, United States). Images were processed with high-pass filtering to increase visual sharpness using ImageJ software 68 . ...
Article
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The development of experimental methodologies that enable investigations of biochemistry at high pressure promises to yield significant advances in our understanding of life on Earth and its origins. Here, we introduce a method for studying lipid membranes at thermodynamic conditions relevant for life at deep sea hydrothermal vents. Using in situ high pressure magic-angle spinning solid state nuclear magnetic resonance spectroscopy (NMR), we measure changes in the fluidity of model microbial membranes at pressures up to 28 MPa. We find that the fluid-phase lateral diffusion of phospholipids at high pressure is significantly affected by the stoichiometric ratio of lipids in the membrane. Our results were facilitated by an accessible pressurization strategy that we have developed to enable routine preparation of solid state NMR rotors to pressures of 30 MPa or greater.
... Note: DBH; diameter at breast height, *; photomicrographs of transverse sections were taken with a digital camera mounted on a microscope for measuring cell morphologies, and then cell dimensions were measured using Image J (Schneider et al. 2012). TROPICS Vol. ...
Article
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Database for radial variations of anatomical characteristics and wood properties in tropical trees (DaRV-tropics)’ is constructed using data collected by the Laboratory of Forest Products and Wood Material Science, School of Agriculture, Utsunomiya University, Japan, to promote effective utilization of forest resources. The concept to construct this database is ‘how we can construct a database easier’ and ‘how we can maintain it easier’. The first edition of DaRV-tropics includes 39 study project data consisted of 298 individual trees from 35 species (24 genera from 14 families) collected from Indonesia, Malaysia, and Thailand. DaRV-tropics includes radial variations of measured traits, such as basic density, compressive strength parallel to grain, length and diameter of wood fiber and vessel element, vessel frequency, and wall thickness of wood fiber, as well as mean values. Our database has made it possible to provide basic information that contributes to the development of research on tropical hardwoods.
... The non-cropped image of the blot in Fig. S11. Densitometry analysis was performed using ImageJ software 50 . For the analysis of PrP res levels in ScN2a, logistic dose-response curves were generated and analyzed using the drc package 51 of the R program 52 to calculate the half-maximal inhibitory concentration (IC 50 ). ...
Article
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In prion diseases, the cellular prion protein (PrPC) forms an abnormal, infectious, and disease-causing form known as PrPSc. Inhibition of prion propagation is a key approach for the treatment of these diseases. We report on a curcumin-based compound, GT863 (formerly known as PE859) that displays therapeutic efficacy when administered orally. GT863 inhibited abnormal prion protein formation in prion-infected neuroblastoma cells in a prion strain dependent manner: effectively for RML prion and marginally for 22 L prion. Treatment with ad libitum GT863-containing feed prolonged the incubation period of intracerebrally RML prion infected Tga20 mice by 217% increase in mean. Although the 263 K prion-infected Tg7 mice were less sensitive to GT863 than RML prion infected Tga20, treatment with ad libitum GT863-containing feed prolonged the incubation period by 39% increase in mean. The mechanism of the anti-prion effectiveness in vivo needs to be elucidated and managed. Nevertheless, GT863 could inspire the development of oral chemotherapy for prion diseases.
... The copyright holder for this preprint this version posted February 13, 2025. ; https://doi.org/10.1101/2025.02.12.637900 doi: bioRxiv preprint was calculated using ImageJ [60], by taking the area of sensitivity around the stimulation site and subtracting the area of the thermode. ...
Preprint
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The thermal grill, in which innocuous warm and cool stimuli are interlaced, can produce a paradoxical burning pain sensation—the thermal grill illusion (TGI). While the mechanisms underlying TGI remain unclear, prominent theories point to spinal dorsal horn integration of innocuous thermal inputs to elicit pain. It remains unknown whether the TGI activates peripheral nociceptors, or solely thermosensitive afferents and is integrated within the spinal cord. Different types of sensitization have established mechanisms and can inform TGI mechanisms: if the TGI elicits (1) primary hyperalgesia, peripheral nociceptors are activated; (2) secondary hyperalgesia in the absence of primary hyperalgesia, spinal integration is required; and (3) brush allodynia, wide-dynamic range neurons are involved in mediating the TGI. Here, we determine whether the TGI elicits primary hyperalgesia, secondary hyperalgesia or brush allodynia. Fifty-two participants underwent individually calibrated phasic thermal grill stimulation. We found that the TGI elicited primary hyperalgesia, but only in participants with component temperatures in the noxious range (<19 °C and >41 °C). The TGI also elicited secondary hyperalgesia, even in participants with strictly innocuous thermal inputs. No participants developed brush allodynia. We observed sex differences in primary hyperalgesia: only males exhibited thermal grill-induced primary hyperalgesia. These findings suggest that the TGI is integrated in the spinal dorsal horn, likely mediated by heat-pinch-cold (HPC) neurons, and, to some degree, by primary nociceptive afferents in males. This study shows that the TGI may have sex-dependent mechanisms and determines that HPC cells are involved in the illusory sensation of pain from innocuous thermal inputs.
... After primary antibody incubation, we incubated the PVDF membranes with either HRP-conjugated goat anti-mouse IgG secondary antibody (1:10,000 dilution, AP181P; Sigma-Aldrich, St. Louis, MO, USA) or HRP-conjugated goat anti-rabbit IgG secondary antibody (1:10,000 dilution, HAF008; Novus Biologicals, Littleton, CO, USA). We incubated PVDF membranes with ECL substrate (Immobilon ECL Ultra Western HRP Substrate; MiliporeSigma, Billerica, MA, USA) after secondary antibody incubation, visualized proteins using a ChemiDoc imager (Analytik Jena US), and quantified protein expression via utilizing version 1.53a NIH ImageJ software [61]. ...
Article
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Background and Objectives: Atherosclerosis is an inflammatory condition that results in cholesterol accumulating within vessel wall cells. Atherosclerotic cardiovascular disease is the leading cause of mortality worldwide due to this disease being a major contributor to myocardial infarctions and cerebrovascular accidents. Research suggests that cholesterol accumulation occurring precisely within arterial endothelial cells triggers atherogenesis and exacerbates atherosclerosis. Furthermore, inflamed endothelium acts as a catalyst for atherosclerotic development. Therefore, enhancing cholesterol removal specifically in pro-inflammatory endothelial cells may be a potential treatment option for atherosclerosis. While we have previously shown that inhibiting the microRNA guide strand miR-33a-5p within pro-inflammatory endothelial cells increases both ABCA1 expression and apoAI-mediated cholesterol efflux, it is unknown whether inhibiting the miR-33a-3p passenger strand in pro-inflammatory endothelial cells causes similar atheroprotective effects. In this study, this is what we aimed to test. Materials and Methods: We used plasmid transfection to knockdown miR-33a-3p expression within cultured pro-inflammatory immortalized mouse aortic endothelial cells (iMAECs). We compared ABCA1 expression and apoAI-mediated cholesterol efflux within these cells to cultured pro-inflammatory iMAECs transfected with a control plasmid. Results: The knockdown of miR-33a-3p expression within pro-inflammatory iMAECs resulted in a significant increase in ABCA1 mRNA expression. However, the inhibition of miR-33a-3p did not significantly increase ABCA1 protein expression within pro-inflammatory iMAECs. Moreover, we failed to detect a significant increase in apoAI-mediated cholesterol efflux within pro-inflammatory iMAECs from miR-33a-3p knockdown. Conclusions: Our results indicative that the knockdown of miR-33a-3p alone does not enhance ABCA1-dependent cholesterol efflux within pro-inflammatory endothelial cells. To gain any atheroprotective benefit from inhibiting miR-33a-3p within pro-inflammatory endothelium, additional anti-atherogenic strategies would likely be needed in unison.
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Understanding the genetic basis of neuron-glia interactions is essential to comprehend the function of glia. Recent studies on Drosophila antennal glia Mz317 has shown their role in olfactory perception. In the antenna, the Mz317-type glia tightly envelops the somas of olfactory sensory neurons and axons already covered by wrapping glia. Here, we investigate candidate genes involved in glial regulation in olfactory reception of Drosophila. Targeted transcriptional profiling reveals that Mz317 glial cells express 21% of Drosophila genes emphasizing functions related to cell junction organization, synaptic transmission, and chemical stimuli response. Comparative gene expression analysis with other glial cell types in both the antenna and brain provides a differential description based on cell type, offers candidate genes for further investigation, and contributes to our understanding of neuron-glia communication in olfactory signaling. Additionally, similarities between the molecular signatures of peripheral glia in Drosophila and vertebrates highlight the utility of model organisms in elucidating glial cell functions in complex systems.
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Species belonging to the genus Leptothrix are widely distributed in the environment and in activated sludge (AS) wastewater treatment plants (WWTPs). They are commonly found in iron-rich environments and reported to cause filamentous bulking in WWTPs. In this study, the diversity, distribution, and metabolic potential of the most prevalent Leptothrix spp. found in AS worldwide were studied. Our 16S rRNA amplicon survey showed that Leptothrix belongs to the general core community of AS worldwide, comprising 32 species with four species being most commonly found. Their taxonomic classification was re-evaluated based on both 16S rRNA gene and genome-based phylogenetic analysis showing that three of the most abundant “ Leptothrix” species represented species in three other genera, Rubrivivax , Ideonella , and the novel genus, Ca . Intricatilinea. New fluorescence in situ hybridization (FISH) probes revealed rod-shaped morphology for the novel Ca . Rubrivivax defluviihabitans and Ca . Ideonella esbjergensis , while filamentous morphology was found only for Ca . Intricatilinea gracilis. Analysis of high-quality metagenome-assembled genomes revealed metabolic potential for aerobic growth, fermentation, storage of intracellular polymers, partial denitrification, photosynthesis, and iron reduction. FISH in combination with Raman microspectroscopy confirmed the in situ presence of chlorophyll and carotenoids in Ca . Rubrivivax defluviihabitans and Ca . Intricatilinea gracilis. This study resolves the taxonomy of abundant but poorly classified “ Leptothrix” species, providing important insights into their diversity, morphology, and function in global AS wastewater treatment systems. IMPORTANCE The genus Leptothrix has been extensively studied and described since the 1880s, with six species currently described but with the majority uncultured and undescribed. Some species are assumed to have a filamentous morphology and can cause settling problems in wastewater treatment plants (WWTPs). Here, we revised the classification of the most abundant Leptothrix spp. present in WWTPs across the world, showing that most belong to other genera, such as Rubrivivax and Ideonella . Furthermore, most do not have a filamentous morphology and are not problematic in WWTPs as previously believed. Metabolic reconstruction, including some traits validated in situ by the application of new fluorescence in situ hybridization probes and Raman microspectroscopy, provided additional insights into their metabolism. The study has contributed to a better understanding of the diversity, morphology, and function of “ Leptothrix ,” which belong to the abundant core community across global activated sludge WWTPs.
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Current research in biology uses evermore complex computational and imaging tools. Here we describe Icy, a collaborative bioimage informatics platform that combines a community website for contributing and sharing tools and material, and software with a high-end visual programming framework for seamless development of sophisticated imaging workflows. Icy extends the reproducible research principles, by encouraging and facilitating the reusability, modularity, standardization and management of algorithms and protocols. Icy is free, open-source and available at http://icy.bioimageanalysis.org/.
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Fiji is a distribution of the popular open-source software ImageJ focused on biological-image analysis. Fiji uses modern software engineering practices to combine powerful software libraries with a broad range of scripting languages to enable rapid prototyping of image-processing algorithms. Fiji facilitates the transformation of new algorithms into ImageJ plugins that can be shared with end users through an integrated update system. We propose Fiji as a platform for productive collaboration between computer science and biology research communities.
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Analysing large amounts of data generated by next-generation sequencing (NGS) technologies is difficult for researchers or clinicians without computational skills. They are often compelled to delegate this task to computer biologists working with command line utilities. The availability of easy-to-use tools will become essential with the generalization of NGS in research and diagnosis. It will enable investigators to handle much more of the analysis. Here, we describe Knime4Bio, a set of custom nodes for the KNIME (The Konstanz Information Miner) interactive graphical workbench, for the interpretation of large biological datasets. We demonstrate that this tool can be utilized to quickly retrieve previously published scientific findings. Availability: http://code.google.com/p/knime4bio/. Contact: richard.redon@univ-nantes.fr
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Data sharing is important in the biological sciences to prevent duplication of effort, to promote scientific integrity, and to facilitate and disseminate scientific discovery. Sharing requires centralized repositories, and submission to and utility of these resources require common data formats. This is particularly challenging for multidimensional microscopy image data, which are acquired from a variety of platforms with a myriad of proprietary file formats (PFFs). In this paper, we describe an open standard format that we have developed for microscopy image data. We call on the community to use open image data standards and to insist that all imaging platforms support these file formats. This will build the foundation for an open image data repository.
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