University of Minnesota. Scientists have developed a first-of-its-kind, 3-D printed guide that helps regrow both the sensory and motor functions of complex nerves after injury. The groundbreaking research has the potential to help more than 200,000 people annually who experience nerve injuries or disease.
This is a 3-D printed nerve regeneration pathway implanted in a rat helped to improve walking in 10 to 12 weeks after implantation. Credit: University of Minnesota College of Science and Engineering
A national team of researchers has developed a first-of-its-kind, 3D-printed guide that helps regrow both the sensory and motor functions of complex nerves after injury. The groundbreaking research has the potential to help more than 200,000 people annually who experience nerve injuries or disease.
Collaborators on the project are from the University of Minnesota, Virginia Tech, University of Maryland, Princeton University, and Johns Hopkins University.
Nerve regeneration is a complex process. Because of this complexity, regrowth of nerves after injury or disease is very rare, according to the Mayo Clinic. Nerve damage is often permanent. Advanced 3D printing methods may now be the solution.
In a new study, published today in the journal Advanced Functional Materials, researchers used a combination of 3D imaging and 3D printing techniques to create a custom silicone guide implanted with biochemical cues to help nerve regeneration. The guide's effectiveness was tested in the lab using rats.
To achieve their results, researchers used a 3D scanner to reverse engineer the structure of a rat's sciatic nerve. They then used a specialized, custom-built 3D printer to print a guide for regeneration. Incorporated into the guide were 3D-printed chemical cues to promote both motor and sensory nerve regeneration. The guide was then implanted into the rat by surgically grafting it to the cut ends of the nerve. Within about 10 to 12 weeks, the rat's ability to walk again was improved.
"This represents an important proof of concept of the 3D printing of custom nerve guides for the regeneration of complex nerve injuries," said University of Minnesota mechanical engineering professor Michael McAlpine, the study's lead researcher. "Someday we hope that we could have a 3D scanner and printer right at the hospital to create custom nerve guides right on site to restore nerve function."
Scanning and printing takes about an hour, but the body needs several weeks to regrow the nerves. McAlpine said previous studies have shown regrowth of linear nerves, but this is the first time a study has shown the creation of a custom guide for regrowth of a complex nerve like the Y-shaped sciatic nerve that has both sensory and motor branches.
"The exciting next step would be to implant these guides in humans rather than rats," McAlpine said. In cases where a nerve is unavailable for scanning, McAlpine said there could someday be a "library" of scanned nerves from other people or cadavers that hospitals could use to create closely matched 3D-printed guides for patients.
In addition to McAlpine, major contributors to the research team include Blake N. Johnson, Virginia Tech; Xiaofeng Jia, University of Maryland and Johns Hopkins University; and Karen Z. Lancaster, Esteban Engel, and Lynn W. Enquist, Princeton University.
This research was funded by grants from the National Institutes of Health, the Defense Advanced Research Projects Agency, the Maryland Stem Cell Research Fund, and the Grand Challenges Program at Princeton University.
To read more about the study entitled "3D Printed Anatomical Nerve Regeneration Pathways," visit the Advanced Functional Materials website.
Note: Materials may be edited for content and length.
Journal Reference:
Blake N. Johnson, Karen Z. Lancaster, Gehua Zhen, Junyun He, Maneesh K. Gupta, Yong Lin Kong, Esteban A. Engel, Kellin D. Krick, Alex Ju, Fanben Meng, Lynn W. Enquist, Xiaofeng Jia, Michael C. McAlpine. 3D Printed Anatomical Nerve Regeneration Pathways. Advanced Functional Materials, 2015;
Abstract
The introduction of affordable, consumer-oriented 3-D printers is a milestone in the current “maker movement,” which has been heralded as the next industrial revolution. Combined with free and open sharing of detailed design blueprints and accessible development tools, rapid prototypes of complex products can now be assembled in one’s own garage—a game-changer reminiscent of the early days of personal computing. At the same time, 3-D printing has also allowed the scientific and engineering community to build the “little things” that help a lab get up and running much faster and easier than ever before.
Figures
Citation: Baden T, Chagas AM, Gage G, Marzullo T, Prieto-Godino LL, et al. (2015) Open Labware: 3-D Printing Your Own Lab Equipment. PLoS Biol 13(3): e1002086. doi:10.1371/journal.pbio.1002086
Funding: This work was supported by the Deutsche Forschungsgemeinschaft (DFG) (Werner Reichardt Centre for Integrative Neuroscience Tübingen, EXC 307 to TE and TB; BA 5283/1-1 to TB) and the U.S. National Institutes of Mental Health Small Business Innovation Research grant #R44 MH093334 to GG and TM. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: Authors GG and TM are founders of Backyard Brains (www.backyardbrains.com), a company specializing in the design and distribution of Open Labware.
Abbreviations:: ABS, Acrylnitrile butadiene styrene; CNC, computer numerical control; DIY, Do It Yourself; EMG, Electromyogram; ERG, Electroretinogram; FOSSFA, Free Software and Open Source Foundation for Africa; GNU, “GNU is not Unix” (recursive acronym); GPIO, general-purpose input-output; IT, information technology; LED, Light Emitting Diode; NIH, National Institutes of Health; PCB, Printed Circuit Board; PLA, Polylactic acid; TReND, Teaching and Research in Natural Sciences for Development (in Africa); UV, Ultraviolet
Applications of 3-D printing technologies (Fig. 1A, Box 1) have become as diverse as the types of materials that can be used for printing. Replacement parts at the International Space Station may be printed in orbit from durable plastics or metals, while back on Earth the food industry is starting to explore the same basic technology to fold strings of chocolate into custom-shaped confectionary. Also, consumer-oriented laser-cutting technology makes it very easy to cut raw materials such as sheets of plywood, acrylic, or aluminum into complex shapes within seconds. The range of possibilities comes to light when those mechanical parts are combined with off-the-shelf electronics, low-cost microcontrollers like Arduino boards [1], and single-board computers such as a Beagleboard [2] or a Raspberry Pi [3]. After an initial investment of typically less than a thousand dollars (e.g., to set-up a 3-D printer), the only other materials needed to build virtually anything include a few hundred grams of plastic (approximately US$30/kg), cables, and basic electronic components [4,5].
Fig 1. Examples of open 3-D printed laboratory tools.A1, Components for laboratory tools, such as the base for a micromanipulator [18] shown here, can be rapidly prototyped using 3-D printing. A2, The printed parts can be easily combined with an off-the-shelf continuous rotation servo-motor (bottom) to motorize the main axis. B1, A 3-D printable micropipette [8], designed in OpenSCAD [19], shown in full (left) and cross-section (right). B2, The pipette consists of the printed parts (blue), two biro fillings with the spring, an off-the-shelf piece of tubing to fit the tip, and one screw used as a spacer. B3, Assembly is complete with a laboratory glove or balloon spanned between the two main printed parts and sealed with tape to create an airtight bottom chamber continuous with the pipette tip. Accuracy is ±2–10 μl depending on printer precision, and total capacity of the system is easily adjusted using two variables listed in the source code, or accessed via the “Customizer” plugin on the thingiverse link [8]. See also the first table.
Box 1. Glossary Open source
A collective license that defines terms of free availability and redistribution of published source material. Terms include free and unrestricted distribution, as well as full access to source code/blueprints/circuit board designs and derived works. For details, see http://opensource.org.
Maker movement
Technology-oriented extension of the traditional “Do-it-Yourself (DIY)” movement, typically denoting specific pursuits in electronics, CNC (computer numerical control) tools such as mills and laser cutters, as well as 3-D printing and related technologies.
3-D printing
Technology to generate three-dimensional objects from raw materials based on computer models. Most consumer-oriented 3-D printers print in plastic by locally melting a strand of raw material at the tip (“hot-end”) and “drawing” a 3-D object in layers. Plastic materials include Acrylnitrile butadiene styrene (ABS) and Polylactic acid (PLA). Many variations of 3-D printers exist, including those based on laser-polymerization or fusion of resins or powdered raw materials (e.g., metal or ceramic printers).
Arduino boards
Inexpensive and consumer-oriented microcontroller boards built around simple processors. These boards offer a variety of interfaces (serial ports, I2C and CAN bus, etc.), μs-timers, and multiple general-purpose input-output (GPIO) pins suitable for running simple, time-precise programs to control custom-built electronics.
Single board computers
Inexpensive single-board computers capable of running a mature operating system with graphical-user interface, such as Linux. Like microcontroller boards, they offer a variety of hardware interfaces and GPIO pins to control custom-built electronics.
It therefore comes as no surprise that these technologies are also routinely used by research scientists and, especially, educators aiming to customize existing lab equipment or even build sophisticated lab equipment from scratch for a mere fraction of what commercial alternatives cost [6]. Designs for such “Open Labware” include simple mechanical adaptors [7], micropipettes (Fig. 1B) [8], and an egg-whisk–based centrifuge [9] as well as more sophisticated equipment such as an extracellular amplifier for neurophysiological experiments [10], a thermocycler for PCR [11], or a two-photon microscope [12]. At the same time, conceptually related approaches are also being pursued in chemistry [13–15] and material sciences [16,17]. See also Table 1.
Table 1. Open Labware designs for a biology lab. doi:10.1371/journal.pbio.1002086.t001
A Culture of SharingMost makers share their designs under an open source license together with detailed assembly instructions in online repositories [20–22] such as the National Institutes of Health (NIH) 3-D print exchange [23] or in peer-reviewed journals [10,17,24–29]. As a result, anyone can freely use and modify them. This open culture, which has transformed the world of software engineering over the past decades, offers several advantages over traditional product design. First, designs are not only free, but are directly shaped by people who will actually use the final product. Second, building your own experimental equipment yields a much deeper understanding of the principles underlying its design and a better awareness of its limits. Third, manufacturing is immediate and local—thus empowering laboratories and schools located in difficult-to-reach places. Fourth, the open source movement is a global phenomenon, connecting people worldwide, often to recruit talented builders and coders from outside the traditional scientific establishment. Still, there are drawbacks. For example, a commercial solution may be preferred if the time and cost of in-house development exceeds the benefits of control and instrument knowledge. Nevertheless, and perhaps counter-intuitively, some open designs are published by commercial companies that offer the parts as well as the assembled product with technical support for a fee, while maintaining the source material online under an open source license. Consumers can more freely balance cost against time to build, while companies gain in customer relations and product feedback.
Quality Control and the Evolution of Open Labware Designs
The flagship of the open source movement, the operation system GNU/Linux [30], is perhaps the best example for the potential of open designs. First available in the 1990s, today it is one of the most widely used system on supercomputers, servers, and mobile phones (Android) and increases its share of home users every year. Critically, Linux is fully open source—anyone can freely access its entire source code, change it, and distribute modifications. Its success flows from the idea that “given enough eyeballs all bugs are shallow” [31]. In other words, a distributed network of collaborators helps to identify and rectify bugs in the system as they arise. Open Labware works the same way: release of a design sparks feedback and refinements from the community. Although Open Labware’s contributor base is still relatively small, feedback can nonetheless markedly improve product design and expand potential applications over time. For example, free online sharing of the designs for a manually controlled 3-D printed micromanipulator (Fig. 2A) [18] provided the starting point for a high-precision motorized version (Fig. 2B) [32]. Subsequently, and in combination with open designs for smartphone lens-adapters offering portable options for field-microscopy [33], both designs led to the “Raspberry Pi-scope” [34]—a self-standing histology microscope based on a Raspberry Pi, equipped with a high-resolution camera module, a low-cost acrylic lens, and the manipulator body to accurately position the sample (Fig. 2C). One next step in this evolution may be to add low-cost fluorescence capability through the addition of a ultraviolet light-emitting diode (UV-LED) and the appropriate filters, perhaps inspired by materials used in the less than US$1 fluorescence-capable Foldscope [24]. Without free-and-open sharing of the complete source materials at all stages of development, this evolution would not have been possible. With educators and researchers increasingly integrating Open Labware approaches into their projects, designs are expected to continuously improve and diversify.
Fig 2. Evolution of an Open Labware design.
A, A 3-D printable micromanipulator with a slanted Z-axis [18], here shown amidst commercial alternatives, initially served as the basis for a motorized version with “real” Z axis [32] (B). B1,2, the three axes are driven by continuous-rotation micro-servos, controlled by an Arduino fitted with a Joystick-shield and a 9V battery. B3, The motorized manipulator offers sufficient precision to target individual hairs on the head of a fruitfly (±5–20 μm during movements, depending on printer precision; <1 μm drift min-1 when stationary). Scale bar 1 mm. C1, The same manipulator build was then converted into a microscope-stage to permit accurate placement and focus of histology samples [34]. The optics are provided by an off-the-shelf, low-power acrylic lens positioned directly above a Raspberry Pi camera module [3]. C2, Image taken with the microscope, showing a slice of mouse brain (hippocampus) stained for cytochrome oxidase C. Scale bar 500 μm.
doi:10.1371/journal.pbio.1002086.g002
Online Resources for Learning and Problem Solving
Although most Open Labware designs are published with detailed assembly instructions aimed at the non-specialist, a basic understanding of concepts in physics, electronics, and computer programming is certainly helpful. Luckily, freely available online resources facilitate self-learning (Table 2). In addition, many makers hone their skills directly at the workbench, simply by attempting to recreate or modify existing designs. Expert community help can be rapidly found in online forums such as Stack Overflow [35], returning to the idea that many eyes lead to rapid problem-solving. In addition, online aggregators gather and summarize information on specific topics, serving as a hub for both information seekers and distributors. Some of them are curated in a centralized manner [36], while others follow the distributed wiki principle [37].
Table 2. Open resources for self-taught learning of programming, electronics, and basic physics. doi:10.1371/journal.pbio.1002086.t002
Application in a Resource-Challenged Context
If a commercial alternative is available, it is usually the compromise between time and money spent that determines whether to build or purchase equipment. While Open Labware designs may benefit any research or education setting [38], one obvious foothold for their possibilities lies in economically deprived schools and universities in the developing world. Here, the introduction of Open Labware may make the crucial difference between having some usable equipment to work with and having none at all. Several benefits of an open model come to light: low cost, local manufacture, and the possibility to customize according to local demands or availability of parts. In sub-Saharan Africa, only a few bases and organizations that offer training in “maker” skills or general promotion of open source principles operate. These include the Free Software and Open Source Foundation for Africa (FOSSFA [39]), Fundi bots [40], as well as a few physical spaces such as FabLabs [41] and Maker- [42] and Hackerspaces [43] scattered mostly around the major cities. In addition, companies selling easily transportable 3-D printers and supplies are rapidly establishing their foothold on the African continent, such that local sourcing of the required hardware is no longer an insurmountable obstacle. However, on the whole, Open Labware possibilities remain poorly established in this context, as attested in a survey taken by 89 biomedical researchers from 12 sub-Saharan African countries in August 2014 (Fig. 3). When asked about their software competency, most respondents indicated that while they are comfortable with “basic” IT usage (defined as using office packages or navigating the web), few were routine users of standard open analysis packages such as R [44], octave [45], or other Python [46] based packages, and programming skills were even less prevalent (Fig. 3A). Awareness and competency in the use of open hardware approaches were even less developed: over 90% of respondents (83/89) had never used 3-D printing or any form of single-board computers or microcontrollers (Fig. 3B).
Fig 3. Open Labware at universities in sub-Saharan Africa.
An online survey was taken by 89 biomedical researchers (MSc. to
Professor) at universities in 12 different sub-Saharan African countries
in August 2014. Researchers rated their own competency and awareness in
aspects of software and hardware usage. A, Software competency rated on
a scale of 1 (low) to 10 (high) in “basic usage such as navigating
office software or the internet,” “usage of open analysis packages such
as R [44], octave [45],
or similar,” and “programming, e.g., using C++, python, or any other
mainstream language.” B, Hardware awareness for possibilities in “3-D
printing” (top) and “single board computers/microcontrollers such as
Raspberry Pi, Arduino, Beagleboard, or similar” rated in four
categories: (i) “I have never heard of this,” (ii) “I have heard of it
but I have no access,” (iii) “I have tried using this at least once,”
(iv) “I am a competent/routine user.”doi:10.1371/journal.pbio.1002086.g003
As part of ongoing efforts to promote scientific education and research in the developing world, we introduced the many possibilities of Open Labware to schools and universities in sub-Saharan Africa (TReND in Africa [47]) and Latin America (Backyard Brains [48]). Different formats were used, ranging from three-week intensive neuroscience summer schools aimed at university graduates in Uganda and Tanzania (four events), to multiday MakerSpace workshops aimed at school students and their teachers in Chile and Mexico (12 events), to outreach events and lectures held at scientific conferences, schools, and public spaces on four continents (more than 100 events). Here, we present some experiences and success stories from this work.
In-Depth Exposure during Multiday Training Events
The longer events, such as the three-week neuroscience summer schools in Africa or the multiday MakerSpace workshops in Latin America, afforded participants the opportunity to get hands-on experience using, assembling, and contributing to the development of Open Labware designs. One popular activity was to assemble existing designs (such as a spikerbox, Fig. 4A [10,49]) from off-the-shelf parts. This required participants to study the circuit diagram, note the identity and polarity of simple electronic components such as chips and capacitors and to solder them in place on the printed circuit board (PCB) (Fig. 4B). The assembled amplifiers were subsequently used to perform classic neurophysiological experiments such as recording of action potentials from the locust [50] or cricket [51,52] extensor tibiae (Fig. 4C). Although initially daunting especially for the many participants without previous contact with any form of electrical engineering, the experience tended to be very motivating across ages and cultures. In particular, it contributed to take away the fear of experimenting with simple electronic circuits or opening up and attempting to modify or repair existing electronic tools in their daily environment. The low cost of required parts also allowed participants to build their own gear, rather than that of their school or university, making them more invested in its success and maintenance. Critically, building equipment from scratch often resulted in a high level of mechanistic understanding and a curiosity to “play” with the finished product to see if it can be improved or modified to better suit a particular purpose. For example, one undergraduate student linked an electromyogram (EMG)-amplifier to an off-the-shelf “robotic limb” [53] via an Arduino microcontroller [1] to remotely control grasping movements by contracting their forearm muscles (Fig. 4D). Clearly, introduction of low-cost and open source electronics and mechanical parts has the potential to open up vast possibilities to resourceful people anywhere in the world, independent of financial means or educational background.
Fig 4. Hands-on exposure to Open Labware in the developing world.
A, Each student assembled a spikerbox [10,49],
an amplifier for neurophysiological experiments, from its off-the-shelf
components. B, Students at a workshop in Dar es Salaam, Tanzania. C,
The assembled amplifiers were subsequently used to perform simple
neurophysiological experiments [51]. Image credit for panels B and C: Horst Schneider. D, One student set up
a spikerbox via an Arduino to trigger closure of a “gripper-hand,” a
low-cost robotic limb, through contractions of their forearm muscles. doi:10.1371/journal.pbio.1002086.g004
During the three-week neuroscience summer schools in Uganda and Tanzania [54] we introduced participants to 3-D printed lab tools such as pipettes [8], manipulators [18,32], and microscope adapters [55] and compared the usefulness of self-built versus commercially available solutions for different types of experiments. For example, although printed pipettes [8] offer lower precision than commercially available ones (±2–10 μl, depending on printer precision) they are nonetheless adequate for many “low precision” tasks such as distributing diluted antibody solutions to different samples or applying mounting media for immunohistochemistry. Similarly, we used different versions of 3-D printed micromanipulators [18,32] to position reference electrodes during neurophysiological experiments, e.g., for Calliphora tangential cell recordings [56], but maintained the recording tungsten electrode on a more stable commercial manipulator. For some types of recordings, such as Drosophila electroretinograms (ERGs) [57], the printed manipulators were adequate to hold both electrodes, and the magnification gained by attaching a low-power acrylic lens to a webcam or smartphone easily sufficed for their accurate placement. We also provided access to pre-installed Raspberry Pi computers [3] running a range of open analysis and office software packages and provided basic training in their use. Hands-on exposure allowed students to judge for themselves which designs would be useful in their own research and teaching activities. In a subsequent survey, students rated the usefulness of every open design described above as at least nine out of ten on average (n = 33). We believe that the introduction of Open Labware possibilities to the African university system may be a highly effective measure towards fostering excellence in research and education on the continent.
Motivating More High School Graduates to Pursue a Career in Science
One pervasive issue in building a sustainable research infrastructure and scientific culture in resource-challenged countries is a perceived limit on career choices afforded by higher education. Traditionally, three disciplines—medicine, civil engineering, or law—are considered the best choices for reliable income, with few individuals enrolling in a natural science subject and fewer still ending up in active research [58,59]. To encourage students to consider a career in science, we (TReND and Backyard Brains) have participated in more than a hundred science outreach events around (Neuro)science, engaging more than 10,000 students, parents, and teachers. In several science camps (“ChileVA! [60]”), we gave two-hour lectures to high school students about the brain and how to record from neurons and muscles. The students not only “tolerated” this unusually long-format science lecture without breaks, but swarmed the demonstration booth afterwards. Clearly, low-cost and portable equipment offers the possibility to perform science demonstrations anywhere, independent of local infrastructure.
In TReND’s African activities, student-alumni of our summer workshops have taken over local science outreach by organizing into regional teams to visit schools and universities in their respective home countries [61]. Many of these young scientists come from similar backgrounds as the students they engage, allowing them to act as powerful role models. Thus, students attending a school that cannot afford the equipment or infrastructure to perform live experiments during science classes are exposed to local scientists with a similar background, handling equipment they know how to build from affordable, local resources. The experience can be very powerful and inspire them to pursue a similar career, as well as offering local teachers ideas to use in future science classes. This approach of “teaching the teachers” also means that the impact of educating a few students at a high level has the potential to trickle-down and achieve a wide impact in the long term.
Policy Recommendations
Clearly, the use and design of Open Labware designs can be a powerful ingredient to foster scientific research, education, and public science engagement. Their evolution spans several disciplines, from computer sciences and mechanical engineering to electronics and biology—thus connecting experts and the wider public across fields and sparking creativity in people of all ages. Their low cost, adaptability and robustness renders designs suitable for a broad range of applications in both teaching and research. Below we present some suggestions for policy implementations to optimize available possibilities.
Integrate more aspects of design and use of Open Labware into traditional science curricula, ideally at an early age.
Establish more hands-on training courses in basic hardware design and programming skills for established scientists and educators.
Establish infrastructural support to afford more students and educators easy and direct access to 3-D printing and related technologies. With 3-D printers starting at a few hundred dollars and their price steadily falling, schools and university departments should not be barred from investing in their own model because of economic reasons.
Provide incentives for companies to invest in an open model of product design. This move promises to spark a new generation of open companies in more direct dialogue with the end user, towards better, individually tailored, and more affordable product design.
Container with all files mentioned in the article that are currently hosted on thingiverse (www.thingiverse.com).
S1 Data. Thingiverse files.
Container with all files mentioned in the article that are currently hosted on thingiverse (www.thingiverse.com).
doi:10.1371/journal.pbio.1002086.s001
(ZIP)
Acknowledgments
We thank the students and voluntary instructors of the many lectures and workshops mentioned in the manuscript, as well as the TReND outreach team, headed by Mahmoud Bukar Maina and Yunusa Mohammed Garba, for their ongoing efforts in promoting science education in Africa. We thank all sponsors of workshops, most notably including the International Brain Research Organization (IBRO), The Company of Biologists, and the Cambridge Alborada Fund.
ORIGINAL:PLOS
Tom Baden,
Andre Maia Chagas,
Greg Gage,
Timothy Marzullo,
Lucia L. Prieto-Godino,
Thomas Euler
March 20, 2015
DOI: 10.1371/journal.pbio.1002086
Scientists have coaxed stem cells to grow the first three-dimensional mini lungs. Previous research has focused on deriving lung tissue from flat cell systems or growing cells onto scaffolds made from donated organs.
In a study published in the online journal eLifethe multi-institution team defined the system for generating the self-organizing human lung organoids, 3D structures that mimic the structure and complexity of human lungs.
Figure 1.Generation of three-dimensional ventral anterior foregut spheroids from endoderm monolayers.
(A)
hESCs were differentiated into foregut endoderm by treating cells with 4
days of Activin A (ACTA) followed by 4 days of NOG+SB. (B) Foregut endoderm (NOG+SB) had high expression of the foregut marker SOX2 while the hindgut marker CDX2
was significantly reduced compared to untreated endoderm controls
(End). NOG+SB monolayers had high expression of ventral anterior foregut
genes NKX2.1 and PAX8 while the posterior foregut marker PDX1 was reduced. The foregut marker HHEX is expressed in the developing liver, biliary system, and thyroid and remained unchanged. (C)
The majority of cells in NOG+SB treated cultures were SOX2 positive
(green) compared to the control, in which only scattered clusters of
cells were SOX2 positive. The scale bar represents 200 µm. (D)
hESCs were differentiated into foregut spheroids by treating cells with
4 days of ACTA and then additional 4–6 days of NOG+SB+FGF4+Ch.
Representative images of a spheroid in a matrigel droplet are shown as a
whole mount image. Scale bar represents 100 µm. (E) Foregut spheroids (NOG+SB+FGF4+Ch) had high expression of the foregut marker SOX2 while the hindgut marker CDX2
was significantly reduced compared to untreated endoderm control (End)
(top panel). Spheroids had high expression of anterior foregut genes NKX2.1 and PAX8 while the posterior foregut marker PDX1 was reduced and HHEX was unchanged (bottom panel). *p < 0.05, error bars represent SEM. (F)
The majority of cells in foregut spheroids are FOXA2+ (green, left
panel) and SOX2+ (white, right panel) and ECAD+ (red, right panel).
Scale bar represent 50 µm.
Neri Oxman and a group of creatives from the MIT mediated matter group in collaboration with Christoph Bader and Dominik Kolb have developed a technology to create digitally manufactured wearables with multi-material 3D printing machinery. the project, entitled ‘wanderers: wearables for interplanetary pilgrims’, was introduced as part of ‘The Sixth Element: Exploring the natural beauty of 3D printing’, at Euromold in Frankfurt.
Each of the wearables are designed to hold life sustaining elements contained within 3D printed vascular structures with internal cavities. according to the mediated matter group, they’ve found a way to embed ‘living matter in the form of engineered bacteria within the 3D structures in order to augment the environment. living matter within these structures will ultimately transform oxygen for breathing, photons for seeing, biomass for eating, biofuels for moving and calcium for building.’
The human brain carries out many tasks at the same time, but how many? Now fMRI data has revealed just how parallel gray matter is.
The human brain is often described as a massively parallel computing machine. That raises an interesting question: just how parallel is it?
Today, we get an answer thanks to the work of Harris Georgiou at the National Kapodistrian University of Athens in Greece, who has counted the number of “CPU cores” at work in the brain as it performs simple tasks in a functional magnetic resonance imaging (fMRI) machine. The answer could help lead to computers that better match the performance of the human brain.
The brain itself consists of around 100 billion neurons that each make up to 10,000 connections with their neighbors. All of this is packed into a structure the size of a party cake and operates at a peak power of only 20 watts, a level of performance that computer scientists observe with unconcealed envy.
fMRI machines reveal this activity by measuring changes in the levels of oxygen in the blood passing through the brain. The thinking is that more active areas use more oxygen so oxygen depletion is a sign of brain activity.
Typically, fMRI machines divide the brain into three-dimensional pixels called voxels, each about five cubic millimeters in size. The complete activity of the brain at any instant can be recorded using a three-dimensional grid of 60 x 60 x 30 voxels. These measurements are repeated every second or so, usually for tasks lasting two or three minutes. The result is a dataset of around 30 million data points.
Georgiou’s work is in determining the number of independent processes at work within this vast data set. “This is not much different than trying to recover the (minimum) number of actual ‘cpu cores’ required to ‘run’ all the active cognitive tasks that are registered in the entire 3-D brain volume,” he says.
This is a difficult task given the size of the dataset. To test his signal processing technique, Georgiou began by creating a synthetic fMRI dataset made up of eight different signals with statistical characteristics similar to those at work in the brain. He then used a standard signal processing technique, called independent component analysis, to work out how many different signals were present, finding that there are indeed eight, as expected.
Next, he applied the same independent component analysis technique to real fMRI data gathered from human subjects performing two simple tasks. The first was a simple visuo-motor task in which a subject watches a screen and then has to perform a simple task depending on what appears.
In this case, the screen displays either a red or green box on the left or right side. If the box is red, the subject must indicate this with their right index finger, and if the box is green, the subject indicates this with their left index finger. This is easier when the red box appears on the right and the green box appears on the left but is more difficult when the positions are swapped. The data consisted of almost 100 trials carried out on nine healthy adults.
The second task was easier. Subjects were shown a series of images that fall into categories such as faces, houses, chairs, and so on. The task was to spot when the same object appears twice, albeit from a different angle or under different lighting conditions. This is a classic visual recognition task.
The results make for interesting reading. Although the analysis is complex, the outcome is simple to state. Georgiou says that independent component analysis reveals that about 50 independent processes are at work in human brains performing the complex visuo-motor tasks of indicating the presence of green and red boxes. However, the brain uses fewer processes when carrying out simple tasks, like visual recognition.
That’s a fascinating result that has important implications for the way computer scientists should design chips intended to mimic human performance. It implies that parallelism in the brain does not occur on the level of individual neurons but on a much higher structural and functional level, and that there are about 50 of these.
Georgiou points out that a typical voxel corresponds to roughly three million neurons, each with several thousand connections with its neighbors. However, the current state-of-the-art neuromorphic chips contain a million artificial neurons each with only 256 connections. What is clear from this work is that the parallelism that Georgiou has measured occurs on a much larger scale than this.
“This means that, in theory, an artificial equivalent of a brain-like cognitive structure may not require a massively parallel architecture at the level of single neurons, but rather a properly designed set of limited processes that run in parallel on a much lower scale,” he concludes.
Anybody thinking of designing brain-like chips might find this a useful tip.
Ref: arxiv.org/abs/1410.7100Estimating The Intrinsic Dimension In fMRI Space Via Dataset Fractal Analysis
