Liverpool will assess Sadio Mane after the forward suffered a hamstring injury in their 2-1 win over Wolves.
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Friday, January 24, 2020
Thursday, January 23, 2020
Study: Commercial air travel is safer than ever
It has never been safer to fly on commercial airlines, according to a new study by an MIT professor that tracks the continued decrease in passenger fatalities around the globe.
The study finds that between 2008 and 2017, airline passenger fatalities fell significantly compared to the previous decade, as measured per individual passenger boardings — essentially the aggregate number of passengers. Globally, that rate is now one death per 7.9 million passenger boardings, compared to one death per 2.7 million boardings during the period 1998-2007, and one death per 1.3 million boardings during 1988-1997.
Going back further, the commercial airline fatality risk was one death per 750,000 boardings during 1978-1987, and one death per 350,000 boardings during 1968-1977.
“The worldwide risk of being killed had been dropping by a factor of two every decade,” says Arnold Barnett, an MIT scholar who has published a new paper summarizing the study’s results. “Not only has that continued in the last decade, the [latest] improvement is closer to a factor of three. The pace of improvement has not slackened at all even as flying has gotten ever safer and further gains become harder to achieve. That is really quite impressive and is important for people to bear in mind.”
The paper, “Aviation Safety: A Whole New World?” was published online this month in Transportation Science. Barnett is the sole author.
The new research also reveals that there is discernible regional variation in airline safety around the world. The study finds that the nations housing the lowest-risk airlines are the U.S., the members of the European Union, China, Japan, Canada, Australia, New Zealand, and Israel. The aggregate fatality risk among those nations was one death per 33.1 million passenger boardings during 2008-2017.
For airlines in a second set of countries, which Barnett terms the “advancing” set with an intermediate risk level, the rate is one death per 7.4 million boardings during 2008-2017. This group — comprising countries that are generally rapidly industrializing and have recently achieved high overall life expectancy and GDP per capita — includes many countries in Asia as well as some countries in South America and the Middle East.
For a third and higher-risk set of developing countries, including some in Asia, Africa, and Latin America, the death risk during 2008-2017 was one per 1.2 million passenger boardings — an improvement from one death per 400,000 passenger boardings during 1998-2007.
“The two most conspicuous changes compared to previous decades were sharp improvements in China and in Eastern Europe,” says Barnett, who is the George Eastman Professor of Management at the MIT Sloan School of Management. In those places, he notes, had safety achievements in the last decade that were strong even within the lowest-risk group of countries.
Overall, Barnett suggests, the rate of fatalities has declined far faster than public fears about flying.
“Flying has gotten safer and safer,” Barnett says. “It’s a factor of 10 safer than it was 40 years ago, although I bet anxiety levels have not gone down that much. I think it’s good to have the facts.”
Barnett is a long-established expert in the field of aviation safety and risk, whose work has helped contextualize accident and safety statistics. Whatever the absolute numbers of air crashes and fatalities may be — and they fluctuate from year to year — Barnett has sought to measure those numbers against the growth of air travel.
To conduct the current study, Barnett used data from a number of sources, including the Flight Safety Foundation’s Aviation Safety Network Accident Database. He mostly used data from the World Bank, based on information from the International Civil Aviation Organization, to measure the number of passengers carried, which is now roughly 4 billion per year.
In the paper, Barnett discusses the pros and cons of some alternative metrics that could be used to evaluate commercial air safety, including deaths per flight and deaths per passenger miles traveled. He prefers to use deaths per boarding because, as he writes in the paper, “it literally reflects the fraction of passengers who perished during air journeys.”
The new paper also includes historical data showing that even in today’s higher-risk areas for commerical aviation, the fatality rate is better, on aggregate, than it was in the leading air-travel countries just a few decades in the past.
“The risk now in the higher-risk countries is basically the risk we used to have 40-50 years ago” in the safest air-travel countries, Barnett notes.
Barnett readily acknowledges that the paper is evaluating the overall numbers, and not providing a causal account of the air-safety trend; he says he welcomes further research attempting to explain the reasons for the continued gains in air safety.
In the paper, Barnett also notes that year-to-year air fatality numbers have notable variation. In 2017, for instance, just 12 people died in the process of air travel, compared to 473 in 2018.
“Even if the overall trendline is [steady], the numbers will bounce up and down,” Barnett says. For that reason, he thinks looking at trends a decade at a time is a better way of grasping the full trajectory of commercial airline safety.
On a personal level, Barnett says he understands the kinds of concerns people have about airline travel. He began studying the subject partly because of his own worries about flying, and quips that he was trying to “sublimate my fears in a way that might be publishable.”
Those kinds of instinctive fears may well be natural, but Barnett says he hopes that his work can at least build public knowledge about the facts and put them into perspective for people who are afraid of airplane accidents.
“The risk is so low that being afraid to fly is a little like being afraid to go into the supermarket because the ceiling might collapse,” Barnett says.
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Blue Cross Blue Shield Medicare Health Plans
What is the Right Blue Cross Blue Shield Medicare Plan for You?
Knowing which health insurance plan will work best for you isn’t always easy. So, let’s take a closer look at some Blue Cross Blue Shield Medicare coverage plans and how they can help you get the health care you need.
What is Medicare?
There’s a lot of talk about Medicare coverage and other issues about health plans. But these conversations often skip past explaining what Medicare is.
Medicare is a federal health insurance program. Medicare health plans offered are for:
- U.S. citizens age 65 and over, or
- Citizens under 65 with certain disabilities or medical conditions
Medicare has different plans referred to as Parts A, B, C, D and private carriers also issue supplemental plans. These plans cover different health care services and needs.
| Medicare Plans | Part A | Part B | Medicare Advantage (Part C) | Part D | Medicare supplement plans |
| Features | Offers inpatient care and home health care services | Offers doctor services, outpatient care, and medical supplies | Coverage may include wellness programs, hearing aids, and vision services | Offers prescription drug coverage | Help with out-of-pocket costs (such as deductibles, copays and coinsurance) not covered by Part A and Part B |
Medicare Part A and Part B Plans
Medicare Part A and Part B are also known as Original Medicare.
Part A covers:
- Inpatient care offered in hospitals or skilled nursing facilities
- me health care services
- Hospice care for the terminally ill
Part B covers:
- Doctorservices
- Outpatient care
- Medical supplies
- Durable medical equipment
- Preventive services
Now, let’s take a deeper look at Blue Cross and Blue Shield Medicare’s Advantage plans and Supplement plans
Medicare Advantage Blue Cross Blue Shield Plans (Part C)
Medicare Advantage plans offer Medicare coverage through private health insurance companies approved by the Center for Medicare and Medicaid Services (CMS).
These plans include health maintenance organizations (HMOs), preferred provider organizations (PPOs), regional PPOs and private fee-for-service plans. An organization in these categories usually has a Medicare contract and is a licensee of the Blue Cross Blue Shield Association.
Medicare Advantage plans provide all Part A and B services and some additional services, such as:
- Wellness programs
- Hearing aids
- Vision services
Also, Medicare Advantage plans (such as PPO plans and HMO plans) usually provide prescription drug coverage.
Some of these plans have a maximum that you would have to pay for out-of-pocket costs each calendar year, a feature not offered through original Medicare.
Medicare Advantage plans have location-based service areas and most have networks of doctors and hospitals. So, be sure to ask your doctors if they are in your health insurance plan’s Medicare Advantage network.
How Can I Enroll in a Blue Cross Blue Shield Medicare Advantage Plan?
You must first enroll in Medicare Part A and Part B before joining a Medicare Advantage plan.
Medicare currently offers people an Open Enrollment Period about once a year. This gives you the chance to review and, if you want, make changes to your Medicare coverage. Some of the changes you can make include:
- Joining a Medicare Advantage plan
- Leaving your Medicare Advantage plan and returning to Original Medicare (Part A and Part B)
- Switching from one Medicare Advantage plan to another
- Adding or changing your prescription drug coverage (Part D) plan if you are in Original Medicare
Medicare Advantage plans can be useful. But what if you find out you have gaps in your Medicare health coverage?
That’s where Medigap comes in.
Blue Cross Blue Shield Medicare Supplement Plans
If you have Medicare Part A and Part B coverage, Medicare Supplement (also called Medigap) plans can help fill the coverage gaps in Medicare Part A and Part B. Some Medigap plans even cover foreign travel emergency services.
Medigap plans are sold by private insurance companies. They can help you with out-of-pocket costs (such as deductibles, copays and coinsurance) not covered by Parts A and B.
How Can I Enroll in Medigap?
You must first enroll in Medicare Part A and Part B before joining a Medigap plan.
Even though Medicare has an annual Open Enrollment Period for Medicare Advantage plans, this doesn’t apply for folks wanting Medigap coverage.
The Open Enrollment Period for a Medigap policy is the six-month period that starts the first day of the month that you turn 65 or older and enrolled in Medicare Part B.
After that, your ability to buy one of the Medigap plans depends on which state you live in. This is balanced by the fact that once you’re enrolled in a Medigap plan, it renews annually as long as you pay your premium and the plan is available.
Medicare Prescription Drug Coverage (Part D)
Medicare prescription drug plans (PDP plans) are offered by private health insurance companies and cover your prescription drug costs for covered medications.
You can select a Medicare PDP plan in addition to:
- Original Medicare (Part A and Part B)
- Original Medicare (Part A and Part B) with a Medigap Plan
Part D coverage is included in most Medicare Advantage (Part C) plans.
Blue Cross Blue Shield Medicare Coverage – Frequently Asked Questions (FAQs)
Q: Can I have the Blue Cross Blue Shield Medicare Advantage plan and a Medicare supplement plan at the same time?
A: No. You must pick between the Medicare Advantage plan (Part C) or a Blue Cross Blue Shield Medicare Supplement plan. It is against federal law for someone to be enrolled in both plans at the same time.
Q: How is Medicare different from Medicaid?
A: Medicaid is a state-based health insurance program that covers a set of benefits and services. The program helps low-income individuals and families, people with disabilities and older folks.
Medicare is a federal health insurance program that covers certain benefits and services. This program is primarily for people 65 and older as well as for people under age 65 who have disabilities and/or certain medical conditions.
Medicaid eligibility differs from Medicare eligibility. Certain rules apply for individuals eligible for both Medicare and Medicaid.
The Bottom Line
Getting original Medicare is a good start for basic health care coverage. But you also have additional options with Blue Cross Blue Shield Medicare to help cover more of your health care needs.
Medicare Advantage plans focus on covering additional health care services, including prescription drug coverage. Medigap (or Medicare Supplement) plans focus on helping to keep your out-of-pocket costs for health care affordable.
Choosing the right Medicare plan for you is straightforward. Get your customized Medicare plan quote and learn more about Medicare Advantage and Medicare Supplemental plans.
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Technique reveals whether models of patient risk are accurate
After a patient has a heart attack or stroke, doctors often use risk models to help guide their treatment. These models can calculate a patient’s risk of dying based on factors such as the patient’s age, symptoms, and other characteristics.
While these models are useful in most cases, they do not make accurate predictions for many patients, which can lead doctors to choose ineffective or unnecessarily risky treatments for some patients.
“Every risk model is evaluated on some dataset of patients, and even if it has high accuracy, it is never 100 percent accurate in practice,” says Collin Stultz, a professor of electrical engineering and computer science at MIT and a cardiologist at Massachusetts General Hospital. “There are going to be some patients for which the model will get the wrong answer, and that can be disastrous.”
Stultz and his colleagues from MIT, IBM Research, and the University of Massachusetts Medical School have now developed a method that allows them to determine whether a particular model’s results can be trusted for a given patient. This could help guide doctors to choose better treatments for those patients, the researchers say.
Stultz, who is also a professor of health sciences and technology, a member of MIT’s Institute for Medical Engineering and Sciences and Research Laboratory of Electronics, and an associate member of the Computer Science and Artificial Intelligence Laboratory, is the senior author of the new study. MIT graduate student Paul Myers is the lead author of the paper, which appears today in Digital Medicine.
Modeling risk
Computer models that can predict a patient’s risk of harmful events, including death, are used widely in medicine. These models are often created by training machine-learning algorithms to analyze patient datasets that include a variety of information about the patients, including their health outcomes.
While these models have high overall accuracy, “very little thought has gone into identifying when a model is likely to fail,” Stultz says. “We are trying to create a shift in the way that people think about these machine-learning models. Thinking about when to apply a model is really important because the consequence of being wrong can be fatal.”
For instance, a patient at high risk who is misclassified would not receive sufficiently aggressive treatment, while a low-risk patient inaccurately determined to be at high risk could receive unnecessary, potentially harmful interventions.
To illustrate how the method works, the researchers chose to focus on a widely used risk model called the GRACE risk score, but the technique can be applied to nearly any type of risk model. GRACE, which stands for Global Registry of Acute Coronary Events, is a large dataset that was used to develop a risk model that evaluates a patient’s risk of death within six months after suffering an acute coronary syndrome (a condition caused by decreased blood flow to the heart). The resulting risk assessment is based on age, blood pressure, heart rate, and other readily available clinical features.
The researchers’ new technique generates an “unreliability score” that ranges from 0 to 1. For a given risk-model prediction, the higher the score, the more unreliable that prediction. The unreliability score is based on a comparison of the risk prediction generated by a particular model, such as the GRACE risk-score, with the prediction produced by a different model that was trained on the same dataset. If the models produce different results, then it is likely that the risk-model prediction for that patient is not reliable, Stultz says.
“What we show in this paper is, if you look at patients who have the highest unreliability scores — in the top 1 percent — the risk prediction for that patient yields the same information as flipping a coin,” Stultz says. “For those patients, the GRACE score cannot discriminate between those who die and those who don’t. It’s completely useless for those patients.”
The researchers’ findings also suggested that the patients for whom the models don’t work well tend to be older and to have a higher incidence of cardiac risk factors.
One significant advantage of the method is that the researchers derived a formula that tells how much two predictions would disagree, without having to build a completely new model based on the original dataset.
“You don’t need access to the training dataset itself in order to compute this unreliability measurement, and that’s important because there are privacy issues that prevent these clinical datasets from being widely accessible to different people,” Stultz says.
Retraining the model
The researchers are now designing a user interface that doctors could use to evaluate whether a given patient’s GRACE score is reliable. In the longer term, they also hope to improve the reliability of risk models by making it easier to retrain models on data that include more patients who are similar to the patient being diagnosed.
“If the model is simple enough, then retraining a model can be fast. You could imagine a whole suite of software integrated into the electronic health record that would automatically tell you whether a particular risk score is appropriate for a given patient, and then try to do things on the fly, like retrain new models that might be more appropriate,” Stultz says.
The research was funded by the MIT-IBM Watson AI Lab. Other authors of the paper include MIT graduate student Wangzhi Dai; Kenney Ng, Kristen Severson, and Uri Kartoun of the Center for Computational Health at IBM Research; and Wei Huang and Frederick Anderson of the Center for Outcomes Research at the University of Massachusetts Medical School.
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Wednesday, January 22, 2020
Using artificial intelligence to enrich digital maps
A model invented by researchers at MIT and Qatar Computing Research Institute (QCRI) that uses satellite imagery to tag road features in digital maps could help improve GPS navigation.
Showing drivers more details about their routes can often help them navigate in unfamiliar locations. Lane counts, for instance, can enable a GPS system to warn drivers of diverging or merging lanes. Incorporating information about parking spots can help drivers plan ahead, while mapping bicycle lanes can help cyclists negotiate busy city streets. Providing updated information on road conditions can also improve planning for disaster relief.
But creating detailed maps is an expensive, time-consuming process done mostly by big companies, such as Google, which sends vehicles around with cameras strapped to their hoods to capture video and images of an area’s roads. Combining that with other data can create accurate, up-to-date maps. Because this process is expensive, however, some parts of the world are ignored.
A solution is to unleash machine-learning models on satellite images — which are easier to obtain and updated fairly regularly — to automatically tag road features. But roads can be occluded by, say, trees and buildings, making it a challenging task. In a paper being presented at the Association for the Advancement of Artificial Intelligence conference, the MIT and QCRI researchers describe “RoadTagger,” which uses a combination of neural network architectures to automatically predict the number of lanes and road types (residential or highway) behind obstructions.
In testing RoadTagger on occluded roads from digital maps of 20 U.S. cities, the model counted lane numbers with 77 percent accuracy and inferred road types with 93 percent accuracy. The researchers are also planning to enable RoadTagger to predict other features, such as parking spots and bike lanes.
“Most updated digital maps are from places that big companies care the most about. If you’re in places they don’t care about much, you’re at a disadvantage with respect to the quality of map,” says co-author Sam Madden, a professor in the Department of Electrical Engineering and Computer Science (EECS) and a researcher in the Computer Science and Artificial Intelligence Laboratory (CSAIL). “Our goal is to automate the process of generating high-quality digital maps, so they can be available in any country.”
The paper’s co-authors are CSAIL graduate students Songtao He, Favyen Bastani, and Edward Park; EECS undergraduate student Satvat Jagwani; CSAIL professors Mohammad Alizadeh and Hari Balakrishnan; and QCRI researchers Sanjay Chawla, Sofiane Abbar, and Mohammad Amin Sadeghi.
Combining CNN and GNN
Quatar, where QCRI is based, is “not a priority for the large companies building digital maps,” Madden says. Yet, it’s constantly building new roads and improving old ones, especially in preparation for hosting the 2022 FIFA World Cup.
“While visiting Qatar, we’ve had experiences where our Uber driver can’t figure out how to get where he’s going, because the map is so off,” Madden says. “If navigation apps don’t have the right information, for things such as lane merging, this could be frustrating or worse.”
RoadTagger relies on a novel combination of a convolutional neural network (CNN) — commonly used for images-processing tasks — and a graph neural network (GNN). GNNs model relationships between connected nodes in a graph and have become popular for analyzing things like social networks and molecular dynamics. The model is “end-to-end,” meaning it’s fed only raw data and automatically produces output, without human intervention.
The CNN takes as input raw satellite images of target roads. The GNN breaks the road into roughly 20-meter segments, or “tiles.” Each tile is a separate graph node, connected by lines along the road. For each node, the CNN extracts road features and shares that information with its immediate neighbors. Road information propagates along the whole graph, with each node receiving some information about road attributes in every other node. If a certain tile is occluded in an image, RoadTagger uses information from all tiles along the road to predict what’s behind the occlusion.
This combined architecture represents a more human-like intuition, the researchers say. Say part of a four-lane road is occluded by trees, so certain tiles show only two lanes. Humans can easily surmise that a couple lanes are hidden behind the trees. Traditional machine-learning models — say, just a CNN — extract features only of individual tiles and most likely predict the occluded tile is a two-lane road.
“Humans can use information from adjacent tiles to guess the number of lanes in the occluded tiles, but networks can’t do that,” He says. “Our approach tries to mimic the natural behavior of humans, where we capture local information from the CNN and global information from the GNN to make better predictions.”
Learning weights
To train and test RoadTagger, the researchers used a real-world map dataset, called OpenStreetMap, which lets users edit and curate digital maps around the globe. From that dataset, they collected confirmed road attributes from 688 square kilometers of maps of 20 U.S. cities — including Boston, Chicago, Washington, and Seattle. Then, they gathered the corresponding satellite images from a Google Maps dataset.
In training, RoadTagger learns weights — which assign varying degrees of importance to features and node connections — of the CNN and GNN. The CNN extracts features from pixel patterns of tiles and the GNN propagates the learned features along the graph. From randomly selected subgraphs of the road, the system learns to predict the road features at each tile. In doing so, it automatically learns which image features are useful and how to propagate those features along the graph. For instance, if a target tile has unclear lane markings, but its neighbor tile has four lanes with clear lane markings and shares the same road width, then the target tile is likely to also have four lanes. In this case, the model automatically learns that the road width is a useful image feature, so if two adjacent tiles share the same road width, they’re likely to have the same lane count.
Given a road not seen in training from OpenStreetMap, the model breaks the road into tiles and uses its learned weights to make predictions. Tasked with predicting a number of lanes in an occluded tile, the model notes that neighboring tiles have matching pixel patterns and, therefore, a high likelihood to share information. So, if those tiles have four lanes, the occluded tile must also have four.
In another result, RoadTagger accurately predicted lane numbers in a dataset of synthesized, highly challenging road disruptions. As one example, an overpass with two lanes covered a few tiles of a target road with four lanes. The model detected mismatched pixel patterns of the overpass, so it ignored the two lanes over the covered tiles, accurately predicting four lanes were underneath.
The researchers hope to use RoadTagger to help humans rapidly validate and approve continuous modifications to infrastructure in datasets such as OpenStreetMap, where many maps don’t contain lane counts or other details. A specific area of interest is Thailand, Bastani says, where roads are constantly changing, but there are few if any updates in the dataset.
“Roads that were once labeled as dirt roads have been paved over so are better to drive on, and some intersections have been completely built over. There are changes every year, but digital maps are out of date,” he says. “We want to constantly update such road attributes based on the most recent imagery.”
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Printing objects that can incorporate living organisms
A method for printing 3D objects that can control living organisms in predictable ways has been developed by an interdisciplinary team of researchers at MIT and elsewhere. The technique may lead to 3D printing of biomedical tools, such as customized braces, that incorporate living cells to produce therapeutic compunds such as painkillers or topical treatments, the researchers say.
The new development was led by MIT Media Lab Associate Professor Neri Oxman and graduate students Rachel Soo Hoo Smith, Christoph Bader, and Sunanda Sharma, along with six others at MIT and at Harvard University’s Wyss Institute and Dana-Farber Cancer Institute. The system is described in a paper recently published in the journal Advanced Functional Materials.
“We call them hybrid living materials, or HLMs,” Smith says. For their initial proof-of-concept experiments, the team precisely incorporated various chemicals into the 3D printing process. These chemicals act as signals to activate certain responses in biologically engineered microbes, which are spray-coated onto the printed object. Once added, the microbes display specific colors or fluorescence in response to the chemical signals.
In their study, the team describes the appearance of these colored patterns in a variety of printed objects, which they say demonstrates the successful incorporation of the living cells into the surface of the 3D-printed material, and the cells’ activation in response to the selectively placed chemicals.
The objective is to make a robust design tool for producing objects and devices incorporating living biological elements, made in a way that is as predictable and scalable as other industrial manufacturing processes.
The team uses a multistep process to produce their hybrid living materials. First, they use a commercially available multimaterial inkjet-based 3D printer, and customized recipes for the combinations of resins and chemical signals used for printing. For example, they found that one type of resin, normally used just to produce a temporary support for overhanging parts of a printed structure and then dissolved away after printing, could produce useful results by being mixed in with the structural resin material. The parts of the structure that incorporate this support material become absorbent and are able to retain the chemical signals that control the behavior of the living organisms.
Finally, the living layer is added: a surface coating of hydrogel — a gelatinous material composed mostly of water but providing a stable and durable lattice structure — is infused with biologically engineered bacteria and spray-coated onto the object.
“We can define very specific shapes and distributions of the hybrid living materials and the biosynthesized products, whether they be colors or therapeutic agents, within the printed shapes,” Smith says. Some of these initial test shapes were made as silver-dollar-sized disks, and others in the form of colorful face masks, with the colors provided by the living bacteria within their structure. The colors take several hours to develop as the bacteria grow, and then remain stable once they are in place.
“There are exciting practical applications with this approach, since designers are now able to control and pattern the growth of living systems through a computational algorithm,” Oxman says. “Combining computational design, additive manufacturing, and synthetic biology, the HLM platform points toward the far-reaching impact these technologies may have across seemingly disparate fields, ‘enlivening’ design and the object space.”
The printing platform the team used allows the material properties of the printed object to be varied precisely and continuously between different parts of the structure, with some sections stiffer and others more flexible, and some more absorbent and others liquid-repellent. Such variations could be useful in the design of biomedical devices that can provide strength and support while also being soft and pliable to provide comfort in places where they are in contact with the body.
The team included specialists in biology, bioengineering, and computer science to come up with a system that yields predictable patterning of the biological behavior across the printed object, despite the effects of factors such as diffusion of chemicals through the material. Through computer modeling of these effects, the researchers produced software that they say offers levels of precision comparable to the computer-assisted design (CAD) systems used for traditional 3D printing systems.
The multiresin 3D printing platform can use anywhere from three to seven different resins with different properties, mixed in any proportions. In combination with synthetic biological engineering, this makes it possible to design objects with biological surfaces that can be programmed to respond in specific ways to particular stimuli such as light or temperature or chemical signals, in ways that are reproducible yet completely customizable, and that can be produced on demand, the researchers say.
“In the future, the pigments included in the masks can be replaced with useful chemical substances for human augmentation such as vitamins, antibodies or antimicrobial drugs,” Oxman says. “Imagine, for example, a wearable interface designed to guide ad-hoc antibiotic formation customized to fit the genetic makeup of its user. Or, consider smart packaging that can detect contamination, or environmentally responsive architectural skins that can respond and adapt — in real-time — to environmental cues.”
In their tests, the team used genetically modified E. coli bacteria, because these grow rapidly and are widely used and studied, but in principle other organisms could be used as well, the researchers say.
The team included Dominik Kolb, Tzu-Chieh Tang, Christopher Voigt, and Felix Moser at MIT; Ahmed Hosny at the Dana-Farber Cancer Institute of Harvard Medical School; and James Weaver at the Wyss Medical Institute of Harvard. It was supported by the Robert Wood Johnson Foundation, Gettylab, the DARPA Engineered Living Materials agreement, and a National Security Science and Engineering Faculty Fellowship.
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Audio explainer: Exploring the fields of bioprinting and biohybrid materials
The following audio excerpt and transcript features an explanation of bioprinting and biohybrid materials by MIT graduate student Rachel Smith of the Mediated Matter Group at the Media Lab. It corresponds with this MIT News article on those subjects.
[INTRO MUSIC] [INTRO]
HOST: 3-D printing is everywhere. From bike parts to fashion, to novelty key chains, to tools and light fixtures. We often see it employed to accelerate production processes and prototyping, but what about the biological potential of printing? You may have heard terms such as bioprinting, bioinks or biomaterials, but what exactly are they? We’ve asked Rachel Smith, a graduate student of the Mediated Matter Group at the MIT Media Lab to explain what bioprinting is and what biohybrid materials are, and to give us some idea of where these fields of study are going.
RACHEL SMITH: Bioprinting and biohybrid materials: though these terms overlap, it is a bit like comparing apples to oranges. Bioprinting is a type of material fabrication process, whereas biohybrid materials are one type of material resulting from fabrication processes like this. Both bioprinting and biohybrid materials involve the use of living cells.
First, lets think about living cells as fabrication materials: something that you could integrate, like other components, into human-made engineering processes and products.
Many cells can naturally replicate, differentiate, and self-organize, and over time, we have also engineered ways to guide their movement, their growth, and the products that they excrete and consume. Thus, you can think of living cells as sensing and computing machines that are extremely sensitive to their surroundings, but we can control and ‘code’ their responses. As a material, they have uniquely responsive and programmable properties.
Bioprinting is the process of printing with living cells. You can include living cells in the ink of a 2D printer, or in the build material for a 3D printer to create tissue-like structures. Currently, 3D-bioprinting can be used to print tissues and organs with the appropriate biological and mechanical properties as the real thing to help with a wide variety of medicinal research. In some cases, researchers print with porous materials that encourage cells to migrate inside and begin to ossify into bone. Another exciting example is 3D printing cardiac cells, which can begin to contract in sync to regenerate mechanical functions of the heart.
Biohybrid materials combine both living and non-living materials to acquire useful properties of both. Currently, the most prominent application for doing this is reconstructing tissues and organs from a combination of synthetic scaffolds and living cells. But, more recently, this idea has expanded to include constructs not found in nature and intended for uses beyond medicine, such as wearables and construction materials.
Already in the works, we have researchers developing biohybrid walls where concrete is blended with mineralizing bacteria for self-healing properties. We have biohybrid fibers spun with microbes, but knit or woven like traditional cloth. The living components in these fibers can produce pigments as dyes, filter heavy metals, or excrete drugs. I like to imagine them being used in fashion, wound dressings, or even environmental remediation. It’s an exciting field to be a part of.
[OUTRO MUSIC] [OUTRO]
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Eboni K. Williams talks State of the Culture and building her dream career
Eboni K. Williams has a reputation for bringing substance, style, and intelligence to whatever endeavor she tackles. Whether it’s hosting on television or REVOLT’s “State of The Culture”, writing inspirational books, or giving back to her community, Williams puts her passion and truth into her work.
TheGrio spoke with the dynamic attorney and media personality about what it takes to find your authentic voice and stay motivated in an ever-complicated world.
“I don’t care if you’re an accountant, if you’re a teacher, if you’re a social worker, whatever your profession. This is not just for media types. Don’t let people tell you what to do with your talent or your gifts,” says Williams.
“You show them. Take that affirmative step. Take that proactive position to make sure that you are articulating for yourself where your gifts are and how wide the scope is. Because many of us are limitless in our talents and abilities, and we [can’t] allow other people to dictate and minimize what that looks like.“
When it comes to standing on your convictions and speaking with courage, Williams offers this advice.
“I’m a woman of faith,” says Williams. “My steps are ordered. So, I appreciate the fact that it’s not going to be for everybody. I know that and I’m good with it. What I would invite people to do, just stay open to the process, stay open to your own process.”
Watch the full interview with Eboni K. Williams above, for more motivational gems and wisdom.
The post Eboni K. Williams talks State of the Culture and building her dream career appeared first on TheGrio.
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Eboni K. Williams talks State of the Culture and speaking your truth
Eboni K. Williams has a reputation for bringing substance, style, and intelligence to whatever endeavor she tackles. Whether it’s hosting on television or REVOLT’s “State of The Culture”, writing inspirational books, or giving back to her community, Williams puts her passion and truth into her work.
TheGrio spoke with the dynamic attorney and media personality about what it takes to find your authentic voice and stay motivated in an ever-complicated world.
“I don’t care if you’re an accountant, if you’re a teacher, if you’re a social worker, whatever your profession. This is not just for media types. Don’t let people tell you what to do with your talent or your gifts,” says Williams.
“You show them. Take that affirmative step. Take that proactive position to make sure that you are articulating for yourself where your gifts are and how wide the scope is. Because many of us are limitless in our talents and abilities, and we [can’t] allow other people to dictate and minimize what that looks like.“
When it comes to standing on your convictions and speaking with courage, Williams offers this advice.
“I’m a woman of faith,” says Williams. “My steps are ordered. So, I appreciate the fact that it’s not going to be for everybody. I know that and I’m good with it. What I would invite people to do, just stay open to the process, stay open to your own process.”
Watch the full interview with Eboni K. Williams above, for more motivational gems and wisdom.
The post Eboni K. Williams talks State of the Culture and speaking your truth appeared first on TheGrio.
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WATCH: Eboni K. Willams talks her inspiring journey, women’s empowerment and message for Black America
Eboni K. Williams has a reputation for bringing substance, style, and intelligence to whatever endeavor she tackles. Whether it’s hosting on television, writing inspirational books, or giving back to her community, Williams puts her passion and truth into her work.
TheGrio spoke with the dynamic attorney and media personality about what it takes to find your authentic voice and stay motivated in an ever-complicated world.
“I don’t care if you’re an accountant, if you’re a teacher, if you’re a social worker, whatever your profession. This is not just for media types. Don’t let people tell you what to do with your talent or your gifts,” says Williams.
“You show them. Take that affirmative step. Take that proactive position to make sure that you are articulating for yourself where your gifts are and how wide the scope is. Because many of us are limitless in our talents and abilities, and we [can’t] allow other people to dictate and minimize what that looks like.“
Watch the full interview with Eboni K. Williams above, for more motivational gems and wisdom.
The post WATCH: Eboni K. Willams talks her inspiring journey, women’s empowerment and message for Black America appeared first on TheGrio.
from TheGrio https://ift.tt/3aDp2yk
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