Using Stem Cells to Heal Broken Bones 06-08

A promising new method for regenerating bones using the body's own stem cells may possibly eliminate the need for bone grafts.

When a fracture will not heal, people are typically left with two options.
One is bone grafting, the other is surgery.

A new treatment that uses gene and stem cell therapies could promise success with a less-invasive procedure.

Researchers led by a team from Cedars-Sinai Medical Center in Los Angeles, tested the therapy on laboratory animals and found that it triggered bones to regrow their own tissue.

If it is found safe in humans, the process could replace bone grafting as the gold standard treatment.

“We are just at the beginning of a revolution in orthopedics,” Dan Gazit, co-director of the Skeletal Regeneration and Stem Cell Therapy Program in the Department of Surgery and the Cedars-Sinai Board of Governors Regenerative Medicine Institute, said in a statement.

The study was published in the journal Science Translational Medicine.

Bone grafts can result in gaps between fracture edges, and often require surgery to relocate bone from other places in the body to fill in the spaces.

Bone can come from the patient or a cadaver.

But healthy bone isn’t always available, and surgeries can lead to other complications.

A stem cell solution

The new method involves implanting a collagen matrix made up of bone-inducing genes into stem cells.

It is inserted into the gap over a two-week span. An ultrasound pulse and microbubbles help the matrix get into the cells.

“Our method relies on the body’s own repair cells [stem cells],” Gadi Pelled, senior author, and an assistant professor of surgery at Cedars-Sinai, told Healthline. “We recruit them to the injury site and then activate them to regenerate bone in an efficient way.”

“The uniqueness of our method is that it is injectable and minimally invasive,” Pelled said.

Researchers found that the fractures were healed eight weeks after the procedure. The bone that grew into the empty space was as strong as surgical bone grafts.

“We showed that our method was equivalent, in terms of fracture healing, to the use of an autograft [bone graft obtained from the patient’s own body], which is the gold standard today,” Gazit said. “Our method does not require the harvest of bone, which often leads to prolonged pain and hospitalization and risk of infection, and that is our advantage.”

Stem cell concerns?

Because the process uses stem cells from the patient’s body without external manipulation, it may not face many of the hurdles that other stem cell treatments come up against.

“But obviously we will need to show that our method is not toxic and is safe to use in people before it is approved for use in the clinic,” added Zulma Gazit, PhD, co-director of the Skeletal Regeneration and Stem Cell Therapy Program in the Department of Surgery and the Cedars-Sinai Board of Governors Regenerative Medicine Institute.

Moving forward

In cases where there are large gaps or fractures unable to heal, the method can be repeated to grow more bone.

That’s something that will need to be reproduced in additional studies, but the latest study is the first to show that this ultrasound-mediated gene delivery can be used to treat nonhealing bone fractures, Pelled added.

David Forsh, an assistant professor of orthopedics at the Icahn School of Medicine at Mount Sinai, and chief of orthopedic trauma at Mount Sinai St. Luke’s, said the breakthrough needs to be reproduced before it goes mainstream.

Similar research has been conducted in the past, but the way this was done is something new, according to his knowledge.

“It sounds good,” Forsh told Healthline. “It’s very promising that they were able to achieve this.”

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What brands need to know about VR and AR [Infographic] 06-07

Virtual reality (VR) and augmented reality (AR) sound like great novelties on the surface, but what can these technologies do to help your brand? The answer might surprise you. According to this infographic, these experiences can do quite a lot to boost your brand, especially if you are in the business of selling products or experiences.

Product manufacturers and venues are already taking advantage of VR and AR technologies as part of their marketing strategy, and even retail stores are using AR to help customers find what they’re looking for.

This infographic, created by MDG Advertising, outlines some of the reasons brands should start paying attention to this emerging trend.

Virtual reality is finally coming into its own

After years of wait-and-see with VR and AR, tech companies are finally starting to make headway into creating experiences that consumers are responding to. Products like Google’s Daydream and Samsung’s Gear VR have opened the door to enable virtually anyone with a smartphone the chance to experience virtual reality from their smartphones.

Gaming consoles like Sony’s PlayStation even has a VR experience through Sony’s PlayStation VR. Oculus, which is now part of the Facebook family, is driving Facebook’s new Spaces experience where people can meet in a virtual environment.

Linden Lab, the creators of the still-popular virtual world Second Life are hard at work creating a whole new virtual world experience with VR at the forefront.

As for brands, hotels like Marriott are offering potential customers virtual tours of their hotels around the world, enabling them to preview their experience before booking their rooms.



Even product-oriented brands like Coca-Cola are using VR to raise brand awareness through providing fun experiences. Just last year, Coca-Cola showed off packaging that converts into a cardboard VR headset for smartphones.

Augmented reality is helping brands help their customers

Have you ever been lost looking for items in a retail store? We all have, but this is where augmented reality can change the game.

Aisle411 is already being tested with retailers like Walgreens and Toys”R”Us. It creates an augmented reality of sorts that shoppers can use like GPS navigation to get them to the product they’re searching for. A tablet mounted to the shopping cart, or the shopper’s own smartphone, this augmented reality experience makes life easier for the shopper, and that is always best for business.
In addition to announcing better support for Virtual Reality at WWDC this week, Apple unveiled a ARKit, a new development platform for creating augmented reality experiences in iOS. This is a big step as Apple has long been considered the tipping point for augmented reality to really take a leap forward.

The success of Niantic’s Pokemon Go last year introduced a lot of new people to the concept of augmented reality. It remains a fun, and addicting game, but the implications of its success were that brands everywhere became aware that augmented reality is well within reach.

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Survey for scaling up Digital Technology 06-07

ARM’s new processors are designed to power the machine-learning machines 06-06

ML plus AI, AR, and VR — ARM is taking on the full set of trendy initialisms

On the eve of Computex, Taiwan’s big showpiece event where PC makers roll out the latest and best implementations of Intel CPUs, mobile rival ARM is announcing its own big news with the unveiling of a new generation of ARM CPUs and GPUs. Official today, the ARM Cortex-A75 is the new flagship-tier mobile processor design, with a claimed 22 percent improvement in performance over the incumbent A73. It’s joined by the new Cortex-A55, which has the highest power efficiency of any mid-range CPU ARM’s ever designed, and the Mali-G72 graphics processor, which also comes with a 25 percent improvement in efficiency relative to its predecessor G71.

The efficiency improvements are evolutionary and predictable, but the revolutionary aspects of this new lineup relate to artificial intelligence: this is the first set of processing components designed specifically to tackle the challenges of onboard AI and machine learning. Plus, last year’s updates to improve performance in the power-hugry tasks of augmented and virtual reality are being extended and elaborated.

Before we dive into the detail of this year’s changes, it’s worth recapping what ARM does and why it’s important. This English company, now owned by Japan’s SoftBank, is responsible for designing the processor architecture of practically every mobile device — you’ll have heard of Qualcomm’s Snapdragon, Samsung’s Exynos, and Apple’s A-series of mobile chips, all of which are built using ARM’s instruction sets and based on ARM’s design blueprints. When we talk about the oncoming wave of mobile AI, mobile VR, and smartphones that can perform machine-learning tasks without sending them off to processor farms up in the cloud, developing the capabilities for those tasks starts with ARM. 

The new Cortex-A75 and A55 are the first Dynamiq CPUs from ARM. Dynamiq is the branding chosen to describe a much more flexible set of design options for silicon vendors like Qualcomm. Where previously ARM allowed for designs that paired a cluster of so-called big CPUs (from its A7x class) and a matched number of little CPUs (from the A5x series), the new design makes it possible to spec a single, mixed-up cluster composed of both big and little CPUs, to a maximum of eight. Thus, chip makers can now have, for example, seven little A55 cores and just one big A75 one: for a favorable mix of long battery life, cost efficiency, and a high ceiling of single-threaded performance when it’s called for.

"50x improvement in AI performance over the next three to five years"

A RM marketing chief John Ronco says he anticipates a "50x improvement in AI performance over the next three to five years thanks to better architecture, micro-architecture, and software optimizations." ARM’s Dynamiq changes include a redesigned memory subsystem and tweaks to how CPU caches work — which has led to a doubling of memory streaming performance on the A55 relative to the A53 preceding it. Given that the A53 has shipped on 1.7 billion devices over the past three years, it’s truly the A55 that will make the biggest difference in achieving Ronco’s ambitious forecast. In most applications, the new mid-range core will be 10 to 30 percent better than previously, offering up to 15 percent better power efficiency and 18 percent better single-thread performance.

But it’s the fact that the new chip designs will be 10 times more configurable, with up to 3,000 different configurations, that will allow chipmakers far greater flexibility to make the most of them by tailoring them to specific tasks.

Interestingly, ARM won’t just be powering machine learning with its new chips, it’ll benefit from ML too. The new designs benefit from an improved branch predictor that uses neural network algorithms to improve data prefetching and overall performance.

The Cortex-A75 makes double-figure performance improvements across the board, with ARM claiming it’s on average 22 percent better than the A73, with 16 percent higher memory throughput, and a 34 percent improvement in its Geekbench score. Single-threaded performance, according to ARM’s Ronco, is up by 20 percent, purely by improving the instructions-per-clock efficiency. The A75 chip is roughly 2.5x the size of the A55, and its intended uses are for infrastructure, automotive, and rich mobile applications. Yes, that means VR, AR, and high-fidelity games, the latter of which ARM’s research has shown have been rapidly increasing in popularity.

A major architectural change with the A75 is the opening of a larger power envelope for chips using this core, scaling up to 2W of power consumption, and thus offering up to 30 percent of extra performance on larger-screen devices. This is entirely targeted at the upcoming Windows on ARM reboot, expected later this year. It’s worth noting that in ARM’s world a "large" screen basically amounts to a laptop — and the company set up a dedicated Large Screen Compute division a year and a half ago to more aggressively target the clamshell devices that Intel has been dominant in. 

As to the new Mali GPU, it has 32 shader cores, 25 percent higher energy efficiency, and a 20 percent better performance density (aka performance per mm² of space). The Mali-G72 is at the heart of ARM’s push toward improving machine learning efficiency, and ARM claims it’s showing itself to be 17 percent better than the G71 in ML benchmarks. The design optimizations from the company are tailored to accelerate inference engines rather than training engines — that’s to say ARM chips will be best at using accumulated ML capabilities rather than developing them, which makes perfect sense for mobile applications. Training AI will be a task better left to Nvidia and AMD graphics cards or Google’s custom TensorFlow TPUs.

The Cortex-A75 and A55 designs were released to ARM’s partners at the end of 2016, so by this point, they’ve all had a few months to decide what to do with them. ARM says a "realistic time window" for new mobile devices powered by its latest designs would be the first quarter of 2018 — though the company is also conscious of a new phenomenon it describes as "China speed," where Chinese phone vendors will put its designs into products almost immediately. The Huawei Mate 9, for example, was released just eight months after ARM distributed the Mali-G71 to partners. This faster Chinese cadence could lead to some A75- and A55-based designs this year, but then bulk of them are likely to arrive with the usual smartphone refresh cycle early next year.



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A New Antibiotic Multitool Could Beat The Toughest Bacteria 06-06

Doctors may soon have a new weapon in the long-running war between antibiotics and bacteria. It’s a Swiss Army knife of a drug that’s tens of thousands of times more effective in lab tests against dangerous antibiotic-resistant bacteria. 

Starting with the discovery of penicillin in 1928, scientists and doctors have been finding and making molecules that weaken or kill bacteria in a range of different ways to help humans survive infections. And as soon as humans started employing these antibiotics, bacteria began evolving to beat those attacks.

That has started to become a huge problem. So-called superbugs like methicillin-resistant Staphylococcus aureus (MRSA) can ward off some of our most potent antibiotics, making infections by these bacteria extremely hard to treat. Not only that, but their existence poses a strategic challenge as well, forcing doctors to think hard about when and where they use certain antibiotics, lest bacteria develop resistance to them and render them less effective.

Vancomycin is one antibiotic that has stayed effective even as others have been brought down by resistant bacteria. That’s because of the way vancomycin works: by latching onto one of the building blocks bacteria use to build their cell walls, like the microscopic equivalent of a bully stealing your shovel in the sandbox and not giving it back. (In this analogy, we’re on the bully’s side.)

By interfering with such a critical cellular process in such a fundamental way, vancomycin makes it hard for bacteria to develop a simple mutation to defeat the antibiotic. That makes vancomycin one of our last lines of defense for treating infections like MRSA that others can’t. It’s why the World Health Organization (WHO) added the drug to its list of essential medicines.

Naturally, some bacteria have found ways to fight vancomycin, the most common being to substitute a different cell wall building block that the antibiotic can’t latch onto. Taking vancomycin out of doctors’ quivers would be a big blow. Which is why the WHO also lists vancomycin-resistant bacteria at number four and five on its list of the most threatening antibiotic-resistant microbes.

So. To try to make sure vancomycin can beat those resistant bacteria, and stay effective for the next few decades—a reasonable lifetime for an antibiotic—chemists Dale Boger, Nicholas Isley and Akinori Okano at the Scripps Research Institute in California opened up the hood to make a few adjustments to the molecule.

After swapping out one part and bolting on a couple others, the group’s souped-up vancomycin was about 25,000 times more potent against resistant bacteria, and it had better endurance. They describe their work in the Proceedings of the National Academy of Sciences.

The major change was to the region of the molecule that grabs those cell wall building blocks, which are called D-alanyl-D-alanine. Resistant bacteria have learned to substitute the very similar D-alanyl-D-lactate, which your standard vancomycin can’t bind to very well, limiting its effectiveness. The researchers changed an oxygen atom for two atoms of hydrogen, making a new version of vancomycin that could hang onto either building block.

Then, because why quit while you’re ahead, they added two new features to the molecule. One weakens bacteria cell walls. The other further disrupts the cell wall building process. Those each made the modified vancomycin 200- and 100-times more effective, respectively.

And not only is their improved vancomycin better at killing bacteria, it appears to be hold up against their attempts to evolve defenses. In the lab, the researchers tested their new molecule against successive generations of bacteria without seeing any appreciable gain in their resistance.
The researchers say their molecule’s multipronged attack is the key to its effectiveness, and its resistance to resistance. “Organisms just can't simultaneously work to find a way around three independent mechanisms of action.

Even if they found a solution to one of those, the organisms would still be killed by the other two,” Boger told the BBC. Because if they can't survive, they won't pass on mutations to future generations.

Like I've said before, keep in mind that a promising molecule is still a long way from doctors having a new drug to rely on. Successful laboratory tests don’t necessarily mean it will work in the real world, or be safe for humans to use, although this group’s work suggests both those things should be true. And it’s a hopeful sign that chemists will be able to keep us at least a step ahead of bacteria in our ongoing clash.

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How Data Mining Facebook Messages Can Reveal Substance Abusers 06-06

Substance abuse changes people’s patterns of behavior, and this is detectable in their social media messages, say researchers.

“A substance use disorder (SUD) is a condition in which recurrent use of substances such as alcohol, drugs and tobacco causes clinically and functionally significant impairment in an individual’s daily life.” So begin Warren Bickel from the Addition Recovery Research Center in Roanoke, Virginia, and a couple of pals, who study this condition.

Substance abuse is a serious concern. Around one in 10 Americans are sufferers. Which is why it costs the American economy more than $700 billion a year in lost productivity, crime, and health-care costs. So a better way to identify people suffering from the disorder, and those at risk of succumbing to it, would be hugely useful.

Bickel and co say they have developed just such a technique, which allows them to spot sufferers simply by looking at their social media messages such as Facebook posts. The technique even provides new insights into the way abuse of different substances influences people’s social media messages.

The new technique comes from the analysis of data collected between 2007 and 2012 as part of a project that ran on Facebook called myPersonality. Users who signed up were offered various psychometric tests and given feedback on their scores. Many also agreed to allow the data to be used for research purposes.

One of these tests asked over 13,000 users with an average age of 23 about the substances they used. In particular, it asked how often they used tobacco, alcohol, or other drugs, and assessed each participant’s level of use. The users were then divided into groups according to their level of substance abuse.

This data set is important because it acts as a kind of ground truth, recording the exact level of substance use for each person.

The team next gathered two other Facebook-related data sets. The first was 22 million status updates posted by more than 150,000 Facebook users. The other was even larger: the “like” data associated with 11 million Facebook users.

Finally, the team worked out how these data sets overlapped. They found almost 1,000 users who were in all the data sets, just over 1,000 who were in the substance abuse and status update data sets, and 3,500 who were in the substance abuse and likes data sets.

These users with overlapping data sets provide rich pickings for data miners. If people with substance use disorders have certain unique patterns of behavior, it may be possible to spot these in their Facebook status updates or in their patterns of likes.

So Bickel and co got to work first by text mining most of the Facebook status updates and then data mining most of the likes data set. Any patterns they found, they then tested by looking for people with similar patterns in the remaining data and seeing if they also had the same level of substance use.

The results make for interesting reading. The team says its technique was hugely successful. “Our best models achieved 86%  for predicting tobacco use, 81% for alcohol use and 84% for drug use, all of which significantly outperformed existing methods,” say Bickel and co.

The technique also identified a wide range of keywords that people with substance abuse disorder are more likely to use in social media posts. “Swear words such as ‘fuck’ and ‘shit,’ sexual words such as ‘horny’ and ‘sex,’ words related to biological process such as ‘blood’ and ‘pain’ are positively correlated with all three types of substance use disorder,” say Bickel and co, referring to tobacco, alcohol and drug use. “In addition, female references such as ‘girl’ and ‘woman,’ prepositions, space reference words such as ‘up’ and ‘down’ are positively correlated with alcohol use, while words related to anger such as ‘hate’ and ‘kill,’ words related to health such as ‘clinic’ and ‘pill’ are positively correlated with drug use.”

The data shows correlations both ways. “A preference for movies such as V for Vendetta and Boondock Saints is positively correlated with alcohol use, while having a hobby, liking cartoons and shows favored by kids or liking movies and brands favored by girls are negatively correlated with drug, alcohol and tobacco use respectively,” say the team.

There are also some surprising correlations. “For example, female references such as ‘girl’ and ‘woman’ are positively related to alcohol use while male references such as ‘man’ and ‘boy’ are negatively related to drug use,” says Bickel and co. This is probably because references to females are more likely to be made by males who are also more likely to use alcohol.

That’s interesting work that immediately suggests a way to identify people who are at risk of substance use disorder—simply look at their Facebook posts and likes. “We believe social media is a promising platform for both studying SUD-related human behaviors as well as engaging the public for substance abuse prevention and screening,” say Bickel and co.

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Revolutionary Discovery About the Human Brain Could Lead to Second-Gen AI 06--03

Teaching Individual Neurons

The Neurophysiology department at the University of Lund has discovered that individual neurons can be taught patterns rather than just respond to a single, specific signal. This means that individual Purkinje cells (cells that control motor movement) are capable of learning, rather than learning being an emergent property (a property that a collection has but individual members do not).

Scientists’ previous understanding was that learning occurred due to an interaction of an entire neural network, however the study states:

Cerebellar control and coordination of motor behaviors may rely more on intracellular mechanisms and less on neuronal network properties than previously thought. It also suggests the capacity for information storage in the individual neuron is vastly greater and of a very different nature than suggested by the dominant paradigm.

The Lund researchers ‘taught’ the cells over a number of hours to associate different signals. Eventually, this meant the cells could learn several reactions in a series. The responses followed the time pattern of the stimuli, for example: They responded to “Signal – brief pause – signal – long pause – signal” with “response – brief pause – response – long pause – response.”
Dan-Anders Jirenhed, a researcher of associative learning at Lund, concluded that, “this means that the brain’s capacity for learning is even greater than previously thought!”

Learning Difficulties and AI

Disruptions in these systems could be responsible for a number of learning difficulties such as Autism, ADHD and language disorders, meaning that scientists may base future treatment therapies on principles derived from the study.

The most exciting application of the discovery, though, is for deep learning and artificial intelligence (AIs), most of which currently use neural networks. In order to build human-like intelligence, researchers need to understand how our minds accrue information.


This study shows that our minds learn on a single-cell basis, rather than through the combined effort of a neural network. Therefore, when building AI, we need to employ a similar model – programming the individual components to learn. If done successfully, this could result in more complex tasks being dealt with in a more efficient way, lessening the divide between human and machine even more.

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