Common food additive causes adverse health effects in mice

A common food additive, recently banned in France but allowed in the U.S. and many other countries, was found to significantly alter gut microbiota in mice, causing inflammation in the colon and changes in protein expression in the liver, according to research led by a University of Massachusetts Amherst food scientist.

“I think our results have a lot of implications in the food industry and on human health and nutrition,” says lead author Hang Xiao, professor and Clydesdale Scholar of Food Science. “The study confirmed a strong linkage between foodborne titanium dioxide nanoparticles (TiO2 NPs) and adverse health effects.”

Along with colleagues at UMass Amherst and in China, Xiao published the research in Small, a weekly, peer-reviewed, interdisciplinary journal that covers nanotechnology.

Gut microbiota, which refers to the diverse and complex community of microorganisms in the gut, plays a vital role in human health. An imbalance of gut microbiota has been associated with a range of health issues, including inflammatory bowel disease, obesity and cardiovascular disease.

Human exposure to foodborne TiO2 NPs comes primarily from a food additive known as E171, which is made up of different-size particles of TiO2, including one-third or more that are nanoscale. E171, which makes products look whiter and more opaque, is found in such food as desserts, candy, beverages and gum. E171 exposure is two to four times higher in U.S. children than in adults, Xiao points out that one study found.

Smaller than 100 nanometers, foodborne nanoscale particles may have unique physiological properties that cause concern. “The bigger particles won’t be absorbed easily, but the smaller ones could get into the tissues and accumulate somewhere,” Xiao says.

In their study, Xiao and his team fed either E171 or TiO2 NPs to two populations of mice as part of their daily diet. One population was fed a high-fat diet similar to that of many Americans, two-thirds of whom are obese or overweight; the other group of mice was fed a low-fat diet. The mice fed a high-fat diet eventually became obese, while the mice on the low-fat diet did not.

“In both the non-obese mice and obese mice, the gut microbiota was disturbed by both E171 and TiO2 NPs,” Xiao says. “The nanosized particles caused more negative changes in both groups of mice.” Moreover, the obese mice were more susceptible to the adverse effects of TiO2 NPs, causing more damage in obese mice than in non-obese ones.

The researchers found TiO2 NPs decreased cecal levels of short-chain fatty acids, which are essential for colon health, and increased pro-inflammatory immune cells and cytokines in the colon, indicating an inflammatory state.

To evaluate the direct health impact of gut microbiota disrupted by TiO2 NP, Xiao and colleagues conducted a fecal transplant study. They gave mice antibiotics to clear out their original gut microbiota and then transplanted fecal bacteria from the TiO2 NP-treated mice to the antibiotic-treated mice. “The results support our hypothesis that including TiO2 NPs in the diet disrupts the homeostasis of the gut microbiota,” Xiao says, “which in turn leads to colonic inflammation in the mice.”

The study also measured levels of TiO2 in human stool samples, finding a wide range. Xiao says further research is needed to determine the health effects of long-term — such as life-long and multigenerational — exposure to TiO2 NPs.

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Machine learning shapes microwaves for a computer’s eyes

Engineers from Duke University and the Institut de Physique de Nice in France have developed a new method to identify objects using microwaves that improves accuracy while reducing the associated computing time and power requirements.

The system could provide a boost to object identification and speed in fields where both are critical, such as autonomous vehicles, security screening and motion sensing.

The new machine-learning approach cuts out the middleman, skipping the step of creating an image for analysis by a human and instead analyzes the pure data directly. It also jointly determines optimal hardware settings that reveal the most important data while simultaneously discovering what the most important data actually is. In a proof-of-principle study, the setup correctly identified a set of 3D numbers using tens of measurements instead of the hundreds or thousands typically required.

The results appear online on December 6 in the journal Advanced Science and are a collaboration between David R. Smith, the James B. Duke Distinguished Professor of Electrical and Computer Engineering at Duke, and Roarke Horstmeyer, assistant professor of biomedical engineering at Duke.

“Object identification schemes typically take measurements and go to all this trouble to make an image for people to look at and appreciate,” said Horstmeyer. “But that’s inefficient because the computer doesn’t need to ‘look’ at an image at all.”

“This approach circumvents that step and allows the program to capture details that an image-forming process might miss while ignoring other details of the scene that it doesn’t need,” added Aaron Diebold, a research assistant in Smith’s lab. “We’re basically trying to see the object directly from the eyes of the machine.”

In the study, the researchers use a metamaterial antenna that can sculpt a microwave wave front into many different shapes. In this case, the metamaterial is an 8×8 grid of squares, each of which contains electronic structures that allow it to be dynamically tuned to either block or transmit microwaves.

For each measurement, the intelligent sensor selects a handful of squares to let microwaves pass through. This creates a unique microwave pattern, which bounces off the object to be recognized and returns to another similar metamaterial antenna. The sensing antenna also uses a pattern of active squares to add further options to shape the reflected waves. The computer then analyzes the incoming signal and attempts to identify the object.

By repeating this process thousands of times for different variations, the machine learning algorithm eventually discovers which pieces of information are the most important as well as which settings on both the sending and receiving antennas are the best at gathering them.

“The transmitter and receiver act together and are designed together by the machine learning algorithm,” said Mohammadreza Imani, research assistant in Smith’s lab. “They are jointly designed and optimized to capture the features relevant to the task at hand.”

“If you know your task, and you know what sort of scene to expect, you may not need to capture all the information possible,” said Philipp del Hougne, a postdoctoral fellow at the Institut de Physique de Nice. “This co-design of measurement and processing allows us to make use of all the a priori knowledge that we have about the task, scene and measurement constraints to optimize the entire sensing process.”

After training, the machine learning algorithm landed on a small group of settings that could help it separate the data’s wheat from the chaff, cutting down on the number of measurements, time and computational power it needs. Instead of the hundreds or even thousands of measurements typically required by traditional microwave imaging systems, it could see the object in less than 10 measurements.

Whether or not this level of improvement would scale up to more complicated sensing applications is an open question. But the researchers are already trying to use their new concept to optimize hand-motion and gesture recognition for next-generation computer interfaces. There are plenty of other domains where improvements in microwave sensing are needed, and the small size, low cost and easy manufacturability of these types of metamaterials make them promising candidates for future devices.

“Microwaves are ideal for applications like concealed threat detection, identifying objects on the road for driverless cars or monitoring for emergencies in assisted-living facilities,” said del Hougne. “When you think about all of these applications, you need the sensing to be as quick as possible, so we hope our approach will prove useful in making these ideas reliable realities.”

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Sensing magnetism in atomic resolution with just a scanning tunneling microscope

Scientists from the University of Strasbourg, France, in close collaboration with colleagues from the research centers in San Sebastián, Spain, and Jülich, Germany, have achieved a breakthrough in detecting the magnetic moments of nanoscale structures. They succeeded in making the magnetic moments visible with a resolution down to the atomic level using a scanning tunneling microscope, a device that has been standard in science for many years. The researchers made it sensitive to magnetic properties by placing a small molecule containing a Nickel atom at the microscope tip. The results published in the current issue of Science opens a novel path to achieve fundamental insights into atomic-scale structures and for the designing of future atomic-scale devices like nanoscale storage devices and quantum simulators.

To explore the world of individual atoms and molecules scientists use microscopes which don’t rely on a ray of light or electrons but can rather be seen as the ultimate version of an analogous record-player. These instruments named scanning probe microscopes use the end of a sharp needle as a tip to ‘read’ the grooves created by atoms and molecules on the supporting surface. To sense the proximity between tip and surface the scientists use a tiny electrical current which starts to flow when both are only separated by a fraction of a nanometer- that is a millions of a millimeter. Regulating the tip to keep this distance enables the topographic imaging by scanning the surface.

While the basic idea of such microscopes have been developed already in the 1980, only during the last decade scientist in different laboratories have learned to expand the capabilities of these microscopes by cleverly designing the very last end of their probing tip. For example, by attaching a small molecule, like CO or hydrogen, an unprecedented increase in spatial resolution was achieved in which the flexibility of the molecule made even chemical bonds visible.

Similarly, the authors of the recent publication in Science also specially crafted the tip apex to bring a novel function to the sharp tip: They made it sensitive to magnetic moments by placing a molecule containing a single Nickel atom, a so-called quantum molecular magnet, at the apex. This molecule can be brought electrically into different magnetic states with ease in a way that it acts like a tiny magnet. While its ground state possesses effectively no magnetic moment, its excited states do have a magnetic moment which senses near-by moments which unprecedented spatial resolution and high sensitivity.

The importance of this achievement is manifold. For the first time, this method makes it possible to image surface structures in combination with their magnetic properties in atomic resolution. The use of a molecule as active sensor makes it very reproducible and easy to implement in instruments used by other groups world-wide working in the field. “Dark” magnetic moments of complex magnetic structures, which are usually difficult to measure, become accessible, which is important for understanding their inner structure. And the method offers another advantage. Because the ground-state of the molecular sensor is non-magnetic, the measurement induces only minimal back-action onto the system under study — important to prevail volatile states at the nanoscale.

In summary, with this work scientists have expanded their nanoscale toolbox with a new tool sensitive to the magnetic properties which will be important for future applications ranging from nanoscale memory-devices to novel materials or applications in the field of quantum simulation and computing.

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Chemistry: Scientist confirm a new ‘magic number’ for neutrons

An international collaboration led by scientists from the University of Hong Kong, RIKEN (Japan), and CEA (France) have used the RI Beam Factory (RIBF) at the RIKEN Nishina Center for Accelerator-base Science to show that 34 is a “magic number” for neutrons, meaning that atomic nuclei with 34 neutrons are more stable than would normally be expected. Earlier experiments had suggested, but not clearly demonstrated, that this would be the case.

The experiments, published in Physical Review Letters, were performed using calcium 54, an unstable nucleus which has 20 protons and 34 neutrons. Through the experiments, the researchers showed that it exhibits strong shell closure, a situation with neutrons that is similar to the way that atoms with closed electron shells, such as helium and neon, are chemically inactive.

While it was once believed that the protons and neutrons were lumped together like a soup within the nucleus, it is now known that they are organized in shells. With the complete filling of a nuclear shell, often referred to as “magic number,” nuclei exhibit distinctive attributes that can be probed in the laboratory. For example, a large energy for the first excited state of a nucleus is indicative of a magic number.

Recent studies on neutron rich nuclei have hinted that new numbers need to be added to the known, canonical numbers of 2, 8, 20, 28, 50, 82, and 126.

Initial tests on calcium 54, also carried out at the RIBF in 2013, had already indicated that the number should exist. During the new experiment, the research focus shifted towards determining its actual strength. In the current experiment, the team around Sidong Chen directly measured the number of neutrons occupying the individual shells in calcium 54 by painstakingly knocking out the neutrons one at a time.

To do this, the group used a beam containing the calcium traveling at around 60% of the speed of light, selected and identified by the BigRIPS isotope separator, and collided the beam into a target of thick liquid hydrogen, or protons, cooled to a tremendously low temperature of 20 K. The detailed shell structure of the isotope was inferred from the cross-sections of the neutrons knocked out as they collided with the protons, allowing the researchers to associate them with different shells.

According to Pieter Doornenbal of the Nishina Center, “For the first time, we were able to demonstrate quantitatively that all the neutron shells are completely filled in 54Ca, and that 34 neutrons is indeed a good magic number.” The finding demonstrates that 34 is a part of the set of magic numbers, though its appearance is restricted to a very limited region of the nuclear chart. Sidong Chen continues “Major efforts in the future will focus on delineating this region. Moreover, for more neutron rich systems, like 60Ca, further magic numbers are predicted. These ‘exotic’ systems are currently beyond the reach of the RIBF for detailed studies, but we believe that thanks to its increasing capabilities, they will become accessible in the foreseeable future.”

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3D Printing Industry

STERNE launches 3D printable antimicrobial silicone for medical prototyping

STERNE, an industrial silicone manufacturer headquartered in the Provence-Alpes-Côte d’Azur region of southern France, has launched a new material for 3D printing. Made for use with the company’s SiO-Shaping system, the new release is an antimicrobial silicone that resists the formation of bacteria both in and outside of a 3D printed product. The material, to be […]

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Author: Beau Jackson


Verdict for China’s efforts on coal emissions

Researchers from China, France and the USA have evaluated China’s success in stemming emissions from its coal-fired power plants (CPPs).

CPPs are one of the main contributors to air pollution in China, and their proliferation over the last 20 years has had significant impacts on air quality and public health.

These impacts led authorities to introduce measures to control emissions from CPPs and reduce their effects.

Writing today in Environmental Research Letters, researchers examined if these policies have been effective, and measured their benefits.

Dr Qiang Zhang, from Tsinghua University, China, is the study’s lead author. He said: “Between 2005 and 2015, the coal-fired power generation of CPPs in China grew by more than 97 percent. In 2010, CPPs’ sulphur dioxide, nitrogen oxide and fine particulate matter (PM2.5) emissions accounted for 33 per cent, 33 per cent and 6 percent of China’s total national emissions, respectively. The large amount of air pollutant emissions from CPPs causes fine particulate air pollution, which contributed 26 percent of the fine particulate nitrate and 22 percent of the fine particulate sulphate ambient concentration in 2012.

“To combat this, China introduced three primary policies for CPPs during 2005-2020. They aimed to improve efficiency energy by promoting large CPPs and decommissioning small plants during 2005-2020; brought in national emission cap requirements by installing of end-of-pipe control devices during 2005-2015; and introduced ultra-low emission standards between 2014-2020.”

To measure the effect these policies had on emissions, the team developed two retrospective emission scenarios based on a high-resolution coal-fired power plant database for China.

They also developed two emission prediction scenarios to forecast the CPPs’ emission changes associated with the implementation of ultra-low emission standards and power generation increments during 2015-2020.

Finally, they evaluated the air quality and health impacts associated with CPPs’ emission changes during 2005-2020, using a regional air quality model and the integrated exposure-response model.

Dr Fei Liu, from the Universities Space Research Association, Goddard Earth Sciences Technology and Research, USA, is the study’s corresponding author. She said: “Our results show that overall, China’s efforts on emission reductions, air quality improvement and human health protection from CPPS between 2005-2020 were effective.

“We found that the upgrading of end-pipe control facilities could reduce PM2.5 exposures by 7.9 ug/m3 and avoid 111,900 premature deaths annually. Meanwhile, the early retirement of small and low-efficiency units could reduce PM2.5 exposures by 2.1 ug/m3 and avoid 31,400 annual premature deaths.

“This suggests similar measures could be taken in countries such as India, to enable the reduction of emissions alongside rapid economic development.”

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