Since individual cells are almost translucent, microscope cameras must detect extremely subtle differences in the light passing through parts of the cell. Those differences are known as the phase of the light. Camera image sensors are limited by what amount of light phase difference they can detect, referred to as dynamic range.

“To see greater detail using the same image sensor, we must expand the dynamic range so that we can detect smaller phase changes of light,” said Associate Professor Takuro Ideguchi from the University of Tokyo Institute for Photon Science and Technology.

The research team developed a technique to take two exposures to measure large and small changes in light phase separately and then seamlessly connect them to create a highly detailed final image. They named their method adaptive dynamic range shift quantitative phase imaging (ADRIFT-QPI) and recently published their results in Light: Science & Applications.

“Our ADRIFT-QPI method needs no special laser, no special microscope or image sensors; we can use live cells, we don’t need any stains or fluorescence, and there is very little chance of phototoxicity,” said Ideguchi.

Phototoxicity refers to killing cells with light, which can become a problem with some other imaging techniques, such as fluorescence imaging.

Quantitative phase imaging sends a pulse of a flat sheet of light towards the cell, then measures the phase shift of the light waves after they pass through the cell. Computer analysis then reconstructs an image of the major structures inside the cell. Ideguchi and his collaborators have previously pioneered other methods to enhance quantitative phase microscopy.

Quantitative phase imaging is a powerful tool for examining individual cells because it allows researchers to make detailed measurements, like tracking the growth rate of a cell based on the shift in light waves. However, the quantitative aspect of the technique has low sensitivity because of the low saturation capacity of the image sensor, so tracking nanosized particles in and around cells is not possible with a conventional approach.

The new ADRIFT-QPI method has overcome the dynamic range limitation of quantitative phase imaging. During ADRIFT-QPI, the camera takes two exposures and produces a final image that has seven times greater sensitivity than traditional quantitative phase microscopy images.

The first exposure is produced with conventional quantitative phase imaging — a flat sheet of light is pulsed towards the sample and the phase shifts of the light are measured after it passes through the sample. A computer image analysis program develops an image of the sample based on the first exposure then rapidly designs a sculpted wavefront of light that mirrors that image of the sample. A separate component called a wavefront shaping device then generates this “sculpture of light” with higher intensity light for stronger illumination and pulses it towards the sample for a second exposure.

If the first exposure produced an image that was a perfect representation of the sample, the custom-sculpted light waves of the second exposure would enter the sample at different phases, pass through the sample, then emerge as a flat sheet of light, causing the camera to see nothing but a dark image.

“This is the interesting thing: We kind of erase the sample’s image. We want to see almost nothing. We cancel out the large structures so that we can see the smaller ones in great detail,” Ideguchi explained.

In reality, the first exposure is imperfect, so the sculptured light waves emerge with subtle phase deviations.

The second exposure reveals tiny light phase differences that were “washed out” by larger differences in the first exposure. These remaining tiny light phase difference can be measured with increased sensitivity due to the stronger illumination used in the second exposure.

Additional computer analysis reconstructs a final image of the sample with an expanded dynamic range from the two measurement results. In proof-of-concept demonstrations, researchers estimate the ADRIFT-QPI produces images with seven times greater sensitivity than conventional quantitative phase imaging.

Ideguchi says that the true benefit of ADRIFT-QPI is its ability to see tiny particles in context of the whole living cell without needing any labels or stains.

“For example, small signals from nanoscale particles like viruses or particles moving around inside and outside a cell could be detected, which allows for simultaneous observation of their behavior and the cell’s state,” said Ideguchi.

The major intervention for NTDs in Ghana is currently mass drug administration of a few repeatedly recycled drugs, which can lead to reduced efficacy and the emergence of drug resistance. Chronic infections of schistosomiasis, onchocerciasis and lymphatic filariasis can be fatal. Schistosomiasis is caused by the blood flukes Schistosome haematobium and S. mansoni. Onchocerciasis, or river blindness, is caused by the parasitic worm Onchocerca volvulus. Lymphatic filariasis, also called elephantiasis, is caused by the parasitic filarial worm Wuchereria bancrofti.

In the new work, Dorcas Osei-Safo of the University of Ghana, and colleagues obtained — from the Ghana Federation of Traditional Medicines Practitioners Association — 15 traditional medicines used for treating NTDs in local communities. The medicines were available in aqueous herbal preparations or dried powdered herbs. In all cases, crude extracts were prepared from the herbs and screened in the laboratory for their ability to treat various NTDs.

Two extracts, NTD-B4-DCM and NTD-B7-DCM, displayed high activity against S. mansoni adult worms, decreasing the movement of the worms by 78.4% and 84.3% respectively. A different extract, NTD-B2-DCM, was the most active against adult Onchocera onchengi worms, killing 100% of males and more than 60% of females. Eight of 26 crude extracts tested, including NTD-B4-DCM and NTD-B2-DCM, also exhibited good activity against trypanosomes — parasites that cause other human diseases but weren’t the original targets of the traditional medicines.

“By embracing indigenous knowledge systems which have evolved over centuries, we can potentially unlock a wealth of untapped research and shape it by conducting sound scientific investigations to produce safe, efficacious and good quality remedies,” the researchers say.

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Desalination membranes remove salt and other chemicals from water, a process critical to the health of society, cleaning billions of gallons of water for agriculture, energy production and drinking. The idea seems simple — push salty water through and clean water comes out the other side — but it contains complex intricacies that scientists are still trying to understand.

The research team, in partnership with DuPont Water Solutions, solved an important aspect of this mystery, opening the door to reduce costs of clean water production. The researchers determined desalination membranes are inconsistent in density and mass distribution, which can hold back their performance. Uniform density at the nanoscale is the key to increasing how much clean water these membranes can create.

“Reverse osmosis membranes are widely used for cleaning water, but there’s still a lot we don’t know about them,” said Manish Kumar, an associate professor in the Department of Civil, Architectural and Environmental Engineering at UT Austin, who co-led the research. “We couldn’t really say how water moves through them, so all the improvements over the past 40 years have essentially been done in the dark.”

The findings were published today in Science.

The paper documents an increase in efficiency in the membranes tested by 30%-40%, meaning they can clean more water while using significantly less energy. That could lead to increased access to clean water and lower water bills for individual homes and large users alike.

Reverse osmosis membranes work by applying pressure to the salty feed solution on one side. The minerals stay there while the water passes through. Although more efficient than non-membrane desalination processes, it still takes a large amount of energy, the researchers said, and improving the efficiency of the membranes could reduce that burden.

“Fresh water management is becoming a crucial challenge throughout the world,” said Enrique Gomez, a professor of chemical engineering at Penn State who co-led the research. “Shortages, droughts — with increasing severe weather patterns, it is expected this problem will become even more significant. It’s critically important to have clean water availability, especially in low-resource areas.”

The National Science Foundation and DuPont, which makes numerous desalination products, funded the research. The seeds were planted when DuPont researchers found that thicker membranes were actually proving to be more permeable. This came as a surprise because the conventional knowledge was that thickness reduces how much water could flow through the membranes.

The team connected with Dow Water Solutions, which is now a part of DuPont, in 2015 at a “water summit” Kumar organized, and they were eager to solve this mystery. The research team, which also includes researchers from Iowa State University, developed 3D reconstructions of the nanoscale membrane structure using state-of-the-art electron microscopes at the Materials Characterization Lab of Penn State. They modeled the path water takes through these membranes to predict how efficiently water could be cleaned based on structure. Greg Foss of the Texas Advanced Computing Center helped visualize these simulations, and most of the calculations were performed on Stampede2, TACC’s supercomputer.

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The research was co-led by Dr Lu Yang, Associate Professor in the Department of Mechanical Engineering (MNE) at CityU and researchers from Massachusetts Institute of Technology (MIT) and Harbin Institute of Technology (HIT). Their findings have been recently published in the scientific journal Science, titled “Achieving large uniform tensile elasticity in microfabricated diamond.”

“This is the first time showing the extremely large, uniform elasticity of diamond by tensile experiments. Our findings demonstrate the possibility of developing electronic devices through ‘deep elastic strain engineering’ of microfabricated diamond structures,” said Dr Lu.

Diamond: “Mount Everest” of electronic materials

Well known for its hardness, industrial applications of diamonds are usually cutting, drilling, or grinding. But diamond is also considered as a high-performance electronic and photonic material due to its ultra-high thermal conductivity, exceptional electric charge carrier mobility, high breakdown strength and ultra-wide bandgap. Bandgap is a key property in semi-conductor, and wide bandgap allows operation of high-power or high-frequency devices. “That’s why diamond can be considered as ‘Mount Everest’ of electronic materials, possessing all these excellent properties,” Dr Lu said.

However, the large bandgap and tight crystal structure of diamond make it difficult to “dope,” a common way to modulate the semi-conductors’ electronic properties during production, hence hampering the diamond’s industrial application in electronic and optoelectronic devices. A potential alternative is by “strain engineering,” that is to apply very large lattice strain, to change the electronic band structure and associated functional properties. But it was considered as “impossible” for diamond due to its extremely high hardness.

Then in 2018, Dr Lu and his collaborators discovered that, surprisingly, nanoscale diamond can be elastically bent with unexpected large local strain. This discovery suggests the change of physical properties in diamond through elastic strain engineering can be possible. Based on this, the latest study showed how this phenomenon can be utilized for developing functional diamond devices.

Uniform tensile straining across the sample

The team firstly microfabricated single-crystalline diamond samples from a solid diamond single crystals. The samples were in bridge-like shape — about one micrometre long and 300 nanometres wide, with both ends wider for gripping (See image: Tensile straining of diamond bridges). The diamond bridges were then uniaxially stretched in a well-controlled manner within an electron microscope. Under cycles of continuous and controllable loading-unloading of quantitative tensile tests, the diamond bridges demonstrated a highly uniform, large elastic deformation of about 7.5% strain across the whole gauge section of the specimen, rather than deforming at a localized area in bending. And they recovered their original shape after unloading.

By further optimizing the sample geometry using the American Society for Testing and Materials (ASTM) standard, they achieved a maximum uniform tensile strain of up to 9.7%, which even surpassed the maximum local value in the 2018 study, and was close to the theoretical elastic limit of diamond. More importantly, to demonstrate the strained diamond device concept, the team also realized elastic straining of microfabricated diamond arrays.

Tuning the bandgap by elastic strains

The team then performed density functional theory (DFT) calculations to estimate the impact of elastic straining from 0 to 12% on the diamond’s electronic properties. The simulation results indicated that the bandgap of diamond generally decreased as the tensile strain increased, with the largest bandgap reduction rate down from about 5 eV to 3 eV at around 9% strain along a specific crystalline orientation. The team performed an electron energy-loss spectroscopy analysis on a pre-strained diamond sample and verified this bandgap decreasing trend.

Their calculation results also showed that, interestingly, the bandgap could change from indirect to direct with the tensile strains larger than 9% along another crystalline orientation. Direct bandgap in semi-conductor means an electron can directly emit a photon, allowing many optoelectronic applications with higher efficiency.

These findings are an early step in achieving deep elastic strain engineering of microfabricated diamonds. By nanomechanical approach, the team demonstrated that the diamond’s band structure can be changed, and more importantly, these changes can be continuous and reversible, allowing different applications, from micro/nanoelectromechanical systems (MEMS/NEMS), strain-engineered transistors, to novel optoelectronic and quantum technologies. “I believe a new era for diamond is ahead of us,” said Dr Lu.

The research at CityU was funded by the Hong Kong Research Grants Council and the National Natural Science Foundation of China.

In the 19th century, scientists and engineers developed the discipline of statistical mechanics, which predicts how groups of simple particles transition between order and disorder, as when a collection of randomly colliding atoms freezes to form a uniform crystal lattice.

More challenging to predict are the collective behaviors that can be achieved when the particles become more complicated, such that they can move under their own power. This type of system — observed in bird flocks, bacterial colonies and robot swarms — goes by the name “active matter.”

As reported in the January 1, 2021 issue of the journal Science, a team of physicists and engineers have proposed a new principle by which active matter systems can spontaneously order, without need for higher level instructions or even programmed interaction among the agents. And they have demonstrated this principle in a variety of systems, including groups of periodically shape-changing robots called “smarticles” — smart, active particles.

The theory, developed by Dr. Pavel Chvykov at the Massachusetts Institute of Technology while a student of Prof. Jeremy England, who is now a researcher in the School of Physics at Georgia Institute of Technology, posits that certain types of active matter with sufficiently messy dynamics will spontaneously find what the researchers refer to as “low rattling” states.

“Rattling is when matter takes energy flowing into it and turns it into random motion,” England said. “Rattling can be greater either when the motion is more violent, or more random. Conversely, low rattling is either very slight or highly organized — or both. So, the idea is that if your matter and energy source allow for the possibility of a low rattling state, the system will randomly rearrange until it finds that state and then gets stuck there. If you supply energy through forces with a particular pattern, this means the selected state will discover a way for the matter to move that finely matches that pattern.”

To develop their theory, England and Chvykov took inspiration from a phenomenon — dubbed dubbed — discovered by the Swiss physicist Charles Soret in the late 19th century. In Soret’s experiments, he discovered that subjecting an initially uniform salt solution in a tube to a difference in temperature would spontaneously lead to an increase in salt concentration in the colder region — which corresponds to an increase in order of the solution.

Chvykov and England developed numerous mathematical models to demonstrate the low rattling principle, but it wasn’t until they connected with Daniel Goldman, Dunn Family Professor of Physics at the Georgia Institute of Technology, that they were able to test their predictions.

Said Goldman, “A few years back, I saw England give a seminar and thought that some of our smarticle robots might prove valuable to test this theory.” Working with Chvykov, who visited Goldman’s lab, Ph.D. students William Savoie and Akash Vardhan used three flapping smarticles enclosed in a ring to compare experiments to theory. The students observed that instead of displaying complicated dynamics and exploring the container completely, the robots would spontaneously self-organize into a few dances — for example, one dance consists of three robots slapping each other’s arms in sequence. These dances could persist for hundreds of flaps, but suddenly lose stability and be replaced by a dance of a different pattern.

After first demonstrating that these simple dances were indeed low rattling states, Chvykov worked with engineers at Northwestern University, Prof. Todd Murphey and Ph.D. student Thomas Berrueta, who developed more refined and better controlled smarticles. The improved smarticles allowed the researchers to test the limits of the theory, including how the types and number of dances varied for different arm flapping patterns, as well as how these dances could be controlled. “By controlling sequences of low rattling states, we were able to make the system reach configurations that do useful work,” Berrueta said. The Northwestern University researchers say that these findings may have broad practical implications for microrobotic swarms, active matter, and metamaterials.

As England noted: “For robot swarms, it’s about getting many adaptive and smart group behaviors that you can design to be realized in a single swarm, even though the individual robots are relatively cheap and computationally simple. For living cells and novel materials, it might be about understanding what the ‘swarm’ of atoms or proteins can get you, as far as new material or computational properties.”

Malaria remains a devastating disease for tropical and subtropical regions, accounting for an estimated 405,000 deaths and 228 million cases in 2018. In natural settings, the female Anopheles gambiae mosquito — the major malaria vector — feeds on blood multiple times in her lifespan. Such complex behavior is regularly overlooked when mosquitoes are experimentally infected with malaria parasites, limiting our ability to accurately describe potential effects on transmission. In the new study, the researchers examine how additional blood feeding affects the development and transmission potential of Plasmodium falciparum malaria parasites in An. gambiae females.

“We wanted to capture the fact that, in endemic regions, malaria-transmitting mosquitoes are feeding on blood roughly every 2-3 days,” says W. Robert Shaw, a lead author of this study. “Our study shows that this natural behavior strongly promotes the transmission potential of malaria parasites, in previously unappreciated ways.”

The results show that an additional blood feed three days after infection with P. falciparum accelerates the growth of the malaria parasite, thereby shortening the incubation period required before transmission to humans can occur. Incorporating these data into a mathematical model across sub-Saharan Africa reveals that malaria transmission potential is likely higher than previously thought, making disease elimination more difficult. In addition, parasite growth is accelerated in genetically modified mosquitoes with reduced reproductive capacity, suggesting that control strategies using this approach, with the aim of suppressing Anopheles populations, may inadvertently favor malaria transmission. The data also suggest that parasites can be transmitted by younger mosquitoes, which are less susceptible to insecticide killing, with negative implications for the success of insecticide-based strategies. Taken together, the results suggest that younger mosquitoes and those with reduced reproductive ability may provide a larger contribution to infection than previously thought.

According to the authors, the findings have important implications for accurately understanding malaria transmission potential and estimating the true impact of current and future mosquito control measures.

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With the Covid-19 pandemic causing illness and death worldwide, better treatments are urgently needed. One shortcut could be to repurpose existing drugs that were originally developed to treat other conditions. Computational methods can help identify such drugs by simulating how different drugs would interact with SARS-CoV-2, the virus that causes Covid-19.

To aid virtual screening of existing drugs, Zhang and colleagues combined multiple computational techniques that simulate drug-virus interactions from different, complimentary perspectives. They used this hybrid approach to screen 1,906 existing drugs for their potential ability to inhibit replication of SARS-CoV-2 by targeting a viral protein called RNA-dependent RNA polymerase (RdRP).

The novel screening approach identified four promising drugs, which were then tested against SARS-CoV-2 in lab experiments. Two of the drugs, pralatrexate and azithromycin, successfully inhibited replication of the virus. Further lab experiments showed that pralatrexate more strongly inhibited viral replication than did remdesivir, a drug that is currently used to treat some Covid-19 patients.

These findings suggest that pralatrexate could potentially be repurposed to treat Covid-19. However, this chemotherapy drug can prompt significant side effects and is used for people with terminal lymphoma, so immediate use for Covid-19 patients is not guaranteed. Still, the findings support the use of the new screening strategy to identify drugs that could be repurposed.

“We have demonstrated the value of our novel hybrid approach that combines deep-learning technologies with more traditional simulations of molecular dynamics,” Zhang says. He and his colleagues are now developing additional computational methods for generating novel molecular structures that could be developed into new drugs to treat Covid-19.

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