Your Brain Under Pressure: 30s Science #brainresearch#brainresearch #neuroscience

 The value of wetlands on the landscape cannot be overstated—they store and filter water, provide wildlife habitat, cool the atmosphere and sequester carbon. Yet, in the farmland area of Canada's Prairies, wetlands are being drained to increase crop production and expand urban development.

While wetlands sequester carbon, they also naturally release greenhouse gases (GHG) into the atmosphere. That means the impact of wetland drainage on net GHG emissions was previously difficult to determine.

Our new study, however, has found that widespread wetland drainage on Prairie farmland releases 2.1 million tons of carbon dioxide equivalent (CO₂-eq) per year. That's equal to more than 5% of Prairie agricultural emissions from the industry as a whole. CO₂-eq is a metric used to compare emissions from different greenhouse gases by converting amounts of those gases to the equivalent amount of carbon dioxide.

Our research team included Darrin Qualman from the National Farmers Union, Sydney Jensen, a then-graduate student at the University of Regina, as well as Murray Hidlebaugh and Scott Beaton, independent farmers in the Canadian Prairies.

Some tout wetland drainage as providing numerous benefits to agriculture. In addition to increasing arable land area, proponents argue that "proper drainage management … reduces the carbon footprint by cutting down equipment operation time, fuel and emissions, reduces the impacts of extreme weather events, and decreases overland flooding and nutrient washouts."

This assertion of the environmental benefits associated with wetland drainage is not supported by science. Our work highlights a large increase in the carbon footprint associated with wetland drainage rather than a reduction, while other work documents impacts on streamflows and nutrient export, and the loss of ducks and other birds.

The impacts of draining wetlands

To quantify the net greenhouse gas (GHG) emissions associated with wetland drainage, our approach was to quantify GHG sources when wetlands are intact, and compare them with sources after drainage takes place to understand the net effect of wetland removal on emissions. The annual rate of wetland loss from existing data (10,820 hectares per year) was used to quantify associated carbon emissions for the region.

Intact wetlands emit GHGs such as carbon dioxide, methane and nitrous oxide, so their removal eliminates these natural emissions from the landscape. The presence of wetlands in fields can also require repeated machinery passes and lead to double fertilization around wetland margins, both of which contribute to GHG emissions.

When wetlands are drained, carbon-rich sediments are exposed to the air, allowing rapid decomposition and the release of carbon dioxide. Drainage also expands cropland area, leading to additional GHG emissions from farming activities on the newly cultivated land. It often requires the removal of rings of willow trees surrounding wetlands, with the resulting debris typically burned or composted, producing further emissions.

Our results show that the amount of carbon dioxide released from exposed soil from drained wetlands far exceeded any other source. This was much larger than emissions when wetlands were intact, including natural wetland emissions and emissions from multiple passes with machinery. Additional emissions from farming the former wetland and the removal of vegetation also made a small contribution to the overall balance.

Overall, we estimate that wetland drainage contributes to an annual increase in emissions of at least 2.1 million tons CO₂-eq (recognizing that stored carbon will be released over a multi-year period). It is worth noting that this includes natural emissions from intact wetlands, but emissions that are not human-caused are not typically targeted in an effort to achieve GHG reductions.

For example, reducing methane emissions from livestock is a strategy to reduce agricultural GHG emissions, but emissions from wild animals are not considered or incorporated in the same way. Our estimate swells to 3.4 million tons of CO₂-eq per year when we exclude natural wetland GHG emissions; this represents an increase of approximately 8% above currently quantified GHG emissions from the agricultural industry in the Prairie provinces.

Canada's GHG Inventory

Canada uses a National Inventory Report to quantify GHG emissions from different jurisdictions and industries, but emissions associated with wetland drainage are not currently included. Emissions of 3.4 million tons of CO₂-eq from a single year of wetland drainage are substantial and exceed several emission sources currently described in the report.

For example, emissions from wetland destruction are greater than agricultural emissions from gasoline combustion in trucks or from poultry and swine manure in the Prairie provinces. Including emissions from wetland drainage in the National Inventory Report would provide a more accurate accounting of total agricultural emissions and better position the country to meet its climate commitments.

Prairie farmers play a key stewardship role in this landscape—preserving wetlands on their land provides a public good. Retaining wetlands would create many additional benefits: maintaining wildlife habitats, groundwater recharge, nutrient retention, as well as drought and flood mitigation. These wetland services help address global and regional crises related to biodiversity loss, climate change, lake eutrophication and flooding.

Research shows there is public willingness to pay to restore wetlands in the Prairie provinces. There is additionally a need to reduce conflict and increase collaboration in conversations on agricultural water management in the Canadian Prairies and develop policies that incentivize and enable landowners to consider the environmental benefits of wetlands in their decision making. By better understanding the costs of GHG emissions resulting from wetland drainage, we can better preserve wetlands in the Canadian Prairies.

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Researchers uncover MraZ 'donut' deformation that triggers bacterial cell division

 A research team led by UAB researcher David Reverter has discovered the molecular mechanism that describes in detail the process regulating cell division in bacteria, based on the binding of the MraZ protein to the dcw gene cluster. The research has been published in Nature Communications.

Cell division is a central process in all living organisms and requires the coordinated action of many proteins and other regulatory elements. In most bacteria, this process is encoded in a gene cluster called the dcw operon, which groups all the genes that produce the proteins necessary to carry out cell division and bacterial wall formation.

These sets of genes are activated by proteins that act as transcription factors: they bind to the promoter region of the gene, the DNA sequence that indicates the point to start transcription, just before the first codon (the basic unit of gene information) that codes for the beginning of the protein sequence.

One of these transcription factors is MraZ, the first gene of the dcw operon in all bacteria. When activated, the necessary proteins (encoded within the genes of the operon) are produced so that the bacteria can divide. It is, therefore, the transcription factor that controls the activity of the operon responsible for cell division in most bacteria. A UAB research team, led by David Reverter, full professor in the Department of Biochemistry and Molecular Biology and researcher at the Institute of Biotechnology and Biomedicine of the UAB (IBB-UAB), has discovered the mechanism that describes in detail the process regulating cell division.

Using structural biology techniques, such as X-ray crystallography and cryo-electron microscopy, the UAB team has discovered the molecular mechanism that describes how this MraZ transcription factor binds to the promoter of the dcw operon of the bacterium Mycoplasma genitalium, a species widely used in research because it has a very small genome.

The promoter of the dcw operon is formed by four "boxes" of six nucleotides with repeated sequences that regulate its transcription. By using cryo-electron microscopy, the researchers were able to see directly, almost at an atomic scale, the specific contacts between the MraZ factor and the bases of the four repeated "boxes" of the dcw operon.

In this way, they discovered that, for the binding of MraZ to the operon to occur, distortion in the structure of MraZ is necessary. "This is a surprising observation. The MraZ protein is an octamer formed by eight identical subunits joined in the shape of a donut, but with a curvature that would never allow the union with the four 'boxes' of the promoter. However, to regulate cell division, we see how the donut breaks and deforms in such a way that four of the subunits can join the four boxes of the promoter," Reverter explains.


This direct observation of the interactions between MraZ and the promoter DNA regulating the initiation of cell division represents a very important advance, since previous approaches to understanding the mechanism have been based only on biochemical studies and computer modeling. Furthermore, the regulatory mechanism discovered by UAB researchers "is universal to most bacteria, since all MraZ proteins are very similar, have the same octamer structure, and the DNA sequences of the promoters of the operons that regulate cell division are also similar," Reverter concludes.

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When AI meets physics: Unlocking complex protein structures to accelerate biomedical breakthroughs

Artificial intelligence (AI) is transforming how scientists understand proteins—these are working molecules that drive nearly every process in the human body, from cell growth and immune defense to digestion and cell signaling. At NUS, researchers are harnessing AI to fast-track discoveries, offering fresh insights into life at the molecular level and new strategies against disease.

A protein's function is dictated by its three-dimensional (3D) shape, which determines how it interacts with other molecules and provides crucial clues to how diseases develop and could be treated. However, determining these structures experimentally is often time-consuming and costly.

A team led by Professor Zhang Yang, who is from NUS' Cancer Science Institute of Singapore, School of Computing and Yong Loo Lin School of Medicine, has developed D-I-TASSER, a new software tool that predicts the 3D shapes of complex proteins more accurately, supporting faster drug discovery, improved disease research and more precise design of targeted therapies.

"For most proteins, we still do not know their 3D structures, and that remains a major blind spot in biology," said Prof Zhang. "A protein's shape determines what it does in the body, but many large, multi-domain proteins are too complex for existing tools to model reliably."

The human body contains about 20,000 different proteins, many of which consist of several connected parts that move and interact with each other. This complexity makes accurate computer modeling difficult and slows progress in understanding disease mechanisms and developing new medicines.

Combining AI and physics to reveal protein structures

To address this challenge, the team developed D-I-TASSER by combining AI with physics-based simulations. The system breaks a complex protein into smaller sections, predicts the shape of each section first, and then uses physical modeling to assemble them into a complete three-dimensional structure, allowing more precise reconstruction of how the protein folds and fits together.

In tests, D-I-TASSER predicted complex protein structures about 13% more accurately than existing state-of-the-art methods. The researchers were also able to generate reliable structural models for most proteins in the human body, including many that were previously difficult to analyze.

"When we can see a protein's structure more clearly, we can better understand what goes wrong in disease and how potential drugs might interact with it," Prof Zhang added.

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On-Chip Spectrometer Applies AI to Laboratory-Grade Sensing

Researchers at the University of California, Davis, have shrunk a lab-grade spectrometer down to the size of a grain of sand, making a tiny spectrometer-on-a-chip that can be integrated into portable devices. The solution addresses the bottleneck posed by the long physical path that results from obtaining measurement of the intensity of each color. The use of a prism or grating, and the long physical light path, makes spectrometers inherently bulky.

“We wanted to take this power [of a spectrometer] out of the lab and put it in your pocket,” said Ahasan Ahamed, a postdoctoral scientist in the lab of Saif Islam, professor and chair of the Department of Electrical and Computer Engineering, and the corresponding paper’s first author. 

In the current work, instead of physically separating each color, the device uses 16 distinct silicon detectors, each engineered to respond slightly differently to incoming light. This is analogous to giving a handful of specialized sensors a mixed drink, with each sensor sampling a different aspect of the drink. The key to deciphering the original recipe is the second part of the invention: artificial intelligence.

The heart of this innovation lies into technological breakthroughs. First, the researchers engineered the surfaces of standard silicon photodiodes with specialized photon-trapping surface textures. Silicon is typically effective at sensing visible light but is poor at sensing near-infrared light, which is critical for many applications. The photon-trapping surface forces near-infrared light to scatter within the thin silicon layer instead of passing straight through. This dramatically increases the likelihood that the silicon absorbs light, making the entire chip sensitive across a broad spectral range.

This microscopic chip uses photon-trapping surface nanostructures and AI to extend silicon's spectral range into the near-infrared, simulating the capabilities of a spectrometer. Courtesy of the University of California, Davis.

Beyond color detection, the design employs high-speed sensors to provide an inherent, ultra-fast capability for measuring photon lifetime. This temporal precision allows the device to capture fleeting light-matter interactions that are invisible to traditional instruments.

Second, the chip uses a powerful fully connected neural network. Since the 16 unique detectors only capture encoded, noisy signals, the AI is trained on thousands of examples to learn the complex, hidden relationship between the detectors' raw outputs and the original, pure light spectrum. The AI addresses this "inverse problem," reconstructing the light spectrum with high accuracy (around 8 nm resolution). This computational method completely removes the need for bulky optics. 

The system has a minimal footprint of 0.4 mm², high sensitivity, and strong noise resistance. The AI augmented chip can maintain signal clarity even in the presence of significant electrical interference, which is a major challenge in portable, low-cost electronics. By extending the sensing range of silicon into the near-infrared spectrum while enabling high performance through machine learning, this technology will bring integrated, real-time hyperspectral sensing across applications ranging from advanced medical diagnostics to environmental remote sensing.

“We are paving the way for a future where your watch or phone doesn’t just take pictures,” Islam said, the project’s principal investigator, “[but] analyzes the chemistry of the world around you.” 

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AI-Enhanced Biophotonic Nanostructures for Ultra-Sensitive Biomedical Imaging and Precision Therapeutics

 

Abstact

Biophotonics has emerged as a transformative interdisciplinary field combining optics, nanotechnology, and biomedical engineering to enable highly sensitive diagnostic and therapeutic technologies. Recent advances in nanostructured photonic materials and artificial intelligence (AI)-driven signal analysis have significantly improved imaging resolution, disease detection accuracy, and targeted therapeutic delivery.

This research proposes an integrated biophotonic platform based on plasmonic nanostructures, multimodal optical sensing, and AI-assisted data interpretation for early-stage disease diagnostics and precision phototherapy. Experimental simulations demonstrate enhanced photon–tissue interaction efficiency, leading to improved sensitivity in detecting cellular-level abnormalities. The proposed system offers a scalable and non-invasive approach suitable for real-time clinical applications, particularly in oncology, cardiovascular disease monitoring, and infectious disease diagnostics.

 The findings highlight the potential of next-generation intelligent biophotonic systems to revolutionize personalized medicine through rapid diagnostics, targeted treatment, and continuous health monitoring.

 Introduction

Biophotonics integrates photonic science with biological and medical applications to develop advanced technologies for disease detection, imaging, and therapy. Conventional diagnostic methods often face limitations in sensitivity, invasiveness, and response time, creating a demand for rapid and non-destructive biomedical technologies. Optical techniques such as fluorescence imaging, Raman spectroscopy, and plasmonic sensing have demonstrated exceptional capabilities in detecting molecular-level changes in biological tissues.

Recent progress in nanophotonics has enabled the development of engineered photonic nanostructures capable of enhancing light–matter interactions, thereby improving detection sensitivity and imaging contrast. Simultaneously, the integration of artificial intelligence into photonic systems enables automated interpretation of complex optical datasets, significantly improving diagnostic reliability and reducing clinical decision time.

This study explores an AI-assisted biophotonic sensing and imaging framework that combines plasmonic nanostructures, adaptive optical imaging modules, and intelligent signal-processing algorithms to achieve ultra-sensitive disease detection and targeted photonic therapy. The proposed approach aims to advance next-generation biomedical technologies that support early diagnosis, personalized treatment strategies, and continuous health monitoring.

 Methodology (Overview)

The research employs computational photonic modeling, nanostructure design optimization, and AI-based spectral data analysis to evaluate system performance. Plasmonic nanoparticle arrays are engineered to maximize optical field enhancement, while machine-learning algorithms process spectral variations associated with pathological biomarkers. Simulation studies assess imaging resolution, sensitivity improvement, and therapeutic light-delivery efficiency.

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Laser‑written glass chip pushes quantum communication toward practical deployment

 As quantum computers continue to advance, many of today’s encryption systems face the risk of becoming obsolete. A powerful alternative—quantum cryptography—offers security based on the laws of physics instead of computational difficulty. But to turn quantum communication into a practical technology, researchers need compact and reliable devices that can decode fragile quantum states carried by light.

A new study from teams at the University of Padua, Politecnico di Milano, and the CNR Institute for Photonics and Nanotechnologies shows how this goal can be approached using a simple material: borosilicate glass. As reported in Advanced Photonics, their work demonstrates a high‑performance quantum coherent receiver fabricated directly inside glass using femtosecond laser writing. The approach provides low optical loss, stable operation, and broad compatibility with existing fiber‑optic infrastructure—key factors for scaling quantum technologies beyond the laboratory.

Why glass?

Continuous‑variable (CV) quantum information processing—used in quantum key distribution (QKD) and quantum random number generation (QRNG)—relies on measuring the amplitude and phase of light waves. These measurements require a coherent receiver that combines a weak quantum signal with a stronger reference beam and analyzes their interference.

Most integrated receivers so far have been implemented on silicon. While silicon is well‑established and highly integrable, it suffers from polarization sensitivity and high optical losses, both of which reduce performance and stability in quantum communication systems.

Glass, on the other hand, is naturally polarization‑insensitive, extremely stable, and allows waveguides to be written in three dimensions with very low propagation loss. Using femtosecond laser micromachining, researchers can draw light‑guiding channels directly inside the material, creating compact photonic circuits without the fabrication complexity of semiconductor foundries.

Inside the laser‑written quantum receiver

The team fabricated a fully tunable heterodyne receiver—an essential component for CV‑QKD and CV‑QRNG—by writing the optical circuit directly into the volume of borosilicate glass. The chip includes:

• Fixed and tunable beam splitters
• Thermo‑optic phase shifters for precise electrical control
• Three‑dimensional waveguide crossings
• Polarization‑independent directional couplers

These elements allow the quantum signal and reference beam to interfere in a controlled manner so that two conjugate quadratures can be measured at once. The device also demonstrates:

• Extremely low insertion loss (≈1 dB)
• Polarization‑independent operation
• Common‑mode rejection ratio above 73 dB, indicating strong suppression of classical noise
• High signal‑to‑noise stability over at least 8 hours of operation

These characteristics meet or exceed those of many silicon‑based photonic receivers.

Two quantum technologies on one chip

Because of the chip’s low loss, tunability, and stability, it can support multiple quantum communication tasks without hardware changes. Using the chip as a heterodyne detector, the team implemented a source‑device‑independent QRNG, meaning the system remains secure even if the incoming optical state is untrusted. Thanks to its high noise suppression and stable quadrature measurements, the chip achieved 42.7 Gbit/s secure random bit generation, which represents a record‑high rate for this security model.

The same device was used to implement a QPSK‑based CV‑QKD protocol, where information is encoded in a four‑point constellation of quantum states. Over a simulated 9.3‑km fiber link, the system reached a 3.2 Mbit/s secret key rate. This performance demonstrates that a glass‑based photonic front end can support state‑of‑the‑art CV‑QKD without the limitations associated with silicon platforms

Platform ready for real‑world deployment

Beyond performance metrics, the work underscores the inherent advantages of glass for integrated quantum photonics:

• Environmental stability: Glass is inert and resistant to thermal and mechanical fluctuations.
• Low‑loss fiber coupling: Waveguides closely match the size of standard telecom fibers.
• 3D design freedom: Circuits can include crossings and complex routing without added scattering.
• Scalability and cost‑effectiveness: Femtosecond laser writing enables rapid prototyping without expensive semiconductor processing steps.

These features support long‑term stability and resilience—qualities that could enable future use in field systems and even space‑based quantum communication missions. The authors emphasize that glass‑based integrated photonics may help bridge the gap between laboratory‑grade experiments and deployable quantum networks.

Leveraging these advantageous properties, the team demonstrated two core applications with the same chip: a source-device-independent QRNG, achieving a record-high secure generation rate of 42.7 Gbit/s, and a QPSK-based CV-QKD system, reaching a 3.2 Mbit/s secure key rate over a simulated fiber link of 9.3 kilometers.

Beyond these achievements, the work highlights the potential of glass-based integrated photonics as a robust and versatile platform for quantum technologies. Glass is inert, stable, and cost-effective, enabling the fabrication of devices inherently resistant to harsh environmental conditions. This novel approach could bridge the gap between laboratory prototypes and deployable quantum communication systems, marking a significant step toward a real-world quantum network infrastructure.

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Biophotonics imaging transforms studies of neuronal activities

 Yuehan Liu is a fifth-year doctoral candidate affiliated with the Biophotonics Imaging Technology Lab (BIT) advised by Xingde Li. She recently gave a talk at SPIE Photonics West BiOS entitled "Two-photon fiberscope with a proactive optoelectrical commutator for rotational resistance-free neuroimaging in freely-behaving rodents." Her talk focused on the recent progress of non-invasive imaging technologies that could revolutionize the study of brain function and diseases.

Biophotonics is an interdisciplinary field that applies photonics — the branch of physics dealing with the creation, transmission, manipulation and reception of light — to biology-related studies, particularly in neuroscience. At the core of biophotonics is the use of photons and optical imaging techniques to study cells and tissue. Unlike traditional biopsy, which requires the extraction of sample cells for examination, biophotonics allows biological cells to be examined while keeping their integrity so that they can be monitored in real time.

"Biophotonics provides alternatives to traditional imaging methods like X-rays or ultrasound. The technology offers a real-time, non-invasive look at biological tissues — with less risk and higher resolution images than ultrasound — and offers advantages over other techniques that might be harmful or require longer processing times," Liu said in an interview with The News-Letter.

Historically, to image live rodents, researchers have had to fix their heads on a stationary bench top to be examined under microscopes. For studies of non-stationary behavior, their heads have to be fixed the entire time, which is suboptimal. Liu highlighted the critical need for advanced optical imaging tools capable of capturing detailed neuronal activity in live, freely moving mice.

"When neuroscientists edit certain genes in mice that are believed to influence behavior, learning or memory, they want to see how these changes manifest in the mice's behavior," she said. “Traditional methods, which require animals to be immobilized, drastically alter their natural behaviors and lead to skewed or unrepresentative data."

Liu's work with the FiberScope, a two-photon microscope, overcomes the constraints of traditional fixed imaging setups. With its compact design that fits all lenses and sensors inside a tube, the FiberScope is small and lightweight yet does not compromise on its ability to produce fast, high-resolution imaging comparable to standard-size microscopes. 

Moreover, Liu and her team have optimized the FiberScope to provide an enlarged field of view with increased scanning range and speed that allows fast and stable imaging of multiple planes. With it, scientists can now observe over a thousand neurons simultaneously, offering insights into neuronal networks in a naturalistic setting. 

"It's a revolutionary technique that allows us to watch neurons firing in living and freely moving rodents in real time, which is a game changer for studying the brain's communication pathways and animal behavior at the cellular level," Liu explained.

The work is laborious, and the success of such sophisticated optical instruments demands extreme precision for each minute component. 

"Making instruments like the FiberScope is more than designing a theoretical optical system; it demands a meticulous level of dexterity and precision that brings it to reality. The lenses we work with are tiny, and every single one needs to be aligned with exacting accuracy. A slight misalignment would render the entire system futile," she said.

Currently, the FiberScope is mostly used to aid basic neuroscience studies, diagnose diseases and create disease models. Yet, the implications of Liu's research extend beyond academic interest, as biophotonics holds promise for enhancing clinical practices, particularly in guiding neurosurgical procedures and improving early disease detection. Still, there is a long way to go from animal tests to being able to apply this technology to humans. 

"We're always pushing the boundaries of what's possible with biophotonics. It’s truly exciting that our research can facilitate neurological research to advance our understanding of the brain," Liu said.

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