Newswise — Winter Storm Fern swept across a large swath of the southern and eastern United States, delivering not just a wide range of snow accumulation, but also snow types. Experts say one big reason for those differences comes from a meteorological phenomenon — that the same amount of total precipitation can deliver very different amounts of measurable snowfall, depending on the underlying conditions.

Barrett Gutter, a meteorologist at Virginia Tech who teaches classes in weather analysis, weather forecasting, and severe weather, explains that the snow-to-liquid ratio (SLR), or how much moisture is in a snowflake, can be impacted by a number of factors.

“Very dry snow, which often occurs in mountainous terrain and higher latitudes, can have SLR values closer to 20:1 (20″ of snow = 1″ of liquid), while very wet snow, which often occurs in the southeast, can have SLR values closer to 6:1,” he says.

This explains why snow totals in the Rocky Mountains are often much higher than along the east coast ranges. But elevation isn’t the only determining factor.

“Lower temperatures throughout the atmosphere will lead to drier and fluffier snow (higher SLR) since there tends to be less moisture available, while higher temperatures (closer to freezing) will lead to wetter and denser snow.”

Other factors that impact snow-to-liquid ratios include the height in which snowflakes form, moisture content, and wind speed.

This led to record snowfall totals in places like Toronto, which received nearly two feet of accumulation, while the mid-Atlantic got less total snow, but several inches of sleet, which settled into hard-packed ice.

About Gutter
Barrett Gutter is a collegiate assistant professor of meteorology. Gutter teaches a wide variety of courses, including Weather Analysis, Weather Forecasting, Severe Weather, and Radar and Satellite Meteorology. He also leads a two-week storm chase field course during the summer.

Interview
To schedule an interview with Barrett Gutter, contact Noah Frank at nafrank@vt.edu or 805-453-2556.

Newswise — Agricultural pest management has traditionally relied on chemical insecticides, but their overuse has led to environmental contamination, health risks, and rapidly evolving pesticide resistance. Meanwhile, natural variation in pest resistance exists within crops and their wild relatives, offering valuable resources for breeding. However, resistance traits are difficult to measure accurately, as they are often scored visually using coarse categories that fail to capture continuous variation. This limits the effectiveness of genome-wide association studies and genomic selection. Advances in deep learning provide new opportunities to extract detailed phenotypic information directly from images, overcoming subjectivity and labor constraints. Based on these challenges, there is a pressing need to conduct in-depth research on AI-enabled phenotyping and genomic breeding for pest resistance.

Researchers from the Chinese Academy of Agricultural Sciences and collaborating institutions report (DOI: 10.1093/hr/uhaf128) on 7 May 2025 in Horticulture Research that deep learning can substantially improve genomic selection for pest-resistant grapevine. The team developed convolutional neural networks to automatically assess insect damage on grape leaves and combined these data with genome resequencing, genome-wide association studies, and transcriptomic analyses. By linking AI-derived phenotypes with genetic markers, the study identifies key resistance genes and demonstrates highly accurate machine-learning-based prediction of pest resistance, offering a new framework for precision breeding.

The study analyzed 231 grapevine accessions subjected to natural infestations of the tobacco cutworm, a major leaf-feeding pest. Deep convolutional neural networks were trained to classify pest damage as mild or severe, achieving over 95% accuracy, while a custom regression model generated continuous damage scores strongly correlated with human assessments. These AI-derived phenotypes enabled more precise genetic analyses than traditional categorical scoring. Genome-wide association studies identified 69 quantitative trait loci and 139 candidate genes linked to pest resistance, many involved in jasmonic acid, salicylic acid, ethylene, and calcium-mediated signaling pathways. By integrating transcriptomic data, the researchers pinpointed key defense genes, including calcium-transporting ATPase ACA12 and the protein kinase CRK3, both strongly induced during herbivore attack. Machine-learning-based genomic selection models further demonstrated high predictive power, reaching 95.7% accuracy for binary traits and strong correlations for continuous traits. Together, these results show that combining deep learning phenotyping with genomics reveals subtle resistance mechanisms and enables reliable prediction of complex, polygenic pest-resistance traits.

“This work highlights how artificial intelligence can fundamentally change plant breeding,” said the study’s senior authors. “By replacing subjective visual scoring with fast, objective deep-learning-based phenotyping, we can capture continuous variation in pest damage that was previously overlooked. When these high-quality phenotypes are integrated with genomics and transcriptomics, they reveal the true polygenic architecture of pest resistance. This approach not only improves prediction accuracy, but also allows breeders to make informed selections much earlier in the breeding cycle.”

The findings have broad implications for sustainable agriculture and crop improvement. AI-driven phenomics enables rapid, large-scale assessment of pest resistance without increasing labor costs, making it suitable for breeding programs worldwide. By identifying resistance genes and accurately predicting pest tolerance, breeders can reduce reliance on chemical pesticides while improving crop resilience. The framework established in grapevine can be readily adapted to other crops and stress traits, supporting the development of automated, data-driven breeding platforms. Ultimately, integrating deep learning, genomics, and machine learning could accelerate the creation of pest-resistant varieties essential for food security under increasing environmental pressure.

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References

DOI

10.1093/hr/uhaf128

Original Source URL

https://doi.org/10.1093/hr/uhaf128

Funding information

This work was supported by the National Key Research and Development Program of China (No. 2023YFD2200702), the project of National Key Laboratory for Tropical Crop Breeding (No. NKLTCB202325), the National Natural Science Foundation of China (No. 32372662), and the Science Fund Program for Distinguished Young Scholars of the National Natural Science Foundation of China (Overseas) to Yongfeng Zhou.

About Horticulture Research

Horticulture Research is an open access journal of Nanjing Agricultural University and ranked number one in the Horticulture category of the Journal Citation Reports ™ from Clarivate, 2023. The journal is committed to publishing original research articles, reviews, perspectives, comments, correspondence articles and letters to the editor related to all major horticultural plants and disciplines, including biotechnology, breeding, cellular and molecular biology, evolution, genetics, inter-species interactions, physiology, and the origination and domestication of crops.

Newswise — Environmental pollutant analysis typically requires complex sample pretreatment steps such as filtration, separation, and preconcentration. When solid materials such as sand, soil, or food residues are present in water samples, analytical accuracy often decreases, and filtration can unintentionally remove trace-level target pollutants along with the solids.

To address this challenge, a joint research team led by Dr. Ju Hyeon Kim at the Korea Research Institute of Chemical Technology (KRICT), in collaboration with Professor Jae Bem You’s group at Chungnam National University, has developed a microfluidic-based analytical device that enables direct extraction and analysis of pollutants from solid-containing samples without any pretreatment.

Water, food, and environmental samples encountered in daily life may contain trace amounts of hazardous contaminants that are invisible to the naked eye. Accurate detection requires selective extraction and concentration of target analytes, a process traditionally achieved using liquid–liquid extraction (LLE). However, conventional LLE requires large volumes of solvents and is difficult to automate. Although liquid–liquid microextraction (LLME) has been introduced to overcome these limitations, its practical application has remained limited because samples containing solid particles still require a filtration step prior to extraction.

Existing analytical approaches typically follow a multistep workflow—solid removal, extraction, and analysis—which increases time and cost while reducing analytical reliability. These limitations pose significant challenges in fields closely related to public health, including environmental monitoring, drinking water safety, and pharmaceutical residue analysis.

The research team overcame these issues by designing a trap-based microfluidic device that confines a small volume of extractant droplet inside a microchamber while allowing the sample solution to flow continuously through an adjacent microchannel. This configuration enables rapid and selective mass transfer of target analytes into the extractant, while solid particles pass through the channel without interference. After extraction, the extractant droplet can be retrieved for downstream analysis.

Using this device, the researchers successfully detected perfluorooctanoic acid (PFOA), a representative per- and polyfluoroalkyl substance (PFAS) increasingly regulated due to environmental and health concerns, as well as carbamazepine (CBZ), an anticonvulsant pharmaceutical compound. Notably, CBZ was extracted directly from sand-containing slurry samples without filtration. PFOA signals were detected within five minutes, and CBZ extracted from slurry samples was clearly identified using high-performance liquid chromatography (HPLC).

The results demonstrate that the proposed microfluidic platform significantly reduces analytical steps while maintaining high reliability, highlighting its potential as a compact and automatable solution for environmental pollution monitoring, food safety inspection, and pharmaceutical and bioanalytical applications.

Dr. Kim noted that “integrating multiple pretreatment steps into a single process offers substantial advantages for on-site analysis and automated systems,” while KRICT President Young-Kuk Lee emphasized that “this technology can enhance the reliability of environmental and food safety analyses that directly impact public health.”

The study was published as a cover article in ACS Sensors (Impact Factor: 9.1; top 3.2% in JCR Analytical Chemistry) in December 2025. Dr. Ju Hyeon Kim (KRICT) and Professor Jae Bem You (Chungnam National University) served as corresponding authors, with Sung Wook Choi as the first author.

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KRICT is a non-profit research institute funded by the Korean government. Since its foundation in 1976, KRICT has played a leading role in advancing national chemical technologies in the fields of chemistry, material science, environmental science, and chemical engineering. Now, KRICT is moving forward to become a globally leading research institute tackling the most challenging issues in the field of Chemistry and Engineering and will continue to fulfill its role in developing chemical technologies that benefit the entire world and contribute to maintaining a healthy planet. More detailed information on KRICT can be found at https://www.krict.re.kr/eng/

The research was supported by the KRICT Core Research Program, the National Research Foundation of Korea, and the Korea–Switzerland Innovation Program.

Newswise — Ground-level ozone is a major air pollutant that threatens human health, ecosystems, and climate stability. Despite aggressive reductions in nitrogen oxides and primary volatile organic compounds, ozone levels continue to exceed air quality standards in many regions. This paradox reflects the complex and nonlinear nature of atmospheric photochemistry, where reactive radicals control ozone formation. Oxygenated volatile organic compounds (OVOCs) are key intermediates in this process, acting as both sources and sinks of radicals. However, most previous studies have measured only a small subset of OVOCs, leaving major uncertainties in radical budgets. Based on these challenges, there is a critical need to systematically investigate how a broader spectrum of OVOCs drives radical cycling and ozone formation.

In a study published (DOI: 10.1016/j.ese.2026.100659) in January 2026 in Environmental Science and Ecotechnology, researchers from the Southern University of Science and Technology, The Hong Kong Polytechnic University, Hong Kong Baptist University, Beijing University of Chemical Technology, and the University of Helsinki investigated how oxygenated volatile organic compounds shape atmospheric chemistry in background air over southern China. Combining intensive field observations with photochemical box modeling, the team examined the role of OVOCs in radical cycling and ozone formation. Their results show that commonly used models relying on limited OVOC measurements substantially misrepresent radical budgets and ozone production under real atmospheric conditions.

The study combined high-resolution field measurements with a detailed photochemical box model to quantify the role of OVOCs in atmospheric radical chemistry. When models were constrained using only three commonly measured OVOCs, simulated hydroxyl radical levels were overestimated by up to 100 percent. By contrast, including measurements of 23 OVOCs brought simulations into close agreement with observations.

The analysis revealed that OVOC photolysis contributed approximately 49–61 percent of total radical production, making it the dominant radical source in background air. Surprisingly, several OVOCs present at relatively low concentrations accounted for a disproportionate share of radical generation. Errors in simulating these compounds caused cascading biases in radical budgets, altering ozone formation pathways.

The study further showed that traditional chemical mechanisms systematically overestimate some OVOCs while underestimating others, masking offsetting errors that appear acceptable when only limited measurements are used. These hidden inaccuracies significantly affect predictions of ozone production rates and sensitivity regimes. Overall, the findings demonstrate that a narrow observational focus can lead to misleading conclusions about the drivers of ozone pollution.

“This work shows that what we don’t measure can matter more than what we do,” said one of the study’s senior authors. “OVOCs have often been treated as secondary products, but our results demonstrate that they are central to controlling radical chemistry and ozone formation. Without comprehensive OVOC observations, models may appear accurate while fundamentally misrepresenting atmospheric processes. Expanding OVOC measurements is therefore essential for designing effective air quality management strategies in regions struggling with persistent ozone pollution.”

These findings have important implications for air pollution control and atmospheric modeling worldwide. Strategies focused solely on reducing traditional ozone precursors may fail if OVOC-driven radical chemistry is ignored. Incorporating comprehensive OVOC measurements can improve model accuracy, guide emission control priorities, and help policymakers identify more effective mitigation pathways. The study also highlights the need to update chemical mechanisms and expand monitoring networks to include reactive OVOC intermediates. Ultimately, recognizing the hidden role of OVOCs may be key to resolving the long-standing challenge of persistent surface ozone pollution in both developing and industrialized regions.

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References

DOI

10.1016/j.ese.2026.100659

Original Source URL

https://doi.org/10.1016/j.ese.2026.100659

Funding information

This research was funded by the Hong Kong Research Grants Council via Theme-Based Research Scheme (T24-504/17-N) and General Research Fund (HKBU 15219621), the National Natural Science Foundation of China (42325504), the National Key Research and Development Program of China (2023YFC3706205), and the Shenzhen Science and Technology Program (JCYJ20220818100611024).

About Environmental Science and Ecotechnology

Environmental Science and Ecotechnology (ISSN 2666-4984) is an international, peer-reviewed, and open-access journal published by Elsevier. The journal publishes significant views and research across the full spectrum of ecology and environmental sciences, such as climate change, sustainability, biodiversity conservation, environment & health, green catalysis/processing for pollution control, and AI-driven environmental engineering. The latest impact factor of ESE is 14.3, according to the Journal Citation Reports^TM 2024.

Newswise — ALBANY, N.Y. (Jan. 22, 2026) — A major winter storm is expected to bring dangerously low temperatures and heavy snow through the weekend across a nearly 2,000-mile stretch of the United States, from the southern Plains to the Northeast. 

The storm is expected to develop on Friday, creating a hazardous mix of heavy snow and ice that could cause power outages for millions of Americans and make roads impassable. 

Allison Finch, lead meteorologist at the University at Albany’s State Weather Risk Communication Center, is closely monitoring the storm. She says snow, freezing rain, sleet, gusty winds and dangerously cold temperatures are all among the hazards expected. 

“From Texas to the Mid-Atlantic states, this storm looks to bring snow and a widespread swath of ice,” Finch said. “Ice is a very impactful hazard to begin with, but when it occurs in areas that doesn’t typically experience it as often, impacts can be exacerbated. Among the impacts is the likelihood of power outages. Anyone who loses a heat source may be impacted since temperatures are not expected to rebound quickly after the storm.” 

Finch points to two main factors fueling the storm — cold air from Canada and moisture moving up from the Gulf of Mexico. 

“A powerful Arctic air mass is sweeping across the U.S. late this week and into next week, bringing temperatures well below average,” Finch said. “At the same time, a large plume of moisture originating from warm ocean waters is being drawn into that Arctic air. When that moisture gets wrapped into the cold air mass, it provides the fuel needed for a widespread and potentially high-impact winter storm.”  

Launched in 2023, the State Weather Risk Communication Center is a first-of-its-kind partnership between UAlbany and the New York State Division of Homeland Security and Emergency Services that leverages the University’s expertise in atmospheric sciences to help emergency managers prepare for and respond to severe weather events. 

The Center provides rapid, tailored, real-time weather information and custom weather services to New York state and local public-sector partners.  

Finch, along with other meteorologists at the State Weather Risk Communication Center, are available to share their insights on this weekend’s winter storm via phone or live/recorded interviews.    

For the latest conditions in New York, follow the NYS Mesonet, a statewide weather observation network operated by UAlbany, which provides real-time data from monitoring sites across the state. 

About the University at Albany:  

The University at Albany is one of the most diverse public research institutions in the nation and a national leader in educational equity and social mobility. As a Carnegie-classified R1 institution, UAlbany faculty and students are advancing our understanding of the world in fields such as artificial intelligence, atmospheric and environmental sciences, business, education, public health, social sciences, criminal justice, humanities, emergency preparedness, engineering, public administration, and social welfare. Our courses are taught by an accomplished roster of faculty experts with student success at the center of everything we do. Through our parallel commitments to academic excellence, scientific discovery and service to community, UAlbany molds bright, curious and engaged leaders and launches great careers.  

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Newswise — WASHINGTON, Feb. 3, 2026 — Arctic sea ice has large effects on the global climate. By cooling the planet, Arctic ice impacts ocean circulation, atmospheric patterns, and extreme weather conditions, even outside the Arctic region. However, climate change has led to its rapid decline, and being able to make real-time predictions of sea ice extent (SIE) — the area of water with a minimum concentration of sea ice — has become crucial for monitoring sea ice health.

In Chaos, by AIP Publishing, researchers from the United States and the United Kingdom reported accurate, real-time predictions of SIE in Arctic regions. Sea ice coverage is at its minimum in September, making the month a critical indicator of sea ice health and the primary target of the work.

“Indigenous Arctic communities depend on the hunting of species like polar bears, seals, and walruses, for which sea ice provides essential habitat,” said author Dimitri Kondrashov. “There are other economic activities, such as gas and oil drilling, fishing, and tourism, where advance knowledge of accurate ice conditions reduces risks and costs.”

The researchers’ approach treats sea ice evolution as a set of atmospheric and oceanic factors that oscillate at different rates — for example, climate memory at long timescales, annual seasonal cycles, and quickly changing weather — while still interacting with one another. They used the National Snow and Ice Data Center’s average daily SIE measurements from 1978 onward to find the relationships between these factors that affect sea ice.

Testing their prediction method live in September 2024, and retroactively for Septembers of past years, the group confirmed their technique is generally accurate and can capture effects from subseasonal to seasonal timescales. They predicted SIE ranging from one to four months out and found their predictions outperformed other models.

In general, long-term climate forecasts tend to be easier and more reliable than short-term predictions. However, by incorporating regional data into their model, the researchers were able to improve short-term ice and weather estimates.

“The model includes several large Arctic regions composing [the] pan-Arctic,” said Kondrashov. “Despite large differences in sea ice conditions from year to year in different regions, the model can pick it up reasonably accurately.”

The group plans to improve their model by including additional oceanic and atmospheric variables, such as air temperature and sea level pressure. These variables can cause fast changes and short-term fluctuations that are not currently reflected in the model, and the researchers hope these additions will further enhance the predictability of summertime Arctic sea ice.

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The article “Accurate and robust real-time prediction of September Arctic sea ice” is authored by Dimitri Kondrashov, Ivan Sudakow, Valerie N. Livina, and QingPing Yang. It will appear in Chaos on Feb. 3, 2026 (DOI: 10.1063/5.0295634). After that date, it can be accessed at https://doi.org/10.1063/5.0295634.

ABOUT THE JOURNAL

Chaos is devoted to increasing the understanding of nonlinear phenomena in all areas of science and engineering and describing their manifestations in a manner comprehensible to researchers from a broad spectrum of disciplines. See https://pubs.aip.org/aip/cha.

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