Climate change and warmer conditions have altered snow-driven extremes and previous studies predict less and slower snowmelt in the northern United States and Canada. However, mixed-phase precipitation—shifting between snow and rain—is increasing, especially in higher elevations, making it more challenging to predict future snowmelt, a dominant driver of severe flooding. Researchers at the University of New Hampshire took a closer look at previous studies, and because geographical areas respond differently to climate change, they found future snowmelt incidences could vary greatly by the late 21st century. Snowmelt could decrease over the continental U.S. and southern Canada but increase in Alaska and northern Canada resulting in larger flooding vulnerabilities and possibly causing major societal and economic consequences including costly infrastructure failures.

“Estimation of future floods can be a tricky business and yet it is important information for those planning future infrastructure,” said Jennifer Jacobs, professor of civil and environmental engineering. “For instance, if a region primarily has floods occurring during the winter, then this work could really help build infrastructure that can handle those future conditions. And, if the floods are decreasing, then the design values should also decrease rather than over design.”

Their study, recently published in the journal Geophysical Research Letters, looked at previous study predictions of change in the snowpack, snowmelt, and runoff to translate it into information that would be helpful for water resources managers, engineering designers, and the general public living in the areas of Northern California, Pacific Northwest, Alaska, and Canada. The researchers used historical maps and regional climate model (RCM) simulations that focused on North America. They found that in the West Coast mountain areas, such as Northern California and the Pacific Northwest, there could be a greater risk of rain-on-snow flooding because these areas are predicted to warm and produce more rain. This could increase the melting of any existing snowpack and lead to larger runoff potential, increasing flooding risk. But this differed in extremely cold regions like Alaska and northern Canada. Researchers found warmer temperatures in these areas could increase the opportunity for moisture that could likely lead to more winter precipitation like snow.

“These findings can be important in helping to develop or modify federal and state governments’ long-term policies for climate adaptation,” said Eunsang Cho, a former UNH doctoral student, now a postdoctoral researcher at NASA’s Goddard Space Flight Center, and lead author of the study. “For example, the current U.S. government standards for water-related infrastructure design are based on liquid precipitation data with very limited guidance on snow or snowmelt information.”

The researchers point out that certain infrastructure policies, like the relicensing of dams, depend on information about extreme weather conditions. This information can help engineers design infrastructure not based on past conditions but to anticipate future conditions. In their previous research, Jacobs and Cho created a map that accounts for snowmelt across the continental U.S. They say this information is already being used by the state of California in their relicensing process.

State-of-the-art technology will allow physicians to identify patients who are at risk for serious illness ahead of time

A new technology developed at Tel Aviv University in Isreal will make it possible, using artificial intelligence, to identify patients who are at risk of serious illness as a result of blood infections. The researchers trained the AI program to study the electronic medical records of about 8,000 patients at Tel Aviv’s Ichilov Hospital who were found to be positive for blood infections. These records included demographic data, blood test results, medical history, and diagnosis. After studying each patient’s data and medical history, the program was able to automatically identify medical files’ risk factors with an accuracy of 82%. According to the researchers, in the future, this model could even serve as an early warning system for doctors, by enabling them to rank patients based on their risk of serious disease. 

Behind this groundbreaking research with the potential to save many lives are students Yazeed Zoabi and Dan Lahav from the laboratory of Prof. Noam Shomron of Tel Aviv University’s Sackler Faculty of Medicine, in collaboration with Dr. Ahuva Weiss Meilik, head of the I-Medata AI Center at Ichilov Hospital, Prof. Amos Adler, and Dr. Orli Kehat. 

The researchers explain that blood infections are one of the leading causes of morbidity and mortality in the world, so it is very important to identify the risk factors for developing serious illness at the early stage of infection with a bacterium or fungus. Most of the time, the blood system is a sterile one, but infection with a bacterium or fungus can occur during surgery, or as the result of complications from other infections, such as pneumonia or meningitis. The diagnosis of infection is made by taking a blood culture and transferring it to a growth medium for bacteria and fungi. The body’s immunological response to the infection can cause sepsis or shock, dangerous conditions that have high mortality rates.

“We worked with the medical files of about 8,000 Ichilov Hospital patients who were found to be positive for blood infections between the years 2014 and 2020, during their hospitalization and up to 30 days after, whether the patient died or not,” explains Prof. Noam Shomron. “We entered the medical files into software based on artificial intelligence; we wanted to see if the AI would identify patterns of information in the files that would allow us to automatically predict which patients would develop serious illness, or even death, as a result of the infection.”

To the researchers’ satisfaction, following their training the AI reached an accuracy level of 82% in predicting the course of the disease, even when ignoring obvious factors such as the age of the patients and the number of hospitalizations they had endured. After the researchers entered the patient's data, the algorithm knew how to predict the course of the disease, which suggests that in the future it will be possible to rank patients in terms of the danger posed to their health – ahead of time.

“Using artificial intelligence, the algorithm was able to find patterns that surprised us, parameters in the blood that we hadn’t even thought about taking into account,” says Prof. Shomron. “We are now working with medical staff to understand how this information can be used to rank patients in terms of the severity of the infection. We can use the software to help doctors detect the patients who are at maximum risk.”

Since the study’s success, Ramot, Tel Aviv University's technology transfer company, is working to register a global patent for the groundbreaking technology. Keren Primor Cohen, CEO of Ramot, says, “Ramot believes in this innovative technology’s ability to bring about a significant change in the early identification of patients at risk and help hospitals reduce costs. This is an example of effective cooperation between the university’s researchers and hospitals, which improves the quality of medical care in Israel and around the world.”