Hannah Dailey ’02 ’06G ’09 PhD, an associate professor of mechanical engineering and mechanics in Lehigh University’s P.C. Rossin College of Engineering and Applied Science, has been named a 2024 recipient of the Presidential Early Career Award for Scientists and Engineers (PECASE) by the Biden administration. The award recognizes exceptional professionals in the fields of science and engineering and is the highest such honor given by the U.S. government.

Rui Zhu, Ph.D., an assistant professor in the School of Industrial and Systems Engineering at the University of Oklahoma, has received a National Science Foundation Faculty Early Career Development Program award to uncover the mechanisms behind cardiac arrhythmias.

Researchers at the University of Toronto have invented a new method that uses DNA sequencing to measure metabolites. The new platform for small molecule sequencing, called “smol-seq”, employs short strands of DNA called aptamers to detect metabolites; this enables rapid and precise analysis of biological compounds, such as sugars, vitamins, hormones and the hundreds of other metabolites that are critical for health.

Advances in nanotechnology may provide solutions to oil spill cleanups in coastal regions that are more effective, safer and work much faster than current methods, according to a new paper in Environmental Science: Nano by a Concordia-led team of researchers. The paper synthesizes, reviews and analyzes between 40 and 50 studies on the subject to provide a big-picture look of the status of nanotechnologies in coastal oil spill response. The researchers also present their own suggestions and identifying research gaps between using nanomaterials in the lab and how they can be used in real-world applications.

Is it better to work in large groups? Smaller ones? With other people who are similar or different? New research from Binghamton University, State University of New York offers insight into these questions — and some of the results are not what you’d expect.

A recent study introduces an innovative method for analyzing body composition using advanced 3D imaging and deep learning techniques. This approach aims to provide more accurate assessments of body fat and muscle distribution, which are crucial for understanding health risks associated with various conditions.

The study, “3D Convolutional Deep Learning for Nonlinear Estimation of Body Composition from Whole Body Morphology,” authored by researchers from Pennington Biomedical Research Center, University of Washington, University of Hawaii and University of California-San Francisco was recently published in NPJ Digital Medicine, a journal of the Nature portfolio.

Key Highlights of the study include:

Advanced Imaging: The researchers utilized 3D imaging technology to capture detailed representations of the body's shape.

Deep Learning Application: By applying sophisticated deep learning algorithms, the study achieved more precise estimations of body composition compared to traditional methods.

Health Implications: Accurate body composition analysis is essential for assessing health risks related to obesity, cardiovascular diseases, and other metabolic disorders.

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A research team led by Professor Kazuaki Sawada and Project Assistant Professor Hideo Doi of the Department of Electrical and Electronic Information Engineering, Toyohashi University of Technology has developed a semiconductor sensor enabling the real-time observation of two types of biomolecule dynamics in solutions. By using semiconductor technology to pattern a thin metal film functioning as a neurotransmitter-sensitive membrane on sensor pixels arranged two-dimensionally in a 2 µm pitch, the sensor captures the movement of hydrogen ions and lactate (neurotransmitters) in a solution as image data. A time resolution of milliseconds and a spatial resolution of several microns (approximately 1/17 the size of a strand of hair) were achieved, and it is expected that the measurement of relation for neurotransmitters and ions distribution which changes temporally and spatially between cells with high spatiotemporal resolution.