New study helps explain recent scarcity of Bay nettles

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A new, long-term study of how environmental conditions affect the abundance and distribution of jellyfish in the nation’s largest estuary helps explain the widely reported scarcity of sea nettles within Chesapeake Bay during the past few months and raises concerns about how a long-term continuation of this trend might harm Bay fisheries as climate continues to warm.

The research, reported in the latest issue of Estuaries and Coasts, used two long-term monitoring surveys to study the interplay between jelly populations and water temperature, salinity, oxygen levels, and other factors. Compiling and analyzing population data for 5 different jelly species in the years between 1984 and 2012, it is the most comprehensive study of Bay jelly populations ever undertaken.

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New study helps explain recent scarcity of bay nettles in the Chesapeake Bay

Wondering why you saw so few jellyfish in the Chesapeake Bay this summer? A new, long-term study of how environmental conditions affect the abundance and distribution of bay jellies helps explain the widely reported scarcity of bay nettles during the past few months and raises concerns about how a long-term continuation of this trend might harm bay fisheries as climate continues to warm.

Lead author Joshua Stone conducted the work as part of his dissertation research at William & Mary’s Virginia Institute of Marine Science, along with VIMS professors Deborah Steinberg and Mary Fabrizio. Steinberg is an expert in the’ecology of jellies and other zooplankton; Fabrizio directs the’VIMS Juvenile Fish and Blue Crab Trawl Survey’that provided one of the datasets used in the study. The other dataset was’a component of the Chesapeake Bay Program. Stone is now an assistant professor at the University of South Carolina. His work at VIMS was supported through a’Virginia Sea Grant Fellowship.

Racial, ethnic discrimination impacts adolescent development, UT study says

Racism and ethnic discrimination negatively affect development in adolescents in aspects ranging from physical health to academic success, according to a recent UT study.’

The study, led by Aprile Benner, a human development and family sciences associate professor, found discrimination has the largest effect on Latinx and Asian communities. The study was based on outside research and previous racial-ethnic discrimination studies done in her lab, Benner said.’

‘There are clear effects of racial and ethnic discrimination on adolescents’ mental health, on how well they are doing in school, how engaged they are and then also on the kinds of choices that they are making,’ Benner said.

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Award helps UB researcher balance both family and career in neuroscience

As far as awards go, the dollar amount was humble. Last spring, UB researcher Caroline E. Bass was notified that she had been chosen to receive a child care award of 400 Euros, approximately $463, to attend the Federation of European Neuroscience Societies (FENS) Forum in Berlin in July.

The FENS-Kavli child care award was open to any scientist with children and could be used either to pay a child care provider at home or to take a child to the meeting and to pay for care while traveling.

With two children, ages 5 and 9, both with special needs and a parent with a serious, chronic health condition, traveling to scientific conferences is, at best, challenging for Bass. The award allowed her to attend the FENS biannual, a critical meeting in her field.

Tech Giants Launch New AI Tools as Worries Mount About Explainability

Concerns about transparency and ethics in artificial intelligence are mounting, prompting cloud services companies to launch new tools that explain the decision-making behind their AI algorithms.

Executives in regulated industries such as accounting and finance say it’s crucial that both data scientists and non-technical business managers understand the processes behind an algorithmic decision. That knowledge could have far-reaching impacts in guarding against potential ethical and regulatory breaches, especially as enterprise-level AI algorithms become widespread.

‘I don’t believe it’s possible for AI to scale in the enterprise beyond hundreds of (experiments) unless you have that explainability,’ said Vinodh Swaminathan, principal of intelligent automation, cognitive and AI at accounting firm KPMG LLP’s innovation and enterprise solutions division.

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