STUDENT ENGAGEMENT THROUGH DATA MAPPING IN AN UNDERGRADUATE ENVIRONMENTAL CHEMISTRY LABORATORY

We are all too familiar with the map visualisations in media depicting the spread and severity of COVID-19 across the world. The representation of statistical data on a map is a powerful tool that can effectively convey factors such as magnitude, density and spatial variations. Analysing data in this format can help identify trends (eg “hotspots”, “patient zero”) from large datasets. Whilst students outside the discipline of geosciences may be familiar with analysing a data map; constructing one would be a rare experience. In our undergraduate environmental chemistry laboratory, students analyse the metal ion content and hardness of water samples collected on campus. We have used Google Maps Application Programming Interface (API)1 to allow students to geotag their results on a Google Map. The resulting bubble map is live and continually updated as students complete the lab and submit their results.2 This map is shared with the cohort so students can view the evolution of data, their contribution to the “project” and generate their own hypotheses as to why certain concentrations may be linked to certain locales (eg. age of building). This approach offers rich context-based learning that could be modified to address other datasets/contexts, locations, and disciplines

Curvature-informed multi-task learning for graph networks

Properties of interest for crystals and molecules, such as band gap, elasticity, and solubility, are generally related to each other: they are governed by the same underlying laws of physics. However, when state-of-the-art graph neural networks attempt to predict multiple properties simultaneously (the multi-task learning (MTL) setting), they frequently underperform a suite of single property predictors. This suggests graph networks may not be fully leveraging these underlying similarities. Here we investigate a potential explanation for this phenomenon: the curvature of each property's loss surface significantly varies, leading to inefficient learning. This difference in curvature can be assessed by looking at spectral properties of the Hessians of each property's loss function, which is done in a matrix-free manner via randomized numerical linear algebra. We evaluate our hypothesis on two benchmark datasets (Materials Project (MP) and QM8) and consider how these findings can inform the training of novel multi-task learning models.Comment: Published at the ICML 2022 AI for Science workshop: https://openreview.net/forum?id=m5RYtApKFO

Will high-resolution global ocean models benefit coupled predictions on short-range to climate timescales?

As the importance of the ocean in the weather and climate system is increasingly recognised, operational systems are now moving towards coupled prediction not only for seasonal to climate timescales but also for short-range forecasts. A three-way tension exists between the allocation of computing resources to refine model resolution, the expansion of model complexity/capability, and the increase of ensemble size. Here we review evidence for the benefits of increased ocean resolution in global coupled models, where the ocean component explicitly represents transient mesoscale eddies and narrow boundary currents. We consider lessons learned from forced ocean/sea-ice simulations; from studies concerning the SST resolution required to impact atmospheric simulations; and from coupled predictions. Impacts of the mesoscale ocean in western boundary current regions on the large-scale atmospheric state have been identified. Understanding of air-sea feedback in western boundary currents is modifying our view of the dynamics in these key regions. It remains unclear whether variability associated with open ocean mesoscale eddies is equally important to the large-scale atmospheric state. We include a discussion of what processes can presently be parameterised in coupled models with coarse resolution non-eddying ocean models, and where parameterizations may fall short. We discuss the benefits of resolution and identify gaps in the current literature that leave important questions unanswered

Parastrongylus cantonensis in a Nonhuman Primate, Florida

Parastrongylus (= Angiostrongylus) cantonensis is a parasitic nematode of Norway rats throughout tropical regions. This parasite is neurotropic and causes disease and death in humans and other mammals. We report the first identification of P. cantonensis as the cause of a debilitating neurologic disease in a captive primate in Florida

Prevalence and Predictors of Vitamin D Insufficiency in Children: A Great Britain Population Based Study

Objectives To evaluate the prevalence and predictors of vitamin D insufficiency (VDI) in children In Great Britain. Design A nationally representative cross-sectional study survey of children (1102) aged 4–18 years (999 white, 570 male) living in private households (January 1997–1998). Interventions provided information about dietary habits, physical activity, socio-demographics, and blood sample. Outcome measures were vitamin D insufficiency (<50 nmol/L). Results Vitamin D levels (mean = 62.1 nmol/L, 95%CI 60.4–63.7) were insufficient in 35%, and decreased with age in both sexes (p<0.001). Young People living between 53–59 degrees latitude had lower levels (compared with 50–53 degrees, p = 0.045). Dietary intake and gender had no effect on vitamin D status. A logistic regression model showed increased risk of VDI in the following: adolescents (14–18 years old), odds ratio (OR) = 3.6 (95%CI 1.8–7.2) compared with younger children (4–8 years); non white children (OR = 37 [95%CI 15–90]); blood levels taken December-May (OR = 6.5 [95%CI 4.3–10.1]); on income support (OR = 2.2 [95%CI 1.3–3.9]); not taking vitamin D supplementation (OR = 3.7 [95%CI 1.4–9.8]); being overweight (OR 1.6 [95%CI 1.0–2.5]); <1/2 hour outdoor exercise/day/week (OR = 1.5 [95%CI 1.0–2.3]); watched >2.5 hours of TV/day/week (OR = 1.6[95%CI 1.0–2.4]). Conclusion We confirm a previously under-recognised risk of VDI in adolescents. The marked higher risk for VDI in non-white children suggests they should be targeted in any preventative strategies. The association of higher risk of VDI among children who exercised less outdoors, watched more TV and were overweight highlights potentially modifiable risk factors. Clearer guidelines and an increased awareness especially in adolescents are needed, as there are no recommendations for vitamin D supplementation in older children

New frog

6 p. : ill. ; 24 cm.Includes 1 bibliographical reference (p. 6)

On the need and use of models to explore the role of economic confidence:a survey.

Empirical studies suggest that consumption is more sensitive to current income than suggested under the permanent income hypothesis, which raises questions regarding expectations for future income, risk aversion, and the role of economic confidence measures. This report surveys a body of fundamental economic literature as well as burgeoning computational modeling methods to support efforts to better anticipate cascading economic responses to terrorist threats and attacks. This is a three part survey to support the incorporation of models of economic confidence into agent-based microeconomic simulations. We first review broad underlying economic principles related to this topic. We then review the economic principle of confidence and related empirical studies. Finally, we provide a brief survey of efforts and publications related to agent-based economic simulation