climate

Environmental sampling for the fungal pathogen Coccidioides spp. in New Mexico

PROJECT SUMMARY/ABSTRACT Coccidioidomycosis (also known as Valley fever) is a fungal disease endemic to the arid and semi-arid portions of the United States. Due to its alarming health impacts, it has recently been deemed as one of the fungal diseases of highest concern by the World Health Organization. Disease cases continue to rise, causing increasing concern and warranting further understanding of this disease. Though New Mexico has been considered endemic to the disease since the 1940s, few cases are reported in the state each year, suggesting cases may be going vastly underreported. Indeed, recent epidemiologic models suggest New Mexico is likely underreporting cases, which may be a result of low disease awareness in the state or a lack of understanding what populations are at risk. Meanwhile, the neighboring state of Arizona reports the highest number of cases in the country, despite similarities in climate and ecology to New Mexico. In general, very little is known about the health burden of coccidioidomycosis and the geographical distribution of the causative fungal pathogen, Coccidioides spp., in New Mexico. Interestingly, both species of Coccidioides are likely endemic to New Mexico and hybridization of the species may occur. This, in combination with a variety of different ecosystems across the state, makes New Mexico an ideal location for studying the ecology of Coccidioides spp. The objective of our proposal is to generate preliminary data to gain a better understanding of the geographical distribution of Coccidioides spp. in New Mexico, including any regions where hybridization of the species may be occurring. To achieve this, we will collect soil samples throughout New Mexico to: (1) identify what ecosystems are conducive for the growth of Coccidioides and each Coccidioides species in New Mexico and (2) assess whether locations directly surrounding New Mexico’s five largest population centers (Albuquerque, Las Cruces, Rio Rancho, Santa Fe, and Roswell) are endemic to Coccidioides spp. The positive impacts of our proposal are an understanding of what populations are at risk for contracting this disease, where future disease surveillance efforts should be targeted in the state, and where future soil samples should be collected to further explore the genomics and phenotypes of Coccidioides spp. and potential for hybridization. This will help us achieve our long-term goal: to understand the ecology and endemicity of Coccidioides spp. to increase disease awareness, mitigate the negative health impacts from coccidioidomycosis, and ultimately protect the health of all Americans.

Prosocial Learning and Motivation across the Lifespan

2024 BACN Early-Career Prize Lecture Many of our decisions affect other people. Our choices can decelerate climate change, stop the spread of infectious diseases, and directly help or harm others. Prosocial behaviours – decisions that help others – could contribute to reducing the impact of these challenges, yet their computational and neural mechanisms remain poorly understood. I will present recent work that examines prosocial motivation, how willing we are to incur costs to help others, prosocial learning, how we learn from the outcomes of our choices when they affect other people, and prosocial preferences, our self-reports of helping others. Throughout the talk, I will outline the possible computational and neural bases of these behaviours, and how they may differ from young adulthood to old age.

Environmental Impact of Research

Research, whether direct or indirect, aims to advance knowledge and change the world for the better. But whether you are spike-sorting with high-performance computers, getting through 100 single-use plastic pipette tips in a day or receiving regular shipments of metal-rich equipment, your research is having a long-term and detrimental impact on the environment. This session will explore how life sciences research contributes to the climate crisis and negatively impacts local and global environments. Practical advice will be given on ways to reduce the footprint of your own research.

Machine Learning as a tool for positive impact : case studies from climate change

Climate change is one of our generation's greatest challenges, with increasingly severe consequences on global ecosystems and populations. Machine Learning has the potential to address many important challenges in climate change, from both mitigation (reducing its extent) and adaptation (preparing for unavoidable consequences) aspects. To present the extent of these opportunities, I will describe some of the projects that I am involved in, spanning from generative model to computer vision and natural language processing. There are many opportunities for fundamental innovation in this field, advancing the state-of-the-art in Machine Learning while ensuring that this fundamental progress translates into positive real-world impact.

On climate change, multi-agent systems and the behaviour of networked control

Multi-agent reinforcement learning (MARL) has recently shown great promise as an approach to networked system control. Arguably, one of the most difficult and important tasks for which large scale networked system control is applicable is common-pool resource (CPR) management. Crucial CPRs include arable land, fresh water, wetlands, wildlife, fish stock, forests and the atmosphere, of which proper management is related to some of society’s greatest challenges such as food security, inequality and climate change. This talk will consist of three parts. In the first, we will briefly look at climate change and how it poses a significant threat to life on our planet. In the second, we will consider the potential of multi-agent systems for climate change mitigation and adaptation. And finally, in the third, we will discuss recent research from InstaDeep into better understanding the behaviour of networked MARL systems used for CPR management. More specifically, we will see how the tools from empirical game-theoretic analysis may be harnessed to analyse the differences in networked MARL systems. The results give new insights into the consequences associated with certain design choices and provide an additional dimension of comparison between systems beyond efficiency, robustness, scalability and mean control performance.

Aviation’s non-CO2 impacts on the climate 2026