Skip To Main Content
Juan Herrera on Muck Rack

Juan Herrera

As seen in: Medium, Nature, Wiley Online Library, MDPI, El Espectador, BioRxiv, Texas Medical Center

Get in touch with Juan

Contact Juan, search articles and posts on X, monitor coverage, and track replies from one place.

Learn more about Muck Rack

Actions

Share this page

Articles

Macroeconomic impacts of local population dynamics and acute climate events: a 20-year global perspective

|
By Danielle N. Medgyesi, Zachary M. Hirsch, Jeremy Porter, Juan Herrera, Jasmina Buresch
| Nature Verified
Abstract Local economies worldwide are largely influenced by fundamental population dynamics including migration. Climate change is a growing threat to macroeconomic stability through both the direct damage from acute hazards and the indirect pressures of climate-induced migration. This study examines two distinct forms of macroeconomic vulnerability via: (1) local population change and (2) exposure to severe climate events.
Open in Who Shared Issue with article?

Detection of Potentially Toxic Elements and Tolerant Native Fungi Presence in an Urban Stream in Argentina

|
By Juan Herrera, Nadia Álvarez, Ludmila López Arias
| Wiley Online Library Verified
1 Introduction Potentially toxic elements (PTEs) such as Cr (chromiun), Cu (copper), Cd (cadmiun), Pb (lead) and Zn (zinc) are recognised pollutants in wastewater owing to their harmful effects on environments and human health when they accumulate in soil and water systems. Common contaminants include nickel (Ni), arsenic (As), Cd, Cu, Zn, Pb and Cr, making PTEs pollution both ubiquitous and biologically hazardous (Angon et al. 2024; Hossain et al. 2025).
Open in Who Shared Issue with article?

The plant-time-bender model: predicting yield through wheat's perception of time

|
By Lukas Roth, Juan Herrera, Lilia Levy Häner, Didier Pellet
| BioRxiv
Abstract To address challenges in food security, a better understanding of crop performance under varying environmental conditions is required. Plant Time Bender (PTB) is a deep learning model that integrates high-throughput field phenotyping data with genomic and environmental information to predict wheat yield. PTB leverages image time series, genetic markers, and environmental covariates to learn genotype-specific responses to temperature and vapor pressure deficit.
Open in Who Shared Issue with article?
See all 12 articles