M. Reyes

Laboratory application of the Aquascope approach of automated imaging and classification for long-term plankton monitoring

The paper evaluates the laboratory application of the Aquascope for automated plankton imaging and classification, highlighting its advantages and limitations compared to traditional methods.

Automated plankton monitoring suggests a key role of microzooplankton and temperature for predicting dynamics of phytoplankton size classes

The paper uses Random Forest models on four years of lake data to show temperature and microzooplankton grazing significantly influence phytoplankton size class dynamics.

Novel orthotopic model of neuroblastoma in RNU homozygous rats

This study develops a novel rat neuroblastoma model to evaluate surgical treatments, showing tumor growth and feasibility of resection.

Underwater dual-magnification imaging for automated lake plankton monitoring

The paper introduces a dual-magnification imaging system for automated plankton monitoring, showing it can accurately detect and characterize plankton species and track ecological dynamics in both lab and field settings.

Deep Learning Classification of Lake Zooplankton

The paper develops deep learning models to classify lake zooplankton images with high accuracy and evaluates their performance on external datasets.

Induction of a regulatory myeloid program in bacterial sepsis and severe COVID-19

The paper identifies a regulatory myeloid program, MS1, associated with sepsis mortality and up-regulated in severe COVID-19, showing its immunosuppressive role and cytokine-dependent induction.

Random access DNA memory in a scalable, archival file storage system

This paper demonstrates a method to enable random access of DNA-encoded files in large-scale archival storage by using silica capsules with barcode labels for efficient file selection.

Early-Career Coordinated Distributed Experiments: Empowerment Through Collaboration

This paper analyzes the benefits and challenges of a coordinated distributed experiment led by early-career researchers and provides recommendations for successful execution.

Quantifying cell densities and biovolumes of phytoplankton communities and functional groups using scanning flow cytometry, machine learning and unsupervised clustering

The predictability of a lake phytoplankton community, from hours to years

This study uses machine learning to assess the predictability of phytoplankton communities in a lake across multiple time scales, finding high predictability from hours to months and novel ecological trade-offs.

Multiplexed enrichment and genomic profiling of peripheral immune cell subsets on a microfluidic chip

This paper presents a microfluidic system for sorting and profiling immune cell subsets using RNA-seq, demonstrating its utility in identifying cell-specific disease signatures in lupus patients.