Today, we are proud to release Rainflow! 🌈⛈️☔️. Rainflow is a Desktop application for macOS designed to help researchers with reproducible analysis and integration of their Flow Cytometry data. Rainflow is available on the App Store now:

Today our lab presented two posters at CoDEx, hosted by @Northwestern_IT !

RT @cpiller: ‘Patent mills’ sell scientists inventorship of bizarre medical devices - crazier and crazier - via @cathleenogrady | Science |….

Congrats @spencercodes on a great thesis seminar!

RT @RetractionWatch: "@ScienceMagazine and Retraction Watch’s investigation suggests authors, journals, and institutions all benefit from t….

RT @Peter_Aldiss: Often find myself thinking of this great paper by @AmaralLab back in 2018 on why most of the genome is ignored. https://t….

Scientists Develop Approach to Accurately Predict Pneumonia Outcomes - Machine-learning approach using health records identifies five pneumonia ‘states’ with predictable prognoses.

Our own Reese Richardson just defended his PhD thesis and received his program's Outstanding Dissertation award! Way to go, Dr. Richardson! 🎊. Thanks to the big event, we finally have an updated lab photo! Some people could not make it and were scaled up by 1000% as penance. 😊

We hope you give it a read!. Article (paywalled): News release:

🌍Broader Impact: This work represents a significant step towards more nuanced, data-informed approaches to ICU patient management, potentially improving mortality prediction and resource allocation in healthcare systems worldwide.

Clinical Significance of States: Our five identified states not only correlate with patient outcomes (e.g., recovery vs. mortality) but also show strong associations with different pneumonia etiologies, including COVID-19. 🦠.

Novel Clustering Approach: We introduced an ensemble clustering method that improves classification stability, even across large, varied datasets. 💭.

✨Highlights: . Common-Sense Ground Truth: We developed a reliable baseline for distinguishing extreme patient states (recovery vs. severe progression), enhancing prediction accuracy.

. to identify five distinct clinical states associated with patient outcomes and disease progression.

🔍 Pneumonia is a leading cause of ICU admissions globally, yet its complexity challenges existing diagnostic and prognostic methods. Our study brings a data-driven approach to pneumonia stratification, leveraging EHR data. .

📢 Exciting New Publication Alert! 🫁. "Robust Extraction of Pneumonia-Associated Clinical States from Electronic Health Records" by Feihong Xu, Félix Morales and Luís Amaral has just been published in the PNAS! . 🧵

RT @Thatsregrettab1: Congratulations to Reese Richardson on his successful PhD defense. For a youngster 😛 he’s already had a huge impact on….

In this piece, our own Reese Richardson, @spencercodes and @amaral construct an ego network around one shady business and uncover a massive hydra. Check it out at the link below!

RT @nickwizzo: Y'day I said that 'Cureus ‘academic channels’ appear purpose-built to be abused by papermills'. Today, Reese Richardson and….

RT @joelving: "It appears the term ‘paper mill’ may fail to fully capture the diversity and scale of activities overseen by these hydra-li….

RT @amaral: Widespread signs of paper milling discovered in materials science and engineering papers | News | Chemistry World https://t.co/….