At the University of Illinois at Urbana-Champaign, a team led by Han Lee integrates deep learning with Photonic Resonator Absorption Microscopy (PRAM) to create LOCA-PRAM. This system automatically identifies single biomarker molecules tagged with gold nanoparticles by analyzing red LED microscopy images and eliminating artifacts. By training the AI model with paired high-resolution SEM validation data, LOCA-PRAM delivers rapid, accurate molecular counts at the point of care for early disease diagnostics.

Key points

• LOCA-PRAM uses context-aware deep neural network to identify gold-nanoparticle–tagged biomarkers in PRAM images.
• Paired SEM imaging provides high-resolution ground truth for AI training, yielding >95% accuracy in nanoparticle localization.
• System achieves single-molecule sensitivity below 0.1 pM concentration with false-positive rates reduced by over 50% in point-of-care tests.

Why it matters: LOCA-PRAM ushers in accessible single-molecule diagnostics, enabling rapid, accurate disease detection at the patient’s side without expert intervention.

A team led by Rabinowitz published in IJ STEM Ed demonstrates how embedding foundational machine learning modules within informal learning settings—such as after-school programs and science clubs—enables high school students to conduct ecological modeling and genetic data analysis, thereby enhancing computational thinking. The curriculum employs supervised and unsupervised learning exercises, scaffolding, and mentorship to incrementally develop students’ abilities to formulate hypotheses and interpret complex data.

Key points

• Accessible programming modules introduce supervised and unsupervised machine learning tasks.
• Informal settings like after-school clubs provide flexible, collaborative environments for data-driven science.
• Curriculum addresses feature selection, overfitting, and evaluation metrics to build robust modeling skills.
• Structured mentorship supports autonomy and growth mindset while preventing cognitive overload.
• Mixed-method assessments show significant gains in students’ computational thinking, data literacy, and STEM interest.

Why it matters: Embedding machine learning into informal science education shifts the paradigm by democratizing access to computational skills and lowering classroom barriers. This scalable model fosters data literacy across diverse youth populations and equips the next generation with tools vital for addressing complex societal and scientific challenges.

Osaka University, in collaboration with Diponegoro University, has engineered cyborg insects equipped with motion and obstacle sensors. This innovative biohybrid approach draws a unique parallel between innate insect behaviors and engineered navigation systems tested in obstacle courses. The 2024 Soft Robotics study presents a promising avenue in search-and-rescue applications, and experts are encouraged to further explore this convergence of biology and technology.