Integrating Machine Learning and Color Chemistry: Developing a High-School Curriculum toward Real-World Problem-Solving.

Artificial intelligence (AI) is rapidly transforming our world, making it imperative to educate the next generation about both the potential benefits and challenges associated with AI. This study presents a cross-disciplinary curriculum that connects AI and chemistry disciplines in the high school classroom. Particularly, we leverage machine learning (ML), an important and simple application of AI to instruct students to build an ML-based virtual pH meter for high-precision pH read-outs. We used a "codeless" and free ML neural network building software - Orange, along with a simple chemical topic of pH to show the connection between AI and chemistry for high-schoolers who might have rudimentary backgrounds in both disciplines. The goal of this curriculum is to promote student interest and drive in the analytical chemistry domain and offer insights into how the interconnection between chemistry and ML can benefit high-school students in science learning. The activity involves students using pH strips to measure the pH of various solutions with local relevancy and then building an ML neural network model to predict the pH value based on color changes of pH strips. The integrated curriculum increased student interest in chemistry and ML and demonstrated the relevance of science to their daily lives and global issues. This approach is transformative in developing a broad spectrum of integration topics between chemistry and ML and understanding their global impacts.

Switchable and Functional Fluorophores for Multidimensional Single-Molecule Localization Microscopy.

Multidimensional single-molecule localization microscopy (mSMLM) represents a paradigm shift in the realm of super-resolution microscopy techniques. It affords the simultaneous detection of single-molecule spatial locations at the nanoscale and functional information by interrogating the emission properties of switchable fluorophores. The latter is finely tuned to report its local environment through carefully manipulated laser illumination and single-molecule detection strategies. This Perspective highlights recent strides in mSMLM with a focus on fluorophore designs and their integration into mSMLM imaging systems. Particular interests are the accomplishments in simultaneous multiplexed super-resolution imaging, nanoscale polarity and hydrophobicity mapping, and single-molecule orientational imaging. Challenges and prospects in mSMLM are also discussed, which include the development of more vibrant and functional fluorescent probes, the optimization of optical implementation to judiciously utilize the photon budget, and the advancement of imaging analysis and machine learning techniques.

Near-atomic, non-icosahedrally averaged structure of giant virus Paramecium bursaria chlorella virus 1.

Giant viruses are a large group of viruses that infect many eukaryotes. Although components that do not obey the overall icosahedral symmetry of their capsids have been observed and found to play critical roles in the viral life cycles, identities and high-resolution structures of these components remain unknown. Here, by determining a near-atomic-resolution, five-fold averaged structure of Paramecium bursaria chlorella virus 1, we unexpectedly found the viral capsid possesses up to five major capsid protein variants and a penton protein variant. These variants create varied capsid microenvironments for the associations of fibers, a vesicle, and previously unresolved minor capsid proteins. Our structure reveals the identities and atomic models of the capsid components that do not obey the overall icosahedral symmetry and leads to a model for how these components are assembled and initiate capsid assembly, and this model might be applicable to many other giant viruses.