Lei Bao

Item response theory is a popular assessment method used in education. It rests on the assumption of a probability framework that relates students’ innate ability and their performance on test questions. Item response theory transforms students’ raw test scores into a scaled proficiency score, which can be used to compare results obtained with different test questions. The scaled score also addresses the issues of ceiling effects and guessing, which commonly exist in quantitative assessment. We used item response theory to analyze the force concept inventory (FCI). Our results show that item response theory can be useful for analyzing physics concept surveys such as the FCI and produces results about the individual questions and student performance that are beyond the capability of classical statistics. The theory yields detailed measurement parameters regarding the difficulty, discrimination features, and probability of correct guess for each of the FCI questions.

The dynamics of proteins are crucial for understanding their mechanisms. However, computationally predicting protein dynamic information has proven challenging. Here, we propose a neural network model, RMSF-net, which outperforms previous methods and produces the best results in a large-scale protein dynamics dataset; this model can accurately infer the dynamic information of a protein in only a few seconds. By learning effectively from experimental protein structure data and cryo-electron microscopy (cryo-EM) data integration, our approach is able to accurately identify the interactive bidirectional constraints and supervision between cryo-EM maps and PDB models in maximizing the dynamic prediction efficacy. Rigorous 5-fold cross-validation on the dataset demonstrates that RMSF-net achieves test correlation coefficients of 0.746 ± 0.127 at the voxel level and 0.765 ± 0.109 at the residue level, showcasing its ability to deliver dynamic predictions closely approximating molecular dynamics simulations. Additionally, it offers real-time dynamic inference with minimal storage overhead on the order of megabytes. RMSF-net is a freely accessible tool and is anticipated to play an essential role in the study of protein dynamics.

Noncoding RNA molecules take part in many biological processes, while metal ions play crucial roles in helping RNAs to perform their functions. However, the statics and dynamics of these metal ions around RNA molecules are still not well understood. In this work, we report a detailed molecular dynamics study of the type-I preQ_{1}-bound riboswitch aptamer domain (PRAD) at different ionic conditions (K^{+}, Na^{+}, and Mg^{2+}). The results show that the structural properties and flexibility of the PRAD molecule greatly influence the distributions and dynamics of metal ions around it. Simultaneously, Na^{+} ions show a stronger competitiveness with Mg^{2+} ions than K^{+} ions, and the three types of metal ions have different modes of interaction with the RNA molecule. Furthermore, we have also investigated specific binding sites of metal ions on the PRAD molecule and found that the dynamics and hydration structures of metal ions located at the ion-binding sites were obviously affected by the RNA structure near these ion-binding sites. These results may be useful to understand the role of the metal ions in noncoding RNA functions.