Particle transport is a fundamental aspect of various systems, from artificial to biological. A common assumption is that particle motion follows Markovian (memoryless) processes in the absence of interaction between particles. However, hydrodynamic memory and the interaction between particles are ubiquitous, leaving many fundamental questions unanswered regarding transport of interacting particles involving hydrodynamic drag in corrugated channels, as described by the fractional Langevin equation. This study examines the hydrodynamic transport of interacting non-Brownian particles moving within a corrugated channel. We propose a method that relies on factors such as temperature, the driving force to alternate between no transport and finite net transport. Of importance is to note that the absence of transport results from the clogging, while the transport consists of collective motion and independent motion. The transport systems investigated in this work suggest the potential for sensor functionality within the system. Our findings may prove valuable for exploring the transport with hydrodynamic memory in various fields, including biology, physics, and chemistry.

RNAs have important biological functions and the functions of RNAs are generally coupled to their structures, especially their secondary structures. In this work, we have made a comprehensive evaluation of the performances of existing top RNA secondary structure prediction methods, including five deep-learning (DL) based methods and five minimum free energy (MFE) based methods. First, we made a brief overview of these RNA secondary structure prediction methods. Afterwards, we built two rigorous test datasets consisting of RNAs with non-redundant sequences and comprehensively examined the performances of the RNA secondary structure prediction methods through classifying the RNAs into different length ranges and different types. Our examination shows that the DL-based methods generally perform better than the MFE-based methods for RNAs with long lengths and complex structures, while the MFE-based methods can achieve good performance for small RNAs and some specialized MFE-based methods can achieve good prediction accuracy for pseudoknots. Finally, we provided some insights and perspectives in modeling RNA secondary structures.

Accurate identification of RNA-ligand binding sites is essential for elucidating RNA function and advancing structure-based drug discovery. Here, we present RLsite, a novel deep learning framework that integrates energy-, structure- and sequence-based features to predict nucleotide-level binding sites with high accuracy. RLsite leverages energy-based three-dimensional representations, obtained from atomic probe interactions using a pre-trained ITScore-NL potential, and models their contextual features through a 3D convolutional neural network (3D-CNN) augmented with self-attention. In parallel, structure-based features, including network properties, Laplacian norm, and solvent-accessible surface area, together with sequence-based evolutionary constraint scores, are mapped along the RNA sequence and used as sequential descriptors. These descriptors are modeled using a bidirectional long short-term memory (BiLSTM) network enhanced with multi-head self-attention. By effectively fusing these complementary modalities, RLsite achieves robust and precise binding site prediction. Extensive evaluations across four diverse RNA-ligand benchmark datasets demonstrate that RLsite consistently outperforms state-of-the-art methods in terms of precision, recall, Matthews correlation coefficient (MCC), area under the curve (AUC), and overall robustness. Notably, on a particularly challenging test set composed of RNA structures containing junctions, RLsite surpasses the second-best method by 7.3% in precision, 3.4% in recall, 7.5% in MCC, and 10.8% in AUC, highlighting its potential as a powerful tool for RNA-targeted molecular design.

Spectrin domains, characterized by a distinctive triple helix structure, are crucial in physiological processes, particularly in maintaining membrane shape and crosslinking cytoskeletons. Previous research on the 16th domain of $\alpha$-spectrin repeats (R16) has yielded conflicting results: bulk experiments showed an unfolding rate approximately two orders of magnitude faster than the zero-force result extrapolated from single-molecule force spectroscopy experiments using atomic force microscopy (AFM). To address this discrepancy, we investigated the folding and unfolding rates of R16 across a broader range of forces using magnetic tweezers (MT). Our findings reveal that AFM results at higher forces cannot be directly extrapolated to the low-force regime due to a nonlinear relationship between force and the logarithm of the unfolding rate. We demonstrated that two-dimensional model, structural-elastic model, and two-pathway model can all effectively explain the experimental data when they capture the core physics of the short unfolding distance at low forces. Our study provides a more comprehensive understanding of the unfolding dynamics of the spectrin domain, resolves previous contradictory experimental results, and highlights the common basis of different theoretical models.

Solution environment can influence the flexible structure of DNA under specific conditions, thereby affecting the stability of nucleic acids and ultimately impacting critical biological processes such as replication and transcription. Intracellular solution environment is variable, and previous studies have demonstrated that it can enhance the stability of DNA structures under certain circumstances. In this work, molecular dynamics simulations were conducted on B-DNA (1ZEW, with a nucleotide sequence of CCTCTAGAGG) derived from human breast cancer cells (MDA-MB231) to explore the effects of ethanol solution on DNA configuration transformation at different temperatures and concentrations. The calculated results indicate that ethanol facilitates the transition of 1ZEW from B-DNA to A-DNA at lower temperature. Furthermore, it is observed that temperature affects DNA structure to some extent, thereby modifying the trend in DNA configuration transformation. At low temperatures, the ethanol can promote the transformation of B-DNA into A-DNA at higher concentrations. While at higher temperatures, the DNA could be in a state of thermal melting. These conclusions presented here can give valuable insights into how ethanol affects B-DNA configuration transformations.

Vesicles of lipid bilayer can adopt a variety of shapes due to different coating proteins. The ability of proteins to reshape membrane is typically characterized by inducing spontaneous curvature of the membrane at the coated area. BAR family proteins are known to have a crescent shape and can induce membrane curvature along their concaved body axis but not in the perpendicular direction. We model this type of proteins as a rod-shaped molecule with an orientation and induce normal curvature along its orientation in the tangential plane of the membrane surface. We show how a ring of these proteins reshapes an axisymmetric vesicle when the protein curvature or orientation is varied. A discontinuous shape transformation from a protrusion shape without a neck to a one with a neck is found. Increasing the rigidity of the protein ring is able to smooth out the transition. Furthermore, we show that varying the protein orientation is able to induce an hourglass-shaped neck, which is significantly narrower than the reciprocal of the protein curvature. Our results offer a new angle to rationalize the helical structure formed by many proteins that carry out membrane fission functions.

Cell division is a fundamental biological process in which a parent cell divides into two daughter cells. The cell cortex, a thin layer primarily composed of actin filaments and myosin motors beneath the plasma membrane, plays a critical role in ensuring proper cell division. In this study, we apply a hydrodynamic model to describe the actin cortex as an active nematic surface, incorporating orientational order arising from actin filament alignment and anisotropic active stress produced by myosin motors. By analyzing the linearized dynamics, we investigate how shape, flow, and stress regulators evolve over time when the surface deviates slightly from a sphere. Our findings reveal that the active alignment of actin filaments, often overlooked in previous studies, is crucial for successful division. Furthermore, we demonstrate that a cortical chiral flow naturally emerges as a consequence of this active alignment. Overall, our results provide a mechanistic explanation for key phenomena observed during cell division, offering new insights into the role of active stress and filament alignment in cortical dynamics.

The composition vector tree (CVTree) method, developed under the leadership of Professor Hao Bailin, is an alignment-free algorithm for constructing phylogenetic trees. Although initially designed for studying prokaryotic evolution based on whole-genome, it has demonstrated broad applicability across diverse biological systems and gene sequences. In this study, we employed two methods, InterList and Hao, of CVTree to investigate the phylogeny and taxonomy of prokaryote based on the 16S rRNA sequences from All-Species Living Tree Project. We have established a comprehensive phylogenetic tree that incorporates the majority of species documented in human scientific knowledge and compared it with the taxonomy of prokaryotes. And the performance of CVTree was also compared with multiple sequence alignment-based approaches. Our results revealed that CVTree methods achieve computational speeds 1-3 orders of magnitude faster than conventional alignment methods while maintaining high consistency with established taxonomic relationships, even outperforming some multiple sequence alignment methods. These findings confirm CVTree's effectiveness and efficiency not only for whole-genome evolutionary studies but also for phylogenetic and taxonomic investigations based on genes.

Spatial transcriptomics technology provides novel insights into the spatial organization of gene expression during embryonic development. In this study, we propose a method that integrates analysis across both temporal and spatial dimensions to investigate spatial transcriptomics data from mouse embryos at different developmental stages. We quantified the spatial expression pattern of each gene at various stages by calculating its Moran's I. Furthermore, by employing time-series clustering to identify dynamic co-expression modules, we identified several developmentally stage-specific regulatory gene modules. A key finding was the presence of distinct, stage-specific gene network modules across different developmental periods: Early modules focused on morphogenesis, mid-stage on organ development, and late-stage on neural and tissue maturation. Functional enrichment analysis further confirmed the core biological functions of each module. The dynamic, spatially-resolved gene expression model constructed in this study not only provides new biological insights into the programmed spatiotemporal reorganization of gene regulatory networks during embryonic development but also presents an effective approach for analyzing complex spatiotemporal omics data. This work provides a new perspective for understanding developmental biology, regenerative medicine, and related fields.

Spiral patterns widely exist in both macroscopic and microscopic systems such as hearts, bacteria, and active matters but have never been reported at molecular length scale. The emergence of spiral patterns has considerable impacts on the trajectories of the objects and thus usually relates to various physical, chemical, biological and physiological processes. In this paper, we show that, down to the length scale of only several Angstroms, asymmetric molecules exhibit spiral patterns in their trajectories within finite time based on the under-damped Langevin equation and demonstrated by molecular dynamics simulation. The key to this observation lies in the asymmetric molecular architecture that leads to a translation-rotation coupling. This finding enriches the knowledge of spiral patterns to the molecular length scale, provides a new insight into the understanding of various processes at the molecular level, and may evoke new ideas on the understanding of the vortices in turbulence.

Chiral active matter exhibits a variety of collective behaviors, including phase separation, which is governed by the rule of "like chiralities attract, while opposite chiralities repel". In this work, we investigate the chiral demixing strategy of double-chiral partial mixture with inter-chiral frustration. We find that the inter-chiral frustration can significantly enhance the chiral demixing of active particles with different chiralities, both during the transient and in the steady state, not only accelerating the progress, but also improving the degree of phase separation. This phenomenon is reminiscent of the phase separation of binary mixtures in condensed matter physics, where the inter-chiral frustration can play a crucial role in the formation of the phase-separated states. We construct the phase diagram of the system and discuss the critical frustration for the enhancement of chiral demixing. Our work presents the first systematic investigation of inter-chiral frustration in self-propelled chiral active matter, filling a critical gap in the field.

We generalize the convex duality symmetry in Gibbs' statistical ensemble formulation, between the Gibbs entropy $\varphi_{V,N}(E)$ as a function of mean internal energy $E$ and Massieu's free entropy $\varPsi_{V,N}(\beta)$ as a function of inverse temperature $\beta$. The duality in terms of Legendre-Fenchel transform tells us that Gibbs' thermodynamic entropy is to the law of large numbers (LLN) for arithmetic sample mean values what Shannon's information entropy is to the LLN for empirical counting frequencies in independent and identically distributed data. Proceeding with the same mathematical logic, we identify the energy of the state $\{u_i\}$ as the conjugate variable to the counting of statistical occurrence $\{m_i\}$ and find a Hamilton-Jacobi equation for the Shannon entropy analogous to an equation of state in thermodynamics. An eigenvalue problem that is reminiscent of certain features in quantum mechanics arises in the entropy theory of statistical counting frequencies of Markov correlated data.