定量方法
N,N,N-Trimethyl chitosan (TMC), a biocompatible and biodegradable derivative of chitosan, is currently used as a permeation enhancer to increase the translocation of drugs to the bloodstream in the lungs. This article discusses the effect…
Deep learning methods have emerged as powerful tools for analyzing histopathological images, but current methods are often specialized for specific domains and software environments, and few open-source options exist for deploying models in…
High-dimensional multimodal data arises in many scientific fields. The integration of multimodal data becomes challenging when there is no known correspondence between the samples and the features of different datasets. To tackle this…
Protein aggregation occurs when misfolded or unfolded proteins physically bind together, and can promote the development of various amyloid diseases. This study aimed to construct surrogate models for predicting protein aggregation via…
Deep learning has been widely used for protein engineering. However, it is limited by the lack of sufficient experimental data to train an accurate model for predicting the functional fitness of high-order mutants. Here, we develop SESNet,…
Advances in deep learning models have revolutionized the study of biomolecule systems and their mechanisms. Graph representation learning, in particular, is important for accurately capturing the geometric information of biomolecules at…
Topological data analysis (TDA) is an emerging field in mathematics and data science. Its central technique, persistent homology, has had tremendous success in many science and engineering disciplines. However, persistent homology has…
Discovering non-linear dynamical models from data is at the core of science. Recent progress hinges upon sparse regression of observables using extensive libraries of candidate functions. However, it remains challenging to model hidden…
Of the vast variety of animal gaits, one of the most striking is the non-planar undulating motion of a sidewinder. But non-planar gaits are not limited to sidewinders. Here we report a new non-planar mode used as an escape strategy in…
While non-invasive sampling is more and more commonly used in capture-recapture (CR) experiments, it carries a higher risk of misidentifications than direct observations. As a consequence, one must screen the data to retain only the…
Machine learning is increasingly used to discover diagnostic and prognostic biomarkers from high-dimensional molecular data. However, a variety of factors related to experimental design may affect the ability to learn generalizable and…
Cholera continues to be a global health threat. Understanding how cholera spreads between locations is fundamental to the rational, evidence-based design of intervention and control efforts. Traditionally, cholera transmission models have…
The movement of metals through the environment links together a wide range of scientific fields: from earth sciences and geology as weathering releases minerals; to environmental sciences as metals are mobilized and transformed, cycling…
Tracking single molecules is instrumental for quantifying the transport of molecules and nanoparticles in biological samples, e.g., in brain drug delivery studies. Existing intensity-based localisation methods are not developed for imaging…
While ischemic stroke is a leading cause of death worldwide, there has been little success translating putative cerebroprotectants from rodent preclinical trials to human patients. We investigated computational image-based assessment tools…
Development and homeostasis in multicellular systems both require exquisite control over spatial molecular pattern formation and maintenance. Advances in spatially-resolved and high-throughput molecular imaging methods such as multiplexed…
As protein therapeutics play an important role in almost all medical fields, numerous studies have been conducted on proteins using artificial intelligence. Artificial intelligence has enabled data driven predictions without the need for…
The complexity of mathematical models in biology has rendered model reduction an essential tool in the quantitative biologist's toolkit. For stochastic reaction networks described using the Chemical Master Equation, commonly used methods…
Connectomics has emerged as a powerful tool in neuroimaging and has spurred recent advancements in statistical and machine learning methods for connectivity data. Despite connectomes inhabiting a matrix manifold, most analytical frameworks…
Vision Transformer (ViT) has achieved outstanding results in computer vision. Although there are many Transformer-based architectures derived from the original ViT, the dimension of patches are often the same with each other. This…