Cryo-electron microscopy (cryo-EM) studies using single particle reconstruction are extensively used to reveal structural information on macromolecular complexes. Aiming at the highest achievable resolution, state of the art electron microscopes automatically acquire thousands of high-quality micrographs. Particles are detected on and boxed out from each micrograph using fully- or semi-automated approaches. However, the obtained particles still require laborious manual post-picking classification, which is one major bottleneck for single particle analysis of large datasets. We introduce MAPPOS, a supervised post-picking strategy for the classification of boxed particle images, as additional strategy adding to the already efficient automated particle picking routines. MAPPOS employs machine learning techniques to train a robust classifier from a small number of characteristic image features. In order to accurately quantify the performance of MAPPOS we used simulated particle and non-particle images. In addition, we verified our method by applying it to an experimental cryo-EM dataset and comparing the results to the manual classification of the same dataset. Comparisons between MAPPOS and manual post-picking classification by several human experts demonstrated that merely a few hundred sample images are sufficient for MAPPOS to classify an entire dataset with a human-like performance. MAPPOS was shown to greatly accelerate the throughput of large datasets by reducing the manual workload by orders of magnitude while maintaining a reliable identification of non-particle images.

The growth of plants is a hydromechanical phenomenon in which cells enlarge by absorbing water, while their walls expand and remodel under turgor-induced tension. In multicellular tissues, where cells are mechanically interconnected, morphogenesis results from the combined effect of local cell growths, which reflects the action of heterogeneous mechanical, physical, and chemical fields, each exerting varying degrees of nonlocal influence within the tissue. To describe this process, we propose a physical field theory of plant growth. This theory treats the tissue as a poromorphoelastic body, namely a growing poroelastic medium, where growth arises from pressure-induced deformations and osmotically-driven imbibition of the tissue. From this perspective, growing regions correspond to hydraulic sinks, leading to the possibility of complex non-local regulations, such as water competition and growth-induced water potential gradients. More in general, this work aims to establish foundations for a mechanistic, mechanical field theory of morphogenesis in plants, where growth arises from the interplay of multiple physical fields, and where biochemical regulations are integrated through specific physical parameters.

During neurodevelopment, neuronal axons navigate through the extracellular environment, guided by various cues to establish connections with distant target cells. Among other factors, axon trajectories are influenced by heterogeneities in environmental stiffness, a process known as durotaxis, the guidance by substrate stiffness gradients. Here, we develop a three-scale model for axonal durotaxis. At the molecular scale, we characterise the mechanical interaction between the axonal growth cone cytoskeleton, based on molecular-clutch-type interactions dependent on substrate stiffness. At the growth cone scale, we spatially integrate this relationship to obtain a model for the traction generated by the entire growth cone. Finally, at the cell scale, we model the axon as a morphoelastic filament growing on an adhesive substrate, and subject to durotactic growth cone traction. Firstly, the model predicts that, depending on the local substrate stiffness, axons may exhibit positive or negative durotaxis, and we show that this key property entails the existence of attractive zones of preferential stiffness in the substrate domain. Second, we show that axons will exhibit reflective and refractive behaviour across interface between regions of different stiffness, a basic process which may serve in the deflection of axons. Lastly, we test our model in a biological scenario wherein durotaxis was previously identified as a possible guidance mechanism in vivo. Overall, this work provides a general mechanistic theory for exploring complex effects in axonal mechanotaxis and guidance in general.