Fractal morphology facilitates Bacillus subtilis biofilm growth

In the late stage of Bacillus subtilis biofilm growth, to adapt the extremely nutrient-lacking environment, the biofilm edge grows into a complex branching structure, which allows the biofilm to expand outward at a faster speed, comparing to the expansion speed of the biofilm edge without branching structure. The fractal analysis shows that the fractal dimension (Fd) decreases along the radius in the biofilm branching structure, as shown in Figs. 1d and 3a. The variation of Fd along the radius is not monotonic, which is because of the texture evolution induced by the bacterial clusters' movement. By using the wide field stereomicroscope and image analysis, we find that the ridges in the mature branching structure are composed of inactive substances, and most of the bacterial clusters move through the valleys. Further analysis shows that bacterial clusters move to the area with the high Succolarity (Suc) value.

Simulation of various biofilm fractal morphologies by agent-based model

Biofilms are clusters of bacteria wrapped in extracellular matrix and polymers. The study of biofilm morphological transformation has been around for a long time and has attracted widespread attention. In this paper, we present a model for biofilm growth based on the interaction force, in which bacteria are treated as tiny particles and locations of particles are updated by calculating the repulsive forces among particles. We adapt a continuity equation to indicate nutrient concentration variation in the substrate. Based on the above, we study the morphological transformation of biofilms. We find that nutrient concentration and nutrient diffusion rate dominate different biofilm morphological transition processes, in which biofilms would grow into fractal morphology under the conditions of low nutrient concentration and nutrient diffusivity. At the same time, we expand our model by introducing a second particle to mimic extracellular polymeric substances (EPS) in biofilms. We find that the interaction between different particles can lead to phase separation patterns between cells and EPSs, and the adhesion effect of EPS can attenuate this phenomenon. In contrast to single particle system models, branches are inhibited due to EPS filling in dual particle system models, and this invalidation is boosted by the enhancement of the depletion effect.

Updating predictions in a complex repertoire of actions and its neural representation

Even though actions we observe in everyday life seem to unfold in a continuous manner, they are automatically divided into meaningful chunks, that are single actions or segments, which provide information for the formation and updating of internal predictive models. Specifically, boundaries between actions constitute a hub for predictive processing since the prediction of the current action comes to an end and calls for updating of predictions for the next action. In the current study, we investigated neural processes which characterize such boundaries using a repertoire of complex action sequences with a predefined probabilistic structure. Action sequences consisted of actions that started with the hand touching an object (T) and ended with the hand releasing the object (U). These action boundaries were determined using an automatic computer vision algorithm. Participants trained all action sequences by imitating demo videos. Subsequently, they returned for an fMRI session during which the original action sequences were presented in addition to slightly modified versions thereof. Participants completed a post-fMRI memory test to assess the retention of original action sequences. The exchange of individual actions, and thus a violation of action prediction, resulted in increased activation of the action observation network and the anterior insula. At U events, marking the end of an action, increased brain activation in supplementary motor area, striatum, and lingual gyrus was indicative of the retrieval of the previously encoded action repertoire. As expected, brain activation at U events also reflected the predefined probabilistic branching structure of the action repertoire. At T events, marking the beginning of the next action, midline and hippocampal regions were recruited, reflecting the selected prediction of the unfolding action segment. In conclusion, our findings contribute to a better understanding of the various cerebral processes characterizing prediction during the observation of complex action repertoires.

Lignin nanoparticles formation by multiscale structure control to regulate morphology and their adsorption, nucleation, and growth on chitin nanofibers

The lignin nanoparticles (LNPs) synthesis relies on lignin polymers with heterogeneous molecules and properties, which impose significant limitations on the preparation and property regulation. The multiscale structure of lignin from monomers to oligomers, provides a potential pathway for precise regulation of its physical and chemical properties. The study addresses this challenge by employing coniferyl alcohol and sinapyl alcohol as monomers and separately utilizing the Zulaufverfaren (ZL) and Zutropfverfaren (ZT) methods to synthesize different types of lignin dehydrogenation polymers (DHPs) including guaiacyl (G)-ZL-DHP, G-ZT-DHP, syringyl (S)-ZL-DHP, and S-ZT-DHP. The investigation highlights the chemical bonds as essential components of lignin primary structure. Additionally, the secondary structure is influenced by branched and linear molecular structures. G unit provides some branching points, which are utilized and amplified in the ZL process of DHPs synthesis. The branched DHPs aggregate at the edge and form rod-like LNPs. While linear DHPs aggregate around the center, presenting polygonal LNPs. The study identifies that the branched LNPs, characterized by more surface charges and lower steric hindrance, can form a stable complex with chitin nanofibers. Emulsions with varying oil-to-water ratios were subsequently prepared, opening a new window for the application of LNPs in fields such as food and cosmetics.

TbsNet: the importance of thin-branch structures in CNNs

The performance of a convolutional neural network (CNN) model is influenced by several factors, such as depth, width, network structure, size of the receptive field, and feature map scaling. The optimization of the best combination of these factors poses as the main difficulty in designing a viable architecture. This article presents an analysis of key factors influencing network performance, offers several strategies for constructing an efficient convolutional network, and introduces a novel architecture named TbsNet (thin-branch structure network). In order to minimize computation costs and feature redundancy, lightweight operators such as asymmetric convolution, pointwise convolution, depthwise convolution, and group convolution are implemented to further reduce the network's weight. Unlike previous studies, the TbsNet architecture design rejects the reparameterization method and adopts a plain, simplified structure which eliminates extraneous branches. We conduct extensive experiments, including network depth, width, etc. TbsNet performs well on benchmark platforms, Top 1 Accuracy on CIFAR-10 is 97.02%, on CIFAR-100 is 83.56%, and on ImageNet-1K is 86.17%. Tbs-UNet's DSC on the Synapse dataset is 78.39%, higher than TransUNet's 0.91%. TbsNet can be competent for some downstream tasks in computer vision, such as medical image segmentation, and thus is competitive with prior state-of-the-art deep networks such as ResNet, ResNeXt, RepVgg, ParNet, ConvNeXt, and MobileNet.