Prediction of single cell mechanical properties in microchannels based on deep learning

Traditional methods for measuring single-cell mechanical characteristics face several challenges,including lengthy measurement times,low throughput,and a requirement for advanced technical skills.To overcome these challenges,a novel machine learning(ML)approach is implemented based on the convolutional neural networks(CNNs),aiming at predicting cells'elastic modulus and constitutive equations from their deformations while passing through micro-constriction channels.In the present study,the computational fluid dynamics technology is used to generate a dataset within the range of the cell elastic modulus,incorporating three widely-used constitutive models that characterize the cellular mechanical behavior,i.e.,the Mooney-Rivlin(M-R),Neo-Hookean(N-H),and Kelvin-Voigt(K-V)models.Utilizing this dataset,a multi-input convolutional neural network(MI-CNN)algorithm is developed by incorporating cellular deformation data as well as the time and positional information.This approach accurately predicts the cell elastic modulus,with a coefficient of determination R^(2)of 0.999,a root mean square error of 0.218,and a mean absolute percentage error of 1.089%.The model consistently achieves high-precision predictions of the cellular elastic modulus with a maximum R^(2)of 0.99,even when the stochastic noise is added to the simulated data.One significant feature of the present model is that it has the ability to effectively classify the three types of constitutive equations we applied.The model accurately and reliably predicts single-cell mechanical properties,showcasing a robust ability to generalize.We demonstrate that incorporating deformation features at multiple time points can enhance the algorithm's accuracy and generalization.This algorithm presents a possibility for high-throughput,highly automated,real-time,and precise characterization of single-cell mechanical properties.

Illumina MiSeq sequencing investigation of Chanhua (Cordyceps cicadae Shing) fungal community structures in different regions

Objective:This study was carried out to elucidate the fungal diversity of Chanhua (Cordyceps cicadae Shing),which is an entomogenous fungal parasite that grows on wingless cicada nymphs.Methods:Samples were collected from 10 different geographical regions including Yibing in Sichuan Province,Jiangsu Province,Jiangxi Province,Anhui Province,Guangdong Province,and Guangxi Autonomous Region.The Illumina MiSeq-based method was used to investigate the Chanhua fungal community structures.Results:The results showed high fungal diversity,with 22 families found in the different samples,Eurotiomycetes was the dominant fungal family associated with Chanhua (composing 36.69% of the sample from Sichuan Mianyang),whereas Cordycipitaceae only composed 0.51% (Sichuan Yibing) to 0.33% (Guangdong) of these samples.Surprisingly,the proportion of Capnodiales was 5.27% in Sichuan Mianyang,whereas the percentage in other regions ranged from 4.35% (Sichuan Yibing) to 2.55% (Zhejiang),and the remaining proportion (more than 80%) was made up of unidentified fungus.Conclusions:These findings contribute to a better understanding of Chanhua from different regions and revealed an unexpectedly and high amount of unidentified diversity of these fungal communities.These results indicate that Chanhua parasitic fungi need to be further analyzed and identified for finding new medical fungi resources.

Bidirectional solid fermentation using Trametes robiniophila Murr. For enhancing efficacy and reducing toxicity of rhubarb (Rheum palmatum L.)

Objective:To investigate the potential of bidirectional solid fermentation of rhubarb (Rheum palmatum L.) for reducing its toxicity and enhancing its medicinal efficacy.Methods:The fungus Trametes robiniophila Murr.was inoculated into rhubarb.The chemical ingredients as well as antioxidant,antibacterial,and anticancer activities of fermented and unfermented rhubarb extracts were then determined.Results:After fermentation,levels of anthraquinone glycosides (purgative ingredients) decreased significantly,while the level of anthraquinone aglycone increased.The level of gallic acid was also reduced after fermentation.Ethanol extract of rhubarb (0.8 mg/mL) exhibited DPPH-scavenging activity of 7.6% ± 0.8% while the blank control (0.8 mg/mL rhubarb)showed 31.3% ± 2.0% activity.Antibacterial activities in fermented samples were found to be enhanced compared with unfermented samples and anticancer activity was evident at concentrations of 2000 and 5000 μg/mL.Conclusion:Bidirectional solid fermentation appears to be an effective processing method that can be used to improve the efficacy and reduce the toxicity of rhubarb.

The Genome of Medicinal Plant Macleaya cordata Provides New Insights into Benzylisoquinoline Alkaloids Metabolism

The overuse of antibiotics in animal agriculture and medicine has caused a series of potential threats to public health. Macleaya cordata is a medicinal plant species from the Papaveraceae family, providing a safe resource for the manufacture of antimicrobial feed additive for livestock. The active constituents from M. cordata are known to include benzylisoquinoline alkaloids （BIAs） such as sanguinarine （SAN） and chelerythrine （CHE）, but their metabolic pathways have yet to be studied in this non-model plant. The active biosynthesis of SAN and CHE in M. cordata was first examined and confirmed by feeding ^13C-labeled tyrosine. To gain further insights, we de novo sequenced the whole genome of M. cordata, the first to be sequenced from the Papaveraceae family. The M. cordata genome covering 378 Mb encodes 22,328 predicted protein-coding genes with 43.5% being transposable elements. As a member of basal eudicot, M. cordata genome lacks the paleohexaploidy event that occurred in almost all eudicots. From the genomics data, a complete set of 16 metabolic genes for SAN and CHE biosynthesis was retrieved, and 14 of their biochemical activities were validated. These genomics and metabolic data show the conserved BIA metabolic pathways in M. cordata and provide the knowledge foundation for future productions of SAN and CHE by crop improvement or microbial pathway reconstruction.