A novel culture system for modulating single cell geometry in 3D

Dedifferentiation of chondrocytes during in vitro expansion remains an unsolved challenge for repairing serious articular cartilage defects. In this study, a novel culture system was developed to modulate single cell geometry in 3D and investigate its effects on the chondrocyte phenotype. The approach uses 2D micropatterns followed by in situ hydrogel formation to constrain single cell shape and spreading. This enables independent control of cell geometry and extracellular matrix. Using collagen I matrix, we demonstrated the formation of a biomimetic collagenous “basket” enveloping individual chondrocytes cells. By quantitatively monitoring the production by single cells of chondrogenic matrix (e.g. collagen II and aggrecan) during 21-day cultures, we found that if the cell’s volume decreases, then so does its cell resistance to dedifferentiation (even if the cells remain spherical). Conversely, if the volume of spherical cells remains constant (after an initial decrease), then not only do the cells retain their differentiated status, but previously de-differentiated redifferentiate and regain a chondrocyte phenotype. The approach described here can be readily applied to pluripotent cells, offering a versatile platform in the search for niches toward either self-renewal or targeted differentiation

Effect of Different Fertilization Rates on Cyanogen and Foliage and Tuber Yields of Cassava

This experiment was conducted to determine the effect of different fertilization rates on the cyanogen and yields of cassava foliage and tuber. Nine fertilization rates, three nitrogen and potassium levels (N: 0, 50, 100 kg/ha and K: 0, 100, 250 kg/ha, respectively) with constant phosphorus level (P: 50 kg/ha) (F-0:N0-P50-K0, F-1:N0-P50-K100, F-2:N0-P50-K250, F-3:N50-P50-K0, F-4:N50-P50-K100, F-5:N50-P50-K250, F-6:N100-P50-K0, F-7:N100-P50-K100, F-8:N100-P50-K250), were applied in the randomized completely block design. After one year experiment, cassava foliage and tuber were harvested, and determined the yields and cyanogen (HCNp) content. The lowest (P < 0.05) HCNp contents and the highest (P < 0.05) foliage, tuber and protein yields were observed in cassava applied with F-4 (N50-P50-K100) and F-5 (N50-P50-K250) in compare with other fertilization rates. Regarding growth characteristics, the plant height (P < 0.05) was also highest in cassava fertilized by F-4 (N50-P50-K100) and F-5 (N50-P50-K250), whereas the leaf numbers per plant and branches number per plant were highest in cassava applied with F-5 (N50-P50-K250) and F-7 (N100-P50-K100), respectively. It could be recommended that the nitrogen (N: 50 kg/ha) and potassium (K: 100-250 kg/ha) should be used to reduce cyanogen contents for safe utilization and increased cassava foliage and tuber yields

Leveraging Machine Learning Models for Peptide-Protein Interaction Prediction

Peptides play a pivotal role in a wide range of biological activities through participating in up to 40% protein-protein interactions in cellular processes. They also demonstrate remarkable specificity and efficacy, making them promising candidates for drug development. However, predicting peptide-protein complexes by traditional computational approaches, such as Docking and Molecular Dynamics simulations, still remains a challenge due to high computational cost, flexible nature of peptides, and limited structural information of peptide-protein complexes. In recent years, the surge of available biological data has given rise to the development of an increasing number of machine learning models for predicting peptide-protein interactions. These models offer efficient solutions to address the challenges associated with traditional computational approaches. Furthermore, they offer enhanced accuracy, robustness, and interpretability in their predictive outcomes. This review presents a comprehensive overview of machine learning and deep learning models that have emerged in recent years for the prediction of peptide-protein interactions.Comment: 46 pages, 10 figure

2021 International Symposium on Transportation Data and Modelling

This project partially sponsored the organization of the 2021 International Symposium on Transportation Data and Modeling(ISTDM 2021), which aims to gather transportation researchers and practitioners across the globe for exploring the frontiers of bigdata, modeling and simulation to advance transportation research to support the connected, cooperative and automated mobility.Due to the COVID-19 pandemic, the conference was held virtually June 21-24, 2021. Its program consisted of 8 keynote talks, and104 regular or lightning talks. It attracted more than 1,100 registrations, and the accumulated number of attendees was more than2960.U.S. Department of Transportation Office of the Assistant Secretary for Research and Technology 1200 New Jersey Avenue, SE Washington, DC 20590http://deepblue.lib.umich.edu/bitstream/2027.42/168542/1/2021_08_06 - 2021 International Symposium on Transportation Data and Modelling Final Report.pdfDescription of 2021_08_06 - 2021 International Symposium on Transportation Data and Modelling Final Report.pdf : Final ReportSEL