Automated Deep Lineage Tree Analysis Using a Bayesian Single Cell Tracking Approach

Single-cell methods are beginning to reveal the intrinsic heterogeneity in cell populations, arising from the interplay of deterministic and stochastic processes. However, it remains challenging to quantify single-cell behaviour from time-lapse microscopy data, owing to the difficulty of extracting reliable cell trajectories and lineage information over long time-scales and across several generations. Therefore, we developed a hybrid deep learning and Bayesian cell tracking approach to reconstruct lineage trees from live-cell microscopy data. We implemented a residual U-Net model coupled with a classification CNN to allow accurate instance segmentation of the cell nuclei. To track the cells over time and through cell divisions, we developed a Bayesian cell tracking methodology that uses input features from the images to enable the retrieval of multi-generational lineage information from a corpus of thousands of hours of live-cell imaging data. Using our approach, we extracted 20,000 + fully annotated single-cell trajectories from over 3,500 h of video footage, organised into multi-generational lineage trees spanning up to eight generations and fourth cousin distances. Benchmarking tests, including lineage tree reconstruction assessments, demonstrate that our approach yields high-fidelity results with our data, with minimal requirement for manual curation. To demonstrate the robustness of our minimally supervised cell tracking methodology, we retrieve cell cycle durations and their extended inter- and intra-generational family relationships in 5,000 + fully annotated cell lineages. We observe vanishing cycle duration correlations across ancestral relatives, yet reveal correlated cyclings between cells sharing the same generation in extended lineages. These findings expand the depth and breadth of investigated cell lineage relationships in approximately two orders of magnitude more data than in previous studies of cell cycle heritability, which were reliant on semi-manual lineage data analysis

Strategy for large???scale monolithic Perovskite/Silicon tandem solar cell: A review of recent progress

For any solar cell technology to reach the final mass-production/commercialization stage, it must meet all technological, economic, and social criteria such as high efficiency, large-area scalability, long-term stability, price competitiveness, and environmental friendliness of constituent materials. Until now, various solar cell technologies have been proposed and investigated, but only crystalline silicon, CdTe, and CIGS technologies have overcome the threshold of mass-production/commercialization. Recently, a perovskite/silicon (PVK/Si) tandem solar cell technology with high efficiency of 29.1% has been reported, which exceeds the theoretical limit of single-junction solar cells as well as the efficiency of stand-alone silicon or perovskite solar cells. The International Technology Roadmap for Photovoltaics (ITRPV) predicts that silicon-based tandem solar cells will account for about 5% market share in 2029 and among various candidates, the combination of silicon and perovskite is the most likely scenario. Here, we classify and review the PVK/Si tandem solar cell technology in terms of homo- and hetero-junction silicon solar cells, the doping type of the bottom silicon cell, and the corresponding so-called normal and inverted structure of the top perovskite cell, along with mechanical and monolithic tandemization schemes. In particular, we review and discuss the recent advances in manufacturing top perovskite cells using solution and vacuum deposition technology for large-area scalability and specific issues of recombination layers and top transparent electrodes for large-area PVK/Si tandem solar cells, which are indispensable for the final commercialization of tandem solar cells

Evidence for Inhibition of Lysozyme Amyloid Fibrillization by Peptide Fragments from Human Lysozyme: A Combined Spectroscopy, Microscopy, and Docking Study

Degenerative diseases, such as Alzheimer’s and prion diseases, as well as type II diabetes, have a pathogenesis associated with protein misfolding, which routes with amyloid formation. Recent strategies for designing small-molecule and polypeptide antiamyloid inhibitors are mainly based on mature fibril structures containing cross β-sheet structures. In the present study, we have tackled the hypothesis that the rational design of antiamyloid agents that can target native proteins might offer advantageous prospect to design effective therapeutics. Lysozyme amyloid fibrillization was treated with three different peptide fragments derived from lysozyme protein sequence R¹⁰⁷–R¹¹⁵. Using low-resolution spectroscopic, high-resolution NMR, and STD NMR-restrained docking methods such as HADDOCK, we have found that these peptide fragments have the capability to affect lysozyme fibril formation. The present study implicates the prospect that these peptides can also be tested against other amyloid-prone proteins to develop novel therapeutic agents