Background and Objective Gingival keratinocytes are used in model systems to investigate the interaction between periodontal bacteria and the epithelium in the initial stages of the periodontal disease process. Primary gingival epithelial cells (GECs) have a finite lifespan in culture before they enter senescence and cease to replicate, while epithelial cells immortalized with viral proteins can exhibit chromosomal rearrangements. The aim of this study was to generate a telomerase-immortalized human gingival epithelial cell line and compare its in vitro behavior to that of human GECs. Material and Methods Human primary gingival epithelial cells were immortalized with a bmi1/hTERT combination to prevent cell cycle triggers of senescence and telomere shortening. The resultant cell-line, Telomerase Immortalized Gingival Keratinocytes (TIGKs), were compared to GECs for cell morphology, karyotype, growth and cytokeratin expression, and further characterized for replicative lifespan, expression of toll-like receptors (TLRs) and invasion by P. gingivalis. Results TIGKs showed morphologies, karyotype, proliferation rates and expression of characteristic cytokeratin proteins comparable to GECs. TIGKs underwent 36 passages without signs of senescence and expressed transcripts for TLRs 1-6, 8 and 9. A subpopulation of cells underwent stratification after extended time in culture. The cytokeratin profiles of TIGK monolayers were consistent with basal cells. When allowed to stratify, cytokeratin profiles of TIGKs were consistent with suprabasal cells of the junctional epithelium. Further, TIGKs were comparable to GECs in previously reported levels and kinetics of invasion by wild type Porphyromonas gingivalis and an invasion defective ΔserB mutant. Conclusion Results confirm bmi1/hTERT-immortalization of primary gingival epithelial cells generated a robust cell-line with similar characteristics to the parental cell type. TIGKs represent a valuable model system for the study of oral bacteria interactions with host gingival cells.
The physical characteristics and atmospheric chemical composition of newly discovered exoplanets are often inferred from their transit spectra, which are obtained from complex numerical models of radiative transfer. Alternatively, simple analytical expressions provide insightful physical intuition into the relevant atmospheric processes. The deep-learning revolution has opened the door for deriving such analytical results directly with a computer algorithm fitting to the data. As a proof of concept, we successfully demonstrate the use of symbolic regression on synthetic data for the transit radii of generic hot-Jupiter exoplanets to derive a corresponding analytical formula. As a preprocessing step, we use dimensional analysis to identify the relevant dimensionless combinations of variables and reduce the number of independent inputs, which improves the performance of the symbolic regression. The dimensional analysis also allowed us to mathematically derive and properly parameterize the most general family of degeneracies among the input atmospheric parameters that affect the characterization of an exoplanet atmosphere through transit spectroscopy.
Recently, Generative Adversarial Networks (GANs) trained on samples of traditionally simulated collider events have been proposed as a way of generating larger simulated datasets at a reduced computational cost. In this paper we point out that data generated by a GAN cannot statistically be better than the data it was trained on, and critically examine the applicability of GANs in various situations, including a) for replacing the entire Monte Carlo pipeline or parts of it, and b) to produce datasets for usage in highly sensitive analyses or sub-optimal ones. We present our arguments using information theoretic demonstrations, a toy example, as well as in the form of a formal statement, and identify some potential valid uses of GANs in collider simulations.
The physical characteristics and atmospheric chemical composition of newly discovered exoplanets are often inferred from their transit spectra which are obtained from complex numerical models of radiative transfer. Alternatively, simple analytical expressions provide insightful physical intuition into the relevant atmospheric processes. The deep learning revolution has opened the door for deriving such analytical results directly with a computer algorithm fitting to the data. As a proof of concept, we successfully demonstrate the use of symbolic regression on synthetic data for the transit radii of generic hot Jupiter exoplanets to derive a corresponding analytical formula. As a preprocessing step, we use dimensional analysis to identify the relevant dimensionless combinations of variables and reduce the number of independent inputs, which improves the performance of the symbolic regression. The dimensional analysis also allowed us to mathematically derive and properly parametrize the most general family of degeneracies among the input atmospheric parameters which affect the characterization of an exoplanet atmosphere through transit spectroscopy.
We address the problem of finding a wombling boundary in point data generated by a general Poisson point process, a specific example of which is an LHC event sample distributed in the phase space of a final state signature, with the wombling boundary created by some new physics. We discuss the use of Voronoi and Delaunay tessellations of the point data for estimating the local gradients and investigate methods for sharpening the boundaries by reducing the statistical noise. The outcome from traditional wombling algorithms is a set of boundary cell candidates with relatively large gradients, whose spatial properties must then be scrutinized in order to construct the boundary and evaluate its significance. Here we propose an alternative approach where we simultaneously form and evaluate the significance of all possible boundaries in terms of the total gradient flux. We illustrate our method with several toy examples of both straight and curved boundaries with varying amounts of signal present in the data.
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