High strength glass ionomer for the ART technique: one-year results

published_or_final_versio

Clinical performance of Dyract AP compomer - six months evaluation

published_or_final_versio

High-strength GICs for the ART technique: two-year results

published_or_final_versio

Pulse-delay light cure on marginal adaptation of compomer

published_or_final_versio

Clinical performance of Dyract AP compomer - 2-year results

published_or_final_versio

Spatial distribution of carrier concentration in un-doped GaN film grown on sapphire (Abstract)

published_or_final_versio

Conventional spark versus nanosecond repetitively pulsed discharge for a turbulence facilitated ignition phenomenon

This work applies both conventional-single-spark-discharge (CSSD) at 500-µs pulse duration time and nanosecond-repetitively-pulsed-discharge (NRPD) at various pulsed-repetitive-frequency PRF = 5–70 kHz to explore a turbulence facilitated ignition (TFI) phenomenon using a pair of pin-to-pin electrodes at an inter-electrode gap of 0.8 mm in randomly-stirred lean n-butane/air mixture with Lewis number ≫ 1. For CSSD, measured laminar and turbulent minimum ignition energies (MIE$_{L}$ and MIE$_{T}$) at 50% ignitability show that MIE$_{L}$≈ 23 mJ > the smallest MIE$_{T}$≈ 19.7 mJ at u′ = 0.9 m/s (TFI) and then MIE$_{T}$≈ 28.6/30.8/36.8 mJ at u′ = 1.4/2.1/2.8 m/s (no TFI), where u′ is the r.m.s turbulent fluctuating velocity. For comparison, all NRPD experiments apply the same total ignition energy E$_{tot}$≈ 23 mJ via a fixed train of 11 pulses, each pulse with 2.2 mJ except for the first pulse with 1 mJ. NRPD results show a cumulatively synergistic effect depending on the coherence between PRF and an inward reactant flow recirculation frequency (f$_{RC}$) inside the torus-like kernel induced by the discharge that could enhance ignition. When PRF is approximately synchronizing with f$_{RC}$, the synergistic effect is most profound at PRF = 20-kHz/40-kHz with very high ignition probability P$_{ig}$ = 90%/85% > 50% in quiescence, whereas lower values of P$_{ig}$ = 42%/34% are found at PRF = 10-kHz/60-kHz. Further, P$_{ig}$ = 0 at PRF = 5-kHz even when 5000 pulses (E$_{tot}$≈ 10 J) are applied. We discover that P$_{ig}$ decreases significantly with increasing u′ for most PRFs (no TFI) except at higher PRF ≥ 60 kHz showing possible TFI. These results are attributed to the interactions between turbulent dissipation, differential diffusion, and synergistic influence, which are substantiated by Schlieren images of initial kernel development and the ignition time determined at one half of the flame critical radius that leads to a self-sustained spherical flame propagation

Regulation of Tcf7l1 DNA Binding and Protein Stability as Principal Mechanisms of Wnt/β-Catenin Signaling

SummaryWnt/β-catenin signal transduction requires direct binding of β-catenin to Tcf/Lef proteins, an event that is classically associated with stimulating transcription by recruiting coactivators. This molecular cascade plays critical roles throughout embryonic development and normal postnatal life by affecting stem cell characteristics and tumor formation. Here, we show that this pathway utilizes a fundamentally different mechanism to regulate Tcf7l1 (formerly named Tcf3) activity. β-catenin inactivates Tcf7l1 without a switch to a coactivator complex by removing it from DNA, which leads to Tcf7l1 protein degradation. Mouse genetic experiments demonstrate that Tcf7l1 inactivation is the only required effect of the Tcf7l1-β-catenin interaction. Given the expression of Tcf7l1 in pluripotent embryonic and adult stem cells, as well as in poorly differentiated breast cancer, these findings provide mechanistic insights into the regulation of pluripotency and the role of Wnt/β-catenin in breast cancer

Constraining cosmology with machine learning and galaxy clustering: the CAMELS-SAM suite

As the next generation of large galaxy surveys come online, it is becoming increasingly important to develop and understand the machine learning tools that analyze big astronomical data. Neural networks are powerful and capable of probing deep patterns in data, but must be trained carefully on large and representative data sets. We developed and generated a new `hump' of the Cosmology and Astrophysics with MachinE Learning Simulations (CAMELS) project: CAMELS-SAM, encompassing one thousand dark-matter only simulations of (100 $h^{-1}$ cMpc)$^3$ with different cosmological parameters ($\Omega_m$ and $\sigma_8$) and run through the Santa Cruz semi-analytic model for galaxy formation over a broad range of astrophysical parameters. As a proof-of-concept for the power of this vast suite of simulated galaxies in a large volume and broad parameter space, we probe the power of simple clustering summary statistics to marginalize over astrophysics and constrain cosmology using neural networks. We use the two-point correlation function, count-in-cells, and the Void Probability Function, and probe non-linear and linear scales across $0.68<$ R $<27\ h^{-1}$ cMpc. Our cosmological constraints cluster around 3-8$\%$ error on $\Omega_{\text{M}}$ and $\sigma_8$, and we explore the effect of various galaxy selections, galaxy sampling, and choice of clustering statistics on these constraints. We additionally explore how these clustering statistics constrain and inform key stellar and galactic feedback parameters in the Santa Cruz SAM. CAMELS-SAM has been publicly released alongside the rest of CAMELS, and offers great potential to many applications of machine learning in astrophysics: https://camels-sam.readthedocs.io.Comment: 40 pages, 22 figures (11 made of subfigures