Cell-induced confinement effects in soft tissue mechanics

The mechanical properties of tissues play a critical role in their normal and pathophysiological functions such as tissue development, aging, injury, and disease. Understanding tissue mechanics is important not only for designing realistic biomimetic materials for tissue engineering and drug testing but also for developing novel diagnostic techniques and medical interventions. Tissues are heterogeneous materials consisting of cells confined within extracellular matrices (ECMs), both of which derive their structural integrity, at least in part, from networks of biopolymers. However, the rheology of purified reconstituted biopolymer networks fails to explain many key aspects of tissue mechanics. Notably, purified networks typically soften under applied compression, whereas many soft tissues like liver, fat, and brain instead stiffen when compressed. While continuum models can readily capture this compression-stiffening behavior, the underlying mechanism is not fully understood. In this perspective paper, we discuss several recently proposed microscopic mechanisms that may explain compression stiffening of soft tissues. These mechanisms include (I) interactions between the ECM and volume-preserving inclusions that promote extension-dominated stiffening of fibrous ECMs when subject to uniform compression, (II) ECM interactions with rigid inclusions under non-uniform compression, (III) other internal physical constraints that cause compression stiffening of cells and ECMs, and (IV) propagation of compressive forces through jammed, compression-stiffening cells. We further identify a few of the many open problems in understanding the structure–function relationship of soft-tissue mechanics

Evaluating deep learning architecture and data assimilation for improving water temperature forecasts at unmonitored locations

Deep learning (DL) models are increasingly used to forecast water quality variables for use in decision making. Ingesting recent observations of the forecasted variable has been shown to greatly increase model performance at monitored locations; however, observations are not collected at all locations, and methods are not yet well developed for DL models for optimally ingesting recent observations from other sites to inform focal sites. In this paper, we evaluate two different DL model structures, a long short-term memory neural network (LSTM) and a recurrent graph convolutional neural network (RGCN), both with and without data assimilation for forecasting daily maximum stream temperature 7 days into the future at monitored and unmonitored locations in a 70-segment stream network. All our DL models performed well when forecasting stream temperature as the root mean squared error (RMSE) across all models ranged from 2.03 to 2.11°C for 1-day lead times in the validation period, with substantially better performance at gaged locations (RMSE = 1.45–1.52°C) compared to ungaged locations (RMSE = 3.18–3.27°C). Forecast uncertainty characterization was near-perfect for gaged locations but all DL models were overconfident (i.e., uncertainty bounds too narrow) for ungaged locations. Our results show that the RGCN with data assimilation performed best for ungaged locations and especially at higher temperatures (>18°C) which is important for management decisions in our study location. This indicates that the networked model structure and data assimilation techniques may help borrow information from nearby monitored sites to improve forecasts at unmonitored locations. Results from this study can help guide DL modeling decisions when forecasting other important environmental variables

The MOSDEF Survey: Untangling the Emission-line Properties of z ∼ 2.3 Star-forming Galaxies

We analyze the rest-optical emission-line spectra of z∼2.3 star-forming galaxies in the complete MOSFIRE Deep Evolution Field (MOSDEF) survey. In investigating the origin of the well-known offset between the sequences of high-redshift and local galaxies in the [O III]5008/Hβ vs. [N II]6585/Hα ("[N II] BPT") diagram, we define two populations of z∼2.3 MOSDEF galaxies. These include the "high" population that is offset towards higher [O III]5008/Hβ and/or [N II]6585/Hα with respect to the local SDSS sequence and the "low" population that overlaps the SDSS sequence. These two groups are also segregated within the [O III]5008/Hβ vs. [S II]6718,6733/Hα and the [O III]4960,5008/[O II]3727,3730 (O32) vs. ([O III]4960,5008+[O II]3727,3730)/Hβ (R23) diagram, which suggests qualitatively that star-forming regions in the more offset galaxies are characterized by harder ionizing spectra at fixed nebular oxygen abundance. We also investigate many galaxy properties of the split sample and find that the "high" sample is on average smaller in size and less massive, but has higher specific star-formation rate and star-formation-rate surface density values and is slightly younger compared to the "low" population. From Cloudy+BPASS photoionization models, we estimate that the "high" population has a lower stellar metallicity (i.e., harder ionizing spectrum) but slightly higher nebular metallicity and higher ionization parameter compared to the "low" population. While the "high" population is more α-enhanced (i.e., higher α/Fe) than the "low" population, both samples are significantly more α-enhanced compared to local star-forming galaxies with similar rest-optical line ratios. These differences must be accounted for in all high-redshift star-forming galaxies -- not only those "offset" from local excitation sequences

The MOSDEF Survey: Neon as a Probe of ISM Physical Conditions at High Redshift

We present results on the properties of neon emission in $z\sim2$ star-forming galaxies drawn from the MOSFIRE Deep Evolution Field (MOSDEF) survey. Doubly-ionized neon ([NeIII]3869) is detected at $\geq3\sigma$ in 61 galaxies, representing $\sim$25% of the MOSDEF sample with H$\alpha$, H$\beta$, and [OIII]$5007$ detections at similar redshifts. We consider the neon emission-line properties of both individual galaxies with [NeIII]3869 detections and composite $z\sim2$ spectra binned by stellar mass. With no requirement of [NeIII]3869 detection, the latter provide a more representative picture of neon emission-line properties in the MOSDEF sample. The [NeIII]3869/[OII]3727 ratio (Ne3O2) is anti-correlated with stellar mass in $z\sim2$ galaxies, as expected based on the mass-metallicity relation. It is also positively correlated with the [OIII]$5007$/[OII]$3727$ ratio (O32), but $z\sim2$ line ratios are offset towards higher Ne3O2 at fixed O32, compared with both local star-forming galaxies and individual H~II regions. Despite the offset towards higher Ne3O2 at fixed O32 at $z\sim2$, biases in inferred Ne3O2-based metallicity are small. Accordingly, Ne3O2 may serve as an important metallicity indicator deep into the reionization epoch. Analyzing additional rest-optical line ratios including [NeIII]$3869$/[OIII]$5007$ (Ne3O3) and [OIII]$5007$/H$\beta$ (O3H$\beta$), we conclude that the nebular emission-line ratios of $z\sim2$ star-forming galaxies suggest a harder ionizing spectrum (lower stellar metallicity, i.e., Fe/H) at fixed gas-phase oxygen abundance, compared to systems at $z\sim0$. These new results based on neon lend support to the physical picture painted by oxygen, nitrogen, hydrogen, and sulfur emission, of an ionized ISM in high-redshift star-forming galaxies irradiated by chemically young, $\alpha$-enhanced massive stars.Comment: 7 pages, 5 figures, accepted to ApJ Letter