Toughening of Thermoresponsive Arrested Networks of Elastin-Like Polypeptides To Engineer Cytocompatible Tissue Scaffolds

Formulation of tissue engineering or regenerative scaffolds from simple bioactive polymers with tunable structure and mechanics is crucial for the regeneration of complex tissues, and hydrogels from recombinant proteins, such as elastin-like polypeptides (ELPs), are promising platforms to support these applications. The arrested phase separation of ELPs has been shown to yield remarkably stiff, biocontinuous, nanostructured networks, but these gels are limited in applications by their relatively brittle nature. Here, a gel-forming ELP is chain-extended by telechelic oxidative coupling, forming extensible, tough hydrogels. Small angle scattering indicates that the chain-extended polypeptides form a fractal network of nanoscale aggregates over a broad concentration range, accessing moduli ranging from 5 kPa to over 1 MPa over a concentration range of 5–30 wt %. These networks exhibited excellent erosion resistance and allowed for the diffusion and release of encapsulated particles consistent with a bicontinuous, porous structure with a broad distribution of pore sizes. Biofunctionalized, toughened networks were found to maintain the viability of human mesenchymal stem cells (hMSCs) in 2D, demonstrating signs of osteogenesis even in cell media without osteogenic molecules. Furthermore, chondrocytes could be readily mixed into these gels via thermoresponsive assembly and remained viable in extended culture. These studies demonstrate the ability to engineer ELP-based arrested physical networks on the molecular level to form reinforced, cytocompatible hydrogel matrices, supporting the promise of these new materials as candidates for the engineering and regeneration of stiff tissues

Mechanical correlates of the third heart sound

AbstractIn seven chronically instrumented conscious dogs, micromanometers measured left ventricular pressure, and ultrasonic dimension transducers measured left ventricular minor-axis diameter; the latter recording was filtered to examine data between 20 and 100 Hz. Acceptable external heart sounds were recorded with a phonocardiographic microphone in four of the seven dogs. With each dog sedatede, intubated and mechanically ventilated, data were obtained during hemodynamic alterations produced by volume loading, phenylephrine, calcium infusion and vena caval occlusion.Damped oscillations were noted consistently in the left ventricular diameter waveform toward the end of rapid ventricular filling. These wall vibrations, assessed by the Altered diameter, correlated well with the third heart sound (S3) on the phonocardiogram. The peak frequency of the wall vibrations increased with increased diastolic pressure (p = 0.004), probably reflecting an increase in myocardlal wall stiffness. In contrast, the amplitude of the vibrations varid directly with left ventricular filling rate (p = 0.0001).Thus, S3seemed to be related specifically to ventricular wall vibrations during rapid filling, and the spectra of the amplitude-frequency relation shifted toward the audible range with increases in diastolic pressure, wall stiffness or filling rate. Spectral analysis of S3may be useful in assessing pathologic chances in myocardial wall properties

Empirically Constrained Color-Temperature Relations. II. uvby

(Abriged) A new grid of theoretical color indices for the Stromgren uvby photometric system has been derived from MARCS model atmospheres and SSG synthetic spectra for cool dwarf and giant stars. At warmer temperatures this grid has been supplemented with the synthetic uvby colors from recent Kurucz atmospheric models without overshooting. Our transformations appear to reproduce the observed colors of extremely metal-poor turnoff and giant stars (i.e., [Fe/H]<-2). Due to a number of assumptions made in the synthetic color calculations, however, our color-temperature relations for cool stars fail to provide a suitable match to the uvby photometry of both cluster and field stars having [Fe/H]>-2. To overcome this problem, the theoretical indices at intermediate and high metallicities have been corrected using a set of color calibrations based on field stars having accurate IRFM temperature estimates and spectroscopic [Fe/H] values. Encouragingly, isochrones that employ the transformations derived in this study are able to reproduce the observed CMDs (involving u-v, v-b, and b-y colors) for a number of open and globular clusters (including M92, M67, the Hyades, and 47Tuc) rather well. Moreover, our interpretations of such data are very similar, if not identical, with those given by VandenBerg & Clem (2003, AJ, 126, 778) from a consideration of BV(RI)c observations for the same clusters. In the present investigation, we have also analyzed the observed Stromgren photometry for the classic Population II subdwarfs, compared our "final" (b-y)-Teff relationship with those derived empirically in a number of recent studies, and examined in some detail the dependence of the m1 index on [Fe/H].Comment: 70 pages, 26 figures. Accepted for publication in AJ (Feb 2004). Postscript version with high resolution figures and complete Table 3 available at http://astrowww.phys.uvic.ca/~jclem/uvb

Enhancing structure relaxations for first-principles codes: an approximate Hessian approach

We present a method for improving the speed of geometry relaxation by using a harmonic approximation for the interaction potential between nearest neighbor atoms to construct an initial Hessian estimate. The model is quite robust, and yields approximately a 30% or better reduction in the number of calculations compared to an optimized diagonal initialization. Convergence with this initializer approaches the speed of a converged BFGS Hessian, therefore it is close to the best that can be achieved. Hessian preconditioning is discussed, and it is found that a compromise between an average condition number and a narrow distribution in eigenvalues produces the best optimization.Comment: 9 pages, 3 figures, added references, expanded optimization sectio

Rapid emplacement of the Central Atlantic Magmatic Province as a net sink for CO2

Recent evidence from the ~ 201.5 Ma Central Atlantic Magmatic Province (CAMP) in the Newark rift basin demonstrates that this Large Igneous Province produced a transient doubling of atmospheric pCO2, followed by a falloff to pre-eruptive concentrations over ~ 300 kyr. This paper confirms the short-term findings from the Newark basin, and tests the million-year effects of the CAMP volcanism on Early Jurassic pCO2 from strata in the corollary Hartford basin of Eastern North America (ENA) also using the pedogenic carbonate paleobarometer. We find pCO2 levels for pre-CAMP background of 2000 ± 700 ppm (at S(z) = 3000 ± 1000 ppm), increasing to ~ 5000 ± 1700 ppm immediately above the first lava flow unit, consistent with observations from the Newark. The longer post-extrusive Portland Formation of the Hartford basin records a fourth pulse of pCO2 to ~ 4500 ± 1200 ppm, about 240 kyr after the last lava recorded in the ENA section. We interpret this fourth increase as due to a major episode of volcanism, and revise the main CAMP duration to 840 ± 60 kyr. The Portland also records a post-eruptive decrease in pCO2 reaching pre-eruptive background concentrations of ~ 2000 ppm in only ~ 300 kyr, and continuing to levels below pre-CAMP background over the subsequent 1.5 Myr following the final episode of eruptions. Geochemical modeling (using modified COPSE code) demonstrates that the rapidity of the pCO2 decreases, and fall to concentrations below background can be accounted for by a 1.5-fold amplification of the continental silicate weathering response due to the presence of the CAMP basalts themselves. These results demonstrate that a continental flood basalt capable of producing a short-term perturbation of the carbon system may actually have an overall net-cooling effect on global climates due to a long-term net-decrease in pCO2 to below pre-eruptive levels, as previous models have suggested followed the emplacement of the Deccan Traps

AN ULTRA-FAINT GALAXY CANDIDATE DISCOVERED in EARLY DATA from the MAGELLANIC SATELLITES SURVEY

We report a new ultra-faint stellar system found in Dark Energy Camera data from the first observing run of the Magellanic Satellites Survey (MagLiteS). MagLiteS J0644-5953 (Pictor II or Pic II) is a low surface brightness (μ = 28.5+1 -1 mag arcsec-2 within its half-light radius) resolved overdensity of old and metal-poor stars located at a heliocentric distance of 45+5 -4 kpc. The physical size (r1/2 = 46+15 -11) and low luminosity (Mv = -3.2+0.4 -0.5 mag) of this satellite are consistent with the locus of spectroscopically confirmed ultra-faint galaxies. MagLiteS J0644-5953 (Pic II) is located 11.3+3.1 -0.9 kpc from the Large Magellanic Cloud (LMC), and comparisons with simulation results in the literature suggest that this satellite was likely accreted with the LMC. The close proximity of MagLiteS J0644-5953 (Pic II) to the LMC also makes it the most likely ultra-faint galaxy candidate to still be gravitationally bound to the LM

DeepWeeds: a multiclass weed species image dataset for deep learning

Robotic weed control has seen increased research of late with its potential for boosting productivity in agriculture. Majority of works focus on developing robotics for croplands, ignoring the weed management problems facing rangeland stock farmers. perhaps the greatest obstacle to widespread uptake of robotic weed control is the robust classification of weed species in their natural environment. the unparalleled successes of deep learning make it an ideal candidate for recognising various weed species in the complex rangeland environment. This work contributes the first large, public, multiclass image dataset of weed species from the Australian rangelands; allowing for the development of robust classification methods to make robotic weed control viable. The DeepWeeds dataset consists of 17,509 labelled images of eight nationally significant weed species native to eight locations across northern Australia. This paper presents a baseline for classification performance on the dataset using the benchmark deep learning models, Inception-v3 and ResNet-50. These models achieved an average classification accuracy of 95.1% and 95.7%, respectively. We also demonstrate real time performance of the ResNet-50 architecture, with an average inference time of 53.4 ms per image. These strong results bode well for future field implementation of robotic weed control methods in the Australian rangelands