4,410 research outputs found
Low temperature storage container for transporting perishables to space station
Two storage containers are disclosed within which food or biological samples may be stored for transfer in a module by the space shuttle to a space station while maintaining the food or samples at very low temperatures. The container is formed in two parts, each part having an inner shell and an outer shell disposed about the inner shell. The space between the shells is filled with a continuous wrap multi-layer insulation and a getter material. The two parts of the container have interlocking members and when connected together are sealed for preventing leakage from the space between the shells. After the two parts are filled with frozen food or samples they are connected together and a vacuum is drawn in the space between the shells and the container is stored in the module. For the extremely low temperature requirements of biological samples, an internal liner having a phase change material charged by a refrigerant coil is disposed in the space between the shells, and the container is formed from glass fiber material including honeycomb structural elements. All surfaces of the glass fiber which face the vacuum space are lined with a metal foil
Why the observed spin evolution of older-than-solar like stars might not require a dynamo mode change
The spin evolution of main sequence stars has long been of interest for basic
stellar evolution, stellar aging, stellar activity, and consequent influence on
companion planets. Observations of older than solar late-type main-sequence
stars have been interpreted to imply that a change from a dipole-dominated
magnetic field to one with more prominent higher multipoles might be necessary
to account for the data. The spin-down models that lead to this inference are
essentially tuned to the sun. Here we take a different approach which considers
individual stars as fixed points rather than just the Sun. We use a
time-dependent theoretical model to solve for the spin evolution of low-mass
main-sequence stars that includes a Parker-type wind and a time-evolving
magnetic field coupled to the spin. Because the wind is exponentially sensitive
to the stellar mass over radius and the coronal base temperature, the use of
each observed star as a separate fixed point is more appropriate and, in turn,
produces a set of solution curves that produces a solution envelope rather than
a simple line. This envelope of solution curves, unlike a single line fit, is
consistent with the data and does not unambiguously require a modal transition
in the magnetic field to explain it.Comment: 8 pages, 4 figures; Accepted for publication in MNRA
Exoplanet atmosphere evolution: emulation with random forests
Atmospheric mass-loss is known to play a leading role in sculpting the
demographics of small, close-in exoplanets. Understanding the impact of such
mass-loss driven evolution requires modelling large populations of planets to
compare with the observed exoplanet distributions. As the quality of planet
observations increases, so should the accuracy of the models used to understand
them. However, to date, only simple semi-analytic models have been used in such
comparisons since modelling populations of planets with high accuracy demands a
high computational cost. To address this, we turn to machine learning. We
implement random forests trained on atmospheric evolution models, including XUV
photoevaporation, to predict a given planet's final radius and atmospheric
mass. This evolution emulator is found to have an RMS fractional radius error
of 1 from the original models and is times faster to evaluate.
As a test case, we use the emulator to infer the initial properties of
Kepler-36b and c, confirming that their architecture is consistent with
atmospheric mass loss. Our new approach opens the door to highly sophisticated
models of atmospheric evolution being used in demographic analysis, which will
yield further insight into planet formation and evolution.Comment: 5 pages, 3 figures. Submitted to MNRAS letter
The effect of irradiation-induced disorder on the conductivity and critical temperature of the organic superconductor -(BEDT-TTF)Cu(SCN)
We have introduced defects into clean samples of the organic superconductor
-(BEDT-TTF)Cu(SCN) in order to determine their effect on the
temperature dependence of the conductivity and the critical temperature . We find a violation of Matthiessen's rule that can be explained by a model
of the conductivity involving a defect-assisted interlayer channel which acts
in parallel with the band-like conductivity. We observe an unusual dependence
of on residual resistivity which is not consistent with the
generalised Abrikosov-Gor'kov theory for an order parameter with a single
component, providing an important constraint on models of the superconductivity
in this material
Network Hawkes Process Models for Exploring Latent Hierarchy in Social Animal Interactions
Group-based social dominance hierarchies are of essential interest in animal
behavior research. Studies often record aggressive interactions observed over
time, and models that can capture such dynamic hierarchy are therefore crucial.
Traditional ranking methods summarize interactions across time, using only
aggregate counts. Instead, we take advantage of the interaction timestamps,
proposing a series of network point process models with latent ranks. We
carefully design these models to incorporate important characteristics of
animal interaction data, including the winner effect, bursting and pair-flip
phenomena. Through iteratively constructing and evaluating these models we
arrive at the final cohort Markov-Modulated Hawkes process (C-MMHP), which best
characterizes all aforementioned patterns observed in interaction data. We
compare all models using simulated and real data. Using statistically developed
diagnostic perspectives, we demonstrate that the C-MMHP model outperforms other
methods, capturing relevant latent ranking structures that lead to meaningful
predictions for real data
DNA-Encoded Antibody Libraries: A Unified Platform for Multiplexed Cell Sorting and Detection of Genes and Proteins
Whether for pathological examination or for fundamental biology studies, different classes of biomaterials and biomolecules are each measured from a different region of a typically heterogeneous tissue sample, thus introducing unavoidable sources of noise that are hard to quantitate. We describe the method of DNA-encoded antibody libraries (DEAL) for spatially multiplexed detection of ssDNAs and proteins as well as for cell sorting, all on the same diagnostic platform. DEAL is based upon the coupling of ssDNA oligomers onto antibodies which are then combined with the biological sample of interest. Spotted DNA arrays, which are found to inhibit biofouling, are utilized to spatially stratify the biomolecules or cells of interest. We demonstrate the DEAL technique for (1) the rapid detection of multiple proteins within a single microfluidic channel, and, with the additional step of electroless amplification of gold-nanoparticle labeled secondary antibodies, we establish a detection limit of 10 fM for the protein IL-2, 150 times more sensitive than the analogue ELISA; (2) the multiplexed, on-chip sorting of both immortalized cell lines and primary immune cells with an efficiency that exceeds surface-confined panning approaches; and (3) the co-detection of ssDNAs, proteins, and cell populations on the same platform
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