Numerical and semi-analytic core mass distributions in supersonic isothermal turbulence

We investigate the influence of the turbulence forcing on the mass distributions of gravitationally unstable cores by postprocessing data from simulations of non-selfgravitating isothermal supersonic turbulence with varying resolution. In one set of simulations solenoidal forcing is applied, while the second set uses purely compressive forcing to excite turbulent motions. From the resulting density field, we compute the mass distribution of gravitationally unstable cores by means of a clump-finding algorithm. Using the time-averaged probability density functions of the mass density, semi-analytic mass distributions are calculated from analytical theories. We apply stability criteria that are based on the Bonnor-Ebert mass resulting from the thermal pressure and from the sum of thermal and turbulent pressure. Although there are uncertainties in the application of the clump-finding algorithm, we find systematic differences in the mass distributions obtained from solenoidal and compressive forcing. Compressive forcing produces a shallower slope in the high-mass power-law regime compared to solenoidal forcing. The mass distributions also depend on the Jeans length resulting from the choice of the mass in the computational box, which is freely scalable for non-selfgravitating isothermal turbulence. Provided that all cores are numerically resolved and most cores are small compared to the length scale of the forcing, the normalised core mass distributions are found to be close to the semi-analytic models. Especially for the high-mass tails, the Hennebelle-Chabrier theory implies that the additional support due to turbulent pressure is important.Comment: 15 pages, 7 figures, submitted to A&

The Unusual Spectral Energy Distribution of a Galaxy Previously Reported to be at Redshift 6.68

Observations of distant galaxies are important both for understanding how galaxies form and for probing the physical conditions of the universe at the earliest epochs. It is, however, extremely difficult to identify galaxies at redshift z>5, because these galaxies are faint and exhibit few spectral features. In a previous work, we presented observations that supported the identification of a galaxy at redshift z = 6.68 in a deep STIS field. Here we present new ground-based photometry of the galaxy. We find that the galaxy exhibits moderate detections of flux in the optical B and V images that are inconsistent with the expected absence of flux at wavelength shortward of the redshifted Lyman-alpha emission line of a galaxy at redshift z>5. In addition, the new broad-band imaging data not only show flux measurements of this galaxy that are incompatible with the previous STIS measurement, but also suggest a peculiar spectral energy distribution that cannot be fit with any galaxy spectral template at any redshift. We therefore conclude that the redshift identification of this galaxy remains undetermined.Comment: 9 pages, 2 figures; To appear in Nature (30 November 2000

Mechanically induced silyl ester cleavage under acidic conditions investigated by AFM-based single-molecule force spectroscopy in the force-ramp mode

AFM-based dynamic single-molecule force spectroscopy was used to stretch carboxymethylated amylose (CMA) polymers, which have been covalently tethered between a silanized glass substrate and a silanized AFM tip via acid-catalyzed ester condensation at pH 2.0. Rupture forces were measured as a function of temperature and force loading rate in the force-ramp mode. The data exhibit significant statistical scattering, which is fitted with a maximum likelihood estimation (MLE) algorithm. Bond rupture is described with a Morse potential based Arrhenius kinetics model. The fit yields a bond dissociation energy De = 35 kJ mol−1 and an Arrhenius pre-factor A = 6.6 × 104 s−1. The bond dissociation energy is consistent with previous experiments under identical conditions, where the force-clamp mode was employed. However, the bi-exponential decay kinetics, which the force-clamp results unambiguously revealed, are not evident in the force-ramp data. While it is possible to fit the force-ramp data with a bi-exponential model, the fit parameters differ from the force-clamp experiments. Overall, single-molecule force spectroscopy in the force-ramp mode yields data whose information content is more limited than force-clamp data. It may, however, still be necessary and advantageous to perform force-ramp experiments. The number of successful events is often higher in the force-ramp mode, and competing reaction pathways may make force-clamp experiments impossible

Complex thermorheology of living cells

Temperature has a reliable and nearly instantaneous influence onmechanical responses of cells.As recently published, MCF-10Anormal epithelial breast cells follow the time–temperature superposition (TTS) principle. Here,wemeasured thermorheological behaviour of eightcommoncell types within physiologically relevant temperatures and appliedTTS to creep compliance curves.Our results showed that superposition is not universal and was seen in four of the eight investigated cell types. For the other cell types, transitions of thermorheological responses were observed at 36 °C.Activation energies (EA)were calculated for all cell types and ranged between 50 and 150 kJmol−1.The scaling factors of the superposition of creep curves were used to group the cell lines into three categories. They were dependent on relaxation processes aswell as structural composition of the cells in response tomechanical load and temperature increase.This study supports the view that temperature is a vital parameter for comparing cell rheological data and should be precisely controlledwhen designing experiments

Scalable and flexible inference framework for stochastic dynamic single-cell models

Understanding the inherited nature of how biological processes dynamically change over time and exhibit intra- and inter-individual variability, due to the different responses to environmental stimuli and when interacting with other processes, has been a major focus of systems biology. The rise of single-cell fluorescent microscopy has enabled the study of those phenomena. The analysis of single-cell data with mechanistic models offers an invaluable tool to describe dynamic cellular processes and to rationalise cell-to-cell variability within the population. However, extracting mechanistic information from single-cell data has proven difficult. This requires statistical methods to infer unknown model parameters from dynamic, multi-individual data accounting for heterogeneity caused by both intrinsic (e.g. variations in chemical reactions) and extrinsic (e.g. variability in protein concentrations) noise. Although several inference methods exist, the availability of efficient, general and accessible methods that facilitate modelling of single-cell data, remains lacking. Here we present a scalable and flexible framework for Bayesian inference in state-space mixed-effects single-cell models with stochastic dynamic. Our approach infers model parameters when intrinsic noise is modelled by either exact or approximate stochastic simulators, and when extrinsic noise is modelled by either time-varying, or time-constant parameters that vary between cells. We demonstrate the relevance of our approach by studying how cell-to-cell variation in carbon source utilisation affects heterogeneity in the budding yeast Saccharomyces cerevisiae SNF1 nutrient sensing pathway. We identify hexokinase activity as a source of extrinsic noise and deduce that sugar availability dictates cell-to-cell variability

SAFER: Development and Evaluation of an IoT Device Risk Assessment Framework in a Multinational Organization

Users of Internet of Things (IoT) devices are often unaware of their security risks and cannot sufficiently factor security considerations into their device selection. This puts networks, infrastructure and users at risk. We developed and evaluated SAFER, an IoT device risk assessment framework designed to improve users' ability to assess the security of connected devices. We deployed SAFER in a large multinational organization that permits use of private devices. To evaluate the framework, we conducted a mixed-method study with 20 employees. Our findings suggest that SAFER increases users' awareness of security issues. It provides valuable advice and impacts device selection. Based on our findings, we discuss implications for the design of device risk assessment tools, with particular regard to the relationship between risk communication and user perceptions of device complexity

Mechanically activated rupture of single covalent bonds: evidence of force induced bond hydrolysis.

We have used temperature-dependent single molecule force spectroscopy to stretch covalently anchored carboxymethylated amylose (CMA) polymers attached to an amino-functionalized AFM cantilever. Using an Arrhenius kinetics model based on a Morse potential as a one-dimensional representation of covalent bonds, we have extracted kinetic and structural parameters of the bond rupture process. With 35.5 kJ mol−1, we found a significantly smaller dissociation energy and with 9.0 × 102 s−1 to 3.6 × 103 s−1 also smaller Arrhenius pre-factors than expected for homolytic bond scission. One possible explanation for the severely reduced dissociation energy and Arrhenius pre-factors is the mechanically activated hydrolysis of covalent bonds. Both the carboxylic acid amide and the siloxane bond in the amino-silane surface linker are in principle prone to bond hydrolysis. Scattering, slope and curvature of the scattered data plots indicate that in fact two competing rupture mechanisms are observed

Cell membrane softening in human breast and cervical cancer cells

Biomechanical properties are key to many cellular functions such as cell division and cell motility and thus are crucial in the development and understanding of several diseases, for instance cancer. The mechanics of the cellular cytoskeleton have been extensively characterized in cells and artificial systems. The rigidity of the plasma membrane, with the exception of red blood cells, is unknown and membrane rigidity measurements only exist for vesicles composed of a few synthetic lipids. In this study, thermal fluctuations of giant plasma membrane vesicles (GPMVs) directly derived from the plasma membranes of primary breast and cervical cells, as well as breast cell lines, are analyzed. Cell blebs or GPMVs were studied via thermal membrane fluctuations and mass spectrometry. It will be shown that cancer cell membranes are significantly softer than their non-malignant counterparts. This can be attributed to a loss of fluid raft forming lipids in malignant cells. These results indicate that the reduction of membrane rigidity promotes aggressive blebbing motion in invasive cancer cells

Detecting heterogeneity in and between breast cancer cell lines

Cellular heterogeneity in tumor cells is a well-established phenomenon. Genetic and phenotypic cell-to-cell variability have been observed in numerous studies both within the same type of cancer cells and across different types of cancers. Another known fact for metastatic tumor cells is that they tend to be softer than their normal or non-metastatic counterparts. However, the heterogeneity of mechanical properties in tumor cells are not widely studied. Here we analyzed single-cell optical stretcher data with machine learning algorithms on three different breast tumor cell lines and show that similar heterogeneity can also be seen in mechanical properties of cells both within and between breast tumor cell lines. We identified two clusters within MDA-MB-231 cells, with cells in one cluster being softer than in the other. In addition, we show that MDA-MB-231 cells and MDA-MB-436 cells which are both epithelial breast cancer cell lines with a mesenchymal-like phenotype derived from metastatic cancers are mechanically more different from each other than from non-malignant epithelial MCF-10A cells. Since stiffness of tumor cells can be an indicator of metastatic potential, this result suggests that metastatic abilities could vary within the same monoclonal tumor cell line.https://doi.org/10.1186/s41236-020-0010-