Real Estate Income and Value Cycles: A Model of Market Dynamics

We develop a theoretical real estate cycles model linking economic fundamentals to real estate income and value. We estimate and test an econometric model specification, based on the theoretical model, using MSA level data for twenty office markets in the United States. Our major conclusion is that cities that exhibit seemingly different cyclical office market behavior may be statistically characterized by our three-parameter econometric specification. The parameters are MSA-specific amplitude, through the CAP rate, cycle duration (peak-to-peak), via the rate of partial adjustments to changing expectations about stabilized NOI and the market trend.

Kinetics of the glass transition of styrene-butadiene-rubber : Dielectric spectroscopy and fast differential scanning calorimetry

The glass transition is relevant for performance definition in rubber products. For extrapolation to high-frequency behavior, time–temperature superposition is usually assumed, although most complex rubber compounds might be outside of its area of validity. Fast differential scanning calorimetry (FDSC) with cooling rates up to 1500 K/s and broadband dielectric spectroscopy (BDS) with frequencies up to 20 MHz are applied here to directly access both kinetics and dynamics of glass formation in a wide frequency range. For the first-time, the relation between the thermal vitrification and the dielectric relaxation is studied on vulcanized styrene-butadiene rubber, showing that both cooling rate and frequency dependence of its glass transition can be described by one single Vogel-Fulcher-Tammann-Hesse equation. The results indicate the validity of the Frenkel-Kobeko-Reiner equation. Another focus is the sample preparation of vulcanized elastomers for FDSC and BDS as well as the temperature calibration below 0°C. © 2020 The Authors. Journal of Applied Polymer Science published by Wiley Periodicals LLC

Chemotaxis: a feedback-based computational model robustly predicts multiple aspects of real cell behaviour

The mechanism of eukaryotic chemotaxis remains unclear despite intensive study. The most frequently described mechanism acts through attractants causing actin polymerization, in turn leading to pseudopod formation and cell movement. We recently proposed an alternative mechanism, supported by several lines of data, in which pseudopods are made by a self-generated cycle. If chemoattractants are present, they modulate the cycle rather than directly causing actin polymerization. The aim of this work is to test the explanatory and predictive powers of such pseudopod-based models to predict the complex behaviour of cells in chemotaxis. We have now tested the effectiveness of this mechanism using a computational model of cell movement and chemotaxis based on pseudopod autocatalysis. The model reproduces a surprisingly wide range of existing data about cell movement and chemotaxis. It simulates cell polarization and persistence without stimuli and selection of accurate pseudopods when chemoattractant gradients are present. It predicts both bias of pseudopod position in low chemoattractant gradients and-unexpectedly-lateral pseudopod initiation in high gradients. To test the predictive ability of the model, we looked for untested and novel predictions. One prediction from the model is that the angle between successive pseudopods at the front of the cell will increase in proportion to the difference between the cell's direction and the direction of the gradient. We measured the angles between pseudopods in chemotaxing Dictyostelium cells under different conditions and found the results agreed with the model extremely well. Our model and data together suggest that in rapidly moving cells like Dictyostelium and neutrophils an intrinsic pseudopod cycle lies at the heart of cell motility. This implies that the mechanism behind chemotaxis relies on modification of intrinsic pseudopod behaviour, more than generation of new pseudopods or actin polymerization by chemoattractant

Anthracyclines, proteasome activity and multi-drug-resistance

BACKGROUND: P-glycoprotein is responsible for the ATP-dependent export of certain structurally unrelated compounds including many chemotherapeutic drugs. Amplification of P-glycoprotein activity can result in multi-drug resistance and is a common cause of chemotherapy treatment failure. Therefore, there is an ongoing search for inhibitors of P-glycoprotein. Observations that cyclosporin A, and certain other substances, inhibit both the proteasome and P-glycoprotein led us to investigate whether anthracyclines, well known substrates of P-gp, also inhibit the function of the proteasome. METHODS: Proteasome function was measured in cell lysates from ECV304 cells incubated with different doses of verapamil, doxorubicin, daunorubicin, idarubicin, epirubicin, topotecan, mitomycin C, and gemcitabine using a fluorogenic peptide assay. Proteasome function in living cells was monitored using ECV304 cells stably transfected with the gene for an ubiquitin/green fluorescent protein fusion protein. The ability of the proteasome inhibitor MG-132 to affect P-glycoprotein function was monitored by fluorescence due to accumulation of daunorubicin in P-glycoprotein overexpressing KB 8-5 cells. RESULTS: Verapamil, daunorubicin, doxorubicin, idarubicin, and epirubicin inhibited 26S chymotrypsin-like function in ECV304 extracts in a dose-dependent fashion. With the exception of daunorubicin, 20S proteasome function was also suppressed. The proteasome inhibitor MG-132 caused a dose-dependent accumulation of daunorubicin in KB 8-5 cells that overexpress P-glycoprotein, suggesting that it blocked P-glycoprotein function. CONCLUSION: Our data indicate that anthracyclines inhibit the 26S proteasome as well as P-glycoprotein. Use of inhibitors of either pathway in cancer therapy should take this into consideration and perhaps use it to advantage, for example during chemosensitization by proteasome inhibitors

Circular RNAs Are the Predominant Transcript Isoform from Hundreds of Human Genes in Diverse Cell Types

Most human pre-mRNAs are spliced into linear molecules that retain the exon order defined by the genomic sequence. By deep sequencing of RNA from a variety of normal and malignant human cells, we found RNA transcripts from many human genes in which the exons were arranged in a non-canonical order. Statistical estimates and biochemical assays provided strong evidence that a substantial fraction of the spliced transcripts from hundreds of genes are circular RNAs. Our results suggest that a non-canonical mode of RNA splicing, resulting in a circular RNA isoform, is a general feature of the gene expression program in human cells

Force-Velocity Measurements of a Few Growing Actin Filaments

The authors propose a new mechanism for actin-based force generation based on results using chains of actin-grafted magnetic colloids

Control of Dendritic Morphogenesis by Trio in Drosophila melanogaster

Abl tyrosine kinase and its effectors among the Rho family of GTPases each act to control dendritic morphogenesis in Drosophila. It has not been established, however, which of the many GTPase regulators in the cell link these signaling molecules in the dendrite. In axons, the bifunctional guanine exchange factor, Trio, is an essential link between the Abl tyrosine kinase signaling pathway and Rho GTPases, particularly Rac, allowing these systems to act coordinately to control actin organization. In dendritic morphogenesis, however, Abl and Rac have contrary rather than reinforcing effects, raising the question of whether Trio is involved, and if so, whether it acts through Rac, Rho or both. We now find that Trio is expressed in sensory neurons of the Drosophila embryo and regulates their dendritic arborization. trio mutants display a reduction in dendritic branching and increase in average branch length, whereas over-expression of trio has the opposite effect. We further show that it is the Rac GEF domain of Trio, and not its Rho GEF domain that is primarily responsible for the dendritic function of Trio. Thus, Trio shapes the complexity of dendritic arbors and does so in a way that mimics the effects of its target, Rac

An Adhesion-Dependent Switch between Mechanisms That Determine Motile Cell Shape

Keratocytes are fast-moving cells in which adhesion dynamics are tightly coupled to the actin polymerization motor that drives migration, resulting in highly coordinated cell movement. We have found that modifying the adhesive properties of the underlying substrate has a dramatic effect on keratocyte morphology. Cells crawling at intermediate adhesion strengths resembled stereotypical keratocytes, characterized by a broad, fan-shaped lamellipodium, clearly defined leading and trailing edges, and persistent rates of protrusion and retraction. Cells at low adhesion strength were small and round with highly variable protrusion and retraction rates, and cells at high adhesion strength were large and asymmetrical and, strikingly, exhibited traveling waves of protrusion. To elucidate the mechanisms by which adhesion strength determines cell behavior, we examined the organization of adhesions, myosin II, and the actin network in keratocytes migrating on substrates with different adhesion strengths. On the whole, our results are consistent with a quantitative physical model in which keratocyte shape and migratory behavior emerge from the self-organization of actin, adhesions, and myosin, and quantitative changes in either adhesion strength or myosin contraction can switch keratocytes among qualitatively distinct migration regimes

Exact Hybrid Particle/Population Simulation of Rule-Based Models of Biochemical Systems

Detailed modeling and simulation of biochemical systems is complicated by the problem of combinatorial complexity, an explosion in the number of species and reactions due to myriad protein-protein interactions and post-translational modifications. Rule-based modeling overcomes this problem by representing molecules as structured objects and encoding their interactions as pattern-based rules. This greatly simplifies the process of model specification, avoiding the tedious and error prone task of manually enumerating all species and reactions that can potentially exist in a system. From a simulation perspective, rule-based models can be expanded algorithmically into fully-enumerated reaction networks and simulated using a variety of network-based simulation methods, such as ordinary differential equations or Gillespie's algorithm, provided that the network is not exceedingly large. Alternatively, rule-based models can be simulated directly using particle-based kinetic Monte Carlo methods. This "network-free" approach produces exact stochastic trajectories with a computational cost that is independent of network size. However, memory and run time costs increase with the number of particles, limiting the size of system that can be feasibly simulated. Here, we present a hybrid particle/population simulation method that combines the best attributes of both the network-based and network-free approaches. The method takes as input a rule-based model and a user-specified subset of species to treat as population variables rather than as particles. The model is then transformed by a process of "partial network expansion" into a dynamically equivalent form that can be simulated using a population-adapted network-free simulator. The transformation method has been implemented within the open-source rule-based modeling platform BioNetGen, and resulting hybrid models can be simulated using the particle-based simulator NFsim. Performance tests show that significant memory savings can be achieved using the new approach and a monetary cost analysis provides a practical measure of its utility. © 2014 Hogg et al