Structural characterization and statistical-mechanical model of epidermal patterns

In proliferating epithelia of mammalian skin, cells of irregular polygonal-like shapes pack into complex nearly flat two-dimensional structures that are pliable to deformations. In this work, we employ various sensitive correlation functions to quantitatively characterize structural features of evolving packings of epithelial cells across length scales in mouse skin. We find that the pair statistics in direct and Fourier spaces of the cell centroids in the early stages of embryonic development show structural directional dependence, while in the late stages the patterns tend towards statistically isotropic states. We construct a minimalist four-component statistical-mechanical model involving effective isotropic pair interactions consisting of hard-core repulsion and extra short-ranged soft-core repulsion beyond the hard core, whose length scale is roughly the same as the hard core. The model parameters are optimized to match the sample pair statistics in both direct and Fourier spaces. By doing this, the parameters are biologically constrained. Our model predicts essentially the same polygonal shape distribution and size disparity of cells found in experiments as measured by Voronoi statistics. Moreover, our simulated equilibrium liquid-like configurations are able to match other nontrivial unconstrained statistics, which is a testament to the power and novelty of the model. We discuss ways in which our model might be extended so as to better understand morphogenesis (in particular the emergence of planar cell polarity), wound-healing, and disease progression processes in skin, and how it could be applied to the design of synthetic tissues

Firm-level evidence on productivity differentials, turnover, and exports in Taiwanese manufacturing

Recent dynamic models of firm entry and exit emphasise the relationship between a firm's productivity and the decision to enter or exit. If firm turnover is driven by productivity differentials then the reallocation of resources across firms at the micro level can have important implications for aggregate or industry-level productivity change. Using comprehensive firm-level panel data from the Taiwanese Census of Manufactures for the years 1981, 1986, and 1991, this paper documents the extent of firm turnover in both the domestic and export markets, uses index numbers to measure differences in total factor productivity between entering, exiting, and continuing firms, and quantifies the contribution of firm turnover to industry productivity improvements. We find significant differences in productivity across manufacturing firms and these differences are reflected in turnover patterns. Cohorts of new firms have lower average productivity than incumbents but are themselves a heterogeneous group. The more productive members of the group survive and, in many cases, their productivity converges to the productivity level of incumbents. Exiting firms are also less productive than survivors. Differences in productivity are also reflected in movements of firms in and out of the export market. Firms that remain exporters over multiple years have the highest productivity while beginning exporters, whether they are new firms or older firms, follow behind them. All are more productive on average than firms that exit the export market who, in turn, are more productive than firms that never exported. These patterns are consistent with the view that both the domestic and export market sort out high productivity from low productivity firms and that the export market is a tougher screen. Unlike the findings for most other countries, the differential productivity between entering and exiting firms is an important source of industry-level productivity improvements in the Taiwan manufacturing sector

Improved delineation of short cortical association fibers and gray/white matter boundary using whole-brain three-dimensional diffusion tensor imaging at submillimeter spatial resolution.

Recent emergence of human connectome imaging has led to a high demand on angular and spatial resolutions for diffusion magnetic resonance imaging (MRI). While there have been significant growths in high angular resolution diffusion imaging, the improvement in spatial resolution is still limited due to a number of technical challenges, such as the low signal-to-noise ratio and high motion artifacts. As a result, the benefit of a high spatial resolution in the whole-brain connectome imaging has not been fully evaluated in vivo. In this brief report, the impact of spatial resolution was assessed in a newly acquired whole-brain three-dimensional diffusion tensor imaging data set with an isotropic spatial resolution of 0.85 mm. It was found that the delineation of short cortical association fibers is drastically improved as well as the definition of fiber pathway endings into the gray/white matter boundary-both of which will help construct a more accurate structural map of the human brain connectome

A computer-based automated algorithm for assessing acinar cell loss after experimental pancreatitis

The change in exocrine mass is an important parameter to follow in experimental models of pancreatic injury and regeneration. However, at present, the quantitative assessment of exocrine content by histology is tedious and operatordependent, requiring manual assessment of acinar area on serial pancreatic sections. In this study, we utilized a novel computer-generated learning algorithm to construct an accurate and rapid method of quantifying acinar content. The algorithm works by learning differences in pixel characteristics from input examples provided by human experts. HE-stained pancreatic sections were obtained in mice recovering from a 2-day, hourly caerulein hyperstimulation model of experimental pancreatitis. For training data, a pathologist carefully outlined discrete regions of acinar and non-acinar tissue in 21 sections at various stages of pancreatic injury and recovery (termed the ''ground truth''). After the expert defined the ground truth, the computer was able to develop a prediction rule that was then applied to a unique set of high-resolution images in order to validate the process. For baseline, non-injured pancreatic sections, the software demonstrated close agreement with the ground truth in identifying baseline acinar tissue area with only a difference of 1%±0.05% (p = 0.21). Within regions of injured tissue, the software reported a difference of 2.5%± 0.04% in acinar area compared with the pathologist (p = 0.47). Surprisingly, on detailed morphological examination, the discrepancy was primarily because the software outlined acini and excluded inter-acinar and luminal white space with greater precision. The findings suggest that the software will be of great potential benefit to both clinicians and researchers in quantifying pancreatic acinar cell flux in the injured and recovering pancreas

Impact of Investor's Varying Risk Aversion on the Dynamics of Asset Price Fluctuations

While the investors' responses to price changes and their price forecasts are well accepted major factors contributing to large price fluctuations in financial markets, our study shows that investors' heterogeneous and dynamic risk aversion (DRA) preferences may play a more critical role in the dynamics of asset price fluctuations. We propose and study a model of an artificial stock market consisting of heterogeneous agents with DRA, and we find that DRA is the main driving force for excess price fluctuations and the associated volatility clustering. We employ a popular power utility function, $U(c,\gamma)=\frac{c^{1-\gamma}-1}{1-\gamma}$ with agent specific and time-dependent risk aversion index, $\gamma_i(t)$, and we derive an approximate formula for the demand function and aggregate price setting equation. The dynamics of each agent's risk aversion index, $\gamma_i(t)$ (i=1,2,...,N), is modeled by a bounded random walk with a constant variance $\delta^2$. We show numerically that our model reproduces most of the ``stylized'' facts observed in the real data, suggesting that dynamic risk aversion is a key mechanism for the emergence of these stylized facts.Comment: 17 pages, 7 figure

Prioritized Sweeping Neural DynaQ with Multiple Predecessors, and Hippocampal Replays

During sleep and awake rest, the hippocampus replays sequences of place cells that have been activated during prior experiences. These have been interpreted as a memory consolidation process, but recent results suggest a possible interpretation in terms of reinforcement learning. The Dyna reinforcement learning algorithms use off-line replays to improve learning. Under limited replay budget, a prioritized sweeping approach, which requires a model of the transitions to the predecessors, can be used to improve performance. We investigate whether such algorithms can explain the experimentally observed replays. We propose a neural network version of prioritized sweeping Q-learning, for which we developed a growing multiple expert algorithm, able to cope with multiple predecessors. The resulting architecture is able to improve the learning of simulated agents confronted to a navigation task. We predict that, in animals, learning the world model should occur during rest periods, and that the corresponding replays should be shuffled.Comment: Living Machines 2018 (Paris, France

Gene and protein expression of glucose transporter 1 and glucose transporter 3 in human laryngeal cancer—the relationship with regulatory hypoxia-inducible factor-1α expression, tumor invasiveness, and patient prognosis

Increased glucose uptake mediated by glucose transporters and reliance on glycolysis are common features of malignant cells. Hypoxia-inducible factor-1α supports the adaptation of hypoxic cells by inducing genes related to glucose metabolism. The contribution of glucose transporter (GLUT) and hypoxia-inducible factor-1α (HIF-1α) activity to tumor behavior and their prognostic value in head and neck cancers remains unclear. The aim of this study was to examine the predictive value of GLUT1, GLUT3, and HIF-1α messenger RNA (mRNA)/protein expression as markers of tumor aggressiveness and prognosis in laryngeal cancer. The level of hypoxia/metabolic marker genes was determined in 106 squamous cell laryngeal cancer (SCC) and 73 noncancerous matched mucosa (NCM) controls using quantitative realtime PCR. The related protein levels were analyzed by Western blot. Positive expression of SLC2A1, SLC2A3, and HIF-1α genes was noted in 83.9, 82.1, and 71.7 % of SCC specimens and in 34.4, 59.4, and 62.5 % of laryngeal cancer samples. Higher levels of mRNA/protein for GLUT1 and HIF-1α were noted in SCC compared to NCM (p<0.05). SLC2A1 was found to have a positive relationship with grade, tumor front grading (TFG) score, and depth and mode of invasion (p<0.05). SLC2A3 was related to grade and invasion type (p<0.05). There were also relationships of HIF-1α with pTNM, TFG scale, invasion depth and mode, tumor recurrences, and overall survival (p<0.05). In addition, more advanced tumors were found to be more likely to demonstrate positive expression of these proteins. In conclusion, the hypoxia/metabolic markers studied could be used as molecular markers of tumor invasiveness in laryngeal cancer.This work was supported, in part, by the statutory fund of the Department of Cytobiochemistry, University of Łódź, Poland (506/811), and by grant fromtheNational Science Council, Poland (N403 043 32/2326)

Domain Wall Holography for Finite Temperature Scaling Solutions

We investigate a class of near-extremal solutions of Einstein-Maxwell-scalar theory with electric charge and power law scaling, dual to charged IR phases of relativistic field theories at low temperature. These are exact solutions of theories with domain wall vacua; hence, we use nonconformal holography to relate the bulk and boundary theories. We numerically construct a global interpolating solution between the IR charged solutions and the UV domain wall vacua for arbitrary physical choices of Lagrangian parameters. By passing to a conformal frame in which the domain wall metric becomes that of AdS, we uncover a generalized scale invariance of the IR scaling solution, indicating a connection to the physics of Lifshitz fixed points. Finally, guided by effective field theoretic principles and the physics of nonconformal D-branes, we argue for the applicability of domain wall holography even in theories with AdS critical points, namely those theories for which a scalar potential is dominated by a single exponential term over a large range

Reduction of seafood processing wastewater using technologies enhanced by swim–bed technology

The increasing growth of the seafood processing industries considerably requires more industrial process activities and water consumption. It is estimated that approximately 10–40 m3 of wastewater is generated from those industries for processing one-tonne of raw materials. Due to limitations and regulations in natural resources utilization, a suitable and systematic wastewater treatment plant is very important to meet rigorous discharge standards. As a result of food waste biodegradability, the biological treatment and some extent of swim-bed technology, including a novel acryl-fibre (biofilm) material might be used effectively to meet the effluent discharge criteria. This chapter aims to develop understanding on current problems and production of the seafood wastewater regarding treatment efficiency and methods of treatment

Decaying Dark Matter in Supersymmetric Model and Cosmic-Ray Observations

We study cosmic-rays in decaying dark matter scenario, assuming that the dark matter is the lightest superparticle and it decays through a R-parity violating operator. We calculate the fluxes of cosmic-rays from the decay of the dark matter and those from the standard astrophysical phenomena in the same propagation model using the GALPROP package. We reevaluate the preferred parameters characterizing standard astrophysical cosmic-ray sources with taking account of the effects of dark matter decay. We show that, if energetic leptons are produced by the decay of the dark matter, the fluxes of cosmic-ray positron and electron can be in good agreements with both PAMELA and Fermi-LAT data in wide parameter region. It is also discussed that, in the case where sizable number of hadrons are also produced by the decay of the dark matter, the mass of the dark matter is constrained to be less than 200-300 GeV in order to avoid the overproduction of anti-proton. We also show that the cosmic gamma-ray flux can be consistent with the results of Fermi-LAT observation if the mass of the dark matter is smaller than nearly 4 TeV.Comment: 24 pages, 5 figure