Shear thickening and jamming of dense suspensions: The “roll" of friction

Particle-based simulations of discontinuous shear thickening (DST) and shear jamming (SJ) suspensions are used to study the role of stress-activated constraints, with an emphasis on resistance to gear-like rolling. Rolling friction decreases the volume fraction required for DST and SJ, in quantitative agreement with real-life suspensions with adhesive surface chemistries and "rough" particle shapes. It sets a distinct structure of the frictional force network compared to only sliding friction, and from a dynamical perspective leads to an increase in the velocity correlation length, in part responsible for the increased viscosity. The physics of rolling friction is thus a key element in achieving a comprehensive understanding of strongly shear-thickening materials.Comment: 5 pages, 4 figures. +Supplemental Material--- --- Supplementary Material also consists of the following movies: ---https://youtu.be/wgrfN7jiu1g (Force network in 3D at volume fraction 0.45, at high stress limit, with and without rolling friction) --- https://youtu.be/SMzTB7CsRsY (Force network in 2D at packing fraction 0.7, at high stress limit, with and without rolling friction

Complexity without chaos: Plasticity within random recurrent networks generates robust timing and motor control

It is widely accepted that the complex dynamics characteristic of recurrent neural circuits contributes in a fundamental manner to brain function. Progress has been slow in understanding and exploiting the computational power of recurrent dynamics for two main reasons: nonlinear recurrent networks often exhibit chaotic behavior and most known learning rules do not work in robust fashion in recurrent networks. Here we address both these problems by demonstrating how random recurrent networks (RRN) that initially exhibit chaotic dynamics can be tuned through a supervised learning rule to generate locally stable neural patterns of activity that are both complex and robust to noise. The outcome is a novel neural network regime that exhibits both transiently stable and chaotic trajectories. We further show that the recurrent learning rule dramatically increases the ability of RRNs to generate complex spatiotemporal motor patterns, and accounts for recent experimental data showing a decrease in neural variability in response to stimulus onset

Understanding Urban Demand for Wild Meat in Vietnam: Implications for Conservation Actions

Vietnam is a significant consumer of wildlife, particularly wild meat, in urban restaurant settings. To meet this demand, poaching of wildlife is widespread, threatening regional and international biodiversity. Previous interventions to tackle illegal and potentially unsustainable consumption of wild meat in Vietnam have generally focused on limiting supply. While critical, they have been impeded by a lack of resources, the presence of increasingly organised criminal networks and corruption. Attention is, therefore, turning to the consumer, but a paucity of research investigating consumer demand for wild meat will impede the creation of effective consumer-centred interventions. Here we used a mixed-methods research approach comprising a hypothetical choice modelling survey and qualitative interviews to explore the drivers of wild meat consumption and consumer preferences among residents of Ho Chi Minh City, Vietnam. Our findings indicate that demand for wild meat is heterogeneous and highly context specific. Wild-sourced, rare, and expensive wild meat-types are eaten by those situated towards the top of the societal hierarchy to convey wealth and status and are commonly consumed in lucrative business contexts. Cheaper, legal and farmed substitutes for wild-sourced meats are also consumed, but typically in more casual consumption or social drinking settings. We explore the implications of our results for current conservation interventions in Vietnam that attempt to tackle illegal and potentially unsustainable trade in and consumption of wild meat and detail how our research informs future consumer-centric conservation actions

Identification of disease-causing genes using microarray data mining and gene ontology

Background: One of the best and most accurate methods for identifying disease-causing genes is monitoring gene expression values in different samples using microarray technology. One of the shortcomings of microarray data is that they provide a small quantity of samples with respect to the number of genes. This problem reduces the classification accuracy of the methods, so gene selection is essential to improve the predictive accuracy and to identify potential marker genes for a disease. Among numerous existing methods for gene selection, support vector machine-based recursive feature elimination (SVMRFE) has become one of the leading methods, but its performance can be reduced because of the small sample size, noisy data and the fact that the method does not remove redundant genes. Methods: We propose a novel framework for gene selection which uses the advantageous features of conventional methods and addresses their weaknesses. In fact, we have combined the Fisher method and SVMRFE to utilize the advantages of a filtering method as well as an embedded method. Furthermore, we have added a redundancy reduction stage to address the weakness of the Fisher method and SVMRFE. In addition to gene expression values, the proposed method uses Gene Ontology which is a reliable source of information on genes. The use of Gene Ontology can compensate, in part, for the limitations of microarrays, such as having a small number of samples and erroneous measurement results. Results: The proposed method has been applied to colon, Diffuse Large B-Cell Lymphoma (DLBCL) and prostate cancer datasets. The empirical results show that our method has improved classification performance in terms of accuracy, sensitivity and specificity. In addition, the study of the molecular function of selected genes strengthened the hypothesis that these genes are involved in the process of cancer growth. Conclusions: The proposed method addresses the weakness of conventional methods by adding a redundancy reduction stage and utilizing Gene Ontology information. It predicts marker genes for colon, DLBCL and prostate cancer with a high accuracy. The predictions made in this study can serve as a list of candidates for subsequent wet-lab verification and might help in the search for a cure for cancers

A Software Tool to Model Genetic Regulatory Networks. Applications to the Modeling of Threshold Phenomena and of Spatial Patterning in Drosophila

We present a general methodology in order to build mathematical models of genetic regulatory networks. This approach is based on the mass action law and on the Jacob and Monod operon model. The mathematical models are built symbolically by the Mathematica software package GeneticNetworks. This package accepts as input the interaction graphs of the transcriptional activators and repressors of a biological process and, as output, gives the mathematical model in the form of a system of ordinary differential equations. All the relevant biological parameters are chosen automatically by the software. Within this framework, we show that concentration dependent threshold effects in biology emerge from the catalytic properties of genes and its associated conservation laws. We apply this methodology to the segment patterning in Drosophila early development and we calibrate the genetic transcriptional network responsible for the patterning of the gap gene proteins Hunchback and Knirps, along the antero-posterior axis of the Drosophila embryo. In this approach, the zygotically produced proteins Hunchback and Knirps do not diffuse along the antero-posterior axis of the embryo of Drosophila, developing a spatial pattern due to concentration dependent thresholds. This shows that patterning at the gap genes stage can be explained by the concentration gradients along the embryo of the transcriptional regulators

A Dynamic Stochastic Model of Frequency-Dependent Stress Fiber Alignment Induced by Cyclic Stretch

BACKGROUND: Actin stress fibers (SFs) are mechanosensitive structural elements that respond to forces to affect cell morphology, migration, signal transduction and cell function. Cells are internally stressed so that SFs are extended beyond their unloaded lengths, and SFs tend to self-adjust to an equilibrium level of extension. While there is much evidence that cells reorganize their SFs in response to matrix deformations, it is unclear how cells and their SFs determine their specific response to particular spatiotemporal changes in the matrix. METHODOLOGY/PRINCIPAL FINDINGS: Bovine aortic endothelial cells were subjected to cyclic uniaxial stretch over a range of frequencies to quantify the rate and extent of stress fiber alignment. At a frequency of 1 Hz, SFs predominantly oriented perpendicular to stretch, while at 0.1 Hz the extent of SF alignment was markedly reduced and at 0.01 Hz there was no alignment at all. The results were interpreted using a simple kinematic model of SF networks in which the dynamic response depended on the rates of matrix stretching, SF turnover, and SF self-adjustment of extension. For these cells, the model predicted a threshold frequency of 0.01 Hz below which SFs no longer respond to matrix stretch, and a saturation frequency of 1 Hz above which no additional SF alignment would occur. The model also accurately described the dependence of SF alignment on matrix stretch magnitude. CONCLUSIONS: The dynamic stochastic model was capable of describing SF reorganization in response to diverse temporal and spatial patterns of stretch. The model predicted that at high frequencies, SFs preferentially disassembled in the direction of stretch and achieved a new equilibrium by accumulating in the direction of lowest stretch. At low stretch frequencies, SFs self-adjusted to dissipate the effects of matrix stretch. Thus, SF turnover and self-adjustment are each important mechanisms that cells use to maintain mechanical homeostasis

Radio Emission from Ultra-Cool Dwarfs

The 2001 discovery of radio emission from ultra-cool dwarfs (UCDs), the very low-mass stars and brown dwarfs with spectral types of ~M7 and later, revealed that these objects can generate and dissipate powerful magnetic fields. Radio observations provide unparalleled insight into UCD magnetism: detections extend to brown dwarfs with temperatures <1000 K, where no other observational probes are effective. The data reveal that UCDs can generate strong (kG) fields, sometimes with a stable dipolar structure; that they can produce and retain nonthermal plasmas with electron acceleration extending to MeV energies; and that they can drive auroral current systems resulting in significant atmospheric energy deposition and powerful, coherent radio bursts. Still to be understood are the underlying dynamo processes, the precise means by which particles are accelerated around these objects, the observed diversity of magnetic phenomenologies, and how all of these factors change as the mass of the central object approaches that of Jupiter. The answers to these questions are doubly important because UCDs are both potential exoplanet hosts, as in the TRAPPIST-1 system, and analogues of extrasolar giant planets themselves.Comment: 19 pages; submitted chapter to the Handbook of Exoplanets, eds. Hans J. Deeg and Juan Antonio Belmonte (Springer-Verlag

Network analyses in systems biology: new strategies for dealing with biological complexity

The increasing application of network models to interpret biological systems raises a number of important methodological and epistemological questions. What novel insights can network analysis provide in biology? Are network approaches an extension of or in conflict with mechanistic research strategies? When and how can network and mechanistic approaches interact in productive ways? In this paper we address these questions by focusing on how biological networks are represented and analyzed in a diverse class of case studies. Our examples span from the investigation of organizational properties of biological networks using tools from graph theory to the application of dynamical systems theory to understand the behavior of complex biological systems. We show how network approaches support and extend traditional mechanistic strategies but also offer novel strategies for dealing with biological complexity

The emerging structure of the Extended Evolutionary Synthesis: where does Evo-Devo fit in?

The Extended Evolutionary Synthesis (EES) debate is gaining ground in contemporary evolutionary biology. In parallel, a number of philosophical standpoints have emerged in an attempt to clarify what exactly is represented by the EES. For Massimo Pigliucci, we are in the wake of the newest instantiation of a persisting Kuhnian paradigm; in contrast, Telmo Pievani has contended that the transition to an EES could be best represented as a progressive reformation of a prior Lakatosian scientific research program, with the extension of its Neo-Darwinian core and the addition of a brand-new protective belt of assumptions and auxiliary hypotheses. Here, we argue that those philosophical vantage points are not the only ways to interpret what current proposals to ‘extend’ the Modern Synthesis-derived ‘standard evolutionary theory’ (SET) entail in terms of theoretical change in evolutionary biology. We specifically propose the image of the emergent EES as a vast network of models and interweaved representations that, instantiated in diverse practices, are connected and related in multiple ways. Under that assumption, the EES could be articulated around a paraconsistent network of evolutionary theories (including some elements of the SET), as well as models, practices and representation systems of contemporary evolutionary biology, with edges and nodes that change their position and centrality as a consequence of the co-construction and stabilization of facts and historical discussions revolving around the epistemic goals of this area of the life sciences. We then critically examine the purported structure of the EES—published by Laland and collaborators in 2015—in light of our own network-based proposal. Finally, we consider which epistemic units of Evo-Devo are present or still missing from the EES, in preparation for further analyses of the topic of explanatory integration in this conceptual framework