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"Schwab, David"
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Right to dream : immigration reform and America's future
\"The DREAM Act, bipartisan legislation first introduced in Congress in 2001, would provide conditional residency for undocumented youth brought to the United States as children. It recognizes that undocumented youth have done nothing wrong and that they should be allowed to work, to go to school, and to travel. The bill makes college more affordable through in-state tuition and gives the undocumented a path to citizenship if they graduate from college or serve in the military. Congress has failed to pass the DREAM Act, and fourteen states have filled the gap by implementing their own laws and policies that provide educational benefits to undocumented students. Right to DREAM makes a compelling argument for the DREAM Act and comprehensive immigration reform. William A. Schwab explores the key issues surrounding this legislation: What are the issues that divide? What do the proponents and opponents of the DREAM Act argue? Is there a middle ground? Is compromise possible? Answering these questions, Schwab explains the legal issues surrounding the education of immigrant children, who immigrates and why, how four waves of immigration have shaped the nation, the effects of immigrants on the U.S. economy and culture, and the process of becoming an American. Schwab analyzes the DREAM Act, deferred action, and immigration policy. He weaves personal stories of undocumented youth throughout the book and advocates for the economic, political, and social benefits of the DREAM Act that would bring undocumented youth out of the shadows and into the mainstream of society.\"--Publisher's website.
Book
Energetic costs of cellular computation
Cells often perform computations in order to respond to environmental cues. A simple example is the classic problem, first considered by Berg and Purcell, of determining the concentration of a chemical ligand in the surrounding media. On general theoretical grounds, it is expected that such computations require cells to consume energy. In particular, Landauer’s principle states that energy must be consumed in order to erase the memory of past observations. Here, we explicitly calculate the energetic cost of steady-state computation of ligand concentration for a simple two-component cellular network that implements a noisy version of the Berg–Purcell strategy. We show that learning about external concentrations necessitates the breaking of detailed balance and consumption of energy, with greater learning requiring more energy. Our calculations suggest that the energetic costs of cellular computation may be an important constraint on networks designed to function in resource poor environments, such as the spore germination networks of bacteria.
Journal Article
Theory of Gating in Recurrent Neural Networks
Recurrent neural networks (RNNs) are powerful dynamical models, widely used in machine learning (ML) and neuroscience. Prior theoretical work has focused on RNNs with additive interactions. However gating i.e., multiplicative interactions are ubiquitous in real neurons and also
central feature of the best-performing RNNs in ML. Here, we show that gating offers flexible control of two salient features of the collective dynamics: (i) timescales and (ii) dimensionality. The gate controlling timescales leads to a novel marginally stable state, where the network functions as a flexible integrator. Unlike previous approaches, gating permits this important function without parameter fine-tuning or special symmetries. Gates also provide a flexible, context-dependent mechanism to reset the memory trace, thus complementing the memory function. The gate modulating the dimensionality can induce a novel, discontinuous chaotic transition, where inputs push a stable system to strong chaotic activity, in contrast to the typically stabilizing effect of inputs. At this transition, unlike additive RNNs, the proliferation of critical points (topological complexity) is decoupled from the appearance of chaotic dynamics (dynamical complexity). The rich dynamics are summarized in phase diagrams, thus providing a map for principled parameter initialization choices to ML practitioners.
Journal Article
Energy consumption and cooperation for optimal sensing
The reliable detection of environmental molecules in the presence of noise is an important cellular function, yet the underlying computational mechanisms are not well understood. We introduce a model of two interacting sensors which allows for the principled exploration of signal statistics, cooperation strategies and the role of energy consumption in optimal sensing, quantified through the mutual information between the signal and the sensors. Here we report that in general the optimal sensing strategy depends both on the noise level and the statistics of the signals. For joint, correlated signals, energy consuming (nonequilibrium), asymmetric couplings result in maximum information gain in the low-noise, high-signal-correlation limit. Surprisingly we also find that energy consumption is not always required for optimal sensing. We generalise our model to incorporate time integration of the sensor state by a population of readout molecules, and demonstrate that sensor interaction and energy consumption remain important for optimal sensing.
Cells exhibit exceptional chemical sensitivity, yet we haven’t fully understood how they achieve it. Here the authors consider the mutual information between signals and two coupled sensors as a proxy for sensing performance and show its optimisation depending on noise level and signal statistics.
Journal Article
Quantifying the Role of Population Subdivision in Evolution on Rugged Fitness Landscapes
Natural selection drives populations towards higher fitness, but crossing fitness valleys or plateaus may facilitate progress up a rugged fitness landscape involving epistasis. We investigate quantitatively the effect of subdividing an asexual population on the time it takes to cross a fitness valley or plateau. We focus on a generic and minimal model that includes only population subdivision into equivalent demes connected by global migration, and does not require significant size changes of the demes, environmental heterogeneity or specific geographic structure. We determine the optimal speedup of valley or plateau crossing that can be gained by subdivision, if the process is driven by the deme that crosses fastest. We show that isolated demes have to be in the sequential fixation regime for subdivision to significantly accelerate crossing. Using Markov chain theory, we obtain analytical expressions for the conditions under which optimal speedup is achieved: valley or plateau crossing by the subdivided population is then as fast as that of its fastest deme. We verify our analytical predictions through stochastic simulations. We demonstrate that subdivision can substantially accelerate the crossing of fitness valleys and plateaus in a wide range of parameters extending beyond the optimal window. We study the effect of varying the degree of subdivision of a population, and investigate the trade-off between the magnitude of the optimal speedup and the width of the parameter range over which it occurs. Our results, obtained for fitness valleys and plateaus, also hold for weakly beneficial intermediate mutations. Finally, we extend our work to the case of a population connected by migration to one or several smaller islands. Our results demonstrate that subdivision with migration alone can significantly accelerate the crossing of fitness valleys and plateaus, and shed light onto the quantitative conditions necessary for this to occur.
Journal Article
α-ketoglutarate coordinates carbon and nitrogen utilization via enzyme I inhibition
Cells must coordinate nutrient uptake for balanced growth, but the mechanism by which this occurs was unknown. Flux measurements and biochemical assays now identify α-ketoglutarate as the key signal in this process that accumulates upon nitrogen limitation and inhibits an enzyme involved in glucose transport.
Microbes survive in a variety of nutrient environments by modulating their intracellular metabolism. Balanced growth requires coordinated uptake of carbon and nitrogen, the primary substrates for biomass production. Yet the mechanisms that balance carbon and nitrogen uptake are poorly understood. We find in
Escherichia coli
that a sudden increase in nitrogen availability results in an almost immediate increase in glucose uptake. The concentrations of glycolytic intermediates and known regulators, however, remain homeostatic. Instead, we find that α-ketoglutarate, which accumulates in nitrogen limitation, directly blocks glucose uptake by inhibiting enzyme I, the first step of the sugar–phosphoenolpyruvate phosphotransferase system (PTS). This inhibition enables rapid modulation of glycolytic flux without marked changes in the concentrations of glycolytic intermediates by simultaneously altering import of glucose and consumption of the terminal glycolytic intermediate phosphoenolpyruvate. Quantitative modeling shows that this previously unidentified regulatory connection is, in principle, sufficient to coordinate carbon and nitrogen utilization.
Journal Article
From intracellular signaling to population oscillations: bridging size‐ and time‐scales in collective behavior
Collective behavior in cellular populations is coordinated by biochemical signaling networks within individual cells. Connecting the dynamics of these intracellular networks to the population phenomena they control poses a considerable challenge because of network complexity and our limited knowledge of kinetic parameters. However, from physical systems, we know that behavioral changes in the individual constituents of a collectively behaving system occur in a limited number of well‐defined classes, and these can be described using simple models. Here, we apply such an approach to the emergence of collective oscillations in cellular populations of the social amoeba
Dictyostelium discoideum
. Through direct tests of our model with quantitative
in vivo
measurements of single‐cell and population signaling dynamics, we show how a simple model can effectively describe a complex molecular signaling network at multiple size and temporal scales. The model predicts novel noise‐driven single‐cell and population‐level signaling phenomena that we then experimentally observe. Our results suggest that like physical systems, collective behavior in biology may be universal and described using simple mathematical models.
Synopsis
A simple two‐variable model in combination with quantitative
in vivo
measurements of single‐cell and population signaling dynamics is used to analyze the emergence of collective cAMP oscillations in
Dictyostelium discoideum
.
Single
Dictyostelium
cells are well described as excitable, oscillatory systems.
A universal, top‐down model reproduces single‐cell and population‐level behaviors.
Model‐based predictions are validated in individual cells and in cellular populations.
Stochasticity drives the emergence and continued coordination of collective behavior.
Graphical Abstract
A simple two‐variable model in combination with quantitative
in vivo
measurements of single‐cell and population signaling dynamics is used to analyze the emergence of collective cAMP oscillations in
Dictyostelium discoideum
.
Journal Article
Lag normalization in an electrically coupled neural network
In this study, the authors show that velocity-dependent lag normalization in the retina is accomplished via a subset of adjacent directionally selective ganglion cells that are electrically coupled, allowing each activated cell to prime its neighbor.
Moving objects can cover large distances while they are processed by the eye, usually resulting in a spatially lagged retinal response. We identified a network of electrically coupled motion–coding neurons in mouse retina that act collectively to register the leading edges of moving objects at a nearly constant spatial location, regardless of their velocity. These results reveal a previously unknown neurophysiological substrate for lag normalization in the visual system.
Journal Article
Nonlinear dendritic integration of electrical and chemical synaptic inputs drives fine-scale correlations
Fine-scale synchrony of neural activity determines the nature of neural coding, but its underlying mechanisms are unclear. Here the authors find that coincident electrical and chemical synaptic inputs are nonlinearly integrated in overlapping retinal ganglion cell dendrites to produce synchronous spiking.
Throughout the CNS, gap junction–mediated electrical signals synchronize neural activity on millisecond timescales via cooperative interactions with chemical synapses. However, gap junction–mediated synchrony has rarely been studied in the context of varying spatiotemporal patterns of electrical and chemical synaptic activity. Thus, the mechanism underlying fine-scale synchrony and its relationship to neural coding remain unclear. We examined spike synchrony in pairs of genetically identified, electrically coupled ganglion cells in mouse retina. We found that coincident electrical and chemical synaptic inputs, but not electrical inputs alone, elicited synchronized dendritic spikes in subregions of coupled dendritic trees. The resulting nonlinear integration produced fine-scale synchrony in the cells' spike output, specifically for light stimuli driving input to the regions of dendritic overlap. In addition, the strength of synchrony varied inversely with spike rate. Together, these features may allow synchronized activity to encode information about the spatial distribution of light that is ambiguous on the basis of spike rate alone.
Journal Article
Meteotsunamis in the Laurentian Great Lakes
The generation mechanism of meteotsunamis, which are meteorologically induced water waves with spatial/temporal characteristics and behavior similar to seismic tsunamis, is poorly understood. We quantify meteotsunamis in terms of seasonality, causes, and occurrence frequency through the analysis of long-term water level records in the Laurentian Great Lakes. The majority of the observed meteotsunamis happen from late-spring to mid-summer and are associated primarily with convective storms. Meteotsunami events of potentially dangerous magnitude (height > 0.3 m) occur an average of 106 times per year throughout the region. These results reveal that meteotsunamis are much more frequent than follow from historic anecdotal reports. Future climate scenarios over the United States show a likely increase in the number of days favorable to severe convective storm formation over the Great Lakes, particularly in the spring season. This would suggest that the convectively associated meteotsunamis in these regions may experience an increase in occurrence frequency or a temporal shift in occurrence to earlier in the warm season. To date, meteotsunamis in the area of the Great Lakes have been an overlooked hazard.
Journal Article