Inference of the genetic network regulating lateral root initiation in Arabidopsis thaliana

Regulation of gene expression is crucial for organism growth, and it is one of the challenges in Systems Biology to reconstruct the underlying regulatory biological networks from transcriptomic data. The formation of lateral roots in Arabidopsis thaliana is stimulated by a cascade of regulators of which only the interactions of its initial elements have been identified. Using simulated gene expression data with known network topology, we compare the performance of inference algorithms, based on different approaches, for which ready-to-use software is available. We show that their performance improves with the network size and the inclusion of mutants. We then analyse two sets of genes, whose activity is likely to be relevant to lateral root initiation in Arabidopsis, by integrating sequence analysis with the intersection of the results of the best performing methods on time series and mutants to infer their regulatory network. The methods applied capture known interactions between genes that are candidate regulators at early stages of development. The network inferred from genes significantly expressed during lateral root formation exhibits distinct scale-free, small world and hierarchical properties and the nodes with a high out-degree may warrant further investigation

Predicting protein functions with message passing algorithms

Motivation: In the last few years a growing interest in biology has been shifting towards the problem of optimal information extraction from the huge amount of data generated via large scale and high-throughput techniques. One of the most relevant issues has recently become that of correctly and reliably predicting the functions of observed but still functionally undetermined proteins starting from information coming from the network of co-observed proteins of known functions. Method: The method proposed in this article is based on a message passing algorithm known as Belief Propagation, which takes as input the network of proteins physical interactions and a catalog of known proteins functions, and returns the probabilities for each unclassified protein of having one chosen function. The implementation of the algorithm allows for fast on-line analysis, and can be easily generalized to more complex graph topologies taking into account hyper-graphs, {\em i.e.} complexes of more than two interacting proteins.Comment: 12 pages, 9 eps figures, 1 additional html tabl

Dynamical Quasicondensation of Hard-Core Bosons at Finite Momenta

Long-range order in quantum many-body systems is usually associated with equilibrium situations. Here, we experimentally investigate the quasicondensation of strongly-interacting bosons at finite momenta in a far-from-equilibrium case. We prepare an inhomogeneous initial state consisting of one-dimensional Mott insulators in the center of otherwise empty one-dimensional chains in an optical lattice with a lattice constant $d$. After suddenly quenching the trapping potential to zero, we observe the onset of coherence in spontaneously forming quasicondensates in the lattice. Remarkably, the emerging phase order differs from the ground-state order and is characterized by peaks at finite momenta $\pm (\pi/2) (\hbar / d)$ in the momentum distribution function.Comment: See also Viewpoint: Emerging Quantum Order in an Expanding Gas, Physics 8, 99 (2015

Approaching the adiabatic timescale with machine-learning

The control and manipulation of quantum systems without excitation is challenging, due to the complexities in fully modeling such systems accurately and the difficulties in controlling these inherently fragile systems experimentally. For example, while protocols to decompress Bose-Einstein condensates (BEC) faster than the adiabatic timescale (without excitation or loss) have been well developed theoretically, experimental implementations of these protocols have yet to reach speeds faster than the adiabatic timescale. In this work, we experimentally demonstrate an alternative approach based on a machine learning algorithm which makes progress towards this goal. The algorithm is given control of the coupled decompression and transport of a metastable helium condensate, with its performance determined after each experimental iteration by measuring the excitations of the resultant BEC. After each iteration the algorithm adjusts its internal model of the system to create an improved control output for the next iteration. Given sufficient control over the decompression, the algorithm converges to a novel solution that sets the current speed record in relation to the adiabatic timescale, beating out other experimental realizations based on theoretical approaches. This method presents a feasible approach for implementing fast state preparations or transformations in other quantum systems, without requiring a solution to a theoretical model of the system. Implications for fundamental physics and cooling are discussed.Comment: 7 pages main text, 2 pages supporting informatio

Coupling Identical one-dimensional Many-Body Localized Systems

We experimentally study the effects of coupling one-dimensional many-body localized systems with identical disorder. Using a gas of ultracold fermions in an optical lattice, we artificially prepare an initial charge density wave in an array of 1D tubes with quasirandom on-site disorder and monitor the subsequent dynamics over several thousand tunneling times. We find a strikingly different behavior between many-body localization and Anderson localization. While the noninteracting Anderson case remains localized, in the interacting case any coupling between the tubes leads to a delocalization of the entire system

Nest-Site Selection and Nest Survival of the Rusty Blackbird: Does Timber Management Adjacent to Wetlands Create Ecological Traps?

Animals are subject to ecological traps when anthropogenic changes create habitat that appears suitable but when selected results in decreased fitness. The Rusty Blackbird (Euphagus carolinus) breeds in boreal wetlands and has declined by 85–95% over the last half century. We studied nest-site selection and daily nest survival rate (DSR) of 43 Rusty Blackbird nests in northern New England and evaluated whether regenerating logged areas adjacent to wetlands created ecological traps. Although nesting adults avoided high-canopied forests and selected areas with dense balsam fir (Abies balasmea) 1 to 3 m high, those characteristics were not associated with DSR. Conversely, the frequency of speckled alder (Alnus incana) and sedges (Cyperaceae) in the nest plot varied with DSR, suggesting that the risk of predation of nests within wetlands was lower. DSR also varied with past logging; nests in plots not harvested recently were 2.3x more likely to fledge young than nests in plots harvested within 20 years. When logging extends to the edges of or into wetlands, the subsequent dense regenerating conifers appear to attract Rusty Blackbirds to nest closer to or within these human-altered uplands, exposing their nests to increased predation not typical of unaltered wetlands. Three surrogates for habitat preference did not differ by timber-management history, suggesting that the birds do not prefer habitats that increase their fitness. Rusty Blackbirds nesting in harvested wetlands may be subject to “equal preference” ecological traps, and we suggest that buffers 75 m wide around the perimeter of suitable wetlands should increase DSR