Reply to the Comment on `Glassy Transition in a Disordered Model for the RNA Secondary Structure'

We reply to the Comment by Hartmann (cond-mat/9908132) on our paper Phys. Rev. Lett. 84 (2000) 2026 (also cond-mat/9907125).Comment: 1 page, no figures. Accepted for publication in Phys. Rev. Let

Zero Temperature Properties of RNA Secondary Structures

We analyze different microscopic RNA models at zero temperature. We discuss both the most simple model, that suffers a large degeneracy of the ground state, and models in which the degeneracy has been remove, in a more or less severe manner. We calculate low-energy density of states using a coupling perturbing method, where the ground state of a modified Hamiltonian, that repels the original ground state, is determined. We evaluate scaling exponents starting from measurements of overlaps and energy differences. In the case of models without accidental degeneracy of the ground state we are able to clearly establish the existence of a glassy phase with $\theta \simeq 1/3$.Comment: 20 pages including 9 eps figure

Scalings of domain wall energies in two dimensional Ising spin glasses

We study domain wall energies of two dimensional spin glasses. The scaling of these energies depends on the model's distribution of quenched random couplings, falling into three different classes. The first class is associated with the exponent theta =-0.28, the other two classes have theta = 0, as can be justified theoretically. In contrast to previous claims, we find that theta=0 does not indicate d=d_l but rather d <= d_l, where d_l is the lower critical dimension.Comment: Clarifications and extra reference

Report of the SNOMS Project 2006 to 2012, SNOMS SWIRE NOCS Ocean Monitoring System. Part 1: Narrative description

The ocean plays a major role in controlling the concentration of carbon dioxide (CO2) in the atmosphere. Increasing concentrations of CO2 in the atmosphere are a threat to the stability of the earth’s climate. A better understanding of the controlling role of the ocean will improve predictions of likely future changes in climate and the impact of the uptake of CO2 itself on marine eco-systems caused by the associated acidification of the ocean waters. The SNOMS Project (SWIRE NOCS Ocean Monitoring System) is a ground breaking joint research project supported by the Swire Group Trust, the Swire Educational Trust, the China Navigation Company (CNCo) and the Natural Environment Research Council. It collects high quality data on concentrations of CO2 in the surface layer of the ocean. It contributes to the international effort to better quantify (and understand the driving processes controlling) the exchanges of CO2 between the ocean and the atmosphere. In 2006 and 2007 a system that could be used on a commercial ship to provide data over periods of several months with only limited maintenance by the ships crew was designed and assembled by NOCS. The system was fitted to the CNCo ship the MV Pacific Celebes in May 2007. The onboard system was supported by web pages that monitored the progress of the ship and the functioning of the data collection system. To support the flow of data from the ship to the archiving of the data at the Carbon Dioxide Information Analysis Center (CDIAC in the USA) data processing procedures were developed for the quality control and systematic handling of the data. Data from samples of seawater collected by the ships crew and analysed in NOC (730 samples) have been used to confirm the consistency of the data from the automated measurement system on the ship. To examine the data collected between 2007 and 2012 the movements of the ship are divided into 16 voyages. Initially The Celebes traded on a route circum-navigating the globe via the Panama and Suez Canals. In 2009 the route shifted to one between Australia and New Zealand to USA and Canada. Analysis of the data is an on going process. It has demonstrated that the system produces reliable data. Data are capable of improving existing estimates of seasonal variability. The work has improved knowledge of gas exchange processes. Data from the crew-collected-samples are helping improve our ability to estimate alkalinity in different areas. This helps with the study of ocean acidification. Data from the 9 round trips in the Pacific are currently being examined along with data made available by the NOAA-PMEL laboratory forming time series from 2004 to 2012. The data from the Pacific route are of considerable interest. One reason is that the data monitors variations in the fluxes of CO2 associated with the current that flows westwards along the equator. This is one of the major natural sources of CO2 from the ocean into the atmosphere

Inference algorithms for gene networks: a statistical mechanics analysis

The inference of gene regulatory networks from high throughput gene expression data is one of the major challenges in systems biology. This paper aims at analysing and comparing two different algorithmic approaches. The first approach uses pairwise correlations between regulated and regulating genes; the second one uses message-passing techniques for inferring activating and inhibiting regulatory interactions. The performance of these two algorithms can be analysed theoretically on well-defined test sets, using tools from the statistical physics of disordered systems like the replica method. We find that the second algorithm outperforms the first one since it takes into account collective effects of multiple regulators

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

A discrete model of water with two distinct glassy phases

We investigate a minimal model for non-crystalline water, defined on a Husimi lattice. The peculiar random-regular nature of the lattice is meant to account for the formation of a random 4-coordinated hydrogen-bond network. The model turns out to be consistent with most thermodynamic anomalies observed in liquid and supercooled-liquid water. Furthermore, the model exhibits two glassy phases with different densities, which can coexist at a first-order transition. The onset of a complex free-energy landscape, characterized by an exponentially large number of metastable minima, is pointed out by the cavity method, at the level of 1-step replica symmetry breaking.Comment: expanded version: 6 pages, 7 figure

Near optimal configurations in mean field disordered systems

We present a general technique to compute how the energy of a configuration varies as a function of its overlap with the ground state in the case of optimization problems. Our approach is based on a generalization of the cavity method to a system interacting with its ground state. With this technique we study the random matching problem as well as the mean field diluted spin glass. As a byproduct of this approach we calculate the de Almeida-Thouless transition line of the spin glass on a fixed connectivity random graph.Comment: 13 pages, 7 figure

Impact of Conservation Agriculture on Soil Erosion in the Annual Cropland of the Apulia Region (Southern Italy) Based on the RUSLE-GIS-GEE Framework

The processes of soil erosion and land degradation are more rapid in the case of inappropriate agricultural management, which leads to increased soil loss rates. Moreover, climatic conditions are one of the most important determining factors affecting agriculture, especially in the Mediterranean areas featuring irregular rainfall and high summer temperatures. Conservation agriculture (CA) can make a significant contribution to reducing soil erosion risk on the annual cropland (ACL) of the Mediterranean region in comparison with conventional management (CM). The objective of this study is to provide soil loss rate maps and calculate the values for each altitude and slope class and their combination for the Apulia region in four annual production cycles for the scenarios CM and CA. The present study estimates the significance of the adoption of CA on soil erosion assessment at regional scale based on the Revised Universal Soil Loss Equation (RUSLE) model. The parameters of the RUSLE model were estimated by using remote sensing (RS) data. The erosion probability zones were determined through a Geographic Information System (GIS) and Google Earth Engine (GEE) approach. Digital terrain model (DTM) at 8 m, ACL maps of the Apulia region, and rainfall and soil data were used as an input to identify the most erosion-prone areas. Our results show a 7.5% average decrease of soil loss rate during the first period of adoption of the four-year crop cycle—from 2.3 t ha−1 y−1 with CM to 2.1 t ha−1 y−1 with the CA system. CA reduced soil loss rate compared to CM in all classes, from 10.1% in hill class to 14.1% for hill + low slope class. These results can therefore assist in the implementation of effective soil management systems and conservation practices to reduce soil erosion risk in the Apulia region and in the Mediterranean basin more generally