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

In cond-mat/9907125 the low-temperature behavior of a model for RNA secondary structure was studied. It is claimed that the model exhibits a breaking of the replica symmetry, since the width of the distribution P(q) of overlaps may converge to a finite value at T=0. The authors used an exact enumeration method to obtain all ground states for a given RNA sequence. Because of the exponential growing degeneracy, only sequences up to length L=256 could be studied. Here it is shown that, in contrast to the previous results, by going to much larger sizes as L=2000 the variance coverges towards zero, i.e. P(q) is a delta-function in the thermodynamic limit.Comment: completely rewritten, comment to cond-mat/9907125 (PRL 84, 2026

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

Effect of soil tillage and crop sequence on grain yield and quality of durum wheat in Mediterranean areas

Conservation agriculture (CA) can be very strategic in degradation prone soils of Mediterranean environments to recover soil fertility and consequently improve crop productivity as well as the quality traits of the most widespread crop, durum wheat, with reference to protein accumulation and composition. The results shown by two years of data in a medium long-term experiment (7-year experiment; split-plot design) that combined two tillage practices (conventional tillage (CT) and zero tillage (ZT)) with two crop sequences (wheat monocropping (WW) and wheat-faba bean (WF)) are presented. The combination ZT + WF (CA approach) induced the highest grain yields (617 and 370 g m(-2) in 2016 and 2017, respectively), principally due to an increased number of ears m(-2); on the other hand, the lowest grain yield was recorded under CT + WW (550 and 280 g m(-2) in 2016 and 2017, respectively). CA also demonstrated significant influences on grain quality because the inclusion of faba bean in the rotation favored higher N-remobilization to the grains (79.5% and 77.7% in 2017). Under ZT and WF, all gluten fractions (gliadins (Glia), high molecular-weight glutenins (GS), and low molecular-weight GS) as well as the GS/Glia ratio increased. In durum wheat-based farming systems in Mediterranean areas, the adoption of CA seems to be an optimal choice to combine high quality yields with improved soil fertility

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

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

Past and future of plant stress detection: an overview from remote sensing to Positron Emission Tomography

Plant stress detection is considered one of the most critical areas for the improvement of crop yield in the compelling worldwide scenario, dictated by both the climate change and the geopolitical consequences of the Covid-19 epidemics. A complicated interconnection of biotic and abiotic stressors affect plant growth, including water, salt, temperature, light exposure, nutrients availability, agrochemicals, air and soil pollutants, pests and diseases. In facing this extended panorama, the technology choice is manifold. On the one hand, quantitative methods, such as metabolomics, provide very sensitive indicators of most of the stressors, with the drawback of a disruptive approach, which prevents follow up and dynamical studies. On the other hand qualitative methods, such as fluorescence, thermography and VIS/NIR reflectance, provide a non-disruptive view of the action of the stressors in plants, even across large fields, with the drawback of a poor accuracy. When looking at the spatial scale, the effect of stress may imply modifications from DNA level (nanometers) up to cell (micrometers), full plant (millimeters to meters) and entire field (kilometers). While quantitative techniques are sensitive to the smallest scales, only qualitative approaches can be used for the larger ones. Emerging technologies from nuclear and medical physics, such as computed tomography, magnetic resonance imaging and positron emission tomography, are expected to bridge the gap of quantitative non disruptive morphologic and functional measurements at larger scale. In this review we analyze the landscape of the different technologies nowadays available, showing the benefits of each approach in plant stress detection, with a particular focus on the gaps, which will be filled in the nearby future by the emerging nuclear physics approaches to agriculture

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

Clostridium cellulovorans metabolism of cellulose as studied by comparative proteomic approach

Clostridium cellulovorans is among the most promising candidates for consolidated bioprocessing (CBP) of cellulosic biomass to liquid biofuels (ethanol, butanol). C. cellulovorans metabolizes all the main plant polysaccharides and mainly produces butyrate. Since most butyrate and butanol biosynthetic reactions from acetyl-CoA are common, introduction of single heterologous alcohol/aldehyde dehydrogenase can divert the branching-point intermediate (butyryl-CoA) towards butanol production in this strain. However, engineering C. cellulovorans metabolic pathways towards industrial utilization requires better understanding of its metabolism. The present study aimed at improving comprehension of cellulose metabolism in C. cellulovorans by comparing growth kinetics, substrate consumption/product accumulation and whole-cell soluble proteome (data available via ProteomeXchange, identifier PXD015487) with those of the same strain grown on a soluble carbohydrate, glucose, as the main carbon source. Growth substrate-dependent modulations of the central metabolism were detected, including regulation of several glycolytic enzymes, fermentation pathways (e.g. hydrogenase, pyruvate formate lyase, phosphate transacetylase) and nitrogen assimilation (e.g. glutamate dehydrogenase). Overexpression of hydrogenase and increased ethanol production by glucose-grown bacteria suggest a more reduced redox state. Higher energy expenditure seems to occur in cellulose-grown C. cellulovorans (likely related to overexpression and secretion of (hemi-)cellulases), which induces up-regulation of ATP synthetic pathways, e.g. acetate production and ATP synthase. Significance: C. cellulovorans can metabolize all the main plant polysaccharides (cellulose, hemicelluloses and pectins) and, unlike other well established cellulolytic microorganisms, can produce butyrate. C. cellulovorans is therefore among the most attractive candidates for direct fermentation of lignocellulose to high-value chemicals and, especially, n-butanol, i.e. one of the most promising liquid biofuels for the future. Recent studies aimed at engineering n-butanol production in C. cellulovorans represent milestones towards production of biofuels through one-step fermentation of lignocellulose but also indicated that more detailed understanding of the C. cellulovorans central carbon metabolism is essential to refine metabolic engineering strategies towards improved n-butanol production in this strain. The present study helped identifying key genes associated with specific catabolic reactions and indicated modulations of central carbon metabolism (including redox and energy balance) associated with cellulose consumption. This information will be useful to determine key enzymes and possible metabolic bottlenecks to be addressed towards improved metabolic engineering of this strain

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