Changes in Defoliation Patterns of Plant Functional Groups under Variable Herbage Allowance in Campos Grasslands

Several studies have evaluated separately forage production, botanical composition, leaf traits and animal performance. However, few of them have focused on defoliation patterns at the level of functional groups (FGs) under different and variable herbage allowance (HA), especially in natural, diverse grasslands. The objective was to evaluate the relationship between HA and leaf traits on defoliation patterns of mature beef cows in the autumn, winter and spring. We evaluated the grazing probability (GP), intensity of defoliation (ID), and leaf traits on 14 species that represent more than 80% of total dry matter of the pasture. The experiment at which we evaluated those traits and responses has been managed under High HA (HHA) and Low HA (LHA) (8 and 5 kg dry matter kg live weight-1, respectively). Four plant FGs (A, B, C and D) were defined according to leaf traits, and a selectivity index (SI) was developed for each group (considering the proportion of grazed and ungrazed species). Grazing patterns shifted across seasons. In the autumn, grazing was concentrated on FGs A, B and C groups (GP = 0.417). While for FG D, represented by high-biomass tussocks, the GP was lower (0.075). During winter, when herbage accumulation rate is limited, the average GP was 0.175. FGs C and D were more defoliated in relation to autumn, and during spring the GP shifted to FG B (0.289). The ID was similar to all FGs and seasons (66 % of leaf removed). In autumn and spring, the SI was affected by FGs and HA while in winter were similar between FGs but higher in HHA. Cows behaved differently in the defoliation pattern, modifying mainly the GP on FGs rather than the ID. Variation in HA across season determined changes in defoliation pattern, allowing to express selectivity in autumn and spring

Respuesta al manejo de factores ecológicos en Coelorachis selloana (hack) en la región noreste de Uruguay

Results from 5 experiments with Coelorachis selloana, a native species of the northeast region of Uruguay are reported; different ecological factors were assessed in paddocks and in the glasshouse. The species was found mainly on clay soils and responded linearly to nitrogen applications up to 100 units; water stress significantly reduced total dry matter production compared with an irrigated control; intensively and frequently cut plants (cut every 2 weeks at 2cm) yielded significantly less dry matter per plant as compared with alleviated ones (cut every 8 weeks); under field conditions its presence increased significantly in conditions with high forage allowance as compared with low forage allowance during the growing season.Se presentan los resultados de 5 experimentos con Coelorachis selloana, una especie nativa de la región noreste de Uruguay; se estudiaron factores ecológicos en condiciones de campo y en el invernáculo. La especie se encontró principalmente en los suelos arcillosos y respondió linealmente a la aplicación de nitrógeno hasta 100 unidades; el estrés hídrico afectó significativa y negativamente su producción total de materia seca comparado con un testigo con riego, la alta frecuencia e intensidad de corte (corte a 2 cm. y 2 semanas) redujeron significativamente su productividad comparado con una situación de alivio (corte cada 8 semanas); en condiciones de campo, una mayor oferta de forraje incrementó significativamente su presencia durante la estación de crecimiento comparado con una menor oferta de forraje

Modularity in Protein Complex and Drug Interactions Reveals New Polypharmacological Properties

Recent studies have highlighted the importance of interconnectivity in a large range of molecular and human disease-related systems. Network medicine has emerged as a new paradigm to deal with complex diseases. Connections between protein complexes and key diseases have been suggested for decades. However, it was not until recently that protein complexes were identified and classified in sufficient amounts to carry out a large-scale analysis of the human protein complex system. We here present the first systematic and comprehensive set of relationships between protein complexes and associated drugs and analyzed their topological features. The network structure is characterized by a high modularity, both in the bipartite graph and in its projections, indicating that its topology is highly distinct from a random network and that it contains a rich and heterogeneous internal modular structure. To unravel the relationships between modules of protein complexes, drugs and diseases, we investigated in depth the origins of this modular structure in examples of particular diseases. This analysis unveils new associations between diseases and protein complexes and highlights the potential role of polypharmacological drugs, which target multiple cellular functions to combat complex diseases driven by gain-of-function mutations

Modeling complex metabolic reactions, ecological systems, and financial and legal networks with MIANN models based on Markov-Wiener node descriptors

[Abstract] The use of numerical parameters in Complex Network analysis is expanding to new fields of application. At a molecular level, we can use them to describe the molecular structure of chemical entities, protein interactions, or metabolic networks. However, the applications are not restricted to the world of molecules and can be extended to the study of macroscopic nonliving systems, organisms, or even legal or social networks. On the other hand, the development of the field of Artificial Intelligence has led to the formulation of computational algorithms whose design is based on the structure and functioning of networks of biological neurons. These algorithms, called Artificial Neural Networks (ANNs), can be useful for the study of complex networks, since the numerical parameters that encode information of the network (for example centralities/node descriptors) can be used as inputs for the ANNs. The Wiener index (W) is a graph invariant widely used in chemoinformatics to quantify the molecular structure of drugs and to study complex networks. In this work, we explore for the first time the possibility of using Markov chains to calculate analogues of node distance numbers/W to describe complex networks from the point of view of their nodes. These parameters are called Markov-Wiener node descriptors of order kth (Wk). Please, note that these descriptors are not related to Markov-Wiener stochastic processes. Here, we calculated the Wk(i) values for a very high number of nodes (>100,000) in more than 100 different complex networks using the software MI-NODES. These networks were grouped according to the field of application. Molecular networks include the Metabolic Reaction Networks (MRNs) of 40 different organisms. In addition, we analyzed other biological and legal and social networks. These include the Interaction Web Database Biological Networks (IWDBNs), with 75 food webs or ecological systems and the Spanish Financial Law Network (SFLN). The calculated Wk(i) values were used as inputs for different ANNs in order to discriminate correct node connectivity patterns from incorrect random patterns. The MIANN models obtained present good values of Sensitivity/Specificity (%): MRNs (78/78), IWDBNs (90/88), and SFLN (86/84). These preliminary results are very promising from the point of view of a first exploratory study and suggest that the use of these models could be extended to the high-throughput re-evaluation of connectivity in known complex networks (collation)