Family-specific scaling laws in bacterial genomes

Among several quantitative invariants found in evolutionary genomics, one of the most striking is the scaling of the overall abundance of proteins, or protein domains, sharing a specific functional annotation across genomes of given size. The size of these functional categories change, on average, as power-laws in the total number of protein-coding genes. Here, we show that such regularities are not restricted to the overall behavior of high-level functional categories, but also exist systematically at the level of single evolutionary families of protein domains. Specifically, the number of proteins within each family follows family-specific scaling laws with genome size. Functionally similar sets of families tend to follow similar scaling laws, but this is not always the case. To understand this systematically, we provide a comprehensive classification of families based on their scaling properties. Additionally, we develop a quantitative score for the heterogeneity of the scaling of families belonging to a given category or predefined group. Under the common reasonable assumption that selection is driven solely or mainly by biological function, these findings point to fine-tuned and interdependent functional roles of specific protein domains, beyond our current functional annotations. This analysis provides a deeper view on the links between evolutionary expansion of protein families and the functional constraints shaping the gene repertoire of bacterial genomes.Comment: 41 pages, 16 figure

A Quali-quantitative evaluation approach to pedodiversity by multivariate analysis: introduction to the concept of "pedocharacter"

A model has been developed for the interpretation of the complexity of pedological systems; this is referred to as “pedocharacter”. The main aim of the model was to reduce the variables able to define soils and their relationships with the environment through the following quali-quantitative approach: i) definition of a fair number of qualitative characters; and ii) development of an analytic function, defined as “Land Relevance of the Factor”

Zipf and Heaps laws from dependency structures in component systems

Complex natural and technological systems can be considered, on a coarse-grained level, as assemblies of elementary components: for example, genomes as sets of genes, or texts as sets of words. On one hand, the joint occurrence of components emerges from architectural and specific constraints in such systems. On the other hand, general regularities may unify different systems, such as the broadly studied Zipf and Heaps laws, respectively concerning the distribution of component frequencies and their number as a function of system size. Dependency structures (i.e., directed networks encoding the dependency relations between the components in a system) were proposed recently as a possible organizing principles underlying some of the regularities observed. However, the consequences of this assumption were explored only in binary component systems, where solely the presence or absence of components is considered, and multiple copies of the same component are not allowed. Here, we consider a simple model that generates, from a given ensemble of dependency structures, a statistical ensemble of sets of components, allowing for components to appear with any multiplicity. Our model is a minimal extension that is memoryless, and therefore accessible to analytical calculations. A mean-field analytical approach (analogous to the "Zipfian ensemble" in the linguistics literature) captures the relevant laws describing the component statistics as we show by comparison with numerical computations. In particular, we recover a power-law Zipf rank plot, with a set of core components, and a Heaps law displaying three consecutive regimes (linear, sub-linear and saturating) that we characterize quantitatively

Pedotechniques strategies to improve soil resilience against the impact of irrigation by municipal wastewater: using zeolitized tuffs as soil amendments

A research was started aiming at evaluating the possible use of natural zeolites as exchange conditioners to improve and make durable the soil resilience against the adverse effects of the use of anomalous wastewater, for irrigation purposes. To satisfy such aims, two zeolitized tuffs (ZTs), viz. a Neapolitan yellow tuff (NYT) and a clinoptilolite bearing tuff (ZCL), were tested as pedotechnical materials to improve soil resilience against the impact of treatment by a ‘dirty’ municipal wastewater (DMW)

Assessing Soil Erosion Susceptibility for Past and Future Scenarios in Semiarid Mediterranean Agroecosystems

The evaluation of soil erosion rate, particularly in agricultural lands, is a crucial tool for long-term land management planning. This research utilized the soil and water assessment tool (SWAT) model to simulate soil erosion in a semiarid watershed located in South Portugal. To understand the evolution of the erosive phenomenon over time, soil erosion susceptibility maps for both historical and future periods were created. The historical period exhibited the highest average soil erosion for each land use, followed by the representative concentration pathways (RCPs) 8.5 and 4.5 scenarios. The differences in soil loss between these two RCPs were influenced by the slightly increasing trend of extreme events, particularly notable in RCP 8.5, leading to a higher maximum value of soil erosion. The research highlighted a tendency towards erosion in the agroforestry system known as “montado”, specifically on Leptosols throughout the entire basin. The study confirmed that Leptosols are most susceptible to sediment loss due to their inherent characteristics. Additionally, both “montado” and farmed systems were found to negatively impact soil erosion rates if appropriate antierosion measures are not adopted. This underscores the importance of identifying all factors responsible for land degradation in Mediterranean watersheds. In conclusion, the study highlighted the significance of assessing soil erosion rates in agricultural areas for effective land management planning in the long run. The utilization of the SWAT model and the creation of susceptibility maps provide valuable insights into the erosive phenomenon’s dynamics, urging the implementation of antierosion strategies to protect the soil and combat land degradation in the region.info:eu-repo/semantics/publishedVersio

Comparing Machine and Deep Learning Methods for Large 3D Heritage Semantic Segmentation

In recent years semantic segmentation of 3D point clouds has been an argument that involves different fields of application. Cultural heritage scenarios have become the subject of this study mainly thanks to the development of photogrammetry and laser scanning techniques. Classification algorithms based on machine and deep learning methods allow to process huge amounts of data as 3D point clouds. In this context, the aim of this paper is to make a comparison between machine and deep learning methods for large 3D cultural heritage classification. Then, considering the best performances of both techniques, it proposes an architecture named DGCNN-Mod+3Dfeat that combines the positive aspects and advantages of these two methodologies for semantic segmentation of cultural heritage point clouds. To demonstrate the validity of our idea, several experiments from the ArCH benchmark are reported and commented

Zeolitized tuffs in pedotechnique for quarry restoration: evaluation of phytonutritional efficiency in ^AUP model horizons

A study was started aiming at assessing the suitability of zeolitized tuff as optimal mineral Human Transported Materials (HTMs) in pedotechnologies for quarry restoration

DAT atypical inhibitors as novel antipsychotic drugs

Despite its classification as a psychiatric disease, schizophrenia is both a behavioral and a biological disorder resulting in neurocognitive dysfunction. Social and economic costs of schizophrenia are extremely high compared to its incidence and prevalence, however, due to a heterogeneous pattern of brain pathology and symptoms and to an unknown etiology, developing an effective treatment has been really challenging. Among the many neurochemical hypothesis, the dysregulation of dopaminergic neurotransmission has been considered as a central dogma of schizophrenia over the last few decades. In fact, patients with this pathology exhibit increased dopamine (DA) synthesis and release in the striatum which seems to correlate with positive symptoms and moreover, most of the effective antipsychotic drugs (APDs) are D2-receptor antagonists. Unfortunately, chronic treatment with APDs is associated with the induction of extrapyramidal side effects (EPS). In order to identify new possible APDs with a novel mechanism of action and potentially less EPS we tested 3 different compounds generated from the structural modification of vanoxerine (or GBR12909), a known atypical inhibitor of the presynaptic DA transporter (DAT) with cocaine-like activity but cardiotoxic properties that have precluded its clinical use. Preliminary in vitro studies showed that DAhLIs (DAT atypical inhibitors) are able to bind to DAT and inhibit DA reuptake. Additionally, our in vivo results showed that DAhLI i) have putative central effects, ii), unlike vanoxerine, reduce novelty-induced locomotor activity, and iii) counteract cocaine stimulating effects, suggesting that DAhLI may potentiate DA reuptake via DAT. These compounds may provide a way to reduce DA extracellular levels and DA neurotransmission with a selective action on active DA synapses, thus with reduced EPS typical of D2 antagonists, representing a new promising class of presynaptic APDs

Approccio gerarchico di machine learning per la segmentazione semantica di nuvole di punti 3D

L’uso di dati 3D, nuvole di punti e mesh, per la documentazione, la valorizzazione e la visualizzazione del patrimonio è diventato sempre più diffuso. Ricchi di informazioni metriche, questi dati 3D soffrono la mancanza di informazioni strutturate quali la semantica e la gerarchia tra le parti. In questo contesto, l'introduzione di metodi automatici di classificazione può svolgere un ruolo essenziale per permettere un utilizzo reale di questi dati nelle operazioni di manutenzione e conservazione del bene culturale, agevolando un migliore utilizzo dei dati ai fini informativi e di analisi. In questo articolo viene presentato un innovativo approccio di classificazione multilivello e multi-risoluzione (MLMR). L'approccio MLMR proposto migliora il processo di apprendimento e ottimizza i risultati della classificazione 3D attraverso un concetto gerarchico. La procedura MLMR viene testata e valutata su due diversi datasets, complessi e di grandi dimensioni: l'Abbazia di Pomposa (Italia) e il Duomo di Milano (Italia). I risultati della classificazione mostrano l'affidabilità e la replicabilità del metodo sviluppato, permettendo l'identificazione di svariate classi architettoniche a diversi livelli di risoluzione geometrica.The recent years saw an extensive use of 3D point cloud data for heritage documentation, valorisation, and visualisation. Although rich in metric quality, these 3D data lack structured information such as semantics and hierarchy between parts. In this context, the introduction of point cloud classification methods can play an essential role for better data usage, model definition, analysis, and conservation. The paper aims to extend a machine learning (ML) classification method with a multi-level and multi-resolution (MLMR) approach. The proposed MLMR approach improves the learning process and optimises 3D classification results through a hierarchical concept. The MLMR procedure is tested and evaluated on two large-scale and complex datasets: the Pomposa Abbey (Italy) and the Milan Cathedral (Italy). Classification results show the reliability and replicability of the developed method, allowing the identification of the necessary architectural classes at each geometric resolution