Water use in Citrus: effect on porosity in water retention in a “Latossolo vermelho-escuro” of Northeast of Estado de Sao Paulo

[Abstract] The objective of this research was to study the porosity, bulk density and retention of water of an Oxisol, located in the Northwestern region of São Paulo state, Brazil. The soil was cultivated with Citrus sp., to which green manure was applied between rows for three years. Each of six species of green manure crops (Crotalaria junceaL., Mucunade eringian a Steph. & Bart., Canavalia ensiformis L. DC., Cajanus cajan L., Lablab purpureum L. and Ricinus communis L.) were seeded for three years (1995, 1996 and 1997) between Citrus rows, plus a treatment with a mix of all six species and a control (natural regrowth af vegetation). The experimental design was a randomized complete block design, with four replications for each of the eight treatments. Water retention, microporosity, macroporosity, total porosity and bulk density were analyzed in the beginning (1995) and end (1997) of the experiment, at three depth ranges (0-0.10; 0.10-0.20 and 0.20-0.40m). We concluded that there were statistically significant differences for bulk density, macroporosity, total porosity and retention of water among the different soil depth ranges; there were no significant differences among treatments though

Analytical methods applied to diverse types of Brazilian propolis

Propolis is a bee product, composed mainly of plant resins and beeswax, therefore its chemical composition varies due to the geographic and plant origins of these resins, as well as the species of bee. Brazil is an important supplier of propolis on the world market and, although green colored propolis from the southeast is the most known and studied, several other types of propolis from Apis mellifera and native stingless bees (also called cerumen) can be found. Propolis is usually consumed as an extract, so the type of solvent and extractive procedures employed further affect its composition. Methods used for the extraction; analysis the percentage of resins, wax and insoluble material in crude propolis; determination of phenolic, flavonoid, amino acid and heavy metal contents are reviewed herein. Different chromatographic methods applied to the separation, identification and quantification of Brazilian propolis components and their relative strengths are discussed; as well as direct insertion mass spectrometry fingerprinting

AI is a viable alternative to high throughput screening: a 318-target study

: High throughput screening (HTS) is routinely used to identify bioactive small molecules. This requires physical compounds, which limits coverage of accessible chemical space. Computational approaches combined with vast on-demand chemical libraries can access far greater chemical space, provided that the predictive accuracy is sufficient to identify useful molecules. Through the largest and most diverse virtual HTS campaign reported to date, comprising 318 individual projects, we demonstrate that our AtomNet® convolutional neural network successfully finds novel hits across every major therapeutic area and protein class. We address historical limitations of computational screening by demonstrating success for target proteins without known binders, high-quality X-ray crystal structures, or manual cherry-picking of compounds. We show that the molecules selected by the AtomNet® model are novel drug-like scaffolds rather than minor modifications to known bioactive compounds. Our empirical results suggest that computational methods can substantially replace HTS as the first step of small-molecule drug discovery

Adubação verde em Citrus: Influência na porosidade e retenção de água de um latossolo vermelho-escuro do noroeste do estado de São Paulo

The objective of this research was to study the porosity, bulk density and retention of water of an Oxisol, located in the Northwestern region of Sn̄o Paulo state, Brazil. The soil was cultivated with Citrus sp., to which green manure was applied between rows for three years. Each of six species of green manure crops (Crotalaria juncea L., Mucuna deeringiana Steph. & Bart., Canavalia ensiformis L. DC., Cajanus cajan L., Lablab purpureum L. and Ricinus communis L.) were seeded for three years (1995, 1996 and 1997) between Citrus rows, plus a treatment with a mix of all six species and a control (natural regrowth af vegetation). The experimental design was a randomized complete block design, with four replications for each of the eight treatments. Water retention, microporosity, macroporosity, total porosity and bulk density were analyzed in the beginning (1995) and end (1997) of the experiment, at three depth ranges (0-0.10; 0.10-0.20 and 0.20-0.40m). We concluded that there were statistically significant differences for bulk density, macroporosity, total porosity and retention of water among the different soil depth ranges; there were no significant differences among treatments though

Selection of powerful radio galaxies with machine learning

Context. The study of active galactic nuclei (AGNs) is fundamental to discern the formation and growth of supermassive black holes (SMBHs) and their connection with star formation and galaxy evolution. Due to the significant kinetic and radiative energy emitted by powerful AGNs, they are prime candidates to observe the interplay between SMBH and stellar growth in galaxies. Aims. We aim to develop a method to predict the AGN nature of a source, its radio detectability, and redshift purely based on photometry. The use of such a method will increase the number of radio AGNs, allowing us to improve our knowledge of accretion power into an SMBH, the origin and triggers of radio emission, and its impact on galaxy evolution. Methods. We developed and trained a pipeline of three machine learning (ML) models than can predict which sources are more likely to be an AGN and to be detected in specific radio surveys. Also, it can estimate redshift values for predicted radio-detectable AGNs. These models, which combine predictions from tree-based and gradient-boosting algorithms, have been trained with multi-wavelength data from near-infrared-selected sources in the Hobby-Eberly Telescope Dark Energy Experiment (HETDEX) Spring field. Training, testing, calibration, and validation were carried out in the HETDEX field. Further validation was performed on near-infrared-selected sources in the Stripe 82 field. Results. In the HETDEX validation subset, our pipeline recovers 96% of the initially labelled AGNs and, from AGNs candidates, we recover 50% of previously detected radio sources. For Stripe 82, these numbers are 94% and 55%. Compared to random selection, these rates are two and four times better for HETDEX, and 1.2 and 12 times better for Stripe 82. The pipeline can also recover the redshift distribution of these sources with σNMAD=0.07 for HETDEX (σNMAD=0.09 for Stripe 82) and an outlier fraction of 19% (25% for Stripe 82), compatible with previous results based on broad-band photometry. Feature importance analysis stresses the relevance of near- and mid-infrared colours to select AGNs and identify their radio and redshift nature. Conclusions. Combining different algorithms in ML models shows an improvement in the prediction power of our pipeline over a random selection of sources. Tree-based ML models (in contrast to deep learning techniques) facilitate the analysis of the impact that features have on the predictions. This prediction can give insight into the potential physical interplay between the properties of radio AGNs (e.g. mass of black hole and accretion rate)