This study explores the chemical composition of different macrophyte species and infers their potential in extracting nutrients and some heavy metals from water as well as the use of macrophytes’ biomass as natural fertilizers. It used a dataset obtained from a previous study composed of 445 samples of chemical concentrations in the dried biomass of 16 macrophyte species collected from the Santana Reservoir in Rio de Janeiro, Brazil. Correlation tests, analysis of variance, and factor analysis of mixed data were performed to infer correspondences between the macrophyte species. The results showed that the macrophyte species can be grouped into three different clusters with significantly different profiles of chemical element concentrations (N, P, K⁺, Ca²⁺, Mg²⁺, S, B, Cu²⁺, Fe²⁺, Mn²⁺, Zn²⁺, Cr³⁺, Cd²⁺, Ni²⁺, Pb²⁺) in their biomass (factorial map from PCA). Most marginal macrophytes have a lower concentration of chemical elements (ANOVA p-value < 0.05). Submerged and floating macrophyte species presented a higher concentration of metallic and non-metallic chemical elements in their biomass (ANOVA p-value < 0.05), revealing their potential in phytoremediation and the removal of toxic compounds (such as heavy metal molecules) from water. A cluster of macrophyte species also exhibited high concentrations of macronutrients and micronutrients (ANOVA p-value < 0.05), indicating their potential for use as soil fertilizers. These results reveal that the plant’s location in the reservoir (marginal, floating, or submerged) is a relevant feature associated with macrophytes’ ability to remove chemical components from the water. The obtained results can contribute to planning the management of macrophyte species in large water reservoirs.

This study investigates the potential of macrophytes as biostimulants in agricultural applications through a two-stage experimental approach. In the first stage, a screening experiment evaluated 12 macrophyte species using ethanolic and potassium chloride extracts at two doses (1 and 5 kg fresh biomass/ha) applied to bioindicator species Cucumis sativus (C3) and Urochloa decumbens (C4). Controlled greenhouse conditions and randomized block designs ensured reliability. Dry biomass was measured 21 days after treatment (DAT), revealing varied macrophyte effects. Ethanolic extracts of Typha domingensis and Egeria densa demonstrated significant biomass increases, particularly for U. decumbens, while potassium chloride extracts often reduced biomass. E. densa was selected for further analysis due to its promising results and ease of selective harvesting. In the second stage, a dose–response experiment assessed the impact of E. densa ethanolic extracts on Phaseolus vulgaris at six doses (0.25 to 4 kg fresh biomass/ha). Optimal results were observed at 1–2 kg/ha, yielding 15% increases in plant height and dry biomass. Higher doses showed diminishing returns. These findings highlight the potential of E. densa as a sustainable biostimulant and a solution for macrophyte overabundance in Brazilian reservoirs, supporting agricultural and environmental objectives.

Background: The skin prick test (SPT) is used to diagnose sensitization to antigens. This study proposes a deep learning approach to infer wheal dimensions, aiming to reduce dependence on human interpretation. Methods: A dataset of SPT images (n = 5844) was used to infer a convolutional neural network for wheal segmentation (ML model). Three methods for inferring wheal dimensions were evaluated: the ML model; the standard protocol (MA1); and approximation of the area as an ellipse using diameters measured by an allergist (MA2). The results were compared with assisted image segmentation (AIS), the most accurate method. Bland–Altman analysis, distribution analyses, and correlation tests were applied to compare the methods. This study also compared the percentage deviation among these methods in determining the area of wheals with regular geometric shapes (n = 150) and with irregular shapes (n = 150). Results: The Bland–Altman analysis showed that the difference between methods was not correlated with the absolute area. The ML model achieved a segmentation accuracy of 85.88% and a strong correlation with the AIS method (ρ = 0.88), outperforming all other methods. Additionally, MA1 showed significant error (13.44 ± 13.95%) for pseudopods. Conclusions: The ML protocol can potentially automate the reading of SPT, offering greater accuracy than the standard protocol.

Background/Objectives: Translation and the formation of membraneless organelles are linked mechanisms to promote cell stress surveillance. LncRNAs responsive to ethanol stress transcr_9136 of the SEY6210 strain and transcr_10027 of the BY4742 strain appear to act on tolerance to ethanol in these strains. Here, we investigate whether the ethanol responsiveness of transcr_9136 and transcr_10027 and their role in ethanol stress are associated with protein biogenesis and membraneless organelle assembly. Methods: SEY6210 transcr_9136∆ and BY4742 transcr_10027∆ and their wild-type counterparts were subjected to their maximum ethanol-tolerant stress. The expression of the transcr_9136, transcr_10027, ILT1, RRP1, 27S, 25S, TIR3, and FAA3 genes was accessed by qPCR. The level of DCP1a, PABP, and eIF4E proteins was evaluated by Western blotting. Bioinformatics analyses allowed us to check whether transcr_9136 may regulate the expression of RRP1 and predict the interaction between transcr_10027 and Tel1p. The cell death rate of SEY6210 strains under control and ethanol stress conditions was assessed by flow cytometry. Finally, we evaluated the total protein yield of all strains analyzed. Results: The results demonstrated that transcr_9136 of SEY6210 seems to control the expression of RRP1 and 27S rRNA and reduce the general translation. Furthermore, transcr_9136 seems to act on cell membrane integrity. Transcr_10027 of BY4742 appears to inhibit processing body formation and induce a general translation level. Conclusions: This is the first report on the effect of lncRNAs on yeast protein synthesis and new mechanisms of stress-responsive lncRNAs in yeast, with potential industrial applications such as ethanol production.

In recent years, the scientific community has worked intensively in the search and development of new drugs to suppress viral infections, such as COVID-19. In fact, a number of active compounds have been tested; however, the absence of significant structure-activity relationships hinders the production of optimized drugs. In this study, molecular modeling techniques were employed to investigate the electronic, structural and chemical reactivity properties of a set α-ketoamides whose antiviral activities have been reported in the literature, aiming to propose new promising derivatives. The local reactivity of the compounds was evaluated via condensed-to-atoms Fukui indexes and molecular electrostatic potential. Multivariate data analysis and random forests machine learning techniques were employed to correlate the antiviral properties and electronic and structural descriptors and identify relevant variables. A series of new derivatives were then proposed and evaluated via density functional theory-based calculations, and docking/molecular dynamics with the target protein of the virus. The results suggest that active derivatives present reduced reactivity towards electrophilic agents on the central core of the molecules and high reactivity on R¹ ligands. Derivatives with higher predicted antiviral activities were proposed based on simple electronic descriptors, and their efficacies are reinforced by docking and molecular dynamics simulations.Communicated by Ramaswamy H. Sarma.

O objetivo deste estudo é identificar características e elaborar modelos que ajudem a compreender como ocorre a propagação de fake news em redes sociais, apontando mecanismos hábeis para a adequação da legislação para o combate à disseminação de fake news em proteção à saúde coletiva. O método de pesquisa foi dividido em três etapas: (1) revisão da legislação atual abordando disseminação de fake news; (2) aplicação e avaliação de respostas a um questionário sociocultural utilizando análises fatoriais multivariadas e aprendizado de máquina para identificar possíveis correspondências entre o comportamento de disseminar informações falsas e características socioculturais; (3) identificação de parâmetros associados à propagação de fake news em rede social utilizando uma abordagem de redes complexas. Os resultados mostraram que a legislação brasileira ainda não possui mecanismos para caracterização de dolo associado à propagação de fake news em redes sociais. A análise das respostas ao questionário indica que a propagação de fake news não se dá por falta de conhecimento. A análise de propagação de fake news em rede social mostrou que a disseminação desse tipo de informação não segue um padrão esperado para redes livres de escala, evidenciando impulsionamento artificial de notícia. Adicionalmente, a análise de parâmetros da rede de propagação de notícias utilizando teoria de redes complexas pode ser utilizada objetivamente para identificar se a propagação ocorre de forma natural ou artificial. A responsabilização pela ação dolosa de propagação de fake news em redes sociais demanda de uma adequação da legislação vigente.