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Abstract Exotic Eucalyptus spp. are the most planted trees species in forest cultivation in Brazil. The red gum lerp psyllid Glycaspis brimblecombei Moore (Hemiptera: Aphalaridae), an invasive exotic pest, reduces the development and wood production of Eucalyptus spp. plantations. The search for natural enemies targeting the egg life stage is beneficial for the integrated pest management of G. brimblecombei. The objective of this work was to test a range of Coccinellidae species that potentially prey on G. brimblecombei eggs. Cycloneda sanguinea Linnaeus, Eriopsis conexa Germar, Harmonia axyridis Pallas, Hippodamia convergens Guerin-Meneville, Jaguarita conjugata Mulsant and Olla v-nigrum Mulsant (Coleoptera: Coccinellidae), reported to be associated with G. brimblecombei infestations, were evaluated. Newly emerged adults of O. v-nigrum and J. conjugata preyed on G. brimblecombei eggs, unlike C. sanguinea, E. conexa, H. axyridis and H. convergens. Predation of eggs by O. v-nigrum was highest, with a mean of 1,016 in 24 hours. These results, evaluating a range of Coccinellidae as predators of G. brimblecombei eggs, confirm the high predation rate of G. brimblecombei eggs by O. v-nigrum and that this ladybug may be important in the integrated management of this pest. Resumo O gênero Eucalyptus ocupa a maior área plantada no setor florestal brasileiro com alta produtividade. O psilídeo de concha Glycaspis brimblecombei Moore (Hemiptera: Aphalaridae), praga exótica, reduz o desenvolvimento e produção de madeira nesses plantios. A busca de inimigos naturais para a fase de ovos de G. brimblecombei é essencial para complementar o manejo integrado e quebrar o ciclo da praga. O objetivo deste trabalho foi avaliar a predação de ovos de G. brimblecombei por seis espécies de Coccinelidae. A predação de ovos de G. brimblecombei por Cycloneda sanguinea Linnaeus, Eriopsis conexa Germar, Harmonia axyridis Pallas, Hippodamia convergens Guerin-Meneville, Jaguarita conjugata Mulsant e Olla v-nigrum Mulsant (Coleoptera: Coccinellidae), comumente associadas com infestações por essa praga, foi avaliada. Adultos recém emergidos de O. v-nigrum e J. conjugata predaram ovos de G. brimblecombei, mas isto não foi observado para C. sanguinea, E. conexa, H. axyridis e H. convergens. A predação de ovos, dessa praga, por O. v-nigrum foi maior em todas as avaliações, com 1.016 após 24 horas. Estes resultados são pioneiros na utilização de coccinelídeos no manejo de pragas do eucalipto. A maior predação de ovos de G. brimblecombei por O. v-nigrum indica que este predador pode ser importante para complementar o manejo integrado dessa praga.

Synthesis, characterization and thermal behavior of some transition metal glycolates, M(C2H3O3)2 [M = Mn(II), Co(II), Ni(II), Cu(II) and Zn(II)], as well as the thermal behavior of glycolic acid and its sodium salt (NaC2H3O3) were investigated employing simultaneous thermogravimetry and differential thermal analysis, infrared spectroscopy (FTIR), simultaneous thermogravimetry–differential scanning calorimetry coupled to FTIR (evolved gas analysis), complexometry and high-resolution electrospray ionization mass spectrometry. The lowest energy model structure of each complex has been proposed by using the density functional theory at the B3LYP/6-311++g(d) level of theory. The results provided information concerning the composition, dehydration, thermal stability, thermal decomposition and identification of the gaseous products evolved during the thermal decomposition of these compounds in dynamic dry air atmosphere.

The trivalent lanthanide isonicotinates were synthesized to obtain stoichiometry Lu(IN) 3 and Ln(IN) 3 ·2H 2 O (Ln = Tb to Lu, and Y; IN = isonicotinate). A deep study of the thermal behavior in oxidant (air) and inert (N 2 ) atmospheres was carried out using the following thermoanalytical techniques: simultaneous thermogravimetry and differential scanning calorimetry (TG–DSC), differential scanning calorimetry (DSC) and evolved gas analysis (EGA by TG–DSC–FTIR). From these results, it was possible to determine that dehydration occurs in a single step and the thermal decomposition of the anhydrous compounds occurs in one or two (air), and two or three steps (N 2 ). The final residues of thermal decomposition were Tb 4 O 7 and Ln 2 O 3 (Ln = Dy to Lu, and Y) in air atmosphere, while in N 2 atmosphere the mass loss is still being observed up to 1000 °C. The identified gaseous products evolved during the thermal decomposition in dynamic dry air and nitrogen atmospheres were water, CO 2 , pyridine and CO. From these thermoanalytical data, it was possible to propose a general equation of thermal decomposition of these compounds in the N 2 atmosphere. DSC curves of Tb and Ho compounds presented endothermic peaks corresponding to a reversible phase transition, confirmed by powder X-ray diffractometry (XRD), not previously reported. In addition, infrared vibrational spectroscopy (IR) suggests the coordination through carboxylate as bridging bidentate ligand toward the heavy trivalent lanthanides metals. This paper is complementary to our previous study involving the series of light trivalent lanthanides isonicotinates.

Characterization, thermal stability, and thermal decomposition of light trivalent lanthanide isonicotinates Ln(L)3·2H2O (Ln = La to Gd, except Pm; L = isonicotinate) were investigated employing simultaneous thermogravimetry and differential scanning calorimetry (TG–DSC), DSC, infrared spectroscopy (FTIR), evolved gas analysis by TG–DSC coupled to FTIR, elemental analysis, and complexometry. The dehydration of these compounds occurs in a single step, and the thermal decomposition of the anhydrous compounds occurs in one or two (air) and two or three steps (N2). The final residues of thermal decomposition were CeO2, Pr6O11, and Ln2O3 (Ln = La, Nd to Gd) in air atmosphere, while in N2 atmosphere the mass loss is still being observed up to 1000 °C. The results also provided information concerning the gaseous products evolved during the thermal decomposition in dynamic dry air and nitrogen atmospheres.

In this work we describe a proposal for a graphene-based nanostructure that modulates electric current even in the absence of a gap in the band structure. The device consists of a graphene p-n junction that acts as a Veselago lens that focuses ballistic electrons on the output lead. Applying external (electric and magnetic) fields changes the position of the output focus, reducing the transmission. Such device can be applied to low power field effect transistors, which can benefit from graphene's high electronic mobility.

Artificial neural networks in drug discovery have shown remarkable potential in various areas, including molecular similarity assessment and virtual screening. This study presents a novel multimodal Siamese neural network architecture. The aim was to join molecular electrostatic potential (MEP) images with the texture features derived from reduced density gradient (RDG) diagrams for enhanced molecular similarity prediction. On one side, the proposed model is combined with a convolutional neural network (CNN) for processing MEP visual information. This data is added to the multilayer perceptron (MLP) that extracts texture features from gray-level co-occurrence matrices (GLCM) computed from RDG diagrams. Both representations converge through a multimodal projector into a shared embedding space, which was trained using triplet loss to learn similarity and dissimilarity patterns. Limitations associated with the use of purely structural descriptors were overcome by incorporating non-covalent interaction information through RDG profiles, which enables the identification of bioisosteric relationships needed for rational drug design. Three datasets were used to evaluate the performance of the developed model: tyrosine kinase inhibitors (TKIs) targeting the mutant T315I BCR-ABL receptor for the treatment of chronic myeloid leukemia, acetylcholinesterase inhibitors (AChEIs) for Alzheimer's disease therapy, and heterodimeric AChEI candidates for cross-validation. The visual and texture features of the Siamese architecture help in the capture of molecular similarities based on electrostatic and non-covalent interaction profiles. Therefore, the developed protocol offers a suitable approach in computational drug discovery, being a promising framework for virtual screening, drug repositioning, and the identification of novel therapeutic candidates.

Background Drug-target networks are receiving a lot of attention in late years, given its relevance for pharmaceutical innovation and drug lead discovery. Different in silico approaches have been proposed for the identification of new drug-target interactions, many of which are based on kernel methods. Despite technical advances in the latest years, these methods are not able to cope with large drug-target interaction spaces and to integrate multiple sources of biological information. Results We propose KronRLS-MKL, which models the drug-target interaction problem as a link prediction task on bipartite networks. This method allows the integration of multiple heterogeneous information sources for the identification of new interactions, and can also work with networks of arbitrary size. Moreover, it automatically selects the more relevant kernels by returning weights indicating their importance in the drug-target prediction at hand. Empirical analysis on four data sets using twenty distinct kernels indicates that our method has higher or comparable predictive performance than 18 competing methods in all prediction tasks. Moreover, the predicted weights reflect the predictive quality of each kernel on exhaustive pairwise experiments, which indicates the success of the method to automatically reveal relevant biological sources. Conclusions Our analysis show that the proposed data integration strategy is able to improve the quality of the predicted interactions, and can speed up the identification of new drug-target interactions as well as identify relevant information for the task. Availability The source code and data sets are available at www.cin.ufpe.br/~acan/kronrlsmkl/ .

This paper presents the quantification, antioxidant and antimicrobial activity of capsaicin, dihydrocapsaicin and the flavonoid chrysoeriol isolated from different extracts (hexane and acetonitrile extracts from whole fruit, peel and seed) of Capsicum frutescens (pimenta malagueta). The acetonitrile extract of the seeds, peel and whole fruits contained capsaicin as a major component, followed in abundance by dihydrocapsaicin and chrysoeriol. The antimicrobial activity of the isolated compounds against seven microorganisms showed chrysoeriol was the most active compound. In the antioxidant test, the acetonitrile extract from the whole fruit showed the highest activity. The antioxidant activity of pimenta malagueta may be correlated with its phenolic content, principally with the most active compound, capsaicin.

In the context of multi-environment trials (MET), genomic prediction is proposed as a tool that allows the prediction of the phenotype of single cross hybrids that were not tested in field trials. This approach saves time and costs compared to traditional breeding methods. Thus, this study aimed to evaluate the genomic prediction of single cross maize hybrids not tested in MET, grain yield and female flowering time. We also aimed to propose an application of machine learning methodologies in MET in the prediction of hybrids and compare their performance with Genomic best linear unbiased prediction (GBLUP) with non-additive effects. Our results highlight that both methodologies are efficient and can be used in maize breeding programs to accurately predict the performance of hybrids in specific environments. The best methodology is case-dependent, specifically, to explore the potential of GBLUP, it is important to perform accurate modeling of the variance components to optimize the prediction of new hybrids. On the other hand, machine learning methodologies can capture non-additive effects without making any assumptions at the outset of the model. Overall, predicting the performance of new hybrids that were not evaluated in any field trials was more challenging than predicting hybrids in sparse test designs.