Caracterizacion sintomatologica y molecular del virus de la mancha anillada del papayo (PRSV) que infecta Carica papaya L. en el norte del Peru

The objective of present study was symptomatic and molecular characterization of the virus that infects Carica papaya L. in areas of northern Peru. To do this, of different fields were collected leaves of C. papaya with mosaic symptoms, chlorosis and distortion the leaf. Sap of these leaves was inoculated mechanically onto virus-free plants of C. papaya, Chenopodium murale, Ch. amaranticolor, Ch. quinoa, Cucumis melo, C. sativus and Cucurbita pepo; which they were kept at room temperature for 45 days, after which young leaves in of C. papaya, mosaic, distortion and reduction of the leaf blade was observed; in the species C. melo, C. sativus and Cucurbita pepo systemic chlorosis. Ch. murale, Ch. amaranticolor and Ch. quinoa they no showed symptoms evident. The plants infected were analyzed by serological technique NCM-ELISA and RT-PCR proving that the virus that is infecting the plantations assessed in the north of Peru, it is the papaya ringspot virus (RSVP).El presente trabajo tuvo como objetivo la caracterización sintomatológica y molecular del virus que infecta Carica papaya L. en zonas del norte peruano. Para ello, de diferentes campos en producción se colectaron hojas tiernas de C. papaya con síntomas de mosaico, clorosis, aclareo de nervaduras y distorsión de la lámina foliar. Savia de estas hojas fue inoculada en forma mecánica sobre plantas libres de virus de C. papaya, Chenopodium murale, Ch. amaranticolor, Ch. quinoa, Cucumis melo, C. sativus y Cucurbita pepo; las que fueron mantenidas a temperatura ambiente durante 45 días al cabo de los cuales en las hojas jóvenes de C. papaya se observó aclareo de nervaduras, mosaico, distorsión y reducción de la lámina foliar; en las especies C. melo, C. sativus y Cucurbita pepo clorosis sistémica, en Ch. murale, Ch. amaranticolor y Ch. quinoa no se evidenciaron sintomas. Las plantas de C. papaya, C. melo, C. sativus y Cucurbita pepo infectadas, fueron analizadas por la técnica serológica de NCM-ELISA y RT-PCR comprobándose que el virus que se encuentra infectando las plantaciones de las zonas evaluadas en el norte del Perú, es el virus de la mancha anillada del papayo (PRSV)

Underwater Adhesion of Multiresponsive Complex Coacervates

International audienceMany marine organisms have developed adhesives that are able to bond under water, overcoming the challenges associated with wet adhesion. A key element in the processing of several natural underwater glues is complex coacervation, a liquid–liquid phase separation driven by complexation of oppositely charged macromolecules. Inspired by these examples, the development of a fully synthetic complex coacervate‐based adhesive is reported with an in situ setting mechanism, which can be triggered by a change in temperature and/or a change in ionic strength. The adhesive consists of a matrix of oppositely charged polyelectrolytes that are modified with thermoresponsive poly(N‐isopropylacrylamide) (PNIPAM) grafts. The adhesive, which initially starts out as a fluid complex coacervate with limited adhesion at room temperature and high ionic strength, transitions into a viscoelastic solid upon an increase in temperature and/or a decrease in the salt concentration of the environment. Consequently, the thermoresponsive chains self‐associate into hydrophobic domains and/or the polyelectrolyte matrix contracts, without inducing any macroscopic shrinking. The presence of PNIPAM favors energy dissipation by softening the material and by allowing crack blunting. The high work of adhesion, the gelation kinetics, and the easy tunability of the system make it a potential candidate for soft tissue adhesion in physiological environments

Tuning the Interactions in Multiresponsive Complex Coacervate-Based Underwater Adhesives

In this work, we report the systematic investigation of a multiresponsive complex coacervate-based underwater adhesive, obtained by combining polyelectrolyte domains and thermoresponsive poly(N-isopropylacrylamide) (PNIPAM) units. This material exhibits a transition from liquid to solid but, differently from most reactive glues, is completely held together by non-covalent interactions, i.e., electrostatic and hydrophobic. Because the solidification results in a kinetically trapped morphology, the final mechanical properties strongly depend on the preparation conditions and on the surrounding environment. A systematic study is performed to assess the effect of ionic strength and of PNIPAM content on the thermal, rheological and adhesive properties. This study enables the optimization of polymer composition and environmental conditions for this underwater adhesive system. The best performance with a work of adhesion of 6.5 J/m2 was found for the complex coacervates prepared at high ionic strength (0.75 M NaCl) and at an optimal PNIPAM content around 30% mol/mol. The high ionic strength enables injectability, while the hydrated PNIPAM domains provide additional dissipation, without softening the material so much that it becomes too weak to resist detaching stress. © 2019 by the authors. Licensee MDPI, Basel, Switzerland

Amino acid classification based spectrum kernel fusion for protein subnuclear localization

<p>Abstract</p> <p>Background</p> <p>Prediction of protein localization in subnuclear organelles is more challenging than general protein subcelluar localization. There are only three computational models for protein subnuclear localization thus far, to the best of our knowledge. Two models were based on protein primary sequence only. The first model assumed homogeneous amino acid substitution pattern across all protein sequence residue sites and used BLOSUM62 to encode <it>k</it>-mer of protein sequence. Ensemble of SVM based on different <it>k</it>-mers drew the final conclusion, achieving 50% overall accuracy. The simplified assumption did not exploit protein sequence profile and ignored the fact of heterogeneous amino acid substitution patterns across sites. The second model derived the <it>PsePSSM </it>feature representation from protein sequence by simply averaging the profile PSSM and combined the <it>PseAA </it>feature representation to construct a kNN ensemble classifier <it>Nuc-PLoc</it>, achieving 67.4% overall accuracy. The two models based on protein primary sequence only both achieved relatively poor predictive performance. The third model required that GO annotations be available, thus restricting the model's applicability.</p> <p>Methods</p> <p>In this paper, we only use the amino acid information of protein sequence without any other information to design a widely-applicable model for protein subnuclear localization. We use <it>K</it>-spectrum kernel to exploit the contextual information around an amino acid and the conserved motif information. Besides expanding window size, we adopt various amino acid classification approaches to capture diverse aspects of amino acid physiochemical properties. Each amino acid classification generates a series of spectrum kernels based on different window size. Thus, (I) window expansion can capture more contextual information and cover size-varying motifs; (II) various amino acid classifications can exploit multi-aspect biological information from the protein sequence. Finally, we combine all the spectrum kernels by simple addition into one single kernel called <it>SpectrumKernel+ </it>for protein subnuclear localization.</p> <p>Results</p> <p>We conduct the performance evaluation experiments on two benchmark datasets: <it>Lei </it>and <it>Nuc-PLoc</it>. Experimental results show that <it>SpectrumKernel+ </it>achieves substantial performance improvement against the previous model <it>Nuc-PLoc</it>, with overall accuracy <it>83.47% </it>against <it>67.4%</it>; and <it>71.23% </it>against <it>50% </it>of <it>Lei SVM Ensemble</it>, against 66.50% of <it>Lei GO SVM Ensemble</it>.</p> <p>Conclusion</p> <p>The method <it>SpectrumKernel</it>+ can exploit rich amino acid information of protein sequence by embedding into implicit size-varying motifs the multi-aspect amino acid physiochemical properties captured by amino acid classification approaches. The kernels derived from diverse amino acid classification approaches and different sizes of <it>k</it>-mer are summed together for data integration. Experiments show that the method <it>SpectrumKernel</it>+ significantly outperforms the existing models for protein subnuclear localization.</p

Thermoresponsive Complex Coacervate-Based Underwater Adhesive

Sandcastle worms have developed protein-based adhesives, which they use to construct protective tubes from sand grains and shell bits. A key element in the adhesive delivery is the formation of a fluidic complex coacervate phase. After delivery, the adhesive transforms into a solid upon an external trigger. In this work, a fully synthetic in situ setting adhesive based on complex coacervation is reported by mimicking the main features of the sandcastle worm's glue. The adhesive consists of oppositely charged polyelectrolytes grafted with thermoresponsive poly(N-isopropylacrylamide) (PNIPAM) chains and starts out as a fluid complex coacervate that can be injected at room temperature. Upon increasing the temperature above the lower critical solution temperature of PNIPAM, the complex coacervate transitions into a nonflowing hydrogel while preserving its volume—the water content in the material stays constant. The adhesive functions in the presence of water and bonds to different surfaces regardless of their charge. This type of adhesive avoids many of the problems of current underwater adhesives and may be useful to bond biological tissues.</p

Uso da Espectroscopia Raman e FT-IR na caracterização do biocarvão em Latossolo Amarelo da Amazônia Central

The Amazonian Latosols are acidic soils shows low activity in clay minerals. However, it is also found anthropogenic soils known as Amazonian Dark Earth (EAD) that provides a potential to develop a sustainable system in agriculture. The majority of TPI soils show fragments of black carbon stemming from an anthropic activity. The presence of these fragments endows the improvements in the physic and chemical characteristics of the soil. In order to reproduce some characteristics of these anthropogenic soils, it is proposed to apply biochar (BC) in a dystrophic Yellow Oxisol in increasing doses from 0; 40; 80 and 120 t.ha-1. The use of Spectroscopy FT-IR and Raman tools and technics can elucidate on the nature of the pyrolised biomass and likewise interfere on the fertility of the soil. Furthermore, it could clarify how the BC contributes to the increase of cation exchange capacity (CEC), the elucidation of its chemical characteristics and how it can act in the development of a sustainable agriculture model for the humid tropics. It was possible to observe that he FT-IR spectra were similar between the treatments and that the BC exhibits similar crystallinity to the carbons of Amazonian Dark Earth

Computational identification of ubiquitylation sites from protein sequences

<p>Abstract</p> <p>Background</p> <p>Ubiquitylation plays an important role in regulating protein functions. Recently, experimental methods were developed toward effective identification of ubiquitylation sites. To efficiently explore more undiscovered ubiquitylation sites, this study aims to develop an accurate sequence-based prediction method to identify promising ubiquitylation sites.</p> <p>Results</p> <p>We established an ubiquitylation dataset consisting of 157 ubiquitylation sites and 3676 putative non-ubiquitylation sites extracted from 105 proteins in the UbiProt database. This study first evaluates promising sequence-based features and classifiers for the prediction of ubiquitylation sites by assessing three kinds of features (amino acid identity, evolutionary information, and physicochemical property) and three classifiers (support vector machine, <it>k</it>-nearest neighbor, and NaïveBayes). Results show that the set of used 531 physicochemical properties and support vector machine (SVM) are the best kind of features and classifier respectively that their combination has a prediction accuracy of 72.19% using leave-one-out cross-validation.</p> <p>Consequently, an informative physicochemical property mining algorithm (IPMA) is proposed to select an informative subset of 531 physicochemical properties. A prediction system UbiPred was implemented by using an SVM with the feature set of 31 informative physicochemical properties selected by IPMA, which can improve the accuracy from 72.19% to 84.44%. To further analyze the informative physicochemical properties, a decision tree method C5.0 was used to acquire if-then rule-based knowledge of predicting ubiquitylation sites. UbiPred can screen promising ubiquitylation sites from putative non-ubiquitylation sites using prediction scores. By applying UbiPred, 23 promising ubiquitylation sites were identified from an independent dataset of 3424 putative non-ubiquitylation sites, which were also validated by using the obtained prediction rules.</p> <p>Conclusion</p> <p>We have proposed an algorithm IPMA for mining informative physicochemical properties from protein sequences to build an SVM-based prediction system UbiPred. UbiPred can predict ubiquitylation sites accompanied with a prediction score each to help biologists in identifying promising sites for experimental verification. UbiPred has been implemented as a web server and is available at <url>http://iclab.life.nctu.edu.tw/ubipred</url>.</p

Subcellular location prediction of proteins using support vector machines with alignment of block sequences utilizing amino acid composition

Background: Subcellular location prediction of proteins is an important and well-studied problem in bioinformatics. This is a problem of predicting which part in a cell a given protein is transported to, where an amino acid sequence of the protein is given as an input. This problem is becoming more important since information on subcellular location is helpful for annotation of proteins and genes and the number of complete genomes is rapidly increasing. Since existing predictors are based on various heuristics, it is important to develop a simple method with high prediction accuracies. Results: In this paper, we propose a novel and general predicting method by combining techniques for sequence alignment and feature vectors based on amino acid composition. We implemented this method with support vector machines on plant data sets extracted from the TargetP database. Through fivefold cross validation tests, the obtained overall accuracies and average MCC were 0.9096 and 0.8655 respectively. We also applied our method to other datasets including that of WoLF PSORT. Conclusion: Although there is a predictor which uses the information of gene ontology and yields higher accuracy than ours, our accuracies are higher than existing predictors which use only sequence information. Since such information as gene ontology can be obtained only for known proteins, our predictor is considered to be useful for subcellular location prediction of newly-discovered proteins. Furthermore, the idea of combination of alignment and amino acid frequency is novel and general so that it may be applied to other problems in bioinformatics. Our method for plant is also implemented as a web-system and available on http://sunflower.kuicr.kyoto-u.ac.jp/~tamura/slpfa.html webcite

A method to improve protein subcellular localization prediction by integrating various biological data sources

<p>Abstract</p> <p>Background</p> <p>Protein subcellular localization is crucial information to elucidate protein functions. Owing to the need for large-scale genome analysis, computational method for efficiently predicting protein subcellular localization is highly required. Although many previous works have been done for this task, the problem is still challenging due to several reasons: the number of subcellular locations in practice is large; distribution of protein in locations is imbalanced, that is the number of protein in each location remarkably different; and there are many proteins located in multiple locations. Thus it is necessary to explore new features and appropriate classification methods to improve the prediction performance.</p> <p>Results</p> <p>In this paper we propose a new predicting method which combines two key ideas: 1) Information of neighbour proteins in a probabilistic gene network is integrated to enrich the prediction features. 2) Fuzzy k-NN, a classification method based on fuzzy set theory is applied to predict protein locating in multiple sites. Experiment was conducted on a dataset consisting of 22 locations from Budding yeast proteins and significant improvement was observed.</p> <p>Conclusion</p> <p>Our results suggest that the neighbourhood information from functional gene networks is predictive to subcellular localization. The proposed method thus can be integrated and complementary to other available prediction methods.</p