Trypanosoma cruzi: establishment of permeable cells for RNA processing analysis with drugs

Pre-mRNA maturation in trypanosomatids occurs through a process called trans-splicing which involves excision of introns and union of exons in two independent transcripts. For the first time, we present the standardization of Trypanosoma cruzi permeable cells (Y strain) as a model for trans-splicing study of mRNAs in trypanosomes, following by RNase protection reaction, which localizes the SL exon and intron. This trans-splicing reaction in vitro was also used to analyze the influence of NFOH-121, a nitrofurazone-derivative, on this mechanism. The results suggested that the prodrug affects the RNA processing in these parasites, but the trans-splicing reaction still occurred.

The sample processing time interval as an influential factor in flow cytometry analysis of lymphocyte subsets

The objective of this paper is to propose a protocol to analyze blood samples in yellow fever 17DD vaccinated which developed serious adverse events. We investigated whether or not the time between sample collection and sample processing could interfere in lymphocyte subset percentage, for it is often impossible to analyze blood samples immediately after collection due to transport delay from collection places to the flow cytometry facility. CD4+CD38+ T, CD8+CD38+ T, CD3+ T, CD19+ B lymphocyte subsets were analyzed by flow cytometry in nine healthy volunteers immediately after blood collection and after intervals of 24 and 48 h. The whole blood lysis method and gradient sedimentation by Histopaque were applied to isolate peripheral blood mononuclear cells for flow cytometry analyses. With the lysis method, there was no significant change in lymphocyte subset percentage between the two time intervals (24 and 48 h). In contrast, when blood samples were processed by Histopaque gradient sedimentation, time intervals for sample processing influenced the percentage in T lymphocyte subsets but not in B cells. From the results obtained, we could conclude that the whole blood lysis method is more appropriate than gradient sedimentation by Histopaque for immunophenotyping of blood samples collected after serious adverse events, due to less variation in the lymphocyte subset levels with respect to the time factor.

Footprints of a trypanosomatid RNA world: pre-small subunit rRNA processing by spliced leader addition trans-splicing

The addition of a capped mini-exon [spliced leader (SL)] through trans-splicing is essential for the maturation of RNA polymerase (pol) II-transcribed polycistronic pre-mRNAs in all members of the Trypanosomatidae family. This process is an inter-molecular splicing reaction that follows the same basic rules of cis-splicing reactions. In this study, we demonstrated that mini-exons were added to precursor ribosomal RNA (pre-rRNA) are transcribed by RNA pol I, including the 5' external transcribed spacer (ETS) region. Additionally, we detected the SL-5'ETS molecule using three distinct methods and located the acceptor site between two known 5'ETS rRNA processing sites (A' and A1) in four different trypanosomatids. Moreover, we detected a polyadenylated 5'ETS upstream of the trans-splicing acceptor site, which also occurs in pre-mRNA trans-splicing. After treatment with an indirect trans-splicing inhibitor (sinefungin), we observed SL-5'ETS decay. However, treatment with 5-fluorouracil (a precursor of RNA synthesis that inhibits the degradation of pre-rRNA) led to the accumulation of SL-5'ETS, suggesting that the molecule may play a role in rRNA degradation. The detection of trans-splicing in these molecules may indicate broad RNA-joining properties, regardless of the polymerase used for transcription.

Digital soil mapping: strategy for data pre-processing

The region of greatest variability on soil maps is along the edge of their polygons, causing disagreement among pedologists about the appropriate description of soil classes at these locations. The objective of this work was to propose a strategy for data pre-processing applied to digital soil mapping (DSM). Soil polygons on a training map were shrunk by 100 and 160 m. This strategy prevented the use of covariates located near the edge of the soil classes for the Decision Tree (DT) models. Three DT models derived from eight predictive covariates, related to relief and organism factors sampled on the original polygons of a soil map and on polygons shrunk by 100 and 160 m were used to predict soil classes. The DT model derived from observations 160 m away from the edge of the polygons on the original map is less complex and has a better predictive performance.

Application of text-mining for updating protein post-translational modification annotation in UniProtKB.

BACKGROUND: The annotation of protein post-translational modifications (PTMs) is an important task of UniProtKB curators and, with continuing improvements in experimental methodology, an ever greater number of articles are being published on this topic. To help curators cope with this growing body of information we have developed a system which extracts information from the scientific literature for the most frequently annotated PTMs in UniProtKB. RESULTS: The procedure uses a pattern-matching and rule-based approach to extract sentences with information on the type and site of modification. A ranked list of protein candidates for the modification is also provided. For PTM extraction, precision varies from 57% to 94%, and recall from 75% to 95%, according to the type of modification. The procedure was used to track new publications on PTMs and to recover potential supporting evidence for phosphorylation sites annotated based on the results of large scale proteomics experiments. CONCLUSIONS: The information retrieval and extraction method we have developed in this study forms the basis of a simple tool for the manual curation of protein post-translational modifications in UniProtKB/Swiss-Prot. Our work demonstrates that even simple text-mining tools can be effectively adapted for database curation tasks, providing that a thorough understanding of the working process and requirements are first obtained. This system can be accessed at http://eagl.unige.ch/PTM/.

Chemical and physical composition of grain-type and food-type soybean for food processing

The objective of this work was to evaluate the chemical and physical characteristics of grains of soybean (Glycine max) cultivars for food processing. The soybean cultivars evaluated were: grain-type - BRS 133 and BRS 258; food-type - BRS 213 (null lipoxygenases), BRS 267 (vegetable-type) and BRS 216 (small grain size). BRS 267 and BRS 216 cultivars showed higher protein content, indicating that they could promote superior nutritional value. BRS 213 cultivar showed the lowest lipoxygenase activity, and BRS 267, the lowest hexanal content. These characteristics can improve soyfood flavor. After cooking, BRS 267 cultivar grains presented a higher content of aglycones (more biologically active form of isoflavones) and oleic acid, which makes it proper for functional foods and with better stability for processing, and also showed high content of fructose, glutamic acid and alanine, compounds related to the soybean mild flavor. Because of its large grain size, BRS 267 is suitable for tofu and edamame, while small-grain-sized BRS 216 is good for natto and for soybean sprouts production. BRS 216 and BRS 213 cultivars presented shorter cooking time, which may be effective for reducing processing costs.

Changes in the isoflavone profile and in the chemical composition of tempeh during processing and refrigeration

The objective of this work was to analyze changes in the isoflavone profile, determined by high performance liquid chromatography, at different processing stages and after refrigeration of tempeh. For tempeh production, clean soybean grains from cultivars BR 36 (low isoflavone content) and IAS 5 (high) were dehulled, and the separated cotyledons were hydrated and then cooked in boiling water for 30 min. Spores of the fungus Rhizopus microsporus var. oligosporus were inoculated in the cooked and cooled cotyledons, and incubated at 32ºC for 6, 12, 18, and 24 hours in perforated polypropylene bags, for fermentation. The resulting tempeh was stored at 4ºC for 6, 12, 18, and 24 hours. After 24-hour fermentation, isoflavone glucosides were 50% reduced, and the aglycone forms in the tempeh from both cultivars was increased. The malonyl forms reduced 83% after cooking. Less than 24 hours of refrigeration did not affect the isoflavone profile of tempeh from either cultivar, which is a good indicator of its quality. The tempeh maintains the high and low isoflavone content of the cultivars, which indicates that cultivar differences in this trait should be considered when processing tempeh.

New kernel functions and learning methods for text and data mining

Recent advances in machine learning methods enable increasingly the automatic construction of various types of computer assisted methods that have been difficult or laborious to program by human experts. The tasks for which this kind of tools are needed arise in many areas, here especially in the fields of bioinformatics and natural language processing. The machine learning methods may not work satisfactorily if they are not appropriately tailored to the task in question. However, their learning performance can often be improved by taking advantage of deeper insight of the application domain or the learning problem at hand. This thesis considers developing kernel-based learning algorithms incorporating this kind of prior knowledge of the task in question in an advantageous way. Moreover, computationally efficient algorithms for training the learning machines for specific tasks are presented. In the context of kernel-based learning methods, the incorporation of prior knowledge is often done by designing appropriate kernel functions. Another well-known way is to develop cost functions that fit to the task under consideration. For disambiguation tasks in natural language, we develop kernel functions that take account of the positional information and the mutual similarities of words. It is shown that the use of this information significantly improves the disambiguation performance of the learning machine. Further, we design a new cost function that is better suitable for the task of information retrieval and for more general ranking problems than the cost functions designed for regression and classification. We also consider other applications of the kernel-based learning algorithms such as text categorization, and pattern recognition in differential display. We develop computationally efficient algorithms for training the considered learning machines with the proposed kernel functions. We also design a fast cross-validation algorithm for regularized least-squares type of learning algorithm. Further, an efficient version of the regularized least-squares algorithm that can be used together with the new cost function for preference learning and ranking tasks is proposed. In summary, we demonstrate that the incorporation of prior knowledge is possible and beneficial, and novel advanced kernels and cost functions can be used in algorithms efficiently.

Identification of the Emerging Issues in Recycled Fiber Processing

The objectives of this research work “Identification of the Emerging Issues in Recycled Fiber processing” are discovering of emerging research issues and presenting of new approaches to identify promising research themes in recovered paper application and production. The projected approach consists of identifying technological problems often encountered in wastepaper preparation processes and also improving the quality of recovered paper and increasing its proportion in the composition of paper and board. The source of information for the problem retrieval is scientific publications in which waste paper application and production were discussed. The study has exploited several research methods to understand the changes related to utilization of recovered paper. The all assembled data was carefully studied and categorized by applying software called RefViz and CiteSpace. Suggestions were made on the various classes of these problems that need further investigation in order to propose an emerging research trends in recovered paper.

Machine Learning and Clinical Text. Supporting Health Information Flow

Fluent health information flow is critical for clinical decision-making. However, a considerable part of this information is free-form text and inabilities to utilize it create risks to patient safety and cost-­effective hospital administration. Methods for automated processing of clinical text are emerging. The aim in this doctoral dissertation is to study machine learning and clinical text in order to support health information flow.First, by analyzing the content of authentic patient records, the aim is to specify clinical needs in order to guide the development of machine learning applications.The contributions are a model of the ideal information flow,a model of the problems and challenges in reality, and a road map for the technology development. Second, by developing applications for practical cases,the aim is to concretize ways to support health information flow. Altogether five machine learning applications for three practical cases are described: The first two applications are binary classification and regression related to the practical case of topic labeling and relevance ranking.The third and fourth application are supervised and unsupervised multi-class classification for the practical case of topic segmentation and labeling.These four applications are tested with Finnish intensive care patient records.The fifth application is multi-label classification for the practical task of diagnosis coding. It is tested with English radiology reports.The performance of all these applications is promising. Third, the aim is to study how the quality of machine learning applications can be reliably evaluated.The associations between performance evaluation measures and methods are addressed,and a new hold-out method is introduced.This method contributes not only to processing time but also to the evaluation diversity and quality. The main conclusion is that developing machine learning applications for text requires interdisciplinary, international collaboration. Practical cases are very different, and hence the development must begin from genuine user needs and domain expertise. The technological expertise must cover linguistics,machine learning, and information systems. Finally, the methods must be evaluated both statistically and through authentic user-feedback.

Evaluation of sample processing methods for the polar contaminant analysis of sewage sludge using liquid chromatography - mass spectrometry (LC/MS)

Monitoring of sewage sludge has proved the presence of many polar anthropogenic pollutants since LC/MS techniques came into routine use. While advanced techniques may improve characterizations, flawed sample processing procedures, however, may disturb or disguise the presence and fate of many target compounds present in this type of complex matrix before analytical process starts. Freeze-drying or oven-drying, in combination with centrifugation or filtration as sample processing techniques were performed followed by visual pattern recognition of target compounds for assessment of pretreatment processes. The results shown that oven-drying affected the sludge characterization, while freeze-drying led to less analytical misinterpretations.

Processing of spent NiW/Al2O3 catalysts

Spent oxidized (500 ºC, 5 h) commercial NiW/Al2O3 catalysts were processed using two different routes: a) fusion with NaOH (650 ºC, 1 h), the roasted mass was leached in water; b) leaching with HCl or H2SO4 (70 ºC, 1-3 h). HCl was the best leachant. In both routes, soluble tungsten was extracted at pH 1 with Alamine 336 (10 vol.% in kerosene) and stripped with 2 mol L-1 NH4OH (25 ºC, one stage, aqueous/organic ratio = 1 v/v). Tungsten was isolated as ammonium paratungstate at very high yield (> 97.5%). The elements were better separated using the acidic route.

The Li+, Na+ and K+ ion exchange reaction process on the surface of mixed oxide SiO2/TiO2/Sb2O5 surface prepared by the sol-gel processing method

The porous mixed oxide SiO2/TiO2/Sb2O5 obtained by the sol-gel processing method presented a good ion exchange property and a high exchange capacity towards the Li+, Na+ and K+ ions. In the H+/M+ ion exchange process, the H+ / Na+ could be described as presenting an ideal character. The ion exchange equilibria of Li+ and K+ were quantitatively described with the help of the model of fixed tetradentate centers. The results of simulation evidence that for the H+ / Li+ exchange the usual situation takes place: the affinity of the material to the Li+ ions is decreased with increasing the degree of ion exchange. On the contrary, for K+ the effects of positive cooperativity, that facilitate the H+ / K+ exchange, were revealed.

Adsorption of hidrogen peroxide on the surface of silica - titania mixed oxide obtained by the sol-gel processing method

This work describes the sol-gel mixed oxide SiO2/TiO2 property, ST, as prepared, and submitted to heat treatment a 773 K, STC. SEM and EDS images show, within magnification used, a uniform distribution of the TiO2 particles in SiO2/TiO2 matrix. Both, ST and STC adsorb hydrogen peroxide on the surface and through EPR and UV-Vis diffuse reflectance spectra, it was possible to conclude that the species on the surface is the peroxide molecule attached to the Lewis acid site of titanium particle surface, alphaTi(H2O2)+. As the material is very porous, presumably the hydrogen peroxide molecule is confined in the matrix pores on the surface, a reason why the adsorbed species presents an exceptional long lived stability.

Detection of beetle damage in forests by X-ray CT image processing

Some beetle species can have devastating economic impacts on forest and nursery industries. A recent example is Anophophora glabripennis, a species of beetle known in the United States as the ''Asian Longhorrned beetle'', which has damaged many American forests, and is a threat which can unintentionally reach south American countries, including Brazil. This work presents a new method based on X-ray computerized tomography (CT) and image processing for beetle injury detection in forests. Its results show a set of images with correct identification of the location of beetles in living trees as well as damage evaluation with time.