Reassessment of the Contribution of Belowground N from Soybean after Testing Different 15N Leaf-Labelling Strategies

Purpose: Soybean is the most important grain crop in Brazil with a mean N accumulation of over 250 kg N ha-1, principally from biological N2 fixation. The residual N benefit depends heavily on the quantity of the belowground N at harvest, much which cannot be directly recovered in roots. The purpose of this study was to investigate different aspects of the 15N leaf-labelling technique to quantify non-recoverable root N (NRRN) derived from senescent roots and nodules (rhizodeposits). Methods: Soybean plants were grown in pots of soil and at 27 days after planting (vegetative stage V4) cut or whole leaves were exposed to highly enriched 15N-labelled urea or glutamine. Seven sequential harvests of the plants and soil were taken until the final grain harvest at 70 days after labelling.Results: After only 48 h, the plants labelled with 15N urea transferred approximately 5% to the soil, while only 1% was found in the roots. Leakage of 15N label was even more pronounced when the leaves were labelled with 15N glutamine. After this initial leakage, the excess 15N deposited in the soil only increased by a further 2.6% of applied label, which suggested that only part of this N represented senescence of roots or nodules.Conclusions At the final harvest, N in roots separated from the soil amounted to 6.4% of total plant N. Discounting the early rapid deposition of 15N-enriched N to the soil, our calculations indicated that at final harvest the total NRRN was 2.8% of total plant N.

Direct incorporation of [ 18F] into aliphatic systems: A promising Mncatalysed labelling technique for PET imaging

Background: One of the challenges in positron emission tomography (PET) is labelling complex aliphatic molecules. Objective: To develop a method of metal-catalysed radiofluorination that is site-selective and works in moderate to good yields under facile conditions. Methods: We report here on the optimisation of an aliphatic C-H to C-18F bond transformation catalysed by a Mn(porphyrin) complex. Results: The successful oxidation of 11 aliphatic molecules including progesterone are reported. Radiochemical Incorporations (RCIs) up to 69% were achieved within 60 min without the need for pre-activation or specialist equipment. Conclusion: The method features mild conditions (60 °C) and promises to constitute a valuable approach to labelling of biomolecules and drug substances.

A simple in situ labelling approach and adequate tools for photo and video quadrats used in underwater ecological studies

Ecological studies use quadrats to gather qualitative (1/0) and quantitative (density and surface coverage) information in terrestrial and marine sciences. Depending on the spatiotemporal scale of the assessment, this could be a pilot or a monitoring survey. For monitoring surveys, it is necessary to develop a code for the quadrat itself (in situ labelling), for the digital file (ex situ codification), and ideally, for both. The design of the quadrat used for these studies must accomplish ergonomics through certain specifications such as: made of highly resistant material; negative-buoyant but lightweight; anticorrosive (specially for marine environments); able to stay positioned on seafloor habitat; and compatible with the in situ labelling technique. The present paper is a comparison of quadrats of different materials and widths, including the implementation of an in situ and ex situ codification technique. Recommendations are made after several test hours sampling with quadrats.

Predicting Solar and Wind Based Computation using Square Difference Labelling Technique

In this paper we have shown that the graph Dbn, C(Ln), T(n, m), K1 + K1,n, balloon of the triangular snake , DHF(n), bull graph (C3), Duplication of the pendant vertex by the edge of bull graph (C3) and one point union of (bull (C3))k is a square difference graph.

The Role of Ensemble Learning in Stock Market Classification Model Accuracy Enhancement Based on Naive Bayes Classifiers

Over the last years, methods of hybrid and ensemble have attracted the attention of the data mining community. Moreover, in the computational intelligence area such as machine learning, constructing and adaptive hybrid models have become essential to achieve good performance. However, the accuracy of stock market classification models is still low, and this has negatively affected the stock market indicators. Furthermore, there are many factors that have a direct effect on the classification models’ accuracies which were not addressed by previous research such as the automatic labelling technique which results in low classification accuracy due to the absence of specific lexicon, and the suitability of the classifiers to the data features and domain. In this research, a proposed model is designed to enhance the classification accuracy by the incorporation of stock market domain expert labelling technique and the construction of an ensemble Naïve Bayes classifiers to classify the stock market sentiments. The methodology for this research consists of five phases. The first phase is data collection, and the second phase is labelling, in which polarity of data is specified and negative, positive or neutral values are assigned. The third phase involves data pre-processing. The fourth phase is the classification phase in which suitable patterns of the stock market are identified by Ensemble Naïve Bayes classifiers, and the final is the performance and evaluation. The classification method has produced a significant result; it has achieved accuracy of more than 89%.