Eliminación de DBP en aceite de onagra mediante arcilla activada modificada por chitosán y CTAB

The pollution of phthalic acid esters (PAEs) in edible oils is a serious problem. In the current study, we attempt to remove dibutyl phthalate ester (DBP) from evening primrose oil (EPO) with modified activated clay. The activated clay, commonly used for de-coloration in the oil refining process, was modified by chitosan and hexadecyl trimethyl ammonium bromide (CTAB). The modifications were characterized by SEM, XRD, and FT-IR. We further tested the DBP adsorption capacity of CTAB/chitosan-clay and found that the removal rate was 27.56% which was 3.24 times higher than with pristine activated clay. In addition, the CTAB/chitosan-clay composite treatment had no significant effect on the quality of evening primrose oil. In summary, the CTAB/chitosan-clay composite has a stronger DBP adsorption capacity and can be used as a new adsorbent for removing DBP during the de-coloration process of evening primrose oil.La contaminación por ésteres de ácido ftálico (PAEs) en los aceites comestibles es un problema grave. En el presente estudio, intentamos eliminar el éster de ftalato de dibutilo (DBP) del aceite de onagra (EPO) con arcilla activada modificada. La arcilla activada, comúnmente utilizada en la decoloración en el proceso de refinación de los aceites, fue modificada con chitosán y bromuro de hexadecil trimetil amonio (CTAB). Las modificaciones se caracterizaron mediante SEM, XRD y FT-IR. Además, probamos la capacidad de adsorción de DBP de CTAB / chitosán-arcilla y descubrimos que la tasa de eliminación era del 27,56%, que era 3,24 veces mayor que la arcilla activada pura. Además, el tratamiento compuesto de CTAB/chitosán-arcilla no tuvo un efecto significativo sobre la calidad del aceite de onagra. En resumen, el compuesto CTAB/chitosán-arcilla tiene una capacidad de adsorción de DBP más fuerte y se puede utilizar como un nuevo adsorbente para eliminar DBP durante el proceso de decoloración del aceite de onagra

Antigenic cross-reactivity between severe acute respiratory syndrome-associated coronavirus and human coronaviruses 229E and OC43

Cross-reactivity between antibodies to different human coronaviruses (HCoVs) has not been systematically studied. By use of Western blot analysis, indirect immunofluorescence assay (IFA), and enzyme-linked immunosorbent assay (ELISA), antigenic cross-reactivity between severe acute respiratory syndrome (SARS)-associated coronavirus (SARS-CoV) and 2 HCoVs (229E and OC43) was demonstrated in immunized animals and human serum. In 5 of 11 and 10 of 11 patients with SARS, paired serum samples showed a ≥4-fold increase in antibody titers against HCoV-229E and HCoV-OC43, respectively, by IFA. Overall, serum samples from convalescent patients who had SARS had a 1-way cross-reactivity with the 2 known HCoVs. Antigens of SARS-CoV and HCoV-OC43 were more cross-reactive than were those of SARS-CoV and HCoV-229E. © 2005 by the Infectious Diseases Society of America. All rights reserved.published_or_final_versio

Joint Protection of Energy Security and Information Privacy for Energy Harvesting: An Incentive Federated Learning Approach

Energy harvesting (EH) is a promising and critical technology to mitigate the dilemma between the limited battery capacity and the increasing energy consumption in the Internet of everything. However, the current EH system suffers from energy-information cross threats, facing the overlapping vulnerability of energy deprivation and private information leakage. Although some existing works touch on the security of energy and information in EH, they treat these two issues independently, without collaborative and intelligent protection cross the energy side and information side. To address the above challenge, this paper proposes a joint protection framework of energy security and information privacy for EH with an incentive federated learning approach. First, we design a federated learning-based malicious energy user detection method according to energy status and behaviors to provide energy security protection. Secondly, a differential privacy-empowered information preservation scheme is devised, where sensitive information is perturbed and protected by the customized demand-based noise. Thirdly, a non-cooperative game-enabled incentive mechanism is established to encourage EH nodes to participate in the joint energy-information protection system. The proposed incentive mechanism derives the optimal energy-information security strategy for EH nodes and achieve a tradeoff between the protection of energy security and information privacy. Evaluation results have verified the effectiveness of our proposed joint protection mechanism

Elevated CO₂ does not increase eucalypt forest productivity on a low-phosphorus soil

Rising atmospheric CO2 stimulates photosynthesis and productivity of forests, offsetting CO2 emissions. Elevated CO2 experiments in temperate planted forests yielded ~23% increases in productivity over the initial years. Whether similar CO2 stimulation occurs in mature evergreen broadleaved forests on low-phosphorus (P) soils is unknown, largely due to lack of experimental evidence. This knowledge gap creates major uncertainties in future climate projections as a large part of the tropics is P-limited. Here,we increased atmospheric CO2 concentration in a mature broadleaved evergreen eucalypt forest for three years, in the first large-scale experiment on a P-limited site. We show that tree growth and other aboveground productivity components did not significantly increase in response to elevated CO2 in three years, despite a sustained 19% increase in leaf photosynthesis. Moreover, tree growth in ambient CO2 was strongly P-limited and increased by ~35% with added phosphorus. The findings suggest that P availability may potentially constrain CO2-enhanced productivity in P-limited forests; hence, future atmospheric CO2 trajectories may be higher than predicted by some models. As a result, coupled climate-carbon models should incorporate both nitrogen and phosphorus limitations to vegetation productivity in estimating future carbon sinks

Accurate Prediction of Protein Structural Class

Because of the increasing gap between the data from sequencing and structural genomics, the accurate prediction of the structural class of a protein domain solely from the primary sequence has remained a challenging problem in structural biology. Traditional sequence-based predictors generally select several sequence features and then feed them directly into a classification program to identify the structural class. The current best sequence-based predictor achieved an overall accuracy of 74.1% when tested on a widely used, non-homologous benchmark dataset 25PDB. In the present work, we built a multiple linear regression (MLR) model to convert the 440-dimensional (440D) sequence feature vector extracted from the Position Specific Scoring Matrix (PSSM) of a protein domain to a 4-dimensinal (4D) structural feature vector, which could then be used to predict the four major structural classes. We performed 10-fold cross-validation and jackknife tests of the method on a large non-homologous dataset containing 8,244 domains distributed among the four major classes. The performance of our approach outperformed all of the existing sequence-based methods and had an overall accuracy of 83.1%, which is even higher than the results of those predicted secondary structure-based methods

Size and frequency of natural forest disturbances and the Amazon forest carbon balance

types: Journal Article; Research Support, Non-U.S. Gov't; Research Support, U.S. Gov't, Non-P.H.S.Copyright © 2014 Macmillan Publishers Limited. All rights reserved.This is an open-access articleForest inventory studies in the Amazon indicate a large terrestrial carbon sink. However, field plots may fail to represent forest mortality processes at landscape-scales of tropical forests. Here we characterize the frequency distribution of disturbance events in natural forests from 0.01 ha to 2,651 ha size throughout Amazonia using a novel combination of forest inventory, airborne lidar and satellite remote sensing data. We find that small-scale mortality events are responsible for aboveground biomass losses of ~1.7 Pg C y(-1) over the entire Amazon region. We also find that intermediate-scale disturbances account for losses of ~0.2 Pg C y(-1), and that the largest-scale disturbances as a result of blow-downs only account for losses of ~0.004 Pg C y(-1). Simulation of growth and mortality indicates that even when all carbon losses from intermediate and large-scale disturbances are considered, these are outweighed by the net biomass accumulation by tree growth, supporting the inference of an Amazon carbon sink.NASA Earth System Science Fellowship (NESSF

'Unite and conquer': enhanced prediction of protein subcellular localization by integrating multiple specialized tools

<p>Abstract</p> <p>Background</p> <p>Knowing the subcellular location of proteins provides clues to their function as well as the interconnectivity of biological processes. Dozens of tools are available for predicting protein location in the eukaryotic cell. Each tool performs well on certain data sets, but their predictions often disagree for a given protein. Since the individual tools each have particular strengths, we set out to integrate them in a way that optimally exploits their potential. The method we present here is applicable to various subcellular locations, but tailored for predicting whether or not a protein is localized in mitochondria. Knowledge of the mitochondrial proteome is relevant to understanding the role of this organelle in global cellular processes.</p> <p>Results</p> <p>In order to develop a method for enhanced prediction of subcellular localization, we integrated the outputs of available localization prediction tools by several strategies, and tested the performance of each strategy with known mitochondrial proteins. The accuracy obtained (up to 92%) surpasses by far the individual tools. The method of integration proved crucial to the performance. For the prediction of mitochondrion-located proteins, integration via a two-layer decision tree clearly outperforms simpler methods, as it allows emphasis of biologically relevant features such as the mitochondrial targeting peptide and transmembrane domains.</p> <p>Conclusion</p> <p>We developed an approach that enhances the prediction accuracy of mitochondrial proteins by uniting the strength of specialized tools. The combination of machine-learning based integration with biological expert knowledge leads to improved performance. This approach also alleviates the conundrum of how to choose between conflicting predictions. Our approach is easy to implement, and applicable to predicting subcellular locations other than mitochondria, as well as other biological features. For a trial of our approach, we provide a webservice for mitochondrial protein prediction (named YimLOC), which can be accessed through the AnaBench suite at http://anabench.bcm.umontreal.ca/anabench/. The source code is provided in the Additional File <supplr sid="S2">2</supplr>.</p> <suppl id="S2"> <title> <p>Additional file 2</p> </title> <text> <p>This file contains scripts for the online server YimLOC. Please note that there scripts only codes for the ready-to-use STACK-mem-DT described in the main text. The scripts do not provide the training process.</p> </text> <file name="1471-2105-8-420-S2.pdf"> <p>Click here for file</p> </file> </suppl

The use of cessation assistance among smokers from China: Findings from the ITC China Survey

<p>Abstract</p> <p>Background</p> <p>Stop smoking medications significantly increase the likelihood of smoking cessation. However, there are no population-based studies of stop-smoking medication use in China, the largest tobacco market in the world. This study examined stop-smoking medication use and its association with quitting behavior among a population-based sample of Chinese smokers.</p> <p>Methods</p> <p>Face-to-face interviews were conducted with 4,627 smokers from six cities in the ITC China cohort survey. Longitudinal analyses were conducted using Wave 1 (April to August, 2006) and Wave 2 (November 2007 to January 2008).</p> <p>Results</p> <p>Approximately 26% of smokers had attempted to quit between Waves 1 and 2, and 6% were abstinent at 18-month follow-up. Only 5.8% of those attempting to quit reported NRT use and NRT was associated with lower odds of abstinence at Wave 2 (OR = 0.11; 95%CI = 0.03-0.46). Visiting a doctor/health professional was associated with greater attempts to quit smoking (OR = 1.60 and 2.78; 95%CI = 1.22-2.10 and 2.21-3.49 respectively) and being abstinent (OR = 1.77 and 1.85; 95%CI = 1.18-2.66 and 1.13-3.04 respectively) at 18-month follow-up relative to the smokers who did not visit doctor/health professional.</p> <p>Conclusions</p> <p>The use of formal help for smoking cessation is low in China. There is an urgent need to explore the use and effectiveness of stop-smoking medications in China and in other non-Western markets.</p

Search for Kaluza-Klein Graviton Emission in ppˉp\bar{p} Collisions at s=1.8\sqrt{s}=1.8 TeV using the Missing Energy Signature

We report on a search for direct Kaluza-Klein graviton production in a data sample of 84 ${pb}^{-1}$ of \ppb collisions at $\sqrt{s}$ = 1.8 TeV, recorded by the Collider Detector at Fermilab. We investigate the final state of large missing transverse energy and one or two high energy jets. We compare the data with the predictions from a $3+1+n$-dimensional Kaluza-Klein scenario in which gravity becomes strong at the TeV scale. At 95% confidence level (C.L.) for $n$=2, 4, and 6 we exclude an effective Planck scale below 1.0, 0.77, and 0.71 TeV, respectively.Comment: Submitted to PRL, 7 pages 4 figures/Revision includes 5 figure