The effect of BCECF on intracellular pH of human platelets

Abstract2′,7′-Bis-(2-carboxyethyl)-5-(and-6)-carboxyfluorescein (BCECF) is frequently used for fluorometric determination of intracellular pH (pHi) and its metabolic changes. Studies of BCECF-loaded platelets have reported different pHi values in the range of 6.98 to 7.35, despite the use of the same probe. It is now shown that intracellular BCECF (BCECFi) content affects pHi, and that its over-loading, leads to significantly lower pHi. Different pHi values can be reproduced by changing BCECFi, as reflected by fluorescence intensity. The major loading factors are: the concentration of the probe parent compound, BCECF acetoxymethyl ester (AM), and whether this ester is partly hydrolyzed externally when applied in plasma. When least affected by BCECF, platelet pHi is 7.34. High BCECFi does not affect ATP content, buffer capacity, activation of Na+H+ exchange by protein kinase C (PKC) and basal PKC activity. On the other hand high BCECFi does inhibit the Na+H+ exchange rate by over 50%. Since the Na+H+ exchange strongly affects platelets pHi, it is proposed that this inhibition accounts, at least partly, for the lowered pHi in BCECF over-loaded platelets

Integrating image caption information into biomedical document classification in support of biocuration.

Gathering information from the scientific literature is essential for biomedical research, as much knowledge is conveyed through publications. However, the large and rapidly increasing publication rate makes it impractical for researchers to quickly identify all and only those documents related to their interest. As such, automated biomedical document classification attracts much interest. Such classification is critical in the curation of biological databases, because biocurators must scan through a vast number of articles to identify pertinent information within documents most relevant to the database. This is a slow, labor-intensive process that can benefit from effective automation. We present a document classification scheme aiming to identify papers containing information relevant to a specific topic, among a large collection of articles, for supporting the biocuration classification task. Our framework is based on a meta-classification scheme we have introduced before; here we incorporate into it features gathered from figure captions, in addition to those obtained from titles and abstracts. We trained and tested our classifier over a large imbalanced dataset, originally curated by the Gene Expression Database (GXD). GXD collects all the gene expression information in the Mouse Genome Informatics (MGI) resource. As part of the MGI literature classification pipeline, GXD curators identify MGI-selected papers that are relevant for GXD. The dataset consists of ~60 000 documents (5469 labeled as relevant; 52 866 as irrelevant), gathered throughout 2012-2016, in which each document is represented by the text of its title, abstract and figure captions. Our classifier attains precision 0.698, recall 0.784, f-measure 0.738 and Matthews correlation coefficient 0.711, demonstrating that the proposed framework effectively addresses the high imbalance in the GXD classification task. Moreover, our classifier\u27s performance is significantly improved by utilizing information from image captions compared to using titles and abstracts alone; this observation clearly demonstrates that image captions provide substantial information for supporting biomedical document classification and curation. Database URL

An effective biomedical document classification scheme in support of biocuration: addressing class imbalance.

Published literature is an important source of knowledge supporting biomedical research. Given the large and increasing number of publications, automated document classification plays an important role in biomedical research. Effective biomedical document classifiers are especially needed for bio-databases, in which the information stems from many thousands of biomedical publications that curators must read in detail and annotate. In addition, biomedical document classification often amounts to identifying a small subset of relevant publications within a much larger collection of available documents. As such, addressing class imbalance is essential to a practical classifier. We present here an effective classification scheme for automatically identifying papers among a large pool of biomedical publications that contain information relevant to a specific topic, which the curators are interested in annotating. The proposed scheme is based on a meta-classification framework using cluster-based under-sampling combined with named-entity recognition and statistical feature selection strategies. We examined the performance of our method over a large imbalanced data set that was originally manually curated by the Jackson Laboratory\u27s Gene Expression Database (GXD). The set consists of more than 90 000 PubMed abstracts, of which about 13 000 documents are labeled as relevant to GXD while the others are not relevant. Our results, 0.72 precision, 0.80 recall and 0.75 f-measure, demonstrate that our proposed classification scheme effectively categorizes such a large data set in the face of data imbalance

Upregulation of Neurotrophic Factors Selectively in Frontal Cortex in Response to Olfactory Discrimination Learning

We have previously shown that olfactory discrimination learning is accompanied by several forms of long-term enhancement in synaptic connections between layer II pyramidal neurons selectively in the piriform cortex. This study sought to examine whether the previously demonstrated olfactory-learning-task-induced modifications are preceded by suitable changes in the expression of mRNA for neurotrophic factors and in which brain areas this occurs. Rats were trained to discriminate positive cues in pair of odors for a water reward. The relationship between the learning task and local levels of mRNA for brain-derived neurotrophic factor, tyrosine kinase B, nerve growth factor, and neurotrophin-3 in the frontal cortex, hippocampal subregions, and other regions were assessed 24 hours post olfactory learning. The olfactory discrimination learning activated production of endogenous neurotrophic factors and induced their signal transduction in the frontal cortex, but not in other brain areas. These findings suggest that different brain areas may be preferentially involved in different learning/memory tasks

A-to-I RNA editing in the earliest-diverging Eumetazoan phyla

© The Author(s), 2017. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Molecular Biology and Evolution 34 (2017): 1890-1901, doi:10.1093/molbev/msx125.The highly conserved ADAR enzymes, found in all multicellular metazoans, catalyze the editing of mRNA transcripts by the deamination of adenosines to inosines. This type of editing has two general outcomes: site specific editing, which frequently leads to recoding, and clustered editing, which is usually found in transcribed genomic repeats. Here, for the first time, we looked for both editing of isolated sites and clustered, non-specific sites in a basal metazoan, the coral Acropora millepora during spawning event, in order to reveal its editing pattern. We found that the coral editome resembles the mammalian one: it contains more than 500,000 sites, virtually all of which are clustered in non-coding regions that are enriched for predicted dsRNA structures. RNA editing levels were increased during spawning and increased further still in newly released gametes. This may suggest that editing plays a role in introducing variability in coral gametes.This work was supported by the Australian Research Council (to PK), the European Research Council (grant 311257), the I-CORE Program of the Planning and Budgeting Committee in Israel (grants 41/11 and 1796/12), and the Israel Science Foundation (1380/14)

Large and unexpected enrichment in stratospheric ^(16)O^(13)C^(18)O and its meridional variation

The stratospheric CO_2 oxygen isotope budget is thought to be governed primarily by the O(1D)+CO_2 isotope exchange reaction. However, there is increasing evidence that other important physical processes may be occurring that standard isotopic tools have been unable to identify. Measuring the distribution of the exceedingly rare CO_2 isotopologue ^(16)O^(13)C^(18)O, in concert with ^(18)O and ^(17)O abundances, provides sensitivities to these additional processes and, thus, is a valuable test of current models. We identify a large and unexpected meridional variation in stratospheric 16O13C18O, observed as proportions in the polar vortex that are higher than in any naturally derived CO_2 sample to date. We show, through photochemical experiments, that lower ^(16)O^(13)C^(18)O proportions observed in the midlatitudes are determined primarily by the O(1D)+CO_2 isotope exchange reaction, which promotes a stochastic isotopologue distribution. In contrast, higher ^(16)O^(13)C^(18)O proportions in the polar vortex show correlations with long-lived stratospheric tracer and bulk isotope abundances opposite to those observed at midlatitudes and, thus, opposite to those easily explained by O(1D)+CO_2. We believe the most plausible explanation for this meridional variation is either an unrecognized isotopic fractionation associated with the mesospheric photochemistry of CO_2 or temperature-dependent isotopic exchange on polar stratospheric clouds. Unraveling the ultimate source of stratospheric ^(16)O^(13)C^(18)O enrichments may impose additional isotopic constraints on biosphere–atmosphere carbon exchange, biosphere productivity, and their respective responses to climate change

siRNA Targeted to p53 Attenuates Ischemic and Cisplatin-Induced Acute Kidney Injury

Proximal tubule cells (PTCs), which are the primary site of kidney injury associated with ischemia or nephrotoxicity, are the site of oligonucleotide reabsorption within the kidney. We exploited this property to test the efficacy of siRNA targeted to p53, a pivotal protein in the apoptotic pathway, to prevent kidney injury. Naked synthetic siRNA to p53 injected intravenously 4 h after ischemic injury maximally protected both PTCs and kidney function. PTCs were the primary site for siRNA uptake within the kidney and body. Following glomerular filtration, endocytic uptake of Cy3-siRNA by PTCs was rapid and extensive, and significantly reduced ischemia-induced p53 upregulation. The duration of the siRNA effect in PTCs was 24 to 48 h, determined by levels of p53 mRNA and protein expression. Both Cy3 fluorescence and in situ hybridization of siRNA corroborated a short t½ for siRNA. The extent of renoprotection, decrease in cellular p53 and attenuation of p53-mediated apoptosis by siRNA were dose- and time-dependent. Analysis of renal histology and apoptosis revealed improved injury scores in both cortical and corticomedullary regions. siRNA to p53 was also effective in a model of cisplatin-induced kidney injury. Taken together, these data indicate that rapid delivery of siRNA to proximal tubule cells follows intravenous administration. Targeting siRNA to p53 leads to a dose-dependent attenuation of apoptotic signaling, suggesting potential therapeutic benefit for ischemic and nephrotoxic kidney injury

How to Get the Most out of Your Curation Effort

Large-scale annotation efforts typically involve several experts who may disagree with each other. We propose an approach for modeling disagreements among experts that allows providing each annotation with a confidence value (i.e., the posterior probability that it is correct). Our approach allows computing certainty-level for individual annotations, given annotator-specific parameters estimated from data. We developed two probabilistic models for performing this analysis, compared these models using computer simulation, and tested each model's actual performance, based on a large data set generated by human annotators specifically for this study. We show that even in the worst-case scenario, when all annotators disagree, our approach allows us to significantly increase the probability of choosing the correct annotation. Along with this publication we make publicly available a corpus of 10,000 sentences annotated according to several cardinal dimensions that we have introduced in earlier work. The 10,000 sentences were all 3-fold annotated by a group of eight experts, while a 1,000-sentence subset was further 5-fold annotated by five new experts. While the presented data represent a specialized curation task, our modeling approach is general; most data annotation studies could benefit from our methodology