Truth Discovery in Crowdsourced Detection of Spatial Events

Postprin

Improving a gold standard: treating human relevance judgments of MEDLINE document pairs

Given prior human judgments of the condition of an object it is possible to use these judgments to make a maximal likelihood estimate of what future human judgments of the condition of that object will be. However, if one has a reasonably large collection of similar objects and the prior human judgments of a number of judges regarding the condition of each object in the collection, then it is possible to make predictions of future human judgments for the whole collection that are superior to the simple maximal likelihood estimate for each object in isolation. This is possible because the multiple judgments over the collection allow an analysis to determine the relative value of a judge as compared with the other judges in the group and this value can be used to augment or diminish a particular judge’s influence in predicting future judgments. Here we study and compare five different methods for making such improved predictions and show that each is superior to simple maximal likelihood estimates

An odd oxygen framework for wintertime ammonium nitrate aerosol pollution in urban areas: NOx and VOC control as mitigation strategies

Wintertime ammonium nitrate aerosol pollution is a severe air quality issue affecting both developed and rapidly urbanizing regions from Europe to East Asia. In the US, it is acute in western basins subject to inversions that confine pollutants near the surface. Measurements and modeling of a wintertime pollution episode in Salt Lake City, Utah demonstrates that ammonium nitrate is closely related to photochemical ozone through a common parameter, total odd oxygen, Ox,total. We show that the traditional NOx‐VOC framework for evaluating ozone mitigation strategies also applies to ammonium nitrate. Despite being nitrate‐limited, ammonium nitrate aerosol pollution in Salt Lake City is responsive to VOC control and, counterintuitively, not initially responsive to NOx control. We demonstrate simultaneous nitrate limitation and NOx saturation and suggest this phenomenon may be general. This finding may identify an unrecognized control strategy to address a global public health issue in regions with severe winter aerosol pollution

Interspecific Proteomic Comparisons Reveal Ash Phloem Genes Potentially Involved in Constitutive Resistance to the Emerald Ash Borer

The emerald ash borer (Agrilus planipennis) is an invasive wood-boring beetle that has killed millions of ash trees since its accidental introduction to North America. All North American ash species (Fraxinus spp.) that emerald ash borer has encountered so far are susceptible, while an Asian species, Manchurian ash (F. mandshurica), which shares an evolutionary history with emerald ash borer, is resistant. Phylogenetic evidence places North American black ash (F. nigra) and Manchurian ash in the same clade and section, yet black ash is highly susceptible to the emerald ash borer. This contrast provides an opportunity to compare the genetic traits of the two species and identify those with a potential role in defense/resistance. We used Difference Gel Electrophoresis (DIGE) to compare the phloem proteomes of resistant Manchurian to susceptible black, green, and white ash. Differentially expressed proteins associated with the resistant Manchurian ash when compared to the susceptible ash species were identified using nano-LC-MS/MS and putative identities assigned. Proteomic differences were strongly associated with the phylogenetic relationships among the four species. Proteins identified in Manchurian ash potentially associated with its resistance to emerald ash borer include a PR-10 protein, an aspartic protease, a phenylcoumaran benzylic ether reductase (PCBER), and a thylakoid-bound ascorbate peroxidase. Discovery of resistance-related proteins in Asian species will inform approaches in which resistance genes can be introgressed into North American ash species. The generation of resistant North American ash genotypes can be used in forest ecosystem restoration and urban plantings following the wake of the emerald ash borer invasion

Methane Clumped Isotopes: Progress and Potential for a New Isotopic Tracer

The isotopic composition of methane is of longstanding geochemical interest, with important implications for understanding petroleum systems, atmospheric greenhouse gas concentrations, the global carbon cycle, and life in extreme environments. Recent analytical developments focusing on multiply substituted isotopologues (‘clumped isotopes’) are opening a valuable new window into methane geochemistry. When methane forms in internal isotopic equilibrium, clumped isotopes can provide a direct record of formation temperature, making this property particularly valuable for identifying different methane origins. However, it has also become clear that in certain settings methane clumped isotope measurements record kinetic rather than equilibrium isotope effects. Here we present a substantially expanded dataset of methane clumped isotope analyses, and provide a synthesis of the current interpretive framework for this parameter. In general, clumped isotope measurements indicate plausible formation temperatures for abiotic, thermogenic, and microbial methane in many geological environments, which is encouraging for the further development of this measurement as a geothermometer, and as a tracer for the source of natural gas reservoirs and emissions. We also highlight, however, instances where clumped isotope derived temperatures are higher than expected, and discuss possible factors that could distort equilibrium formation temperature signals. In microbial methane from freshwater ecosystems, in particular, clumped isotope values appear to be controlled by kinetic effects, and may ultimately be useful to study methanogen metabolism

Multi-Emotion Estimation in Narratives from Crowdsourced Annotations

Emotion annotations are important metadata for narrative texts in digital libraries. Such annotations are necessary for automatic text-to-speech conversion of narratives and affective education support and can be used as training data for machine learning algorithms to train automatic emotion detectors. However, obtaining high-quality emotion annotations is a challenging problem because it is usually expensive and time-consuming due to the subjectivity of emotion. Moreover, due to the multiplicity of “emotion”, emotion annotations more naturally fit the paradigm of multi-label classification than that of multi-class classification since one instance (such as a sentence) may evoke a combination of multiple emotion categories. We thus investigated ways to obtain a set of high-quality emotion annotations ({instance, multi-emotion} paired data) from variable-quality crowdsourced annotations. A common quality control strategy for crowdsourced labeling tasks is to aggregate the responses provided by multiple annotators to produce a reliable annotation. Given that the categories of “emotion” have characteristics different from those of other kinds of labels, we propose incorporating domain-specific information of emotional consistencies across instances and contextual cues among emotion categories into the aggregation process. Experimental results demonstrate that, from a limited number of crowdsourced annotations, the proposed models enable gold standards to be more effectively estimated than the majority vote and the original domain-independent model