Water Activities and Osmotic Coefficients of Aqueous Solutions of Five Alkyl-Aminium Sulfates and Their Mixtures with H2SO4 at 25 oC

Alkylaminium sulfates are frequently detected in ambient aerosols, and are believed to be important in the nucleation of new particles in the atmosphere, despite their comparatively low gas phase concentrations. In this study water activities and osmotic coefficients have been measured, using a chilled mirror dew point technique, of aqueous mixtures of sulfuric acid and the following alkylaminium sulfates: methylaminium, ethylaminium, dimethylaminium, diethylaminium and trimethylaminium sulfate. The solutions were prepared by mixing solutions of the five corresponding amines and aqueous sulfuric acid and determining the exact aminium to sulfate molar ratios by ion chromatography. The results were correlated using an extended Zdanovskii-Stokes-Robinson equation to enable concentration/water activity relationships to be calculated over the entire composition range from pure aqueous sulfuric acid to pure aqueous aminium sulfates. Comparisons with water activities of ammonium sulfate/sulfuric acid mixtures showed very similar behavior for cation:sulfate ratios of 1:1 (the bisulfate salts) and lower, but that osmotic coefficients for the 2:1 ratio (the aminium sulfates) were much greater than for ammonium sulfate. These results differ from those obtained in another recent study (S. L. Clegg, C. Qiu, and R. Zhang [2013] Atmos. Environ. 73:145–158). The relative values of the osmotic coefficients, in concentrated solutions, suggest that the numbers of methyl or ethyl groups in the aminium ion may have a stronger lowering effect on water activity than the alkyl chain length

Dietary preferences of brachyuran crabs from Taiwan for marine or terrestrial food sources: evidence based on fatty acid trophic markers

Background Trophic interactions are key processes, which determine the ecological function and performance of organisms. Many decapod crustaceans feed on plant material as a source for essential nutrients, e.g. polyunsaturated fatty acids. Strictly herbivorous feeding appears only occasionally in marine decapods but is common in land crabs. To verify food preferences and to establish trophic markers, we studied the lipid and fatty acid composition of the midgut glands of two marine crab species (Grapsus albolineatus and Percnon affine), one semi-terrestrial species (Orisarma intermedium, formerly Sesarmops intermedius), and one terrestrial species (Geothelphusa albogilva) from Taiwan. Results All species showed a wide span of total lipid levels ranging from 4 to 42% of the dry mass (%DM) in the marine P. affine and from 3 to 25%DM in the terrestrial G. albogilva. Triacylglycerols (TAG) were the major storage lipid compound. The fatty acids 16:0, 18:1(n-9), and 20:4(n-6) prevailed in all species. Essential fatty acids such as 20:4(n-6) originated from the diet. Terrestrial species also showed relatively high amounts of 18:2(n-6), which is a trophic marker for vascular plants. The fatty acid compositions of the four species allow to clearly distinguish between marine and terrestrial herbivorous feeding due to significantly different amounts of 16:0, 18:1(n-9), and 18:2(n-6). Conclusions Based on the fatty acid composition, marine/terrestrial herbivory indices were defined and compared with regard to their resolution and differentiating capacity. These indices can help to reveal trophic preferences of unexplored species, particularly in habitats of border regions like mangrove intertidal flats and estuaries

Decoding face recognition abilities in the human brain

Why are some individuals better at recognising faces? Uncovering the neural mechanisms supporting face recognition ability has proven elusive. To tackle this challenge, we used a multi-modal data-driven approach combining neuroimaging, computational modelling, and behavioural tests. We recorded the high-density electroencephalographic brain activity of individuals with extraordinary face recognition abilities—super-recognisers—and typical recognisers in response to diverse visual stimuli. Using multivariate pattern analyses, we decoded face recognition abilities from 1 second of brain activity with up to 80% accuracy. To better understand the mechanisms subtending this decoding, we compared representations in the brains of our participants with those in artificial neural network models of vision and semantics, as well as with those involved in human judgments of shape and meaning similarity. Compared to typical recognisers, we found stronger associations between early brain representations of super-recognisers and mid-level representations of vision models as well as shape similarity judgments. Moreover, we found stronger associations between late brain representations of super-recognisers and representations of the artificial semantic model as well as meaning similarity judgments. Overall, these results indicate that important individual variations in brain processing, including neural computations extending beyond purely visual processes, support differences in face recognition abilities. They provide the first empirical evidence for an association between semantic computations and face recognition abilities. We believe that such multi-modal data-driven approaches will likely play a critical role in further revealing the complex nature of idiosyncratic face recognition in the human brain

Decoding face recognition abilities in the human brain

Why are some individuals better at recognizing faces? Uncovering the neural mechanisms supporting face recognition ability has proven elusive. To tackle this challenge, we used a multimodal data-driven approach combining neuroimaging, computational modeling, and behavioral tests. We recorded the high-density electroencephalographic brain activity of individuals with extraordinary face recognition abilities—super-recognizers—and typical recognizers in response to diverse visual stimuli. Using multivariate pattern analyses, we decoded face recognition abilities from 1 s of brain activity with up to 80% accuracy. To better understand the mechanisms subtending this decoding, we compared representations in the brains of our participants with those in artificial neural network models of vision and semantics, as well as with those involved in human judgments of shape and meaning similarity. Compared to typical recognizers, we found stronger associations between early brain representations of super-recognizers and midlevel representations of vision models as well as shape similarity judgments. Moreover, we found stronger associations between late brain representations of super-recognizers and representations of the artificial semantic model as well as meaning similarity judgments. Overall, these results indicate that important individual variations in brain processing, including neural computations extending beyond purely visual processes, support differences in face recognition abilities. They provide the first empirical evidence for an association between semantic computations and face recognition abilities. We believe that such multimodal data-driven approaches will likely play a critical role in further revealing the complex nature of idiosyncratic face recognition in the human brain

The Construction and Validation of an Abridged Version of the Autism-Spectrum Quotient (AQ-Short)

This study reports on the development and validation of an abridged version of the 50-item Autism-Spectrum Quotient (AQ), a self-report measure of autistic traits. We aimed to reduce the number of items whilst retaining high validity and a meaningful factor structure. The item reduction procedure was performed on data from 1,263 Dutch students and general population adults. The resulting 28-item AQ-Short was subsequently validated in 3 independent samples, both clinical and controls, from the Netherlands and the UK. The AQ-Short comprises two higher-order factors assessing ‘social behavioral difficulties’ and ‘a fascination for numbers/patterns’. The clear factor structure of the AQ-Short and its high sensitivity and specificity make the AQ-Short a useful alternative to the full 50-item version

Provenance for Lattice QCD workflows

We present a provenance model for the generic workflow of numerical Lattice Quantum Chromodynamics (QCD) calculations, which constitute an important component of particle physics research. These calculations are carried out on the largest supercomputers worldwide with data in the multi-PetaByte range being generated and analyzed. In the Lattice QCD community, a custom metadata standard (QCDml) that includes certain provenance information already exists for one part of the workflow, the so-called generation of configurations. In this paper, we follow the W3C PROV standard and formulate a provenance model that includes both the generation part and the so-called measurement part of the Lattice QCD workflow. We demonstrate the applicability of this model and show how the model can be used to answer some provenance-related research questions. However, many important provenance questions in the Lattice QCD community require extensions of this provenance model. To this end, we propose a multi-layered provenance approach that combines prospective and retrospective elements