115 research outputs found
10 years of BAWLing into affective and aesthetic processes in reading: what are the echoes?
Reading is not only âcoldâ information processing, but involves affective and
aesthetic processes that go far beyond what current models of word
recognition, sentence processing, or text comprehension can explain. To
investigate such âhotâ reading processes, standardized instruments that
quantify both psycholinguistic and emotional variables at the sublexical,
lexical, inter-, and supralexical levels (e.g., phonological iconicity, word
valence, arousal-span, or passage suspense) are necessary. One such
instrument, the Berlin Affective Word List (BAWL) has been used in over 50
published studies demonstrating effects of lexical emotional variables on all
relevant processing levels (experiential, behavioral, neuronal). In this
paper, we first present new data from several BAWL studies. Together, these
studies examine various views on affective effects in reading arising from
dimensional (e.g., valence) and discrete emotion features (e.g., happiness),
or embodied cognition features like smelling. Second, we extend our
investigation of the complex issue of affective word processing to words
characterized by a mixture of affects. These words entail positive and
negative valence, and/or features making them beautiful or ugly. Finally, we
discuss tentative neurocognitive models of affective word processing in the
light of the present results, raising new issues for future studies
Autonomous detection and anticipation of jam fronts from messages propagated by inter-vehicle communication
In this paper, a minimalist, completely distributed freeway traffic
information system is introduced. It involves an autonomous, vehicle-based jam
front detection, the information transmission via inter-vehicle communication,
and the forecast of the spatial position of jam fronts by reconstructing the
spatiotemporal traffic situation based on the transmitted information. The
whole system is simulated with an integrated traffic simulator, that is based
on a realistic microscopic traffic model for longitudinal movements and lane
changes. The function of its communication module has been explicitly validated
by comparing the simulation results with analytical calculations. By means of
simulations, we show that the algorithms for a congestion-front recognition,
message transmission, and processing predict reliably the existence and
position of jam fronts for vehicle equipment rates as low as 3%. A reliable
mode of operation already for small market penetrations is crucial for the
successful introduction of inter-vehicle communication. The short-term
prediction of jam fronts is not only useful for the driver, but is essential
for enhancing road safety and road capacity by intelligent adaptive cruise
control systems.Comment: Published in the Proceedings of the Annual Meeting of the
Transportation Research Board 200
Verification of doppler coherence imaging for 2D ion velocity measurements on DIII-D
Coherence Imaging Spectroscopy (CIS) has emerged as a powerful tool for investigating complex ion phenomena in the boundary of magnetically confined plasma devices. The combination of Fourier-transform interferometry and high-resolution fast-framing cameras has made it possible to make sensitive velocity measurements that are also spatially resolved. However, this sensitivity makes the diagnostic vulnerable to environmental effects including thermal drifts, vibration, and magnetic fields that can influence the velocity measurement. Additionally, the ability to provide an absolute calibration for these geometries can be impacted by differences in the light-collection geometry between the plasma and reference light source, spectral impurities, and the presence of thin-films on in-vessel optics. This paper discusses the mitigation of these effects and demonstration that environmental effects result in less than 0.5 km/s error on the DIII-D CIS systems. A diagnostic comparison is used to demonstrate agreement between CIS and traditional spectroscopy once tomographic artifacts are accounted for.This material is based upon work supported by the U.S.
Department of Energy, Office of Science, Office of Fusion
Energy Sciences, using the DIII-D National Fusion Facility, a
DOE Office of Science user facility, under Award Nos. DEFC02-04ER54698 (DIII-D), DE-AC52-07NA27344 (LLNL),
and DE-AC05-00OR22725 (ORNL). DIII-D data shown in
this paper can be obtained in digital format by following the
links at https://fusion.gat.com/global/D3D DMP
Discrete Emotion Effects on Lexical Decision Response Times
Our knowledge about affective processes, especially concerning effects on cognitive demands like word processing, is increasing steadily. Several studies consistently document valence and arousal effects, and although there is some debate on possible interactions and different notions of valence, broad agreement on a two dimensional model of affective space has been achieved. Alternative models like the discrete emotion theory have received little interest in word recognition research so far. Using backward elimination and multiple regression analyses, we show that five discrete emotions (i.e., happiness, disgust, fear, anger and sadness) explain as much variance as two published dimensional models assuming continuous or categorical valence, with the variables happiness, disgust and fear significantly contributing to this account. Moreover, these effects even persist in an experiment with discrete emotion conditions when the stimuli are controlled for emotional valence and arousal levels. We interpret this result as evidence for discrete emotion effects in visual word recognition that cannot be explained by the two dimensional affective space account
Proteomic analysis of the Plasmodium male gamete reveals the key role for glycolysis in flagellar motility.
BACKGROUND: Gametogenesis and fertilization play crucial roles in malaria transmission. While male gametes are thought to be amongst the simplest eukaryotic cells and are proven targets of transmission blocking immunity, little is known about their molecular organization. For example, the pathway of energy metabolism that power motility, a feature that facilitates gamete encounter and fertilization, is unknown.
METHODS: Plasmodium berghei microgametes were purified and analysed by whole-cell proteomic analysis for the first time. Data are available via ProteomeXchange with identifier PXD001163.
RESULTS: 615 proteins were recovered, they included all male gamete proteins described thus far. Amongst them were the 11 enzymes of the glycolytic pathway. The hexose transporter was localized to the gamete plasma membrane and it was shown that microgamete motility can be suppressed effectively by inhibitors of this transporter and of the glycolytic pathway.
CONCLUSIONS: This study describes the first whole-cell proteomic analysis of the malaria male gamete. It identifies glycolysis as the likely exclusive source of energy for flagellar beat, and provides new insights in original features of Plasmodium flagellar organization
Membrane Topology and Predicted RNA-Binding Function of the âEarly Responsive to Dehydration (ERD4)â Plant Protein
Functional annotation of uncharacterized genes is the main focus of computational methods in the post genomic era. These tools search for similarity between proteins on the premise that those sharing sequence or structural motifs usually perform related functions, and are thus particularly useful for membrane proteins. Early responsive to dehydration (ERD) genes are rapidly induced in response to dehydration stress in a variety of plant species. In the present work we characterized function of Brassica juncea ERD4 gene using computational approaches. The ERD4 protein of unknown function possesses ubiquitous DUF221 domain (residues 312â634) and is conserved in all plant species. We suggest that the protein is localized in chloroplast membrane with at least nine transmembrane helices. We detected a globular domain of 165 amino acid residues (183â347) in plant ERD4 proteins and expect this to be posited inside the chloroplast. The structural-functional annotation of the globular domain was arrived at using fold recognition methods, which suggested in its sequence presence of two tandem RNA-recognition motif (RRM) domains each folded into ÎČαÎČÎČαÎČ topology. The structure based sequence alignment with the known RNA-binding proteins revealed conservation of two non-canonical ribonucleoprotein sub-motifs in both the putative RNA-recognition domains of the ERD4 protein. The function of highly conserved ERD4 protein may thus be associated with its RNA-binding ability during the stress response. This is the first functional annotation of ERD4 family of proteins that can be useful in designing experiments to unravel crucial aspects of stress tolerance mechanism
TESTLoc: protein subcellular localization prediction from EST data
Abstract Background The eukaryotic cell has an intricate architecture with compartments and substructures dedicated to particular biological processes. Knowing the subcellular location of proteins not only indicates how bio-processes are organized in different cellular compartments, but also contributes to unravelling the function of individual proteins. Computational localization prediction is possible based on sequence information alone, and has been successfully applied to proteins from virtually all subcellular compartments and all domains of life. However, we realized that current prediction tools do not perform well on partial protein sequences such as those inferred from Expressed Sequence Tag (EST) data, limiting the exploitation of the large and taxonomically most comprehensive body of sequence information from eukaryotes. Results We developed a new predictor, TESTLoc, suited for subcellular localization prediction of proteins based on their partial sequence conceptually translated from ESTs (EST-peptides). Support Vector Machine (SVM) is used as computational method and EST-peptides are represented by different features such as amino acid composition and physicochemical properties. When TESTLoc was applied to the most challenging test case (plant data), it yielded high accuracy (~85%). Conclusions TESTLoc is a localization prediction tool tailored for EST data. It provides a variety of models for the users to choose from, and is available for download at http://megasun.bch.umontreal.ca/~shenyq/TESTLoc/TESTLoc.html</p
Imbalanced Multi-Modal Multi-Label Learning for Subcellular Localization Prediction of Human Proteins with Both Single and Multiple Sites
It is well known that an important step toward understanding the functions of a protein is to determine its subcellular location. Although numerous prediction algorithms have been developed, most of them typically focused on the proteins with only one location. In recent years, researchers have begun to pay attention to the subcellular localization prediction of the proteins with multiple sites. However, almost all the existing approaches have failed to take into account the correlations among the locations caused by the proteins with multiple sites, which may be the important information for improving the prediction accuracy of the proteins with multiple sites. In this paper, a new algorithm which can effectively exploit the correlations among the locations is proposed by using Gaussian process model. Besides, the algorithm also can realize optimal linear combination of various feature extraction technologies and could be robust to the imbalanced data set. Experimental results on a human protein data set show that the proposed algorithm is valid and can achieve better performance than the existing approaches
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