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Institution

University of Haifa

EducationHaifa, Israel
About: University of Haifa is a education organization based out in Haifa, Israel. It is known for research contribution in the topics: Population & Poison control. The organization has 7558 authors who have published 27141 publications receiving 711629 citations. The organization is also known as: Haifa University & Universiṭat Ḥefah.


Papers
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Journal ArticleDOI
24 Nov 2006-Science
TL;DR: The positional cloning of Gpc-B1, a wheat quantitative trait locus associated with increased grain protein, zinc, and iron content, is reported here, and reduction in RNA levels of the multiple NAM homologs by RNA interference delayed senescence by more than 3 weeks and reduced wheat grain protein and zinc content.
Abstract: Enhancing the nutritional value of food crops is a means of improving human nutrition and health. We report here the positional cloning of Gpc-B1, a wheat quantitative trait locus associated with increased grain protein, zinc, and iron content. The ancestral wild wheat allele encodes a NAC transcription factor (NAM-B1) that accelerates senescence and increases nutrient remobilization from leaves to developing grains, whereas modern wheat varieties carry a nonfunctional NAM-B1 allele. Reduction in RNA levels of the multiple NAM homologs by RNA interference delayed senescence by more than 3 weeks and reduced wheat grain protein, zinc, and iron content by more than 30%.

1,377 citations

Journal ArticleDOI
29 Nov 2012-Nature
TL;DR: An integrated and ordered physical, genetic and functional sequence resource that describes the barley gene-space in a structured whole-genome context and suggests that post-transcriptional processing forms an important regulatory layer.
Abstract: Barley (Hordeum vulgare L.) is among the world's earliest domesticated and most important crop plants. It is diploid with a large haploid genome of 5.1 gigabases (Gb). Here we present an integrated and ordered physical, genetic and functional sequence resource that describes the barley gene-space in a structured whole-genome context. We developed a physical map of 4.98 Gb, with more than 3.90 Gb anchored to a high-resolution genetic map. Projecting a deep whole-genome shotgun assembly, complementary DNA and deep RNA sequence data onto this framework supports 79,379 transcript clusters, including 26,159 'high-confidence' genes with homology support from other plant genomes. Abundant alternative splicing, premature termination codons and novel transcriptionally active regions suggest that post-transcriptional processing forms an important regulatory layer. Survey sequences from diverse accessions reveal a landscape of extensive single-nucleotide variation. Our data provide a platform for both genome-assisted research and enabling contemporary crop improvement.

1,347 citations

Journal ArticleDOI
TL;DR: The SVM approach as represented by Schoelkopf was superior to all the methods except the neural network one, where it was, although occasionally worse, essentially comparable.
Abstract: We implemented versions of the SVM appropriate for one-class classification in the context of information retrieval. The experiments were conducted on the standard Reuters data set. For the SVM implementation we used both a version of Schoelkopf et al. and a somewhat different version of one-class SVM based on identifying "outlier" data as representative of the second-class. We report on experiments with different kernels for both of these implementations and with different representations of the data, including binary vectors, tf-idf representation and a modification called "Hadamard" representation. Then we compared it with one-class versions of the algorithms prototype (Rocchio), nearest neighbor, naive Bayes, and finally a natural one-class neural network classification method based on "bottleneck" compression generated filters.The SVM approach as represented by Schoelkopf was superior to all the methods except the neural network one, where it was, although occasionally worse, essentially comparable. However, the SVM methods turned out to be quite sensitive to the choice of representation and kernel in ways which are not well understood; therefore, for the time being leaving the neural network approach as the most robust.

1,293 citations

Journal ArticleDOI
01 Mar 2009-Brain
TL;DR: The hypothesis that emotional empathic abilities (involving the mirror neuron system) are distinct from those related to cognitive empathy and that the two depend on separate anatomical substrates is tested.
Abstract: Recent evidence suggests that there are two possible systems for empathy: a basic emotional contagion system and a more advanced cognitive perspective-taking system. However, it is not clear whether these two systems are part of a single interacting empathy system or whether they are independent. Additionally, the neuroanatomical bases of these systems are largely unknown. In this study, we tested the hypothesis that emotional empathic abilities (involving the mirror neuron system) are distinct from those related to cognitive empathy and that the two depend on separate anatomical substrates. Subjects with lesions in the ventromedial prefrontal (VM) or inferior frontal gyrus (IFG) cortices and two control groups were assessed with measures of empathy that incorporate both cognitive and affective dimensions. The findings reveal a remarkable behavioural and anatomic double dissociation between deficits in cognitive empathy (VM) and emotional empathy (IFG). Furthermore, precise anatomical mapping of lesions revealed Brodmann area 44 to be critical for emotional empathy while areas 11 and 10 were found necessary for cognitive empathy. These findings are consistent with these cortices being different in terms of synaptic hierarchy and phylogenetic age. The pattern of empathy deficits among patients with VM and IFG lesions represents a first direct evidence of a double dissociation between emotional and cognitive empathy using the lesion method.

1,290 citations

Journal ArticleDOI
TL;DR: In this paper, the authors examine two explanatory models for the relative lack of conflict between democracies: the normative model suggests that democracies do not fight each other because norms of compromise and cooperation prevent their conflicts of interest from escalating into violent clashes, and the structural model asserts that complex political mobilization processes impose institutional constraints on the leaders of two democracies confronting each other to make violent conflict impossible.
Abstract: Democratic states are in general about as conflict- and war-prone as nondemocracies, but democracies have rarely clashed with one another in violent conflict. We first show that democracy, as well as other factors, accounts for the relative lack of conflict. Then we examine two explanatory models. The normative model suggests that democracies do not fight each other because norms of compromise and cooperation prevent their conflicts of interest from escalating into violent clashes. The structural model asserts that complex political mobilization processes impose institutional constraints on the leaders of two democracies confronting each other to make violent conflict unfeasible. Using different data sets of international conflict and a multiplicity of indicators, we find that (1) democracy, in and of itself, has a consistent and robust negative effect on the likelihood of conflict or escalation in a dyad; (2) both the normative and structural models are supported by the data; and (3) support for the normative model is more robust and consistent.

1,215 citations


Authors

Showing all 7747 results

NameH-indexPapersCitations
Markku Laakso162945142292
M.-Marsel Mesulam15055890772
Michael Levin11198645667
Peter Schmidt10563861822
Eviatar Nevo9584840066
Uri Alon9144254822
Dan Roth8552328166
Simon G. Potts8224931557
Russell G. Foster7931823206
Leo Radom7960434075
Stevan E. Hobfoll7427135870
Larry Davidson6945920177
Alan R. Templeton6724928320
Uri Gneezy6521129671
Benny Pinkas6415621122
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
202394
2022304
20211,978
20201,822
20191,579
20181,505