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Institution

University of Notre Dame

EducationNotre Dame, Indiana, United States
About: University of Notre Dame is a education organization based out in Notre Dame, Indiana, United States. It is known for research contribution in the topics: Population & Large Hadron Collider. The organization has 22238 authors who have published 55201 publications receiving 2032925 citations. The organization is also known as: University of Notre Dame du Lac & University of Notre Dame, South Bend.


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Journal ArticleDOI
TL;DR: In this paper, the authors explore the impact on employee attitudes of their perceptions of how others outside the organization are treated (i.e., corporate social responsibility) above and beyond the impact of how employees are directly treated by the organization.
Abstract: We explore the impact on employee attitudes of their perceptions of how others outside the organization are treated (i.e., corporate social responsibility) above and beyond the impact of how employees are directly treated by the organization. Results of a study of 827 employees in eighteen organizations show that employee perceptions of corporate social responsibility (CSR) are positively related to (a) organizational commitment with the relationship being partially mediated by work meaningfulness and perceived organizational support (POS) and (b) job satisfaction with work meaningfulness partially mediating the relationship but not POS. Moreover, in order to address limited micro-level research in CSR, we develop a measure of employee perceptions of CSR through four pilot studies. Employing a bifactor model, we find that social responsibility has an additional effect on employee attitudes beyond environmental responsibility, which we posit is due to the relational component of social responsibility (e.g., relationships with community).

419 citations

Journal ArticleDOI
TL;DR: The security of the scheme is based on pseudorandom functions, without reliance on the Random Oracle Model, and it is shown how to handle extensions proposed by Crampton [2003] of the standard hierarchies to “limited depth” and reverse inheritance.
Abstract: Hierarchies arise in the context of access control whenever the user population can be modeled as a set of partially ordered classes (represented as a directed graph). A user with access privileges for a class obtains access to objects stored at that class and all descendant classes in the hierarchy. The problem of key management for such hierarchies then consists of assigning a key to each class in the hierarchy so that keys for descendant classes can be obtained via efficient key derivation.We propose a solution to this problem with the following properties: (1) the space complexity of the public information is the same as that of storing the hierarchy; (2) the private information at a class consists of a single key associated with that class; (3) updates (i.e., revocations and additions) are handled locally in the hierarchy; (4) the scheme is provably secure against collusion; and (5) each node can derive the key of any of its descendant with a number of symmetric-key operations bounded by the length of the path between the nodes. Whereas many previous schemes had some of these properties, ours is the first that satisfies all of them. The security of our scheme is based on pseudorandom functions, without reliance on the Random Oracle Model.Another substantial contribution of this work is that we are able to lower the key derivation time at the expense of modestly increasing the public storage associated with the hierarchy. Insertion of additional, so-called shortcut, edges, allows to lower the key derivation to a small constant number of steps for graphs that are total orders and trees by increasing the total number of edges by a small asymptotic factor such as O(log*n) for an n-node hierarchy. For more general access hierarchies of dimension d, we use a technique that consists of adding dummy nodes and dimension reduction. The key derivation work for such graphs is then linear in d and the increase in the number of edges is by the factor O(logd − 1n) compared to the one-dimensional case.Finally, by making simple modifications to our scheme, we show how to handle extensions proposed by Crampton [2003] of the standard hierarchies to “limited depth” and reverse inheritance.

418 citations

Journal ArticleDOI
V. M. Abazov1, Brad Abbott2, M. Abolins3, Bobby Samir Acharya4  +601 moreInstitutions (73)
TL;DR: In this article, the authors reported the observation of the X(3872) in the J/psipi(+)pi(-) channel with decaying to mu(+)mu(-), in p (p) over bar collisions at roots=1.96 TeV.
Abstract: We report the observation of the X(3872) in the J/psipi(+)pi(-) channel, with J/psi decaying to mu(+)mu(-), in p (p) over bar collisions at roots=1.96 TeV. Using approximately 230 pb(-1) of data collected with the Run II D0 detector, we observe 522+/-100 X(3872) candidates. The mass difference between the X(3872) state and the J/psi is measured to be 774.9+/-3.1(stat)+/-3.0(syst) MeV/c(2). We have investigated the production and decay characteristics of the X(3872) and find them to be similar to those of the psi(2S) state.

418 citations

Journal ArticleDOI
16 Mar 2015-eLife
TL;DR: Using shotgun metagenomic data from wild baboons, it is found that social group membership and social network relationships predicted both the taxonomic structure of the gut microbiome and the structure of genes encoded by gut microbial species.
Abstract: The digestive system is home to a complex community of microbes—known as the gut microbiome—that contributes to our health and wellbeing by digesting food, producing essential vitamins, and preventing the growth of harmful bacteria. The recent development of rapid genome sequencing techniques has made it much easier to identify the species of microbes found in the gut microbiome, and how this microbiome's composition varies between individuals. Studies in humans and other primates suggest that direct contact during social interactions may alter the composition of the gut microbiome in an individual. This could explain why there is a strong association between social interactions and health in humans and other social animals. However, similarities in the gut microbiomes of individuals within a social group could also be due to a shared diet or a common environment. The information collected during long-term studies of wild primates offers an opportunity to analyze and assess the influence of diet, environment and social interaction on the gut microbiome. Here, Tung et al. studied the gut microbiomes of 48 wild baboons belonging to two different social groups in Amboseli, Kenya. Using a technique called shotgun metagenomic sequencing, they sequenced DNA extracted from samples of feces collected from individual baboons. The sequence data revealed that an individual's social group and social network can predict the species found in its gut microbiome. This remained the case even when other factors—such as diet, kinship, and shared environments—were taken into account. Tung et al.'s findings suggest that direct physical contact during social interactions may be important in transmitting gut microbiomes between members of the same social group. However, scientists still don't know whether this exchange is good or bad for the health of the baboons. Future work will try to understand whether baboons benefit from acquiring gut microbes from their group members, and if the gut microbes of some social groups are better than others.

417 citations

Journal ArticleDOI
TL;DR: The structure and location of the allosteric activator site agrees with the pattern of alternate genetic splicing of the PK gene in multicellular eukaryotes that distinguishes between a non-regulated isozyme and the regulated fetal isozymes.

417 citations


Authors

Showing all 22586 results

NameH-indexPapersCitations
George Davey Smith2242540248373
David Miller2032573204840
Patrick O. Brown183755200985
Dorret I. Boomsma1761507136353
Chad A. Mirkin1641078134254
Darien Wood1602174136596
Wei Li1581855124748
Timothy C. Beers156934102581
Todd Adams1541866143110
Albert-László Barabási152438200119
T. J. Pearson150895126533
Amartya Sen149689141907
Christopher Hill1441562128098
Tim Adye1431898109010
Teruki Kamon1422034115633
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
2023115
2022543
20212,777
20202,925
20192,774
20182,624