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Institution

University of Aberdeen

EducationAberdeen, United Kingdom
About: University of Aberdeen is a education organization based out in Aberdeen, United Kingdom. It is known for research contribution in the topics: Population & Randomized controlled trial. The organization has 21174 authors who have published 49962 publications receiving 2105479 citations. The organization is also known as: Aberdeen University.


Papers
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Journal ArticleDOI
TL;DR: It is proposed that the brain processes stimuli by forming increasingly complex functional cliques and cavities that evolve in a stereotypical sequence toward peak complexity.
Abstract: The lack of a formal link between neural network structure and its emergent function has hampered our understanding of how the brain processes information. We have now come closer to describing such a link by taking the direction of synaptic transmission into account, constructing graphs of a network that reflect the direction of information flow, and analyzing these directed graphs using algebraic topology. Applying this approach to a local network of neurons in the neocortex revealed a remarkably intricate and previously unseen topology of synaptic connectivity. The synaptic network contains an abundance of cliques of neurons bound into cavities that guide the emergence of correlated activity. In response to stimuli, correlated activity binds synaptically connected neurons into functional cliques and cavities that evolve in a stereotypical sequence towards peak complexity. We propose that the brain processes stimuli by forming increasingly complex functional cliques and cavities.

364 citations

Journal ArticleDOI
TL;DR: Using phenotypic variation to measure the similarity between the populations of a species one should analyse the variation in several characters simultaneously simultaneously, known as multivariate analysis.
Abstract: Summary 1. The study of geographic variation and the racial affinities between populations is of central importance to systematics and evolutionary theory. When using phenotypic variation to measure the similarity between the populations of a species one should analyse the variation in several characters simultaneously. This is a statistical procedure and is known as multivariate analysis. Multivariate analysis of phenotypic variation, unlike some other methods, has the advantage of not being dependent on living specimens. 2. To obtain an adequate sample at each locality, and an adequate distribution of localities within a given geographic area, can be a major problem. The pooling of data from adjacent localities is discussed. 3. There are several sources of phenotypic variation within a species, e.g. sexual and ontogenetic variation. Failure to eliminate the non-geographic sources of variation can confuse the assessment of the similarity between populations. 4. Correlation between characters can reflect similar genetic control and/or similar patterns of geographic variation, the biological interpretation being influenced by whether the data come from one locality or many. 5. The influences of environmental induction and genetic control cannot easily be separated. Also, some characters may not be entirely homologous throughout the range of the species. 6. Most studies rely on far too few characters of a too restricted type to give an ‘overall’ assessment of the phenotypic similarity. This is one of the most neglected aspects of the study of geographic variation. 7. The various forms of clinal and categorical variation, the precise nature and position of sharp transition (hybrid) zones, the relationship between non-adjacent as well as adjacent populations and the phenotypic divergence between island populations, etc., all come under the heading of geographic variation. The ideal technique should be able to elucidate all types of geographic variation but some techniques can only be used effectively with a few of them. Moreover, techniques may be limited in their application because they require the data to conform to certain models, e.g. normal distribution. 8. The degree of phenotypic similarity between populations can be measured by a wide range of similarity coefficients. Comparison between even a small series of populations produces a large set (or matrix) of similarity coefficients that is difficult to interpret. However, the relationships between populations can be summarized in several ways and these may be loosely grouped into four categories; (i) network diagrams, (ii) contours and isometric plots, (iii) hierarchical clusters, and (iv) ordination methods. These methods are explained and their advantages and limitations discussed. 9. The hierarchical (dendritic) model of cluster analysis is unsuitable for analysing all but a few types of geographic variation. 10. There are several types of ordination technique. They all aim to summarize the variation of many characters in a reduced number of axes. One can either emphasize the biological interpretation of each separate axis, or treat the analysis as a classifying technique and assess the grouping of the populations in the space defined by the axes. Considerable care is needed in interpreting the results of both of these approaches. If correctly applied, ordination techniques generally can be used to analyse all the forms of geographical variation and are therefore recommended. Contrary to current practice they can be used with a large number of characters. The advantages and limitations of the various ordination techniques are discussed. 11. Contours and their three-dimensional isometric plots can be used to portray geographic variations in the information obtained from a multivariate analysis. However, contours and isometric plots are limited in their applicability and the amount of information they can convey. 12. The sophistication of some multivariate methods should not be allowed to cloak the scientific inadequacies of a study. The use of more than one technique and variety in the choice of pertinent parameters may be of value in indicating the reliability of the results.

364 citations

Journal ArticleDOI
TL;DR: In this article, the active cathode material in commercial rechargeable lithium batteries, is shown to be a p-type semiconductor, associated with the presence of a small concentration of CO{sup 4+} ions.
Abstract: LiCoO{sub 2} the active cathode material in commercial rechargeable lithium batteries, is shown to be a p-type semiconductor, associated with the presence of a small concentration of CO{sup 4+} ions. Its conductivity at room temperature can be increased by over two orders of magnitude, to {approximately}0.5 S/cm, by partial substitution of CO{sup 3+} by Mg{sup 2+} and compensating hole creation. The electrochemical performance of LiMg{sub 0.05} Co{sub 0.95}O{sub 2} is comparable to that of LiCoO{sub 2}; a small reduction in capacity, associated with a reduction in Co{sup 3+} content, occurs but good reversibility is retained and, in contrast to LiCoO{sub 2}, the Mg-doped material is single phase throughout the charge/discharge cycle.

363 citations

Journal ArticleDOI
TL;DR: Active local educational interventions involving secondary care specialists and structured referral sheets are the only interventions shown to impact on referral rates based on current evidence.
Abstract: Background The primary care specialist interface is a key organisational feature of many health care systems. Patients are referred to specialist care when investigation or therapeutic options are exhausted in primary care and more specialised care is needed. Referral has considerable implications for patients, the health care system and health care costs. There is considerable evidence that the referral processes can be improved.

363 citations


Authors

Showing all 21424 results

NameH-indexPapersCitations
Paul M. Thompson1832271146736
Feng Zhang1721278181865
Ian J. Deary1661795114161
Peter A. R. Ade1621387138051
David W. Johnson1602714140778
Pete Smith1562464138819
Naveed Sattar1551326116368
John R. Hodges14981282709
Ruth J. F. Loos14264792485
Alan J. Silman14170892864
Michael J. Keating140116976353
David Price138168793535
John D. Scott13562583878
Aarno Palotie12971189975
Rajat Gupta126124072881
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
2023141
2022362
20212,195
20202,118
20191,846
20181,894