1,720,968 research outputs found
Cerebellum Lecture: the Cerebellar Nuclei—Core of the Cerebellum
Get PDFThe cerebellum is a key player in many brain functions and a major topic of neuroscience research. However, the cerebellar nuclei (CN), the main output structures of the cerebellum, are often overlooked. This neglect is because research on the cerebellum typically focuses on the cortex and tends to treat the CN as relatively simple output nuclei conveying an inverted signal from the cerebellar cortex to the rest of the brain. In this review, by adopting a nucleocentric perspective we aim to rectify this impression. First, we describe CN anatomy and modularity and comprehensively integrate CN architecture with its highly organized but complex afferent and efferent connectivity. This is followed by a novel classification of the specific neuronal classes the CN comprise and speculate on the implications of CN structure and physiology for our understanding of adult cerebellar function. Based on this thorough review of the adult literature we provide a comprehensive overview of CN embryonic development and, by comparing cerebellar structures in various chordate clades, propose an interpretation of CN evolution. Despite their critical importance in cerebellar function, from a clinical perspective intriguingly few, if any, neurological disorders appear to primarily affect the CN. To highlight this curious anomaly, and encourage future nucleocentric interpretations, we build on our review to provide a brief overview of the various syndromes in which the CN are currently implicated. Finally, we summarize the specific perspectives that a nucleocentric view of the cerebellum brings, move major outstanding issues in CN biology to the limelight, and provide a roadmap to the key questions that need to be answered in order to create a comprehensive integrated model of CN structure, function, development, and evolution
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Get PDFWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Connectometrics: developing and applying statistical network science towards understanding nanoscale connectomes
Get PDFNeuroscientists have made tremendous progress in our ability to measure the structure of neural circuits. Detailed maps of neural wiring – termed connectomes – are increasingly studied in neuroscience because they have the potential to improve our understanding of how structure relates to function, how the structure of the brain is generated, how it changes with evolution or disease or experience. Despite this progress in measuring connectomes at the scale of individual neurons and the synapses between them, techniques for extracting meaning from these complicated datasets have lagged behind.
This thesis focuses on developing methods for improving our understanding of connectomics data, with a focus on the application of these methods to the connectome of a Drosophila larva brain. In particular, this thesis describes methods for characterizing the general structure of a connectome, including analysis of connectivity-based cell types, characterization of feedforward and feedback structure in a biological neural network, and tools for assessing sensory convergence within the brain using network traversal methods. Then, this thesis presents novel methods for comparing and aligning connectome datasets via a focus on the comparison of the left and right hemispheres of this Drosophila larva brain. The first method enables a statistical comparison of cell type connection probabilities in the left and the right hemispheres, enabling quantitative assessment of which parts of the two connectome datasets have the most significant deviations. The second method enables high-accuracy automated prediction of neuron-to-neuron pairings across the two sides of this connectome by augmenting techniques for graph matching. The tools used and developed as part of this thesis are also made available to the wider neuroscience community and beyond, via the development of documented, tested, open-source Python code to implement these algorithms.
Taken together, this thesis represents an advancement in the algorithmic analysis of connectome data. This kind of analysis will be particularly important going forward, as larger and more complicated connectomes are generated, and in particular, when multiple connectome samples are collected which require quantitative comparison
Connectometrics: developing and applying statistical network science towards understanding nanoscale connectomes
Get PDFNeuroscientists have made tremendous progress in our ability to measure the structure of neural circuits. Detailed maps of neural wiring – termed connectomes – are increasingly studied in neuroscience because they have the potential to improve our understanding of how structure relates to function, how the structure of the brain is generated, how it changes with evolution or disease or experience. Despite this progress in measuring connectomes at the scale of individual neurons and the synapses between them, techniques for extracting meaning from these complicated datasets have lagged behind.
This thesis focuses on developing methods for improving our understanding of connectomics data, with a focus on the application of these methods to the connectome of a Drosophila larva brain. In particular, this thesis describes methods for characterizing the general structure of a connectome, including analysis of connectivity-based cell types, characterization of feedforward and feedback structure in a biological neural network, and tools for assessing sensory convergence within the brain using network traversal methods. Then, this thesis presents novel methods for comparing and aligning connectome datasets via a focus on the comparison of the left and right hemispheres of this Drosophila larva brain. The first method enables a statistical comparison of cell type connection probabilities in the left and the right hemispheres, enabling quantitative assessment of which parts of the two connectome datasets have the most significant deviations. The second method enables high-accuracy automated prediction of neuron-to-neuron pairings across the two sides of this connectome by augmenting techniques for graph matching. The tools used and developed as part of this thesis are also made available to the wider neuroscience community and beyond, via the development of documented, tested, open-source Python code to implement these algorithms.
Taken together, this thesis represents an advancement in the algorithmic analysis of connectome data. This kind of analysis will be particularly important going forward, as larger and more complicated connectomes are generated, and in particular, when multiple connectome samples are collected which require quantitative comparison
Dispelling the Myths Behind First-author Citation Counts
Get PDFWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
koamabayili/VECTRON-author-checklist: VECTRON author checklist
No full textWe have done our best to complete the author checklist relating to the use of animals in the hut study. Note that the objective for the hut study was to evaluate the IRS treatment applications for residual efficacy against Anopheles mosquitoes, including the local An. coluzzii mosquito population. Cows were only used to attract mosquitoes into the huts and no tests were carried out directly on the cows. The author checklist is intended for use with studies where experiments are carried out on animals, which is why we have had such difficulty in completing this for the hut study, as many of the questions do not relate to how the cows were used
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