The Experts below are selected from a list of 4785 Experts worldwide ranked by ideXlab platform
Vince Grolmusz - One of the best experts on this subject based on the ideXlab platform.
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Graph Theoretical Analysis Reveals: Women’s Brains Are Better Connected than Men’s
PLOS ONE, 2015Co-Authors: Balázs Szalkai, Bálint Varga, Vince GrolmuszAbstract:Deep graph-theoretic ideas in the context with the graph of the World Wide Web led to the definition of Google’s PageRank and the subsequent rise of the most Popular Search Engine to date. Brain graphs, or connectomes, are being widely explored today. We believe that non-trivial graph theoretic concepts, similarly as it happened in the case of the World Wide Web, will lead to discoveries enlightening the structural and also the functional details of the animal and human brains. When scientists examine large networks of tens or hundreds of millions of vertices, only fast algorithms can be applied because of the size constraints. In the case of diffusion MRI-based structural human brain imaging, the effective vertex number of the connectomes, or brain graphs derived from the data is on the scale of several hundred today. That size facilitates applying strict mathematical graph algorithms even for some hard-to-compute (or NP-hard) quantities like vertex cover or balanced minimum cut. In the present work we have examined brain graphs, computed from the data of the Human Connectome Project, recorded from male and female subjects between ages 22 and 35. Significant differences were found between the male and female structural brain graphs: we show that the average female connectome has more edges, is a better expander graph, has larger minimal bisection width, and has more spanning trees than the average male connectome. Since the average female brain weighs less than the brain of males, these properties show that the female brain has better graph theoretical properties, in a sense, than the brain of males. It is known that the female brain has a smaller gray matter/white matter ratio than males, that is, a larger white matter/gray matter ratio than the brain of males; this observation is in line with our findings concerning the number of edges, since the white matter consists of myelinated axons, which, in turn, roughly correspond to the connections in the brain graph. We have also found that the minimum bisection width, normalized with the edge number, is also significantly larger in the right and the left hemispheres in females: therefore, the differing bisection widths are independent from the difference in the number of edges.
Balázs Szalkai - One of the best experts on this subject based on the ideXlab platform.
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Graph Theoretical Analysis Reveals: Women’s Brains Are Better Connected than Men’s
PLOS ONE, 2015Co-Authors: Balázs Szalkai, Bálint Varga, Vince GrolmuszAbstract:Deep graph-theoretic ideas in the context with the graph of the World Wide Web led to the definition of Google’s PageRank and the subsequent rise of the most Popular Search Engine to date. Brain graphs, or connectomes, are being widely explored today. We believe that non-trivial graph theoretic concepts, similarly as it happened in the case of the World Wide Web, will lead to discoveries enlightening the structural and also the functional details of the animal and human brains. When scientists examine large networks of tens or hundreds of millions of vertices, only fast algorithms can be applied because of the size constraints. In the case of diffusion MRI-based structural human brain imaging, the effective vertex number of the connectomes, or brain graphs derived from the data is on the scale of several hundred today. That size facilitates applying strict mathematical graph algorithms even for some hard-to-compute (or NP-hard) quantities like vertex cover or balanced minimum cut. In the present work we have examined brain graphs, computed from the data of the Human Connectome Project, recorded from male and female subjects between ages 22 and 35. Significant differences were found between the male and female structural brain graphs: we show that the average female connectome has more edges, is a better expander graph, has larger minimal bisection width, and has more spanning trees than the average male connectome. Since the average female brain weighs less than the brain of males, these properties show that the female brain has better graph theoretical properties, in a sense, than the brain of males. It is known that the female brain has a smaller gray matter/white matter ratio than males, that is, a larger white matter/gray matter ratio than the brain of males; this observation is in line with our findings concerning the number of edges, since the white matter consists of myelinated axons, which, in turn, roughly correspond to the connections in the brain graph. We have also found that the minimum bisection width, normalized with the edge number, is also significantly larger in the right and the left hemispheres in females: therefore, the differing bisection widths are independent from the difference in the number of edges.
Bálint Varga - One of the best experts on this subject based on the ideXlab platform.
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Graph Theoretical Analysis Reveals: Women’s Brains Are Better Connected than Men’s
PLOS ONE, 2015Co-Authors: Balázs Szalkai, Bálint Varga, Vince GrolmuszAbstract:Deep graph-theoretic ideas in the context with the graph of the World Wide Web led to the definition of Google’s PageRank and the subsequent rise of the most Popular Search Engine to date. Brain graphs, or connectomes, are being widely explored today. We believe that non-trivial graph theoretic concepts, similarly as it happened in the case of the World Wide Web, will lead to discoveries enlightening the structural and also the functional details of the animal and human brains. When scientists examine large networks of tens or hundreds of millions of vertices, only fast algorithms can be applied because of the size constraints. In the case of diffusion MRI-based structural human brain imaging, the effective vertex number of the connectomes, or brain graphs derived from the data is on the scale of several hundred today. That size facilitates applying strict mathematical graph algorithms even for some hard-to-compute (or NP-hard) quantities like vertex cover or balanced minimum cut. In the present work we have examined brain graphs, computed from the data of the Human Connectome Project, recorded from male and female subjects between ages 22 and 35. Significant differences were found between the male and female structural brain graphs: we show that the average female connectome has more edges, is a better expander graph, has larger minimal bisection width, and has more spanning trees than the average male connectome. Since the average female brain weighs less than the brain of males, these properties show that the female brain has better graph theoretical properties, in a sense, than the brain of males. It is known that the female brain has a smaller gray matter/white matter ratio than males, that is, a larger white matter/gray matter ratio than the brain of males; this observation is in line with our findings concerning the number of edges, since the white matter consists of myelinated axons, which, in turn, roughly correspond to the connections in the brain graph. We have also found that the minimum bisection width, normalized with the edge number, is also significantly larger in the right and the left hemispheres in females: therefore, the differing bisection widths are independent from the difference in the number of edges.
Irmina Masłowska - One of the best experts on this subject based on the ideXlab platform.
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ECIR - Phrase-based hierarchical clustering of web Search results
Lecture Notes in Computer Science, 2003Co-Authors: Irmina MasłowskaAbstract:The paper addresses the problem of clustering text documents coming from the Web. We apply clustering to support users in interactive browsing through hierarchically organized Search results as opposed to the standard ranked-list presentation. We propose a clustering method that is tailored to on-line processing of Web documents and takes into account the time aspect, the particular requirements of clustering texts, and readability of the produced hierarchy. Finally, we present the user interface of an actual system in which the method is applied to the results of a Popular Search Engine.
Rick Manrow - One of the best experts on this subject based on the ideXlab platform.
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using the internet to Search for cancer clinical trials a comparative audit of clinical trial Search tools
Contemporary Clinical Trials, 2008Co-Authors: Nancy L Atkinson, Sandra L Saperstein, Holly A Massett, Colleen Ryan Leonard, Lakshmi Grama, Rick ManrowAbstract:Advancing the clinical trial reSearch process to improve cancer treatment necessitates helping people with cancer identify and enroll in studies, and reSearchers are using the power of the Internet to facilitate this process. This study used a content analysis of online cancer clinical trial Search tools to understand what people with cancer might encounter. The content analysis revealed that clinical trial Search tools were easy to identify using a Popular Search Engine, but their functionality and content varied greatly. Most required that users be fairly knowledgeable about their medical condition and sophisticated in their web navigation skills. The ability to Search by a specific health condition or type of cancer was the most common Search strategy. The more complex tools required that users input detailed information about their personal medical history and have knowledge of specific clinical trial terminology. Search tools, however, only occasionally advised users to consult their doctors regarding clinical trial decision-making. This, along with the complexity of the tools suggests that online Search tools may not adequately facilitate the clinical trial recruitment process. Findings from this analysis can be used as a framework from which to systematically examine actual consumer experience with online clinical trial Search tools.
