4,614 research outputs found
Local pre-processing for node classification in networks : application in protein-protein interaction
Network modelling provides an increasingly popular conceptualisation in a wide range of domains, including the analysis of protein structure. Typical approaches to analysis model parameter values at nodes within the network. The spherical locality around a node provides a microenvironment that can be used to characterise an area of a network rather than a particular point within it. Microenvironments that centre on the nodes in a protein chain can be used to quantify parameters that are related to protein functionality. They also permit particular patterns of such parameters in node-centred microenvironments to be used to locate sites of particular interest. This paper evaluates an approach to index generation that seeks to rapidly construct microenvironment data. The results show that index generation performs best when the radius of microenvironments matches the granularity of the index. Results are presented to show that such microenvironments improve the utility of protein chain parameters in classifying the structural characteristics of nodes using both support vector machines and neural networks
Analysis of the Effects of Dietary Pattern on the Oral Microbiome of Elite Endurance Athletes
Although the oral microbiota is known to play a crucial role in human health, there are few studies of diet x oral microbiota interactions, and none in elite athletes who may manipulate their intakes of macronutrients to achieve different metabolic adaptations in pursuit of optimal endurance performance. The aim of this study was to investigate the shifts in the oral microbiome of elite male endurance race walkers from Europe, Asia, the Americas and Australia, in response to one of three dietary patterns often used by athletes during a period of intensified training: a High Carbohydrate (HCHO; = 9; with 60% energy intake from carbohydrates; ~8.5 g kg day carbohydrate, ~2.1 g kg day protein, 1.2 g kg day fat) diet, a Periodised Carbohydrate (PCHO; = 10; same macronutrient composition as HCHO, but the intake of carbohydrates is different across the day and throughout the week to support training sessions with high or low carbohydrate availability) diet or a ketogenic Low Carbohydrate High Fat (LCHF; = 10; 0.5 g kg day carbohydrate; 78% energy as fat; 2.1 g kg day protein) diet. Saliva samples were collected both before (Baseline; BL) and after the three-week period (Post treatment; PT) and the oral microbiota profiles for each athlete were produced by 16S rRNA gene amplicon sequencing. Principal coordinates analysis of the oral microbiota profiles based on the weighted UniFrac distance measure did not reveal any specific clustering with respect to diet or athlete ethnic origin, either at baseline (BL) or following the diet-training period. However, discriminant analyses of the oral microbiota profiles by Linear Discriminant Analysis (LDA) Effect Size (LEfSe) and sparse Partial Least Squares Discriminant Analysis (sPLS-DA) did reveal changes in the relative abundance of specific bacterial taxa, and, particularly, when comparing the microbiota profiles following consumption of the carbohydrate-based diets with the LCHF diet. These analyses showed that following consumption of the LCHF diet the relative abundances of and spp. were decreased, and the relative abundance of spp. was increased. Such findings suggest that diet, and, in particular, the LCHF diet can induce changes in the oral microbiota of elite endurance walkers
Bipartite Graph Learning for Autonomous Task-to-Sensor Optimization
NPS NRP Technical ReportThis study addresses the question of how machine learning/artificial intelligence can be applied to identify the most appropriate 'sensor' for a task, to prioritize tasks, and to identify gaps/unmet requirements. The concept of a bipartite graph provides a mathematical framework for task-to-sensor mapping by establishing connectivity between various high-level tasks and the specific sensors and processes that must be invoked to fulfil those tasks and other mission requirements. The connectivity map embedded in the bipartite graph can change depending on the availability/unavailability of resources, the presence of constraints (physics, operational, sequencing), and the satisfaction of individual tasks. Changes can also occur according to the valuation, re-assignment and re-valuation of the perceived task benefit and how the completion of a specific task (or group of tasks) can contribute to the state of knowledge. We plan to study how machine learning can be used to perform bipartite learning for task-to-sensor planning.Naval Special Warfare Command (NAVSPECWARCOM)N9 - Warfare SystemsThis research is supported by funding from the Naval Postgraduate School, Naval Research Program (PE 0605853N/2098). https://nps.edu/nrpChief of Naval OperationsĀ (CNO)Approved for public release. Distribution is unlimited.
Effect of H on the crystalline and magnetic structures of the YCo3-H(D) system. I. YCo3 from neutron powder diffraction and first-principles calculations
This paper reports investigations into the influence of hydrogen on the magnetic properties of the YCo3-H
system. We report results on the magnetic structure and magnetic transitions of YCo3 using a combination of
neutron powder diffraction measurements and first-principles full potential augmented plane wave + local
orbital calculations under the generalized gradient approximation. The ferromagnetic and ferrimagnetic structures
are examined on an equal footing. However, we identify that, no matter which structure is used as the
starting point, the neutron diffraction data always refines down to the ferrimagnetic structure with the Co2
atoms having antiparallel spins. In the ab initio calculations, the inclusion of spin-orbit coupling is found to be
important in the prediction of the correct magnetic ground state. Here, the results suggest that, for zero external
field and sufficiently low temperatures, the spin arrangement of YCo3 is ferrimagnetic rather than ferromagnetic
as previously believed. The fixed spin moment calculation technique has been employed to understand
the two successive field-induced magnetic transitions observed in previous magnetization measurements under
increasing ultrahigh magnetic fields. We find that the magnetic transitions start from the ferrimagnetic phase
ļæ½0.61ļæ½B/Coļæ½ and terminate with the ferromagnetic phase ļæ½1.16ļæ½B/Coļæ½, while the spin on the Co2 atoms
progressively changes from antiparallel ferrimagnetic to paramagnetic and then to ferromagnetic. Our neutron
diffraction measurements, ab initio calculations, and the high field magnetization measurements are thus
entirely self-consistent
Bipartite Graph Learning for Autonomous Task-to-Sensor Optimization
NPS NRP Project PosterThis study addresses the question of how machine learning/artificial intelligence can be applied to identify the most appropriate 'sensor' for a task, to prioritize tasks, and to identify gaps/unmet requirements. The concept of a bipartite graph provides a mathematical framework for task-to-sensor mapping by establishing connectivity between various high-level tasks and the specific sensors and processes that must be invoked to fulfil those tasks and other mission requirements. The connectivity map embedded in the bipartite graph can change depending on the availability/unavailability of resources, the presence of constraints (physics, operational, sequencing), and the satisfaction of individual tasks. Changes can also occur according to the valuation, re-assignment and re-valuation of the perceived task benefit and how the completion of a specific task (or group of tasks) can contribute to the state of knowledge. We plan to study how machine learning can be used to perform bipartite learning for task-to-sensor planning.Naval Special Warfare Command (NAVSPECWARCOM)N9 - Warfare SystemsThis research is supported by funding from the Naval Postgraduate School, Naval Research Program (PE 0605853N/2098). https://nps.edu/nrpChief of Naval OperationsĀ (CNO)Approved for public release. Distribution is unlimited.
Bipartite Graph Learning for Autonomous Task-to-Sensor Optimization
NPS NRP Project PosterThis study addresses the question of how machine learning/artificial intelligence can be applied to identify the most appropriate 'sensor' for a task, to prioritize tasks, and to identify gaps/unmet requirements. The concept of a bipartite graph provides a mathematical framework for task-to-sensor mapping by establishing connectivity between various high-level tasks and the specific sensors and processes that must be invoked to fulfil those tasks and other mission requirements. The connectivity map embedded in the bipartite graph can change depending on the availability/unavailability of resources, the presence of constraints (physics, operational, sequencing), and the satisfaction of individual tasks. Changes can also occur according to the valuation, re-assignment and re-valuation of the perceived task benefit and how the completion of a specific task (or group of tasks) can contribute to the state of knowledge. We plan to study how machine learning can be used to perform bipartite learning for task-to-sensor planning.Naval Special Warfare Command (NAVSPECWARCOM)N9 - Warfare SystemsThis research is supported by funding from the Naval Postgraduate School, Naval Research Program (PE 0605853N/2098). https://nps.edu/nrpChief of Naval OperationsĀ (CNO)Approved for public release. Distribution is unlimited.
Bipartite Graph Learning for Autonomous Task-to-Sensor Optimization
NPS NRP Executive SummaryThis study addresses the question of how machine learning/artificial intelligence can be applied to identify the most appropriate 'sensor' for a task, to prioritize tasks, and to identify gaps/unmet requirements. The concept of a bipartite graph provides a mathematical framework for task-to-sensor mapping by establishing connectivity between various high-level tasks and the specific sensors and processes that must be invoked to fulfil those tasks and other mission requirements. The connectivity map embedded in the bipartite graph can change depending on the availability/unavailability of resources, the presence of constraints (physics, operational, sequencing), and the satisfaction of individual tasks. Changes can also occur according to the valuation, re-assignment and re-valuation of the perceived task benefit and how the completion of a specific task (or group of tasks) can contribute to the state of knowledge. We plan to study how machine learning can be used to perform bipartite learning for task-to-sensor planning.Naval Special Warfare Command (NAVSPECWARCOM)N9 - Warfare SystemsThis research is supported by funding from the Naval Postgraduate School, Naval Research Program (PE 0605853N/2098). https://nps.edu/nrpChief of Naval OperationsĀ (CNO)Approved for public release. Distribution is unlimited.
Inflammatory bowel disease-associated colorectal cancer: translational risks from mechanisms to medicines
The cumulative impact of chronic inflammation in patients with inflammatory bowel diseases predisposes to the development of inflammatory bowel disease-associated colorectal cancer [IBD-CRC]. Inflammation can induce mutagenesis, and the relapsingāremitting nature of this inflammation, together with epithelial regeneration, may exert selective pressure accelerating carcinogenesis. The molecular pathogenesis of IBD-CRC, termed the āinflammationādysplasiaācarcinomaā sequence, is well described. However, the immunopathogenesis of IBD-CRC is less well understood. The impact of novel immunosuppressive therapies, which aim to achieve deep remission, is mostly unknown. Therefore, this timely review summarizes the clinical context of IBD-CRC, outlines the molecular and immunological basis of disease pathogenesis, and considers the impact of novel biological therapies
Bayesian High-Redshift Quasar Classification from Optical and Mid-IR Photometry
We identify 885,503 type 1 quasar candidates to i<22 using the combination of
optical and mid-IR photometry. Optical photometry is taken from the Sloan
Digital Sky Survey-III: Baryon Oscillation Spectroscopic Survey
(SDSS-III/BOSS), while mid-IR photometry comes from a combination of data from
the Wide-Field Infrared Survey Explorer (WISE) "ALLWISE" data release and
several large-area Spitzer Space Telescope fields. Selection is based on a
Bayesian kernel density algorithm with a training sample of 157,701
spectroscopically-confirmed type-1 quasars with both optical and mid-IR data.
Of the quasar candidates, 733,713 lack spectroscopic confirmation (and 305,623
are objects that we have not previously classified as photometric quasar
candidates). These candidates include 7874 objects targeted as high probability
potential quasars with 3.5<z<5 (of which 6779 are new photometric candidates).
Our algorithm is more complete to z>3.5 than the traditional mid-IR selection
"wedges" and to 2.2<z<3.5 quasars than the SDSS-III/BOSS project. Number counts
and luminosity function analysis suggests that the resulting catalog is
relatively complete to known quasars and is identifying new high-z quasars at
z>3. This catalog paves the way for luminosity-dependent clustering
investigations of large numbers of faint, high-redshift quasars and for further
machine learning quasar selection using Spitzer and WISE data combined with
other large-area optical imaging surveys.Comment: 54 pages, 17 figures; accepted by ApJS Data for tables 1 and 2
available at
http://www.physics.drexel.edu/~gtr/outgoing/optirqsos/data/master_quasar_catalogs.011414.fits.bz2
and
http://www.physics.drexel.edu/~gtr/outgoing/optirqsos/data/optical_ir_quasar_candidates.052015.fits.bz
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