Exercise intensity-dependent effects of arm and leg-cycling on cognitive performance

Physiological responses to arm and leg-cycling are different, which may influence psychological and biological mechanisms that influence post-exercise cognitive performance. The aim of this study was to determine the effects of maximal and submaximal (absolute and relative intensity matched) arm and leg-cycling on executive function. Thirteen males (age, 24.7 ± 5.0 years) initially undertook two incremental exercise tests to volitional exhaustion for arm-cycling (82 ± 18 W) and leg-cycling (243 ± 52 W) for the determination of maximal power output. Participants subsequently performed three 20-min constant load exercise trials: (1) arm-cycling at 50% of the ergometer-specific maximal power output (41 ± 9 W), (2) leg-cycling at 50% of the ergometer-specific maximal power output (122 ± 26 W), and (3) leg-cycling at the same absolute power output as the submaximal arm-cycling trial (41 ± 9 W). An executive function task was completed before, immediately after and 15-min after each exercise test. Exhaustive leg-cycling increased reaction time (p 0.05). Improvements in reaction time following arm-cycling were maintained for at least 15-min post exercise (p = 0.008, d = -0.73). Arm and leg-cycling performed at the same relative intensity elicit comparable improvements in cognitive performance. These findings suggest that individuals restricted to arm exercise possess a similar capacity to elicit an exercise-induced cognitive performance benefit

Visualization of heterogeneous data

Abstract — Both the Resource Description Framework (RDF), used in the semantic web, and Maya Viz u-forms represent data as a graph of objects connected by labeled edges. Existing systems for flexible visualization of this kind of data require manual specification of the possible visualization roles for each data attribute. When the schema is large and unfamiliar, this requirement inhibits exploratory visualization by requiring a costly up-front data integration step. To eliminate this step, we propose an automatic technique for mapping data attributes to visualization attributes. We formulate this as a schema matching problem, finding appropriate paths in the data model for each required visualization attribute in a visualization template. Index Terms—Data integration, RDF, attribute inference.

A unique bacteriohopanetetrol stereoisomer of marine anammox

Anaerobic ammonium oxidation (anammox) is a major process of bioavailable nitrogen removal from marine systems. Previously, a bacteriohopanetetrol (BHT) isomer, with unknown stereochemistry, eluting later than BHT using high performance liquid chromatography (HPLC), was detected in ‘Ca. Scalindua profunda’ and proposed as a biomarker for anammox in marine paleo-environments. However, the utility of this BHT isomer as an anammox biomarker is hindered by the fact that four other, non-anammox bacteria are also known to produce a late-eluting BHT stereoisomer. The stereochemistry in Acetobacter pasteurianus, Komagataeibacter xylinus and Frankia sp. was known to be 17β, 21β(H), 22R, 32R, 33R, 34R (BHT-34R). The stereochemistry of the late-eluting BHT in Methylocella palustris was unknown. To determine if marine anammox bacteria produce a unique BHT isomer, we studied the BHT distributions and stereochemistry of known BHT isomer producers and of previously unscreened marine (‘Ca. Scalindua brodeae’) and freshwater (‘Ca. Brocadia sp.’) anammox bacteria using HPLC and gas chromatographic (GC) analysis of acetylated BHTs and ultra high performance liquid chromatography (UHPLC)-high resolution mass spectrometry (HRMS) analysis of non-acetylated BHTs. The 34R stereochemistry was confirmed for the BHT isomers in Ca. Brocadia sp. and Methylocella palustris. However, ‘Ca. Scalindua sp.’ synthesise a stereochemically distinct BHT isomer, with still unconfirmed stereochemistry (BHT-x). Only GC analysis of acetylated BHT and UHPLC analysis of non-acetylated BHT distinguished between late-eluting BHT isomers. Acetylated BHT-x and BHT-34R co-elute by HPLC. As BHT-x is currently only known to be produced by ‘Ca. Scalindua spp.’, it may be a biomarker for marine anammox

The Fourteenth Data Release of the Sloan Digital Sky Survey: First Spectroscopic Data from the extended Baryon Oscillation Spectroscopic Survey and from the second phase of the Apache Point Observatory Galactic Evolution Experiment

The fourth generation of the Sloan Digital Sky Survey (SDSS-IV) has been in operation since July 2014. This paper describes the second data release from this phase, and the fourteenth from SDSS overall (making this, Data Release Fourteen or DR14). This release makes public data taken by SDSS-IV in its first two years of operation (July 2014-2016). Like all previous SDSS releases, DR14 is cumulative, including the most recent reductions and calibrations of all data taken by SDSS since the first phase began operations in 2000. New in DR14 is the first public release of data from the extended Baryon Oscillation Spectroscopic Survey (eBOSS); the first data from the second phase of the Apache Point Observatory (APO) Galactic Evolution Experiment (APOGEE-2), including stellar parameter estimates from an innovative data driven machine learning algorithm known as "The Cannon"; and almost twice as many data cubes from the Mapping Nearby Galaxies at APO (MaNGA) survey as were in the previous release (N = 2812 in total). This paper describes the location and format of the publicly available data from SDSS-IV surveys. We provide references to the important technical papers describing how these data have been taken (both targeting and observation details) and processed for scientific use. The SDSS website (www.sdss.org) has been updated for this release, and provides links to data downloads, as well as tutorials and examples of data use. SDSS-IV is planning to continue to collect astronomical data until 2020, and will be followed by SDSS-V.Comment: SDSS-IV collaboration alphabetical author data release paper. DR14 happened on 31st July 2017. 19 pages, 5 figures. Accepted by ApJS on 28th Nov 2017 (this is the "post-print" and "post-proofs" version; minor corrections only from v1, and most of errors found in proofs corrected

Learning to Learn: How to Continuously Teach Humans and Machines

Our education system comprises a series of curricula. For example, when we learn mathematics at school, we learn in order from addition, to multiplication, and later to integration. Delineating a curriculum for teaching either a human or a machine shares the underlying goal of maximizing the positive knowledge transfer from early to later tasks and minimizing forgetting of the early tasks. Here, we exhaustively surveyed the effect of curricula on existing continual learning algorithms in the class-incremental setting, where algorithms must learn classes one at a time from a continuous stream of data. We observed that across a breadth of possible class orders (curricula), curricula influence the retention of information and that this effect is not just a product of stochasticity. Further, as a primary effort toward automated curriculum design, we proposed a method capable of designing and ranking effective curricula based on inter-class feature similarities. We compared the predicted curricula against empirically determined effectual curricula and observed significant overlaps between the two. To support the study of a curriculum designer, we conducted a series of human psychophysics experiments and contributed a new Continual Learning benchmark in object recognition. We assessed the degree of agreement in effective curricula between humans and machines. Surprisingly, our curriculum designer successfully predicts an optimal set of curricula that is effective for human learning. There are many considerations in curriculum design, such as timely student feedback and learning with multiple modalities. Our study is the first attempt to set a standard framework for the community to tackle the problem of teaching humans and machines to learn to learn continuously

Empirical Definition of Object-oriented Programming Competencies

International large-scale educational investigations and the focus on learners' competencies powered a veritable revolution in teaching and learning approaches as well as in educational research methodologies. In the relatively young field of computer science education research, however, there is a considerable lack of empirical studies on the definition and measurement of competencies. The central goal of the presented research project is to identify, describe, and measure competencies for object-oriented programming, in particular for implementing abstract data types. We use an automated assessment system to evaluate and score a large number of students' solutions of programming tasks. Item Response Theory analyses of the results identify subsets of tasks suitable for defining typical programming competencies. Further qualitative analyses reveal the internal structure of the competencies and allow a classification in a competency structure model. This article presents in detail our rigorous methodology and exemplary results for the empirical definition and decomposition of the competency named "Ability to implement the abstract data type Binary Search Tree"

Dark carbon fixation in the Arabian Sea oxygen minimum zone contributes to sedimentary organic carbon (SOM)

In response to rising CO2concentrations and increasing global sea surface temperatures,oxygen minimum zones (OMZ), or“dead zones”, are expected to expand. OMZs are fueled by highprimary productivity, resulting in enhanced biological oxygen demand at depth, subsequent oxygen depletion, and attenuation of remineralization. This results in the deposition of organic carbon‐rich sediments. Carbon drawdown is estimated by biogeochemical models; however, a major process is ignored: carbon fixation in the mid‐and lower water column. Here, we show that chemoautotrophic carbon fixation is important in the Arabian Sea OMZ; and manifests in a13C‐depleted signature of sedimentary organic carbon. We determined theδ13C values of Corg deposited in close spatial proximity but over a steepbottom‐water oxygen gradient, and theδ13C composition of biomarkers of chemoautotrophic bacteriacapable of anaerobic ammonia oxidation (anammox). Isotope mixing models show that detritus fromanammox bacteria or other chemoautotrophs likely forms a substantial part of the organic matter depositedwithin the Arabian Sea OMZ (~17%), implying that the contribution of chemoautotrophs to settling organicmatter is exported to the sediment. This has implications for the evaluation of past, and future, OMZs:biogeochemical models that operate on the assumption that all sinking organic matter is photosynthetically derived, without new addition of carbon, could significantly underestimate the extent of remineralization. Oxygen demand in oxygen minimum zones could thus be higher than projections suggest, leading to a more intense expansion of OMZs than expected