The effects of a 10-day altitude training camp at 1828 meters on varsity cross-country runners

International Journal of Exercise Science 10(1): 97-107, 2017. Altitude training has been shown to alter blood lactate (BL) levels due to alterations resulting from acclimatization. This study aims to estimate the impact of altitude training on BL changes immediately following an incremental treadmill test and during recovery before and after 10-day altitude training at approximately 1828 meters. Eight varsity cross-country runners performed an incremental treadmill test (ITT), pre and post-altitude training. Resting and post-warm-up BL values were recorded. During ITT, heart rate (HR), oxygen saturation (SpO2), and time to exhaustion were monitored. BL was also measured post-ITT at 0, 2, 4, 6, and 8 minutes. The average of all BL values was higher following altitude intervention (8.8 ± 4.6 mmol/L) compared to pre-intervention (7.4 ± 3.3 mmol/L). These differences were statistically significant (t(6) = -2.40, p = .026). BL immediately (0 minutes) after the ITT was higher following the altitude intervention (13.6 ± 3.6 mmol/L) compared to pre-intervention (9.7 ± 3.8 mmol/L) and was statistically significant (t(7) = -3.30, p = .006). Average HR during the ITT was lower following the altitude intervention (176.9 ± 11.1 bpm) compared to pre (187 ± 9.5 bpm), these differences were statistically significant (t(28)= 18.07, p

Non-linear Pattern Matching with Backtracking for Non-free Data Types

Non-free data types are data types whose data have no canonical forms. For example, multisets are non-free data types because the multiset $\{a,b,b\}$ has two other equivalent but literally different forms $\{b,a,b\}$ and $\{b,b,a\}$. Pattern matching is known to provide a handy tool set to treat such data types. Although many studies on pattern matching and implementations for practical programming languages have been proposed so far, we observe that none of these studies satisfy all the criteria of practical pattern matching, which are as follows: i) efficiency of the backtracking algorithm for non-linear patterns, ii) extensibility of matching process, and iii) polymorphism in patterns. This paper aims to design a new pattern-matching-oriented programming language that satisfies all the above three criteria. The proposed language features clean Scheme-like syntax and efficient and extensible pattern matching semantics. This programming language is especially useful for the processing of complex non-free data types that not only include multisets and sets but also graphs and symbolic mathematical expressions. We discuss the importance of our criteria of practical pattern matching and how our language design naturally arises from the criteria. The proposed language has been already implemented and open-sourced as the Egison programming language

Enzyme-coated Janus nanoparticles that selectively bind cell receptors as a function of the concentration of glucose

A method is proposed for controlling the number of nanoparticles bound to cell membranes via RGDS peptide-integrin interactions. It consists of propelling nanoparticles bearing the peptides with enzymes (glucose oxidase), which disrupts biomolecular interactions as a function of the concentration of enzyme substrate (glucose)

Jet Acceleration by Tangled Magnetic Fields

We explore the possibility that extragalactic radio jets might be accelerated by highly disorganized magnetic fields that are strong enough to dominate the dynamics until the terminal Lorentz factor is reached. Following the twin-exhaust model by Blandford & Rees (1974), the collimation under this scenario is provided by the stratified thermal pressure from an external medium. The acceleration efficiency then depends on the pressure gradient of that medium. In order for this mechanism to work there must be continuous tangling of the magnetic field, changing the magnetic equation of state away from pure flux freezing (otherwise conversion of Poynting flux to kinetic energy flux is suppressed). This is a complementary approach to models in which the plasma is accelerated by large scale ordered fields. We include a simple prescription for magnetic dissipation, which leads to tradeoffs among conversion of magnetic energy into bulk kinetic energy, random particle energy, and radiation. We present analytic dynamical solutions of such jets, assess the effects of radiation drag, and comment on observational issues, such as the predicted polarization and synchrotron brightness. Finally, we try to make the connection to observed radio galaxies and gamma-ray bursts.Comment: 15 pages, 10 figures, accepted for publication in Ap

Engineering alcohol oxidases for substrate scope and their application in flow and cascade biocatalysis

Alcohol oxidases have significant advantages over alcohol dehydrogenases (ADHs) for biocatalytic oxidation of alcohols: they don’t require addition of (expensive) nicotinamide cofactors (or a recycling system for cofactor regeneration) and the catalytic reaction is irreversible. Although alcohol oxidases generate hydrogen peroxide when they turn over, this issue can be alleviated by addition of catalase, which, not only removes the peroxide, but also creates more oxygen for cofactor regeneration. Alcohol oxidases are perceived to have a limited substrate scope preventing their wider use in synthesis. Thus, we present the engineering of two alcohol oxidases for increased substrate scope, one for the selective oxidation of primary alcohols and one for secondary alcohol oxidation. Please click Additional Files below to see the full abstract

New constraints on micro-seismicity and stress state in the western part of the North Anatolian Fault Zone : Observations from a dense seismic array

Major funding was provided by the UK Natural Environment Research Council (NERC) under grant NE/I028017/1 and partially supported by Boğaziçi University Research Fund (BAP) under grant 6922. We would like to thank all the project members from the University of Leeds, Boğaziçi University, Kandilli Observatory, Aberdeen University and Sakarya University. I would also like to thank Prof. Ali Pinar and Dr. Kıvanç Kekovalı for their valuable comments. Some of the figures were generated by GMT software (Wessel and Smith, 1995).Peer reviewedPostprin

Neural ODEs as a discovery tool to characterize the structure of the hot galactic wind of M82

Dynamic astrophysical phenomena are predominantly described by differential equations, yet our understanding of these systems is constrained by our incomplete grasp of non-linear physics and scarcity of comprehensive datasets. As such, advancing techniques in solving non-linear inverse problems becomes pivotal to addressing numerous outstanding questions in the field. In particular, modeling hot galactic winds is difficult because of unknown structure for various physical terms, and the lack of \textit{any} kinematic observational data. Additionally, the flow equations contain singularities that lead to numerical instability, making parameter sweeps non-trivial. We leverage differentiable programming, which enables neural networks to be embedded as individual terms within the governing coupled ordinary differential equations (ODEs), and show that this method can adeptly learn hidden physics. We robustly discern the structure of a mass-loading function which captures the physical effects of cloud destruction and entrainment into the hot superwind. Within a supervised learning framework, we formulate our loss function anchored on the astrophysical entropy ($K \propto P/\rho^{5/3}$). Our results demonstrate the efficacy of this approach, even in the absence of kinematic data $v$. We then apply these models to real Chandra X-Ray observations of starburst galaxy M82, providing the first systematic description of mass-loading within the superwind. This work further highlights neural ODEs as a useful discovery tool with mechanistic interpretability in non-linear inverse problems. We make our code public at this GitHub repository (https://github.com/dustindnguyen/2023_NeurIPS_NeuralODEs_M82).Comment: 9 Pages, 2 Figures, Accepted at the NeurIPS 2023 workshop on Machine Learning and the Physical Science

An efficient and adaptive test of auditory mental imagery

The ability to silently hear music in the mind has been argued to be fundamental to musicality. Objective measurements of this subjective imagery experience are needed if this link between imagery ability and musicality is to be investigated. However, previous tests of musical imagery either rely on self-report, rely on melodic memory, or do not cater in range of abilities. The Pitch Imagery Arrow Task (PIAT) was designed to address these shortcomings; however, it is impractically long. In this paper, we shorten the PIAT using adaptive testing and automatic item generation. We interrogate the cognitive processes underlying the PIAT through item response modelling. The result is an efficient online test of auditory mental imagery ability (adaptive Pitch Imagery Arrow Task: aPIAT) that takes 8 min to complete, is adaptive to participant’s individual ability, and so can be used to test participants with a range of musical backgrounds. Performance on the aPIAT showed positive moderate-to-strong correlations with measures of non-musical and musical working memory, self-reported musical training, and general musical sophistication. Ability on the task was best predicted by the ability to maintain and manipulate tones in mental imagery, as well as to resist perceptual biases that can lead to incorrect responses. As such, the aPIAT is the ideal tool in which to investigate the relationship between pitch imagery ability and musicality