Characteristics of Thiamin and Its Relevance to the Management of Heart Failure

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/141593/1/ncp0487.pd

Alien Registration- Gearing, Harriet A. (Woolwich, Sagadahoc County)

https://digitalmaine.com/alien_docs/9165/thumbnail.jp

Spin torque, tunnel-current spin polarization and magnetoresistance in MgO magnetic tunnel junctions

We examine the spin torque (ST) response of magnetic tunnel junctions (MTJs) with ultra-thin MgO tunnel barrier layers to investigate the relationship between the spin-transfer torque and the tunnel magnetoresistance (TMR) under finite bias. We find that the spin torque per unit current exerted on the free layer decreases by less than 10% over a bias range where the TMR decreases by over 40%. We examine the implications of this result for various spin-polarized tunneling models and find that it is consistent with magnetic-state-dependent effective tunnel decay lengths.Comment: 4 pages, 3 figure

Metabolic and Nutritional Aspects of Acute Renal Failure in Critically Ill Patients Requiring Continuous Renal Replacement Therapy

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/141368/1/ncp0176.pd

Influence of dynamic content on visual attention during video advertisements

Purpose Dynamic advertising, including television and online video ads, demands new theory and tools developed to understand attention to moving stimuli. The purpose of this study is to empirically test the predictions of a new dynamic attention theory, Dynamic Human-Centred Communication Systems Theory, versus the predictions of salience theory. Design/methodology/approach An eye-tracking study used a sample of consumers to measure visual attention to potential areas of interest (AOIs) in a random selection of unfamiliar video ads. An eye-tracking software feature called intelligent bounding boxes (IBBs) was used to track attention to moving AOIs. AOIs were coded for the presence of static salience variables (size, brightness, colour and clutter) and dynamic attention theory dimensions (imminence, motivational relevance, task relevance and stability). Findings Static salience variables contributed 90% of explained variance in fixation and 57% in fixation duration. However, the data further supported the three-way interaction uniquely predicted by dynamic attention theory: between imminence (central vs peripheral), relevance (motivational or task relevant vs not) and stability (fleeting vs stable). The findings of this study indicate that viewers treat dynamic stimuli like real life, paying less attention to central, relevant and stable AOIs, which are available across time and space in the environment and so do not need to be memorised. Research limitations/implications Despite the limitations of small samples of consumers and video ads, the results of this study demonstrate the potential of two relatively recent innovations, which have received limited emphasis in the marketing literature: dynamic attention theory and IBBs. Practical implications This study documents what does and does not attract attention to video advertising. What gets attention according to salience theory (e.g. central location) may not always get attention in dynamic advertising because of the effects of relevance and stability. To better understand how to execute video advertising to direct and retain attention to important AOIs, advertisers and advertising researchers are encouraged to use IBBs. Originality/value This study makes two original contributions: to marketing theory, by showing how dynamic attention theory can predict attention to video advertising better than salience theory, and to marketing research, showing the utility of tracking visual attention to moving objects in video advertising with IBBs, which appear underutilised in advertising research

Effect of an acute dose of omega-3 fish oil following exercise-induced muscle damage

Purpose The purpose of this double-blind, placebo-controlled study was to examine the effect of two fish oil supplements, one high in EPA (750 mg EPA, 50 mg DHA) and one low in EPA (150 mg EPA, 100 mg DHA), taken acutely as a recovery strategy following EIMD. Methods Twenty-seven physically active males (26 ± 4 year, 1.77 ± 0.07 m, 80 ± 10 kg) completed 100 plyometric drop jumps to induce muscle damage. Perceptual (perceived soreness) and functional (isokinetic muscle strength at 60° and 180° s−1, squat jump performance and countermovement jump performance) indices of EIMD were recorded before, and 1, 24, 48, 72, and 96h after the damaging protocol. Immediately after the damaging protocol, volunteers ingested either a placebo (Con), a low-EPA fish oil (Low EPA) or a high-EPA fish oil (High EPA) at a dose of 1 g per 10 kg body mass. Results A significant group main effect was observed for squat jump, with the High EPA group performing better than Con and Low EPA groups (average performance decrement, 2.1, 8.3 and 9.8%, respectively), and similar findings were observed for countermovement jump performance, (average performance decrement, 1.7, 6.8 and 6.8%, respectively, p = 0.07). Significant time, but no interaction main effects were observed for all functional and perceptual indices measured, although large effect sizes demonstrate a possible ameliorating effect of high dose of EPA fish supplementation (effect sizes ≥0.14). Conclusion This study indicates that an acute dose of high-EPA fish oil may ameliorate the functional changes following EIMD

Scaling metagenome sequence assembly with probabilistic de Bruijn graphs

Deep sequencing has enabled the investigation of a wide range of environmental microbial ecosystems, but the high memory requirements for {\em de novo} assembly of short-read shotgun sequencing data from these complex populations are an increasingly large practical barrier. Here we introduce a memory-efficient graph representation with which we can analyze the k-mer connectivity of metagenomic samples. The graph representation is based on a probabilistic data structure, a Bloom filter, that allows us to efficiently store assembly graphs in as little as 4 bits per k-mer, albeit inexactly. We show that this data structure accurately represents DNA assembly graphs in low memory. We apply this data structure to the problem of partitioning assembly graphs into components as a prelude to assembly, and show that this reduces the overall memory requirements for {\em de novo} assembly of metagenomes. On one soil metagenome assembly, this approach achieves a nearly 40-fold decrease in the maximum memory requirements for assembly. This probabilistic graph representation is a significant theoretical advance in storing assembly graphs and also yields immediate leverage on metagenomic assembly

TOPSAN: a collaborative annotation environment for structural genomics

<p>Abstract</p> <p>Background</p> <p>Many protein structures determined in high-throughput structural genomics centers, despite their significant novelty and importance, are available only as PDB depositions and are not accompanied by a peer-reviewed manuscript. Because of this they are not accessible by the standard tools of literature searches, remaining underutilized by the broad biological community.</p> <p>Results</p> <p>To address this issue we have developed TOPSAN, The Open Protein Structure Annotation Network, a web-based platform that combines the openness of the wiki model with the quality control of scientific communication. TOPSAN enables research collaborations and scientific dialogue among globally distributed participants, the results of which are reviewed by experts and eventually validated by peer review. The immediate goal of TOPSAN is to harness the combined experience, knowledge, and data from such collaborations in order to enhance the impact of the astonishing number and diversity of structures being determined by structural genomics centers and high-throughput structural biology.</p> <p>Conclusions</p> <p>TOPSAN combines features of automated annotation databases and formal, peer-reviewed scientific research literature, providing an ideal vehicle to bridge a gap between rapidly accumulating data from high-throughput technologies and a much slower pace for its analysis and integration with other, relevant research.</p