3,213 research outputs found
The moderating effect of gender on ideal-weight goals and exercise dependence symptoms
Background and aims:
Exercise dependence is implicated in the development of eating disorders and muscle dysmorphic disorder. Although conceptually these disorders represent similar pathologies they largely affect different genders and result in opposite body composition, appearance, and ideal-weight goals (i.e., to gain or lose/maintain weight). Therefore, understanding individuals' ideal-weight goals related to engaging in exercise while simultaneously examining gender differences in exercise dependence symptoms may help to identify those whom may be most at-risk for eating disorders and muscle dysmorphic disorder. The purpose of our study was to examine the moderating effect of gender for exercise dependence symptoms in relation to weight gain, loss, or maintenance goals.
Methods:
Self-reported exercise behavior and exercise dependence symptoms (i.e., Exercise Dependence Scale) were assessed in 513 undergraduate students.
Results:
Our analysis revealed a moderating effect for gender on ideal-weight goals and a gender difference in exercise dependence symptoms. Specifically, men who were dissatisfied with their current weight reported more exercise dependence symptoms than women.
Conclusions:
These results support a growing body of research and extend our understanding of the relationships among exercise dependence and gender specific body-focused psychiatric disorders
Dynamic Behavioral Mixed-Membership Model for Large Evolving Networks
The majority of real-world networks are dynamic and extremely large (e.g.,
Internet Traffic, Twitter, Facebook, ...). To understand the structural
behavior of nodes in these large dynamic networks, it may be necessary to model
the dynamics of behavioral roles representing the main connectivity patterns
over time. In this paper, we propose a dynamic behavioral mixed-membership
model (DBMM) that captures the roles of nodes in the graph and how they evolve
over time. Unlike other node-centric models, our model is scalable for
analyzing large dynamic networks. In addition, DBMM is flexible,
parameter-free, has no functional form or parameterization, and is
interpretable (identifies explainable patterns). The performance results
indicate our approach can be applied to very large networks while the
experimental results show that our model uncovers interesting patterns
underlying the dynamics of these networks
Automated Determination of [Fe/H] and [C/Fe] from Low-Resolution Spectroscopy
We develop an automated spectral synthesis technique for the estimation of
metallicities ([Fe/H]) and carbon abundances ([C/Fe]) for metal-poor stars,
including carbon-enhanced metal-poor stars, for which other methods may prove
insufficient. This technique, autoMOOG, is designed to operate on relatively
strong features visible in even low- to medium-resolution spectra, yielding
results comparable to much more telescope-intensive high-resolution studies. We
validate this method by comparison with 913 stars which have existing
high-resolution and low- to medium-resolution to medium-resolution spectra, and
that cover a wide range of stellar parameters. We find that at low
metallicities ([Fe/H] < -2.0), we successfully recover both the metallicity and
carbon abundance, where possible, with an accuracy of ~ 0.20 dex. At higher
metallicities, due to issues of continuum placement in spectral normalization
done prior to the running of autoMOOG, a general underestimate of the overall
metallicity of a star is seen, although the carbon abundance is still
successfully recovered. As a result, this method is only recommended for use on
samples of stars of known sufficiently low metallicity. For these
low-metallicity stars, however, autoMOOG performs much more consistently and
quickly than similar, existing techniques, which should allow for analyses of
large samples of metal-poor stars in the near future. Steps to improve and
correct the continuum placement difficulties are being pursued.Comment: 8 pages, 7 figures; accepted for publication in A
The Aes protein and the monomeric alpha-galactosidase from Escherichia coli form a non-covalent complex. Implications for the regulation of carbohydrate metabolism.
Aes, a 36-kDa acetylesterase fromEscherichia coli, belongs to the hormone-sensitive lipase family, and it is involved in the regulation of MalT, the transcriptional activator of the maltose regulon. The activity of MalT is depressed through a direct protein-protein interaction with Aes. Although the effect is clear-cut, the meaning of this interaction and the conditions that trigger it still remain elusive. To perform a comparative thermodynamic study between the mesophilic Aes protein and two homologous thermostable enzymes, Aes was overexpressed in E. coli and purified. At the last step of the purification procedure the enzyme was eluted from a Mono Q HR 5/5 column as a major form migrating, anomalously, at 56 kDa on a calibrated Superdex 75 column. A minor peak that contains the Aes protein and a polypeptide of 50 kDa was also detected. By a combined analysis of size-exclusion chromatography and surface-enhanced laser desorption ionization-time of flight mass spectrometry, it was possible to demonstrate the presence in this peak of a stable 87-kDa complex, containing the Aes protein itself and the 50-kDa polypeptide in a 1:1 ratio. The homodimeric molecular species of Aes and of the 50-kDa polypeptide were also detected. The esterase activity associated with the 87-kDa complex, when assayed with p-nitrophenyl butanoate as substrate, proved 6-fold higher than the activity of the major Aes form of 56 kDa. Amino-terminal sequencing highlighted that the 50-kDa partner of Aes in the complex was the α-galactosidase from E. coli. TheE. coli cells harboring plasmid pT7-SCII-aesand, therefore, expressing Aes were hampered in their growth on a minimal medium containing raffinose as a sole carbon source. Because α-galactosidase is involved in the metabolism of raffinose, the above findings suggest a potential role of Aes in the regulation of carbohydrate metabolism in E. coli
Near-Infrared Spectroscopy of Carbon-Enhanced Metal-Poor Stars. I. A SOAR/OSIRIS Pilot Study
We report on an abundance analysis for a pilot study of seven Carbon-Enhanced
Metal-Poor (CEMP) stars, based on medium-resolution optical and near-infrared
spectroscopy. The optical spectra are used to estimate [Fe/H], [C/Fe], [N/Fe],
and [Ba/Fe] for our program stars. The near-infrared spectra, obtained during a
limited early science run with the new SOAR 4.1m telescope and the Ohio State
Infrared Imager and Spectrograph (OSIRIS), are used to obtain estimates of
[O/Fe] and 12C/13C. The chemical abundances of CEMP stars are of importance for
understanding the origin of CNO in the early Galaxy, as well as for placing
constraints on the operation of the astrophysical s-process in very
low-metallicity Asymptotic Giant Branch (AGB) stars.
This pilot study includes a few stars with previously measured [Fe/H],
[C/Fe], [N/Fe],[O/Fe], 12C/13C, and [Ba/Fe], based on high-resolution optical
spectra obtained with large-aperture telescopes. Our analysis demonstrates that
we are able to achieve reasonably accurate determinations of these quantities
for CEMP stars from moderate-resolution optical and near-infrared spectra. This
opens the pathway for the study of significantly larger samples of CEMP stars
in the near future. Furthermore, the ability to measure [Ba/Fe] for (at least
the cooler) CEMP stars should enable one to separate stars that are likely to
be associated with s-process enhancements (the CEMP-s stars) from those that do
not exhibit neutron-capture enhancements (the CEMP-no stars).Comment: 27 pages, including 5 tables, 6 figures, accepted for publication in
The Astronomical Journa
Randomized sham-controlled trial of repetitive transcranial magnetic stimulation in treatment-resistant obsessive–compulsive disorder
In open trials, 1-Hz repetitive transcranial magnetic stimulation (rTMS) to the supplementary motor area (SMA) improved symptoms and normalized cortical hyper-excitability of patients with obsessive–compulsive disorder (OCD). Here we present the results of a randomized sham-controlled double-blind study. Medication-resistant OCD patients (n=21) were assigned 4 wk either active or sham rTMS to the SMA bilaterally. rTMS parameters consisted of 1200 pulses/d, at 1 Hz and 100% of motor threshold (MT). Eighteen patients completed the study. Response to treatment was defined as a ≽25% decrease on the Yale–Brown Obsessive Compulsive Scale (YBOCS). Non-responders to sham and responders to active or sham rTMS were offered four additional weeks of open active rTMS. After 4 wk, the response rate in the completer sample was 67% (6/9) with active and 22% (2/9) with sham rTMS. At 4 wk, patients receiving active rTMS showed on average a 25% reduction in the YBOCS compared to a 12% reduction in those receiving sham. In those who received 8-wk active rTMS, OCD symptoms improved from 28.2±5.8 to 14.5±3.6. In patients randomized to active rTMS, MT measures on the right hemisphere increased significantly over time. At the end of 4-wk rTMS the abnormal hemispheric laterality found in the group randomized to active rTMS normalized. The results of the first randomized sham-controlled trial of SMA stimulation in the treatment of resistant OCD support further investigation into the potential therapeutic applications of rTMS in this disabling condition
Digitalizing Art: Transforming Marketing Efforts
This research is part of a semester-long project where the undergraduate Business students from Molloy College pitch their business and marketing recommendations to a local non-profit. The theme emerges from a pedagogical philosophy that it is essential to learn the significance that business has upon society while working with a neighborhood non-profit. For example, having students enter into the business world where profit is a healthy by-product and is not the main focus toward today’s career development is critical in developing tomorrow’s ethical and social leaders. The learning activity leading to this learning outcome requires the undergraduate business students to experience this type of real-world project. The students are presented with an opportunity to study the most prevalent issues their assigned non-profit is facing and they are to collectively make a set of solution-driven recommendations that will ultimately lead to social good
Neuraghe: Exploiting CPU-FPGA synergies for efficient and flexible CNN inference acceleration on zynQ SoCs
Deep convolutional neural networks (CNNs) obtain outstanding results in tasks that require human-level understanding of data, like image or speech recognition. However, their computational load is significant, motivating the development of CNN-specialized accelerators. This work presents NEURAghe, a flexible and efficient hardware/software solution for the acceleration of CNNs on Zynq SoCs. NEURAghe leverages the synergistic usage of Zynq ARM cores and of a powerful and flexible Convolution-Specific Processor deployed on the reconfigurable logic. The Convolution-Specific Processor embeds both a convolution engine and a programmable soft core, releasing the ARM processors from most of the supervision duties and allowing the accelerator to be controlled by software at an ultra-fine granularity. This methodology opens the way for cooperative heterogeneous computing: While the accelerator takes care of the bulk of the CNN workload, the ARM cores can seamlessly execute hard-to-accelerate parts of the computational graph, taking advantage of the NEON vector engines to further speed up computation. Through the companion NeuDNN SW stack, NEURAghe supports end-to-end CNN-based classification with a peak performance of 169GOps/s and an energy efficiency of 17GOps/W. Thanks to our heterogeneous computing model, our platform improves upon the state-of-the-art, achieving a frame rate of 5.5 frames per second (fps) on the end-to-end execution of VGG-16 and 6.6fps on ResNet-18
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