416 research outputs found
The Taiwan ECDFS Near-Infrared Survey: Ultra-deep J and Ks Imaging in the Extended Chandra Deep Field-South
We present ultra-deep J and Ks imaging observations covering a 30' * 30' area
of the Extended Chandra Deep Field-South (ECDFS) carried out by our Taiwan
ECDFS Near-Infrared Survey (TENIS). The median 5-sigma limiting magnitudes for
all detected objects in the ECDFS reach 24.5 and 23.9 mag (AB) for J and Ks,
respectively. In the inner 400 arcmin^2 region where the sensitivity is more
uniform, objects as faint as 25.6 and 25.0 mag are detected at 5-sigma. So this
is by far the deepest J and Ks datasets available for the ECDFS. To combine the
TENIS with the Spitzer IRAC data for obtaining better spectral energy
distributions of high-redshift objects, we developed a novel deconvolution
technique (IRACLEAN) to accurately estimate the IRAC fluxes. IRACLEAN can
minimize the effect of blending in the IRAC images caused by the large
point-spread functions and reduce the confusion noise. We applied IRACLEAN to
the images from the Spitzer IRAC/MUSYC Public Legacy in the ECDFS survey
(SIMPLE) and generated a J+Ks selected multi-wavelength catalog including the
photometry of both the TENIS near-infrared and the SIMPLE IRAC data. We
publicly release the data products derived from this work, including the J and
Ks images and the J+Ks selected multiwavelength catalog.Comment: 25 pages, 25 figures, ApJS in pres
Predicting microRNA precursors with a generalized Gaussian components based density estimation algorithm
<p>Abstract</p> <p>Background</p> <p>MicroRNAs (miRNAs) are short non-coding RNA molecules, which play an important role in post-transcriptional regulation of gene expression. There have been many efforts to discover miRNA precursors (pre-miRNAs) over the years. Recently, <it>ab initio </it>approaches have attracted more attention because they do not depend on homology information and provide broader applications than comparative approaches. Kernel based classifiers such as support vector machine (SVM) are extensively adopted in these <it>ab initio </it>approaches due to the prediction performance they achieved. On the other hand, logic based classifiers such as decision tree, of which the constructed model is interpretable, have attracted less attention.</p> <p>Results</p> <p>This article reports the design of a predictor of pre-miRNAs with a novel kernel based classifier named the generalized Gaussian density estimator (G<sup>2</sup>DE) based classifier. The G<sup>2</sup>DE is a kernel based algorithm designed to provide interpretability by utilizing a few but representative kernels for constructing the classification model. The performance of the proposed predictor has been evaluated with 692 human pre-miRNAs and has been compared with two kernel based and two logic based classifiers. The experimental results show that the proposed predictor is capable of achieving prediction performance comparable to those delivered by the prevailing kernel based classification algorithms, while providing the user with an overall picture of the distribution of the data set.</p> <p>Conclusion</p> <p>Software predictors that identify pre-miRNAs in genomic sequences have been exploited by biologists to facilitate molecular biology research in recent years. The G<sup>2</sup>DE employed in this study can deliver prediction accuracy comparable with the state-of-the-art kernel based machine learning algorithms. Furthermore, biologists can obtain valuable insights about the different characteristics of the sequences of pre-miRNAs with the models generated by the G<sup>2</sup>DE based predictor.</p
Regulation of CLC-1 chloride channel biosynthesis by FKBP8 and Hsp90β.
Mutations in human CLC-1 chloride channel are associated with the skeletal muscle disorder myotonia congenita. The disease-causing mutant A531V manifests enhanced proteasomal degradation of CLC-1. We recently found that CLC-1 degradation is mediated by cullin 4 ubiquitin ligase complex. It is currently unclear how quality control and protein degradation systems coordinate with each other to process the biosynthesis of CLC-1. Herein we aim to ascertain the molecular nature of the protein quality control system for CLC-1. We identified three CLC-1-interacting proteins that are well-known heat shock protein 90 (Hsp90)-associated co-chaperones: FK506-binding protein 8 (FKBP8), activator of Hsp90 ATPase homolog 1 (Aha1), and Hsp70/Hsp90 organizing protein (HOP). These co-chaperones promote both the protein level and the functional expression of CLC-1 wild-type and A531V mutant. CLC-1 biosynthesis is also facilitated by the molecular chaperones Hsc70 and Hsp90β. The protein stability of CLC-1 is notably increased by FKBP8 and the Hsp90β inhibitor 17-allylamino-17-demethoxygeldanamycin (17-AAG) that substantially suppresses cullin 4 expression. We further confirmed that cullin 4 may interact with Hsp90β and FKBP8. Our data are consistent with the idea that FKBP8 and Hsp90β play an essential role in the late phase of CLC-1 quality control by dynamically coordinating protein folding and degradation
Is the Relationship between Body Size and Trophic Niche Position Time-Invariant in a Predatory Fish? First Stable Isotope Evidence
Characterizing relationships between individual body size and trophic niche position is essential for understanding how population and food-web dynamics are mediated by size-dependent trophic interactions. However, whether (and how) intraspecific size-trophic relationships (i.e., trophic ontogeny pattern at the population level) vary with time remains poorly understood. Using archival specimens of a freshwater predatory fish Gymnogobius isaza (Tanaka 1916) from Lake Biwa, Japan, we assembled a long-term (>40 years) time-series of the size-dependence of trophic niche position by examining nitrogen stable isotope ratios (δ15N) of the fish specimens. The size-dependence of trophic niche position was defined as the slope of the relationship between δ15N and log body size. Our analyses showed that the slope was significantly positive in about 60% of years and null in other years, changing through time. This is the first quantitative (i.e., stable isotope) evidence of long-term variability in the size-trophic relationship in a predatory fish. This finding had implications for the fish trophic dynamics, despite that about 60% of the yearly values were not statistically different from the long-term average. We proposed hypotheses for the underlying mechanism of the time-varying size-trophic relationship
Design of a Quality of Service-Based Load Balancing Relay Selection Mechanism for Long Term Evolution-Advanced Systems
Serving as the fourth generation mobile communication standard, Long Term Evolution-Advanced provides various technical support to achieve high transmission speed. In particular, relays are an essential technology supported by the standard. Because a relay uses the resources within a communication system, user devices adopt the optimal relay method as the transmission pathway to optimize resource utilization. According to the quality of service required by various user applications, this paper fabricates a method for selecting the optimal load-balancing transmission pathway for user devices
Assessing the Decision-Making Process in Human-Robot Collaboration Using a Lego-like EEG Headset
Human-robot collaboration (HRC) has become an emerging field, where the use of a robotic agent has been shifted from a supportive machine to a decision-making collaborator. A variety of factors can influence the effectiveness of decision-making processes during HRC, including the system-related (e.g., robot capability) and human-related (e.g., individual knowledgeability) factors. As a variety of contextual factors can significantly impact the human-robot decision-making process in collaborative contexts, the present study adopts a Lego-like EEG headset to collect and examine human brain activities and utilizes multiple questionnaires to evaluate participants’ cognitive perceptions toward the robot. A user study was conducted where two levels of robot capabilities (high vs. low) were manipulated to provide system recommendations. The participants were also identified into two groups based on their computational thinking (CT) ability. The EEG results revealed that different levels of CT abilities trigger different brainwaves, and the participants’ trust calibration of the robot also varies the resultant brain activities
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