Miracle-dz/UniFAR-FTUs huggingface dataset:
Human Paraoxonase 1 Is the Enzyme Responsible for Pilocarpine Hydrolysis

Pilocarpine has been widely used in ophthalmic preparations for the treatment of glaucoma and in oral preparations for the treatment of radiation-induced xerostomia and Sjögren syndrome. The major metabolic pathways of pilocarpine in human are hydrolysis and hydroxylation. It was found that CYP2A6 is responsible for the 3-hydroxylation, but the enzymes responsible for the hydrolysis have not been characterized. In this study, we attempted to identify esterases responsible for pilocarpine hydrolysis. Pilocarpine hydrolase activities in human liver microsomes and plasma were stimulated by the addition of CaCl2, suggesting that the calcium-dependent esterase, paraoxonase (PON), was responsible for pilocarpine hydrolysis. To confirm this hypothesis, the pilocarpine hydrolase activity was measured using the recombinant human PONs (PON1, PON2, and PON3) established in this study, and the result was that only PON1 showed pilocarpine hydrolase activity. The effect of PON1 polymorphism (Q192R) on pilocarpine hydrolase activity was analyzed using recombinant human PON1 192Q and 192R and human plasma from 50 volunteers. The results showed that recombinant PON1 192R revealed significantly higher catalytic efficiency than PON1 192Q. In human plasma, the activity of the R/R genotype (117.0 ± 25.2 pmol · min−1 · μl−1, n = 23) was significantly higher than those of the Q/R and Q/Q genotypes (97.3 ± 21.0 pmol · min−1 · μl−1, n = 20 and 90.4 ± 26.2 pmol · min−1 · μl−1, n = 7, respectively). It is suggested that this polymorphism affects pilocarpine hydrolase activity. In this study, we found that human PON1 is the major enzyme for the catalytic efficiency of pilocarpine hydrolysis.
Humanoid behaviour learning through visuomotor association by self-imitation

Learning by imitation renders a means for more natural human-robot interaction and is potentially the primary form of teaching. Imitation provides a potential means of automatically programming complex systems without extensive trials. This paper presents a method of imitation learning based on mapping the demonstrator's motion patterns to the observer self-posture. The self-posture is acquired through observing and recognizing the mirror image of its own body posture while performing the action in front of a mirror. First, different kinds of behavioural actions are learned through motor babbling by randomly performing different actions. The actions are learned through a novel probabilistic model called Topological Gaussian Adaptive Resonance Hidden Markov Model. During learning, the observer also extracts visual features through optical flow from self-observation in a mirror image. Second, after learning, a visuo-motor association is developed through novel Topological Gaussian Adaptive Resonance Associative Memory. Finally, after learning the demonstrator performs a similar action in front of the robot and the he robot recalls the corresponding motor command from the memory.
Stellar structure and mass loss on the upper asymptotic giant branch

ABSTRA C T We examine the envelope properties of asymptotic giant branch (AGB) stars as they evolve on the upper AGB and during the early post-AGB phase. Because of the high mass-loss rate, the envelope mass decreases by more than an order of magnitude. This makes the density profile below the photosphere much shallower, and the entropy profile much steeper. We discuss the possible role of these changes in the profiles in the onset of the high mass-loss rate (superwind) and the large deviation from spherical mass loss at the termination of the AGB. We concentrate on the idea that the shallower density profile and steeper entropy profile allow the formation of cool magnetic spots, above which dust forms much more easily.
Pharmacists' Perceptions of the Barriers and Facilitators to the Implementation of Clinical Pharmacy Key Performance Indicators

Background In hospitals around the world, there has been no consensus regarding which clinical activities a pharmacist should focus on until recently. In 2011, a Canadian clinical pharmacy key performance indicator (cpKPI) collaborative was formed. The goal of the collaborative was to advance pharmacy practice in order to improve patient outcomes and enhance the quality of care provided to patients by hospital pharmacists. Following a literature review, which indicated that pharmacists can improve patient outcomes by carrying out specific activities, and an evidence-informed consensus process, a final set of eight cpKPIs were established. Canadian hospitals leading the cpKPI initiative are currently in the early stages of implementing these indicators. Objective To explore pharmacists' perceptions of the barriers and facilitators to the implementation of cpKPIs. Methods Clinical pharmacists employed by the Nova Scotia Health Authority were invited to participate in focus groups. Focus group discussions were audio-recorded and transcribed, and data was analyzed using thematic analysis. Findings Three focus groups, including 26 pharmacists, were conducted in February 2015. Three major themes were identified. Resisting the change was comprised of documentation challenges, increased workload, practice environment constraints, and competing priorities. Embracing cpKPIs was composed of seeing the benefit, demonstrating value, and existing supports. Navigating the unknown was made up of quality versus quantity battle, and insights into the future. Conclusions Although pharmacists were challenged by documentation and other changes associated with the implementation of cpKPIs, they demonstrated significant support for cpKPIs and were able to see benefits of the implementation. Pharmacists came up with suggestions for overcoming resistance associated with the implementation of cpKPIs and provided insights into the future of pharmacy practice. The identification of barriers and facilitators to cpKPI implementation will be used to inform the implementation process on a local and national level.
The Fractional Fourier Transform and Applications

This paper describes the “fractional Fourier transform,” which admits computation by an algorithm that has complexity proportional to the fast Fourier transform algorithm. Whereas the discrete Fourier transform (DFT) is based on integral roots of unity $e^{{{ - 2\pi i} / n}} $, the fractional Fourier transform is based on fractional roots of unity $e^{ - 2\pi i\alpha } $ where $\alpha $ is arbitrary. The fractional Fourier transform and the corresponding fast algorithm are useful for such applications as computing DFTs of sequences with prime lengths, computing DFTs of sparse sequences, analyzing sequences with noninteger periodicities, performing high-resolution trigonometric interpolation, detecting lines in noisy images, and detecting signals with linearly drifting frequencies. In many cases, the resulting algorithms are faster by arbitrarily large factors than conventional techniques.
Stability Analysis of Closed-loop Algorithms for Cancelling Nonlinear Memory Effects

In a nonlinear memory system, using a nonlinear adaptive filter as a predistorter is an effective compensation technique. Predistorters based on closed-loop algorithms typically exhibit better linearity than those based on indirect learning algorithms. However, the steady state of a closed-loop algorithm may be affected by external noise or error signals. In this paper, the long-term stability of a closed-loop algorithm is analyzed using perturbation and iteration analyses. It is shown that because of accumulation of historical errors, it is difficult for typical closed-loop algorithms to converge when the number of iterations becomes large. To address this problem, a modified closed-loop algorithm is proposed. By reducing the effect of accumulated historical errors, the long-term stability of the algorithm is largely improved, as verified by numerical simulations.
Binaural sound source localization based on upward zero-crossing points detection in reverberant fields

This paper mainly deals with the problem of sound source localization based on upward zero-crossing points detection with two microphones. Gammatone filter bank are used to divide the signal into different frequencies, SNR can be obtained by the statistical properties of the time difference samples, and then reliable time difference samples can be selected according to the SNR limitation. Finally, the practical time difference is achieved by the analysis of the time difference samples. Consequently, the time differences are transformed into azimuths by use of geometrical relationship. Experiments are conducted with different sounds, and sounds with wider spectrum seem to perform more precisely; Evidence also shows that the method is robust to sound signals with different SNR, and experiments in different rooms reveal that reverberation apparently degrades the localization accuracy.
Persistence of Orally Administered Lactobacillus Strains in the Gut of Infant Mice

The present study tested the persistence of orally administered bacteria in the gut of suckling mice. We used three bacterial strains: one strain of Lactobacillus johnsonii (designated strain Ms1) that was previously isolated from the mouse stomach, and two strains of L. plantarum, (strain No. 14 and JCM 1149T). We detected L. johnsonii Ms1, but neither strain of L. plantarum, in the gut 7 days after administration when the organisms were administered on days 0, 1, 3 or 7 of neonatal life. None of the strains was detected in the gut 7 days after the administration on days 14 or 28 of neonatal life. L. johnsonii Ms1 and L. plantarum JCM 1149T exhibited similar levels of in vitro association with gut tissues, with both strains showing association that was significantly higher than that of L. plantarum No. 14. In a separate experiment, the number of total bacteria and lactobacilli in the gut, as estimated by real-time quantitative PCR, was significantly higher in 14- and 21-day-old mice than in 0- and 7-day-old mice. In addition, the number of total bacteria was higher in 21-day-old mice than in 14-day-old mice, and the number of lactobacilli was higher in 7-day-old mice than in 0-day-old mice. These results suggest that gut persistence of administered bacteria in infant mice is species- or strain-specific and is affected by the development of indigenous microbiota. In addition, gut persistence of administered bacteria may not always depend on the tissue association capacity.
Applying Clustering Analysis on Grouping Similar OLAP Reports

On Line Analysis Processing (OLAP) is a common solution that modern enterprises use to generate, monitor, share, and administrate their analysis reports. When daily, weekly, and/or monthly reports are generated or published by the OLAP operators, the report readers can only rely on their smart eyes to find out hidden rules, similar reports, or trend inside the potentially huge amount of reports. Data mining is a well-developed field for finding hidden rules inside the data itself. However, there is few techniques focus on finding hidden rules, similarity, or trend using OLAP reports as the unit of analysis. In this paper, we explore how to use clustering analysis on OLAP reports in order to automatically and effectively find the grouping knowledge of OLAP reports. We also address the appropriate presentation of this grouping knowledge to OLAP users.
Inverted Index based Modified Version of KNN for Text Categorization

This research proposes a new strategy where documents are encoded into string vectors and modified version of KNN to be adaptable to string vectors for text categorization. Traditionally, when KNN are used for pattern classification, raw data should be encoded into numerical vectors. This encoding may be difficult, depending on a given application area of pattern classification. For example, in text categorization, encoding full texts given as raw data into numerical vectors leads to two main problems: huge dimensionality and sparse distribution. In this research, we encode full texts into string vectors, and modify the supervised learning algorithms adaptable to string vectors for text categorization.
THE ROLE OF THE N-TERMINUS ON THE ENZYMATIC ACTIVITY OF dUTPase FROM DICTYOSTELIUM DISCOIDEUM

The enzyme deoxyuridine triphosphatase (dUTPase) (EC 3.6.1.23) converts dUTP to dUMP, thus shifting the dUTP to dUMP ratio in the cell. The molecule dUTP is subject to mis-incorporation into DNA due to lack of distinguishing by DNA polymerase. Uracil incorporation can be repaired with base excision repair mechanisms but may create overwhelming DNA strand breaks proving to be detrimental to the cell. Most dUTPases of eukaryotes are homotrimeric and contain five highly conserved motifs responsible for catalysis and substrate binding. Many dUTPases of eukaryotes possess a leading and core region in their sequence. The core region is composed of the five highly conserved motifs while the leading region is described as the N-terminal end of the enzyme with an uncharacterized role. Due to its similarity to mammalian cellular biology, the eukaryotic soil amoeba Dictyostelium discoideum was used as a model organism for understanding characteristics of dUTPase. The leading region of the D. discoideum dUTPase is 37 residues at the N-terminal end. Two separate sequences of dUTPase, a predicted native sequence (full-length version) and a sequence lacking the 37 N-terminal residues (core version) were cloned and expressed in the organism Escherichia coli. A comparative study format was utilized to understand the role of the N-terminal leading region in relation to kinetic constants, pH and temperature optimum, metal dependence, and renaturation potential. Data revealed the core version to be superior to the full-length due to a lower K m (0.44-fold lower) and greater k cat /K m (1.35-fold higher) than the full-length version. 2.6: Activity assays and data analysis 23 2.6. 1: Full-length and core kinetic data acquisition and analysis 23 2.7: Measurement of dUMP product formed in end-point assays 26 2.8: Temperature and pH optima 30 2.
Polynomial Fuzzy Radial Basis Function Neural Network Classifiers Realized with the Aid of Boundary Area Decision

In the area of clustering, there are numerous approaches to construct clusters in the input space. For regression problem, when forming clusters being a part of the overall model, the relationships between the input space and the output space are essential and have to be taken into consideration. Conditional Fuzzy C-Means (c-FCM) clustering offers an opportunity to analyze the structure in the input space with the mechanism of supervision implied by the distribution of data present in the output space. However, like other clustering methods, c-FCM focuses on the distribution of the data. In this paper, we introduce a new method, which by making use of the ambiguity index focuses on the boundaries of the clusters whose determination is essential to the quality of the ensuing classification procedures. The introduced design is illustrated with the aid of numeric examples that provide a detailed insight into the performance of the fuzzy classifiers and quantify several essentials design aspects.
A study of code reuse and sharing characteristics of Java applications

Presents a detailed characterization of Java application and applet workloads in terms of reuse and sharing of Java code at the program, class and method level. In order to expose more sharing opportunities, techniques for detecting code equivalence (even in the presence of minor code changes or constant pool index differences) are also proposed and examined. The analyzed application workload consists of the recently released SPECjvm98 benchmarks, and the applet workload is derived from three extensive searches of the Internet between May 1997 and May 1998 using an enhanced Web crawler. Analysis of these workloads reveals several new code sharing and optimization opportunities.
Monocular Depth by Nonlinear Diffusion

Following the phenomenological approach of gestaltists, sparse monocular depth cues such as T- and X-junctions and the local convexity are crucial to identify the shape and depth relationships of depicted objects. According to Kanizsa, mechanisms called a modal and modal completion permit to transform these local relative depth cues into a global depth reconstruction. In this paper, we propose a mathematical and computational translation of gestalt depth perception theory, from the detection of local depth cues to their synthesis into a consistent global depth perception. The detection of local depth cues is built on the response of a line segment detector (LSD), which works in a linear time relative to the image size without any parameter tuning. The depth synthesis process is based on the use of a nonlinear iterative filter which is asymptotically equivalent to the Perona-Malik partial differential equation (PDE). Experimental results are shown on several real images and demonstrate that this simple approach can account a variety of phenomena such as visual completion, transparency and self-occlusion.
A rateless code for dynamic decode-and-forward relaying in wireless relay networks

In this paper, we propose a novel rateless coding scheme for dynamic decode-and-forward (DDF) relaying in wireless relay networks. The proposed rateless code is developed to overcome the high error floor problem caused by the Luby Transform (LT) code-based encoding scheme in Raptor codes. The improvement is achieved by providing each symbol with approximately equal protection in the belief propagation (BP) decoding, and by designing a special full rank parity-check matrix in the encoding process. Simulation results show that the proposed scheme considerably outperforms the existing rateless coding scheme in wireless relay networks with DDF relaying protocols.
Fixation of Atmospheric Nitrogen: Synthesis of Heterocycles with Atmospheric Nitrogen as the Nitrogen Source.

Dry air is the source of molecular nitrogen for reactions with TiL4 , Li, and TMSCl (L = Cl, OiPr; TMS = trimethylsilyl). The nitrogen-titanium complexes thus prepared can be used to synthesize indoles, pyrroles, and lactams from carbonyl compounds. Applying this method to 1 provides access to 2, the key compound in the synthesis of (±)-lycopodine.
Mequindox-Induced Kidney Toxicity Is Associated With Oxidative Stress and Apoptosis in the Mouse

Mequindox (MEQ), belonging to quinoxaline-di-N-oxides (QdNOs), is a synthetic antimicrobial agent widely used in China. Previous studies found that the kidney was one of the main toxic target organs of the QdNOs. However, the mechanisms underlying the kidney toxicity caused by QdNOs in vivo still remains unclear. The present study aimed to explore the molecular mechanism of kidney toxicity in mice after chronic exposure to MEQ. MEQ led to the oxidative stress, apoptosis, and mitochondrial damage in the kidney of mice. Meanwhile, MEQ upregulated Bax/Bcl-2 ratio, disrupted mitochondrial permeability transition pores, caused cytochrome c release, and a cascade activation of caspase, eventually induced apoptosis. The oxidative stress mediated by MEQ might led to mitochondria damage and apoptosis in a mitochondrial-dependent apoptotic pathway. Furthermore, upregulation of the Nrf2-Keap1 signaling pathway was also observed. Our findings revealed that the oxidative stress, mitochondrial dysfunction, and the Nrf2-Keap1 signaling pathway were associated with the kidney apoptosis induced by MEQ in vivo.
Semi-Supervised Methods for Improving Keyword Search of Unseen Terms

We present a semi-supervised language modeling technique to improve search performance on words without training data. Probabilities estimated from automatic transcripts of a large corpus of in-domain audio are added to an existing LM. Requiring only pronunciations and indomain audio, our method achieves 70% of the possible gain without any supervised data or development data. This method is contrasted with techniques using additional resources. If human effort is available, we also present a transcription regime to efficiently close the gap.
The Globular Cluster System of the Spiral Galaxy NGC 7814

We present the results of a wide-field photometric study of the globular cluster (GC) system of the edge-on Sab spiral NGC 7814. This is the first spiral to be fully analyzed from our survey of the GC systems of a large sample of galaxies beyond the Local Group. NGC 7814 is of particular interest because a previous study estimated that it has 500–1000 GCs, giving it the largest specific frequency (SN) known for a spiral. Understanding this galaxy's GC system is important in terms of our understanding of the GC populations of spirals in general and has implications for the formation of massive galaxies. We observed the galaxy in BVR filters with the WIYN 3.5 m telescope and used image classification and three-color photometry to select GC candidates. We also analyzed archival Hubble Space Telescope (HST) Wide Field Planetary Camera 2 images of NGC 7814, both to help quantify the contamination level of the WIYN GC candidate list and to detect GCs in the inner part of the galaxy halo. Combining HST data with high-quality ground-based images allows us to trace the entire radial extent of this galaxy's GC system and determine the total number of GCs directly through observation. We find that rather than being an especially high-SN spiral, NGC 7814 has ≲200 GCs and SN ∼ 1, making it comparable to the two most well-studied spiral galaxies, the Milky Way and M31. We explore the implications of these results for models of the formation of galaxies and their GC systems. The initial results from our survey suggest that the GC systems of typical elliptical galaxies can be accounted for by the merger of two or more spirals, but that for highly luminous elliptical galaxies, additional physical processes may be needed.
POLARIMETRIC SAR IMAGE SEGMENTATION USING AFFINITY FUNCTION FROM PROBABILISTIC BOUNDARIES AND PATCH FEATURES

We investigate the segmentation of high resolution polarimetric Synthetic Aperture Radar (PolSAR) images of urban areas. The segmentation strategy in [1] is applied in this paper. Spectral graph segmentation has the advantage of capturing non-local property. The probabilistic boundaries and patch features are integrated for spectral graph segmentation. Accurate boundaries extraction and efficient patch features improve the segmentation. On the other hand, a better segmentation corresponds to a refined binary boundary map. Gradients of amplitude, texture, PolSAR CFAR edges and gradient magnitude are incorporated to produce an accurate probabilistic boundary map. These gradient features are combined in a supervised manner. The combination rules are learned from ground truth data using a logistic regression classifier. For a test image, a soft boundary map is generated by the classifier using all the gradient features. Probabilistic boundaries and patch features are integrated into affinity matrix construction [1] in spectral graph segmentation. Probabilistic boundary map provides an estimate of intervening contours. Learning of affinity function is treated as a supervised classification problem. Eigen decomposition of the affinity matrix results in spectral segmentations [2].