Miracle-dz/UniFAR-FTUs huggingface dataset:
Molecular Basis for Modulation of Metabotropic Glutamate Receptors and Their Drug Actions by Extracellular Ca2+

Metabotropic glutamate receptors (mGluRs) associated with the slow phase of the glutamatergic signaling pathway in neurons of the central nervous system have gained importance as drug targets for chronic neurodegenerative diseases. While extracellular Ca2+ was reported to exhibit direct activation and modulation via an allosteric site, the identification of those binding sites was challenged by weak binding. Herein, we review the discovery of extracellular Ca2+ in regulation of mGluRs, summarize the recent developments in probing Ca2+ binding and its co-regulation of the receptor based on structural and biochemical analysis, and discuss the molecular basis for Ca2+ to regulate various classes of drug action as well as its importance as an allosteric modulator in mGluRs.
Large-Scale Candidate Gene Analysis of HDL Particle Features

Background HDL cholesterol (HDL-C) is an established marker of cardiovascular risk with significant genetic determination. However, HDL particles are not homogenous, and refined HDL phenotyping may improve insight into regulation of HDL metabolism. We therefore assessed HDL particles by NMR spectroscopy and conducted a large-scale candidate gene association analysis. Methodology/Principal Findings We measured plasma HDL-C and determined mean HDL particle size and particle number by NMR spectroscopy in 2024 individuals from 512 British Caucasian families. Genotypes were 49,094 SNPs in >2,100 cardiometabolic candidate genes/loci as represented on the HumanCVD BeadChip version 2. False discovery rates (FDR) were calculated to account for multiple testing. Analyses on classical HDL-C revealed significant associations (FDR<0.05) only for CETP (cholesteryl ester transfer protein; lead SNP rs3764261: p = 5.6*10−15) and SGCD (sarcoglycan delta; rs6877118: p = 8.6*10−6). In contrast, analysis with HDL mean particle size yielded additional associations in LIPC (hepatic lipase; rs261332: p = 6.1*10−9), PLTP (phospholipid transfer protein, rs4810479: p = 1.7*10−8) and FBLN5 (fibulin-5; rs2246416: p = 6.2*10−6). The associations of SGCD and Fibulin-5 with HDL particle size could not be replicated in PROCARDIS (n = 3,078) and/or the Women's Genome Health Study (n = 23,170). Conclusions We show that refined HDL phenotyping by NMR spectroscopy can detect known genes of HDL metabolism better than analyses on HDL-C.
Visual topological SLAM and global localization

Visual localization and mapping for mobile robots has been achieved with a large variety of methods. Among them, topological navigation using vision has the advantage of offering a scalable representation, and of relying on a common and affordable sensor. In previous work, we developed such an incremental and real-time topological mapping and localization solution, without using any metrical information, and by relying on a Bayesian visual loop-closure detection algorithm. In this paper, we propose an extension of this work by integrating metrical information from robot odometry in the topological map, so as to obtain a globally consistent environment model. Also, we demonstrate the performance of our system on the global localization task, where the robot has to determine its position in a map acquired beforehand.
Kinematic analysis and dynamic control of a 3-PUU parallel manipulator for cardiopulmonary resuscitation

The concept of a medical parallel manipulator applicable to chest compression in the process of cardiopulmonary resuscitation (CPR) is proposed in this paper. According to the requirement of CPR action, a three-prismatic-universal-universal (3-PUU) translational parallel manipulator (TPM) is designed for such applications. And a thorough analysis involving the issues of kinematics, dynamics, and control have been performed for the 3-PUU TPM. Not only the inverse and forward kinematics problems are solved in closed-form, but also the Jacobian matrix is derived analytically, along with the manipulator workspace generated in view of the physical constraints imposed by mechanical joints. Based on the principle of virtual work with a simplifying hypothesis adopted, the dynamic modeling is performed, and dynamic control utilizing computed torque method is implemented, at last the simulation results illustrate the well performance of the control algorithm. The research work provide a sound base for the development of a medical manipulator to assist in CPR operation
SAR polar format implementation with MATLAB

Transform coding based on the Karhunen-Loeve transform (KLT), the discrete cosine transform (DCT), and the discrete wavelet transform (DWT) is well-understood for optical images. Transform coding applied to synthetic aperture radar (SAR) data, however, has not been well studied. This paper shows the results of compressing SAR images when it is available in polar formats. We compare the compression results based on six performance criteria-mean-squared error (MSE), mean absolute error, peak signal-to-noise ratio (SNR), energy compaction, transform gain, and compression ratio (CR).
Large Deviations of Vector-valued Martingales in 2-Smooth Normed Spaces

In this paper, we derive exponential bounds on probabilities of large deviations for “light tail” martingales taking values in finitedimensional normed spaces. Our primary emphasis is on the case where the bounds are dimension-independent or nearly so. We demonstrate that this is the case when the norm on the space can be approximated, within an absolute constant factor, by a norm which is differentiable on the unit sphere with a Lipschitz continuous gradient. We also present various examples of spaces possessing the latter property.
Identification of Nonpoint Sources of Fecal Pollution in Coastal Waters by Using Host-Specific 16S Ribosomal DNA Genetic Markers from Fecal Anaerobes

ABSTRACT We describe a new PCR-based method for distinguishing human and cow fecal contamination in coastal waters without culturing indicator organisms, and we show that the method can be used to track bacterial marker sequences in complex environments. We identified two human-specific genetic markers and five cow-specific genetic markers in fecal samples by amplifying 16S ribosomal DNA (rDNA) fragments from members of the genus Bifidobacterium and theBacteroides-Prevotella group and performing length heterogeneity PCR and terminal restriction fragment length polymorphism analyses. Host-specific patterns suggested that there are species composition differences in the Bifidobacterium andBacteroides-Prevotella populations of human and cow feces. The patterns were highly reproducible among different hosts belonging to the same species. Additionally, all host-specific genetic markers were detected in water samples collected from areas frequently contaminated with fecal pollution. Ease of detection and longer survival in water made Bacteroides-Prevotella indicators better than Bifidobacterium indicators. Fecal 16S rDNA sequences corresponding to our Bacteroides-Prevotellamarkers comprised closely related gene clusters, none of which exactly matched previously published Bacteroides orPrevotella sequences. Our method detected host-specific markers in water at pollutant concentrations of 2.8 × 10−5 to 2.8 × 10−7 g (dry weight) of feces/liter and 6.8 × 10−7 g (dry weight) of sewage/liter. Although our aim was to identify nonpoint sources of fecal contamination, the method described here should be widely applicable for monitoring spatial and temporal fluctuations in specific bacterial groups in natural environments.
Speech bandwidth expansion based on deep neural networks

This paper proposes a new speech bandwidth expansion method, which uses Deep Neural Networks (DNNs) to build high-order eigenspaces between the low frequency components and the high frequency components of the speech signal. A four-layer DNN is trained layer-by-layer from a cascade of Neural Networks (NNs) and two Gaussian-Bernoulli Restricted Boltzmann Machines (GBRBMs). The GBRBMs are adopted to model the distribution of spectral envelopes of the low frequency and the high frequency respectively. The NNs are used to model the joint distribution of hidden variables extracted from the two GBRBMs. The proposed method takes advantage of the strong modeling ability of GBRBMs in modeling the distribution of the spectral envelopes. And both the objective and subjective test results show that the proposed method outperforms the conventional GMM based method.
Wanted : A Unified Model for Search in Social Media

Social Media, including wikis, micro-blogging, forums and social network services, plays an indispensable role in providing a platform to support user-generated and user-disseminated information through social interactions. Social Media provides participants faster and wider information spread and burst compared to traditional media channels, which has attracted significant attention from the research community. In addition, due to its diversity of representation and operation mechanisms, different kinds of tasks are usually supported by different Social Media services or sometimes combinations of them. For example, blogs are usually maintained by individuals while forums are maintained by “moderators” and all users may participate in discussions by writing posts and replies. The differences in design and structure permit them to serve different purposes. Therefore, people use blogs to express personal feelings, opinions and comments but use forums to discuss problems and receive suggestions, though there is no strict line between these two types of Social Media. Due to this diversity, research in Social Media in recent years mainly focuses in two directions: how to effectively support specific tasks that one or several particular Social Media services provide as the example mentioned above, and how to improve the effectiveness of search engines that can take advantage of rich information that Social Media generates. In both directions, research work is usually conducted on a small number of types of Social Media services. In fact, most of them focus on only one type of Social Media. For instance, work has been done to help Community Question Answering portals (CQA) like Yahoo! Answers to retrieve similar question-answer pairs more effectively (e.g., [4]). Researchers also pay attention to social bookmarking systems and try to use them to help users organize information. Blogs are used to track opinions and sentiments while micro-blogging tools like Twitter are used to identify emerging trends and hot topics. Although certain success has been shown in these distinct types of Social Media, little research work has been done to discuss whether or not we need a unified way to deal with all types of Social Media, which could then support notyet-imagined tasks rather than developing separate models for each Social Media service. Here, a unified model should provide a “baseline” model which
A two step linear inversion strategy for imaging simple shapes

In this paper an innovative procedure for the localization and reconstruction of objects composed of elementary shapes is described. Sparse backscattered multifrequency field data are assumed to be known. The strategy is based on two steps in order to decrease the computational costs and to allow the use of few measurement points (with respect of Number of Degrees of Freedom). At first an Interferometric-Migration (IM) strategy is introduced with the purpose of reducing the dimension of the solution space. The second step is founded on the assumption that the elementary shapes are known and their geometrical features can characterized by a small amount of parameters. Then an Interferometric-MUSIC (I-MUSIC) algorithm is applied by combining results at single frequency. The overall approach is numerically tested in a 2D geometry for electrically large objects and has been proved robust against uncertainties on data.
Artificial gene networks for objective comparison of analysis algorithms

MOTIVATION Large-scale gene expression profiling generates data sets that are rich in observed features but poor in numbers of observations. The analysis of such data sets is a challenge that has been object of vigorous research. The algorithms in use for this purpose have been poorly documented and rarely compared objectively, posing a problem of uncertainty about the outcomes of the analyses. One way to objectively test such analysis algorithms is to apply them on computational gene network models for which the mechanisms are completely know. RESULTS We present a system that generates random artificial gene networks according to well-defined topological and kinetic properties. These are used to run in silico experiments simulating real laboratory microarray experiments. Noise with controlled properties is added to the simulation results several times emulating measurement replicates, before expression ratios are calculated. AVAILABILITY The data sets and kinetic models described here are available from http://www.vbi. vt.edu/~mendes/AGN/as biochemical dynamic models in SBML and Gepasi formats.
On the nonlinear relationship between dissolved cadmium and phosphate in the modern global ocean: Could chronic iron limitation of phytoplankton growth cause the kink?

I report two vertical profiles of dissolved cadmium (Cd) and phosphate (PO4) from the Bering Sea: one from a high‐nutrient, low‐chlorophyll (HNLC) area, in which phytoplankton growth is limited by iron (Fe) availability, and one in highly productive waters near the continental shelf, where Fe is sufficient. At both stations, dissolved Cd and PO4 display nutrient‐like profiles and are strongly correlated with depth. The surface‐water dissolved Cd : PO4 ratio in the Fe‐limited HNLC (0.21 ± 0.03 nmol µmol−1) is significantly lower than the ratio in the productive Fe‐replete station (0.31 ± 0.02 nmol µmol−1). A simple model based on the results of previously published laboratory culture studies by others and field incubation experiments with natural phytoplankton assemblages is proposed relating the availability of Fe to the Cd : phosphorus content of phytoplankton, the dissolved Cd : PO4 of ocean surface waters, and the slope of Cd : PO4 in the nutricline. The model is consistent with available data and suggests that the nonlinearity or kink in the global dissolved Cd versus PO4 relationship exists because of chronic Fe‐limiting conditions in high‐latitude HNLC areas in the modern ocean.
A social network representation for Collaborative Filtering Recommender Systems

Collaborative Filtering Recommender Systems are one of the most used and well-known tools in the e-commerce area because they are adaptive and they do not need information about the recommended items. Although many studies have been proposed to take advantage of the information gathered by this kind of recommender systems, none have focused on the use of social network analysis. In this contribution we present a first approach that shows some of the advantages and results that can be obtained applying this methodology.
Towards the implementation of natural prosthetic elbow motion using upper limb joint coordination

The control of an active prosthetic elbow is problematic for most transhumeral amputees and a functional solution providing intuitive control over active multi-joint prosthetic upper limbs is yet to be found. The method in this paper uses IMU-based upper arm kinematics to predict the elbow motion based on upper limb joint coordinations during pointing movements. A RBFN-based regression was performed to model the shoulder/elbow coordination. The prediction results indicate that such an approach is ready to be implemented on current transhumeral prostheses equipped with embedded motion sensors like IMUs. Different algorithm training methods to obtain better prediction performance are also investigated.
Retrieval of IVUS images using contextual information and elastic matching

We present a content‐based image retrieval system of medical images of bodies with high elasticity, that is, high inter‐ and intrasubject variability in shape. The system is based on a rich feature space that is able to describe all the relevant aspects of an image, including local, global, and contextual information. For including relative spatial relations between the structures (i.e., contextual information) we do not need to obtain very accurate segmentations of the image, in contrast to the majority of methods employed for this kind of description. We also obtain invariance to spatial deformations of the same type of object along different instances. This is achieved by applying efficient registrations of the images before their comparison. The incorporation of all these components represents an innovative and powerful way of comparing and retrieving medical images. Validated results are reported on a database of 168 intravascular ultrasound images, showing the appropriateness of our approach for images of such high complexity. © 2005 Wiley Periodicals, Inc. Int J Int Syst 20: 541–559, 2005.
Discovering Novelty in Time Series Data

Modern manufacturing plants rely heavily on the use of automation. Automated facilities use sensors to measure material state and react to data patterns, which correspond to physical events. Many patterns can be predefined either by careful analysis or from domain experts. Instances of these patterns can then be discovered through techniques such as pattern recognition. However, this approach will fail to detect events that have not been predefined, potentially causing expensive production errors. A solution to this dilemma, novelty detection, allows for the identification of interesting data patterns embedded in otherwise normal data. This paper describes several novelty detection methods for time series data that have been proposed in the literature.
The Discovery of My Completeness Proofs

§1. Introduction. This paper deals with aspects of my doctoral dissertation which contributed to the early development of model theory. What was of use to later workers was less the results of my thesis, than the method by which I proved the completeness of first-order logic—a result established by Kurt Gödel in his doctoral thesis 18 years before. The ideas that fed my discovery of this proof were mostly those I found in the teachings and writings of Alonzo Church. This may seem curious, as his work in logic, and his teaching, gave great emphasis to the constructive character of mathematical logic, while the model theory to which I contributed is filled with theorems about very large classes of mathematical structures, whose proofs often by-pass constructive methods. Another curious thing about my discovery of a new proof of Gödel's completeness theorem, is that it arrived in the midst of my efforts to prove an entirely different result. Such “accidental” discoveries arise in many parts of scientific work. Perhaps there are regularities in the conditions under which such “accidents” occur which would interest some historians, so I shall try to describe in some detail the accident which befell me. A mathematical discovery is an idea, or a complex of ideas, which have been found and set forth under certain circumstances. The process of discovery consists in selecting certain input ideas and somehow combining and transforming them to produce the new output ideas. The process that produces a particular discovery may thus be represented by a diagram such as one sees in many parts of science; a “black box” with lines coming in from the left to represent the input ideas, and lines going out to the right representing the output. To describe that discovery one must explain what occurs inside the box, i.e., how the outputs were obtained from the inputs.
An evaluation of white- and gray-box testing tools for C#, C++, Eiffel, and Java

Automating the process of software testing is a very popular research topic and of real interest to industry. Test automation can take part on different levels, e.g., test execution,testcasegeneration,testdatageneration.Thissur- vey gives an overview of state-of-the art test data generation tools, either academic or commercial. The survey focuses on white- and gray-box techniques. The list of existing tools was filtered with respect to their public availability, their maturity, and activity. The remaining seven tools, i.e., Agi- tarOne, CodePro AnalytiX, AutoTest, C++test, Jtest, RAN- DOOP, and PEX, are briefly introduced and their evaluation results are summarized. For the evaluation we defined 31 benchmark tests, which check the tools capabilities to gener- ate test data that satisfies a given specification: 24 primitive type benchmarks and 7 non-primitive type and more com- plex with respect to the specification benchmarks. Most of the commercial tools implement a test data strategy that uses constant values found in the method under test or values that are slightly modified by means of mathematical operations. This strategy turns out to be very effective. In general, all tools that combine multiple techniques perform very well. For example PEX uses constraint solving techniques, but in cases where the constraint solver reaches its limitations it uses random based techniques to overcome those limita- tions. Especially, the two commercial tools AgitarOne and PEX that combine multiple approaches to test data genera- tionareabletopassall31tests.Thissurveyreflectsthestatus in 2011.
FAST STARTING HOLLOW CATHODES FOR SOUNDING ROCKET-BASED TETHER EXPERIMENTS

Data are presented on cathode temperature profiles, electron emission profiles under no-flow conditions, and timed starting tests with gas flow. Correlations between the temporal behavior of the noflow emission current and the starting time are made, and a model is presented of the barium/bariumoxide generation and surface diffusion processes that describe the electron emission profile and starting time behavior of a hollow cathode. Future tests for evaluating effects of fast conditioning/starting sequences are described. A stand alone test pallet containing all of the sub-systems required to quickly start and operate a hollow cathode has been built and is described. The test pallet is intended for use on a sounding rocket mission and its sub-systems include gas storage and delivery, power conditioning, transducers and sensors, and an embedded micro-controller.
Local Loperamide Injection Reduces Mechanosensitivity of Rat Cutaneous, Nociceptive C-Fibers

Loperamide reverses signs of mechanical hypersensitivity in an animal model of neuropathic pain suggesting that peripheral opioid receptors may be suitable targets for the treatment of neuropathic pain. Since little is known about loperamide effects on the responsiveness of primary afferent nerve fibers, in vivo electrophysiological recordings from unmyelinated afferents innervating the glabrous skin of the hind paw were performed in rats with an L5 spinal nerve lesion or sham surgery. Mechanical threshold and responsiveness to suprathreshold stimulation were tested before and after loperamide (1.25, 2.5 and 5 µg in 10 µl) or vehicle injection into the cutaneous receptive field. Loperamide dose-dependently decreased mechanosensitivity in unmyelinated afferents of nerve-injured and sham animals, and this effect was not blocked by naloxone pretreatment. We then investigated loperamide effects on nerve conduction by recording compound action potentials in vitro during incubation of the sciatic nerve with increasing loperamide concentrations. Loperamide dose-dependently decreased compound action potentials of myelinated and unmyelinated fibers (ED50 = 8 and 4 µg/10 µl, respectively). This blockade was not prevented by pre-incubation with naloxone. These results suggest that loperamide reversal of behavioral signs of neuropathic pain may be mediated, at least in part, by mechanisms independent of opioid receptors, most probably by local anesthetic actions.