Miracle-dz/UniFAR-FTUs huggingface dataset:
Kinin Infusion Prevents Renal Inflammation, Apoptosis, and Fibrosis via Inhibition of Oxidative Stress and Mitogen-Activated Protein Kinase Activity

The progression of renal disease displays several characteristics, including proteinuria, apoptosis, inflammation, and fibrosis. In this study, we investigated the effect of long-term infusion of kinin in protection against salt-induced renal damage in Dahl salt-sensitive rats. Dahl salt-sensitive rats were fed a high-salt diet for 2 weeks and were then infused with bradykinin (500 ng/h) via subcutaneously implanted minipumps for 3 weeks. Kinin infusion attenuated salt-induced impaired renal function as evidenced by reduced proteinuria, serum creatinine, and blood urea nitrogen levels without apparent effect on blood pressure. Morphological analysis indicated that kinin administration reduced salt-induced glomerular sclerosis, tubular dilatation, luminal protein cast formation, and interlobular arterial thickness. Kinin also significantly lowered collagen I, III, and IV deposition and their mRNA levels. Moreover, kinin reduced interstitial monocyte/macrophage accumulation, as well as tubular cell apoptosis and caspase-3 activity. Protection of renal injury by kinin was associated with increased renal NO levels and reduced nicotinamide adenine dinucleotide/nicotinamide adenine dinucleotide phosphate oxidase activities and superoxide generation. Suppression of oxidative stress by kinin was accompanied by reduced transforming growth factor-&bgr;1 protein and mRNA levels, as well as decreased phosphorylation of mitogen-activated protein kinases. This is the first study to demonstrate that kinin infusion can directly protect against salt-induced renal injury without blood pressure reduction by inhibiting apoptosis, inflammation, and fibrosis via suppression of oxidative stress, transforming growth factor-&bgr;1 expression, and mitogen-activated protein kinase activation.
SO(2) and SO(3), omni-directional personal mobility with link-driven spherical wheels

There are many researches about mobile robot with a varied wheel. Mobile robots or mobilities with wheels such like a car or bike have non-holonomic constraint and then it is difficult to move toward wheel's axis direction. Namely, it is impossible to move toward omni-direction due to the constraints by wheel's mechanical structure. Omni-directional movement means the mobility can move toward any direction at anytime. This paper adapted link-driven spherical wheel as an omnidirectional wheel and solved singularity problem caused by mechanical structure of it. We developed omni-directional mobilities( SO(2) and SO(3)) installing link-driven spherical wheels and experimented omni-directional movement with designed controllers.
A Structured P2P System with Match Path and Probability Balance Tree

Excellent P2P network infrastructure should support scalability and efficient search simultaneity. The intelligent routing mechanisms have flexible query expression but hold low scalability and recall rate. Contrastively, DHTs are scalable but either difficult to organize or too expensive for maintenance. A new structured P2P model is proposed in which resource storages are organized by match path and random access method. When an approximately balance distributed character is obtained, nodes will keep high search efficiency and low maintenance cost. The system supports various query models, has load balancing capability and high scalability, and ensures search range of each node can cover entire network. The system runs without any center service while all nodes simultaneously take index storage, data storage and routing task to drive network by only maintaining a little local information
Online Learning with Pairwise Loss Functions

Efficient online learning with pairwise loss functions is a crucial component in building large-scale learning system that maximizes the area under the Receiver Operator Characteristic (ROC) curve. In this paper we investigate the generalization performance of online learning algorithms with pairwise loss functions. We show that the existing proof techniques for generalization bounds of online algorithms with a univariate loss can not be directly applied to pairwise losses. In this paper, we derive the first result providing data-dependent bounds for the average risk of the sequence of hypotheses generated by an arbitrary online learner in terms of an easily computable statistic, and show how to extract a low risk hypothesis from the sequence. We demonstrate the generality of our results by applying it to two important problems in machine learning. First, we analyze two online algorithms for bipartite ranking; one being a natural extension of the perceptron algorithm and the other using online convex optimization. Secondly, we provide an analysis for the risk bound for an online algorithm for supervised metric learning.
Role of Guanylyl Cyclase Modulation in Mouse Cone Phototransduction

A negative phototransduction feedback in rods and cones is critical for the timely termination of their light responses and for extending their function to a wide range of light intensities. The calcium feedback mechanisms that modulate phototransduction in rods have been studied extensively. However, the corresponding modulation mechanisms that enable cones to terminate rapidly their light responses and to adapt in bright light, properties critical for our daytime vision, are still not understood. In cones, calcium feedback to guanylyl cyclase is potentially a key step in phototransduction modulation. The guanylyl cyclase activity is modulated by the calcium-binding guanylyl cyclase activating proteins (GCAP1 and GCAP2). Here, we used single-cell and transretinal recordings from mouse to determine how GCAPs modulate dark-adapted responses as well as light adaptation in mammalian cones. Deletion of GCAPs increased threefold the amplitude and dramatically prolonged the light responses in dark-adapted mouse cones. It also reduced the operating range of mouse cones in background illumination and severely impaired their light adaptation. Thus, GCAPs exert powerful modulation on the mammalian cone phototransduction cascade and play an important role in setting the functional properties of cones in darkness and during light adaptation. Surprisingly, despite their better adaptation capacity and wider calcium dynamic range, mammalian cones were modulated by GCAPs to a lesser extent than mammalian rods. We conclude that a disparity in the strength of GCAP modulation cannot explain the differences in the dark-adapted properties or in the operating ranges of mammalian rods and cones.
Implementation of a cost efficient SSL based on an Angular beamformer SRP-PHAT

Among Sound Source Localization (SSL) methods, beamformed microphone arrays using Steering Response Power(SRP) has received significant attention. Yet, its application is stumbled by its computational complexity which cannot meet low-power/low-cost applications requirements. In this paper, Angular beamforming is presented. By applying this beamforming approach to the conventional SRP-PHAT one can find that the complexity of SRP-PHAT will decreased by two orders of magnitudes with a mere three fold reduction in resolution. The proposed method is implemented both on FPGA and 0.18um CMOS technology for a practical case.
Least Squares Channel Estimation for IEEE 802.16e Systems Based on Adaptive Taps Selection

IEEE 802.16 is an emerging technology for broadband cellular wireless communications. In the context of high capacity systems for mobile terminals, implementation issues like channel estimation become more challenging because of the sparsity of the pilots grid (w.r.t. the channel coherence bandwidth) and the time varying nature of the channel. This paper proposes a novel algorithm for channel estimation that is characterized by a good trade-off between complexity and performance. The algorithm, that is suitable for pilot-aided OFDM transmission, is applied here to IEEE 802.16e systems. Numerical results show that a meaningful performance gain (in terms of mean squared error and error probability) is provided by this estimation procedure, especially in the low SNR (Signal to Noise Ratio) region.
Discovering OLAP dimensions in semi-structured data

With the standard OLAP technology, cubes are constructed from the input data based on the available data fields and known relationships between them. Structuring the data into a set of numeric measures distributed along a set of uniformly structured dimensions may be unrealistic for applications dealing with semi-structured data. We propose to extend the capabilities of OLAP via content-driven discovery of measures and dimensional characteristics in the original dataset. New structural elements are discovered by means of data mining and other techniques and are therefore prone to changes as the underlying dataset evolves. In this work we focus on the challenge of generating, maintaining, and querying such discovered elements of the cube. We demonstrate the benefits of our approach by providing OLAP to the public stream of user-generated content of the popular microblogging service Twitter. We were able to enrich the original set by discovering dynamic characteristics such as user activity, popularity, messaging behavior, as well as classifying messages by topic, impact, origin, method of generation, etc. Application of knowledge discovery techniques coupled with human expertise enable structural enrichment of the original data beyond the scope of the existing methods for generating multidimensional models from relational or semi-structured data.
Birthweight and Body Size throughout Life in Relation to Sex Hormones and Prolactin Concentrations in Premenopausal Women

The association of birthweight and body size throughout life with premenopausal breast cancer risk may be due, in part, to relationships with sex hormones. Therefore, we assessed whether birthweight, body shape at ages 5 and 10, body mass index (BMI) at age 18 and adulthood, adult waist circumference and waist-to-hip ratio (WHR), and attained height were associated with the plasma concentrations of estrogens, androgens, progesterone, prolactin, and sex hormone–binding globulin (SHBG) in 592 premenopausal women, ages 33 to 52 years old, from the Nurses' Health Study II. About 85% of women provided blood samples during follicular and luteal menstrual phases; other women had a single untimed sample. We observed few associations between sex hormone levels and birthweight or body shape in childhood. However, adult BMI was inversely associated with SHBG (P trend < 0.001) and positively associated with free testosterone (P trend < 0.001) concentrations. Adult BMI was not associated with follicular or luteal free estradiol levels (P trend ≥ 0.15) because it was inversely associated with total estradiol levels (P trend < 0.001 for follicular and luteal estradiol levels). Testosterone, androstenedione, and progesterone were inversely associated with BMI. Comparing women with a BMI of ≥30 versus <20 kg/m2, levels were higher by 53% for free testosterone and lower by 51% for SHBG, 39% for follicular estradiol, 20% for luteal estradiol, 14% for androstenedione, 13% for testosterone, and 20% for progesterone. We observed no clear associations between BMI at age 18, waist circumference, WHR, or height, and sex hormone concentrations. Our results suggest that effects on premenopausal sex hormone levels may be one mechanism through which adult adiposity, but not birthweight or childhood body size, affects premenopausal breast cancer risk. (Cancer Epidemiol Biomarkers Prev 2006;15(12):2494–501)
Accelerated 3 D phase-contrast imaging using adaptive compressed sensing with no free parameters

Introduction Compressed Sensing (CS) theory [1] allows one to use the sparsity of images in a transform domain (e.g. wavelets) effectively to enable image reconstruction with sub-Nyquist sampled data. While CS methodologies present several potential benefits for medical imaging applications (e.g. reduced scan times, higher resolution imaging, etc.), their successful implementation on clinical scanners will require robust algorithms that can automatically account for patient-to-patient and anatomical variability in the images. Typical image reconstruction algorithms employing CS theory in the literature are iterative in nature and solve the problem of minimizing a cost function consisting of an L2-norm error (or data consistency) term and one or more L1-norm terms that impose the transform domain sparsity constraints. In particular, two classes of algorithms are commonly used: (1) Non-linear conjugate gradient (NLCG) [2], and (2) iterative soft thresholding (ST) [3] approaches. The NLCG method has good convergence properties, but is sensitive to the choice of relative weights on the L1-norm penalty terms that have to be selected empirically on a case-by-case basis. NLCG algorithms may also involve computationally intensive cost function / derivative evaluations and line searches. The ST approach in the wavelet domain is leaner to implement but is also sensitive to the choice of the thresholding parameter and is slow to converge. In this paper we describe a robust and fully data driven approach to CS reconstruction in the ST framework that uses a Nesterov type [4,5] 2-step optimal gradient scheme for fast convergence properties along with adaptive wavelet denoising for imposition of the L1-sparsity constraint. For illustration we combine this CS algorithm with image domain parallel imaging (SENSE) and apply it to brain vasculature phase-contrast imaging. The reconstructed angiograms were scored blindly for image quality by an experienced neuroradiologist. Methods In describing our approach for combining the CS algorithm with parallel imaging, the data acquired for each of the echoes from a 3D phase contrast pulse sequence may be represented as f y j CSP FT j , Φ Φ = where yj is a vector of k-space measurements from the j-th coil, f is the image to be determined, ΦFT is the Fourier
Autoregressive modeling based feature extraction for Aurora3 DSR task

Techniques for analysis of speech, that use autoregressive (all-pole) modeling approaches, are presented here and compared to generally known Mel-frequency cepstrum based feature extraction. In the paper, ﬁrst, we focus on several possible applications of modeling speech power spectra that increase the performance of ASR system mainly in case of large mismatch between training and testing data. Then, the attention is payed to the different types of features that can be extracted from all-pole model to reduce the overall word error rate. The results show that generally used cepstrum based features, which can be easily extracted from all-pole model, are not the most suitable parameters for ASR, where the input speech is corrupted by different types of real noises. Very good recognition performances were achieved e.g., with discrete or selective all-pole modeling based approaches, or with decorrelated line spectral frequencies. The feature extraction techniques were tested on SpeechDat-Car databases used for front-end evaluation of advanced distributed speech recognition (DSR) systems.
A Regulatory Role for CD37 in T Cell Proliferation1

CD37 is a leukocyte-specific protein belonging to the tetraspanin superfamily. Previously thought to be predominantly a B cell molecule, CD37 is shown in this study to regulate T cell proliferation. CD37-deficient (CD37−/−) T cells were notably hyperproliferative in MLR, in response to Con A, or CD3-TCR engagement particularly in the absence of CD28 costimulation. Hyperproliferation was not due to differences in memory to naive T cell ratios in CD37−/− mice, apoptosis, or TCR down-modulation. Division cycle analyses revealed CD37−/− T cells to enter first division earlier than wild-type T cells. Importantly, proliferation of CD37−/− T cells was preceded by enhanced early IL-2 production. We hypothesized CD37 to be involved in TCR signaling and this was supported by the observation that CD4/CD8-associated p56Lck kinase activity was increased in CD37−/− T cells. Remarkably, CD37 cross-linking on human T cells transduced signals that led to complete inhibition of CD3-induced proliferation. In the presence of CD28 costimulation, CD37 engagement still significantly reduced proliferation. Taken together, these results demonstrate a regulatory role for CD37 in T cell proliferation by influencing early events of TCR signaling.
Anaphora Resolution in Hindi: Issues and Challenges

Anaphora occurs very frequently in written texts and spoken dialogues. Almost all NLP applications such as machine translation, information extraction, automatic summarization, question answering system, natural language generation, etc., require successful identification and resolution of anaphora. Though the significant amount of work has been done in English and other European languages, the computational work, in reference to Hindi, is lagging far behind. In this paper, we present a review of work done in the field of anaphora resolution in Hindi. We also cover different issues and challenges in developing computational models for Hindi. The paper covers issues related to syntactic/semantic structure of Hindi and influence of cases on pronouns, mainly personal pronoun. The work is mainly dedicated to pronominal anaphora. As the pronouns in Hindi do not differentiate gender, it is the verb and case markers that signify different genders. Influence of case markers in deciding the antecedent is explored. Pronoun resolution in context of EHMT (English-Hindi Machine Translation) systems is demonstrated to substantiate the need of anaphora resolution for NLP application.
Manhattan scene understanding using monocular, stereo, and 3D features

This paper addresses scene understanding in the context of a moving camera, integrating semantic reasoning ideas from monocular vision with 3D information available through structure-from-motion. We combine geometric and photometric cues in a Bayesian framework, building on recent successes leveraging the indoor Manhattan assumption in monocular vision. We focus on indoor environments and show how to extract key boundaries while ignoring clutter and decorations. To achieve this we present a graphical model that relates photometric cues learned from labeled data, stereo photo-consistency across multiple views, and depth cues derived from structure-from-motion point clouds. We show how to solve MAP inference using dynamic programming, allowing exact, global inference in ∼100 ms (in addition to feature computation of under one second) without using specialized hardware. Experiments show our system out-performing the state-of-the-art.
Ieee Transactions on Visualization and Computer Graphics Simplification of Three-dimensional Density Maps

— We consider scientific datasets that describe density functions over three-dimensional geometric domains. Such datasets are often large and coarsened representations are needed for visualization and analysis. Assuming a tetrahedral mesh representation, we construct such representations with a simplification algorithm that combines three goals: the approximation of the function, the preservation of the mesh topology, and the improvement of the mesh quality. The third goal is achieved with a novel extension of the well known quadric error metric. We perform a number of computational experiments to understand the effect of mesh quality improvement on the density map approximation. In addition, we study the effect of geometric simplification on the topological features of the function by monitoring its critical points.
Probabilistic integration of diffuse noise suppression and dereverberation

This paper deals with joint suppression of diffuse noise and reverberation, to enhance perceived speech quality and speech recognition performance. Although diffuse noise and reverberation are both omnipresent in the real world, conventional methods have modeled only one while neglecting the other. In contrast, we propose a novel joint suppression method that employs a unified probabilistic model of observed signals affected by both diffuse noise and reverberation. Through likelihood maximization, this unified model enables proper parameter estimation that takes into account both diffuse noise and reverberation. As a byproduct, we also propose a novel method for diffuse noise suppression. Experimental results demonstrate the effectiveness of the proposed joint suppression method in terms of dereverberation and denoising.
N-[(2-Hydroxy-1-naphthyl)(2-hydroxyphenyl)methyl]acetamide

In the asymmetric unit of the title compound, C19H17NO3, there are two crystallographically independent molecules, which are connected to each other by O—H⋯O hydrogen bonds, forming molecular chains as well as cyclic centrosymmetric R 2 2(16) dimers.
Own body perception based on visuomotor correlation

This work proposes a plausible approach for a humanoid robot to define its own body parts based on the correlation of two different sensory signals: vision and proprioception. The high correlation between the motions in vision and proprioception informs the robot that the visually attractive object is related to the motor function of its own body. When the robot finds the highly motor-correlated object during head-arm movements, the visuomotor cues such as the body posture and visual features are stored in a visuomotor memory. Then, developmentally, the robot defines the motor-correlated objects as the own-body parts without prior knowledge on the body appearances and kinematics. It is also adaptable to extended body parts such as a grasped tool. The body movements are generated by stochastic motor babbling. The visuomotor memory biases the babbling to keep the own-body parts in sight. This memory-based bias towards the own-body parts helps the robot explore the large head-arm joint space. The acquired visuomotor memory is also used to anticipate the own-body image from the motor commands in advance of the body movement. The proposed approach was evaluated with two humanoid platforms; iCub and James.
Multifunctional and lightweight load-bearing composite structures for satellites

Within the framework of the German national project multiSat multifunctional composite structures for satellite applications are developed. The objective is the integration of passive and active functions into the load-bearing spacecraft structure by using suitable materials, components and mechanisms. The passive functions include heat transfer, radiation shielding and protection against space debris impacts, whereas the active functions comprise electric energy and data transfer and vibration reduction. Due to their multi-layer build-up composite materials are suitable for functional integration since each layer can be defined and designed to provide one or more specific functions. The concept of a multifunctional structure allows for the reduction of the overall satellite mass and of installation space required for subsystems. It also opens up new opportunities for highly integrative and standardized production processes and lower total costs and time for manufacturing, qualification and launch. This paper describes the development and design of a concept for a multifunctional sandwich panel and the results of the analyses, numerical simulations and experiments conducted at coupon level.