Miracle-dz/UniFAR-FTUs huggingface dataset:
Mechanism of action of the C4 nephritic factor. Deregulation of the classical pathway of C3 convertase

Three mechanisms that regulate the formation and function of the classical pathway C3 convertase (C4b2a) have been elucidated: (a) an intrinsic decay of the enzyme that is temperature dependent; (b) an extrinsic decay mediated by the effect of the serum protein C4b binding protein (C4-bp); and (c) inactivation of C4b by the proteolytic action of C4b/C3b inactivator (C4b/C3bINA), which cleaves that alpha' chain of C4b to yield C4d (alpha 2) and C4c (alpha 3, alpha 4, beta, and gamma chains). A fourth mechanism described here is based on the observation that the IgG fraction of the serum of certain patients with glomerulonephritis contains a protein termed C4 nephritic factor (NFc), which prevents the intrinsic decay of C4b2a. This protein, which prolongs the half-life of surface-bound C4b2a from 7.5 min to greater than 5 h, increases the use of C3 and C5. It also inhibits the decay produced by C4-bp by preventing the dissociation of C2a from the C4b2a complex. Additionally, the C2b/C3bINA alone, or in the presence of C4- bp, fails to cleave the alpha' chain of C4b in the surface-bound stabilized C4b2a complex. This protective property of NFc requires the presence of C2a, because C4b was not protected unless it was bound to C2a. Thus in the presence of NFc, the three natural controls of the function of the classical pathway convertase, intrinsic decay, extrinsic decay, and proteolytic cleavage, are bypassed.
MR-Apriori: Association Rules algorithm based on MapReduce

Traditional Association Rules algorithm has computing power shortage in dealing with massive datasets. In order to overcome these problems, a distributed association rules algorithm based on MapReduce programming model named MR-Apriori is proposed. In this paper, we introduce the MapReduce programming model of Hadoop platform and Apriori algorithm of data mining, propose the detailed procedure of MR-Apriori algorithm. Theoretical and experimental results show MR-Apriori algorithm make a sharp increase in efficiency.
Total variation blind deconvolution

In this paper, we present a blind deconvolution algorithm based on the total variational (TV) minimization method proposed. The motivation for regularizing with the TV norm is that it is extremely effective for recovering edges of images as well as some blurring functions, e.g., motion blur and out-of-focus blur. An alternating minimization (AM)implicit iterative scheme is devised to recover the image and simultaneously identify the point spread function (psf). Numerical results indicate that the iterative scheme is quite robust, converges very fast (especially for discontinuous blur), and both the image and the psf can be recovered under the presence of high noise level. Finally, we remark that psf's without sharp edges, e.g., Gaussian blur, can also be identified through the TV approach.
Arginase-2 Mediates Diabetic Renal Injury

OBJECTIVE To determine 1) whether renal arginase activity or expression is increased in diabetes and 2) whether arginase plays a role in development of diabetic nephropathy (DN). RESEARCH DESIGN AND METHODS The impact of arginase activity and expression on renal damage was evaluated in spontaneously diabetic Ins2Akita mice and in streptozotocin (STZ)-induced diabetic Dilute Brown Agouti (DBA) and arginase-2–deficient mice (Arg2−/−). RESULTS Pharmacological blockade or genetic deficiency of arginase-2 conferred kidney protection in Ins2Akita mice or STZ-induced diabetic renal injury. Blocking arginases using S-(2-boronoethyl)-l-cysteine for 9 weeks in Ins2Akita mice or 6 weeks in STZ-induced diabetic DBA mice significantly attenuated albuminuria, the increase in blood urea nitrogen, histopathological changes, and kidney macrophage recruitment compared with vehicle-treated Ins2Akita mice. Furthermore, kidney arginase-2 expression increased in Ins2Akita mice compared with control. In contrast, arginase-1 expression was undetectable in kidneys under normal or diabetes conditions. Arg2−/− mice mimicked arginase blockade by reducing albuminuria after 6 and 18 weeks of STZ-induced diabetes. In wild-type mice, kidney arginase activity increased significantly after 6 and 18 weeks of STZ-induced diabetes but remained very low in STZ-diabetic Arg2−/− mice. The increase in kidney arginase activity was associated with a reduction in renal medullary blood flow in wild-type mice after 6 weeks of STZ-induced diabetes, an effect significantly attenuated in diabetic Arg2−/− mice. CONCLUSIONS These findings indicate that arginase-2 plays a major role in induction of diabetic renal injury and that blocking arginase-2 activity or expression could be a novel therapeutic approach for treatment of DN.
Modeling and Stability Analysis of Networked Robot System with Network-induced Delay and Data Dropout

To the networked robot system (NRS) that drops packets randomly, we considered the problem of optimal linear quadratic Gaussian (LQG) control and analyzed the stability of a NRS by modeling it. By presenting a mathematical model based on a packet-dropped setting, we extended the familiar LQG separation principle, proposed an optimal algorithm irrespective of the packet drop pattern of the networked robot. For the case of packet drops occurring according to a Markov chain, the stability analysis was carried out. We used the uncertainty threshold principle to show that under certain conditions there was a rate for dropped packets for which an undisturbed networked control system with imperfect state observation was mean square stable, used a sub-optimal method to simplify the calculation of the estimator and controller, got the solution to the Riccati-like equation and guaranteed the mean square stability of the NRS with perfect state information.
Artificial prediction markets as a tool for syndromic surveillance

A range of data sources across the internet, such as google search terms, twitter topics and Facebook messages, amongst others, can be viewed as kinds of sensors from which information might be extractable about trends in the expression of matters of concern to people. We focus on the problem of how to identify emerging trends after the original textual data has been processed into a quantitative form suitable for the application of machine learning techniques. We present some preliminary ideas, including an agent-based implementation and some early results, about the application of artificial prediction markets to such data, taking the specific domain of syndromic surveillance (early stage recognition of epidemics) as an example, using publicly available data sets.
Analysis of spatial domain information for footstep recognition

This study reports an experimental analysis of footsteps as a biometric. The focus here is on information extracted from the spatial domain of signals collected from an array of piezoelectric sensors. Results are related to the largest footstep database collected to date, with almost 20 000 valid footstep signals and more than 120 persons. A novel feature approach is proposed, obtaining three-dimensional images of the distribution of the footstep pressure along the spatial course. Experimental work is based on a verification mode with a holistic approach based on principal component analysis and support vector machines, achieving results in the range of 6–10% equal error rate (EER) depending on the experimental conditions of quantity of data used in the client models (200 and 40 signals per model, respectively). Also, this study includes the analysis of two interesting factors affecting footstep signals and especially spatial domain features, namely, sensor density and the special case of high heels.
Recurrent neural networks for minimum infinity-norm kinematic control of redundant manipulators

This paper presents two neural network approaches to minimum infinity-norm solution of the velocity inverse kinematics problem for redundant robots. Three recurrent neural networks are applied for determining a joint velocity vector with its maximum absolute value component being minimal among all possible joint velocity vectors corresponding to the desired end-effector velocity. In each proposed neural network approach, two cooperating recurrent neural networks are used. The first approach employs two Tank-Hopfield networks for linear programming. The second approach employs two two-layer recurrent neural networks for quadratic programming and linear programming, respectively. Both the minimal 2-norm and infinity-norm of joint velocity vector can be obtained from the output of the recurrent neural networks. Simulation results demonstrate that the proposed approaches are effective with the second approach being better in terms of accuracy and optimality.
MagR Alone Is Insufficient to Confer Cellular Calcium Responses to Magnetic Stimulation

Magnetic manipulation of cell activity offers advantages over optical manipulation but an ideal tool remains elusive. The MagR protein was found through its interaction with cryptochrome (Cry) and the protein in solution appeared to respond to magnetic stimulation (MS). After we initiated an investigation on the specific role of MagR in cellular response to MS, a subsequent study claimed that MagR expression alone could achieve cellular activation by MS. Here we report that despite systematically testing different ways of measuring intracellular calcium and different MS protocols, it was not possible to detect any cellular or neuronal responses to MS in MagR-expressing HEK cells or primary neurons from the dorsal root ganglion and the hippocampus. By contrast, in neurons co-expressing MagR and channelrhodopin, optical but not MS increased calcium influx in hippocampal neurons. Our results indicate that MagR alone is not sufficient to confer cellular magnetic responses.
A new real-valued diploid genetic algorithm for optimization in dynamic environments

Many real-world problems are dynamic, requiring an optimization algorithm which is able to continuously track a changing optimum over the time. Using a diploidy and dominance is one method to enhance the performance of genetic algorithms in dynamic environment. Diploid genetic algorithm has two chromosomes in each individual. In this paper, for the first time, a real-valued diploid genetic algorithm is proposed. Its new dominance mechanism is based on a simple function with homogeneous outputs. In addition, a new dominance change mechanism is added to the algorithm. Hence, when environment change occurs, it can increase diversity to respond more quickly to the changes. Other diploid genetic algorithms in literature are discrete and they have never been tested by Moving Peak Benchmark (MPB) which is continuous and dynamic. For the first time, the proposed approach is tested by MPB. Results are compared with other diploid genetic algorithms showing that proposed algorithm significantly outperforms previous approaches.
Efficient computation of the Levenberg-Marquardt algorithm for feedforward networks with linear outputs

An efficient algorithm for the calculation of the approximate Hessian matrix for the Levenberg-Marquardt (LM) optimization algorithm for training a single-hidden-layer feedforward network with linear outputs is presented. The algorithm avoids explicit calculation of the Jacobian matrix and computes the gradient vector and approximate Hessian matrix directly. It requires approximately 1/N the floating point operations of other published algorithms, where N is the number of network outputs. The required memory for the algorithm is also less than 1/N of the memory required for algorithms explicitly computing the Jacobian matrix. We applied our algorithm to two large-scale classification problems - the MNIST and the Forest Cover Type databases. Our results were within 0.5% of the best performance of systems using pixel values as inputs to a feedforward network for the MNIST database. Our results were achieved with a much smaller network than other published results. We achieved state-of-the-art performance for the Forest Cover Type database.
Robust Real-Time Registration of RGB-D Images using Multi-Resolution Surfel Representations

Fast and robust registration of 3D scans is required in many approaches to perception in robotics such as pose tracking or simultaneous localization and mapping. We propose a novel efficient method to register RGB-D images. We convert the image content into a multi-resolution surfel representation and exploit the dense image neighborhood to construct such views at high frame-rates on a single CPU. Our approach registers views using an efficient and robust variant of the Iterative Closest Points algorithm. We evaluate our method on a recently published benchmark dataset and achieve results beyond the state-of-the-art. We also report on the successful public demonstration of our method at RoboCup 2011.
Experiment with Discourse Structure Builder engine

In this paper, some experiments with a Rule-Based Discourse Structure Builder (DSB) Engine are presented. The DSB discussed here is a Rule-Based Engine that identifies discourse relations based on centering status of entities in the text, rather than on cohesive devices. In the design of the DSB Engine some abstract notions have been defined. Addressing these notions according to a set of theories in natural language processing provides an engine for revealing discourse structure. Here in this paper, experiments with one of the possible settings are presented. The results show that the engine reveals discourse structures of the texts significantly. Although the Rule Set that was used in this experiment was a hand-tuned one, it produced Discourse Trees with 80% accuracy comparing to the ones suggested by human annotators.
Designing Effective Interaction with Large High-Resolution Displays

Large high-resolution displays do not restrict designs to traditional desktop displays, creating new opportunities for various research communities. However, we cannot fully exploit the benefits of large high-resolution displays by merely presenting a huge amount of information. Rather, we should develop usable and useful input devices and interaction techniques that accommodate distinct characteristics afforded by such emerging technologies. Large high-resolution displays presents a number of interaction challenges not well addressed by traditional input devices (represented by keyboard and mouse) and graphical user interface metaphors (represented by WIMP).
Antisense-Mediated Knockdown of NaV1.8, but Not NaV1.9, Generates Inhibitory Effects on Complete Freund's Adjuvant-Induced Inflammatory Pain in Rat

Tetrodotoxin-resistant (TTX-R) sodium channels NaV1.8 and NaV1.9 in sensory neurons were known as key pain modulators. Comparing with the widely reported NaV1.8, roles of NaV1.9 on inflammatory pain are poorly studied by antisense-induced specific gene knockdown. Here, we used molecular, electrophysiological and behavioral methods to examine the effects of antisense oligodeoxynucleotide (AS ODN) targeting NaV1.8 and NaV1.9 on inflammatory pain. Following complete Freund's adjuvant (CFA) inflammation treatment, NaV1.8 and NaV1.9 in rat dorsal root ganglion (DRG) up-regulated mRNA and protein expressions and increased sodium current densities. Immunohistochemical data demonstrated that NaV1.8 mainly localized in medium and small-sized DRG neurons, whereas NaV1.9 only expressed in small-sized DRG neurons. Intrathecal (i.t.) delivery of AS ODN was used to down-regulate NaV1.8 or NaV1.9 expressions confirmed by immunohistochemistry and western blot. Unexpectedly, behavioral tests showed that only NaV1.8 AS ODN, but not NaV1.9 AS ODN could reverse CFA-induced heat and mechanical hypersensitivity. Our data indicated that TTX-R sodium channels NaV1.8 and NaV1.9 in primary sensory neurons played distinct roles in CFA-induced inflammatory pain and suggested that antisense oligodeoxynucleotide-mediated blocking of key pain modulator might point toward a potential treatment strategy against certain types of inflammatory pain.
StressSense: detecting stress in unconstrained acoustic environments using smartphones

Stress can have long term adverse effects on individuals' physical and mental well-being. Changes in the speech production process is one of many physiological changes that happen during stress. Microphones, embedded in mobile phones and carried ubiquitously by people, provide the opportunity to continuously and non-invasively monitor stress in real-life situations. We propose StressSense for unobtrusively recognizing stress from human voice using smartphones. We investigate methods for adapting a one-size-fits-all stress model to individual speakers and scenarios. We demonstrate that the StressSense classifier can robustly identify stress across multiple individuals in diverse acoustic environments: using model adaptation StressSense achieves 81% and 76% accuracy for indoor and outdoor environments, respectively. We show that StressSense can be implemented on commodity Android phones and run in real-time. To the best of our knowledge, StressSense represents the first system to consider voice based stress detection and model adaptation in diverse real-life conversational situations using smartphones.
Fast Trigonometric Polynomial Approach for Computing the Shape from Shading

Abstract Several recently developed techniques for recons- truction surface shape from shading information estimate surface slopes with out ensuring that they integrable. This paper presents a new approach for estimating the shape of three dimension 3-D obje- ct from its two dimension 2-D shade image in ter-ms of a approximating the height map by a second order trigonometric polynomial. The proposed approach satisfies the integrability condition and does not need any boundary condition assumptio-ns, we have designed a stand-alone, flexible Matlab implementation that enables to evaluation the proposed approach. The experiments on real images show the approaches ability to reconstruc-tion the surface from SFS. Keywords: Computer vision, shape from shading, trigonometric polynomial approximate, gray sca- le, integrability. 1. Introduction Shape-from-Shading (SFS) is a fundamental prob-lem in Computer Vision. The common way to obtain shape information is to solve the image irradiance equation, which relates the reflectance map to image intensity. As this task is nontrivial, most of the works in the field employ simplifying assumptions, and in particular the assumption that projection of scene points during a photographic process is orthographic [5,17,1,13,16,21]. This resulted in low stability of reconstruction algorit-thms. The SFS problem consists of computing the three dimensional shape of a surface from the brightness variations in a black and white image of that surface. Pioneered by Horn [6], this problem has been central in the field of computer vision since the early days, because of several reasons, the interest in this problem has slightly decreased at the end of the 90s. First, due to the difficulty of the problem, progress in SFS research is very slow. Second, until recently, the results obtained on real images have been very disappointing. For example, in [21], Zhang et al. acknowledge failure. Third, the various con constraints imposed by the existing solutions to the SFS problem limit its applications. The SFS problem consists of recovering the shape of a scene from a single grey-level image, by means of the analysis of the shading. The craze for SFS in the past seems to have subsided, probably because of rather disappointing results on real images [21]. Nevertheless, several recent works [2,3,9,10,11] have (independently) attempted to modelize SFS in a more realistic way, in particular by considering perspective projection. The present work fits into this scheme; we outline a new modeling of the SFS problem and validate it through a practical application. Our final purpose is to design a system that “unwarps” the image, taken by a digital camera and we attempt to change this situation completely and hope to revive the interest of the community for this old problem and its applications
Structural feature selection for wrapper methods

The wrapper approach to feature selection requires the as- sessment of several subset alternatives and the selection of the one which is expected to have the lowest generalization error. To tackle this prob- lem, practitioners have often recourse to a search procedure in a very large space of subsets of features aiming to minimize a leave-one-out or more in general a cross-validation criterion. It has been previously discussed in literature, how this practice can lead to a strong bias selection in the case of high dimensionality problems. We propose here an alternative method, inspired by structural identification in model selection, which re- places a single global search by a number of searches into a sequence of nested spaces of features with an increasing number of variables. The pa- per presents some promising, although preliminary results on several real nonlinear regression problems.
Pervasive Radio Mapping of Industrial Environments Using a Virtual Reality Approach

Wireless communications in industrial environments are seriously affected by reliability and performance issues, due to the multipath nature of obstacles within such environments. Special attention needs to be given to planning a wireless industrial network, so as to find the optimum spatial position for each of the nodes within the network, and especially for key nodes such as gateways or cluster heads. The aim of this paper is to present a pervasive radio mapping system which captures (senses) data regarding the radio spectrum, using low-cost wireless sensor nodes. This data is the input of radio mapping algorithms that generate electromagnetic propagation profiles. Such profiles are used for identifying obstacles within the environment and optimum propagation pathways. With the purpose of further optimizing the radio planning process, the authors propose a novel human-network interaction (HNI) paradigm that uses 3D virtual environments in order to display the radio maps in a natural, easy-to-perceive manner. The results of this approach illustrate its added value to the field of radio resource planning of industrial communication systems.
N A ] 6 M ay 2 00 5 A cell-centered finite volume approximation for second order partial derivative operators with full matrix on unstructured meshes in any space dimension

Finite volume methods for problems involving second order operators with full diffusion matrix can be used thanks to the definition of a discrete gradient for piecewise constant functions on unstructured meshes satisfying an orthogonality condition. This discrete gradient is shown to satisfy a strong convergence property on the interpolation of regular functions, and a weak one on functions bounded for a discrete H 1 norm. To highlight the importance of both properties, the convergence of the finite volume scheme on a homogeneous Dirichlet problem with full diffusion matrix is proven, and an error estimate is provided. Numerical tests show the actual accuracy of the method.
Power and Discrete Rate Adaptation for Energy Harvesting Wireless Nodes

Energy Harvesting (EH) nodes, which harvest energy from the environment in order to communicate over a wireless link, promise perpetual operation of a wireless network with battery-powered nodes. In this paper, we address the throughput optimization problem for a rate-adaptive EH node that chooses its rate from a set of discrete rates and adjusts its power depending on its channel gain and battery state. First, we show that the optimal throughput of an EH node is upper bounded by the throughput achievable by a node that is subject only to an average power constraint. We then propose a simple transmission scheme for an EH node that achieves an average throughput close to the upper bound. The scheme's parameters can be made to account for energy overheads such as battery non-idealities and the energy required for sensing and processing. The effect of these overheads on the average throughput is also analytically characterized.