Miracle-dz/UniFAR-FTUs huggingface dataset:
Replication of Subgenomic Hepatitis C Virus Rnas in a Hepatoma Cell Line

into these strains to determine if yeast growth was affected in the absence of PKR. No retarded or stimulated growth phenotype was observed in these strains in the presence of E2. 25. RY1-1 carries two chromosomal copies of PKR at the Leu2 locus (18) and has wild-type eIF2␣. members of the Lai laboratory for helpful discussions. Confocal microscopy was performed at the cell biology core laboratory of University of Southern Cali-fornia Liver Center. Supported by a NIH grant (AI 40038) and by a postdoctoral fellowship to D.R. T. from the NIH. An estimated 170 million persons worldwide are infected with hepatitis C virus (HCV), a major cause of chronic liver disease. Despite increasing knowledge of genome structure and individual viral proteins, studies on virus replication and pathogenesis have been hampered by the lack of reliable and efficient cell culture systems. A full-length consensus genome was cloned from viral RNA isolated from an infected human liver and used to construct subgenomic selectable replicons. Upon transfection into a human hepatoma cell line, these RNAs were found to replicate to high levels, permitting metabolic radiolabeling of viral RNA and proteins. This work defines the structure of HCV replicons functional in cell culture and provides the basis for a long-sought cellular system that should allow detailed molecular studies of HCV and the development of antiviral drugs. HCV is a plus (ϩ) strand RNA virus that causes acute and chronic liver diseases (1). Although the acute phase of infection is usually associated with mild symptoms, most patients fail to clear the virus and contract persistent infection that frequently leads to chronic liver disease, including cirrhosis and hepatocellular carcinoma. Given the high prevalence of the virus, HCV has become a focus of intensive research. Originally cloned in 1989 (2), the viral genome is now well characterized. It has a length of ϳ9. 6 kb and its single, long open reading frame (ORF) encodes a ϳ3000 –ami-no acid polyprotein (3) (Fig. 1A). The ORF is flanked at the 5Ј end by a nontranslated region (NTR) that functions as an internal ri-bosome entry site (IRES) and at the 3Ј end by a highly conserved sequence essential for genome replication (4). The structural proteins are in the NH 2-terminal region of the polyprotein and the nonstructural proteins (NS) 2 to 5B in the remainder. By analogy to related ϩstrand RNA viruses, replication occurs by means of a minus (Ϫ) strand RNA …
Knowledge Management In Transition Economies: Selected Key Issues And Possible Research Avenues

Most of the existing literature on knowledge and knowledge management has a Western or Japanese origin. While this literature provides important insights and findings, these may not necessarily be successfully replicable in other contexts and may lose some of their relevance when applied there. Such and similar observations have given a rise to a substantial number of knowledge management studies conducted in non-Western settings, among those transition economies. We highlight three particular challenges to the knowledge management literature in transition economies: moving beyond the extension of existing knowledge to instead developing contextualized and distinct knowledge; shifting from studying unidirectional knowledge transfer to examining multidirectional knowledge flows; and changing emphasis from studying knowledge transfer to studying knowledge creation.
A missing data estimation approach for small size image sequence

Data missing is a frequently encountered problem for structure-from-motion (SFM). In this paper, a sub-sequence based approach is proposed to deal with the missing data estimation problem for small size image sequence. In the proposed method, the sub-sequences are first extracted from the original sequence. Further, multiple weaker estimators are constructed by means of the column space fitting (CSF) algorithm. Finally, the missing entries are estimated by a linear programming based weighted model. Experimental results on several widely used image sequences demonstrate the effectiveness and feasibility of the proposed algorithm.
Simple Error Scattering Model for improved Information Reconciliation

Implementations of quantum key distribution as available nowadays suffer from inefficiencies due to post processing of the raw key that severely cuts down the final secure key rate. We present a simple model for the error scattering across the raw key and derive "closed form" expressions for the probability of a parity check failure, or experiencing more than some fixed number of errors. Our results can serve for improvement for key establishment, as information reconciliation via interactive error correction and privacy amplification rests on mostly unproven assumptions. We support those hypotheses on statistical grounds.
Towards Efficient Information Gathering Agents

Information gathering agents are required in many software agent applications to answer queries, posed by other agents, using a variety of available information sources. We formally consider the problem of designing information gathering agents, and make two important contributions. First, we examine the key issue of integrating knowledge from external sites into our knowledge base, and present an expressive language for this purpose. A noteworthy feature of our language is its ability to capture the knowledge that some external sites have complete information of a certain kind, using rich semantic constraints. Given a query on the knowledge base, it is important for the agent to first determine the set of external sites that contain information relevant to answering the query, and then access those sites. Our second contribution is to show that, given a query and the descriptions of the external sites in our language, it is possible to determine minimal subsets of sites that are needed to answer the query.
Genetic Mapping of Resistance to Meloidogyne arenaria in Arachis stenosperma: A New Source of Nematode Resistance for Peanut

Root-knot nematodes (RKN; Meloidogyne sp.) are a major threat to crops in tropical and subtropical regions worldwide. The use of resistant crop varieties is the preferred method of control because nematicides are expensive, and hazardous to humans and the environment. Peanut (Arachis hypogaea) is infected by four species of RKN, the most damaging being M. arenaria, and commercial cultivars rely on a single source of resistance. In this study, we genetically characterize RKN resistance of the wild Arachis species A. stenosperma using a population of 93 recombinant inbred lines developed from a cross between A. duranensis and A. stenosperma. Four quantitative trait loci (QTL) located on linkage groups 02, 04, and 09 strongly influenced nematode root galling and egg production. Drought-related, domestication and agronomically relevant traits were also evaluated, revealing several QTL. Using the newly available Arachis genome sequence, easy-to-use KASP (kompetitive allele specific PCR) markers linked to the newly identified RKN resistance loci were developed and validated in a tetraploid context. Therefore, we consider that A. stenosperma has high potential as a new source of RKN resistance in peanut breeding programs.
Decoding and Reconstructing Color from Responses in Human Visual Cortex

How is color represented by spatially distributed patterns of activity in visual cortex? Functional magnetic resonance imaging responses to several stimulus colors were analyzed with multivariate techniques: conventional pattern classification, a forward model of idealized color tuning, and principal component analysis (PCA). Stimulus color was accurately decoded from activity in V1, V2, V3, V4, and VO1 but not LO1, LO2, V3A/B, or MT+. The conventional classifier and forward model yielded similar accuracies, but the forward model (unlike the classifier) also reliably reconstructed novel stimulus colors not used to train (specify parameters of) the model. The mean responses, averaged across voxels in each visual area, were not reliably distinguishable for the different stimulus colors. Hence, each stimulus color was associated with a unique spatially distributed pattern of activity, presumably reflecting the color selectivity of cortical neurons. Using PCA, a color space was derived from the covariation, across voxels, in the responses to different colors. In V4 and VO1, the first two principal component scores (main source of variation) of the responses revealed a progression through perceptual color space, with perceptually similar colors evoking the most similar responses. This was not the case for any of the other visual cortical areas, including V1, although decoding was most accurate in V1. This dissociation implies a transformation from the color representation in V1 to reflect perceptual color space in V4 and VO1.
Partitioning and Scheduling of Task Graphs on Partially Dynamically Reconfigurable FPGAs

This paper proposes a new model for the partitioning and scheduling of a specification on partially dynamically reconfigurable hardware. Although this problem can be solved optimally only by tackling its subproblems jointly, the exceeding complexity of such a task leads to a decomposition into two phases. The partitioning phase is based on a new graph-theoretic approach, which aims to obtain near optimality even if performed independently from the subsequent phase. For the scheduling phase, a new integer linear programming formulation and a heuristic approach are developed. Both take into account configuration prefetching and module reuse. The experimental results show that the proposed method compares favorably with existing solutions.
A Domain Robust Approach For Image Dataset Construction

There have been increasing research interests in automatically constructing image dataset by collecting images from the Internet. However, existing methods tend to have a weak domain adaptation ability, known as the "dataset bias problem". To address this issue, in this work, we propose a novel image dataset construction framework which can generalize well to unseen target domains. In specific, the given queries are first expanded by searching in the Google Books Ngrams Corpora (GBNC) to obtain a richer semantic description, from which the noisy query expansions are then filtered out. By treating each expansion as a "bag" and the retrieved images therein as "instances", we formulate image filtering as a multi-instance learning (MIL) problem with constrained positive bags. By this approach, images from different data distributions will be kept while with noisy images filtered out. Comprehensive experiments on two challenging tasks demonstrate the effectiveness of our proposed approach.
Filter feature selection performance comparison in high-dimensional data: A theoretical and empirical analysis of most popular algorithms

The key idea behind feature selection is to find a subset of features that produce similar or better results as the original set while being more compact. Algorithms in this topic can be grouped in filter, wrapper and hybrid, however for very high dimensional data it has been found that the filter approach is better due to being less computational expensive. In this paper we provide a study about how information explosion has caused an impact on solutions for feature selection. A theoretical analysis is reviewed followed by an empirical comparison of 10 of the most popular filter algorithms with datasets ranging from 2400 up to 100,000 features in order to observe algorithm performance, scalability and detect current open problems. Results suggest that some of the current most popular solutions may become obsolete in the future due to the increase in dataset complexity.
Variable selection for functional linear models with functional predictors and a functional response

We consider a variable selection problem for functional linear models where both multiple predictors and a response are functions. Especially we assume that variables are given as functions of time and then construct the historical functional linear model which takes the relationship of dependences of predictors and a response into consideration. Unknown parameters included in the model are estimated by the maximum penalized likelihood method with the L1 penalty. We can simultaneously estimate and select variables given as functions using the L1 type penalty. A regularization parameter involved in the regularization method is decided by a model selection criterion. The effectiveness of the proposed method is investigated by simulation studies and real data analysis.
Electronic publishing, storage, dissemination and retrieval of a scientific journal through the Web

The initial part of the paper describes the general architecture of a prototype system developed in the context of the IRIDES project for the electronic publishing, storage, dissemination and retrieval of The Computer Journal, published by Oxford University Press. The core of the paper is devoted to one of the components of the overall system: AUCTOR. AUCTOR is a system for the automatic authoring of HTML hypertext representing the content of the journal's articles. After a detailed description of the system, considerations of the automatically authored hypertext are given. AUCTOR in particular, and IRIDES in general, can be considered as the initial component of a complete environment to collect different collections of documents and services for the implementation of a modern digital library.
Trend of Industrial Robot Share in Different Branches of Industry in America

This paper provides an analysis of trend of the industrial robot share in different industrial branches in America. The automation and modernization of the manufacturing process in whomever industrial branch is not possible without industrial robot application. This paper shows how much the manufacturing process is automatized in different industrial branches in America. The annual and total (operational stock) industrial robot application has been analysed in following manufacturing process: handling material operations, welding, assembly, processing, dispensing and the other undefined, where robots couldn't be classified in the manufacturing process. The industrial robot application of the leading American countries has been analysed, with the developed industry or in the developing process, where the automation and modernization of the manufacturing process is performed. This paper provides the comparative analysis of the industrial robot application in last three years, at the annual level and the total number of the industrial robot applications. There has been given the percentage reference of the industrial robot application in different industrial branches in 2010. It also provides the prediction analysis of the industrial robot application in following period in America till 2014.
Advances in Greedy Algorithms

ing and non-profit use of the material is permitted with credit to the source. Statements and opinions expressed in the chapters are these of the individual contributors and not necessarily those of the editors or publisher. No responsibility is accepted for the accuracy of information contained in the published articles. Publisher assumes no responsibility liability for any damage or injury to persons or property arising out of the use of any materials, instructions, methods or ideas contained inside. After this work has been published by the In-Teh, authors have the right to republish it, in whole or part, in any publication of which they are an author or editor, and the make other personal use of the work. © 2008 In-teh www.in-teh.org Additional copies can be obtained from: publication@ars-journal.com First published November 2008 Printed in Croatia A catalogue record for this book is available from the University Library Rijeka under no. 120115050 Advances in Greedy Algorithms, Edited by Witold Bednorz p. cm. ISBN 978-953-7619-27-5 1. Advances in Greedy Algorithms, Witold Bednorz
Hypohydration effect on finger skin temperature and blood flow during cold-water finger immersion.

This study was conducted to determine whether hypohydration (Hy) alters blood flow, skin temperature, or cold-induced vasodilation (CIVD) during peripheral cooling. Fourteen subjects sat in a thermoneutral environment (27 degrees C) during 15-min warm-water (42 degrees C) and 30-min cold-water (4 degrees C) finger immersion (FI) while euhydrated (Eu) and, again, during Hy. Hy (-4% body weight) was induced before FI by exercise-heat exposure (38 degrees C, 30% relative humidity) with no fluid replacement, whereas during Eu, fluid intake maintained body weight. Finger pad blood flow [as measured by laser-Doppler flux (LDF)] and nail bed (T(nb)), pad (T(pad)), and core (T(c)) temperatures were measured. LDF decreased similarly during Eu and Hy (32 +/- 10 and 33 +/- 13% of peak during warm-water immersion). Mean T(nb) and T(pad) were similar between Eu (7.1 +/- 1.0 and 11.5 +/- 1.6 degrees C) and Hy (7.4 +/- 1.3 and 12.6 +/- 2.1 degrees C). CIVD parameters (e.g., nadir, onset time, apex) were similar between trials, except T(pad) nadir was higher during Hy (10.4 +/- 3.8 degrees C) than during Eu (7.9 +/- 1.6 degrees C), which was attributed to higher T(c) in six subjects during Hy (37.5 +/- 0.2 degrees C), compared with during Eu (37.1 +/- 0.1 degrees C). The results of this study provide no evidence that Hy alters finger blood flow, skin temperature, or CIVD during peripheral cooling.
Mathematical Methods for Supervised Learning

Let ρ be an unknown Borel measure defined on the space Z := X × Y with X ⊂ IR and Y = [−M,M ]. Given a set z of m samples zi = (xi, yi) drawn according to ρ, the problem of estimating a regression function fρ using these samples is considered. The main focus is to understand what is the rate of approximation, measured either in expectation or probability, that can be obtained under a given prior fρ ∈ Θ, i.e. under the assumption that fρ is in the set Θ, and what are possible algorithms for obtaining optimal or semi-optimal (up to logarithms) results. The optimal rate of decay in terms of m is established for many priors given either in terms of smoothness of fρ or its rate of approximation measured in one of several ways. This optimal rate is determined by two types of results. Upper bounds are established using various tools in approximation such as entropy, widths, and linear and nonlinear approximation. Lower bounds are proved using KullbackLeibler information together with Fano inequalities and a certain type of entropy. A distinction is drawn between algorithms which employ knowledge of the prior in the construction of the estimator and those that do not. Algorithms of the second type which are universally optimal for a certain range of priors are given.
Real-time dynamic power management based on Pearson's Correlation Coefficient

An autonomous robotic platform should be able to perform long-range and long-endurance missions, which energy limitation is one of the most important challenges. Studies show that motion is not the only power consumer. Management of all power resources is therefore important for these systems. Moreover, many applications for control of autonomous platform are being developed and one important aspect is the excess of information, frequently redundant, that imposes a great computational cost in data processing and consequently power consummation. In our previous works, we have proposed a visual-perception system based on an automatic image discarding method as a simple solution to improve the performance of a real-time navigation system. In this paper, we propose a new environment observer method based on Pearson's Correlation Coefficient. This monocular-vision system permits that some logical components may be shut down to save processor energy consumption, and/or to make the CPU available for running concurrent processes. Nevertheless, this method may be extended to other sensors and components. Our real-time perception system has been evaluated from real data obtained by our intelligent vehicle. It is not based on previous knowledge of the environment neither camera calibration.
Twenty-Four-Hour Real-Time Continuous Monitoring of Cerebral Edema in Rabbits Based on a Noninvasive and Noncontact System of Magnetic Induction

Cerebral edema is a common disease, secondary to craniocerebral injury, and real-time continuous monitoring of cerebral edema is crucial for treating patients after traumatic brain injury. This work established a noninvasive and noncontact system by monitoring the magnetic induction phase shift (MIPS) which is associated with brain tissue conductivity. Sixteen rabbits (experimental group n = 10, control group, n = 6) were used to perform a 24 h MIPS and intracranial pressure (ICP) simultaneously monitored experimental study. For the experimental group, after the establishment of epidural freeze-induced cerebral edema models, the MIPS presented a downward trend within 24 h, with a change magnitude of −13.1121 ± 2.3953°; the ICP presented an upward trend within 24 h, with a change magnitude of 12–41 mmHg. The ICP was negatively correlated with the MIPS. In the control group, the MIPS change amplitude was −0.87795 ± 1.5146 without obvious changes; the ICP fluctuated only slightly at the initial value of 12 mmHg. MIPS had a more sensitive performance than ICP in the early stage of cerebral edema. These results showed that this system is basically capable of monitoring gradual increases in the cerebral edema solution volume. To some extent, the MIPS has the potential to reflect the ICP changes.
Confirmation of strong magnetic field amplification and nuclear cosmic ray acceleration in SN 1006

It is shown that the nonlinear kinetic model of cosmic ray (CR) acceleration in supernova remnants (SNRs) fits the shell-type nonthermal X-ray morphology, obtained in Chandra observations, in a satisfactory way. The set of empirical parameters is the same which reproduces the dynamical properties of the SNR and the spectral characteristics of the emission produced by CRs. The extremely small spatial scales of the observed X-ray distribution are due to the large effective magnetic field Bd ∼ 100 µG in the interior, which is also required to give a good fit for the spatially integrated radio and X-ray synchrotron spectra. The only reasonably thinkable condition for the production of such a large effective field strength is an efficiently accel- erated nuclear CR component. Therefore the Chandra data confirm the inference that SN 1006 indeed accelerates nuclear CRs with the high efficiency required for SNRs to be considered as the main Galactic CR sources.
Transmitter Equalization for 4 Gb / s Signalli ng

To operate a serial channel over copper wires at 4Gb/s, we incorporate an 4GHz FIR equalizing filter into a differential transmitter. The equalizer cancels the frequency-dependent attenuation caused by the skin-effect resistance of copper wire giving a frequency response that is flat to within 5% over the band from 200MHz to 2GHz even over wires with 6dB of high-frequency attenuation. All but the last stage of the transmitter operates at 400MHz. The transmitter output stage uses a stable 10-phase 400MHz clock to sequence an array of drivers that implement the FIR filter. This paper introduces the concept of digital-signal equalization, describes the system design and circuit design of our equali zing transmitter, and presents simulation results from a 4Gb/s 0. 5μm CMOS transmitter.