Miracle-dz/UniFAR-FTUs huggingface dataset:
The synthesis and secretion of immunoglobulins by lymphoid cells in the sheep. The primary response to salmonella lipopolysaccharide

The primary response of the popliteal node to Salmonella lipopolysaccharide was studied in the sheep. All three classes of immunoglobulin IgG1, igG2, and IgM were produced by both free-floating cells in the lymph and by cells within the pymph node throughout the immune responce which extended over a period of at least 20 days. Most of the immunoglobulins were found to be nonspecific for the antigen when tested by a binding assay. It was calculated from the binding assay that far more antigen-specific IgG molecules were produced than IgM molecules. The proportion of IgM and IgG1 which showed affinity for Salmonella organisms increased throughout the response. IgG2 had no affinity for the antigen until around 480 h after challenge. When a hemagglutination assay was used to measure antibody production, most of the specific antibody produced during the response was found to be IgM. Blast cells produced most of the immunoglobulin during the first 4 days of the response, and these cells were responsible for almost all of the IgM production. Differences were observed in the relative amounts of IgG and IgM produced by the cells within the node and by free-floating cells in the efferent lymph. The free-floating cells in lymph synthesized and secreted relatively more IgM and relatively less IgG than did cells within the lymph node. Both populations of cells, however, secreted much more IgG than IgM.
Inhibition of VEGF-C Modulates Distal Lymphatic Remodeling and Secondary Metastasis

Tumor-associated lymphatics are postulated to provide a transit route for disseminating metastatic cells. This notion is supported by preclinical findings that inhibition of pro-lymphangiogenic signaling during tumor development reduces cell spread to sentinel lymph nodes (SLNs). However, it is unclear how lymphatics downstream of SLNs contribute to metastatic spread into distal organs, or if modulating distal lymph transport impacts disease progression. Utilizing murine models of metastasis, longitudinal in vivo imaging of lymph transport, and function blocking antibodies against two VEGF family members, we provide evidence that distal lymphatics undergo disease course-dependent up-regulation of lymph transport coincidental with structural remodeling. Inhibition of VEGF-C activity with antibodies against VEGF-C or NRP2 prevented these disease-associated changes. Furthermore, utilizing a novel model of adjuvant treatment, we demonstrate that antagonism of VEGF-C or NRP2 decreases post SLN metastasis. These data support a potential therapeutic strategy for inhibiting distant metastatic dissemination via targeting tumor-associated lymphatic remodeling.
Fast interactive segmentation using color and textural information Segmentation interactive basée sur la couleur et la texture

This work presents an efficient method for image multi-zone segmentation under human supervision. As most of the segmentation methods, our procedure relies on an energy minimization and this energy contains a data-consistent term and a coherence term. In this work, the pixel-by-pixel data-consistent term is based on a Support Vector Machine designed in order to deal with color and textural information. For the coherence term, our contribution is to take into account the gradient information of the image through the use of a Canny edge detector. To minimize the energy, as in Blake et al. ([1]), we use a Graph-Cut algorithm. Finally, we obtain excellent segmentation results at low computational cost. We compare our results on an image data bank on-line1. 1 http://research.microsoft. com/vision/cambridge/i3l/segmentation/ GrabCut.htm
A Bayesian network classifier for word-level reading assessment

To automatically assess young children’s reading skills as demonstrated by isolated words read aloud, we propose a novel structure for a Bayesian Network classifier. Our network models the generative story among speech recognition-based features, treating pronunciation variants and reading mistakes as distinct but not independent cues to a qualitative perception of reading ability. This Bayesian approach allows us to estimate the probabilistic dependencies among many highly-correlated features, and to calculate soft decision scores based on the posterior probabilities for each class. With all proposed features, the best version of our network outperforms the C4.5 decision tree classifier by 17% and a Naive Bayes classifier by 8%, in terms of correlation with speaker-level reading scores on the Tball data set. This best correlation of 0.92 approaches the expert inter-evaluator correlation, 0.95.
A Systematic Review of Bilateral Upper Limb Training Devices for Poststroke Rehabilitation

Introduction. In stroke rehabilitation, bilateral upper limb training is gaining ground. As a result, a growing number of mechanical and robotic bilateral upper limb training devices have been proposed. Objective. To provide an overview and qualitative evaluation of the clinical applicability of bilateral upper limb training devices. Methods. Potentially relevant literature was searched in the PubMed, Web of Science, and Google Scholar databases from 1990 onwards. Devices were categorized as mechanical or robotic (according to the PubMed MeSH term of robotics). Results. In total, 6 mechanical and 14 robotic bilateral upper limb training devices were evaluated in terms of mechanical and electromechanical characteristics, supported movement patterns, targeted part and active involvement of the upper limb, training protocols, outcomes of clinical trials, and commercial availability. Conclusion. Initial clinical results are not yet of such caliber that the devices in question and the concepts on which they are based are firmly established. However, the clinical outcomes do not rule out the possibility that the concept of bilateral training and the accompanied devices may provide a useful extension of currently available forms of therapy. To actually demonstrate their (surplus) value, more research with adequate experimental, dose-matched designs, and sufficient statistical power are required.
Cosmic microwave background power spectrum estimation and map reconstruction with the expectation-maximization algorithm

We apply the iterative expectation-maximization algorithm (EM) to estimate the power spectrum of the cosmic microwave background (CMB) from multifrequency microwave maps. In addition, we are also able to provide a reconstruction of the CMB map. By assuming that the combined emission of the foregrounds plus the instrumental noise is Gaussian distributed in Fourier space, we have simplified the EM procedure, finding an analytical expression for the maximization step. By using the simplified expression, the CPU time can be greatly reduced. We test the stability of our power spectrum estimator with realistic simulations of Planck data, including point sources and allowing for spatial variation of the frequency dependence of the Galactic emissions. Without prior information about any of the components, our new estimator can recover the CMB power spectrum up to scales l≈ 1500 with less than 10 per cent error. This result is significantly improved if the brightest point sources are removed before applying our estimator. In this way, the CMB power spectrum can be recovered up to l≈ 1700 with 10 per cent error and up to l≈ 2100 with 50 per cent error. This result is very close to the one that would be obtained in the ideal case of only CMB plus white noise, for which all our assumptions are satisfied. Moreover, the EM algorithm also provides a straightforward mechanism for reconstructing the CMB map. The recovered cosmological signal shows a high degree of correlation (r= 0.98) with the input map and low residuals.
Interleukin-1β and Interleukin-6 in Arthritis Animal Models: Roles in the Early Phase of Transition from Acute to Chronic Inflammation and Relevance for Human Rheumatoid Arthritis

Tumor necrosis factor-α (TNF-α) is the major target of the therapeutic approach in rheumatoid arthritis. A key issue in the approach to chronic arthritis is the understanding of the crucial molecules driving the transition from the acute phase to the chronic irreversible phase of the disease. In this review we analyzed five experimental arthritis animal models (antigen-induced arthritis, adjuvant-induced arthritis, streptococcal cell wall arthritis, collagen-induced arthritis and SKG) considered as possible scenarios to facilitate interpretation of the biology of human rheumatoid arthritis. The SKG model is strictly dependent on interleukin (IL)-6. In the other models, IL-1β and IL-6, more than TNF-α, appear to be relevant in driving the transition, which suggests that these should be the targets of an early intervention to stop the course toward the chronic form of the disease.
Lexicons Modelling Inherent Lexical Meaning . Lexicons Modelling Meaning Relations . Lexicons Modelling Predicate-argument Structure

Wechsler, Steven 2005. Resultatives under the 'event-argument homomorphism' model of telicity. 110. Semantics in computational lexicons 1. Representation and computation 2. Building semantic lexicons 3. Multilingual and cross-lingual aspects 4. Interfaces and interoperability 5. Conclusion and outlook 6. References Abstract This chapter gives an overview of work on the representation of semantic information in lexicon resources for computational natural language processing (NLP). It starts with a broad overview of the history and state of the art of different types of semantic lexicons in Computational Linguistics, and discusses their main use cases. Section 2 is devoted to questions of how to construct semantic lexicons for Computational Linguistics. We discuss diverse modelling principles for semantic lexicons and methods for their construction, ranging from largely manual resource creation to automated methods for learning lexicons from text, semi-structured or unstructured. Section 3 addresses issues related to the cross-lingual and multilingual creation of broad-coverage semantic lexicon resources. Section 4 discusses interoperability, i.e., the combination of lexical (and other) resources describing different meaning aspects. Section 5 concludes with an outlook on future research directions. The development of semantic lexicons in and for computational semantic processing has been shaped by two complementary aspects of lexical meaning. Since words are combined to form complex phrases that express specifi c meanings, lexical meaning clearly relates to structural aspects of meaning in compositional meaning construction, with phenomena such as argument structure, quantifi er or adverbial scope, presupposition projection , or anaphoric reference (cf. article 82 (von Stechow) Syntax and semantics). But more importantly, the lexicon plays its primary role in the representation of the lexical meaning of individual words that build the basis for constructing complex meanings, and that can serve as a basis for recognising and modelling paraphrases, lexically driven entailments, or creative meaning extensions such as metaphor. 64 XXII. Semantics and computer science Computational theories of grammar have been studied extensively in the course of the last decades, with a strong focus on formal modelling and effi cient computational processing of compositional meaning construction. Particular focus was put on the design of expressive semantic formalisms, ranging from classical predicate logic to dynamic semantic formalisms, and the design of principled meaning construction methods for diverse grammar frameworks (for an overview see Müller 2010). Since all major computational grammar formalisms are lexicalised, it is the computational semantic lexicon in conjunction with compositional meaning construction principles that needs to account for …
Global information processing in gene networks: Fault tolerance

In this paper we study the fault tolerance of gene networks. We assume single gene knockouts and investigate the effect this kind of perturbation has on the communication between genes globally. For our study we use directed scale-free networks resembling gene networks, e.g., signaling or protein-protein interaction networks, and define a Markov process based on the network topology to model communication. This allows us to evaluate the spread of information in the network and, hence, detect differences due to single gene knockouts in the gene-gene communication asymptotically.
The duality of knowledge

Knowledge Management (KM) is a field that has attracted much attention both in academic and practitioner circles. Most KM projects appear to be primarily concerned with knowledge that can be quantified and can be captured, codified and stored - an approach more deserving of the label Information Management. Recently there has been recognition that some knowledge cannot be quantified and cannot be captured, codified or stored. However, the predominant approach to the management of this knowledge remains to try to convert it to a form that can be handled using the 'traditional' approach. In this paper, we argue that this approach is flawed and some knowledge simply cannot be captured. A method is needed which recognises that knowledge resides in people: not in machines or documents. We will argue that KM is essentially about people and the earlier technology driven approaches, which failed to consider this, were bound to be limited in their success. One possible way forward is offered by Communities of Practice, which provide an environment for people to develop knowledge through interaction with others in an environment where knowledge is created nurtured and sustained.
Approach to unwrap a 3D fingerprint to a 2D equivalent

Fingerprints are the most widely used biometric feature for human identification because of their accuracy and uniqueness. Traditional fingerprint acquisition techniques are contact based and result in poor quality images. The new generation of non-contact based scanners capture high resolution and detailed 3D fingerprint scans, which addresses many of the problems of traditional fingerprint acquisition techniques. The majority of existing fingerprint databases available today are 2D, so there is a need for backward compatibility for the 3D scans captured. In order to solve this interoperability issue, I present an algorithm to unwrap the 3D fingerprint to its 2D equivalent image to be used in an Automatic Fingerprint Identification System.
Data-Driven Coherence Models

How groups maintain and revise their beliefs and attitudes in the face of new information is a basic research question in human social behavior and communications, as well as having a range of applications in crafting effective communications in such areas as health interventions, political campaigns and advertising. In this paper, we argue for a coherence based approach for modeling group belief revision processes and as a framework for studying belief and attitude change. Coherence models have a rich history of applicability in the psychological sciences where they have been used to explain a range of belief maintenance processes in the individual. Given that processes of social comparison and pressure can homogenize a cohesive group’s beliefs, we argue in this paper to extend the application of coherence models to modeling group belief systems. Additionally, we address challenges in constructing and using such belief models. Typically, creating accurate models of either an individual or group’s beliefs requires the painstaking engagement of domain experts. We present and demonstrate a method for producing them from data and exploring potential vectors of attitude change in their subpopulations.
A budget constrained scheduling of workflow applications on utility Grids using genetic algorithms

Over the last few years, grid technologies have progressed towards a service-oriented paradigm that enables a new way of service provisioning based on utility computing models. Users consume these services based on their QoS (quality of service) requirements. In such ldquopay-per-userdquo grids, workflow execution cost must be considered during scheduling based on users' QoS constraints. In this paper, we propose a budget constraint based scheduling, which minimizes execution time while meeting a specified budget for delivering results. A new type of genetic algorithm is developed to solve the scheduling optimization problem and we test the scheduling algorithm in a simulated grid testbed.
Analysis of Physiological, Technical, and Tactical Analysis during a Friendly Football Match of Elite U19

The main objective was to analyze a friendly match of youth elite soccer players identifying the variance of tactical and physiological response parameters during the game. In addition, detecting the impact of both halves on player performance. For the purposes of this study twenty-two U19 players were analyzed playing 11v11. Activity profile, heart rate (HR and HRmax), grouped in five different zones were analyzed via Bluetooth technology, technical performance was analyzed by the Team Sport Assessment Procedure (TSAP), and tactical performance was measured by Social Network Analysis. A comparison of heart rate responses showed significant main effects in the halves (p = 0.001; ηp2 = 0.623). A comparison between tactical position and technical performance had significant main effects (p = 0.001; ηp2 = 0.390). Tactical position showed statistically significant effects on tactical prominence (p = 0.002; ηp2 = 0.296). Therefore, fatigue is a component distinguished in technical/tactical parameters, such as volume of play and efficiency index. Results suggest that fatigue effects may constrain technical performance and, for that reason, the use of instruments to monitor the fatigue effect during matches may be suggested.
An Efficient Goalie Strategy Using Twin Hidden Markov Models

Recently robosoccer has proved to be one of the most successful test beds for multi-agent behavior. In robosoccer, agents take actions based on the feedback they receive from the environment. In this paper, we propose a novel concept to select high level actions in a multi-agent environment. We conduce the whole problem to that of a defense strategy of goalie as it has got more scope for behavior recognition and action selection. We first characterize the various behaviors and actions so as to suit the HMM model. We then describe how these twin HMMs (Behavior Hidden Markov Model and Action Hidden Markov Model) are used in two different phases to recognize behaviors and select actions. Finally we present an algorithm that would implement the complete defense strategy of a goalie in key game situations.
Serial Concatenation of RS Codes with Kite Codes: Performance Analysis, Iterative Decoding and Design

In this paper, we propose a new ensemble of rateless forward error correction (FEC) codes. The proposed codes are serially concatenated codes with Reed-Solomon (RS) codes as outer codes and Kite codes as inner codes. The inner Kite codes are a special class of prefix rateless low-density parity-check (PRLDPC) codes, which can generate potentially infinite (or as many as required) random-like parity-check bits. The employment of RS codes as outer codes not only lowers down error-floors but also ensures (with high probability) the correctness of successfully decoded codewords. In addition to the conventional two-stage decoding, iterative decoding between the inner code and the outer code are also implemented to improve the performance further. The performance of the Kite codes under maximum likelihood (ML) decoding is analyzed by applying a refined Divsalar bound to the ensemble weight enumerating functions (WEF). We propose a simulation-based optimization method as well as density evolution (DE) using Gaussian approximations (GA) to design the Kite codes. Numerical results along with semi-analytic bounds show that the proposed codes can approach Shannon limits with extremely low error-floors. It is also shown by simulation that the proposed codes performs well within a wide range of signal-to-noise-ratios (SNRs).
Research on the Traffic Situation Identification Method of the Urban Periphery Road Network

Recognition of traffic situation in the urban periphery road network is a very important fundamental problem and a valuable source of information for traffic flow macro control and management especially with the urban scale expansion and traffic volume increasing rapidly nowadays. At present, researches on traffic flow situation identification are mainly focus on intercity expressway traffic, lack of the research work on urban freeway which is significantly different from urban freeway, and also rare of the comparison and comprehensive traffic flow situation identification method of these two different function types road. In this paper, Fuzzy C Means clustering algorithm and wavelet analysis was adopted for classification of urban expressway and ring freeway respectively. Then the unified situation identification method is given for the urban periphery road network.
Reversible Logic Synthesis via Biconditional Binary Decision Diagrams

Reversible logic synthesis is an emerging research area to aid the circuit implementation for multiple nano-scale technologies with bounded fan-out. Due to the inherent complexity of this problem, several heuristics are proposed in the literature. Among those, reversible logic synthesis using decision diagrams offers an attractive solution due to its scalability and performance. In this paper, we exploit a novel, canonical, Biconditional Binary Decision Diagram (BBDD) for reversible logic synthesis. Using BBDD, for multiple classes of Boolean functions, superior circuit performance is achievable due to its compacter presentation. We discuss theoretical and experimental studies in comparison with state-of-the-art reversible logic synthesis based on decision diagrams.
Quality measurement of unwrapped three-dimensional fingerprints: A neural networks approach

Traditional biometric systems based on the fingerprint characteristics acquire the biometric samples using touch-based sensors. Some recent researches are focused on the design of touch-less fingerprint recognition systems based on CCD cameras. Most of these systems compute three-dimensional fingertip models and then apply unwrapping techniques in order to obtain images compatible with biometric methods designed for images captured by touch-based sensors. Unwrapped images can present different problems with respect to the traditional fingerprint images. The most important of them is the presence of deformations of the ridge pattern caused by spikes or badly reconstructed regions in the corresponding three-dimensional models. In this paper, we present a neural-based approach for the quality estimation of images obtained from the unwrapping of three-dimensional fingertip models. The paper also presents different sets of features that can be used to evaluate the quality of fingerprint images. Experimental results show that the proposed quality estimation method has an adequate accuracy for the quality classification. The performances of the proposed method are also evaluated in a complete biometric system and compared with the ones obtained by a well-known algorithm in the literature, obtaining satisfactory results.
Drowsy Detection On Eye Blink Duration Using Algorithm

— this paper presents an automatic drowsy driver monitoring and accident prevention system that is based on monitoring the changes in the eye blink duration. Our proposed method detects the drowsiness in eyes using the proposed mean sift algorithm. Our new method detects eye blinks via a standard webcam in real-time YUY2_640x480 resolution. Experimental results in the eye-blink database showed that the proposed system detects eye blinks with 99.4% accuracy with a 1% false positive rate.