Miracle-dz/UniFAR-FTUs huggingface dataset:
Inhibition of the asthmatic allergen challenge response by the CRTH2 antagonist OC000459

CRTH2 (chemoattractant receptor expressed on T-helper (Th) type 2 cells) is a G-protein-coupled receptor expressed by Th2 lymphocytes and eosinophils that mediates prostaglandin (PG)D2-driven chemotaxis. We studied the efficacy of the oral CRTH2 antagonist OC000459 in steroid-naïve asthmatic patients. A randomised, double-blind, placebo-controlled, two-way crossover study of 16 days’ treatment with OC000459 (200 mg twice daily) on the late (LAR) and early (EAR) asthmatic responses to bronchial allergen challenge was conducted, with 16 subjects completing the study. There was a 25.4% (95% CI 5.1–45.6%) reduction in the LAR area under the curve (AUC) for change in forced expiratory volume in 1 s with OC000459 compared with placebo (p=0.018) but no effect on the EAR. Sputum eosinophil counts at 1 day post-allergen challenge were lower after OC000459 treatment (p=0.002). PGD2-induced blood eosinophil shape change ex vivo was assessed at day 7 (n=7). The AUC of eosinophil shift for OC000459 was lower than placebo; the mean difference was -33.6% (95% CI -66.8– -0.4%; p=0.048). OC000459 treatment inhibited LAR and post-allergen increase in sputum eosinophils. This CRTH2 antagonist appears to inhibit allergic inflammation in asthma.
A declarative approach to enable flexible and dynamic service compositions

Service Oriented Architecture (SOA) is considered the best solution to develop distributed applications that compose existing services to provide new added-value services for their users. However, we claim that the fully potential of SOA is still to be reached and it is our belief that this is caused by the same tools used to build service orchestrations. To overcome this situation we are developing a new declarative language for service orchestration, enhanced with an innovative runtime system which simplifies the development of flexible and robust service compositions.
Array Antenna based Localization Using Spatial Smoothing Processing

An array antenna based localization using spatial smoothing processing (SSP) is proposed for wireless security and monitoring, referred to as array sensor. The proposed method is based on the array sensor that exploits an array antenna at the receiver to detect the propagation environment of interest. If an event occurs, e.g., human motion, the propagation environment is changed. Thus the eigenvector and eigenvalue spanning the signal subspace that is inherent to its environment changes as well. Using a machine learning technique based on the eigenvector and eigenvalue, we can detect the event accurately. The proposed method is improved from our previous work which uses only a limited number of signal subspace features. The basic idea of this work is the extension of the dimension of the signal subspace by using SSP without increasing the number of array element. In addition, this work investigates the impact of the array antenna placement on localization performance. The experimental results show that the proposed SSP based method achieves a 41.83 % improvement in localization accuracy, and a 1.24 m improvement in root mean square error (RMSE) compared to the previous method.
Solid-phase translation and RNA–protein fusion: a novel approach for folding quality control and direct immobilization of proteins using anchored mRNA

A novel cell-free translation system is described in which template-mRNA molecules were captured onto solid surfaces to simultaneously synthesize and immobilize proteins in a more native-state form. This technology comprises a novel solid-phase approach to cell-free translation and RNA–protein fusion techniques. A newly constructed biotinylated linker-DNA which enables puromycin-assisted RNA–protein fusion is ligated to the 3′ ends of the mRNA molecules to attach the mRNA-template on a streptavidin-coated surface and further to enable the subsequent reactions of translation and RNA–protein fusion on surface. The protein products are therefore directly immobilized onto solid surfaces and furthermore were discovered to adopt a more native state with proper protein folding and superior biological activity compared with conventional liquid-phase approaches. We further validate this approach via the production of immobilized green fluorescent protein (GFP) on microbeads and by the production and assay of aldehyde reductase (ALR) enzyme with 4-fold or more activity. The approach developed in this study may enable to embrace the concept of the transformation of ‘RNA chip-to-protein chip’ using a solid-phase cell-free translation system and thus to the development of high-throughput microarray platform in the field of functional genomics and in vitro evolution.
Combining quantitative 2D and 3D image analysis in the serial block face SEM: application to secretory organelles of pancreatic islet cells

A combination of two‐dimensional (2D) and three‐dimensional (3D) analyses of tissue volume ultrastructure acquired by serial block face scanning electron microscopy can greatly shorten the time required to obtain quantitative information from big data sets that contain many billions of voxels. Thus, to analyse the number of organelles of a specific type, or the total volume enclosed by a population of organelles within a cell, it is possible to estimate the number density or volume fraction of that organelle using a stereological approach to analyse randomly selected 2D block face views through the cells, and to combine such estimates with precise measurement of 3D cell volumes by delineating the plasma membrane in successive block face images. The validity of such an approach can be easily tested since the entire 3D tissue volume is available in the serial block face scanning electron microscopy data set. We have applied this hybrid 3D/2D technique to determine the number of secretory granules in the endocrine α and β cells of mouse pancreatic islets of Langerhans, and have been able to estimate the total insulin content of a β cell.
The Synthetic Bacterial Lipopeptide Pam3CSK4 Modulates Respiratory Syncytial Virus Infection Independent of TLR Activation

Respiratory syncytial virus (RSV) is an important cause of acute respiratory disease in infants, immunocompromised subjects and the elderly. However, it is unclear why most primary RSV infections are associated with relatively mild symptoms, whereas some result in severe lower respiratory tract infections and bronchiolitis. Since RSV hospitalization has been associated with respiratory bacterial co-infections, we have tested if bacterial Toll-like receptor (TLR) agonists influence RSV-A2-GFP infection in human primary cells or cell lines. The synthetic bacterial lipopeptide Pam3-Cys-Ser-Lys4 (Pam3CSK4), the prototype ligand for the heterodimeric TLR1/TLR2 complex, enhanced RSV infection in primary epithelial, myeloid and lymphoid cells. Surprisingly, enhancement was optimal when lipopeptides and virus were added simultaneously, whereas addition of Pam3CSK4 immediately after infection had no effect. We have identified two structurally related lipopeptides without TLR-signaling capacity that also modulate RSV infection, whereas Pam3CSK4-reminiscent TLR1/2 agonists did not, and conclude that modulation of infection is independent of TLR activation. A similar TLR-independent enhancement of infection could also be demonstrated for wild-type RSV strains, and for HIV-1, measles virus and human metapneumovirus. We show that the effect of Pam3CSK4 is primarily mediated by enhanced binding of RSV to its target cells. The N-palmitoylated cysteine and the cationic lysines were identified as pivotal for enhanced virus binding. Surprisingly, we observed inhibition of RSV infection in immortalized epithelial cell lines, which was shown to be related to interactions between Pam3CSK4 and negatively charged glycosaminoglycans on these cells, which are known targets for binding of laboratory-adapted but not wild-type RSV. These data suggest a potential role for bacterial lipopeptides in enhanced binding of RSV and other viruses to their target cells, thus affecting viral entry or spread independent of TLR signaling. Moreover, our results also suggest a potential application for these synthetic lipopeptides as adjuvants for live-attenuated viral vaccines.
Methods for Retrieval of Forest Parameters from Satellite Remote Sensing NOTAT / NOTE

FOREMMS Methods for retrieval of forest parameters from satellite remote sensing 2 Norsk Regnesentral NOTAT SAMBA/26/01 Sammendrag/Abstract: One of the key questions in the FOREMMS project, is what kind of forest parameters satellite sensors can provide. In this note, we review previous approaches to estimation of forest parameters from existing satellite optical data, and discuss basic methodological technology and further research needs. The most fundamental parameter to FOREMMS is the forest cover, which can be derived from a classification of forest type or land cover. This review also includes methods for estimation of forest coverage fraction, and classification of forest type from time series. Biomass and wood volume can be estimated by regression models or by kNN interpolation models. Leaf Area Index (LAI) is a key biophysical variable. Many methods are based on empirical relationship between vegetation indices and LAI. However, the most promising methods seem to be the physically founded ones that have been developed for the production of the standard MODIS LAI product. Detection of forest changes and damages involves time-series of images and separation of effects like seasonal responses of the vegetation, inter-annual variability, and directional change. Deforestation and afforestation can be detected by comparing a classified forest image to the CORINE land cover database. Biodiversity can be mapped from three parameters named 'dominance', 'contagion' and 'fractal dimension'. We then discuss classification algorithms and algorithms for estimating a continuous parameter. For classification we suggest the Gaussian ML and Haslett's contextual MAP classifier. We also briefly discuss multi-resolution analysis, multi-temporal image classification models and data fusion models. For estimation of a continuous parameter we suggest general regression models that can use local models for spatial context, e.g. by using Markov random field models. This approach would use the same methodological framework as proposed for spatial context and ancillary data in image classification. FOREMMS Methods for retrieval of forest parameters from satellite remote sensing 3 Norsk Regnesentral NOTAT SAMBA/26/01 FOREMMS Methods for retrieval of forest parameters from satellite remote sensing 4 Norsk Regnesentral NOTAT SAMBA/26/01 Preface This report is written as a part of the FOREMMS project. Its purpose was initially to give a general documentation and description of the state-of-the-art for forest parameter retrieval from satellite remote sensing. During the process of studying literature and writing this text, the specification and goal of the FOREMMS system has been developing and changing into a system that are more focused, …
Development of multi-axial force sensing system for haptic feedback enabled minimally invasive robotic surgery

This paper presents a multi-axial force sensing system for haptic feedback of minimally invasive robotic surgery. The system consists of a 3-axial force sensor located on the wrist of surgical grasper and a torque sensor which plays a role of pulley for driving tendons. The 3-axial force sensor measures magnitude and direction of manipulation forces when the grasper grasps and pulls the tissue, while the torque sensor measures grasping force transferred through the driving tendons. The sensors and a grasper mechanism are designed and manufactured. For the calibration and verification of the prototype sensors, a testbed which simulates tissue manipulation is built and experiments are performed to evaluate the sensing system.
Evaluation of User Model Using Partial Order Relation

The traditional personalized recommendation system measures the quality of recommendation performance by the indicators of precision and recall. User model based on user browsing behavior plays an increasingly important role in personalized recommendation systems. The evaluation of user model is significant for personalized recommendation systems, which mainly focuses on the contents of subjects. Taking into account user's priority for the subjects, we urgently need to make an evaluation for the performance of user model. In this paper, an evaluation framework on performance of priority in user model is introduced, and the similarity calculation method of interest structure is designed. According to this, the evaluation method of stability of update mechanism for user model over an interval is proposed. Ten users verify the similarity of interest priority between their user models and actual interests by priority precision (p_pre) and priority recall (p_rec), and test the degrees of stability of user models for two months in the experiment when users' interests changed continually. The results show that the proposed method is more successful than traditional method in the measurement of interest priority.
Clustering Based on Context Similarity

The discovery of word categories is an important step in statistical grammar induction systems. Word categories can be considered as clusters containing words with similar grammatical or semantic behavior. Having a metric space of words, the clustering algorithm will place similar words into the same cluster, whereas dissimilar ones are clustered into different groups. In this paper we propose an approximate word clustering method based on context similarity. The context of a word is defined here as the set of sentences containing the word. The similarity of two words is measured with the similarity of the corresponding context sets. For the calculation of the context-based distance of two words, a hierarchical agglomerative clustering algorithm has been developed, and is presented here.
Development of a Small-Sized, Flexible, and Insertable Fiber-Optic Radiation Sensor for Gamma-Ray Spectroscopy

We fabricated a small-sized, flexible, and insertable fiber-optic radiation sensor (FORS) that is composed of a sensing probe, a plastic optical fiber (POF), a photomultiplier tube (PMT)-amplifier system, and a multichannel analyzer (MCA) to obtain the energy spectra of radioactive isotopes. As an inorganic scintillator for gamma-ray spectroscopy, a cerium-doped lutetium yttrium orthosilicate (LYSO:Ce) crystal was used and two solid-disc type radioactive isotopes with the same dimensions, cesium-137 (Cs-137) and cobalt-60 (Co-60), were used as gamma-ray emitters. We first determined the length of the LYSO:Ce crystal considering the absorption of charged particle energy and measured the gamma-ray energy spectra using the FORS. The experimental results demonstrated that the proposed FORS can be used to discriminate species of radioactive isotopes by measuring their inherent energy spectra, even when gamma-ray emitters are mixed. The relationship between the measured photon counts of the FORS and the radioactivity of Cs-137 was subsequently obtained. The amount of scintillating light generated from the FORS increased by increasing the radioactivity of Cs-137. Finally, the performance of the fabricated FORS according to the length and diameter of the POF was also evaluated. Based on the results of this study, it is anticipated that a novel FORS can be developed to accurately measure the gamma-ray energy spectrum in inaccessible locations such as narrow areas and holes.
Real Time Click Fraud Prevention using multi-level Data Fusion

from the viewpoint of Dempster-Shafer evidence theory, information obtained from different sources can be considered as pieces of evidence, and as such, multi-sensor based CCFDP (Collaborative Click Fraud Detection and Prevention) system can be viewed as a problem of evidence fusion. In this paper we detail the multi level data fusion mechanism used in CCFDP for real time click fraud detection and prevention. Prevention mechanisms are based on blocking suspicious traffic by IP, referrer, city, country, ISP, etc. Our system maintains an online database of these suspicious parameters. We have tested the system with real world data from an actual ad campaign where the results show that use of multi-level data fusion improves the quality of click fraud analysis.
Ergonomics-Inspired Reshaping and Exploration of Collections of Models

This paper examines the following question: given a collection of man-made shapes, e.g., chairs, can we effectively explore and rank the shapes with respect to a given human body-in terms of how well a candidate shape fits the specified human body? Answering this question requires identifying which shapes are more suitable for a prescribed body, and how to alter the input geometry to better fit the shapes to a given human body. The problem links physical proportions of the human body and its interaction with object geometry, which is often expressed as ergonomics guidelines. We present an interactive system that allows users to explore shapes using different avatar poses, while, at the same time providing interactive previews of how to alter the shapes to fit the user-specified body and pose. We achieve this by first constructing a fuzzy shape-to-body map from the ergonomic guidelines to multi-contacts geometric constraints; and then, proposing a novel contact-preserving deformation paradigm to realize a reshaping to adapt the input shape. We evaluate our method on collections of models from different categories and validate the results through a user study.
Cloud-Based Impact for Mobile and Pervasive Environments : A Survey

Mobile Cloud Computing (MCC) which combines mobile computing and cloud computing, has become one of the industry buzz words and a major discussion thread in the IT world since 2009. Despite increasing usage of mobile computing, exploiting its full potential is difficult due to its inherent problems such as resource scarcity, frequent disconnections, and mobility. MCC integrates the cloud computing into the mobile environment and overcomes obstacles related to the performance (e.g., battery life,storage, and bandwidth), environment (e.g., heterogeneity, scalability, and availability), and security (e.g., reliability and privacy) discussed in mobile computing. As MCC is still at the early stage of development, it is necessary to grasp a thorough understanding of the technology in order to point out the direction of future research. With the latter aim, this paper presents a review on the background and principle of MCC, characteristics, recent research work, and future research trends.
Distributed Data Collection in Large-Scale Asynchronous Wireless Sensor Networks Under the Generalized Physical Interference Model

Wireless sensor networks (WSNs) are more likely to be d-pistributed asynchronous systems. In this paper, we investigate the achievable data collection capacity of realistic distributed asynchronous WSNs. Our main contributions include five aspects. First, to avoid data transmission interference, we derive an ℜ0-proper carrier-sensing range (ℜ0-PCR) under the generalized physical interference model, where ℜ0 is the satisfied threshold of data receiving rate. Taking ℜ0-PCR as its carrier-sensing range, any sensor node can initiate a data transmission with a guaranteed data receiving rate. Second, based on ℜ0-PCR, we propose a Distributed Data Collection (DDC) algorithm with fairness consideration. Theoretical analysis of DDC surprisingly shows that its achievable network capacity is order-optimal and independent of network size. Thus, DDC is scalable. Third, we discuss how to apply ℜ0-PCR to the distributed data aggregation problem and propose a Distributed Data Aggregation (DDA) algorithm. The delay performance of DDA is also analyzed. Fourth, to be more general, we study the delay and capacity of DDC and DDA under the Poisson node distribution model. The analysis demonstrates that DDC is also scalable and order-optimal under the Poisson distribution model. Finally, we conduct extensive simulations to validate the performance of DDC and DDA.
Distributed Beamforming Using Multi-Antenna Backscattering Network

: - This paper proposes two novel beam-forming methodologies, which employ passive RF devices to enhance received signal through a passive, scattering based beam forming. Currently available backscattering systems, e.g. the radio frequency identification (RFID), suffer from a poor communication in an environment with multiple passive devices. The intermediate antennas create severe scattering environment with nulls. In contrast, the proposed approach changes the scattering properties of a subset of the multiple antennas such that the received signal strength and quality is maximized. The schemes employ Taguchi system and Learning Automaton to quickly identify such a suitable set of scattering intermediate devices. The theoretical limits of these schemes are studied mathematically and in simulations. For a 100-device network, the proposed schemes achieve power improvements over the line of sight equal to 37.5dB and 33dB for Taguchi and Learning Automaton-based schemes respectively. Moreover, the schemes are shown to perform well in an environment with the Rayleigh fading.
M3 spectral analysis of lunar swirls and the link between optical maturation and surface hydroxyl formation at magnetic anomalies

We examined the lunar swirls using data from the Moon Mineralogy Mapper (M3). The improved spectral and spatial resolution of M3 over previous spectral imaging data facilitates distinction of subtle spectral differences, and provides new information about the nature of these enigmatic features. We characterized spectral features of the swirls, interswirl regions (dark lanes), and surrounding terrain for each of three focus regions: Reiner Gamma, Gerasimovich, and Mare Ingenii. We used Principle Component Analysis to identify spectrally distinct surfaces at each focus region, and characterize the spectral features that distinguish them. We compared spectra from small, recent impact craters with the mature soils into which they penetrated to examine differences in maturation trends on‐ and off‐swirl. Fresh, on‐swirl crater spectra are higher albedo, exhibit a wider range in albedos and have well‐preserved mafic absorption features compared with fresh off‐swirl craters. Albedoand mafic absorptions are still evident in undisturbed, on‐swirl surface soils, suggesting the maturation process is retarded. The spectral continuum is more concave compared with off‐swirl spectra; a result of the limited spectral reddening being mostly constrained to wavelengths less than ∼1500 nm. Off‐swirl spectra show very little reddening or change in continuum shape across the entire M3 spectral range. Off‐swirl spectra are dark, have attenuated absorption features, and the narrow range in off‐swirl albedos suggests off‐swirl regions mature rapidly. Spectral parameter maps depicting the relative OH surface abundance for each of our three swirl focus regions were created using the depth of the hydroxyl absorption feature at 2.82 μm. For each of the studied regions, the 2.82 μm absorption feature is significantly weaker on‐swirl than off‐swirl, indicating the swirls are depleted in OH relative to their surroundings. The spectral characteristics of the swirls and adjacent terrains from all three focus regions support the hypothesis that the magnetic anomalies deflect solar wind ions away from the swirls and onto off‐swirl surfaces. Nanophase iron (npFe0) is largely responsible for the spectral characteristics we attribute to space weathering and maturation, and is created by vaporization/deposition by micrometeorite impacts and sputtering/reduction by solar wind ions. On the swirls, the decreased proton flux slows the spectral effects of space weathering (relative to nonswirl regions) by limiting the npFe0 production mechanism almost exclusively to micrometeoroid impact vaporization/deposition. Immediately adjacent to the swirls, maturation is accelerated by the increased flux of protons deflected from the swirls.