Miracle-dz/UniFAR-FTUs huggingface dataset:
Pig Bone Marrow-Derived Macrophages Resemble Human Macrophages in Their Response to Bacterial Lipopolysaccharide

Mouse bone marrow-derived macrophages (BMDM) grown in M-CSF (CSF-1) have been used widely in studies of macrophage biology and the response to TLR agonists. We investigated whether similar cells could be derived from the domestic pig using human rCSF-1 and whether porcine macrophages might represent a better model of human macrophage biology. Cultivation of pig bone marrow cells for 5–7 d in presence of human rCSF-1 generated a pure population of BMDM that expressed the usual macrophage markers (CD14, CD16, and CD172a), were potent phagocytic cells, and produced TNF in response to LPS. Pig BMDM could be generated from bone marrow cells that had been stored frozen and thawed so that multiple experiments can be performed on samples from a single animal. Gene expression in pig BMDM from outbred animals responding to LPS was profiled using Affymetrix microarrays. The temporal cascade of inducible and repressible genes more closely resembled the known responses of human than mouse macrophages, sharing with humans the regulation of genes involved in tryptophan metabolism (IDO, KYN), lymphoattractant chemokines (CCL20, CXCL9, CXCL11, CXCL13), and the vitamin D3-converting enzyme, Cyp27B1. Conversely, in common with published studies of human macrophages, pig BMDM did not strongly induce genes involved in arginine metabolism, nor did they produce NO. These results establish pig BMDM as an alternative tractable model for the study of macrophage transcriptional control.
Adaptive Learning Object Selection in Intelligent Learning Systems

Adaptive learning object selection and sequencing is recognized as among the most interesting research questions in intelligent web-based education. In most intelligent learning systems that incorporate course sequencing techniques, learning object selection is based on a set of teaching rules according to the cognitive style or learning preferences of the learners. In spite of the fact that most of these rules are generic (i.e. domain independent), there are no well-defined and commonly accepted rules on how the learning objects should be selected and how they should be sequenced to make “instructional sense”. Moreover, in order to design highly adaptive learning systems a huge set of rules is required, since dependencies between educational characteristics of learning objects and learners are rather complex. In this paper, we address the learning object selection problem in intelligent learning systems proposing a methodology that instead of forcing an instructional designer to manually define the set of selection rules, it produces a decision model that mimics the way the designer decides, based on the observation of the designer’s reaction over a small-scale learning object selection case.
Classification and Monitoring of Reed Belts Using Dual-Polarimetric TerraSAR-X Time Series

Synthetic aperture radar polarimetry (PolSAR) and polarimetric decomposition techniques have proven to be useful tools for wetland mapping. In this study we classify reed belts and monitor their phenological changes at a natural lake in northeastern Germany using dual-co-polarized (HH, VV) TerraSAR-X time series. The time series comprises 19 images, acquired between August 2014 and May 2015, in ascending and descending orbit. We calculated different polarimetric indices using the HH and VV intensities, the dual-polarimetric coherency matrix including dominant and mean alpha scattering angles, and entropy and anisotropy (normalized eigenvalue difference) as well as combinations of entropy and anisotropy for the analysis of the scattering scenarios. The image classifications were performed with the random forest classifier and validated with high-resolution digital orthophotos. The time series analysis of the reed belts revealed significant seasonal changes for the double-bounce–sensitive parameters (intensity ratio HH/VV and intensity difference HH-VV, the co-polarimetric coherence phase and the dominant and mean alpha scattering angles) and in the dual-polarimetric coherence (amplitude), anisotropy, entropy, and anisotropy-entropy combinations; whereas in summer dense leaves cause volume scattering, in winter, after leaves have fallen, the reed stems cause predominately double-bounce scattering. Our study showed that the five most important parameters for the classification of reed are the intensity difference HH-VV, the mean alpha scattering angle, intensity ratio HH/VV, and the coherence (phase). Due to the better separation of reed and other vegetation (deciduous forest, coniferous forest, meadow), winter acquisitions are preferred for the mapping of reed. Multi-temporal stacks of winter images performed better than summer ones. The combination of ascending and descending images also improved the result as it reduces the influence of the sensor look direction. However, in this study, only an accuracy of ~50% correct classified reed areas was reached. Whereas the shorelines with reed areas (>10 m broad) could be detected correctly, the actual reed areas were significantly overestimated. The main source of error is probably the challenging data geocoding causing geolocation inaccuracies, which need to be solved in future studies.
The Effect of Metacognitive Strategy Instruction on EFL Learners ’ Reading Comprehension Performance and Metacognitive Awareness

As learners have an important role in new teaching methodologies, raising their awareness of learning strategies and helping them utilize these strategies is a crucial aim of teachers. One type of these learning strategies is metacognitive strategies including planning, self-monitoring and selfevaluation. The present study aimed at examining the effect of metacognitive (planning & self-monitoring) strategy instruction on EFL learners’ reading comprehension performance (on authentic and inauthentic texts) and their metacognitive awareness. To this end, two tests (TOEFL and a reading comprehension test) and Strategy Inventory for Language Learning (SILL) were administered to 93 male and female EFL learners in four phases of this study. At the first phase, TOEFL was administered to all the students both to homogenize students regarding language proficiency and to validate the reading comprehension test. At the second phase, SILL was administered to two experimental and one control groups before strategy instruction. SILL assesses the frequency with which the subjects use a variety of techniques for foreign language learning. At the third phase, two experimental groups received five sessions of instruction on metacognitive strategies, one on planning and the other on self-monitoring strategy based on the Cognitive Academic Language Learning Approach (CALLA). Both experimental and control groups worked on authentic and inauthentic texts (some articles from
Limitations of Using Constraint Set Utility in Semi-Supervised Clustering

Semi-supervised clustering algorithms allow the user to incorporate background knowledge into the clustering process. Often, this background knowledge is specified in the form of must-link (ML) and cannot-link (CL) constraints, indicating whether certain pairs of elements should be in the same cluster or not. Several traditional clustering algorithms have been adapted to operate in this setting. We compare some of these algorithms experimentally, and observe that their performances vary significantly, depending on the data set and constraints. We use two previously introduced constraint set utility measures, consistency and coherence, to help explain these differences. Motivated by the correlation between consistency and clustering performance, we also examine its use in algorithm selection. We find this consistency-based approach to be unsuccessful, and explain this result by observing that the previously found correlation between utility measures and clustering performance is only present when we look at results of different data sets jointly. This limits the use of these constraint set utility measures, as often we are interested in using them in the context of a particular data set.
Geostatistical Methodology to Characterize Volcanogenic Massive and Stockwork Ore Deposits

The Zambujal ore deposit, Neves-Corvo mine, is a zoned volcanogenic sulfide deposit of copper and zinc, with massive ores at the top and stockwork ores at the bottom. Metal grades are strongly zoned by ore types. The main methodology hereby proposed combines geostatistical techniques so that an improved stochastic geological model of the Zambujal deposit encompassing morphology and grades is presented. The model of the morphology was made in two main steps. First, a 3D solid of the boundaries was created and then a 3D grid model of the local sulfide proportion was simulated. This latter variable was modeled by using rock specific gravity as a proxy. After that, a conditional grid model of relative copper grades, i.e., recalculated metal grades assuming only the sulfide content, is also simulated in accordance with the morphology. At the end, the new tool, metal tonnage cut-off surface, is proposed, which combines copper grades within massive ores and stockwork ores. To validate the results found, the global tonnages of copper obtained by Ordinary Kriging and the proposed methodology are compared.
Person Authentication by Handwriting in air using a Biometric Smart Pen Device

The phenomenon of writing reflects developed fine motor skills of a person. Specially, the dynamics of writing can be used for the characterization of the individual writer or written object. Person authentication based on the dynamics of handwriting on pad and in air using biometric Smart Pen (BiSP) device are presented in this paper. We regard hand movement with pen in space as handwriting (or hand gesturing) in air. The acquisition device BiSP is a ballpoint pen equipped with a diversity of sensors, measuring the acceleration and tilt angle of the pen, the grip forces of fingers holding the pen, and the forces and vibrations in the refill during writing, drawing and hand gesturing in air or on any solid pad. For classification, queries and references of multi-dimensional time-series provided by BiSP are matched by fast dynamic time warping DTW. To judge the biometric authentication of a writer, the recognition score rates of PIN words and single characters are computed. It was found that the score rates of performance are better 99.99% and the response time is below 2 seconds for a population of 40 enrolled persons. The proposed handwriting in air is a promising authentication method because performance of writing on paper pad and in air differ marginally. It can create a new dimension of freedom in unconstrained data input, and access control and can compete with the emerging hand gesture recognition methods.
Enhancement of image-to-image co-registration accuracy using spectral matching methods

Two of the important stages in the production of maps from remotely sensed imagery are rectification and classification. Residual geometric errors following rectification produce a “component” of the apparent classification error. Hence, besides being of value in its own right, reduction of the rectification error will enhance the quality of the classification stage, and the classification accuracy statistics will more closely refer to “pure” classification error rather than classification error due to pixel mismatch. In rectification to ground control points (GCPs) there is a natural limit of what can be achieved in rectification of an image, and this is determined by the cost of collecting good GCPs. However, if we are concerned with a sequence of images, and are concerned primarily with estimating change and growth, the rectification is image-to image, and the process becomes one of pixel matching, and the only cost is processing time. We present a spectrally-based pixel-matching algorithm which seems to offer considerable scope for very accurate pixel-to-pixel and hence image-to-image matching. An algorithm is used which maximizes local correlations in each spectral band at each co-registration point: a multivariate anisotropic spatial auto-correlation approach. The results are demonstrated and validated in a case-study of a 1987 Landsat5 TM image, 1997 Landsat5 TM image and a 2000Landsat7 ETM image, all of the same forested region in China.
Rainfall-Runoff Modelling using Modified NRCS-CN,RS and GIS -A Case Study

Study of rainfall and runoff for any area and modeling it, is one of the important aspects for planning and development of water resources. The development of water resources and its effective management plays a vital role in development of any country more particularly in India, which is an agricultural based economy. Hence it is intended to develop a model of Rainfall and runoff to a river basin and also apply the methodology to Sarada River Basin which has drainage area of 1252.99 Sq.km. The basin is situated in Vishakhapatnam district of Andhra Pradesh, India. The rainfall and runoff data has been collected from the gauging stations of the basin apart from rainfall data from nearby stations. MNRCS-CN method has been adopted to calculate runoff. Various hydrological parameters like soil information, rainfall, land use and land cover (LU/LC) were considered to use in MNRCS-CN method. The depth of runoff has been computed for different land use patterns using, IRS-P4LISS IV data for the study area. Based on the analysis, land use/land cover pattern of Sarada River Basin has been prepared. The land use/land cover patterns were also visually interpreted and digitized using ERDAS IMAGINE software. The raster data was processed in ERDAS and geo-referenced and various maps viz. LU/LC maps, drainage map, contour map, DEM (Digital elevation model) have been generated apart from rainfall potential map using GIS tool. The estimated runoff using MNRCS-CN model has been simulated and compared with that of actual runoff. The performance of the model is found to be good for the data considered. The coefficient of determination R 2 value for the observed runoff and that of the computed runoff is found to be more than 0.72 for the selected watershed basin.
Acting in virtual reality

Three pairs of professional actors and a director each met in a shared non-immersive virtual reality system over a two-week period to rehearse a short play. The actors and director never met one another physically until a short time before a live rehearsal in front of an audience. The actors were represented by avatars which could be controlled to make a range of facial expressions, and some body movements, including navigation through the space. The study examined the extent to which virtual reality could be used by the actors and director to rehearse their later live performance. Four indicators captured by questionnaires show that over the period of the four days their sense of presence in the virtual rehearsal space, their co-presence with the other actor, and their degree of cooperation all increased. Moreover their evaluation of the extent to which the virtual rehearsal was similar to a real rehearsal also increased. Debriefing sessions with the actors and director are reported, which suggest that a performance level was reached in the virtual rehearsal which formed the basis of a successful live performance.
Divergence of Volatile Anesthetic Effects in Inhibitory Neurotransmitter Receptors

Background The mechanism of volatile anesthetic (VA) action is unknown. Inhibitory receptors for the neurotransmitters &ggr;-aminobutyric acid (GABA) or glycine are typically positively modulated by VAs and may be important targets for their action. The existence of a GABA receptor subtype (&rgr;), which is uniquely inhibited by VAs, suggested a chimeric receptor approach to identify portions of these proteins that may be necessary for anesthetic effects. Methods A silent mutation resulting in the addition of a unique restriction enzyme recognition site was introduced in GABA receptor type A &agr;2, glycine &agr;1, and &rgr; subunit cDNAs. Chimeras were constructed by rejoining restriction digest fragments and were expressed in Xenopus oocytes. Modulation of submaximal agonist-evoked peak currents by the VAs chloroform, enflurane, halothane, or isoflurane was measured using two-electrode voltage clamp. Results Four chimeras were constructed and designated glyrho, rhogly, &agr;2rho, and rho&agr;2. Glyrho formed glycine-gated receptors with currents that were enhanced by chloroform or halothane but were inhibited by enflurane or isoflurane. Chimeras rhogly and rho&agr;2 each formed GABA-gated receptors with currents that were inhibited by chloroform or halothane but enhanced by enflurane or isoflurane. Conclusions These data show, for the first time, functional divergence of VA action on a single protein target. The VAs in this study fall into two distinct groups with respect to their effects on these receptors. This grouping parallels the chemistry of these compounds. Our results support the involvement of multiple protein domains in the mechanism of VA modulation of GABA and glycine receptors.
Automatic extraction of property norm-like data from large text corpora

Traditional methods for deriving property-based representations of concepts from text have focused on either extracting only a subset of possible relation types, such as hyponymy/hypernymy (e.g., car is-a vehicle) or meronymy/metonymy (e.g., car has wheels), or unspecified relations (e.g., car--petrol). We propose a system for the challenging task of automatic, large-scale acquisition of unconstrained, human-like property norms from large text corpora, and discuss the theoretical implications of such a system. We employ syntactic, semantic, and encyclopedic information to guide our extraction, yielding concept-relation-feature triples (e.g., car be fast, car require petrol, car cause pollution), which approximate property-based conceptual representations. Our novel method extracts candidate triples from parsed corpora (Wikipedia and the British National Corpus) using syntactically and grammatically motivated rules, then reweights triples with a linear combination of their frequency and four statistical metrics. We assess our system output in three ways: lexical comparison with norms derived from human-generated property norm data, direct evaluation by four human judges, and a semantic distance comparison with both WordNet similarity data and human-judged concept similarity ratings. Our system offers a viable and performant method of plausible triple extraction: Our lexical comparison shows comparable performance to the current state-of-the-art, while subsequent evaluations exhibit the human-like character of our generated properties.
Body position changes redistribute lung computed-tomographic density in patients with acute respiratory failure.

Ten patients with parenchymal acute respiratory failure (ARF) underwent computed tomography (CT) scans while in the supine and prone positions. At equal levels of positive end-expiratory pressure, the authors measured the changes of CT density in dorsal and ventral basilar lung regions induced by the change of position as well as alterations of gas exchange. The level of venous admixture did not change with body position. The CT scan image of each lung was fractionated into ten levels from dorsal to ventral, each constituting 10% of the lung height. After measuring each lung fraction, the volume, the average CT number, its frequency distribution, and the expected normal value, we computed the lung tissue mass, the excess tissue mass, and the fraction of normally inflated tissue (excess tissue mass = amount of "tissue," which includes edema, cells, and blood in excess of the expected normal value). We also estimated the superimposed hydrostatic pressure on each lung region. We found that the excess lung tissue mass is independent of position. However, in patients in the supine position, lung CT density increased and regional inflation decreased from ventral to dorsal, suggesting progressive deflation of gas-containing alveoli along the gravity gradient. A similar ventral-dorsal deflation pattern occurred within 10 min in patients in the prone position. We conclude that the lung in patients with ARF behaves like an elastic body with a diffusely increased mass; dependent lung regions are compressed by the pressure of overlying structures. (ABSTRACT TRUNCATED AT 250 WORDS)
Document Retrieval and Routing Using the INQUERY System

The INQUERY retrieval and routing system, which is based on the Bayesian inference net retrieval model, has been described in a number of papers (5,4,10,11). In the TREC experiments this year, a number of new techniques were introduced for both the ad-hoc retrieval and routing runs. In addition, experiments with Spanish retrieval were carried out.
Improved Data-Aided Channel Estimation in LTE PUCCH Using a Tensor Modeling Approach

NA In 3rd. Generation Partnership Project (3GPP) Long Term Evolution (LTE) systems, when no resources has been assigned in the uplink to a given user, the control information associated with Layers 1 and 2 in the protocol stack is conveyed back to the LTE base station (also known as eNodeB) through the so-called Physical Uplink Control Channel (PUCCH). In this work we consider the Format 2 of LTE PUCCH which conveys information about the channel status. At the eNodeB, conventional receivers generally resort to reference signals (RS), or pilot symbols, to perform channel estimation prior to symbol detection. In this paper, we propose a tensor modeling approach for a Data-Aided (DA) channel estimation in PUCCH. First, we formulate the practical channel estimation problem in PUCCH using the Parallel Factor (PARAFAC) tensor model. Based in this model, we resort to the Alternating Least Squares (ALS) algorithm as a DA-based channel estimator. Contrary to conventional RS-based channel estimation operating only on reference signals, the proposed algorithm also simultaneously exploits the energy of the data symbols of all the users, which is contained in PUCCH slots in order to iteratively estimate the user channel coefficients. As will be shown in our simulation results, improved channel estimation accuracy is obtained.
Hearing New Voices: Registered Nurses and Health Technicians Experience Caring for Chronic Pain Patients in Primary Care Clinics

Recent national estimates from the U.S. reveal that as many as one-third of all Americans experience chronic pain resulting in high prevalence rates of visits to primary care clinics (PCC). Indeed, chronic pain appears to be an emerging global health problem. Research has largely ignored the perspective of PCC staff other than physicians in providing care for patients with chronic pain. We wanted to gain insights from the experiences of Registered Nurses (RNs) and Health Technicians (HTs) who care for this patient population. Krippendorff’s method for content analysis was used to analyze comments written in an open-ended survey from fifty-seven primary care clinic staff (RNs-N=27 and HTs-N=30) respondents. This represented an overall response rate of 75%. Five themes emerged related to the experience of RNs and HTs caring for patients with chronic pain: 1) Primacy of Medications and Accompanying Clinical Quandaries; 2) System Barriers; 3) Dealing with Failure; 4) Primacy of Patient Centered Care; and 5) Importance of Team Based Care. This study demonstrates that nursing staff provide patient-centered care, recognize the importance of their role within an interdisciplinary team and can offer valuable insight about the care of patients with chronic pain. This study provides insight into strategies that can mitigate barriers to chronic pain management while sustaining those aspects that RNs and HTs view as essential for improving patient care for this vulnerable population in PCCs.