Miracle-dz/UniFAR-FTUs huggingface dataset:
Gibraltar’s position in the Dynamic Model of Postcolonial English

This article examines the emergence of local identity and language use in the British Overseas Territory of Gibraltar, from its annexation in 1704 to the present day. Contrary to popular opinion, it shows that the founding population of British Gibraltar was divided along racial and linguistic grounds, and only in the 19th century evolved into a cohesive Spanish-speaking community, before its subsequent development into the bilingual English- and Spanish-speaking society of the present day. Through the analysis of census data, reportage and colonial government records, the article shows that the Dynamic Model of Postcolonial English in Schneider (2007) aptly captures the spread of English knowledge on the Rock. The population’s persistent attachment to its British identity, and its framing of Gibraltar English as a variety of British English, are however theoretically problematic. The article concludes that local identity and language use are dependent as much on the territory’s relationship with Spain as the United Kingdom.
Regimen complexity and medication nonadherence in elderly patients

Objective: To assess whether the number of daily administrations of individual drugs, as a measure of regimen complexity, contributes to the profile of an elderly patient who adheres poorly to the prescribed therapy. Population: Six hundred ninety patients over 64 years who were consecutively admitted to 11 acute medical care and three long term/rehabilitation wards in Italy. Main outcome measure: Self-reported adherence to drugs taken at home before admission was measured by a single question assessment for each listed drug supplemented with a latter question about the circumstances of the missed administration. For cognitively impaired patients the question was put to patients’ relatives or caregivers. Methods: A structured multidimensional assessment was performed to identify nonadherence and its potential correlates. Correlates of nonadherence were identified by multivariable logistic regression. Results: We recorded 44 cases (6.4%) of nonadherence to at least one drug. Being assisted by foreign caregivers (OR 2.17; 95% CI 1.02–4.63) and the use of at least one multiple daily dosing drug (OR 2.99; 95% CI 1.24–7.17) were significant independent correlates of medication nonadherence, while age, selected indexes of frailty and the cumulative number of prescribed drugs were not. Conclusion: Regimen complexity and type of assistance are independent correlates of medication nonadherence.
Neocortical Temporal Lobe Epilepsy

Complex partial seizures (CPSs) can present with various semiologies, while mesial temporal lobe epilepsy (mTLE) is a well-recognized cause of CPS, neocortical temporal lobe epilepsy (nTLE) albeit being less common is increasingly recognized as separate disease entity. Differentiating the two remains a challenge for epileptologists as many symptoms overlap due to reciprocal connections between the neocortical and the mesial temporal regions. Various studies have attempted to correctly localize the seizure focus in nTLE as patients with this disorder may benefit from surgery. While earlier work predicted poor outcomes in this population, recent work challenges those ideas yielding good outcomes in part due to better localization using improved anatomical and functional techniques. This paper provides a comprehensive review of the diagnostic workup, particularly the application of recent advances in electroencephalography and functional brain imaging, in neocortical temporal lobe epilepsy.
A Neural-Network-Based Approach to White Blood Cell Classification

This paper presents a new white blood cell classification system for the recognition of five types of white blood cells. We propose a new segmentation algorithm for the segmentation of white blood cells from smear images. The core idea of the proposed segmentation algorithm is to find a discriminating region of white blood cells on the HSI color space. Pixels with color lying in the discriminating region described by an ellipsoidal region will be regarded as the nucleus and granule of cytoplasm of a white blood cell. Then, through a further morphological process, we can segment a white blood cell from a smear image. Three kinds of features (i.e., geometrical features, color features, and LDP-based texture features) are extracted from the segmented cell. These features are fed into three different kinds of neural networks to recognize the types of the white blood cells. To test the effectiveness of the proposed white blood cell classification system, a total of 450 white blood cells images were used. The highest overall correct recognition rate could reach 99.11% correct. Simulation results showed that the proposed white blood cell classification system was very competitive to some existing systems.
Differential Online Exposure to Extremist Content and Political Violence: Testing the Relative Strength of Social Learning and Competing Perspectives

The present study applies Social Learning (Differential Association) Theory to the explanation of political violence, focusing on exposure to extremist content through new social media (NSM) and controlling for key variables derived from rival theories. Data are gathered using (a) a paper-and-pencil study among high school students, and (b) a web survey targeting youths between 16 and 24 years old. A total of 6020 respondents form the dataset. Binary logistic regression is used to analyze the data. Results show that even when controlling for background variables, strain variables, personality characteristics, moral values, and peer influences, the statistical association between measures of extremism through NSM (ENSM) and self-reported political violence remains significant and fairly constant. The most persistent effects are found for those measures where individuals actively seek out extremist content on the Internet, as opposed to passive and accidental encounters using NSM. Furthermore, offline differential associations with racist and delinquent peers are also strongly and directly related to self-reported political violence, as are some mechanisms from rival perspectives. This indicates that political violence can only partially be explained by social learning and suggests that the impact of ENSM is mediated by real-world associations and that the offline world has to be taken into account.
Sequential Treatment of SH‐SY5Y Cells with Retinoic Acid and Brain‐Derived Neurotrophic Factor Gives Rise to Fully Differentiated, Neurotrophic Factor‐Dependent, Human Neuron‐Like Cells

Abstract: A rapid and simple procedure is presented to obtain nearly pure populations of human neuron‐like cells from the SH‐SY5Y neuroblastoma cell line. Sequential exposure of SH‐SY5Y cells to retinoic acid and brain‐derived neurotrophic factor in serum‐free medium yields homogeneous populations of cells with neuronal morphology, avoiding the presence of other neural crest derivatives that would normally arise from those cells. Cells are withdrawn from the cell cycle, as shown by 5‐bromo‐2′‐deoxyuridine uptake and retinoblastoma hypophosphorylation. Cell survival is dependent on the continuous presence of brain‐derived neurotrophic factor, and removal of this neurotrophin causes apoptotic cell death accompanied by an attempt to reenter the cell cycle. Differentiated cells express neuronal markers, including neurofilaments, neuron‐specific enolase, and growth‐associated protein‐43 as well as neuronal polarity markers such as tau and microtubule‐associated protein 2. Moreover, differentiated cultures do not contain glial cells, as could be evidenced after the negative staining for glial fibrillary acidic protein. In conclusion, the protocol presented herein yields homogeneous populations of human neuronal differentiated cells that present many of the characteristics of primary cultures of neurons. This model may be useful to perform large‐scale biochemical and molecular studies due to its susceptibility to genetic manipulation and the availability of an unlimited amount of cells.
Enhancement of dissolution profile by solid dispersion (kneading) technique

This article investigates enhancement of the dissolution profile of valdecoxib using solid dispersion with PVP. The article also describes the preparation of fast-dissolving tablets of valdecoxib by using a high amount of superdisintegrants. A phase solubility method was used to evaluate the effect of various water-soluble polymers on aqueous solubility of valdecoxib. Polyvinyl pyrrolidone (PVP K-30) was selected and solid dispersions were prepared by the method of kneading. Dissolution studies, using the USP paddle method were performed for solid dispersions of valdecoxib. Infrared (IR) spectroscopy, differential scanning calorimetry (DSC), and x-ray diffractometry (XRD) were performed to identify the physicochemical interaction between drug and carrier, hence its effect on dissolution. Tablets were formulated containing solid dispersion products and compared with commercial products. IR spectroscopy, XRD, and DSC showed no change in the crystal structure of valdecoxib. Dissolution of valdecoxib improved significantly in solid dispersion products (<85% in 5 minutes). Tablets containing solid dispersion exhibited better dissolution profile than commercial tablets. Thus, the solid dispersion technique can be successfully used for improvement of dissolution of valdecoxib.
A Cross‐domain Analysis of Change in Students’ Attitudes toward Science and Attitudes about the Utility of Science

Cross‐domain analysis was used to examine changes in two attitudinal dimensions: students’ attitudes towards science and attitudes about the utility of science over the middle school and high school years. A sample of 444 students was selected from the Longitudinal Study of American Youth. The results show that the overall trend for students’ attitudes about the utility of science is positive; however, attitudes towards science decline over the middle school and high school years. Important time‐varying predictors of both attitudes include science self‐concept, peer attitudes, teacher encouragement of science, and, to a lesser extent, student participation in science activities.
Chaotic Loss Cones , Black Hole Fueling and the M • − σ Relation

In classical loss cone theory, stars are supplied to a central black hole via gravi-tational scattering onto low angular momentum orbits. Much higher feeding rates are possible if the gravitational potential near the black hole is non-axisymmetric and the orbits are chaotic. Motivated by recently published, self-consistent models , we evaluate stellar capture rates in triaxial nuclei. Rates are found to exceed those in collisional loss cone models by as much as several orders of magnitude and are comparable to capture rates in spherical galaxies under conditions of continuous loss cone replenishment. In a triaxial nucleus with a steep, ρ ∼ r −2 density cusp, feeding drops off as ∼ t −1/2 as the centrophilic orbits are depopu-lated. The mass captured by the black hole after a given time scales as σ 5 with σ the stellar velocity dispersion, and the accumulated mass in 10 10 yr is of the correct order to reproduce the M • − σ relation. Stellar disruption rates at the current epoch would be greatly enhanced, as would the decay rates of binary supermassive black holes, even if only a small fraction of the stars are on chaotic orbits.
On the Usefulness of a Multilevel Logistic Regression Approach to Person-Fit Analysis

The logistic person response function (PRF) models the probability of a correct response as a function of the item locations. Reise (2000) proposed to use the slope parameter of the logistic PRF as a person-fit measure. He reformulated the logistic PRF model as a multilevel logistic regression model and estimated the PRF parameters from this multilevel framework. An advantage of the multilevel framework is that it allows relating person fit to explanatory variables for person misfit/fit. We critically discuss Reise's approach. First, we argue that often the interpretation of the PRF slope as an indicator of person misfit is incorrect. Second, we show that the multilevel logistic regression model and the logistic PRF model are incompatible, resulting in a multilevel person-fit framework, which grossly violates the bivariate normality assumption for residuals in the multilevel model. Third, we use a Monte Carlo study to show that in the multilevel logistic regression framework estimates of distribution parameters of PRF intercepts and slopes are biased. Finally, we discuss the implications of these results and suggest an alternative multilevel regression approach to explanatory person-fit analysis. We illustrate the alternative approach using empirical data on repeated anxiety measurements of cardiac arrhythmia patients who had a cardioverter-defibrillator implanted.
Performance optimization for pedestrian detection on degraded video using natural scene statistics

Abstract. We evaluate the effects of transmission artifacts such as JPEG compression and additive white Gaussian noise on the performance of a state-of-the-art pedestrian detection algorithm, which is based on integral channel features. Integral channel features combine the diversity of information obtained from multiple image channels with the computational efficiency of the Viola and Jones detection framework. We utilize “quality aware” spatial image statistics to blindly categorize distorted video frames by distortion type and level without the use of an explicit reference. We combine quality statistics with a multiclassifier detection framework for optimal pedestrian detection performance across varying image quality. Our detection method provides statistically significant improvements over current approaches based on single classifiers, on two large pedestrian databases containing a wide variety of artificially added distortion. The improvement in detection performance is further demonstrated on real video data captured from multiple cameras containing varying levels of sensor noise and compression. The results of our research have the potential to be used in real-time in-vehicle networks to improve pedestrian detection performance across a wide range of image and video quality.
QoS-Aware Transmission Policies for OFDM Bidirectional Decode-and-Forward Relaying

Two-way relaying can considerably improve spectral efficiency in relay-assisted bidirectional communications. However, the benefits and flexible structure of orthogonal frequency division multiplexing (OFDM)-based two-way relay systems is much less exploited. Moreover, most of existing works have not considered quality-of-service (QoS) provisioning for two-way relaying. In this paper, we consider the OFDM-based bidirectional transmission where a pair of users exchange information via the assistance of a decode-and-forward (DF) relay. Each user can communicate with the other via three transmission modes: direct transmission, one-way relaying, and two-way relaying. We jointly optimize the transmission policies, including power allocation, transmission mode selection, and subcarrier assignment in order to maximize the weighted sum rates of the two users with diverse QoS guarantees. This is formulated as a mixed integer programming problem. By using the dual method, we efficiently solve the problem in an asymptotically optimal manner. Simulation results show that the proposed resource allocation scheme can substantially improve system performance compared with conventional schemes. A number of interesting insights are also obtained via comprehensive simulations.
Assembling a network out of ambiguous patches

Many graph mining and network analysis problems rely on the availability of the full network over a set of nodes. But inferring a full network is sometimes non-trivial if the raw data is in the form of many small patches or subgraphs, of the true network, and if there are ambiguities in the identities of nodes or edges in these patches. This may happen because of noise or because of the nature of data; for instance, in social networks, names are typically not unique. Graph assembly refers to the problem of reconstructing a graph from these many, possibly noisy, partial observations. Prior work suggests that graph assembly is essentially impossible in regimes of interest when the true graph is Erdős-Rényi. The purpose of the present paper is to show that a modest amount of clustering is sufficient to assemble even very large graphs. We introduce the G(n, p; q) random graph model, which is the random closure over all open triangles of a G(n, p) Erdős-Rényi, and show that this model exhibits higher clustering than an equivalent Erdős-Rényi. We focus on an extreme case of graph assembly: the patches are small (1-hop egonets) and are unlabeled. We show that in realistic regimes, graph assembly is fundamentally feasible, because we can identify, for every edge e, a subgraph induced by its neighbors that is unique and present in every patch containing e. Using this result, we build a practical algorithm that uses canonical labeling to reconstruct the original graph from noiseless patches. We also provide an achievability result for noisy patches, which are obtained by edge-sampling the original egonets.
Gate Sizing Minimizing Delay and Power/Area

In this work we present a gate sizing tool based on Geometric Programming. The optimization can be done targeting both, delay and power/area minimization. In order to qualify our approach, the ISCAS’85 benchmark circuits are mapped for 350nm and 45nm technologies using typical standard cell libraries. Next, the mapped circuit is sized using our tool and the result is comparated to the original mapped circuit. The speed is increased by 21% and 4.5%, on average, for 45nm and 350nm technology, respectively, keeping the same area and power values of the sizing provided by standard-cells library. For power/area optimization, where the delay was restricted to the delay value found at delay minimization, the reduction was 28.2% in area and 27.3% in power consumption, on average, considering 45nm technology and 29.9% in area and 28.5% in power, on average, considering 350nm technology.
A risk of meaning transfer: Are negative associations more likely to transfer than positive associations?

Celebrity endorsement is a common influence tactic used by marketers. By linking their brands with cultural entities such as celebrity endorsers, marketers attempt to acquire positive meanings and personality traits associated with the entity. Entities, however, often have both positive and negative associations. For example, a celebrity can be both smart and arrogant, or sexy and ditsy. We highlight a risk of meaning transfer: negative associations are more likely to transfer to a brand than positive associations. Three studies show that brands are more likely to acquire the negative than the positive personality traits associated with a celebrity endorser and that negative associations transfer even under conditions that inhibit the transfer of positive associations.
Adaptively Scaling the Metropolis Algorithm Using Expected Squared Jumped Distance

A good choice of the proposal distribution is crucial for the rapid con- vergence of the Metropolis algorithm. In this paper, given a family of parametric Markovian kernels, we develop an adaptive algorithm for selecting the best kernel that maximizes the expected squared jumped distance, an objective function that characterizes the Markov chain. We demonstrate the effectiveness of our method in several examples.
Contrastive Unsupervised Word Alignment with Non-Local Features

Word alignment is an important natural language processing task that indicates the correspondence between natural languages. Recently, unsupervised learning of log-linear models for word alignment has received considerable attention as it combines the merits of generative and discriminative approaches. However, a major challenge still remains: it is intractable to calculate the expectations of non-local features that are critical for capturing the divergence between natural languages. We propose a contrastive approach that aims to differentiate observed training examples from noises. It not only introduces prior knowledge to guide unsupervised learning but also cancels out partition functions. Based on the observation that the probability mass of log-linear models for word alignment is usually highly concentrated, we propose to use top-$n$ alignments to approximate the expectations with respect to posterior distributions. This allows for efficient and accurate calculation of expectations of non-local features. Experiments show that our approach achieves significant improvements over state-of-the-art unsupervised word alignment methods.
Retrospective study of clinical complications occurring after arterial punctures in 111 dogs

The clinical complications occurring after 111 dogs had undergone arterial punctures were reviewed in relation to the dogs' breed, bodyweight, age, sex and underlying diseases. Seven of the dogs had moderate to extensive ecchymoses, which were significantly more common in dogs under 3-5 kg in bodyweight and in dogs with disorders of the cardiovascular system.
Rough concept lattice based ontology similarity measure

With the rapid development of the semantic web, it is likely that the number of ontologies will greatly increase during the next few years, which leads to the arising demand for rapid ontology mapping. In this paper, a novel similarity measure method based on rough set and concept lattice is proposed to realize ontology mapping tasks. A reference concept lattice is first constructed with the combination of two normalized contexts. Rough set theory is then employed to calculate the similarity measure of the two ontology nodes. With a specified threshold, the final result of ontology mapping can be obtained. Compared with other mapping algorithms, the proposed ontology mapping method is featural and structural, and the experiment shows the performance of the mapping method.
Dysregulated DNA Methyltransferase 3A Upregulates IGFBP5 to Suppress Trophoblast Cell Migration and Invasion in Preeclampsia

Preeclampsia is a unique multiple system disorder during human pregnancy, which affects ≈5% to 8% of pregnancies. Its risks and complications have become the major causes of maternal and fetal morbidity and mortality. Although abnormal placentation to which DNA methylation dysregulation is always linked is speculated to be one of the reasons causing preeclampsia, the underlying mechanisms still remain elusive to date. Here we revealed that aberrant DNA methyltransferase 3A (DNMT3A) plays a critical role in preeclampsia. Our results show that the expression and localization of DNMT3A are dysregulated in preeclamptic placenta. Moreover, knockdown of DNMT3A obviously inhibits trophoblast cell migration and invasion. Mechanistically, IGFBP5 (insulin-like growth factor–binding protein 5), known as a suppressor, is upregulated by decreased DNMT3A because of promoter hypomethylation. Importantly, IGFBP5 downregulation can rescue the defects caused by DNMT3A knockdown, thereby, consolidating the significance of IGFBP5 in the downstream of DNMT3A in trophoblast. Furthermore, we detected low promoter methylation and high protein expression of IGFBP5 in the clinical samples of preeclamptic placenta. Collectively, our study suggests that dysregulation of DNMT3A and IGFBP5 is relevant to preeclampsia. Thus, we propose that DNMT3A and IGFBP5 can serve as potential markers and targets for the clinical diagnosis and therapy of preeclampsia.