Miracle-dz/UniFAR-FTUs huggingface dataset:
Cigarette Smoke-Induced Pulmonary Inflammation and Emphysema Are Attenuated in CCR6-Deficient Mice1

Chronic obstructive pulmonary disease (COPD) is mainly caused by cigarette smoking, and is characterized by an increase in inflammatory cells in the airways and pulmonary tissue. The chemokine receptor CCR6 and its ligand MIP-3α/CCL20 may be involved in the recruitment of these inflammatory cells. To investigate the role of CCR6 in the pathogenesis of COPD, we analyzed the inflammatory responses of CCR6 knockout (KO) and wild-type mice upon cigarette smoke (CS) exposure. Both subacute and chronic exposure to CS induced an increase in cells of the innate and adaptive immune system in the bronchoalveolar lavage, both in CCR6 KO and wild-type mice. However, the accumulation of dendritic cells, neutrophils, and T lymphocytes, which express CCR6, was significantly attenuated in the CCR6 KO mice, compared with their wild-type littermates. In the lung tissue of CCR6 KO mice, there was an impaired increase in dendritic cells, activated CD8+ T lymphocytes, and granulocytes. Moreover, this attenuated inflammatory response in CCR6 KO mice offered a partial protection against pulmonary emphysema, which correlated with an impaired production of MMP-12. Importantly, protein levels of MIP-3α/CCL20, the only chemokine ligand of the CCR6 receptor, and MCP-1/CCL2 were significantly increased upon CS exposure in wild-type, but not in CCR6 KO mice. In contrast, CCR6 deficiency had no effect on the development of airway wall remodeling upon chronic CS exposure. These results indicate that the interaction of CCR6 with its ligand MIP-3α contributes to the pathogenesis of CS-induced pulmonary inflammation and emphysema in this murine model of COPD.
Heterogeneous nucleation and crystal growth on curved surfaces observed by real-space imaging

We present a real-space imaging study of homogeneous and heterogeneous crystal nucleation and growth in colloidal suspensions of slightly charged and polydisperse particles. Heterogeneous crystallization is observed close to curved surfaces with radii of curvature, R, in the range from 4 to 40 particle diameters, d. Close to a curved surface, we find crystal nucleation and growth to be suppressed for R ≲ 10d. For R ≳ 15d, fast crystal growth is observed similar to that on a flat wall (R = ∞). We use the purely topological method of shortest path rings to determine the orientation of the crystal on the length scale of the nearest neighbor distance. Crystal nuclei forming close to a curved surface are oriented analogous to crystal growth on a flat wall with hexagonal planes parallel to the wall. While the smallest nuclei appear to be unaffected by the surface, larger nuclei are found to be suppressed for radii of curvature R ≲ 10d. The critical nucleus size in the vicinity of a curved surface is found to be about the same as for homogeneous nucleation.
Fibrinolytic Gene Polymorphism and Ischemic Stroke

Background and Purpose— The tissue-type plasminogen activator (tPA) −7351C>T and the plasminogen activator inhibitor type 1 (PAI-1) -675 4G >5G polymorphisms influence transcriptional activity. Both variants have been associated with myocardial infarction, with increased risk for the T and 4G allele, respectively. In this study we investigated the possible association between these polymorphisms, the respective plasma protein levels, and ischemic stroke. Methods— In the Sahlgrenska Academy Study on Ischemic Stroke (SAHLSIS), 600 patients with acute ischemic stroke aged 18 to 69 years and 600 matched community controls were recruited. Stroke subtype was determined using Trial of Org 10172 in Acute Treatment criteria. Results— There were no associations between individual genetic variants and ischemic stroke. The multivariate-adjusted odds ratio for overall ischemic stroke was 1.11 (95% CI 0.87 to 1.43) for tPA T allele carriers, and 0.84 (95% CI, 0.64 to 1.11) for subjects homozygous for the PAI-1 4G allele. When genotypes were combined, a protective effect for the tPA CC/PAI-1 4G4G genotype combination was observed (odds ratio 0.65, 95% CI 0.43 to 0.98; P<0.05). Plasma levels of tPA and PAI-1 antigen at follow-up were independently associated with overall ischemic stroke. tPA-antigen differed by stroke subtype and was highest among those with large-vessel disease and cardioembolic stroke. Conclusions— Neither the tPA −7351C>T nor the PAI-1 to 675 4G >5G polymorphism showed significant association with ischemic stroke. For the tPA CC/PAI-1 4G4G genotype combination, a protective effect was observed. Collectively, these results are consistent with a more complex role for tPA and PAI-1 in the brain as compared with the heart.
A Randomized Trial Examining Housing First in Congregate and Scattered Site Formats

Objective No previous experimental trials have investigated Housing First (HF) in both scattered site (SHF) and congregate (CHF) formats. We hypothesized that CHF and SHF would be associated with a greater percentage of time stably housed as well as superior health and psychosocial outcomes over 24 months compared to treatment as usual (TAU). Methods Inclusion criteria were homelessness, mental illness, and high need for support. Participants were randomised to SHF, CHF, or TAU. SHF consisted of market rental apartments with support provided by Assertive Community Treatment (ACT). CHF consisted of a single building with supports equivalent to ACT. TAU included existing services and supports. Results Of 800 people screened, 297 were randomly assigned to CHF (107), SHF (90), or TAU (100). The percentage of time in stable housing over 24 months was 26.3% in TAU (reference; 95% confidence interval (CI) = 20.5, 32.0), compared to 74.3% in CHF (95% CI = 69.3, 79.3, p<0.001) and 74.5% in SHF (95% CI = 69.2, 79.7, p<0.001). Secondary outcomes favoured CHF but not SHF compared to TAU. Conclusion HF in scattered and congregate formats is capable of achieving housing stability among people experiencing major mental illness and chronic homelessness. Only CHF was associated with improvement on select secondary outcomes. Registration Current Controlled Trials: ISRCTN57595077.
Partial lagrange stability for time-varying linear systems

In this paper, we apply Wazewski inequality and Gronwall-Bellman inequality to study Lagrange stability of time varying linear systems with respect to partial and whole variables. We derive sufficient conditions for system partial stability, equi-stability and uniform stability in the Lagrange sense. As corollaries, we obtain easily verifiable stability criteria involving only the coefficients of the right-hand side and its eigenvalues.
Trace Prediction and Recovery with Unlexicalized PCFGs and Slash Features

This paper describes a parser which generates parse trees with empty elements in which traces and fillers are co-indexed. The parser is an unlexicalized PCFG parser which is guaranteed to return the most probable parse. The grammar is extracted from a version of the PENN treebank which was automatically annotated with features in the style of Klein and Manning (2003). The annotation includes GPSG-style slash features which link traces and fillers, and other features which improve the general parsing accuracy. In an evaluation on the PENN treebank (Marcus et al., 1993), the parser outperformed other unlexicalized PCFG parsers in terms of labeled bracketing f-score. Its results for the empty category prediction task and the trace-filler co-indexation task exceed all previously reported results with 84.1% and 77.4% f-score, respectively.
Restricted Variance Interaction Effects: What They Are and Why They Are Your Friends

Although interaction hypotheses are increasingly common in our field, many recent articles point out that authors often have difficulty justifying them. The purpose of this article is to describe a particular type of interaction: the restricted variance (RV) interaction. The essence of the RV interaction is that, as the value of one variable in a system changes, certain values of another variable in the system become less plausible, thus restricting its variance. This, in turn, influences relationships between that variable and other variables. These types of interactions are quite common, even if they are not recognized as RV interactions, and they exist at every level of analysis. The advantage of the RV interaction is that, as compared with other interaction types, it is relatively simple to justify. The different forms of RV interaction do, however, contain complexities of which a researcher must be aware. This article explains and illustrates the forms that RV interactions can take and their often counterintuitive implications. It also describes how one should go about testing them. Our intention is to help researchers strengthen and focus their interaction arguments.
Ieee Transactions on Information Forensics and Security 1 Face Spoofing Detection Based on Multiple Descriptor Fusion Using Multiscale Dynamic Binarized Statistical Image Features

—Face recognition has been the focus of attention for the past couple of decades and, as a result, a significant progress has been made in this area. However, the problem of spoofing attacks can challenge face biometric systems in practical applications. In this work, an effective countermeasure against face spoofing attacks based on a kernel discriminant analysis approach is presented. Its success derives from three innovations. First it is shown that the recently proposed multiscale dynamic texture descriptor based on binarized statistical image features on three orthogonal planes (MBSIF-TOP) is effective in detecting spoofing attacks, showing promising performance compared to existing alternatives. Next, by combining MBSIF-TOP with a blur-tolerant descrip-tor, namely the dynamic multiscale local phase quantization representation (MLPQ-TOP), the robustness of the spoofing attack detector can be further improved. The fusion of the information provided by MSIF-TOP and MLPQ-TOP is realized via a kernel fusion approach based on a fast kernel discriminant analysis (KDA) technique. It avoids the costly eigen-analysis computations by solving the KDA problem via spectral regression. The experimental evaluation of the proposed system on different databases demonstrates its advantages in detecting spoofing attacks in various imaging conditions, compared to existing methods.
Hierarchical Regularization Cascade

We present a hierarchical approach for information sharing among different classification tasks, in multitask and multi-class settings. We propose a top-down iterative method, which begins by posing an optimization problem with an incentive for large scale sharing among all classes. This incentive to share is gradually decreased, until there is no sharing and all tasks are considered separately. The method therefore exploits different levels of sharing within a given group of related tasks, without having to make hard decisions about the grouping of tasks. In order to deal with large scale problems, with many tasks and many classes, we extend our batch approach to online setting and provide regret analysis of the algorithm. Based on the structure of shared information discovered in the joint learning settings, we propose two different knowledge-transfer methods for learning novel tasks. The methods are designed to work within the very challenging large scale settings. We tested our methods extensively on synthetic and real datasets, showing significant improvement over baseline and state-of-the-art methods.
Satisfacción estudiantil en la valoración de la calidad educativa universitaria

Assessing student satisfaction contributes to successful decision-making when it comes to managing the quality of university education. The aim of this study was to identify the satisfaction of undergraduate students in two academic areas: Administration and Economics and Engineering and Architecture at a public university in the south of Mexico regarding elements of educational quality. These include the teaching-learning process, students´ perception of the treatment they receive from those who they must interact with during their studies, university infrastructure, and students´ self-realization. Based on non-experimental, cross-sectional, descriptive and correlational method of research, the study took a quantitative approach with a probabilistic sampling of a population of 7676 students. Using a questionnaire with a Likert scale, the confidence level was evaluated by means of the Cronbach Alpha Coefficient, which reports a value of . 840, and was validated with a factorial confirmation through the SPSS-AMOS program. This research made it possible to identify areas of opportunity to increase student satisfaction with infrastructure and noted positive feedback on the respectful treatment students receive from their tutors. It also highlighted the opportunities for personal development the institution offers to students and statistically specified significant satisfaction differences between age groups, campuses, study programs and marital status.
Immigrant adolescents’ roots and dreams: Perceived mismatches between ethnic identities and aspirational selves predict engagement

ABSTRACT Immigrant adolescents are generally more ambitious but not as likely to achieve their career/occupational aspirations as their peers. The present study draws on self-discrepancy and social identity literatures to define and explore the role of mismatches between ethnic and aspiration-related ideal selves. In two samples recruited in Canada, 73% of immigrant adolescents aspired to a university-bound career (e.g., physician, engineer). As expected, adolescents reporting larger ethnic/ideal self-discrepancies were less engaged towards their aspiration (Sample 1, n = 73) and viewed school less favorably (Sample 2, n = 125). The present analyses suggest that actual/ideal self-discrepancies mediate these associations, thus extending prior findings and highlighting the role of self-discrepancies in immigrant adolescents’ experiences.
Optimal Speed Profiles for Passenger Cars with Gear Shifting

Fuel consumption in passenger vehicles has become more important due to the significant increase of fuel cost and the awareness of the environmental impact. Thus finding optimal speed profiles and gear shift strategies to cover a distance in order to decrease fuel consumption is an important issue. The optimization problem is presented and solved by means of dynamic programming and using a quasistatic vehicle model approach. Different models are presented in order to solve the problem with different assumptions. For one model the gear shifts are assumed to be instantaneous, and for another model used the gear shift maneuver requires a certain time. For the second model, the influence of taking into account a realistic way to compute the losses during a gear shift maneuver is also developed and studied. Comparing the results provided by the two models, this method to compute the gear shift losses in the optimization problem has influence in the optimal speed profiles compared to the models that do not take into account losses during the gear shift. There are two engine models used in this project, a Willans approximation engine model and a map based engine model. The differences between the results from each engine model are explained and illustrated. Moreover, the influence of the trip length is studied, stating that without a time constraint of the driving mission, the time constraint has no influence on the results. This investigation highlights the importance of selecting a suitable discretization. A smart choice of the discretization is explained in order to have the coasting solution achievable in the deceleration phase. In order to study the results in different road situations, the optimization problem is solved by changing the road topology. It is stated, like in previous research papers, that keeping constant speed to drive through a hill is optimal, if no braking is required to maintain constant speed. Finally some vehicle parameters are changed in order to observe its sensitivity on the optimization results. Results for several vehicle masses and engine sizes are illustrated and compared. Regarding the engine size it is shown that it has a significant influence on the fuel consumption as well as on the optimal speed profile.
An analogue-sensitive approach identifies basal body rotation and flagellum attachment zone elongation as key functions of PLK in Trypanosoma brucei

The Polo-like kinase homologue in Trypanosoma brucei (TbPLK) regulates the assembly of a series of organelles necessary for positioning the parasite's flagellum. An analogue-sensitive strategy is used to acutely and specifically inhibit the kinase with a small molecule, making it possible to identify novel TbPLK functions.
A framework of metacognitive scaffolding in learning through facebook

The purpose of this study is to develop a framework of metacognitive scaffolding in learning through Facebook. A quantitative research design of a multiple-case study method was adopted in this research. Initially, a survey was conducted to identify student’s metacognitive abilities. 80 master degree students from the Educational Technology Program were randomly selected to answer the survey. Next, 37 students from the earlier survey were selected as respondents, which further contributed to the development of the framework. These 37 students comprised three cohorts of students from three subsequent semesters. The study identified the dominant mechanism of metacognitive scaffolding prompted by the instructor, along with the dominant types of online interaction triggered by the students in an online discussion. The data was analyzed by using content analysis technique. Besides that, 4 mobile applications or apps were developed by the researcher as the technological tool used in the learning. The mobile apps as well as the learning activities used in the current study follow the project-based learning approach. Data was collected from a General Metacognition Questionnaire (GMQ) survey, observation through Facebook discussions and through a performance test. The results show that the students’ metacognitive abilities in learning are high (µ=3.86). Besides that, this study points out that the instructor often guides the students to focus on their learning process. The findings also demonstrate that students often give opinions rather than giving examples from their existing ideas; they are also unlikely to compare similarities and differentiate facts. Pearson correlation analysis shows a significant correlation between metacognitive scaffolding and students’ types of online interaction with the students’ learning performance. A data mining analysis using a decision tree technique was used to project a predictive model in order to suggest the mechanisms of metacognitive scaffolding appropriate to be used by the instructor; such a technique must be able to contribute to students’ performance in learning. Finally, the proposed framework recommends a series of rules that serves as a shortcut for the instructor to produce meaningful learning among students through metacognitive scaffolding. These rules were derived from data mining, i.e, association rule analysis. These rules also mentioned the types of student online interactions that actually represent their learning process, particularly in terms of their interactivity in Facebook discussions.
Local and global statistics of clear‐air Doppler radar signals

A refined theoretical analysis of the clear‐air Doppler radar (CDR) measurement process is presented. The refined theory builds on the Fresnel‐approximated (as opposed to Fraunhofer‐approximated) radio wave propagation theory, and turbulence statistics like locally averaged velocities, local velocity variances, local dissipation rates, and local structure parameters are allowed to vary randomly within the radar's sampling volume and during the dwell time. A local version of the moments theorem and the random Taylor hypothesis are used to derive first‐principle formulations of all higher moments of the Doppler cross‐spectrum. The mth moment is written as a convolution integral of a spectral sampling function and a generalized, mth‐order refractive‐index spectrum or, alternatively, as a convolution integral of a lag‐space sampling function and a spatial cross‐covariance function of the local refractive‐index fluctuations and their local mth‐order time derivatives. Closed‐form expressions of the first three moments (i.e., m = 0, 1, 2) of the Doppler spectrum for the monostatic, single‐signal case are derived. This refined theory, or “local sampling theory,” enables one to correctly interpret CDR observations that are collected under conditions where the applicability of the traditional “global sampling theory” is questionable. The commonly used global sampling assumptions (Bragg‐isotropy, homogeneity, and stationarity of all turbulence statistics within the sampling volume and during the dwell time) may be invalid for small‐scale intermittency in the mixed layer, for refractive‐index sheets corrugated by gravity waves or instabilities, and for layered turbulence in the stably stratified atmosphere.
Neuron Unreliable Evoked Responses in Autism

Ilan Dinstein,1,* David J. Heeger,2 Lauren Lorenzi,1 Nancy J. Minshew,3 Rafael Malach,4 and Marlene Behrmann1 1Department of Psychology, Baker Hall, Carnegie Mellon University, Pittsburgh, PA 15213, USA 2Department of Psychology and Center for Neural Science, New York University, New York, NY 10003, USA 3Department of Neurology, University of Pittsburgh, 3471 Fifth Avenue, Pittsburgh, PA 15213, USA 4Department of Neurobiology, Weizmann Institute of Science, Rehovot 76100, Israel *Correspondence: ilan@cns.nyu. edu http://dx.doi. org/10.1016/j.neuron. 2012.07. 026
Bayesian Variable Selection Under Collinearity Joyee Ghosh

In this article we highlight some interesting facts about Bayesian variable selection methods for linear regression models in settings where the design matrix exhibits strong collinearity. We first demonstrate via real data analysis and simulation studies that summaries of the posterior distribution based on marginal and joint distributions may give conflicting results for assessing the importance of strongly correlated covariates. The natural question is which one should be used in practice. The simulation studies suggest that summary statistics that evaluate the importance of correlated covariates jointly are more appropriate, and some priors may be more adversely affected in such a setting. To obtain a better understanding behind the phenomenon we study some toy examples with Zellner’s g-prior. The results show that strong collinearity may lead to a multimodal posterior distribution over models, in which joint summaries are more robust than marginal summaries. Thus we recommend a routine examination of the correlation matrix and calculation of the joint inclusion probabilities for correlated covariates, in addition to marginal inclusion probabilities, for assessing the importance of covariates in Bayesian variable selection.