Miracle-dz/UniFAR-FTUs huggingface dataset:
Comparison of LDL cholesterol concentrations by Friedewald calculation and direct measurement in relation to cardiovascular events in 27,331 women.

BACKGROUND National Cholesterol Education Program (NCEP) guidelines recommend development of direct assays for LDL cholesterol (LDL-C) measurement, but it is unclear how these assays compare with Friedewald calculation in predicting cardiovascular disease (CVD). METHODS In a study of 27 331 healthy women with triglycerides <or=4.52 mmol/L (<or=400 mg/dL), baseline fasting Friedewald LDL-C was compared with fasting and nonfasting direct homogenous measurement for incident CVD during an 11-year period. RESULTS Fasting LDL-C measurements obtained by the 2 methods were highly correlated (r = 0.976, P < 0.001). Compared with fasting Friedewald LDL-C, mean fasting direct LDL-C was 0.15 mmol/L (5.6 mg/dL) lower and nonfasting direct LDL-C 0.30 mmol/L (11.5 mg/dL) lower, both P < 0.0001. The adjusted hazard ratio per 1-SD increment was 1.23 [95% CI 1.15-1.32; 1-SD 0.88 mmol/L (34.1 mg/dL)] for fasting direct LDL-C and 1.22 [95% CI 1.14-1.30; 1-SD 0.90 mmol/L (34.9 mg/dL)] for fasting Friedewald. Nonfasting LDL-C was not associated with CVD by either method. Fasting LDL-C measurements fell into the same NCEP risk category with either method for 79.3% of participants, whereas they differed by 1 NCEP category for 20.7% of participants, with most classified into a lower-risk category by direct LDL-C. CONCLUSIONS The association of LDL-C with CVD by the 2 methods was nearly identical in fasting samples. However, the lower direct LDL-C concentrations may misclassify many individuals into a lower NCEP category. Moreover, the lack of association of nonfasting direct LDL-C with CVD raises questions regarding the clinical utility of a direct assay for LDL-C in nonfasting blood samples.
From reticence to resistance: understanding educators’ engagement with indigenous environmental issues in Canada

Abstract Educators who introduce critical socio-ecological issues into learning contexts often experience formidable internal and external challenges. This is especially true when intersecting Indigenous and environmental issues are involved. Compounding such difficulties in Canada is an inadequate level of pre-service, curricular, resource, and research support in this area. As such, while an increasing number of bold educators are incorporating discussion of Indigenous environmental issues, activism, and related history, law, and policy into their teaching practice, many others are interested, but remain understandably reticent. This study explored the experiences of educators in a variety of contexts across Canada with attempting to incorporate critical consideration of Indigenous environmental issues into their teaching practice. Findings include discussion of challenges encountered, successful strategies employed, the societal significance of these considerations, and future research possibilities.
Unsupervised change detection on SAR images using fuzzy hidden Markov chains

This work deals with unsupervised change detection in temporal sets of synthetic aperture radar (SAR) images. We focus on one of the most widely used change detector in the SAR context, the so-called log-ratio. In order to deal with the classification issue, we propose to use a new fuzzy version of hidden Markov chains (HMCs), and thus to address fuzzy change detection with a statistical approach. The main characteristic of the proposed model is to simultaneously use Dirac and Lebesgue measures at the class chain level. This allows the coexistence of hard pixels (obtained with the classical HMC segmentation) and fuzzy pixels (obtained with the fuzzy measure) in the same image. The quality assessment of the proposed method is achieved with several bidate sets of simulated images, and comparisons with classical HMC are also provided. Experimental results on real European Remote Sensing 2 Precision Image (ERS-2 PRI) images confirm the effectiveness of the proposed approach.
Early Quantitative Software Reliability Prediction Using Petri-nets

In a competitive business landscape, large organizations such as insurance companies and banks are under high pressure to innovate, improvise and distinguish their products and services while continuing to reduce the time-to market for new product introductions. Generating a single view of the customer is vital from different perspectives of the systems developer over a period of time because of the existence of disconnected systems within an enterprise. Therefore, to increase revenues and cost optimization, it is important to build enterprise systems more closely with the business requirements by reusing the existing systems. While building distributed based applications, it is important to take into account the proven processes like Rational Unified Process (RUP) to mitigate risks and increase the reliability of systems. Experiences in developing applications in Java Enterprise Edition (JEE) with customized RUP have been presented in this paper. RUP is adopted into an onsite-offshore development model along with ISO 9001 and SEICMM Level 5 standards. This paper provides an RUP approach to achieve increased reliability with higher productivity and lower defect density along with competitiveness through cost effective custom software solutions. Quantitative software reliability prediction is done using Generalized Stochastic Petri Nets, based on the RUP implemented prototype obtained from the PoC of a financial application prior to the actual implementation of the application development.
RDF Triple Stores and a Custom SPARQL Front-End for Indexing and Searching (Very) Large Semantic Networks

With growing interest in the creation and search of linguistic annotations that form general graphs (in contrast to formally simpler, rooted trees), there also is an increased need for infrastructures that support the exploration of such representations, for example logical-form meaning representations or semantic dependency graphs. In this work, we lean heavily on semantic technologies and in particular the data model of the Resource Description Framework (RDF) to represent, store, and efficiently query very large collections of text annotated with graph-structured representations of sentence meaning. Our full infrastructure is available under open-source licensing, and through this system demonstration we hope to receive feedback on the general approach, explore its application to additional types of meaning representation, and attract new users and possibly co-developers.
Can We Hide in the Web? Large Scale Simultaneous Age and Gender Author Profiling in Social Media Notebook for PAN at CLEF 2013

Would you target your audience differently, knowing the real age and gender of the text authors on your website forum? This paper examines hundreds of thousands of online documents, e.g. chat lines or blog posts, showing that computers are capable to address this task better than humans, without relying on content stereotypes. Pointing out that age and gender profiling are not independent problems, we approach the task as a multiclass classification problem, combining the age and gender information to define six classes. Utilizing a wide range of stylistic and content features and a large number of readability measures we demonstrate the high predictive abilities of the parts of speech, the punctuation and the amount of emotions and slang used in the text, independently of the topic discussed.
Accelerating large vocabulary continuous speech recognition on heterogeneous CPU-GPU platforms

While prior works have demonstrated the effectiveness of Graphic-Processing Units (GPUs) for limited vocabulary speech recognition, these methods were unsuitable for recognition with large language models. To overcome this limitation, previously we introduced a novel “on-the-fly rescoring” approach in which search was performed over a WFST-network composed with a unigram language model on the GPU, and partial hypotheses were rescored on-the-fly using a large language model stored on the CPU. In this paper, we extend our previous algorithm to enable on-the-fly rescoring to be performed over an H-level network composed with any n-gram language model, and show that using a longer language model history in the H-level network improves decoding speed. We demonstrate that large language models can be applied on-the-fly with no degradation in decoding speed, realizing a LVCSR system that performs recognition over 22× faster than a CPU implementation with no loss in recognition accuracy.
Therapeutic alliance and adherence to a plant-based eating plan to treat chronic disease

Abstract Background: Systematic reviews show that a plant-based diet offers many benefits to patients with a variety of chronic illnesses. However, more research is needed to show how plant-based diets are successfully prescribed by physicians and what supports are essential for adherence. The primary research questions in this study were: Is therapeutic alliance correlated with adherence to the eating plan? ; Does a change in therapeutic alliance result in a change in adherence? ; and How do patients view the doctor-patient relationship and adherence? Methods: This multiple methods feasibility study combined cross-sectional pre-post and six-month follow-up survey, a focus group and case study methodology. Results: Patients rated their relationship with their PCP as high, and most patients (78.3%) strictly adhered to the eating plan. Strictness in following the plan was positively correlated with therapeutic alliance (r = 0.346, p = 0.025). Both the PCP and the support group members attributed the doctor-patient relationship as central to the change process. Patients also emphasized their ownership of the plan, a supportive community environment and a positive change in health as motivation for adherence. Conclusion: Adherence rates, ameliorated by the doctor-patient relationship, indicated that the diet is a feasible option for the treatment of chronic disease.
Semi-supervised low-rank mapping learning for multi-label classification

Multi-label problems arise in various domains including automatic multimedia data categorization, and have generated significant interest in computer vision and machine learning community. However, existing methods do not adequately address two key challenges: exploiting correlations between labels and making up for the lack of labeled data or even missing labels. In this paper, we proposed a semi-supervised low-rank mapping (SLRM) model to handle these two challenges. SLRM model takes advantage of the nuclear norm regularization on mapping to effectively capture the label correlations. Meanwhile, it introduces manifold regularizer on mapping to capture the intrinsic structure among data, which provides a good way to reduce the required labeled data with improving the classification performance. Furthermore, we designed an efficient algorithm to solve SLRM model based on alternating direction method of multipliers and thus it can efficiently deal with large-scale datasets. Experiments on four real-world multimedia datasets demonstrate that the proposed method can exploit the label correlations and obtain promising and better label prediction results than state-of-the-art methods.
Effect of FDC-SP on the phenotype expression of cultured periodontal ligament cells

Introduction Recently, a novel protein, follicular dendritic cell secreted protein (FDC-SP), has been identified in human periodontal ligament (PDL) tissue and a biomolecular study suggested that the expression of FDC-SP might be associated with the expression of the PDL phenotype. The purpose of this study was to test the effect of FDC-SP on the proliferation and phenotype of PDL cells. Material and methods Periodontal ligament cells obtained following the 3rd passage were exposed to various concentrations of FDC-SP. The cell proliferation was monitored by 3-[4,5-dimethylthiazol-2-yl]-2,5-diphenyl tetrazolium bromide(MTT) assay. Then, as a measure of osteogenic activity, the alkaline phosphatase (ALP) activity was recorded after 4, 7, and 14 days using p-nitrophenylphosphate as a substrate. Finally, total RNA was extracted and RT-PCR was performed for gene analysis. Results The results indicated that PDL cells exposed to 50 ng/ml FDC-SP could proliferate more rapidly. RT-PCR results showed that the mRNA expression of epidermal growth factor receptor (EGFR) was obviously upregulated and the mRNA expression of osteocalcin (OCN) and bone sialoprotein (BSP) were downregulated in PDL cells exposed to FDC-SP. Moreover, two groups of PDL cells exposed to FDC-SP showed a significant decrease of ALP activity during all the culture days. Conclusions In sum, the findings observed in this study suggest that FDC-SP in PDL cells could positively affect the proliferation and act as a fibroblastic phenotype stabilizer by inhibiting their differentiation into mineralized tissue-forming cells.
Support Vector Machine Active Learning for Music Mood Tagging

Active learning is a subfield of machine learning based on the idea that the accuracy of an algorithm can be improved with fewer training samples if it is allowed to choose the data from which it learns. We present the results for Support Vector Machine (SVM) active learning experiments for music mood tagging based on a multi-sample selection strategy that chooses samples according to their proximity to the boundary, their proximity to points in the training set and the density around them. The influence of those key active learning parameters is assessed by means of ANalysis Of Variance (ANOVA). Using these analyses we demonstrate the efficiency of active learning compared to typical fulldataset batch learning: our method allows to tag music by mood more efficiently than a regular approach, requiring fewer instances to obtain the same performance than using random sample selection methods.
Colored Point Set Matching Under Noise in 3D

Let A and B be two colored points sets in R 3 , with |A| ≤ |B| . We propose a process for determining matches, in terms of the bottleneck distance, between A and subsets of B under color preserving rigid motion, assuming that the position of all colored points in both sets contains a certain amount of ”noise”. Our algorithm consists of two main stages. In the ﬁrst one we generate all, up to a certain equivalence, possible motions that bring A close to some subset B 0 of B and seek for a matching between sets A and B 0 . To look for these possible matchings we use a bipartite matching algorithm that uses skip octrees for neighborhood queries. Additionaly, we also present a lossless ﬁltering algorithm to discard those subsets of B where there can not appear any matches and thus, improve the eﬃciency of our process. This algorithm determines a number of c andidate zones which are regions that contain a subset S of B such that A may match one or more subsets B 0 of S . We use a compressed octree to have easy access to the subsets of B related to candidate zones and store geometric information that is used by the lossless ﬁltering algorithm in each octree node.
Similarity based filtering of point clouds

Denoising surfaces is a a crucial step in the surface processing pipeline. This is even more challenging when no underlying structure of the surface is known, id est when the surface is represented as a set of unorganized points. In this paper, a denoising method based on local similarities is introduced. The contributions are threefold: first, we do not denoise directly the point positions but use a low/high frequency decomposition and denoise only the high frequency. Second, we introduce a local surface parameterization which is proved stable. Finally, this method works directly on point clouds, thus avoiding building a mesh of a noisy surface which is a difficult problem. Our approach is based on denoising a height vector field by comparing the neighborhood of the point with neighborhoods of other points on the surface. It falls into the non-local denoising framework that has been extensively used in image processing, but extends it to unorganized point clouds.
Robot localization using 3D-models and an off-board monocular camera

Robot localization is one of the most common tasks in autonomous systems. Unlike most vision-based methods which perform localization from cameras mounted on the robot, we propose a system which uses an off-board camera to achieve localization. For this purpose, we use a similarity measure between a camera image and a synthetic image generated using a 3D object model. In contrast to other methods, a previous training period is not necessary as we use a model of shading appearance based on the surface curvature of the 3D model. Assuming a reasonably planar area which is observed by an off-board camera, an initial position estimation in the camera image (based on 2D blob tracking) makes it possible for the 3D model to be rendered near the object in the image. From this initial guess, the model is then compared against the real image, such that it can converge to the true vehicle pose. Experiments are performed with a forklift-like robot in an outdoor industrial environment, considering different illumination conditions. The results are compared against a laser-based ground-truth, and illustrate the applicability of the method with a low average error.
Looking Through Tinted Glasses: Depression and Social Anxiety Are Related to Both Interpretation Biases and Inflexible Negative Interpretations

Interpretation bias is often theorized to play a critical role in depression and social anxiety. To date, it remains unknown how interpretation bias exerts its toxic effects. Interpretation inflexibility may be an important determinant of how distorted interpretations affect emotional well-being. This study investigated interpretation bias and inflexibility in relation to depression severity and social anxiety. Participants (N = 212) completed a novel cognitive task that simultaneously measured bias and inflexibility in the interpretation of unfolding ambiguous situations. Depression severity was associated with increased negative and decreased positive interpretation biases. Social anxiety was associated with increased negative interpretation bias. Critically, both symptom types were related to reduced revision of negative interpretations by disconfirmatory positive information. These findings suggest that individuals with more severe depression or social anxiety make more biased and inflexible interpretations. Future work examining cognitive risk for depression and anxiety could benefit from examining both these factors.
2HiGWAS: a unifying high-dimensional platform to infer the global genetic architecture of trait development

Whole-genome search of genes is an essential approach to dissecting complex traits, but a marginal one-single-nucleotide polymorphism (SNP)/one-phenotype regression analysis widely used in current genome-wide association studies fails to estimate the net and cumulative effects of SNPs and reveal the developmental pattern of interplay between genes and traits. Here we describe a computational framework, which we refer to as two-side high-dimensional genome-wide association studies (2HiGWAS), to associate an ultrahigh dimension of SNPs with a high dimension of developmental trajectories measured across time and space. The model is implemented with a dual dimension-reduction procedure for both predictors and responses to select a sparse but full set of significant loci from an extremely large pool of SNPs and estimate their net time-varying effects on trait development. The model can not only help geneticists to precisely identify an entire set of genes underlying complex traits but also allow them to elucidate a global picture of how genes control developmental and dynamic processes of trait formation. We investigated the statistical properties of the model via extensive simulation studies. With the increasing availability of GWAS in various organisms, 2HiGWAS will have important implications for genetic studies of developmental compelx traits.
Impacts of sharing production information on supply chain dynamics

Information sharing and coordination between buyers and vendors have been considered as useful strategies to improve supply chain performance. Some researches, however, have reported that not all players in a supply chain are benefited under such strategies. Therefore, the debate is not about whether or not production information should be shared in the supply chain, but about what information to share and how to share most cost-effectively to maximize the mutual benefits of the supply chain as a whole and the individual business players. In this paper a multi-agent simulation model is developed to investigate the issue. The multiagent model supports more comprehensive and flexible modeling capability than traditional modeling approach. Several experiments are designed and conducted to investigate the impacts of different levels of inter-agent cooperation and information sharing between supply chain partners on various performance indicators
Association of joint space narrowing with impairment of physical function and work ability in patients with early rheumatoid arthritis: protection beyond disease control by adalimumab plus methotrexate

Objectives Tumour necrosis factor inhibition plus methotrexate is believed to inhibit radiographic progression independent of inflammation. This analysis assessed whether these protective effects are exerted on bone (joint erosion; JE) and/or cartilage (joint space narrowing; JSN), and what the independent effects of JE/JSN progression are on longer-term patient-reported outcomes. Methods PREMIER was a 2-year, randomised, controlled trial of adalimumab plus methotrexate (ADA+MTX) versus the monotherapies. The impact of treatment on the relationships between time-averaged disease activity (TA-DAS28(CRP)) and changes in JE/JSN and associations of JE/JSN with the disability index of the health assessment questionnaire (HAQ-DI) at baseline and weeks 52 and 104 were assessed through non-parametric approaches of analysis of variance and quantile regression. JE/JSN association with employment status was evaluated at baseline and weeks 52 and 104 through logistic regression. Results Increasing tertiles of TA-DAS28(CRP) were associated with JE and JSN progression in the monotherapy groups, a phenomenon largely absent in ADA+MTX-treated patients. Although JSN was not associated with HAQ-DI at baseline, it was at 52 and 104 weeks. In contrast, JE was not associated with HAQ-DI at any time point examined. Odds of being employed at baseline, 52 weeks and 104 weeks were significantly associated with lower JSN, but not JE, scores. Conclusions ADA+MTX inhibited both JE and JSN progression independently of disease activity. JSN played a more prominent role in patient-reported outcomes than JE. Preventing the onset or worsening of JSN probably represents a critical aspect of effective disease management of early rheumatoid arthritis patients.