Miracle-dz/UniFAR-FTUs huggingface dataset:
Evolution of Spontaneous Atherosclerotic Plaque Rupture With Medical Therapy: Long-Term Follow-Up With Intravascular Ultrasound

Background—Ruptured coronary atheromatous plaque is generally considered to involve a high risk of subsequent clinical events. Few data are available on the natural evolution of non–culprit-lesion ruptured plaque. We therefore used serial intravascular ultrasound (IVUS) to study how such lesions, detected in the context of a first acute coronary syndrome with elevated troponin I levels, develop. Methods and Results—Fourteen patients with 28 distinct plaque ruptures (2±1 per patient) without significant associated stenosis (minimal lumen cross-sectional area >4 mm2) were included and systematically treated with 40 mg statin and antiplatelet agent (clopidogrel and aspirin for ≥9 months). Mean clinical and IVUS follow-up was 22±13 months (median, 22 months). No clinical event related to the lesion under study occurred. On final IVUS examination, half (14 of 28) of the ruptured plaques had healed, and the degree of stenosis tended to diminish (stenosis, 22±17% versus 29±17% at baseline; P=0.056). No healing-prediction criterion could be identified. Conclusions—Nearly 2 years of follow-up found that spontaneous coronary atheromatous plaque rupture without significant stenosis detected on first acute coronary syndrome healed without significant plaque modification in 50% of cases with medical therapy.
EXIT chart optimization of Turbo-FSK: Application to Low Power Wide Area networks

Low Power Wide Area (LPWA) networks aim at connecting low cost devices with high constraints on the link budget. Focusing on the physical layer, the combination of Frequency Shift Keying (FSK) modulation and coding in the same process has been shown efficient when associated to a turbo receiver. This paper considers the optimization of Turbo-FSK parameters for the LPWA context using the EXtrinsic Information Transfer (EXIT) Chart analysis. After adapting the metric to the FSK waveforms, EXIT chart performance results are extended and compared to short packet lengths using extensive Bit Error Rate (BER) computations.
Learning Subcategorization

A method to identify the subcategorized constituents of a verb (its complements) automatically in a sentence is useful in various areas of Natural Language Processing (e.g. automatic acquisition of subcategorization lexicons, parsing, acquisition of verb semantics, information retrieval). I will describe a method for subcategorization identification that uses memorybased learning. Train and test material is extracted from the Penn Treebank II Wall Street Journal Corpus. It makes use of the part-of-speech tags of the words in the sentence and the chunks: base (i.e. non-recursive) noun phrases, adjectival and adverbial phrases. In the training material, each word or chunk has an associated class that describes its relation to the verb; whether it is directly dependent on the verb (the attachment), and if so, what its syntactic category and grammatical function are (e.g. predicative NP, temporal adjunct PP). All the training examples are stored in memory. Then the memory-based learner classifies new test instances by extrapolating from the class of the most similar training example. In doing so, it achieves a generalization accuracy of 88.3% on the word/chunk level, which corresponds to 91% of correctly identified subcategorization frames. LEARNING SUBCATEGORIZATION
First spectra of the W UMa system V524 Monocerotis

We present the first high-resolution spectra of the W UMa contact binary V524 Mon. The spectra of the two com- ponents are very similar, resembling a G5 and a K0. We find the radial velocities and rotational velocities consistent with corotation. We estimate the radii and the masses and derive a mass ratio M2/M1 = 2.1. We confirm that V524 Mon is a W-type contact system, likely enclosed in a common convective envelope, as found by Samec & Loflin (2003). We do not find evidence for the expected level of emission in the chromospheric Ca  H and K lines, neither Hα, indicating that the magnetic activity is much weaker than expected or that other processes are hampering chromospheric emission.
Environmental Exposure of the Mouse Germ Line: DNA Adducts in Spermatozoa and Formation of De Novo Mutations during Spermatogenesis

Background Spermatozoal DNA damage is associated with poor sperm quality, disturbed embryonic development and early embryonic loss, and some genetic diseases originate from paternal de novo mutations. We previously reported poor repair of bulky DNA-lesions in rodent testicular cells. Methodology/Principal Findings We studied the fate of DNA lesions in the male germ line. B[a]PDE-N2-dG adducts were determined by liquid chromatography-tandem mass spectrometry, and de novo mutations were measured in the cII-transgene, in Big Blue®mice exposed to benzo[a]pyrene (B[a]P; 3×50 mg/kg bw, i.p.). Spermatozoa were harvested at various time-points following exposure, to study the consequences of exposure during the different stages of spermatogenesis. B[a]PDE-N2-dG adducts induced by exposure of spermatocytes or later stages of spermatogenesis persisted at high levels in the resulting spermatozoa. Spermatozoa originating from exposed spermatogonia did not contain DNA adducts; however de novo mutations had been induced (p = 0.029), specifically GC-TA transversions, characteristic of B[a]P mutagenesis. Moreover, a specific spectrum of spontaneous mutations was consistently observed in spermatozoa. Conclusions/Significance A temporal pattern of genotoxic consequences following exposure was identified, with an initial increase in DNA adduct levels in spermatozoa, believed to influence fertility, followed by induction of germ line de novo mutations with possible consequences for the offspring.
Schools, accountability and transparency—approaching the Nordic school evaluation practices through discursive institutionalism

ABSTRACT Over recent decades we have witnessed a growing emphasis on educational quality assurance and evaluation (QAE) around the globe. The trend, not only to intensify evaluative measurements, but also to publish school-specific indicators, has become visible also in the Nordic countries. In Sweden, Denmark and Norway, the governments have launched web-portals, in which various indicators can be observed and compared at the school level. However in Finland, the data is published only at a general level. In this article we compare the discourses of educational experts on comprehensive school QAE policies and practices in four Nordic countries, Sweden, Denmark, Norway and Finland. Our aim is to clarify how the discursive practices reflect the current evaluation and publication policies and how the discourses construct the rationales of educational governance. We have approached our data (58 interviews) from the framework of discursive institutionalism, which sees both the underlying ideas and beliefs, and the discursive practices as the dynamic factors behind institutional change. We argue, that in all the Nordic countries these discursive practices take place in a balancing discursive triad between global competence, neo-liberal accountability pressures and the traditions of the egalitarian Nordic comprehensive school—however with varying country-specific rationales on school accountability and transparency.
A Study of an Emotional Labor Training Program for Classroom Teachers

Emotional labor refers to the efforts workers engage in to manage the expression of their feelings in order to meet organizational goals or norms. Although education researchers have established emotional labor among classroom teachers, the nuances and effects of emotional labor in classroom settings still requires more study and understanding. In particular, as researchers have identified the connections between emotional labor and stress among educators, they have posited that providing instruction on the constructs of emotional labor may help to decrease those feelings of stress. Researchers have not yet studied this idea. The aim of this study was to fill that gap by creating and evaluating an in-service training program for educators that teaches about the constructs of emotional labor. The study design incorporated both qualitative and quantitative measures to determine not only if teachers can increase their understanding of emotional labor constructs through in-service training, but also how they apply these new understandings in their daily practice. The participants included 22 K-5 classroom teachers from an elementary school in the Mid-Atlantic region of the United States. Over the course of 10 weeks, the teachers participated in five 30-minute long training sessions that were delivered via direct instruction, whole group discussion, and small group discussion. They completed a pre-test and post-test around the first direct instruction training session to determine if their understanding of emotional labor increased after the training. As the training program continued over the course of four more sessions, the participants completed journal entries, which were analyzed to determine how the teachers were recognizing and understanding emotional labor in their practice. The analysis of the journal entries and post-test results serve to extend the field of emotional labor research, because it established that this group of teachers increased their understanding of emotional labor and applied their new learning to their practice. The findings from this study may also be interpreted as a call to action for further research, because the participants requested additional training during which they could talk with colleagues about how to manage the stress they feel related to emotional labor.
Computational modelling of 5‐HT receptor‐mediated reorganization of the brainstem respiratory network

Brainstem respiratory neurons express the glycine α3 receptor (Glyα3R), which is a target of modulation by several serotonin (5‐HT) receptor agonists. Application of the 5‐HT1A receptor (5‐HT1AR) agonist 8‐OH‐DPAT was shown (i) to depress cellular cAMP, leading to dephosphorylation of Glyα3R and augmentation of postsynaptic inhibition of neurons expressing Glyα3R ( Manzke et al., 2010 ) and (ii) to hyperpolarize respiratory neurons through 5‐HT‐activated potassium channels. These processes counteract opioid‐induced depression and restore breathing from apnoeas often accompanying pharmacotherapy of pain. The effect is postulated to rely on the enhanced Glyα3R‐mediated inhibition of inhibitory neurons causing disinhibition of their target neurons. To evaluate this proposal and investigate the neural mechanisms involved, an established computational model of the brainstem respiratory network ( Smith et al., 2007 ), was extended by (i) incorporating distinct subpopulations of inhibitory neurons (glycinergic and GABAergic) and their synaptic interconnections within the Bötzinger and pre‐Bötzinger complexes and (ii) assigning the 5‐HT1AR‐Glyα3R complex to some of these inhibitory neuron types in the network. The modified model was used to simulate the effects of 8‐OH‐DPAT on the respiratory pattern and was able to realistically reproduce a number of experimentally observed responses, including the shift in the onset of post‐inspiratory activity to inspiration and conversion of the eupnoeic three‐phase rhythmic pattern into a two‐phase pattern lacking the post‐inspiratory phase. The model shows how 5‐HT1AR activation can produce a disinhibition of inspiratory neurons, leading to the recovery of respiratory rhythm from opioid‐induced apnoeas.
In Vivo Activation of the p53 Pathway by Small-Molecule Antagonists of MDM2

MDM2 binds the p53 tumor suppressor protein with high affinity and negatively modulates its transcriptional activity and stability. Overexpression of MDM2, found in many human tumors, effectively impairs p53 function. Inhibition of MDM2-p53 interaction can stabilize p53 and may offer a novel strategy for cancer therapy. Here, we identify potent and selective small-molecule antagonists of MDM2 and confirm their mode of action through the crystal structures of complexes. These compounds bind MDM2 in the p53-binding pocket and activate the p53 pathway in cancer cells, leading to cell cycle arrest, apoptosis, and growth inhibition of human tumor xenografts in nude mice.
Öz-Yönetimli Öğrenme Becerileri Ölçeği: Geçerlik ve Güvenirlik Çalışması

Bu arastirmanin amaci universite ogrencilerinin oz-yonetimli ogrenme becerilerini belirlemeye yonelik bir olcme araci gelistirmektir. Bu amacla oncelikle, ilgili alanyazina dayali olarak kavramsal cerceve olusturulmus, bu alanda yurt ici ve yurt disinda yapilmis olan olcek gelistirme calismalari incelenmistir. Bu incelemenin ardindan, olcege iliskin olcutler ortaya konmus ve oz-yonetimli ogrenme becerilerine sahip bireylerin ozelliklerini ifade eden maddelerden olusan 72 maddelik madde havuzu olusturulmustur. Uzman goruslerinin ardindan yapilan duzenlemeler sonucunda, olcegin 53 maddelik deneme formu olusturulmustur. Bu form, Hacettepe ve Baskent Universitelerinde ogrenim gormekte olan 753 universite ogrencisine uygulanarak, veriler acimlayici faktor analizine tabi tutulmus ve yapilan analiz sonucunda olcegin 21 madde ve dort boyuttan (gudulenme, oz-izleme, oz-kontrol ve ozguven) olustugu ortaya konmustur. Ayrica, acimlayici faktor analizinin uygulandigi ogrencilerin disinda yer alan ve Hacettepe ve Baskent Universitelerinin ortak olan bolumlerinin birinci ve dorduncu siniflarinda ogrenim gormekte olan 2600 ogrenciye olcek uygulamasi yapilarak olcegin yapi gecerliginin belirlenmesi amaclanmistir. Toplanan veriler dogrulayici faktor analizine tabi tutulmustur. Analiz sonucunda olcege iliskin uyum indekslerinin iyi uyum gosteren ve kabul edilebilir degerler arasinda oldugu gorulmus ve olcegin 4 faktorlu ve 21 maddeden olusan yapisi bir model olarak dogrulanmistir. Olcegin, Cronbach Alpha ic tutarlik katsayisi kullanilarak hesaplanan guvenirligi ise . 895 bulunmustur. Sonuc olarak gelistirilen “Oz-Yonetimli Ogrenme Becerileri Olcegi”nin, gecerli bir olcme araci oldugu ve oz-yonetimli ogrenme becerilerini olcmede guvenilir puanlar verecegi soylenebilir.
The NextGen Model Atmosphere Grid for 3000 ≤ Teff ≤ 10,000 K

We present our NextGen Model Atmosphere grid for low-mass stars for effective temperatures larger than 3000 K. These LTE models are calculated with the same basic model assumptions and input physics as the VLMS part of the NextGen grid so that the complete grid can be used, e.g., for consistent stellar evolution calculations and for internally consistent analysis of cool star spectra. This grid is also the starting point for a large grid of detailed NLTE model atmospheres for dwarfs and giants. The models were calculated from 3000 to 10,000 K (in steps of 200 K) for 3.5 ≤ log g ≤ 5.5 (in steps of 0.5) and metallicities of -4.0 ≤ [M/H] ≤ 0.0. We discuss the results of the model calculations and compare our results to the Kurucz grid. Some comparisons to standard stars like Vega and the Sun are presented and compared with detailed NLTE calculations.
Rangegram: A novel payload based anomaly detection technique against web traffic

Application specific intrusion detection methods are used to detect network intrusions targeted at applications. Normally such detection methods require payload or packet content analysis. One of the prominent method of payload modeling and analysis is sequence or ngram modeling. Normally ngrams generated from a packet are compared with a database of ngrams seen during training phase. Depending on the number of ngrams found or not found in the packet it is labeled either as normal or anomalous. Previous methods use either presence or absence of ngram in training dataset or use frequency of its occurrence in the entire training dataset. This approach results into many false positives and false negatives. In this paper we propose a novel payload analysis technique for the detection of Zero day attacks against web traffic. We consider the minimum and maximum occurrence frequency of a particular ngram from a packet in training dataset and find deviations from this range to detect anomalies. Experiments on a large dataset has shown good detection rate with low false positives.
MULTI-FEATURE BACKGROUND MODELLING AND SUBTRACTION USING KDA WITH SUPPORT VECTOR MACHINE 1 SOWMYA

Background subtraction is a crucial step in many automatic video content analysis applications. While numerous acceptable techniques have been proposed so far for background extraction, there is still a need to produce more efficient algorithms in terms of adaptability to multiple environments, noise resilience, and computation efficiency. This paper propose a pixel-wise background modeling and subtraction technique using multiple features,where generative and discriminative techniques are combined for classification. In our algorithm, color, gradient and Haarlike features are integrated to handle spatio-temporal variations for each pixel. A pixel-wise generative background model is obtained for each feature efficiently and effectively by the technique called Kernel Density Approximation(KDA). Background subtraction is performed in a discriminative manner using a Support Vector Machine (SVM) over background likelihood vectors for a set of features. The proposed algorithm is robust to shadow, illumination changes, spatial variations of background. Comparision of the performance of the algorithm is made with other density-based methods using several different feature combinations and modeling techniques, both quantitatively and qualitatively. Keywords—Background modelling and subtraction, Haarlike features, support vector machine, kernel density approximation
Effects of Surgical Correction for the Treatment of Adult Acquired Flatfoot Deformity: A Computational Investigation

Computational models of the foot/ankle complex were developed to predict the biomechanical consequences of surgical procedures that correct for Stage II adult acquired flatfoot deformity. Cadaveric leg and foot bony anatomy was captured by CT imaging in neutral flexion and imported to the modeling software. Ligaments were approximated as tension only springs attached at insertion sites. Muscle contraction of the gastrocnemius/soleus complex was simulated through force vectors and desired external loads applied to the model. Ligament stiffnesses were modified to reflect Stage II flatfoot damage, followed by integration of corrective osteotomies—medializing calcaneal osteotomy (MCO) and Evans and calcaneocuboid distraction arthrodesis (CCDA)—to treat flatfoot. Joint angles, tissue strains, calcaneocuboid contact force, and plantar loads were analyzed. The flatfoot simulation demonstrated clinical signs of disease evidenced by degradation of joint alignment. Repair states corrected these joint misalignments with MCO having greatest impact in the hindfoot, and Evans/CCDA having greatest effect in the mid‐ and forefoot. The lateral procedures unevenly strained plantar structures, while offloading the medial forefoot, and increased loading on the lateral forefoot, which was amplified by combining with MCO. The Evans procedure raised calcaneocuboid joint contact force to twice intact levels. Computational results are in agreement with clinical and experimental findings. The model demonstrated potential precursors to such complications as lateral tightness and arthritic development and may thus be useful as a predictor of surgical outcomes. © 2011 Orthopaedic Research Society Published by Wiley Periodicals, Inc. J Orthop Res 29: 1047–1054, 2011
4 Pixel-Based Artificial Neural Networks in Computer-Aided Diagnosis

Artificial neural networks (ANNs) play an essential role in the medical imaging field, including medical image analysis and computer-aided diagnosis (CAD) (Doi 2005; Giger and Suzuki 2007), because objects such as lesions and organs in medical images may not be represented accurately by a simple equation. For example, a lung nodule is generally modeled as a solid sphere, but there are nodules of various shapes and nodules with internal inhomogeneities, such as spiculated nodules and ground-glass nodules. A polyp in the colon is modeled as a bulbous object, but there are also polyps which exhibit a flat shape (Lostumbo, Wanamaker et al. 2010). Thus, diagnostic tasks in medical images essentially require “learning from examples (or data). ” One of the most popular uses of ANNs in medical image analysis is the classification of objects such as lesions into certain classes (e.g., abnormal or normal, lesions or non-lesions, and malignant or benign). The task of ANNs here is to determine “optimal” boundaries for separating classes in the multi-dimensional feature space which is formed by input features (e.g., contrast, area, and circularity) obtained from object candidates. Machine-learning algorithms for classification include linear discriminant analysis (Fukunaga 1990), quadratic discriminant analysis (Fukunaga 1990), multilayer perceptron (Rumelhart, Hinton et al. 1986), and support vector machines (Vapnik 1995). Such machine-learning algorithms were applied to lung nodule detection in chest radiography (Shiraishi, Li et al. 2006) and thoracic CT (Armato, Giger et al. 2001; Arimura, Katsuragawa et al. 2004), classification of lung nodules into benign or malignant in chest radiography (Aoyama, Li et al. 2002) and thoracic CT (Aoyama, Li et al. 2003), detection of microcalcifications in mammography (Wu, Doi et al. 1992), classification of masses into benign or malignant in mammography (Huo, Giger et al. 1998), polyp detection in CT colonography (Yoshida and Nappi 2001; Jerebko, Summers et al. 2003), determining subjective similarity measure of mammographic images (Muramatsu, Li et al. 2005; Muramatsu, Li et al. 2006; Muramatsu, Li et al. 2007), and detection of aneurysms in brain MRI (Arimura, Li et al. 2006). Recently, as available computational power increased dramatically, pixel/voxel-based ANNs (PANNs) emerged in medical image processing/analysis which use pixel/voxel values in images directly instead of features calculated from segmented regions as input
Compact wideband HMSIW bandpass filter with defected ground structure

A compact wideband bandpass filter based on the half mode substrate integrated waveguide (HMSIW) is presented in this paper. Two double-slit complementary split-ring resonators (DS-CSRRs) are etched on the bottom plane, which can improve the out-of-band rejection. The microstrip line is used to feed the HMSIW cavity and DS-CSRRs. A bandpass prototype with the center frequency of 7.6GHz is designed and fabricated. The measured fractional bandwidth is 53%. Good agreement is observed between simulation and measurement.
Automatic feature extraction and face synthesis in facial image coding

A real-time method that automatically extracts facial feature points is proposed. Based upon this technique, the authors also present a procedure to encode facial images efficiently. For clarity the proposed method is divided into an analysis part and synthesis part. The analysis part includes face tracking, feature extraction, and pose estimation. All of these procedures are applicable for live video applications. In the synthesis part, a new face image is synthesized using one base image and a small number of feature images. The current performance of feature extraction is about 8 frames per second on a Pentinum II 233 MHz PC with an image size 320/spl times/240.
Glass object segmentation by label transfer on joint depth and appearance manifolds

We address the glass object localization problem with a RGB-D camera. Our approach uses a nonparametric, data-driven label transfer scheme for local glass boundary estimation. A weighted voting scheme based on a joint feature manifold is adopted to integrate depth and appearance cues, and we learn a distance metric on the depth-encoded feature manifold. Local boundary evidence is then integrated into a MRF framework for spatially coherent glass object detection and segmentation. The efficacy of our approach is verified on a challenging RGB-D glass dataset where we obtained a clear improvement over the state-of-the-art both in terms of accuracy and speed.