Miracle-dz/UniFAR-FTUs huggingface dataset:
Formation and contraction of a microfilamentous shell in saponin- permeabilized platelets

To study the mechanism of granule centralization in platelets, we permeabilized with saponin in either EGTA (5 mM) or calcium (1 or 10 microM). Under all conditions, platelets retained 40-50% of their total actin and greater than 70% of their actin-binding protein (ABP) but lost greater than 80% of talin and myosin to the supernatant. Thin sections of platelets permeabilized in EGTA showed a microfilament network under the residual plasma membrane and throughout the cytoplasm. Platelets permeabilized in calcium contained a microfilament shell partly separated from the residual membrane. The shell stained brightly for F-actin. A less dense microfilament shell was also seen in sections of ADP-stimulated intact platelets subsequently permeabilized in EGTA. In the presence of 1 mM ATP gamma S and calcium, myosin was retained (70%) and was localized by indirect immunofluorescence in bright central spots that also stained intensely for F-actin. Electron micrographs showed centralized granules surrounded by a closely packed mass of microfilaments much like the structures seen in thrombin- stimulated intact platelets subsequently permeabilized in EGTA. Permeabilization in calcium, ATP, and okadaic acid, produced the same configuration of centralized granules and packed microfilaments; myosin was retained and the myosin regulatory light chain became phosphorylated. Microtubule coil disassembly before permeabilization did not inhibit granule centralization. These results suggest a possible mechanism for granule centralization in these models. The cytoskeletal network first separates from some of its connections to the plasma membrane by a calcium-dependent mechanism not involving ABP proteolysis. Phosphorylated myosin interacts with the microfilaments to contract the shell moving the granules to the platelet's center.
Cell-free DNA detected by “liquid biopsy” as a potential prognostic biomarker in early breast cancer

As conventional biomarkers for defining breast cancer (BC) subtypes are not always capable of predicting prognosis, search for new biomarkers which can be easily detected by liquid biopsy is ongoing. It has long been known that cell-free DNA (CF-DNA) could be a promising diagnostic and prognostic marker in different tumor types, although its prognostic value in BC is yet to be confirmed. This retrospective study evaluated the prognostic role of CF-DNA quantity and integrity of HER2, MYC, BCAS1 and PI3KCA, which are frequently altered in BC. We collected 79 serum samples before surgery from women at first diagnosis of BC at Forlì Hospital (Italy) from 2002 to 2010. Twenty-one relapsed and 58 non-relapsed patients were matched by subtype and age. Blood samples were also collected from 10 healthy donors. All samples were analyzed by Real Time PCR for CF-DNA quantity and integrity of all oncogenes. Except for MYC, BC patients showed significantly higher median values of CF-DNA quantity (ng) than healthy controls, who had higher integrity and lower apoptotic index. A difference nearing statistical significance was observed for HER2 short CF-DNA (p = 0.078, AUC value: 0.6305). HER2 short CF-DNA showed an odds ratio of 1.39 for disease recurrence with p = 0.056 (95% CI 0.991-1.973). Our study suggests that CF-DNA detected as liquid biopsy could have great potential in clinical practice once demonstration of its clinical validity and utility has been provided by prospective studies with robust assays.
Clustering model for progression of ocular hypertension to primary open angle glaucoma based on rough mereology

This paper presents clustering model for progression of ocular hypertension to primary open angle glaucoma using classification approach based on rough mereology. Our model comprises two phases: Clustering and prediction. We start by Clustering that utilises a rough mereology theory for clustering into one of three obtained classes, namely low, mid, and high and generates its association rules, followed by a prediction phase. Experimental results show that the employed rough mereology provides prediction accuracy 92.85%, which is nearest to the prediction accuracy of random forest classifier are 93% but the performance of rough mereology classifier O(n) is less than performance of random forest classifier O(nlogn), also is better prediction accuracy compared to other machine learning techniques including Bayes net, multi-layer perceptron, radial basis function and naive Bayes tree classifiers. Keywords-Rough Mereology; Rough Inclusion; Rule Generation; Glaucoma; primary open angle glaucoma; retinal fiber layer; machine learning; pattern classification; random forest classification.
The effect of superfluid hydrodynamics on pulsar glitch sizes and waiting times

Pulsar glitches, sudden jumps in frequency observed in many radio pulsars, may be the macroscopic manifestation of superfluid vortex avalanches on the microscopic scale. Small scale quantum mechanical simulations of vortex motion in a decelerating container have shown that such events are possible and predict power-law distributions for the size of the events, and exponential distributions for the waiting time. Despite a paucity of data, this prediction is consistent with the size and waiting time distributions of most glitching pulsars. Nevertheless a few object appear to glitch quasi-periodically, and exhibit many large glitches, while a recent study of the Crab pulsar has suggested a cut-off deviations from a power-law distribution for smaller glitches. In this paper we incorporate the results of quantum mechanical simulations in a macroscopic scale superfluid hydrodynamics simulation. We show that the effect of vortex coupling to the neutron and proton fluids in the neutron star naturally leads to deviations from power-law distributions for sizes and from exponential distributions for waiting times. In particular we predict a cut-off in the size distribution for small glitches.
Evaluation of surface roughness standards applying Haralick parameters and Artificial Neural Networks

This paper presents a methodology for roughness analysis based on surface characteristics of images obtained from optical and electronic microscopes. Texture analysis has been widely used in different fields, such as medical image analysis, visual interpretation of remote sensing images, image search and industrial quality inspection of manufactured products, which is the focus of this paper. We present a novel method of analysis based on texture characteristics. The Haralick descriptors are used to describe the surface texture and to classify its roughness. The primary roughness standards are evaluated and classified according to several features considering these descriptors. The set of values is the input of a multilayer perceptron artificial neural network. The obtained results show that it can be possible to advance this methodology in order to develop a roughness digital measurement system for the evaluation of surface ending process.
Orchestration framework for automated Ajax-based web application testing

The web today is a growing universe of interlinked web pages and web apps, teeming with videos, photos, and interactive content. Over time web technologies have evolved to give web developers the ability to create new generations of useful and immersive web experiences. The most important technology in realizing this shift is Ajax (Asynchronous Javascript and XML). Ajax is a major breakthrough in the web development area even though it is rather complex to set it up and test. Tools like CRAWLJAX automatically generate test cases and apply pipelined oracle comparators along with generated DOM templates. But with the huge amount of data and test cases this comes at the cost of accuracy and speed. This paper explores the webapplication automated testing. In this research, orchestration framework has been introduced to effectively control automated task and orchestrated process. The orchestrator has been further implemented and demonstrated on a prototype production test. The functionalities and implementation of the orchestrator has been discussed in detail in this paper.
Time trend in hospitalised chronic lower respiratory diseases among Danish building and construction workers, 1981–2009: a cohort study

Objectives To show trends in age-standardised hospital admission ratios (SHR) for chronic lower respiratory diseases, estimated for Danish construction workers over three time periods (1981–1990, 1991–2000, 2001–2009). Design Within consecutive cohorts of all male building and construction workers in Denmark, selected occupations: bricklayers, carpenters, electricians, painters, plumbers and ‘other construction workers’ were followed up for hospitalisation due to chronic lower respiratory diseases. SHR was calculated for each occupation and time period. Time trend was calculated for construction workers at large using Poisson regression. Setting Denmark. Participants All gainfully employed male building and construction workers aged 20 or more. Primary and secondary outcome measures Age-standardised and gender-standardised hospitalisation ratios (SHR). Results The number of hospitalised construction workers at large was reduced from 1134 in the first 10-year period to 699 in the last 9-year period. Among all Danish males, it was, however, even more reduced as reflected in the expected number that was down from 1172 to 617. Hence, SHR increased from 97 during 1981–1990, 100 during 1991–2000 to 113 during 2001–2009. It means that SHR increased with an average rate of 0.76% per year (95% CI 0.28 to 1.24) during the study period. A low SHR=72 (95% CI 60 to 87) was found among carpenters in 1981–1990. From 2001 to 2009, high SHRs were found among painters (SHR=147; 95% CI 111 to 192) and plumbers (SHR=132; 95% CI 101 to 171). In general, the selected groups of construction workers had, however, a low or average SHR due to chronic lower respiratory diseases. Conclusions The number of hospitalised workers, suffering from chronic lower respiratory diseases, was reduced over time for construction workers, but for all economically active men, it was reduced even more. Therefore, SHR due to chronic lower respiratory diseases increased over time in the construction industry at large.
Fuzzy subgroup mining for gene associations

When studying the therapeutic efficacy of potential new drugs, it would be much more efficient to use predictors in order to assess their toxicity before going into clinical trials. One promising line of research has focused on the discovery of sets of candidate gene profiles to be used as toxicity indicators in future drug development. In particular genomic microarrays may be used to analyze the causality relationship between the administration of the drugs and the so-called gene expression, a parameter typically used by biologists to measure its influence at gene level. This kind of experiments involves a high throughput analysis of noisy and particularly unreliable data, which makes the application of many data mining techniques very difficult. In this paper we explore a fuzzy formulation of the a priori algorithm, a technique whose crisp version is commonly used to mine for subgroups in large datasets; the purpose is to extend the original method, already suitable to deal with large amount of data, in a way that naturally allows the user to deal with the intrinsic imprecision in the data. The algorithm is tested on real data coming from experimental genomic data.
Component analysis of a Sentiment Analysis framework on different corpora

Sentiment Analysis (SA) is the computational study of people's opinions about certain topics. With the massive growth of web 2.0 technologies, many sources of data and corpora are available for SA. There are some recent frameworks proposed in this field that can deal with different corpora. This paper presents a component analysis of recently proposed sentiment analysis framework. The framework components are divided to three stages, each of which contains many alternatives. The first stage is the text processing which include “handling negations, removing stopwords, and using selective words of part-of-speech tags”. The second stage is the feature extractions which are “unigrams and bigrams”. The third stage is the text classification which was done using “Naïve Bayes and Decision Tree” classifiers. It is important to analyze the components of the framework to configure which scenario is better for each corpus used. The analysis is enhanced by applying the framework components on the benchmark corpus movie reviews in addition to the prepared corpora from online social network sites and a review site. The results show that applying all the stages of text processing techniques ultimately decrease the classifiers' training time with no significant penalty in accuracy. The results also show that “Naïve Bayes” gives higher accuracy in case of balanced benchmark corpus while “Decision tree” classifier is better for imbalance data from social network.
An Efficient and Flexible Dynamic Remote Attestation Method

Remote attestation is an important characteristic of trusted computing technology which provides reliable evidence that a trusted environment actually exists. In this paper, we present a new paradigm that leverages software attack graph for dynamic remote attestation. The major contributions of this paper are two-folds: (1) From the angle of attack, we propose a method to reduce the number of objects to be monitored. (2)We propose a flexible and complete execution status information collection method at operating system level. (3)Software attack graph is introduced as the basis for remote attestation. Analysis shows that our method is complete, efficient and flexible.
Materials and Methods Secondhand Smoke Exposure and Inflammatory Markers in Nonsmokers in the Trucking Industry

Research Secondhand smoke (SHS) is a complex mixture of chemicals generated by the burning of tobacco. SHS is associated with a number of adverse health effects, including chronic respiratory symptoms, lung cancer, and cardio vascular disease (CVD) [Barnoya and Association (AHA) has determined that SHS is a cause of fatal CVD (Taylor et al. 1992). Smoking prevalence has declined among adults in the United States (CDC 2000, 2005); however , among the major blue-collar occupations (e.g., construction workers, motor vehicle opera tors, freight handlers, mechanics), it remains higher than those of many other occupational groups (Bang and Kim 2001; Gerlach et al. 1997; Lee et al. 2007), suggesting that this is a popu lation with high potential for SHS exposure (Wortley et al. 2002). Several studies of systemic inflammatory markers, which have been associated with susceptibility to CVD (Blake and Ridker 2002; Saadeddin et al. 2002; Tousoulis et al. 2007), have shown positive relationships with active smoking (Melbye et al. 2007; Saito et al. 2003; Wannamethee et al. 2005). However, there is limited and inconsistent evidence of an association between SHS exposure and inflam-A study of adults in Greece (Panagiotakos et al. 2004) and another study of British elderly (Jefferis et al. 2010) without clinical evidence of CVD suggested that exposure to SHS is related to an increase in C-reactive protein (CRP) levels , whereas this association was not found among never-smoking adults in the U.S. general population (Clark et al. 2008; Venn and Britton 2007). These studies have used either self-reports or biomarkers to assess SHS exposure. Cotinine in biological fluids has been used most widely as a biomarker (Benowitz 1999; CDC 2006). It is generated from nicotine in the liver and released into the bloodstream, and thus blood levels of cotinine should closely reflect the level of nicotine from inhaled SHS (Benowitz 1999). The average half-life of cotinine is about 17 hr, and coti-nine levels remain relatively stable throughout the day (Benowitz 1996). In the present study, we examined the relationship of SHS exposure meas ured by plasma cotinine with systemic inflammatory markers that included high-sensitivity CRP (hs-CRP) and inter-leukin-6 (IL-6), and with soluble intercellular adhesion molecule-1 (sICAM-1), a marker of vascular endothelial activation and inflammation , among non smoking workers in the U.S. trucking industry. In addition, we evaluated the relationship between self-reported SHS exposure and plasma cotinine concentrations. Study participants. Participants were from two volunteer groups …
Graviton propagator from Loop Quantum Gravity

Loop quantum gravity provides a background independent quantisation of General Relativity, where the microscopical structure of spacetime shows discrete and quantum properties. This microscopical quantum geometry has led to new insights in problems such as the big bang and black hole singularities. However, the study of the semiclassical limit of the theory is still a key open issue. In this talk, I will present recent developments suggesting that the correct low energy physics emerges. In particular, I will describe how the calculations of the graviton propagator in loop quantum gravity agree at large scales with the conventional free propagator of linearised General Relativity
Towards a theory of narrative for interactive fiction

The current paradigm for synthetic reality based interactive fiction features familiar kinds of worlds realistically presented. This approach is unsatisfying in that it excludes several interesting classes of worlds, such as worlds where the user experiences the subjective reality of a character with a substantially different personality. We wish to extend the current paradigm to include classes of worlds such as these. To achieve this, we survey cinema technique and develop a theory of narrative and roughly sketch a system architecture to support enriched interactive fiction based on analogy with film techniques. This work is significant because it brings us closer to our goal of making interactive fiction into a rich, high quality artistic medium. PA lS 3 a U , h 0 r S " reaCh6d a ^ ***** ° f C 0 m p W e r S d e n c e Mellon University. Pittsburgh S. Smith was supported by an Office of Naval Research Graduate Fellowship. The views expressed in this paper are those of the authors alone. Towards a Theory of Narrative for Interactive Fiction Sean Smith Joseph Bates School of Computer Science Carnegie Mellon University Pittsburgh, PA 15213
Improved methods for vocal tract normalization

This paper presents improved methods for vocal tract normalization (VTN) along with experimental tests on three databases. We propose a new method for VTN in training: by using acoustic models with single Gaussian densities per state for selecting the normalization scales the need for the models to learn the normalization scales of the training speakers is avoided. We show that using single Gaussian densities for selecting the normalization scales in training results in lower error rates than using mixture densities. For VTN in recognition, we propose an improvement of the well-known multiple-pass strategy: by using an unnormalized acoustic model for the first recognition pass instead of a normalized model lower error rates are obtained. In recognition tests, this method is compared with a fast variant of VTN. The multiple-pass strategy is an efficient method but it is suboptimal because the normalization scale and the word sequence are determined sequentially. We found that for telephone digit string recognition this suboptimality reduces the VTN gain in recognition performance by 30% relative. On the German spontaneous scheduling task Verbmobil, the WSJ task and the German telephone digit string corpus SieTill the proposed methods for VTN reduce the error rates significantly.
Early burst detection for memory-efficient image retrieval

Recent works show that image comparison based on local descriptors is corrupted by visual bursts, which tend to dominate the image similarity. The existing strategies, like power-law normalization, improve the results by discounting the contribution of visual bursts to the image similarity. In this paper, we propose to explicitly detect the visual bursts in an image at an early stage. We compare several detection strategies jointly taking into account feature similarity and geometrical quantities. The bursty groups are merged into meta-features, which are used as input to state-of-the-art image search systems such as VLAD or the selective match kernel. Then, we show the interest of using this strategy in an asymmetrical manner, with only the database features being aggregated but not those of the query. Extensive experiments performed on public benchmarks for visual retrieval show the benefits of our method, which achieves performance on par with the state of the art but with a significantly reduced complexity, thanks to the lower number of features fed to the indexing system.
Characterization of a Novel Coronavirus Associated with Severe Acute Respiratory Syndrome

In March 2003, a novel coronavirus (SARS-CoV) was discovered in association with cases of severe acute respiratorysyndrome (SARS). The sequence of the complete genome of SARS-CoV was determined, and the initial characterization of the viral genome is presented in this report. The genome of SARS-CoV is 29,727 nucleotides in length and has 11 open reading frames, and its genome organization is similar to that of other coronaviruses. Phylogenetic analyses and sequence comparisons showed that SARS-CoV is not closelyrelated to anyof the previouslycharacterized coronaviruses.
Towards Semantic Validation of a Derivational Lexicon

Derivationally related lemmas like friendN ‐ friendlyA ‐ friendshipN are derived from a common stem. Frequently, their meanings are also systematically related. However, there are also many examples of derivationally related lemma pairs whose meanings differ substantially, e.g., objectN ‐ objective N . Most broad-coverage derivational lexicons do not reflect this distinction, mixing up semantically related and unrelated word pairs. In this paper, we investigate strategies to recover the above distinction by recognizing semantically related lemma pairs, a process we call semantic validation. We make two main contributions: First, we perform a detailed data analysis on the basis of a large German derivational lexicon. It reveals two promising sources of information (distributional semantics and structural information about derivational rules), but also systematic problems with these sources. Second, we develop a classification model for the task that reflects the noisy nature of the data. It achieves an improvement of 13.6% in precision and 5.8% in F1-score over a strong majority class baseline. Our experiments confirm that both information sources contribute to semantic validation, and that they are complementary enough that the best results are obtained from a combined model.
SVM detection for superposed pulse amplitude modulation in visible light communications

A support vector machine (SVM)-based data detection for 8-superposed pulse amplitude modulation in visible light communication is proposed and experimentally demonstrated. In this work, the SVM detector contains three binary classifiers with different classification strategies. And the separating hyperplane of each SVM is constructed by training data. The experiment results show that the SVM detection offers 35% higher data rates when compared with the traditional direct decision method.
Isolated question words recognition from speech queries by using Artificial Neural Networks

Most of the research works in Information Extraction focus only on written language processing, in which a few are devoted to the study of Spoken Language Information Extraction. This paper discusses a novel technique for recognition of the isolated question words from Malayalam (one of the south Indian languages) speech query. We have created and analyzed a database consisting of 250 isolated question words. Discrete Wavelet Transform (DWT) is used for the feature extraction purpose and Artificial Neural Network (ANN) is used for classification and recognition. A recognition accuracy of 80% could be achieved from this experiment