Miracle-dz/UniFAR-FTUs huggingface dataset:
Quantitative assessment of hepatic fibrosis in an animal model with magnetic resonance elastography

Chronic liver disease is a world‐wide problem that causes progressive hepatic fibrosis as a hallmark of progressive injury. At present, the gold standard for diagnosing hepatic fibrosis is liver biopsy, which is an invasive method with many limitations, including questionable accuracy and risks of complications. MR elastography (MRE), a phase‐contrast MRI technique for quantitatively assessing the mechanical properties of soft tissues, is a potential noninvasive diagnostic method to assess hepatic fibrosis. In this work, MRE was evaluated as a quantitative method to assess the in vivo mechanical properties of the liver tissues in a knockout animal model of liver fibrosis. This work demonstrates that the shear stiffness of liver tissue increases systematically with the extent of hepatic fibrosis, as measured by histology. A linear correlation between liver stiffness and fibrosis extent was well‐defined in this animal model. An additional finding of the study was that fat infiltration, commonly present in chronic liver disease, does not significantly correlate with liver stiffness at each fibrosis stage and thus does not appear to interfere with the ability of MRE to assess fibrosis extent. In conclusion, MRE has the potential not only for assessing liver stiffness, but also for monitoring potential therapies for hepatic fibrosis. Magn Reson Med 58:346–353, 2007. © 2007 Wiley‐Liss, Inc.
The quadratic Fock functor

We construct the quadratic analogue of the boson Fock functor. While in the first order case all contractions on the 1--particle space can be second quantized, the semigroup of contractions that admit a quadratic second quantization is much smaller due to the nonlinearity. Within this semigroup we characterize the unitary and the isometric elements.
Spatial Disaggregation of Agricultural Production Data by Maximum of Entropy /доклад на 10 конгрессе ЕААЕ, Exploring Diversity in the European Agri-Food System, Zaragoza, Spain, 28-31 August 2002

In this paper we develop a dynamic data-consistent way for estimating agricultural land use choices at a disaggregate level (district-level), using more aggregate data (regional-level). The disaggregation procedure requires two steps. The first step consists in specifying and estimating a dynamic model of land use at the regionallevel. In the second step, we disaggregate outcomes of the aggregate model using maximum entropy (ME). The ME disaggregation procedure is applied to a sample of California data. The sample includes 6 districts located in Central Valley and 8 possible crops, namely: Alfalfa, Cotton, Field, Grain, Melons, Tomatoes, Vegetables and Subtropical. The disaggregation procedure enables the recovery of land use at the district-level with an out-sample prediction error of 16%. This result shows that the micro behavior, inferred from aggregate data with our disaggregation approach, seems to be consistent with observed behavior.
Model-Based Mitigation of Availability Risks

The assessment and mitigation of risks related to the availability of the IT infrastructure is becoming increasingly important in modern organizations. Unfortunately, present standards for risk assessment and mitigation show limitations when evaluating and mitigating availability risks. This is due to the fact that they do not fully consider the dependencies between the constituents of an IT infrastructure that are paramount in large enterprises. These dependencies make the technical problem of assessing availability issues very challenging. In this paper we define a method and a tool for carrying out a risk mitigation activity which allows us to assess the global impact of a set of risks and to choose the best set of countermeasures to cope with them. To this end, the presence of a tool is necessary, due to the high complexity of the assessment problem. Our approach can be integrated in present risk management methodologies (e.g. COBIT) to provide a more precise risk mitigation activity. We substantiate the viability of this approach by showing that most of the input required by the tool is available as part of a standard business continuity plan, and/or by performing a common tool-assisted risk management.
Pharmacokinetics of single dose intravenous propacetamol in neonates: effect of gestational age

Aim: To investigate the pharmacokinetics and pharmacodynamics of single dose propacetamol in preterm and term infants on the first day of life. Methods: Neonates were stratified by gestational age. Preterm (< 37 weeks) and term (37–41 weeks) infants received a single dose of propacetamol in the first 24 hours of life when they had minor, painful procedures or as additional treatment in infants receiving opioids. Blood samples were taken from an arterial line, and pain was evaluated by a multidimensional pain scale. Results were reported as mean (SD). Student’s t and Wilcoxon tests were used to compare the groups. Results: Thirty neonates were included, 10 of which were term infants. Serum half life was 277 (143) minutes in the preterm infants and 172 (59) minutes in the term infants (p < 0.05). Clearance was 0.116 (0.08) litre/kg/h in the preterm infants and 0.170 (0.06) litre/kg/h in the term infants (p < 0.05). Gestational age correlated with serum half life (r = −0.46). No effect of sex or administration of prenatal steroids was found on the pharmacokinetics of paracetamol. In neonates who only received propacetamol (n = 15), the level of analgesia seemed to be associated with the therapeutic (> 5 mg/l) level. Conclusions: A correlation was found between gestational age and the serum half life of propacetamol. The maturational trend of clearance and half life in preterm and term neonates is in line with data on the pharmacokinetics of propacetamol beyond the newborn period.
Report on the CONALD Workshop on Learning from Text and the Web

An increasing fraction of the world's information and data is now represented in textual form. For example, the World Wide Web, online news feeds, and other Internet sources contain a tremendous volume of textual information. The goal of the CONALD workshop on Learning from Text and the Web was to explore computer methods for automatically extracting, clustering and classifying information from text and hypertext sources. The workshop included ten oral paper presentations, an organized discussion by a panel of distinguished researchers, and a handful of other contributed papers. The workshop provided a good survey of the state of the art in machine learning methods applied to text processing tasks. The presented work involved a wide array of learning approaches, including nite-state-machine induction HD, MMK], neural networks that can accept advice from users SER], relational learning methods Moo, SC], statistical clustering algorithms GS, Hof, LV, YPC], boosting methods ADW], algorithms for learning with hierarchical classes Hof, MG], and active learning methods LT, NM]. A principal limitation of many of these approaches is that they do not directly reeect attempts to develop formal models of the text phenomenon of interest. The research presented at the workshop also spanned a broad range of application tasks, in-presentation of documents in information retrieval systems GS, Hof], collaborative ltering dVN], lexicon learning GBGH], query reformulation KK], text generation Rad] and analysis of the statistical properties of text MA]. In short, the state of the art in learning from text and the web is that a broad range of methods are currently being applied to many important and interesting tasks. There remain numerous open research questions, however. Broadly, the goals of the work presented at the workshop fall into two overlapping categories: (i) making textual information available in a structured format so that it can be used for complex queries and problem solving, and (ii) assisting users in nding, organizing and managing information represented in text sources. As an example of research aimed at the former goal, Muslea, Minton and Knoblock MMK] have developed an approach to learning wrappers for semi-structured Web sources, such as restaurant directories. Their method is able to induce extraction rules from small numbers of labeled examples. These learned extraction rules are then applied so that Web pages can be treated like structured databases. As an example of work geared toward the latter goal, Shavlik and Eliassi-Rad SER] have developed an approach to …
A2: Agnostic Aggregation in wireless sensor networks

Data aggregation has been developed to take benefits from either spatial or temporal correlation of collected data. In wireless sensor networks, data aggregation is a key mechanism to reduce energy consumption and to increase capacity. Most current aggregation functions are designed for specific network topology or data pattern, with prior assumptions. Concretely, these functions are developed with fixed models or parameters, so they fail to adapt against non-anticipated data variations or dynamic scenarios. In this work, we propose a dynamic forecasting function: Agnostic Aggregation (A2), which can predict values with self-tuned model based on temporal correlation. With A2, sensor nodes are able to adjust model to fit the latest time series better. Compared with previous works, the proposed A2 can deliver packets to the sink with higher accuracy, and is more robust to independent and consecutive errors. We also investigate the influence of system parameters on the performance of A2, and highlight the optimal choices. Comparison results verify that A2 improves the recovery fidelity and reduces model updates than other aggregation functions.
Analyzing data from sponsored search markets from an agent-based and complex systems perspective

This paper provides a comprehensive study of the structure and dynamics of online advertising markets, based on a dataset of sponsored search queries provided by Microsoft Research. The study was performed using techniques from agent-based computing, data mining and from the emergent discipline of complex systems analysis. First, we look at how the display rank of a URL link influences its click frequency, for both sponsored search and organic search. Second, we study the market structure that emerges from these queries, especially the market share distribution of different advertisers. We show that the sponsored search market is highly concentrated, with less than 5% of all advertisers receivin g over 2/3 of the clicks in the market. Furthermore, we show that both the number of ad impressions and the number of clicks follow power law distributions of approximately the same coefficient. Ho wever, we find this result does not hold when studying the same distri bution of clicks per rank position, which shows considerable variance, due to the way advertisers divide their budget on different
Body Mass Index and Etiology of Intracerebral Hemorrhage

Background and Purpose— Extremes of body mass index (BMI) are associated with increased incidence of intracerebral hemorrhage (ICH). Because ICH can result from different vessel pathologies, we investigated whether the effect of BMI depends on ICH etiology. Methods— We analyzed 384 consecutive ICH cases (188 lobar ICH and 196 deep ICH) and 388 control subjects enrolled between 2004 and 2009 in an ongoing single-center prospective study of primary ICH. ICH was categorized as lobar or deep based on CT imaging at admission. BMI was calculated based on subjects' height and weight at enrollment. We constructed multivariate logistic regression models including BMI and known predictors of ICH occurrence. Analyses were performed separately for lobar and deep ICH subjects versus control subjects. Results— Low BMI (<18.5 kg/m2) and very high BMI (>30.0 kg/m2) were associated with deep ICH risk (OR, 1.76; P=0.011 and OR, 1.75; P=0.013, respectively), whereas no effect was found for lobar ICH. Furthermore, sex-stratified analyses uncovered that among low BMI individuals, males were at higher ICH risk (OR, 2.85; P=0.041) but females were not (OR, 0.89; P=0.54, respectively). Conclusions— Extremes of BMI are associated with increased risk of deep ICH, but not lobar ICH, suggesting a role for BMI in the vascular pathologies underlying deep ICH, but not in pathologies such as cerebral amyloid angiopathy that cause ICH in the lobar brain regions.
Lower expressed miR-198 and its potential targets in hepatocellular carcinoma: a clinicopathological and in silico study

Purpose To investigate the clinicopathological value and potential roles of microRNA-198 (miR-198) in hepatocellular carcinoma (HCC). Methods Ninety-five formalin-fixed paraffin-embedded HCC and the para-cancerous liver tissues were gathered. Real-time reverse transcription quantitative polymerase chain reaction was applied to determine the miR-198 expression. The association between the miR-198 expression and clinicopathological features was examined. Meanwhile, potential target messenger RNAs of miR-198 in HCC were obtained from 14 miRNA prediction databases and natural language processing method, in which we pooled the genes related to the tumorigenesis and progression of HCC and classified them by their frequency. The selected target genes were finally analyzed in the Gene Ontology analysis and Kyoto Encyclopedia of Genes and Genomes pathway. Results miR-198 expression was significantly lower in HCC than that in adjacent noncancerous liver tissues (1.30±0.72 vs 2.01±0.58, P<0.001). Low miR-198 expression was also correlated to hepatitis C virus infection (r=−0.48, P<0.001), tumor capsular infiltration (r=−0.43, P<0.001), metastasis (r=−0.26, P<0.010), number of tumor nodes (r=−0.25, P=0.013), vaso-invasion (r=−0.24, P=0.017), and clinical tumor node metastasis stage (r=−0.23, P=0.024). Altogether, 1,048 genes were achieved by the concurrent prediction from at least four databases and natural language processing indicated 1,800 genes for HCC. Further, 127 overlapping targets were further proceeded with for pathway analysis. The most enriched Gene Ontology terms in the potential target messenger RNAs of miR-198 were cell motion, cell migration, cell motility, and regulation of cell proliferation in biological process; organelle lumen, membrane-enclosed lumen, and nuclear lumen in cellular component; and enzyme binding, protein domain-specific binding, and protein kinase activity in molecular function. Kyoto Encyclopedia of Genes and Genomes analysis showed that these target genes were obviously involved in focal adhesion and pathways in cancer. Conclusion Lower expression of miR-198 was related to several clinicopathological parameters in HCC patients. miR-198 might play a regulatory role through its target genes in the development of HCC.
Semi-orthogonal MARC with half duplex relaying: A backward compatible cooperative network with interference channels

This paper proposes a full-rate cooperative scheme adapted to the slow fading semi-orthogonal multiple access relay channel (MARC). It is assumed that the sources transmit on orthogonal channels and the relay is half duplex transmitting on the same channels as the one of the sources. The sources employ LDPC to encode their messages before transmitting them, while the relay uses a simple demodulate-and-forward strategy to transmit the combination of the received codewords. The presence of the relay does not affect the time-scheduling of the sources. Therefore, this scheme is backward compatible to existing non-cooperative systems. In order to cancel the interference at the destination, we propose a joint network-channel decoder that uses maximum a posteriori (MAP) detection and channel decoding. Through numerical results, we show the benefits of joint network-channel decoder. We also show that the proposed scheme significantly outperforms, in terms of BER, the non-cooperative system as well as the classical relay-assisted orthogonal channel network and that it achieves the maximum code diversity.
Classification of delayed enhancement scar islands by means of their local subendocardial transmurality

Delayed Enhancement Magnetic Resonance (DE-MR) has clinical relevance in the diagnosis and prognosis of several cardiomyopathies with different etiologies, since they often lead to different scar configurations. However, to the best of our knowledge very little research has been devoted to the computational characterization of the scar islands detected by DE-MR and its location within the myocardial wall. Our purpose is to classify between subendocardial or transmural (S/T) scar islands and mid-wall or subepicardial (M/E) ones by means of their local subendocardial transmurality (LSTM). The results suggest that inspecting local magnitudes such as LSTM within isolated scar islands in DE-MR provides useful information with respect to the diagnosis of different types of cardiomyopathies and might help in the development of computer assisted diagnostic frameworks for nonischemic cardiomyopathies.
Development of a Scale to Measure Social Capital in Recreation and Sport Clubs

ABSTRACT Social capital's measurement has been limited and an effective scale is needed. This research employed focus groups and interviews and a panel of experts to provide understanding and items for a social capital scale in sport and recreation setting. After a pilot study the Club Social Capital Scale (CSCS) was completed by 1,079 members of sport and recreation clubs. This 42-item scale included the factors trust, friendship, acceptance, reciprocity, norms, and governance. Exploratory factor analysis resulted in a 20-item four component (governance, norms, friendship-acceptance, and trust-reciprocity) CSCS. Confirmatory factor analysis confirmed the potential four or five factor model and the hierarchical structure. The CSCS is the first psychometrically developed instrument to measure the factors underlying social capital rather than its outcomes. It can inform policy makers or sport and recreation administrators to establish baseline social capital in their organization and the efficacy of interventions or changes in policy.
An Empirical Study of Immune System Based On Bipartite Network

Immune system is the most important defense system to resist human pathogens. In this paper we present an immune model with bipartite graphs theory. We collect data through COPE database and construct an immune cell- mediators network. The act degree distribution of this network is proved to be power-law, with index of 1.8. From our analysis, we found that some mediators with high degree are very important mediators in the process of regulating immune activity, such as TNF-alpha, IL-8, TNF-alpha receptors, CCL5, IL-6, IL-2 receptors, TNF-beta receptors, TNF-beta, IL-4 receptors, IL-1 beta, CD54 and so on. These mediators are important in immune system to regulate their activity. We also found that the assortative of the immune system is -0.27. It reveals that our immune system is non-social network. Finally we found similarity of the network is 0.13. Each two cells are similar to small extent. It reveals that many cells have its unique features. The results show that this model could describe the immune system comprehensive.
New Immigrants’ Perceptions of Ethnic Small Businesses

Recent immigrants in pro-immigration countries face the burden of economic instability due to lack of host country work experience, resulting in job search in the unskilled labor market as cashiers, grocery packers or waiters. At the same time, many other immigrants have taken on entrepreneurship by starting businesses that reflect deep linkages to their mother country (nationalism) in terms of products, network and cultural work environment, establishing a source of unskilled employment for other immigrants. However, there are both practical and theoretical needs to understand intergroup phenomena occurring when an immigrant intends to apply for work in a business of different ethnicity. This paper explores job seekers’ perceptions of nationalistic ethnic businesses concerning intergroup feelings (prejudice and group competition), social identity issues (nationalism) and perceived job opportunities. Results on an experiment of immigrants in Montreal indicated that, consistent with the main hypothesis, nationalistic businesses are deemed more prejudiced by job seekers than non-nationalistic ones. Interestingly, the evidence also suggested that job seekers regard each business ethnicity differently and that those prior ethnic impressions affected levels of expected prejudice. Moreover, individual variables such as job seeker’s openness to experience and ethnic identification revealed marginally significant impact on perceived prejudice. This work helps enlighten literature in intergroup conflict in the context of job search and ethnic business and enhance literature on immigrant experience. Important practical implications for recent immigrants, ethnic businesses and immigration policies are discussed in favor of reducing perceived prejudice in multicultural countries.
Motion from " X " by Compensating CCY , , *

This paper analyzes the geometry of the visual motion estimation problem in relation to transformations of the input (images) that stabilize particular output functions such as the motion of a point, a line and a plane in the image. By casting the problem within the popular "epipolar geometry", we provide a common framework for including constraints such as point, line of plane fixation by just considering "slices" of the parameter manifold. The models we provide can be used for estimating motion from a batch using the preferred optimization techniques, or for defining dynamic filters that estimate motion from a causal sequence. We discuss methods for performing the necessary compensation by either controlling the support of the camera or by pre-processing the images. The compensation algorithms may be used also for recursively fitting a plane in 3-D both from point-features or directly from brightness. Conversely, they may be used for estimating motion relative to the plane independent of its parameters. *Research sponsored by NSF NYI Award, NSF ERC in Neuromorphic Systems Engineering at Caltech, ONR grant N00014-93-1-0990. This work is registered as CDS technical report n. CIT-CDS 95-009, March 1995.
Bayesian Flexible Shape Matching with Applications to Structural Proteomics

We introduce a method for flexible shape registration using Bayesian changepoint analysis. Our approach is particularly suitable for shapes containing “hinge”-like flexibility, and is motivated by problems in structural proteomics and bioinformatics. We define a class of flexible shape spaces and an associated Procrustes-type metric, along with a highly efficient algorithm for computing flexible shape distance. We use this distance to define distributions over flexible shapes, and develop Bayesian models for several variants including both affine and rigid-body component transformations. We demonstrate the approach on several examples arising from protein structure analysis, including structure alignment and function discovery.