Miracle-dz/UniFAR-FTUs huggingface dataset:
In Situ Hepatitis C NS3 Protein Detection Is Associated with High Grade Features in Hepatitis C-Associated B-Cell Non-Hodgkin Lymphomas

Hepatitis C Virus (HCV) infection is associated with the B-cell non-Hodgkin lymphomas (NHL), preferentially marginal zone lymphomas (MZL) and diffuse large B-cell lymphomas (DLBCL). While chronic antigenic stimulation is a main determinant of lymphomagenesis in marginal zone lymphomas (MZL), a putative role of HCV infection of B-cells is supported by in vitro studies. We performed a pathological study within the "ANRS HC-13 LymphoC" observational study focusing on in situ expression of the oncogenic HCV non structural 3 (NS3) protein. Lympho-C study enrolled 116 HCV-positive patients with B-NHL of which 86 histological samples were collected for centralized review. Main histological subtypes were DLBCL (36%) and MZL (34%). Almost half of DLBCL (12/26) were transformed from underlying small B-cell lymphomas. NS3 immunostaining was found positive in 17 of 37 tested samples (46%). There was a striking association between NS3 detection and presence of high grade lymphoma features: 12 out of 14 DLBCL were NS3+ compared to only 4 out of 14 MZL (p = 0.006). Moreover, 2 among the 4 NS3+ MZL were enriched in large cells. Remarkably, this study supports a new mechanism of transformation with a direct oncogenic role of HCV proteins in the occurrence of high-grade B lymphomas.
The Netherlands Twin Register Biobank: A Resource for Genetic Epidemiological Studies

Abstract In 2004 the Netherlands Twin Register (NTR) started a large scale biological sample collection in twin families to create a resource for genetic studies on health, lifestyle and personality. Between January 2004 and July 2008, adult participants from NTR research projects were invited into the study. During a home visit between 7:00 and 10:00 am, fasting blood and morning urine samples were collected. Fertile women were bled on day 2–4 of the menstrual cycle, or in their pill-free week. Biological samples were collected for DNA isolation, gene expression studies, creation of cell lines and for biomarker assessment. At the time of blood sampling, additional phenotypic information concerning health, medication use, body composition and smoking was collected. Of the participants contacted, 69% participated. Blood and urine samples were collected in 9,530 participants (63% female, average age 44.4 (SD 15.5) years) from 3,477 families. Lipid profile, glucose, insulin, HbA1c, haematology, CRP, fibrinogen, liver enzymes and creatinine have been assessed. Longitudinal survey data on health, personality and lifestyle are currently available for 90% of all participants. Genome-wide SNP data are available for 3,524 participants, with additional genotyping ongoing. The NTR biobank, combined with the extensive phenotypic information available within the NTR, provides a valuable resource for the study of genetic determinants of individual differences in mental and physical health. It offers opportunities for DNA-based and gene expression studies as well as for future metabolomic and proteomic projects.
A Theory of Causal Responsibility Attribution

People frequently reward and punish other people if they perceive them to be responsible for the implementation of events that they like or dislike. However, the determinants of such responsibility perceptions are not well understood within economics. In this paper, I propose a notion of causal responsibility as one determinant of responsibility perceptions. The notion attempts to objectively capture the causal importance of a person’s action for the implementation of an event when the implementation depends on the interaction of multiple persons and, potentially, moves of nature. I incorporate the notion in a framework of responsibility preferences and study its implication for behavior and equilibria in strategic settings. Finally, I show that the notion can explain experimentally elicited punishment and reward patterns in multi-agent situations that are not well-explained by existing social preference theories.
Small-scale structure of the galactic cirrus emission

We examined the Fourier power spectrum characteristics of cirrus structures in 13 sky fields with faint to bright cirrus emission observed with ISOPHOT in the 90-200 µm wavelength range in order to study variations of the spectral index α . W e found that α varies from field to field with -5.3 ≤ α ≤-2.1. It depends on the absolute surface brightness and on the hydrogen column density. We also found different spectral indices for the same sky region at different wavelengths. Longer wavelength measurements show steeper power spectra. This can be explained by the presence of dust at various temperatures, in particular of a cold extended component. For the faintest areas of the far-infrared sky we derive a wavelength-independent spectral index of α = -2.3 ± 0.6 for the cirrus power spectrum. The application of the correct spectral index is a precondition for the proper disentanglement of the cirrus foreground component of the Cosmic Far-Infrared Background and its fluctuations.
An Empirical Test of Rubinstein's Similarity Definitions for Choice Under Risk

This paper tests two alternative definitions of probability similarity using experimental data. These definitions are key components of Rubinstein’s [14] larger axiomatic model for risky choice under similarity effects. The results of our tests strongly support absolute difference similarity measure over relative (ratio) similarity. As developed by Rubinstein, the evidence argues against an axiomatic reduced-form model that treats preference and similarity jointly.
Finnish-French Fundamental Cultural Antagonisms in Organising

Seeking to challenge the belief that within-West cultural differences should be seen as insignificant in organisations, this paper seeks to demonstrate how two given Western European ‘organising cultures’ (i.e. Finnish culture and French culture, as they are expressed in the process of organising) can contrast, if not conflict, with each other. Further, it aims to help the reader realise what kinds of fundamental ‘cultural antagonisms’ these contrasting organising behaviours may come from, to help her/him understand ‘the other culture’ better, and thus allow for a first step towards an improvement of Finnish-French intercultural interactions in organisational contexts. After shortly introducing what should be understood here as ‘cultural antagonisms’, the paper addresses four fundamental Finnish-French antagonisms, regarding the vision of the organisation (‘functionalist vs. personalist’), the relative importance of ‘consensus vs. dissensus’, the typical trade-off between reliability and flexibility, and the striking differences in communication, respectively. These four fundamental antagonisms are found to be closely interrelated and integrated, serving as explanation, justification and legitimisation for each other. That does not mean, however, that differences, however striking they may be, should merely be a threat to co-operation: some implications introduced at the end of the paper suggest that, provided people are aware of them, cultural antagonisms can also be seen as opportunities for a more fruitful work interaction.
Evaluating Influence Diagrams using LIMIDs

We present a new approach to the solution of decision problems formulated as influence diagrams. The approach converts the influence diagram into a simpler structure, the LImited Memory Influence Diagram (LIMID), where only the requisite information for the computation of optimal policies is depicted. Because the requisite information is explicitly represented in the diagram, the evaluation procedure can take advantage of it. In this paper we show how to convert an influence diagram to a LIMID and describe the procedure for finding an optimal strategy. Our approach can yield significant savings of memory and computational time when compared to traditional methods.
Effects of Feeding Methylthio Butyric Acid Isopropyl Ester on Postpartum Performance and Metabolism in Dairy Cows

The present experiment aimed to evaluate the effect of HMBi on the production performance and metabolism in dairy cows. Thirty multiparous Holstein dairy cows under similar conditions were randomly assigned to three dietary treatments; i) Control, a basal diet; ii) T1, a basal diet plus HMBi (0 g prepartum and 18 g postpartum); and iii) T2, a basal diet plus HMBi (10 g prepartum and 18 g postpartum). Treatments were initiated 21 d before expected calving and continued through 91 d postpartum. HMBi was top-dressed onto the total mixed ration of each cow. Treatments did not affect dry matter intake, plasma urea nitrogen, peak milk yield, days to peak milk yield, nonesterified fatty acid, glutamate pyruvate transaminase, glutamic oxalaetic transaminase, milk fat content, milk protein content, milk lactose content, and milk solid non-fat content. The milk composition yields were increased by the HMBi-supplemented treatment. The T1 and T2 treatments increased the yields of 4% fat-corrected milk yield, milk fat, milk protein, and milk lactose compared with the control. Although there was no difference in the milk composition of the control and T2-treated cows, the T2-treated cows exhibited higher milk fat yield (increased by 74 g/d), lower milk urea nitrogen (reduced by 3.41%), and plasma β-hydroxy butyrate than the control cows. The results indicate that HMBi supplementation to diet has beneficial effects, and that there is no difference between supplementation at prepartum and starting only at parturition.
LCG-MRF-Based Segmentation of MRI Brain Images

Segmentation of MRI brain images plays a critical role in medical image processing and analysis. In this paper, a new method based on Markov Random Field (MRF) is proposed for segmentation of MR brain images. We consider the low-level MRF as a linear combination of Gaussians (LCG) with positive and negative component, and we use the modified Expectation-maximization (MEM) algorithm to estimate the mean, variance and proportion for each distribution.The MEM algorithm is sensitive to initial parameters, so we improve the method of initialization. In high-level MRF, we use Potts model to describe the label image. By using the Bayesian maximum a posterior (MAP) rule, the segmentation problem is converted to precisely identify the models parameters. The MAP estimation is obtained using the Metroplis algorithm to search the optimization. The experimental results show that the proposed method is effective for segmentation of MR brain images.
Cooperative Cognitive Automobiles

Safety requirements are among the most ambitious challenges for autonomous guidance and control of automobiles. A human-like understanding of the surrounding traffic scene is a key element to fulfill these requirements, but is a still missing capability of today's intelligent vehicles. Few recent proposals for driver assistance systems approach this issue with methods from the AI research to allow for a reasonable situation evaluation and behavior generation. While the methods proposed in this contribution are lend from cognition in order to mimic human capabilities, we argue that in the long term automated cooperation among traffic participants bears the potential to improve traffic efficiency and safety beyond the level attainable by human drivers. Both issues are major objectives of the Transregional Collaborative Research Centre 28 'cognitive automobiles,' TCRC28 that is outlined in the paper. Within this project the partners focus on systematic and interdisciplinary research on machine cognition of mobile systems as the basis for a scientific theory of automated machine behavior.
On the Computational Complexity of MSE-OFDM System using Pseudo Random Cyclic Prefix

A new multicarrier system, termed multi-symbol encapsulated orthogonal frequency division multiplexing (MSE-OFDM), was proposed earlier, in which one cyclic prefix (CP) is used for multiple OFDM symbols. The motivation of this proposal is to address the disadvantages of OFDM system, i.e., the high peak to average power ratio and sensitivity to carrier frequency offset at the same time. Although more robust performance and higher bandwidth efficiency are achieved, the implementation of the MSE-OFDM receiver is substantially more complicated than OFDM. In this paper, we propose a new cyclic prefix using pseudo random sequence for MSE-OFDM. With the new introduced cyclic prefix, the inter-symbol interference (ISI) within one MSE-OFDM frame can be easily canceled in time domain. It is shown that the implementation complexity of the MSE-OFDM receiver with the new cyclic prefix is comparable to the traditional OFDM. The performance of the proposed technique is also analyzed and verified through numerical simulations.
Regulating Bank Capital Structure to Control Risk

Since the Basle capital regulations of 1988 and the Federal Deposit Insurance Corporation Improvement Act (FDICIA) of 1991, capital requirements have been a critical part of bank regulation. These requirements limit leverage by requiring and encouraging banks to hold minimum levels of equity. This article uses the agency theory of corporate finance to study how capital requirements control bank risk taking. It finds that existing capital regulations can be made more effective if augmented with financial instruments like warrants or convertible debt.
Uncovering the Nature of the X-Ray Transient 4U 1730–22: Discovery of X-Ray Emission from a Neutron Star in Quiescence with Chandra

The X-ray transient 4U 1730-22 has not been detected in outburst since 1972, when a single ~200 day outburst was detected by the Uhuru satellite. This neutron star or black hole X-ray binary is presumably in quiescence now, and here we report on X-ray and optical observations of the 4U 1730-22 field designed to identify the system's quiescent counterpart. Using the Chandra X-ray Observatory, we have found a very likely counterpart. The candidate counterpart, CXOU J173357.5-220156, is close to the center of the Uhuru error region and has a thermal spectrum. The 0.3-8 keV spectrum is well described by a neutron star atmosphere model with an effective temperature of 131 ± 21 eV. For a neutron star with a 10 km radius, the implied source distance is 10 kpc, and the X-ray luminosity is 1.9 × 1033 (d/10 kpc)2 ergs s-1. Accretion from a companion star is likely required to maintain the temperature of this neutron star, which would imply that it is an X-ray binary, and therefore almost certainly the 4U 1730-22 counterpart. We do not detect an optical source at the position of the Chandra source down to R > 22.1, and this is consistent with the system being a low-mass X-ray binary at a distance greater than a few kpc. If our identification is correct, 4U 1730-22 is one of the 5 most luminous of the 20 neutron star transients that have quiescent X-ray luminosity measurements. We discuss the results in the context of neutron star cooling and the comparison between neutron stars and black holes in quiescence.
Progressive Abstraction Refinement for Sparse Sampling

Monte Carlo tree search (MCTS) algorithms can encounter difficulties when solving Markov decision processes (MDPs) in which the outcomes of actions are highly stochastic. This stochastic branching can be reduced through state abstraction. In online planning with a time budget, there is a complex tradeoff between loss in performance due to overly coarse abstraction versus gain in performance from reducing the problem size. Coarse but unsound abstractions often outperform sound abstractions for practical budgets. Motivated by this, we propose a progressive abstraction refinement algorithm that refines an initially coarse abstraction during search in order to match the abstraction to the sample budget. Our experiments show that the algorithm combines the strong performance of coarse abstractions at small sample budgets with the ability to exploit larger budgets for further performance gains.
Laplace approximation with Gaussian Processes for volatility forecasting

Generalized Autoregressive Conditional Heteroscedascity (GARCH) models are ad hoc methods very used to predict volatility in financial time series. On the other hand, Gaussian Processes (GPs) offer very good performance for regression and prediction tasks, giving estimates of the average and dispersion of the predicted values, and showing resilience to overfitting. In this paper, a GP model is proposed to predict volatility using a reparametrized form of the Ornstein-Uhlenbeck covariance function, which reduces the underlying latent function to be an AR(1) process, suitable for the Brownian motion typical of financial time series. The tridiagonal character of the inverse of this covariance matrix and the Laplace method proposed to perform inference allow accurate predictions at a reduced cost compared to standard GP approaches. The experimental results confirm the usefulness of the proposed method to predict volatility, outperforming GARCH models with more accurate forecasts and a lower computational burden.
A Fast Learning Agent Based on the Dyna Architecture

In this paper, we present a rapid learning algorithm called Dyna-QPC. The proposed algorithm requires considerably less training time than Q-learning and Table-based Dyna-Q algorithm, making it applicable to real-world control tasks. The Dyna-QPC algorithm is a combination of existing learning techniques: CMAC, Q-learning, and prioritized sweeping. In a practical experiment, the Dyna-QPC algorithm is implemented with the goal of minimizing the learning time required for a robot to navigate a discrete statespace containing obstacles. The robot learning agent uses Q-learning for policy learning and a CMAC-Model as an approximator of the system environment. The prioritized sweeping technique is used to manage a queue of previously influential state-action pairs used in a planning function. The planning function is implemented as a background task updating the learning policy based on previous experience stored by the approximation model. As background tasks run during CPU idle time, there is no additional loading on the system processor. The Dyna-QPC agent switches seamlessly between real and virtual modes with the objective of achieving rapid policy learning. A simulated and an experimental scenario have been designed and implemented. The simulated scenario is used to test the speed and efficiency of the three learning algorithms, while the experimental scenario evaluates the new Dyna-QPC agent. Results from both simulated and experimental scenarios demonstrate the superior performance of the proposed learning agent.
Ultrasound elastography based on multiscale estimations of regularized displacement fields

Elasticity imaging is based on the measurements of local tissue deformation. The approach to ultrasound elasticity imaging presented in this paper relies on the estimation of dense displacement fields by a coarse-to-fine minimization of an energy function that combines constraints of conservation of echo amplitude and displacement field continuity. The multiscale optimization scheme presents several characteristics aimed at improving and accelerating the convergence of the minimization process. This includes the nonregularized initialization at the coarsest resolution and the use of adaptive configuration spaces. Parameters of the energy model and optimization were adjusted using data obtained from a tissue-like phantom material. Elasticity images from normal in vivo breast tissue were subsequently obtained with these parameters. Introducing a smoothness constraint into motion field estimation helped solve ambiguities due to incoherent motion, leading to elastograms less degraded by decorrelation noise than the ones obtained from correlation-based techniques.
Reactive behavior in object-oriented applications: an analysis and a research roadmap

Reactive applications are difficult to implement. Traditional solutions based on event systems and the Observer pattern have a number of inconveniences, but programmers bear them in return for the benefits of OO design. On the other hand, reactive approaches based on automatic updates of dependencies - like functional reactive programming and dataflow languages - provide undoubted advantages but do not fit well with mutable objects. In this paper, we provide a research roadmap to overcome the limitations of the current approaches and to support reactive applications in the OO setting. To establish a solid background for our investigation, we propose a conceptual framework to model the design space of reactive applications and we study the flaws of the existing solutions. Then we highlight how reactive languages have the potential to address those issues and we formulate our research plan.
18 Another Look at Preposition Stranding: English and Swedish Discourse Patterns

Preposition Stranding (henceforth PS) is a syntactic construction that can be found across Germanic languages (Bolinger 1977, 1978). PS involves a non-canonical word order, in which a preposition and its object noun phrase (henceforth NP) are not adjacent, hence they appear to be “stranded”. PS includes three sub-types of constructions: pseudo-passives, relative clauses and wh-constructions (Koopman 2000). The existence of PS in Southern and Western Germanic languages, such as German and Dutch, is considered a controversial matter (van Riemsdijk 1990, 1998; Maling & Zaenen 1985; Truswell 2009). However, PS is certainly attested in English and most Scandinavian languages including Swedish, Norwegian, Danish, and Icelandic (Ǻfarli 1992; Takami 1992; van Riemsdijk 1998; Law 2005; Klingvall 2012). Interestingly, some of these works observe that PS seems to be an uncommon construction in Swedish (Takami 1992, Klingvall 2012). For this reason, in this paper we concentrate on English and Swedish, in order to address whether this phenomenon can receive a unified account across both languages, regardless of its language-related frequency. We start by discussing some preliminary examples in (1)-(8)1: