Miracle-dz/UniFAR-FTUs huggingface dataset:
Association of Killer Cell Immunoglobulin-Like Receptor Genes with Hodgkin's Lymphoma in a Familial Study

Background Epstein-Barr virus (EBV) is the major environmental factor associated with Hodgkin's lymphoma (HL), a common lymphoma in young adults. Natural killer (NK) cells are key actors of the innate immune response against viruses. The regulation of NK cell function involves activating and inhibitory Killer cell Immunoglobulin-like receptors (KIRs), which are expressed in variable numbers on NK cells. Various viral and virus-related malignant disorders have been associated with the presence/absence of certain KIR genes in case/control studies. We investigated the role of the KIR cluster in HL in a family-based association study. Methodology We included 90 families with 90 HL index cases (age 16–35 years) and 255 first-degree relatives (parents and siblings). We developed a procedure for reconstructing full genotypic information (number of gene copies) at each KIR locus from the standard KIR gene content. Out of the 90 collected families, 84 were informative and suitable for further analysis. An association study was then carried out with specific family-based analysis methods on these 84 families. Principal Findings Five KIR genes in strong linkage disequilibrium were found significantly associated with HL. Refined haplotype analysis showed that the association was supported by a dominant protective effect of KIR3DS1 and/or KIR2DS1, both of which are activating receptors. The odds ratios for developing HL in subjects with at least one copy of KIR3DS1 or KIR2DS1 with respect to subjects with neither of these genes were 0.44[95% confidence interval 0.23–0.85] and 0.42[0.21–0.85], respectively. No significant association was found in a tentative replication case/control study of 68 HL cases (age 18–71 years). In the familial study, the protective effect of KIR3DS1/KIR2DS1 tended to be stronger in HL patients with detectable EBV in blood or tumour cells. Conclusions This work defines a template for family-based association studies based on full genotypic information for the KIR cluster, and provides the first evidence that activating KIRs can have a protective role in HL.
Moral Hazard Contracting and Private Credit Markets

This paper studies the impact of credit markets on optimal contracting, when the agent's "interim preference" over upcoming contracts is private information because personal financial decisions affect it via the wealth effect. The main result is a severe loss of incentive provision: equilibrium contracts invariably cause the agent to shirk (i.e., exert minimal effort) if the agent's private financial decision precedes moral hazard contracting. The basic intuition is that committing on another private variable, other than the effort level, exposes the parties to further exploitation of efficient risk-sharing by relaxing the incentive constraint that was binding ex ante, unless the risk-sharing was fully efficient to begin with. Copyright The Econometric Society 2004.
Influence of internal versus external egalitarian goals on stereotype accessibility following goal-behaviour discrepancies

The mechanism through which goals influence stereotype activation/control is unclear. This thesis aimed to shed some light on the mechanism through which internally-generated and externally-imposed goals influence stereotype activation by applying Fishbach and colleagues’ model of goal progress. Across six experiments, I demonstrated that (1) internal, but not external, egalitarian goals result in less stereotype accessibility (consistent with egalitarian goal pursuit) following a potentially large goal–behaviour discrepancy (Experiment 2), (2) external, but not internal, egalitarian goals result in less stereotype accessibility (consistent with egalitarian goal pursuit) when contemplating past success (Experiment 1), but not following a potentially small goal–behaviour discrepancy or a potential goal–behaviour match (Experiments 3 & 4), and (3) internal egalitarian goals result in concern over including ambiguous faces in the ingroup, but only following a potential goal–behaviour discrepancy. External egalitarian goals irrespective of goal–behaviour discrepancy size, and internal egalitarian goals following a potential goal–behaviour match, both result in concern over accurate categorisation (Experiment 6). These findings suggest that the source of a goal (i.e., internal vs. external), and discrepancy size for internal, but not external, goals, are important parts of the mechanism through which goals influence social categorisation and stereotype activation.
Morphological Characterization and Effective Thermal Conductivity of Dual-Scale Reticulated Porous Structures

Reticulated porous ceramic (RPC) made of ceria are promising structures used in solar thermochemical redox cycles for splitting CO2 and H2O. They feature dual-scale porosity with mm-size pores for effective radiative heat transfer during reduction and µm-size pores within its struts for enhanced kinetics during oxidation. In this work, the detailed 3D digital representation of the complex dual-scale RPC is obtained using synchrotron submicrometer tomography and X-ray microtomography. Total and open porosity, pore size distribution, mean pore diameter, and specific surface area are extracted from the computer tomography (CT) scans. The 3D digital geometry is then applied in direct pore level simulations (DPLS) of Fourier’s law within the solid and the fluid phases for the accurate determination of the effective thermal conductivity at each porosity scale and combined, and for fluid-to-solid thermal conductivity from 10−5 to 1. Results are compared to predictions by analytical models for structures with a wide range of porosities 0.09–0.9 in both the strut’s µm-scale and bulk’s mm-scale. The morphological properties and effective thermal conductivity determined in this work serve as an input to volume-averaged models for the design and optimization of solar chemical reactors.
Holistic Interstitial Lung Disease Detection using Deep Convolutional Neural Networks: Multi-label Learning and Unordered Pooling

Accurately predicting and detecting interstitial lung disease (ILD) patterns given any computed tomography (CT) slice without any pre-processing prerequisites, such as manually delineated regions of interest (ROIs), is a clinically desirable, yet challenging goal. The majority of existing work relies on manually-provided ILD ROIs to extract sampled 2D image patches from CT slices and, from there, performs patch-based ILD categorization. Acquiring manual ROIs is labor intensive and serves as a bottleneck towards fully-automated CT imaging ILD screening over large-scale populations. Furthermore, despite the considerable high frequency of more than one ILD pattern on a single CT slice, previous works are only designed to detect one ILD pattern per slice or patch. To tackle these two critical challenges, we present multi-label deep convolutional neural networks (CNNs) for detecting ILDs from holistic CT slices (instead of ROIs or sub-images). Conventional single-labeled CNN models can be augmented to cope with the possible presence of multiple ILD pattern labels, via 1) continuous-valued deep regression based robust norm loss functions or 2) a categorical objective as the sum of element-wise binary logistic losses. Our methods are evaluated and validated using a publicly available database of 658 patient CT scans under five-fold cross-validation, achieving promising performance on detecting four major ILD patterns: Ground Glass, Reticular, Honeycomb, and Emphysema. We also investigate the effectiveness of a CNN activation-based deep-feature encoding scheme using Fisher vector encoding, which treats ILD detection as spatially-unordered deep texture classification.
An Efficient Tree Search Decoder with Column Reordering for Underdetermined MIMO Systems

An efficient tree search decoder for underdetermined MIMO systems is presented. The decoder employs a novel column reordering strategy for the channel matrix in the reduction process, which can significantly reduce the computational cost. Simulation results show that this new algorithm is much more efficient than current approaches for a square constellation higher than 4QAM.
The Effects of Health Insurance and Self-Insurance on Retirement Behavior

\tThis paper presents and estimates a dynamic programming model model of retirement behavior that accounts explicitly for the effects of health cost volatility and and health insurance on retirement behavior. The model includes a savings decision so that we are able to analyze whether self-insurance is an important mechanism for understanding how greatly individuals value health insurance. We present preliminary estimates from the Health and Retirement Survey. We find that the possibility of self-insurance significantly lessens an individual's valuation of health insurance. Therefore, failure to account for self-insurance potentially leads to overestimates of the effect of health insurance and Medicare on retirement behavior.
Economic and care considerations of Marfan syndrome

ABSTRACT Introduction: Marfan syndrome is a rare multisystem disease of the connective tissue, which affects multiple organ systems. advances in healthcare have doubled the life-expectancy of patients over the past three decades. to date, there is no comprehensive review that consolidates economic considerations and care for marfan patients. Areas covered: Present research suggests that there may be a link between treatment pattern, disease progression and economic costs of Marfan syndrome. It indicates that an early detection of the disease and preventive interventions achieve a dual aim. From a patient perspective, it may reduce the amount of emergency surgery or intervention, and inpatient stays. In addition, it slows disease progression, lowers lifestyle restrictions, reduces psychological stress, and improves health-related quality of life. Expert commentary: Early detection and preventive measures are likely to achieve a dual aim by simultaneously containing costs and reducing the number and length of inpatient stays.
All digital baseband 50 Mbps data recovery using 5× oversampling with 0.9 data unit interval clock jitter tolerance

In this paper, an all digital baseband data recovery algorithm using oversampling technique is presented. Our algorithm uses 5× clock to sample incoming data once around the middle. Sampling occurs exactly on the third clock after a data edge. If no data edge sensed, sampling occurs after five clocks from the previous sample. The system is designed to receive 50 Mbps data bit rate and uses a 250 MHz local clock to do the oversampling process. After injecting the clock with jitter at various magnitudes and frequencies, the design showed around 0.9 data Unit Interval (UIdata) jitter tolerance at frequencies higher than 25 MHz, in addition to a low Bit Error Rate (BER ≪ 10−11). The setup is implemented on an Altera Stratix II GX Field Programmable Gate Array (FPGA) while Agilent 81250 parallel Bit Error Ratio Tester (parBERT) is used to measure BER using Pseudo Random Bit Sequences (PRBS). Using 0.18 µm CMOS process, the design consumes as low power as 5 µW, which makes it effective for low power applications such as wireless image sensor nodes.
A novel assessment of population structure and gene flow in grey wolf populations of the Northern Rocky Mountains of the United States

The successful re‐introduction of grey wolves to the western United States is an impressive accomplishment for conservation science. However, the degree to which subpopulations are genetically structured and connected, along with the preservation of genetic variation, is an important concern for the continued viability of the metapopulation. We analysed DNA samples from 555 Northern Rocky Mountain wolves from the three recovery areas (Greater Yellowstone Area, Montana, and Idaho), including all 66 re‐introduced founders, for variation in 26 microsatellite loci over the initial 10‐year recovery period (1995–2004). The population maintained high levels of variation (HO = 0.64–0.72; allelic diversity k = 7.0–10.3) with low levels of inbreeding (FIS < 0.03) and throughout this period, the population expanded rapidly (n1995 = 101; n2004 = 846). Individual‐based Bayesian analyses revealed significant population genetic structure and identified three subpopulations coinciding with designated recovery areas. Population assignment and migrant detection were difficult because of the presence of related founders among different recovery areas and required a novel approach to determine genetically effective migration and admixture. However, by combining assignment tests, private alleles, sibship reconstruction, and field observations, we detected genetically effective dispersal among the three recovery areas. Successful conservation of Northern Rocky Mountain wolves will rely on management decisions that promote natural dispersal dynamics and minimize anthropogenic factors that reduce genetic connectivity.
Personal and Group Environment Factors of Water Polo Players

This study examined to see if Division I female water polo players (n = 113) had a distinct personality profile when compared to their counterpart of other female college students (n = 170). Also, this study analyzed to see the impact personality traits and team cohesion variables had on overall athletic satisfaction in female water polo players. The measures used were as follows: for personality, the Personal Style Inventory for College Students (PSI, Lounsbury & Gibson, 2008); for team cohesion, Group Environment Questionnaire (GEQ, Carron, Widmeyer, & Brawley, 1985) ; and for athletic satisfaction, Athletes Satisfaction Questionnaire (ASQ, Riemer & Chelladurai, 1998). The results looking at the differences between means and effect sizes in regard to the Big Five Personality traits in water polo players and other college students showed a significant difference in 3 out of the 5 traits (conscientiousness, emotional stability, and openness). Then, when looking at overall athletic satisfaction in their athletic experience a hierarchical multiple regression analysis was performed. The Big Five Personality traits only accounted for 7% of the variance (statistically insignificant change in R 2 [coefficient of determination], p = .16), while the four team cohesion measures accounted for 55% (p < .001) of the variance, a large effect. In particular, a part correlation showed that significant results between athletic satisfaction and Individual attraction to the group-Task (ATG-T, rpart [part correlation]= .37, p < .001) and Group Integration-Task (GI-T, rpart [part correlation]= .29, p < .001). These findings have implications to help players and coaches understand if they would be a good fit for a Division I water polo team and to understand how to best make a team successful. Future research can also be done to see if these results are generalizable to other similar team sports as well (i.e. soccer, hockey, etc.).
Cross-language Information Filtering : Word Sense Disambiguation vs . Distributional models

The exponential growth of the Web is the most influential factor that contributes to the increasing importance of text retrieval and filtering systems. Anyway, since information exists in many languages, users could also consider as relevant documents written in different languages from the one the query is formulated in. In this context, an emerging requirement is to sift through the increasing flood of multilingual text: this poses a renewed challenge for designing effective multilingual Information Filtering systems. How could we represent user information needs or user preferences in a language-independent way? In this paper, we compared two content-based techniques able to provide users with cross-language recommendations: the first one relies on a knowledge-based word sense disambiguation technique that uses MultiWordNet as sense inventory, while the latter is based on a dimensionality reduction technique called Random Indexing and exploits the so-called distributional hypothesis in order to build language-independent user profiles. Since the experiments conducted in a movie recommendation scenario show the effectiveness of both approaches, we tried also to underline strenghts and weaknesses of each approach in order to identify scenarios in which a specific technique fits better.
Spectral Correlates in Emotion Labeling of Sustained Musical Instrument Tones

Music is one of the strongest inducers of emotion in humans. Melody, rhythm, and harmony provide the primary triggers, but what about timbre? Do the musical instruments have underlying emotional characters? For example, is the well-known melancholy sound of the English horn due to its timbre or to how composers use it? Though music emotion recognition has received a lot of attention, researchers have only recently begun considering the relationship between emotion and timbre. To this end, we devised a listening test to compare representative tones from eight different wind and string instruments. The goal was to determine if some tones were consistently perceived as being happier or sadder in pairwise comparisons. A total of eight emotions were tested in the study. The results showed strong underlying emotional characters for each instrument. The emotions Happy, Joyful, Heroic, and Comic were strongly correlated with one another. The violin, trumpet, and clarinet best represented these emotions. Sad and Depressed were also strongly correlated. These two emotions were best represented by the horn and flute. Scary was the emotional outlier of the group, while the oboe had the most emotionally neutral timbre. Also, we found that emotional judgment correlates significantly with average spectral centroid for the more distinctive emotions, including Happy, Joyful, Sad, Depressed, and Shy. These results can provide insights in orchestration, and lay the groundwork for future studies on emotion and timbre.
Sparse Neural Networks Topologies

We propose Sparse Neural Network architectures that are based on random or structured bipartite graph topologies. Sparse architectures provide compression of the models learned and speed-ups of computations, they can also surpass their unstructured or fully connected counterparts. As we show, even more compact topologies of the so-called SNN (Sparse Neural Network) can be achieved with the use of structured graphs of connections between consecutive layers of neurons. In this paper, we investigate how the accuracy and training speed of the models depend on the topology and sparsity of the neural network. Previous approaches using sparcity are all based on fully connected neural network models and create sparcity during training phase, instead we explicitly define a sparse architectures of connections before the training. Building compact neural network models is coherent with empirical observations showing that there is much redundancy in learned neural network models. We show experimentally that the accuracy of the models learned with neural networks depends on expander-like properties of the underlying topologies such as the spectral gap and algebraic connectivity rather than the density of the graphs of connections.
AUDITORYEXPERIENCEDOES NOT SHAPE SEXUAL PREFERENCESFOR SONGS IN FEMALENORTHERN CARDINALS

Bird song is typically used by males to attract females. As a consequence of the vocal learning process, the acoustic morphology of male songs shows marked geographic variation. Whether females use variation in male songs to choose mates has been controversial (reviewed by Catchpole & Slater, 1995). In some species, the song types that females produce when treated hormonally have been considered to be the song types they prefer in the context of mate choice. To examine this notion, I investigated the song type preferences of female northern cardinals (Cardinalis cardinalis) using a more direct measure, copulation solicitation display. Unlike females of many other species of songbirds in the Temperate zone, female cardinals naturally develop songs by imitating adults just as males do, allowing direct identi cation of when and what song types are memorized by females for vocal production. I sought to determine if the memory trace formed for vocal performance is identical with the memory trace that guides song type preference in a sexual context, and whether females truly form
A Parsimonious Approach to Modeling Animal Movement Data

Animal tracking is a growing field in ecology and previous work has shown that simple speed filtering of tracking data is not sufficient and that improvement of tracking location estimates are possible. To date, this has required methods that are complicated and often time-consuming (state-space models), resulting in limited application of this technique and the potential for analysis errors due to poor understanding of the fundamental framework behind the approach. We describe and test an alternative and intuitive approach consisting of bootstrapping random walks biased by forward particles. The model uses recorded data accuracy estimates, and can assimilate other sources of data such as sea-surface temperature, bathymetry and/or physical boundaries. We tested our model using ARGOS and geolocation tracks of elephant seals that also carried GPS tags in addition to PTTs, enabling true validation. Among pinnipeds, elephant seals are extreme divers that spend little time at the surface, which considerably impact the quality of both ARGOS and light-based geolocation tracks. Despite such low overall quality tracks, our model provided location estimates within 4.0, 5.5 and 12.0 km of true location 50% of the time, and within 9, 10.5 and 20.0 km 90% of the time, for above, equal or below average elephant seal ARGOS track qualities, respectively. With geolocation data, 50% of errors were less than 104.8 km (<0.94°), and 90% were less than 199.8 km (<1.80°). Larger errors were due to lack of sea-surface temperature gradients. In addition we show that our model is flexible enough to solve the obstacle avoidance problem by assimilating high resolution coastline data. This reduced the number of invalid on-land location by almost an order of magnitude. The method is intuitive, flexible and efficient, promising extensive utilization in future research.
Stabilization of nonlinear sandwich systems via state feedback—Discrete‐time systems

A recent paper (IEEE Trans. Aut. Contr. 2010; 55(9):2156–2160) considered stabilization of a class of continuous‐time nonlinear sandwich systems via state feedback. This paper is a discrete‐time counterpart of it. The class of nonlinear sandwich systems consists of saturation elements sandwiched between linear systems. We focus first on single‐layer sandwich systems, which consist of a single saturation sandwiched between two linear systems. For such systems, we present necessary and sufficient conditions for semi‐global and global stabilization problems by state feedback, and develop design methodologies to achieve the prescribed stabilization. We extend the results to single‐layer sandwich systems subject to additional actuator saturation. Finally, we discuss further extension to general multi‐layer sandwich systems with an arbitrary number of saturations sandwiched between linear systems, both with and without actuator saturation. The design methodologies can be viewed as extensions of classical low‐gain design methodologies developed during the 1990s in the context of stabilizing linear systems subject to actuator saturation. Copyright © 2010 John Wiley & Sons, Ltd.