Miracle-dz/UniFAR-FTUs huggingface dataset:
The Functional UGT1A1 Promoter Polymorphism Decreases Endometrial Cancer Risk

UDP-glucuronosyltransferase (UGT) 1A1 is involved in the inactivation of estradiol (E2) and its oxidized metabolites. These metabolites have been shown to contribute to the development of endometrial cancer in animal studies. Thus UGT1A1 represents a candidate gene in endometrial carcinogenesis. In this study, we established the substrate specificity of UGT1A1 for E2 and its 2- and 4-hydroxylated metabolites. Intrinsic clearances indicated that UGT1A1 had a preference for the glucuronidation of 2-hydroxyestradiol, a metabolite associated with antiproliferative activity. Expression analysis demonstrated that UGT1A1 is present in the nonmalignant endometrium. Subsequently, we sought to determine whether the common UGT1A1 promoter allele, UGT1A1*28 [A(TA)7TAA], which decreases gene transcription, was associated with endometrial cancer risk in a case-control study nested within the Nurses’ Health Study (222 cases, 666 matched controls). Conditional logistic regression demonstrated a significant inverse association with the UGT1A1*28 allele and endometrial cancer risk. Compared with women homozygous for the UGT1A1*1 [A(TA)6TAA] allele, the adjusted odds ratio (OR) was 0.81 [95% confidence interval (CI), 0.56–1.16] for the UGT1A1*1/*28 genotype and 0.40 (95% CI, 0.21–0.75) for the homozygous UGT1A1*28 genotype (Ptrend = 0.007). There was a suggestion of an interaction by menopausal status [OR = 0.39 (95% CI, 0.18–0.85) for premenopausal women and OR = 0.79 (95% CI, 0.55–1.13) for postmenopausal women who carry the UGT1A1*28 allele (Pinteraction = 0.05)]. These observations suggest that lower expression of UGT1A1 decreases the risk of endometrial cancer by reducing the excretion of 2-hydroxyestradiol, the antiproliferative metabolite of E2, in the endometrium.
Event detection in evolving networks

This paper describes a methodology for finding and describing significant events in time evolving complex networks. We first group the nodes of the network in clusters, according to their similarity in terms of a set of local properties such as degree and clustering coefficient. We then monitor the behavior of these groups over time, looking for significant changes on the size of the groups. These events are notable since they show that the position of a number of nodes in the network has changed. We describe this evolution by extracting the correspondent transition patterns. We examined our methodology on three different real network datasets. Our experiments show that the discovered rules are significant and can describe the occurring events.
What are Grade Twelve Students Reading? Surveying Saskatchewan English Language Arts B30 Teachers

The purpose of this research study is to investigate how the recent English Language Arts (ELA) curriculum renewal has been adopted throughout Saskatchewan by looking specifically at the full-length texts taught in the ELA B30 course throughout 2014-2015, as a possible indicator. The basis of this research study stems from two prior Canadian studies: “The Constancy of the School ‘Canon’: A Survey of Texts used in Grade 10 English Language Arts in 2006 and 1996” by Mackey, Vermeer, Storie, and DeBlois (2012), as well as Allison Skerrett’s 2010 study, “Of Literary Import: A Case of Cross-National Similarities in the Secondary English Curriculum in the United States and Canada. ” While these two studies are based in Alberta and Ontario, at the time of this research project, there is no information regarding what texts are commonly studied in Saskatchewan. This qualitative bounded case study draws upon mixed methods for data collection. ELA B30 teachers were surveyed on-line, and following that, ELA consultants and/or coordinators from a variety of Saskatchewan school divisions were interviewed. The focus was to learn, as an indicator, what full-length texts are used in ELA B30 and to further unpack how the curriculum renewal process occurred in Saskatchewan classrooms, and whether or not teachers felt supported to change their text selections. Teachers and coordinators found the Saskatchewan Ministry of Education support to be lacking in the implementation process of the curriculum renewal, and data from this research study revealed that full-length text selections are primarily dated, maledominated, and reflect the voices of British or American male authors. To conclude, a
An Efficient Decomposition Algorithm for Static , Stochastic , Linear and Mixed-Integer Linear Programs with Conditional-Value-at-Risk Constraints

LIMITED DISTRIBUTION NOTICE: This report has been submitted for publication outside of IBM and will probably be copyrighted if accepted for publication. It has been issued as a Research Report for early dissemination of its contents. In view of the transfer of copyright to the outside publisher, its distribution outside of IBM prior to publication should be limited to peer communications and specific requests. After outside publication, requests should be filled only by reprints or legally obtained copies of the article (e.g. , payment of royalties). Copies may be requested from IBM T. J. We present an efficient decomposition algorithm for single-stage, stochastic linear programs, where conditional value at risk (CVaR) appears as a risk measure in multiple constraints. It starts with a well-known nonlinear, convex reformulation of conditional value at risk constraints, and establishes the connection to a combinatorially large polyhedral representation of the convex feasible set induced by the above refor-mulation. The algorithm is developed as a column-generation procedure in the dual space of the resulting polyhedral representation. In the special case, where CVaR appears in the objective function alone, it offers an alternative view into CVarMin, a breakthrough algorithm in the literature, which specialized the L-shaped algorithm of two-stage stochastic programs with recourse for CVaR minimization. CVarMin concentrated on CVaR in the objective function, which led naturally to a two-stage interpretation. The optimization problem with multiple CVaR constraints does not have a natural two-stage interpretation, nevertheless we explicitly extend the polyhedral ideas of CVaR minimization and integrated chance constraints to the case of multiple CVaR constraints. We also present an algorithm that engineers the decomposition scheme to address mixed-integer linear programming problems with multiple CVaR constraints.
Optimizing snake locomotion in the plane

We develop a numerical scheme to determine which planar snake motions are optimal for locomotory efficiency, across a wide range of frictional parameter space. For a large coefficient of transverse friction, we show that retrograde travelling waves are optimal. We give an asymptotic analysis showing that the optimal wave amplitude decays as the power of the coefficient of transverse friction. This result agrees well with the numerical optima. At the other extreme, zero coefficient of transverse friction, we propose a triangular direct wave that is optimal. Between these two extremes, a variety of complex, locally optimal motions are found. Some of these can be classified as standing waves (or ratcheting motions).
Automated diagnostic systems: arterial disorders detection

The paper includes illustrative and detailed information about implementation of automated diagnostic systems and feature extraction/selection from signals recorded from arteries. The major objective of the paper is to be a guide for the readers, who want to develop an automated diagnostic systems for detection of arterial disorders. Toward achieving this objective, this paper present the techniques which should be considered in developing automated diagnostic systems. The author suggest that the content of the paper will assist to the people in gaining a better understanding of the techniques in the detection of arterial disorders.
The evolution of Eclipse

We present a metrics-based study of the evolution of Eclipse, an open source integrated development environment, based on data from seven major releases, from releases 1.0 to 3.3. We investigated whether three of the laws of software evolution were supported by the data. We found that Eclipse displayed continual change and growth, hence supporting laws 1 and 6. Six size indicators, out of eight, closely followed trend models. Four were linear and two superlinear. We found evidence of increasing complexity (law 2) in only two indicators, out of five. At subproject level, size and complexity are not distributed uniformly, and subproject size can be modelled as a negative exponential function of the rank position. We encountered a range of different size and complexity trends across subprojects. Our approach and results can help in evaluating the future evolution of Eclipse, the evolution of other systems and in performing comparisons.
Mechanisms of auditory signal decoding in the progressive aphasias

The primary progressive aphasias (PPA) are a diverse group of neurodegenerative disorders that selectively target brain networks mediating language. The pathophysiology of PPA remains poorly understood, but emerging evidence suggests that deficits in auditory processing accompany and may precede language symptoms in these patients. In four studies, I have probed the pathophysiology of auditory signal decoding in patient cohorts representing all major PPA syndromes – nonfluent variant PPA (nfvPPA), semantic variant PPA (svPPA), and logopenic variant PPA (lvPPA) – in relation to healthy age-matched controls. In my first experiment, I presented sequences of spoken syllables manipulated for temporal regularity, spectrotemporal structure and entropy. I used voxel-based morphometry to define critical brain substrates for the processing of these attributes, identifying correlates of behavioural performance within a cortico-subcortical network extending beyond canonical language areas. In my second experiment, I used activation functional magnetic resonance imaging (fMRI) with the same stimuli. I identified network signatures of particular signal attributes: nfvPPA was associated with reduced activity in anterior cingulate for processing temporal irregularity; lvPPA with reduced activation of posterior superior temporal cortex for processing spectrotemporal structure; and svPPA with reduced activation of caudate and anterior cingulate for processing signal entropy. In my third experiment, I manipulated the auditory feedback via which participants heard their own voices during speech production. Healthy control participants spoke significantly less fluently under delayed auditory feedback, but patients with nfvPPA and lvPPA were affected significantly less. In my final experiment, I probed residual capacity for dynamic auditory signal processing and perceptual learning in PPA, using sinewave speech. Patients with nfvPPA and lvPPA showed severely attenuated learning to the degraded stimuli, while patients with svPPA showed intact early perceptual processing, but deficient integration of semantic knowledge. Together, these experiments represent the most concerted and comprehensive attempt to date to define the pathophysiology of auditory signal decoding in PPA.
Hierarchical Reinforcement Learning and Hidden Markov Models for Task-Oriented Natural Language Generation

Surface realisation decisions in language generation can be sensitive to a language model, but also to decisions of content selection. We therefore propose the joint optimisation of content selection and surface realisation using Hierarchical Reinforcement Learning (HRL). To this end, we suggest a novel reward function that is induced from human data and is especially suited for surface realisation. It is based on a generation space in the form of a Hidden Markov Model (HMM). Results in terms of task success and human-likeness suggest that our unified approach performs better than greedy or random baselines.
Python-based Distributed Programming with Trickle

Trickle is a an extension to the Python program- ming language that provides explicit but simple mechanisms to write distributed scripts and programs. Trickle links to - gether remote Python interpreters running on heterogeneou s machines so that work can be deployed and results collected. A Trickle program interacts with remote interpreters by inj ect- ing functions or classes. Remote objects can be instantiate d and invoked synchronously or asynchronously. Also, the in- jected code need not reside on the remote interpreters; code is dynamically transferred as needed. Trickle leverages of f of Python's list comprehensions and generators to simplify parallel invocation, result gathering, and dynamic schedu l- ing. The Trickle run-time system uses a broadcast mechanism to find eligible Trickle virtual machines. Python programme rs can immediately use Trickle to dispatch work to idle machine s with minimal setup and easy to learn mechanisms. This paper describes the Trickle extension interface, its implementa tion, and presents an example application and its performance.
Noise Reduction Technique for ECG Signals Using Adaptive Filters

The ECG finds its importance in the detection of cardiac abnormalities. Noise reduction in ECG signal is an important task of biomedical science. ECG signals are very low frequency signals of about 0.5Hz-100Hz and digital filters are very efficient for noise removal of such low frequency signals. In this Paper an adaptive filter for high resolution ECG Signal is presented which estimate the deterministic component of the ECG Signal and remove the noise. The filter needs two input: the signal (primary input) and an impulse correlated with the deterministic component (reference input). Several signals to noise ratio were considered and the effect of shape variation was also studied. The adaptive filters essentially minimizes the mean-squared error between a primary input, which is the noisy ECG, and a reference input, which is either noise that is correlated in some way with the noise in the primary input or a signal that is correlated only with ECG in the primary input. Different filter structures are presented to eliminate the diverse forms of noise: 60Hz power line interference, baseline wander, muscle noise and the motion artifact. Finally, we have applied these algorithms on real ECG signals obtained from the MIT-BIH data base. Simulation results are also shown. Performance of filters is analyzed based on SNR and MSE.
Reliability challenges for electric vehicles: From devices to architecture and systems software

Today, modern high-end cars have close to 100 electronic control units (ECUs) that are used to implement a variety of applications ranging from safety-critical control to driver assistance and comfort-related functionalities. The total sum of these applications is several million lines of software code. The ECUs are connected to different sensors and actuators and communicate via a variety of communication buses like CAN, FlexRay and now also Ethernet. In the case of electric vehicles, both the amount and the importance of such electronics and software are even higher. Here, a number of hydraulic or pneumatic controls are replaced by corresponding software-implemented controllers in order to reduce the overall weight of the car and hence to improve its driving range. Until recently, most of the software and system design in the automotive domain - as in many other domains - relied on an always correctly functioning or a zero-defect hardware implementation platform. However, as the device geometries of integrated circuits continue to shrink, this assumption is increasingly not true. Incorporating large safety margins in the design process results in very pessimistic design and expensive processors. Further, the processors in cars - in contrast to those in many consumer electronics devices like mobile phones - are exposed to harsh environments, extreme temperature variations, and often, strong electromagnetic fields. Hence, their reliability is even more questionable and must be explicitly accounted for in all layers of design abstraction - starting from circuit design to architecture design, to software design and runtime management and monitoring. In this paper we outline some of these issues, currently followed practices, and the challenges that lie ahead of us in the automotive and electric vehicles domain.
Design of Energy Management Strategy in Hybrid Electric Vehicles by Evolutionary Fuzzy System Part II: Tuning Fuzzy Controller by Genetic Algorithms

This paper presents the second part of a two-part paper on development of an evolutionary fuzzy energy management strategy for parallel hybrid vehicles. In this part, we utilized genetic algorithms (GA) to optimize the parameters of the fuzzy controller. In addition, we employed a novel method to cope with the difficulties often encountered in designing a fitness function of GA. The simulation study reveals that the proposed "evolutionary fuzzy system" based energy management strategy provide a platform of new energy management system and gives improved performance of a parallel hybrid vehicle
The qTSN Positive Effect on Panicle and Flag Leaf Size of Rice is Associated with an Early Down-Regulation of Tillering

The qTSN4 was identified as rice QTL (Quantitative Traits Locus) increasing total spikelet number per panicle and flag leaf area but potentially reducing panicle number depending on the environment. So far, this trade-off was mainly observed at grain maturity and not specifically studied in details, limiting the apprehension of the agronomic interest of qTSN4. This study aimed to understand the effect of qTSN4 and of the environment on panicle sizing, its trade-off with panicle number, and finally plant grain production. It compared two high yielding genotypes to their Near Isogenic Lines (NIL) carrying either QTL qTSN4 or qTSN12, two distinct QTLs contributing to the enlarged panicle size, thereafter designated as qTSN. Traits describing C sink (organ appearance rate, size, biomass) and source (leaf area, photosynthesis, sugar availability) were dynamically characterized along plant and/or panicle development within two trials (greenhouse, field), each comparing two treatments contrasting for plant access to light (with or without shading, high or low planting densities). The positive effect of qTSN on panicle size and flag leaf area of the main tiller was confirmed. More precisely, it could be shown that qTSN increased leaf area and internode cross-section, and in some cases of the photosynthetic rate and starch reserves, of the top 3–4 phytomers of the main tiller. This was accompanied by an earlier tillering cessation, that coincided with the initiation of these phytomers, and an enhanced panicle size on the main tiller. Plant leaf area at flowering was not affected by qTSN but fertile tiller number was reduced to an extent that depended on the environment. Accordingly, plant grain production was enhanced by qTSN only under shading in the greenhouse experiment, where panicle number was not affected and photosynthesis and starch storage in internodes was enhanced. The effect of qTSN on rice phenotype was thus expressed before panicle initiation (PI). Whether early tillering reduction or organ oversizing at meristem level is affected first cannot be entirely unraveled. Further studies are needed to better understand any signal involved in this early regulation and the qTSN × Environment interactions underlying its agronomic interest.
Stochastic optimization methods applied to BP network based fault diagnosis problems of rotating machinery

BP network has been successfully used in the fault diagnosis of rotating machinery, however BP network's drawbacks, such as low convergence rate and its easy fall into local optima have restricted its wider applications, especially to those complex multimodal problems. Two of the recently proposed stochastic optimization methods: adaptive particle swarm optimization (APSO) and adaptive genetic algorithms (AGA) are discussed. And the way that BP network's initial weights and bias are optimized by those two methods is also carefully discussed. Compared with standard particle swarm optimization(SPSO), APSO solves the premature convergence problem better by giving particles a spatial extension and adaptive mutation. In this paper, firstly APSO and AGA are used to optimize the initial weights of BP network, then the APSO-BP and AGA-BP networks are used to diagnose the turbo-pump faults, and the experimental results show many advantages in convergence speed and accuracy. The comparison between AGA and APSO is also discussed.
A Young-Planet Search in Visible and Infrared Light: DN Tauri, V836 Tauri, and V827 Tauri

In searches for low-mass companions to late-type stars, correlation between radial velocity variations and line bisector slope changes indicates contamination by large starspots. Two young stars demonstrate that this test is not sufficient to rule out starspots as a cause of radial velocity variations. As part of our survey for substellar companions to T Tauri stars, we identified the ~2 Myr old planet host candidates DN Tau and V836 Tau. In both cases, visible-light radial velocity modulation appears periodic and is uncorrelated with line bisector span variations, suggesting close companions of several MJup in these systems. However, high-resolution, infrared spectroscopy shows that starspots cause the radial velocity variations. We also report unambiguous results for V827 Tau, identified as a spotted star on the basis of both visible-light and infrared spectroscopy. Our results suggest that infrared follow-up observations are critical for determining the source of radial velocity modulation in young, spotted stars.
The Evolution of Supernovae in Circumstellar Wind-Blown Bubbles. I. Introduction and One-Dimensional Calculations

Mass loss from massive stars (≳8 M☉) can result in the formation of circumstellar wind-blown cavities surrounding the star, bordered by a thin, dense, cold shell. When the star explodes as a core-collapse supernova (SN), the resulting shock wave will interact with this modified medium around the star, rather than the interstellar medium. In this work we first explore the nature of the circumstellar medium around massive stars in various evolutionary stages. This is followed by a study of the evolution of SNe within these wind-blown bubbles. The evolution depends primarily on a single parameter Λ, the ratio of the mass of the dense shell to that of the ejected material. We investigate the evolution for different values of this parameter. We also plot approximate X-ray surface brightness plots from the simulations. For very small values Λ ≪ 1 the effect of the shell is negligible, as one would expect. Values of Λ ≲ 1 affect the SN evolution, but the SN "forgets" about the existence of the shell in about 10 doubling times or so. The remnant density profile changes, and consequently the X-ray emission from the remnant will also change. The initial X-ray luminosity of the remnant is quite low, but interaction of the shock wave with the dense circumstellar shell can increase the luminosity by 2-3 orders of magnitude. As the reflected shock begins to move inward, X-ray images will show the presence of a double-shelled structure. Larger values result in more SN energy being expended to the shell. The resulting reflected shock moves quickly back to the origin, and the ejecta are thermalized rapidly. The evolution of the remnant is speeded up, and the entire remnant may appear bright in X-rays. If Λ ≫ 1, then a substantial amount of energy may be expended in the shell. In the extreme case the SN may go directly from the free expansion to the adiabatic stage, bypassing the Sedov stage. Our results show that in many cases the SNR spends a significant amount of time within the bubble. The low density within the bubble can delay the onset of the Sedov stage and may end up reducing the amount of time spent in the Sedov stage. The complicated density profile within the bubble makes it difficult to infer the mass-loss properties of the pre-SN star by studying the evolution of the resulting SNR.