dataesr/scientific-paragraphs-categorization huggingface dataset:
Homozygote marker SNPs and those with low coverage (less than 15×) were excluded. Major allele fraction (μ) representing allelic imbalance for each marker SNP was computed, and an average of allelic imbalances for each gene was calculated for individual tumor samples. Marker SNPs with extreme μ values (μ >0. 75) were not included in the analysis. Level 3 SNP array data were downloaded from the TCGA portal, and GISTIC version 2. 0. 16 was used to identify copy number variations (CNVs) for each sample. Samples with low or high CNV levels, as presented in the gene-based GISTIC module report, were excluded from the analysis of the corresponding gene. For each risk SNP, allelic imbalance for the target transcripts was compared between heterozygote (AB) and homozygote (AA and BB) samples. For a given risk SNP and target gene, we used Levene's test, a more robust test than the F test, for equality of variances when the risk SNP was not in linkage disequilibrium with any of the marker SNPs on that gene (r 2 <0.
ER + MCF-7 and BT-474 or ER -Bre-80 cells were transfected with equimolar amounts of luci-ferase reporter plasmids and 50 ng of pRL-TK transfection control plasmid with Lipofectamine 3000. The total amount of transfected DNA was kept constant at 600 ng for each construct by the addition of pUC19 as a carrier plasmid. Luciferase activity was measured 24 h after transfection by the Dual-Glo Luciferase Assay System. To correct for any differences in transfection efficiency or cell lysate preparation, firefly luciferase activity was normalized to Renilla luciferase activity, and the activity of each construct was measured relative to the promoter-only construct, which had a defined activity of 1. Statistical significance was tested by log transforming the data and performing two-way ANOVA followed by Dunnett's multiple-comparisons test in GraphPad Prism.
Acknowledgments We thank all the individuals who took part in these studies and all the researchers, clinicians, technicians and administrative staff who have enabled this work to be carried out. This study would not have been possible without the contributions of the following:  A. Berchuck  (OCAC),  R. A. Eeles ,  A. A. Al Olama ,  Z. Kote-Jarai  and  S. Benlloch  (PRACTICAL),  C. Luccarini  and the staff of the  Centre for Genetic Epidemiology Laboratory , the staff of the CNIO genotyping unit,  D. C. Tessier ,  F. Bacot ,  D. Vincent ,  S. LaBoissière ,  F. Robidoux  and the staff of the  McGill University and Génome Québec Innovation Centre ,  S. F. Nielsen ,  B. G. Nordestgaard  and the staff of the  Copenhagen DNA laboratory , and  J. M. Cunningham ,  S. A. Windebank ,  C. A. Hilker ,  J.
Meyer  and the staff of the  Mayo Clinic Genotyping Core Facility. Normal human tissues from the  Susan G. Komen for the Cure Tissue Bank at the Indiana University Simon Cancer Center (Indianapolis)  were used in this study. We thank the contributors, including  Indiana University  who collected samples used in this study, as well as the donors and their families, whose help and participation made this work possible. We also acknowledge  National Institute for Health Research (NIHR)  support to the  Royal Marsden Biomedical Research Centre. Funding for the iCOGS infrastructure came from the  European Community 's  Seventh Framework Programme  under grant agreement  223175  ( HEALTH-F2-2009-223175 ) (COGS),  Cancer Research UK  ( C1287/A10118 ,  C1287/A10710 ,  C12292/A11174 ,  C1281/ A12014 ,  C5047/A8384 ,  C5047/A15007 ,  C5047/A10692  and  C8197/A16565 ), the  US National Institutes of Health (NIH ;  CA128978 ,  CA192393 ,  CA116167 ,  CA176785  and an  NIH Specialized Program of Research Excellence (SPORE) in Breast Cancer  ( CA116201 )) and  Post-Cancer GWAS initiative  ( 1U19 CA148537 ,  1U19 CA148065  and  1U19 CA148112 , the  GAME-ON initiative) , the  US Department of Defense  ( W81XWH-10-1-0341 ), the  Canadian Institutes of Health Research (CIHR)  for the  CIHR Team in Familial Risks of Breast Cancer , the  Komen Foundation  for the Cure, the  Breast Cancer Research Foundation  and the  Ovarian Cancer Research Fund.
3 The predicted disbursements instrument is constructed as follows. We start with a set of approximately 60,000 loans from official creditors to developing country governments, as recorded in the Debtor Reporting System database of the World Bank. For each loan we have the year of the original commitment, and the schedule of subsequent annual actual disbursements. For each loan we construct a 10-yeardisbursement profile, i. e. the fraction of the original commitment that is disbursed in year 0, 1,2,. ,10, with year zero corresponding to the approval year of the loan. We next sort loans into creditor-decade-region-specific bins and average disbursement profiles across loans within bins. We then apply these typical disbursement profiles to individual loans within each bin, and construct predicted disbursements for each loan, i.
The quality of the nuclear data is essential for AREVA NP. Indeed, many AREVA NP activities such as reactor design, safety studies or reactor instrumentation use them as input data. So, the nuclear data can be considered as a key element for AREVA NP. AREVA NP's contribution in the improvement of the nuclear data consists in a joint effort with the CEA. It means a financing and a sharing of information which can give an orientation to the future research axis. The aim of this article is to present the industrial point of view from AREVA NP on the research on nuclear data. Several examples of collaborations with the CEA which have resulted in an improvement of the nuclear data are presented.
AREVA NP actions inside the I3P Inside the I3P, the "direct" research on the nuclear data is mainly performed by the CEA which get a funding from CEA, EDF and AREVA NP. AREVA NP is responsible of the physical validation of its own calculation codes which make use of the nuclear data libraries provided by the CEA. This validation is based on a lot of experimental and power reactor measurements: reactivity, fission rate distribution, depleted pin inventory, boron let-down curve, temperature coefficient, flux map, rod worth… AREVA NP then provides to the CEA a feedback on the basis of the EDF and the worldwide nuclear fleet which can give an orientation to the future research axis on the nuclear data.
CEA actions inside the I3P The CEA is an important actor inside the European project JEFF. It proposes many new nuclear data evaluations which are then integrated in the new releases of the JEFF library. The new libraries are then transmitted to AREVA NP in binary file readable by AREVA NP codes and more precisely by ARCADIA. The CEA also designs critical mock-up in experimental reactors. It performs measurements and provides the results to AREVA NP and EDF. These experiments can then be added to the physical validation base of AREVA NP codes. The last but not least action from the CEA is an expert advice. It can give an efficient support to the industrial needs on the nuclear data.
Introduction The urban fleet of vehicles is rapidly evolving from a collection of sensor platforms that provide information to drivers and upload filtered sensor data (e. g. global positioning system (GPS) location and road conditions) to the Internet cloud to a network of autonomous vehicles (AUVs) that exchange their sensor inputs with each other in order to optimize a well-defined utility function. This function, in the case of autonomous cars, is a prompt delivery of the passengers to destinations with maximum safety and comfort and minimum impact on the environment. In other words, one is witnessing in the vehicle fleet the same evolution from sensor web (i. e. sensors are accessible from the Internet to get their data) to the Internet of Things (IoT, the components with embedded sensors are networked with each other and make intelligent use of the sensors).
Vehicular ad hoc network contents A vehicular ad hoc network (VANET) has enjoyed a variety of applications, from safety and comfort to entertainment and commercial services. This section discusses three important characteristics of application of content items in VANET and discusses their impacts on an emerging application, a connected, AUV. First, vehicles in emerging applications share a huge amount of sensor data (contents) and collaborate to complete a common task. On-board sensors record a myriad of physical phenomena, and vehicle applications collect such sensor records, even from neighboring vehicles, to produce value-added services. 1 In the CarSpeak application, a vehicle accesses sensors on neighboring vehicles in the same manner as it accesses its own. 2  The vehicle then runs an autonomous driving application using the sensor collection.
Local validity indicates that vehicle-generated content has its own spatiotemporal scope of validity to consumers. For instance, a speed-warning message near a sharp corner is only valid to vehicles approaching the corner, say within 500 m. Likewise, road congestion information may invalidate after 30 min. The location relevance implies the scalability of the data collection/storage/ processing applications, since old data is discarded. It also implies that the data should be kept on the vehicles rather than uploaded to the Internet, leading to enormous spectrum savings. Thus, this property will be key to the scalability of the AUV concept, given the huge amount of data collected by AUV sensors.
Instantiating the Internet of Vehicles Vehicles with embedded sensors generate copious amounts of data every second. At the same time, roads are instrumented with smart dust components,  6  radiofrequency identification (RFID) tags,  7  and embedded microcontrollers. These things constitute a vehicle grid, that is, an intelligent transportation infrastructure analogous to the energy grid for intelligent power generation and distribution. The next trend we want to report is the emergence of the vehicular fog. It instantiates the Internet of Vehicles by inter-networking all things that sense and move in the grid and by coordinating them to provide a computing environment. That is, the vehicular fog emerges as a computing and networking model for the systematic implementation of protocols and services required for vehicle applications in the grid.
mercedes-benz. com/_ en/intelligent_drive/detail. html). Academia has also demonstrated meaningful results by running autonomous driving tests. 9, 10 In the future, as addressed in Kumar et al. ,  2  accessing sensors of neighboring vehicles will significantly improve the accuracy and safety of the driving. The vehicular fog will provide the ideal system environment for the coordinated deployment of the sensor aggregation, fusion, and database sharing applications required by the future AUVs.
Vehicular fog and AUV challenges The evolution from manually operated to AUV will pose several new challenges. Some of these challenges come from the massive deployment of sensors on the AUV and the huge amount of data that the AUV picks up from the environment. Other challenges result from the fact that the AUV ''drives itself autonomously'' while the driver may be busy with background activities and not capable of intervening immediately in case of emergencies. After all, a much-advertised AUV benefit is the ability of the driver to engage in other activities as if he or she were on a train-''with wheels. '' In this section, we review these challenges and their impacts on vehicular protocols and applications and more generally on the vehicular fog architecture design.
Recovery from infrastructure failure. The AUVs depend on the infrastructure (e. g. WiFi access points, RSUs, and LTE) for several non-safety functions such as advanced sensor data processing and intelligent transport. In the case of a major infrastructure failure caused by an earthquake, say, some of these functions must be taken over by human drivers. However, there is a gray period, between when a massive infrastructure failure occurs and when the human takes over of navigation, during which the AUV systems must deal with the problems on their own. This is a very critical window because the AUVs only know about their immediate neighbors. After the disaster, they have lost knowledge of the neighbors beyond the reach of their sensors, which was provided by an Internet transaction server (ITS) server.
System issues and virtualization. Virtualization is one of the most fundamental features in the Internet cloud. It also plays an important role in the vehicular fog, especially in the support of AUVs. Because of the rich assortment of sensors on-board, the AUV fleet may be required to perform ''data mining'' like tasks such as recognizing a fugitive in a certain geographic area. The AUVs can do some initial filtering and correlation of images of interest. But, for final processing, these data must be uploaded to a virtual image of the pattern recognition process in the Internet cloud. Another important function of virtualization is the customization of the sensor platform to different applications, often executed also for the privacy of the drivers. For example, the car manufacturer can access all CAN bus sensors and cameras, while a neighbor vehicle may access only the outward pointing camera.
Security and privacy A major incentive for participants in the vehicular fog is to protect data and allow users to decide what information could be exposed and what information should be kept private. Moreover, functions, data, and trust validations of mobile applications can be delegated to a vehicular fog, if mobile devices and mobile users become temporarily disconnected. The fog also provides protection from devices that have been penetrated by the adversary, or exhibit uncontrolled, disruptive behavior. In addition to the common security requirements like confidentiality, integrity, privacy, and authentication, the AUV is very vulnerable to vicious attacks that may, say, disable the steering or the brake system. The latter attacks are of concern with normal cars with a human driver in control.
g. identification,  34  encryption,  35, 36  access control,  33, 37, 38  and signature  39, 40  ). There, a network node is identified and qualified by a set of attributes (e. g. ''UCLA,''''CS,'' and ''Professor''), not by an IP address. This way, a node does not need to memorize the identity of potential communication partners, handling scalability issue in a modern, large network. Attributes can be used in the content discovery, which is comparable with the index and keywords in the Internet search. A search engine collects web data using crawlers, does indexing, and creates a search database. Then, users send a query with several keywords to the engine to find contents of interest. NDN, designed initially for the Internet, executes the content search using one attribute (i. e. content name) at the network layer.
Bound-states A spectroscopic and theoretical study of the HC3N-H2 dimer was reported by  Michaud et al. (2011). The boundstate energies of the complex were computed by these authors using scaled and unscaled versions of their PES. These calculations were performed in two-dimensions (2D) using the Lanczos iterative procedure applied to the averaged M11 PES for para-H2 and to the three PES (corresponding to the three hydrogen orientation) separately for ortho-H2. The results were compared with high-resolution microwave spectroscopy measurements. Theoretical and experimental transition frequencies were shown to be in good agreement, within a few percent or better (see below). We have also computed the bound-state energies supported by the W07 PES using the coupled-channel approach, as implemented in the BOUND program  (Hutson 1994).
Quantum calculations The scattering quantum calculations were conducted at the full coupled-channel level using the MOLSCAT program  (Hutson & Green 1995). The spherical harmonics expansion of the W07 PES, including 97 angular functions, was employed and the coupled differential equations were solved using the hybrid modified log-derivative Airy propagator. Total energies up to 2120 cm -1 were considered using a fine grid below 200 cm -1 (with increments as small as 0. 01 cm -1 ) for the correct assessment of resonances close to thresholds. The total number of collision energies was 324 for para-H2(j2 = 0) and 344 for ortho-H2(j2 = 1). The highest rotational level of HC3N in the basis set was j1 = 44 while one single rotational level of H2 was included for both the para (j2=0) and ortho (j2=1) modifications.
Creating the stimuli The study used three types of stimuli. First, we selected a number of recordings of sounds produced by manufactured products and basic mechanical interactions: the referent sounds. Second, we recorded vocal imitations of these referent sounds performed by human imitators. Third, we created auditory sketches using an algorithm that synthesizes a sparsfied spectrotemporal representation of the referent sounds. The selection of referent sounds and the recording of vocal imitations was part of a larger project (http://skatvg. iuav. it, last retrieved on October 13, 2016). The following paragraphs summarize the selection of referent sounds, recording of vocal imitations, and the creation of auditory sketches. Data are available at https://zenodo. org/record/57468#. V4T1a6uM67A (last retrieved on November 4, 2016).
The literature in product sound quality highlights three types of sources for such sounds: road vehicles, cars, motorcycles, buses, etc. (see for instance  [56] [57] [58] ), domestic appliances (refrigerators, airconditioning, etc. see for instance  [59] [60] [61] ) and alarm sounds  [62] [63] [64]. The selection of referent sounds therefore sampled through these categories. 2. 1. 2 Selection of the referent sounds. The sound of manufactured products and mechanical interactions were selected from commercial or freely available databases (Hollywood Edge, Blue Box, Sound Ideas, Freesound, etc. ), as well as from our previous studies  [3, 54, 55, 65]. The selection of referent sounds followed three criteria. First, the selection balanced sounds across different morphological profiles  [66]. Morphological profiles represent the temporal evolution of a sound's acoustic properties.
The vocal imitations The recording of vocal imitations was included in a larger project, where we recorded a large number of vocal and gestural imitations of a large variety of sounds. 1 The following paragraphs describe the part of the recording sessions that are relevant to the current study. Whereas there exists a similar attempt at collecting a large amount of imitations of sounds using on-line procedures  [68] , our approach controlled precisely the recording procedure and the quality of the recordings in a laboratory setting. The database of imitations has been registered with the French National Commission on Informatics and Liberty (CNIL 1890949). Overall, the procedure consisted for the imitators to listen to the referent sounds and record a vocal or a gestural imitation. Setup. The imitators used a custom-made Max/MSP v.
The auditory sketches We created auditory sketches based on the method proposed by  Suied et al. (2013)   [53]. It consists in three parts: 1. Computing a time-frequency representation of the signal inspired by models of peripheral auditory processing; 2. Selecting the most important elements of the representation based on a given criterion; 3. Inverting the representation. Based on the results of  [53] , we used the auditory spectrogram proposed by  [70]  and a simple maximum-peaking algorithm to select the most important elements of the representation (maximum-peaking creates fewer artifacts that the peak-picking method used by  [53] ). We used the NSL toolbox for signal representation and inversion (http://www. isr. umd. edu/Labs/NSL/Software. htm, last retrieved on September 15, 2015).
Phonetic overview To further analyze why certain vocal imitations are well identified and some others are not, we listened to the two best-and two worst-identified imitations for each morphological profile and reported a description of their characteristic elements in Table  4. These descriptions are the results of a consensus between three of the authors, using annotations of the vocal imitations made by trained phoneticians, using a system specifically developed for the database of imitations  [67]. Some cases are easy to interpret. For the discrete morphological profiles (impulsive and repeated) in both families, the duration and timing of the elements composing the sounds appears to be critical, regardless of the acoustic quality of each element. For example, the bestidentified imitations of switches were made of a rapid succession of two clicks, whereas the worst-identified imitations were made of only one click, or a slower succession of two clicks.
Heating and ventilating radiometers are mandatory to limit snow accumulation and the build-up of frost and freezing rain. Heating and ventilation were performed for 5 min every hour prior to measurements. This protocol of instrumentation and measurement is similar to that employed near Umiujaq  (Domine et al. , 2015) , which was operated in the same manner with a CR1000 data logger (Campbell Scientific). Briefly, hourly.
Thermal conductivity measurements The NP method has been used extensively for soils for a long time  (Devries, 1952). Sturm and Johnson (1992)  and  Morin et al. (2010)  discussed in detail the heated NP method in snow. The automatic operation of the TP08 needles in Arctic snow and the data analysis have been detailed in  Domine et al. (2015)  and only a brief summary will be given here. For a measurement cycle, the 10 cm long needles were heated at constant power (0. 4 W m -1 ) for 150 s. The temperature was monitored by a thermocouple at the centre of the heated zone. Heat loss is a function of the thermal conductivity of the medium. A plot of the thermocouple temperature as a function of ln(t), where t is time, theoretically yields a linear curve after an initiation period of 10 to 20 s.
Factors such as non-homogeneous snow may result in poor-quality heating curves, which were discarded. Thermal conductivity measurements were performed once every 2 days to take into account the slow evolution of this variable and to minimise thermal perturbation to the snow. Measurements were performed at 05:00 to minimise the risk of having warm snow. The NP heating sequence takes place regardless of whether the NP is covered by snow or not. The k snow data that we determined took place in air were discarded. When an NP is not snow covered, even a slight wind produces an erratic heating curve. Should there be very little wind, snow height measurement, the value of k snow and the presence of convection often allow the detection of a non-covered probe. Incorrect or uncertain determination of NP snow coverage is nevertheless still possible, and this will be discussed when relevant.
The general trend is an increase of k soil with depth, while temperature expectedly decreases. Soil grain size distribution was obtained from 5 samples taken around 10 cm depth. The average data show a bimodal size distribution with modes centred at 17 and 59 µm. If the standard 50 µm size limit between sand and silt is used, then our sample is 65 % silt and 35 % sand by mass, so that the soil here is a mixture of silt and fine sand. Figure  5  shows the evolution of the k soil and temperature measured every other day by the NP at 10 cm depth between www. the-cryosphere. net/10/2573/2016/ The Cryosphere, 10, 2573-2588, 2016 28 July 2014 and 26 June 2015. Figure  5  also shows hourly soil temperature and volume water content at 10 cm depth measured by a 5TM probe located about 1 m away. Both temperature measurements show similar variations, but the 5TM is 1 to 2 • C warmer, in part due to a positive 0.