dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements We wish to acknowledge the wild mouse genetic repository of Montpellier (http://www. isem. univ-montp2. fr/recherche/les-plate-formes) and  Dr Kerstin Musolf  at the  Konrad Lorenz Institute of Ethology in Vienna. We are grateful to  Dr Philip Brownridge  for excellent instrumentation support, and  Marco Perriat-Sanguinet  for his commitment to the well-being of the mouse colony. G. G. and C. M. S. acknowledge funding from the  French National Research Agency (ANR , No. 2010BLAN171401-AssortMate ). J. L. H. and R. J. B. acknowledge funding from the  Biotechnology and Biological Sciences Research Council, UK  (grants  BBC503897  and  BB/J002631/1 ), and from the  Natural Environment Research Council  (grant  NE/G018650/1 ). This is publication ISEM number  2017-041.
Sodium is a very important element for the research and analysis of astrophysical, laboratory, and technological plasmas, but neither theoretical nor experimental data on Stark broadening of Na IV spectral lines are present in the literature. Using the modified semiempirical method of Dimitrijević and Konjević, here Stark widths have been calculated for nine Na IV transitions. Na IV belongs to the oxygen isoelectronic sequence, and we have calculated Stark widths belonging to singlets, triplets, and quintuplets, as well as with different parent terms. This is used to discuss similarities within one transition array with different multiplicities and parent terms. Additionally, calculated widths will be implemented in the STARK-B database (http://stark-b. obspm. fr) which is also a part of the Virtual Atomic and Molecular Data Center (VAMDC-http://www.
Introduction In spite of the fact that sodium is important for the research and analysis of various astrophysical (e. g. ,  [1] ), laboratory (e. g. ,  [2] ), and technological (e. g. ,  [3] ) plasmas, Stark broadening data for its different ionization stages are very scarce or even missing. Experimental results exist only for Na I  [4]. There are also a number of theoretical results for neutral sodium. For example, semiclassical perturbation (SCP) Stark broadening parameters from  [5]  have been used for non-LTE calculations for neutral Na in late-type stars  [6]. For Na II, there are the semiclassical results of Jones et al. [7]  published also by Griem  [8] , while for Na VI, Na IX, and Na X, there are theoretical SCP results in the STARK-B database  [9, 10]. There are also quantum mechanical calculations of Stark width for one Na VII, and one Na VIII spectral line  [11] , and that is all.
Our values of Stark widths for Na IV spectral lines will be added to the STARK-B database  [9, 10] , which is also a part of the set of databases included in the Virtual Atomic and Molecular Data Center (VAMDC)  [23, 24] , enabling a much better and easier search and mining of atomic and molecular data. We hope that the new Stark broadening data obtained in this work will be of interest for a number of problems in astrophysics and for the diagnostics of laboratory plasmas, as well as for the investigation of laser-produced inertial fusion plasma and for plasma in industry (e. g. , laser welding or plasma-based light sources).
Purpose: This study aimed to describe treatment patterns and estimate health care resource utilization and associated costs among Japanese women with dysmenorrhea, using a claims database. Methods: This was a retrospective analysis using health insurance data from the Japan Medical Data Center, assessing female patients aged 18-49 years with newly diagnosed primary or secondary dysmenorrhea. Treatment pattern analyses focused on hormonal medications, analgesics, hemostatic agents, traditional Chinese medicine (TCM), and gynecological surgeries. Data were collected on health care resource utilization and costs associated with medications, imaging procedures, and inpatient and outpatient care in both patients and matched controls. Results: The analysis included 6,315 women with dysmenorrhea (3,441 primary; 2,874 secondary).
Study design and population This was a retrospective claims analysis of the Japan Medical Data Center (JMDC) database, which includes deidentified medical (inpatient and outpatient) and pharmacy claims from up to 3. 0 million beneficiaries (both employees and their dependents) from 2005 onward. The JMDC database covers nearly 10% of the 30 million individuals in the Japanese Social Insurance System, equating to approximately 2. 5% of the total Japanese population. Data were extracted for patients with records between July 1, 2009, and June 30, 2014, and included information on patient demographics, diagnoses, drug prescriptions, medical procedures, medical facility characteristics, and reimbursement costs. Included patients were women between ages 18 and 49 years who had two separate diagnoses of dysmenorrhea within 3 months.
For the resource utilization analysis, matched controls were identified among females in the JMDC database who did not suffer from dysmenorrhea (ie, no diagnosis between July 1, 2009, and June 30, 2014). Controls and cases were matched 2:1 by year of birth (within 5 years), Charlson Comorbidity Index (CCI) at baseline (±15 days from index date), and at least the same 18 months of follow-up period.
Resource utilization and cost calculations The resources used and their associated costs were calculated based on the 1-year follow-up period after the index date for each patient. Data were collected on inpatient costs, outpatient costs, and costs associated with both prescriptions and imaging procedures. From this information, total health care costs were calculated. Resource utilization data collected in this analysis included the number of inpatient admissions, cumulative length of stay, number of outpatient visits, and number of imaging procedures. Imaging procedures, which included echography, computed tomography scan, and magnetic resonance imaging, were determined to be related to a diagnosis of dysmenorrhea when they occurred within a 1-month period around the date of the diagnosis (±15 days).
All analyses were performed using SAS Analytics Pro release 9. 3 and R (version 3. 2. 1, SAS Institute, Cary, NC, USA) software (for time-to-event data). In general, a p-value of ≤0. 05 was determined to represent statistical significance. This study was approved by the Ethics Committee of the nonprofit organization, Clinical Research Promotion Network Japan. The information available in the JMDC database is anonymized. The informed consent of patients was not applicable based on the Ethical Guidelines for Epidemiological Research issued by the Ministry of Health, Welfare and Labor.
Our findings were generally compatible with the guidelines for gynecological practice by the JSOG and JAOG 2011 edition. 10 Although such guidance suggests NSAID treatment for initial pain relief, our findings show relatively low levels of NSAID use. One of the reasons may be that many patients may have used over-the-counter therapies including NSAIDs, which are not captured by the database. 1 Persistence on LEPs was higher in this analysis than for any of the other therapies assessed, including TCM therapies. It was found by adding up the proportions of patients without defined treatment (13. 0% of both primary and secondary dysmenorrhea cases) and those with hemostatic agents (11. 2%) and NSAIDs (1. 6%) that around 25% of patients diagnosed with dysmenorrhea received no reimbursed treatment or short-term treatments only.
22 The prevalence of dysmenorrhea in this JMDC database was only 1. 6%, and it is possible that the restriction to patients with at least two recorded diagnoses within 3 months led to the exclusion of less severe cases. Compared to a study by Tanaka et al,  1  which was a population-based survey of Japanese women aged 15-49 years, patients in this analysis had a higher rate of LEP utilization. One potential explanation is an increase in the general utilization of LEPs over time that has been captured by our more recently collected JMDC data. Comparatively, our analysis also has a more restrictive patient sample than that of Tanaka et al, 2 who surveyed women with menstrual symptoms in general, as our study only included patients who consulted for dysmenorrhea at least two times within 3 months, and not for any other menstrual symptoms.
However, the JMDC pharmacy costs might be less than the pharmacy costs that patients actually paid, as the analysis excluded nonreimbursed therapies, such as NSAIDs and COCs. Therefore, it is uncertain that the total pharmacy costs paid by patients and health insurance are below the willingness-to-pay amount for dysmenorrhea treatments. Study limitations include those related to any retrospective claims data analysis, including the lack of data supporting the specific reason for each treatment choice. Furthermore, dysmenorrhea is certainly underreported in claims databases and women included in this study may represent severe cases consulting a practitioner. It was previously estimated that 64. 6% of women with menstrual symptoms do not seek medical care. 1  In addition, treatment patterns observed in this analysis may not fully reflect all medications taken to treat dysmenorrhea if they are not reimbursed by insurance, such as COCs or over-the-counter pain medications such as NSAIDs.
As a result, women who were identified as having 'no treatment' may have received nonreimbursed or over-the-counter therapies during the study period; this could have artificially increased the observed 'no treatment' rate. Because the JMDC is an administrative database, its diagnostic reporting may be incomplete as physicians may report codes for which reimbursement is provided instead of the full or actual diagnoses. For example, NSAIDs prescribed for diagnosis with other pain symptoms not associated with menstruation were not included in this study. Patients who require surgery may not receive a diagnosis of dysmenorrhea as they will instead receive a diagnostic code for the underlying condition requiring surgery (eg, fibroids). The burden of the disease was estimated over the first year of follow-up and not over the whole available period.
Evaluation of the efficacy of TiONts-DTX combined with rT To evaluate the benefit of nanohybrids combined with RT, 50 µL of TiONts-DTX (containing 12 µM of DTX grafted onto 10 µg of TiONts, corresponding to 60 µmol DTX/g TiONts in the designed nanohybrid and from TGA measurement) was injected into the tumors. After 24 h of incubation, the tumors were irradiated with three daily fractions of 4 Gy using a brachytherapy iridium projector (Microselectron HDR; Elekta). When the tumors reached a mean tumor volume of around 200 mm 3 , the mice were randomized according to their individual tumor volume into eight groups (vehicle IT injection ± RT, DTX ± RT, TiONts ± RT and TiONts-DTX ± RT), with each containing seven mice, using Vivo Manager ® software (Biosystemes, Couternon, France). The behavior and health status of the mice were monitored twice a week, and the tumor volumes were measured using calipers until tumor growth recovery (to a maximum of 2,000 mm 3 ).
Generation of Dicer_KO mESCs using CRISPR/ Cas9 Dicer_KO mESCs were generated from E14TG2a mESCs using a paired CRISPR/Cas9 strategy as described in  [34]. Specific CRISPR/Cas9 sgRNAs have been generated using the E-CRISPR software  [35]  or chosen from an established library  [36]  and cloned into the plasmid pX330-U6-Chimeric_BB-CBh-hSpCas9  [37]  using the BbsI restriction site. mESCs were single cell sorted 48 h after transfection. All the primers used for the CRISPR/Cas9 are described in Table  S1. All newly generated plasmids are described in Table  S2. All designs are based on the latest mouse genome assembly (GRCm38/mm10) provided by the UCSC Genome browser http://genome. ucsc. edu/.
FeatureCounts (v1. 4. 5-p1)  [42]  was used to count reads for genes (Ensembl GRCm38. 78), ignoring reads on overlapping region and the plot was generated using GGPLOT2 (v1. 0. 1)  [43]. TETOOLKIT (v1. 5)  [44]  was used to count reads for repeat elements, accounting for multiple-hit reads and RPKM were calculated by using edgeR  [45]. Complete RNA sequencing data of WT and Dicer_KO mESCs are available on the NCBI GEO database (GEO: GSE78971 for WT and GEO: GSE78973 for Dicer_KO).
Exit from pluripotency assay Cells were plated in six-well plate at a density of 4500 cellsÁcm À2 and cultured in 2i medium (N 2 B 27 (Cellartis) complemented with 50 UÁmL À1 of penicillin and 0. 05 mgÁmL À1 of streptomycin) containing or not the following inhibitors cocktail: PD032591 at 1 lM final concentration, CHIR99021 at 3 lM final concentration, and 1000 UÁmL À1 of leukemia inhibitory factor (LIF). The alkaline phosphatase (AP) staining was performed using the Leukocyte Alkaline Phosphatase kit (Sigma, St. Louis, MO, USA). For the clonal AP quantification, entire six-well plates used for AP staining assays were first scanned to capture the total plate area in a single image. Images were then processed using the ImageJ software  [48]. The number of AP-positive colonies was calculated on threshold intensity (default parameters) of inverted regions that were user-selected (full wellidentical areas.
In the case of retrotransposition events, mESCs will express EGFP. In the negative control (pL1 JM111 ), the L1 RP element has been replaced by the L1 JM111 element. The L1 JM111 element is a nonfunctional L1 transgene consisting in a human L1 mutated in ORF1 (*)  [70] , abrogating its retrotransposition activity. (D) Retrotransposition assay experiment design and time line in mESCs. (E) Flow cytometry gating strategy for the analysis of GFPpositive cells in (F). We first selected the mESC population and subsequently excluded the doublets in both dimensions. The data from the first triplicate of D13 mESCs transfected with the pL1 RP (week 6) plasmid were used to represent the gating strategy. The gating for EGFPpositive and EGFP-negative cells was determined by analyzing cells transfected with: a plasmid coding EGFP and a puromycin-resistance gene and a plasmid coding only a puromycin-resistance gene respectively described in (B).
Acknowledgements We thank  Dr T. Beyer  and  D. Spies  for the critical reading of the manuscript and for fruitful discussions. We thank  Dr R. Freimann  for his advice and expertise concerning the single cell sorting of mESCs. We thank  Louise V eron  and Lea F€ uglister for their great help during the PCR screening of the Dicer_KO candidates. We also thank  Dr O'Carrol  for the gift of the anti-L1_ORF1 antibody, and  Dr Zhang  for the gift of the pX330-U6-Chimeric_BB-CBh-hSpCas9 plasmid. Technical support by the  Functional Genomics Center Zurich  and the  ETH Flow Cytometry Core Facility  is gratefully acknowledged. This work was supported by a core grant from ETH-Z (supported by  Roche ) and a grant from  Novartis Stiftung für Biologisch-Medizinische Forschung  ( 13B063 ). MB is supported by a PhD fellowship from the  ETH-Z foundation  ( ETH-21 13-1 ), DC-S by the  NCCR RNA & Disease , funded by the  Swiss National Science Foundation  and RPN by a  Swiss National Science Foundation  grant ( 31003A_153220 ).
Acknowledgements Financial support from the  Centre National de la Recherche Scientifique , the  Université de Toulouse  and the  Agence Nationale de la Recherche  ( ANR-JCJC-2012-POGO ) is gratefully acknowledged. We thank  Umicore AG & Co  for a generous gift of gold precursors. UPPA,  MCIA  ( Mésocentre de Calcul Intensif Aquitain ) and IDRIS under Allocation 2016 (i2016080045) made by  Grand Equipement National de Calcul Intensif (GENCI)  are acknowledged for computational facilities. L. E. thanks the  Xunta de Galicia  for financial support through the  I2C program.
The updated CONSORT NPT extension recommends reporting details of whether and how the clustering by care providers or centers was addressed in the sample size calculation (item 7a) and the statistical analysis (item 12a). Difficulties of Blinding. Blinding of patients and care providers is frequently impossible in trials assessing NPTs and often relies on complex methods when it CONSORT Nonpharmacologic Treatments RESEARCH AND REPORTING METHODS is possible (9, 31). Some of these methods rely on blinding other care providers who do not perform the intervention but who will care for the patients after the intervention. To account for this, item 11a was modified slightly. If blinding is not possible in a trial, the updated CONSORT NPT extension recommends reporting this information explicitly and providing a description of any attempts to limit bias, such as collection of data by an independent researcher (item 11c).
Funding was provided by the research funds of the  Centre d'Epidemiologie Clinique, Assistance Publique des Hopitaux de Paris , and the  METHODS Team of the Centre of Research in Epidemiology and Statistics Sorbonne Paris Cite-CRESS-UMR1153. Disclosures:  Dr. Ravaud  reports that he is a member of the CONSORT Group, director of EQUATOR France, and a member of the EQUATOR steering group.
Acknowledgments JMA, TIB, SH, JL, NJS and LP initiated discussions on the paper; LC and LP developed the simulation model; JMA led the writing; all authors contributed to discussions and revising drafts of the paper. This paper arose from a workshop " Biosecurity in Mountains and Northern Ecosystems: Current Status and Future Challenges " from 3-5 June 2015, organized by the  Mountain Invasion Research Network (MIREN)  and supported through funding by the  Mountain Research Initiative (MRI) of the University of Bern (Switzerland) , the  Marcus Wallenberg Foundation for International Scientific Collaboration , the  Oscar and Lili Lamms Remembrance Foundation , the  Arctic Research Centre at Umeå University (ARCUM) , and the  Climate Impacts Research Centre (CIRC). JMA received funding from the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement No.
This paper presents the analysis and discussion of the off-site localization competition track, which took place during the Seventh International Conference on Indoor Positioning and Indoor Navigation (IPIN 2016). Five international teams proposed different strategies for smartphone-based indoor positioning using the same reference data. The competitors were provided with several smartphone-collected signal datasets, some of which were used for training (known trajectories), and others for evaluating (unknown trajectories). The competition permits a coherent evaluation method of the competitors' estimations, where inside information to fine-tune their systems is not offered, and thus provides, in our opinion, a good starting point to introduce a fair comparison between the smartphone-based systems found in the literature.
However, most of these evaluations are carried out in rather restricted areas; usually, a few university departments easily accessible to the researcher  [8]. A second related problem is that the raw data collected and used during the evaluation of the positioning system is not made available to the research community, making it impossible to confirm the claimed results. Both issues make a critical comparison of Indoor Positioning Systems (IPS) developed by different groups not feasible in a rigorous manner, since collected signals, context and scenarios may change in an uncontrolled way. A better way to compare positioning algorithms under the same experimental conditions would be using a repository of prerecorded data in a large variety of buildings and contexts. An inspiration for this approach is the Machine Learning Repository of the University of California Irvine (UCI)  [9]  and Kaggle  [10] , both created for evaluating machine learning algorithms with common databases.
Although some related databases  [11] [12] [13] [14] [15] [16] [17] [18] [19] [20] [21] [22] , interesting initiatives for benchmarking  [23]  and even competitions (Microsoft-IPSN  [24] [25] [26] [27] , EvAAL  [28] [29] [30] [31] [32]  and EVARILOS  [33, 34] ) for better evaluation of IPS already exist, no one encompasses the wide diversity in technologies, contexts and scenarios currently found in this field. A competition is a convenient way to initiate IPS comparisons and reach a worldwide recognition of datasets for fair evaluation. The first off-site indoor location competition was the EvAAL-ETRI Indoor Location  [35, 36] , which was held during the Sixth International Conference on Indoor Positioning and Indoor Navigation  (IPIN 2015). The third track, called "Wi-Fi fingerprinting in large environments" was off-site, and the competitors had access to a large Wi-Fi fingerprint database, the UJIIndoorLoc  [15] , to which they had to apply their positioning estimation approaches offline.
In the current edition, the organizers decided to make the off-site competition more challenging by creating a new database including the data provided by all sensors embedded in typical smartphones, acquired by different people moving in different types of buildings. The aim of this paper is to contribute to the definition and creation of a new framework for the evaluation of smartphone-based IPS based on our experience at the IPIN2016 off-site competition. We will: 1. describe the database and the evaluation criteria used 2. analyze and compare the competing IPS under equal evaluation conditions 3. present the experiences and suggestions from the competitors to enhance the evaluation framework 4. discuss directions to improve a repository that could be used as a universal reference for testing smartphone-based IPS The remaining of this paper is organized as follows.
• Multiple sources of information. The provided data logfiles were captured by using several conventional modern smartphones and a dedicated Android application named GetSensorData  [37]. The logfiles contained all of the available signals that were captured in real time with a smartphone: Wi-Fi Received Signal Strengh (RSS), inertial data, magnetic field, GPS, and pressure, among others. • Continuous motion and recording process. While recording the logfiles with the smartphone, the person moved along a continuous trajectory passing by some known landmarks that were recorded in the logfiles. • Realistic walking style. The person recording the data moved in a natural way: most of the time walking forward at normal speed, but occasionally taking 90 or 180 degree turns (e. g. , at corridor ends), moving backward or laterally at certain points (e.
g. , if giving way at door accesses), changing floors through elevators and stairs, etc. The user speed was approximately constant when recording the data with eventual stops at some positions. • Phone holding. The phone was hand held at all times by the user, either stable in front of his face or chest (typical position for reading or typing with the phone), or with the arm downwards while holding the phone in his hand (producing a natural arm swing if walking). No pocket use, calling or any strange handling conditions were considered while collecting data. • Realistic environment and diversity The competition took place in four different buildings (see Figure  1 ) that were not modified by installing any additional hardware. Moreover, different smartphones were used to gather the data so the competition was not attached to the features of any particular smartphone.
Description of Datasets (Logfiles) The stream of sensor data generated in the phone is sequentially registered at the time it is obtained in a plain text file (called a logfile). Each line in the logfile corresponds to a single sensor reading, beginning with an initial header identifier (such as WIFI, ACC, or MAGN, among others) that determines the kind of sensor reading, and several fields separated by semicolons with different values. Additionally, those lines starting with the POSI header identifier does not belong to a sensor reading and they contain the ground truth location provided by the users. A parser in Matlab code (version 1, CSIC, Arganda del Rey, Madrid, Spain)  [37]  was provided to competitors in order to facilitate data rearrangement in the preferred format. Figure  2  shows an excerpt of a logfile.