dataesr/scientific-paragraphs-categorization huggingface dataset:
Reconstruction of contributions from different mtDNA haplogroups. Given that human mtDNA sequences present in sewage samples are likely to be diverse mixtures of general populations living in a specific area, it can be of great interest to decompose the available reads to differently weighted contributions of various mtDNA haplogroups. To achieve this, we used a computational tool called mixemt  37. The algorithm uses the database provided by PhyloTree. org (Phylotree Build 17) that describes the defining mutations of over 5000 mtDNA haplotypes. After aligning short reads of the investigated samples to the reference sequence of the human mitochondrion, the pipeline assigns a value to each read and mtDNA haplotype pair that describes how consistent the variants in the given read are with the given mtDNA haplotype, while accounting for sequencing errors.
Identified mtDNA haplotypes were grouped into mtDNA haplogroups for easier comparison with available data. comparison with available mtDnA haplogroup composition data. Relevant information about the mtDNA haplogroup distribution of the investigated cities was gathered from literature. We aimed to compare our data with city-, or region-specific results of different studies whenever possible, however, in the absence of such particular information, the country level distributions were used for reference. Given the lack of specific mtDNA haplogroup distribution data for US cities, we collected census data available about the ethnic composition of the cities and used the results of Just et al. 65  to convert these ratios to a distribution of mtDNA haplogroups belonging to four broad biogeographic ancestry categories.
Acknowledgements The authors are grateful to the collaborators of the  Global Sewage Surveillance project consortium  for providing samples and to  G. Vattay  and  S. Laki  for help with data processing. This study has received funding from the  European Union 's  Horizon 2020 research and innovation program  under Grant Agreement No. 643476  (COMPARE), the  World Health Organization  and The  Novo Nordisk Foundation  ( NNF16OC0021856 : Global Surveillance of Antimicrobial Resistance ).
Furthermore, for each item threshold parameter, the probability that a patient chooses the higher of the adjacent response options is described using a logistic function of the distance between these parameters on the latent variable. In the PCM, all item response functions have the same slope, whereas for the GPCM an additional parameter is introduced which allows the slopes of items to differ. In the first step of the IRT analysis, goodness of fit of the PCM and GPCM was compared using a likelihood ratio test for nested models. Marginal maximum likelihood estimates of the GPCM parameters and the means and variances of the different groups of respondents were obtained using the Multidimensional Item Response Theory (MIRT) package  [22]. In the initial item response model, which will be referred to as the unadjusted model, a single set of item parameters is used to describe response behavior of patients in all countries.
| Atmospheric conditions We used the Yala Basecamp (Yala BC) station as a reference, and to calibrate model parameters. We chose this station, since it has the most consistent data record, and is intermediate in elevation (5,090 m. a. s. l. ). The mean meteorological conditions for Yala BC are summarized in Figures 2 and 3. During the dry season, relative humidity increases from sunrise to sunset, after which it drops again, coinciding with the wind direction reversing from upvalley to down-valley (not shown). During the monsoon, the air is practically saturated at night, with a slight average drop in relative humidity during the day, when temperatures increase rapidly. LW in has a diurnal cycle in the dry season that better resembles the relative humidity curve than the temperature curve.
Sicart et al. , 2010 note a similar change in their C parameter with altitude, which they attribute to changing temperature and humidity profiles. Our observations also show that the originally proposed parameter of C = 1. 24 provides a somewhat better description to LW in than the value of C = 1. 15, as determined by  Sicart et al. , 2010    our stations in the Himalaya, indicating that a higher value of C is needed. The model by  Dilley and O'Brien (1998)  generally has a lower bias, as indicated in Figure  5 , but also does not capture the data points with high LW in. Furthermore, it overestimates LW in at the very lowest values. ε eff = (1 + a Á (1 -τ atm )) Á ε clear + b Á (1 -τ atm ) For the full time series, including cloud cover, we assess the S and UM parameterizations, as described above.
These intermediate points show only a slight correlation with shortwave transmissivity, with a large scatter and many data points having very high transmissivities (see Figure  7c ). Also note that at high emissivities, there is almost no dependence of LW in on the transmissivity, in contrast to what the UM and S models assume. Figure  7  also shows that relative humidity is generally good at separating out the two branches. Especially at night, when there are no transmissivity measurements possible, thick local clouds form only at very high relative humidity. We chose a simple parameterization using only temperature and relative humidity, which can represent both the clearsky and the cloudy branch: LW in =c 1 +c 2 RH +c 3 σT 4 ð8Þ We tried multiple relations that included temperature and relative humidity, but the best ones gave similar results as the above simple relationship.
| Impact of the calibration site We have now compared our new parameterization, calibrated at Yala Basecamp, to the S and UM models, calibrated using data from the Andes and Alps. Hence, it is possible that a calibration site in the Himalaya will change the S and UM models and will improve performance in the Himalaya. We tested the ability of all three models to reproduce the measured LW in by fitting the model to the measurements for all stations separately using linear least-squares. The fitted parameters for Yala Basecamp for the S and UM models are given in Table  5. The performance metrics from this exercise are summarized in Table  6. It is clear that the S and UM models indeed show much better performance of all errors when fitted to the data at the site itself, compared with the literature values of the parameters.
ACKNOWLEDGEMENTS The authors wish to acknowledge the tremendous efforts that have been put into maintaining the sensor setups used in this study over multiple years. This includes numerous porters and guides as well as local and international research staff, including  Arbindra Khadka ,  Gunjan Silwal  and  Joseph Shea. We are furthermore grateful to  Pascal Buri  for initial discussions on this subject. Khumbu data have been collected within the framework of the Ev-K2- CNR  Project in collaboration with the  Nepal Academy of Science and Technology  as foreseen by the  Memorandum of Understanding between Nepal and Italy , and thanks to contributions from the  Italian National Research Council , the  Italian Ministry of Education, University and Research  and the  Italian Ministry of Foreign Affairs.
Meteorological observations on Saint-Sorlin and Zongo glaciers are conducted in the framework of the  French GLACIOCLIM program  (https://glacioclim. osug. fr)and we thank  D. Six  and  A. Rabatel  for providing the data. We thank the anonymous reviewers and editor for their careful reading of the manuscript, and their constructive suggestions. We acknowledge funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation program  (grant number  676819 ) and the  Netherlands Organization for Scientific Research Innovational Research Incentives Schemes VIDI  ( 016. 181. 308 ). P. W. acknowledges the support of the  French Service d'Observation CRYOBS-CLIM , the  French National Research Agency (ANR)  through  ANR-13-SENV-0005-04/05-PRESHINE , and a grant from  Labex OSUG@2020  (Investissements d'avenir - ANR10 LABX56 ).
Statistical Analysis Results were expressed as the mean ± standard deviation of the determination. An analysis of the variance (ANOVA) of the results was carried out in order to determine significant differences (p < 0. 05) between the different treatments and the concentration of carotenoid in non-digested smoothies and that obtained in the different phases of the in vitro gastrointestinal digestion. For all samples, carotenoid analyses were performed at least in triplicate (n = 3-5). All data were examined with SPSS 24. 0 for Windows (SPSS Inc. ; Chicago, IL, USA).
Data collection At inclusion, the following characteristics were recorded for each patient: sex, age, disease duration, smoking status, UC characteristics according to Montreal Classification, extraintestinal manifestation, prior received treatments (steroids, thiopurine, methotrexate, infliximab, adalimumab and golimumab) and reasons for discontinuation (primary failure, loss of response or intolerance), disease activity according to the partial Mayo and the Lichtiger scores, endoscopic activity measured with the endoscopic Mayo subscore and UC endoscopic index of severity (UCEIS), severe endoscopic lesions, C-reactive protein (CRP), haemoglobin, and albumin levels.
Analysis of the radio signal The expected electric field is obtained with the code SELFAS3, developed at Subatech. It is based on the SELFAS2 version of the code  [7, 8]  but we significantly improved the computation in 2018. First, we are not using anymore the common US Standard model for the description of the atmosphere. This model is static and does not include day/night effects nor seasonal effects. We are now using the data from the GDAS  [9]  to estimate the precise atmospheric status at the time of the detection of our events at the location of Nançay. These data allow us to compute the air density and air refractive index which are key ingredients to obtain with high precision the electric field emitted by the secondary particles  [10]. We also use the exact formula for the electric field estimation, valid at all frequencies  [11].
The typical accuracy we obtain at the end of the analysis chain is 15 m on the core position, 20% on the primary energy and 20 g/cm 2 on the X max. Fig. 2  shows the reconstruction performances on an event detected by CODALEMA on March 9 th , 2017. As the CODALEMA radio stations are the electric field gradient in 20-80 MHz is very small: the shower parameters reconstruction is not well constrained by these data alone. At higher frequency (120-200 MHz), the electric field variations are much stronger due to the Cherenkov-like amplification at ∼ 140 m of the shower axis. This permits a much better shower reconstruction.
These low frequency antennas are triggered by the scintillator array and data are produced by a scope with a sampling rate of 500 MHz on 8 bits, in a time window of 2 ms. The associated amplifier is a modified version of the LONAMOS used in the 57 radio stations of CODALEMA. In 18 months, we detected 18 showers. We observed that the detection range is higher than in the 20-80 MHz band as predicted by the SELFAS3 simulation  [14] : in these events, the maximum distance between any pair of low frequency antennas is always larger than for the CODALEMA antennas. We did not detect the sudden death signal so far. This could be due to the fact that the Nançay observatory has an altitude of 180 m: the atmospheric depth at this altitude is of the order of 1000 g/cm 2 for vertical showers and the number of 26th Extended European Cosmic Ray Symposium IOP Conf.
In Fig. 4 , we present an event detected on January 1 st , 2018. The shower is coming from the south-west, as indicated by the blue arrow. The radio stations of CODALEMA that detected the shower are indicated by the colored circles, with the same color code than in Fig. 2. The 6 EXTASIS antennas involved in the event are indicated by the colored triangles; the arrival direction estimated using the low-frequency data only is in very good agreement with the direction obtained by the CODALEMA data. We see that the ground pattern of the EXTASIS antennas has a much larger area than the pattern of the CODALEMA stations: this confirms the more extended lateral width of the signal at lowfrequency compared to the signal in the 20-80 MHz band. It is very interesting to focus on the polarization angle of the electric in all triggered CODALEMA stations.
ABSTRACT Introduction: This first-time-in-humans study assessed the safety, pharmacokinetics (PK), pharmacodynamics (PD), and clinical activity of GSK2879552 in patients with relapsed or refractory SCLC. Methods: This phase I, multicenter, open-label study (NCT02034123) enrolled patients (18 years old) with relapsed or refractory SCLC (after 1 platinum-containing chemotherapy or refusal of standard therapy). Part 1 was a dose-escalation study; Part 2 was a dose-expansion study. Dose escalations were based on safety, PK, and PD. The primary end point (Part 1) was to determine the safety, tolerability, and recommended dose and regimen of GSK2879552. Secondary end points were to characterize PK and PD parameters and measure disease control rate at week 16. Part 2 was not conducted. Results: Between February 4, 2014, and April 18, 2017, a total of 29 patients were allocated to one of nine dose cohorts (0.
Study Design This was a phase I, open-label, multicenter, nonrandomized, two-part study designed to evaluate the safety, tolerability, PK, PD, and clinical activity of GSK2879552 given orally to patients with relapsed or refractory SCLC (200858, NCT02034123). The study was planned as two parts: Part 1 was a dose-escalation study to determine the recommended dose for Part 2 based on safety, tolerability, and PK and PD data; Part 2 was to be a dose-expansion study planned to further evaluate the safety and tolerability of GSK2879552 at the recommended dose and determine clinical activity (Supplementary Fig. 1 ). This study was conducted at three centers in the United States, one center in France, and four centers in Spain. The first patient was enrolled on February 4, 2014, and the last patient visit was completed on April 18, 2017.
Sufficient numbers of patients were enrolled in each cohort to ensure that data for at least one patient who had completed the first 28 days of dosing were available before defining a new dose and/or schedule and initiating the subsequent cohort. The doselimiting toxicity (DLT) period was 28 days. After the second cohort, the dosing recommendation for the next cohort was made by using the Neuenschwander Continual Reassessment Method based on the observed DLT data for all the patients treated under the same dosing schedule (further details are included in the Supplementary Materials). The dose increment was no more than 100% of the current dose in the absence of any safety signals and no more than 50% of the current dose if AEs were reported. A safety review was conducted to evaluate safety parameters, such as those in the primary end point (i.
g. , dose, C max ) and safety (e. g. , platelet levels in blood) and efficacy parameters. Safety Assessments. Safety assessments included the monitoring of AEs, clinical laboratory test results, heart rate, blood pressure, temperature, 12-lead electrocardiogram findings, Eastern Cooperative Oncology Group performance status, and physical examination findings. PK Assessments. Serial blood samples were collected for PK analysis on day 1 and day 15 before a dose to 24 hours after a dose. Most patients did not receive a dose on day 2 to allow for PK sampling at 48 hours after a dose to better characterize the terminal t1/ 2 of GSK2879552. Further predose samples were collected regularly throughout the study. The GSK2879552 concentration-time data were summarized by planned time point and dose cohort.
Statistical Methods No formal hypotheses testing was conducted; all statistical analyses for safety and efficacy were descriptive. PK and PD parameters were summarized using descriptive statistics by dose cohort. Dose proportionality for C max and AUC after single and repeated administration was evaluated by using power models. Platelet nadir values expressed as percent change from baseline were plotted graphically against summary exposure measures (e. g. , dose, C max , AUC obtained on day 1). Only dose cohorts with repeat daily dosing were included in the analysis (i. e. , 0. 25-mg-3. 0-mg daily dose cohorts). On the basis of the nature of the relationship, a sigmoidal maximum effect (E max ) model was fitted to the data to obtain the slope, with the exposure producing 50% of the E max (ED50) and E max.
PK Analyses The PK concentration-time data showed a rapid increase in GSK2879552 concentration, with the maximum concentration achieved within 2 hours (Fig. 1 ). Single-and repeat-dose PK parameters are summarized in Table  4. GSK2879552 was eliminated slowly, with an average terminal phase effective t1/ 2 of 18 hours at the 2-mg daily dose. C max and AUC values generally increased in a dose-proportional manner over the 0. 25mg to 3. 0-mg daily dose range at both day 1 and day 15 (Supplementary Table  3 ). Accumulation ratio based on AUC was assessed for the GSK2879552 1. 0-mg, 2. 0-mg, and 3. 0-mg daily groups, as these cohorts each had at least three patients. The results showed a moderate accumulation of GSK2879552 with daily administration from day 1 to day 15, which is in line with the terminal phase effective t1/ 2 (Supplementary Table  4 ).
Acknowledgments This study (200858, NCT02034123) was funded by  GlaxoSmithKline. The authors would like to thank  Andre Acusta  and  Ahmed Khaled  for their assistance with study analysis. Medical writing support in the form of developing drafts based on author input, editorial assistance, and submission of the final article was provided by  Leigh O'Connor, PhD , and  Clare Slater, PhD , CMPP, of  Fishawack Indicia Ltd ,  United Kingdom , and was funded by  GlaxoSmithKline. Dr. Dhar ,  Dr. Ferron-Brady ,  Dr. Park ,  Dr. Mohammad ,  Dr. Kruger ,  Dr. Govindan , and  Dr. Wu  contributed to the study conception or design. Dr. Bauer ,  Dr. Besse ,  Dr. Garrido ,  Dr. Trigo ,  Dr. Martinez-Marti ,  Dr. Moreno , and  Dr. Kruger  were involved in acquisition of data. Dr. Collingwood,  Dr. Dhar ,  Dr.
For the energy calibration we used a 207 Bi source. When the detector is triggered, a gate generator  is creating a time window for the energy integration of the analog to digital converters (ADCs). The time window is much larger than the expected time between the deposition of the kinetic energy of the positron and the annihilation process. Therefore, a significant probability of full-energy deposition of an annihilation gamma would lead to additional beta spectra shifted by 511 keV or 1022 keV. In Fig. 2  we show hypothetical contributions to the spectrum, if the full energy of one or two annihilation gammas would be detected with a 10% chance. By fitting, we get an upper limit on the full energy detection probability for one gamma of 1%. This test measurement was performed with a single plastic scintillator without backscatter detector and in a different magnetic field configuration than used during the PERKEO II data taking, resulting in different systematics.
Data Fitting The experimental permeability values P app,pig were fitted to the in vivo permeability P eff,human values using a four parameter logistic equation (SigmaPlot software, version 12. 5-Systat Software Inc. ; San Jose, CA, USA) as used by Sjöberg et al. , where y 0 is the minimum value of P eff,human , X 50 is the P app,pig when P eff,human is at half the maximum value, a is a scaling factor and b is the slope factor  [28]. Conventional approaches to evaluate intestinal drug absorption use the permeability coefficient as the reference parameter. This does not take into account drug retention in the membrane. The quantification of drug deposition in addition to permeation is routinely carried out in investigations into the transport of drugs across other biological membranes  [63] [64] [65] [66].
Atenolol, 9. Terbutaline, 10. Furosemide and 11. Hydrochlorothiazide (for n, see Table  2 ). A 100 µM solution of each drug was prepared in (i) KBR, (ii) FaSSIF V2 and (iii) FeSSIF V2. A four parameter logistic model was used to derive the fit between P eff,human and P app,pig (see Section 2. 5. 2)  [28]. As the P eff,human values are obtained for more molecules, further experiments can be carried out with the porcine intestine to determine the corresponding P app,pig and so to expand the dataset.
). A 100 µM solution of 138 each drug was prepared in (i) KBR, (ii) FaSSIF V2 and (iii) FeSSIF V2. A four parameter logistic model 139 was used to derive the fit between Peff,human and Papp,pig (see Section 2. 5. 2) [28]. 140As the Peff,human values are obtained for more molecules, further experiments can be carried out 141 with the porcine intestine to determine the corresponding Papp,pig and so to expand the dataset.
SixTrack Version 5 is a major SixTrack release that introduces new features, with improved integration of the existing ones, and extensive code restructuring. New features include dynamic-memory management, scattering-routine integration, a new initial-condition module, and reviewed post-processing methods. Existing features like on-line aperture checking and Fluka-coupling are now enabled by default. Extensive performance regression tests have been developed and deployed as part of the new-release generation. The new features of the tracking environment developed for the massive numerical simulations will be discussed as well.
Main features SixTrack [1] is a 6D single-particle symplectic tracking code for studying dynamic aperture (DA) or evaluating the performance of beam-intercepting devices like collimators  [2]. SixTrack is licensed as GNU Lesser General Public License software and is under very active development in the GitHub repository  [3]. Extensive restructuring and new features have been added in the last years. The SixDesk runtime environment manages SixTrack simulations from input generation, job queue management (using HTCondor  [4]  in the CERN BATCH service [5] and customised software in the CERN BOINC server  [6] ), to collecting and post-processing results. This paper presents a summary of the existing features in the last SixTrack and SixDesk releases and focuses on some of the most recent developments.
SixTrack tracking maps are optimised for speed and numerical reproducibility. Scattering maps are being re-factored to be numerically reproducible, thus increasing the type of simulations that can be ported to LHC@Home  [8]. SixTrack can also be linked with the BOINC library  [6]  to use the volunteer computing project LHC@Home. SixTrack can compute linear and non-linear optical functions using a 5D matrix code and a 6D truncated power series algebra (TPSA) tracking code. Coupled Twiss parameters, using the Mais-Ripken formalism  [9] , can be extracted along the lattice. The optical parameters are optionally used in the beam-beam elements for self-consistent simulations. Differently from other codes, SixTrack uses (σ = s -β 0 c t) as the longitudinal coordinate during tracking to avoid rounding errors associated to the relativistic β when updating time delays in drifts and ζ = β β 0 σ, δ = P -P 0 P 0 as conjugate canonical variables in 6D optics calculations.