dataesr/scientific-paragraphs-categorization huggingface dataset:
Communication The Ubiquity airMAX-AC system relies on the WiFi communication of BULLET-AC devices to a single ROCKET base station. As stated before, each detection unit produces 465 kB/s of data at most. We require that the ROCKET device works only at 60% of its capacity to ensure an efficient working network (corresponding to a 62. 5 MB/s data flow at 100%). Consequently up to 75 detection units can be connected to the same ROCKET base station. Therefore the GP300 array will be divided in 5 districts. The five ROCKET stations will be installed atop the DAQ room, located at one corner of the radio array. Finally Ubiquity AirFiber will connect the DAQ room to Lenghu (distant by some 90 km) and the outside ethernet, at a speed of 2 Gbps.
Acknowledgements. The GRAND and GRANDProto300 projects are supported by the  Natural Science Fundation of China  (Nos. 11135010 , 11375209 ), the  Chinese Ministery of Science and Technology , the  APACHE  grant ( ANR-16-CE31-0001 ) of the  French Agence Nationale de la Recherche , and the  France China Particle Physics Laboratory.
In order to solve the subproblem of power control, the previous pilot allocation remains unchanged. So, the subproblem becomes a geometric programming problem that can be solved by convex optimization software such as MOSEK. Through solving two subproblems alternately, the minimum spectral efficiency can be increased with lower computational complexity. Notation: Boldface lower and uppercase symbols represent column vectors and matrices, respectively. (•) T denotes the transpose of matrix. (•) H denotes the conjugate transpose of matrix. I K is the identity matrix of size K. C m×n is the set of m × n complex matrix. E{•} denotes expectation. CN (•, •) stands for the circulary symmetric complex Gaussian distribution. O(•) denotes the order of complexity.
Yes, overlying water from the sediment cores was measured and in good agreement with the bottom water DIC and TA concentrations. This information will be mentioned in the revised manuscript. L. 108-110: At what temperature are the pH data presented, in situ or 25 degrees? Please add. The pH samples were measured at 25 • C and the pH values recalculated to in situ temperature, salinity, and pressure using CO2sys. The pH data is presented at in situ temperature and pressure. This information will be provided in the revised Method section. L. 114: What are the 'main redox species'? Specify. In the results only DIC and TA lander data are presented. This part of the sentence will be removed from the paper as the results from the in situ voltammetric sensors in the lander chamber (iron, manganese, sulfide) are not used in this manuscript.
Yet the two profiles (short and long cores) for station A and Z match at some point at depth. Furthermore, the profiles measured at station A and Z are comparable with DIC, TA, and SO42-profiles measured in other campaigns (e. g. :  Rassmann et al. , 2016; Pozzato et al. , 2017). Despite the different shapes of the individual profiles, the DIC/TA and DIC/SO42-ratios (and if we would look at them also the DIC/Ca ratios) are always the same. The CH4 core was a different core and the symbols do not indicate that CH4 was measured on the same core. This information will be provided in the revised manuscript. C: Maybe identifying the SMTZ would be easier if only data from a single core are used. The concentrations of dissolved methane were measured in another core than the concentrations of DIC, TA, and SO42-for practical reasons, as it more efficient to use horizontal mini-cores at each depth in the sediment core to sample this insoluble gas (see methods).
L. 390-391: This statement can be sharpened. Coupled nitrification-denitrification does not produce TA in a net sense, so any net TA production from denitrification must come from riverine nitrate inputs. Can you use e. g. monitoring data to make an estimate about the importance of this? In microtidal systems such that Rhône River delta, haline stratification is strong, such that riverine nitrate is confined in the surface river plume (e. g. , 0. 5 to 1 m,  Many et al. , 2018 PiO)  where it is either diluted or consumed by phytoplankton growth. The marine origin of the bottom water is assessed by their salinity (37. 5-38. 0; Table  2 ) and their C18 low nitrate concentrations  (Bonin et al. , 2002, Wat. Res. 36, 722-732). Furthermore, nitrate profiles presented in  Pastor et al. (2011)  all show an increase of concentration in the porewater indicating that the nitrate flux is directed from the sediment to the water column.
These concepts will be added to the revised manuscript to clarify this point. L. 526-529: I agree that microniches can be important, but do your Ca2+ porewater profiles give any indication of CaCO3 dissolution at the top of the sediment? In the sediment surface layer at station A and Z, Ca2+ concentrations either decrease (St. A) or remain constant with a further decrease deeper (St. Z), thus providing no sign of carbonate dissolution in the upper layers. Furthermore, with the uncertainty of 2-3 % as reported in the method section (line 191), it is difficult to detect minor changes of C25 Ca2+ linked to small dissolution of carbonate. This evidence for low CaCO3 dissolution from the data collected will be higlighted in the revised manuscript. L. 534-539: Is FeS the dominant form of solid-phase S in the sediment, or is pyrite also present in substantial amounts? Only FeS was measured in these sediments in a limited number of cores as these analyses take considerable time.
With an annual budget of several billion euros, this plan to generalize open access and to structure and open up research data is part of a European movement under an association of scientific organizations called "cOAlition S", supported by the European Commission, advocating a policy to oblige European researchers to publish studies funded by national or supranational European organizations in Gold OA journals or platforms. Under this "Plan S" (https://www. coalition-s. org), readers would enjoy open access, totally free of charge, to articles as soon as they are accepted for publication, facilitating public access to research and transparency. This European initiative, not taken up in either America or China -an awkward detail… -, has, however, been widely criticized, notably in relation to the roles of scientific societies and medical publishers  [25] , and the launch, initially planned for January 2020, has been postponed to 2021, with a possible further postponement to 2024 (https://www.
Inhibition of drug efflux transporters The overexpression of drug transporters on the membrane of cancer cells is a major cause of multi-drug resistance to cancer chemotherapy. Among the many transporters, ABCC10 (also known as MRP7) can be inhibited by a variety of drugs including certain kinase inhibitors (lapatinib, erlotinib, imatinib) and different natural products such as CEP. This natural product can reverse completely the resistance to paclitaxel  Weber and Opatz (2019) antioquine, aromoline, baluchistine, berbacolorflammine, berbibuxine, calafatimine, cocsoline, cocsulinine, colorflammine, curacautine, curine, cycleahomine, cycleanine, cycleatjehine, daphnine, daphnoline, daurisoline, dihydrosecocepharanthine, dihydrothalictrinine, faralaotrine, funiferine, gilletine, hernandezine, johnsonine, krukovine, liensinine, limacine, limacusine, macolidine, macoline, malekulatine, norpanurensine, obaberine, oblongamine, osornine, oxandrine, phaeantharine, racemosidine, repandine, sinomenine, temuconine, thalbadenzine, thalfine, thalibrunine, thalictine, thalictrinine, thalidasine, thalidezine, thalmethine, thalmine,, tiliageine, tubocurine a Number of citations in the PubMed database (May 2019).
Discussion and perspective CEP is a well-established alkaloid, known for 85 years and the only cyclic BBIQ used in the clinic to treat Human diseases. But surprisingly it has not been extensively studied. For example, a search through of the PubMed database gave 331 references for CEP vs. 910 for the related BBIQ tetrandrine which is not used clinically (Table  1 ). As presented here, CEP was discovered in 1934, launched in Japan in 1951 and remains used in the clinic today for the treatment of a variety of diseases and symptoms (Fig. 3 ). It was initially considered as a promising drug for the treatment of tuberculosis, but today the alkaloid is principally used for its anti-inflammatory, immuno-modulatory properties, notably for the treatment of anemia, alopecia and snake bites. The drug can be administered in large dose and over a long term, either orally or intravenously.
Acknowledgments The photography of the plant Stephania japonica (Fig. 1a) was kindly provided by  Prof. Frédéric Dupont ,  Faculty of Pharmacy, University of Lille, France. The photo of the caudex of S. rotunda (Fig. 1b) was given by  Mr Florian Beaucourt  ( Pierre Fabre Botanical Institute, Cambounetsur-Sor, France ). The photos of Stephania cephalantha (Fig. 1c-d) were provided by  Prof. Sovanmoly Hul ,  Muséum National d'Histoire Naturelle, Paris (France) , Collection: Plantes vasculaires (P), specimens  P02467444 & P02384134. I thank  Mr Bruno David ,  Mr François Sautel  ( Pierre Fabre Research Institute ) and  Prof. Jean-François Goossens  ( Faculty of Pharmacy, University of Lille ) who provided the contacts. The CEP market data in Fig. 5 were graciously collected by  Mr François Noailles  ( Pierre Fabre SA, France ).
A possible charm UHE neutrino discover A more convincing and testable approach is offered in the present article. It is based on a noisy charm atmospheric component smearing ICECUBE data and hiding the minor astrophysical signals. Many authors agreed that such a charmed atmospheric neutrino component must exist and plays role  [20] , but most of them considered the charm signals as secondary component, of one third or less of the present observed flux, in the ICECUBE rate. Therefore, most authors, remained convinced of the main UHE neutrino astrophysical nature. We remind anyway that the charmed atmospheric spectra derived by Cosmic Rays, CR, models is not well defined. It is known within a factor two. Moreover, it is dependent on the real, but unknown, PeVs energy CR composition, leaving much room for its eventual underestimated relevance.
Sample data and tau events: 2018-2019 The ICECUBE event data has not been published in detail for several years. Only maps and energy-coordinate spectra have been released in conferences in 2018-2019. We will use the 103 HESE event data of 2018  [21, 22] , see Fig. 1 , and the most updated UHE neutrino map  [7]  of 2019,   [21, 22]  On the vertical axis the HESE events arrival direction, in sin(Declination) within celestial coordinate, while on the horizontal one the deposited EMequivalent energy released in the detector. The energy lower bound threshold has been reduced to 100 TeV, see dotted line (or even at 90 TeV as in the colored areas); the two τ candidates to inspect are just among 27 events above 100 TeV and 9 above 200 TeV, for a total of 36 events. Adding the events in the range 90-100 TeV the total number rises to 42.
Upward-Downward tracks anisotropy and asymmetry A very recent August 2019 ICECUBE map shown in ICRC, is containing more useful updated information. There are hundreds of recent UHECR events (by AUGER and TA, Telescope Array), all the ICECUBE cascade HESE events (76 events) and muon HESE neutrino tracks  (26) , reaching nearly 86 total tracks, see Fig. 4. In this figure we overlap the HESE muon tracks with 2018 map data  [7, 21]. This comparision permited us to disentangle through-going muon tracks (by UHE neutrino, crossing ICECUBE from side to side) from HESE contained muon tracks. This disentanglement is shown by differnt colors for the event numbers in Fig. 4. We grouped the events in following ranges of zenith angles above and below the horizon: above (0 to 15) • , (15 to 30) • , (30 to 45) • , (45 to 60) • and (60 to 90) • ; and below (0 to -15) • , (-15 to -30) • , (-30 to -45) • , (-45 to -60) • , (-60 to -90) •.
(E) Relative spike rate as a function of laser power for different photoinhibition methods. Pyramidal neurons within 0. 4 mm from laser center across all cortical depth. Mean ± s. e. m. across neurons, bootstrap (Materials and methods). Two datasets (lines) are shown for VGAT-ChR2-EYFP: the line spanning 0. 5-10. 5 mW shows data from barrel cortex, n = 153; the line spanning 0. 1-1. 5 mW shows data from ALM, n = 188; PV-IRES-Cre x Ai32, data from barrel cortex, n = 16; Sst-IRES-Cre x Ai32, data from barrel cortex, n = 65; two datasets (lines) are shown for PV-IRES-Cre x ReaChR: the line spanning 0. 5-10. 5 mW shows data from barrel cortex, n = 211; the line spanning 0. 3-3 mW shows data from ALM, n = 55; PV-IRES-Cre, ChR2 virus injection, data from barrel cortex, n = 78; Emx1-IRES-Cre x Ai35D, data from barrel cortex, n = 26; Emx1-IRES-Cre x Camk2a-tTA x Ai79, data from barrel cortex, n = 176.
In Figure  8 , we quantified the half-max radius of photoinhibition when the relative spike rate is 0. 1 at laser center (i. e. 90% activity reduction). Bootstrap was performed over neurons to establish 90% confidence intervals around the spatial spread estimates in Figure  8. For each round of bootstrapping, repeated 2000 times, we randomly sampled with replacement neurons in the dataset. We computed the half-max radius on the resampled datasets. The bootstrap created a distribution of half-max radius estimates. The confidence interval in Figure  8  encompassed 90% of the observed half-max radiuses from bootstrap. To quantify the time course of inactivation, we computed the average population Peristimulus Time Histogram (PSTH) aligned to photostimulus onset (Figure  11 ). To quantify the onset time of inactivation, we estimated the time when the pyramidal neuron PSTH reached its minimum post stimulus onset (18.
4 ± 1. 6 ms, mean ± s. e. m. ). To quantify the earliest time inactivation occurred (3. 95 ± 1. 7 ms), we estimated the first time bin (10 ms bin) in which the spike rate differed significantly (p<0. 05 two-tailed t-test) from baseline. To quantify photoinhibition lag from excitation of the interneurons, we computed the earliest time bin in which FS neuron PSTH significantly deviated from baseline spike rate (1. 05 ± 0. 2 ms) and subtracted this onset time from the photoinhibition onset time (lag: 2. 90 ms). Bootstrap was performed over neurons to obtain standard error of the mean. For each round of bootstrapping, repeated 1000-10000 times, we randomly sampled with replacement neurons in the dataset. We computed the means of the resampled datasets. The standard error of the mean was the standard deviation of the mean estimates from bootstrap.
Bootstrap was also used to evaluate whether ChR2-assisted photoinhibition had significantly faster onset latency than ReaChR-assisted photoinhibition (Figure  11C ). The neuronal dataset was re-sampled with replacement, and the onset latencies were recalculated from the resampled datasets. The p-value reported is the fraction of times when bootstrap produced a resampled dataset in which ReaChR-assisted photoinhibition had a faster onset latency than ChR2-assisted photoinhibition, (one-tailed test against the null hypothesis that ChR2-assisted photoinhibition had significantly faster onset latency). To quantify rebound activity, for each neuron, we computed spike rate in a 500 ms window after photostimulus offset, subtract the baseline spike rate (computed in a 500 ms time window before photostimulus onset), and normalized this spike rate difference to the baseline spike rate.
Data and code availability Rosa26-CAG-LNL-GtACR1-ts-FRed-Kv2. 1 mice are available at The Jackson Laboratory (stock #033089). Electrophysiology data and code used are available at Github (https://github. com/ NuoBCM/PhotoinhibitionCharaterization; copy archived at https://github. com/elifesciences-publications/PhotoinhibitionCharaterization).
Acknowledgements We thank  Mathias Mahn  and  Ofer Yizhar  for sharing the GtACR1-ts-FRed-Kv2. 1 construct. We thank  Mingshan Xue  for comments on the manuscript,  David Golomb  for critical discussions,  Susan Michael  and  Amy Hu  for histology,  Tim Harris ,  Brian Barbarits ,  Wei-Lung Sun ,  Anthony Leonardo  for help with silicon probe recordings. This work was supported by  Howard Hughes Medical Institute (KS) ,  Helen Hay Whitney Foundation  fellowship ( NL, HI), Sir Henry Wellcome Postdoctoral Fellowship (SC) , the  Robert and Janice McNair Foundation  (NL),  Whitehall Foundation  (NL),  Alfred P Sloan Foundation  (NL),  Searle Scholars Program  (NL),  NIH   NS104781  (NL), the  Pew Charitable Trusts  (NL), and Simons Collaboration on the Global Brain (# 543005 , KS, NL).
Data availability Rosa26-CAG-LNL-GtACR1-ts-FRed-Kv2. 1 mice are available at The Jackson Laboratory (stock #033089). Electrophysiology data and code used are available at Github (https://github. com/ NuoBCM/PhotoinhibitionCharaterization; copy archived at https://github. com/elifesciences-publications/PhotoinhibitionCharaterization).
Secondary goals were 1/ to identify factors associated with secondary transfer (considered as undertriage), and 2/ to evaluate the proportion of patients deceased in lower level facilities before admission to a level 1 TC (considered as undertriage) and calculate a population-based undertriage. over the country, sharing the registry for research and public health issues. Even though datasets are de-identified, the National Commission for data protection (CNIL) has imposed restrictions on data sharing since they contain sensitive information on trauma. In the present study, the Traumabase data have been linked to data owned by the Ministry of the Interior. Restrictions to the sharing of these data are ruled by the body of the Road Safety Office in the Ministry of the Interior and a special convention has been signed to secure the data.
Population and databases This study included all the patients involved in a RTA in the I ˆle de France region between January 1, 2015 and December 31, 2017. All patients admitted to one of the six level 1 TCs of the region were prospectively registered in the regional trauma registry (Traumabase, http://traumabase. eu). The Traumabase obtained approval from the Advisory Committee for Information Processing in Health Research (CCTIRS, 11. 305bis) and from the National Commission for data protection (CNIL, 911461), and meets the national institutional review board requirements (Comitede Protection des Personnes Paris VI, Paris France). The information is given to the patients (or next of kin, in case of comatose patients) about the existence of the trauma registry, but the institutional review board waived the need for informed consent.
Data are anonymized from the time they are collected in the case report file. Sociodemographic, clinical, biological and therapeutic data (from the prehospital phase to the discharge of hospital) are systematically collected for all recorded patients. In parallel, for all RTA involving any vehicle in France, the Police collects descriptive variables on the road user, vehicle, location and conditions of the crash. These road accident analysis reports (RAAR) are sent to the Ministry of the Interior once the 30-day survival status is completed. The RAARs constitute a nation-wide database of more than 10 million accidents since 1953. Records from the RAAR and Traumabase were linked in order to evaluate the frequency of patients deceased in non-TC facilities (considered as inappropriate triage).
Population based analyses The linkage of administrative databases to medical registries has blossomed in epidemiological studies in the last decade  [40]. Trauma registries have historically provided the main source of data  [41] , targeting the most severely injured patients transported to a TC and focusing on hospital-centered outcomes. Linking to non-research database (e. g. administrative) now allows creating population-based injury databases, spanning all phases of care, gathering data on severe and non-severe patients. This is the first study in France enabling the drawing of the regional triage profile and the estimation of the death rate on scene within the studied trauma system. First, the rate of undertriage leading to death was low (0. 15%, 95%CI [0. 12-0. 19]). Then, if we admit that the systemic undertriage was over 1.
Limitations Our study presents some limitations. First, its retrospective design precluded the measurement and adjustment on confounding factors such as provider experience, quality, timing, or appropriateness of care at the initial facility which were not available in the databases. Second, multiple imputation was used to handle with missing data for most of the variables except for prehospital physiological variables for which data were not missing at random. Nevertheless, as the rate of missing data was low (max: 5. 5%) for these variables, the potential resulting bias was likely to be limited. Third, a potential for selection bias (survivor bias) may have been introduced by severely injured patients deceased in local hospitals before transfer to TC. However, efforts have been made to evaluate it using the linkage of Traumabase and RAAR, and return to the archives from the 8 central dispatch centers of the region but adjustment could not be performed on these data as confounding variables were not available.
A large observational study in France is about to begin and will allow to have an integrated database across all facilities of the participating regions to answer these points. An Australian study even showed that, in a well-resourced and coordinated trauma network, mortality was lower in non-TC when adjusted for age and injury severity  [46]. Fourth, a probabilistic linkage had to be performed between the Traumabase and RAAR as both datasets did not have a unique common identifier and the performances of the matching algorithm we designed could not be evaluated before implementation. However, this method has already been used to match EMS records to hospital outcomes in the US  [47, 48]  and has been validated among injured patients  [49]. Fifth, the external validity of this kind of regional study is a complex issue but the present data shed light onto the triage process in an exclusive physican-staffed trauma system.
Supporting information S1 File. Database linking process. (DOCX) S2 File. Sensitivity analysis: Results of the multivariate analysis to identify variables associated to the secondary transfer with only "complete cases" for prehospital physiologic variables. Complete cases included 3868 patients in the direct group and 262 in the secondary transfer), SAP remained in the final model but not HR and SpO2. As it can be seen by comparing to Table  4 , the values of the OR of all the identified predictors were marginally modified in this sensitivity analyses. Data missing not at random are known to introduce bias, so we chose to withdraw these prehospital variables and we showed that it had no meaningful impact on our results with these sensitivity analyses.