dataesr/scientific-paragraphs-categorization huggingface dataset:
Conclusions We present in this work the results of vertical profiles of temperature and CO 2 density from solar occultation observations by the NOMAD/SO channel in its diffraction order 149 during the first year of TGO science operations. For this purpose we adapted a well tested inversion scheme on Earth limb sounding experiments to solar occultation data on Mars for the first time. This scheme is used in a sequential manner to retrieve diverse targets from the NOMAD/SO data. Results on aerosol opacity's and physical properties, and on H 2 O and CO abundance profiles are presented in companion papers in this special issue. The analysis starts with a pre-processing phase, which permits to clear the SO calibrated transmittance of remaining issues like spectral shifts and bendings. This is followed by the actual retrieval, following an iterative application of a precise forward model, which uses micro-windows to eliminate severe contamination from adjacent diffraction orders, and solves temperature and density consistently, with incorporation of hydrostatic adjustments in each iteration.
The inversion achieves best-fits with a high degree of convergence in the dataset analyzed here. The scheme is robust against a priori assumptions and permits meaningful retrievals typically up to about 90 km altitude. The atmospheric thermal structure during the two Mars seasons in MY34 (the Martian perihelion half-year period) is characterized by the strong MY34 GDS episode, a well documented event peaking around Ls 195 • -210 •  (Guzewich et al. , 2019; Montabone et al. , 2020). Our results show that the GDS warmed the atmosphere significantly at all sounded altitudes, with episodes where 180 K are observed at 80 km altitude in the NH and up to 100 km in the SH. The thermal impact is clearly observed during an extended decay phase up to about Ls 250 •. During this period the retrievals are difficult in the troposphere due to the strong dust opacity.
A warm layer is also observed in the evening terminator but more sporadically. These two global results, a colder mesosphere and a warm layer around 80 km were also found very recently by  Belyaev et al. (2021). Regarding the latitudinal and local time variations, our data is patchy and the TGO orbital mapping does not allow us to build pure latitude or pure local time cross sections. Instead, some boxing is required, which are not free from a mixture of effects. However, with the help of scan-to-scan comparisons with the GCM and of global maps created with the GCM specifically for this comparisons, it seems that our NOMAD retrievals do track the expected thermal structure typical of equinoxes and solstices during the observed period. The main conclusions regarding local time variations is the clearly stronger tidal activity in the morning terminator, mentioned above.
A second retrieval product is the atmospheric CO 2 density, whose global distribution shows very clear latitudinal effects, especially when looking at a single altitude, like 70 km. On top of these, a seasonal trend seems also present, although the occurrence of the GDS in the early Southern Spring dominates the seasonal pattern and hides the expected maximum around Ls 270 • that should be typical of non-GDS Mars years. It will be very interesting to revisit this when an extended SO dataset covering several MY are processed. Another interesting product of our retrieval scheme is the so called "LOS correction", which permits to revisit the hydrostatic assumption, based on using a given pressure level at a given altitude, usually taken from the climatology or a priori dataset. In our case, the correction indicates that the LMD MGCM pressures at the lowermost tangent altitude of our retrieval range are globally correct, except for a small correction of a couple of km.
This agreement is better in dust-clear conditions, when our sounding can be extended to the lower troposphere, below 20 km. Below this altitude, the lower the sounding, the smaller the correction. In some scans the altitude adjustment to reconcile NOMAD and GCM can reach up to 10 km. We plan to continue the analysis of NOMAD/SO data by extending our retrieval scheme to the whole SO dataset available to date, covering two full Mars Years. A few issues remain to be improved also and we plan to tackle them, in a joint effort with other NOMAD teams. These include a better characterization of the SO measurement noise in order to eliminate remaining systematics, and the combination of two different SO orders. For example, the combination of orders 149 and 165, this one with much stronger absorption lines from the CO 2 2.
Acknowledgments The  IAA/CSIC  team acknowledges financial support from the  State Agency for Research of the Spanish MCI through the 'Center of Excellence Severe Ochoa ' award for the  Instituto de Astrofísica de Andalucia  ( DEV-2017-0709 ) and funding by grants  PGC2018-101836-B-100  ( MCI/AEI/FEDER, EU) ,  PID2019-110689RB-I00/AEI/10. 13039/501100011033 , and  RTI2018-100920-J-I00. ExoMars is a space mission of the  European Space Agency (ESA)  and  Roscosmos. The NOMAD experiment is led by the  Royal Belgian Institute for Space Aeronomy (IASB-BIRA ), assisted by Co-PI teams from  Spain (IAA-CSIC), Italy (IN AF-IA PS ), and the  United Kingdom (Open University ). This project acknowledges funding by the  Belgian Science Policy Office (BELLS) , with the financial and contractual coordination by the  ESAU Prod ex Office  ( PEA 4000103401 ,  4000121493 ) as well as by  UK Space Agency  through grants  ST/V002295/1 ,  ST/V005332/1  and  ST/S00145X/1  and  Italian Space Agency  through grant  2018-2-HHS.
0. US investigators were supported by the  National Aeronautics and Space Administration. This work was supported by the  Belgian Funds de la Recherche Scientific -FIRS  under grant number  30442502  ( ET HOME ). This project has received funding from the  European Union   Horizon 2020 research and innovation program  under grant agreement No  101004052  ( Road Map  project). We want to thank the  LMD  and LATMOS teams for the continuous development of the LMD-MGCM. the  Local Solar Time  of the observations and the central panels show the densities in log scale. The bottom panels show the density variation at 70 km altitude through the mission, including latitudinal and seasonal variations (see text); red dots: NOMAD densities, blue dots: GCM data. Figure  11. Distribution of the retrieved LOS correction with altitude of all the retrieved profiles (left panel) and the same but in absolute values (right panel).
Catalyst Characterization Elementary analysis of the calcined samples was performed by inductively coupled plasma optical emission spectroscopy (ICP-OES) using a SPECTRO ARCOS ICP-OES instrument. Prior to the analysis, the samples were digested with concentrated nitric and hydrofluoric acid using a microwave heating system. Textural properties of the catalysts were obtained by N 2 physisorption at -196 • C on a Micromeritics Tristar II automated gas sorption system, operated with a software MicroActive version 4. 06. Before the analysis, the samples were outgassed under a vacuum at 300 • C for 3 h. The specific surface areas were calculated using the B. E. T. method in the 0. 10-0. 25 p/p 0 interval. The T plot method was used for calculating the microporous volume and surface area. The mesopore size distribution was plotted by using the B.
Acknowledgments: The  Chevreul Institute  is acknowledged for its help in the development of this work through the  ARCHI-CM  project supported by the  "Ministère de l'Enseignement Supérieur de la Recherche et de   l'Innovation" , the  region "Hauts-de-France"  and the  ERDF program  of the  European Union  and the " Métropole Européenne de Lille.
Nephrologists or outpatient nurses measured the patients' blood pressure, weight, and height and calculated the BMI. All patients were prescribed standard blood and urine tests (as recommended by the French health authorities for routine CKD care), which were performed at their usual laboratory at study entry, or at a centralized laboratory for the measurement of CRP and creatinine. We recorded data on the serum hemoglobin, CRP, urea, creatinine, and albumin levels and the ACR. The eGFR was calculated from serum creatinine using the Chronic Kidney Disease-Epidemiology Collaboration (CKD-EPI) equation [37], and the ACR was classified according to the Kidney Disease Improving Global Outcomes (KDIGO) guidelines  [38].
Assuming that data were missing at random, we performed multiple imputation with chained equations (MICE)  [39] , implemented with the MICE package in R software (version 4. 1. 2, R Foundation for Statistical Computing, Vienna, Austria)  [40]. Given the proportion of missing data, 32 complete datasets were imputed. The imputation model included all the variables present in the models (UTs, PPI, and each model's covariates). Linear regression models were generated for each dataset, and pooled regression coefficients were obtained according to Rubin's rules. The threshold for statistical significance was p < 0. 05. All statistical analyses were performed with R software.
The FE-I4 chip was configured to consider only signals in a region of interest (ROI) compatible with the DUT geometries. The trigger logic was handled by a programmable Trigger Logic Unit (TLU)  [25]. Moreover, a NIM logic circuit was implemented to generate a busy signal covering the time the oscilloscope needed to read out its data buffer. Dry ice-based cooling was used to maintain, but not control, the temperature of the DUTs between -43 °C to -25 °C. The monitoring of the temperature and humidity was performed with Pt100 probes.
JINST 18 P05005 CNM-0, which has been placed next to the cooling box. It has been calibrated in the lab and in beam tests to a time resolution of 54. 8 ps at room temperature. Its time resolution at a temperature of -30 °C is 35 ps. For triggering on a particle, the second MALTA sensor is used in coincidence with a scintillator placed at the back of the telescope. If the two sensors record a signal, an FPGA-based triggering system records the telescope data from all six planes and the waveforms from both the DUT and the LGAD used as a timing reference. A remote-controlled cooling system was used to maintain the temperature of the DUTs at -20 °C in a hermetic box.
For data collected at DESY with a 5 GeV electron beam, the procedure was slightly different because electrons may be scattered in the equipment around the DUTs. As shown in figure  2 , 3D-proto-tracks are reconstructed from the three MIMOSA planes before the DUTs on the beam trajectory ("upstream triplets"), and other 3D-proto-tracks are reconstructed from the three MIMOSA planes after the DUTs ("downstream triplets"). The downstream triplets must coincide with a hit in the FE-I4 plane. -6 - ) and other 3D-proto-tracks are reconstructed from the three MIMOSA planes after the DUTs ("downstream triplets"). The downstream triplets must coincide with a hit in the FE-I4 plane. A complete track is considered to be found if a downstream triplet matches an upstream triplet in the central region with a minimal distance of approach of 150 μm.
Oscilloscope data processing A complete description of the LGAD waveform processing can be found in  [11]. In summary, the procedure is the following: after the start and stop points of the pulse were determined, the pedestal was computed in a region outside the pulse where no signal was expected. This pedestal was subtracted from all the data points of the waveform on an event-by-event basis. After that subtraction, the start and stop points were re-computed. Several waveform properties can be extracted at this step such as the maximum of the pulse amplitude, the charge, the rise time, the jitter, the signal-to-noise ratio and the time of arrival (TOA). The maximum of the pulse amplitude was estimated as the sample with the maximum amplitude. The collected charge was defined as the integral of the pulse in the signal region divided by the trans-impedance of the read-out board and the gain of the voltage amplifier.
Data selection In all subsequent analyses, background events were removed with a timing cut using a 2 ns window from the maximum point of the distribution of the difference between the TOA of the DUT and that of the SiPM. Additionally, the signal amplitude in the SiPM was required to be three times higher than the noise level. The value of the discriminator of the future ALTIROC chip corresponds to a collected charge of 2 fC, as explained in section 1. The efficiency and the time resolution were computed using this threshold in the analysis. To measure the global efficiency and the collected charge of the sensors, another condition was added: only the fiducial region of 0. 5x0. 5 mm2 in the center of the DUTs was used.
Acknowledgments The authors gratefully acknowledge CERN and the  SPS  staff for successfully operating the  North Experimental Area  and for continuous supports to the users. Measurements leading to the SPS results have been carried out using a MALTA beam telescope as part of the  CERN ATLAS R&D programme. The authors thank MALTA team for their successful running of the telescope during the data-taking. The measurements leading to these results have been performed at the  Test Beam Facility at DESY Hamburg (Germany) , a member of the  Helmholtz Association (HGF). The authors gratefully acknowledge  DESY  staff for their support to the users. This work was partially funded by  MINECO ,  Spanish Government , under grant  RTI2018-094906-B-C21. This project has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Sklodowska-Curie  grant agreement No.
754510. This work was partially funded by: the  Spanish Government , under grant  FPA2015-69260-C3-2-R  and  SEV-2012-0234  ( Severo Ochoa  excellence programme); and by the  H2020  project  AIDA-2020 , GA no. 654168. This work is partially supported by "the  Fundamental Research Funds for the Central Universities" of China  (grant  WK2030040100 ), the  National Natural Science Foundation of China  (No. 11961141014 ) and the  Chinese Academy of Sciences  (Grant NO. :  GJJSTD20200008 ).
METHODS A 10 min online survey (online supplemental appendix 1) was administered between 20 June 2022 and 18 July 2022. HCPs were recruited from a proprietary vendor panel (a database built over time to include members of the public who have indicated willingness to take part in surveys through open recruitment and direct campaigns) using an external sampling team to locate eligible respondents and invite them to participate via email. Eligibility criteria included being an emergency department physician, general surgeon, internal medicine physician, critical care physician or pulmonologist; being qualified for 3-30 years; spending the majority of clinical time in a hospital; and spending at least 50% of professional time in direct patient care. Participants indicated consent in the survey introduction and were remunerated according to fair market value after completion.
Acknowledgements The authors extend their to gratitude to the participants in this research. They also thank  Rebecca West  and  Phoebe Seltzer  for their support in finalising the manuscript. Contributors RD, AF, EF and SU conceived the research and secured funding. EF, SP and SU managed data collection, and conducted analysis and interpretation of the data. All authors contributed equally to writing and revising the manuscript. Funding This research was funded by  bioMérieux  (no award/grant number).
2 Whether a consumer owns a car (or not) or rents their apartment (or not) can generate situations with even greater contrast. In a nutshell, over the same period and in the same country, different people experience different inflation rates. Here, we study how this heterogeneity helps explain the distribution of well-being in France between 2016 and 2022. For the empirical analysis, we exploit some newly available survey data, representative of the French population. The data set is a longitudinal cross-section from more than 40,000 individuals, collected by the National Statistical Office (INSEE) and the CEPREMAP Well-Being Observatory. In order to measure well-being, we use material satisfaction, intended as self-reported satisfaction with personal living standards. In spite of being declarative measures, data on subjective well-being have been shown to be statistically reliable  (OECD, 2013, chap.
Official inflation and perceived inflation Even though the importance of studying inflation inequality is acknowledged, measuring its well-being cost poses some serious methodological challenges. The most natural approach would be to construct an objective heterogeneous inflation rate, based on individual consumption weights and individual price indexes, and then combine it with individual-level material satisfaction. To the best of our knowledge, no existing dataset allows for this, for a simple reason: consumption datasets do not monitor the material satisfaction of respondents. Typically, two types of consumption data sets are used to estimate the level of inflation inequalities: micro-expenditure surveys and scanner data. Both approaches have abundantly shown important levels of inflation inequality  (Jaravel, 2021).
Based on American scanner data,  Kaplan and Schulhofer-Wohl (2017)  document "massive heterogeneity in inflation rates at the household level" (ibid, p. 36) and show that lowerincome households have experienced higher inflation rates over the period of study  (2004 to 2013). Recent data suggest that inflation inequalities have been widening since the beginning of the inflation crisis  (Orchard, 2022; Avtar et al. , 2022). These estimates should be interpreted as lower-bounds. Micro-expenditure datasets contain no information on individual price indexes, so that consumers living, say, in the capital or in a rural area are assumed to face the same price index, thus attenuating the dispersion of the inflation rates. 3 Scanner data monitor price variations for shopping items, but not for other goods that can significantly contribute to inflation inequalities (e.
, 2007 , for a discussion). 7 Section 6 discusses the assumptions that are needed to map perceived inflation effects into actual inflation effects. 8 The dataset is available upon request to the CEPREMAP Well-being Observatory. respondent, the new datasets contain only a subsample of the individuals and only up to December 2018. More details are provided in section 5. 9 We use the following definitions: Material satisfaction = the answer, on a scale from 0 to 10, to the following question: "To what extent are you satisfied with your standard of living?" [0 = not satisfied at all, 10 = completely satisfied]. 10 Perceived inflation = inflation as measured by the Opinion Price Index (OPI). Perceived inflation is elicited from the preliminary question "Would you say that over the last twelve months prices have i) risen a lot; ii) risen moderately; iii) risen slightly; iv) stayed about the same; v) fallen" and the subsequent question on the value of the increase / decrease of prices (in percentage, rounded to the first decimal).
This gap is an order of magnitude larger than the one associated with a 1% difference in income. 13 Importantly, the estimated effect is due only to inequalities within the same period. This is a first empirical hint that previous macroeconomic analysis based on time effects might have overlooked an important aspect of the well-being cost of inflation. We now move to the longitudinal dimension, and use the whole available dataset, from June 2016 to March 2022. We also consider the threat of omitted variable bias, and introduce a richer set of explanatory variables. We study a regression of the form: h it = αOPI it + βlogincome it + X ′ it γ + Z ′ it δ + T ′ t τ + ϵ it (1) where h it is the level of current satisfaction with the standard of living reported by individual i at quarter t; OP I it is the level of perceived inflation he or she reports; logincome it is his/her adjusted household monthly income in the standard log form; X ′ it is a set of socio-demographic variables that includes a dummy variable for imputed OPI, age, age 2 , sex, education level, marital status, region of residence, size of urban unit of residence, employment status (employed, inactive, unemployed, retired, student or civil servant), category of occupation (managers, intermediate, clerks, workers); Z ′ it is a set of subjective variables.
In our dataset, although income is measured as the monthly level for the household, while prices are measured by individuals as growth rates over a year, a comparison between their estimated marginal effects can offer some interesting insights. Our results are along the same lines of  Boes et al. (2007). According to our linear regressions, the effect of perceived inflation is about twice as high as under nominal equivalence (this is in line with previous measures of loss aversion using satisfaction data, see  Boyce et al. 2013). For instance, for a household earning e2,000 monthly, the well-being cost of a 1-percentage-point increase in perceived inflation (i. e. a e20 loss in purchasing power) equals the loss brought about by a e52 loss in monthly income. Overall, these results show a non-negligible impact of perceived inflation rates on material satisfaction.
Yet, recent research reveals that inflation inequalities have been widening since the beginning of the inflation crisis  (Avtar et al. , 2022) , as predicted by the positive relationship between aggregate inflation and inflation dispersion  (Orchard, 2020). Our dataset corroborates this concern: the standard deviation of the OPI distribution moved from 2. 7 in March 2020 to 9. 7 in March 2022. In  22  The daunting task of matching subjective survey answers with detailed micro-data about consumption bundles and itemlevel prices has already been undertaken before. In a terrific project, the Chicago Booth Expectations and Attitudes Survey interviewed over 90,000 households of the Kilts Nielsen Consumer Panel, that allows to construct a household CPI for nondurable consumption basket  (D'Acunto et al.
50 They target multicopy ribosomal DNAs  28, 29, 33, 36, 39, [43] [44] [45] 47, 49, [51] [52] [53] [54] [55] [56] [57] [58] [59]  (rDNAs) or species-specific genes,  27, 41, 42, 46, 48, [60] [61] [62] [63] [64] [65] [66] [67] [68] [69] [70] [71] [72] [73]  mostly single copy, except for the region identified in the NCBI database as a 'copia-like retrotransposon'  1, 37, 38, 74, 75  with approximately 60 copies per Coccidioides genome. 74  The spec ic PCRs targeting multicopy rDNAs, such as the internal transcribed spacer (ITS) region or the.
| Interpretation of the results For the sensitivities, we compared the percentage of true positive PCR signals detected in each panel, the lowest detected concentration and absolute amount of DNA, and we transformed the latter into genome equivalents (GE). For the calculation of the absolute amount of DNA, and the GEs, the volume of sample per reaction was considered (Tables  3  and 4 ). We compared the percentage of false positive results detected in each panel for the specificities. GE For the graphical presentation, we used the GraphPad Prism 9. 1. 0 software.
Current lexica and machine learning based sentiment analysis approaches still suffer from a two-fold limitation. First, manual lexicon construction and machine training is time consuming and error-prone. Second, the prediction's accuracy entails sentences and their corresponding training text should fall under the same domain. In this article, we experimentally evaluate four sentiment classifiers, namely support vector machines (SVMs), Naive Bayes (NB), logistic regression (LR) and random forest (RF). We quantify the quality of each of these models using three real-world datasets that comprise 50,000 movie reviews, 10,662 sentences, and 300 generic movie reviews. Specifically, we study the impact of a variety of natural language processing (NLP) pipelines on the quality of the predicted sentiment orientations.