dataesr/scientific-paragraphs-categorization huggingface dataset:
We also removed datasets during which a 83m Kr source was left open for calibration. Data within 50 days of the end of neutron calibrations were also removed to reduce neutron-activated backgrounds and better constrain the background at low energies. After the other selections, this data would have only added ∼ 10 days of live time; thus, for simplicity, it was removed rather than fit separately like the SR1 dataset. With these selections, the effective SR2 live time for this analysis is 24. 4 days, with an average ER background reduction of 20% in  (1, 30)  keV as compared to SR1. A profile likelihood analysis was then performed on SR2 with a similar background model as SR1, denoted as B SR2. Since we are primarily interested in using this data set to test the tritium hypothesis, we focus on the tritium results.
Only the latter depend on the β particle energy. The outer radiative corrections R(Z, W ) take into account the internal bremsstrahlung process, by which the β particles lose energy in the electromagnetic field of the nucleus. For allowed transitions, analytical corrections were derived in  [150, 151]  and are implemented in the first version of BetaShape as described in  [62]. Finally, the spectral shape is modified by applying the screening correction S(W, Z). The BetaShape program includes an analytical correction based on the work of Bühring  [152]  that is more precise than Rose's correction. The most realistic, spatially varying screened potentials at the time were of Hulthén type (see  [153]  and references therein). Bühring first developed a version of the Dirac equation that correctly includes Hulthén's potentials but simplified the angular momentum dependency, allowing analytical solutions to be established  [154].
Application to the Transitions of Interest These different models have been applied to the ground-state to ground-state transitions in 212 Pb, 214 Pb, and 85 Kr decays. The resulting spectra are similar to each other in the major part of the energy range, except at low energy. The differences are illustrated in Fig. 15  for the low energy region of the 214 Pb β spectrum. The yellow curve is the GEANT4 RDM model as described in A 1 and the non-physical discontinuity due to the screening correction is clearly visible at 12 keV. The red curve is from IAEA LiveChart, thus generated by the first version of the BetaShape program as described in A 2. One can see the effect of Bühring screening correction that tends to decrease the emission probability. The cyan and blue curves were determined as described in A 3, without and with the atomic exchange correction, respectively.
Acknowledgements. We thank  Dr. Roland Purtschert  at  Climate and Environmental Physics, University of Bern , for measurements of the 37 Ar concentration at  LNGS  and for useful information regarding its production and transport. We also thank  Dr. Robin Grössle  of the  Institute for Nuclear Physics -Tritium Laboratory at Karlsruhe Institute of Technology (KIT)  and  Dr. Richard Saldanha  of  Pacific Northwest National Laboratory (PNNL ) for informative discussions. We gratefully acknowledge support from the  National Science Foundation ,  Swiss National Science Foundation ,  German Ministry for Education and Research ,  Max Planck Gesellschaft, Deutsche Forschungsgemeinschaft ,  Netherlands Organisation for Scientific Research (NWO) ,  Weizmann Institute of Science, ISF, Fundacao para a Ciencia e a Tecnologia ,  Région Pays de la Loire ,  Knut and Alice Wallenberg Foundation ,  Kavli Foundation ,  JSPS Kakenhi in Japan , the  Abeloe Graduate Fellowship  and  Istituto Nazionale di Fisica Nucleare.
Sedentary Behavior and Physical Activity Levels We performed this evaluation with a validated triaxial accelerometer (ActiGraph GT3X+, MTI)  [16]. Participants wore the device for 7 consecutive days of assessment during the waking hours. To be considered valid, days of data collection needed to have at least 10 hours of continuous monitoring, starting at the moment of awakening. Participants used the accelerometer until bedtime, except during showering and aquatic activities. Nonwearing time and the thresholds for the intensity of physical activity were evaluated as previously described  [17]. We defined wearing time as 24 hours minus nonwearing time. Periods of zero counts for 60 or more consecutive minutes were considered as nonwearing time. To be considered as valid data for analysis, volunteers needed to use the device for at least 4 days (10 hours/day), including a weekend day.
Qualitative Data Analysis We performed qualitative analysis of the perceptions of all participants included in the SMART in terms of the protocol and the use of the app for physical activity. The exchange of text messages with participants was performed through the free instant messaging app WhatsApp. We transcribed to text all the text messages exchanged between participants and researchers. The proceedings used were adapted from qualitative research in health  [18]. To assimilate the content of the material, we conducted a free-floating reading of the transcribed material, followed by an exhaustive reading until recording units were extracted. Finally, we organized and analyzed the material according to the literature recommendations. Conversations with participants via the app occurred two times a week and were usually initiated by the researcher.
It has been shown to increase adherence and involvement in physical activity intervention programs, overcome barriers, and increase motivation to achieve goals  [25]. This intervention proved to be effective in a 10-week physical training program submitted through WhatsApp, along with sending encouraging messages and responses to any questions from participants. Muntaner-Mas et al proved that it was feasible and had good adherence, even without the use of behavior change techniques, although the researchers emphasized the importance of such techniques  [26]. This result is in line with our study design, in which we performed a SMART owing to the adaptive characteristic of this design. Furthermore, the availability of researchers allied with the weekly text messages may have contributed to participants' adhesion to the intervention since they could stop using the app or stop monitoring or trying to increase their daily steps.
| MATERIAL AND METHODS 2. 1 | Clinical data of patients and ethical compliance GE affected patients (P1, P2, P3, and P4) and their available family members were referred to the Human Genetics at the University of Oldenburg (Oldenburg, Germany), CHU de Bordeaux, Hospices Civils de Lyon (Lyon, France), and Erasmus MC University Medical Center (Rotterdam, the Netherlands). All examined relatives of P1 originated from Germany, whereas P2, P4, and their relatives originated from France. The family of P3 settled in the Netherlands, but the grandparents of this patient were of Russian Caucasian origin. The patients and their relatives were informed about possible consequences of the study, agreed to participate in the study, and signed informed written consents before the project started. The study adhered to the tenets of the Declaration of Helsinki and was approved by local ethics committees (Hannover Medical School (MHH) ethics committee, OE9515; Medizinische Ethik-Kommission University Oldenburg, Comité de protection des personnes Bordeaux-Outre Mer III, France; Erasmus MC, METC-2012387).
broad insti tute. org/) and Genome Aggregation Database (GnomAD, https://gnomad. broad insti tute. org/). The sequence variants following X-linked and autosomal recessive modes of inheritance were considered for further analyses. Sequencing reads encompassing selected sequence variants were additionally evaluated with Integrated Genomics Viewer (IGV, http:// softw are. broad insti tute. org/softw are/igv). Trio-WES analysis was performed for family 2, including P2 and unaffected parents. Preparation of gDNA libraries, exome capture, sequencing, and data analysis were made with IntegraGen SA (IntegraGen SA), using SureSelect XT Clinical Research Exome in-solution enrichment methodology (Agilent technologies, Santa Clara, California, USA). Pairedend sequencing runs (2 × 75 bp) were performed on Illumina HiSeq4000 (Illumina).
The detailed explanations of the process have been previously described  (Gnirke et al. , 2009). A bioinformatic pipeline, including base calling was performed for each WES data using the Real-Time Analysis software version 2. 7. 7 (Illumina) sequence pipeline with default parameters. Sequence reads were mapped to the hg19/GRCh37 using Elandv2e (CASAVA1. 8. 2; Illumina), and duplicated reads were removed. Furthermore, CASAVA1. 8. 2 was used to call single-nucleotide variants (SNVs) and short insertions/deletions (max. size is 300 bp). Variants' filtering was performed on Eris v3. 0 (IntegraGen SA). Only variants with an appropriate familial segregation were considered, under the hypothesis of an autosomal recessive, X-linked and a dominant disease with de novo germinal pathogenic variants.
Variants with minor allele frequency (MAF) ≥1. 5% in ExAc as well as noncoding variants were not included into further analyses. Finally, mapped reads were visualized with Alamut Visual Software 2. 9. 0 (Interactive Biosoftware). Trio-WES was applied in family 3 using the Agilent Sure Select platform (Clinical Research Exome V2). A 150 bp pairedend sequencing run was performed on HiSeq4000 (Illumina). Sequencing data were demultiplexed by CASAVA software (Illumina) and reads were mapped to the hg19/GRCh37 by Burrows-Wheeler Aligner (http://bio-bwa. sourc eforge. net/). Variants were detected with Genome Analysis Toolkit (http:// www. broad insti tute. org/gatk/). Subsequently, sequence variants were filtered with Cartagenia software package (Agilent Technologies), considering reads' quality (read depth ≥10), MAF ≤ 1.
5% (dbSNP, ESP6500, the 1,000 Genome project, GoNL, or the ExAc database), and location (within an exon and 10 bp areas encompassing introns). Variants were further selected in regard to an autosomal recessive, X-linked or de novo dominant inheritance. Trio-WES analyses of family 4 using DNA samples from P4 and healthy parents were performed on a HiSeq 1000 Sequencing System (Illumina). The sequencing was done with a 2 × 150 bp high output sequencing kit after enrichment with SeqCap EZ MedExome kit (Roche), according to manufacturer's specifications. Sequence quality was assessed with FastQC 0. 11. 5 (Babraham Bioinformatics). After sequencing, reads were mapped to the hg19/GRCh37, using BWA-MEM (http:// bio-bwa. sourc eforge. net), sorted and indexed (Samtools, http://samto ols. sourc eforge.
net). Duplicates were flagged (Sambamba, https://www. open-bio. org/wiki/Sambamba) and coverage was calculated (Picard-Tools, https://broad insti tute. github. io/picar d/). Variant calling was performed with GATK 3. 7 Haplotype Caller (https://gatk. broad insti tute. org/ hc/en-us) and variants were annotated with SnpEff (http:// snpeff. sourc eforge. net/), dbNSFP (http://sites. google. com/ site/jpopg en/dbNSFP), GnomAD, ClinVar (https://www. ncbi. nlm. nih. gov/clinv ar/), Human Gene Mutation Database (HGMD), Variome Great Middle East (http://igm. ucsd. edu/ gme/data-brows er. php) Mutation Tester (http://www. mutat ionta ster. org/), Combined Annotation Dependent Depletion (CADD, https://cadd. gs. washi ngton. edu/), as well as with an in-house-database.
1 Cycle Sequencing Kit on the ABI Prism 3130xl Genetic Analyzer (Applied Biosystem). Sanger sequencing data were analyzed with SeqScape software (Applied Biosystem) and SnapGene software (GSL Biotech). Results of high throughput sequencing from P2, P3, and P4 were not additionally verified by Sanger sequencing as the GABRE gene, especially the nucleotide positions of the identified pathogenic sequence variants were very well covered in the trio-WES data.
| Multiple species protein alignments and in silico predictions of GABRE protein domains Multiple sequence alignments were performed with the ClustalW2 tool (http://www. ebi. ac. uk/Tools /msa/clust alw2/). Amino acid sequences of GABRE proteins were compared between human (GenBank: NP_004952. 2), mouse (GenBank: NP_059065. 2), Norway rat (GenBank: NP_075579. 1), rabbit (GenBank: XP_002721372. 1), hedgehog tenrec (GenBank: XP_004715681. 1), Sunda flying lemur (GenBank: XP_008589892. 1), dog (GenBank: XP_549340. 3), Californian Sea Lion (GenBank: XP_027465429. 1), Chinese rufous horseshoe bat (GenBank: XP_019566807. 1), and lesser ferret (GenBank: XP_004776709. 1). Publicly available tools to predict protein domains, such as NCBI Conserved Domain Search (https://www. ncbi. nlm. nih. gov/Struc ture/cdd/wrpsb.
cgi), PROSITE (http://prosi te. expasy. org), and SMART (http://smart. embl-heide lberg. de) were used to in silico estimate locations of the mutated amino acids in the GABRE protein, found in our patients. Additionally, MetaDome data (https://github. com/cmbi/ metadome) were used to predict pathogenicity and improve interpretation of the newly identified GABRE-variants. MetaDome aligns information of population variations from the ExAc and pathogenic variants from the HGMD with Pfam protein domains. Generally, MetaDome visualizes metadomain information and gene-wide profiles of genetic tolerance based on 56'319 human transcripts, 71'419 protein domains, 12'164'292 genetic variants from GnomAD, and 34'076 pathogenic variants from ClinVar  (Wiel et al. , 2019).
00004409 ExAc (Table  S1 ). In silico predictions of the glutamic acid into lysine substitution at the amino acid position 222 in the GABRE protein suggested a likely pathogenic variant and showed M-CAP = 0. 387 and SIFT = 0. 78 (VARVIS Version 1. 12; Limbus Medical Technologies GmbH). Sanger sequencing was additionally performed in P1 and the available family members. The analyses confirmed the co-segregation of the c. 664G>A (NM_004961. 3) variant in the GABRE gene in the family of P1 (Figure  1a ; Figure  S1a ). In family 2, a trio-WES was performed. The sequenced data for P2 resulted in 9. 69 Gb raw data and showed Q30score of 97%, and 95% of bases were covered at 25×. After variant filtering, only one variant was identified as a potential candidate for GE (Table  S2 ). This variant was located on the X-chromosome at position 151'974'625.
The identified GABREvariant in P4 was predicted to lead to an exchange of valine into isoleucine (NM_004961. 3: c. 1045G>A, NP_004952. 2: p. Val349Ile) and was not found in the ExAc/GnomAD database. SIFT score and Mutation Tester showed scores of 0. 92 and 0. 947, respectively (Table  S1 ). The score of the CADD was estimated to 6. 512. These predictions support the pathogenicity of the variant in the GABRE gene detected in P4. In total, 20 x coverage of each WES was obtained for about 93% of sequencing reads.
| Bioinformatic predictions of the identified variants in the GABRE protein To evaluate the evolutionary conservation of the GABRE protein, multiple protein alignments of different species were performed and showed a full conservation at amino acid 1, 133, 222, and 349 in regard to the human reference protein (NP_004952. 2; P2, P3, P1, P4; Figure  2 ). In silico predictions of the GABRE protein domains revealed that the genetic variants identified in P1 (NP_004952. 2: p. Glu222Lys) and P3 (NP_004952. 2: p. Tyr133*) were both located in the neurotransmitter-gated ion-channel ligand binding domain (PF02931; Figures  2c  and 3b ). This protein domain plays an important role in ligand binding, which in turn leads to changes in protein conformation and channel opening  (Richter et al. , 2012). The inflow of chloride into the cell is thought to result an inhibitory postsynaptic potential.
The variant identified in P2 (NP_004952. 2: p. Met1?) was located at the signal peptide site of the GABRE protein, which determines the localization and the transport pathway of the protein within the cell  (Blobel, 1980). The missense variant found in P4 (NP_004952. 2: p. Val349Ile) was mapped to the neurotransmitter-gated ion-channel transmembrane region domain (PF02932). This protein domain is essential for ion channel formation  (Figures 2c and 3b; Chen et al. , 2018). Tolerance analyses of the missense variants described herein (NM_004961. 3: P1 c. 664G>A, NM_004961. 3: P4 c. 1045G>A) revealed that the mutated amino acids were located in functional regions of the GABRE protein with intolerant and slightly intolerant impacts (Figure  3a ). Moreover, we also tested the impact of previously reported missense variants (NP_004952.
Starting in 1969, and with three launches a week, the Uccle (Brussels, Belgium) ozonesonde dataset is one of longest and densest of the world. Moreover, as the only major change was the switch from Brewer-Mast to Electrochemical Concentration Cell (ECC) ozonesonde types in 1997 (when the emissions of ozone depleting substances peaked), the Uccle time series is very homogenous. In this paper, we briefly describe which efforts have been taken during the first three decades of the 50 years of ozonesonde observations to guarantee the homogeneity between ascent and descent profiles, under changing environmental conditions (e. g. SO 2 ), and between the different ozonesonde types. This paper focusses on the 50 years long Uccle ozonesonde dataset and aims to demonstrate its past, present and future relevance to ozone research in two application areas: (i) the assessment of the temporal evolution of ozone from the surface to the (middle) stratosphere, and (ii) as backbone for validation and stability analysis of both stratospheric as well as tropospheric satellite ozone retrievals.
The strength and uniqueness of the ozonesonde measurements, and in particular of our long-term and very dense Uccle dataset, lie in combining all those different aspects of ozone research. In this paper, we will first give a description of the ozonesonde measurements at Uccle from a historical point of view (Sect. 2) and describe briefly which data processing has been applied to the ozonesonde measurements used in this paper (Sect. 3). In Section 4, we assess the time evolution of ozone at Uccle at different vertical layers against the background of recent findings in ozone variability. The fifth section illustrates the important role of the Uccle data for the validation of satellite ozone retrievals. Finally, in Section 6, concluding remarks and perspectives are given.
In the course of time different radio sounding systems have been used. A major change occurred in 1990 when digital data transmission at high sampling rate was introduced, which allowed a higher vertical resolution of the profiles (not only at significant and standard pressure levels). To normalize the integrated ozone amount of the ozone soundings, the Dobson spectrophotometer (no. 40, D40) at Uccle was used since July 1971; before that date an interpolation of values from other Dobson stations in the European network was employed. In 1984, the Uccle site was equipped with a single Brewer UV spectrophotometer (no. 16), and with a double  Brewer instrument (no. 178)  in September 2001, to provide total ozone column measurements.
The impact of the boundary layer SO 2 concentrations on the ozone measurements As SO 2 has even stronger absorption bands than ozone in the UV 305-340 nm wavelength range used for the total ozone determination with a Dobson spectrophotometer, total ozone amounts might be overestimated with this instrument in case of a considerable total vertical SO 2 column amount, as discussed by  Komhyr and Evans (1980). In the suburban area of Uccle, the SO 2 densities near the ground were quite elevated at the start of the ozone measurements, but showed a steep decrease from the late 1960s to the early 1990s (Fig. S1 ). A comparison between quasi-simultaneous total ozone observations at Uccle with a Dobson and a Brewer spectrophotometer showed that there was no drift in the difference between the two datasets if https://doi.
org/10. 5194/acp-2020-724 Preprint. Discussion started: 2 December 2020 c Author(s) 2020. CC BY 4. 0 License. the effect of SO 2 was taken into account  (De Backer and De Muer, 1991). It was also found that the SO 2 correction of the Dobson spectrophotometer D40 data had a significant effect on the calculated total ozone trend (De  Muer and De Backer, 1992) , and made this trend consistent with the one derived from reprocessed Total Ozone Mapping Spectrometer (TOMS) satellite data for a sub-period in which both datasets were available. Ozonesonde measurements by the KI method are sensitive to interference by oxidizing or reducing agents (e. g. Tarasick et al. , 2020, and references therein). In particular, SO 2 can cause an important reduction in the ozone detected of 100%, and excess SO 2 can accumulate in the cathode solution, affecting ozonesonde measurements well above the polluted boundary layer  (Komhyr, 1969 , De Muer and De Backer, 1993 , Morris et al.
An alternative, homogenized, Uccle ozonesonde corrected dataset is available by request from the authors for the ECC time series since 1997  (Van Malderen et al. , 2016) , following the principles of the Ozonesonde Data Quality Assessment (O3S-DQA) activity  (Smit et al. , 2012) , but is not used here to maintain the consistency over the entire time series. Differences between both versions of corrected Uccle ECC ozonesonde data, in comparison with nearby De Bilt (the Netherlands, 175 km north of Uccle), are highlighted in  Van Malderen et al. (2016). For the BM ozonesondes, the applied PRESTO corrections include (i) a correction for SO 2 interference on the ozone soundings (imperative to have reliable lower-tropospheric ozone trend estimates for the period 1969-1996, see Fig. S3 ), (ii) a correction for a negative background current caused by impurities in the sensor before October 1981, (iii) a correction for box temperatures depending on the insulating capacity of the Styrofoam boxes (a short discussion of those additional corrections and the proper references are given in the appendix A of  Van Malderen et al.
, 2015). Tropospheric ozone is measured with ozonesondes, by commercial aircraft, with different types of ground-based remote sensing instruments and with satellite instruments. Besides clear regional differences, the distribution and trends of ozone in the troposphere are not always consistent between these different datasets, and even not between different retrieval methods of the same satellite (e. g. Cooper et al. , 2014 , Gaudel et al. , 2018). In fact, measuring the vertical profile of tropospheric  (also done in e. g. Petetin et al. , 2016). We used simple linear trends based on the monthly anomalies at different altitude levels (see Fig. 5 ) for the period 1995-2018, as there is no consensus on the used proxies to account for natural variability in a multiple linear regression model as for stratospheric ozone (e.
Near the surface, the De Bilt trend is in better agreement with the Frankfurt trend, but the local surface ozone production and destruction and the boundary layer dynamics can vary substantially between the three sites considered here. In the upper troposphere, the ozone concentration trends deviate more between the different datasets, both in magnitude and sign, with larger trend uncertainties. At these altitudes, the aircraft could be very distant from Frankfurt (or Munich) airport, as the ascent/descent profiles stop/start at about 400 to 500 km from the airport. The measurements at these altitudes are hence representing large areas. Therefore, the closer agreement between the Uccle and De Bilt trends above 8 km compared to the IAGOS trend might be ascribed as well to a similar source region.