dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements The authors would like to thank the divisions of clinical hematology, hematology laboratory and pathology of Nancy ( Dr Hélène Busby ,  Pr Hervé Sartelet ,  Dr Ludovic Dubouis ), Poitiers, Angers, Reims ( Dr Pascale Cornillet-Lefebvre ),  Clermont-Ferrand  ( Dr Lauren Veronèse  and  Dr Albane Ledoux-Pilon ),  Tours (Dr Flavie Arbion) ,  Avicenne ,  Saint-Louis  ( Dr Véronique Meignin ) and  Pitié-Salpêtrière  ( Dr Frédéric Charlotte  and  Pr Isabelle Brocheriou ). The authors would like to thank the tumor libraries biological resource centers of Nancy ( BB-0033-00035 ), Poitiers ( BB-0033-00068 ),  Caen  ( Pr Xavier Troussard), Tours, Clermont-Ferrand, Angers  ( BB -0033-00038 ),  Reims-Champagne-Ardenne, Besançon  ( Franck Monnien ,  Dr Etienne Daguindau ) and  Bordeaux  ( Marie-Pierre Fort ,  Dr Fontanet Bijou ) who provided us with the biological material.
The authors would like to thank  Véronique Saunier  ( direction of research at University Hospital of Nancy ) for supporting the project. RNA sequencing was performed by the GenomEast platform, a member of the " France Génomique "  consortium  ( ANR-10-INBS-0009 ). The authors would like to thank  Louis Staudt  ( Center for Cancer Genomics, National Cancer Institute, Bethesda, MD 20892, USA ) for giving access to the data published in Schmitz and colleagues (2018) and Wright and colleagues (2020). The authors would like to thank the members of ICGC the MMML-seq consortium for contribution to the generation of the DLBCL omics datasets and the  MMML-seq consortium  for data access. The authors would like to thank  Pr Catherine Wu  and  Dr Erin Parry  for shared expertise and language editing, and  Dr Cath Carsberg  for English language editing.
2 | DNA methylation comparative analysis with CLL and de novo DLBCL shows that RS is a heterogeneous and hypomethylated entity. a Unsupervised principal component analysis of the adjusted DNAm values of RS, CLL, and DLBCL. Geometrical centers are represented by bigger circles of the same color. (FDR < 0. 01; beta-value differential >30%; moderated t test). f Density map of DNAm between high CLL-derived and DLBCL-like RS. Smoothed beta-value densities from the EPIC dataset. Scale from blue (no density) to yellow (medium density) and red b Boxplots (high density). g Boxplots showing general methylation levels for high CLL-derived of sample-averaged methylation levels with all 397,769 CpGs. RS (n = 58) versus (n = 33), low CLL-derived (n = 12), and DLBCL-like RS (n = 13), de novo DLBCLs U-CLL (n = 112): p = 7.
75 (NS) NOTCH1 21/58 36 18/45 40 3/13 23 p = 0. 33 (NS) SF3B1 12/58 22 12/45 27 0/13 0 p = 0. 05 (NS) EGR2 11/58 19 11/45 24 0/13 0 p = 0. 055 (NS) Nature Communications | (2023) 14:309 82][9][10]42lored an independent gene expression dataset containing RS samples, untransformed CLLs, and EBV-positive DLBCL cell lines (GSE103265). The 215-gene set allowed unequivocal clustering of RS and CLL samples, well separated from DLBCLs (Supplementary Fig. 22). To cross-validate the previously described DNAm-based classifier (LPS) with the gene expression-derived classifier (LCS), we explored array-based DNAm and transcriptome-sequencing data of the ICGC MMML-Seq consortium (both classifiers can be used independently). Four (6. 2%) DLBCL samples with classical DLBCL morphology showed extreme DNAm and gene expression scores, suggesting a CLL-like RS profile (Supplementary Fig.
23). Applying the gene expression-based classifier to array-based gene expression data of 430 DLBCL from the MMML-network identified 31 samples (7. 2%) with a statistically significant score (see "Methods"). Next, we mined four large external cohorts of de novo DLBCL, including 1342 samples[8][9][10]42. As with previous datasets, gene expression-based LCS distributions were biased toward overrepresenting extreme positive values (Supplementary Fig. 24). Our transcriptomic classifier identified 35/420 (8. 3%; series from Lenz and colleagues)42, 8/137 (5. 8%; series from Chapuy and colleagues)8, 13/223 (5. 8%; series from Dubois and colleagues).
Study design and population Our study analyses cross-sectional baseline data from the ENT Prospective International Cohort of Patients with PCD (EPIC-PCD), the first PCD cohort focused on upper airway disease manifestations  [26]. We set up EPIC-PCD in February 2020 to follow PCD patients at their routine ENT consultations. Participants did not undergo additional testing for our study purposes. EPIC-PCD is hosted at the University of Bern (clinicaltrials. gov identifier: NCT04611516). For our collaborative study, 12 participating centres (Amsterdam, Ankara, Berlin, Bern, Cyprus, Istanbul, Leuven, Liège, Oslo, Paris, Southampton and Valencia) in 10 countries contributed data. For our analysis, we included data entered in the database by 31 July 2022 for participants with PCD of all ages who underwent ENT examinations and completed symptoms questionnaires at the same visit or within 2 weeks.
We created a composite outcome variable for sinonasal disease consisting of three variables: patient-reported headache while bending down as a proxy for sinusitis, ENT examination findings of nasal polyps and facial pain. Each of them scored either 0 (absence) or 1 ( presence). Total scores ranged from 0 to 3. We assessed factors possibly associated with sinonasal disease such as age, age of diagnosis, sex, study centre, smoking status of either active or passive smoke exposure, and season when ENT consultations occurred in a multivariable ordinal logistic regression model. We chose factors based on clinical importance and data availability. There was collinearity of age and age of diagnosis so it was not possible to include both in our main model; separate models showed similar results so we included age.
Study population By the end of July 2022, 448 (89%) of 505 invited patients with PCD enrolled in the EPIC-PCD cohort (figure  1 ). Of these participants, 384 (54% male) with median age 16 years (IQR 9-22) entered in the database fulfilled eligibility criteria for ENT consultation and completed a FOLLOW-PCD questionnaire at the same visit or within 2 weeks (table  1 ). 247 (64%) participants were children, 137 (36%) adults and 134 (35%) had situs inversus totalis. With regard to participant diagnostic status, 257 (67%) had a PCD positive diagnosis based on ERS guidelines  [31]  with a bi-allelic PCD-causing mutation or a hallmark defect identified by TEM (supplementary tables S1-2); 59 (15%) had highly likely PCD. The remaining 68 (18%) participants had probable PCD diagnosed with typical symptoms and with at least one pathological test indicating PCD.
Discussion Our study benefitted from data from the first prospective, multicentre, international ENT cohort of patients with PCD. Even though we performed our study during the COVID-19 pandemic, with much lower prevalence of viral infections, most participants reported chronic nasal symptoms, most commonly rhinorrhea. Our results showed sinonasal symptoms, and clinical examination findings indicated chronic inflammation that was also more common with increasing age. Overall, QoL, as measured by SNOT-22, was moderately affected by CRS (median score 31; IQR 23-45). Anterior rhinoscopic or endoscopic findings, such as nasal polyps, hypertrophic turbinates and deviated septum, as well as facial pain at examination, were more commonly found among adults than children. We found the risk of sinonasal disease increased with age and was associated with study centre.
Strengths and limitations Our study's main strength includes our use of data from a large, prospective, international cohort with an overall recruitment rate of 89%. We are the first to describe patient-reported sinonasal symptoms and sinonasal examination findings obtained during the same consultation for PCD. Another strength is our use of FOLLOW-PCD, which allowed standardised records of disease-specific information and comparisons between participating centres. We excluded participants if their data were not yet entered in the study database or they did not meet the eligibility criteria. We have no reason to think exclusions were not random or affected the representation of our study population, but participants with more sinonasal symptoms might be more willing to join EPIC-PCD when invited.
Acknowledgements: We thank all people with PCD and their families participating in EPIC-PCD and PCD support organisations (especially  PCD Family Support Group UK, Association ADCP France ,  Kartagener Syndrom und Primäre Ciliäre Dyskinesie e. V. Deutschland/Deutschschweiz , and  Asociación Nacional de Pacientes con Discinesia Ciliar Primaria DCP España/PCD Spain)  for their close collaboration. We also thank all researchers from the participating centres involved in enrolment, data collection and data entry who work closely with us (listed below as collaborators of the EPIC-PCD study). We are grateful for everyone who contributed to translations of the https://doi. org/10. 1183/23120541. 00701-2022.
SAMPLE SIZE ESTIMATION The sample size was calculated using Raosoft sample size calculator. The computation is based on a 50% response distribution, 5% margin of error, and 95% Confidence Interval (CI). This online software formula is for descriptive study sample size estimation. 13, 14  The research team chose this software calculator because Raosoft, Inc. form and survey software comprise a database management system with great strength and reliability that communicates with other proprietary formats. In addition, Raosoft database is a highly robust, proven system with high data integrity and security. 13, 14  The sample size was calculated using the formula Based on the assumption of a population size of 45,000 clients and attendants in one month in OPD in twenty-four health facilities in the Acholi subregion, the minimum sample size based on the above assumptions was 396 participants.
DATA COLLECTION Our research team collected data from participants using face-to-face questionnaire interviews, strictly following Uganda's standard COVID-19 infection, prevention, and control (IPC) guidelines. We used a questionnaire constructed in English and consisted of questions on participants' perceptions on COVID-19 vaccines and vaccinations in the Acholi sub-region (Additional file 1). The study instrument (questionnaire) was developed, grounded on literature reviews and discussions with the research team. 17, 18  Study participants were recruited by a systematic sampling approach at twenty-four health facility outpatients' departments by our research teams. Every third attendant or attendee of OPDs were selected for interviews every day for a week until the required sample was obtained.
Before data collection, the questionnaire was pretested among outpatients in Gulu Regional Referral Hospital, a hospital situated in northern Uganda. The internal validity of the questionnaire was determined as Cronbach's α= 0. 772. Furthermore, the questionnaire was designed to minimize lethargy in participants' responses. In addition, participants were assured of confidentiality and privacy of their responses to reduce potential biases introduced by self-reported data and the fear of reprisals. DATA MANAGEMENT Data obtained from participants were de-identified. Only the principal investigator and supervisors had access to and were stored in a database with restricted access. The database was later archived at the Department of Surgery, Faculty of Medicine, Gulu University.
This argument is because new variants may emerge and affect the unvaccinated population, thus, destroying the successes already achieved by the Ugandan Ministry of Health. Because of many uncertainties about COVID-19 vaccines among Ugandan communities, and concerns that COVID-19 vaccines were developed rapidly, it is not surprising that there is a high level of skepticism on the vaccine among the study population. With the unsuspecting and unforeseen side effects, it would be a balanced decision to weigh-in-the argument that it would not be wise to vaccinate children who have a longer life ahead-considering inadequate data on longterm effects of COVID-19 vaccines on Ugandan children. However, a report from the Vaccine portal states that serious side effects that would cause long-term health problems are implausible following COVID-19 vaccination.
Data Conversion and Preparation The baseband voltage data were captured by Mark 6 recorders 156  at the telescopes, and the disk packs shipped to the correlator at the Max Planck Institute for Radio Astronomy in Bonn, Germany, for post-processing. The voltage data selected for the pulsar search were reduced into multichannel intensity time series (commonly referred to as a filterbank) written in search-mode PSRFITS format, a standard FITS specification for pulsar data  (Hotan et al. 2004 ). 157  This used the software tool MPIvdif2psrfits, 158  an upgraded version of the original toolkit developed under the ALMA Pulsar Mode Project 159   (Liu et al. 2019 ) that incorporates parallel processing capability using OpenMPI. 160  The resulting properties of the PSRFITS products are presented in Table  1.
To locate and clean the features, we first produced a time series collapsing all of the frequency channels of the PSRFITS file with the routine prepdata of the software package PRESTO (Ransom 2011), 161  downsampling the time resolution to 32 μs, and visually inspected each scan, marking the center of the first appearance of the broad feature. Then, 2 s wide windows around the first broad feature position, and every 18. 192 s thereafter, were replaced with the mean level of the time series calculated outside the marked windows. Next, we use a moving window of width 10 s that clips negative narrow spikes below a threshold of five times the standard deviation of the samples within the window. The flagged data due to the phasing features correspond to ≈11% of the total. Before combining the two frequency sub-bands (low and high) to obtain a full-bandwidth data set, we remove slow variations of the mean power level in the time series with a "running-fit" filter that fits and subtracts a first-order polynomial in a running window of 10 s.
Periodicity Search in the Fourier Domain The Fourier-domain pipeline predominantly searches for pulsars by detecting and analyzing peaks in the Fourier spectrum of the time series. We use the software PRESTO, which includes the capability to search for accelerated pulsars, like those orbiting Sgr A * or other companions. The acceleration parameter space is searched using a template-matching algorithm by recovering the power spread over contiguous Fourier bins  (Ransom et al. 2002). This power spread is the result of the Doppler effect when a pulsar changes its radial velocity during an observation. A limitation of the template-matching algorithm is that it loses efficiency for observations covering a substantial fraction of the binary period (P b ). This limiting fraction is about 0. 1P b (given a companion mass of 1.
Periodicity Search with Fast Folding Algorithm Unlike the Fourier-domain pipeline, the FFA searches for pulsars by folding the time series at different trial periods, forming a sequence of profiles, and testing the significance of each profile using boxcar matched filters. The pulse width and arbitrary total pulse power produced by the filters are used to calculate the signal-to-noise ratio (S/N FFA ) of the pulse profiles (see, e. g. ,  Cameron et al. 2017, for details). We used the software RIPTIDE 168   (Morello et al. 2020)  as the basis for the FFA search pipeline. We include an acceleration search by resampling the time series using SIGPYPROC at a series of acceleration trials using a custom pipeline  (Wongphechauxsorn et al. 2024). This implementation of the FFA pipeline assumes a constant acceleration during an observation.
The RIPTIDE software uses a matched filter to detect the pulse in the folded data, making the number of folding profile bins (N bin ) an important parameter. If the duty cycle is less than one bin, the sensitivity will be reduced. Morello et al. (2020)  demonstrated that the number of trials is proportional to N bin 3. Folding with many profile bins consequently results in more trials and longer processing times. Furthermore, the acceleration step between trials is proportional to N bin ; thus, the total number of trials is proportional to N bin 4. Our FFA pipeline uses the cleaned time series generated following the description in Section 2. 2. We applied the FFA acceleration search to the time series for periods from = P 1 s min to = P 1025 max s, using N bin = 128. As a result, the pipeline provides full sensitivity to duty cycles down to about 1%).
The vertical line over the yellow star marks the range of known variability of PSR J1745-2900. Middle: histogram of the luminosity distribution. Right: histogram of the luminosity distribution in a cumulative layout. potentially visible. Furthermore, the single-pulse analysis may detect pulsars through bright single pulses. A second finding shows that the percentage of detected pulsars can decrease by up to ∼50%-60% when using the empirically derived sensitivity limits versus the theoretical ones. This sensitivity loss is substantial, and is caused by the noise characteristics, which are far from showing the Gaussian statistics that the radiometer equation assumes. From the three stations, the data from the IRAM 30 m seems to be less affected by this loss of sensitivity. This is apparent in Figure  2 , where the empirical limit for this station is flatter, and starts rising at longer spin periods as compared to the ALMA and LMT cases.
The highest fraction of detectable population in the EHT 2017 data comes from ALMA, which, despite its superb sensitivity, could detect only around 2. 2% of the brightest pulsars in the simulated population with a á ñ 1 -that we consider as the most accurate. This fraction is lower by a factor ∼2 compared to the one yielded by ALMA in 2017 Global Millimeter very long baseline interferometry (VLBI) observations, of about 4%, despite the use of only 2 GHz of bandwidth  (Liu et al. 2021 ). In the case of the IRAM 30 m Telescope and the LMT, less than 0. 4% and 0. 1% of the simulated pulsars, respectively, would be detectable with the EHT 2017 data. For the IRAM 30 m, previous searches at 3 and 2 mm yielded theoretical population coverage fractions of 1%-2%  (Torne et al. 2021 ). The differences arise from a combination of different search parameters (e.
In practice, as already mentioned in Section 2. 3, the range of acceleration and its derivative explored in the search would also place a constraint on the types of orbits around Sgr A * that the search is able to detect. For a given time span of the observation, the constraint can be estimated by calculating the maximum absolute z-and w-values in a range of orbits and comparing with those used in the search, as demonstrated in  Liu et al. (2021;  see also  Eatough et al. 2021 ). Here, as case studies, we carried out the same practice for two time spans of the observation, T obs = 10. 2 and 3. 2 hr, which correspond to the April 7 observation of ALMA and IRAM 30 m, respectively, being the longest and shortest time spans in our data set. We focus on a full sensitivity signal recovery based on 32 summed harmonics (see Section 2.
e. , a projected distance of ≈0. 1 pc;  Rea et al. 2013 ). This pulsar was not detected because it is highly variable and was likely on a weak-emission state during the EHT 2017 campaign. PSR J1745-2900 would have been detected by the IRAM 30 m Telescope, and even phased ALMA (even offset from the Values of zmax (upper row) and wmax (lower row) required for the Fourier-domain search to be conducted with optimal sensitivity in orbits around Sgr A * with different periods and eccentricity (e). Here we use two pulsar spin periods, which are typical values for ordinary pulsars and MSPs, respectively. The time spans of the observations are assumed to be 10. 2 and 3. 2 hr, which are in turn the longest and shortest in our analyzed data sets. center of its synthetic beam), if it were in a bright-emission episode (PSR J1745-2900 can reach S ∼ 6 mJy at 1.
In principle, it is possible to increase the search sensitivity by coherently summing the data collected with the entire EHT array, forming tied array beams toward areas in the vicinity of Sgr A * (see, e. g. ,  Bassa et al. 2016). This may also effectively mitigate some of the systematics present in individual telescope data because they should not correlate between stations. A downside is that a large number of beams needs to be formed, which requires substantial computing power. For instance, given the longest baseline of the EHT 2017 observation, of the order of 20 million beams would be needed to cover an area of 0 1 around Sgr A *. These beams will also have to be contained by the smallest beam in the array, which is the synthetic beam of ALMA for the EHT 2017 campaign. This could be improved if multibeaming capability of ALMA becomes available.
To improve the feasibility of coherent beam forming of the EHT array, the beams can be directed toward compact objects that may be identified by imaging observations. Alternatively, incoherently adding the data and/or coherently summing only a subset of the array may also be considered as an option to increase the system sensitivity. Table  6  summarizes the delivered equivalent diameters of the array under different ways of summing the telescope signals. For coherent addition, the summation of the entire array will deliver a sensitivity equivalent to a 111 m dish, while summing only the most sensitive four telescopes in the array will still yield an equivalent diameter of 107 m, only 7% less in sensitivity. The sky area where a coherent beam may be formed is restricted by the smallest beam size of the array, which is from one of the interferometers.
If summing only the single-dish telescopes, the sky coverage can then be enlarged to the smallest beam size of the single dishes. This will give an equivalent diameter of 64 m, approximately 60% more sensitive than LMT, the largest single dish in the array. For incoherent summation, adding the data from the entire array will give an equivalent diameter of 81 m. However, only a few percent difference is expected from summing only the top three sensitive telescopes, i. e. , ALMA, LMT, and the NOrthern Extended Millimeter Array (NOEMA). 174  Similarly, incoherent summation of all single-dish telescopes in the array will deliver an equivalent diameter of 52 m-only 8% improvement in sensitivity from the LMT. This is largely due to the significant difference in gain among the telescopes. Finally, the sensitivity of the LMT during the EHT 2017 campaign is far from its full capacity, mainly due to the underillumination of its surface.
Summary and Conclusions We carried out a search for pulsars and fast transients in the GC with the 2017 Event Horizon Telescope observations, the first search at an observing wavelength of λ = 1. 3 mm (ν = 228 GHz). The search used data from phased ALMA, LMT, and the IRAM 30 m Telescope, the most sensitive telescopes in the EHT array of 2017. Periodicity searches both in the Fourier domain and with an FFA were conducted to each individual data set collected on three different nights, with acceleration and jerk search incorporated to cope with the potential orbital motion of pulsars around Sgr A * or a binary companion. Single-pulse searches were also performed to the whole data set. These searches did not detect any pulsars or transients. We estimated the search sensitivity both theoretically and in practice by injecting artificial signals into the real data.