dataesr/scientific-paragraphs-categorization huggingface dataset:
The resulting best-fit values (or upper limits) for the photon flux (cm -2 s -1 ), energy flux (GeV cm -2 s -1 ), and associated spectral shape are saved to the LCR database, for both the fixed and free photon index analyses. This procedure ultimately allows for a user-selectable detection threshold and spectral fit method, as the flux estimates and upper limits between 1 TS 4 are both recorded in the LCR database, as well as the results from both the fixed and free spectral analyses. In Figure  4  we compare, for the case of the bright quasar Ton 599, the best-fit flux values found using a fixed spectral index versus leaving the spectral index free to vary. The colors in the plot refer to the three cadences analyzed. The results are generally stable, with greater uncertainties for lower fluxes with higher cadences due to lower statistics in the ROI in each time bin.
Usage Caveats In this section we provide a number of caveats to be mindful of when using the LCR data for scientific research. This list is also available and will be updated periodically on the LCR data portal. 1. The LCR provides fit results from likelihood analyses that both did and did not converge. However, it is important that the end user is aware that results from analyses that did not converge should be considered suspect and not be used for higher-level analyses (e. g. , multifrequency cross-correlation, power spectral density, or studies of variability). The convergence status for a particular time bin is recorded in the MINUIT Return Code parameter and nonconvergent analyses are hidden by default, but are optionally accessible to the user. A review of the current version of the LCR fit results for the first 14 years of mission data shows that the analyses for 0.
7% of time bins, for all sources, did not converge when the spectral indices were held fixed, and that ∼35% did not converge when the spectral indices were free to vary. 2. From its definition, Equation (4), TS is a manifestly positive quantity. However, negative TS values can sometimes be obtained when the parameters reach the  ] in time bins obtained with the fitting pipeline keeping the spectral index fixed vs. letting the spectral index be free. The empty circles indicate results from analyses that did not converge, i. e. , MINUIT Return Code q ≠ 0. MINUIT (James 1994) is the optimizer adopted in the likelihood analysis. The plot shows the fluxes measured for the quasar Ton 599. In this case, the fit for the highest-flux point (with TS ∼ 4) converged when the spectral index was set free, but did not converge when the spectral index was fixed.
83 http://healpix. jpl. nasa. gov/  (Górski et al. 2005). 84 Fermitools, as part of the maximum likelihood calculation, automatically accounts for the exposure, as described on the official mission web page at https://fermi. gsfc. nasa. gov/ssc/data/analysis/documentation/Cicerone/ Cicerone_Data_Exploration/livetime_and_exposure. html. The module gtexpmap is used to compute the exposure when performing unbinned analyses, as for the LCR analysis pipeline. In this section, we compute the exposure through the gtexpcube2 module, which is used when performing binned likelihood analyses. We stress that this is for purely illustrative purposes; the exposure maps computed in this section were not used in the LCR likelihood analyses. limits of their allowed intervals without having maximized the likelihood profile.
We invite the community to use the LCR data products, and report any issues or suggestions to the LCR contacts at the Fermi Science Support Center. A brief guide for navigating the LCR to navigate the website is provided in the Appendix. In the future, the LCR source list and the resulting lightcurve data will be updated with every main release of the Fermi-LAT source catalog, with the next LCR version planned to be reprocessed entirely and released alongside the 5FGL catalog. In each new release, the LCR will continue following the latest analysis recommendations of the FSSC. Due to the computational expense of reanalyzing the full-mission lightcurves for the entire LCR sample, the LCR sample will not be updated for each catalog subrelease (e. g. , the newly available 4FGL-DR3;  Abdollahi et al.
2022 ; and the upcoming 4FGL-DR4), or for model and software updates (e. g. , related to diffuse background components, PSF, and Fermitools), unless we think that their impact on the lightcurves is significant. Currently, the LCR does not provide permanent identifiers that allow distinguishing between different versions due to data reprocessing. This will be implemented in a future version of the repository. In conclusion, in this era of large surveys, the Fermi-LAT is the only high-energy gamma-ray observatory to continuously monitor variable sources, providing all-sky coverage to identify gamma-ray counterparts to transient events at other wavelengths. We expect that the LCR will greatly enhance the usefulness of LAT data to the time-domain, multimessenger, and multiwavelength communities.
M. N. and J. V. acknowledge that the material is based upon work supported by NASA under award number 80GSFC21M0002. D. K. and M. N. acknowledge support to this work from NASA Fermi GI Program under grant number 80NSSC23K0242. A. B. is supported by the NASA Postdoctoral Program at NASA Goddard Space Flight Center, administered by Oak Ridge Associated Universities. INFN and ASI personnel performed in part under ASI-INFN Agreements No. 2021-43-HH. 0. Work at NRL is supported by NASA. The Fermi-LAT Collaboration acknowledges generous ongoing support from a number of agencies and institutes that have supported both the development and the operation of the LAT as well as scientific data analysis. These include the National Aeronautics and Space Administration and the Department of Energy in the United States, the Commissariat à l'Energie Atomique and the Centre National de la Recherche Scientifique/Institut National de Physique Nucléaire et de Physique des Particules in France, the Agenzia Spaziale Italiana and the Istituto Nazionale di Fisica Nucleare in Italy, the Ministry of Education, Culture, Sports, Science and Technology (MEXT), High Energy Accelerator Research Organization (KEK) and Japan Aerospace Exploration Agency (JAXA) in Japan, and the K.
Overall results Data obtained from the analysis of the POCIS extracts were used to determine the presence of the considered contaminants at the various stages of the wastewater treatment. Due to the complex sorption mechanism of the different chemical classes onto the POCIS sorbent, as well as probable fluctuations in the site's conditions (flow, pH, temperatures), a certain degree of variability was expected in the accumulation of the analytes in the duplicated deployments  (Alvarez et al. , 2014). Nevertheless, results characterized by a relative standard deviation (RSD) >30 %, related to the analyte mass sampled by the duplicated POCIS, had low reliability and were considered with caution. Thirty-two out of the 56 considered analytes were detected in at least one of the POCIS extracts, 31 of them in the WWTP influent.
Acknowledgements The authors are grateful to  Water JPI  and the  Spanish Agencia Estatal de Investigación (AEI)  for the project  MARadentro  ( PCI2019-103603 ),  Consorci d'Aigües de la Costa Brava Girona (CACBG)  for the access to the facility and water supply, to the staff of the  Palamós WWTP  for their help, and  M. P. Serra-Roig  for her help in the analysis of the samples.
| Sampled colonies We sampled 265 honey bee colonies from two different sub- DNA extraction and pool sequencing of the sampled colonies are described in detail by  Guichard et al. (2021). Briefly, approximately 500 workers per colony were shredded in a DNA extraction solution. Pair-end sequencing was performed on an Illumina™ HiSeq 3000 or a NovaSeq™ 6000 platform. To significantly decrease computing time, the pool sequence analysis was restricted to an informative marker panel including 7,023,977 genome-wide SNPs, as previously described by  Wragg et al. (2022). Raw reads from pool sequencing of the 265 colonies were aligned to the honey bee reference genome Amel_HAV3. 1, Genbank assembly accession GCA_003254395. 2  (Wallberg et al. , 2019). After the alignment, the resulting BAM files were converted into pileup files using the samtools mpileup utility  (Li et al.
| Admixture Queen admixture levels and genetic distances (F ST ) between the subspecies were determined using the program Admixture 1. 23  (Alexander et al. , 2009). We ran Admixture for 100 iterations increasing K from 2 to 10. Convergence between independent runs at the same K was monitored by comparing the resulting log-likelihood scores (LLs) following 100 iterations, and was inferred from stabilized LLs with less than 1 LL unit of variation between runs. Cross validation error estimation for each K was performed to determine the optimal number of clusters. Admixture results increasing K from 2 to 7 were visualized with the program Distruct 1. 1  (Rosenberg, 2004)  and integrated in the dynamic population network, as described above.
| Runs of homozygosity Continuous homozygous segments were determined with an overlapping window approach implemented in PLINK v. 1. 9 (Chang   (Wallberg et al. , 2019). Differences between subspecies and subpopulations were investigated using t-tests (for the two subspecies) et and ANOVA with post hoc Tukey's honestly significant difference (HSD) tests at a significance level of α < 0. 05 as implemented in the R package multcompView  (Graves et al. , 2015). We also correlated F ROH with the admixture proportions at K = 2 for each subspecies as implemented in the statistical computing software R (R Core Team, 2020). Furthermore, we compared F ROH of 74 SL_CH queens with pedigree-based inbreeding coefficients (F PED ). Pedigree-based inbreeding coefficients of the selected queens were calculated following the method described by  Brascamp and Bijma (2014)  based on the pedigree information of 1082 A.
| Homozygosity islands and gene functions Homozygosity islands of the three different groups (CAR, SL_CH, and CS_CH) were determined based on overlapping homozygous regions present in more than 50% of the queens with <10% admixture applying the R package detectRUNS  (Biscarini et al. , 2019). Considering the small sample size, we used all CAR with admixture proportions <10% for the identification of breed-specific ROH islands. Finally, we used the NCBI genome data viewer (https://www. ncbi. nlm. nih. gov/genom e/gdv/), and the reference genome assembly Amel_HAv3. 1  (Wallberg et al. , 2019)  to identify genes located in ROH islands and specified the known functions of the identified genes by conducting a literature review.
ACK N OWLED G M ENTS We thank  Lucie Genestout  ( Labogena ) for coordinating the  Beestrong  project,  Christina Sann  and  Yannick Poquet (Labogena)  for organizing the sampling in Savoie, and  Alice Revel (Agroscope)  for her help in organizing the sampling in Switzerland. We thank  Rachid Mahla  ( Labogena ) for DNA extraction and  Olivier Bouchez  ( GeT-PlaGe ) for sequencing. Sequencing was performed in collaboration with the  GeT platform, Toulouse (France) , a partner of the National Infrastructure France Genomique, thanks to support by the  Commissariat aux Grands Investissements  ( ANR-10-INBS-0009 ). Sequence alignments and allele frequencies estimations were performed on the computer cluster of the  Genotoul bioinformatics platform Toulouse Midi-Pyrénées  (www. bioinfo. genot oul.
Participants We conducted a retrospective analysis, incorporating longitudinal follow-up data from patients with optic disc oedema who were recruited from the ophthalmology departments of Marseille University Hospitals, including both The Hospital of La Timone and North Hospital between December 2015 and December 2021. Opt-out consent was obtained from all patients. This is a method used in the recruitment of participants into research where information is provided to the potential participant regarding the research and the involvement, and where their participation is presumed unless they express to decline to participate. All patients, irrespective of age, with optic disc oedema secondary to elevated ICP at initial presentation, were included. Additional inclusion criteria were a recent diagnosis of papilloedema (less than 1 week after first finding of papilloedema if diagnosis was made with an in-office ophthalmologist), feasible OCT examination, and OCT follow-up for at least 3 months.
Clinical measures The following clinical data were collected: age, sex, symptoms (headache, nausea, vomiting, visual blur, diplopia, tinnitus, visual eclipses, epilepsy) and their speed of onset, body mass index (BMI), aetiology of papilloedema, presence of peripapillary haemorrhage at initial presentation, association with sixth cranial pair palsy (abducens nerve), visual field, visual acuity, opening ICP at lumbar puncture in strict lateral decubitus and therapeutic management. Initial diagnosis of IIH was confirmed according to the revised Dandy criteria. 10  OCT protocol OCT data were collected using a Heidelberg Spectralis Spectral Domain OCT (Heidelberg Engineering, Germany), Spectralis software V. 5. 3. 3. 0, Eye Explorer software V. 1. 6. 4. 0. The following OCT data were collected as part of a routine clinical OCT protocol: RNFL, total peripapillary retinal thickness (RT) (TR: total retina), ganglion cell layer (GCL) and scleral canal size.
The RNFL was measured using a 3. 4 mm circular scan around the optic nerve with the segmentation algorithm and the standardised eye-tracking enabled device protocol (TrueTrack). The TR was measured using the same image as for the RNFL, and corresponded to the thickness between the internal limiting membrane (ILM) and Bruch's membrane (BM). RNFL and TR were automatically segmented by the device. Spectralis software of Heidelberg OCT was used to perform manual corrections of RNFL and TR segmentation when the automatic segmentation by the OCT showed incorrect layer segmentation of the ILM, RNFL or BM (figure  1 ). The verifications and modifications of the RNFL segmentations were performed by two ophthalmologist. The GCL was analysed when a macular OCT was available and automatically segmented by the device.
It was thoroughly checked, and in cases of inaccurate automatic segmentation, the values were not considered. OCT data were collected several times during the follow-up: at diagnosis, at 3 months, at 6 months, at 12 months and at the patient's last visit. Our primary outcome measure was the presence of optic nerve atrophy defined by the presence of an RNFL beyond 95% of the OCT database CI, and/or severe sectoral atrophy defined by a value beyond 99% of the OCT database CI Statistical analysis Data were analysed using RStudio software V. 4. 2. 0 (2022-04-22 ucrt) (R Core Team 2022. R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria) and the ggplot and dplyr packages. Descriptive results are presented using the mean±SD, minimum and maximum values, or the number and associated percentage (%) dependent on the data.
Cohort details The study included data for 222 eyes across 113 patients with IH, 93 of whom were at La Timone Hospital and 20 at the North Hospital in Marseille, France. Data from 54 eyes were subsequently excluded from the correlation analyses: 34 were lost to follow-up due to having a follow-up of less than 3 months, 16 eyes were removed due to lack of OCT data, 3 eyes had an optic disc dysversion and 1 eye had intravitreal haemorrhage. Out of the 168 eyes finally included, 35 showed optic atrophy according to the primary outcome.
Demographic and clinical data The average age of the patients was 24. 3 years (±16,72), with a mean BMI of 25. 9 kg/m 2. The majority of patients had bilateral involvement, with only four patients presenting unilateral involvement, and mean follow-up was 13. 9 months. The cohort included more women (64. 6%). The various aetiologies of IH in the cohort are detailed in figure  2. The main causes of IH, accounting for more than 80% of cases, were IIH (43. 36%), intracranial tumours (29. 20%) and cerebral venous thrombosis (13. 27%). Among the IIH cases, only 3 were classified as fulminant, 15 as subacute and 31 as chronic. Of the intracranial tumours, these were found to be gliomas (10/33), meningiomas (5/33), cysts (5/33), medulloblastomas (3/33), craniopharyngiomas (3/33), metastases (2/33), pineal tumours (2/33), haemangioblastoma (1/33), schwannoma (1/33) and neuroblastoma (1/33).
Hence, we employed this data to evaluate the MGCL. This strong association between the alteration of the GCL and the final optic atrophy, which is consistent with the study by Marzoli et al, indicates that, in patients with IH, signs of optic neuropathy can be identified even in the presence of papilloedema. 21  Thus, OCT, which is already useful for quantifying papilloedema, may show retinal ganglion cell damage in an early phase of the disease. 22  The sex ratio in our study in favour of females is explained by a preponderance of females in IIH and cerebral venous thrombosis with the important role of sex hormones. 2324  However, male or female sex is neither a risk factor nor a protective factor for optic atrophy, which partly contradicts the Bruce et al study in which men with IIH were twice as likely as women to develop severe visual loss, but this study used visual acuity as the principal outcome.
Open access OCT is a marker of treatment efficacy and disease progression in IH. 26  It is a quick and easy alternative to repeated lumbar punctures for monitoring patients with IIH, and facilitates monitoring of disease progression and response to treatment. In addition, several studies have shown that the rise in ICP correlates with the hike in RNFL during the follow-up of these patients. 27 28  However, this study shows some limitations. It is a retrospective study and some clinical data from the interview or clinical examination were not always specified, such as the presence of certain symptoms or peripapillary haemorrhages. Also, children were mostly excluded from the study because OCT was often impossible to perform due to lack of compliance with the examination. The prevalence of IIH was low in our cohort because the more serious aetiologies (tumours, thromboses) are overestimated in connection with the region's reference neurosurgery department attached to our ophthalmology department.
Acknowledgements This article was the subject of  Ruben Attia 's thesis, but it has not yet been published to date. To my dear coauthors, a heartfelt thank you for your invaluable contribution. Contributors Guarantor : RA. Realisation/acquisition of data/redaction/statistical analysis: RA. Conception: NS. Planning: TD. Interpretation of data: PT and J-PS. Data analysis: BA and SD. Reviewing/finalising the manuscript: RF and KM. Funding The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors. Competing interests None declared. Patient and public involvement Patients or the public were not involved in the design, or conduct, or reporting, or dissemination plans of our research. Patient consent for publication Not applicable.
In this paper, we describe a new approach to the near-realtime detection and characterization of unexpected events, which relies on the principal component analysis (PCA) of IASI radiance spectra. By analyzing both the IASI raw and compressed spectra, we applied a PCA-granule-based method on various past, well-documented extreme events such as volcanic eruptions, fires, anthropogenic pollution, and industrial accidents. We demonstrate that the method is well suited to the detection of spectral signatures for reactive and weakly absorbing gases, even for sporadic events. Consistent long-term records are also generated for fire and volcanic events from the available IASI/Metop-B data record. The method is running continuously, delivering email alerts on a routine basis, using the near-real-time IASI L1C radiance data.
The PCA is based on the eigenvalue decomposition of the matrix S , as follows: S = E E T , ( 1 ) where E is the matrix m × m of eigenvectors and the diagonal matrix of their associated eigenvalues. The representation of a measured spectrum y in the eigenspace E is obtained by the following: p = E T N -1 (y -y). (2) p (dimension m) is the vector of the principal component scores. The analysis consists of representing the multidimensional IASI spectra in a lower-dimensional space, which accounts for most of the variance seen in the data. This space is spanned by a truncated set of the eigenvectors of the data covariance matrix. By noise-normalizing the spectra prior to the application of the PCA, the ability to fit the data is enhanced by avoiding giving too much weight to variance caused by noise.
Acknowledgements. Adrien Vu Van acknowledges funding from  SPACIA SA  through an  ANRT   CIFRE doctoral grant. IASI is a joint mission of  EUMETSAT  and the  Centre National d'Etudes Spatiales (CNES, France). The IASI Level 1C data are distributed in nearreal time by  EUMETSAT  through the EUMETCast system distribution. The authors acknowledge the AERIS data infrastructure (https://www. aeris-data. fr,last access:  19 April 2023 ), for providing access to the IASI Level 1 radiance and Level 2 concentration data used in this study. Financial support. This research has been supported by the  French Association Nationale de la Recherche et de la Technologie  (grant no. 2019/0196 ). Review statement. This paper was edited by  Diego Loyola  and reviewed by  Santtu Mikkonen  and one anonymous referee.
Data availability. IASI L2 SO 2 , NH 3 , and CO data are available at https://doi. org/10. 25326/42 (Clarisse, 2019), https://doi. org/10. 25326/11 (Clarisse et al. , 2018), and https://doi. org/10. 25326/64 (Clerbaux et al. , 2020) , respectively. The volatile organic carbon (VOC) retrievals are processed by Franco Bruno (bruno. franco@ulb. be) and Lieven Clarisse (lieven. clarisse@ulb. be) at ULB and are available upon request.
Histological assessment For histological evaluation the fruit sectors were divided at equal distances along the radius into three concentrically oriented zones: the area closest to the fruit Exterior (zone Ex), the intermediate or Middle zone (zone Md), and the area closest to the fruit Interior (Zone In) as depicted in Figure  1. Two square images per zone, each 0. 06 cm2, were observed in each histological section for Stage I, and six images per zone for Stage II. Microscopic images were acquired with an Olympus BX51 light microscope and recorded by a digital camera (Olympus SC50) attached to the microscope using Cell Sense Imaging Software (Olympus). All parameters were first determined for the three different fruitflesh zones, and then overall (globally) for the total area studied. The global values were calculated by considering the proportional area contribution of each zone.
Individual stone cells and stone-cell clusters were observed. Stone-cell clusters, referred to as SSCs following the nomenclature of Nii et al. [14] , were considered as such when the cluster contained more than three adjoining stone cells. Individual stone cells and those found in groups of two or three, in all cases separated from other stone cells by at least one parenchyma cell, were designated as isolated. Given the relatively small number of isolated stone cells (Figure  2D ), the majority of the stone cell analyses utilized only the SCC's. A macro was developed for use with Image J software (V. 2. 1. 0) to automatically determine the size of the SCCs, the number of SCCs and isolated stone cells, the area occupied by the SCCs, and the number of SCCs per area. In addition, average stone cell size was calculated from the data.