dataesr/scientific-paragraphs-categorization huggingface dataset:
Although the precision of the CHEOPS secondary eclipse light curves would be sufficient for individual occultation fits, the missing data points in between CHEOPS observations -when the target cannot be observed due to the Earth being in the line of sight of the target -make it hard to get precise timings, especially if parts of the ingress or egress are missing. When mid-occultation times are derived from multiple occultation light curves, the reference time of conjunction is chosen to be as close to the middle of the data set as possible. This is done because the occultation observations are relatively evenly spaced in each data set. The resulting mid-occultation times can be found in Table  5. Only a portion of the data is shown here, the full table is available at the CDS. Timing analysis.
The occultation data from the various sources is also inconclusive in differentiating the three models because the scatter of the data points is too large (see Fig. 3 ). Further high-precision observations of this target will be necessary in the future to constrain the models further and to differentiate between them. KELT-9 is observed in TESS Sector 55 in camera 3, from 5 August to 1 September, 2022, which could enable better fits and might replace the apsidal precession model as the best model, as this is heavily influenced by the Spitzer transit data point. Using the results from the orbital decay fit, we can calculate Ṗ = (-24. 42 ± 10. 66) ms yr -1 , assuming the observed trend is true and only caused by this effect. Calculating the 95% confidence lower limit on the orbital decay timescale τ = P | Ṗ| yields τ > (2.
KELT-16 b Transit fitting. For KELT-16 b, we fitted a total of 151 transits. All of these, except those of CHEOPS, were modelled individually with TLCM, as many different telescopes and filters were used for the ground-based observations. Even though there are many transit observations, they offer less precision in comparison to KELT-9 b. The CHEOPS light curves suffer from gaps in between observations, especially if they are located near the ingress or egress phase of the transit (see Fig. 4 ), meaning the resulting mid-transit times cannot be as precise as the data would allow  (Barros et al. 2013; Borsato et al. 2021 ). Because of this, a combined model was created and applied to the final transit fitting of all seven CHEOPS light curves. The previously unpublished observed transits from CHEOPS are shown in Fig.
In addition, the dP/dN of the orbital decay model also agrees with a constant period within 1 σ, with the resulting eccentricity e of the apsidal precession model agreeing with a circular orbit at 2 σ. The dω/dN parameter agrees with a Keplerian orbit at only just over 4 σ. Comparing our orbital decay fit results to earlier results from the literature, we find that our finding of dP/dN = (-2. 73 ± 3. 77) × 10 -10 days per orbit A124, page 9 of 17 A&A 669, A124 (2023) Fig. 4. KELT-16 b transits observed with CHEOPS. The x-axis shows the orbital phase of the transits and the y-axis shows the normalised flux. There is an offset of 0. 015 in terms of normalised flux between the individual transits. The data points (black dots) were corrected for the roll angle of the satellite using PYCHEOPS and fitted with TLCM.
Conclusions We homogeneously analysed new CHEOPS and TESS photometric data and re-analysed archival photometric data for KELT-9 b, KELT-16 b, WASP-4 b, and HD 97658 b using stateof-the-art software, namely TLCM and PYCHEOPS, and developed our own MCMC routines for the analysis of this data. Transit fitting was done using TLCM, occultation fitting using BATMAN in combination with an MCMC approach, and the TTVs for each system were fitted using MCMC algorithms for three different models. The first of these models is a constantperiod model assuming a Keplerian orbit, the second is an orbital-decay model accounting for a changing orbital period, and the third is an apsidal-precession model assuming that the orbit is slightly eccentric, leading to a precessing orbit that can mimic orbital decay for short baselines but can be distinguished using secondary eclipses or with long baselines.
Acknowledgements. We thank the anonymous referee for their helpful comments. The CHEOPS photometric data underlying this article is publicly accessible using the CHEOPS archive browser (https://cheops-archive. astro. unige. ch/archive_browser/),at the DACE website in the CHEOPS database, or via PYCHEOPS using the file keys provided in Table 1. The TESS data used in this publication is publicly available at the MAST archive. The timing data for the various planets and the photometric CHEOPS data is available with the online version of this article and at the  CDS. CHEOPS is an ESA mission in partnership with Switzerland with important contributions to the payload and the ground segment from  Austria, Belgium, France, Germany, Hungary, Italy, Portugal, Spain, Sweden , and the  United Kingdom.
The CHEOPS Consortium would like to gratefully acknowledge the support received by all the agencies, offices, universities, and industries involved. Their flexibility and willingness to explore new approaches were essential to the success of this mission. JVH acknowledges the support of the  DFG priority programme SPP 1992 "Exploring the Diversity of Extrasolar Planets  ( SM 4862-1 )". S. C. C. B. acknowledges support from FCT through FCT contracts nr. IF/01312/2014/CP1215/CT0004. This project has received funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  (project  FOUR ACES. P. M. acknowledges support from  STFC  research grant number  ST/M001040/1. L. M. S. gratefully acknowledges financial support from the  CRT foundation  under Grant No.
2018. 2323'Gaseous or rocky?Unveiling the nature of small worlds'. B. -O. D. acknowledges support from the  Swiss National Science Foundation  ( PP00P2 -190080 ). This work was granted access to the HPC resources of MesoPSL financed by the  Region Ile de France  and the project  Equip@Meso  (reference  ANR-10-EQPX-29-01 ) of the programme Investissements d'Avenir supervised by the  Agence Nationale pour la Recherche. Y. A. and M. J. H. acknowledge the support of the  Swiss National Fund  under grant  200020_172746. We acknowledge support from the  Spanish Ministry of Science and Innovation  and the  European Regional Development Fund  through grants MDM-2017-0737  Unidad de Excelencia Maria de Maeztu-Centro de Astrobiología (INTA-CSIC) , as well as the support of the  Generalitat de Catalunya/CERCA  programme.
D. S. D. is supported in the form of work contract ( DL 57/2016/CP1364/CT0004 ) funded by national funds through FCT. M. F. and C. M. P. gratefully acknowledge the support of the  Swedish National Space Agency  ( DNR 65/19 ,  174/18 ). DG gratefully acknowledges financial support from the  CRT foundation  under Grant No. 2018. 2323"Gaseousor rocky?Unveiling the nature of small worlds". M. G. is an F. R. S. -FNRS Senior Research Associate. SH gratefully acknowledges CNES funding through the grant  837319. K. G. I. is the  ESA   CHEOPS  Project Scientist and is responsible for the  ESA CHEOPS Guest Observers Programme. She does not participate in, or contribute to, the definition of the  Guaranteed Time Programme  of the CHEOPS mission through which observations described in this paper have been taken, nor to any aspect of target selection for the programme.
M. L. acknowledges support of the  Swiss National Science Foundation  under grant number  PCEFP2_194576. LBo, GBr, VNa, IPa, GPi, RRa, GSc, VSi , and TZi acknowledge support from  CHEOPS ASI-INAF  agreement no. 2019-29-HH. 0. This work was also partially supported by a grant from the  Simons Foundation (PI Queloz , grant number  327127 ). IRI acknowledges support from the  Spanish Ministry of Science and Innovation  and the  European Regional Development Fund  through grant  PGC2018-098153-B-C33 , as well as the support of the  Generalitat de Catalunya/CERCA  programme. S. G. S. acknowledge support from  FCT  through  FCT  contract nr. CEECIND/00826/2018  and  POPH/FSE (EC). GyMSz acknowledges the support of the  Hungarian National Research, Development and Innovation Office (NKFIH)  grant  K-125015 , a a PRODEX Experiment Agreement No.
Introduction: iGlarLixi (insulin glargine 100 U/ mL plus lixisenatide) has demonstrated glycaemic efficacy and safety in adults with inadequately controlled type 2 diabetes mellitus (T2DM). Per the European Medicines Agency's product label, iGlarLixi should be injected once a day within 1 h prior to a meal, preferably the same meal every day when the most convenient meal has been chosen. It is however unknown whether iGlarLixi administration timing affects glycaemic control and safety, as clinical trial evidence is mainly based on pre-breakfast iGlarLixi administration. Therefore, we assessed the effectiveness and safety of iGlarLixi in clinical practice, according to its administration timing. Methods: Data were pooled from two prospective observational studies including 1303 European participants with T2DM inadequately controlled on oral antidiabetic drugs with or without basal insulin who initiated iGlarLixi therapy for 24 weeks.
Data Collection and Assessments Study-related data were collected at baseline, at 12 weeks and at 24 weeks. Baseline demographics and clinical characteristics in this analysis included age, sex, duration of diabetes, body weight and/or body mass index (BMI), diabetic complications and cardiovascular comorbidities, and details of prior glucose-lowering medications. Data on iGlarLixi treatment, such as iGlarLixi dose, timing of injection, used pen, and concomitant use of other glucoselowering medications were also collected during the study period. The primary endpoint of this analysis was the change in HbA1c from baseline to week 24. Secondary effectiveness endpoints included HbA1c change from baseline to week 12, proportions of participants achieving HbA1c targets of \ 7. 0% (\ 53 mmol/mol), \ 7.
Such benefit may not be seen with a pre-dinner iGlarLixi administration since the time interval between dinner and breakfast is usually much longer than 4-5 h. To the best of our knowledge, this work represents the first analysis in which the daily administration time of iGlarLixi was prospectively recorded and data regarding glycaemic control were systematically collected and analysed. In such a way, our study addresses the clinical question concerning the impact of iGlarLixi administration timing on its effectiveness and safety. Among other strengths of this analysis are the large data set coming from clinical practice and the analytical methods used to assess the change in HbA1c. Indeed, the change in HbA1c from baseline to week 24 was evaluated using a MMRM that adjusted for several factors including baseline HbA1c, age, baseline BMI, and prior insulin use.
ACKNOWLEDGEMENTS Funding. This study (including the  Rapid Service fee/Open Access  fee) was funded by  Sanofi (Paris, France). Medical Writing Support. Medical writing support in accordance with Good Publication Practice (GPP3) guidelines (http://www. ismpp. org/gpp3) was provided by  Thomas Rohban, MD , and  Magalie El Hajj, PharmD , of  Partner 4 Health (Paris, France ) and was funded by  Sanofi.
Other contributions are obtained by determining the maximum contamination from residual pp collisions with samples of pure pNe collisions and pure pp collisions. The neon purity systematic uncertainty corresponds to the contamination from collisions between the beam and elements different from neon, coming from standard outgassing. It is quantified using data samples recorded with no neon injection. Since the tracking and particle identification efficiencies are determined using pp control samples, the differences between the track multiplicity in pNe and pp collisions are considered as systematic uncertainties. The tracking and particle identification systematic uncertainties also take into account the size of the pp control samples. The PV reconstruction systematic uncertainty corresponds to the variation of the efficiency over the whole z PV range, and to the difference between the PV reconstruction efficiency evaluated using the simulation and a data-driven approach exploiting the well-reconstructed φ → K + K -decay.
Correlated leading order (NLO) in the heavy-flavour cross-section, with or without a 1% intrinsic charm (IC) contribution  [32]. The J/ψ production cross-section is also compared to the D 0 production cross-section extracted from the same dataset, in the same kinematical conditions  [7]. Several systematic uncertainties cancel in the J/ψ /D 0 cross-section ratio, related to the PV and track reconstruction efficiencies, the contamination from residual pp collisions, the neon purity and the luminosity determination. The ratio of J/ψ and D 0 cross-sections is σ J/ψ σ D 0 = (1. 06 ± 0. 02 ± 0. 09)%, where the first uncertainty is statistical and the second systematic. The ratio takes into account the branching fractions  [26]  of J/ψ → μ + μ -and D 0 → K -π +. The J/ψ -to-D 0 cross-section ratio as a function of y and p T is shown in Fig.
We conducted an observational retrospective study including patients admitted to 5 ICUs of the Dijon University Hospital (France) from January 1, 2015, to December 12, 2021, with a diagnosis of severe CAP. Inclusion criteria were: age > 18 years, admission to ICU with a diagnosis of CAP (clinical respiratory signs, new infiltrate on chest imagery within 48 hours following admission to hospital) with at least one test for Aspergillus (serum or alveolar antigen, bronchoalveolar lavage fluid (BALF) or tracheal aspirate culture or PCR) during the ICU stay. ICU stays for pneumonia were identified in the French hospital discharge database by primary diagnoses according to the International Classification of Diseases (ICD)-10 codes. The accuracy of the diagnosis was checked in individual medical files by a trained clinician, and patients were excluded if they did not meet the inclusion criteria.
Cases were defined as having at least one of the following positive Aspergillus biomarkers: galactomannan index in serum > 0. 5 and/or BALF > 1, and/or positive culture and/or PCR for Aspergillus from BALF or tracheal aspirate samples. We used the definition of IPA from Blot et al. to classify cases as colonization (definition not met) or putative IPA (definition met)  (8). We included between 2 and 3 control patients per case randomly sampled from the cohort of CAP patients with a negative Aspergillus test and matched on ICU admission ± 14 days. SAPS-II scores were prospectively calculated at ICU admission and extracted from medical files. Demographic data were retrospectively collected from medical files. We defined 90 ICU-free days from admission as 90 minus the number of days in the ICU (range, 0-90 days); 0 ICU-free days were assigned if death occurred within those 90 days.
To account for matching, a random effect on the matching identifier was added to the model. Models were adjusted on age and variables with a p-value < 0. 2 in the bivariate analysis, and log-linearity was tested using splines and Akaike Information Criteria (AIC). A p-value < 0. 05 was considered statistically significant. Analyses were performed using R (v4. 1. 3) and GraphPad Prism (v. 9. 1. 1) software. During the study period, 148 cases (115 colonized and 33 putative IPA) and 273 matched controls with severe pneumonia and negative Aspergillus biomarkers were included (Figure  1A ). BALF or tracheal aspirate were performed in 95%, 98% and 100% of non-colonized, colonized and putative IPA patients respectively (p = 0. 032). Median time from ICU admission to Aspergillus identification was 2 (0-11) and 5 (3-9) days for colonized and putative IPA patients, respectively (p = 0.
ACKNOWLEDGEMENTS The work was supported in part by grants from  NSFC  ( 91754205 ,  91957204 ,  31771523  and  M-1040 ) to QZ; MOST ( 2019YFA0508602 ) to QZ and  Shanghai Municipal Science and Technology Project  ( 20JC1411100 ,  19XD1402200 ) to QZ,  National Natural Science Foundation of China  ( 32070734 ),  Natural Science Foundation of Shanghai  ( 20ZR1430800 ), grants from  State Key Laboratory of Oncogenes and Related Genes  ( KF2101-93 ) to JZ. This work is also supported by  Shanghai Frontiers Science Center of Cellular Homeostasis and Human Diseases  and the innovative research team of high-level local universities in Shanghai ( SHSMU-ZDCX20212000 ) and  Shanghai Science and Technology Commission  ( 20JC1410100 ). GK is supported by the  Ligue contre le Cancer  (équipe labellisée);  Agence National de la Recherche (ANR) -Projets blancs; AMMICa  US23/CNRS UMS3655 ; Association pour la recherche sur le cancer (ARC);  Cancéropôle Ile-de-France ;  European Research Council Advanced Investigator Grand "ICD-Cancer", Fondation pour la Recherche Médicale (FRM) ; a donation by Elior;  Equipex Onco-Pheno-Screen ;  European Joint Programme on Rare Diseases (EJPRD) ;  European Research Council (ICD-Cancer) ,  European Union   Horizon 2020 Projects Oncobiome and Crimson ;  Fondation Carrefour ;  Institut National du Cancer (INCa) ;  Institut Universitaire de France ;  LabEx Immuno-Oncology  ( ANR-18-IDEX-0001 ); a  Cancer Research ASPIRE Award  from the  Mark Foundation ; the  RHU Immunolife; Seerave Foundation ;  SIRIC Stratified Oncology Cell DNA Repair and Tumor Immune Elimination (SOCRATE) ; and  SIRIC Cancer Research and Personalized Medicine (CARPEM).
Clinical and biological data The demographic, clinical and biological data of the recipients and donors were collected in the ASTRE database. Post-KT hemodialysis session follow-up data was obtained from the electronic health record system by the Department of Medical Information of Poitiers University Hospital. Serum creatinine was determined daily, by enzymatic method in the Biochemistry department of Poitiers University Hospital. To establish the renal function trajectories following transplantation, we considered all the creatinine determinations during the hospital stay from 24 h after the transplant to hospital discharge.
Follow-Up and Outcome Patients were followed up as were all the patients included in the French ASTRE cohort and the subsequent information was reported in the ASTRE database. The main study outcome was occurrence of a graft loss from 3month post-KT to last updating in July 2020 or to patient death. Secondary outcomes were creatinine values and HLA antibodies at 1-year post transplant, as well as HLA antibodies and acute cell rejection at latest news.
5 mg/dL, which fell to 3 mg/dL within 10 days post-KT. Patients classified in Class 3 ("good recovery", n = 45, 10%) and patients in Class 4 ("optimal recovery," n = 159, 37%) had the lowest creatinine values at day 1, 5, and 5. 5 mg/dL respectively; they both dropped to approximately 1. 1-1. 3 mg/dL at day 8 and day 4 respectively. After this pronounced diminution, "good recovery" patients' creatinine remained stable, whereas "optimal recovery" patients' creatinine showed a small rise of approximately 0. 5 mg/dL from day 5 to day 10. LCMM performed on the database excluding creatinine values reported within the 24 h following a hemodialysis session gave comparable results, identifying four classes with similar trajectories (n = 23 in Class 1, n = 204 in Class 2, n = 44 in Class 3 and n = 164 in Class 4).
If the main functions of LGG-1 reside in its capacity to bind multiple proteins, the localization to autophagosome membrane through lipidation is an efficient but not unique way to gather cargoes and autophagy complexes. Furthermore, the possibility that non-positive LGG-1/LGG-2 autophagosomes could mediate cargo degradation questions the use of Atg8/GABARAP/LC3 family as a universal marker for autophagosomes. Overall, our results confirm the high level of plasticity and robustness of autophagosome biogenesis. spectrometry. The present work has benefited from the facilities and expertise of the I2BC proteomic platform (Proteomic-Gif, SICaPS) supported by IBiSA, Ile de France Region, Plan Cancer, CNRS and Paris-Sud University as well as the core facilities of Imagerie-Gif, member of IBiSA), supported by "France-BioImaging" and the Labex "Saclay Plant Science".
, 2011) ), anti-LGG-2 (1:200 rabbit), anti-Tubulin (1:1000 mouse; Sigma, 078K4763), anti-SEL-1 (1:8000, rabbit), anti-CDC-48. 1 (1:5000, rabbit) and revealed using HRP-conjugated antibodies (1: 5000 promega W401B and 1:10000 promega W4021) and the Super Signal Pico Chemiluminescent Substrate  (Thermo Scientific, 34579). Signals were revealed on a Las3000 photoimager (Fuji) and quantified with Image Lab software. For cellular fractionation, 4000 age-synchronized worms (day 1 of adulthood) were collected from NGM/OP-50 plates, washed three times with M9 buffer and transferred to NGM plates without OP-50 to induce starvation. Worms were starved at 20°C for 7 hours, and then transferred to fractionation buffer (50 mM Tris-HCl pH 7. 4, 150 mM NaCl, 1 mM DTT, 1 mM PMSF, and protease inhibitor cocktail).
5 to 1 µm. Images were analyzed, quantified and processed using ImageJ or Fiji softwares. For live imaging samples were mounted on a 2% agarose pad and larvae were immobilized by 40 mM sodium azide. For MitoTracker staining, adult worms were transferred to NGM agar plates containing 3. 7 µM of Red CMXRos (Molecular Probes, Invitrogen) and incubated for overnight in the dark.
Briefly, peptides were loaded on an Aurora analytical column (ION OPTIK, 25cm x75µm, C18, 1. 6µm) and eluted with a gradient of 0-35% of solvent B for 100 min. Solvent A was 0. 1 % formic acid and 2% acetonitrile in water, and solvent B was 99. 9% acetonitrile with 0. 1% formic acid. MS and MS/MS spectra were recorded and converted into mgf files. Proteins identification were performed with Mascot search engine (Matrix science, London, UK) against a database composed of all LGG-1 sequences including the wild-type and mutant sequences. Database searches were performed using semi-trypsin cleavage specificity with five possible miscleavages. Methionine oxidation was set as variable modification. Peptide and fragment tolerances were set at 15 ppm and 0. 05 Da, respectively. A peptide mascot score threshold of 13 was set for peptide identification.
Quantification and statistical analysis All experiments were done at least three times. All data summarization and statistical analyses were performed by using either the GraphPad-Prism or the R software (www. r- (I, J) Boxplots of the number of ATG-18::GFP puncta (P)  (n=13, 24, 10, 14, 28, 36, 27, 20 )  and the mean fluorescent intensity (Q)  (n= 42, 45, 55, 140, 145, 130,115, 100).
Modern specimens The modern referential is based on the one used in   1 , Fig. 1 ) ranging from MIS3 to the beginning of the Holocene (for site descriptions, see Supplementary Material 1). Due to the larger number of upper M3 preserved in the fossil dataset, and because most of the sites contain more M3 than m1, results of fossil classification will be presented only on M3. All other results for the first lower molar are detailed in the supplementary material (Supplementary Material 2).
Data acquisition Each tooth was photographed with a Macro objective (Canon EOS6D mark II, macroscopic objective MP-E 65mmf/2. 8) using a standardized protocol to avoid any between-picture deformation. Each outline was then drawn directly from pictures using Image J v. 1. 52a (Schneider et al. 2012). Landmark placement was automatized using the protocol of  Navarro et al. (2018). Two landmarks were placed manually on the first lower molar (m1) to orientate the teeth. One landmark was placed manually on the centre of the third upper molar (M3) and was used as a pivot point around which the outline was rotated. After these procedures, all landmarks (lm m1: n ¼ 14, lm M3: n ¼ 11) were placed automatically at the extreme tips of salient and re-entrant angles. Furthermore, some additional semi-landmarks were placed on the anterior (slm m1, n ¼ 20) and posterior (slm M3, n ¼ 50) loops to describe their morphological variability (Fig.