dataesr/scientific-paragraphs-categorization huggingface dataset:
10a ). Fig. 11b  shows the difference between several-area homogenization (case A) and complete homogenization (case B). One should note that the former method does not add computing time, hence leads to a significant improvement. The independent quarter modeling (case C) does not present so much difference with our reference (case A) as shown on Figs. 11c  and 11d , respectively. The results obtained with CASMO5/SIMULATE5 show the same behavior between case A and case B (see Fig. 12a ). As for the comparison between case A and case C, maximum discrepancies are still for 6BA and 15BA assemblies, but where DRAGON5/DONJON5 overestimates case C fission rates compared to case A, CASMO5/SIMU-LATE5 seems to underestimate them and vice versa. These conclusions come from Fig. 12b , which was reconstructed from data provided in Bahadir (2020).
DESIGN, SETTING, AND PARTICIPANTS Using an interrupted time series design with data from 2016 to 2020, this study analyzed 50 260 referral requests submitted during the year before and the year after eConsult implementation at 19 New York City Health + Hospitals (NYC H+H) specialty clinics that spanned 7 NYC H+H hospital facilities and 6 unique specialties. EXPOSURES Referral request was submitted to a specialty clinic in the year following eConsult implementation.
1, 3, 5, 6 New models of care that can bridge these gaps are sorely needed to achieve equitable access to specialty care for patients in a safety-net system. These problems were mirrored in the largest safety-net system in the United States, New York City Health + Hospitals (NYC H+H), estimated to have provided more than 2 million specialty visits in 2015. 7 Historically, NYC H+H had a fragmented electronic medical record system and no standardized process for ambulatory specialty referrals. In 2016, expansion of an ongoing primary care patient access initiative led to improved data collection for specialty wait times, which demonstrated lengthy wait times and notable variation within and across specialties. This led to NYC H+H ambulatory care clinics prioritizing improvement of communication between primary care and specialty clinicians and standardization of referral workflows.
[16] [17] [18] [19] [20] [21]  To our knowledge, there has been no prior evaluation of a systemwide, multispecialty eConsult implementation that uses data before and after eConsult implementation to evaluate temporal trends in wait times and other outcomes. To address this evidence gap, we examined referral requests before and after eConsult adoption at several NYC H+H specialty clinics to understand the associations of the program with access to specialty care for patients within the NYC H+H system.
Data Sources To analyze the association of the eConsult program with access to specialty care, we used a database of all referral requests submitted to 19 NYC H+H specialty clinics through the QuadraMed and Epic EHR systems between January 2016 and February 2020. We linked each request to all related outpatient specialty encounters (eg, appointments scheduled and follow-up visits, see eMethods in the Supplement). The database included referrals across 19 separate clinical departments representing 6 separate specialties (cardiology, endocrinology, gastroenterology, neurology, nephrology, and urology) at 7 NYC H+H hospital facilities (Bellevue, Elmhurst, Jacobi, Lincoln, Metropolitan, North Central Bronx, and Woodhull). For each referral request, the database captured the date of the referral request, whether an appointment was scheduled following the referral request, and whether a follow-up visit occurred related to the referral.
eConsult and Referral Study Sample For each referral request, we limited our sample to encounters with a scheduling action (ie, an action by administrative staff to schedule an appointment) within 30 days of the referral request. Among the remaining encounters, we identified the first triage decision, date of the first appointment scheduled for the matching specialty clinic (which may have been different than the date of a completed visit), and the date of the first completed office visit occurring within 365 days of the referral request to the matching specialty clinic for each patient. We then limited our sample to referral requests from each specialty clinic that occurred between the period 12 months before and 12 months after eConsult adoption at the specialty clinic. We also further excluded referral requests based on the quality of data recorded for those requests (eMethods in the Supplement).
Statistical Analysis We compared the characteristics of patients with referrals and unadjusted rates of outcomes in the pre-eConsult and post-eConsult periods with bivariate statistical tests. We then conducted an interrupted time series analysis of the eConsult program using the database of eConsult requests and linked encounters. We conducted adjusted analyses with individual referral-level multivariate linear regression and the margins command in Stata (v. 15, StataCorp) to calculate adjusted proportions and means for each outcome over time. For the adjusted results, our key quantity of interest was an indicator for whether a referral request was submitted to a clinic after eConsult was adopted at the clinic (ie, the post-eConsult period), which provides an estimate of the average change in the particular outcome in the post-eConsult period compared with the pre-eConsult period.
Discussion In the first 12 months after implementation of eConsult at NYC H+H, the program made significant progress on several of its goals. The average wait times for specialty appointments fell by 8. 2 days, a  a Adjusted differences and P values were calculated using a linear regression and the margins function in Stata (v. 15). The regression included an indicator for whether the referral occurred in the 12-month post-eConsult adoption period at the specialty clinic the patient was being referred to, with specialty clinic fixed effects. b Referrals that resolved without a face-to-face visit (n = 3074) are excluded. c Referrals that resolved without a face-to-face visit (n = 3074) or without an appointment scheduled (n = 12 574) are excluded. d Referrals that resolved without a face-to-face visit (n = 3074) or that occurred within 90 days of the end of the study period (n = 726) are excluded.
Analysis of placental morphology and glycogen Formalin fixed-paraffin embedded placental tissue was sectioned at 5 μm through the mid-line using a Leitz 1512 rotary microtome (Leica). Placental sections were stained with haematoxylin and eosin to assess gross morphological structure and layer proportions. Placental glycogen was stained using a periodic acid Schiff (PAS) stain. Briefly, sections were dewaxed and rehydrated before incubation with 0. 5% periodic acid (Sigma, UK) for 90 min. Sections were then washed and incubated in Schiff's reagent (Sigma, UK) for 15 min, before dehydrating and mounting in DPX. The percentage of positive magenta staining was measured by a blinded operator using Image J software. Total placental glycogen content was determined using the protocols of  Lo et al.
Statistical analysis Fetal and placental data were analysed using generalised linear mixed model analysis, factoring for dam, littersize and uterine position, using SPSS  [27]. Percentiles for NPD data were calculated using the formula (Z-critical × SD) + mean; where Z-critical value was -1. 2816 (10th centile) and 1. 2816 (90th centile) and statistical significance was determined using Chi 2 -test. Analysis and graphical interpretations of all other data were performed using GraphPad Prism (v7. 0). Where appropriate, normality was assessed using Shaprio-Wilk and Kolmogorov-Smirnov tests, in the case data was normally distributed, one-way ANOVA with Tukey's post-hoc test was performed, and where data were not normally distributed, Kruskall-Wallis test with Dunns multiple comparison test was performed.
Placental glycogen content Glycogen content of the placenta was analysed with a biochemical assay and PAS staining. The placental glycogen content of whole placental tissue was not significantly different between any of the diet groups (Fig. 4A ). The PAS stained placenta revealed the majority of glycogen positive staining was localised to the junctional zone, and this was significantly reduced in MD-LPD placentas when compared to NPD Fig. 2. Paternal diet impacted fetal and placental parameters at E17. 5. Fetal weights (A) and their distributions (B), placental weights (C) and their distributions (D), the fetal:placental weight ratio (E) and yolk sac (F). Fetal organs were dissected to allow calculations of the heart:fetal weight ratio (G) and liver:fetal weight ratio (H). Data presented as dot plot where each data point is average of entire litter, with line representation of mean ± SEM (N = 6-7 litters; n = 53-59 fetuses), statistical significance determined using a generalised linear mixed model analysis, *p < 0.
Objectives: This study explores changes in the bone homeostasis by testing the N-terminal collagen type I extension propeptide (PINP) marker for osteo-formation and the carboxy-terminal region of collagen type I (CTX-I) marker for osteo-resorption in patients taking tocilizumab for polymyalgia rheumatica (PMR). Methods: Twenty patients were included in the prospective open-label TENOR study (Clinicaltrials. gov NCT01713842) and received three monthly tocilizumab infusions, followed by corticosteroids starting at week (W) 12. PINP and CTX-I were tested at inclusion (W0), after tocilizumab but before steroid initiation (W12), at the end of the protocol (W24) and were compared to healthy controls. Information regarding disease activity, bone mineral density using scanographic bone attenuation correlation (SBAC), inflammatory parameters and interleukin (IL)-6 levels were collected during the follow-up of the patients.
Sample collection Twenty untreated early PMR patients (median age 67 years old, range 55-79 years old), 13 males and 7 females, participating in a proof of concept study: the TENOR study (registered on Clinicaltrials. gov NCT01713842) at Brest and Nantes University Hospital, France, were included in this study. Inclusion and exclusion criteria have been described previously  [2]. Tocilizumab was given as three 8 mg/kg intravenous infusions in monotherapy, at baseline and then at week (W) 4 and at W8. From W12 to W24, patients received low-dose prednisone (0. 15 mg/kg/day with predefined tapering). Disease activity was measured by determining the PMR activity score (PMR-AS)  [15, 16]. Blood samples were collected at inclusion and before 1st tocilizumab infusion (W0), at W12 (under tocilizumab infusions only) and at W24 (under steroids).
Bone turnover markers at baseline in PMR Characteristics of the 20 patients with PMR included in the study were previously described  [2, 18]  and they were matched in age and sex with 97 healthy controls (Table  1 , Appendix A -Table  S1 ). None of the patients was treated with anti-osteoporotic drug during the 24 weeks of the study. Data about alcohol intake, smoking, calcium intake, familial history of fracture, early menopause and amenorrhea, were not available. Among the 17 patients with 18 FDG-PET-CT at inclusion, mean SABC was 118 ± 10HU and among them, 6/17 (35. 3%) presented a high risk of fracture with an SABC ≥ 145 (Appendix A, Table  S2 ). Only one patient had one vertebral fracture at inclusion and no fracture occurs during follow-up. The bone formation marker PINP and the bone resorption marker CTX-I were assayed.
In PMR, data are scarce with regards to bone formation and resorption, but a decrease in bone formation markers and an increase in bone resorption markers have been described in early untreated PMR  [25]. On one hand, bone formation was evaluated by the PINP serum level and was decreased compared to healthy controls. Such a difference was not observed in our study using age and sex matched healthy controls, which may be explained in part by differences in the selection of the control groups. On the other hand, bone resorption markers used were urinary free pyridinoline and deoxypyridinoline, and an increase in these markers was suggested to be induced by inflammation as reported by another group  [1]. In our study, the serum level of CTX-I was higher at baseline, when compared to healthy controls and subsequently, the CTX-I levels were further decreased at the end of the treatment.
Although CTX-I changes were evaluated early at W12 and W24 in our study, and bone markers evaluated after 6 months of prolonged corticosteroid therapy in Dolan's study, both studies have concluded that there is an effect of the therapy on bone resorption markers. The data available in rheumatoid arthritis (RA) patients are heterogeneous, and with some parallels with regards to our report in PMR. Indeed in RA, the use of tocilizumab was effective in decreasing the CTX-I serum level in several studies  [26, 27] , PINP serum levels were either unmodified  [27]  or increased with tocilizumab therapy  [26] [27] [28]. Baseline serum levels of CTX-I and PINP were concordant between studies and correspond to the concentrations measured in ours (0. 3-0. 4 ng/mL and 35-45 ng/mL, respectively). The decrease of CTX-I in the studies performed in RA, and not in our study, could be explained by differences between patient selection and, in our study, we have selected an untreated and early PMR population.
Acknowledgements The authors thank the rheumatologists and general practitioners who referred their patients to the TENOR study. They are grateful to the  Clinical Investigation Centre at Brest University Medical School  for centralising the material. We are thankful to  Dr. Wesley H. Brooks  ( University of South Florida, USA ) for editorial assistance, to  Valérie Pedron  ( IDS, France ) for technical assistance, and to  Genevieve Michel  and  Simone Forest  for their help in typing of the paper.
All residual DNA is archived at INRAE Sophia-Antipolis (France). Correction, annotation, and alignment were performed manually using BioEdit Geneious R10 software. The comparison of nucleotide sequences with sequences available in the NCBI database (GenBank) was performed using Blastn  [41]  with standard settings. Analysis of sequence data was done with the MegaX software [42], using the neighbour joining (NJ) method  [43] , with Bootstrap values based on 100 replications. Nucleotide distances in NJ trees were estimated by the Kimura's two-parameters method  [44].
This identification was further confirmed by result of the partial sequencing of the COI gene from three specimens resulting in two available sequences but one unique COI haplotype of 600 bp. This haplotype does not have a stop codon in the predicted partial protein. These two sequences were deposited in GenBank (Table  2 ). This identification was further confirmed by result of the partial sequencing of the COI gene from three specimens resulting in two available sequences but one unique COI haplotype of 600 bp. This haplotype does not have a stop codon in the predicted partial protein. These two sequences were deposited in GenBank (Table  2 ). The best NCBI matches (100% identity) were obtained with the sequences gb |MK097190. 1|, |MK097189. 1|, |MN615594. 1|, and |MN615595. 1|, which correspond to T.
Acknowledgments: The authors acknowledge  Felise Gutierrez  for the first English revision of the manuscript. Many thanks to  Lily Cesari ,  Nicolas Bonetti ,  Claire Caravel , and  Melina Archinard  for their technical assistance in field and laboratory work. Special acknowledgment to all volunteers who participated in punctual exposition or sending sampled egg masses. The authors thank the different reviewers of this manuscript for their valuable comments and English improvement. Additionally, we acknowledge the  Plant-BioS Microscopy Facility of the Institute Sophia AgroBiotech UMR INRA   1355-CNRS7254 - Université Côte d'Azur  for access to instruments and technical advice.
For lung cancer screening, several national trials, including the National Lung Screening Trial (NLST, USA)  [4] , Nederlans-Leuvens Longkanker Screenings Onderzoek (Nelson, Netherlands)  [5] , International Early Lung Cancer Action Program (iELCAP, USA)  [6] , Pan-Canadian Early Detection of Lung Cancer Study (PanCan, Canada)  [7]  or scientific societies, including the American College of Radiology (Lung-RADS)  [8] , give guidelines to manage pulmonary nodules in lung cancer screening, and follow the same policy of being less aggressive on small nodules, to avoid false-positive results. Nonetheless, follow-up CT examinations are needed in all these guidelines, as visual discriminators (essentially size, location and shape) have limited specificity. Artificial intelligence and especially radiomics are promising techniques to overcome these challenges.
Radiomics extracts a high number of imaging features to achieve better diagnostic performance  [9, 10]. Machine-learning algorithms are used to compare radiomics extracted features with a large database of known malignant or benign pulmonary nodules, mostly from screening trials  [11]. Given the increasing number of chest CT examinations, limitation of the radiation dose is of paramount importance. Low-dose chest CT and ultra-low dose (ULD) chest CT, which do not have a clear definition in the literature, are associated with a marked decrease in dose-length product (DLP)  [12] [13] [14] , play an important role in reducing the global burden of radiation. However, the diagnostic performance of radiomics algorithm with low dose CT, as compared to the reference standard, is unknown, and one might fear that the degraded image quality of low-dose CT has an impact on the performance of the radiomics algorithm  [15].
Radiomics of lung nodules Nodule analysis was performed with a research version of the RevealAI-Lung software (Mindshare Medical). RevealAI-Lung is a software analytics platform based on machine learning, trained with a population of lung nodules from low-dose CT and their known outcomes from the NLST database, to aid risk assessment of lung nodules. When the radiologist indicates a lung nodule, the software calculates image features from the indicated location and from the full image. The software calculates many features (> 1 k each) from multiple three-dimensional views of the scan and then uses ensemble machine learning training techniques to relate these features to known clinical outcomes. In the end, it provides a relative malignancy risk as a "malignancy similarity index" (mSI), a variable ranging from 0 to 1.
Data collection Several data data were collected in a dedicated spreadsheet. They include patient sex, age and height (cm); dose length product (DLP) (mGy. cm); standard deviation (SD) of the air inside the trachea; number of pulmonary nodules; nodule size; nodule location; nodule type (solid, part-solid, ground glass); margins (regular, spiculated); nodule calcifications (presence or absence); follow-up CT data; results of histopathological analysis and mSI.
Statistical analysis Statistical analysis was performed using R statistical software (v 3. 4. 5) (R Core Team 2019). Descriptive statistics were used to present results. The qualitative concordance (i. e. classification in B, U or M categories) between FD-CT and ULD-CT mSI results was evaluated using Cohen weighted kappa test  [20]. The quantitative concordance (i. e. the number itself) between FD and ULD CT. mSI was evaluated using intraclass correlation coefficient (ICC). A graphical representation of the quantitative concordance was performed using Bland and Altman method  [21]. Correlation between noise and mSI difference was evaluated using Spearman correlation test. Differences in radiation dose and noise between FD and ULD CT were evaluated using Student t test. A P < 0. 05 was considered to indicate statistically significant differences.
004-0. 993) with FD CT and 0. 536 ± 0. 366 (SD) (range: 0. 039-1) with ULD-CT. With FD CT, 5/32 (15. 6%) nodules were classified "B", 23/32 (71. 9%) nodules were classified "U", and 4/32 (12. 5%) nodules were classified "M". With ULD CT, 7/32 (21. 9%) nodules were classified "B", 14/32 (43. 8%) nodules were classified "U", and 11/32 (34. 3%) nodules were classified "M". An example of mSI provided by the radiomics software on lung nodules is provided.
Discussion In the present retrospective study including 99 pulmonary nodules in 68 patients, we evaluated the intra-patient concordance of a radiomics-based software analysis for nodule characterization between FD and ULD chest-CT. This concordance was qualitatively and quantitatively good (Kappa =0. 60 and ICC = 0. 82). These findings suggest that the impact of image quality for quantitative radiomics-based analysis is most likely minor and does have a limited effect on the final analysis, at least in our cohort. In addition, we did not find any correlation between noise difference and mSI difference between FD and ULD chest CT, meaning that the degraded signal-to-noise ratio does not correlate with the slight absolute mSI difference we observed. Artificial intelligence, deep learning and radiomics are relatively recent terms in modern radiology.
Messerli et al. have already evaluated the applicability of dedicated software on degraded chest-CT acquisition, with computer-aided-detection of solid pulmonary nodule on ULD chest-CT, with similar sensitivity of 68% versus 70% with FD chest CT  [22]. Ohno et al. demonstrated that iterative reconstruction algorithm significantly improves nodule detection rate with computer aided detection on ULD chest-CT, meaning that relatively "basic" software applications can accommodate a degraded image quality  [23]. However, radiomics software are working on a more complex and deep scale  [24]. They generate complex high-dimensional data from CT or other imaging techniques, and extract quantitative descriptors from images which can be divided in first-order statistical outputs: distribution of values of individual voxels; second-order statistical outputs: textures feature for intra-tumoral and inter-tumoral heterogeneity; and higher-orders statistical method.
Then, all these data are compared with machine-learning on large database of known histological tumors, and promise to increase the accuracy of diagnosis, prognosis and prediction of tumor response  [24]. The promise is to have a far better reproducibility than radiologists, whose visual discriminators are limited and subjective  [11]. While radiomics of pulmonary nodules is facilitated by the low attenuation of the surrounding lung, it has also been developed and evaluated in non-chest imaging, as for prostate cancer; hepatocellular carcinoma; glioblastoma and more  [24]. Despite all the aforementioned advantages and promises, radiomics has also some limitations on the reproducibility of results. Most of radiomics software have been trained on the same dataset, specifically LIDC-IDRI  [25] , NLST  [4]  and from the National Cancer Institute released in 2017, which limits the generalization of the results to other populations.
A third study studied the effect of simulated dose reduction on chest CT of patients with known lung cancer  [29]. This study showed a significant deviation in all radiomics features  [29]. This may suggest that the use of radiomics software with different acquisition protocols on different CT equipment may lead to inconsistent results. Our study has some limitations due to the retrospective design, evaluated only on one CT model and manufacturer, and with only one type of radomics software. The fact that the software we evaluated was trained on the NLST database, meaning only low dose chest CT (below 1. 5mSv, so still about 6 times the dose of ULD-CT in our cohort) were used, might have favored the similarity of results between FD and ULD chest CT examinations in our study. Further studies with more patients and CT acquisitions from different centers and manufacturers are needed.