dataesr/scientific-paragraphs-categorization huggingface dataset:
Anti-inflammatory activity of resveratrol derivatives Anti-inflammatory activity of the produced compounds was evaluated using LPS-induce RAW 264. 7 cells model. In order to determine the cytotoxicity of stilbenes, the viability was measured after cells treatment at 1 to 20 µM. Resveratrol and quadrangularine B did not show significant cytotoxicity at all tested concentrations, whereas δ-viniferin, oxistilbenin F and oxistilbenin G exhibited cellular toxicity at 15 μM (data not shown). On one hand, as shown in the Fig. 2A , resveratrol, δ-viniferin, oxistilbenin F and oxistilbenin G reduced NO production by LPS-stimulated macrophages, with IC50 of 17. 9, 8. 7, 9. 2 and 9. 8 µM, respectively. On the other hand, quadrangularin B exhibited a moderate inhibitory activity against NO at the highest concentration.
Background Carcinoid syndrome, a rare condition in patients with neuroendocrine tumours, characterised by flushing and diarrhoea, severely affects patients' quality of life. The current carcinoid syndrome standard of care includes somatostatin analogues, but some patients experience uncontrolled symptoms despite somatostatin analogue therapy. Telotristat ethyl is a novel treatment approved by the European Medicines Agency (EMA) and US FDA that significantly reduces bowel movement frequency in patients with uncontrolled carcinoid syndrome. Objective We developed a model to evaluate the 5-year budget impact of introducing telotristat ethyl to standard care in Swedish patients with uncontrolled carcinoid syndrome. Methods Treatment response in the 12-week phase III TELESTAR trial (NCT01677910) informed telotristat ethyl efficacy; subsequently, health states were captured by a Markov model using 4-week cycles.
TELESTAR open-label extension data informed telotristat ethyl discontinuation. The number of treatment-eligible patients was estimated from literature reviews reporting the prevalence, incidence and mortality of carcinoid syndrome. A Swedish database study informed real-world costs related to carcinoid syndrome and carcinoid heart disease costs. Telotristat ethyl market share was assumed to increase annually from 24% (year 1) to 70% (year 5). Results Over the 5-year model horizon, 44 patients were expected to initiate telotristat ethyl treatment. The cumulative net budget impact of adding telotristat ethyl to current standard of care was €172,346; per-year costs decreased from €66,495 (year 1) to €29,818 (year 5). Increased drug costs from adding telotristat ethyl were offset by reduced costs elsewhere.
Model Inputs Initial TE treatment efficacy was informed by data from the 12-week double-blind treatment period of the phase III TELESTAR trial (NCT01677910). A total of 135 patients entered the trial and were randomised to one of three study arms: TE 500 mg, TE 250 mg, or placebo, each three times daily. All patients remained on the dose of SSA therapy that they were receiving upon entry. Full details of the TEL-ESTAR study design and results have been published previously  [38]. For this analyses, a durable response during either stage (the initial 12 weeks of treatment or the 4-week Markov cycles) was defined as a ≥ 30% reduction in bowel movement frequency for ≥ 50% of the time, matching the definition used in TELESTAR  [38]. The target population for treatment with TE has been defined as patients who have not adequately responded to SSA treatment.
Cost Inputs The unit cost of TE in Swedish krona (SEK) (10,100. 00) [43]  was converted to € using the European Central Bank conversion rate as of 28 November 2018 (€1 = SEK10. 2823). Therefore, the unit cost of TE in Sweden is approximately €11. 69 per 250 mg pill, cumulating in a total cost of €982. 27 over 4 weeks (three pills per day). Patients in the SSA + TE arm during the initial treatment period and those in the durable response and inadequate response to TE health states within the Markov model incurred this cost. Real-world CS and CaHD-related costs, including healthcare resource use (frequency per month), drug acquisition and SSA dosage (cost of average dose) for patients with controlled and uncontrolled CS were obtained from a retrospective Swedish database study  [33]  (Table  1 ).
A limitation of our study was the lack of readily available data, which is often a challenge when conducting budget impact analyses of treatments for orphan diseases  [59, 60]. For example, prevalence and incidence data specific to Sweden were not available, so this analysis used data from a systematic review conducted for Europe and the USA to estimate the size of the target population  [44]. The derivation of cost inputs for patients with controlled and uncontrolled CS from Swedish databases presented another limitation for this study. It was assumed that patients demonstrating a durable response to TE would have the same resource use as patients with controlled CS, and patients without a durable response to TE would have that of patients with uncontrolled CS (despite the fact that the definition of a durable response relates to reductions in symptoms of diarrhoea).
Materials and methods The sample for this study consisted in 954 adolescents (488 boys, 466 girls) recruited in New Caledonia. Participant ages ranged from 10. 5 to 16. 1 years (M = 13. 22, SD = 1. 23). Full descriptive information about the sample is presented in Table  S1  and Table  S2. Ethics approval for this project was obtained from the Consultative Ethics Committee of New Caledonia (CCE 2018-06 001) and the project was conducted in accordance with the requirements of the Declaration of Helsinki. Eight secondary public schools were randomly selected to obtain a representative repartition between rural and urban areas, which is 63% and 37%, respectively, in New Caledonia. The selection criterion was school size (N > 150) to ensure sufficient data in a single field trip. One or two classes were then randomly selected in each of four grades (levels) by a staff member, for a total of approximately 150 adolescents (6 groups with a mean of 25 students per division).
We obtained 89% of the expected data due to parental refusal, no return of the parental agreement or absence of the participants on the days of intervention. Participants completed two anonymous online questionnaires consisting of the measures described above and additional measures on body image or self-esteem, which are not reported here. Data were collected over two consecutive days in order not to disrupt the students' schedules too much. There were missing data because some participants were absent on at least one of the two consecutive days or because they used a wrong anonymous number. Adolescents with missing data (N = 108; 10. 2%) were then excluded from analyses (Figure  S1 ). Parents gave informed written consent prior to the adolescents' participation in the study, and all participants received written debriefing information at the end of the study.
The questions were asked on tablets distributed by the team and sometimes computerized in class so that the answers were automatically recorded in the database. The sleep duration was determined with the following four questions: "What time do you fall asleep on school days?", "What time do you fall asleep on the weekend?", "What time do you wake up in the school week?" and "What time do you wake up on the weekend?" There were 13 available categories for the time an adolescent might fall asleep, from "Around 9 pm or before" to "Around 3 am or later" with a 30-minute interval between each category. There were 15 available categories for the wake-up time from "Around 5 am or before" to "Around midday or later" with a 30-minute interval between each category. Answers were converted to numerical values by using the median value of the time interval in the categorized answer or by using 30 min before (respectively, after) for the first (respectively, the last) category.
Participants from minor ethnic groups (approximately 5% of the whole sample) were then clustered in the category "Other". Socioeconomic status (SES) was indexed on the basis of the occupation of the household reference person (defined as the householder with the highest income) using the National Statistics Socio-Economic Classification  [59]. In this study, three categories were retained: managerial and professional occupations (high SES), intermediate occupations (intermediate SES), and routine and manual occupations (low SES). Based on data from the last census in New Caledonia  [60]  , area of residence was determined using a European standard  [61]. Densely populated areas comprising at least 50,0 0 0 inhabitants in a continuous zone with more than 500 inhabitants per km 2 were classified as urban.
4 and ≤ -0. 4 were considered significant when naming patterns. The patterns produced were transformed to remove skew and converted to z-scores, so factor scores had a standardised mean of zero. Data factorability was assessed using the Kaiser-Meyer-Olkin (KMO) measure of sampling adequacy and Bartlett's test of sphericity. Cronbach's alpha coefficient was used to determine the internal consistency. All participants received a score for all five patterns, with positive scores indicating high consumption of foods associated with that pattern and negative scores indicating lower consumption. For example, the higher the Fruits and vegetables score, the higher an individual's frequency of consuming fruits, vegetables, tubers, soup and fish. To determine which factors were significantly associated with each dietary pattern, we computed linear mixed models with each dietary pattern score as the criterion variable.
Concerning the short FFQ used, social desirability bias may have led overweight adolescents to overreport or underreport food consumption  [85]. Moreover, the design of the FFQ did not allow us to determine the total energy intake. This FFQ was also validated in a specific population in Australia, so it may not reflect some of the specificities of the New Caledonian adolescent diet. Also, we did not assess PA, which can have an impact on weight status. Concerning sleep time duration, FFQ and screen use, we obtained self-declarative data and the results should thus be considered with caution. Despite these limitations, this is the first study to examine concomitantly the impacts of sleep duration, screen time and diet on overweight in a pluri-ethnic Pacific population of adolescents.
DNA sequencing, variant discovery and filtering Details of our sequencing protocols and variant analyses are provided in the Supplementary Appendix. In brief, targeted enrichment and sequencing of the 147 gene panel was undertaken using DNA extracted from peripheral blood. Single nucleotide variants (SNV) and indels were identified according to strict bioinformatic protocols, aligned with 1000 Genomes Project (1000g)  23  sequence sets, and variant pathogenicity assessed using SIFT  24  , Polyphen-2  25  and Combined Annotation Dependent Depletion (CADD) v1. 3 26 scores. SNVs were filtered according to their population frequencies within the Exome Aggregation Consortium (ExAC) dataset, employing 1% and 0. 1% in the case of putative biallelic mutations, or 0. 01% and 0. 001% for heterozygous variants in the case of indels or SNVs respectively, as our thresholds (threshold (heterozygous) = threshold (homozygous) 2 i.
Pt16, carrying a C3 substitution, demonstrated early onset lupus nephritis with C1q deposits and thrombotic microangiopathy. C3 was undetectable on several assessments. Heterozygous variants in C3 have been reported as possibly causal in SLE  32. Other predicted damaging variants were recorded in RNASEH2C, ACP5, C5, C8B and C9. In this group of eight patients the sex ratio was 1:1, similar to the subset of probands described above, but differing from the overall composition of the cohort (Figure  S2  in the Supplementary Appendix), and the median age at onset was earlier than in the cohort overall. To test if the accumulation of heterozygous KLC variants was specifically associated with lupus status, we applied the same algorithm to our control group as used in the lupus data set. In so doing we observed a statistically significant excess of rare, predicted damaging variants in KLC genes within the pediatric SLE population compared to controls, with an odds ratio of 14.
10 in the SLE cohort (Figure  3A ). These data comprised all variants in our gene panel in the SLE and 1000g cohort datasets, generated respectively by targeted and whole genome sequencing. Considering that sequence coverage and indel analysis could be different between our in-house derived sequence and those of the 1000g, we carried out the similar assessment but restricted to SNVs within the coding regions of the KLC gene set (Figure  S1 , Figure  S3A  and Supplemental table 3 in the Supplementary Appendix). Again, rare and predicted damaging variants in KLC genes were significantly increased in the SLE population (p = 4. 11x10 -4 ).
Acknowledgements YJC acknowledges the  European Research Council  ( GA 309449 ; and  786142 -E-T1IFNs ),  ERA-NET Neuron  ( MR/M501803/1 ) and a state subsidy managed by the  National Research Agency (France)  under the  "Investments for the Future" program  bearing the reference  ANR-10-IAHU-01. AB acknowledges the  French ANR program   14-CE14-0026 LUMUGENE  and the " Jeune Chercheur " support from  Hospices Civils de Lyon. TAB acknowledges the  National Institute for Health Research (NIHR)  ( NIHR   Transitional Research Fellowship ,  Dr Tracy Briggs ,  TRF-2016-09-002 ). The views expressed are those of the author(s) and not necessarily those of the  NHS , the  NIHR  or the Department of Health. J. M. E. is funded by a postdoctoral research fellowship from the  Health Education England Genomics Education Programme (HEE GEP).
The feeding station was designed to avoid feces and urine dropping into the food jar and also to avoid food spillage. Analysis first proceeded through a MATLAB-developed software, filtering out high frequency oscillations in food jar weights and collecting the start time, end time and weight amplitude of the stepwise reductions in food jar weights. Analyzed data were exported to Excel for further analysis. Meals were defined by stepwise reductions in food jar weight of at least 0. 02 g in amplitude (i. e. , twice the balance precision) and 30 s in duration.
Indirect Calorimetry and Locomotor Activity Indirect calorimetry recording was performed with an indirect open-circuit calorimeter Oxylet Physiocage system (Panlab, Cornella, Spain), equipped with a laser absorption O 2 sensor, an infrared technology CO 2 sensor, and weight transducers for the continuous assessment of the food and water consumption. Room air flowed through each chamber at a rate of 450 mL/min. The O 2 and CO 2 levels were measured during 3 min sampling periods every 30 min and data were analyzed with the METABOLISM software. Oxygen consumption (VO 2 ), and carbon dioxide production (VCO 2 ) were expressed in mL/min/kg 0. 75. The respiratory quotient (RQ) was determined by the ratio V CO2 /V O2 and energy expenditure (EE) was calculated according to the formula EE (kcal/day/kg 0.
Image Acquisition and Data Processing Fluorescence images were acquired by confocal microscopy (Zeiss LSM 710). In double-labeling experiments, images were sequentially acquired. All images were further processed in Adobe Photoshop 6. 0, with only contrast and brightness adjustments. Photomicrographs labeling were taken on a Nikon Eclipse E600 light microscope using a DXM 1200 Camera equipped with ACT-1 software. The microscope was set at a specific illumination level, as was the camera exposure time. c-Fos-positive nuclei were counted using NIH ImageJ software. Labeling and counting were performed on every fourth coronal section throughout the rostro ¬¬-¬caudal axis from the brainstem to the hypothalamus.
Acknowledgments: The authors acknowledge  Elise Courvoisier  and  Coraline Airault  for their support in qPCR experiments ( Plateforme Analyse et Valorisation de la Biodiversité, Marseille ), as well as  Elena Dossi  for her help for the slice preparations and EtBr uptake assays. The authors are grateful to  Alain Tonetto  ( Plateforme de Recherche Analytique Technologique et Imagerie, Marseille ) for his assistance in confocal imaging. We thank  Jérôme Espejo  for his precious contribution to animal's maintenance and management ( Animalerie Saint Jérôme ). We would also like to express our gratitude to  Ayush Srivastava  for the English editing of the revised manuscript.
Acknowledgements The authors would like to thank  R. Wyatt  ( University of Exeter ),  I. Kusmartseva  and  M. Padilla  ( University of Florida ) for the preparation and imaging of the pancreas sections. Funding RM is funded by grants from  The Leona M. and Harry B. Helmsley Charitable Trust  (Helmsley no. 1901-03689 ),  JDRF  ( 2-SRA-2016-164-Q-R ), the  Fondation Francophone pour la Recherche  sur le D i a b è t e , t h e F o n d a t i o n p o u r l a R e c h e r c h e M é d i c a l e ( EQU20193007831 ) and the  Agence Nationale de la Recherche  ( ANR-19-CE15-0014-01 ). SJR is funded by grants from  Diabetes UK  ( 16/ 0005480 ),  JDRF  ( 5-CDA-2014-221-A-N; 2-SRA-2018-474-S-B ) and the  MRC  ( MR/P010695/1 ). RM and SJR are funded and by the  Innovative Medicines Initiative 2 Joint Undertaking  under grant agreements  115797  and  945268  (INNODIA and  INNODIA HARVEST).
3D gait analysis (3DGA) is used as part of clinical care for children with CP; gait data are typically collected for both barefoot walking and walking with the orthosis. This provides information about the effectiveness of the orthosis in positively changing the gait pattern and supports clinicians' decisions around changes in orthotic prescription  [7]. 3DGA measurements are accurate, but interpretation of this complex information can be challenging  [8, 9]. Previous researchers have attempted to summarize 3DGA data into a single measure of "quality" of the gait pattern. Schwartz and colleagues  [10]  developed the Gait Deviation Index (GDI), which quantifies the differences in kinematics between a patient's gait cycle and a reference dataset. Because the GDI is a single measure of the gait deviation between a child with CP and typically developing (TD) children, this index does not show the levels at which the gait deviations are present.
This method can process large quantities of data without exclusively relying on a priori knowledge of predictive variables  [11]. For example, previous studies were able to predict which children with CP would benefit from rectus femoris muscle transfer surgery or hamstring-lengthening surgery in crouch gait with 88% and 73% accuracy, respectively, by combining 3DGA and musculoskeletal modeling  [12, 13]. More recently, researchers  [14]  developed a statistical orthosis selection model using a supervised learning model, i. e. , a random forest algorithm. Model development was carried out using retrospective data from gait analysis performed on children with CP who wore one of five orthotic designs bilaterally. Their model recommended a specific AFO design or no AFO depending on the GDI improvement seen in their training dataset.
Participants Kinematic data from children with CP collected as part of standard clinical care at the AUT Motion Analysis Laboratory, Auckland, New Zealand, were retrospectively analyzed. The AUT Motion Analysis Laboratory used a Qualisys motion capture system (Qualisys AB, Göteborg, Sweden) with nine cameras and a modified Helen Hayes marker set  [15]. Children were asked to walk (1) barefoot and (2) with their prescribed orthosis at their self-selected speed on a 10 m runway, with mean gait data derived from a minimum of five representative gait trials for each condition. Inter and intra-rater reliability was routinely assessed in the clinic, with marker placement accuracy quality checked prior to data collection for each child in each condition. A total of 98 children with CP, GMFCS levels I to III (age: 10.
3 ± 3. 3 years), were included in the analysis. Selected children had already been prescribed either unilateral orthoses (n = 27), bilateral orthoses (n = 56), or no orthoses (n = 15) based on the decisions of a multi-disciplinary team in consultation with the child and their guardian  [1]. Exclusion criteria were any history of orthopedic surgery or botulinum toxin A injections within the past 6 months. We also included barefoot walking kinematic data from 25 typically developing (TD) children as a control reference dataset. Given that many children with CP have asymmetric involvement, we decided to treat the two lower limbs as independent variables, including the uninvolved unilateral limb that was not prescribed an orthosis.
Model Input The model input data consisted of the gait variable scores (GVSs) extracted from 3DGA performed walking barefoot  [8]. The GVS were determined as follows: for each joint, we calculated the RMS difference across time for one gait cycle (51 values, representing a gait cycle from 0 to 100% every 2%) between a child with CP's gait cycle and the average gait cycle from our TD children reference dataset. For the hip, knee, and ankle range of motion in the sagittal plane, we differentiated between the GVS for flexion (when the patient displayed higher flexion angles than the TD reference dataset) and the GVS for extension (when the patient displayed higher extension angles than the TD reference dataset). Foot progression angle was also differentiated in the same way (a score for greater outward angles and a score for greater inward angles).
Data-Driven Model The model was developed in Python version 3. 4 using an in-house code and the SOMPY library  [16]. The model consisted of a self-organizing map (SOM), which is an unsupervised data-driven model that represents high-dimensional data in a two-dimensional (2D) map representation by grouping participants together based on shared features/similar input (Figure  1 )  [17]. SOM is a type of artificial neural network that uses a neighborhood function to preserve topological properties of the input space, giving an insight into the topographical relationships of data  [17]. SOMs differ from other artificial neural networks (NN) as SOMs apply competitive learning as opposed to error-correction learning NN. The concept of the SOM is very similar to other clustering algorithms in the sense that it computes distance between reference elements (centroid for k-means, each node of the grid in the SOM case), but reduces the requirement for a priori knowledge and allows for dynamic updates while maintaining previously discovered patterns.
It is important to note that such an approach allows the best neighbors to be found for a given sample. In the present study, after the SOM allocated each limb into a unit on the grid based on neighborhood similarities (Figure  1 ), a k-means clustering algorithm was used to partition the resulting SOM map into distinct clusters, in which each observation belongs to the cluster with the nearest mean  [18]. A sensitivity analysis was performed to determine the number of clusters needed for this dataset.
Relationships between AFO Types and Gait Pattern In children with CP, 3DGA data collected when wearing their prescribed orthosis were also evaluated. In New Zealand, all orthoses are custom-molded to the shape of the foot with posting as needed to align the forefoot and hind foot neutrally and set the floor to shank angle to neutral. The posting on the Ankle Foot Orthosis (AFO is on the outside of the AFO, i. e. , the forefoot and hind foot are set as close to neutral as possible and the floor to shank angle is also set as close to neutral as Figure  1. The self-organizing map (SOM) grouped together participants with similar inputs. The map was a 10 × 10 square units; each limb was localized in one of the 100 square units. A square unit could have multiple limbs (the number in each unit is displayed in the square unit).