dataesr/scientific-paragraphs-categorization huggingface dataset:
The participants were selected by purposing sampling and the data collection was made with the help of brochures and picture cards and a problem tree exercise. Additionally, qualitative in-depth semistructured interviews were conducted with 30 key persons and target individuals (health care workers, teachers, school leaders, and persons working in areas with a risk for transmission), to gain insight into personal knowledge and experiences with hepatitis and general insights of the community. The interviews explored knowledge, attitudes, behaviors around viral hepatitis, sexual and other practices that may lead to transmission. In FG the anthropologist collected data in 12 sites, covering all ethnic communities (Figure  1 ). He used an ethnographic approach with 20 participatory observations, as well as 52 in-depth face-toface semi-structured interviews with respondents aged ≥18, about their own experiences, and 27 interviews with informants with expertise in different aspects of the topic (teachers, HCWs, community spokespersons or chiefs, traditional practitioners, and SWs), all recruited through informants or acquaintances or during participatory observations.
Acknowledgments We would like to thank the community leaders' acknowledgement, approval, and support for this study. Without them this study and the  MaHeVi  project would not have been possible. Furthermore we especially thank the key informants and members of the focus groups, the local health care workers on both sides of the river:  Medical Mission in Suriname ,  Centres Délocalisés de Prévention et de Soins in FG. We thank the  Ink Link  for the development of posters and other visual communication materials. Last but not least we are grateful for all involved in interviewing and administration:  Paul Brousse ,  Valentin Dufit ,  Leila Adriouch ,  Rikesh Bisnajak ,  Mariette Jordaan ,  Deborah Hordijk ,  Anfernee Neus , and  Edinio Flink.
Handgrip strength The handgrip strength was measured using a Camry Electronic Hand Dynamometer (Model: EH101). The participant sat with the elbow exed at a 90° angle with the forearm and the wrist in a neutral position (Fig. 2 ). Three maximum efforts were performed with the dominant hand with a rest period of 30 seconds between each trial. Participants were asked to press as fast and hard as possible. The best performance was retained for data analysis  [36] [37] [38].
Flexibility To assess the exibility of the trunk and the hamstrings, the ngertip-to-oor (FTF) test was used (Fig. 3 ). This valid, reliable, and practical test is commonly used in low back pain prevention  [39]. For this test, the vineyard workers were standing on a 20 cm high box with their feet together  [40]. Then, participants bent forward as far as possible while keeping their knees straight. The distance (cm) between the oor and the middle nger was recorded as an index of trunk forward bending exibility  [40]. Participants performed two trials in a row and the best one was used for data analysis  [39, 40]. The exibility of the upper extremities was evaluated with the back scratch test. This test gives an overall measure of the range of motion of the shoulders  [41]. Participants were standing with one hand behind their back, stretching as far as possible up the spinal column.
Heart Rate Each vineyard worker was equipped with a heart rate (HR) monitor Geonaute Onmove 500, placed on the wrist. Due to the nature of the working task, participants placed the monitor on the arm they wanted. The HR was continuously recorded during the entire duration of the experimentation. For the statistical analysis, HR data was analyzed in original units (beats per minute [bpm])  [42]. Heart recordings were visually inspected for data quality. Participants were asked to rest during 3min before the start of the warm-up session. The mean value of the third min of rest was saved for T0  [43]  and was considered as the resting heart rate. For T1, the mean value of the 15min of the warm-up was retained  [43]. For T2, T3, and T4, the mean value of the past hour was calculated. At the workplace, the Relative Heart Rate (RHR) is frequently chosen as an indicator of physical workload  [44].
Strengths and limitations The cluster-randomized design of this study could be considered as a strength since participants were randomly allocated to the four groups and were blinded to their allocation. This design allows intervention at the gang level, thus reducing some of the risks of biases associated with non-randomized studies. This study further followed the SPIRIT guidelines, registered the trial in ClinicalTrials. gov (NCT05425693), and followed a protocol study published in 2022  [27]. As described in the protocol study, the team members who supervised warm-up sessions to workers graduated with a master's degree in sport science and specialized in MSDs prevention among vineyard workers. In addition, the research team member responsible for the data analysis was blinded to the participant's allocation.
Acknowledgments Authors are grateful to the vineyard-workers and to the direction of  Château Calon-Ségur ,  Château Camensac ,  Chateau Clarke ,  Château Haut-Bages Liberal ,  Château Lynch-Bages ,  Château Palmer ,  Château Phélan Ségur , and the  Vignobles de Larose  for their active participation in this study.
The first step to train the ANN is to generate a dataset. One important aspect that we considered to build it is the fabrication constraint that imposes a maximum aspect ratio of 2 between the height and width of the resonators. The dataset consists of 810 simulations generated by uniformly sweeping the four parameters indicated in Figure  1. The period P ranges from 250 nm and 510 nm. For the height H, the range spans from 100 nm to 425 nm. In order to ensure that the ratios H/W and H/(P-W) remain less than 2, the width W varies between H/2 and (P-H)/2, and therefore, the height H must obey the inequality H/2 < (P-H)/2. Additionally, the parameter S ranges from 0 to 0. 8, with intervals of 0. 1. The simulations were performed on an Intel® Xeon® W-2125 Processor operating at 4. 0 GHz and allocating 8 threads, taking a total of 45 minutes and 48 seconds.
The description of the 2D Python MEEP Finite-Difference Time-Domain (FDTD) simulations are described in the supplementary information. Subsequently, a feedforward ANN surrogate model was trained to forecast the reflection spectral response, considering the resonator's geometry as input. In this study, we leverage the surrogate model for two distinct purposes. Firstly, it enables rapid estimation of the reflection spectrum, achieving significant computational speed-ups compared to full wave FDTD simulations. Secondly, the surrogate model is crucial in the inverse design process, playing a critical role in computing optimal solutions. As a result, the surrogate model's performance is of paramount importance, as it must deliver exceptional precision to provide the most accurate approximations possible.
The dataset is divided into two subsets: 769 simulations to update the weights of the ANN and 41 simulations for validation. The optimizer of choice was Adam with Mean Square Error (MSE) loss and batch size of 512  42   Figure  3  illustrates the generalization capacity of the surrogate model. Despite being trained on a relatively small dataset, the model is able to reproduce the spectra associated to the geometries that lie beyond the training dataset. This accomplishment can be attributed to the integration of wavelength as an input variable and the meticulous training methodology employed to mitigate overfitting. The demonstrated efficiency of this approach underscores the potential for constructing dependable surrogate models even when confronted with restricted training data.
QF := f 0 ω (2) The optimization scenario illustrated in Figure  4  depicts the inverse design process. The desired spectrum is represented by the cyan curve in the top left corner. In this stage, we employ two complementary approaches for inverse design. The first approach involves a straightforward search through a dataset, represented by the arrow pointing to the right of the target spectrum. For each simulation in the dataset, we calculate the MSE loss between the target spectrum and the simulated spectrum. By doing so, we can identify the design that produces the lowest MSE. Although this design is the best within the dataset, it fails to accurately match the desired response, as indicated in Figure  5. To address this limitation, we utilize a fully differentiable surrogate model and perform gradient optimization to finely adjust the parameters.
As it can be seen in Figure  5  running the back-propgation yields interesting designs. However, still this approach is not able to produce vivid colors along all the visible regime. Further the resonances are mainly associated with higher order modes appearing at shorter wavelengths (see last column) that allows for non-pure color. The optimized solution, along with the best design found within the dataset, are both stored for later comparison. The second complementary approach, indicated by the downward arrow from the target spectrum in Figure  4 , involves the utilization of a MVANN to generate robust solutions for the inverse design problem. A MVANN is essentially an ANN capable of producing multiple solutions based on a single input. In the context of our study, the adoption of the MVANN proves highly advantageous as it effectively addresses the challenge of multiple parameter responses corresponding to a given objective target  33.
To ensure reasonable accuracy, we choose to employ a model that yields 20 outputs. The detailed architecture and training procedure of the MVANN are provided in the supplementary information section. After acquiring the set of 20 distinct 4/10 Figure  4. Optimization diagram for the inverse design methodology, where the target reflection spectrum is specified in the cyan curve. To achieve the desired outcome, two complementary paths are identified. The first path involves exploiting the dataset scan and searching for the best design that will be subjected to a back-propagation optimization to fine-tune the parameters, as indicated in the top blocks. On the other hand, the second path involves passing the target through the MVANN to obtain 20 designs. These designs are then validated using the surrogate model, and the solution that presented the least MSE compared to the target is chosen.
Finally, a back-propagation optimization is performed on that design. Ultimately, the best solution among the four selected designs from the two paths is chosen. Further details can be found in the text. designs, a validation process is conducted using the surrogate model. Among these designs, the one that exhibits the lowest MSE loss when compared to the target is initially stored for future comparison. To further improve the results, this best design is subjected to back-propagation optimization. By employing a combination of the dataset search method indicated by the top blocks in Figure  4  and the MVANN approach, followed by back-propagation indicated by the bottom blocks in Figure  4 , we successfully identify four robust designs. This entire process, encompassing both approaches, takes approximately 100 seconds to complete.
A field profile for one of the optimized geometries is presented in the supplementary information section. Additionally, a numerical comparison between FDTD calculations and COMSOL has been included to verify our results. Furthermore, we have 5/10 Figure  5. Results from the dataset search using a Lorentzian lineshape in frequency as target for the dataset search for the wavelengths of 420 nm 480 nm, 500 nm, 530 nm, 650 nm 650 nm with ω = 0. 01 from left to right, top to bottom. The metric used to evaluate the results was the MSE (L) between the target spectrum and the closest one in the dataset, while the geometric parameters (P, H, W and S) were described in Figure  1. Blue and red curves represent the best design without and with back-propagation optimization on best design found in the dataset.
A comparison of sharp resonances and broader resonances can be found in the supplementary information section. conducted a full-wave simulation with finite structure to identify the number of required periods to retrive the results of a single unitcell simulation with periodic boundary conditions (for more details, please refer to the supplementary section). To the best of our knowledge, the optimized metasurface designs yield the most vivid color filter reported so far in the literature  8, [34] [35] [36]. It is worth mentioning that the entire optimization process took 2 hours, 47 minutes, and 41 seconds. Among this time, 1 hour, 29 minutes, and 57 seconds were dedicated to simulations, 1 hour, 25 minutes, and 9 seconds were allocated for back-propagation optimization, 8 minutes and 30 seconds were spent on predictions using both the surrogate model and the MVANN, and 330 milliseconds were utilized for searching the best design within the dataset.
Conclusion In conclusion, our research presents a novel DL methodology for optimizing resilient designs of vivid color filter metasurfaces. By utilizing a surrogate model constructed from a dataset of only 810 simulations, our approach demonstrates exceptional efficiency in the optimization process. The numerical tool developed in this study enables the cost-effective fabrication of structural color filters by exploring a wide range of narrow line shapes that exhibit high-quality resonances, aligning with desired spectral reflection responses. Notably, our methodology expands the color gamut beyond the conventional RGB colors, offering unprecedented versatility in color generation. Furthermore, our DL approach successfully respects fabrication constraints, ensuring practical feasibility. The achievements of our research significantly contribute to the field of optical device design.
Synthesis We summarized data related to matching and concordance assessment in both narrative and tabular formats. We reported percentages for categorical variables and medians with ranges for continuous variables. For the methodological quality of guidelines, we calculated the scaled domain scores according to the AGREE II instrument manual, where scores range from 0 to 100%. The six domains of the instrument are as follows: scope and purpose, stakeholder involvement, rigour of development, clarity of presentation, applicability and editorial independence.
D) Statistical analyses The data were anonymized by the Saujon spa team and were entered into Excel software. Statistical populations were described by means of Tukey's boxes showing medians, 25 and 75 percentiles. The means of the scores of each questionnaire were compared between D1 and D18 by paired two-tailed Student's t tests, with an alpha probability lower than 0. 01. Similarly, correlation investigations with Pearson correlations were conducted between the scores of each questionnaire taken in pairs on D1 and D18.
Enrichment analyses were conducted with missMethyl package and the KEGG database. Our results show that CA was associated with psychosis (Composite: OR = 3. 09; p = <0. 001; abuse: OR = 2. 95; p<0. 001; neglect: OR = 3. 25; p=<0. 001). None of the CpG sites signi cantly mediated the adversity-psychosis association after Bonferroni correction (p<8. 1x10-8). However, 28, 34 and 29 differentially methylated probes associated with 21, 27, 20 genes passed a less stringent discovery threshold (p<5x10-5) for composite, abuse, and neglect respectively, with a lack of overlap between abuse and neglect. These included genes previously associated with psychosis in EWAS studies, such as PANK1, SPEG TBKBP1, TSNARE1 or H2R. Downstream gene ontology analyses did not reveal any biological pathways that survived false discovery rate correction.
Study Design And Participants The sample was drawn from the EU-GEI (European Network of National Schizophrenia Networks Studying Gene-Environment Interactions) multi-centre study. The EU-GEI study is a multicentre incidence and case-sibling-control study of genetic and environmental determinants of psychotic disorders  16. The current study was based on participants from work the 'Incidence and rst-episode' 16 work package of the EU-GEI study. For the analyses presented in this paper, only participants who had complete data on DNAm and CA using the CTQ were included, with no restriction on site or ethnicity. Patients and controls were recruited from 17 different sites. Cases and controls were not related. During the case ascertainment period, participants, aged 18-64 years, were invited to take part in the study if they presented to mental healthcare services with a rst presentation of psychosis.
Socio-demographic Characteristics Socio-demographic data were collected using the Medical Research Council (MRC) Socio-demographic Schedule modi ed version (Mallett et al. , 2002), and supplemented with additional information from medical records on educational attainment, employment, marital and living status. Ethnicity was selfascribed using categories employed by the 2001 UK Census (http://www. ons. gov. uk/ons/guidemethod/census/census-2001/index. html). Childhood Trauma Questionnaire (Ctq) CTQ  19, 20  was used to measure the exposure to past experiences of abuse (sexual, physical, and emotional), neglect (physical and emotional) and to calculate a composite measure of cumulative exposure of the ve types of experiences. This self-reported instrument enquires about such types of events occurring prior to the age of 18, with answers ranging from 'never true', through 'rarely true', 'sometimes true', 'often true', to 'very true'.
This yields a total score and ve sub-scores for physical abuse, emotional abuse, sexual abuse, physical neglect, and emotional neglect. The reliability and validity of the CTQ have been demonstrated previously  19. For this study, data were dichotomised for each childhood adversity domain (0 = 'absent' and 1 = 'present'), based on the moderate to severe cut-off score from the CTQ Manual 20 as follows: ≥13 for emotional abuse; ≥10 for physical abuse; ≥8 for sexual abuse; ≥15 for emotional neglect; and ≥ 10 for physical neglect. We used a composite category involving the cumulative exposure to any form of the ve adversities (score ranging 0-5), to abuse subtypes (score ranging 03), and to neglect subtypes (score ranging 0-2).
Genome-wide Dna Methylation Analysis Illumina EPIC chip processing Genomic DNA was extracted using standard protocols  21, 22. Whole-blood genomic DNA diluted with water (50 ng/µl) was treated with sodium bisul te using the EpiTect® Bisul te Kit from QIAGEN® following the manufacturer's protocol. DNAm was assessed using the Illumina In nium HumanMethylationEPIC BeadChip kit (Illumina, Inc. , San Diego, California) and quanti ed on an Illumina HiScan System (Illumina, Inc. ). The level of methylation is expressed as a 'beta' value (β-value), ranging from 0 (no cytosine methylation) to 1 (complete cytosine methylation). 23. Data pre-processing and downstream statistical analyses were conducted using R version 3. 6. 0  24.
05 in 1% of the samples identi ed by the lter function within the watermelon R package  25  were removed (N = 1); (viii) bead count per CpG was performed; (ix) testing case control differences in terms of epigenetic age, cell composition, smoking status; comparing the signal between blood and buccal samples. At a later stage, using a previous EPIC quality control pipeline https://github. com/PGC-PTSD-EWAS/EPIC_QC, it was detected that an additional 3330 CpG probes corresponded to probes located in SNPs, and these were removed prior to nal analyses. DNAm was explored in 614719 probes at EWAS level after quality control procedures. From the initial EU-GEI sample that had data on DNAm, 49 participants were excluded because of missing or unreliable data on CTQ, 1 participant because of missing smoking score, and another 1 due to missing information on age.
Gene Ontology Pathway Analysis Illumina UCSC gene annotation was used to create a test gene list from the mediating DMPs (P < 5e-05). Gene ontology and pathway analysis were performed, using the missMethyl package  32  , which takes into account the variable number of EPIC probes associated with each gene. Downstream KEGG pathways analyses were also performed to provide further insights into potential relevant biological processes associated with the signi cant DMPs (P < 5e-05), according to previous studies  33. Independent pathways with FDR < 0. 05 were considered to be signi cantly associated with CA subtypes, and pathways with p < 0. 05 were also reported.
Gene Ontology Enrichment Analyses The genes associated with differentially methylated probes that passed the nominally signi cant threshold (P < 5e-5) (for composite adversity, abuse and neglect) were identi ed. Exploratory downstream enrichment analyses were performed in those genes using missMethyl package with the KEGG data set. The top 10 ranked biological pathways based on DMPs located in the gene body are summarised in the SM (S. Tables 8, 9 and 10 respectively). None of the pathways survived the FDR adjustment. (paste Table  3  here).
INTRODUCTION Ce document a pour but de résumer sous forme graphique les principaux éléments descriptifs obtenus à partir de la base de données du Registre de Dialyse Péritonéale et hémodialyse à domicile (RDPLF) en France métropolitaine en 2022. Aucune discussion n'est prévue dans ce rapport dont le but est avant tout de fournir une documentation de base aux lecteurs pour illustrer leurs propres travaux ou présentations. Les diapositives originales peuvent être obtenues auprès du secrétariat du RDPLF. Ces tableaux et graphiques peuvent être librement recopiés sous réserve de citer le présent document avec son DOI. Les originaux peuvent aussi être obtenus sous forme de fichiers PowerPoint auprès du secrétariat du RDPLF.
Le Réseau Epidemiologie Information en Nephrologie (REIN) est exhaustif pour la France, il est possible de connaître l'exhaustivité des patients de dialyse péritonéale (DP) inclus dans le RDPLF en comparant le nombre de patients traités au 31 décembre dans chaque base de données. D'après le rapport 2019 de REIN [1] 97,8 % des patients français sont inclus en 2020. Au 1er Janvier 2022 le nombre de patients traités était identique dans les deux registres (communication personnelle Cécile Couchoud et Mathilde Lassalle). La base de données du RDPLF est structurée en plusieurs modules décrits par ailleurs [2,3]. Différents tableaux et graphiques inclus dans ce texte ont été obtenus à partir du module d'analyse statistiques en libre accès du RDPLF [4]. DESCRIPTION DE LA POPULATION Nombre de patients prévalents en 2022 (traités au moins 1 jour) : 3955 patients.
e. , radar system) with a synchronized acquisition, and (4) a Vicon Nexus software to capture data, manage data acquisition, manage the calibration of the cameras, visualize, and record the results. Twenty-six healthy subjects, aged between 22 and 60, volunteered to participate in this study, and gave their informed consent. Tey were asked to perform three TUG trials at slow, normal, and fast speed, leading to a total of 9 trials per subject. Within the framework of our experiment, the Doppler radar system was placed into the backrest of the chair. It was connected to an electronic circuit in order to amplify its output and flter out noise. Te amplifcation circuit transforms the radar output from millivolts (mV) to volts (V). Te fltration consists of a bandpass flter with two cutof frequencies of 5 Hz and 500 Hz.