dataesr/scientific-paragraphs-categorization huggingface dataset:
The sex of the babies was given by the parents and was consistent with their civil status. We collected no data on race nor ethnicity. Recordings were conducted inside each family's private home during several 48-h sessions at 15 days, 1. 5 months, 2. 5 months, and 3. 5 months following birth. Because of variations in parental availability and the well-known decrease in the frequency of infant crying with age 1 , some babies could not be recorded at each of the four ages (Supplementary Table  1 ). We thus obtained recordings from 17 infants at 15 days, 24 infants at 1. 5 and 2. 5 months, and 12 infants at 3. 5 months. We used automated sound recorders equipped with omnidirectional microphones (Song-Meter SM4 Acoustic Recorder©, Wildlife Acoustics, Inc. , Concord, MA, USA). The acoustic recorder was placed in the baby's room at a height of 1.
In 75% of the cases, the action that stopped the crying matched the parental assessment of the cry cause (for example, parents indicated "hunger," and feeding their child did indeed stop the cry). When the parent's assumed cause did not match the action that stopped the cry, we coded the cause of the cry based on that action and not on the parental assessment. We made this choice to increase the reliability and objectivity of labeling cry causes. Extraction of cries. Cries were selected and edited following a six-step process (see Fig. 1 ). In step 1, we manually extracted audio cry sequences from each 48-h recording session using Praat software version 6. 1. 16  47. Each cry sequence corresponded to a single questionnaire entry completed by the parents, and thus to a single crying bout and cause.
0. 0  48. We looked for segments that were a minimum 50 ms in duration, separated from other cries by at least 100 ms. More details on the segmentation algorithm are available in the documentation of the soundgen function segment, and the R code for segmentation is provided in the supplements (scripts. zip, file prep. R). In step 5, we removed all non-cry vocalizations produced by infants (e. g. , unvoiced grunts, coughing), thus retaining only cries, operationalized as at least partly voiced episodes of vocalizing that were different from steady background noise and that satisfied the following four conditions: (1) minimum 20% voiced; (2) median pitch > 150 Hz; (3) duration > 250 ms; and (4) Wiener entropy < 0. 6. These inclusion criteria yielded the lowest rate of false positives (2% of non-cry vocalizations) in a randomly drawn sample of 100 selected and 100 rejected cry syllables.
First, recordings were taken in real-life environments, wherein background noise was inevitable and needed to be removed to ensure high-quality audio for robust acoustic analysis. Cry sequences were thus manually cut to remove background noise (steps 2-3), resulting in spliced cry sequences. Second, by measuring acoustic parameters over an entire sequence, we would have introduced a greater degree of acoustic variation, making it difficult to assess the contributions of individual acoustic parameters. The cries collected for this study are now available in the form of a new cry databank (EnesBabyCries1, Supplementary Data). This bank contains two sets of recordings (EnesBabyCries1A and EnesBabyCries1B). EnesBabyCries1A contains the sequences of recordings from step 3 (i. e. , after removal of noisy parts).
EnesBabyCries1B contains the cries from step 6 (after segmentation and selection of the cries; these cries are the ones used in the present research). This databank is anonymized. No clues to the identity of the baby have been retained in the recordings. The metadata accompanying the cries are: (1) the age of the recorded baby; (2) the baby's biological sex; (3) the cause of the cry as stated by the parent; (4) the parental action that ended the cry. We obtained parental permission to make these recordings and information public. The databank EnesBabyCries1 is available here: https://osf. io/ru7na/. Acoustic analysis. The acoustic structure of each of the 39201 cries was represented by ten key acoustic variables. The choice of these variables was based on a large body of research in animal communication and the human voice sciences, implicating these acoustic variables as markers to speaker identity, speaker physical traits, and/or motivation and emotion  9, [49] [50] [51].
The model was: mvbind(duration, entropy, HNR, jitter, pitch_iqr, pitch_median, roughness, shimmer, spectralCentroid, voiced) ~cause *age + (cause * age|baby) + (1|sequence). We used a Random Forest classifier, randomForest package in R, version 4. 7-1  54  , with the same 10 acoustic predictors and with stratification per cause (i. e. , without over-representing more common causes) to predict cry cause from acoustics using machine learning. Two-thirds of the available observations were used for training, and one-third for testing. Crucially, we trained our models on cries from one set of recording sessions and tested them not only on different cries but also on cries taken from different recording sessions. Because cries from the same recording session may share not only the same internal baby's state but also background noises and other confounds, training, and testing on different sessions ensures that the model learns the categories of interest (causes of crying, babies' identities, etc.
) rather than session-specific acoustic signatures. The Random Forest algorithm is stochastic, and the training sample was chosen at random; therefore, we ran each model 100 times with different training and test sets and summarized its performance by median accuracy and 95% coverage intervals. Considering the large dataset of cries (39201 cries), it was essential to develop an effective method to visualize similarities across cries based on various categories such as individual babies, age groups, or cry causes. The crux of this problem is obtaining a reliable and intuitive measure of similarity across cries. To solve this issue, we present a method based on dynamic time warping Fig. 1  The six steps of the cry selection and editing process. A Recordings were conducted inside each family's private home during several 48-h sessions at 15 days, 1.
The EnesBabyCries1 database contains the recordings after step 3 (EnesBabyCries1A) and after step 5 (EnesBabyCries1B). The database is available online at https://osf. io/ru7na/ (see Supplementary Data for a description of the database). (DTW) of frame-by-frame acoustic features (fundamental frequency, harmonicity, etc. ). To reduce the dimensionality of the resulting distance matrix, we use the state-of-the-art algorithm of Uniform Manifold Approximation and Projection (UMAP) implemented in R with the uwot package version 0. 1. 11  55. UMAP is conceptually similar to traditional unsupervised methods for dimensionality reduction, such as principal components, multidimensional scaling, and more recent techniques like tSNE, but UMAP uses rigorous mathematical methods of topological analysis, preserves global structure and withincluster distances, and scales well to very large datasets, as recently demonstrated in complex comparative analyses of animal vocal acoustics  56.
In the subsequent test session, listeners indicated whether each cry was due to hunger, discomfort, or isolation in a three-alternative forced-choice task. In Experiment 2, listeners were explicitly trained on the cause of a subset of cries. For each cry in this training phase, listeners indicated if the cry was due to hunger, discomfort, or isolation in a three-alternative forced-choice task, after which they received explicit feedback regarding the actual cause of the cry. They were informed that their goal was to use this explicit feedback to learn to recognize the cause of baby cries in a forthcoming test. The final 30 trials, which included cries the participants had not heard during the training phase, were considered the testing phase. Unaggregated, trial-by-trial responses in both psychoacoustic experiments were analyzed using Bayesian mixed models fit with the brms package  53.
2b ). However, accurate estimation of actual age from a single cry of any given baby was low. Indeed, a Random Forest classifier (randomForest R package  54  ; see Methods), using the same ten acoustic predictors, could recognize a baby's age group with a moderate accuracy of about 40% [35, 43], with an Odds Ratio to This lack of robust age signatures may be explained by strong inter-individual variation across infants. As a next step, we therefore precisely analyzed individual signatures in cries and their development in early ontogeny. Acoustic encoding of baby identity. We observed individual cry signatures across infants. A Random Forest model using the same ten acoustic predictors but normalized across all observations instead of within each baby achieved an accuracy of ~28% (95% CI [23, 31]) when discriminating among all 24 babies from a single cry and across all ages.
Further corroborating this result, we observed some clustering by baby identity at each age in an unsupervised UMAP projection, a two-dimensional representation of the acoustic similarity between the analyzed cries (Fig. 3a ). Our results also show that individual signatures of human infants remain relatively stable throughout the first months of life. Indeed, models trained on cries recorded at a given age can still identify individual babies if tested with their cries recorded at other ages (Fig. 3b ). Nevertheless, we observed developmental drift in cry signatures: the greater the difference in baby age between training and test datasets, the less reliable the model's recognition of infant identity (Fig. 3b ). This indicates that cry signatures, while specific to each baby and relatively stable throughout early ontogeny, nevertheless change to some degree with age.
The dataset includes 15540 cries of six babies who were recorded at all four ages. Columns show the training-age group, and rows show the testing-age group. The greater the difference in age between training and testing, the less accurately the model can recognize the identity of a given baby. c Scatterplots showing the drift in individual babies' acoustic characteristics over the first 4 months of life. While acoustic characteristics change with age, they do so consistently for each baby (one data point = one baby). Not all babies were recorded at all four ages, so sample sizes vary across age groups, shown as n within each scatterplot. The strength of pairwise relationships between the same vocal parameter at two different ages is shown in each scatterplot as the r correlation coefficient.
Acknowledgements Funding was provided by the  ANR  ( BABYCRY  project  n°ANR-19-CE28-0014-01 ,  France ), the  Lyon IDEX Fellowship  (D. R. ), the  Labex CeLyA (France) , the  University of Saint-Etienne (France) , the  Centre National de la Recherche Scientifique (CNRS, France) , the  Institut National de la Santé et de la Recherche Médicale (Inserm, France) , the  Swedish Research Council (Vetenskapsrådet ; grant  2020-06352  to A. A. ) and the  Institut universitaire de France  (F. L. , N. M. , and D. R. ). The funders had no role in study design, data collection, analysis, decision to publish, or preparation of the manuscript. We thank the babies, their parents, and all the other adults who participated in this study.
However, it is legitimate to use a single parameter, as the mean % tail DNA, as well as to apply statistical parametric tests to highlight differences between datasets, only when the underlying distribution of the variable is normal  (Møller and Loft, 2014) , which is not necessarily the case. Relevant scenarios can be identified, where considering the full distribution of % tail DNA, instead of its average value only, becomes very important, for instance: i) cells exposed to any agent that, for its specific nature (e. g. , insolubility) and low concentration, is expected not to affect the whole population; ii) cells exposed to low and inhomogeneously distributed ionizing radiation dose. These scenarios share as a common aspect the inhomogeneity of the action of the potentially genotoxic agent, leaving a large fraction of unexposed cells, as well as the possibility of different sub-groups of cells being subject to highly varying exposure levels.
5 μm in water), which means that only cells in close contact with a particle ("hit" by the particle) can receive radiation dose, and thus possibly be subject to radiation-induced DNA damage  (Mentana et al. , 2023) ; iii) particles might not be homogeneously distributed and create aggregates, which also adds to a final inhomogeneity in their action. Overall, in the light of the above discussion, when applying the comet assay to investigate the genotoxic potential of radioactive particles, a better insight could be gained considering the full data distribution, in terms of number of cells scored with different % tail DNA values in the exposed population. In this study, we therefore present a novel analysis of the full dataset collected in  (Lamartiniere et al. , 2022) , applying the single cell gel electrophoresis technique to human lung cells exposed to hydrogenated or tritiated stainless steel and cement particles.
Materials and methods Full information on the experimental protocol for application of the comet assay on BEAS-2B cells is given in  (Lamartiniere et al. , 2022). Cells were stained with a 0. 1 mg/ml propidium iodide solution. The slides were examined with fluorescent microscopes (Carl ZEISS Axio Imager A2, Bayern, Germany or Olympus BX40; Rungis, France) at 400× final magnification. The digital pictures were analyzed using a CCD ANDOR Zyla camera (Andor Bioimaging, Nottingham, UK) and the determination of the % tail DNA was performed using the Komet Software 6. 0 or 7. 0 (Andor Bioimaging, Nottingham, UK). All the details related to the data processing using the Komet Software are given in the provider webpage at https://andor. oxinst. com/. More detailed information, following recommendations to report on the comet assay as given in  (Møller et al.
3 to 6 independent experiments were performed, depending on the particle type. In each experiment, for each exposure condition, the % tail DNA was scored for a total number of 100 nucleoids. In  (Lamartiniere et al. , 2022)  results were presented as average % tail DNA ± SEM (mean value ± standard error of the mean) among all experiments for each exposure condition. The data treatment presented in this work is explained in all its steps in Fig. 2  with a subset of conditions chosen as examples. The starting point is the full distribution of the number of nucleoids scored with different % tail DNA values for each exposure condition. Histograms with a 5% tail DNA bin width are obtained from such distributions. Cells are considered as undamaged if the corresponding % tail DNA value is found below 10%.
Analysis of the full distribution in comet data The full dataset from the comet assay applied to BEAS-2B cells in different exposure conditions in  (Lamartiniere et al. , 2022)  was made available for the analysis. Experiments were performed for cells exposed for 2 and 24 h to: hydrogenated or tritiated (1 kBq/μg) SS316L stainless steel particles, at different concentrations in the range 0 (control) -100 μg/ml, and HTO at the activity level of 100 kBq/ml; hydrogenated or tritiated (0. 4 kBq/μg) cement particles, at different concentrations in the range 0 (control) -200 μg/ml, and HTO at the activity level of 8 kBq/ml. for the control conditions (0 μg/ml particle concentration) to those for cells after 24 h administration of 50 μg/ml of hydrogenated (Fig. 2a ) and tritiated (Fig. 2b ) stainless steel particles.
% tail DNA is expected to be sharply peaked at 0 (intact nuclei). However, cells with damaged DNA can also be found in the control, either as a consequence of replicative stress or of the assay protocol itself. For the analysis presented in this work, only cells with % tail DNA ≥ 10% have been considered as having an appreciable level of DNA damage. This threshold should be adapted to the cell population under study: in our dataset, obtained with BEAS-2B cells, it leads on average to a ~ 80% of cells being undamaged in the control condition, which is deemed reasonable for a healthy and proliferating cell population. A lower value could also be used: however, if the threshold for an appreciable level of DNA damage is set to 5% in our data, only ~60% of BEAS-2B cells are undamaged in the control condition, which seems too low.
More generally, the comet data treatment proposed in this work seems to be more sensitive than standard analysis procedures, and might give more correct estimations of genotoxicity. Its robustness should be tested, applying the same analysis to different datasets. Of note, the common visual scoring of comets, i. e. sorting comets into classes with increasing tail intensities, also gives data that, in principle, can be analyzed with this approach, though a lower sensitivity is expected (because of fewer intensity classes with respect to the number of 5% tail DNA bins as here proposed). We expect that, in spite of a higher difficulty of data treatment with respect to standard averaging, important insight on genotoxicity could be gained with this approach both for exposure to agents with an inhomogeneous action, including radioactive particles (also see  (Baiocco et al.
Acknowledgements The authors would like to thank  M. Payet  ( CEA, IRFM, 13108 Saint Paul lez Durance, France ) and  D. Slomberg  ( Aix-Marseille Univ ,  CNRS ,  IRD ,  INRAE ,  Coll. De France , CEREGE, 13545 Aix-en-Provence, France).
Statistical analyses Datasets were analyzed using frequencies or percentages for categorical variables, and mean ± standard deviation (SD) for quantitative variables. Analyses were based on the nonparametric test, Wilcoxon test, or Mann-Whitney test according to the dependence between samples. p < 0. 05 was considered significant. With regard to nociception assessment, we first considered each group independently. We compared the mean values of the HWT for the left TMJ at different times (day 0 vs. day 2, day 0 vs. day 7, day 0 vs. day 14, day 0 vs. day 21, and day 0 vs. day 30), and compared the mean values of the HWT for the TMJ on each side of the rat. Then, we compared the mean values of the HWT for the TMJ on the same side in both groups at different times. With respect to PET assessment, we compared the mean SUV of each group to that of a control group of rats who did not receive an intra-articular injection.
Study design and participants The STRIPPS study (no. NCT03532321) was a 1-year follow-up multicenter, prospective study investigating the individual and organizational factors that predict occupational exposure to blood among 730 HCWs in Paris university hospitals. 10  The study was carried out in 4 general-care hospitals between February 2018 and July 2019 and included nurses, nursing assistants, midwives, and physicians as participants. We included both permanent and fixedterm contract HCWs with work contracts lasting at least 1 year, matching the survey duration. We excluded those with contracts <1 year, as well as external personnel (eg, nursing float pool). The sample size was a convenience sample based on a previous study. 9  Data were collected from these HCWs by randomly selecting 8 wards per participating hospital from those that employed at least 30 HCWs.
Data collection Data were collected in all participating individuals, every 4 months for a total follow-up period of 1 year, by 2 different interviewers. The collection times were designated as t0, t1, t2, and t3, corresponding to the first collection during the inclusion visit and follow-up visits at 4, 8, and 12 months, respectively. For the first collection (t0), dates and times of visits were randomly assigned for each ward. For subsequent collections, individual appointments were made considering different work shifts (day and night) to ensure a comprehensive representation of HCW schedules. Data were collected through questionnaire-based interviews at both the ward and individual levels. Potential participants were informed of the study through an information letter and gave verbal consent at the beginning of each interview.
Statistical analyses First, we conducted a descriptive analysis to summarize the data collected at the individual and ward levels. We assessed changes in individual-level variables over time using 2-sided student tests for continuous variables and χ 2 tests for categorical variables. Next, we identified factors associated with AEBs in participating HCWs. Bivariate analyses were conducted on all individual-level variables to determine which variables were relevant for inclusion in the multivariate analysis. Variables with P ≤. 20 were considered for inclusion in the model. Based on these results, we performed a multivariate analysis with longitudinal data to investigate the association between risk factors and the outcome variable. We used a linear mixed-effects model, including the hospital as a random effect to account for the unobserved heterogeneity across hospitals.
The longitudinal design provided a comprehensive view of the occupational blood exposure effects over time, allowing for trend analyses. Moreover, a combination of diverse metrics, including individual and organizational factors, as well as 2 levels of data (ward level and HCW level), enriched the    understanding of factors influencing HCW health outcomes. We used validated scales for measuring stress, fatigue, overinvestment, social support, and human resources data (absenteeism, turnover) to facilitate the identification of contributing factors. Finally, the study was conducted in 2019, before the COVID-19 pandemic, which significantly altered healthcare organization worldwide. 39  Hence, our findings over the 12-month period were likely unaffected by the effects of the pandemic, an essential consideration when interpreting the results.
This study had several limitations. Data collection regarding physicians' work characteristics, particularly work hours and shifts, was imprecise. Self-report bias and accident underreporting might have influenced our findings, with potential recall or social desirability bias. Accident underreporting could result from HCW hesitation to report incidents of exposure due to fear of retaliation or reluctance to admit mistakes. This issue may hinder supervisors from providing necessary support and resources, making it difficult to track and prevent future incidents of exposure. We were not able to measure the underreporting of AEBs. In addition, our findings did not establish a direct link between stress, fatigue, and AEBs, possibly due to the presence of confounding factors. Measurements were taken at a single time point, possibly inducing measurement bias.
Although AEB data were collected over a longer 4-month period, conclusive evidence of a relationship between stress and fatigue and infectious risks did not emerge. The study findings have important implications for healthcare organizations, clinicians, and future research in HCW safety. By understanding factors associated with AEBs, facilities can develop targeted interventions addressing risks related to occupation, age, work schedule, shift rotation, and supervisor support. These efforts could include additional training, safer work schedules, and promoting a supportive organizational culture. Healthcare facilities should consider the risks of outsourcing staff and should ensure that HCWs are trained and familiar with infection control protocols to minimize AEBs and other adverse outcomes.
Acknowledgments. We acknowledge the support and funding provided by  Assistance Publique -Hôpitaux de Paris (Département de la Recherche Clinique et du Développement). We also express our gratitude to all the HCWs who participated in this study. Financial support. This study was funded by a grant from  Programme de Recherche sur la Performance du Système de Soins -PREPS 2016 (Ministère de la Santé).