dataesr/scientific-paragraphs-categorization huggingface dataset:
Some limitations of our study need to be acknowledged before interpreting the results. First, parents selfreported paternity leave uptake and post-partum depression, which could induce response error; however, we found the prevalence of paternity leave and post-partum depression in the ELFE data to be consistent with other national estimates. 7, 8 Second, fathers who take paternity leave are likely to differ from those who do not, making the use of observational data to test the health consequences potentially complicated. The ELFE cohort study collected data regarding participants' personal, family, and child's characteristics, which we accounted for in our statistical analyses. Although we were able to statistically balance paternity leave groups on many observed characteristics, some potential confounders were unmeasured, such as fathers' history of depression, physical health, attitudes, and personality traits, as well as engagement in the mother's prenatal care and birth preparation during pregnancy.
29 These characteristics might partly explain the relationship between paternity leave and parental depression during the postnatal period, although we believe that by controlling for other characteristics, including fathers' demographic and socioeconomic features, we accounted for part of the underlying variability in study outcomes. Lastly, non-respondents differed in terms of certain socio economic characteristics, which might lead to bias. However, because depression is most frequent among men who experience socioeconomic disadvantage, the role of paternity leave might be stronger than we report. Our study had several strengths. First, data were from a large nationally representative cohort of families in which the father was eligible for paternity leave based in France, which includes participants from different social backgrounds.
Acknowledgments The ELFE survey is a joint project between the  French Institute for Demographic Studies (INED)  and the  National Institute of Health and Medical Research , in partnership with the French blood transfusion service (Etablissement français du sang); Santé publique France; the  National Institute for Statistics and Economic Studies ; the  Direction générale de la santé (Ministry of Health and Social Affairs) ; the  Direction générale de la prévention des risques (Ministry for the Environment) ; the  Direction de la recherche, des études, de l'évaluation et des statistiques (Ministry of Health and Social Affairs) ; the  Département des études, de la prospective et des statistiques (Ministry of Culture ); and the Caisse nationale des allocations familiales; with the support of the  Ministry of Higher Education and Research and the Institut national de la jeunesse et de l'éducation populaire.
Via the RECONAI ELFE receives a government grant managed by the  National Research Agency  under the  Investissements d'avenir programme  ( ANR-11-EQPX-0038  and  ANR-19-COHO-0001 ). KMB  receives European Research Council  funding (ERC Consolidator  101001420 ). The authors would also like to acknowledge the help of  Thierry Siméon  from the  INED  for his work on developing survey weights for the ELFE cohort.
The latter, however, is challenging because of the absence of the sequence under study in the databases usually employed in mass spectrometry (MS)-based proteomics  [35]. In order to investigate the link between light chain sequence and aggregation behaviour, we have developed a complete de novo protein sequencing workflow based on a combination of top-down  [36]  and bottom-up proteomics with specific data analysis for patient-derived light chains  [37]. The light chains investigated in this study were derived from 10 patients presenting light chains in their urine (2 isotype lambda and 8 isotype kappa) and represent a sub-set of a larger collection from a patient cohort at the University Hospital Düsseldorf, which has already been subjected to an initial biochemical and biophysical characterisation  [13].
LC sequence characterisation and protease identification All LC sequences were determined using a dedicated mass spectrometry workflow described in  [37]. This workflow is based on the combination of bottom-up and top-down proteomics experiments with appropriate data analysis. It is important to note that the sample numbers are not identical between the present manuscript and the manuscript with the sequencing results  [37] , but the latter contains a correspondence table. Briefly, light chain samples were solubilised in 8 M urea, then reduced (5 mM TCEP, 30 min) and alkylated (10 mM iodoacetamide, 30 min in the dark). After urea dilution, digestion was carried out for 3 h at 37°C (1:20) and was stopped by adding 5% FA. Peptides were desalted on Sep-Pak C18 SPE cartridge. Digests were analysed in LC-MS/MS on a Q-Exactive Plus.
Sedimentation velocity (SV) experiments were performed in a XL-A ProteomeLab ultracentrifuge equipped with absorbance optics. Experiments were conducted in An-60-Ti-rotor at 20°C using a rotor speed of 60,000 rpm. Solutions of 35 μM of IgLC samples were investigated and the radial scans were acquired at a wavelength which ensures an optimal resolution, at an absorbance of ca. 1 OD. Scans have a radial step size of 0. 002 cm, i. e. radial resolution of 500 data points/cm. The sedimentation boundaries were analysed with SEDFIT (version 16p35)  [73]. The c(s) model for sedimentation coefficient distributions as solutions to the Lamm equation implemented in SEDFIT was applied to the data. This model provides an approximation of a weight-average frictional ratio f/f 0 for all particles of a distribution and based on which sedimentation coefficients can be translated into diffusion coefficients and/or molecular mass.
An extension to the two-dimensional sedimentation coefficient distribution can be achieved with the c(s, f/f 0 ) model, providing weight-average frictional ratios for each sedimentation coefficient of the distribution with colour-coded signal intensities  [74]. During aggregation, we observed a loss of signal due to sedimentation of large assemblies during acceleration of the centrifuge to final speed for SV analysis. Absorbance was first measured at 3000 rpm to allow for quantification of the loss of material to 60,000 rpm. The wavelength for detection was varied to optimise the signal for SV analyses. The final c(s) curves were adjusted to have signals representative for the concentration left at the first time point of data acquisition. The AUC experiments were conducted as single measurements, if the run did not indicate any disturbances.
The final urea concentrations were 0, 0. 67, 1. 34, 2. 01, 2. 68, 3. 35, 4. 02, 4. 69 and 5. 36 M and the buffer concentration was in all cases 30 mM Tris buffer. From our initial tests, we found that the technical replicates of these experiments with the Panta instrument were very reproducible, with temperatures of unfolding and onset of turbidity usually within 0. 1-0. 2°C for technical replicates. We therefore did not routinely perform technical replicates of these experiments. The data reported in Table  1  stems from individual experiments. The data was visualised as the ratio of the intrinsic fluorescence emission intensity at 350 nm over the intensity at 330 nm. For the thermal unfolding at the different protein concentrations, the melting temperature (T m ) and the temperature of aggregation onset (T agg ), as well as the cumulant radius, were automatically determined by the instrument software.
For fragmentation, electron transfer dissociation with 10 ms of reaction injection time and a supplemental higherenergy collisional dissociation with normalised collision energy (NCE) of 10% (EThcD) was used. All data were processed with ProSightPC v4. 1 (Thermo Scientific) and Proteome Discoverer v2. 4 (Thermo Scientific) using the ProSightPD 3. 0 node. Spectral data were first deconvoluted and deisotoped using the cRAWler algorithm. Spectra were then searched using a two-tier search tree with searches against the corresponding LC sequences. The search 1 consists of a ProSight Absolute Mass search with MS1 tolerance of 10 ppm and MS2 tolerance of 5 ppm. The search 2 is a ProSight Biomarker search with MS1 tolerance of 10 ppm and MS2 tolerance of 5 ppm. Identifications with E-values better than 1e-10 (-log (E-value) = 10) were considered as confident hits.
Modelling and data and sequence analysis The sequences of the IgLC samples of this study were parsed with IMGT, the international ImMunoGeneTics information system and were aligned in order to investigate the amino acid changes between the germline sequences and the sequences under study. The sequences were also analysed using different online bioinformatic tools (ZipperDB, Tango, Pasta, CamSol), which have been developed to predict the aggregation propensity/solubility of proteins based on their amino acid sequences. All the light chain sequences were modelled using the Lyra software  [79] , and insertions and deletions were refined using the modeller  [80]  software using default parameters. The structure renderings were created using PyMol  [81]. All the substrates for Cathepsin B and D were retrieved from the Merops database  [82].
Since limited available experimental evidence was present in the Merops database on the other proteases, they were removed from further analyses. The 4 residues preceding and following the proteolytic sites were used to build two position-specific weight matrices (PSSMs) for Cathepsin B and D with the Biopython  [83]  motifs packages using a pseudocount of 5 and the background distribution of the human proteome. All the residues in the light chain sequences were scored using the PSSMs, and at each position a cleavage logodds score was defined as the largest of the log-odds scores obtained for the two PSSMs. We then tested if the cleavage sites potentially produced by the proteases support the peptides experimentally identified by mass spectrometry. To do so, we identified the regions in the sequences proximal to the N-and C-terminal residues from the experimental peptide and tested if the cleavage log-odds scores within such region was significantly larger than outside.
The proximal region was defined as the union of all residues that are at most two residues before a peptide's N terminal and at most one residue after it, plus all residues that are at most one residue before a peptide's C-terminal and at most two residues after it, in the corresponding light chain sequence. By including the residues near the observed peptide termini, we account for the potential effect of exopeptidases, and for imprecisions in the data used for training the PSSMs. The cleavage scores in the proximal regions were then compared to the scores outside of it by performing a onetailed Mann-Whitney U test, and in all cases, the results were under the significance threshold of 0. 05. The plots visualising these results were generated with the Python Matplotlib library. In order to investigate the correlations between the experimental quantities measured of the IgLCs, numerical values were needed for all measurements.
4), d T agg /d log(c) (pH 7. 4), cumulant radius (pH 7. 4, 70°C), dimerization by AUC, fraction refold by DSC, ΔG (37°C) and digestability by trypsin. Model training was done using a grid search with 4-fold cross validation using negative mean squared error as the loss function. A separate validation set was not created because of the low number of data points. This analysis was performed in Python 3. 7 using packages Numpy, Pandas, SKLearn, Matplotlib and Seaborn. of the κ light chains of our study. The framework regions (FR) are marked with blue, the complementarity determining regions (CDRs) with red. application of the c(s, f/f0)-model determined by sedimentation velocity experiments at 60. 000 rpm. We find that the form factors of the dimers differ within the sample set. The dimers can appear very globular e.
Statistical analysis Statistical analysis was performed using the SPSS 23. 0 ® software package [(Armonk, NY, USA)]. The quantitative variables were expressed by average and standard deviations. The categorical variables were expressed by median, maximum and minimum values, or frequency. The normality of the tested parameters was assessed by a Kolmogorov-Smirnov test. Univariate analysis (independent t-tests) and χ 2 tests were used to compare quantitative and qualitative data of the arthrofibrosis and control ACLr groups. Results were considered significant at p < 0. 05. The effect of each risk factor was tested separately as categorical variable (under or upper the cutoff)  (24). The prediction for the group with arthrofibrosis was assessed using binary ascendant logistic Wald's regression (inclusion probability ≤ 0.
Statistical Analysis Procedure We used SPSS-25 software for statistical treatment. We used p-values at 0. 05 to indicate statistical significance. To investigate developmental trajectory of depression from early childhood to high school in HIP children, we analyzed the correlations between anamnestic variables and depression in the three classes independently but also all retrospective anamnestic variables from early childhood as a single group. Thus, the Spearman's ρ test (ρ) for non-parametric correlations was used. The Pearson's chi-square test (χ 2 ) was used to compare qualitative variables in the groups. To compare differences of quantitative means between groups (primary, middle and high school) at the MDI-C scale, we used the analysis of variance (ANOVA) test. To compare the MDI-C profile of depression in two groups (depressed and not depressed), we used the Kruskal-Wallis H because the Kolmogorov-Smirnov Z test for normality showed a p < 0.
Conclusions It is important to emphasize that depressive symptoms in HIP children could be linked to endogenous or exogenous social adjustment factors in childhood. It may also be associated with challenges in identifying and acknowledging their high abilities, as well as difficulties in developmental psychomotor skills and or psychological disorders which can worsen the intensity of depression. Studies in the literature were mainly focused on unicentric cross-sectional studies on children and/or adolescents, with bias of different inclusion criteria, and not always based on an IQ test or examining the possible associated neurodevelopmental disorders or involved recruiting children from clinical out-patients and from specific school programs for HIP children existing in the USA. The main strength of this current study is the provision of a database at a national level with in-depth, anamnestic data and the administration of a standardized assessment to evaluate all the dimensions of the depression symptomatology.
Population France has 67 million inhabitants, 14. 6 million of whom were under the age of 18 years on January 1, 2019 (14 million in mainland France), according to the INSEE (Institut National de la Statistique et des Etudes Economiques, National Institute for Statistics and Economic Studies). This study concerned individuals under 18 years of age on December 31, 2018, living in mainland France with national health insurance coverage and at least one healthcare reimbursement from health insurance in 2018. Children of the same sex resulting from a multiple birth (twins, triplets, etc. ) could not be identified unambiguously in the database and were excluded. For the sake of homogeneity, we also excluded children of different sexes resulting from a multiple birth. In addition, we excluded children whose month of birth could not be identified and those who died before the end of the 1-year study period.
The list of LTDs is published and activated by decree after expertise from the HAS (Haute Autorit e de Sant e, French National Authority for Health)  [23, 24]. A guide is also sent to the patient and information is available on the HAS website  [23]. The data in the SNDS are linked to data concerning public and private hospital stays via the national hospital discharge database through a pseudonymized identification number. Hospital stays can include short-stay hospitalizations (SSHs) and stays in psychiatric hospitals or rehabilitation facilities and home hospitalization. Normal stays in hospitals/maternity units for childbirth without the need for neonatal care were excluded. Information is also collected for pediatric reanimation, neonatology, and ED visit, with or without a subsequent SSH.
Data for the SSH primary diagnosis are expressed as a rate for the population of individuals with at least one SSH by age, sex, and LTD status. SAS software (version 7. 13, SAS Institute Inc, Cary, NC, USA) was used for statistical analysis. R software  (3. 4. 3)  was used for the analyses by d epartement. The CNAM has permanent access to the pubic SNDS database by decree and with authorization from the Commission nationale de l'informatique et des libert es (CNIL; the French data protection authority).
Chronic long-term diseases Information about diseases in children is generally obtained through parent-reported data, including a broad definition of symptoms and common chronic diseases, such as in primary-care diagnosis databases and school surveys  [17, 25, 26]. A study of individuals aged 17 years or younger seen at US primary-care clinics between 2016 and 2018 reported that the six most frequently identified chronic conditions were obesity/being overweight (36. 7%), eczema (15. 8%), asthma (12. 7%), food allergies (4. 7%), attention deficit-hyperactivity disorder (4. 1%), and hypertension (4. 1%)  [26]. A definition for special healthcare needs was proposed in the United States: children with a chronic physical, developmental, behavioral, or emotional condition requiring health and related services of a type or amount beyond that required by children generally  [27].
In surgical procedures, a frequent obstacle is the constrained room for maneuvering surgical instruments within the designated surgical area. To overcome this challenge, the creation of innovative, compact surgical tools necessitates a thorough examination of the available workspace. Traditionally, computed tomography scans (CT scans) are employed for space assessment. CT scans utilize a blend of X-ray technology and computer processing to generate intricate visuals of the body's internal structures, encompassing bones, muscles, organs, and blood vessels. In comparison to conventional X-rays, CT scans offer a more comprehensive and precise dataset. One common medical procedure is total mesorectal excision. It is used when there is rectal cancer or inflammation that requires removing part or all of the rectum.
To conduct our analysis, we compiled data from a cohort of patients who had undergone colon surgery. The volume of the abdominal workspace is notably influenced by various factors, including the patient's age, gender, weight, and physiological attributes. To exemplify our research, we opted to focus on data obtained from a specific male patient, aged 65, with a height of 1. 66 m and a weight of 55 kg. This particular patient was selected due to his unique physiological characteristics, which yielded the most confined workspace volume for the flexible surgical stapler We then proceeded to acquire CT scan data for the general surgical site, encompassing three anatomical planes: Axial, Coronal, and Sagittal. These data points were spaced at 3. 33-millimeter intervals for each plane. A skilled surgeon delineated the dimensional parameters of the surgical site on the data, denoted by red and blue markings in Figure  2 , respectively, employing Vue PACS software, (version 12.
1. 6. 1005, CARESTREAM Health, New York, USA). Initially, these data underwent analysis to approximate the maximum workspace volume available for the flexible stapler. Subsequently, we pinpointed the most confined cross-sectional area within the surgical site, which came out to be 2613 mm 2 in the axial plane, 8036 mm 2 in the coronal plane and 6875 mm 2 in the sagittal plane. Using this dataset, a three-dimensional model was crafted to simulate the placement of the stapler and its laparoscopic insertion. This simulation enables us to ascertain the optimal size of the tool that can be effectively utilized within the most restricted available space. By taking these factors into account, we can develop a surgical stapler capable of adeptly and precisely navigating challenging anatomical spaces during minimally invasive procedures.
Motion Simulations In this section, we suggest conducting a simulation study using the SIROPA library  [25]  within the MAPLE software (Version 2023, Maplesoft, Waterloo, Canada). The primary aim of this investigation is to examine the behavioural characteristics of the mechanism. It is crucial to emphasize that, at this juncture, specific model parameters are undefined, requiring a thorough selection of materials and a comprehensive comprehension of the system's workspace. To visually illustrate the bending motion of the mechanism in response to different force magnitudes, we refer to Figure  6 , where red curve depict the central spine and blue lines depict the tendons/cable. This figure provides a visual representation of the bending motion, as it responds to force magnitudes ranging from f 1f 2 = 0 + to 17 N.
To carry out this optimization and determine the beam's curvature, we harnessed the capabilities of the MAPLE software (Version 2023, Maplesoft, Waterloo, Canada) optimization package, utilizing cable forces and spring tensions as crucial inputs. When we introduce friction into the system, a notable change occurs in the tension levels of the two cables within each segment. This alteration has a direct impact on the curvature, which is visually evident in Figure  7  (right). It is essential to acknowledge that our model, while informative, maintains a simplification in which it assumes a consistent curvature angle across all segments. j 1 o 1 i 1 i 2 o 2 j 2 j 3 o 3 i 3 i 4 o 4 j 4 j 1 o 1 i 1 i 2 o 2 j 2 j 3 o 3 i 3 i 4 o 4 j 4 Implementing a tensegrity mechanism for the creation of a flexible stapler in the confined and intricate workspace of a patient's body brings about a reduction in the cross-sectional dimensions of each individual segment.
Funding: This research is funded by the  Indian Council of Medical Research, New Delhi, India  under the " Senior Research Fellowship " awarded to  Mr. Dhruva Rajesh Khanzode  (File no. 5/3/ 8/46/ITR-F/2022). This research was supported by the project  New smart and adaptive robotics solutions for personalized minimally invasive surgery in cancer treatment -ATHENA, funded by  European Union-NextGenerationEU and Romanian Government , under  National Recovery and Resilience Plan for Romania , contract no. 760072/23. 05. 2023 , code  CF 16/15. 11. 2022 , through the  Romanian Ministry of Research, Innovation and Digitalization , within Component 9, investment  I8.
Methods This study used the Covidom© cohort, a prospective cohort of ambulatory COVID-19 patients in the greater Paris area, who used the Covidom© telemedicine solution for home monitoring  [3]. The Covidom© solution consists of a user-friendly web application for patients and a regional control center system that manages alerts. Patients register with a physician's help and complete daily self-administered monitoring questionnaires for 30 days after symptom onset. Patient answers are classified into four categories by an automated algorithm: No alert, Orange alert, Red alert, and Grey alert. The Covidom regional control center operates from 8 a. m. to 8 p. m. , 7 days a week, and handles alerts generated by patient answers. Trained responders and a supervising physician does not cover public sharing of data for unknown purposes.