dataesr/scientific-paragraphs-categorization huggingface dataset:
1. 1) defined on training dataset and then applied on test dataset values. The F1 score (harmonic mean of recall and precision) was used as model accuracy measure due to unbalanced data. The survival probability rendered by the linear SVM machine learning model with the 9 genes significantly associated with 28-day mortality was tested in a multivariate logistic regression analysis with confounding clinical factors found to be significantly associated to 28-day mortality in the univariate analysis (i. e. age, Charlson and SOFA scores). The SAPS II score was excluded from the analysis as age and severity were captured by age and SOFA score. As the number of events of interest in the combined train and test cohorts was 52; the inclusion of three confounding factors in such multivariate analysis appeared appropriate.
Association with 28-day mortality We further investigated the association between IPP markers and the 28-day mortality. A logistic regression univariate analysis was performed on the training dataset composed of 216 patients, we identified 9 genes that were significantly associated with 28-day mortality (Table  2 ), namely ADGRE3, C3AR1, CD177, CD74, CIITA, IL10, IL1R2, OAS2 and TDRD9. Among those, ADGRE3, CD74 and CIITA were significantly downregulated in non-survivors when compared to survivors, while all other markers were upregulated in non-survivors. Among cellular parameters, only CD3 T cells count was significantly associated with 28-day mortality in a logistic regression model (p=0. 031). Descriptive boxplots of the 9 genes that compose the panel along with clinical scores and age regarding with 28-day survival status are presented in Figure  2.
Consistent with the existing literature, age, SOFA, SAPS II and Charlson scores were significantly associated with mortality in univariate analysis. This 9-mRNA signature was then used in five different machine learning models to predict the 28-day survival status in the training dataset. We selected the best performing machine learning model based on the area under the precision-recall curve (Table  3 ). We found that the linear support vector machine learning model presented the best AUPRC (0. 431) and second best AUROC (0. 744). We then tested the tuned models on the test dataset (93 patients) to confirm results obtained in training dataset. The AUPRC calculated was 0. 431 while the AUROC reached 0. 764 (Table  3 ). The ROC curves generated on the training and test datasets are shown in Figure  3.
Overall, results from the test dataset confirm the robustness of those obtained on the training dataset to predict mortality. The 9-mRNA signature was further tested in a logistic regression multivariate analysis with the following confounding factors: age, SOFA score and Charlson score. The signature remained significantly associated with 28-day mortality with an odds ratio per interquartile range of 3. 78 (Table  4 ). In order to test the added value of a 9-mRNA signature, we then examined individual 28-day mortality prediction performance of each mRNA in the signature set as well as age, SOFA and SAPS II scores, and T cell count using logistic regression models. We found that the 9-mRNA signature presented the best AUPRC (and AUROC) when compared to all other individual parameters. Using the Youden threshold, we calculated the sensitivity, specificity, positive predictive value, negative predictive value and F1 score for each parameter (Table  5 ).
(A) Area Under the Receiver Operating Characteristics curve (AUC) calculated on the training dataset of 216 patients and the independent test set of 93 critically ill COVID-19 patients using the 9-mRNA panel along with the age at their admission in ICU. The 95% confidence interval (grey) is calculated using bootstrap with 1000 repetitions. (B) Probability of 28-day mortality from linear SVM model trained on 9-gene panel (left) and 9-gene panel combined with age (right) on the entire cohort (n=309) of patients and healthy donors (n=49). p-values were generated using a Mann-Whitney-Wilcoxon test between survivors and non-survivors. Tardiveau et al. 10. 3389/fimmu. 2022. 1022750.
Data collection and questionnaire The questionnaire was developed in English and translated into Chichewa, the local language in southern Malawi spoken by the majority of the population. It included questions on knowledge and awareness of cervical cancer, risk factors and prevention, whether the women had ever received cervical cancer screening, and if so details about the screening (including date, location, reasons) and treatment if they received any. Cervical cancer screening was confirmed both by oral history of the interviewee (self-reported) and by the presence of a health passport that contained this information (verified by health passport). The questionnaire was contextadapted based on two models to measure knowledge and awareness of cervical cancer, the 'Cervical Cancer Awareness Measure Toolkit' and the 'Health Belief Model Scale for Cervical Cancer'  [35, 36].
Data analysis Data were analysed using Stata version 14. 1 (Stata Corp, Texas, USA). Screening and treatment coverage were calculated stratified by age, weighted for survey design. Proportion of women reporting facilitators and barriers to screening were also reported. Chi square and designbased F tests were used to assess relationship between participant characteristics and screening status in the different geographical areas. Interviews were weighted based on the inverse of probability of selection separately by stratum. In the pooled analysis artificial clusters using the administrative boundaries of the wards were introduced in Blantyre due to the different sampling methodology between the three strata.
Data collection Data for the meta-analysis were extracted from literature published between 1980 and January 2021, collected from Web of Science, Google Scholar, and Vol:. (1234567890)  Scopus database. Details of the keywords used are shown in Online Resource 1. We selected only studies that spectrally selectively attenuated solar radiation to measure the photodegradation of surface leaf litter in terrestrial ecosystems. We excluded any studies that did not separate single wavebands or that did not allow the effect of single wavebands to be calculated due to the lack of a control treatment. Since one of our aims was to understand the effects of spectral composition on mass loss under ambient sunlight, all studies employing supplemental radiation were excluded. Moreover, as we aimed to examine the correlation between photodegradation rate and litter traits, we retained only studies employing leaf litter from a single species, while we excluded studies using litter mixtures.
More details about study selection are found Online Resource 1. We considered dark treatments to be only treatments blocking more than 95% of the solar spectrum. We extracted data concerning litter mass loss and initial litter traits. Where data were not presented in tables, we extracted them directly from the figures using WebPlotDigitizer 4. 2  (Rohatgi 2019). We retained a total of 30 articles which produced a total of 325 datapoints. The list of retained studies is shown in Online Resource 2. Several papers included comparisons of multiple plant species (A list of the 114 species included is shown in Online Resource 3), field sites and spectral treatments. The effects of five spectral regions were calculated: 1) UV radiation (280-400 nm); 2) UV-B radiation (280-315 nm); 3) UV-A radiation (315-400 nm); 4) blue light (400-490 nm) and 5) the full spectrum of visible light and UV radiation.
Details about the categorisation of these data and complementary information are shown in Online Resource 4 and in the dataset  (Pieristè et al. 2021). The climate at each study site was defined according to the updated Koppen-Geiger climate classification using the map provided by  Beck et al. 2018 , which divides the globe into five major climatic zones further separated into subdivisions based on temperature and precipitation. Details of the climate classification are shown in Online Resource 5. In order to estimate global-scale quantities of C released from surface litter by photodegradation, we extracted data from the Soil Respiration Data Base (SRDB) (Bond-Lamberty and Thomson 2010) on the annual litter carbon flux from each of the biomes corresponding to the location of studies in the meta-analysis.
Statistical analysis The effect sizes, expressed as log response ratio (lnRR) of mass loss, were computed with the function 'escalc' from the package 'metafor' (ver. 2. 1-0)  (Viechtbauer 2019) , which uses sample sizes, standard deviations and means of the original studies and presents bias correction for small sampling. For each study, we selected only the final collection date to avoid the potential issue of time-dependent effect sizes. We used a three-level mixed effect model using study ID and effect size ID as random factors as described in  (Assink and Wibbelink 2016) , with categorical variables "Ecosystem", "Decay", "Climate", "Habit", "Life form" and "Latitude" as fixed factors. The use of multilevel modelling in meta-analyses is a robust method for dealing with the problem of dependent effect sizes  (Assink and Wibbelink 2016; Cheung 2014; Noortgate et al.
2013). We used this method to test the overall effect of exclusion of each spectral region and the effect of the categorical variables with the function rma. mv() from the package 'metafor' (ver. 2. 1-0)  (Viechtbauer 2019) , employing the Knapp and Hartung correction method for random meta-analyses  (Assink and Wibbelink 2016; Knapp and Hartung 2003). From these models we obtained Vol. : (0123456789) the estimated average lnRR which we used to calculate the percentage change to better interpret the magnitude effect with the formula from Pustejovsky 2018. Following the same multi-level approach, we analysed the correlation between the rate of photodegradation (effect size = lnRR) and the initial litter traits, climatic variables during the study period and absolute latitude, using them as continuous moderators in the model.
The litter traits considered were those initial traits reported in each study: carbon content (C); nitrogen content (N); carbon to nitrogen ratio (C:N); lignin content; lignin to nitrogen ratio (Lig:N) and specific leaf area (SLA). The climatic variables used were average (Tmean), minimum (Tmin) and maximum temperature (Tmax), and cumulative precipitation (PP), over the study period. These data were obtained from the NASA Langley Research Center POWER Project funded through the NASA Earth Science Directorate Applied Science Program (Sparks 2018) using the "nasapower" R package version 3. 0. 1  (Sparks 2020 ). The paucity of published studies from certain climates, ecosystems, latitude, etc. has the potential to introduce bias into the meta-analysis. To assess the risk of bias, we explored the dataset of retained studies to identify over-and under-represented categories.
Bias analysis and bias exploration We did not find bias in any of the datasets used to calculate the effect of each spectral region: full-spectrum (F 1,74 = 0. 265, p-value = 0. 608); blue light (F 1,49 = 0. 336, p-value = 0. 565); UV-A radiation (F 1,32 = 0. 226, p-value = 0. 638); UV-B radiation (F 1,43 = 0. 03, p-value = 0. 857) and UV radiation (F 1,116 = 0. 345, p-value = 0. 558). UV radiation was the most studied spectral region (20 studies, n = 118), while blue light (5 studies, n = 51) and UV-A radiation (5 studies, n = 34) were under-represented in our dataset (Fig. 1a ). Most studies were carried out at latitudes between 30° and 50°North and South, while data from high latitudes were lacking (Fig. 1b ). Grassland and shrubland ecosystems were more studied than woodlands and open areas (Fig.
However, unlike  Day and Bliss (2019) , Vol:. (  1234567890 ) we did not identify UV-A radiation as the most effective spectral region driving carbon emission through photodegradation across all studies in our meta-analysis. This could be due to the fact that most studies testing the effect of UV-A radiation were located in cool moist temperate broadleaf forest biomes at high latitudes characterised by low UV-A radiation  (Aphalo et al. 2012; Grifoni et al. 2008). We estimated annual carbon flux from litter attributable to photodegradation driven by different spectral regions, applying the percentage contributed by photodegradation to the gross annual carbon flux lost from litter in each biome obtained from the SRDB dataset (Bond-Lamberty and Thomson 2010). This produced an estimate of photodegradation driven by the full spectrum of sunlight of up to 5-61 g C m -2 per year according to biome type (Table  4 ), while blue light would potentially be responsible for 4-55 g C m -2 per year according to biome type (Table  4 ).
Introduction: Adolescent idiopathic scoliosis (AIS) surgeries are major paediatric procedures requiring multidisciplinary management. Enhanced recovery after surgery (ERAS) programs, with proven benefits in adults, remain poorly developed in paediatrics. The main objective of this Before/After study was to evaluate the impact of an ERAS program implementation for AIS on length of stay (LOS) and postoperative recovery. Methods: The ERAS protocol included intrathecal morphine, standardised multimodal analgesia and multidisciplinary measures for early recovery. Retrospective data from adolescents operated between 2015 and 2017 (''Before ERAS'' group) were compared with data from patients benefiting from the ERAS program (''After ERAS'' group). Patients treated for neuromuscular scoliosis were not included.
Population The ''Before ERAS'' group included the patients undergoing surgical scoliosis correction between January 2015 and January 2017. The patients of the ''After ERAS'' group were taken in charge after the implementation of the ERAS Protocol, from April 2018 to January 2020. Data of all the paediatric patients during these two study periods underwent a retrospective chart review (''Before ERAS'' group) or a retrospective analysis of prospectively collected data (''After ERAS'' group).
Collected data Demographic data included age, sex, weight, height, degree of scoliosis and number of instrumented vertebral levels. Intraoperative data included perioperative analgesic modalities used, estimated blood loss, perioperative blood transfusion require-ments and duration of surgery. Postoperative data included the duration of effective morphine patient controlled analgesia (PCA) placement, the resumption of feeding and bowel movements, the first mobilisation, the presence of vomiting within the first 24 h and the length of hospital stay (LOS). The duration of surgery was defined, based on the anaesthetic records, from the incision to the end of surgical wound dressing. The LOS was defined from the day of the surgery (postoperative day -POD 0) until hospital discharge. Complications were determined on the basis of surgical reports and included any redo-surgery for septic or mechanical reasons or re-hospitalisation directly related to the scoliosis surgery.
A multinomial logistic regression on the global population evaluated significant covariates that should be included in the propensity score estimation model. Finally, weight, BMI, thoracic scoliosis degree, erythropoietin (EPO) protocol, preoperative haemoglobin, preoperative pain and preoperative gabapentine were matched in the propensity score analysis. The selection process was done without replacement, subjects were not returned to the sample after being matched. The maximum allowable difference between propensity scores, the caliper, was equal to 0. 1. After a propensity score, data of the two groups were analysed by the paired student or Wilcoxon tests for quantitative data and the Mac Nemar test for qualitative data. p Values below 0. 05 were considered statistically significant. The statistical analysis was carried out in collaboration with the medical information service of Montpellier using SAS software, SAS Institute.
However, the postoperative analgesic effect of adjuvant ketamine in patients undergoing scoliosis correction surgery for AIS is controversial  [21]. Double epidural analgesia has shown its effectiveness in pain management for scoliosis surgery  [22, 23]. However, centres implementing ERAS protocols have tended to abandon epidural analgesia, using only intrathecal morphine  [24]. In addition, the Cochrane database published in 2019 concludes that there is medium and low quality evidence indicating a slight additional reduction in pain, up to 72 h after scoliosis surgery, with epidural analgesia, in comparison to systemic analgesia  [25]. However, it is likely that the efficacy and safety of epidural analgesia depends on the experience of the healthcare team. We decided to combine intrathecal opioid administration with double epidural analgesia, historically used in our centre, to optimise postoperative pain management.
Contrastingly, the Iron Age samples (Turkmenistan_IA and Ksirov_Kushan individuals) are located close to modern Indo-Iranian populations, although slightly negative values on the first axis and positive values on the third axis suggest an addition of Baikal ancestry in the present-day Indo-Iranians. Finally, it appears from this PCA (Fig. 1c ) that ancient and present-day Indo-Iranian populations from Central Asia form together a cline between Iranian Neolithic farmers and Central_Steppe Bronze Age, with a clear shift in ancestry toward Steppe between Bronze Age and Iron Age as observed before  18  , and a smaller shift toward eastern Asian ancestry between Iron Age and present-day. This shift is more pronounced for Tajiks than Yaghnobis. To confirm our initial observations and identify genetic structures, we performed an unsupervised clustering analysis using ADMIXTURE  47  on the same dataset used for the PCA (see Supplementary fig.
S4 , S5 and S6). Consistently with the PCA, we evidenced in all modern Indo-Iranians the presence of a genetic component maximized in Iran Neolithic farmers (Iran_N, dark green; mean value for Yaghnobis: 37%; 25% for Tajiks), of another maximized in Eastern European Hunter-Gatherers (EEHG) and Western Scandinavian Hunter-Gatherers (WSHG) (pale green; mean value for Yaghnobis: 13%; for Tajiks: 10%) and of a third component (dark blue; mean value for Yaghnobis: 36%; for Tajiks: 29%) that is not completely maximized in any population of our dataset, but is found in present-day Europeans and in Anatolian Neolithic farmers (Anatolia_N). In addition, a fourth component maximized in Baikal Hunter-Gatherers (BHG: Shamanka_EN) and largely present in all modern Turko-Mongol populations (red; 50% on average) is also inferred to a lower extent in the modern Indo-Iranian populations, with a significantly smaller proportion in Yaghnobis than in Tajiks (mean value respectively 7% and 14%; t-test p-value = 2.
2 ). Bronze Age Central Steppe pastoralists show a similar profile except for a significant increase in Iranian ancestry, and Western Steppe pastoralists have the beige component maximized in Western European Hunter-Gatherers (WEHG), which is absent in modern Indo-Iranian populations. Thus, modern Indo-Iranian speaking populations appear as midway between Central Steppe and southern Central Asia Bronze Age populations, quite similarly to the Turkmenistan Iron Age individuals, with a limited impulse from eastern and southern Asian groups. Population continuity within the Indo-Iranians. To formally test for genetic continuity with Iron Age southern Central Asia and the limited admixture with Baikal-related populations at the source of the presentday Indo-Iranian speaking populations, we performed D-statistics, f3-statistics and qpAdm modelling on the same dataset used for the PCA et ADMIXTURE analyses as well as on a dataset formed by shotgun sequences from 3 Yaghnobis (TJY), 19 Tajiks (TJE) and 24 Turkmens (TUR)  48  as well as the ancient genomes for a final set of ~ 700k SNPs.
We identified and characterized gene flows that occurred since the Iron Age by computing D-statistics of the form D(Mbuti, Ancient population ; Turkmenistan_IA, present day Indo-Iranian) for every ancient population in our dataset (Fig. 3 , Table  S5 ). These statistics are expected positive when gene flows occurred from the Ancient population to the present-day Indo-Iranians. For the Yaghnobis, only one individual, an Iron Age individual from Nepal genetically close to East Asian populations (Nepal_Chokhopani_2700BP. SG)  45  , has a significantly positive D-statistic (Z > 3). Tajik individuals (TJE) display a higher number of ancient populations (N = 41) for which D-statistic is positive; the common characteristic of these ancient populations is to exhibit a large amount of BHG ancestry, consistently with the ADMIXTURE analysis (Fig.
10 -16 ) high amount of Baikal (red component, mean 50%) and East Asian ancestry (pink component, maximized in the Han population). Turkmens, for their part, display a completely different pattern, with an amount of Baikal component (mean value: 22%) closer to the proportion in Tajiks (mean value: 15%) and almost no East Asian component. They do not show as much South Asia related ancestry as Tajiks, suggesting that the admixture with South Asian populations occurred or continued after Turkmens split from the remainder of the Indo-Iranian group. We have established genetic affinity profiles with ancient populations for all Central Asia populations including Turkmens of the first dataset, based on f3-outgroup statistics of the form f3(Mbuti; Ancient pop, Presentday pop) (Fig. 5 ; Table  S8 ).
The populations falling under the name Andronovo form a complex group. Indeed, when screening the individuals used under the label Andronovo in our dataset, we note that they all belong to one site, Kytmanovo  52, 59  , which is eastward, but show a genetic profile very close to the Sintashta individuals, whose area expanded near the Caspian Sea. Individuals from other cultures belonging to the Andronovo complex have been sequenced  17, 18  but overall they form a moderately heterogenous genetic group. Moreover, some studies have shown that Steppe groups can be labelled similarly but be different genetically, such as, for instance, Srubnaya Alakulskaya individuals being closer to Andronovo individuals than to Srubnaya from the Samara region  28. The nomadic populations from the end of the Bronze and Iron Age being very genetically heterogenous, we suspect that the source of the Western steppe ancestry found in Iron Age southern Central Asia may not be sampled yet.
Methods Compiling and merging genomic data. We selected 3102 published ancient human genomes from Eurasia (Table  S1 ) from Paleolithic to Middle Age, whom DNA sequencing data generated with whole genome shotgun or hybridization capture technics, from the merge dataset v42. 4 available at https:// reich. hms. harva rd. edu/ allen-ancie nt-dna-resou rce-aadr-downl oadab le-genot ypes-prese nt-day-and-ancie nt-dna-data. We retained non-related individuals with more than 10,000 SNPs hit on the 1240k panel. We also added ancient individuals from two recent publications about Middle East  37  and Mongolian Steppe  38. The ancient dataset was classified based on geographical, chronological, and ancestry criteria. The individuals from the steppe with known ancestry (usually related to their localization) were labelled as Western_Steppe, Central_Steppe or Eastern_Stepppe respectively referring to a population with a close genetic affinity with Western European Hunter Gatherer (WEHG; Loschbour, LaBrana) or Eastern European HG (EEHG ; Popovo HG, Sidelkino, Karelia HG, Samara HG); to individuals with a higher ancestry from Western Siberian HG (WSHG ; Tyumen Hg and Sosonivoy HG), a strong affinity with Baikal HG (BHG, Shamanka HG) or with eastern non-Africans; to populations exhibiting an East Asian component, although not all of them do like for example Andronovo population.