dataesr/scientific-paragraphs-categorization huggingface dataset:
4 µm. Cat. N • 665641). The cells were incubated at 37 • C for 30 min, and then 800 µL of TMCM medium was added to the bottom of the well, and 200 µL in the inserts. The cells were then let incubate at 37 • C in a humid atmosphere with 5% CO 2. Fresh culture medium was supplied every 3 to 4 days. The cells were exposed to TGF-β2 (5 ng/mL) for 48 h and LT and Y-27632 for 24 h, according to the same protocol as for the two-dimensional model. At 7 days, the 3D cultures obtained were fixed with 4% paraformaldehyde for one hour. Actin was stained with phalloidin (1/100), and the nuclei were stained with DAPI (1/1000) before analysis using confocal microscopy. Confocal laser scanning microscopy was performed using an Olympus IX81 confocal microscope coupled to Fluoview software (Olympus, Ver 4. 2), and the images were acquired at 200× magnification.
Study Design and Participants Institutional review board approval was granted for this study (institutional review board number COS-RGDS-2021-07-005). All participants gave valid consent to participate. We conducted a retrospective analysis of prospectively collected data from consecutive patients who underwent ACLR performed by the senior author between September 1, 2017, and December 31, 2020. Patients were considered for study eligibility if they had experienced symptomatic instability after ACL injury and subsequently underwent ACLR with hamstring tendon autograft either with or without a concomitant ALLR. The decision to perform ALLR in addition to isolated ACLR was based on published indications  15  and patient and/or surgeon preference. Patients were excluded if they had a history of ipsilateral knee surgery, if they underwent other concomitant procedures at the time of ACLR (e.
5 seconds, it scores 1 point. Moreover, if pain prevents the test, no points are given. The final score was calculated as a percentage of the maximum available points. If the final score was less than 50 points, return to sport was discouraged. If the score ranged between 66 and 80 points, pivoting noncontact sports were allowed. If the score ranged between 65 and 80 points, pivoting non-contact sports were allowed. If the score was higher than 80 points, the patient was allowed to return to contact sports. A review of medical notes was used to extract data regarding patient characteristics and technical details of surgery.
Statistical Analyses Statistical analyses were conducted using SPSS software (version 27. 0; IBM) for MacOS (Apple); the level of significance was set at P <. 05. For the evaluation of the K-STARTS scores and subscales, nonparametric rank-based statistical methods were used: the Wilcoxon signed rank test for continuous data and the McNemar test for binary outcomes. Multivariate logistic regression was performed to identify factors influencing any variables that were significantly different between groups.
Procedures The participants first performed some initial tests (week 1) consisting of an assessment of maximal finger strength (MFS), stamina, and endurance on a hangboard fitted with strain gauges (SmartBoard, Peypin d'Aigues, France). This hangboard (Figure  1 ) provides real-time feedback about the vertical force applied on it allowing precise modulation of the force intensity (1 N accuracy). Data concerning the force applied to the holds were recorded and analyzed using the SmartBoard app (50 Hz). For the following 4 weeks (weeks 2-5), they followed one of the three training protocols [except for the CT group (n = 12)] on the instrumented hangboard with 2 sessions per week, with at least 1 day rest between sessions. Participants were instructed to continue their climbing activity normally outside of the study without increasing or decreasing their current practice.
Statistical Analysis Data are reported as mean ± SD. One-way ANOVA was performed to evaluate differences of the descriptive characteristics between the groups. The effects of training intensity on MFS, stamina, and endurance, were assessed by comparing the CT, F60, F80, and F100 groups using a twofactor repeated measure ANOVA (training × group), with Tukey post-hoc analysis when ANOVAs were significant. In addition, effect sizes (eta squared, η 2 ) were calculated. To evaluate the relationship between initial levels of MFS, stamina, and endurance and their benefits after training, Pearson test correlations were assessed. Significance level was set at p < 0. 05. Statistical tests were processed using STATISTICA software (version 6, StatSoft, Inc. ).
Patients and Study Design We retrospectively analyzed data from 220 participants in the French ECOS study. ECOS is a 5-year, phase IV open-label study that ran between November 2010 and February 2016 in 24 countries, with the objective of describing "real-world" rhGH treatment in short children who were using the Easypod ™ electronic drug delivery device  (16, 19). Thirty pediatric endocrinology departments from across France participated in the French part of the ECOS study and included patients between January 2011 and December 2015. Patients were aged 2 to 16 years, and none had growth plate fusion. Eligible patients had a baseline visit and then two to four study visits per year, depending on local routine clinical practice. The duration of follow-up ranged from a minimum of 6 months to a maximum of 5 years.
565). Treatment data (the daily rhGH dose and the frequency of injections performed by each patient) were recorded and collected via the Easypod ™ device. The device has a skin sensor that should prevent injections into a plant or a toy but does not necessarily prevent injections into a pet, for example. Furthermore, it is recommended that injections be performed under parental supervision, although there is no way to ensure that this actually happens. Baseline and outcome measures were obtained by physician data entry into clinical report forms.
Safety The safety data came from the main ECOS France study. Of the 201 patients with safety data, 10 (5. 0%) reported a total of 13 serious adverse events (SAEs). Nine of the 10 patients required hospitalization. Two patients had SAEs that led to study discontinuation (1. 0%). None of the SAEs was fatal. The SAEs were hemorrhagic diarrhea; viral meningitis, viral myositis, pyrexia and headache, velopharyngeal insufficiency, adenoidal disorder and tympanic membrane disorder, sleep apnea syndrome, respiratory failure, gynecomastia, cancer recurrence, and an anaphylactic reaction. Three patients (1. 5%) reported at least one treatment-related SAE: cancer recurrence for the first, adenoidal disorder and tympanic membrane disorder for the second, and gynecomastia for the third.
Study design This study was conducted in compliance with the protocol, in accordance with the International Conference on Harmonisation Good Clinical Practice (CPMP/ICH 135/35) together with such other good clinical practice requirements and the ethical principles that have their origin in the Declaration of Helsinki, as well as with all currently applicable laws and regulations of the country where the study was conducted. Informed consent was obtained from all subjects during the screening period. The trial was registered with Clinicaltrials. gov, number NCT03045926 on the 08/02/2017 (https:// clini caltr ials. gov/ ct2/ show/ NCT03 045926). The clinical trial was a prospective, open label, noncomparative, multi-center, international study with chronic treatment of diosmectite (Smecta ® , 3 g) TID over 5 weeks, whose first purpose was to assess the level of elemental impurities (e.
Reads mapping Read cleaning, filtering and mapping were performed with the METEOR software suite (parameters: -c smart_ shared_reads)  [33]  that relies on Bowtie2 for read mapping  [34]. First, quality control was performed with AlienTrimmer (parameters: -k 10 -l 45 -m 5 -p 40)  [35] : sequencing adapters were removed and low quality reads were trimmed or discarded. Then, reads mapped to the human genome (identity ≥ 90%) were also discarded. Food genome traces were removed by the same method. Remaining reads were mapped to the Integrated Gene Catalogue (IGC)  [25] , comprising 9. 9 million of genes (default parameters of Bowtie2). Uniquely mapped reads (reads mapped to a unique gene in the catalogue) were attributed to their corresponding genes. The shared reads (reads that mapped with the same alignment score to multiple genes in the catalogue) were attributed according to the ratio of their unique mapping counts of the captured genes.
Metagenomic species The IGC has been previously clustered into 1438 MetaGenomic Species (MGS; clusters of > 500 co-abundant genes belonging to the same microbial species)  [26]. Taxonomical annotation of MGS was performed using an in-house pipeline. First, all genes are aligned on public databases (ncbi, wgs)  [37]  using blastn (version 2. 7. 1, task = megablast, word_size = 16)  [38]. The 20 best hits for each gene were kept. A species-level assignment was given if > 50% of the genes matched the RefSeq reference genome of a given species, with a mean identity ≥ 95% and mean gene length coverage ≥ 90%. The remaining MGS were assigned to a higher taxonomic level (genus to superkingdom), if > 50% of their genes had the same annotation. Relative abundance of an MGS was computed as the mean abundance of its 50 'marker' genes (that is, the genes that correlate the most altogether in terms of abundance).
1; a non-adjusted p-value is significant if inferior to 0. 05. Bray-Curtis dissimilarity was computed on the log-10 transformed MGS abundance table with the package vegan v2. 5. 7  [40]. Principal Coordinates Analysis was performed on the Bray-Curtis dissimilarity with the package ade4 v1. 7. 16  [41]. Analysis of similarity (ANO-SIM) between groups was performed with the package vegan  [40]. Effect size was computed using the Cliff 's Delta with the R package 'effsize'  [42]. This measure gives an information similar to log-fold change but is comprised between -1 and 1 (0: no effect; + 1 or -1: large effect). Magnitude of the effect size d is assessed in Romano et al. [43]  as negligible if |d|< 0. 147, small if |d|< 0. 33, medium if |d|< 0. 474, and large otherwise. Individuals were segregated based on k-means method, a classification method used to create groups of individuals without prior knowledge of number of classes.
To ensure women follow the recommended screening guidelines specific to their risk profile, information on CC burden by age, ethnicity and HIV status (positive, negative and unknown) is crucial. The South African Match (SAM) study provides a unique opportunity to achieve such an objective. The SAM database contains linked routinely collected HIV and cancer records of clients seeking care in South African public health care facilities  [9]. A cross-sectional sub-study of the SAM study assessed the burden of cancers attributable to HIV  [10]. The current short report builds on this analysis to determine the association of HIV infection, age, ethnicity and calendar period with CC compared to non-infection related cancer.
Data sources We linked cancer data from the NCR to routinely collected HIV related data from the National Health Laboratory Service (NHLS). The NCR collects data from both private and public laboratories on pathology-confirmed cancer diagnoses  [11, 12]. NHLS HIV data are collected through a network of laboratories that provides pathology services to the public health sector of South Africa. Data from the NHLS represent 80% of the South African population. We excluded data from the private sector in the NCR dataset as we assumed that women who had their cancer diagnosed in the public sector would potentially get their HIV care in the private sector as well. From the linkage, only 0. 08% (n = 6/7,550) of CC cases diagnosed in the private sector matched to the NHLS HIV data.
Variables and data management We defined CC as per International Classification of Disease for Oncology Volume 3 classification (topography C53)  [13]. Since we were unable to select an undiseased comparison group, we selected controls from women with non-infection related cancers in the NCR database. This has been done before by the Johannesburg Case-Control Study  [14]. The following cancers as per topography were used as the comparison group: Breast (C50), Endometrium (C54-55), Colon (C18-20), Oesophagus (C15), Ovaries (C56), Mucinous (C56 & 8470/3, 8471/3, 8472/3, 8473/3), Clear cell (C56 & 8310/3), Lung Cancer (C33-34), Myeloma (C90), Pancreas (C25), Myeloid Leukaemia (ICD-10 C92), Placenta (C58. 9), Melanoma (C43), Bone (C40-41), Larynx (C32), Thyroid (C73), Kidney (C64), Brain (C71), Peritoneum and retroperitoneum (C48), Endocrine gland (C75), Small Intestine (C17), Meninges (C70), Thymus (C37), Fallopian tube (C57.
Data Analysis We described our study population by HIV status, age, ethnicity, year of cancer diagnosis stratified by CC diagnosis versus non-infection related cancer. We performed a test of proportions to determine any differences between categories. We treated age as a continuous variable and used Kruskal-Wallis to test for differences in median age at CC diagnosis by HIV status. We used logistic regression to determine the factors associated with CC compared to non-infection related cancer in women living in South Africa. All analyses were done using Stata (version 16, College Station, TX, USA).
Discussion The results of this cross-sectional analysis of national South African cancer registry data show that women diagnosed with CC and with HIV were a decade younger than women with CC who were HIV negative. Diagnosis of CC was associated with being Black, HIV positivity, early calendar period  (2004) (2005) (2006)  and the ages 30-49 years. The percentage of women diagnosed with CC with a known HIV status increased from 15% in 2004 to approximately 50% in 2014, and over half of the women diagnosed with a CC had an unknown HIV status. Our study's main limitation was the high percentage of CC diagnoses with an unknown HIV status. Given that we assigned HIV status from routinely collected HIV data, we might have missed individuals tested for HIV through point of care tests not recorded by the NHLS as well as those tested by the private sector.
However, it essential to note that we excluded CC cases diagnosed in the private sector. Only 0. 08% of CC cases in the private sector were assigned an HIV results. We then assumed that those CC cases diagnosed in the private sector would have their HIV tests also done in the private sector (and hence not recorded in NHLS). As a result, we might have introduced some selection bias as use of the private sector is largely associated with better socio-economic status. Identifiable data of the national population of women to link to the CC cases as well as HIV data were not available; therefore, our assessment on ethnicity might not reflect the true distribution in the South African population. However, we still believe that even though we had no access to these population estimates, our study provides useful information by demonstrating that a large proportion of women diagnosed with CC are unaware of their HIV status and the disparities in CC by ethnicity.
We and others observed a similar pattern of earlier presentation of CC in women living with HIV than among HIV negative women  [15] [16] [17] [18] [19]. Between 2008 and 2010, a study used NHLS data to determine the age at diagnosis of invasive and pre-invasive CC in the South African province of Limpopo  [19]. In that study, the mean age at invasive CC diagnosis was 41. 3 years (standard deviation (SD): 9. 7) for HIV positive, and 59. 1 years (SD: 14. 7) for HIV negative women  [19]. A nationwide study of the NHLS cervical cancer dataset from 2014 to 2016 established that the peak age at CC diagnosis was 36-40 years amongst HIV positive women and >60 years in HIV negative women  [20]. In Zambia, the median age at CC diagnosis was 40 years for HIV negative women and 35 years for HIV positive women  [21].
Acknowledgments and funding The authors would like to acknowledge  NHLS  and the  NCR. This work was supported by grants from the  U. S. Civilian Research & Development Foundation  ( CRDF Global ), the  National Institutes of Health administrative supplement to Existing NIH Grants and Cooperative Agreements (Parent Admin Supp)  (The  South African HIV Cancer Match Study ;  U01AI069924 ,  PI Matthias Egger, co-PI Julia Bohlius) PEPFAR supplement (PI Matthias Egger) , the  Swiss National Science Foundation  (The South African HIV cancer  Match Study ,  320030_169967 ,  PI Julia Bohlius ); ACCHIVe -(The Cervical Cancer Prevention and  Care Cascade ,  177319 ,  PI Julia Bohlius) ;  ESTHER Switzerland (Linkage Partnership to Control Cervical Cancer in Southern Africa ,  20G26 ,  PI Julia Bohlius ).
That was the only case found of such a relationship between two breeds. For instance, Apulo-Calabrese, Basque, Bísara, Casertana, Krškopolje, and Nero Siciliano matched 100% to Sarda, but Sarda pigs were aligned only to Moravka and Nero Siciliano, (vi) Wild Boar was assigned mainly to Sarda and a small number to Nero Siciliano. The second most frequent breed to be assigned was Moravka with Black Slavonian, Landrace, Sarda, Schwabisch-Hällisches Schwein and Swallow-Bellied Mangalitsa being assigned to this breed. These results were, in general, consistent and the sample size in the TRN set only marginally influenced the assignment of the breeds (Fig. 6 ). It is interesting that even with 30% of the dataset (~ 340 pigs), assignments were fairly consistent with results obtained utilizing the full dataset (~ 1,138 pigs).
To support local pig farming, the TREASURE project joined researchers from nine countries and twenty-four research institutes to collect data from twenty European indigenous breeds. Previous genomic analyses of the aforementioned breeds were focused on linkage disequilibrium analysis and selection signatures detection using genome-wide SNP markers  12  , as well as genome sequencing data  40. Studies on genetic diversity have also been performed, whether based on a candidate genes approach  41  or a runs of homozygosity method  42. The present work complements these studies by further investigating the proportion of ancestry shared among these breeds, together with three of the most representative commercial breeds as well as a joined dataset of Wild Boar, originating from nine countries. To address the question of potential breed traceability via genomic data, we further investigated the ability to predict the breed of origin by SNP markers.
To explain this, it can be hypothesized that in a population such as Mora Romagnola, characterized by a low number of individuals and high level of inbreeding, there may be different response when investigating loci under selective pressure compared to neutral loci. Nevertheless, slight differences among the PCA and admixture were also observed. For instance, the PCA scatterplot of the first two axes (Fig. 1a ) clustered Turopolje close to Alentejana and Iberian; however, admixture analysis showed that ancestries were shared with Black Slavonian, Cinta Senese and Sarda (Fig. 2b , Table  S2 ). Regarding the closeness of some local with the cosmopolitan breeds as revealed by PCA (i. e. , Duroc with Mora Romagnola; Large White with Lithuanian White Old Type), the reason for this could be the sharing some parts of the genome linked to phenotypic characteristics and origin of Lithuanian White pigs; however, the amount of variability explained by the first PCs is largely limited with respect to the overall genetic variability possessed by populations in the entire dataset.
Moreover, although in PCA based on the scatterplot of the first two PCs (Fig. 1a ) Duroc and Mora Romagnola were closely placed, the two breeds had common ancestries close to zero (Fig. 2b , Table  S2 ). Admixture analysis revealed common ancestries shared between some indigenous and the commercial breeds. More precisely, Duroc shared ancestries mainly with Cinta Senese, Iberian and Sarda; Landrace with Bísara, Moravka, Nero Siciliano and Sarda; and Large White with Lithuanian White Old Type, Nero Siciliano, Sarda, and Lithuanian Indigenous Wattle. Regarding Wild Boars, our dataset consisted of a set of 51 samples from seven European countries, Tunisia, and Russia, to capture as much variability and to avoid country-specific bias. Indeed, a recent study investigating the history of the domesticated European pigs indicated an interbreeding between the local pig breeds and Wild Boars  43.
To resemble a practical application, the efficiency of the DAPC method was evaluated using an external validation. Furthermore, to assess the effect of sample size, the analyses were repeated several times with subsets of the dataset ranging from 30 to 90%. Although the correct assignment of the breeds was > 90% in all subsets, the variation of the correct assignment decreased with increased sample size, indicating a more robust model (Fig. 4 ). This level of correct reassignment of pigs is higher than the one reported by Muñoz et al. 41  , where there were many breeds with percentages of correct reassignment < 80%. Moreover, the actual differences might be even higher, since in that analysis an external validation was not considered and the whole data were analysed simultaneously. The correct reassignment was further improved for the Moravka, Nero Siciliano and Sarda breeds that had the lowest values in the DAPC analysis by Muñoz et al.
41. However, in that study only a limited number of 39 SNPs in candidate genes was used. Using the complete dataset, the majority of the breeds were correctly assigned to its breed of origin, with the exceptions of Black Slavonian, Cinta Senese, Krškopolje, Lithuanian White Old Type, Moravka and Turopolje, with the lowest value (86%) being observed for Black Slavonian (Fig. 3 ). In the case of Black Slavonian, there were some cases where animals were classified either as Cinta Senese or Turopolje. This was consistent with the shared ancestries found among the breeds, even at a low degree (Table  S2 ). The relation among these breeds was further highlighted with the un-supervised DAPC, in which Black Slavonian was assigned mainly as Cinta Senese, followed by Sarda and Turopolje. It should be noted that discrepancies between our results and previous genomic analyses on the same set of breeds were to some extent expected.
Methods Animals and genomic data. Our initial pig genomic data (n = 1,195) were obtained from three sources: (i) 20 European indigenous breeds (n = 987) reared in 9 countries (Croatia: Black Slavonian, Turopolje; France: Basque, Gascon; Germany: Schwabisch-Hällisches Schwein; Italy: Apulo Calabrese, Casertana, Cinta Senese, Mora Romagnola, Nero Siciliano, Sarda; Lithuania: Indigenous Wattle, White Old Type; Portugal: Alentejana, Bísara; Serbia: Moravka, Swallow-Bellied Mangalitsa; Slovenia: Krškopolje pig; Spain: Iberian, Majorcan Black), and retrieved from the European funded project TREASURE (https:// treas ure. kis. si/). Blood samples were collected from each institution by specialized professionals, following standard guidelines. No interventions with animals were applied that would require ethical protocols (according to Directive 2010/63/EU-2010) (more details on sampling method can be found in Muñoz et al.
12  ), (ii) three commercial breeds including Duroc (n = 53), Landrace (n = 52) and Large White (n = 52) and (iii) a sample of Wild Boars (n = 51) from Finland, Hungary, Italy, Spain, Poland, Russia, The Netherlands, Tunisia, and Greece was carefully selected from the Dryad Digital Repository: DOI: 10. 5061/dryad. 30tk6 (https:// doi. org/ 10. 5061/ dryad. 30tk6)  44. Further details on the selection of the Wild Boars are provided in the Supplementary Information. In addition, a small Spanish Wild Boar sample (n = 7) was also added  12. All pigs from the indigenous and the three commercial breeds were genotyped with the GeneSeek Genomic Profiler (GGP) 70 K HD porcine genotyping chip containing 68,516 SNPs. The Wild Boars were genotyped with the Illumina 60 K SNP data  45. The merged data contained 42,464 autosomal SNP.