dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgments We would like to thank the women who agreed to participate in this study. We are very grateful to all the doctors and nursing staff of Mahosot and Mother and  Child Hospitals  and the staff of the  Microbiology Laboratory, Mahosot Hospital. We thank  Daniel Paris ,  Mayfong Mayxay ,  Viengmon Davong ,  Anisone Chanthongthip ,  Anan Sakpaserth ,  Buavanh Sensathid ,  Ampai Tanganuchitcharnchai ,  Suthatip Jintawon , and the late  Thaksinaphone Thaojaikong , for their kind technical laboratory support. We are very grateful to  Rose McGready ,  Punam Mangtani  and  Francois Nosten  for their support and advice.
Overall, 33 (39 %) patients received adjuvant radiotherapy. Clinical data were obtained from the medical records and questionnaires designed specifically for our previous studies conducted in the same cohort  [7, 8]. Histopathological data on 76 tumors were obtained from two previous studies  [7, 8]. Nine new tissue specimens were added (collected from consecutive patients treated in 2007) and newly reviewed with the same pathological criteria. Tumor type (pT), depth of invasion (measured from the epithelial-dermal junction of the adjacent, most superficial dermal papillae to the deepest point of invasion), tumor grade according to the GOG (Gynecological Oncology Group) and lymph nodes status (pN) were verified by the same two independent pathologists (without knowledge of the disease outcome).
Statistical analysis To determine statistically significant differences between the variables, the Mann-Whitney U test was used. Overall survival (OS) curves were estimated using the Kaplan-Meier method and compared using a two-sided log-rank test. Independent variables were first analyzed with univariate analysis. Variables shown by univariate analysis to be significantly associated with survival were entered into a Cox proportional hazards regression model for multivariate analysis. A p value <0. 05 was considered significant. All analyses were performed using Statistica 10 software (Stat Soft Inc. USA).
Statistical analysis Statistical analysis will be conducted on intention to treat (ITT) using the Stata software, V. 13 (StataCorp, College Station, TX, USA). A two-sided p value of <0. 05 will be considered to indicate statistical significance (except interim analysis). Baseline characteristics will be presented for each randomised group as the mean±SD or the median (IQR) according to the statistical distribution for continuous data, and as the number of patients and associated percentages for categorical parameters. Comparisons between independent groups will be analysed using the χ 2 or Fisher's exact test for categorical variables (notably unplanned readmission and primary outcome: percentage of patients realising the full perioperative oncological treatment) and Student's t-test or Mann-Whitney's test for quantitative parameters (notably weight loss, body mass index, albumin, body impedance, length of stay, VO 2peak , VT, 6MWT, depressive and anxious symptoms evaluated using HADS, quality of Life according to EQ-5D).
ETHICS AND DISSEMINATION Approval In accordance with the Declaration of Helsinki and French regulations on clinical trials, the study was presented to an independent ethics committee, the 'Comité de Protection des Personnes Sud Est 6' (reference: AU1228, IRB00008526, Clermont-Ferrand, France). The approval of the committee was obtained on 7 March 2016. The protocol was declared to the competent French authority ("Agence Nationale de Sécurité du Médicament et des produits de santé", Saint Denis, France) and registered under number 2015 A01733-46. Authorisation was obtained on 21 December 2015. Any substantial change in the protocol or in the informed consent form will be presented to both authorities as well as first inclusion, interim analyses and end of study. Data monitoring will be performed as per French regulations requirements.
As required by the IRB, a Safety Monitoring Committee (DSMC) has been set up. Data will be collected at each trial visit (every 2 months) and at the interim analyses, regarding any adverse events (AE) and serious AE. All serious AE causally related to treatment procedures will be reported to the relevant ethics committees, the lead site and the independent data and DSMC for their review and recommendations. The DSMC comprises independent clinicians with an interest in prehabilitation and a statistician. Overview is carried out through the review of AE and serious AE, all of which are reported at the regular committee meetings. Each meeting determines the Board's recommendation to the Steering Committee as to whether the study is safe to continue. The study is currently registered on the clinical trials website under the number NCT02780921.
Data collection and quality management Experienced and trained study coordinators will be dedicated to data acquisition, coding, security and storage, under the responsibility of investigators. Each study data will be anonymised. Data will be recorded in paper case report forms at the time of each patient contact. These will be faxed to the study lead site for checking followed by entry into the secure study database. Data will be collected and managed using REDCap electronic data capture tools hosted at the University Hospital of Clermont-Ferrand. 18 Research Electronic Data Capture (REDCap) is a secure, web-based application designed to support data capture for research studies, providing: (1) an intuitive interface for validated data entry; (2) audit trails for tracking data handling and export procedures; (3) automated export procedures for seamless data downloads to common statistical packages; and (4) procedures for importing data from external sources.
It was then subcultured on 5% sheep blood-enriched agar (bioMérieux, Marcy L'Etoile, France). Strain SIT14 grew after 7 days of incubation at 37°C. Agar-grown colonies were white and concave with a diameter ranging from 0. 2 to 0. 6 mm. Bacterial cells were Gram positive and coccus shaped, ranging in diameter from 0. 7 to 0. 9 μm. Strain SIT14 was catalase positive but oxidase negative. The 16S rRNA gene was sequenced using the fD1-rP2 primers as previously described  [5] , using a 3130-XL sequencer (Applied Biosciences, Saint Aubin, France). Strain SIT14 exhibited a 90. 5% sequence identity with Finegoldia magna strain ATCC 29328 T (GenBank accession no. NR_074677. 1), the phylogenetically closest species with standing in nomenclature (Fig. 1 ). Finegoldia magna is a Gram-positive anaerobic bacterium that was originally isolated from an abdominal wound and was described in 2008  [6].
Spatial and geometric constraints are added to avoid false detections. In  [31, 32]  the tumors are detected as outliers with respect to the normal brain tissue characteristics of a registered healthy atlas. Spatial constraints are modeled via Markov Random Fields  [31]  or level sets  [32]. In  [33] , we proposed a MRF based coupled tumor segmentation and registration framework where atlas based prior knowledge is introduced through the registration task. Our method combines local and global informations. Tumor detection is based on a learned classifier and spatial constraints are enforced using a Markov Random Field model, while the statistical atlas of uneven repartition in the brain adds global prior information on the most probable location of tumor voxels. The method is tested on a challenging clinical database with variable quality and poor resolution, and yields promising results that demonstrate the clinical potential of the atlas.
Database Construction and Preprocessing We conducted a retrospective study at a single institution on a series of 210 DLGG patients. This study was reviewed and approved by the Institutional Review Board (Institut Regional du Cancer de Montpellier -Val d'Aurelle, ID number ICM-URC-2015/35). The patient information was anonymized and de-identified prior to analysis. Patients aged over 18 at diagnosis were included if they fulfilled the following criteria: surgery for a DLGG performed by one of the 3 neurosurgeons of the neuro-oncology unit (HD, LB or YY) between January 1st, 2006 and August 30th, 2012; FLAIR weighted MRI images performed before any oncological treatment (chemotherapy or radiation therapy) available and exploitable. Exclusion criteria were as follows: no histological diagnosis available; low-grade gliomatosis cerebri; tumor volume over 150 cm 3 or multicentric; DLGG located in the spinal cord or in the brainstem; delay >3 years between the first MRI performed (at diagnosis) and the first MRI available.
The age at diagnosis (i. e. at the time of the first MRI performed), the age at the time of the first symptoms and at the time of the first MRI available were collected for each patient. For incidental tumors (n = 16) the age at the time of the first symptoms was considered to be the age at the time of the first MRI. The MRI performed at diagnosis was collected for each patient. When that MRI was not available, a later MRI could be used if the patient had not yet received any oncological treatment at the time when it was performed (biopsy possible). The OSIRIX 1 software was used to perform the image manual segmentation on the FLAIR weighted sequences for each patient. Segmentations were performed by HD and AD. A region of interest (ROI) was defined for each patient, determining for each voxel of each slide of the FLAIR sequence if it belongs to the ROI (i.
e. to the tumor) or not. The complete database and expert segmentations are available online at http://dbgliomas-gradeii. net/ and on figshare at http://dx. doi. org/10. 6084/m9. figshare. 1550871. Due to our patient selection constraints for atlas construction, all images are acquired in a clinical setting and have therefore variable quality (often low resolution) and progression of the disease. This makes all image processing tasks (tumor segmentation, deformable registration) particularly challenging. Preprocessing involves intensity regularization for the segmentation task. One of the main issues with MRI imaging is that the intensities of the same anatomical tissues can be very different from one image to another, and even within the same image. Many algorithms have been developed to correct the intensity inhomogeneity  [34].
Probabilistic Atlas Construction A High anatomical variability exists between the brains of different individuals. In order to compare the tumors' positions in the brain, we need to compensate such variability and bring all the MRI images in the same reference coordinate space. To this end, we perform affine registration towards the same reference pose on all the volumes. While deformable registration would yield better correspondences, the presence of the tumor alters the quality of the registration. The most straightforward approach to deformable registration in such a setting, which we used in a previous work on a much smaller database  [35] , is to mask the pathology while performing the registration. It has been shown that it can lead to important errors in registration  [33]  and therefore cannot be used reliably on a large database.
Existing methods developed specifically for registration in the presence of tumours  [33, 36]  currently lack reliability and scalability to be used systematically on our large database, notably due to the variable quality of our data. Let us consider N images I i (x), i 2 [1,. , N] featuring a DLGG, and the healthy reference pose A(x). All images are affinely registered to the reference pose. We call M i (x) the corresponding binary tumor segmentation map obtained by manual segmentation, after affine registration. Registration brings all the tumors (segmentation maps) in the same coordinate system. The next step is to build a graph expressing the statistical relationships between the tumors' positions. Using an arbitrary distance measure, we can build a complete graph G where the nodes correspond to the tumors and the edges' strength corresponds to the distance between the tumors.
The atlas provides a precise description of the tumors locations and spatial extension in each cluster (prior probability maps and clusters positions). The insula is undoubtedly a predominant location for the DLGG as more than a third of the tumors are situated in this area (37% of the data). This result, as well as the quasi-total absence of tumors in or near the occipital and prefrontal lobes, is in accordance with what was observed in  [17]. The proposed atlas and complete database are available for download at http://db-gliomas-gradeii. net/. Several applications for this atlas are imaginable. The proposed atlas constitutes a robust method for correlations between DLGG topography and their characteristics. The patient's age at diagnosis and first symptoms was considered in this paper, and a study with respect to the tumors' molecular biology is currently ongoing.
99; 9. 48]) for the age at the time of the first symptoms and [-0. 74; 9. 79] for at the time of MRI diagnosis, respectively). The small effective in the "SMA group" (n = 18) could partly explain our lack of statistic power. If such a difference does exist for DLGG involving the SMA, it would reinforce the fact that this area has specific biological, architectonic and functional properties, with particular interactions between glial cells and neurons that could, maybe, explain in parts earlier gliomatogenesis  [17]. This study should be pursued with a larger database in order to establish whether those tendencies are confirmed. Furthermore, we have now designed a pipeline for correlating the position of the tumor with any parameter through the use of the probabilistic atlas. It is straightforward to extend the study to investigate the correlation of other parameters (molecular biology for instance) to the tumor location.
Furthermore, it would be interesting to couple this atlas with information obtained from functional imaging and study the relationship between tumors' position and brain functions. This could enable to identify compensable regions and predict the future functional reorganization of the brain depending on the location of the tumor. All of this would be extremely useful for surgery planning. Eventually, the same method could also be applied to other kinds of lesions, perhaps by changing the distance measure for one that is more adapted to the type of lesion. We presented in this paper an application as a spatial position prior for tumor detection and segmentation. We managed to efficiently detect and characterize (i. e. assign to a cluster) the tumors from a sizable dataset and despite some unclear boundaries and heterogeneous appearances.
As stated in the introduction, DLGG have different behaviors depending on their location. We could be able to infer the evolution of the tumor depending on the cluster it is assigned to. Obtaining the best possible tumor segmentation is not the main focus of this paper. The proposed method was used to highlight the clinical relevance of the probabilistic atlas. We therefore used simple unary costs (boosting) while much more elaborated approaches from the literature could have been considered. It is therefore difficult to compare our results with the current state of the art from the BRATS challenge  [50]. On top of that, we are using a very different and challenging database (monomodal data, variable quality, low resolution) due to our clinical setting. Segmentation can be harder for small tumors, that do not offer an important contrast and are sometimes even difficult to detect visually.
Study Design and Patient Eligibility We included HIV-infected women (defined here as ≥15 years old), who started an ART regimen of efavirenz, tenofovir, and lamivudine between 1 September 2011 and 15 October 2013 and later returned to care after that (Figure  1 ). Our data came from 13 health facilities in Malawi that used electronic medical records systems (EMRS) during the study period and entered pharmacy data prospectively at the point of care. At most study facilities, women started ART in antenatal care and were referred to ART clinics 6 weeks post partum. Ethical approval was granted by the Malawi National Health Sciences Research Committee and the Cantonal Ethics Committee of Bern, Switzerland.
Data Sources Healthcare workers use an EMRS with barcode scanners and touchscreens to record data at the point of care  [18]. Pharmacy claims are registered with barcode scanners. The EMRS records type of ART regimen, date of drug dispensation, number of returned pills, and number of pills dispensed. One facility had viral load data for 498 women (27%), and we compared adherence with viral suppression in this subgroup (Figure  1 ). Date of delivery is recorded in paper-based birth registers for HIVexposed children and not in maternal ART records. We entered paper records into a database and used probabilistic record linkage to merge maternal and infant records. For the subgroup of women we linked, we compared antepartum and postpartum adherence (Figure  1 ).
We used the Kaplan-Meier method to estimate retention in care and the probability that women would maintain adequate adherence. We split follow-up time into 3-month intervals and determined the proportions of women with adherence ≥90% or <90% in each interval. For the subset of women who could be linked to infants (Figure  1 ), we determined proportions of women with adherence ≥90% or <90% during pregnancy and in 3-month intervals post partum. We used pooled logistic regression with clusterbased robust standard errors to calculate adjusted hazard ratios (aHRs) for adequate adherence. Analysis was adjusted for age, pregnancy and breastfeeding status at the start of ART, facility type, and WHO clinical stage. We used Stata software, version 14, for all statistical analyses.
To minimize the risk of incorrectly documented drug dispensation, healthcare workers used barcode scanners and recorded drug dispensation prospectively at the point of care  [18] , except during occasional outages of the electronic system, when data were collected on paper forms and entered into the system retrospectively. Data entry may not always have been complete, but it is unlikely that we seriously underestimated adherence because retrospective data entry was rare. Our earlier analysis of data from the same cohort revealed that women with poor adherence have a higher risk of LTF  [7]. We used IPCW to adjust for informative censoring and enable a valid comparison of antepartum and postpartum adherence  [25, 26]. The date of delivery was not recorded in maternal ART records, so we could not calculate antepartum and postpartum adherence for all women.
By linking maternal and infant records, we determined date of delivery for about 20% of the women. This subset was not representative of all women who started ART during pregnancy; those in the subset had slightly better adherence, probably because women who adhere to ART are more likely to enroll their infants in care. Adherence levels for the antepartum and postpartum period may therefore be slightly overestimated. A meta-analysis of studies published before 2012 compared maternal antepartum and postpartum adherence and found poor and deteriorating adherence after delivery  [8]. More recent data suggest high and constant levels of adherence during pregnancy and breastfeeding. The PROMOTE trial and the Kisumu Breastfeeding Study included pregnant women regardless of clinical or immunological criteria.
Acknowledgments. We thank all patients, doctors and study nurses in the participating facilities. We thank  Marcel Zwahlen  from the  Institute of Social and Preventive Medicine  and  Constantin T. Yiannoutsos  from  Indiana University  for advice on statistical analysis. Author contributions. A. D. H. wrote the first draft of the study protocol; all authors critically reviewed the study protocol and contributed to its final version. A. D. H. performed statistical analyses, with interpretation of results by all authors. A. D. H. wrote the first draft of the report, which was revised by M. E. , H. T. , A. J. , K. T. , L. S. -V. , J. E. , N. P. , J. J. v. O. , and  O. K. A. D. H. and M. T. M. extracted the data and did data management. M. T. M. and A. S. did probabilistic record linkage. A. J.
coordinates monitoring and evaluation of the Malawian ART/prevention of mother-to-child transmission program and implemented data collection. O. J. G. coordinates electronic data collection. F. C. heads the HIV and  AIDS Department of Malawi's Ministry of Health. F. C. and O. K. are the principal investigators of the study. All authors reviewed and approved the final version of the manuscript for submission. Disclaimer. The content is solely the responsibility of the authors and does not necessarily represent the official views of the sponsors. Financial support. This work was supported by the  Bill and Melinda Gates Foundation (Global Health  grant  OPP1090200 ), the  United States Agency for International Development-National Institutes of Health initiative Partnerships for Enhanced Engagement in Research Health  (grant  AID-OAA-A-11-00012 ), and the  National Institute of Allergy and Infectious Diseases  (grant  5U01-AI069924 ).
The  Malawi Ministry of Health 's  HIV/ AIDS Programme  is funded by the  Fight AIDS, Tuberculosis and Malaria and President's Emergency Plan for AIDS Relief. Baobab Health Trust  is funded by the  Centers for Disease Control and Prevention. O. K. was supported by a  PROSPER  fellowship grant from the  Swiss National Science Foundation  (grant  3233B-150934 ). Potential conflicts of interest. All authors: No reported conflicts. All authors have submitted the  ICMJE Form for Disclosure of Potential Conflicts of Interest. Conflicts that the editors consider relevant to the content of the manuscript have been disclosed.
No individual thyroid cancer biomarker has been found with sufficient sensitivity and specificity  [2]. However, a panel comprised of GAL3, CK19 and HBME1 is by far the most studied to date and offers some improvement over individual marker performance alone  [2]. In the future, it would be important to evaluate the diagnostic performance of proNGF in comparison and combination with the currently used biomarkers. In terms of gene expression, NGF mRNA abundance has not been reported to be linked to a particular clinicopathological parameter in thyroid cancer. Before investigating proNGF protein levels by immunohistochemistry, we have performed a data mining of NGF gene expression, using cBioportal  [24] , of thyroid datasets in The Cancer Genome Atlas (TCGA) database  [25]. NGF mRNA upregulation was found in only 23 out of 507 patient cases, representing only 5% of the total number of cases.
Goodness of fit was tested using G 2 Chi-squared statistics (comparing the log likelihood to that obtained from the saturated model). Nested models were compared by calculating differences in G 2 statistics and were used to assess removal (a non-significant reduction in fit) of terms to the model. The prognostic value of the biomarker was expressed using the area under the receiver-operating characteristic (AUROC) curve; values close to 0. 5 indicate performance close to chance, while values close to 1 indicate near perfect discrimination. These models were fitted using SAS (SAS Institute, North Carolina, USA).