dataesr/scientific-paragraphs-categorization huggingface dataset:
Methods Patient population. Validation and test data for this research included a total of 5,393 patients from four different datasets, three of which originated from clinical trials. The first dataset, which is referred to as 'Trophos' , was a clinical trial for olesoxime, a drug developed by Trophos  21  which included 512 patients. After excluding samples with missing data, 431 patients remained. The second dataset, 'Exonhit' , was a clinical trial for pentoxifylline, a drug produced by Exonhit Pharma  22  which included 400 patients. Given the considerable negative effect of the tested treatment on survival time, patients that received the treatment were excluded from outcome analysis. Nevertheless, these patients were included in dimension reduction as projection calculation is solely based on baseline features.
Following the exclusion of incomplete samples and patients having received the treatment, data from 345 patients were included in the dimension reduction phase and 172 patients were retained for 1-year survival analysis. The third database was 'PRO-ACT' , funded by the ALS Therapy Alliance and released in 2012 as part of the DREAM Phil Bowen ALS prediction Prize4Life competition. PRO-ACT consists of pooled data from 16 completed phase II-III clinical trials and one observational study  23. The original sample size was 10,723, reduced to 3,971 after discarding samples with missing data. The fourth dataset was population-based and contained RW patient data. These data were obtained from the database of the Paris tertiary referral centre for ALS collected between September 1999 and April 2008.
The original sample size was 1,377 which was reduced to 646 after the removal of incomplete samples. Baseline patient feature distribution for 1-year survival analysis is presented for each cohort in Table  1. Additional information on each dataset is provided as supplementary information. Clinical predictors and outcomes. The primary outcome was 1-year survival. Overall survival (in months), and 1-year functional loss (using the validated ALS Functional Rating Scale (ALSFRS)) were secondary outcomes. Each outcome had a specific data scope: 1-year survival was a binary variable and was predicted for patients dying within 12 months or with an available ALSFRS score at t+12 months. 1-year functional loss was predicted for patients that survived at t+12 months with an ALSFRS score at that time.
Patients who died or had invasive ventilation within the first year were assigned an ALSFRS score of 0 at year 1. Overall survival (in months) was used for patients when such information was available but provides a limited understanding of true patient survival given patient monitoring ended at t+12 months for most data. The choice of predictors was based on feature completeness after database cross-referencing. Predictors include gender, region of onset (spinal/bulbar), age, symptom duration, baseline ALSFRS score, baseline weight, and estimated functional decline rate  24. The functional decline rate was estimated using the following formula: with ALSFRS baseline , the ALSFRS score recorded at baseline, ALSFRS maximum , the maximum score for the ALSFRS (40) and symptom duration , time in months between symptom onset and baseline.
Missing data management. Missing feature analysis focused solely on baseline predictors and outcomes (overall survival, 1-year survival, and 1-year ALSFRS). Table  3  presents missing data ratio per feature for all datasets. Features which were not available in all datasets, such as testing and biological lab results, were discarded. ALSFRS sub-scores were not recorded for Trophos patients and were discarded as a whole. Outcome features can easily be missing due to loss to follow up or death. Features at time t+3 were less available than at baseline. Data collection was not disclosed for PRO-ACT data which aggregates multiple clinical trials and this prevented the identification of missing data patterns. Due to data collection differences between the cohorts, we did not perform missing data imputation and opted for complete case analysis.
Sample size of development data for 1-year survival was 4,574. Functional loss and overall survival analyses had lower sample sizes: respectively 4,382, a 4% drop with regards to 1-year survival sample size, and 1,612, a 65% drop with regards to 1-year survival. Sample size of validation data for 1-year survival, functional loss and overall survival were respectively 646, 541 and 447. 1-year survival rates zones were identified by dividing the UMAP projection space into multiple small square cells. A local assessment of the survival rate was calculated for each cell based on the development samples belonging to that cell. Confidence bounds were derived at a 95% confidence level using the area sample size and the following formula  25  : with α = 1-confidence level , z α , the value for 2 normal distribution, P, the outcome probability and N, the sample size.
(2) width = 2z α P(1 -P) N  Cell sample size directly influenced the cell survival rate. The less populated a cell, the wider the probability confidence interval, and the less reliable the analysis of cell membership. Cells were combined to form three equally populated zones with sample sizes sufficient to bound survival rates' confidence intervals. These zones were designed to have distinct survival rates. Validation data were projected onto the UMAP projection space to check if distribution patterns observed for development data still held. RW patients were then assigned to their corresponding survival rate zone. Validation data zone assignment was assessed with regards to actual survival. The model was compared to logistic regression and random forest models. Models were trained on two different subsets of features: all of the baseline features and specifically age and baseline ALSFRS features.
Three examples are provided for more details and presented in Fig. 3d:  • Patient A (ID 4922) is a 41-years-old woman with a spinal onset, baseline weight is 84 kg, baseline ALSFRS score is 36, symptom duration is estimated at 6. 5 months, hence estimated baseline ALSFRS decline rate is assessed at -0. 6 ALSFRS points per month. This information is used to compute the spatial coordinates of Subsequent analysis of patients' A, B and C status after one year are that patient A and B survived a year while patient C died within the first year. A refined division of the projection space was also carried out and is presented in the supplementary information section. Analysis of the model with additional data-external data testing. The prognosis model was assessed using external data. Patient distribution within the projection space was examined with regards to outcome variables.
Discussion Our study demonstrated the utility of UMAP for survival analysis in ALS. We have successfully applied this non-linear dimension reduction method to ALS clinical trial data to predict overall survival, 1-year survival and 1-year functional loss. Our results showed that limited patient information, collected early in the course of the disease, was sufficient to obtain a relevant low-dimensional patient projection with regards to key outcome variables (survival and functional loss). These input features correlated with the different outcomes of interest, thus explaining the observed distribution patterns. These correlations persisted for external RW patients. Oneyear survival patient distribution patterns were used to identify zones with distinct survival rates. We proposed a simple 1-year survival estimation model which fared well against the tested machine learning models although performance metrics could only be grossly approximated.
The benefit of our approach with regards to standard machine learning methods is threefold. First, our model is simple; it uses only simple probabilities and readily available clinical features. Second, we limit prognosis error by providing a coarse prognosis estimate. Third, our model is easily updated and improves with additional data. No learning was required for our model to work as UMAP is a dimension reduction method. Given dimension reduction was performed on baseline features, As this study evaluated pre-existing datasets we faced a number of constraints. PRO-ACT data are not uniformly recorded; for instance, vital capacity may be available in litres or percent, and slow and forced vital capacities are inconsistently documented. Units for weight are not clearly labelled as pounds or kilograms.
A weight value of 99 without an associated unit may equally be interpreted as kilograms or pounds. These inconsistencies concern 26% of PRO-ACT patients. Inclusion criteria for all datasets pooled within PRO-ACT are not Content courtesy of Springer Nature, terms of use apply. Rights reserved comprehensively documented; 6 out the 23 pooled clinical trial names were not disclosed. Available trial data also suffer from inclusion bias, as patients with marked cognitive or behavioural impairment often face worse prognosis  [26] [27] [28]  , and are often excluded from or drop out of clinical trials. Missing data imputation was omitted and our model was trained solely on complete case samples. Although generally recommended in medical settings, data imputation seemed hazardous in this specific data context, specifically working with PRO-ACT.
Multiple imputation methods often assume that the missingness patterns are missing at random, i. e. that they depend on other observed variables in the dataset. This information is difficult to verify and these data imputation methods are often performed on the biggest feature subset available so as to improve the odds of such a hypothesis being true. Given the differences in the data collection process and the limited feature subset shared between the different datasets, data imputation could not have been carried out on the global data structure. Data imputation at a dataset level would not have been productive and would have led to significant additional noise in data given small sample size and significant missing feature ratio for each dataset. Even advanced multiple imputation methods such as Quartagno et al.
29  which deal with missing data imputation at a study level (for meta-analysis purposes) require knowing the collection process for each study in scope, which we cannot access for PRO-ACT as features could be missing due to loss to follow up or due to clinical trial setup. Furthermore, as UMAP is a neighbourhood-based approach, data imputation can be seen as adding data where it is missing. This would have induced sample similarity in cases where little information was known on the subjects, creating visual artefacts of similar patients within the projection space and adding significant bias to the visual representation. Our spatial distribution approach would have had a more limited performance had we worked with imputed data that would have artificially created spatial proximity. Another data constraint was that lack of availability of established prognostic indicators in at least one of the four datasets, such as ALSFRS sub scores, cognitive profile, Riluzole intake, vital capacity  30  , time to generalisation  31  or weight loss, which is considered more relevant than absolute weight at baseline  32.
Although each cell carried limited survival information on its own; combined, they were useful in understanding the differences in spatial distribution. Sample size was crucial as it directly influenced the level of detail for the projection space division. A larger data sample would be required to define more zones with distinct survival rates. Dividing the projection space in three was deemed the most appropriate approach given the patient distribution and sample size. Based on the available data, we had to deal with the trade-off between prognosis personalisation and narrow confidence bounds for survival. Testing on external RW data was necessary to assess model ability to scale up and model validity as it was designed using trial patients. Only minor differences were observed when assessing zone membership.
A large number of patients were assigned to the low survival rate zone. This is clearly explained by the fact that clinical trials have inclusion criteria which select less severe patients. Additional RW data could correct this bias and limit the resulting over-optimistic prognosis it entails. In conclusion, we have successfully implemented a simple 1-year survival model partially based on a novel non-linear unsupervised learning method. Further work will be needed to extend our analyses to other prognosis outcomes, such as functional loss and clinical staging systems. Given the relatively low incidence of ALS compared to other neurodegenerative conditions, robust international collaborations are necessary to collect large datasets and build precision models  42. Notwithstanding the constraints of the available data, we have demonstrated that combining UMAP with a probabilistic and spatial distribution analysis, important correlations can be unravelled.
Acknowledgements This article reported its findings as advised in the TRIPOD (Transparent Reporting of a multivariate prediction model for Individual Prognosis or Diagnosis) statement 43. As such, the following organisations and individuals within the  PRO-ACT Consortium  contributed to the design and implementation of the PRO-ACT Database and/or provided data, but did not participate in the analysis of the data or the writing of this report:  Neurological Clinical Research Institute (NCRI) ,  Massachusetts General Hospital (MGH) ;  Northeast ALS Consortium ; Novartis; Prize4Life;  RegeneronPharmaceuticals, Inc. ,  Sanofi ;  Teva Pharmaceuticals Industries, Ltd. VG, GL, J-FP-P, and FD contributions were made within a  SORBONNE UNIVERSITE/CNRS  and FRS Consulting partnership which received funding from  MESRI  grant  CIFRE 2017/1051.
9,10 rHuPH20 increases subcutaneous tissue permeability and facilitates drug dispersion and absorption, enabling subcutaneous administration of large volumes. 11, 12 he Phase III COLUMBA study (ClinicalTrials. gov Identifier: NCT03277105) demonstrated that DARA SC (1800 mg flat dose) and DARA IV (16 mg/kg) monotherapy for RRMM have comparable efficacy, pharmacokinetics (PK) and safety. 13, 14 In the primary analysis of COLUMBA, DARA SC monotherapy was non-inferior to DARA IV monotherapy in terms of the pre-defined noninferiority criteria [overall response rate (ORR) and DARA maximum trough concentration (C trough )]. 14 With longer median follow-up, responses to DARA SC monotherapy deepened; DARA SC had a similar safety profile compared with DARA IV, with a statistically significant reduction in infusion-related reaction (IRR) rates (12Á7% vs.
Discussion In this Phase II study, the pre-specified primary endpoints for each cohort were met with DARA SC plus VRd, VMP or Rd. Continued clinical activity was demonstrated with longer follow-up; no new safety concerns were identified, and IRR rates were low (≤9% for any cohort). Importantly, the median duration of administration of DARA SC was only 5 min for all infusions across all cohorts. 7] [18]  Among transplant-eligible patients with NDMM, DARA SC plus VRd as induction therapy led to response rates in PLEIADES that were nearly identical to those for DARA IV plus VRd induction in the Phase II GRIFFIN study  16  (ClinicalTrials. gov Identifier: NCT02874742; ≥VGPR: 71Á6% and 71Á7%, respectively, ≥CR: 16Á4% and 19Á2%). The D-VMP cohort in PLEIADES included patients with transplant-ineligible NDMM; responses were again similar to those from the Phase III ALCYONE study (ClinicalTrials.
gov Identifier: NCT02195479) of DARA IV plus VMP (median follow-up, 16Á5 months). 17 ORR rates were 89Á6% and 90Á9%, respectively, ≥VGPR: 77Á6% and 71Á1%; ≥CR: 47Á8% and 42Á6%. In the D-Rd cohort (patients with RRMM), response rates were comparable to those in the Phase III POLLUX study (Clini-calTrials. gov Identifier: NCT02076009) of DARA IV plus Rd (median follow-up, 13Á5 months). 18 ORR rates were 93Á8% and 92Á9%, ≥VGPR: 78Á5% and 75Á8%; ≥CR: 38Á5% and 43Á1%. Comparable response rates were supported by consistent relative dose intensities of daratumumab between DARA SC combination therapies in PLEIADES and those previously reported for DARA IV. [17] [18] [19]  While care should be taken with direct comparisons of clinical trials, these data suggest equivalent clinical activity of DARA SC and DARA IV combination therapies with standard-of-care regimens, including an immunomodulatory drug, proteasome inhibitor and/or alkylating agent.
Equivalence is also supported by the Phase III COLUMBA study demonstrating non-inferiority of DARA SC monotherapy compared with DARA IV monotherapy. 14 The ongoing Phase III PERSEUS (ClinicalTrials. gov NCT03710603) study of DARA SC plus VRd versus VRd in transplant-eligible NDMM will assess response and long-term outcomes before and after ASCT. The MRD-negativity (10 -5 ) rates for the DARA SC cohorts in PLEIADES were generally comparable with previous data from the corresponding DARA IV regimens. DARA SC plus VMP was associated with 16Á4% MRD negativity compared with 22Á3% for D-VMP in ALCYONE,  17  and DARA SC plus Rd was associated with 15Á4% MRD negativity compared with 26Á2% for D-Rd in POLLUX. 20 The slight variations seen in the MRD-negativity rates between the DARA IV studies and PLEIADES DARA SC cohorts should be considered within the context that the DARA IV studies had fewer patients with high-risk cytogenetics, and had longer followup, allowing clinical responses to daratumumab (including MRD negativity) to deepen over time.
MRD negativity was not assessed in the DARA SC plus VRd cohort because patients only received four 21-day cycles of induction therapy before proceeding to ASCT, which was performed off study. Due to the short duration of the study and follow-up, the study protocol did not require patients in the VRd cohort to undergo an invasive bone marrow procedure for MRD analysis. Overall, the efficacy of DARA SC combination therapies in PLEIADES was similar to data from previous studies of DARA IV combination therapies, which have consistently shown superior outcomes for patients with MM across lines of therapy. 7] [18]  No new safety concerns were identified; however, injection-site reactions did occur infrequently. 7] [18]  Reduced IRR rates for DARA SC cohorts in PLEIADES versus DARA IV is consistent with lower IRR rates seen for DARA SC monotherapy in COLUMBA: DARA SC, 12Á7% versus DARA IV, 34Á5% (P < 0Á0001).
Acknowledgements This study was sponsored by  Janssen Research and Development, LLC. The authors would like to acknowledge the patients participating in this study and their families, the staff members at the study sites, the data and safety monitoring committees and the staff members who were involved in data collection and analyses. The authors would like to acknowledge  Jesus San-Miguel  of the  Cl ınica Universidad de Navarra-CIMA, IDISNA, CIBERONC, Pamplona, Spain , for contributions to the study. Medical writing and editorial support were provided by  J. Matthew Kuczmarski, PhD , of MedErgy, and were funded by  Janssen Global Services, LLC. Ajai Chari served as a consultant for, was a member of a board of directors or advisory committees for, and received research funding from  Janssen ,  Celgene ,  Novartis ,  Amgen, Bristol-Myers Squibb, Pharmacyclics, Karyopharm, Sanofi, Seattle Genetics, OncoPeptides and Millennium/Takeda.
(2013). We added eight cycles of 15 s at 94°C, 15 s at 53°C, and 30 s at 72°C with a final extension at 72°C for 5 min to incorporate the M13 primer. PCR reactions were analyzed using LI-COR Model 4300 DNA and microsatellite sizes were determined with Saga 3. 1. 2 software. for each locus. Finally, frequency of null alleles (Na) for each population and locus was estimated. Genetic structure analysis started with computing a principal coordinate analysis (PCA) on genetic frequencies to plot the samples in a reduced dimensional map and identify the most divergent locations (i. e. , MPAs). We then computed a hierarchical analysis of molecular variance (AMOVA) carried out with 999 permutations to corroborate the statistical baseline of the main divergence observed in the PCA. In addition, we calculated the F ST values between all sites also to validate the structure and we made a sequential Bonferroni adjustment for multiple tests  (Sokal & Rohlf, 1995).
| Data analysis F ′ ST index, which is a standardized measure of population differentiation. This distance is allowing comparison between studies with different types of markers and different effective population sizes  (Hedrick, 2005; Meirmans, 2006; Meirmans & Hedrick, 2011). All calculations were made with GenAlEx 6. 5  (Peakall & Smouse, 2006)  and with Genetix  (Belkhir, Borsa, Chikhi, Raufaste, & Bonhomme, 2001). Lastly, we investigated the genetic structure using a Bayesian approach considering all individuals within the same genetic entity. STRUCTURE 2. 3. 4 was used to optimize the variance in each genetic population. The analysis was conducted with 100,000 generations for burnin and 500,000 recorded generations for each K, repeated, for K = 1 to K = 9, 10 times for each K. Then, we used sampling locations as prior (model LOC-PRIOR,  Hubisz, Falush, Stephens, & Pritchard, 2009)  for three regions as locality priorities.
However, both Hardy-Weinberg equilibrium departures and null alleles resulted from heterozygotes excess. Micro-Checker data analysis pooling all individuals in one group found only one null allele (Pvm15). Nonetheless, it was a false positive because some of the organisms had an interallele at this Locus (204 bp). All loci had negative inbreeding coefficient (F IS ) values, which indicated that individuals were less related than expected under a random mating model. The total number of alleles, average alleles, and number of private alleles were higher in the Mexican Caribbean sites (Table  2 ). A total of 26 private alleles were distributed across F I G U R E 1 Map of sampled Marine Protected Areas (MPAs) from north to south and from left to right: Tuxpan (TUX), Veracruz (VER), Cozumel (COZ), Chinchorro (CHI), Xcalak (XKL), Guatemala (GUA), Utila (UTI), Chiriviche (VEN), and Los Roques (ROQ).
Animal Material The sponge referenced under the number P281-TRAR04 was collected in the Tuamotu archipelago in French Polynesia during the sampling cruise Tuam'2011  [73]  aboard the R/V Alis, off the coast of Raroia (2011/05/13, 16 • 00. 551'S; 142 • 27. 243'W), using SCUBA between 45 and 55 m deep. It was identified as Pseudoceratina sp. (OTU QM2081) and a reference specimen is deposited at the Queensland Museum (Brisbane, Australia) under the accession number QM G333336. Samples (230 g) were deep frozen immediately aboard and kept at -20 • C until processing to obtain deliver 86 g of freeze-dried powder.
Acknowledgments: We thank the  French Polynesian authorities , as well as the communities, for allowing us to collect in the  Tuamotu Archipelago. We acknowledge the crew of the  R/V ALIS  and  IRD 's diving team (SEOH  IRD Noumea, New Caledonia ) for their on-field help. We thank  Bassler  for kindly providing us the mutant strains. Financial support from  CNRS  is gratefully acknowledged.