dataesr/scientific-paragraphs-categorization huggingface dataset:
Statistical analysis The distribution of the quantitative data was investigated. Non-parametric tests were used in case of non-normality of the distributions. For DWB results (using SAS Version 9. 4), the time evolution across the eight time points was investigated. To this aim, a mixed model was fitted to the data to test. The covariates included in the model was the time (considered as a qualitative variable, "T0" was considered as the reference) and an interaction term with the group indicator ("LIP", "DEXA" or "HA" were as the group reference). This statistical method allowed the comparison of response curves between treatments while accounting for repeated data within individuals. This statistical approach was applied to test for time differences in each group, as well as for group differences at each time point.
Data were compared using a Mann-Whitney U-test for nonparametric values (#: p<0. 0001 was considered as significant). (C) Global score obtained for the right operated knee from all study groups. Each open dot represents the OARSI histological global score for one rat. Groups were compared to LIP-treated animals using non-parametric Kruskal-Wallis test. Since positive (p = 0. 0225), it was followed by multiple comparison Dunn's test ( � : p<0. 05 versus vehicle-treated group was considered as significant). Data are presented as median (horizontal bar) and interquartile range (Q1-Q3; vertical bars). Open dots correspond to individual values. https://doi. org/10. 1371/journal. pone. 0231240. g004 rats, none of the animals treated with the test items showed any significant difference in the cartilage degeneration score in both outer and inner zones of cartilage (Fig 7A, left and right panels).
CRPC in the absence of detectable metastases with conventional imaging is classified as nonmetastatic CRPC (nmCRPC)  [7]. Of 249,053 Japanese patients with prostate cancer included in the largescale, Medical Data Vision health claims database, 1236 cases were identified as nmCRPC between 2003 and 2018, although limitations of claims databases may underestimate this proportion (0. 50%)  [8]. An earlier analysis of population cancer registries from 28 countries reported the 5-year prevalence of nmCRPC in Asia (including Japan, China, India, Russia, and Turkey) as 3% in 2013  [9]. Patients with nmCRPC are at substantial risk of progressing to metastatic CRPC (mCRPC)  [5]. In a 2005 study by Smith et al. , 33% of patients with nmCRPC developed bone metastases within 2 years of follow-up  [10].
Study design and patients The design and patient eligibility of this randomized, double-blind, placebo-controlled, phase III trial (NCT02200614) have been reported previously  [26]. Briefly, patients aged ≥ 18 years had histologically or cytologically confirmed adenocarcinoma of the prostate, CRPC, a baseline PSA level of ≥ 2 ng/mL, a PSA doubling time (PSADT) of ≤ 10 months, and an Eastern Cooperative Oncology Group (ECOG) performance status of 0 or 1. Patients were excluded if they had a history of metastatic disease or distant metastases detected by whole body radionuclide bone scan and computed tomography (CT), or magnetic resonance imaging (MRI) of the pelvis, abdomen and chest; presence of pelvic lymph nodes < 2 cm in the short axis below the aortic bifurcation was allowed. Patients with prior seizures or conditions predisposing to seizure were permitted to enter this trial.
Statistical analysis Statistical analysis of efficacy and safety endpoints in the Japanese patient population was conducted as previously described for the global population  [26]. Briefly, MFS, all secondary endpoints, and time to PSA progression were analyzed using a stratified log-rank test. Kaplan-Meier curves, including median survival times and their 95% CI were calculated; the HR was calculated with a Cox proportionalhazards model. The percent of patients with a PSA response was analyzed using the Cochran-Mantel-Haenszel test. Statistical analysis and subject data listings were performed with SAS ® for UNIX (SAS Institute Inc. , Cary, NC, USA). Incomplete event occurrence dates were imputed as the earliest possible date. Efficacy was evaluated in the intention-to-treat population, which comprised all randomized patients.
Acknowledgements This study was funded by  Bayer AG and Orion Pharma. The authors would like to thank the patients, their families, and all investigators involved in this study. Medical writing support, including assisting authors with the development of the outline and initial draft and incorporation of comments was provided by  Charlotte Simpson, PhD , and editorial support, including fact checking, referencing, figure preparation, formatting, proofreading, and submission was provided by  Annabel Ola, MSc , both of  Scion, London, UK , supported by  Bayer AG  according to Good Publication Practice guidelines (Link). The Sponsor was involved in the study design, collection, analysis, and interpretation of data, as well as data checking of information provided in the manuscript. However, the ultimate responsibility for opinions, conclusions, and data interpretation lies with the authors.
The gene expression of regenerating S. ciliatum is as dynamic as postlarval development A long-standing question in the field of sponge regeneration is whether and to what degree regeneration recapitulates embryonic developmental pathways. Extensive morphological similarities between regeneration and PLD have driven us to explore the gene expression pattern of both processes. We sequenced regenerating samples at seven time points spanning from day 1 to day 24 after dissociation, including 1, 2, 3-4, 6-8, 10-14, 16-18 (PJUV) and 21-24 (JUV) dpd. For PLD, the RNA sequencing (RNA-seq) datasets spanning seven time points were collected from previously published data (deposited in ENA under accession numbers PRJEB7138 and PRJEB5970) including amphiblastula (swimming larva), flat post larva (S1), solid post larva (S2), hollow post larvae (S3), S4 juvenile, S5 juvenile and asconoid juvenile or young syconoid (YS) stages  (Fortunato et al.
, 2014a). For comparative analysis we processed both data sets through similar pipelines, as described in the Materials and Methods section. To assess the global transcriptomic profile underlying PLD and regeneration, we performed principal component analysis (PCA) on individual data sets. We found that both regeneration and PLD follow similar PCA distribution and, surprisingly, the transcriptional changes during regeneration were as variant as PLD (Fig. 5A, B ). We also performed hierarchical clustering on both datasets to define the major clusters and identified two major clusters that we classified as early and late stages (Fig. 5C, D ). From PCA and hierarchical clustering analysis we observed that clusters in regeneration were organized in a hierarchy consistent with that observed in the PLD.
Anti-α-tubulin (green) staining with the nuclei counterstained with DAPI (blue). A, archaeocytes or amoebocytes; C, choanocytes; O, Ostia; Os, osculum; P, pinacocyte; PO, porocytes; S, sclerocytes. exhibits dynamic gene expression equivalent to PLD. Of these dynamically expressed genes, approximately 50% of genes (4553) were commonly expressed in both data sets (Fig. 5H ; Table  S2 ), suggesting that half of the PLD genes were deployed during regeneration. As regeneration employs 50% of PLD genes, we intended to compare the expression profiles of these DEGs. We subjected the candidates to Fuzzy c-means clustering  (Kumar and Futschik, 2007)  to group the genes based on their expression profiles and generated seven clusters for each dataset (Fig. 6A, B ; Table  S3 ). After clustering the genes, we analysed the correlation of these clusters among regeneration and PLD by combing all 14 clusters and subjecting them to sample correlation analysis.
As observed from the dendrogram, the gene clusters with similar expression patterns between regeneration and PLD probably form a single branch (Fig. 6C ). For example, regeneration cluster R6 and PLD cluster D5 were grouped together: notably, these clusters had comparable expression profiles (Fig. 6A, B ). Similar results were also reproduced through correlation analysis: the box including R6 and D5 had a relatively high correlation value (Fig. 6D ). We found that the majority of the regeneration gene clusters exhibited overlap with specific PLD clusters (Fig. 6C, D ), suggesting that regeneration partly resembles PLD by displaying similar gene expression profiles. Overall, we found that late stages from both regeneration and PLD had a high correlation, thus the major variation in these data is among the early stages.
Because regeneration does not start from a zygote or amphiblastula larva, early regeneration stages do not necessarily need to follow a similar pattern of gene expression profile to normal PLD. The early primmorphs were not close to normal swimming larva in Comparison of DEGs between PLD and regeneration datasets. The similarity matrix for significantly differentially expressed genes (Padj (FDR)<0. 01 for any time point comparison against day 1 for regeneration and S1 for PLD). The yellow box highlights the similarity between late stages of regeneration (days 10-14, PJUV and JUV) with S5 and YS stages of PLD. (H) The number of DEGs in regeneration is highly comparable with PLD and ∼50% of genes are commonly expressed. A Fisher's exact test was carried out using the R package GeneOverlap to test the statistical significance between two gene lists.
As the tenrecs (and cages) were checked daily, we found detached loggers within 24 h and the corresponding data were omitted from analyses. Loggers for recording T a were fixed to the inside of each cage (to control the preciseness of the climate chamber) and each nest box (T n in the analyses). Temperature readings were averaged for every hour. As T skin of the tenrecs is very flexible and sometimes low even in the non-torpid state, it was not possible to define a torpor/non-torpor threshold for T skin. However, T skin was always either almost at T a , or distinctly above it. Thus, the animals were considered to be torpid when T skin was at or only slightly above T n (T skin -T n ≤ 2 • C), as confirmed by the obvious drop and increase in MR at the beginning and end of each torpor bout and of each activity phase (summer) or arousal (winter) (see Figure  1 ).
Measurement of Metabolic Rate Metabolic rate was measured via oxygen consumption with two differential oxygen analyzers (OxBox, FIWI, Vienna University, Vienna, Austria). Air was drawn at a rate of 50 L/h from the nest boxes, which served as respiratory chambers and were connected to the oxygen analyzers with airtight tubes (Tygon tubing ST, Saint-Gobain, Charny, France). Dividers in the middle of the nest boxes lessened air mixing between ambient air and the inside of the nest boxes. A gas splitter (RM Gas Flow Multiplexer, Sable Systems, Las Vegas, NV, United States) was used to rotate air flow amongst four different nest boxes. Thus, a total of eight animals could be measured at the same time using the two oxygen analyzers. During a 1-h long rotation cycle, all nest boxes were measured for 15 min and data stored every minute, and once per hour reference air was measured for 5 min to obtain a baseline value.
Hourly mean maximal T skin during arousals was highest for S const24 (Table  1 ); the highest overall recorded T skin was 32. 8 • C. T skin in the other treatments was slightly, but significantly lower (Tables  1, 2 ). In the S const24 condition, the animals maintained elevated T skin for about 14 h per day during the activity phase, but only for about 9 h, and significantly shorter, under S fluc19-28 , due to the passive heating phase under the fluctuating conditions (Table  2 ). Arousal or activity phases were significantly shortest in the S fluc19-28 treatment (about 7 h; Table  2 ) and intermediate in the W fluc14-24 treatment (Figure  2 ). The timing of the arousals differed significantly from a random distribution for all treatments (Figure  3 ). We excluded W cost12 from arousal data analysis as only two full arousals were recorded.
METHODS: Patients included into the BEYOND-SWIFT cohort (Bernese-European Registry for Ischemic Stroke Patients Treated Outside Current Guidelines With Neurothrombectomy Devices Using the Solitaire FR With the Intention for Thrombectomy; https://www. clinicaltrials. gov; Unique identifier: NCT03496064) were analyzed (n=2386). Association between baseline characteristics and ATG was evaluated using mixed linear regression analysis. The effect of increasing symptom-onset-to-admission and ATG intervals on successful reperfusion (defined as Thrombolysis in Cerebral Infarction [TICI] 2b-3) was evaluated using logistic regression analysis adjusting for potential confounders.
BEYOND-SWIFT Registry The BEYOND-SWIFT (Bernese-European Registry for Ischemic Stroke Patients Treated Outside Current Guidelines With Neurothrombectomy Devices Using the Solitaire FR With the Intention for Thrombectomy) is an investigator-initiated, international, multicenter observational registry evaluating the outcome of mechanical thrombectomy patients (https://www. clinicaltrials. gov; Unique identifier: NCT03496064). Details of the registry have been published previously. 6 In short, 7 comprehensive stroke centers included patients treated with a Medtronic market-released neurothrombectomy device (applied as first-line device) for patients presenting with large vessel occlusion acute ischemic stroke. Table  I  in the Data Supplement provides an overview of included patients and rates of available follow-up data for each center.
Written informed consent was obtained from patients, or the institutional review board waived the need for patient consent, according to regulations at each center/country (Table  I  in the Data Supplement). In Bern, ethical approval was given for pooling and analysis of the registry data (Kantonale Ethikkommission Bern, Bern, Switzerland; Local Ethics Committee Study Identifier: 2018-00766). For the present analysis, we also included 437 patients from another German center. 7 f 2397 patients included into the registry, 2127 were treated for anterior circulation large vessel occlusion strokes. Of these, there were 1953 patients with full documentation of STA, ATG, and STG, and 1949 had available angiography runs to evaluate reperfusion success. Using the same selection criteria for the additional German center, of 547 patients, 437 were included.
Cohort Two thousand three hundred eighty-six patients were included (median age, 74. 7 years; IQR, 62. 2-82. 0; 51. 2% women; Table  1 ), of which 2008 (84. 2%) were successfully reperfused (TICI 2b-3, including 54. 4% with TICI 2c/3 reperfusions). Rates of successful reperfusion and TICI 2c/3 differed across centers (Figure I in the Data Supplement) and by year of patient treatment (odds ratio per year increase after 2015, 1. 16 [95% CI, 1. 05-1. 28]; Figure II in the Data Supplement). Additionally, we observed an association between interventional technique applied and reperfusion success, with the highest rates of successful reperfusion or TICI 2c/3 observed in patients treated with stent retriever and a balloon guide catheter (Figure III in the Data Supplement). Median STA delay was 150 minutes (IQR, 72-265 minutes), whereas median ATG interval was 73 minutes (IQR, 47-102 minutes).
Manifold learning techniques have become increasingly valuable as data continues to grow in size. By discovering a lower-dimensional representation (embedding) of the structure of a dataset, manifold learning algorithms can substantially reduce the dimensionality of a dataset while preserving as much information as possible. However, state-of-the-art manifold learning algorithms are opaque in how they perform this transformation. Understanding the way in which the embedding relates to the original high-dimensional space is critical in exploratory data analysis. We previously proposed a Genetic Programming method that performed manifold learning by evolving mappings that are transparent and interpretable. This method required the dimensionality of the embedding to be known a priori, which makes it hard to use when little is known about a dataset.
alone, out of the 29,500 total all-time mentions  1. MaL, also called nonlinear dimensionality reduction, is an unsupervised learning problem based on the observation that most high-dimensional data is only artificially high -that is, most data can be represented in a much lower-dimensional space by discovering the intrinsic structure within a dataset, called an (embedded) manifold  [5]. MaL algorithms use this assumption to attempt to discover the best low-dimensional embedding that can be found for a dataset, such that as much high-dimensional structure as possible is maintained in the lower-dimensional space. A common categorisation of MaL algorithms is based on whether they are "embedding" or "mapping" methods  [20]. Embedding methods directly optimise the lowdimensional manifold by treating the task essentially as a numerical optimisation problem, typically producing only the final embedding as a result.
Mapping methods, in contrast, provide a mapping or model that converts the original feature space into the low-dimensional manifold. These mapping methods have two particularly desirable properties: they can be re-used on future instances without having to reoptimise; and they have the potential to be interpretable in terms of the original highdimensional features, which is of particular importance in many exploratory data mining tasks such as medical diagnosis, automatic credit score calculation, and trustworthiness of climate change models  [31]. Mapping methods also have the potential to be more efficient/higher-performing than embedding ones: on datasets with a very large number of instances, it becomes difficult to optimise each instance's embedded values directly, and so learning a mapping for all instances may be more viable.
For true exploratory data analysis, this information is unlikely to be available. In this paper, we aim to develop GP-MaL further by proposing a multi-objective (MO) version (GP-MaL-MO) that can automatically find a good trade-off between the dimensionality of the embedded manifold and the amount of structure that is maintained within it. More specifically, this paper will: -Introduce a revised GP multi-tree representation that allows for a variable number of trees in an individual, as well as new crossover and mutation operators that allow for the addition/removal of trees; -Formulate GP-based MaL as a multi-objective problem with two objectives representing manifold quality and manifold dimensionality; -Evaluate the trade-off (Pareto front) between these tasks on a variety of commonlyused datasets; -Compare GP-MaL-MO to existing MaL methods across a range of different embedded manifold sizes; and -Investigate if GP-MaL-MO has the potential to evolve interpretable manifolds.
Unsupervised Learning Unsupervised learning  [14]  is a category of machine learning in which there are no pre-existing labels (or "guidance") available for datasets. In contrast to supervised learning, which attempts to learn to re-create the provided labels, in unsupervised learning algorithms must discover inherent structure or useful intrinsic patterns within a dataset. The most common and prototypical example of an unsupervised learning problem is cluster analysis  [1] , where data is grouped into a number of clusters that contain instances that are closely related by some aspect of their feature distributions. Other common tasks include anomaly detection  [6] , blind signal separation  [8] , and -core to this work -nonlinear dimensionality reduction  [20].
Dimensionality Reduction Dimensionality Reduction (DR) is, simply put, the task of reducing a high-dimensional space (or dataset) to a smaller one  [24]. DR techniques are most commonly used when datasets are too large to be used directly with existing machine learning techniques, or when we wish to understand the structure of a dataset, which is infeasible at even relatively small dimensionality. The two most common DR approaches are feature selection and feature construction (FC) (or extraction)  [23]. FS is the more intuitive of the two: FS methods seek to find a subset of the original feature set that maintains properties of the data sufficiently well. For example, in a classification task, FS may be performed by finding the smallest subset of features possible that does not result in a meaningful decrease in classification accuracy  [25].
FS and FC have seen wide application in supervised learning tasks  [41, 39] , including EC-based FS  [43]  and FC  [32, 42]. FS and FC have also been used in unsupervised learning tasks  [12] , but little research has considered EC-based approaches for these tasks  [2]. In particular, only a few papers have used EC-based FC for unsupervised learning: mostly in clustering tasks  [4, 7] , but also for the creation of benchmark datasets  [38, 21].
Manifold Learning Manifold learning is the task of discovering the inherent structure within a highdimensional dataset, so that that it can be represented by a much lower-dimensional space  [3]. MaL is an unsupervised learning problem, as only the relationships between instances are considered in searching for this structure. "Embedding" MaL methods can be regarded as a type of optimisation problem, where the feature values of the low-dimensional space (embedding) are optimised according to some cost function based on the high-dimensional space. The "mapping" MaL methods, however, naturally represent a FC problem: given a number of features (the highdimensional space), the method must search for the best way to combine the these features into a low-dimensional space, using a cost function to measure the quality of a mapping.
GP Representation We utilise the same fundamental GP representation as in GP-MaL: a multi-tree GP, where each individual I contains a list of t = |I| trees, I = [T 1 , T 2 ,. , T t ], each of which represents a single dimension in the low-dimensional manifold. In other words, each tree is a single constructed feature, which represents some part of the highdimensional structure of the dataset. Unlike in GP-MaL, GP-MaL-MO does not fix t in advance for all individuals in the population. Instead, it randomly initialises the number of trees in each individual, and then allows individuals to gain/lose trees throughout the evolutionary process via a mutation operator. The terminal set consists only of the d real-valued input features, each uniformly scaled to the range [0,1] to reduce bias. Unlike in GP-MaL, we do not include ephemeral random constants as their removal did not reduce performance.
GP-MaL proposed a fitness function that measures the similarity between an instance's neighbour orderings in the high-dimensional space (N) and low-dimensional space (N ): Similarity(N, N ) = ∑ a∈N Agreement(|Pos(a, N) -Pos(a, N )|) (1) where Pos(a, X) gives the index of neighbour a in the ordering X, and the Agreement function gives higher results for smaller deviations between the orderings. An Agreement function based on a Gaussian weighting was used to punish larger deviations more harshly. The fitness was then calculated as the normalised similarity across all pairs of instances in the dataset. GP-MaL-MO uses a similar approach, but instead of using a Gaussian weighting, we instead use Spearman's rank correlation coefficient  [11, p. 502 ]. This eliminates the need to set the µ and θ hyper-parameters of the Gaussian weighting, while also being a more well-understood method of comparing rankings.
Hence, we calculate the Cost (lack of quality): Cost(N, N ) = 1 -Correlation(N, N ) 2 (3) This gives a cost function in the range [0,1], where 0 represents a perfect preservation of an instance's neighbourhood ordering, and 1 a complete reversal. The cost of a given GP individual, I, is then the mean cost across all instances in the dataset (X): Cost(I) = 1 n ∑ x∈X Cost(N x , N x ) (4) GP-MaL-MO hence aims to minimise both the two conflicting objectives of Cost and the number of trees (t).