dataesr/scientific-paragraphs-categorization huggingface dataset:
Pielou evenness index was calculated as the Simpson index/ln (observed OTUs) in R. Non-metric Multidimensional Scaling (NMDS) was constructed with function metaMDS with the vegan package, performed with 1000 random iterations and based on Bray-Curtis dissimilarity matrix for the taxonomic relative abundance and the metabolic prediction tables. Permutational multivariate analysis of variance (PERMANOVA) considered surface or incubation mode (nested) as factors with 999 permutations. Biomarkers were discovered with linear discriminant analysis (LDA) with effect size (LEfSe)  (Segata et al. , 2011)  software using 5% for alpha significant value for Kruskal-Wallis test, and an LDA higher than 2. 0. LEfSe software was used to detect discriminant groups between the three surfaces incubated in dynamic mode, as well as between all surfaces incubated in the four incubation modes after 1 year.
, 2015)  software based on the relative abundance of OTU table (with assigned taxonomy with Silva database 123) for KEGG orthology enzymes, normalized by the 16S rRNA copy number. The functional prediction matrix was matched with a KEGG orthology (KO) table, corresponding KO with subsystems and metabolism (in-house script). Mean abundances of specific subsystems were used for statistical analyses of 2-way ANOVA using surface and mode of incubation as factors. Graphical representation (ggplot2) and statistical analysis were performed with R (version 3. 4. 2). Differences between time, surface or mode of immersion were tested with one or two-way ANOVA and Tukey HSD post hoc test. An unweighted pair group method with arithmetic mean (UPGMA) dendrogram based on Bray-Curtis dissimilarity was used to test grouping between samples with R functions.
, 2018). Here, we assumed that the greater similarity observed between samples in terms of function rather than taxonomy was due to a mixture of factors: redundancy, ecological coherence between taxa in higher ranks  (Philippot et al. , 2010)  and limited database information. Regarding the latter, the fraction of taxonomic units explained (FTU) metric indicated that function prediction was obtained from 65% of the community on average. Notwithstanding the bias inherent in functional prediction from taxonomic data, general trends regarding adhesion to the surface over time, and greater in modes with lesser shear stress, were observed. Cell adhesion depends on the surface hydrophobicity, roughness and chemistry. Furthermore, adhesion is facilitated by cell motility, the presence of flagella or pili, or the presence of an outer membrane and the LPS in the gram negative bacteria  (Gutman et al.
Using the automatic timing function of PlantScreen TM Scheduler (PSI, Drásov, Czechia), the phenotyping protocol was programmed to always start at the same time of the diurnal cycle (after 3 h of illumination in the climate-controlled growth chamber). Phenotyping protocol was recorded twice prior to biostimulant application in days 1 and 3 (pre-T measurements); three times post first biostimulant application in days 6, 8, and 10 (post T1 application) and twice post second biostimulant application in days 13 and 15 (post T2 application). The acquired images were automatically processed using Plant Data Analyzer (PSI, Drásov, Czechia) and the raw data exported into CSV files were provided as input for further analysis.
1 mm, 1. 8 µm) and a linear gradient (5-95% methanol in water, 34 min run time) foe elution, with a flow of 220 µL min - 1  at 35 • C. The mass spectrometric acquisition was done in positive polarity and extended linear dynamic range SCAN (100-1000 m/z). Features deconvolution and post-acquisition processing were done in Agilent Profinder B. 06. After mass and retention time alignment, compounds annotation was achieved using the 'findby-formula' algorithm based on monoisotopic accurate mass, isotopes spacing and isotopes ratio, with a mass accuracy tolerance of <5 ppm. The database PlantCyc 12. 5 (Plant Metabolic Network 1 ) was used for annotation purposes. Based on the strategy adopted, identification was carried out according to Level 2 (putatively annotated compounds) of COSMOS Metabolomics Standards Initiative 2.
Data Management and Statistical Analysis The data processing pipelines Plant Data Analyser (PSI, Drásov, Czechia) includes pre-processing, segmentation, feature extraction and post-processing. Values for projected shoot area were calculated from images taken in the visible light spectrum and correspond to volume estimation which were used as a proxy for the estimated biomass of the plants. Data were processed using MVApp application (mmjulkowska/MVApp: MVApp. pre-release_v2. 0; Julkowska et al. , unpublished). Using the MVApp, outliers were identified with the interquartile range rule as plants whose volume had a value 1. 5 times away from the mean. Those plants were removed from the data set. Statistical differences between treatments and time points were determined by one-way analysis of variance (ANOVA) with post hoc Tukey's Honest Significant Difference (HSD) test (P-value < 0.
05) performed using appropriate scripts in MVApp tool. Data are displayed as mean ± standard deviation of the six independent plants per treatment. Interpretation of metabolomic data was formerly carried out using Mass Profiler Professional B. 12. 06 as previously described  (Salehi et al. , 2018). Briefly, compound abundance was Log2 transformed and normalized at the 75th percentile and baselined against the median. Unsupervised hierarchical cluster analysis was formerly carried out using the fold-change based heatmap, setting similarity measure as 'Euclidean' and 'Wards' linkage rule. Thereafter, the dataset was exported in SIMCA 13 (Umetrics, Malmo, Sweden), UVscaled and elaborated for Orthogonal Projections to Latent Structures Discriminant Analysis (OPLS-DA) modeling. This latter multivariate supervised statistic allowed separating variance into predictive and orthogonal (i.
Metabolomics Analysis of Tomato Leaves for Understanding the Mode of Action of Selected PHs A metabolomic approach was used, following phenotyping analysis, aimed to strengthen at the molecular level the effects of the PHs on morpho-physiological traits and plant growth. Indeed, the understanding of the mechanisms through which PHs act on a plant can effectively support their actual implementation into agricultural practices and possibly suggest specific contexts for their optimal and profitable use. With this aim, an untargeted UHPLC/QTOF-MS analysis was performed and multi variate statistics used to point out similarities/dissimilarities among metabolomic profiles of the PH-treated plants. The combination of a high-performance untargeted profiling, together with a rather comprehensive database (PlantCyc), resulted in a large dataset (overall, almost 1600 compounds annotated).
A large chemical diversity was represented within the dataset, including compounds from a wide variety of biochemical classes and metabolic processes. The whole dataset, together with individual compounds' abundance and composite mass spectra, is provided in supplementary material (Supplementary Table  S8 ). As a first step of interpretation, a fold-change based hierarchical clustering was carried out (Figure  5 ). This unsupervised approach allowed producing two main clusters, one comprising all replications from the control and another including all PH-treated samples. In this latter, two further sub-clusters were evident, with products A and B being mixed together and with treatment I representing a separate sub-cluster. This unsupervised (i. e. , naïve) classification of metabolomic profiles, based on individual fold-change values for each compound annotated, suggested that the PH treatments imposed a change in the plant metabolomic profile, and that treatments A and B induced a more comparable effect whereas treatment I had a more distinctive effect.
ACKNOWLEDGMENTS We thank  Jaromír Pytela  for helping with the preparation of plant material for the phenotyping experiments at  Photon Systems Instruments (PSI) Research Center (Drásov, Czechia ) and  Zuzana Benedikty  for useful discussions during optimization phase of the phenotyping experiments at  PSI (Drásov, Czechia) ,  Petr Polach  for helping with raw images re-processing at  PSI (Drásov, Czechia ).
Co-interventions For patients undergoing arthroplasty, the choice of the surgical implant is left to the surgeon's discretion in both accelerated and standard care groups. All other perioperative management (monitoring, fluids, type of anaesthesia, analgesia and transfusions) and postoperative care are at the discretion of the attending anaesthesiologist, surgeon and medical specialist. Study personnel record data on co-interventions. Study investigators strongly encourage appropriate venous thromboembolism prophylaxis in all randomised patients. We also advocate early mobilisation within 12 hours of hip surgery in all randomised patients, unless medically or surgically contraindicated.
Functional independence measure (FIM) motor domain is determined at 30 days and 1 year, as validated in hip fracture patients. 20 The phone administration of the SF-36 questionnaire has also been validated in hip arthroplasty patients. 21 Research personnel are trained in administering the CAM, FIM and SF-36. Research personnel record all the trial data on case report forms with information entered directly into an electronic data capture programme (iDataFax).
Sample size The overall type 1 error rate for the two co-primary outcomes will be 5% (0. 05) and this will be partitioned between the two co-primary outcomes, taking into account the overlap between the outcomes (ie, all-cause mortality is a subset of the composite). Assuming a 20% overlap, with the pre-specified α of 0. 04 for the first co-primary outcome (all-cause mortality at 90 days), the α of 0. 012 for the second co-primary outcome (composite) was calculated via simulation. The sample size calculations were performed using a time-to-event analysis (Cox proportional hazards model comparison with two equal groups), two-sided α=0. 05 using Power Analysis and Sample Size software V. 13 (2014) (see table  1 ). With a sample size of 3000 patients, the HIP ATTACK Trial will have 88% power to detect a relative risk reduction (RRR) of 30% (ie, a HR of 0.
EthICs And dIssEMInAtIon We require documentation of Research Ethics Committee or Institutional Review Board (REC/IRB) approvals before sites are activated to enrol patients. All committees are described in detail in the Supplement File under the Supplemental Trial Groups and Investigators section. Investigators are informed of any protocol amendments, and REC/IRBs are requested to approve them. Research personnel or good clinical practice trained healthcare professionals participating in the study obtain written informed consent (online supplementary appendix 2) for each patient before randomisation. All data are stored on a centrally encrypted, high-security computer system and kept strictly confidential. The online supplementary appendix 3, presents the list of the HIP ATTACK trial participant sites and countries.
dissemination policy The knowledge dissemination plan includes traditional modes of dissemination (ie, publication in a policy-driving journal, national/international conference presentations) as well as the engagement of influential medical organisations (ie, emergency medicine and orthopaedic surgery organisations). Broader dissemination will be performed by the HIP ATTACK public website (http:// www. hipattacktrial. com), Twitter account (@HIPAT-TACKTrial), Facebook page and LinkedIn Profile. The Reducing Global Perioperative Risk Multimedia Resource Centre, linked to Elsevier's entire online global readership, will disseminate slide and audioinstructional videos, full-text articles, links to abstracts and data summaries. dIsCussIon Hip fractures are a worldwide health concern due to their high incidence, poor outcomes and high health economic costs.
MiRNAs are involved in a wide range of regulatory functions in both biological and pathological processes: cell/tissue development, differentiation, metabolism, homeostasis, proliferation and apoptosis, immune responses, and cancer development, progression, and response to therapy  [12]. MiRNA biogenesis and functions can be affected by: (i) epigenetic modifications, (ii) alteration in protein activity involved in this process  [13] [14] [15] [16] , (iii) single nucleotide polymorphisms (SNPs) in miRNA genes  [17, 18] , (iv) hereditary mutations in the miRNA seed region  [19] [20] [21] ; (v) somatic mutation in miRNA genes  [22, 23] , and (vi) somatic copy-number variation of miRNA genes  [24]. Information about all the discovered miRNAs can be found in databases such as miRBase (www. mirbase.
An AUC of 1 refers to perfect separability, whereas a value of 0. 5 shows no capacity to discriminate the datasets. Articles with methodological deficiencies were excluded from the review. Besides the described distinction between free (mainly protein-associated) and exosome-/ microvescicle-/LDL-associated circulating miRNAs and their distinct functions, from biomarker-like (free circulating miRNAs) vs. hormone-like (encapsulated circulating miRNAs)  [53] , based on the aim of this review, only free-circulating miRNAs were considered.
The same plasma miR-21 upregulation has been associated with miR-106 and miR-20a upregulation, and miR-223 downregulation. MiR-106a/miR-130b and miR-106a/miR-223 ratios, alone or in combination, effectively discriminate between lPC and BPH patients (AUC: of 0. 81, 0. 77, and 0. 84, respectively) with specificity higher than that of PSA  [231]. A subsequent study identified another plasma miRNA panel for PC diagnosis, composed of miR-4289, miR-326, miR-152-3p, and miR-98-5p, all upregulated in PC patients compared to healthy controls. ROC analysis revealed that each miRNA of this panel has diagnostic potential, but accuracy is highly increased for the whole panel (AUC = 0. 82-0. 95) [232]. However, according to the Cancer Genome Atlas (TCGA) dataset, miR-152-3p expression in PC tissue may be downregulated compared to adjacent normal tissue.
81), and high-risk from low-risk subjects (AUC = 0. 71)  [235]. WB miR-18a, a previously identified oncomir, discriminated PC patients from both BPH and healthy groups (AUC = 0. 81), and its high circulating levels are correlated with more aggressive tumor behavior and advanced clinical stage  [236]. Another set of serum miRNAs (let-7c, let-7e, let-7i, miR-26a-5p, miR-26b-5p, miR-25-3p, miR-18b-5p), found altered in PC tissue, discriminate PC patients from BPH. Of these, miR-25-3p, miR-18b-5p, and their combination gave the highest sensitivities and specificities in predicting PC (AUC: 0. 79, 0. 87, and 0. 92, respectively)  [237]. Based on the TCGA database, circulating miR-200b and miR-200c have been validated as potential diagnostic markers for PC (AUC: 0. 57 and 0. 62, respectively). Compared to healthy controls, in plasma from PC patients, miR-200b was downregulated and is correlated with bone metastasis, whereas miR-200c was upregulated and is associated with more aggressive behavior  [238].
Context. Understanding the evolutionary history of the Magellanic Clouds requires an in-depth exploration and characterization of the stellar content in their outer regions, which ultimately are key to tracing the epochs and nature of past interactions. Aims. We present new deep images of a shell-like over-density of stars in the outskirts of the Small Magellanic Cloud (SMC). The shell, also detected in photographic plates dating back to the fifties, is located at ∼ 1. 9 • from the center of the SMC in the north-east direction. Methods. The structure and stellar content of this feature were studied with multi-band, optical data from the Survey of the MAgellanic Stellar History (SMASH) carried out with the Dark Energy Camera on the Blanco Telescope at Cerro Tololo Inter-American Observatory.
We also investigate the kinematic of the stars in the shell using the Gaia Data Release 2. Results. The shell is composed of a young population with an age ∼ 150 Myr, with no contribution from an old population. Thus, it is hard to explain its origin as the remnant of a tidally disrupted stellar system. The spatial distribution of the young main-sequence stars shows a rich sub-structure, with a spiral arm-like feature emanating from the main shell and a separated small arc of young stars close to the globular cluster NGC 362. We find that the absolute g-band magnitude of the shell is M g,shell = -10. 78 ± 0. 02, with a surface brightness of µ g,shell = 25. 81 ± 0. 01 mag arcsec -2 Conclusions. We have not found any evidence that this feature is of tidal origin or a bright part of a spiral arm-like structure.
Canon 200 lens data In our data, the SMC over-density was first detected in deep images taken with an equipment consisting of a Canon EF 200 mm f/2. 8L II USM and a SBIG STL-11000M CCD camera during two observing runs in 2009 August and September at the European Southern Observatory (La Silla, Chile). This setup provided a total field of view (FoV) of 10 • × 7 • and a pixel scale of 9. 27 arcsec pixel -1. A set of 58 individual exposures of 300 sec were taken in a Luminance filter (see, e. g. , Fig. 1  in  Martínez-Delgado et al. 2015) , with a total exposure time of 290 minutes. This first image is showed in Fig. 1  (panel B), with the SMC over-density marked with label d. Standard data reduction procedures for bias subtraction and flat fielding were carried out using the CCDRED package in the Image Reduction and Analysis Facility (IRAF 1  ).
SMASH data SMASH  (Nidever et al. 2017 ) was carried out using the Dark Energy Camera  (DECam, Flaugher et al. 2015)  on the 4-m Blanco Telescope at Cerro Tololo Inter-American Observatory, Chile. The survey, covering 480 deg 2  of the sky, aims to explore the extended stellar populations of the Clouds and their interaction history. For our current work we use a small subset of the SMASH observations. The region of the observed shell in the Canon images is well covered by SMASH in Fields 9 (central coordinates (J2000): α =01:01:27. 40, δ =-70:43:05. 51), 14 (α =01:20:26. 78, δ =-71:15:32. 76), and 15 (α =01:24:33. 52, δ =-72:49:30. 00), which are part of a ring of fields surrounding the central part of the SMC. Each DECam field covers a FoV of 3 deg 2. Each of these fields was observed in the ugriz bands, with deep observations of 999s in u, i, and z, and 801s in g and r.
In addition, for each field we obtained three shorter exposures (60s) with large offsets to tie all the chips together photometrically and to allow us to cover some of the gaps between the CCDs. Details on the data processing and photometry can be found in  Nidever et al. (2017). The photometry of the DECam images was carried out using the photometry program DAOPHOT  (Stetson 1987)  as incorporated into IRAF. In order to identify point sources in the photometry we imposed the following cuts in the DAOPHOT parameters: -1. 0 < SHARP < 1. 0 and χ 2 < 3. 0. We also required that the Sextractor  (Bertin & Arnouts 1996)  stellar probability index was PROB > 0. 8. Since the Fields 9, 14, and 15 cover a contiguous part of the sky and overlap with each other, we took care to avoid double entries in the final combined catalog.
A full SFH derivation, which is beyond the scope of this paper, would be necessary. Additional considerations brought up by the analysis of the young cluster (see Sec. The metallicity of the oldest isochrone is Z=0. 001 dex, whereas we have adopted a metallicity of Z=0. 002 dex for the younger isochrones (see text). Bottom left: i 0 vs. gi 0 synthetic CMD computed using the BaSTI stellar population synthetic program web tool for a constant SFH from 13. 5 Gyr to 30 Myr ago. The solid lines are the same isochrones as shown in the top row. The horizontal dashed line corresponds to our absolute magnitude cut of M i = 3 mag for deriving the colour function (see text). Bottom right: i 0 luminosity functions of the shell, ConF1, and the synthetic CMD. The horizontal dashed line again corresponds to M i = 3 mag and demonstrates that at fainter magnitudes completeness starts to become an issue for the shell and ConF1 CMDs.
Stellar Kinematics Using the Gaia DR2 data  (Gaia Collaboration et al. 2018) , proper motions for thousands of stars in the SMC outer region have recently became available. We use this catalog to investigate the kinematic signature from the shell-like feature. From the DR2 database, we select stars surrounding the SMC and apply a series of astrometric cuts. We start by applying a parallax cut of ω < 0. 2 mas in order to remove foreground MW stars. Next we use a cut to the renormalized unit weight error (as described in the gaia technical note GAIA-C3-TN-LU-LL-124-01) of 1. 40 and a cut for the color excess of the stars (as described in Lindegren et al. 2018 by Equation C. 2). As astrometric precision has a strong relationship with the magnitude of the stars, we additionally apply a cut of G < 18.
Acknowledgements. We thank  David Hogg  for his help with the astrometry solution of the image used in this work. DMD thanks  Prof. Ken Freeman  for a discussion about the detection of the SMC shell in the photographic plates of the Clouds taken in the 1950s. DMD also thanks the hospitality and fruitful discussion about this work with the European Southern Observatory Garching headquarters staff during his stay as part of the  ESO visitor program  in September 2017. DMD, EKG, BL and LI acknowledge support by  Sonderforschungsbereich (SFB)   881  " The Milky Way System " of the  German Research Foundation (DFG) , particularly through sub-projects  A2 ,  A3  and  A5. DMD acknowledges support from the  Spanish MINECO  grant  AYA2016-81065-C2-2. This project used data obtained with the  Dark Energy Camera (DECam) , which was constructed by the  Dark Energy Survey  (DES) collaboration.
M-RC and CB acknowledge support from the  European Research Council (ERC)  under the European Union s  Horizon 2020 research and innovation program  (grant agreement No. 682115 ). BCC acknowledges the support of the  Australian Research Council  through  Discovery  project  DP150100862. T. d. B. acknowledges support from the  European Research Council  ( ERC StG-335936 ). This work has been supported by the  Spanish Ministry of Economy and Competitiveness (MINECO)  under grant  AYA2014-56795-P. MS acknowledges support from the  ADAP  grant  NNX14AF81G. R. R. M. acknowledges partial support from project  BASAL AFB-170002 as well as FONDECYT  project  N • 1170364. Y. C. acknowledges support from  NSF  grant  AST 1655677. Based on observations at  Cerro Tololo Inter-American Observatory, National Optical Astronomy Observatory  which is operated by the  Association of Universities for Research in Astronomy (AURA)  under a cooperative agreement with the  National Science Foundation.
Research Article Overall, these data suggest that spreading the nanocubes from the point of injection to the periphery under MH contributed to the direct heat damage. It also promoted the spreading of DOXO all over the tumor. Moreover, the nanocube heating likely improved the permeability of the cells/ stromal to DOXO, resulting in complete tumor suppression. In Vivo Biodistribution and Clearance of TR-Cubes. Together with the efficacy, a MRI biodistribution study on healthy mice (with no tumors) provided information on the clearance and biocompatibility of pristine TR-cubes at a relevant dose for hyperthermia. A 1 mg portion of iron per mouse was injected intravenously (i. v. ), which corresponds to 1. 5 times the dose needed for intratumoral MH. The state of health of the animals was monitored after injection, and MRI was used to obtain images of them at different time points over 8 months.