dataesr/scientific-paragraphs-categorization huggingface dataset:
The loss of information is less. The third option is a data transformation, which must have the following properties. This transformation must be continuous, dilate the gaps between the weak values, and contract those between the strong values in order to make the distribution more symmetrical and thus rebalance the weight of useful information. Several possibilities exist. The first is to apply an inverse function (1/x), which results in reversing the order of the data but is only applicable when the extremes correspond to strong values, without values close to zero. A second option is a transformation by the square root or any other power between 0 and 1. This transformation will have the effect of dilating the weak values and contracting the strong values but may be insufficient, or less efficient than a logarithmic transformation, as we observed on the Occitanie dataset.
Impact on Process Analysis The main difference between PCA on raw data and transformed data, and in particular on log-transformed data, is that the effect of extreme values is reduced. The dispersion of the observations is thus distributed in the first score plots of the PCA, making it possible to better represent the significance of the factorial axes, without the interfering effect of a small number of samples. This results in better visualization and efficiency of the analysis, which is measured, on the Occitanie database treated in this work, by the percentage of variance explained by the first four factorial axes, increasing from 60 to 71. 5%. With or without transformation of the data, the first score plot PC1-PC2, which carries about half of the information, reflects the same realities (minerality and fecal contamination of the water), the same processes, but in a much more efficient way with log conditioning.
For illustration, if we look at the EC parameter in the Occitanie region, the range is between 20 and 50 µS cm -1 for mountainous crystalline basement areas, 250 and 500 µS cm -1 for karst environments, and 800 and 1000 µS cm -1 within coastal plains. In this case, the log transformation can be very useful to simply approximate the set of values to a normal distribution. (2) Although the deviation from a normal distribution is troublesome, the consequences are less severe than for the calculation of principal components. Indeed, in the processing of large databases such as SISE-EAUX  [25] , dimensional reduction is an essential step for the synthetic mapping of groundwater quality data at the regional scale. It is part of a proven procedure for grouping groundwater bodies that are similarly more or less vulnerable to contamination via similar mechanisms  [29].
The more information the main factorial axes convey, the more efficient the dimensional reduction, and the better the clustering of groundwater body families and consequently the analysis of the mechanisms responsible for the variability of the chemical composition of the water within each groundwater body family. Log transformation of data is, therefore, necessary for large databases that are likely to have extreme values, not for approximation to a normal distribution, but for a better dimensional reduction in the data hyperspace and clustering of groundwater bodies, which will facilitate analysis of the processes responsible for quality variability. The advances of this study carried out on Occitanie are currently being introduced in other French administrative regions (Provence-Alpes-Côte d'Azur, Bourgogne-Franche Comté, Auvergne-Rhône-Alpes, etc.
) as well as in the territory of Corsica. Taking into account the extreme values in the databases, but attenuating their weight, allows a better vision of the spatial and temporal variability, leading to a better grouping of groundwater bodies intended for human consumption. Quality monitoring and surveillance by health agencies can thus be optimized, resulting in lower costs. Subject to the existence of similar databases in other European countries, the procedure could be applied there. Although correlation is not proof of causality, the multiple correlations illustrate that the way in which the data are conditioned can reveal a correlation between fecal contamination and all the other parameters. The calculation of the R 2 (Table  1 ) quantifies this correlation and illustrates that Log conditioning is the most efficient.
The square root conditioning reveals this correlation, but compared to the Log conditioning, it explains only half of the variance (26% vs. 49%). Such a proportion in the explained variance highlights that the extreme values monopolize a large part of the useful information conveyed by the factorial axes, masking the influence of the bulk of the database, which is revealed by the Log transformation. We also notice that the few samples with extreme values do not follow the same correlations between parameters as the majority of the other observations in the database. Indeed, if these correlations had been similar, the logarithmic or square root transformation would decrease their coordinates on the principal components, but would not change the percentage of variance explained. The increase in explained variance, therefore, reflects a reduction in statistical background noise  [30]  and an improvement in the definition of principal components for the majority of observations.
We can also note an effect on the mapping of the main PCs. If the reduced sill/Nugget ratio increases between the raw data, square root and log transformation, this reflects that the essential and structured information is concentrated on the first factorial axes. In other words, with the log transformation, useful and spatially well-structured information is concentrated in the first axes while unstructured variability is redistributed to the following PCs. With the raw data, the unhelpful and spatially ill-structured information already contaminates the first factorial axes. Thus, the information contained in the extracted database is better structured in terms of variance distribution but also in terms of spatial distribution after log transformation rather than using the raw data without conditioning.
Conclusions In large-dimension multiparametric databases on the quality of water intended for human consumption, the risk of the appearance of extreme values is high, especially for metals and bacteriological parameters. A few water analyses concentrate most of the information, which disturbs the study of the rest of the information. To this effect, the Log transformation appears to be the best option allowing (1) to reduce, without eliminating or limiting them, the influence of extreme values which otherwise have an important impact on the global variance, even if they are few in number;  (2)  to highlight certain correlations between bacteriological and physicochemical parameters; (3) to significantly increase the readability of the factorial axes as well as the identification and the cartographic representation of the associated processes responsible for the variance within the database.
FRAILTY INDEX. We used the Rockwood cumulative deficit approach to construct a 41-item frailty index (FI). 3] [24]  Overall, at least 30 items are required, and these should cover a range of body systems and be associated with health but not part of normal aging (although deficits should generally increase with age). The items included were derived from medical history, vital signs, the EuroQoL-5 Domain questionnaire (quality-of-life measures, including functional status), and laboratory data (Supplemental Table  1 ). Each patient was assigned a score for each nonmissing item, and the FI score was calculated as the sum of these scores divided with the total number of nonmissing items, with higher scores indicating greater frailty. Binary variables were scored as 0/1 (absent/present); ordinal variables were scored from 0 to 1 (1 indicating the greatest severity); and continuous variables were categorized and scored as 0/1 (normal/abnormal).
Patients with $20% missing items were excluded. 10, 11, 26, 27  Prespecified safety analyses included hypotension, an elevation of serum creatinine, an elevation of serum potassium, angioedema, and liver-related adverse events. We also examined falls and fractures using data from investigator-reported adverse events. 2 ). However, there was a trend toward a greater reduction in total HF admissions and hospitalizations due to cardiovascular or any causes with sacubitril/ valsartan in patients with FI class 3 (most frail). When examined as a continuous variable, there was an interaction between FI and the effect of treatment on total HF admissions (P interaction < 0. 001). As with the SAFETY ANALYSES. Increasing frailty was associated with an increased risk of adverse events, except for hypotension, which was inversely associated with frailty, and discontinuation of trial treatment (both sacubitril/valsartan and valsartan) (Supplemental Table  4 ).
favorable than expected. These findings challenge the common reluctance of clinicians to introduce new therapies to patients who are perceived to be frail because of doubts about the benefit of treatments in these patients. More education is needed about the importance of frailty, its assessment, and implications in cardiology. FUNDING SUPPORT AND AUTHOR DISCLOSURESThe PARAGON-HF trial was funded by Novartis. Dr McMurray is supported by British Heart Foundation Centre of Research Excellence Grant RE/18/6/34217. Dr Butt has received advisory board honoraria from Bayer, outside the submitted work. Dr Jhund has received consulting fees from Novo Nordisk AS, Novartis, AstraZeneca, and Boehringer Ingelheim. Dr Anand has received consulting fees from Amgen, ARCA Biopharma, AstraZeneca, Boston Scientific Corporation, Boehringer Ingelheim, LivaNova, Novartis, and Zensun.
3 3 favorable than expected. These findings challenge the common reluctance of clinicians to introduce new therapies to patients who are perceived to be frail because of doubts about the benefit of treatments in these patients. More education is needed about the importance of frailty, its assessment, and implications in cardiology. FUNDING SUPPORT AND AUTHOR DISCLOSURESThe PARAGON-HF trial was funded by Novartis. Dr McMurray is supported by British Heart Foundation Centre of Research Excellence Grant RE/18/6/34217. Dr Butt has received advisory board honoraria from Bayer, outside the submitted work. Dr Jhund has received consulting fees from Novo Nordisk AS, Novartis, AstraZeneca, and Boehringer Ingelheim. Dr Anand has received consulting fees from Amgen, ARCA Biopharma, AstraZeneca, Boston Scientific Corporation, Boehringer Ingelheim, LivaNova, Novartis, and Zensun.
Moreover, powder density was determined by helium pycnometry, using a Micrometrics Accupyc II 1340 pycnometer (Nocross, GA, USA) at 23 • C. The structural properties were analyzed by the X-ray diffraction (XRD) technique (Bruker D8 Advance with DAVINCI design, Karlsruhe, Germany) using Cu-Kα radiation (λ = 1. 5406 Å). The XRD pattern acquisition conditions are: angular step of 0. 02 • , time per step of 0. 3 s, angle range 20 • -80 •. The morphology of the copper particles was investigated using a scanning electron microscope (SEM) type LEO 1530VP (Oxford, UK) at an accelerating voltage of 10 kV. This SEM is equipped with an energy-dispersive X-ray spectroscopy (EDS) system, controlled by INCA Energy software, for composition analyses. Particles' cross-sections were prepared by FIB, type Zeiss Crossbeam 550 (Oberkochen, Germany), to reveal their internal microstructure.
Devices and Test Set-Up Device Under Test (DUT) The devices under test (DUT's) are commercial vertical planar-gate SiC power MOSFETs in a TO-247 molded packages. For this device, nominal maximum ratings are VDS = 900 V, VGS from -4 V to +15 V, IDS = 23 A and on-resistance (Ron) is 120 mΩ. Electrical characterizations, including IDS-VGS and IGS-VGS measurements, have been performed before and after irradiation for each device. To evaluate the irradiation effect, a dedicated test board following the MIL-STD-750E test standard has been designed and fabricated. The test board containing DUTs is placed under the radiation beam while drain and gate voltages are controlled. Source measurement units (SMUs) are used to supply voltages and to measure currents in the control room. The SMUs were controlled and data were collected via Ethernet and USB interfaces.
Acknowledgements: The authors thank  Yannick Coppel  ( LCC-Toulouse ) for recording CP-MAS spectra,  David Neumeyer  ( CEMES ) for recording TGA spectra,  Christian Bourgerette  (CEMES) and  Isabelle Seguy  ( LAAS ) for help in spin-coating experiments. We gratefully acknowledge  Nathalie Saffon , from the  Institut de Chimie de Toulouse , who solved the X-ray structure. Vladimír Vrkoslav (IOCB, Prague) is gratefully acknowledged for the MALDI experiments. Colin Martin  ( NAIST-CEMES ) is thanked for comments and corrections of the manuscript. A. J. acknowledges funding from the  Foundation EXPERIENTIA  and from  ERDF/ESF  " UOCHB MSCA Mobility " (No. CZ. 02. 2. 69/0. 0/0. 0/17 050/0008490 ). This research has received funding from the  EraNET Cofund Initiatives QuantERA  under the European Union's  Horizon 2020 research and innovation programme  grant agreement  ORQUID.
Conclusions Unit 18 at the Castillo Cave entrance is extremely interesting from the chronological point of view given the numerous published 14 C dates and their homogeneity, with an average value of > 42 ka. Furthermore, the archeological data provide the identification of a transitional industry between the end of the Middle and the beginning of the Upper Paleolithic, as summarized in the "El Castillo Cave: Unit 18" section. Taking into account the solid chronology of El Castillo Cave Unit 18, we must consider that the human group, or groups, represented by the teeth of these children corresponds to recent Neanderthals (MIS 3), as do other fossils found in the Iberian Peninsula (e. g. ,  Garralda 2005; Rosas et al. 2006; Lorenzo et al. 2012; Wood et al. 2013; Garralda et al. 2014; Trinkaus and Walker 2017) , many of which lack such solid chronostratigraphic data.
Acknowledgements We thank the Direction of the MUPAC which authorized access to the fossils, and the kind collaboration of the Museum restorer E. Pereda. We are also deeply grateful to  P. Saura ,  R. Asiaín , and  L. Drak  for several pictures of El Castillo fossils. We are especially indebted to  Drs. D. Frayer  and  B. Vandermeersch  for their discussions and support. The authors also gratefully acknowledge the curators and institutions for providing access to original specimens, particularly to  J. -J. Cleyet-Merle  at the  Musée National de Préhistoire in Les Eyzies ,  H. de Lumley  at the  Institut de Paléontologie Humaine ,  R. Kruszinsky  and  C. Stringer  at the  Natural History Museum in London ,  J. Radovčić  at the  Hrvatski Prirodoslovni Muzej in Zagreb , and  C. Duarte  at the  Instituto Português do Património Arquitectónico (Conservaçaõ e Restauro) in Lisbon.
Morphology Four scops-owl individuals from Príncipe (including the vouchered specimen) were captured in the field. These were measured together with representatives of four of the five species of the Afro-Palearctic clade (sensu  Pons et al. 2013)  and O. brucei obsoletus  (Cabanis, 1875). The latter was included due to the potential affinities of O. brucei (Hume, 1872) with African species  (Pons et al. 2013). The Arabian Scops-Owl O. pamelae Bates, 1937 was not included, but we measured individuals of O. senegalensis feae  (Salvadori, 1903)  from Annobón Island, considered by some authors as a distinct species  (Collar and Boesman 2020; Gill et al. 2021). The morphometric dataset includes measurements collected from museum specimens and living individuals measured in the field (Table  1 ).
For the statistical analyses males and females were treated together due to the low sampling size, and the fact that most museum specimens were not sexed. Morphometric differences were explored using a Principal Component Analysis (PCA), performed using the FactoMineR package  (Lê et al. 2008 ) and carried out using R v. 3. 6 (R Core Team 2017) in RStudio v. 1. 1. 447 (RStudio Team 2015). Measurements were size-standardised to prevent the dominance of variables invol ving larger measurement units, thus allowing comparisons between variables. The wing formula was not used in the analyses. Since several individuals had missing data, to maximise the number of analysed specimens of each species only the following variables were used in the PCA: Bilen, Binares, Biwid, Tarlen, Wilen, and Tailen.
A constant of 3000 was added to each computed value to ensure that the dataset only included positive numbers. All measurements were logtransformed (base-10) to normalise distributions (McDonald 2014). PCA was performed using the FactoMineR package  (Lê et al. 2008 ) using R v. 3. 6 (R Core Team 2017) in RStudio v. 1. 1. 447 (R Studio  Team 2015). Measurements were size-standardised to make the variables comparable. Welch's ANOVA and Games-Howell post-hoc comparisons were used to test whether the groups differed from each other  (McDonald 2014).
Molecular data Blood samples were collected non-destructively from the brachial vein of mist-netted individuals and were stored in 96% ethanol for genetic analysis (see molecular dataset: Table  4 ). Blood samples of the scops-owl from Príncipe were collected at Boca do Inferno (1°36. 16'N, 7°24. 06'E, ca. 300 m a. s. l. ) (n = 2) and close to Ribeira Porco (1°33. 03'N, 7°22. 29'E, ca. 100 m a. s. l. ) (n = 2), both localities within Príncipe Obô Natural Park (Fig. 1 ; Suppl. material 9). We obtained four blood samples of O. hartlaubi (Giebel, 1872) from São Tomé (Suppl. material 9), and were lent one blood sample of O. senegalensis feae (Annobón), and one blood sample of Moheli Scops-Owl O. moheliensis Lafontaine & Moulaert 1998. We were lent additional samples from museum specimens, including toe-pad samples for one of each of the two subspecies of the Sandy Scops-Owl, O.
Successfully amplified products were purified and sequenced using dye-labelled dideoxy terminator cycle sequencing on an ABI 3730XL automated sequencer at Macrogen Inc. 1870) ] was performed in seven fragments of ca. 180-200 bp using custom-made primers (see Suppl. material 10). PCR products were cycle-sequenced in both directions using the Big Dye Terminator v. 3. 1. Sequences were read on an ABI 3100 capillary sequencer (Applied Biosystems, Foster City, CA, USA). Samples amplified and sequenced at Muséum national d'Histoire naturelle, Paris (MNHN) (Table  4 ) followed  Pons et al. (2013). Chromatograms of newly generated sequences were checked by eye, edited and aligned using BioEdit v. 7. 0. 5. 3  (Hall 1999). All newly determined sequences were submitted to GenBank (accession numbers provided in Table  4 ).
Dataset 1 ND2 sequences of the species belonging to the Afro-Palearctic clade and of O. brucei, whose taxonomic affinities are not resolved, although it appears to be closer to African and Indian Ocean islands species than to the Asian species  (Pons et al. 2013). This alignment contained 16 sequences belonging to the candidate species from Príncipe, the five species of the Afro-Palearctic clade, including the mainland and Annobón subspecies of O. senegalensis, and O. brucei. This dataset was used to compute the mean genetic distances matrix (uncorrected p-distance in percentage, using the pairwise deletion option) within and between taxa. Distances were computed using MEGA, v. 7. 0. 21 (Kumar et al. 2016).
Dataset 2 Molecular dataset used for the phylogenetic and divergence time analyses. This dataset comprised 51 individuals from 39 taxa and a final concatenated sequence alignment of 12,925 bp. This dataset was built with the sequences produced in this study together with previously available sequences. We used homologous sequences of the Tawny Owl Strix aluco Linnaeus, 1758 and of the Eurasian Eagle-Owl Bubo bubo (Linnaeus, 1758) for outgroup rooting. The input files for phylogenetic inference were prepared in "Pipelogeny"  (Muñoz-Pajares et al. 2019). Sequences were automatically aligned using the mafft algorithm  (Katoh and Standley 2013). The best model of molecular evolution and the best partition scheme to analyse the molecular dataset was identified using PartitionFinder2 v. 2. 1. 1  (Lanfear et al.
Datasets 3-6 The alignments of each nuclear gene were analysed separately to obtain evidence for genetic differentiation of lineages from unlinked loci and, hence, to provide further support to their status as distinct species following the criterion of genealogical concordance  (Avise and Ball 1990; Avise and Wollenberg 1997). These datasets contained the nuclear markers KIAA1239, MYO2, TGFB2 and TTN. In these four datasets we included all species of the Afro-Palearctic clade (sensu  Pons et al. 2013 ) and, when available, se quences of O. brucei. We trimmed all sequences to equal length (KIAA1239: 653 bp, MYO2: 131 bp, TGFB2: 403 bp, and TTN: 706 bp). Sequences were phased using the PHASE algorithm (v. 2. 1. 1) with default settings  (Stephens et al. 2001)  as implemented in the software DnaSP (v.
6. 12. 01; Rozas et al. 2017). PHASE parameters were 1000 iterations, one thinning interval and 100 burn-in iterations and a posterior probability threshold of 0. 95 to determine the most probable inferred haplotypes for each nuclear sequence. Analyses were repeated three times with different seed values. Haplotype network reconstruction of phased sequences was performed using the software TCS, v. 1. 21  (Clement et al. 2000). This software applies the method of  Templeton et al. (1992)  to calculate the minimum number of mutational steps between haplotypes, computing the probability of parsimony for pairwise differences until the probability exceeds 0. 95. This analysis was used to detect the occurrence and extent of haplotype sharing in the studied Otus species. The resulting networks were plotted using the online tool tcsBU  (Múrias dos Santos et al.
Molecular differentiation The ND2 sequences (Dataset 1; 1037 pb) of the four samples of Otus bikegila sp. nov. were identical (Table  5 ). Otus bikegila sp. nov. is a distinct mitochondrial lineage (Dataset 2; Fig. 6 ), belonging to the Afro-Palearctic clade. The genetic distance between this species and the other taxa included in Dataset 1 ranged between 4. 1% (O. bikegila sp. nov. vs. O. hartlaubi and O. scops) and 9. 1% (O. bikegila sp. nov. vs. O. brucei obsoletus). The smallest genetic distances were observed between O. senegalensis senegalensis and O. senegalensis feae (0. 7%), followed by O. senegalensis feae and O. pamelae (3. 0%); the highest value was recorded between O. bikegila sp. nov and O. brucei/O. hartlaubi (9. 1%). The concatenated sequences of the phylogenetic dataset (Dataset 2; 12,925 bp; Suppl.
material 2) were optimally partitioned in seven partitions (Suppl. material 15). The topology of the majority rule consensus tree (Dataset 2; Fig. 6 ) is largely in agreement with previously published phylogenies of the genus Otus  (Fuchs et al. 2008 ;  Bioacoustic parameters -F1: frequency at start; F2: frequency at end; F3: frequency at 25% of total duration; F4: frequency at midpoint; F5: frequency at 75% of total duration; F6: frequency at maximum amplitude; F7: maximum frequency; F8: minimum frequency; DT1: total duration; DT2: time to maximum amplitude; DT3: time to maximum frequency; DT4: internote interval; DF1: frequency drop from start to end; DF2: frequency range; DFT1: slope from 25% to 75% of total duration; DFT2: slope from midpoint to end. Pons et al. 2013) , and increased the resolution of these by resolving some po lytomies.