dataesr/scientific-paragraphs-categorization huggingface dataset:
Such comparisons may miss broader trends that may be apparent by comparing HEAs against the full range of structural metal alloys. The purpose of this paper is to explore the world of CCAs, which also include HEAs, by comparing their mechanical properties against a broader range of commercial structural alloys. We illustrate the effect of alloy composition (alloy family, number of components, phases and effect of Al, Cr and Cu) on mechanical properties using a dedicated database and property charts that summarize key findings and show trends and profiles. This database reflects the state of the art of the field of HEAs and CCAs by using published mechanical properties data  [4,. The majority of published CCA studies provide only hardness and compression data of as-cast alloys, and a growing number gives tensile properties of thermomechanically processed alloys.
The database contains a total of 325 CCA alloys with properties that are not equally populated for every alloy due to the lack of literature data: CES software  [71]  was used to browse the database and to plot properties or combinations of properties and to select constraints to screen and rank materials behavior. The reported results are divided into two sections. In the first one we show a panoramic view of the world of CCAs, and illustrate how they compare with commercial structural alloys. In the second part, we explore a closer view of the effects of composition on mechanical properties. We rely on the notion of material-property spaces and materials indices as defined by M. Ashby  [72].
Materials network To organize the world of CCAs into families, we represent 110 alloy compositions reported in the literature as a network (Figure  2 ). As a social network analysis maps relationships between people, this alloy network analysis (ANA) visually shows relationships between the alloys. Each node is a unique alloy, and a line linking two nodes indicates the sharing of at least one common element. The strength of a link is proportional to the number of elements shared between two nodes. Modularity  [73]  detects alloy families, defined as groups of densely interconnected nodes (alloys) that share many common elements and have sparser connections with the rest of the network. The spatialization of nodes is obtained using the ForceAtlas2 algorithm implemented in the Gephi software  [74] , in which nodes repulse each other while links act as springs attracting the nodes they connect.
In the extreme, the 4f transition metal alloy family (lanthanides) shares no elements with any of the other four alloy families, so the 4f family bubble is totally isolated from the others (no overlap and no links). Based on this network analysis, we have developed a material database with a tree-like structure. The universe of CCAs contains the 5 families found from the ANA, plus ceramic CCAs (oxides, borides, carbides, and nitrides). Each family encompasses two or more classes, each characterized by a unique formula (e. g. AlxCoCrFeNi), and each class encloses members having the same elements but with different proportions. Each member (alloy) is characterized by a set of properties and a listing of these properties makes up a record for that material. The database contains a total of 325 alloys with their mechanical and physical properties.
7b ). Here, the alloy strategy is defined by the combination of N principle elements that, by definition, each have significant concentrations. The alloy density is thus likely to trend toward average values  [78, 79]. Fig. 7b  shows that the density range is largest for the pure metallic elements (N=1) from Li (534 kg/m 3 ) to W (1930 kg/m 3 ), and continually decreases as N increases. The density limits also show a systematic averaging trend, so that the minimum and maximum densities for N=6+ are contained within the minimum and maximum densities for N=4-5, which are contained within the N=2-3 dataset (from 1770 to 20100 kg/m 3 for the commercial alloys Mg-12Li and W-50Re, respectively), which are contained within N=1. The overall density for each dataset trends toward higher values as N increases, but always remains within the range exhibited by the pure elements.
There is no significant difference in the mid-point densities for the N=4-5 and N=6+ datasets. These bubbles are centered on a density near 7,700 kg m -3 (7. 7 g/cm 3 ), which is the average density of all metallic elements excluding noble metals and actinides that are not included in this dataset. It's more difficult to anticipate trends for yield strength as a function of N. As with density, Fig. 7b  shows that the range in room temperature yield strengths decreases and the center of gravity of each bubble increases with increasing N. However, the maximum yield strength increases with increasing N, while the maximum density decreases with increasing N. In fact, CCAs with N=2-3 have specific room temperature yield strengths higher than any pure element with equivalent density, CCAs with N=4-5 have specific yield strengths higher than any with N=2-3 at equivalent density, and alloys with N=6+ principle elements have specific yield strengths higher than any alloy with N<6 at the same density.
Future directions for material-property databases development A detailed description of the balance of properties exhibited by maraging steels was given earlier in this manuscript to emphasize that a single performance index is inadequate to effectively compare different classes of alloys. Indeed, materials selection involves trade-offs between coupled multiple objectives defining several indices and multiple constraints setting property limits. A robust assessment must compare many properties for a given application over a range of relevant temperatures. The main purpose of the present analysis is not to show that any CCA alloy class is superior to existing, commercial alloys, but rather to identify in which property domains experimental CCAs have already achieved some success, so that future efforts can focus on evaluating other properties requirements for which data are not yet available.
ACKNOWLEDGEMENTS SG would like to acknowledge  DGA  ( Direction Générale de l'Armement ) for support through the  ERE program  ( ERE 2015 60 0013 ). The authors thank  Adam Pilchak  for the support provided for this work. SG thanks  Raghavan Srinivasan  for the arrangements that were made to host him at  Wright State University. Work by  ONS  was supported through the  Air Force  onsite contract No. FA8650-15-D-5230  managed by  UES, Inc. We thank  M. Ashby  for providing helpful comments.
Materials and Methods 2. 1. Coculture Experiments. Samples of cartilage were obtained during the knee joint replacement surgery from patients with a grade IV OA, which according to the Kellgren and Lawrence five grades (0-IV), system is classified as severe and characterized by prominent osteophyte formation and articular surface erosion to the subchondral bone with gross geometric changes. The study protocol was approved by the Lithuanian Bioethics Committee at III "Respublikine Vilniaus universitetine ligonine", and informed consents were obtained from the patients included into the study. Cartilage tissue was dissected from the locations with morphologically similar lesions. The removed pieces of the cartilage tissue were further chopped into the small explants of 1-3 mm on each side, mixed, and divided into the portions of 120 mg weight, which according to the published data  [18]  and the observed cell yields during our established enzymatic chondocyte isolation procedure, best correspond to ~5 × 10 5 chondrocytes.
ELISA. Protein levels of TIMP-1, TIMP-2, MMP-1, MMP-3, MMP-13, MMP-2, MMP-9, IL-1β, TGFβ3, TGFβ2, thrombospondin-1 (R&D Systems), TIMP-3, VCAM-1 (Boster Biological Technology), TGFβ1 (IBL International), MIA (Roche), IL-8 (Invitrogen), and aggrecan (DIAsource ImmunoAssays S. A. ) in supernatants were measured on days 3 and 7 by commercially available ELISA kits according to the manufacturer's instructions. IL-6 was measured by flow cytometry using Diaclone DIAplex IL-6 kit (Gen-Probe), and glycosaminoglycans were measured by blyscansulphated glycosaminoglycan assay (Biocolor). ELISA data were normalized by subtracting the values of medium from the samples. 2. 3. Total MMP Activity. Total MMP activity was determined in the supernatants on days 3 and 7, using SensoLyte 520 Generic MMP Assay Kit (Anaspec, USA), according to the manufacturer's protocol.
Each RNA sample was controlled for genomic DNA contamination by reactions without reverse transcriptase (RT), and reagent contamination was checked by the reactions without template (NTC). Individual gene expression efficiencies were calculated with the LinRegPCR program  [22]. Average gene expression efficiency for every single gene was calculated from the different runs and used for later relative gene expression quantification. The gene expression ratio (CE + ASC versus CE) was calculated using M. W. Pfaffl equation with the real reaction efficiencies  [23]. For normalization of gene expression, the geometric mean of two reference genes-RPS9 and B2M, was used. 2. 6. Histology. Zero to 14-point histological-histochemical grading system (HHGS) or Mankin score for the evaluation of excised and analyzed osteoarthritic cartilage samples was used.
0 at +98°C for 20 min and endogenous peroxidase blocking with 0. 3% hydrogen peroxide for 15 min at room temperature. ABC staining kit (Santa Cruz) and 3. 3-diaminobenzidine as a chromogen were used. Stained sections were evaluated and blindly scored independently by two histology experts. Scoring system applied in histochemical and immunohistochemical evaluation: 0-no stained profiles; 1-few stained profiles/weak specific staining; 2-stained profiles occupy about 50% of analysed microscopic field/moderate specific staining; 3-stained profiles occupy about 75% of analysed microscopic field/strong specific staining; 4-stained profiles occupy 100% of analysed microscopic field/very strong specific staining. 2. 7. Statistical Analysis. Data were analysed using SPSS 15. 0 software and nonparametric Kruskal-Wallis or Mann-Whitney U test was used for comparisons.
Eligible population in the COHERE dataset The Collaboration of Observational HIV Epidemiological Research Europe (COHERE) is a collaboration of 40 HIV cohorts (circa 2015) within the EuroCoord network (www. cohere. org; www. Eurocoord. net) representing over 330,000 HIV-1-positive individuals. The HIV controllers project was endorsed by 28 cohorts and the dataset pooled in 2014 included 216,614 patients. Eligible HIV-positive individuals from the COHERE database were those who had at least 5 HIV RNA viral load (VL) measurements, were followed for more than 5 years after HIV diagnosis, were enrolled after 01/01/1996 and less than 8 years after HIV diagnosis and were aged 15 years or more at diagnosis. Enrolment date was defined as the earliest of the date of first CD4 cell counts or first VL measurement.
36, respectively). Only 15,552 (14%) of the 111,073 eligible individuals had test results available to determine their HCV status in COHERE database. Among those with a known HCV status, HIV-HCV coinfection was identified in 69. 7% of HICs and 59. 5% of non-HICs (data not shown). Among the 1067 HICs, 86 experienced loss of virological control, 293 initiated ART and 13 died during virological control (3 from non-AIDS defining infections, 2 cardiovascular diseases, 1 chronic hepatitis C, 1 carcinoma, 1 mental disorder related to drug abuse and 5 unknown/unreported causes of death). The remaining 675 HICs maintained virological control up to the end of follow-up and their follow-up was therefore right-censored. The probabilities of loss of virological control 2, 6 and 10 years after confirmation of HIC status were 2.
One limitation of our study is the lack of confirmation of ART-naïve status as no drug dosage was systematically conducted. However, to prevent considering patients on ART as HICs, we removed those who experienced suspicious concomitant and large VL decrease and CD4 increase while no ART was declared on the database. Another limitation is that we could not study the role of different HIV subtypes on control duration since data were not complete. Finally, it is possible that the history of viral rebounds is not complete in this database, given the irregular VL monitoring in some patients. However, the CD4 level may have provided information regarding the risk of virological progression in these individuals. The evolution of recommendations regarding ART initiation may reduce the chance to conduct studies on such a large number of HICs in the future.
The analyses of polymorphism in the DGRP shed some light on this question, demonstrating that antagonistic polymorphisms are maintained at least over periods of tens to hundreds of years, or hundreds to a few thousand generations. In order to assess signals of balancing selection over longer time spans, we repeated these analyses with data from a population in D. melanogaster's ancestral Sub-Saharan distribution range, in Zambia (ZI, 197 genomes from phase 3 of the Drosophila Population Genomics Project 37 ; see also Sup. Fig. 5 , which repeats the ZI analyses with identical results using 118 genomes from South Africa  38  ). Just as in the DGRP, we found that antagonism generated a clear signature of balancing selection in this ancestral population sample. Analyses based on binary categories showed that antagonistic SNPs had significantly higher MAFs in ZI compared to non-antagonistic SNPs (P=0.
Fitness measurements Lifetime adult reproductive fitness of males and females of each line was measured using assays designed to mimic the LHM rearing regime. For male fitness, we measured competitive fertilisation success by setting up competition vials containing 5 hemiclonal males from a given line, 10 competitor bw males and 15 virgin bw females. After two days, bw females were isolated into individual vials containing no additional yeast and left to oviposit for 18 hours. On day 12 post egg-laying, progeny were scored for eye colour. Male fitness was calculated as the proportion of offspring sired by the 5 hemiclonal males (those with wildtype eye-colour), combining progeny data from the 15 oviposition vials. This assay was repeated 5 times in a blocked design; estimates for each line were therefore based on fitness measurements from 25 hemiclonal males.
Female fitness was measured as competitive fecundity. Competition vials containing 5 virgin hemiclonal females from a given line, 10 competitor bw females and 15 bw males were set up. Two days later, the 5 hemiclonal females were isolated into individual vials and left to oviposit for 18 hours. These vials were immediately chilled at 4°C and fecundity was measured by counting the number of eggs laid per female. This assay was replicated 5 times in a blocked design; each line estimate therefore measured the fitness of 25 hemiclonal females. Fitness data were subjected to quality control and pre-processing in preparation for quantitative genetic and association analysis. Male fitness data from competition vials where not all 5 focal males were present at the end of the assay were removed from further analysis.
Quality control of whole-genome sequences We used previously published whole genome sequences generated from the hemiclonal lines analysed here  20. Details about DNA extraction, library preparation, sequencing, read processing and SNP calling are provided in the original publication. Prior to the association analysis performed here, further site-level quality filtering steps were performed in vcftools  55  and PLINK  26. First, individual variant calls based on depth<10 and genotype quality<30 were removed. Second, individuals with>15% missing positions were removed. Third, positions with poor genotype information (<95% call rate) across all retained individuals were discarded. Finally, given the relatively small sample size of the dataset as a whole and the low power of an association test for rare variants, we retained only common variants (MAF>0.
05) for further analysis. From an initial dataset of 220 hemiclones containing 1,312,336 SNPs, this yielded a quality-filtered dataset of 765,980 SNPs from 203 hemiclones. To detect outliers, we examined LHM's population structure using principal components analysis (PCA). Overlapping SNP positions from the 203 LHM genomes and from an outgroup population (Drosophila Genetic Reference Panel, or DGRP  34  ) consisting of 205 whole-genome sequenced individuals were used as input to construct a genetic similarity matrix. This set of SNPs was pruned for linkage disequilibrium (LD) such that no two SNPs with r 2 >0. 2 within 10Kb remained. The leading PC axes were extracted in LDAK ("Linkage-Disequilibrium Adjusted Kinships"  56  ). After removal of one outlier (see Sup. Fig. 1A ), the final dataset used for association analysis contained 202 individuals and 765,764 SNPs.
Functional analyses of antagonistic loci We used the variant effect predictor (Ensembl VEP  63  ) to map SNPs to functional categories. We partitioned total antagonistic ℎ #$% " into functional subsets, and estimated the observed proportion of ℎ #$% " contributed by each subset using REML in LDAK (settings as above). We then used a permutation test to compare observed and expected ℎ #$% " for each functional category, where we shifted genome-wide annotations to a random starting point along a 'circular genome'. This procedure breaks the relationship between each SNP and its annotation while preserving the order of annotations and their associated LD structure  64. ℎ #$% " was re-calculated via REML for each of 1,000 permuted datasets and two-tailed P-values determined as the sum of permuted estimates with more extreme absolute values than the observed.
As a complementary approach, we compared the proportion of antagonistic SNPs to the proportion of all SNPs mapping to each functional category. We then assessed enrichment for each functional category in turn using Z-tests. We also used the VEP to map SNPs to genes. We included extended gene regions (+/-5kb of gene coordinates, VEP default) in our gene definition. To gain preliminary insights into the functions of antagonistic genes we used the Gorilla  65  Gene Ontology tool, with FDR correction for multiple testing across GO terms. All genes covered in the final SNP dataset were used as the background set. To examine the relationship between antagonistic genes and sex-biased gene expression we used the Sebida online database  66  to annotate genes as having either sex-biased or unbiased expression profiles (meta-class identifier).
We then used a c 2 test to compare the sex-biased expression status of antagonistic and nonantagonistic genes. We additionally examined the quantitative degree of sex-bias using this same dataset. We took the absolute value of the log2 transformed 'M_F' bias variable, such that large values indicate more extreme sex bias in expression irrespectively of whether this bias is towards males or females. We compared the distributions of this variable between antagonistic and non-antagonistic genes using a Wilcoxon Rank-Sum test. To assess the degree of overlap between antagonistic genes identified here and those associated with sexually antagonistic expression patterns in a previous study  16  , we included only genes covered in both datasets, and only those genes in both datasets that were adult-expressed.
To determine whether genes were adultexpressed we used the Drosophila gene expression atlas (FlyAtlas 67 ). Conservatively, we considered a gene 'adult-expressed' if its transcript was detected as present in at least one library of one adult-derived sample. We then used a c " test to assess the degree of overlap between the datasets. We used the tissue-specificity index (t) to compare pleiotropy between antagonistic and non-antagonistic genes. We used gene expression data from FlyAtlas 67 to get average expression values for each gene and in each tissue and then calculated t as: where is the proportional expression level of the gene in tissue and is the number of tissues. We compared values of t for antagonistic and nonantagonist genes using a Wilcoxon rank-sum test. As an additional proxy for pleiotropy we examined the number of proteinprotein interactions (PPIs) between antagonistic and non-antagonistic genes.
We used the physical interactions table from FlyBase 68 to summarise the total number of PPIs for all genes and then compared candidate and non-candidate genes using a original dataset of 765,764 SNPs to 36,319 "LD-independent" SNPs. For each of these SNPs, we then estimated MAFs in each comparison population. We assigned MAF=0 to sites which were monomorphic in a comparison population and those where a comparison population was polymorphic for variants other than those segregating at that site in the LHM. We then used this LD-independent dataset to compare MAF between antagonistic and non-antagonistic SNPs. We did this using a Monte Carlo approach where, 1,000 times, we paired the To test for significant differences in LD between antagonistic pairs and the two control pairs, we modelled variation in r 2 as a declining exponential function of chromosomal distance, and assessed differences in residual r 2 (once distance was regressed out) using Wilcoxon Rank-Sum tests.
Acknowledgements We are grateful to  Aida Andrés  for her suggestions on the analysis of balancing selection and to  Laurent Keller ,  Andrew Pomiankowski  and  François Balloux  for helpful comments on earlier versions of the manuscript. FR was funded by a  London NERC   DTP studentship  ( NERC  grant  NE/L002485/1 ), MSH was funded by a  UCL IMPACT  PhD studentship, EHM, TMP, IF and FCI by a  European Research Council   Grant  ( 280632 ) and a  Royal Society University Research Fellowship  and MR and KF by a  Natural Environment Research Council   research grant  ( NE/G0189452/1 ).
Pvalues for deviations in polymorphism between antagonistic and non-antagonistic sites were then calculated by comparing, in each population, the mean MAF of the 226 antagonistic SNPs to the null MAF distribution. A second analysis used the same LD-independent dataset but considered the whole spectrum of P-values, rather than a binary split of SNPs into antagonistic/nonantagonistic categories. To this end, we performed binning in two dimensions, by residual LHM MAF (20 quantiles) and P-values (100 quantiles). We then drew one SNP from each of these MAF/P-value bins (2,000 SNPs in total), recorded the MAF for each in the comparison population of interest, and finally correlated these MAF values with P-values of the associated SNPs in the LHM using a Spearman's rank correlation. Under the hypothesis of antagonism-mediated balancing selection, SNPs with low P-values should tend to have higher MAFs in the population under consideration than SNPs with high P-values.
3C  and Table  S2 ). Conversely, genes whose expression is repressed under filament-inducing conditions were repressed upon SKN7 overexpression (eg ALS2/4, ALD5, ASR1, ASR2, CHT3, MNN1, PIR1, RHD1/BMT2, RME1, YWP1, C1_05160C, C2_07630C, C4_02930W; Fig. 3C ). Treatment of the negative control strain CEC3785 with ATc had a mild impact on gene expression at 2h, since only 18 and 20 genes were upregulated and downregulated with maximum fold-change values of 1. 9 and -1. 8, respectively (Table  S3 ), and only one overlaped with Skn7 OE data. At 4 h, the number of upregulated and downregulated genes was 44 and 27, respectively, with maximum foldchange values of 2. 5 and -1. 9, and only 4 overlaped with Skn7 OE data (Table  S3 ). The genes present in both analyses were not considered further. We validated our data using RT-qPCR analyses on selected genes (CPH1, CZF1, EED1, SFL2 and UME6 encoding regulators of morphogenesis; HWP1 and IHD1 that encode hyphal-specific cell wall proteins; TSA1 and GPX2 encoding effectors of oxidative stress response and SKN7 as a control).