dataesr/scientific-paragraphs-categorization huggingface dataset:
Discussion In this work, we have shown that the statistical robust and the UP design methods are theoretically nearly equivalent when the size of the database is sufficiently large to embrace the variability of the resonant TX RF field across the population of interest. Despite its apparent complexity, the statistical robust design algorithm defined by equation (  13 ) has a rather straightforward implementation as the general term of the summation across voxels depends explicitly on the mean vector and covariance matrix of the TX RF field (see equation (  14 )). As compared to the database-driven UP design methodology (Gras et al. [14] ), an important difference is the involvement of the covariance matrix C(r) which has not equivalent in the original formulation. To our knowledge, the design of UPs using the SRD formalism, originally applied by Alotto and colleagues for the design of electromagnetic structures, has not been proposed so far for RF pulse design and could represent some advantages.
First, as the SRD computation time does not depend on the size (number or subjects) of the database, this approach enables integration of potentially very large databases, which is a priori necessary to ensure optimal UP performance. Secondly, equipped with this model, one can likewise compute probabilities for obtaining a certain deviation from a target flip angle. Doing such estimations using a database of TX field maps would require unrealistically large number of subjects. e theory presented in this work defines the performance of a FA shimming pulse as the coefficient of variation (std/mean) of the flip angle. Furthermore, in the linear STA regime, it is shown that this CV cannot be lower than a certain value g ∞ which depends on the local second-order statistics of the control field.
Finally, the coefficient of variation metric for UP ∞ (see equation (  23 )), being characteristic of the TX array architecture, can potentially serve as a guide for coil-design purposes. e present study was performed with the B + 1 data collected on 40 subjects, from which a subgroup of 35 subjects was used to compute statistics. Given the high dimension of the covariance matrix of the control field for an eightfold TX array system, it was necessary to use the Ledoit-Wolf correction for the estimation of the covariance matrix of the control field (to obtain positive definite estimates). However, we can still expect a bias in the estimation of the covariance matrix. As a result, it is possible that the CV analysis reported here (lower bound and CV maps for the different k T -point pulses computed in this study) can deviate from the analysis that one would obtain with a larger database to estimate the control field second-order statistics.
Conclusions We have presented in this work a statistical robust approach to design calibration-free pTX universal pulses for brain imaging at ultrahigh field. Experimentally, this method has been shown to provide comparable FA-NRMSE results as the conventional database-driven UP design, but presents a significant advantage in terms of computational efficiency when the size of the design database exceeds 4. Furthermore, in contrast with the database-driven design of UP, the computation time of the statistical robust UP does not depend on the size of this database and consequently enables integration of a very large design database. is is an important criterion to ensure optimal UP performance. Furthermore, the theoretical lower bound for the coefficient of variation of the flip angle derived in this work, g ∞ , appears as a valuable analytical performance limit to assess the FA shimming performance of a given pulse.
Acknowledgments is research received funding from the  RETP program (Research Equipment and Technology Platforms) of the Leducq Foundation , the  European Research Council  under the European Union's  Seventh Framework Program  ( FP7/ 2013-2018 ), and  ERC  Grant Agreement # 309674  and Proof of Concept # 700812. e authors are grateful to  Dr. B. irion  for his valuable input regarding the estimation of the covariance matrix of the control field.
02× at targeted SNPs (ranging from 0. 04× to 5. 39× per individual) and a median number of 466,049 targeted SNPs covered at least once per individual. We obtained age estimates by either direct radiocarbon dating (n = 53), previously reported radiocarbon dates (n = 13), or archeological context and radiocarbon dates from the same burial site (n = 4). The estimated ages range from 4100 years BCE to 1500 years CE (Fig. 1 , Supp. Data 1A). We pragmatically grouped the data into broad periods: Middle/Late Neolithic ('Sar-MN', 4100-3500 BCE, n = 6), Early Copper Age ('Sar-ECA', 3500-2500 BCE, n = 3), Early Middle Bronze Age ('Sar-EMBA', 2500-1500 BCE, n = 27), and Nuragic ('Sar-Nur', 1500-900 BCE, n = 16). For the post-Nuragic sites, there is substantial genetic heterogeneity within and among sites, and so we perform analysis per site when grouping is necessary ('Sar-MSR' and 'Sar-VIL' for the Phoenician and Punic sites of Monte Sirai, n = 2; and Villamar, n = 6; 'Sar-ORC002' for a Punic period individual from the interior site of S'Orcu 'e Tueri, n = 1; 'Sar-AMC' for the Roman period site of Monte Carru near Alghero, n = 3; 'Sar-COR' for the early medieval individuals from the site of Corona Moltana, n = 2; and 'Sar-SNN' for the medieval San Nicola necropoli, n = 4).
For our sample from the Middle Neolithic through the Nuragic (n = 52 individuals), we were able to infer mtDNA haplotypes for each individual and Y haplotypes for 30 out of 34 males. The mtDNA haplotypes belong to macro-haplogroups HV (n = 20), JT (n = 19), U (n = 12), and X (n = 1), a composition broadly similar to other European Neolithic populations. For Y haplotypes, we found at least one carrier for each of three major Sardinia-specific Y founder clades (within the haplogroups I2-M26, G2-L91, and R1b-V88) that were identified previously based on modern Sardinian data  51. More than half of the 31 identified Y haplogroups were R1b-V88 or I2-M223 (n = 11 and 8, respectively, Supp. Fig. 6 , Supp. Data 1B), both of which are also prevalent in Neolithic Iberians  14. Compared with most other ancient populations in our reference dataset, the frequency of R1b-V88 (Supp.
Notably, J1-L862 first appears in Levantine Bronze Age individuals within the ancient reference dataset and is at about 5% frequency in Sardinia today. We used individual-level qpAdm models to further investigate the presence of these new ancestries (Supp. Data 3). In addition to the original Neolithic Anatolian (Anatolia-N) and Hunter Gatherer (WHG) sources that were sufficient to model ancient Sardinians through the Nuragic period, we fit models with representatives of Steppe (Steppe-EMBA), Neolithic Iranian (Iran-N), and Neolithic North-African (Morocco-EN) ancestry as sources. We observe the presence of the Steppe-EMBA (point estimates ranging 0-20%) and Iran-N components (point estimates ranging 0-25%) in many of the post-Nuragic individuals (Supp. Fig. 14 ). All six individuals from the Punic Villamar site were inferred to have substantial levels of ancient North-African ancestry (point estimates ranging 20-35%, Supp.
After indexing PCR  62  and differential amplification cycles, the DNA was shotgun sequenced on Illumina platforms. Samples showing sufficient aDNA preservation where captured for mtDNA and ≈1. 24 million SNPs across the human genome chosen to intersect with the Affymetrix Human Origins array and Illumina 610-Quad array  47. The resulting enriched libraries were also sequenced on Illumina machines in single-end or paired-end mode. Sequenced data were preprocessed using the EAGER pipeline  64. Specifically, DNA adapters were trimmed using AdapterRemoval v2  65  and paired-end sequenced libraries were merged. Sequence alignment to the mtDNA (RSRS) and nuclear (hg19) reference genomes was performed with BWA 66 (parameters -n 0. 01, seeding disabled), duplicates were removed with DeDup 64 and a mapping quality filter was applied (MQ≥30).
For genetic sexing, we compared relative X and Y-chromosome coverage to the autosomal coverage with a custom script. For males, nuclear contamination levels were estimated based on heterozygosity on the X-chromosome with the software ANGSD  67. After applying several standard ancient DNA quality control metrics, retaining individuals with endogenous DNA content in shotgun sequencing >0. 2%, mtDNA contamination <4% (average 1. 6%), and nuclear contamination <6% (average 1. 1%), and after inspection of contamination patterns (Supp. Figs. 2 3 4 5 ), we generated genotype calls for downstream population genetic analyses for a set of 70 individuals. To account for sequencing errors we first removed any reads that overlapped a SNP on the capture array with a base quality score <20. We also removed the last 3-bp on both sides of every read to reduce the effect of DNA damage on the resulting genotype calls  68.
We used custom python scripts (https:// github. com/mathii/gdc3) to generate pseudo-haploid genotypes by sampling a random read for each SNP on the capture array and setting the genotype to be homozygous for the sampled allele. We then screened for first degree relatives using a pairwise relatedness statistic, and identified one pair of siblings and one parent-offspring pair within our sample (Supp. Fig. 12 ). Processing of mtDNA data. Data originating from mtDNA capture were processed with schmutzi  69  , which jointly estimates mtDNA contamination and reconstructs mtDNA consensus sequences that were assigned to the corresponding mtDNA haplogroups using Haplofind 70 (Supp. Data 1C). The consensus sequences were also compared with rCRS  71  to build a phylogenetic tree of ancient Sardinian mitogenomes (Supp.
Data 1D) using a maximum parsimony approach with the software mtPhyl (http://eltsov. org/mtphyl. aspx). We assigned haplogroups following the nomenclature proposed by the PhyloTree database build 17 (http:// www. phylotree. org)  72 and for Sardinian-specific haplogroups  36. Inference of Y haplogroups. To determine the haplotype branch of the Y chromosome of male ancient individuals, we analyzed informative SNPs on the Yhaplotype tree. For reference, we used markers from https://isogg. org/tree (Version: 13. 238, 2018). We merged this data with our set of calls and identified markers available in both to create groups of equivalent markers for sub-haplogroups. Our targeted sequencing approach yielded read count data for up to 32,681 such Ylinked markers per individual. As the conventions for naming of haplogroups are subject to change, we annotated them in terms of carrying the derived state at a defining SNP.
We analyzed the number of derived and ancestral calls for each informative marker for all ancient Sardinian individuals and reanalyzed male ancient West Eurasians in our reference dataset. Refined haplotype calls were based on manual inspection of ancestral and derived read counts per haplogroup, factoring in coverage and error estimates. Merging newly generated data with published data. Ancient DNA datasets from Western Eurasia and North Africa: We downloaded and processed BAM files from several ancient datasets from continental Europe and the Middle East 9,10,13,48-50. To minimize technology-specific batch effects in genotype calls and thus downstream population genetic inference, we focused on previously published ancient samples that had undergone the capture protocol on the same set of SNPs targeted in our study.
We processed these samples through the same pipeline and filters described above, resulting in a reference dataset of 972 ancient samples. Throughout our analysis, we used a subset of n = 1,088,482 variants that was created by removing SNPs missing in more than 90% of all ancients individuals (Sardinian and reference dataset) with at least 60% of all captured SNPs covered. This ancient dataset spans a wide geographic distribution and temporal range (Fig. 2d ). For the PCA (Fig. 2a, b ), we additionally included a single low-coverage ancient individual (label "Pun") dated to 361-178 BCE from a Punic necropolis on the west Mediterranean island of Ibiza  58. We merged individuals into groups (Supp. Data 1E, F). For ancient samples, these groups were chosen manually, trying to strike a balance between reducing overlap in the PCA and keeping culturally distinct populations separate.
We used geographic location to first broadly group samples into geographic areas (such as Iberia, Central Europe, and Balkans), and then further annotated each of these groups by different time periods. Contemporary DNA datasets from Western Eurasia and North Africa: We downloaded and processed the Human Origins dataset to characterize a subset of Eurasian and North-African human genetic diversity at 594,924 autosomal SNPs  7. We focused on a subset of 837 individuals from Western Eurasia and North Africa. Contemporary DNA dataset from Sardinia: We merged in a whole-genome sequence Sardinian dataset (1577 individuals  32  ) and called genotypes on the Human Origin autosomal SNPs to create a dataset similar to the other modern reference populations. For analyses on province level, we used a subset where at least three grandparents originate from the same geographical location and grouped individuals accordingly (Fig.
2c , n = 1085 in total). Principal components analysis. We performed principal components analysis (PCA) on two large-scale datasets of modern genotypes from Western Eurasia and North Africa (837 individuals from the Human Origins dataset) and Sardinia (1577 individuals from the SardiNIA project). For both datasets, we normalized the genotype matrix by mean-centering and scaling the genotypes at each SNP using the inverse of the square-root of heterozygosity  73. We additionally filtered out rare variants with minor allele frequency (p min <0:05). To assess population structure in the ancient individuals, we projected them onto the pre-computed principal axes using only the non-missing SNPs via a leastsquares approach, and corrected for the shrinkage effect observed in highdimensional PC score prediction  74  (see Supp.
4]. We then ran five replicates of ADMIX-TURE for values of K = 2, …, 11. We display results for the replicate that reached the highest log-likelihood after the algorithm converged (Supp. Figs. 19-22). Estimation of f-statistics. We measured similarity between groups of individuals through computing an outgroup-f 3 statistic 77 using the scikit-allel packages's function average_patterson_f3, https://doi. org/10. 5281/zenodo. 3238280). The outgroup-f 3 statistic can be interpreted as a measure of the internal branch length of a three-taxa population phylogeny and thus does not depend on genetic drift or systematic error in one of the populations that are being compared  77. We used the ancestral allelic states as an outgroup, inferred from a multi-species alignment from Ensembl Compara release 59, as annotated in the 1000 Genomes Phase3 sites vcf  78.
We fixed the ancestral allele counts to n = 10 6 to avoid finite sample size correction when calculating outgroup f 3. The f 3 and f 4 -statistics that test for admixture were computed with scikit-allel using the functions average_patterson_f3 and average_patterson_d that implement standard estimators of these statistics  77. We estimated standard errors with a blockjackknife over 1000 markers (blen=1000). For all f-statistics calculations, we analyzed only one allele of ancient individuals that were represented as pseudohaploid genotypes to avoid an artificial appearance of genetic drift-that could for instance mask a negative f 3 signal of admixture. Estimation of F ST -coefficients. To measure pairwise genetic differentiation between two populations (rather than shared drift from an outgroup as the outgroup f 3 statistic does), we estimated average pairwise F ST and its standard error via block-jackknife over 1000 markers, using average_patterson_fst from the package scikit-allel.
A) Single-source models to assess continuity of each Sardinian period with the previous one (see main text for guide to abbreviations). (B) Results of two-way models of admixture for Cagliari (a representative present-day sample). (C) Results of three-way models showing multiple eastern Mediterranean populations that can produce viable models (Results shown with individuals from Lombardy [Bergamo in the Human Origins array (HOA) dataset, see Methods] as one of several possible proxies for north Mediterranean ancestry, see part C). (C) Results of three-way models showing multiple north Mediterranean populations that can produce viable models (Results shown with Jewish individuals from Turkey ['Turkish-Jew' in the dataset] used as one of several possible proxies for east Mediterranean ancestry, see part B).
Acknowledgements We thank  Maanasa Rhagavan  for in-depth feedback on drafts,  Anna Di Rienzo  and  Goncalo Abecasis  for helpful discussions, and  Magdalena Zoledziewska  for useful comments and early assistance. We would like to thank  Antje Wissgott ,  Cäcilia Freund  and other members of the wet laboratory and computational teams at MPI-SHH in Jena. We thank  Nadin Rohland ,  Éadaoin Harney ,  Shop Mallick , and  Alan Cooper  for contributing to generating the data for the four samples processed at the  Australian Centre for Ancient DNA  and in D. R. 's ancient DNA laboratory. We also thank  Dan Rice  and members of the  Novembre lab  for helpful discussion and feedback. IRGB-CNR would like to thank the  Consortium SA CORONA ARRUBIA DELLA MARMILLA for making available equipment and scientific instruments within the program  " Laboratori Dna del Museo Naturalistico del Territorio -Giovanni Pusceddu ".
Data availability The aligned sequences and processed genotype calls (including read counts) from the data generated in this study are available through the European Nucleotide Archive (ENA, accession number PRJEB35094). Processed read counts and pseudo-haploid genotypes are available via the European Variation Archive (EVA, accession number PRJEB36033) in variant call format (VCF). The contemporary Sardinia data used to support this study have allele frequency summary data deposited to EGA (accession number EGAS00001002212). The disaggregated individual-level sequence data (n = 1577) used in this study is a subset of 2105 samples (adult volunteers of the SardiNIA cohort longitudinal study) from Sidore et al. and are available from dbGAP under project identifier phs000313 (v4. p2). The remaining individual-level sequence data originate from a case-control study of autoimmunity from across Sardinia, and per the obtained consent and local IRB, these data are available for collaboration by request from the project leader (Francesco Cucca, Consiglio Nazionale delle Ricerche, Italy).
Acknowledgements. We acknowledge ISCRA for awarding us access to the supercomputer facilities at  CINECA, Italy , where the calculations were performed. The French involvement in MMS is supported by  CNES  and  CNRS. Financial support. This project ( Francesco Califano ) has received funding from the  European Unions   Horizon 2020 research and innovation programme  under grant agreement no. 776262  (AIDA, http://www. aida-space. eu,last access:  19 February 2020 ). Review statement. This paper was edited by  Minna Palmroth  and reviewed by  Johan De Keyser  and one anonymous referee.
Notably, CAR-T cell therapy is hampered in HCC by the limited migration of T cell into the tumor, the immunosuppressive tumor microenvironment and the lack of well-defined tumor antigens  [4]. Concerning immune checkpoint inhibitors, the phase III randomized controlled trials comparing nivolumab versus sorafenib in first line or pembrolizumab versus placebo did not reach their pre-specified endpoints. The lack of efficacy for these two PD-1 inhibitors suggests that a better stratification of HCC patients based on the immune microenvironment is required to identify the patients who may benefit from immune-based therapies  [5]. By improving the current immune classification of HCC, the study of Fujita et al. may help to identify the patients who most likely could respond to immunotherapy. Importantly, this proposed immunological classification is validated in Asian and Western datasets and therefore could be applied to a large cohort of patients independently from HCC etiology.
, 2017). The aim of this work was to investigate the skin microbiome of clinically unaffected skinfolds in HS. Bacterial cultures were performed to identify bacteria to the species level which cannot always be obtained by16S sequencing and to semi-quantify bacterial bioburden. A major finding of this study is the decreased abundance of coagulase negative staphylococci (CoNS) of HS skinfolds and particularly of Staphylococcus epidermidis, that was quantitatively observed in bacterial cultures and confirmed by 16S sequencing. These data are in line with the reported low prevalence of coagulase negative staphylococci biofilms in the stratum corneum in HS  (Ring et al. , 2017). S. epidermidis is suspected to be a key guardian of the skin barrier  (Nakamizo et al. , 2015). It interacts with skin innate immune system and induces IL-17A1 CD81 T cells, that home to the epidermis and enhance innate barrier immunity by stimulating the production of antimicrobial peptides by keratinocytes, thus limiting the invasion of bacterial pathogens  (Lai et al.
, 2011). Similar sequences were clustered into OTUs using Uclust, with a minimum identity of 0. 97. Representative sequences of each OTU were aligned and taxonomy was assigned using the RDP classifier. Statistical analysis. All statistical analyses were performed using the R software v 3. 5 (www. R-project. org). Comparison of bacterial culture profiles and bacterial culture abundance was performed using the Fisher exact test and the Wilcoxon rank sum test, respectively. Alpha diversity indexes, plots and analysis of variance were generated with the Phyloseq package (McMurdie and Holmes, 2013) using non-normalized OTU counts. Hierarchical ascendant clustering and principal component analysis (PCA) were performed after logarithmic transformation of relative abundances using the pheatmap and ade4 package, respectively.
Dendrograms were generated using the Ward aggregation criterion. Validity of clustering and number of centroids to choose for the data were determined using the average silhouette score. Results were figured in a heatmap chart where samples and species are re-ordered according to dendrogram. To compare taxa relative abundance, we used a negative binomial regression model to take into account overdispersion of taxa counts between samples (R Bioconductor edgeR package). All statistical tests were considered as significant if the p-value was below 0. 05. For multiple comparisons procedures, p-values were adjusted by the false discovery rate (FDR) method.
P. K. , manuscript in preparation). As expected, the two datasets were highly consistent and correlated (Extended Data Fig. 1a, b ). The nAb titers declined substantially in both groups within 8 weeks after the third SOSIP/3M-052 immunization, and only a few animals had detectable titers on the day of the fourth immunization 40 weeks later (at week 80) (Extended Data Fig. 1a ). We also measured nAb titers against the replication-competent SHIV-BG505 virus produced in HEK293T cells, using a molecular clone, in week 84 serum. The titers were about threefold lower in comparison to the data derived using BG505. T332N pseudovirus, but the median titers did not differ significantly between the groups (Extended Data Fig. 1c ). In addition to potent antibody responses, SOSIP/3M-052 immunizations induced modest CD4 + T cell responses (Extended Data Fig.