dataesr/scientific-paragraphs-categorization huggingface dataset:
Study characteristics We identified 173,430 titles and abstracts in our search spanning from January  Data from 61 of 134 LMICs and from 36 of 63 vulnerable HRP countries were included. A large proportion of studies included in the descriptive analysis were conducted in LMIC (397/ 965, 41%) and in vulnerable HRP countries (206/965, 21%) (Table  1 ). Of studies included in the meta-analysis and meta-regression, these proportions were 30% (137/459) and 14% (66/ 459), respectively. Of the 66 (30%) meta-analyzed studies in HRP countries, 20 had test performance values below 90% sensitivity or 97% specificity and were included due to exception criteria. Among the 965 studies included in the descriptive analysis, 42% (402/965) reported results at a local level, 36% (345/965) at a national level, and 23% (218/965) at a subnational level.
Discussion We synthesized data from over 800 seroprevalence studies worldwide (43% from LMICs) published up to May 2022 (search dates: January 1, 2020 to May 20, 2022), providing global and regional estimates of SARS-CoV-2 seroprevalence over time with substantial representation of regions with limited available seroprevalence data. We estimate that approximately 59. 2% of the global population had antibodies against SARS-CoV-2 in September 2021 (35. 9% when excluding vaccination). Global seroprevalence has risen considerably over time, from 7. 7% a year before, in June 2020. Our findings provide evidence of regional and temporal variation in the overall seroprevalence, over 80% in SEAR and AFR in late 2021 and over 90% in AMR HIC and EUR HIC in early 2022. In WPR, there was a paucity of high-quality population-based studies in 2021, and estimated seroprevalence was as low as 30.
1371/journal. pmed. 1004107. g003 following the emergence of variants in regions with available data (e. g. , 6% (July 2020) to 41% (April 2021) in AFR following the Beta variant and 12% (February 2021) to 75% (August 2021) in SEAR following the Delta variant), demonstrating the substantial number of infections caused by more transmissible variants. In HIC regions, the increases in overall seroprevalence were driven by increased vaccine coverage in early 2021 (e. g. , 6% (January 2021) to 95% (August 2021) in AMR HIC and 7% (January 2021) to 72% (August 2021) in EUR HIC), while we also observed increases in infection-induced seroprevalence following the Omicron variant (e. g. , 7% (December 2021) to 34% (March 2022) in AMR HIC and 18% (October 2021) to 48% (March 2022) in EUR HIC). Another possibility for regional variation is the potential crossreactivity of antibodies against P.
This and other work have shown that the use of PHSM was associated with reduced SARS-CoV-2 infections, especially when implemented early and limiting population mobility  [51] [52] [53]. Our model also found that subnational studies have higher estimates than national studies; one hypothesis for this is that subnational studies are often concentrated on cities or areas with denser populations, which may contribute to increased transmission of the virus. Further research is needed to validate this hypothesis. Finally, our results suggest that blood donors and residual sera studies may be good proxies for the general population, as there was no statistical difference between seroprevalence estimates in these sample frames compared to household and community samples. In line with the equity principles of the Unity initiative, our dataset had global coverage, including a broad range of LMICs (one-third of studies included in our dataset 1, n = 177) and vulnerable HRP countries (14% of included studies).
Strengths and limitations Our regional and global meta-analysis estimates are timely, robust, and geographically diverse with estimates from all WHO regions. The laboratory and epidemiological standardization enabled by the SEROPREV protocol, as well as the analysis of only studies assessed to have low or moderate risk of bias using a validated risk of bias tool  [25] , enabled high-quality and comparable data. Despite this effort, there are still methodological differences between the metaanalyzed studies that may reduce their comparability. For example, 14% of studies in our analysis dataset (66/459, 18 of which were household based) were convenience samples, which are less representative than population-based probability samples. To limit this bias, we required Unity-aligned convenience samples to have a clearly defined sample frame (i.
Finally, at certain points in time, our meta-analysis estimates were driven by studies from specific countrieseither very populous countries (i. e. , SEAR: India, AMR HIC: USA, AMR LMIC: Brazil, WPR: China), or countries in regions with scarce data during the time in question (e. g. , EMR: 2 countries in early 2021). We also could not produce global estimates for late 2021/early 2022 due to the delays between when studies conducted their sampling (we extracted from the "sampling midpoint"), and when these results were later published or released within our search dates.
Acknowledgments We thank colleagues at partner organizations including  WHO/Dubai Logistics Team ;  Myrna Charles ,  Kathleen Gallagher ,  Amen Ben Hamida ,  Christopher Murrill ,  Toni Whistler ,  Venkatachalam Udhayakumar  ( US Centers for Disease Control and Prevention );  Eeva Broberg ,  Erika Duffell ,  Maria Keramarou  and  Pasi Penttinen  ( European Centre for Disease Prevention and Control ),  Vincent Richard  ( Institut Pasteur  and the  Institut Pasteur International Network) ; WHO regional offices ( Jacob Barnor ,  Alina Guseinova ,  Jason M Mwenda ,  Dmitriy Pereyaslov ,  Harimahefa Razafimandimby , and  WHO HQ (Michael Ryan ). We also thank team members from Gabon:  Rafiou Adamou ,  Ayola A Adegnika ,  Samira Z Assoumou ,  Rosemary A Audu ,  Paulin E Ndong ,  Paulin N Essone ,  Edgard B Ngoungou.
In this paper, a comprehensive validation exercise of 3D physics-based numerical simulations (PBS) of seismic wave propagation is presented for a low-to-moderate seismicity area in the south east of France, within the Rhône River Valley, that hosts several operating nuclear installations. This area was hit on Nov 11, 2019, by an unusually damaging Mw 4. 9 earthquake (Le Teil event). The numerical code SPEED (http://speed. mox. polimi. it/), developed at Politecnico di Milano, Italy, was used to validate the simulations against the available recordings. When comparing simulations with records, a good to excellent agreement was found up to 8-10 Hz, showing that, even without a very detailed 3D numerical model of the medium, the PBS may provide realistic broadband predictions of earthquake ground motion.
, 2013) , which has been extensively used in the recent past to perform PBS validated on different real earthquakes  (Paolucci et al. , 2015; Evangelista et al. , 2017; Infantino et al. , 2020; Sangaraju et al. , 2021)  and to construct a prototype of a near-source simulated accelerograms dataset with the aim of complementing recordings datasets, still relatively sparse in such near-source conditions  (Paolucci et al. , 2021). After a brief overview of the case study in Section 2, the 3D numerical model is introduced in Section 3, while the veri cation and numerical convergence tests are discussed in Section 4. In Section 5, simulated ground motion is successfully compared with the recorded one on a broad frequency range, up to about 8 Hz, where the convergence tests have shown that the accuracy of numerical wave propagation is reasonably preserved.
The region is characterized by a low-to-moderate seismicity, with instrumental earthquakes of maximum magnitude ranging between 3 and 4 (see orange circles in Fig. 1 , from SI-HEX  (Cara et al. , 2015)  updated catalogue, https://www. franceseisme. fr/). The most signi cant historical earthquakes in the region (as indicated by purple dots in Fig. 1 ) occurred south of Le Teil in 1773, 1873 the 1923, with maximum macroseismic intensities up to I max =VII MSK (SISFRANCE database, www. sisfrance. net). The August 8, 1873 earthquake, at around 8 km southward from Le Teil, was the largest shock ever felt in this region, with an estimated Mw of around 4. 1 and a focal depth of about 3 km (FCAT catalogue, Manchuel et al. , 2018). An earthquake was located near Le Teil in November 1923, with an inferred Mw of around 3 and I max =IV MSK.
From a seismotectonic point of view, the epicenter of the Le Teil earthquake is located at the boundary between the Massif Central crystalline basement and the sedimentary basin of South-Eastern France bordering the Alps mountain range. The tectonic evolution of this region was marked by several deformation phases since 200 million years (Ma), which have produced a complex structural pattern in a compressional stress regime with around 100-km-long system of faults (i. e. , the Cevennes Fault System -CFS) striking NE-SW and dipping to the southeast  (Delouis et al. , 2019; Ritz et al. 2020). The earthquake was generated by the seismic rupture of a segment of the La Rouvière fault (LRF, see red line in Fig. 1 ), which is located at the North-Eastern part of the CFS. The LRF was not identi ed as a potentially active fault in the Database of Potentially Active Faults for Metropolitan France -BDFA (https://bdfa.
Veri cation analyses with Hisada code As a preliminary step of the modelling procedure, simulations were performed using the Hisada code, based on the analytical integration of Green's functions  (Hisada and Bielak, 2003). This code allows to compute the ground motions in a horizontally layered half-space originating from a nite-fault kinematic source model, providing solutions with a maximum frequency resolution of about 1-2 Hz at most. Hisada's solution has been used in a preliminary phase to calibrate and validate the crustal model pro le, the assumptions on the quality factor, the slip model on the extended fault and the source time function adopted. Concerning the source, two different parametrizations have been tested: a point source (shown herein) and an extended fault (not shown in this paper).
0s), where simulated ground motions exceed signi cantly the empirical predictions. Such a trend is consistent with the ndings from residual analysis of Montélimar recordings with respect to recorded datasets in Europe and in Italy; the systematic underestimation of the empirical GMMs for the long-period spectral ordinates is even more pronounced for the vertical components of ground motion, for which both simulations and recordings tend to be systematically above the 84th percentile of the empirical predictions; at intermediate periods (see SA(0. 5s)-H and SA(0. 2s)-V) a better agreement is found between numerical simulations and empirical predictions.
Conclusions In paper, a comprehensive validation exercise of physics-based numerical simulations (PBS) of seismic wave propagation is presented. The target area is an industrial region in South-Eastern France within the Rhône Valley, which hosts several operating nuclear power plants (Cruas and Tricastin). This low-to-moderate seismicity area was hit by an unusually shallow (1 km depth) Mw 4. 9 earthquake rupturing the La Rouvière fault, near Le Teil, at about 15 km distance from the Cruas NPP. The recordings available for the Le Teil earthquake, although limited in number, were used for validation of the PBS. The numerical code SPEED was used for this purpose. In the recent past, this numerical code underwent several successful validations with earthquakes in a wide range of magnitudes, spanning from 3 to 6.
0 genotype data (dataset 1). Ancient individuals (shown with a black contour) are projected and their fill colour matches the colour of present-day individuals from the same geographical area. c, DyStruct results for dataset 1 displaying only a subset of the individuals included in the full analysis (Supplementary Fig. 1e ). Newly generated individuals are highlighted in bold in the legend. Country and language information are displayed as colour bars to the left of the inferred ancestry components.
2 ). We next used a model-based clustering method (DyStruct) to infer shared ancestry  40. The results for the best supported number of clusters in each of the tested datasets (Supplementary Fig. 1a, b ) show that ancient Wallaceans shared ancestry with Papuan-speaking groups from New Guinea (dark blue component) and multiple Asian groups whose ancestry can be partitioned into three main components (Fig. 1c ; see full results in Supplementary Fig. 1e, f ). One component (yellow) is present at high frequencies in Austronesian-speaking groups from Taiwan, the Philippines and Indonesia, and ancient individuals from Taiwan; a second component (mango) is maximized in Polynesian-speaking groups from the Pacific and ancient individuals from the same region; and a third component (dark red) is widespread in present-day and ancient individuals from SEA.
NATurE EcOLOgY & EvOLuTION recently published pre-Neolithic individual from Sulawesi (Leang Panninge)  42  (Supplementary Fig. 9  and Supplementary Table  6 ). The non-significant results are probably due to low amounts of data available for Leang Panninge and/or low amount of Papuan ancestry in Liang Bua and Topogaro (Supplementary Fig. 10 ). Nonetheless, tests involving Leang Panninge consistently exhibited the lowest F 4 values. Therefore, despite being from Wallacea, this ancient individual was not a good proxy for the Papuan-related ancestry of the newly reported ancient Wallaceans. We further investigated potential differences in ancestry sources and proportions among ancient Wallaceans using the qpAdm software  41. Our results indicate that whereas ancient individuals from the North Moluccas can be modelled as having both Papuan-and Austronesian-related ancestry, ancient individuals from NTT and Sulawesi were either consistent with or required a three-wave model, with additional SEA-related ancestry (Fig.
An analogous test based on the three main ancestry components observed in the North Moluccas (Papuan, Taiwan-Austronesian and Pacific-Austronesian) does not provide compelling evidence for backflow from the Pacific since the AHG inferred that Papuan ancestry was introduced into a population that already had both Austronesian-related components (Supplementary Table  10 ). This result suggests that drift had a more important role in the occurrence and distribution of the two Austronesian-related components. Finally, we investigated the timing of admixture using the software DATES (Supplementary Table  11 ), applicable to ancient DNA from single individuals  44. With time series ancient data, we expected to reconcile previous admixture time estimates, despite gene flow complexity. Using Papuans and a pool of Asian groups as sources, we found that estimates for the oldest individuals from the Northern Moluccas (2,150 BP) and NTT (2,600 BP) are very similar (approximately 3,000 BP, adjusting for the archaeological age of each sample; Fig.
The enriched DNA product was sequenced on an Illumina HiSeq 4000 instrument with 75 cycles single reads or 50 cycles paired-end reads according to the manufacturer's protocol (at the Max Planck Institute for the Science of Human History) or on a HiSeq 2500 with 75 paired-end reads (at the Max Planck Institute for Evolutionary Anthropology). The output was demultiplexed using in-house scripts requiring either a perfect match of the expected and observed index sequences (Max Planck Institute for Evolutionary Anthropology samples) or allowing a single mismatch between the expected and observed index sequences (Max Planck Institute for the Science of Human History samples). Genomic data processing. Preprocessing of the sequenced reads was performed using EAGER v. 1. 92. 55 (ref. 70 ). The resulting reads were clipped to remove residual adaptor sequences using Clip&Merge v.
1. 7. 6  71  and AdapterRemoval v. 2 (ref. 72 ). Clipped sequences were then mapped against the human reference genome hg19 using the Burrows-Wheeler Aligner v. 0. 7. 12 (ref. 73 ), disabling seeding (-l 16,500) and allowing for 2 mismatches (--n 0. 01). Duplicates were removed with DeDup v. 0. 12. 2 (ref. 70 ). Additionally, a mapping quality filter of 30 was applied using SAMtools v. 1. 3 (ref. 74 ). Different sequencing runs and libraries from the same individuals were merged and duplicates were removed and sorted again using SAMtools v. 1. 3 (ref. 74 ). Genotype calling was performed separately for trimmed and untrimmed reads using pileupCaller v. 8. 6. 5 (https://github. com/stschiff/ sequenceTools), a tool that randomly draws one allele at each of the targeted SNPs covered at least once.
For the UDG-treated libraries produced at the Max Planck Institute for the Science of Human History, two bases were trimmed on both ends of the reads. For libraries produced at the Max Planck Institute for Evolutionary Anthropology (without UDG treatment), the damage plots were inspected to determine the number of bases to trim from each read. For all libraries, the residual damage extended 8 base pairs into the read, after which it was below 0. 05%, and trimmed accordingly. We combined the genotypes keeping all transversions from the untrimmed genotypes and transitions only from the trimmed genotypes to eliminate problematic, damage-related transitions overrepresented at the ends of reads. The generated pseudo-haploid calls were merged with previously published ancient data  38, 39, 42, 48, [75] [76] [77] [78] [79]  , present-day genomes from the Simons Genome Diversity Project  80  , and worldwide populations genotyped on the Affymetrix Human Origins array  38, 41, 50, 75, 76, [81] [82] [83] [84] [85] [86].
For the PCA and DyStruct analyses, we additionally merged the data with populations from ISEA genotyped on the Affymetrix 6. 0 (refs. 46, 87 ) (dataset 1) or Affymetrix Axiom Genome-Wide Human Array 30 (dataset 2), filtering out SNPs with a missing rate higher than 10%. Related individuals were excluded if they exhibited a proportion of identity by descent (IBD) higher than 0. 3, computed in PLINK v. 1. 9 (ref. 88 ) as P(IBD = 2) + 0. 5 × P(IBD = 1). We additionally pruned datasets 1 and 2 for linkage disequilibrium with PLINK v. 1. 9, removing SNPs with r 2 > 0. 4 in 200 kilobase windows, shifted at 25-SNP intervals. After pruning, a total of 89,597 and 65,880 SNPs remained in datasets 1 and 2, respectively. Y-chromosome haplogroups were identified by calling the SNPs covered on the Y chromosome of all male individuals using the pileup from the Rsamtools v1.
3. 89 ackage and by recording the number and form of derived and ancestral SNPs overlapping with the International Society of Genetic Genealogy SNP index v. 14. 07 (https://github. com/Integrative-Transcriptomics/DamageProfiler)  90. Authentication of ancient DNA. The typical features of ancient DNA were inspected with DamageProfiler v. 0. 3. 1 (http://bintray. com/apeltzer/EAGER/ DamageProfiler)  70. Sex determination was performed by comparing the coverage on the targeted X-chromosome SNPs to the coverage on the Y-chromosome SNPs, both normalized by the coverage on the autosomal SNPs 71 (Supplementary Table  2 ). For male individuals, ANGSD v. 0. 919 was run to measure the rate of heterozygosity of polymorphic sites on the X chromosome after accounting for sequencing errors in the flanking regions  91.
This provides an estimate of nuclear DNA contamination in males since they are expected to have only one allele at each site. For both male and female individuals, mtDNA-captured data were used to jointly reconstruct the mtDNA consensus sequence and estimate contamination levels with contamMix v. 1. 0-10 69 (Supplementary Table  2 ) using an in-house pipeline (https://github. com/alexhbnr-mitoBench-ancientMT  39 ). Statistical analyses. PCAs were carried out using smartpca v. 10210 (ref. 92 ) based on present-day Asian and Oceanian populations from datasets 1 and 2. Ancient individuals were projected onto the calculated components using the options lsqproject: YES and numoutlieriter: 0. We used DyStruct v. 1. 1. 0 (ref. 40 ) to infer shared genetic ancestry taking into account archaeological age.
The uncalibrated radiocarbon dates of each ancient sample were converted to generations, assuming a generation time of 29 years  93. For each dataset (1 and 2), we performed 25 independent runs, using 2-15 ancestral populations (K). To compare runs for different values of K, a subset of loci (5%) was held out during training and the conditional log-likelihood was subsequently evaluated (Supplementary Fig. 1a, b ). Within the best K, the run with the highest objective function was selected (Supplementary Fig. 1c, f ). To formally test population relationships we used the f 4 -statistics implemented in the ADMIXTOOLS software v. 4. 1  41. This analysis was carried out using the admixr v. 0. 9. 1 R package  94. To evaluate differences in f 4 -statistics for individuals from NTT and the North Moluccas, we built a Bayesian linear regression model: B OBS,i ∼ Normal (B TRUE,i , B SE,i ) B TRUE,i ∼ Normal(μ i , σ) μ i = α REGION[i] + β REGION[i] A TRUE,i A OBS,i ∼ Normal (A TRUE,i , A SE,i ) A TRUE,i ∼ Normal (0, 1) α REGION[i] ∼ Normal (0, 1) β REGION[i] ∼ Normal (0, 10) σ ∼ Exponential (1) The model was stratified by region (NTT versus North Moluccas) and takes into account measurement error in both A OBS and B OBS variables (corresponding to the f 4 -statistics displayed on the x and y axes of the biplots, respectively)  95.
The parameters μ and σ represent the mean and s. d. A OBS and B TRUE correspond to the unobserved true values of A and B. Both variables were standardized. The posterior distribution was obtained via Hamiltonian Monte Carlo approximation as implemented in the R package rethinking v2. 21 (https://github. com/ rmcelreath/rethinking), using 6 chains of 4,000 samples. We used a non-centred parameterization of the error model to aid in posterior exploration. Convergence of the chains was assessed by inspection of the trace plots, Rhat and the effective number of samples. All of these criteria indicate reliable sampling. All Rhat values were equal to 1. 00 and all effective number of samples values were above 500. This procedure was applied to each pair of f 4 -statistics separately. The code is available at https://github.
Thus, the covariance of the recent ancestry C with the ratio of the two older ancestries A and B should be zero. The AHG approach thus involves estimating the covariance of the frequencies of A/B with C, A/C with B and B/C with A across all individuals in the population; the covariance closest to 0 then indicates the order of admixture events. We used the DyStruct ancestry proportions for each ancient and present-day Wallacean individual included in dataset 1 and 2 (Supplementary Tables  8  and 9 ) to calculate the covariances between ancestry components as indicated in Supplementary Table  10. The sequence of admixture events was then determined by the configuration that produced the smallest absolute value of the covariance estimate. The time since admixture was estimated based on the decay of ancestry covariance using the software DATES v.
Acknowledgements We thank  R. Radzeviciute ,  A. Wissgott , the  Max Planck Institute for Evolutionary Anthropology lab technicians  and the  Max Planck Institute for Evolutionary Anthropology Sequencing and Bioinformatics Groups  for their excellent support,  C. Jeong  for valuable comments,  L. Iasi  for helpful discussions on admixture dating and  R. McElreath  for help with statistics. This research was supported by the  Max Planck Society. K. N. , S. C. and A. P. were supported by the  European Research Council   Starting Grant  'Waves' (no. ERC758967 ). The research conducted on the samples from  Liang Toge ,  Liang Bua  and Komodo was part of a  New Zealand Fast-Start Marsden Grant  (no. 18-UOO-135 ). The research conducted on the Jareng Bori site was part of a joint project between the  Australian National University Universitas Gadjah Maja  funded by an  ARC Laureate  Project no.