dataesr/scientific-paragraphs-categorization huggingface dataset:
We examined the log posterior between the chains and combined results from both using the rEEMSplot package 0. 1. 0. We then plotted surfaces of effective migration rates across each landscape to identify geographic barriers and areas of connectivity. Thirdly, we ran the ResDisMapper program 1. 1  [64]  (https://rdrr. io/github/takfung/ ResDisMapper/man/ResDisMapper-package. html), which estimates resistance to dispersal across landscapes. ResDisMapper 1. 1 calculates IBD residuals for each dyad of individuals and maps resistance values based on their deviations from the general IBD trend. Genetic data were input into ResDisMapper 1. 1 using the GENEPOP format, and a Euclidean distance matrix was used to produce IBD residuals using the "relative similarity method" (Method 1). We ran ResDisMapper 1.
Ecological Determinants of Genetic Diversity We calculated three parameters of genetic diversity to determine which ecological factors affected genetic diversity of the two rodents across the landscapes. The three individual-based parameters were the inbreeding coefficient (F IS ), observed heterozygosity (H O ), and expected heterozygosity (H E ). These three parameters were calculated using the VCFtools --het command  [57]  for each individual E. myoxinus and R. rattus. Generalized additive models (GAMs) were built for each of these dependent variables (Link function = identity, family = Gaussian) using maximum likelihood estimation in the R package mgcv 1. 8. 42  [65]  and the fixed factors species (E. myoxinus, R. rattus), region (ANP, MCF), sex (male, female), forest type (continuous, forest fragment), body mass (a proxy for body condition), forest area (of the fragment where an individual was captured), distance to edge (of an individual to the nearest forest edge), distance to continuous forest (of a fragment edge to the nearest continuous forest edge, a proxy for isolation), and the percentage of edge habitat (area of the fragment within 50 m of the forest edge).
Normality of all continuous variables was assessed using Shapiro-Wilk's tests  [66]. If a variable was not normally distributed, it was transformed using either log or square root transformations, and Q-Q plots were visually inspected to ensure improvement. We tested for correlations between all continuous predictor variables (body mass, forest area, distance to edge, edge percentage, and distance to continuous forest) to ensure independence of the fixed factors included in a given model (Table  S4 ). We modeled each genetic diversity parameter (a) with the complete dataset (n = 123 individuals) and (b) just with the individuals caught in forest fragments (n = 90 individuals) to also test for variables that are not relevant for continuous forest sites (i. e. , 'percentage edge habitat' and 'distance to continuous forest').
RADseq Dataset After filtering with the STACKS pipeline, we recovered 42,855-59,901 RAD loci per individual with a mean depth of 9. 79x coverage (5. 05x-23. 82x per individual) for E. myoxinus individuals (n = 52) from ANP, and 53,070-69,657 RAD loci per individual with a mean depth of 10. 09x coverage (8. 57x-12. 39x per individual) for E. myoxinus individuals (n = 14) from MCF (Table  S2 ). We recovered 40,542-49,097 RAD loci per individual with a mean depth of 9. 68x coverage (5. 40x-19. 43x per individual) for R. rattus individuals (n = 43) from ANP, and 34,451-48,855 RAD loci per individual with a mean depth of 10. 42x coverage (5. 90x-15. 04x per individual) for R. rattus individuals (n = 38) from MCF. SNP sets were generated for each species separately and individuals were grouped for different downstream analyses based on need (Table  S2 ).
Acknowledgments: We thank the  Ministère de l'Environnement et du Developpement Durable (MEDD) , the  Direction du Système des Aires Protégées (DSAP) , and  Madagascar National Parks (MNP)  for permission to conduct research in ANP and MCF. We are grateful to  Mamy Razafitsalama ,  Travis Steffens ,  Solofonirina Rasoloharijaona ,  Rindrahatsarana Ramanankirahina ,  Helena Teixeira ,  Ariel Rodriguez ,  Tobias van Elst , the late  Elke Zimmermann , the organizations Planet Madagascar, DBCAM,  Operation Wallacea , and the people of the villages of Maevatanimbary, Andranohobaka,  Ambarindahy ,  Mariarano ,  Ambolodihy , and  Antanambao  for technical and logistical support. We finally thank  Leonie Baldauf ,  Fernand Fenomanantsoa ,  Jack O'Connor ,  Quinn Parker ,  Olivia Pilmore-Bedford ,  Herinjatovo Rakotondramanana ,  Onjaniaina Gilbert Razafindramasy ,  Simon Rohner , and  Harry Skinner  for assistance in data collection.
Microscopy and cell length measurement Cells were grown in liquid media at 25 • C for temperature sensitive strains ( permissive temperature ) and 32 • C for remaining strains until mid-logarithmical growth phase. They were then incubated in a 37 • C shaking water bath. Eight hours after temperature shift cells were fixed in 70% ethanol and imaged using Delta Vision Core System Microscope ( 100 × 1. 4 numerical aperture UplanSApo objective and a cascade2 EMCCD camera ). Deconvolution was performed using the enhanced ratio method in softWoRx software. Lengths of 400 cells per strain were assessed using FIJI Software and analyzed using PRISM Software.
Funding Portuguese Fundação Ciência e Tecnologia (FCT)  [ PTDC / BEX-BCM / 5179 / 14 ];  Howard Hughes Medical Institute IECS  (to M. G. F. );  Université Côte d'Azur -Académie  4 (Installation Grant:  Action 2 -2019 ) (to M. G. F. );  Fondation pour la Recherche Médicale (Equipe labélisée FRM)  [ EQU201903007804 ]; [ SFRH / BD / 114401 / 2016  to M. G. F. ]; [ PTDC BEX-BCM / 5179 / 14 , to J. M. E. ]; ' Ligue Nationale Contre le Cancer' (LNCC) (Equipe labélisée)  to S. C. ;  Agence Nationale de la Research  ( ANR-16-CE12 TeloMito  and  ANR-20-CE12 TeloRPA ) to S. C. Funding for open access charge:  Agence Nationale de la Recherche (ANR).
Introduction Intra-site spatial approaches have long been recognised as a very useful, even necessary, tool when trying to understand and interpret Palaeolithic archaeological assemblages. Such approaches can provide key information on both anthropogenic activities and the actions of various natural agents, since all of these usually impact the spatial distribution of the remains. Consequently, intra-site spatial analyses provide valuable data on site formation (including both depositional and postdepositional processes) and past human behaviour. Ethnoarchaeological studies have demonstrated the ability of humans to generate a recognisable spatial pattern and shown that the distribution of remains is related to the social behaviour of human groups  (Yellen 1977a, b; Binford 1978a, b; O'Connell 1987; O'Connell et al.
Unlike previous works within this area, we selected a specific archaeolevel, Ob, identified as a result of a previous exhaustive archaeostratigraphic study. Another singularity of this work is the simultaneous application of a great variety of analyses to both lithic and faunal remains. In summary, our main objectives are to: (1) characterise the point pattern of the lithic and faunal remains in Ob; (2) contribute to the dissection of the palimpsest; (3) improve our knowledge of the deposit's formation processes and postdepositional disturbance; (4) provide an optimal basis for the integration of lithic and faunal data; (5) produce quantitative data that facilitates the diachronic study of the site; and (6) take the first steps towards such a diachronic comparison.
Clustering pattern As a first step, we used density maps generated using the kernel algorithm as an informal approach to spatial cluster analysis  (Baxter et al. 1997). In this task, we considered the radius suggested from the results of Besag's L (r) functions. Kernel density estimation (KDE) uses point data to create a smoothed density map taking into account the proximity between the points  (Baxter et al. 1997). We used QGIS software to create the density maps, generating both 2D and 3D maps. KDE has been applied to different archaeological contexts in several previous studies, including recent examples relating to Palaeolithic sites (e. g. ,  Keeler 2010; Moseler 2011; Nigst and Antl-Weiser 2012; Blasco et al. 2016; Spagnolo et al. 2016 Spagnolo et al. , 2019 Spagnolo et al. , 2020a, b, c;, b, c; Romagnoli and Vaquero 2016; Sánchez-Romero et al.
2016 , 2020 , 2021; de la Torre and Wehr 2018; Giusti et al. 2018; Marín et al. 2019). As a second step, a k-means analysis was performed, after calculating the ideal number of clusters using the elbow method  (Kintigh and Ammerman 1982; Baxter 2015; Kasambara 2017). In elbow graphs, the point where the curves abruptly change trend indicates the ideal number of clusters, which must be read on the x-axis. We used the R language and environment to calculate the ideal number of clusters (R Core Development Team 2011), and CrimeStat software (National Institute of Justice, Washington, DC, USA) to carry out the k-means analysis. This analysis allowed us to classify all the items in different sectors and identify the main clusters (one cluster within each sector). Then, we visualised the results in QGIS.
Fabric analysis During the fieldwork, the general orientation data (NS, NESW, EW, NWSE, or SQUARE -in the case of items with the same length and width) of each remain was recorded prior to its removal. To assign an orientation to the remains, the theoretical north of the site, located on the wall at the bottom of the rock shelter, was taken as a reference. In line with previous work  (Bertran and Texier 1995; Bertran and Lenoble 2002; McPherron 2005; Benito-Calvo and de la Torre 2011; Domínguez-Rodrigo et al. 2014; Sánchez-Romero et al. 2016; de la Torre and Wehr 2018; Giusti et al. 2018; Spagnolo et al. 2020a, c) , we selected a subsample of remains appropriate for the orientation analysis, comprising those larger than 2 cm and with an elongation index (length/ width) > 1. 6. As in the case of the size categories, we generated 2D and 3D density maps for each orientation (NS, NESW, EW, and NWSE) and quantified the number of items with each orientation in the different sectors and clusters.
Digital elevation model (DEM) We used the depth values of the remains as a proxy to reconstruct the morphology of the base of the archaeolevel Ob. From a database containing the Z-values of both lithic and faunal remains, a grid file was generated using SGA GIS software. During this process, we indicated that, in the case of multiple Z-values for the same pixel, the lower value would be selected. Next, the grid file was interpolated (Multilevel B-Spline Interpolation) using the same software. The result of the interpolation was represented in 3D by applying the QGIS tool "QGis 2threejs. " Finally, using the same interpolation, a terrain analysis (Analytical Hillshading) was performed. As a result, we obtained detailed 2D and 3D images of the surface of archaeolevel Ob. Similar methods have been used by other authors to produce palaeotopographic reconstructions (e.
, 2022; Bell, Hernandez, et al. , 2021; Bell, La Femina, et al. , 2021; Sandanbata et al. , 2022) ; this would be the case for Curtis caldera. Gusman et al. (2020)  proposed a different mechanism for the Curtis earthquakes to explain datasets of tsunami and broadband seismic waveforms. They separately attributed the tsunami generation to a hydrofracturing source at a shallow depth and the vertical-T CLVD seismic radiation to a deeper source. On the other hand, we showed that a trapdoor faulting within the caldera, involving ring-faulting and sill-like reservoir deformation at a shallow depth, reproduced the tsunami and long-period seismic waveforms. Tsunami waveforms expected for the two mechanisms seem indistinguishable from the available tide-gauge data. Also, given the difficulty in determining source depths of shallow seismic sources  (Ekström et al.
We assume the centroid location at (178. 56°W, 30. 54°S) and the depth at 2. 5 km in the crust, and apply the same filter, as done in the source modeling (see Text S2. 3). We start the inversion with a half duration th and a centroid time shift tc reported in the GCMT catalog, and grid-search optimal values for th = tc. During the inversion process, we select seismic data yielding a single-record normalized root-mean-square (NRMS) misfit ≤ 1. 0, which is calculated by fg 1 A - 1 A g/g 1 A g, where  1 A and  1 A are synthetic and observed data in the inversion window at the i-th station, respectively. The selected datasets are composed of 29 and 33 records of the 2009 and 2017 earthquakes, respectively (Figures  S10  and S11 ). Table  S2  shows the obtained deviatoric moment tensor of the 2009 and 2017 earthquakes.
Acknowledgments The authors thank  Zhongwen Zhan  for helpful discussion. This study is funded by the  JSPS KAKENHI  (Grant numbers:  JP17J02919 ,  JP20J01689 , and  JP19K04034 ), by the  JST J-RAPID  ( JPMJJR1805 ). Osamu Sandanbata's travel to  California Institute of Technology  was supported by  Oversea Internship Program of Earthquake Research Institute , the  University of Tokyo.
, 2021 ) , based on Balas instance preprocessing  ( Balas et al. , 1995 ) , Held and Karp branching framework (see e. g. Morin & Marsten, 1976 ) , and the combinatorial lower bounds from  Salman, Ekstedt, & Damaschke (2020)  , (iv) the public PCGTSPLIB benchmark library proposed in  Salman et al. (2020)  as an extension of the well-known SOPLIB library. According to the literature  ( Khachay et al. , 2021; Salman et al. , 2020 ) , 12 out of 40 instances of this library were solved to optimality. Meanwhile, their solutions can be found within a competitive time by Gurobi solver supplied with our extension of the L1PCATSPxy compact model, previously introduced in  Sarin et al. (2005)  for the PCATSP, built-in cutting planes, and PCGLNS primal heuristic. In addition, we should mention the branch-and-cut algorithm proposed recently in  Yuan et al.
Implementation The proposed algorithm is implemented on top of the Gurobi 9. 3 framework. Primal heuristic and cutting planes are provided as user callback functions. For the sampled models, all the parameters of the heuristic separation including sample sizes and learning rates are tuned within preliminary testing stage. All the built-in Gurobi heuristics and cutting plane algorithms are disabled, while other parameters of the solver keep their default values. The suggested implementation is carried out in Python 3 leveraging Net-workX software package for internal graph processing tasks and fully cross-platform. All source code together with the reported experimental results are open for public access at  Khachai (2022).
Extensive sequencing data and high-quality components are the foundation of pangenome construction Advances in sequencing technology provide a database and technical support for the research on eukaryotic pangenomes (Fig. 1 ). Although the next-generation sequencing (NGS) technology created by Illumina has greatly improved the single throughput and the ability to detect genomic variation in a high throughput way  [20, 22] ; however, NGS has the major defect of short read length, and using PCR during the process can lead to the base bias, which reduces the ability to detect complex genomes. By contrast, TGS, as represented by PacBio technology, has a read length of up to 80 kb on a highthroughput basis, greatly improving the detection and analysis capabilities of complex regions and large SVs of the genome  [19, 20, 23].
Although TGS has the advantages of long read length and high accuracy, its application is currently constrained by its expensive cost and dearth of bioinformatics data analysis software  [20]. In addition, a long-read sequencing technology, synthetic long read (SLR), whose sequencing cost and error incidence are lower than those of TGS, is widely used for cell sequencing  [20]. These technologies have enabled many individual genomes of species to be sequenced. To date, this has yielded over 1. 2 million genomes and a vast amount of the genomic data. With the advent of these technologies, a variety of reference genomes have been successively unfolded. Taking domesticated animals as an example, the chromosomescale assemblies such as ARS1  [24] , Saanen_v1  [25] , Sscrofa11. 1  [26] , ARS-UCD1.
To address the deluge of data from whole-genome wide sequencing and analysis, genomic databases such as the NCBI databases, the goat genome database, the bovine genome database and variation databases such as GGVD  [31] , PigVar  [32]  and BGVD  [33]  have been established. Overall, the development of sequencing technology has greatly promoted the volume of gene sequencing data available for each species, enabling genome research related to diversification and deep refinement and facilitating the development of pangenome research in eukaryotes.
Multi-population pangenomics uses the high-quality TGS-genomes of multiple representative varieties to further help people decode the genetic mechanism of the differentiated phenotypes of polyploid plants and accelerate the breeding process of plants  [57, 75, 76]. Another important improvement is the creation of a "super pan-genome" for plants, which extends the concept of the pan-genome to the genus level, promotes the analysis of scientific issues such as inter-individual gene exchange and genome evolution after polyploidy, and provides a theoretical basis for the rational utilization and preservation of wild germplasm resources  [76, [82] [83] [84]. A recently published article on the pangenome of tomato extends the concept of pangenome to a new field  [85]. This article demonstrates the significant benefit of pangenomic genetic diversity in retrieving the "missing heritability" from three aspects.
2%) was revealed that is currently lacking in the bovine reference genome. The newly assembled genomes of two African breeds fill the current blank in the lack of available high quality reference genomes for African cattle. By comparing different datasets, it was confirmed that the graph genome, which could represent the genetic diversity of cattle breeds throughout the world, has more power for SV calling and prediction of novel functional regions than linear references. Importantly, it was the first to provide direct evidence that pangenome can facilitate the downstream analysis of genomics. It additionally affirmed that the development of ideal pangenomes ought to involve excellent and complete genomes as skeletons, like the utilization of TGS and telomere-to-telomere (T2T) assemblies. Furthermore, a study using 898 cattle generated the biggest cattle pangenome (57 breeds) and found 83 Mb of non-reference sequences.
First, representative samples should be selected to extract high-quality DNA for TGS or NGS sequencing. Second, the high-quality genomes or haplotype-resolved assemblies are generated to construct the graph pangenome, then performs SV detection and identification. Thirdly, newly generated or existing NGS data in the database are compared with the pangenome for genotyping. Combining with multi-omics data, the SV affecting the important economic traits of livestock and poultry will be mined and verified from multiple angles and levels. Finally, the molecular markers found should first be validated at the cellular level. If possible, gene editing technology can be used to verify the effect of the functional gene on phenotype at the individual level.
Those cytokines were chosen by a panel of experts (FB, JFT, JPM) given their known role in malaria pathogenesis. In patients with SM, the same cytokines were assayed using the same method on days 1 and 2. Statistics. Continuous data are described as median and interquartile range [IQR], and qualitative variables as number and percentage. Cytokine values were also described with boxplots according to the different patient groups. For cytokines below the detection limit (DL), imputation was made using the single value of DL/2. This method is commonly used as an imputation method in the presence of detection limit and has been reported in similar studies  17. Comparisons between two groups (UM vs SM or LSM vs VSM) were performed with Student t-test or Mann-Whitney test as appropriate for continuous variables, and with chi-square test or Fisher exact test as appropriate for categorical variables.
To compare plasmatic cytokines level between the three groups UM, LSM and VSM, the non-parametric Kruskal-Wallis's test was used. The association between plasmatic cytokines level and some of biological data was measured using Spearman correlation coefficients. All statistical analyses were performed using SAS version 9. 4 software (SAS Institute Inc. , Cary, NC). 1 ). All patients, and groups with uncomplicated malaria versus severe malaria. Of the 278 patients with cytokine data, 134 had UM and 144 SM, including 68 with VSM and 76 with LSM (see the Supplementary Fig. S1  for Flow-Chart). The SM group was characterized by older age, a higher proportion of patients with comorbidities, a longer time from symptom onset to hospital admission, higher parasitemia on day 0, and a higher pfHRP2 level (Table  1 ).
Acknowledgements This work was supported by a research grant from the  French Ministry of Health  ( Programme Hospitalier de Recherche Clinique , PHRC, # AOR05007 ). The sponsor was the public organization  Département de la Recherche Clinique et du Développement, Assistance Publique-Hôpitaux de Paris. The trial is registered on ClinicalTrials. gov (# NCT00372684 ). We thank all the nurses, physicians, and biologists of the  PALUREA Study Group  for excellent patient care. We are grateful to  Prof. Jean-Marc Cavaillon  for scientific advice based on his considerable expertise. We are indebted to the  Centre Hospitalier de Versailles  for editorial assistance.
To achieve this objective, we used a discrete BN, with each node representing a CVRF or a medical condition. Both the BN structure and its probability tables are learnt from a large dataset originating from annual health assessments. Importantly, we characterize the uncertainty about such probabilities through (higher-order) distributions. While the typical objective in the aforementioned literature is the analysis of CVD risk scores, here we emphasize the predictive assessment of various CVRFs and further integrate additional factors such as depression, sleep duration or socioeconomic status, and analyze them through a BN-based framework. One of the main advantages of our model is that it allows assessing predictions for each factor, or sets of factors, using probabilistic inference. This can be used as a complementary decision-support tool to facilitate diagnosis and treatment by allowing practitioners to better foresee patients' conditions.
Moreover, it can also serve as a validating tool to check how likely the patient's condition could have been forecast by the model based on past data. The paper is structured as follows: Section 2 describes the dataset used to structure and calibrate the BN; in section 3, we formulate the BN model describing the structure learning process as well as the marginal distribution for each node; section 4 presents several uses of the model; and, finally, section 5 summarizes and discusses our findings. An appendix briefly describes a freely available software to perform predictions based on our proposed BN.
Data set Data were extracted from the annual work health assessments of adult workers affiliated with a private insurance provider in Spain, from 2012 to 2016 and were anonymized and secured. Data were complemented with census information based on postal code, allowing us to infer the associated socioeconomic status (combining occupation, economic activity and professional situation averaged across the entire postal code) and education level (level 0 to 4, where 0 represents illiterate people and 4 represents people with a university degree or higher). We removed outliers, duplicates, misrecorded and missing values, and retained only the last assessment available for each individual leading to a total of 205,087 health assessments over the 4-year period. The variables relevant in this study are listed in Table  1  and are grouped as follows: • Non-modifiable CVRFs: sex, age, education, and socioeconomic status.