dataesr/scientific-paragraphs-categorization huggingface dataset:
Microbiome sequencing 16S RNA sequencing reads were processed using QIIME (v1) (Quantitative Insights Into Microbial Ecology)  [77]  to analyze the 16S rRNA. First, paired-end raw reads were merged using fastq-join from ea-utils package  [78]. Then, with usearch software  [79] , merged FASTQ reads were filtered (-fastq_trunclen 253 and -fastq_maxee 0. 5). Using QIIME environment, the metadata mapping file was constructed and validated (validate_mapping_file. py) and QIIME labels were added (add_qiime_labels. py). We applied open-reference OTUs (Operational Taxonomic Units) picking (pick_open_reference_otus. py). Summary statistics were computed using biom summarize-table. The resulting dataset was rarefied to an even depth of 10,000 sequences per extract (6 extracts were excluded in diversity analysis: G191_5782, G344_5827, G314_5824, G489_5834, G489_5835, G344_5828).
05. 2019. These were indexed using malt-build to build a custom database. Malt-run was then used with minimum percent identity (--minPercentIdentity) set to 95, the minimum support (--minSupport) parameter set to 10, and the top percent value (--topPercent) set as 1, other parameters were set to default. The resulting rma6 files were visualized with MEGAN6  [81]  and clustered in a Principal Coordinate analysis (PCoA) and Neighbor Joining Tree analysis according to microbial composition on the species level (Supplementary Figs. S14  and S15 ).
Acknowledgments We are grateful to  S. Gatti ,  F. Levréro ,  C. Genton ,  R. Cristescu  for their assistance with sample collection on site. We thank the teams of the  ECOFAC program  ( EU ) and  African Parks Networks  for logistic assistance and permission to work in Odzala-Kokoua National Park. The storage and extraction of fecal samples were performed in the molecular ecology platform (UMR 6553 Ecobio,  CNRS/UR1 ) dedicated to non-invasive samples.
Funding C. F. is supported by  "la Caixa " PhD fellowship, fellowship code  LCF/BQ/DE15/ 10360006. M. K. is supported by  "la Caixa" Foundation  ( ID 100010434 ), fellowship code  LCF/BQ/PR19/11700002. J. N is supported by the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement no. 676154  ( ArchSci2020 ) and an  EMBO  short-term fellowship  STF-8036. P. F. is supported by the  Innovation Fund Denmark. H. R. S is supported by The  Danish Council for Independent Research | Natural Sciences. A. N. is supported by  BFU2015-68649-P (MINECO/FEDER , UE). M. T. P. G. is supported by the  Danish Basic Research Foundation  award  DNRF143. T. M. -B is supported by funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  (grant agreement No.
864203 ),  BFU2017-86471-P  ( MINECO/FEDER, UE) ,  "Unidad de Excelencia María de Maeztu ", funded by the  AEI  ( CEX2018-000792-M ),  Howard Hughes International Early Career and Secretaria d'Universitats i Recerca and CERCA Programme del Departament d'Economia i Coneixement de la Generalitat de Catalunya  ( GRC 2017 SGR 880 ). P. L. G. , N. M. and D. V. are supported by the  French National agency  for research via the  ANR-11-JVS7-015   IDiPop  project. D. H. is supported by  Wellcome   Investigator Award  ( 202802/Z/ 16/Z ) and works in the  Medical Research Council Integrative Epidemiology Unit at the University of Bristol , which is supported by the  Medical Research Council  ( MC_UU_00011/1-7 ). This long-term research on gorillas was funded by the  ECOsystèmes FORestiers program (Ministère de l'Ecologie et du Développement Durable France) , the  Espèces-Phares program  ( DG Environnement, UE ) and  Lundbeck Foundation Visiting Professorship   R317-2019-5  grant to T.
However, it is very sensitive to the scanner effects introduced by different MRI equipments (magnetic field strength, gradient strength), image acquisition protocols  [13, 14] , sequences, pixel size  [15]  and the signal-to-noise ratio (SNR)  [16]. Besides, unlike other imaging modalities (e. g. , Hounsfield Unit (HU) in Computed Tomography (CT), Standardized Uptake Value (SUV) in Positron Emission Tomography (PET)), the intensities of pixels in an MRI image do not have a clear physical meaning and lack of a standard and quantifiable interpretation  [17] [18] [19] , which makes the comparison between MRI images difficult and the reproducibility of MRI radiomics even more challenging. Harmonization methods  [20]  have been proposed to remove those undesirable scanner effects or solve the incomparability among MRI images, thus improving the reproducibility of multicenter radiomic studies on MRI datasets.
Phantoms A homogeneous phantom was designed to mimic cerebrospinal fluid and opacified blood vessels, while a heterogeneous phantom was designed to mimic the brain white matter. Both the homogeneous and heterogeneous phantoms were scanned by two clinical MRI scans (Optima MR450w 1. 5T and Discovery MR750w 3T, both from GE Healthcare, Milwaukee, WI, USA), with different FOVs (24, 18, and 12 cm) and different matrices (256 × 256, 256 × 128, and 128 × 128 pixels). Only T1 clinical sequences were considered. More details about the image acquisition devices and protocols can be found in paper  [12]  by Ammari et al. who studied the same phantom dataset.
Radiomic Features Using the above processing, all the ROIs are two-dimensional. For each 2D ROI, 92 radiomic features  [47]  were extracted from the original MRI images without any image filters applied, using pyradiomics  [48]  (https://pyradiomics. readthedocs. io/en/latest/, accessed on 30 November 2020, version 3. 0. 1), an open-source Python package for the extraction of radiomic features from medical imaging. These radiomic features consist of 18 first-order statistics features, 23 Gray Level Cooccurence Matrix (GLCM) features, 16 Gray Level Run Length Matrix (GLRLM) features, 16 Gray Level Size Zone Matrix (GLSZM) features, 5 Neighbouring Gray Tone Difference Matrix (NGTDM) features and 14 Gray Level Dependence Matrix (GLDM) features. When extracting radiomic features, we directly used the fixed bin number strategy and took 32 bins for discretization, motivated by its good performance in  [19].
All these intensity normalization methods can be regarded as harmonization methods acting on MRI images, and their detailed definitions can be found in  [21]. Note that brain masks were needed when performing the intensity normalization. We directly used the public Python codes for brain mask extraction (https://pypi. org/project/deepbrain/, accessed on 30 November 2020, version 0. 1) and for intensity normalization (https://github. com/jcreinhold/intensity-normalization, accessed on 2 December 2020, version 1. 4. 3). For phantom data, the ROI masks were passed as normalization masks for Z-Score. Regarding the ComBat method which works on the extracted radiomic features, three variants would be investigated, standard ComBat without using Empirical Bayes (EB), parametric ComBat using parametric EB and non-parametric ComBat using nonparametric EB for more robust data adjustments.
The public Python codes (https://github. com/Jfortin1/neuroCombat, accessed on 30 November 2020, version 0. 2. 7) were used in our experiments. Note that we do not consider the covariates of interest (i. e. , X = 0) in ComBat method, because all of our data are pair-wised, namely, they are from the same individuals or same phantoms, but with different acquisition settings.
5T vs. 3T). As expected, the image histograms are almost the same after applying N4 bias field correction. We though infer that the results would be different for MRI images affected with severe bias field effects, and we leave it to be further investigated in future studies. In the following experiments, we do not use N4 bias field correction as a preprocessing method as it has no obvious impact on our dataset. (d) Use N4 bias field correction and image resampling. Figure  6. Impact of preprocessing methods on image histograms of brain MRI images of patient data, scanned with 1. 5T and 3T. The subfiures show the image histograms of the MRI images (a) without using any preprocessing methods (raw data), (b) with only using the N4 bias field correction, (c) with only using the image resampling (1 mm × 1 mm × 1 mm), and (d) with using both the N4 bias field correction and image resampling.
Impact of Image Resampling on the Removal of Scanner Effects Image resampling ensures the same voxel size (1 mm × 1 mm × 1 mm in our study) in MRI images. From the DiffFeatureRatio values presented in Figures  3 4 5 , we observe that image resampling has a clearly different impact on the radiomic feature reproducibility for different dataset and different scanner effects. For the healthy volunteer data with different magnetic field strengths, image resampling leads to slightly bigger DiffFeatureRatio values, meaning more significantly different feature distributions. For other cases, namely for patient data and phantom data, as well as healthy volunteer data with different FOVs and matrices, image resampling helps to decrease DiffFeatureRatio values. Especially for healthy volunteer data with different FOVs, image resampling helps to reduce the DiffFeatureRatio from 0.
Different kinds of data were studied, including in vitro data and in vivo data, to make our conclusions robust and trustworthy. The in vitro data consists of homogeneous phantoms to mimic cerebrospinal fluid and opacified blood vessels, and the heterogeneous phantoms to mimic brain white matter. The in vivo data are composed of brain MRI images collected from some healthy volunteers, as well as some brain MRI images from clinical patients who were treated in a cancer hospital, Gustave Roussy Cancer Campus. Regarding the image preprocessing methods, N4 bias field correction has no obvious impact on the reproducibility of radiomic features in our healthy volunteer and patient data experiments. It may be explained by the fact there is no obvious bias field effects exist in these brain MRI images.
They will still preserve useful biological information. Besides, regarding three different variants of ComBat method (standard ComBat, parametric ComBat and nonparametric ComBat), by applying the intensity normalization methods, the harmonization result appeared more robust to the choice among variants of ComBat. Some results achieved in our paper can be found to be consistent with the previously published papers. For example, Um et al. [38]  investigated the impact of image preprocessing on the scanner dependence of radiomic features in glioblastoma multiforme datasets. Among these image preprocessing methods, they reported that bias field correction did not have a significant impact on the scanner effects caused by different scanner manufactures or different scanner magnetic field strengths.
We achieved the same conclusion on our datasets, but claimed that it may be due to the non-obvious bias field effects. Besides, the authors also reported that isotropic resampling (1 mm × 1 mm × 1 mm) decreased the number of features dependent on different magnetic field strengths (1. 5T vs. 3T), while we observed the same phenomenon on our phantom data and patient data, despite a slightly increased amount of magnetic field strength dependent features for volunteer data. Carré et al. [19]  reported in their paper that, with the fixed bin number discretization strategy, WhiteStripe and Z-Score methods achieved the same percentages of the robust features with the raw images, for both T1w-gd (post-contrast T1-weighted) and T2w-flair MRI sequences. We observed similar results on our T1-weighted sequence datasets (with 32 fixed bins for discretization).
On the one hand, our paper studies both the intensity normalization methods working on MRI images and the ComBat method working on radiomic features, and how they work together to remove the non-biological scanner effects caused by different image acquisition settings. On the other hand, we study not only the Z-Score and Nyúl methods, but also the intensity normalization methods depending on WM intensities and thus dedicated to brain MRI images, including WhiteStripe, FCM-based, GMM-based and KDE-based normalization. Lastly, we also emphasize here the preciousness and rareness of our healthy volunteer dataset, in which the healthy volunteers have two MRI scans on two different scanners (Optima MR450w 1. 5T and Discovery MR750w 3T) in a very short time interval (within 40 min). Unlike the clinical patient data, the differences in radiomic features can only come from the differences in image acquisition settings.
Thirdly, the radiomic features were extracted using the fixed bin number strategy, with 32 fixed bins. The conclusions may be different for fixed bin size feature extraction strategy, or for fixed bin number strategy but with a different fixed bin number  [19]. Besides, our experiments were done based on T1 sequences of the brain MRI, the results on other clinical MRI sequences may be different. So the conclusions in our paper would need to be further assessed on other independent datasets, on other clinical MRI sequences, on some radiomic models with clinical problems to be solved and so on.
Introduction The SARS-CoV-2 virus was first recorded in China  [3]  in 2019. At least 3 million deaths caused by the virus  [1]  have been recorded by Mai 2021. The virus further has impacted social life and economies around the globe  [2]. Even though successful vaccine campaigns are run, especially in the western world, the virus has since underwent several escape mutations that might jeopardize them in the future  [4]. As such constant monitoring of the viruses evolution is needed. As the virus sequence databases are growing faster, more efficient solutions are needed in order to rapidly infer phylogeny. More than 1. 5 million sequenced samples are openly available to researchers and can be readily downloaded from the internet. Having our in house developed MNHN-Tree-Tools  [6]  toolkit at hand, we grasped this opportunity and investigated the diversity of the virus.
Machine and software requirements This tutorial was performed on a 64bit Intel (R) architecture Linux machine consisting of 28 real and due to hyper threading 56 virtual cores. Due to the nature MNHN-Tree-Tools it is imperative that the CPU cores of the machine support the AVX instruction set. The machine further was equipped with 128 gigabytes of random access memory (RAM). MNHN-Tree-Tools was installed on the machine as described in its manual 1   [6]. We suggest that the reader following this tutorial uses a similar machine, although different configurations might work.
Covid-19 sequence retrieval On the 16th of september 2021 we downloaded 1 442 669 nucleotide sequences of the SARS-CoV-2 virus from the NCBI SARS-CoV-2 Resources website 2. The sequences were selected to match a minimum length of 29 500 bases pairs and do not exceed a length of 32 300 bases pairs. Further only sequences obtained from human, homo sapiens, host identified by taxid:9606 in the database were selected. We instructed the NCBI website to prepare the data in FASTA format, containing only the SARS-CoV-2 Pango linage identifier as well as the NCBI accession code in the sequence identifier lines of the FASTA file. No further filtering of the FASTA raw resources was performed. The obtained FASTA file is stored as covid-ncbi-16-sept-2021. fasta.
6 mer -pca7v > \ clusters / covid -ncbi -16 -sept -2021 -6 mer -pca -7 v -0. 5 -0. 1 -6 -output In order to visualize the results we have to create a tree in Newick format 3  from the adaptive clustering run that we have performed beforehand. This is done by invoking split sets to newick as follows: mkdir clusters / covid -ncbi -16 -sept -2021 -6 mer -pca -7 v -0. 5 -0. 1 -6 -additions cd clusters s p l i t _ s e t s _ t o _ n e w i c k 0 0 \ covid -ncbi -16 -sept -2021 -6 mer -pca -7 v -0. 5 -0. 1 -6 -00* > \ covid -ncbi -16 -sept -2021 -6 mer -pca -7 v -0. 5 -0. 1 -6 -additions / full -tree. dnd The resulting full-tree. dnd file allows us to use common software tools that visualize Newick trees such as the Newick utilities  [8]. The resulting tree is shown in its entirety in figure  1. The different shades are proportional to the logarithm of the number of sequences found in the tree nodes.
From white for 0 sequences, to full black for the 1 442 669 sequences contained in our dataset. In order to visualize the tree with the shading outlined above a map that can be supplied to the Newick utilities  [8]  has to be created. The tree map for split set tool using the final tree layer, containing all sequences, in our case number 49, is perfect for such a task: t r e e _ m a p _ f o r _ s p l i t _ s e t covid -ncbi -16 -sept -2021 -6 mer -pca -7 v -0. 5 -0. 1 -6 -0049 \. / covid -ncbi -16 -sept -2021. fasta 1 2 1 ,#000000 \ covid -ncbi -16 -sept -2021 -6 mer -pca -7 v -0. 5 -0. 1 -6 -00* > \ covid -ncbi -16 -sept -2021 -6 mer -pca -7 v -0. 5 -0. 1 -6 -additions / full -log -gradient. map Using the FASTA file annotations for the split set from annotation tool which is part of MNHN-Tree-Tools were created.
We first processed the downloaded FASTA file with general shell utilities available on our Linux system. In order for the following commands to work the reader has to make sure that Pango linage is in the "$1" row according to the awk tool in the FASTA annotation lines. The commands above extract all Pango lineage codes into a text file. In order to create valid annotation from this text file we created a small utility in C whose source code is outlined in the following: This tool allows us to create annotation files containing either 1 if a certain sequence belongs to a certain variant, or 0 if it does not. We invoke our utility like the following to create annotations for the alpha, beta, gamma and delta variants: Using the sets created with this tool that indexed the sequences by the afformentioned annotations for alpha, beta, gamma and delta variants we are able to use the tree map split set tool, again part of MNHN-Tree-Tools, to create color maps that allow us to highlight the different variants in our tree using the newick utilities  [8].
Visulization of the different Covid-19 variants In the dataset lineages B. 1. 1. 7 Q. 1 Q. 2 Q. 3 Q. 4 Q. 5 Q. 6 Q. 7 # include < stdio. h > # include <. / a. out covid -ncbi -16 -sept -2021. strains \ B. 1. 1. 7 Q. 1 Q. 2 Q. 3 Q. 4 Q. 5 Q. 6 Q. 7 Q. 8 > covid -ncbi - for i in alpha beta gamma delta do t r e e _ m a p _ f o r _ s p l i t _ s e t. / covid -ncbi -16 -sept -2021. $i -split -set \. / covid -ncbi -16 -sept -2021. fasta 1 2 2 ,#000000 \ covid -ncbi -16 -sept -2021 -6 mer -pca -7 v -0. 5 -0. 1 -6 -00* > \ covid -ncbi -16 -sept -2021 -6 mer -pca -7 v -0. 5 -0. 1 -6 -additions / $i -opposition -map done All trees were finally visualized using the newick utilities  [8]. A multicolored tree highlighting both the alpha and gamma variant in a single tree was created by overlaying and postprocessing the results using the GIMP program.
Discussion The data that we obtained is probably skewed as most of the sequences are either of the alpha or delta variant, and most sequences in the dataset originate from the United States with way more populus countries such as India significantly underrepresented. Intrigingly we find very well that beta and gamma variants are mostly related to clades in which the alpha variant labeled sequences reside. Looking at figures 1, 2 and 3 we clearly can identify on the upper left a delta clade and on the upper right an alpha clade. This finding further is replicated by two clades on the lower right side of the figures 1, 2 and 3. In this lower right area the tree develops two boot like structures, where the left boot contains alpha, but intriguingly also beta and gamma sequences, while the right boot contains only delta sequences.
A tree provided by the https://nextstrain. org website, shown in figure  4 , looks different, but seems to highlight the same features in the sense that alpha, beta and gamma sequences seem closer related than delta sequences. An in depth comparison between our algorithm, tree and data with the one provided by Nextstrain  [5] , albeit interesting lies out of the scope of this tutorial. The interested reader is referred to the algorithmic supplement provided with MNHN-Tree-Tools  [6]. We conclude that MNHN-Tree-Tools and its efficiency handling millions of sequences can be useful in studying the evolution of SARS-CoV-2 and its phylogenetics. The public dataset available here provides fast results. These results should however be clearly inspected, and readers are referred to use more comprehensive and cleaned data with this algorithm.
The thermal expansion properties of crystalline organic compounds are investigated by data mining of the Cambridge Structural Database (CSD). The mean volumetric thermal expansion coefficient is 168. 8 Â 10 À6 K À1 and the mean uniaxial thermal expansion coefficient is 71. 4 Â 10 À6 K À1 , based on 745 and 1129 different observations, respectively. Normal and anomalous coefficients can be identified using these values and the associated standard deviations. The anisotropy of the thermal expansion is also evaluated and found to have a very broad distribution. 4719 different structures, comprising 4093 different molecular compounds and 626 additional polymorphs have been analyzed on their thermal expansion properties. Approximately 34% of these structures may have at least one orthogonal axis with negative thermal expansion, much more than generally believed.
32 An exceptional negative volumetric thermal expansion in 1,4-diazabicyclo[2. 2. 2]octane hydrochloride was reported by Szafrański (2013);  33  the highest reported negative uniaxial thermal expansion was found in chloranilic acid pyrazine. 34 n this paper we focus on thermal expansion properties of crystalline organic materials determined by diffraction techniques, using data mining of the Cambridge Structural Database (CSD). 35 It is shown that unusual thermal expansion properties exist for a number of crystalline compounds which have not been recognized as such before. The CSD contains at the time of writing somewhat more than one million of entries containing the results of diffraction experiments under a variety of conditions, and thus also possibly unexplored thermal expansion properties of crystalline materials.
Crystal structures have been occasionally measured at different temperatures, either for one study by one research group, or by different research groups for unrelated studies. A CSD survey on density properties of organic compounds as a function of temperature has been published in 2007 on the 2004 database version with 325 709 entries by a manual search and treatment in an external spreadsheet program. 36 In total 373 unique structures were found with temperature dependent data. The CSD Python API  37  gives the possibility to extract the data in a more systematic way and to treat the results numerically and statistically. For the present study the 5. 41 version of the CSD was used.
g. the b-glycine polymorph). Sometimes polymorphism is not recognized, which could lead to erroneous attributions in thermal expansion coefficients. One case is refcode MEBVOH with two reported structures at two different temperatures. The publication reports two 'polyforms' MEB-VOH and MEBVOH1 with the same space group and very close cell parameters. 38 The two structures are most probably in reality the two enantiomeric forms of the same molecule, but can be easily overlaid without inversion using the MERCURY program. 39 Since the atomic displacement parameters of the two structures were in the normal range for the reported temperatures, they were retained for the present analysis. Temperature information. Each entry has also a 'temperature' key, which could be empty if the corresponding cif tag is empty or not dened and if there is no indication in the original publication at which temperature the experiment was performed.