dataesr/scientific-paragraphs-categorization huggingface dataset:
No powder structures were included. 2. 1. 3 Computational details. Density functional theory (DFT) simulations were applied using the Crystal14 software developed by Dovesi and coworkers. 20 We used the PBESOL0 functional  21  in conjunction with the POB triple-zeta valence + polarization basis set for C, O, P, and H elements,  22  and Mo was treated with a small-core effective-core pseudopotential of the Hay-Wadt type described by Corà et al. 23  The geometries of the single molecules and crystal structures were opti-mized using default convergence criteria. Optimizations of the crystal structures included relaxing both the atomic coordinates and the parameters of the cell. Lattice energy is defined as the difference in energy between the crystal and the free molecule in its relaxed form.
Packing energy is calculated as the difference in energy between the crystal and the individual molecule in the same conformation as in the  crystal. The difference between the lattice energy and packing energy is defined as the strain energy which is attributed to the deformation of the molecule within the lattice. The geometries of the single molecules and crystal structures were optimized using default convergence criteria and the shrink parameter was set to 2. Input files for the calculations and optimized structures can be found online at https://github. com/fxcoudert/citable-data.
Cambridge Structural Database analysis These two C ipso -P-M-P torsion angles (φ-angles) are also what we consider to be the best descriptors for the different conformations of the cis-MĲPPh 3 ) 2 -fragment when we searched the CSD to see which of these two conformations was the most common, or if there were indeed other conformations to consider as well. Torsion angles were selected for all cisbis-triphenylphosphine fragments (P-M-P angles 90-105°), first for all the compounds, giving 1428 structures, and then with the restriction that the metal should be 6-coordinated just as the title compound, leaving us with 287 hits. The data are displayed in Fig. 6. What emerges from this is that for lower coordination numbers, as for cyclopentadienyl complexes for example, there is more space for the PPh 3 to spread around the metal ion and this seems to generate a clear preference for a 15°/ 60°conformation.
The Nikon oil immersion microscope objective was a 60x with a N. A. of 0. 80. Bacteria motion is recorded with a camera Nikon Digital Sight DS-Qi1Mc (1280 × 1024 pixels) at a frame rate of 10 frames per second (fps), leading to a time between frames ∆ =. t s 0 1 , with a spatial resolution of 0. 106 μm/pixel. From movies of the bacterial suspensions, the center of mass → r t ( ) of a spiroplasma at the instant of time t was obtained as the center of mass of the scattered light intensity from the bacterium (Fig. 1a ) using a home made Matlab code. The instantaneous velocity of the center of mass of the bacteria is obtained using finite differences of the displacement of its center of mass after a time ∆t: → = → + ∆ -→ ∆ V t r t t r t t ( ) ( ) ( ) The orientation θ(t) of the bacterium is obtained from the orientation of the long axis of the scattered light intensity.
Background: Reporting mortality and lost to follow-up (LTFU) by age is essential as older HIV-positive patients might be at risk of long-term effects of living with HIV and/or taking antiretroviral therapy (ART). As age effects might not be linear and might impact HIV outcomes in the oldest more severely, people living with HIV (PLHIV) aged 50-59 years and PLHIV aged >60 years were considered separately. Setting: Seventeen adult HIV/AIDS clinics spread over nine countries in West Africa. Methods: Data were collected within the International Epidemiological Databases to Evaluate AIDS West Africa Collaboration. ART-naïve PLHIV-1 adults aged >16 years initiating ART and attending ≥2 clinic visits were included (N=73,525). Age was divided into five groups: 16-29/30-39/40-49/50-59/≥60 years. The age effect on mortality and LTFU was evaluated with Kaplan-Meier curves and multivariable Cox proportional hazard regressions.
Study design Data collected between 2006 and 2016 within the International Epidemiological Databases to Evaluate AIDS (IeDEA) West Africa Collaboration of the US National Institutes of Health (https://www. iedea. org/regions/west-africa/) were analyzed. 7 Since 2006, a network of adult and pediatric HIV clinics has been established in West Africa as part of the global IeDEA Collaboration. The aim of this multicenter collaboration is to get a fair description of the care and treatment patterns and their trends over time, collecting routine information on demographics, clinical, biological, and therapeutic skills. It currently involves 17 adult and 11 pediatric HIV/AIDS clinics spread over ten countries in West Africa: Benin, Burkina Faso, Cote d'Ivoire, Ghana, Guinea, Guinea Bissau, Mali, Nigeria, Senegal, and Togo.
Study definitions Mortality and LTFU were the primary outcomes. Mortality was defined as the number of deaths that occurred during that period. Death was recorded through reports by clinic staff, relatives, peers, or friends. LTFU was defined as no contact within 6 months before the cohort closure date and no knowledge of death or transfer. The database closing date corresponds to the date of the most recent follow-up visit. For each patient, we documented the following: the CD4 cell count (cells by microliter) at ART initiation, the initial clinical stage using Centers for Disease Control and Prevention (CDC) or WHO definitions depending on the study sites (A/B/C or I/II/III/IV disease stages, respectively), the initial hemoglobin (Hb; low when <7. 5 g/dL), and the body mass index (BMI; low when <18 kg/m² and high when ≥25 kg/m²).
Patients' characteristics The vast majority (94%) of the ART-naïve PLHIV in the IeDEA West Africa database were available for this analysis (Figure  S1 ). Patients' characteristics at ART initiation are presented by age groups in Table  1. Among the 73,525 patients included in this analysis, 1,736 (2. 4%) were aged ≥60 years and 6,792 (9. 2%) were aged between 50 and 59 years. The median age was 36. 5 years [IQR: 30. 4-43. 7]. For 50-59 and ≥60 years age groups, the median age was 53. 3 (IQR=51. 3-55. 7) and 63. 6 (IQR=61. 3-67. 0), respectively. Women accounted for 65. 8% of overall but the proportion of men increased with age (51. 5% among the 50-year old and above). A third of the patients aged ≥60 years old (34%) had advanced HIV disease (baseline clinical stage WHO III/IV or AIDS; overall sample: 28.
limitations The main strength of the present study is the inclusion of multicenter data from 17 different sites from 9 West African countries, with a large sample size, including in the older group. However, some limitations have to be mentioned. First, data were extracted from routine monitoring, leading to missing data, including for CD4 level, disease stage, viral load, and Hb level. The causes of death were not documented. Second, due to the retrospective nature of our data, information on other factors that can influence the probability of death or LTFU, such as economic status, social support, cognitive impairment, and depression was not available. In addition, ART adherence was not evaluated, and its impact on outcomes could not be investigated. Third, the reasons why patients were LTFU were not systematically collected, leading to an underestimation of the mortality risk and to an overestimation of LTFU, particularly in the oldest groups who are at higher risk of death.
Data availability Complete data for this study cannot be posted in a supplemental file or a public repository because of legal and ethical restrictions. The Principles of Collaboration under which the IeDEA multiregional collaboration was founded and the regulatory requirements of the different countries' Institutional Review Boards require the submission and approval of a project concept sheet by the IeDEA Executive Committee and the principal investigators from participating regions. All datasets provided by IeDEA are deidentified according to HIPAA Safe Harbor guidelines, with the exception of dates in some of the regions. Disclosure of a person's HIV status can be highly stigmatizing, and since reidentification of deidentified datasets may be possible when they are combined with publicly available datasets (see work of Dr Latanya Sweeney), IeDEA promotes the signing of a Data Use Agreement before HIV clinical data can be released.
Acknowledgments The IeDEA West Africa region: Site investigators and cohorts: Adult cohorts:  Marcel Djimon Zannou ,  CNHU, Cotonou, Benin ;  Armel Poda ,  CHU Souro Sanou ,  Bobo Dioulasso ,  Burkina Faso ;  Fred Stephen Sarfo  and  Komfo Anokeye Teach -.
Subsequently, multivariate logistic regression analysis was performed with VTE as the dependent variable; biomarker variables proven significant at the univariate logistic regression analysis were examined as possible independent variables. Using a stepwise procedure, at the final multivariate logistic model all variables with p-value less than 0. 10 were retained; the area under the ROC curve (AUC) was estimated to describe the fit of the multivariate model. Data were analyzed using the STATA/SE version 13 statistical software (Stata Corp. , College Station, TX, USA).
VTE events occurred across all risk groups for VTE despite administration of thromboprophylaxis based on current recommendations. The study design did not allow detection of potential differences in efficacy and safety of thromboprophylaxis with aspirin or LMWH in patients stratified as intermediate/low-or high-risk for VTE. As a result, the data confirm that the residual rate of symptomatic VTE events remains high and underline the need for an improved tool for the selection of patients eligible for thromboprophylaxis and an alternative antithrombotic strategy  8, 9. The study was prospective and the clinical features of the derivation cohort responded to the principal generalizability criteria for risk assessment tools  [24] [25] [26]. Moreover, it included routine pre-entry VTE status with Doppler echography and vigorous assessment of VTE risk.
Computational studies improving the detection of the functional preferences of environmental communities are important for gaining insight into ecosystem changes  [10] [11] [12] [13] [14] [15]. Ideally, they shall quantitatively relate genetic information with environmental factors in order to understand how these factors affect the genetic material and the dynamics of the expression from one environment to another, from one community to another. Therefore, they demand the development of appropriate tools to zoom in metabolic activities and to compare environments in detail being as precise as possible in evaluating the quantity of genetic material (gene protein sequences or transcripts) associated to a given function. Over the past years, a lot of effort has been devoted to the creation of integrated systems for the computational analysis of metagenomic (MG) and metatranscriptomic (MT) datasets.
In practice, they often do not match reference databases or they match with very low significance scores  [34] , leading to a poor functional annotation  [32]. A second difficulty is that environmental coding sequences are fragmented and annotation of partial information becomes harder due to a much reduced sequence length. In this respect, since protein-coding sequences might be too long compared to reads, in environmental sequence classification, one can either realise a simultaneous alignment and assembly of reads using reference proteins or probabilistic protein sequence profiles  [35] [36] [37]  hoping to improve the sensitivity to detect significant matches, or can focus on annotating protein domains directly on sequencing reads  [38]. Indeed, domains are functional units, much shorter than proteins: even though their sizes vary from a few tens up to several hundreds of amino acids, 90% of the known domains are smaller than 200 aa with mean size of 100 aa  [39] [40] [41].
Despite their short length, they are sufficiently precise to inform us about the potential functional activity of the communities. In particular, direct read annotation will become increasingly important in the future, due to its contribution in the design of computationally efficient and precise assembly algorithms  [42]. With the production of larger and larger MG/MT datasets and the exploration of new environments (possibly gathering many unknown species), contig reconstruction might become even more challenging if realised without the help of domain annotation. Here, we introduce MetaCLADE, a new generation method for the annotation of protein domains in MG/MT reads. MetaCLADE uses a multi-source annotation strategy  [43] , where each known domain is represented by a few hundred probabilistic models and an intelligent algorithmic strategy filters the high number of hits produced by the models, retaining only the most reliable ones.
These models, called clade-centered models (CCMs), span regions of the protein sequence space that are usually not well represented in a model based on a global sequence consensus (SCM)  [44] [45] [46] [47]. They might highlight motifs, structural or physico-chemical properties characteristic of divergent homologous sequences. Hence, if a domain is associated to many divergent homologs, CCMs are expected to describe properties that could be missed by the SCM representing a global consensus. For this reason, CCMs should help in finding diverged homologous sequences in species that might be phylogenetically distant. The great improvement in annotation obtained with the multi-source strategy compared to the mono-source strategy, employed by the two most commonly used annotation tools HMMer  [47]  and HHblits  [48, 49] , was proven for CLADE in  [43, 50]  for genomes and, more generally, for datasets of complete coding sequences.
Many different validation tests have been developed in  [43] , where, in particular, it has been shown that many of the annotations realised with CLADE based on domains present in Pfam24 and considered false positives by Pfam24 were finally validated by the Pfam27 release, this augmenting the confidence on CLADE annotation of protein sequences. With the introduction of MetaCLADE, however, we push forward this idea and we show that CCMs can also be used to successfully annotate fragmented coding sequences in MG/MT datasets, where domain divergence and species variability might be very large. Under the hypothesis that the most populated functional classes define community preferences, the high-quality quantification of domains in reads reached with Meta-CLADE allows us to infer domain functional classification and the functional importance of species in a community.
Second, it simplifies the hit selection so that domain annotation would be less sensitive to sequencing errors and hit length. In order to show how MetaCLADE performs with respect to known domain annotation methods, we realised multiple comparative analyses of MG/MT datasets and demonstrated that MetaCLADE improves domain annotation and provides an improved resolution of the functional activity of a community, while clarifying preferences and missing functional features. We compared MetaCLADE against InterProScan  [51, 52] , a tool that combines different protein signature recognition methods and different domain databases; UProC  [38] , a fast and sensitive algorithm that applies directly to raw reads; and the profile-based assembler HMM-GRASPx  [37]. These are either new or most commonly used metagenomic annotation tools and, as they are based on Pfam, we can fairly compare MetaCLADE to them.
Results Protein domains found in short MG and MT reads can be used as precise markers of the functional activity of an environment. We show that MetaCLADE highly improves current annotation methods and reaches a very fine degree of accuracy in annotation. MetaCLADE workflow is illustrated in Fig. 1a. It takes a dataset of reads (with an average size between 100 and 500 nucleotides) in input and searches for domains using a library of more than two million probabilistic models (CCMs and SCMs)  [43]  associated to almost 15,000 Pfam domains. For each domain, hundreds of CCMs have been generated from homologous sequences representing the entire phylogenetic tree of life. The spread of species whose sequences were used for CCM construction is illustrated by the phylogenetic tree in Fig. 1b , where each leaf corresponds to a different species.
All computational details of the approach (i. e. , algorithms, statistical models and parameter thresholds) are described in the "Methods" section. Differences between MetaCLADE and CLADE are listed in the "Discussion" section. Annotated MG and MT datasets can be explored to learn about the functional activity of the community. For this, one has to properly evaluate the performance of the methods and this was done on simulated datasets and on several published MG/MT datasets. MetaCLADE's improvement over HMMer was shown on a simulated dataset, generated from 56 completely sequenced genomes with the addition of sequencing errors, and Fig. 1  The MetaCLADE workflow. a The MetaCLADE workflow is described in the rectangular green box: the two MetaCLADE main steps are illustrated in white boxes.
b Phylogenetic tree of species that generated the CCMs used in MetaCLADE  [43]. c Histogram reporting the number of CCMs available in MetaCLADE, organised by clades on five ocean MT samples. Five more environmental samples, produced with 454 GS FLX Titanium technology, demonstrated that MetaCLADE annotation highly improves over InterProScan (run with different libraries). One more environmental sample, produced with Illumina HiSeq 2000 technology, confirmed MetaCLADE's good performance on datasets of much shorter reads. To compare MetaCLADE against two state-of-the-art annotation tools designed for short sequences, HMM-GRASPx and UProC, we used environmental samples, a simulated dataset previously employed to evaluate HMM-GRASPx, and two datasets simulated directly from MG sequences. These datasets are different in terms of average read length and species coverage, and they highlight the complementarity of MetaCLADE compared to UProC with respect to reads of shorter (100 bp) and longer (200 bp) size.
Comparison of MetaCLADE and HMMer on a simulated metagenomic dataset We simulated a 454-read dataset from 56 fully sequenced genomes-belonging to archaea and bacteria-and accounting for a total size of 187 Mbp (NCBI's accession numbers and genome sizes are shown in Additional file 1: Table  S2. ) Genomes have been fragmented with MetaSim  [53]  and the outcoming clones have been parsed with FlowSim  [54] , to simulate realistic insertion and deletion errors expected during DNA sequencing. FlowSim was used with the error model of the "454 Titanium FLX" sequencing platform (error rate ∼ 1%). This resulted in about 500,000 reads, with an average size of 523 bp, that were given as input to MetaCLADE. Even though 454 is becoming nowadays a sequencing platform less and less used, we considered it in order to compare MetaCLADE to HMMer on reads that are not short.
Functional annotation of large oceanic metatranscriptomic samples Domain identification allows to highlight the main functional activities of a community through the identification of the functions supported by the most abundant domains, but also to compare communities and organisms. A more accurate zooming into functional activities hopefully leads to capture missing features of communities' behaviour. The functional annotation of domains in the five oceans MT datasets in  [57]  demonstrates a sharp difference in relative abundance of domains found by MetaCLADE compared to HMMer (hmmscan) (Fig. 2a ). MetaCLADE shows that the larger amount of domains it detects falls coherently in functional classes of interest for specific environments, reaching a much better resolution of sign ificant terms among all Metagenomic GO-Slim functional classes.
Red corresponds to > 150 domain hits. Each box in the tree graph is coloured independently of its position in the tree graph because each domain is associated to a single box. There is no cumulative effect in the counting. d Hierarchical tree graph of GO-terms for "ion transport" obtained with HMMer. The GO-terms are associated to domains identified by HMMer in the ANT sample, compared to c. e Distribution of species originating CCMs used to annotate the five MT datasets. Due to the different number of reads in the datasets (and hence, the variable number of identified domains), we report the proportion of species, organised in clades, for each dataset Note the red colour, representing the highest abundance, given to the node "ion transport" in the Meta-CLADE tree graph of Fig. 2c  compared to the yellow colour, corresponding to a weaker abundance, in the HMMer tree graph of Fig.
Improved annotation of MG/MT datasets compared to InterProScan We ran MetaCLADE and InterProScan  [61]  with five different libraries (Pfam  [55, 61, 62] , Gene3D  [63] , TIGR-FAM  [64] , PRINTS  [65]  and ProSite  [66] ) on five publicly available MG and MT datasets (listed in Table  1 ;   [57]. An orange dot is located above all positions in the profile when one of the three top residues with the highest frequency appears as highest frequency residue in the corresponding position of the conservation profile in b. b Profile generated from the alignment of 371 environmental sequences annotated by MetaCLADE with CCMs of the rhodopsin-like domain and missed by HHMer. The letter height in the logo is proportional to the number of sequences in the alignment that contain the letter at a specific position, and the letter thickness is proportional to the number of gaps in the alignment at that position.
c Rhodopsin fragment sequence from the dinoflagellate Prorocentrum donghaiense found in the NR database and matching, with E value 3e-22 and sequence identity 78%, the longest environmental sequence among the 371 annotated by MetaCLADE. Note that the fragment has been aligned to the profile in b for a visual inspection of conserved positions. d Conservation profile HMM of the Pfam bacterial rhodopsin domain PF01036 (fragment). As in c, positions are aligned with the profile in b for best visualisation. An orange dot is located above a position in the profile when one of the three top residues with the highest frequency appears as highest frequency residue in the corresponding position of the conservation profile in b see Additional file 1). For these datasets, the sequenced reads had been pre-processed with the EBI Metagenomics pipeline  [67]  leading on which the annotation has been realised.
These datasets differ in number of reads and difficulty (measured by the number of annotated domains that could be identified). The performances of InterProScan on the five domain libraries differ greatly (Table  1 ). The first observation comes from the number of annotated domains shared by the five libraries, which is very reduced, indicating the complementarity of their domain models (see Fig. 4a  for the Puerto Rico Rainforest dataset and Additional file 1: Figures  S7A-S10A  for the other four datasets). One observes that Pfam   S11. The list of the most abundant identified domains is given in Additional file 1: Table  S6. The domain ranked first is the ABC transporter type 1. The presence in the MG dataset of annotated sequences presenting some sequence similarity to the known ABC transporters is an indicator of potential metabolic activities that we wish to discover.