dataesr/scientific-paragraphs-categorization huggingface dataset:
STAR+METHODS Detailed methods are provided in the online version of this paper and include the following:  then amplified using a mix of gene specific primers containing unique identifiers. Illumina sequencing adapters and sample-specific indexes were added during a second round of PCR. Samples were quantified using fluorometry (Qubit; Life Technologies), pooled at approximately equimolar concentrations, and the sample pool was re-quantified before loading onto an Illumina MiSeq. Paired-end MiSeq reads were merged with PANDAseq  (Masella et al. , 2012). GL assignment, junction identification, and other basic Ab information was determined using AbStar (www. github. com/briney/abstar). Sequences were assigned to clonal lineages using Clonify  (Briney et al. , 2016a). PCIN63 lineage sequences were clustered at 97.
5% identity with USEARCH  (Edgar, 2010) , and the size of each cluster was recorded. Cluster centroids were used to generate a multiple sequence alignment with MAFFT  (Katoh et al. , 2005) , and a tree file was calculated with FastTree using default settings  (Price et al. , 2010). The phylogenetic tree was drawn in Python using the ETE Toolkit  (Huerta-Cepas et al. , 2010). d KEY RESOURCES.
AID hotspots analysis Frequency and distribution of AID hotspots in PCIN63 lineage HCs, including both mAb and NGS sequences as well as the identified UCA, variable gene region was compared to all IGHV1-2 heavy chain alleles in the IMGT database (http://www. imgt. org/genedb). Analysis was performed considering the entire Ab sequence, or each antibody region (CDR1, CDR2, FR1, FR2, FR3) separately. Individual mutations were considered to have occurred in an AID hotspot if the mutated nucleotide fell within a sequence region encoding an AID hotspot motif (RGYW, or the reverse complement WRCY  (Rogozin et al. , 2001)  in the germline V gene sequence. Separately for each time point, the mean frequency of AID hotspots was determined by counting the number of AID hotspots in each sequence (considering both NGS and mAb sequences) and dividing by the total number of sequences obtained at the time point.
Single genome amplification (SGA), sequencing and cloning HIV-1 RNA was isolated from plasma using the QIAGEN QIAamp Viral RNA kit, and reverse transcribed to cDNA using SuperScript III Reverse Transcriptase (Invitrogen, CA). The envelope genes were amplified from single genome templates  (Salazar-Gonzalez et al. , 2008)  and amplicons were directly sequenced using the ABI PRISM Big Dye Terminator Cycle Sequencing Ready Reaction kit (Applied Biosystems, Foster City, CA) and resolved on an ABI 3100 automated genetic analyzer. The full-length env sequences were assembled and edited using Sequencher v. 4. 5 software (Genecodes, Ann Arbor, MI). Selected envelope amplicons were cloned into the expression vector pcDNA 3. 1 (directional) (Invitrogen) by re-amplification of SGA first-round products using Pfu Ultra II enzyme (Stratagene) with the EnvM primer, 59-TAGCCCTTCCAGT CCCCCCT TTTCTTTTA-39  (Gao et al.
Viral RNA was extracted (Viral RNA Mini Kit, QIAGEN) and subjected to RT-PCR (SuperScript III First Strand, Thermo Fisher). The cDNA was used as template to generate HIV-1 env amplicons, which were then purified (QIAquick, QIAGEN). Replicate PCR reactions for each sample were visualized, quantitated (2100 Bioanalyzer System, Agilent Biosciences) and pooled by sample. Preparation and sequencing of SMRTbell template libraries of approximately 2. 6-kb insert size were performed according to the manufacturer's instructions (Pacific Biosciences). CCS sequences were constructed using the PacBio SMRTportal software (version 2. 3). The Full-Length Envelope Analysis (FLEA) pipeline  (Eren et al. , 2018)  was used to error correct these CCS reads, and cluster them into near-identical clusters, inferring High Quality Consensus Sequences (HQCSs) for each cluster.
Envelope phylogenies, as well as the dynamics of amino acid frequency evolution, were inferred from these HQCSs. MAFFT (v7. 164b (Katoh and Standley, 2013) , with manual curation, was used to create a multiple sequence alignment. Gappy regions were manually removed when reconstructing phylogenies, since their alignment is uncertain. Phylogenies were reconstructed with FastTree v2. 1  (Price et al. , 2010) , and visualized with FigTree (http://tree. bio. ed. ac. uk/software/figtree/). Frequency kinetic plots and similar analyses were created with custom Mathematica scripts. Selected full-length autologous env gene sequences were synthesized for representative clones of each time point using GeneArtÒ gene synthesis services (Life Technologies), then cloned into pcDNA3. 1 vector (Life Technologies) for pseudovirus production.
Surface plasmon resonance (SPR) SPR experiments were performed on a Proteon XPR36 instrument (Bio-Rad) using GLC sensor chips (Bio-Rad) and 1x HBS buffer (Teknova) supplemented with 1 mg/mL BSA). Chips were prepared using the Human Antibody Capture Kit (GE Healthcare) according to manufacturer's instructions. For kinetic measurements, approximately 100 RUs of the indicated mAbs were captured onto the sensor surface. 4-fold dilution series of the indicated analytes were flowed over the captured mAbs for 120 s, followed by buffer injections for 600 s. After each cycle, surfaces were regenerated by four injections of 3 M magnesium chloride with 180 s contact times. Data were analyzed using the ProteOn Manager software (Bio-Rad). Following interspot and column double referencing, data were fitted to a Langmuir 1:1 binding model or equilibrium binding model as appropriate.
DATA AND CODE AVAILABILITY The accession numbers for the PCIN63-UCA and PCIN63-66A to PCIN63-77F heavy chain sequences reported in this paper are GenBank: MK_749197, MK_749198, MK_749199, MK_749200, MK_749201, MK_749202, MK_749203, MK_749204, MK_749205, MK_749206, MK_749207, MK_749208, MK_749209, MK_749210, MK_749211, MK_749212, MK_749213, MK_749214, MK_749215, MK_749216, MK_749217, MK_749218, MK_749219. The accession numbers for the PCIN63-UCA and PCIN63-66A to PCIN63-77F light chain sequences reported in this paper are GenBank: MK_749220, MK_749221, MK_749222, MK_749223, MK_749224, MK_749225, MK_749226, MK_749227, MK_749228, MK_749229, MK_749230, MK_749231, MK_749232, MK_749233, MK_749234, MK_749235, MK_749236, MK_749237, MK_749238, MK_749239, MK_749240, MK_749241. The MiSeq PCIN63 Ab lineage heavy and light chain NGS dataset are publicly available online through BioProject: PRJNA545346.
The accession numbers for the PC63 Env clones and HQCS reported in this paper are GenBank: MK_749242, MK_749243, MK_749244, MK_749245, MK_749246, MK_749247, MK_749248, MK_749249, MK_749250, MK_749251, MK_749252, MK_749253, MK_749254, MK_749255, MK_749256, MK_749257, MK_749258, MK_749259, MK_749260, MK_749261, MK_749262, MK_749263, MK_749264, MK_749265, MK_749266, MK_749267, MK_749268, MK_749269, MK_749270, MK_749271, MK_749272, MK_749273, MK_749274, MK_749275, MK_749276, MK_749277, MK_749278, MK_749279, MK_749280, MK_749281, MK_749282, MK_749283, MK_749284, MK_749285, MK_749286, MK_749287, MK_749288, MK_749289, MK_749290, MK_749291, MK_749292, MK_749293, MK_749294, MK_749295, MK_749296.
, 2016: PCIN63 (Black), DRVI07 (Violet), VRC01-class bnAbs (Blue), HCDR3 dominated bnAbs (Red), VH1-46+ bnAbs (Brown). Top left inlet shows heavy (VH) and light chains (VL) putative V-genes nucleotide variation from IMGT database reference (% SHM). Middle left inlet shows length the HCDR3s, with mean ± SEM showed as lines. Bottom left inlet shows heterologous neutralization breadth (coverage in percentage of viruses neutralized) as reported in CATNAP database (www. hiv. lanl. gov/content/ immunology/neutralizing_ab_resources. html) and heavy chains (VH) V-genes nucleotide variation from IMGT database reference (percentage of SHM). Top right inlet shows light chain V-gene usage. Bottom right inlet shows logogram of the LCDR3 amino acid sequences for PCIN63 and other VRC01-class Abs. Residues common or unique to each category are colored in blue and red, respectively.
ACKNOWLEDGMENTS IAVI's work is made possible by generous support from many donors including: the  Bill & Melinda Gates Foundation , the  Ministry of Foreign Affairs of Denmark ,  Irish Aid , the  Ministry of Finance of Japan  in partnership with The  World Bank , the  Ministry of Foreign Affairs of the Netherlands , the  Norwegian Agency for Development Cooperation (NORAD) , the  United Kingdom Department for International Development (DFID) , and the  United States Agency for International Development (USAID). The full list of IAVI donors is available at www. iavi. org. This study was made possible by the generous support of the American people through  USAID. The contents are the responsibility of the  International AIDS Vaccine Initiative  and do not necessarily reflect the views of USAID or the  United States Government.
Diffusion decision model fitting For each of the DDM parameters (i. e. , v, a, and NDT), we fitted an intercept and three slopes, corresponding to the two main effects-valence and feedback informationand their interaction. This allowed us to test the effects of the experimental manipulations on the model parameters. To account for all levels of variability, we used a threelevel version of the hierarchical Bayesian DDM, where the first level corresponds to the participants, the second corresponds to the experiments, and the third corresponds to the whole dataset, thus mimicking the meta-analysis approach described for the Bayesian analysis of the variance. To fit the Bayesian DDM and estimate its joint posterior distribution, we used stan, a probabilistic programming language for Bayesian parameter estimation  (Carpenter et al.
, 2017). In particular, we ran four independent chains with 10,000 samples each, and discarded the first half of each chain. To test for convergence, we checked that the R statistic  (Gelman & Rubin, 1992 )-a measure of convergence across chains-was lower than 1. 01 for all parameters. See the Appendix B for details about the prior distributions. To test the reliability of the parameter estimates, we performed parameter recovery on a simulated dataset  (Palminteri et al. , 2017)  (see the Appendix C). Finally, to assess the model fit of the DDM, we computed the posterior predictive distributions  (Gelman et al. , 1996)  for mean accuracy and RTs, as well as for RT quantiles (separately for correct and incorrect responses; Fig. B2 ).
Left column: 95% Bayesian credible intervals of the estimated posterior distributions of the effects of the experimental manipulations (i. e. , feedback information, outcome valence, and their interaction) on the DDM parameter coefficients at the dataset level. Right column: estimated mean parameters at the dataset level, separately by context and non-decision time varied across experiments, with a lower threshold in experiment 4, and a higher non-decision time in experiment 3 (see Fig. B1 , top row). In Fig. B1  we report the posterior distributions of the group parameters separately for experiments as well as for the overall dataset.
Discussion In the present study, we looked at how different RL contexts (i. e. , partial vs. full feedback, and gains vs. losses) affect accuracy and RTs. To do so, we used different methods and a relatively large dataset, composed of four separate experiments carried out in different centers. First, we used a meta-analytic Bayesian approach to the analysis of variance of accuracy and RTs. Replicating previous reports  (Palminteri et al. , 2016; Salvador et al. 2017) , we showed that participants were slower in the loss (as compared to the gain) domain, and that they were more accurate and faster when complete (as compared to partial) feedback was provided. Interestingly, the similar accuracy observed in the gain and loss domains is at odds with the notion of loss aversion  (Kahneman & Tversky, 1979 ): If in our task "losses loomed greater than gains", we would expect higher accuracy in the loss domain.
Methods Observational trial on DU (OT1) is one of the five trials of the DeSScipher project (ClinicalTrials. gov; OT1 Identifier: NCT01836263,  April 19, 2013). The DeSScipher project [10] was based on the use of the EUSTAR (European Scleroderma Trials and Research group) long-term databank MEDS (Minimal Essential Data Set) accessible online  [11]. The structure of the multicentre and international, prospective, longitudinal EUSTAR database has been described previously  [12]. In OT1, the efficacy of different vasoactive/vasodilating drugs on DU prevention and healing was analysed considering a large number of clinical items on cutaneous lesions of the upper and lower limbs. A tailored approach of DU classification according to their main features was adopted: DU associated digital pitting scars (DPS), DU associated with calcinosis, DU due to loss of tissue not associated with DPS or calcinosis (Pure DU) (Fig.
1 ) and DU associated with gangrene  [9]. Since recurrent DUs are a challenge in clinical practice, these data were also collected in the OT1. For the purpose of the DeSScipher observational trials, the MEDS online database was extended and adapted according to the needs of the individual projects. The OT1-specific DeSScipher dataset included more than 30 supplementary clinical items in addition to three items on upper limb lesions contained in the original MEDS online database (digital ulcers, pitting scars on fingertips and gangrene). OT1 data on DU were collected prospectively from April 2013 to November 2016. At the time of the analysis (November 2016), clinical data on 1749 patients enrolled into OT1 were stored in the database. Out of the items contained in the database, only those on cutaneous lesions were selected.
Results OT1 contained 35 clinical items on upper and lower limb cutaneous lesions; 18 were on the upper limb DU distal to PIP (Table  1 ). The items on upper limb DU distal to the PIP and their data completeness are shown in italic letters in Table  1. The survey on usefulness of the items that reached ≥ 60% of completeness in the OT1 was concluded by a total of 84/148 (56. 8%) EUSTAR centres. The items that obtained the highest score as essential and feasible for DU evaluation in everyday clinical practice (Table  2 ) were the following: Number of DU defined as due to loss of tissue (pure ulcers) (level of agreement 92%) Recurrent DU (84%) Number of new DU (74%) A significant number of centres (64%) agreed that the DU classification adopted in OT1  [9]  was useful to identify DU and their characteristics fundamental to shape the management in everyday clinical practice.
In fact, it may help to identify patients with more severe DU disease burden that may require more intensive treatment, as already suggested  [3]. The aim of the OT1 was to evaluate the best vasodilating/vasoactive therapy for DU prevention and healing through observational non-interventional design. In order to distinguish between different types of DU that might have different response to the treatment, OT1 needed to classify DU. Since there is no universally accepted classification of DU in SSc, OT1 adopted the one proposed by Amanzi et al. [9]  based on observations extrapolated from real life data on more than 1500 DU  [9]. Our study has shown that this classification may be useful and feasible in everyday clinical practice as indicated by 64% of the participating centres. The strength of this study is that the items were already pre-selected based on the analysis of data availability in the DeSScipher project, the first prospective multi-centric European study that expressly addressed DU management in SSc, with a study population of more than 1600 SSc patients.
More than 80 expert centres in SSc management across the world were included in this survey. The limitation of this project is that the online survey was based on the opinion of a single expert of each individual centre. In addition, the survey contained only clinical items (DU features assessed by the clinical history and/or simple clinical examination). Several laboratory and instrumental items have been collected in the OT1 database. The importance of some of these parameters for DU management, such as capillaroscopic findings shown to be risk factors for DU occurrence  [20, 21] , should be addressed in the future. Our findings now need prospective validation using data-driven approach on large SSc cohorts in order to confirm the real usefulness of these essential items and the role of the proposed DU classification and categorisation in real-life clinical practice.
In highly social top predators, group living is an ecological strategy that enhances individual fitness, primarily through increased foraging success. Additive mortality events across multiple social groups in populations may affect the social structure, and therefore the fitness, of surviving individuals. This hypothesis was examined in a killer whale (Orcinus orca) population that experienced a 7-y period of severe additive mortality due to lethal interactions with illegal fishing vessels. Using both social and demographic analyses conducted on a unique long-term dataset encompassing periods before, during, and after this event, results indicated a decrease in both the number and the mean strength of associations of surviving individuals during the additive mortality period. A positive significant correlation between association strength and apparent survival suggested that the fitness of surviving individuals was impacted by the additive mortality event.
Significance Sociality provides various demographic and ecological benefits to animals. However, social systems can be greatly disrupted if populations experience an additive mortality event. Here, we investigated how the social structure of a highly social species, the killer whale, was impacted by a human-induced mortality event (lethal interactions with illegal fisheries), and how these social impacts affected the survival of individuals. Using a unique long-term dataset, we showed that the loss of individuals in social groups resulted in weaker associations among surviving individuals, which may have modified their fitness during and after the additive mortality event. This study therefore highlights the importance of sociality in the resilience of populations to demographic stress and has major implications for the conservation of highly social species.
Leveraging a unique long-term photoidentification dataset, the present study elucidates the impact of additive mortality events on the social structure of a long-lived, highly social species using longitudinal individual life histories. Using social network analyses, we investigated association patterns among individuals over time to assess how a period of severe demographic stress impacted the survival and sociality of Crozet killer whales. Under the assumption of optimal group size, we hypothesized that the rapid loss of individuals due to lethal responses of illegal fishers decreased the survival of remaining individuals and forced these individuals to interact with other social groups. The acute fisheriesinduced additive mortality that took place at the Crozet Islands provides an opportunity to test this hypothesis and to assess the importance of sociality on the survival of a highly social predator.
Methods Photoidentification Data. Individual photoidentification data have been collected since 1964 from two zones: Possession Island (46°24′S, 51°46′E) in the Crozet Archipelago and aboard longliners operating in the Crozet Exclusive Economic Zone  (55). Photographs were checked for quality, georeferenced, and archived with an observation number. An observation corresponded to a period of continuous presence of killer whales in the same area paired with photoidentification effort, which ended either because the whales left or because photoidentification was stopped (e. g. , due to bad weather). Individual killer whales were identified from unique fin shapes, saddle patches, and the presence of any natural marks  (56). Photographs were assigned a quality index (Q) ranging from 0 (low) to 2 (high) based on distance, focus, lighting, and angle criteria.
The mean HWI (the sum of the HWI of an individual divided by the number of individuals it had associated with) and the mean sum of the associations (equivalent to an index of gregariousness) were calculated for each individual over the study period (1987-2014; 221 identified killer whales) and for three subperiods depending on periods of presence of illegal vessels fishing Patagonian toothfish in Crozet. These subperiods are: (i) preillegal fishing (1987-94; 71 identified killer whales); (ii) illegal fishing (1996-2002; 57 identified killer whales); and (iii) postillegal fishing (2002-14; 147 identified killer whales). Social analyses were performed using the software SOCPROG 2. 7  (60). Generalized additive mixed models (GAMM) were used to investigate changes in the mean HWI over the study period with a beta likelihood, which is adapted for continuous variables bounded between 0 and 1  (61).
Photographic effort was included in all models with an interaction with the year  (62). Year was added as a fixed variable to test for difference over time, and individual identity was included as a random variable. Periods (preillegal fishing, illegal fishing, and postillegal fishing) were also added as a fixed variable. GAMMs were fitted with package mgcv version 1. 8-22 in the software R 3. 4. 2 (63, 64). The best model was then determined by Akaike Information Criterion  (65). Demographic Analyses. To investigate the effects of social relationships, social group composition, and size on survival probability we used capture-markrecapture (CMR) models. Apparent survival rates (i. e. , the complement of mortality and emigration out of the study area) and recapture probabilities of marked individuals over the whole study period (1987-2014) were estimated using the Cormack-Jolly-Seber model (CJS)  (66).
Sampling occasions were defined as years (n = 25) and an individual was considered captured in a sampling occasion if it was photoidentified during at least one observation conducted in the corresponding year. The CMR dataset included 221 individuals. The CJS model Φ(t)p(t) is a time-dependent one, where the survival probability (Φ) and the recapture probability (p) vary over time. The underlying assumptions of the CJS model are  (67, 68) : (i) recapture probability (p) is the same for each individual at occasion t; (ii) survival probability (Φ) is identical for all of the marked individuals between occasion t and t + 1; (iii) individual markings do not change and are not overlooked; (iv) observations (capture occasions) are short compared with the time interval between successive observations (capture occasions); (v) emigration is permanent; and (vi) individuals are independent regarding survival and capturability.
When β 1 < 0 or β 1 > 0, the covariate HWI has a negative or positive effect, respectively, on survival probability. For the effect of social group size, we built a model where social group size was included as a group effect since social group size was an integer variable ranging from 1 to 9. The inclusion of fixed (observation effort, HWI, and social group size) and random effects (time and group ID) was assessed using a Bayesian model selection approach and the widely applicable information criterion (WAIC). All models (see complete model list in Table  2 ) were fitted with Jags version 4-8 and package rjags (jags code in SI Appendix; ref. 72). We used weakly informative priors: normal priors centered on zero with scale set to 1. 5 for intercept parameters and scale set to 0. 5 for slope parameters.
Half-normal priors with scale set to 0. 5 were used for SD parameters. Three chains were run with a warmup of 10,000 iterations, followed by another 10,000 iterations (with a thinning factor of 10). Parameter convergence was assessed with Gelman-Rubin r statistics. Posterior inferences were based on the pooled sample of 3,000 values (1,000 per chain). Data Availability. The datasets analyzed in this study are available in Figshare at https://figshare. com/s/f1147c49815b57963144.