dataesr/scientific-paragraphs-categorization huggingface dataset:
Gene Id p Value Ratio SASP Atlas SenMayo Irradiated preadipocytes Irradiated fibroblasts Protease inhibitor fibroblasts Inducible RAS Fibroblast Proteins were selected from available datasets (SASP atlas) as statistically upregulated by senescence stimuli in conditioned media. BOULET et al. | 9 of 24 Irradiated Irradiated Gene Id Gene Id p Value p Value Ratio Ratio SASP Atlas SASP Atlas SenMayo SenMayo Irradiated preadipocytes preadipocytes Gene Id p Value Ratio SASP Atlas Protease Protease Inducible Inducible Irradiated Irradiated Irradiated SenMayo preadipocytes Irradiated Irradiated Protease inhibitor inhibitor Inducible RAS RAS Irradiated epithelial epithelial epithelial Irradiated fibroblasts fibroblasts inhibitor fibroblasts fibroblasts RAS Fibroblast Fibroblast epithelial cells cells cells CPA3 INHBA POSTN 0.
2. 5 µg samples of RNA were reverse transcribed to cDNA using the SuperScript IV First-Strand Synthesis System (Invitrogen), following the manufacturer protocol. cDNA was diluted 1:10 in RNase-free water before use for RT-qPCR. Primers used in this study are listed in Supplementary Table  S1  and include primers for 3 housekeeping genes. All reactions were performed using Thermo Fisher Maxima SYBR Green/ROX qPCR Master Mix (2X) on the Applied Biosystems QuantStudio 3 machine with QuantStudio software for cycling and analysis. The DDCt Method was performed against WT values at each age to determine relative gene expression and fold-change.
The continuity of Z-line striations as a metric of myofibrillar structure We next quantified the continuity of myofibrillar Z-line striations as a metric of myofibrillar structure. The quantitative assessment of Z-line striations in each image was performed using a custom MATLAB routine established by  Morris. (2021)  for profiling myofibrillar structure in developing cardiomyocytes and skeletal myotubes and adapted here for mature skeletal muscle fibers. In brief, multi-channel Nikon confocal fluorescence images were converted into RGB TIFFs, and the Cy5 channel containing phalloidin-633 for actin was output to a new image stack. To decrease processing time, each image was cropped to the fiber of interest and rotated to align the myofiber long-axis to the x-axis of the frame. The maximal myofiber boundary was identified with an Otsu's threshold  (Otsu, 1979)  of the maximum intensity projection (max-IP) of the z-stack (Figure  4A ).
Molecular structure and software. The molecular structure of α-tubulin interacting with pironetin was obtained from the Protein Data Bank (www. rcsb. org) under the PDB code 5FNV. It corresponds to a high-resolution structure (2. 61 Å resolution) determined by X-ray diffraction  [21]. We used the GOLD 5. 3 package from the Cambridge Crystallographic Data Centre (Cambridge, UK) to software perform the docking experiments, with the support of the molecular graphic system provided by Discovery Studio Visualizer, Biovia 2020 (Dassault Systèmes BIOVIA Discovery Studio Visualizer 2020; San Diego, CA, USA, Dassault Systèmes, 2020). For the determination of the potential drug-binding sites on αtubulin, we used the web server CASTp 3. 0 (Computed Atlas of Surface Topography of proteins) which is well suited to define cavities on a protein surface and the specific amino acid positioning, important for the interaction.
Molecular modeling procedure. For model building and evaluation, we used an iterative multi-step procedure. The BOSS software (Biochemical and Organic Simulation System, available at http://zarbi. chem. yale. edu/software. html) was used to perform a Monte Carlo (MC) conformational search of the different ligands  [24]. Within BOSS, a general purpose molecular mechanics engine capable of performing various calculations, the MS simulation was realized in the isothermal-isobaric (NPT, constant number of particles, constant pressure, and constant temperature) ensemble. The analysis of the conformations defines the optimum geometry for each natural product. Thus, the minimum-energy conformers were identified through energy minimization. The next step refers to the calculation of the free energy of hydration (ΔG) for a given structure of the compound.
The knowledge of ΔG is useful to investigate the origin of hydrophobic interactions. For this, the molecular mechanics/generalized Born surface area (MM/GBSA) procedure was used according to a described procedure  [25]. MC search and computation of ΔG were performed within BOSS using the xMCGB script according to previously described procedures  [25, 26]. In our docking procedure, the pironetin-binding site on tubulin was defined as the ligandbinding site for all naturally-occurring plagiochilins and derivatives. With the 5FNV structure, the following flexible amino acids during the docking with GOLD procedure were defined: Phe135, Phe202, Leu248, Leu252, Phe255, Gln256, Leu259, Cys316, Lys352, and Leu378, based on shape complementarity criteria. Shape complementarity and geometry considerations favor a docking grid defined from the volume of the central amino acid.
Comparative docking of plagiochilins to α-tubulin The crystal structure of the reference natural product pironetin bound to an α/β-tubulin dimer was used as a template (PDB: 5FNV) to study the binding of plagiochilins to α-tubulin. Each of the 24 compounds was docked into the pironetin site and the most energetically favorable conformation was identified in each case. There is a small binding pocket in the center of the protein, sufficiently deep to accommodate completely a ligand like plagiochilin A, as represented in Figure  3. The compound is fully engaged into the cavity, inserting its epoxide unit deep into the pocket. All portions of the molecule contributed to the protein interaction, with notably three potential H-bonds with residues Ser241, Gln256 and Lys352, in addition to an array of van der Waals contacts and alkyl/π-alkyl interactions.
Methods and Results: This observational retrospective study included adult patients with an index diagnosis of incident AF during January 1, 2016 through June 30, 2018, in the Optum datasets. The baseline and follow-up periods were 1 year before/after the index date, respectively. The primary objective was identification of the following predefined symptom reports: dyspnea or shortness of breath; syncope, presyncope, lightheadedness, or dizziness; chest pain; fatigue; and palpitations. In an exploratory analysis, the incidence rates of symptom reports and cardiovascular hospitalization were assessed in propensity-matched patient cohorts with incident AF receiving first-line dronedarone or sotalol. Among 30 447 patients with an index AF diagnosis, the NLP algorithm identified at least 1 predefined symptom in 9734 (31.
Using these EMR data for clinical analyses has proven to be challenging. 5, 6 Advances in machine learning algorithms have the potential to improve the efficiency and accuracy of using unstructured EMR data for large-scale clinical research. In the present study, we used natural language processing (NLP) to evaluate information contained in EMRs of patients with AF to provide clinically relevant data that could be used to inform research and treatment decisions. This study was designed to explore the feasibility of using unstructured data within EMR to characterize symptomatic status among patients with AF in the United States, evaluate temporal AF symptom profiles in these patients, and determine whether the NLP methodology can be used to differentiate outcomes between antiarrhythmic drug (AAD) treatment groups.
| Data source This observational retrospective study used the Optum-Humedica deidentified EMR database, linked to the Optum ® Clinformatics ® claims data set (i. e. , Optum Integrated), to identify symptom reports in patients with AF. Optum Integrated allows for the assessment of treatment patterns using information from pharmacy and medical insurance claims, EMR, demographic data, and related documentation. Optum's EMR database encompasses over 80 million patients from all four census regions in the United States, with at least 5 million patients from each region. Data are derived from more than 140 000 physicians at more than 600 hospitals and 6500 clinics. On average, patients contributed 4 years of medical history each to the database.
| Study design The study design is summarized in Figure  1. Adults diagnosed with incident AF in the United States in the period from January 1, 2016   through June 30, 2018 (identification period) were identified, as described below, in the Optum Integrated datasets. The index date was the earliest AF diagnosis during the identification period. A 1year "lookback" before the index date (baseline period) was included for the determination of concomitant medications, comorbidities, and demographic characteristics and for patient selection. Patients were tracked longitudinally for up to 12 months of follow-up from the index date for assessment of symptom reports. Thus, data from the period from January 1, 2015 through June 30, 2019 (study period) were used in this analysis.
| Study objectives The primary objectives of the study were to characterize AF-related symptoms among patients with incident AF, derived from the Optum EMR database of structured NLP terms, and to describe the characteristics (demographics and medical history, including procedures, comorbidities [Supporting Information: To evaluate the utility of NLP to identify differences between treatments, an exploratory analysis was also performed in which patient characteristics and symptom reports were evaluated for patients with prescription claims for dronedarone or sotalol as their first AAD following AF diagnosis. Dronedarone and sotalol were chosen for this purpose as they have overlapping pharmacologic properties and contraindications. As a result, patients prescribed these two drugs are more likely to be similar than patients prescribed Class I-C drugs (contraindicated in coronary artery disease) or amiodarone, which is more often used in heart failure and is to some degree discouraged as a first-line agent.
| Patient characteristics Of 143 625 patients identified from the EMR database with an index diagnosis of AF, 30 447 were found to meet the eligibility criteria for inclusion in the study analyses, as shown in Figure  3. Characteristics in the 12-month baseline period (i. e. , before index date) are shown in Table  1. Median follow-up duration (time from index date to last documented date) for patients in the incident AF cohort was 25. 2 months (interquartile range: 13. 7-35. 8 months) and mean follow-up was 25. 0 months (standard deviation: 14. 0 months); however, followup for capturing symptom reports was limited to 12 months. Cardiovascular-related comorbidities were common, with coronary artery disease in 32. 7%, hypertension in 70. 3%, and diabetes in 28. 1% of patients. The mean CHA 2 DS 2 -VASc score was 3.
| DISCUSSION In this proof-of-concept study, we found that NLP successfully identified the presence of 1 or more pre-defined symptoms in 32% of new-onset AF patients identified in a linked EMR-claims database. The most frequently documented symptoms were dyspnea/shortness of breath, followed by syncope, presyncope, lightheadedness, or dizziness. Chest pain, fatigue, and palpitations were also commonly captured. The frequency of each documented symptom decreased by nearly 50% at 3-6 and 6-12 months after diagnosis, compared with 0-3 months. In an exploratory sub-analysis, we showed that symptoms decreased over time to a similar degree in a propensitymatched subset of patients treated with dronedarone or sotalol. Given the unique methodology involved, it is difficult to compare our results with other studies reporting on the frequency and nature of symptoms reported by new-onset AF patients.
The term "palpitation" to some degree may represent medical shorthand for a number of sensations experienced by patients and described with various words (e. g. racing, skipping, pounding etc. ). The NLP approach studied herein will benefit from additional refinement and validation in other data sources. Despite these potential limitations, we believe NLP has the potential to augment the type of information available for research using administrative data, enabling investigation into symptoms and response to treatment in large EMR databases. Alternatively, the only other method would be to use disease specific surveys to prospectively assess patient report outcomes. Although this would be more precise, it is costly and limited in its scope of reach, while the use of NLP would provide information on population-level symptoms with antiarrhythmic drug therapy, thereby allowing electrophysiologists that use these therapies to measure their effectiveness.
1, 2 The present study supports the use of NLP methodology to conduct studies using symptomology as an endpoint using large, unstructured datasets. It also shows that the same analytic tools can be used to ascertain symptom-benefit over F I G U R E 5 Incidence rates of predefined symptoms reported in patients with incident AF prescribed first-line dronedarone or sotalol post-diagnosis (propensity-matched cohorts; n = 409 each). AF, atrial fibrillation; CI, confidence interval. time with defined rhythm-based treatment approaches that can complement traditional clinical outcomes-based analyses. In the current study, among propensity-matched patients with AF prescribed dronedarone or sotalol, the incidence of symptom reports for dyspnea or shortness of breath, chest pain, and fatigue was lower in the dronedarone cohort compared with the sotalol cohort at all time periods assessed.
| Limitations The claims database used for our study, like all administrative databases, may be prone to coding inaccuracies that could have affected the identification of AF patients or the ascertainment of their baseline characteristics. In particular, the accuracy of ICD-10 coding to distinguish between clinically defined AF patterns is not well-validated. For this reason, we excluded patients for whom selected codes suggested likely permanent or long-standing persistent AF, as described above, and chose not to stratify our results according to AF sub-types. Nonetheless, potential coding errors remain a limitation. Our study was not intended to assess quality of life in AF. Existing, validated quality of life instruments are better suited for this purpose. 17, 18 The European Heart Rhythm Association symptom scale alternatively provides clinician ratings on the impact of AF on patients symptoms and functioning.
19 While the use of NLP allows for an examination of symptoms and their relationship to treatment, it is unlikely to facilitate drawing conclusions about symptom severity or functional impairment in the absence of systematic patientreported outcome data collection. 20 hierarchy or weighting was not applied to assessment of physician notes in the EMR; so, for example, a note from a cardiology clinic, more likely to evaluate cardio-pulmonary symptoms, would have the same weight as a note from a urology clinic, much less likely to evaluate these symptoms. The comparative analysis of two AADs with class II and III properties was exploratory. Analysis of symptom report and clinical outcomes in propensity-matched groups was performed based on date of AF diagnosis (and not treatment initiation). Differences in symptom report rates and outcomes between patients receiving dronedarone versus sotalol reflect relative effects between study groups and should not be directly attributed to treatment effects.
ACKNOWLEDGMENTS All authors contributed to the concept and design of the analysis, interpretation of the data, drafting/reviewing this paper, and confirming accuracy, and approved the final manuscript for submission. Under the direction of the authors and in accordance with GPP3 guidelines, medical writing and editorial support was provided by  Meenakshi Subramanian, PhD , CMPP TM and  Barrie Anthony, PhD ,  CMPP TM , Evidence Medical Affairs (Philadelphia, PA, USA ) and was funded by  Sanofi. We thank  Charlotte Singh, MD ,  CMPP TM (Sanofi)  for critical review of this manuscript. This study was funded by  Sanofi.
Acknowledgement We would like to thank all the workshop participants for giving of their time and thoughts to this process. We would not have been able to do this without your valuable insights. In particular, we would like to acknowledge the contribution made by  Jon Lopez  who attended the inperson workshop in Cape Town and the advisory board, including  Clement Yow Mulalap. Thanks also to  Claire Homewood  from  Care Creative  who was our creative guide,  Ravi Maharaj  who helped with the artistic aspect of online workshop 2 and  Sandile Fanana  who documented the creative process for the in-person workshop arts session.
Spatial niche partitioning We first explored differences in pollinator composition across fields by calculating pollinator species beta diversity as the "Horn-Morisita" similarity index. This quantitative index ranges from 0 (no similarity) to 1 (complete similarity) and is sensitive to changes in relative abundance rather than to absolute abundance changes. Similarity was analysed with vegdist function of "vegan" package (2. 4À5 v) (Okansen, 2013). Differences in the spatial niche partitioning within fields were tested with generalised or linear mixed models (GLMM or LMM) in the 25 fields with the location of the transect (centre versus edge) as fixed factor, and the abundances of each pollinator guild pooled over rounds (honey bees, bumble bees, solitary bees, hoverflies) as each of the response variables for a different model.
Temporal niche partitioning To test for temporal niche partitioning along the course of the day using focal observations we first assumed that the relationship between pollinator visitation rates and daytime are not linear because we expected visits to peak at midday, when temperature values are maximum. Therefore, using the data from the five temporal experimental fields, we used four GLMMs to test the effect of temperature and daytime plus their quadratic terms as fixed factors on the number of visits for the three dominant taxa (A. mellifera, B. terrestris and B. lapidarius) and for solitary bees as a group, as response variables. Field site was included as a random factor. Solitary bees were considered as a single group because the individuals were difficult to identify at the species level during focal plant observations.
Models were fitted either with a negative binomial or with Poisson distribution depending on taxon. Temperature and time were not correlated (Spearman correlation r = 0. 05; p = 0. 6). To further test for differences across groups of species, we re-analysed the relationship between temperature and activity of the different species with a GLMM with species as a fixed factor and visitation temperature as a response variable. Field site was included as a random factor. We selected temperature as it was an important variable in the initial models and the relationship observed was linear for all species. To see pairwise differences in the range of activity between species, we use post hoc analysis with Tukey contrast within the "multcomp" package (v. 1. 3. 5)  (Hothorn et al. , 2008).
All statistical analyses were performed in R v. 3. 3. 3 (R Core Team 2013). We used "dplyr" package  (Wickham et al. , 2018; 0. 7. 7 version)  and "Lubridate"  (Grolemund & Wickham, 2011; 1. 7. 4 version)  package for data treatment. Model fit was evaluated using the 'dHarma' package  (Hartig, 2017; 0. 1. 5 version)  and "MASS" package  (Venables & Ripley, 2002; 7. 3À4. 8 version)  for negative binomial fit. For paired comparison tests between treatments in seed production we used Tukey posthoc analysis with "multcomp" package (v. 1. 3. 5)  (Hothorn et al. , 2008). For graphic visualization we used "yarrr" package (Philips, 2018; 0. 1À5 version) and "ggplot2" (Wickham, 2016; 2. 2. 1 version) for data plots; "visreg" package (Brehenny & Burchet, 2016; 2. 4. 1 version) for regression model visualization; and "fields"  (Nychka et al.
Pollinator community structure association with sunflower seed production We found that pollinator evenness is the main factor associated with an increase in sunflower seed production. This result suggests that an even community, and not necessarily the most abundant, could produce higher seed production in sunflowers. Note that evenness and abundance are weakly correlated in our dataset (R = 0. 57), and abundance refers mostly to a single dominant species, the honeybee. Garibaldi et al. (2013)  found that an increase in wild insect visitation increases seed production twice as much as a similar increase in honeybee visitation in 41 cropping systems worldwide, thereby showing the efficiency of the pollinating insect assemblage. Interestingly, we observed that some even communities were not particularly rich in species, but the few guilds represented enhanced seed production only when they were balanced in terms of frequency.
METHOD DETAILS Data acquisition and pre-processing MEG of the eyes-closed awake resting state The sampling rate was 600 Hz, and the data were band-pass filtered between 1 and 80 Hz. Power-line interferences were removed using a 60-Hz notch filter designed in MATLAB (Mathworks). The sensor array consisted of 275 axial first-order gradiometers. Two dysfunctional sensors were removed, leaving 273 sensors in the analysis. Analysis was performed directly on the axial gradiometer waveforms. EEG of the eyes-closed awake resting state EEG was recorded using the g. tec HIamp system (g. tec, Austria) with 64 gel-based electrodes (AgCl electrolyte gel). Electrodes were positioned according to the standard 10/20 system with linked ears reference. Impedances of all electrodes were kept below 5 k U. Data were pre-processed using a combination of the EEGLAB MATLAB toolbox  81  routines and custom code.
After high-pass filtering (cut-off 1 Hz), a customized adaptive filter was applied to suppress line-noise. This was followed by Artifact Subspace Reconstruction,  82  re-referencing to the mean, and low-pass filtering (cutoff 60 Hz). Subsequently, an ICA (independent component analysis) algorithm was applied to the data. 83  The resulting ICs were evaluated automatically for artifacts by combining spatial, spectral and temporal analysis of ICs. ICs identified as containing ocular, muscular, or cardiac artifacts were removed from the data Sleep EEG Full-night unattended PSG recordings were performed with EOG (2 channels), EEG (19 channels in 7 subjects, Ag/AgCl electrodes placed according to the 10-20 International System referred to linked-ear lobes: Fp2, F4, C4, P4, O2, F8, T4, T6, Fz, Cz, Pz, Fp1, F3, C3, P3, O1, F7, T3, T5; 25 channels in 3 subjects: CP3, CP4, C5, C6, C2, C1, FC4, FC3, F4, C4, P4, O2, F8, T4, T6, Fz, Cz, Pz, F3, C3, P3, O1, F7, T3, T5), EMG of the submentalis muscle, ECG, body position monitor, and signal for SpO2 (pulse-oximetry O2-saturation).
To avoid noise-related events while preserving most of relevant events, in this study, we used threshold values h > 2:9 SD. To ensure a similar event rate across different sets of recordings (4-min MEG, 40-min MEG, 3-min EEG), we used the following h values: 3. 3 SD for 4-min MEG; 3 SD for 40-min MEG, and 3 SD for 3-min EEG. In each excursion beyond the threshold, a single event was identified at the most extreme value (maximum for positive excursions and minimum for negative excursions). Data were binned using a time window e = 2T meg = 3:3 ms and e = T eeg = 4 ms for MEG and EEG data, respectively. T meg = 1:67 ms was the sampling interval for MEG recordings, while T eeg = 4 ms was the sampling interval for EEG recordings. A neural activity cascade, or avalanche, was defined as a continuous sequence of time bins in which there was at least one event on any sensor, ending with a time bin with no events on any sensor.