dataesr/scientific-paragraphs-categorization huggingface dataset:
2014). Display songs are complex sequences of short vocal units called 'syllables', given at land and characterizing the breeding season  (Favaro et al. 2014 (Favaro et al. , 2015)). Display songs can be further divided into ecstatic display songs (vocalized mostly by males to attract females and territorial defence) and mutual display songs (vocalized by established pairs within their nest, primarily for recognition when joining after one of the members comes back from a foraging trip at sea)  (Jouventin 1982 , Favaro et al. 2014). Ecstatic and mutual display songs are acoustically distinct vocalizations  (Favaro et al. 2014)  and can be readily identified by visual inspection of spectrograms (Fig. S1 ). By analysing a dataset of environmental recordings collected in a wild African Penguin colony using an autonomous recording system, we aimed to provide the first quantitative description of the diurnal variation in vocal activity of this species during the breeding season.
Statistical analyses To investigate whether the number of penguin vocal displays has an effect on the ecoacoustic indices, we built a series of generalized linear mixed effect models (GLMMs) using the 'lmer' package  (Bates at al. 2014 ) in R v. 4. 0. 0 (R Core Team 2017). In turn, we included the values of the indices as the response variable and the number of ecstatic display songs (hereafter EDS) and mutual display songs (hereafter MDS) as fixed factors. We included the day and the hour of the day as random effects to control for repeated measurements. We used the timestamps to group recordings in 30-min bins (e. g. all recordings between 00:00 and 00:30 h were grouped in time bin 1, recordings between 00:31 and 01:00 h in time bin 2). Before running the models, we confirmed the absence of collinearity among predictors by examining the variance inflation factors (vif package,  Fox & Weisberg 2011).
2000). These findings have strategic implications for future applications of passive acoustic monitoring to the African Penguin, as they allow sampling (recording) to be concentrated on these specific hours to optimize the energy consumption and data storage on recording devices. We then assessed the validity of several ecoacoustic indices against visual inspection of spectrograms. Confirming our expectations, we found that the magnitude of variation of the HI is influenced by the number of display songs occurring within a recording. This index relies on the combined calculation of both the temporal entropy and the spectral entropy of the sound  (Sueur et al. 2008) , and does not take into account the overall amplitude of the signal  (Depraetere et al. 2012). From a practical standpoint, the values of the HI can vary between 0 and 1, with low values indicating an acoustic scene characterized by pure tones (or noise) and high values a scene dominated by harmonics, where the acoustic energy is apportioned across frequency bands  (Sueur et al.
Such important information might then be instrumental in assisting with decision-making and conservation policies for this species, which is nowadays considered in most critical need of conservation among penguins  (Boersma et al. 2019). We hope that our study will stimulate further research in this direction. The study was partially supported by the Regione Lazio (Italy) through a 'Torno Subito' scholarship (no. J86G16000160009) awarded to Eleonora Cresta and by an IDEXLYON fellowship (ANR-16-IDEX-0005) awarded to Prof. David Reby. The University of Turin provided the acoustic recording equipment through a System s. p. a. research grant for bioacoustics (no. PES_D_RIC_N_COMP_16_01). SANCCOB is supported by a wide range of local and international donors, including international zoos and aquaria, foundations and trusts, corporations and individuals.
coli may be spread horizontally to other bacteria  [8]. Antimicrobial resistance dynamics may be assessed by monitoring resistance in commensal E. coli, a widely accepted AMR indicator  [9] [10] [11] [12]. Livestock data on AMR are classically collected from (a) diseased animals (clinical data) and (b) healthy animals (non-clinical data). Epidemiological cut-off values (ECOFFs)  [13]  and clinical breakpoints (CBPs)  [14]  are used to interpret antimicrobial susceptibility testing (AST) results. Whilst ECOFFs are preferred for monitoring and surveillance objectives contrasting the wild-type and non-wild type populations, CBPs define a microorganism as susceptible, susceptible-increase exposure, or resistant depending on the probability of a therapeutic treatment succeeding  [10, 15]. At European level, most resistance data on non-clinical isolates from food producing animals are collected according to the Commission Implementing Decision 2013/652/EU showing, therefore, a high degree of harmonization.
Data Collection and Processing Phenotypic AMR data of E. coli were collected from different sources between 2014 and 2017. Caecal samples from broilers, turkeys, fattening pigs, and calves without underlying pathologies originated from the German Zoonoses-Monitoring program (ZoMo), the French antimicrobial surveillance program in healthy animals coordinated by the French Agency for Food, Environmental and Occupational Health & Safety (ANSES), the Norwegian monitoring program for antimicrobial resistance in bacteria from feed, food and animals (NORM-VET), and the UK AMR surveillance program coordinated by the Veterinary Medicines Directorate (VMD). Diagnostic submission isolates originated from different sample types from the German Resistance Monitoring of Veterinary Pathogens (GERM-Vet) in the same animal categories (i.
Regarding the latter databases, several limitations were encountered on overlaps of (a) antimicrobials, (b) animal categories and (c) time ranges across countries. As it can be observed in Figure  1 , the largest coincidence of antimicrobials and animal categories between 2014 and 2017 was shown between Germany and France. For the UK the overlap with these antimicrobial/bacterial and animal population data was restricted to two antimicrobials (ampicillin and tetracycline) in broilers. In Norway, the number of clinical isolates that could be included was limited. Combinations of isolate type (clinical vs. non-clinical), antimicrobial, animal category, country, and year (e. g. , clinical resistance data for ampicillin in broilers from Germany in 2014) with fewer than 25 isolates were excluded.
The study analyzed ampicillin, gentamicin, nalidixic acid, and tetracycline in broilers, turkeys, and calves. This reflects the overlap between animal categories and between the test panels in the monitoring systems on clinical and non-clinical isolates from Germany and France between 2014 and 2017. Although colistin was also tested in clinical and non-clinical isolates in Germany and France, it was not included in the analysis, as clinical resistance data from France are produced by disk diffusion, which is considered not reliable to test colistin susceptibility  [34, 35]. Only broiler data were considered from the UK and Norway due to the limited number of clinical isolates from the animal categories. UK data on clinical and non-clinical isolates were also statistically analyzed, but results were included separately because of the limited overlap on the antimicrobial panel (i.
e. , ampicillin and tetracycline) and animal categories (i. e. , broiler). Resistance data on clinical isolates from Norway were limited, and therefore only univariable analyses were performed comparing data on clinical and non-clinical isolates from broilers in 2016. For descriptive purposes, data on antimicrobial use per animal category from Germany, France, and the UK were assessed between 2014 and 2017. However, different AMU measurements are used. Moreover, the animal categories for which data are available differ between countries. In Germany, therapy frequency (TF) is used, expressing animal exposure to antimicrobials in days under treatment. In France, the Animal Level of Exposure to Antimicrobials (ALEA) is based on sales data. German and French data on use were assessed from the respective national reports  [36, 37].
UK data on usage of penicillin (amoxicillin and phenoxymethylpenicillin) and tetracycline antibiotic classes in the broiler sector originated from the British Poultry Council and were provided as grams of active ingredients by the VMD (data shown as kg of active ingredients in Appendix A). It was not possible to acquire AMU data by antimicrobial in all countries. Therefore, AMU data were collected at the antimicrobial class level providing uniformity. Due to the differences in the animal populations covered between AMU and AMR in France, AMU data were not included in the statistical models. Additionally, an estimate of antimicrobial consumption from 2018 expressed as treated live weight per kg for broilers and turkeys was provided in France based on a sample of 10 volunteer producing organizations.
Statistical Analysis Data were analyzed applying "the Konstanz information Miner (KNIME)" tool (Version 4. 1. 2) and the software "R" (Version 3. 6. 3) using the CRAN packages "ROCR" and "pscl". Several analyses were performed adopting different logistic regression approaches: (a) univariable and multivariable analyses for each antimicrobial (ampicillin, nalidixic acid, tetracycline, and gentamicin), each animal category (broilers, turkeys, and calves), and each country (Germany and France) were performed including the isolate type variable (clinical vs. non-clinical) and the year as independent variables to assess differences between clinical and non-clinical isolates. (b) Univariable analyses for each antimicrobial, animal category, country, and isolate type were performed including the year as an independent variable to assess resistance trends.
The year in the model was analyzed as a numeric variable. The separate analyses for the United Kingdom were restricted by the limited overlap of the antimicrobial panel (i. e. , ampicillin and tetracycline; gentamicin and nalidixic acid were not covered) and animal categories (i. e. , broiler) according to Figure  1. In Norway, univariable analyses were performed per antimicrobial for broilers in 2016 including the isolate type variable as a factor in order to assess differences between clinical and nonclinical isolates. The outcome variable (i. e. , susceptible (y = 0) or resistant (y = 1)) was the qualitative categorization of (a) IZD data and (b) MIC data applying the NRI method. Univariable and multivariable models showed significant results with a p-value lower than 0. 05. For the explanatory factor "isolate type", an odds ratio (OR) < 1 indicated a lower fraction of resistance in the clinical isolates compared to non-clinical isolates.
Direct comparisons of susceptible and resistant isolates based on different standards without using any statistical method to overcome the lack of harmonization of AMR on laboratory methodologies might be possible if the same CBPs were accidentally shown. In this study, the NRI method was applied as a straightforward approach to interpret AST results. NRI cut-offs for MIC values were calculated by using all broth microdilution data from France, Germany, the United Kingdom, and Norway for E. coli. Resulting NRI cut-offs and published EUCAST ECOFFs were similar. According to the results, identical cut-offs were obtained for gentamicin (2 mg/L) and nalidixic acid (8 mg/L). A difference of one dilution step in the cut-offs was estimated for ampicillin (NRI = 16 mg/L; ECOFF = 8 mg/L) and tetracycline (NRI = 4 mg/L; ECOFF = 8 mg/L).
Membrane-fusion factors Rab1 and Rint1 localize to LDs To determine if the membrane-fusion machinery identified by the screen acts directly at LDs, we analyzed the localization of these factors in cells. Review of published data  (Krahmer et al. , 2018)  supported localization of specific SNARE proteins at LDs; proteomic analysis of liver LDs showed that three of the four SNARE orthologs required for late ERTOLD in Drosophila cells (i. e. , Stx5 (ortholog of Syx5), Bet1l (ortholog of bet1), and Ykt6) were enriched in LD fractions (Figure  S4C ). However, the site of SNARE protein action is difficult to analyze because these proteins act transiently in numerous membrane-fusion reactions. We therefore focused on the upstream factors, Rab1 and Rint1. To analyze their localizations, we expressed tagged versions of Rab1 or Rint1 in Drosophila cells, induced LDs by growing cells for 20 hours in oleate-containing medium, and analyzed their distribution by confocal microscopy.
Systematic identification of late ERTOLD cargoes Identification of the machinery for late ERTOLD protein targeting enabled us to systematically identify proteins that depend on this trafficking route to access LDs. For this, we individually depleted members of the ERES or ERTOLD membrane fusion machinery (Tango1, Trs20, Rab1, Rint1, Syx5 or Bet1) from cells, isolated LDs, and analyzed their composition by mass spectrometry-based proteomics. Seipin knock-out cells and RNAi treatment targeting LacZ were included as controls. As expected, protein levels of each factor targeted by RNAi were dramatically reduced (Figure  S7A ). Filtering the data for the set of LD proteins  (Krahmer et al. , 2013)  with two or four consecutive predicted transmembrane domains that may form membrane-embedded hairpins, we found ~10 proteins whose abundance in LD fractions was reduced by depletion of late ERTOLD targeting machinery components.
Analysis of genome-scale imaging screen A custom Matlab analysis pipeline was built to analyze screen images. Nuclei, cell, and LD compartments were obtained using supervised machine-learning methods, specifically Random Forest pixel classifiers (http://github. com/HMS-IDAC/PixelClassifier). Three different models were trained, one for each compartment, using separate sets of annotated images (about seven images per model). Nuclei and cells were segmented from the Cy5 channel, and LDs formed the DAPI channel. Nuclei mask was used for segmenting cells as markers in a watershed algorithm. LD masks were post-processed by selecting only the intersection with cell masks. LD objects were then associated with cell objects, depending on the area of intersection. Finally, the signal in the FITC channel was corrected for auto-fluorescence by subtracting the mean value of control images and was quantified inside and outside the LD mask in each segmented cell.
Analysis of mass spectrometry data The mass spectrometry analyzer operated in data-dependent acquisition mode with a top 10 method at a mass-over-charge (m/z) range of 300-2000 Da. Mass spectrometry data were analyzed by MaxQuant software version 1. 5. 2. 8  (Cox and Mann, 2008)  using the following setting: oxidized methionine residues and protein N-terminal acetylation as variable modification, cysteine carbamidomethylation as fixed modification, first search peptide tolerance 20 ppm, main search peptide tolerance 4. 5 ppm. Protease specificity was set to trypsin with up to 2 missed cleavages allowed. Only peptides longer than 6 amino acids were analyzed, and the minimal ratio count to quantify a protein is 2. The false discovery rate was set to 5% for peptide and protein identifications.
Database searches were performed using the Andromeda search engine integrated into the MaxQuant software  (Cox et al. , 2011)  against the UniProt Drosophila melanogaster database containing 20,981 entries (December 2018). "Matching between runs" algorithm with a time window of 0. 7 min was employed to transfer identifications between samples processed using the same nanospray conditions. Protein tables were filtered to eliminate identifications from the reverse database and common contaminants. To identify proteins regulated by different genotypes, the MaxQuant output files were exported to Perseus 1. 5. 1. 6 (Tyanova et al. , 2016). Known contaminant and decoy sequences were removed. Projection and clustering of the dataset was performed using principal component analysis to identify potential sample outlier.
Data and Code Availability Original screen images and quantification results will be available at the Lipid Droplet Knowledge Portal [http://lipiddroplet. org/; (Mejhert et al. , 2021)]. All original code (used for quantification of screen images) has been deposited at Harvard Dataverse and is publicly available. DOIs are listed in the key resources table. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE  (Perez-Riverol et al. , 2019)  partner repository with the dataset identifier PXD027283. (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.
surgery, radiation therapy, or a combination of both. The choice of these strategies depends on the primary therapy. Screening for distant metastases should be performed before deciding on curative treatment. If primary therapy consisted of surgery alone, radiotherapy represents an effective salvage strategy in cases of vaginal or central pelvic recurrence. In these cases, a combination of EBRT and brachytherapy, preferably image guided, is usually required. Large recurrences should be evaluated for excision, followed by radiotherapy. Alternatively, chemotherapy may be considered to decrease the volume of the recurrence and hence improve the chances of complete surgical resection. Additional chemotherapy with radiotherapy is being evaluated in a recently completed GOG trial (GOG-0238, NCT00492778).
16 | 4. 5. 7. 8. 9. 1645789 surgery, radiation therapy, or a combination of both. The choice of these strategies depends on the primary therapy. Screening for distant metastases should be performed before deciding on curative treatment. If primary therapy consisted of surgery alone, radiotherapy represents an effective salvage strategy in cases of vaginal or central pelvic recurrence. In these cases, a combination of EBRT and brachytherapy, preferably image guided, is usually required. Large recurrences should be evaluated for excision, followed by radiotherapy. Alternatively, chemotherapy may be considered to decrease the volume of the recurrence and hence improve the chances of complete surgical resection. Additional chemotherapy with radiotherapy is being evaluated in a recently completed GOG trial (GOG-0238, NCT00492778).
Materials and methods Protocol, data source and search PRISMA reporting guidelines were followed, and the protocol was registered in the International prospective register of systematic reviews (PROSPERO, number CRD42018085522). Electronic databases (Elsevier Medical Database (EMBASE) and the US National Library of Medicine database (MEDLINE via the PubMed portal) were used to search for relevant papers until March 13,  2018. The full search string can be found in Supplementary Data 1. The search was designed to identify controlled trials with AGI drugs together with a standardized carbohydrate load or mixed meal, studying effects on acute PPG and/or PPI responses.
Data extraction and quality assessment Data from included publications were extracted by one researcher (M. A. ) and a random subsample of 10% was checked by another researcher (C. R. ). Information on the study design, population, drug, dosage, test meal and outcome measures (glucose and insulin incremental, or total area under the curve (AUC), mean postprandial level, variance or p-values) were extracted. If no AUC or mean postprandial level was reported, postprandial response data per timepoint were extracted. Data from figures (i. e. bars for AUC, and responses per timepoint from graphs) were extracted using the Microsoft Excel add-in tool TM Image-to-data (tushar-mehta. com). If studies reported responses to multiple sequential meals, data from responses to the first meal were extracted.
Characteristics of included studies The search retrieved 1811 publications and an additional 15 potentially relevant publications were found manually and added to the database for screening (Fig. 1 ). After removal of duplicates, 1341 records were screened based on titles and abstracts; 176 full-text publications were finally assessed for eligibility. The 66 included publications (Supplementary Table  1 ) comprised 134 comparisons, of which 127 comparisons had information on PPG and 106 comparisons had information on PPI. For PPG, the number of comparisons per drug was 64, 48, 6 and 8 for acarbose, miglitol, voglibose and emiglitate, respectively. For PPI, the number of comparisons per drug was 46, 42, 10 and 8 for acarbose, miglitol, voglibose and emiglitate, respectively. Most of the comparisons were among individuals without diabetes (84 out of 134) (Supplementary Table  1 ).
A strength of this study is the comprehensive set of studies included because data from all studies identified as eligible according to inclusion and exclusion criteria were extracted and included in the analyses. A limitation is the lack of standardization in PPG and PPI responses, as different studies have applied different measurement durations and higher or lower frequency of measurements during the postprandial response. This may have led to a greater uncertainty around effect sizes and hence reduced the statistical power to identify effects and differences in effects between subgroups. In addition, the accurate assessment of the PPG/PPI response is ultimately related to the number of time points included in the individual studies. Two of the papers  29, 41  included in our analysis were from studies with only three time points; however, the response to these interventions was within the range reported of other studies.
Acknowledgements We thank  Linda Schoonmade  from  VU University Amsterdam  for designing and executing the literature search strategy. We thank  Dr. Matthieu Flourakis  for his support on finalising the project. Author details 1 Former employee of  Unilever Research and Development, Vlaardingen , The Netherlands. 2  Department of Epidemiology and Biostatistics, VU University Medical Center, Amsterdam Public Health Research Institute, Amsterdam, The Netherlands. 3 Department of Human Biology, NUTRIM, School of Nutrition and Translational Research in Metabolism, Maastricht University, Maastricht , The Netherlands. 4 Former employee of  Nestle Research Center, Lausanne, Switzerland. 5ILSI Europe, Brussels, Belgium. 6Mondēlez International R&D, Nutrition Department, Saclay, France. 7Department of  Nutritional Sciences, University of Surrey, Guildford, UK.
Related Literature A number of research projects have focused on applying machine learning algorithms to Twitter data in order to forecast opinion poll results or election outcomes. We discuss some of them here and refer the interested reader to Gayo-Avello (2013);  Phillips et al. (2017)  for more in-depth reviews of the literature. A pioneer work in this area was that of  Tumasjan et al. (2011)  whose model achieved a mean average error (MAE) of 1. 65% when predicting results of the 2009 German federal election. Authors used Twitter mention counts as an direct indicator of a candidate's popularity, a method that has been considered by several other works as well, often in combination with a sentiment analysis of tweets content  (O'Connor et al. , 2010; Saleiro et al. , 2016; Garcia et al.
Finally, in our previous work we developed closed-form formulas for the distribution of opinion at any step and convergence time to equilibrium in the case where stubborn nodes are present in a strongly connected network  (Vendeville et al. , 2021). Our contributions. In this paper we propose a new model for the forecast of elections outcome, based on official results of previous elections. Our method is based on the voter model with stubborn nodes and uses theoretical results developed in a previous work of ours  (Vendeville et al. , 2021). We apply it to the United Kingdom general elections and in the United States presidential elections and achieve an MAE of 4. 74%. To the best of our knowledge this is the first time such work is conducted. All code used is available online.