dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2017) and are specified on a daily timescale. Additional details on fire NMOG emissions are provided in Sect. 3. A sensitivity analysis, described in Sect. 4, uses FINNv1. 5. Anthropogenic emissions (including fossil and biofuel sources) follow the year-specific Community Emissions Data System (CEDS) global inventory  (Hoesly et al. , 2018). Trash burning emissions are from  Wiedinmyer et al. (2014). Aircraft emissions are from the Aviation Emissions Inventory Code (AEIC) inventory  (Stettler et al. , 2011; Simone et al. , 2013). Biogenic emissions are calculated online from the Model of Emissions of Gases and Aerosols from Nature version 2. 1 (MEGANv2. 1) emissions framework  (Guenther et al. , 2012). A typical source-attribution method in models zeroes out a specific source and differences that simulation from the baseline.
Exploring observational constraints on fire NMOGs There are limited observational constraints on fire-influenced NMOGs and OHR. We use observations of OHR made at the Amazon Tall Tower Observatory (ATTO) and of VOCs from the FIREX-AQ and ARCTAS campaigns in addition to measurements of both VOCs and OHR from the DC3 campaign. Previous work has shown that the plume-chasing sampling strategy of the WE-CAN 2018 field campaign limits the suitability of this dataset for 3D model evaluation  (Carter et al. , 2021). While the Korea-United States Air Quality (KORUS-AQ) campaign included airborne OHR measurements and some fire influence (median concentration of acetonitrile, a biomass burning tracer  (Lobert et al. , 1990) , of ∼ 165 ppt, parts per trillion; Fig. S6 ), the campaign is dominated by anthropogenic sources, which recent work shows may confound the acetonitrile signal  (Huangfu et al.
Conclusions Recent work has suggested that NMOGs from fires may be a large source but noted that we did not yet have a framework in our models to fully characterize them and their reactivity  (Akagi et al. , 2011). Our work provides a first estimate of fire NMOGs globally and regionally and their contribution to reactivity. We updated fire NMOG EFs to Andreae (2019) from  Akagi et al. (2011). We also expanded the model representation by adding new fire NMOGs (e. g. , lumped furans, phenol, ethene), prioritized for their reactivity using data from the FIREX lab studies and their chemistry. We used a suite of recent observations from the lab (FIREX) to towers (ATTO) to aircraft campaigns (FIREX-AQ, ARCTAS, DC3) to con-strain and test our model representation. We show that observations support the additions made to the model.
Data availability. The GEOS-Chem model is publicly available at https://doi. org/10. 5281/zenodo. 4618180 (The International GEOS-Chem User Community, 2021). The DC3 campaign data are available at https://www. eol. ucar. edu/field_projects/dc3 (last access: 18 February 2018), and the ARCTAS campaign data are available at https://www-air. larc. nasa. gov/cgi-bin/ArcView/arctas (last access: 18 February 2018). ATTO data are available at https:// attodata. org/ (last access: 4 June 2021). FIREX-AQ data are available at https://www-air. larc. nasa. gov/cgi-bin/ArcView/firexaq (last access: 16 April 2022). Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/acp-22-12093-2022-supplement.
Images are representative of 3 mice per group with similar results. Scale bar = 40 μm. Source data are provided as a Source Data file. Ppar activation rescues defective fatty acid utilization and lipid droplet accumulation in differentiated Chkb -/ -myocytes in culture. To determine whether the decreased fatty acid utilization and increased lipid droplet accumulation in Chkb -/- myocytes is mediated by a Ppar signaling pathway(s) we treated Chkb -/-myocytes with the Ppara agonists ciprofibrate and fenofibrate, the pan Ppar agonist bezafibrate, and the Pparb/d specific agonist GW501516 and assessed the capacity of the myocytes to oxidize fatty acid. Quantification of maximal respiration showed that all the Ppar agonists enhanced maximal respiration of Chkb -/-myocytes (Fig. 6A, C, D, F ), with the specific Pparb/d agonist (GW501516) able to increase basal respiration in Chkb -/-myocytes to the same level as Chkb +/+ myocytes (Fig.
For D values are means ± SEM. n = 5 random images from 3 independent experiments for each group. Two-sided student's t-test did not result in p values less than 0. 05. Representative traces of oxygen consumption rates (OCRs) of primary Chkb +/+ and Chkb -/-myocytes at day 4 (E, F) and day 8 (G, H) of differentiation driven with glucose/glutamine/pyruvate or palmitate as stated. Bovine serum albumin (BSA) alone was used as control for palmitate-BSA complex driven OCRs. Oligomycin(O), FCCP(F), rotenone (R) and antimycin A (AA) were sequentially injected to assess mitochondrial respiratory states. Data are mean ± SD. For E, n = 5 (Chkb +/+ ) and n = 5 (Chkb -/-) wells per group. For (F), n = 5 (BSA Chkb +/+ ), n = 5 (BSA Chkb -/-), n = 5 (Palmitate-BSA Chkb +/+ ), n = 5 (Palmitate-BSA Chkb -/-).
no. 4368814, Applied Biosystems, MA, USA). Quantitative real-time RT-PCR assays were performed on the Bio-Rad CFX96 Touch Real-Time PCR Detection (Bio-Rad®, California, USA) System using TaqMan™ Fast Advanced Master Mix (Cat. no. 4444557) and TaqMan™Gene Expression Assays (Cat. no. 4331182, ThermoFisher Scientific) for Chka (RRID: Mm00442759_m1), Chkb Cpt1b (Exon boundary7-8)(RRID: Mm01308102_g1), Cnsk2a2 (RRID: Mm01243455_m1), Cpt1b (RRID: Mm00487191_g1), Gapdh (RRID: Mm99999915_g1), Icam1 (RRID: Mm00516023_m1), Ppara (RRID: Mm00440939_m1), Ppard (RRID: Mm00440940_m1), Pparg (RRID: Mm00440940_m1) and Tgfb1 (RRID: Mm011778820_m1). Reactions were run in triplicate. For qPCR analysis, Light Cycler® 96 Instrument Software, Version 1. 1. 1 was used. Microarray analysis of Ppar targets. Mature RNA was isolated using Qiagen RNeasy Plus Mini Kit (Cat.
no 74134) according to the manufacturer's instructions. RNA quality was determined using a spectrophotometer and was reverse transcribed using a cDNA conversion kit. The cDNA was used on the real-time RT2 Profiler PCR Array (QIAGEN, Cat. no. PAMM-149Z) in combination with RT2 SYBR® Green qPCR Mastermix (Cat. no. 330529). CT values were exported to an Excel file to create a table of CT values. This table was then uploaded on to the data analysis web portal at http://www. qiagen. com/geneglobe. Samples were assigned to controls and test groups. CT values were normalized based on a/an Manual Selection of reference genes. The data analysis web portal calculates fold change/ regulation using delta delta CT method, in which delta CT is calculated between Fig. 6  Ppar activation rescues defective fatty acid utilization and normalizes the choline and phosphocholine levels in differentiated Chkb -/- myocytes in culture.
For standardization, we used the SPLASH® Lipidomix® Mass Spec Standard as which includes deuterium labeled forms of all the major lipid classes. High resolution tandem mass spectrometry and lipidomics. Lipid analyses were carried out using a Q-Exactive Orbitrap mass spectrometer controlled by X-Calibur software 4. 0 (ThermoScientific, MO, USA) with an acquisition HPLC system. The following parameters were used for the Q-Exactive mass spectrometer -sheath gas: 40, auxiliary gas: 5, ion spray voltage: 3. 5 kV, capillary temperature: 250 °C; mass range: 200-2000 m/z; full scan mode at a resolution of 70,000 m/z; top-1 m/z and collision energy of 35 (arbitrary unit); isolation window: 1 m/z; automatic gain control target: 1e5. The instrument was externally calibrated to 1 ppm using ESI negative and positive calibration solutions (ThermoScientific, MO, USA).
Tune parameters were optimized using a mixture of lipid standards (Avanti Polar Lipids, Alabama, USA) in both negative and positive ion mode Thermo Scientific™ LipidSearch™ software version 4. 2 was used for lipid identification and quantitation. First, the individual data files were searched for product ion MS/MS spectra of lipid precursor ions. MS/MS fragment ions were predicted for all precursor adduct ions measured within ±5 ppm. The product ions that matched the predicted fragment ions within a ±5 ppm mass tolerance was used to calculate a match-score, and those candidates providing the highest quality match were determined. Next, the search results from the individual positive or negative ion files from each sample group were aligned within a retention time window (±0. 2 min) and the data were merged for each annotated lipid.
Data cleanup and statistical analysis of lipids. Thermo Scientific™ LipidSearch™ software version 4. 2 was used for lipid identification and quantification. An inhouse script written in R (version 4. 0. 2) was used for data QC analysis, normalization, and plotting. The data was filtered to exclude any peak concentration estimates with a signal to noise ratio (SNR parameter) of less than 2. 0 or a peak quality score (PQ parameter) of less than 0. 8. If this exclusion resulted in the removal of two observation within a biological triplicate, the remaining observation was also excluded. The individual concentrations were then gathered together by lipid identity (summing together the concentration of multiple mass spectrometry adducts where these adducts originated from the same molecular source and averaging together biological replicates) and grouped within the broader categories of AcCa, TG, DG, PC, PE, PG, CL, PI, PS.
Images were converted to 8-bit and the total corrected cellular fluorescence for the green channel was measured in random 100 cells per group using FIJI (NIH; ImageJ 2. 0. 0-rc-69/1. 52n) software. The total corrected cellular fluorescence (TCCF) = integrated density-(area of selected cell × mean fluorescence of background readings), was calculated and compared between groups  43. To study the effect of Ppar agonist treatments on exogenous fatty acid utilization and storage, 4 days after differentiation, myocytes were treated with or without ciprofibrate (50 μM), bezafibrate (500 μM), and fenofibrate (25 μM) in the medium for 48 h. On day 6 of differentiation the medium with or without drugs Fig. 7  Ppar activation normalizes lipid droplet accumulation and decrease injury in differentiated Chkb -/-myocytes in culture.
Seahorse analysis of mitochondrial function. Oxygen consumption rate (OCR) was measured as previously described  44  using a Seahorse XF24 extracellular flux analyzer (Seahorse Biosciences, North Billeric, MA, USA). Agilent Seahorse Wave Desktop software version 2. 6 was used to transforms complex cellular metabolism data into publishable results. Isolated skeletal myoblasts were cultured on Matrigel® coated 24-well Seahorse XF24 plates at a density of 40,000 cells/well and differentiated into myocyte as described earlier. Experiments were performed on days 4 and 8 of differentiation in vitro. For fatty acid oxidation analysis, the day prior to the assay growth medium was exchanged for substrate-limited medium (DMEM without glucose, glutamine, sodium pyruvate or bicarbonate (Agilent, Cat. no.
Ultrathin sections were stained with 2% uranyl acetate for 30 min and lead citrate for 4 min and viewed with a JEOL JEM 1230 Transmission Electron Microscope at 80 kV. Images were captured using a Hamamatsu ORCA-HR digital camera. Three mice per genotype for each timepoint were evaluated. The mitochondrial content was determined from the images at 10,000× magnification using Image J software, (Fiji), (ImageJ 2. 0. 0-rc-69/1. 52n), and calculated as mitochondria count/field by blinded investigators. Point counting was used to estimate mitochondrial volume density and mitochondrial cristae density based on standard stereological methods  45, 46. Only mitochondria profiles of acceptable quality defined as clear visibility and no or few missing spots of the inner membrane were included. Using ImageJ software, a point grid was digitally layered over the micrographic images at 20,000× or 40,000× magnification for mitochondrial volume density and cristae density calculations respectively.
Peak integration was performed using Skyline, an open-source multiple reaction monitoring (MRM) analysis software (version 21. 0). An in-house script written in R (version 4. 0. 2) was used for data QC analysis, normalization, and plotting. Briefly, for each metabolite, the peak intensities of samples were normalized to the mean of the corresponding intensities of the two nearest flanking QC pool samples (which consisted of a mix of all samples included in the analysis) to reduce drift. For lipidomics analysis all samples were adjusted to a final concentration of 120 μg/ml and spiked with 10 μl of internal standard (Avanti Polar Lipids Inc; Catalog Number-330707). 500 μl of chloroform was added to each sample, vortexed for 30 min, and centrifuged to separate phases (5 min at 6000 rpm). The bottom organic phase was transferred into a new Eppendorf and dried under a nitrogen stream.
Problem 1: Combating artifacts In experimental animals, SPW-Rs can be confidently measured with linear electrode arrays, as the sharp wave and ripple components are simultaneously recorded across CA1 layers (str. oriens, pyramidal layer and str. radiatum). The current source-density (CSD) profiles of both events can be identified by automated detection thresholds, then visually inspected by expert operators  5. Recording across layers facilitates rejection of artifacts, which are observed as power increases across simultaneously recorded sites. These curated data are considered to be "ground truth," useful for training and testing detection algorithms (https://github. com/buzsakilab/buzcode/blob/ master/detectors/detectEvents/detect_swr/detect_swr. m). Deviating from this ideal scenario, reliable identification of SPW-Rs in humans with sparse electrode coverage introduces daunting challenges.
Data-driven, automated approaches to SPW-R detection Instead of relying on arbitrary thresholds, several laboratories have developed automated detection methods  8, 81. For example, the distribution of ripple frequency events exceeding 20 ms can modeled against background 'noise' and the termination of the SPW-R envelope occurs with return to the session mean  81. Yet, even this method is inadequate if quantitative SPW-R counts are compared across sleep and wake because these states have different background LFP power. Supervised machine learning approaches, such as Recurrent Neural Networks (RNN) with Long Short-Term memory (LSTM) layers or Convolutional Neural Networks (CNN), can learn from curated datasets to recognize distinct features of SPW-R events  82, 83. Alternatively, unsupervised techniques can separate SPW-R from pathological events  84, 85.
Distinguishing between physiological and pathological ripples The use of thresholds on low-level features such as frequency, duration, or amplitude can be problematic in a dataset containing both physiological and pathological events. While some have proposed non-overlapping frequency bands to distinguish between physiological ripples (80-250 Hz) and pathological ripples ("p-ripples"; 250-500 Hz)  121  , neither amplitude  147  nor frequency range can reliably separate physiological SPW-Rs from p-ripples  147. P-ripples can possess broadband peaks overlapping with the physiological ripple band (80-200 Hz) but analysis of the statistical distribution of p-ripples discloses strong spectral variability leaking into both the high and the low-frequency band in both rodents  54  and humans  148, 149.
121), slower and low amplitude p-ripples are more difficult to be separated from SPW-R. Thus, spectral variability could be used to identify p-ripples (Fig. 7   54, 148  ). Finally, SPW-R and pathological highfrequency oscillations (HFO) may be separated during NREM sleep based on their differing phase relationship with slow-wave activity  158, 159. However, the tradeoff for stricter inclusion criteria is data loss. Combining automated detection of both SPW-Rs and p-ripples with expert validation can be more effective than a simple thresholding approach. However imperfect, these automated detection algorithms have the benefit of applying objective criteria across datasets and laboratories, in contrast to subjective judgments by human operators  12, 111, 158. As is the case with SPW-R detection, machine learning techniques have also been applied to identify p-ripples, and IEDs  54, [160] [161] [162].
Detection and confidence estimation (animals, humans) Instead of approaching SPW-R detection with a pre-defined and arbitrary bandpass filter, we recommend first inspecting the broadband data recorded from the CA1 subfield and looking for endogenous narrow-band peaks in activity. Power spectral density analysis of the detected individual events should reveal a significant narrow peak in the SPW-R frequency band, riding on the broadband 1/f frequency-power distribution. Calculating and reporting spectra from wider temporal windows, would also characterize the arousal state. Ideally, putative SPW-Rs detected during an experimental task should be compared with those found during NREM sleep. When unit recordings are available, SPW-R-unit histograms during both awake and NREM sleep should be compared.
Data and code sharing (animals, humans) Public sharing of well-curated datasets would facilitate comparison of different detection methods and provide 'ground truth' material to develop automatic clustering methods. In datasets from closed-loop experiments, SPW-Rs detected and missed should be reanalyzed with offline methods, and report false positive and false negative rates. While errors are an inevitable consequence of any physiological analysis, error reporting will increase the confidence in findings. The above recommendations will advance progress in SPW-R research. These recommendations could also apply to reliably identify neurophysiological events involved in cognition, sensorimotor behavior, and brain-machine interface applications. However, we recognize that ideal conditions are often not feasible due to cost and time and may not be crucial to every experiment.
Acknowledgements This work was funded by  K23NS104252 (A. A. L. ) R01 MH117777  (E. B. , J. W. R. )  Whitehall Foundation (KH)   5F31NS120783-02 (Z. L. ) 1U19NS104590 (A. L. ) R01NS106611-02 (J. S. , M. K. ) MTEC-20-06-MOM-013  (J. S. , M. K. )  1U19NS107609-01  (I. S. , J. L. ) 1U19NS104590 (A. L. , J. S. F. , I. S. ) 1U19NS107609 (E. A. B. , J. W. R. , J. J. L. , I. S. ) La Caixa  LCF/PR/HR21/ 52410030  (A. N. O. , L. dl. P) European Research Council Consolidator Grant  101001121  (B. P. S. ) U. S. - Israel BSF  grant  2017015  (RM) U01-NS113198 (J. J. ) NSF CAREER IOS-1844935  (M. vdM. ) 1R01NS121764-01  (B. L. M. )  R01 MH122391 (G. B. ) F30MH126483  (J. A. G. )  Fondation pour la Recherche Médicale   EQU202103012768  (M. Z. )  1R16-NS131108-01  (L. L. ).
| Study design and participants The IMAgiNE EURO study is a cross-sectional study reported according to the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines for cross-sectional studies. 29 The STROBE checklist is included as supporting information Table  1. Women aged 18 and over who gave birth from March 1, 2020, up to the end of the data collection period (February 7, 2022) were invited to participate in an anonymous online survey. Consent to participate was requested and obtained before women answered the questionnaire and was recorded online. Women who did not match the inclusion criteria or who did not give birth in a facility in the WHO European region were excluded. The online survey was available in more than 20 languages. Women were invited to respond in their preferred language regardless of which country they gave birth in.
Four countries (France, Italy, Portugal, Romania) had an adequate sample to look at country data (Table 2 and supporting information Table 5). In three out of four countries (Italy, Portugal, Romania) the rate of instrumental births was higher in private hospitals compared with public hospitals, with the largest gaps observed in Portugal (72. 0%vs 46. 0%; aOR 3. 27; 95% CI, 2. 43-Study flow diagram. a Missing information on the 40 WHO standards-based quality measures and key sociodemographic variables. 18793479, 2022, S1, Downloaded from https://obgyn. onlinelibrary. wiley. com/doi/10. 1002/ijgo. 14458 by Cochrane France, Wiley Online Library on [26/12/2022]. See the Terms and Conditions (https://onlinelibrary. wiley. com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License Characteristics of survey respondents comparing private versus public facilities.
However, in contrast to other continents  16  , few studies have assessed climate change risks along the 300,000 km African coastline that spans 39 countries  3, 4, 17. Even sparser is information about the future of the continent's cultural and natural heritage sites, many of which are found in the coastal zone. We assess exposure of African heritage sites (AHS) to coastal flooding and erosion along the entire African coastline. We create the first continent-wide, digitized, geospatial database of 284 coastal AHS, combining 71 cultural World Heritage Sites and 213 natural World Heritage Sites that are either already recognized, or currently under consideration by the United Nations Educational, Scientific and Cultural Organization (UNESCO) World Heritage Centre and the Ramsar Convention on Wetlands of International Importance  [18] [19] [20] [21].
1 ). North Africa has the largest number of exposed sites (23 of a total of 109; Extended Data Fig. 1 ). West Africa has 18 sites exposed, Southern Africa has 7, while East Africa and Small Island Developing The number of sites threatened by a 100-year coastal extreme event is projected to more than triple under moderate emissions, reaching 191 (very likely range: 191-196) by 2050 (Table  1 ). Considering the median estimates, 68, 47, 24, 23, 16 and 13 exposed sites are found in North, West, Southern, SIDS, Central, and East Africa, respectively. High emissions will increase the total number of exposed sites by 2050 to 198 (very likely range: 198-210), with 4 of the 7 additional sites (for the median estimate) found in North Africa and 3 distributed among the SIDS, Southern and West Africa.
Methods General. We assess the exposure of African natural and cultural heritage sites to coastal hazards (specifically coastal floods and erosion) during the twenty-first century. We generate a database of heritage sites in Africa by compiling, validating and correcting existing information. Then, we overlay the heritage dataset with hazard maps describing the evolution of coastal flooding and sandy beach erosion during the twenty-first century. We use two greenhouse gas emission scenarios: a moderate (RCP 4. 5) and a high emissions (RCP 8. 5) scenario  49  , both resulting in global warming above the Paris Agreement long-term temperature target of 2 °C.
Heritage sites definition. We consider all African sites included in the UNESCO World Heritage List of 2020  19  and the Ramsar Sites Information Service  18, 21. Poor representation of African sites on the World Heritage List is a known issue  50, 51  , therefore we also consider African sites included in the UNESCO World Heritage Tentative List 20. These sites have been proposed, recognized and endorsed as holding potential 'Outstanding Universal Value'. Our study focusses on 71 cultural and 213 natural heritage sites found within 38 of the 39 African countries with a coastline  52. Accurate maps and coordinates of most Ramsar sites are available in the official database; in contrast to World Heritage Sites, which are not always described by accurate maps and coordinates, often intentionally, to protect fragile heritage sites from looting.