dataesr/scientific-paragraphs-categorization huggingface dataset:
For the binding challenge we generated a reference set that we called DisProt-binding. Positive labels are those residues annotated as binding in the DisProt database, whereas all labels not positive are assigned as negatives. Notice that 232 targets have at least one annotation of binding in the DisProt database. Because DisProt-binding is composed of all 646 targets considered in the analysis, the majority of targets (that is, 646 -232 = 414) do not contain positive labels. Predictions. Most predictors output a series of score and state pairs per residue of the input sequence. Scores are floating point numbers while states are binary labels predicting whether a residue is in a disordered or structured state. If scores are missing, states will be used as scores. If states are missing, they are generated by applying a threshold to scores.
By default, thresholds are inferred from states. When states are not available and a threshold is not specified by the authors of the method, we set the threshold to 0. 5. This ensures correct default threshold estimates for any distribution of scores. Prediction scores are rounded to the third decimal figure, which sets the number of possible thresholds to 1,000. Bootstrapping samples the whole dataset with replacements 1,000 times. Resampling is done at the label (residue) level. Confidence intervals are calculated on Student's t-distribution at alpha set to 0. 05.
We apply this framework to analyze a large-scale outflow of BB smoke plumes from Siberia toward Europe that occurred in July 2016. We use AAOD and SSA data derived from OMI (Ozone Monitoring Instrument) satellite measurements in the near-UV range along with 550 nm AOD and carbon monoxide (CO) columns retrieved from MODIS (Moderate Resolution Imaging Spectroradiometer) and IASI (Infrared Atmospheric Sounding Interferometer) satellite observations, respectively, to infer changes in the optical properties of Siberian BB aerosol due to its atmospheric aging and to get insights into the processes underlying these changes. Using the satellite data in combination with simulated data from the CHIMERE CTM, we evaluate the enhancement ratios (EnR) that allow isolating AAOD and AOD changes due to oxidation and gas-particle partitioning processes from those due to other processes, including transport, deposition, and wet scavenging.
, 2009)  to account for the effects of dilution. To analyze and interpret the satellite observations, we use the CHIMERE chemistry transport model  (Mailler et al. , 2017)  in combination with an external module, OPTSIM  (Stromatas et al. , 2012) , enabling Mie calculations of the light absorption and extinction for both core-shell and homogeneous morphologies of particles. The CHIMERE simulations are also used to estimate the photochemical age of BB aerosol. A mechanistic (highly simplified) representation of evolution of the organic fraction of BB aerosol within the VBS framework  (Robinson et al. , 2007)  is used to simulate the behavior of mass concentrations of primary and secondary organic components of BB aerosol. It should be noted that there is currently no uniform way to represent atmospheric transformations of BB OA and its optical properties in models.
The data are retrieved assuming a set of five different vertical locations of the aerosol center mass: at the surface and 1. 5, 3. 0, 6. 0, and 10 km above the surface. We also used the "final" AAOD OMAERUV product, which is derived using the OMI-CALIOP (Cloud-Aerosol Lidar with Orthogonal Polarization) joint dataset  (Torres et al. , 2013)  for the monthly climatology of the aerosol layer height. An important feature of the OMAERUV data product is that the AAOD data are less affected by sub-pixel cloud contamination than the AOD and SSA data, due to partial cancellation of errors in the AOD and SSA retrievals. Accordingly, the quality assured AOD and SSA data (which are associated with the quality flag "0") are much less abundant than the quality assured AAOD data (which are associated with the quality flag "0" and "1").
As noted above, the results of the experiments performed in C17 do not directly apply to the case of BB aerosol from Siberian fires. Hence, it does not seem infeasible that oxidation of VOC emissions from Siberian fires indeed effectively yields more SOA than oxidation of VOCs from the combustion of beech logs. Enthalpies of vaporization (∆H vap ) of the surrogate OA species are evaluated using the estimates derived by May et al. (2013) from thermodenuder measurements of fresh BB aerosol. The same ∆H vap values (depending on the volatility) are used both for POA and SOA species. The optical properties of BB aerosol particles (for both 'bb_vbs' and 'bb_trc' scenarios) are evaluated using the OPTSIM software  (Stromatas et al. , 2012)  under the assumption that any particle is composed of a spherical BC core surrounded by a concentric shell consisting of homogeneously mixed organic components, inorganic salts and water.
To characterize the effects of water uptake by BB aerosol particles on our computations of their optical properties, we evaluated the hygroscopicity parameter κ org  (Petters and Kreidenweis, 2007)  for BB OA by assuming that all the SOA species feature a constant hygroscopicity parameter, κ org  (Petters and Kreidenweis, 2007) , of 0. 2 and that the POA species are hydrophobic. The chosen value of κ org for SOA is representative of the mid-range of the set of κ org measurements for oxidized organic compounds generated in a flow reactor  (Lambe et al. , 2011). Also, we took into account the water uptake by inorganic salts using the equilibrium concentrations calculated in CHIMERE with the ISORROPIA module  (Nenes et al. , 1998). Finally, we would like to emphasize once again that our simplified parameterization of the physical, chemical, and optical properties of BB aerosol is not aimed at a quantitatively accurate representation of the actual very complex processes.
, 2018) , we expected that the modeled evolution of AOD is not sensitive to k soa and also not sensitive to the correction factor for BC emission, F BC. Therefore, the parameters ξ 1 -ξ 3 , as well as the correction factors for the POA and CO emissions from fires, can be adjusted independently of k soa and F BC. Specifically, we required that the nonlinear trend (see Eq. 5) in EnR for the simulated AOD be consistent with the corresponding nonlinear trend in the MODIS observations: �𝑦 𝑒𝑥𝑡 𝑜𝑏𝑠 (𝑡 𝑒 ) -𝑦 𝑒𝑥𝑡 𝑠𝑖𝑚 (𝑡 𝑒 )� < ∆ 𝑒𝑥𝑡 𝑜𝑏𝑠 (𝑡 𝑒 ) + ∆ 𝑒𝑥𝑡 𝑠𝑖𝑚 (𝑡 𝑒 ), 𝑡 𝑒 ∈ [𝑡 𝑒 𝑚𝑖𝑛 ; 𝑡 𝑒 𝑚𝑎𝑥 ], (8) where    (  ) is the approximation of EnR ext for the AOD observations,    (  ) is a similar approximation for the AOD simulations, ∆   and ∆   are the 95% confidence intervals for    and    , respectively, and    and    are the minimum and maximum values of the photochemical age in the selected dataset.
In particular, one should take into account that AAOD is determined by BC, POA, and SOA species, each of which is characterized by a different value of the imaginary refractive index, and therefore equal relative changes in the mass concentrations of these components will have different impacts on AAOD. Furthermore, changes in the POA and SOA mass concentrations can affect AAOD through the lensing effect  (Lack et al. , 2010). To examine these factors, we ran the OPTSIM module for several limiting cases representing different optical properties of the individual components of BB aerosol simulated within the 'bb_vbs" scenario. The main results are presented in Fig. 10 , where the evolution of EnR abs simulated under the different assumptions is compared with the corresponding trends derived from the OMI observations.
Despite being rather trivial, this test confirms the integrity of our simulations and indicates that evolution of AAOD in our simulations is almost fully determined by transformations of the organic fraction of BB aerosol. Note that in this case, the average AAOD in the selected dataset is much lower than in the base case: we estimated that "pure" BC (without the lensing effect) accounts on average for only 31% of the total absorption (see Sect. S5). Figure  10b  presents results for the opposite sensitivity test, where the contribution of BC to the BB aerosol absorption was disregarded. In other words, AAOD was computed under the assumption that BC is not absorbing. In this case, the evolution of EnR abs is similar to that in the base case (see Fig. 7a ), except that the amplitude of a variation of the nonlinear approximation is much larger in the test case.
The analysis was based on the use of OMI AAOD and SSA retrievals combined with MODIS AOD retrievals. These retrievals were used together with BB aerosol simulations and the CO columns derived from IASI observations to evaluate the enhancement ratios (EnR) for AAOD and AOD, which allows isolating the effects of oxidation and gas-particle partitioning processes from those of other processes, including transport, deposition, and wet scavenging. The simulations were performed with the CHIMERE chemistry transport model combined with the OPTSIM module enabling evaluation of the BB aerosol optical properties under the assumption of a core-shell morphology of the particles. The OA oxidation and gas-particle partitioning processes were represented in the simulation in the framework of a highly simplified but adjustable VBS scheme.
Importantly, the evolution of BrC was simulated consistently with the evolution of the POA and SOA species, based on the assumptions that the POA species are much more absorptive in the near-UV wavelength range than the SOA species and that the imaginary refractive index for a given SOA "virtual" species is constant. The EnR estimates and SSA data were analyzed as a function of modelbased estimates of the BB aerosol photochemical age, which -in the situation considered -is found to be strongly associated with the geographical location of a BB plume. We found that, while the EnR for AAOD does not change significantly during the first 20-30 hours of daytime evolution, the EnR for AOD strongly (by more than a factor of 2) increases during the same period. The increase in EnR for AOD is accompanied by a statistically significant increase in SSA.
the results and to the preparation of the manuscript. NAG contributed to the analysis of satellite observations and conducted a part of the numerical experiments. MOA contributed to the discussion of the results and to the preparation of the manuscript. Financial support. The analysis and simulations of the BB aerosol evolution was supported by the Russian Science Foundation (grant agreement no. № 19-77-20109). The development and validation of the simplified VBS scheme was performed with support from the Russian Foundation for Basic Research (grant no. 18-05-00911).
Data availability. The OMAERUV data product  (Torres, 2006) , the MOD04/MYD04  (Levy and Hsu, 2015)  and MYD14/MOD14 datasets (Giglio and Justice, 2015a, b)  are available through the NASA Earth Data Search (https://searchearthdata. nasa. gov/, last access: 19 April 2020). The CO column amounts retrieved from the IASI measurements (Clerbaux et al. , 2009)  are from the ESPRI data center (http://cds-espri. ipsl. fr/etherTypo/index. php?id=1707&L=1, last access: 19 April 2020). The CHIMERE chemistry transport model (CHIMERE-2017) is available at http://www. lmd. polytechnique. fr/chimere/, last access: 20 April 2020, and the OPTSIM software is available at https://www. lmd. polytechnique. fr/optsim/, last access: 20 April 2020.
Bellhop Model Since Meddies evolve near and below the oceanic thermocline, we use an acoustic ray model which is suitable to simulate sound propagation in the deep ocean: the Bellhop acoustic model  [26]. The Bellhop program package is part of the Toolbox AcTUP-Acoustic Toolbox User-interface & Post-processor to Matlab. This toolbox provides the user with a working environment, automating the creation of code-specific initialization files and guesses, as well as selection guidelines for code specific parameters  [27]. An analysis dataset is used to establish the environment for acoustic modeling and simulation. Bellhop is based on the theory of normal modes, and it uses the boundary element method to.
Acoustic Model 2. 2. 1. Bellhop Model Since Meddies evolve near and below the oceanic thermocline, we use an acoustic ray model which is suitable to simulate sound propagation in the deep ocean: the Bellhop acoustic model  [26]. The Bellhop program package is part of the Toolbox AcTUP-Acoustic Toolbox User-interface & Post-processor to Matlab. This toolbox provides the user with a working environment, automating the creation of code-specific initialization files and guesses, as well as selection guidelines for code specific parameters  [27]. An analysis dataset is used to establish the environment for acoustic modeling and simulation. Bellhop is based on the theory of normal modes, and it uses the boundary element method to obtain the transmission loss. The program calculates the transmission loss (TL) due to discrete modes, specifically those which propagate only via total reflection and refraction.
Acknowledgments: We would like to express our deep and sincere gratitude to LOPS, IUEM organization for accommodating the present research. This work would not have been possible without their accommodation and support. We are also grateful to the  University of Hormozgan, Iran  for providing the opportunity for Hassantabar to join this research team. The authors thank the three anonymous Reviewers for providing valuable comments to improve the manuscript. D. Ciani  acknowledges the support of  Rosalia Santoleri.
Funding: This work was supported financially by two grants: 1. Incoming Mobility grant from  UBO ;  2. ISblue  project , Interdisciplinary graduate school for the blue planet  ( ANR-17-EURE-0015 ) and co-funded by a grant from the  French government  under the program " Investissements d'Avenir"; 3. Iran' s Ministry of Science and Research, University of Hormozgan. Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable.
Methods and findings We searched MEDLINE, EMBASE, and 10 other databases from inception to November 26, 2017, updated on December 17, 2020, to identify systematic reviews or meta-analyses of randomized controlled trials (RCTs) and observational studies investigating effects of MHT, including estrogen-alone therapy (ET) and estrogen plus progestin therapy (EPT), in perimenopausal or postmenopausal women in all countries and settings. All health.
Literature search and eligibility criteria We searched MEDLINE, EMBASE, Cochrane Database of Systematic Reviews, Database of Abstracts of Reviews of Effects, ISI Web of Science, CINAHL, Google Scholar, Allied and Complementary Medicine Database, Global Health, PsycINFO, CAB International, and WHO Global Health Library from inception to November 26, 2017. No language restriction was applied. Table A in S1 Text presents the detailed search strategies for each electronic database. Two investigators independently screened the titles and/or abstracts and reviewed full-text articles for eligibility. Any discrepancies were resolved by discussion. References of the included articles were also manually checked to identify additional eligible articles. Articles were selected for inclusion if they met the following Population, Intervention or Exposure, Comparator, Outcome, Study design (PI[E]COS) criteria: (1) population: perimenopausal or postmenopausal women of any ethnicity in any country or setting; (2) intervention or exposure: any type of MHT, including estrogen-alone therapy (ET) and estrogen plus progestin therapy (EPT), at any dose, duration, and route of administration; (3) comparator: placebo or no treatment; (4) outcome: any health outcome or indicator, including menopausal symptoms; and (5) study design: systematic review, with or without meta-analysis, of randomized controlled trials (RCTs) or observational epidemiological studies (cohort and case-control design).
Data extraction and quality assessment Two investigators independently extracted data by outcome using a customized data extraction form. For each systematic review, we extracted title of the review, citation details (e. g. , author list, journal, year of publication), country, PI(E)COS elements, number of included studies, and meta-analysis method (where applicable). For each individual study included in each meta-analysis, we extracted first author, year of publication, country, study design, phase of prevention, age of participants, menopausal status, type of MHT, route of administration, length of follow-up (where applicable), outcome examined, number of events for binary outcomes or means and standard deviations for continuous outcomes and total number of participants in intervention and control groups in RCTs, number of cases and controls in casecontrol studies or total population in cohort studies, type of effect estimate (mean difference, standardized mean difference, risk ratio [RR], odds ratio, incidence rate ratio, or hazard ratio), and effect estimate with 95% confidence interval (CI).
For systematic reviews without metaanalysis, we abstracted only key findings or conclusions. Given the issues (e. g. , incomplete data, poor data quality) in a sample of included metaanalyses  [25] [26] [27] [28]  as well as the high prevalence of data extraction errors from empirical evaluations  [29] , we devoted extensive efforts to obtaining and validating the data for the individual studies in each meta-analysis. We developed 2 separate protocols for extracting data from included systematic reviews of RCTs and of observational studies based on the assessment of 5 included reviews  [25] [26] [27] [28] 30]  (Figs A and B in S1 Text). In brief, for meta-analyses of RCTs that provided insufficient or inadequate data for individual studies, the full texts of these studies were retrieved, from which data were extracted.
In addition, outcome data from the Women's Health Initiative (WHI) trials were updated based on the most recent publications  [31] [32] [33] [34]. For meta-analyses of observational studies, we extracted all data from individual studies regardless of the availability of data in the meta-analyses, and then compared these data with the data in the meta-analyses. We verified the inclusion of individual studies in each metaanalysis by checking the eligibility of each study against the PI(E)COS criteria of the metaanalysis, and studies were further excluded if found not to meet the criteria. In a few cases (e. g. , no references provided), the review authors were contacted to request additional information. Two investigators independently assessed the methodological quality of included systematic reviews using the updated 16-item AMSTAR 2 (A MeaSurement Tool to Assess systematic Reviews) instrument  [35].
A subgroup analysis by MHT type was conducted to assess whether the effect varied qualitatively between ET and EPT. A qualitative difference means that the effects of ET and EPT do not point in the same direction  [51, 52]. We use the term MHT to encompass ET and EPT, and results are presented for MHT, unless a qualitative difference was indicated between ET and EPT, in which case results are presented separately for them. We grouped the data according to whether the intervention or exposure was primary or secondary prevention. We defined primary prevention as reducing the risk of occurrence of a disease among individuals who do not have that disease at the beginning of follow-up, and secondary prevention as reducing the severity or progression of a disease among individuals who already have that disease or the risk of recurrence among individuals who have a history of that disease.
The analyses were conducted separately for RCTs and observational studies. For observational studies, the analyses were stratified by recency of MHT use (ever, current, or past). Finally, based on the amount and the consistency of the body of evidence, we graded the evidence from metaanalyses as consistent, highly suggestive, suggestive, controversial, or insufficient, following the criteria described in Tables  1  and 2. All estimates were converted to the RR scale  [48, [53] [54] [55] , and results are presented on that scale, except where otherwise noted. All statistical analyses were performed using R software (version 4. 0. 1) [56]. The R scripts are available in S4 and S5 Texts, and the datasets are available at Open Science Framework (https://osf. io/dsy37/).
(PDF) S2 Text. Updated database search, from November 27, 2017, to December 17, 2020. Table A: Characteristics of systematic reviews and/or meta-analyses on menopausal hormone therapy and multiple outcomes, updated on December 17, 2020. Fig A: Flow chart for study selection process, updated on December 17, 2020. (PDF) S3 Text. Statistical analysis protocol. (PDF) S4 Text. R scripts for meta-analyses of randomized controlled trials. (PDF) S5 Text. R scripts for meta-analyses of observational epidemiological studies. (PDF).
Results: Supplementary results for umbrella review on menopausal hormone therapy and women's health. Table A: Search strategies used to retrieve papers from different databases. Table B: Characteristics of included systematic reviews and/or meta-analyses of randomized controlled trials on menopausal hormone therapy and multiple outcomes. Table C: Characteristics of included systematic reviews and/or meta-analyses of observational epidemiological studies on menopausal hormone therapy and multiple outcomes. Table D: Quality assessment of included systematic reviews and/or meta-analyses of randomized controlled trials on menopausal hormone therapy and multiple outcomes. Table E: Quality assessment of included systematic reviews and/or meta-analyses of observational epidemiological studies on menopausal hormone therapy and.
Acknowledgments We would like to thank the  Biomedical Library at University of Gothenburg  for retrieving the full-text articles. We also thank  Sarah M. Greising  ( University of Minnesota, US ),  Mohammad Hassan Murad  ( Mayo Clinic, US ), and  James M. Whedon  ( Southern California University of Health Sciences, US ) for providing additional data or information. We thank  Muwada Bashir Awad Bashir  ( University of Gothenburg, Sweden ) for assistance with data extraction, and  Yarong Tian  ( University of Gothenburg, Sweden ) for advice on drawing figures. They were not compensated for their contribution.
Boardman HM, Hartley L, Eisinga A, Main C, Roque i Figuls M, Bonfill Cosp X, et al. Hormone therapy for preventing cardiovascular disease in post-menopausal women. Cochrane Database Syst Rev. 2015; 2015(3):CD002229. https://doi. org/10. 1002/14651858. CD002229. pub4 PMID: 25754617.
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Rudic JS, Poropat G, Krstic MN, Bjelakovic G, Gluud C. Hormone replacement for osteoporosis in women with primary biliary cirrhosis. Cochrane Database Syst Rev. 2011; 2011(12):CD009146. https://doi. org/10. 1002/14651858. CD009146. pub2 PMID: 22161447.