dataesr/scientific-paragraphs-categorization huggingface dataset:
To this goal we applied to the WOA global dataset the delta values of environmental predictions between 2006-2015 (present-day) and 2090-2099 (end of the century) Earth system models (Supporting Information FigureS7; see Materials and methods). Our results suggest that climate change may lead to a general decrease of diatom richness (with a mean negative variation of 4%, down to a negative minimum of 56%), with a narrowing of hotspot regions (Figure7a, e. g. , Tropical Pacific) and a relocation of richer communities towards the poles. A remarkable example are the peaks of increased richness in the subtropical zones, which at present display very low richness (Figure7b, Supporting Information FigureS7). We analysed the impact of the different environmental changes through computation of the contribution of the different variables on the global scale prediction at the present time and at the end of the century (Figure8a).
ACK N OWLED G M ENTS We thank  Dr Olivier Jaillon  and  Dr Lucie Zinger  for their suggestions. We thank  Dr Marion Gehlen  for her support on analysis of the climate model outputs. G. B. and L. Cam acknowledge a fellowship funded by the  Stazione Zoologica Anton Dohrn (SZN)  within the  SZN-Open University Ph. D. program. R. P. was supported by the  Italian MIUR Flagship Project RITMARE  and the  European Union 's  Horizon 2020 Research and Innovation Program EMBRIC  ( GA 654008 ). E. S. was partially supported by a grant from the  Ministero dell'Istruzione dell'Università e della Ricerca   RITMARE  project, and by a postdoc fellowship 'Lina Rizzo' by the  Accademia Nazionale Dei Lincei (Rome, Italy). F. d'O. acknowledges support by the  NASA/ CNES TOSCA BIOSWOT-AdAC  project.
Abstract. Iceland is a highly active source of natural dust. Icelandic dust has the potential to directly affect the climate via dust-radiation interaction and indirectly via dust-cloud interaction, the snow/ice albedo effect and impacts on biogeochemical cycles. The impacts of Icelandic dust depend on its mineralogical and chemical composition. However, a lack of data has prevented an accurate assessment of the role of Icelandic dust in the Earth system. Here, we collected surface sediment samples from five major Icelandic dust hotspots. Dust aerosols were generated and suspended in atmospheric chambers, and PM 10 and PM 20 fractions were collected for further analysis. We found that the dust samples primarily consist of amorphous basaltic materials ranging from 8 wt % (from the Hagavatn hotspot) to 60 wt %-90 wt % (other hotspots).
The samples were placed on a flat silicon monocrystalline wafer, which was loaded on the sample holder, a reflection spinner (1 s rotation time). The mineral identification was performed in Highscore Plus 3. 0  (Degen et al. , 2014)  using the ICSD-Pan (Inorganic Crystal Structure Database) and COD (Crystallography Open Database) databases. The quantitative analysis of the mineral phases was conducted in MAUD (Material Analysis Using Diffraction). MAUD is an XRD program based on the Rietveld refinement method, which uses least-square procedures to minimise the differences between the observed and calculated diffractograms  (Lutterotti et al. , 1999). The phase files identified with Highscore Plus were loaded as references in MAUD. It is also possible to determine the proportion of amorphous phase with the Rietveld method  (Lutterotti et al.
X-ray fluorescence (XRF) analysis PM 10 and PM 20 samples were analysed by wavelengthdispersive X-ray fluorescence (WD-XRF) to determine the elemental composition of the dust aerosols. The WD-XRF analyses were performed using a PW-2404 spectrometer by Panalytical available at LISA. Excitation X-rays are produced by a Coolidge tube (I max = 125 mA, V max = 60 kV) with a Rh anode. The primary X-ray spectrum is controlled by inserting filters (Al, at different thickness) between the anode and the sample. Each element was analysed three times, with specific conditions (voltage, tube filter, collimator, analysing crystal and detector), lasting 8 to 10 s. Data were collected for 24 elements (Cl, S, Ca, Fe, Na, Mg, Al, Si, P, K, Ti, Mn, Zn, Cr, V, Ba, Co, Cu, Nd, Ni, Sr, Cd, As, Pb) using the SuperQ software.
Chemical weathering index The degree of chemical weathering of Icelandic dust was calculated based on the elemental composition. This is important to evaluate the presence in the samples of secondary minerals such as clays. The chemical index of alteration (CIA)  (Nesbitt and Young, 1984)  was considered most suitable for the present datasets based on the chemical compositions. The CIA is interpreted as a measure of the degree of weathering of aluminium silicate minerals (in particular feldspars) into clay minerals. In this study, the CIA was calculated according to  Price and Velbel (2003)  using the molecular proportion of the element oxides, which were obtained by dividing the weight percentage of each oxide by the molecular weight of the oxide: CIA= Al 2 O 3 Al 2 O 3 + CaO + Na 2 O + K 2 O × 100.
X-ray absorption near edge structure (XANES) analysis To qualitatively examine the Fe speciation in the dust samples, XANES spectra at the Fe K-edge were collected at the Diamond Light Source beamline I18. A Si(111) doublecrystal monochromator was used in the experiments. The beam size was 400 µm × 400 µm. The XANES spectra were Note that the data uncertainty was estimated using the error propagation formula: ∼ 12 % for the elemental oxide mass percentage and ∼ 14 % for CIA and the element ratios. collected from 7000 to 7300 eV at a resolution varying from 0. 2 eV for 3 s in proximity to the Fe K-edge (7100-7125 eV) to 5 eV for 1 s from 7100 to 7300 eV. Powder samples were suspended in methanol and deposited on Kapton ® tape. Filter samples were loaded without prior preparation. The analysis was repeated three times.
We measured the XANES spectra of PM 20 fractions and mineral standards, including hematite and goethite standards, magnetite, feldspar standards (Clay Mineral Society), natural pyroxene, and olivine. Data were processed using the Athena program, part of the software package Demeter (version 0. 9. 26)  (Ravel and Newville, 2005). For comparison, we also collected the XANES spectra for mineral dust from the western Sahara and Mali regions  (Shi et al. , 2011b).
The total mass is calculated as the sum of the element oxides mass from the XRF measurements. The densities of the crystalline phases are from http://webmineral. com/ (last access: 2 January 2020). The density of the amorphous glass was estimated for each sample as ρ glass = ρ dust - j w j CR × ρ j CR w glass , (4) where ρ dust is the density of the sample, ρ j CR and w j CR are respectively the density and the mass fraction of the crystalline phases, and w glass is the mass fraction of glass. The density of the samples (fraction <63 µm) was measured by a He pycnometer and varied from 2. 80 to 3. 1 g cm -3. The volume-average refractive index was calculated at selected wavelengths (470, 520, 590 and 660 nm). The reference complex refractive indices of the individual minerals are reported in Table  S2 ; when necessary the original data were interpolated at the selected wavelengths.
Discussion Our data indicate that Icelandic dust has a basaltic composition with relatively high Fe 2 O 3 and TiO 2 contents (Table  1 , Fig. S3 ). The principal crystalline mineral phases observed are Ca-rich plagioclase and pyroxene, while the amorphous fraction, mostly aluminosilicate glass, is 60 %-90 % (except for the Hagavatn hotspot, which is only 15 %). Glacial processes produce fine sediments (glacial flour), which accumulate into glaciofluvial floodplains where the dust hotspots are located  (Arnalds, 2010; Arnalds et al. , 2016). Our results are consistent with the composition of the volcanic systems supplying the dust hotspots: Bárðarbunga and Kverkfjöll volcanic systems (Dyngjusandur), Katla volcanic system (Mýrdalssandur, Maelifellssandur and Landeyjasandur), and Eldborgir and Skersli volcanic centres (Hagavatn)  (Arnalds et al.
Implications for the soluble Fe deposition to the ocean The subpolar North Atlantic Ocean including the Iceland Basin is seasonally Fe limited  (Moore et al. , 2006; Nielsdottir et al. , 2009; Ryan-Keogh et al. , 2013). Achterberg et al. (2013)  reported that deposition of volcanic ash from the 2010 Eyjafjallajökull eruption caused a significant perturba- Fe psol /FeT (%) performed using the standard major axis regression. The regression line in blue was calculated including the whole dataset: Icelandic dust (this study) and northern African dust  (Shi et al. , 2011b). The regression line in light blue was calculated using only the northern African dust data  (Shi et al. , 2011b). tion to the biogeochemistry of the Iceland Basin. Elevated dissolved Fe concentration and nitrate depletion were observed locally followed by an early spring bloom  (Achterberg et al.
Here we provide a comprehensive dataset including chemical composition, mineralogy, Fe speciation and Fe solubility of Icelandic dust, which can be fed to global models to estimate the flux of atmospheric soluble Fe to the sub-Arctic and Arctic oceans and to assess the radiative effect of Icelandic dust. Our analysis suggests that the shortwave absorption properties of Icelandic dust are determined by the high magnetite content, possibly leading to a stronger direct positive radiative effect in the Arctic than previous estimates. The original measurements of the spectral optical properties together with the size distribution conducted at CESAM will provide further insight into the absorption properties and long-range transport of Icelandic dust, which is necessary to estimate the radiative impact of Icelandic dust and its contribution to Arctic warming.
Figure7. Correlation of the chemical index of alteration (CIA) with (FeA + FeD)/FeT (%) performed using the standard major axis regression. Data for African dust are fromShi et al. (2011b). The regression line in blue was calculated including the whole dataset: Icelandic dust (this study) and northern African dust(Shi et al. ,  2011b). The regression line in light blue was calculated using only the northern African dust data(Shi et al. , 2011b).
Acknowledgements. Clarissa Baldo  is funded by the  Natural Environment Research Council (NERC) CENTA  studentship (grant no. NE/L002493/1 ). This paper is partly funded by the  NERC  highlight topic project ( NE/S00579X/1 ). This project has received funding from the  European Union 's  Horizon 2020 research and innovation programme  through the  EUROCHAMP-2020 Infrastructure Activity  under grant agreement No  730997. Part of this work was sup-.
Financial support. support This research has been supported by the Natural Environment Research Council (grant no. NE/L002493/1), the Natural Environment Research Council (grant no. NE/S00579X/1), the European Commission, Horizon 2020 (grant no. EUROCHAMP-2020 (730997)), the European Cooperation in Science and Technology (grant no. CA16202) and the Icelandic Research Fund (Rannís) (grant no. 207057-051).
In the data reported to date, results from such pseudovirus neutralization assays have correlated well with measurements made using live SARS-CoV-2  [1, 12, 27, 39]. However, the detailed protocols and reagents to perform such assays are not yet widely available to the scientific community. Here, we filled this gap by providing a detailed description of how to pseudotype lentiviral particles with Spike. We explained how these pseudotyped particles could be used to conveniently measure Spike-mediated cell entry via fluorescent or luciferase reporters, and to quantify the neutralizing activity of human plasma. Finally, we described all the necessary experimental reagents and make them available in the BEI Resources reagent repository (https://www. beiresources. org/).
Plasmids The sequences of all plasmids used in this study are available in Genbank format in Supplementary File S1 and are also at https://github. com/jbloomlab/SARS-CoV-2_lentiviral_ pseudotype/tree/master/plasmid_maps. The plasmids themselves are available in BEI Resources (https://www. beiresources. org/) with the following catalog numbers: • pHAGE2-EF1aInt-ACE2-WT (BEI catalog number NR52512): Lentiviral backbone plasmid expressing the human ACE2 gene (GenBank ID for human ACE2 is NM_021804) under an EF1a promoter with an intron to increase expression.
• HDM-IDTSpike-fixK-HA-tail (BEI catalog number NR52513): Plasmid expressing under a CMV promoter the Spike from SARS-CoV-2 strain Wuhan-Hu-1 (Genbank NC_045512) codon-optimized using IDT, with the Spike cytoplasmic tail replaced by that from the HA protein of A/WSN/1933 (H1N1) influenza, and the Kozak sequence in the plasmid fixed compared to an earlier version of this plasmid. • HDM-IDTSpike-fixK (BEI catalog number NR-52514): Plasmid expressing under a CMV promoter the Spike from SARS-CoV-2 strain Wuhan-Hu-1 (Genbank NC_045512) codon-optimized using IDT and the Kozak sequence in the plasmid fixed compared to an earlier version of this plasmid.
• HDM-nCoV-Spike-IDTopt-ALAYT (BEI catalog number NR-52515): plasmid expressing under a CMV promoter the Spike from SARS-CoV-2 strain Wuhan-Hu-1 (Genbank NC_045512) codon-optimized using IDT, with the Spike containing two mutations in the cytoplasmic tail such that the last five amino acids are ALAYT. • pHAGE-CMV-Luc2-IRES-ZsGreen-W (BEI catalog number NR-52516): Lentiviral backbone plasmid that uses a CMV promoter to express luciferase followed by an IRES and ZsGreen. Note that all of these plasmids have ampicillin resistance. The only plasmid used in this study that was not in the BEI Resources catalog is the HDM-VSVG plasmid that expressed VSV G under a CMV promoter, and was used to create the positive control lentivirus pseudotyped with VSV G. However, numerous VSV G expressing plasmids are available from AddGene and other repositories.
8. Plot the data. For our analysis, we first subtracted out the background signal (average of the "virus only" and "virus + 293Ts" wells) and then calculated the "maximum infectivity" for each plate as the average signal from the wells without serum ("virus + cells" wells). We then calculated the "fraction infectivity" for each well, as the luciferase reading from each well divided by the "maximum infectivity" for that plate. For the curves shown in Figure  4 , we then fit and plotted the fraction infectivity data using the neutcurve Python package (https://jbloomlab. github. io/neutcurve/). This package fits a three-parameter Hill curve, with the top baseline being a free parameter and bottom baseline fixed to zero. Waltham, MA, USA). iv. Incubate plate for 2 min at room temperature in the dark, then measure luminescence using a plate reader.
Women still earn less than men and continue to perform the bulk of domestic activities. Several studies documented a negative individual wage-housework relation, suggesting that gender discrepancies in housework may explain the gender wage gap. Less attention has been paid to the role of the partner's unpaid work and to the extent that intra-household inequalities relate to inequalities outside the house. The present study attempts to fill this gap in the literature. We exploit EU-SILC 2010 data for Germany and Italy and PSID 2009 data for the US. Results suggest the importance of accounting for a partner's housework when evaluating the determinants of individual wages and the gender wage gap. Women seem not to profit from their partners' housework; instead, women's non-market work increases their partners' earnings while decreasing their own earnings.
Data and sample The empirical analysis exploits 2009 PSID data for the US and EU-SILC data from the 2010 ad hoc module on the intra-household sharing of resources for Germany and Italy. The EU-SILC ad hoc module is one of the few datasets that provide information on the time spent on household activities by both members of a couple and contains a large set of individual and job-related variables. Unfortunately, it is not possible to link these data with the panel dimension of EU-SILC and a sufficiently high number of observations is available only for Germany and Italy. 3  We restricted the sample to heterosexual, married/cohabiting, and employed individuals between the ages of 25 and 59, but also included non-working women to control for female selection into employment. We focused on employees 4 and excluded those who spent less than five or more than 60 weekly hours on market activities and more than 70 weekly hours on housework  (Hersch and Stratton, 2002).
18 The contributors have worked both on methodology and applications of QSAR modeling for most of their professional life. Some of the co-authors have pivoted their research into other areas where QSAR-like approaches have not been used before, illustrating the main theme of this paper by their own careers. We engaged other scientists who work in areas where data modeling was not common but who have started using QSAR-like methods in their research. We are confident that many fields that employ statistical modeling approaches will benefit significantly from the experience accumulated within the QSAR community in the last 55 years. We start this contribution by discussing fundamental concepts of QSAR, such as chemical similarity. We describe the impact of recent advances, such as deep learning (DL), on traditional areas of QSAR modeling, such as drug discovery and development and chemical safety prediction.
We then reflect how the complexity of algorithms and the size, diversity, and complexity of chemical bioactivity data have grown. We also illustrate how modern computational methods are capable of modeling multiple bioactivity endpoints simultaneously, addressing the issue of multi-objective optimization. We then extend traditional boundaries of QSAR by summarizing recent, exciting developments in organic synthesis planning and retrosynthetic pathway prediction, advances in robotic chemistry, and applications of machine learning to quantum chemistry. Finally, to further illustrate the breadth of applicability of modern QSAR approaches, we discuss their use in materials and nanomaterials science, regenerative medicine, and health care. Throughout the discussion, we identify methodological similarities between drug discovery approaches and those employed in other areas.
We further propose that experience and best practice of data curation, model development, and validation accumulated by the QSAR community provides valuable guidance for many areas where statistical and machine learning data modeling is applied. This broad, platform applicability of QSAR algorithms and protocols across all data-rich areas of modern science underpins the appeal of QSAR as a robust, predictive data analysis and modelling tool. We advise contemporary chemists to become familiar with the major computational approaches discussed in this contribution. To this end, borrowing from a recent ''In the Pipeline'' blog by Derek Lowe,  19  ''it is not that machines are going to replace chemists. It's that the chemists who use machines will replace those that don't''! We hope that this paper will stimulate experimental scientists to consider deeper integration of computational methods and models into their research projects, to consider how the data they generate will be modelled when planning experiments and will serve as useful reference for computational chemists as well.
Clearly, QSAR modeling is an established and useful computational chemistry approach. However, many practitioners still consider it limited to modeling and prediction of chemical bioactivities and/or properties. One aim of this Perspective is to outline the opportunities presented by recent and emerging developments in artificial intelligence (AI), machine learning (ML) and other approaches to modeling Big Data within the traditional QSAR modeling. However, our prime objective is to emphasize the impact that QSAR methods and approaches have, or will shortly have, on many modern data-driven areas of molecular research beyond traditional QSAR areas. We called this paper QSAR without borders, to emphasize the plausible impact that many data modeling approaches developed and practiced by the QSAR community may have on many areas of the scientific pursuit.
Chemical similarity Classical QSAR is defined by linear (regression) models derived from a set of small molecules sharing the same (target-specific) biological activity. A QSAR model predicts changes in potency as a function of structural modifications. 1, 20 The evolution of QSAR modeling from linear to more complex machine learning models addressing non-linear relationships between chemical structure and bioactivity was discussed in a paper co-written by one of the founders of classical QSAR, Prof. Toshio Fujita in 2016. 20 Chemical bioactivity data employed in model development are generally derived from investigations of analog series from medicinal chemistry. These sets of compounds usually share a common core structure (scaffold) and carry different substituents (R-groups) at one or more sites.
In contrast, SAR discontinuities  21   This journal is © The Royal Society of Chemistry 2020 small structural modifications lead to very large biological potency changes, not consistent with the SPP and falling outside the applicability domain of linear QSAR models. Fig. 3  shows small sets of active compounds that are characterized by SAR continuity and discontinuity, respectively. ''Activity cliffs'' are formed by analogs displaying the largest potency differences in a compound series for the smallest change in structure. 23 The existence of activity cliffs in compound data sets is a major factor limiting QSAR predictions, often much greater than intrinsic limitations of modeling. 23 Strikingly similar observations have also been made in bioinformatics where some pairs of proteins with high sequence similarity possess very different structures and functions.
24 This analogy is one of many that methodologically bridge between QSAR and other fields that rely on data analytics. It should be noted that activity cliffs may be sensitive to both the choice of descriptors and the degree of the experimental variability. Importantly, SAR discontinuity limits QSAR modeling regardless of molecular representations and descriptors that are used when the corresponding compounds are close structural analogs. Activity landscapes of compound data sets might be ''flattened'' by using large numbers of features as molecular representations such that compounds become increasingly dissimilar (i. e. , their distances in feature space increase). However, introducing artificial dissimilarity results in a loss of SAR information (and often leads to overfitting of regression models).
occur when In QSAR modeling the presence of SAR continuities and discontinuities in sets of active compounds is not mutually exclusive. Rather, continuous and discontinuous SARs coexist in many data sets  21  resulting in the presence of adjacent gently sloped and rugged regions in activity landscapes (Fig. 3 ). Focusing potency predictions around local regions of SAR continuity can often lead to QSAR models with high predictive power. To this end, numerical SAR analysis methods can be used to identify compound subsets having desirable SAR characteristics. 25 Numerical similarity in QSAR is mostly quantified using the Tanimoto coefficient or related similarity metrics, which provide continuous similarity values, typically in the interval [0,1]. Numerical measures assess whole-molecule similarity on the basis of chosen descriptors.