dataesr/scientific-paragraphs-categorization huggingface dataset:
Representation of [Cu(BINap-SQ2)] obtained from very poorly diffracting crystals Despite several recrystallization attempts, diffraction efficiency of the obtained single crystals was very weak. Nevertheless, a single crystal was selected, mounted onto a cryoloop and transferred into a cold nitrogen gas stream. One phi-scan was launched to collect data for unit-cell parameters determination. The measure was performed at 200K with a Bruker Kappa APEXII CCD diffractometer using a graphite-monochromated Mo-Kα radiation (λ = 0. 71073 Å) and Bruker APEX2 suite. The 2θmax value did not exceed 32°. Unit cell determination was carried out with SAINT program. A data integration with SAINT and scaling and absorption correction with SADABS were performed with this incomplete dataset. S7. S8. Representative procedure for the aziridination Scheme S6.
ACKNOWLEDGMENTS The authors would like to thank Sorbonne Universite(SU), Universitede Strasbourg, CNRS, IR-RPE CNRS FR3443 RENARD network (CW X-band EPR with Dr. J. -L. Cantin, INSP UMR CNRS 7588, SU and X-band EPR in Lille). SU is acknowledged for an Emergence grant (M. D. -E. M. and K. C. ) and CSC for a PhD fellowship (Y. R. ). The authors gratefully acknowledge the COST Action 27 CM1305 ECOSTBio and FrenchBIC.
The greatly accelerated economic growth during the Anthropocene has resulted in astonishing improvements in many aspects of human well-being, but has also caused the acceleration of risks, such as the interlinked biodiversity and climate crisis. Here, we report on another risk: the accelerated infectious disease risk associated with the number and geographic spread of human infectious disease outbreaks. Using the most complete, reliable, and up-to-date database on human infectious disease outbreaks (GIDEON), we show that the number of disease outbreaks, the number of diseases involved in these outbreaks, and the number of countries affected have increased during the entire Anthropocene. Furthermore, the spatial distribution of these outbreaks is becoming more globalized in the sense that the overall modularity of the disease networks across the globe has decreased, meaning disease outbreaks have become increasingly pandemic in their nature.
, 2014). To expand the previous analysis  (Smith et al. , 2014)  to the beginning of the Anthropocene, we investigated whether the number of disease outbreaks has increased since the Second World War. In addition, we examined whether the global pattern of infectious disease outbreaks changed possibly due the increasing connectivity of human populations. In other words, have the disease outbreaks. CC-BY-NC-ND 4. 0 International license made available under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is The copyright holder for this preprint this version posted April 20, 2020. ; https://doi. org/10. 1101/2020. 04. 20. 049866 doi: bioRxiv preprint 4 become more globalized in the sense that these outbreaks are increasingly shared by countries worldwide? To investigate these questions, we used a the most complete, reliable, and up-to-date global dataset (GIDEON Informatics, 2020) which had already been used in the previous analysis  (Smith et al.
, 2014). This dataset can be used to enumerated the recorded annual number of disease outbreaks. To investigate the changing global patterns of disease outbreaks, we used this dataset to calculate two measures which have been recently introduced into ecological and parasitological studies. These two measures, namely modularity and centrality, quantify the connectivity of bipartite networks. Modularity is defined as the extent to which nodes (specifically, sites and species for presenceabsence matrices) in a compartment are more likely to be connected to each other than to other nodes of the network  (Thébault, 2013). The calculation of a modularity measure is useful for global phenomena because it allows the overall level of compartmentalization (or fragmentation) into compartments (or clusters, modules, subgroups, or subsets) of an entire dataset to be quantified.
Eigenvector centrality considers countries to be highly central if the connected countries to them through shared outbreaks are connected to many other well-connected countries  (Bonacich and Lloyd, 2001; Wells et al. , 2020). Modularity and centrality analyses have been used to investigate various ecological, parasitological and epidemiological questions (e. g. ,  Tylianakis et al. , 2007; Jordán, 2009; González et al. , 2010; Anderson and Sukhdeo, 2011; Bascompte and Jordano, 2014; Poisot et al. , 2014; Bordes et al. , 2015; Genrich et al. , 2017). Using a widely used world dataset on infectious disease outbreaks, we here present results which demonstrate that the accelerated number of disease outbreaks and their increased global spread are two further threatening aspects of the accelerated infectious disease risk associated with the globalization process which characterizes the Anthropocene.
Methods Data on GDP and mobility (trade, traffic, transport, travel, and tourism). The data on gross domestic product (GDP) per capita was taken from the World Bank database. It was calculated by dividing a country's GDP by its midyear population; the numbers are in current United States of America (USA) dollars from 1961 to 2018. As measures of mobility, we searched for various numerical examples of the increase in global trade, traffic, transport, travel, and tourism during the Anthropocene. The data on the air freight and air passengers were taken from the World Bank database  (World Bank, 2020a; World Bank, 2020b). The database yielded the number of air passengers and the air freight in million metric tons (MT) per km from 1970 to 2017 separately for each country. The data on container movements were provided by Container Trades Statistics and the Institute of Shipping  Economics and Logistics (in litt.
Data on infectious disease outbreaks. To collate the total number of infectious disease outbreaks over the years 1940-2018, we extracted the relevant data from the medical database called GIDEON (GIDEON Informatics, 2020) which contains information on the presence and occurrence of epidemics of human infectious diseases in each country as well as the number of surveys conducted in each country. The GIDEON data are curated as records of confirmed outbreaks, are continually updated using various sources such as WHO and Promed, and are accessible via subscription. This dataset is generally considered to be the most complete, reliable, and up-to-date in the world and has been regularly used in previous macro-scale studies of infectious disease, epidemics, and pathogen diversity (e. g. ,  Smith et al.
, 2007; Dunn et al. , 2010; Morand et al. , 2013; Morand et al. , 2014; Poisot et al. , 2014; Smith et al. , 2014; Morand, 2015; Morand and Walther, 2018). Each row in the GIDEON dataset specifies the disease 'species', the year and the country of the outbreak. The 'annual total outbreak number' is simply the annual total number of outbreaks regardless of the disease designation and including all countries. The 'annual total disease number' uses the same data for each year as the 'annual total outbreak number' but then counts only the different infectious diseases which had at least one outbreak in that respective year. The 'annual total country number' uses the same data for each year as the 'annual total outbreak number' but then counts only the different countries which had at least one outbreak in that respective year.
Statistical analyses. In our case, we built yearly bipartite networks of presence-absence matrices which link countries with all the recorded epidemic outbreaks. We then transformed these bipartite networks where separate nodes from countries were connected with nodes of epidemic outbreaks into unipartite networks using the tnet package  (Opsahl, 2009)  in R (R Development Core Team, 2019). The calculation of modularity of bipartite networks of shared epidemic outbreaks among countries allowed us to identify modules of countries that share common epidemic outbreaks in each respective year (see Introduction)  (Blondel et al. , 2008; Bordes et al. , 2017). We calculated our modularity measure of unipartite network for each year using igraph  (Lehoucq et al. , 2019). High modularity calculated in this context means that an epidemic remains relatively constrained within a few countries while low modularity means that an epidemic has spread across relatively more countries.
Limitations of this study Before we discuss the implications of our results, we address possible limitations and biases. While GIDEON is generally acknowledged to be the most complete, reliable, and up-to-date global dataset on infectious disease outbreaks, we nevertheless should consider that (1) there may have been some underreporting in the early part of the Anthropocene, and (2) recent outbreaks may not have been entered into GIDEON yet. (1) There may have been some underreporting of infectious disease outbreaks during the early parts of the Anthropocene in developing countries. However, the imposition in the 1980s of the socalled Washington Consensus imposed by the International Monetary Fund, World Bank and United States Department of the Treasury and the resulting the structural adjustment programs dramatically decreased the public health and education capabilities of developing countries (e.
Acknowledgements This work was part of theFutureHealthSEA project funded by the  French ANR  ( ANR-17-CE35-0003-01 ). S. M. is supported by the  Thailand International Cooperation Agency (TICA)  " Animal Innovative Health ". We sincerely thank  Dieter Stockmann  from the  Institute of Shipping Economics and Logistics (ISL)  and  Claire Thackeray  from  Container Trades Statistics (CTS)  for providing data about container movements,  Jean Tournadre  for providing data about ship numbers, and  Ting-Wu Chuang  for providing references.
The WAT scenes represent 14 % to 19 % of all the IIR pixels. Clear-sky conditions are defined as cloud-free scenes with the "Was Cleared" flag at 1 km resolution equal to zero, with no aerosol layers or only low (<7 km) semi-transparent "not dusty" layers. Dusty layers are those identified as dust, polluted dust, or dusty marine  (Kim et al. , 2018)  and are discarded because they may have a signature in the IIR channels  (Chen et al. , 2010). For comparison with the previous categories, the clear-sky conditions represent 20 % of the cases for daytime data and 15 % for nighttime data. It is noted that 6 % to 10 % of the pixels are rejected as "clear sky" in V4 due to the presence of single-shot cleared clouds. These pixels would have been accepted by the V3 algorithm: they represent 25 % and 35 % of the V3 clear-sky conditions for daytime and nighttime data, respectively.
05 µm and 10. 6 µm (τ a,12 + τ a,10 ; Part I), are shown in Sect. 3. 2. The subsequent improvements in the microphysical indices and in the performance of the microphysical algorithm are discussed in Sect. 3. 3, where we also illustrate changes in the effective diameters (D e ) reported in V3 and V4. We recall that D e is defined as D e = (3/2) × (V /A), where V is the total volume of the size distribution and A is the corresponding projected area  (Foot, 1988; Mitchell, 2002). The V4 algorithm uses two ice habit models from the "TAMUice2016" database  (Bi and Yang, 2017; Yang et al. , 2013) , namely the severely roughened solid column (SCO) and severely roughened eight-element column aggregate (CO8) models, and the model used for the retrievals is selected according to the relationship between β eff 12/10 and β eff 12/08.
IIR retrieved D e is the mean of the D e 12/10 and D e 12/08 effective diameters when these two values can be retrieved from the respective β eff 12/k; that is, D e = (D e 12/10 + D e 12/08) /2. Both D e 12/10 and D e 12/08 are reported in the product for users interested in specific analyses. The V4 look-up tables (LUTs) that relate microphysical index and effective diameter are computed using the fast discrete ordinate method (FASDOM)  (Dubuisson et al. , 2008)  model and bulk single scattering properties derived using an idealized gamma particle size distribution. In V3, the LUTs were derived using single scattering properties of the "solid column" and "aggregate" ice habit models from the database described in  Yang et al. (2005) , with no particle size distribution. We showed in Part I that, everything else being equal, the size distribution introduced in V4 increases retrieved D e.
As illustrated in Part I, the microphysical indices are very sensitive to D e smaller than 50 µm and the sensitivity decreases progressively up to D e = 120 µm, which is considered the sensitivity limit of our retrievals in ice clouds. In Sect. 3. 4, we show examples of V4 D e and ice water path (IWP) microphysical retrievals and comparisons with MODIS retrievals are presented in Sect. 3. 5. https://doi. org/10. 5194/amt-14-3277-2021 Atmos. Meas. Tech. , 14, 3277-3299, 2021 Because of numerous changes in the CALIOP V4 algorithms, the cloud layers reported in the V3 and V4 CALIOP data products are not identical, so direct comparisons of the V3 and V4 IIR data products could be misleading. In order to isolate the changes due to the IIR algorithm, the V3 emissivities (hereafter V3_comp) for clouds reported in CALIOP V4 were recomputed using the V3 computed background radiances reported in the V3 product and the V3-like blackbody temperatures derived directly from the centroid temperatures, T c , which are available in the V4 product along with the V4 blackbody temperatures.
Inter-channel effective emissivity differences We recall that effective emissivity retrievals preferably use background radiances observed in neighboring clear-sky pixels and otherwise use radiances computed by FASRAD. In order to evaluate V4 computed background radiances, we first examined ε eff 12 -k at ε eff,12 ∼ 0 in ST clouds by separating retrievals that used observed radiances (V4_obs) and those that used computed radiances (V4_comp). The results are reported in Table  3 , where ε eff 12 -k at ε eff,12 ≈ 0 is also reported for V3_comp for reference. As in V3 (G13), V4 inter-channel biases are minimum when the background radiance can be determined from observations (V4_obs), which represents 30 % of the retrievals in ST clouds for this dataset. When the background radiance is computed (V4_comp, 70 % of the cases), median ε eff 12 -k is similar for both channel pairs and smaller than 0.
This peak compares well with the mode at 36 µm noted by  Dolinar et al. (2019)     for single-layered ice clouds with no detectable precipitation as retrieved using the combined CloudSat-CALIPSO 2C-ICE product. IWP (Fig. 8b ) is found between 0. 03 and 100 g m -2 in ST clouds, with the slightly larger daytime values being explained by the cloud selection and the slightly larger optical depths in the daytime dataset (Table  4 ). The medium val-ues are around 3 g m -2 , with peaks in the distributions at 3 and 8 g m -2 for nighttime and daytime data, respectively, and the median relative uncertainty is 50 %. As noted by  Berry and Mace (2014) , the CloudSat radar is typically insensitive to these thin layers, so microphysical retrievals in combined CloudSat-CALIPSO products such as 2C-ICE rely on parameterization of the radar reflectivity  (Deng et al.
, 2015)  rather than on actual observations. Combining CALIOP and IIR observations appears to be a suitable alternative approach to characterize these thin layers. The estimated cloud radiative temperature (T r ) is at an equivalent altitude located between the CALIOP cloud base and cloud top (Part I). While in the case of ST clouds, IIR D e is a layer average diameter, IIR D e in opaque clouds is mostly representative of the portion of the cloud seen by CALIOP before the signal is totally attenuated. These opaque clouds have median ε eff,12 equal to 0. 95 at night but only 0. 86 for daytime data, with median IIR τ vis equal to 5. 6 and 3. 8, respectively. Median D e in opaque clouds is around 60 µm and the distributions peak at 50 µm. It is larger than in ST clouds, which is consistent with retrievals based on AIRS thermal infrared data  (Guignard et al.
Even though this was not the original intent, comparing median D e with or without setting N (D) 1 to 0 also illustrates the impact of possible vertical inhomogeneities of D e within the cloud layer  (Zhang et al. , 2010). Nevertheless, the overall impact of vertical variations on β eff 12/10 also depends on the in-cloud IIR     2018 ) during the SPARTICUS (blue) and TC4 (red) field experiments using N (D) 1 unmodified (solid) or N(D) 1 = 0 (dashed). This is the same dataset as the one in Fig. 9. for ST (Fig. 12c ) and opaque (Fig. 12e ) clouds. The vertical lines are between the 25th and 75th percentiles. Similarly, Fig. 12d  and f show the corresponding τ vis values. Only one MODIS τ vis is shown because the retrievals from both pairs of MODIS channels are nearly identical. For ST clouds, MODIS 2.
1 D e is larger than IIR by 15 µm on average. IIR and MODIS 3. 7 D e are in good agreement for T r <205 K, where D e is <40 µm and IIR τ vis is <0. 5, and they progressively depart from each other as T r increases and MODIS 3. 7 increases and approaches MODIS 2. 1. MODIS τ vis is larger than IIR by 0. 3 to 0. 2. This small but systematic bias is not seen when comparing CALIOP and IIR (not shown). The MODIS 2. 1 D e -T r relationships are similar for ST and opaque clouds, which is not the case for MODIS 3. 7 and IIR. For opaque clouds, IIR D e is larger than in ST clouds and is in good agreement with MODIS 2. 1 at T r <225 K. MODIS 3. 7 D e exhibits a similar increase with temperature as seen with the two other datasets, but it is shifted by -10 µm. At T r >225 K, MODIS D e 2. 1 continues to increase up to 100 µm at 255 K, whereas IIR remains stable around 60 µm and MODIS 3.
Comparisons with MODIS IIR confident retrievals in liquid water clouds were compared with MODIS Collection 6 retrievals from the visible-2. 1 µm and visible-3. 7 µm pairs of channels for clouds also classified as liquid water by MODIS. The results are shown in Fig. 17 , following the same presentation as in Fig. 12  for ice clouds. Again, cloud centroid altitude is chosen to be higher than 4 km, and, as previously for ice clouds, the comparisons shown in Fig. 17c -f are limited to those pixels for which the IIR, MODIS 2. 1 and MODIS 3. 7 retrievals (orange curves in Fig. 17a  and b ) were all successful. As seen in Fig. 17a , T r spans between 235 and 280 K, and most of these sampled clouds are composed of supercooled droplets. In ST clouds, the three datasets show an increase of median D e (Fig.
Acknowledgements. The authors are grateful to  NASA LaRC, SSAI (Science Systems and Applications, Inc. ), the  Centre National d'Etudes Spatiales (CNES) , and  Institut National des Sciences de l'Univers (INSU)  for their support. We thank the  AERIS infrastructure  for providing access to the CALIPSO products and for data processing during the development phase. We thank  Brian Getzewich  and  Tim Murray  for the processing of the version 4 IIR Level 2 data at  NASA LaRC. Review statement. This paper was edited by  Vassilis Amiridis  and reviewed by four anonymous referees.
Data availability. The version 3 IIR Level 2 track products used in this paper are available at https://doi. org/10. 5067/IIR/CALIPSO/ L2_Track-Beta-V3-01 (last access: 14 September 2020) (NASA,  2011)  and the version 4 IIR Level 2 track products are available at https://doi. org/10. 5067/CALIOP/CALIPSO/CAL_IIR_L2_ Track-Standard-V4-20 (last access: 14 September 2020) (NASA, 2020). The IIR Level 2 track products are also available from the AERIS/ICARE Data and Services Center (http://www. icare. univ-lille. fr, AERIS/ICARE, last access: 22 April 2021) (AERIS/ICARE, 2021). For comparisons with MODIS Collection 6, co-location and MODIS visible-2. 1 µm data are from the CALTRACK-5km_MYD06. v1. 01 products and MODIS visible-3. 7 µm data were extracted from the Collection 6 MYD06 products. These products are available from the AERIS/ICARE Data and Services Center (http://www.
Acknowledgements The authors would like to thank  Patricio Ledesma Figueroa ,  Araceli Rodriguez Morales  and  Emanuela Marchesi  for data management. Contributors Conception and design of the study: JM-B and JAL-M. Collection and assembly of clinical data: JM-B, NH, GG, JM-T, AR, CV, SS, AL-P, LD, IC-G and JAL-M. Collection and execution of correlative studies: JM-B and DSM. Data analysis and interpretation of results: JM-B, NH, AG, HP-V, VE-T, EdA, PC, MP-C, JD, ML-A, DSM and JAL-M. Writing and revision of the manuscript: all authors. Manuscript final approval: all authors. Funding This work was supported by  GEIS  and  ISG. BMS  and  Pfizer  provided drug supply and partial funding for shipping. Translational studies were partially funded by  Beca Buesa  from the  GEIS group. Competing interests JM-B reports research grants from  PharmaMar ,  Eisai ,  Immix BioPharma  and  Novartis , outside the submitted work; honoraria for advisory board participation and expert testimony from  PharmaMar ,  Eli Lilly and Company, Bayer  and  Eisai ; and research funding for clinical studies (institutional) from  PharmaMar ,  Eli Lilly and Company, AROG, Bayer ,  Eisai, Lixte, Karyopharm, Deciphera ,  GSK ,  Novartis, Blueprint, Nektar, Forma, Amgen and Daiichi Sankyo.
EdA reports personal fees and non-financial support from  Roche ,  BMS  and  PharmaMar , and personal fees from  Bayer , outside the submitted work. ML-A declares institutional research grants from  PharmaMar ,  Eisai ,  Immix BioPharma  and  Novartis , outside the submitted work. DSM reports institutional research grants from  PharmaMar ,  Eisai ,  Immix BioPharma  and  Novartis , outside the submitted work, and travel support from  PharmaMar ,  Eisai ,  Celgene ,  Bayer  and  Pfizer. JAL-M reports honoraria for advisory board participation and travel support from  PharmaMar ,  Eli Lilly and Company, Bayer ,  Eisai ,  Novartis ,  BMS ,  MSD ,  Roche ,  Celgene ,  Pierre Fabre ,  Pfizer ,  GSK ,  Daiichi Sankyo, Amgen , and  Chobani. All  other authors declare no relevant relationship to disclose.
Genomic studies have provided a more precise estimation of the genomic alterations present in CDH1, the gene coding for E-cadherin  (5) (6) (7) (8) : approximately 90% of ILC samples have heterozygous CDH1 deletion, mostly characterized by the loss of the long arm of chromosome 16, and approximately 60% exhibit a mutation in the other allele. Researchers have recently used this characteristic as a potential therapeutic opportunity  (10). Indeed, inhibiting ROS1 is synthetically lethal in cells with CDH1 defects, and this strategy is currently being explored in a phase II trial (ClinicalTrials. gov Identifier: NCT03620643). In addition, the various genomic studies have further highlighted the increased prevalence of mutations in ER þ /HER2 À primary ILC in several genes such as AKT1, ARID1A, CDH1, FOXA1, HER2, HER3, PIK3CA, and TBX3 genes as compared with ER þ /HER2 À primary IDC  (5) (6) (7) (8).
A recent study used data generated by Foundation Medicine on unmatched primary or metastatic samples from patients with metastatic ILC and reported a critical role of NF1 in mediating endocrine resistance  (17). Our group investigated ESR1 mutations in metastatic samples from patients with ER þ ILC and reported a similar frequency and distribution as compared with metastatic samples from patients with ER þ IDC  (18). While immunotherapy based on checkpoint inhibition is currently being tested in patients with metastatic ILC (ClinicalTrials. gov Identifier: NCT03147040), the levels of immune infiltration in ILC metastases remain undetermined. Here, we therefore first centrally characterized the levels of sTILs in a unique European multicentric series of matched primary and metastatic samples from patients with metastatic ER þ ILC, further referred to as EuroILC.