dataesr/scientific-paragraphs-categorization huggingface dataset:
They proposed a network of gephyrin molecules linking the glycine receptors. Unfortunately, only the structure of five amino acids of the receptor was resolved, so it is difficult to draw any conclusion from this dataset. Gephyrin also interacts with the GABA A receptor through its α2-subunit  33  and α3-subunit. 34 It is unclear if gephyrin binds the α1-subunit of the GABA A receptor; some experiments failed to show any interaction,  35  but others showed a weak interaction. 36 Maric et al. 37  co-crystallized segments of the α3-subunit of the GABA A receptor with segments of gephyrin, and identified the undecapeptide T 367 FNIVGTTYPIN 381 from the GABA A receptor as important for interaction with gephyrin. They showed that there were similarities between the binding of the GABA A receptor and of the glycine receptor to gephyrin: T 367 FNIVGTT 374 from the GABA A receptor, and F 398 SIVGSL 404 the glycine receptor β-subunit adopted similar conformations.
The key GABARAP amino acids involved are Ile 21, Tyr 25, Ile 32, Lys 46, Lys 48, Tyr 49, Leu 50, Phe 60, and Leu 63 (PDB dataset 3DOW). The ALFY dodecapeptide 27  binds to GABARAP overlapping sites 32 and 33, and surrounding site 11 (PDB dataset 3WIM). The KBTBD6 undecapeptide 28  binds to GABARAP overlapping sites 11, 32, and 33, and surrounding sites 12 and 41 (PDB dataset 4XC2).
Dictionary of hydration patches used for protein-protein interactions from PDB files related to GABARAP. The first half of the table lists the interaction between GABARAP and another protein, with the relevant PDB dataset or relevant publication shown in parenthesis. The second half of the table lists the interaction between GABARAP molecules ("self-interaction") in any oligomer; the relevant PDB dataset or relevant publication is listed with the chains involved. Parentheses () around a site number means it is partial. [other]indicates that lots of the amino acids are not near a hydration patch.
ACKNOWLEDGEMENTS We thank the  Francis Crick Institute  for use of Swarmdock, the  CHARMM-GUI team at Lehigh University  for use of the CHARMM-GUI input generator,  Nigel Unwin  for useful scientific discussions, and  Stuart Rankin  and  Michael Rutter  for computational support. Some of the simulations in this work were carried out on the  Darwin Supercomputer of the University of Cambridge High Performance Computing Service using Strategic Research Infrastructure Funding  from the  Higher Education Funding Council for England. SV was funded by the  UK Medical Research Council  grant  ML/L007266/1. BWJI acknowledges financial support from the  UK Engineering and Physical Sciences Research Council   Centre for Doctoral Training in Computational Methods for Materials Science under Grant  No.
site 42. 42 The key GABARAP amino acids involved are Ile 21, Tyr 25, Ile 32, Lys 46, Lys 48, Tyr 49, Leu 50, Phe 60, and Leu 63 (PDB dataset 3DOW). The ALFY dodecapeptide 27  binds to GABARAP overlapping sites 32 and 33, and surrounding site 11 (PDB dataset 3WIM). The KBTBD6 undecapeptide 28  binds to GABARAP overlapping sites 11, 32, and 33, and surrounding sites 12 and 41 (PDB dataset 4XC2).
Chlorophyll a Samples for determination of Chl a concentrations were collected by filtering 550 mL seawater on GF/F filters (Whatman, UK). Filters were frozen and stored in liquid nitrogen, and Chl a was extracted in methanol and measured fluorometrically (Turner Designs Trilogy Optical kit) (Le  Bouteiller et al. , 1992). Satellite-derived surface Chl a concentrations at the LD stations were used from before and after the cruise sampling at the LD stations. Satellite Chl a data are added as supplementary video files (Supplement, Videos S1, S2, S3).
Acknowledgements. This research is a contribution of the  OUT-PACE (Oligotrophy from UlTra-oligotrophy PACific Experiment ) project (https://outpace. mio. univ-amu. fr/,last access: June 2018) funded by the  Agence Nationale de la Recherche  (grant  ANR-14-CE01-0007-01 ), the  LEFE-CyBER program  ( CNRS-INSU) , the  Institut de Recherche pour le Développement (IRD) , the  GOPS program (IRD)  and the  CNES  ( BC T23 ,  ZBC 4500048836 ). The OUTPACE cruise (https://doi. org/10. 17600/15000900) was managed by the  MIO (OSU Institut Pytheas, AMU)  from  Marseilles (France). The authors thank the crew of the  R/V L'Atalante  for outstanding shipboard operations. Gilles Rougier  and  Marc Picheral  are warmly thanked for their efficient help in CTD rosette management and data processing, as is  Catherine Schmechtig  for the LEFE-CyBER database management.
Data availability. All data and metadata are available from the French INSU/CNRS LEFE CYBER database (scientific coordinator: Hervé Claustre; data manager and webmaster: Catherine Schmechtig) at the following web address: http://www. obs-vlfr. fr/proof/php/outpace/outpace. php (INSU/CNRS LEFE CYBER, 2017). The Supplement related to this article is available online at https://doi. org/10. 5194/bg-15-3893-2018-supplement.
Here we utilize long-term reanalyses and reconstruction datasets to investigate the 'early twentieth-century Arctic warming' (ETCAW) which reached its peak warming at the beginning of the 1940s but started around 1910. Compared to the present Arctic warming, the ETCAW was mainly confined to the European Atlantic sector  (Scherhag 1939 , Bengtsson et al 2004 , Wood and Overland 2010 , Bekryaev et al 2010). In the vertical, recent maximums of temperature anomalies are mostly found at the surface whereas the maximum warming of the ETCAW was located in the mid troposphere  (Grant et al 2009 , Brönnimann et al 2012). Possible warming mechanisms include warm air advection by southerly winds (Wood and Overland 2010, Wegmann et al 2017) as well as increased winter sea-surface temperatures (SSTs)  (Johannessen et al 2004 , Bengtsson et al 2004 , Semenov and Latif 2012).
Analyzing 26 simulated Arctic warming events,  Beitsch et al (2014)  found a triggering warming ocean signal that induces atmospheric changes, including increased stationary eddy heat transport into the Arctic domain. Recently, Tokinaga et al (2017)  were able to reproduce the large-scale ETCAW by introducing historical SST forcing in an atmosphere-ocean coupled global climate models (AOGCM), underlining the importance of SSTs in the onset and evolution of the ETCAW. Paleoclimatic data suggest that, until the beginning of the twenty-first century, the ETCAW was unique in magnitude and rate for at least the last 1500 years in the Arctic domain  (Kaufman et al 2009 , Pages 2 K Consortium 2013 , Opel et al 2013). Therefore, the ETCAW offers a unique opportunity to investigate feedbacks of global climate during Arctic warming periods.
The goal of this study is to demonstrate the consistency between both periods, of the sea ice decline, the covariability of the sea ice with atmospheric temperature and heat fluxes, and their impact on the Arctic region. For this purpose, we compare decadal temperature trend patterns among the two warming events (ETCAW and present) and the dynamical conditions leading to the associated WACS pattern. We focus on the boreal winter (DJF) temperatures and their relationship with September sea ice extent in the BKS region using regression analysis for quantifying the role of different mechanisms. The paper is organized as follows: section 2 describes the data and methods used. In section 3 we introduce the ETCAW and the respective WACS patterns, whereas section 4 investigates the dynamical process behind that pattern.
Reconstruction data For near-surface air temperature reconstruction we use the National Aeronautics and Space Administration (NASA) Goddard Institute for Space Studies (GISS) Surface Temperature Analysis (GIS-TEMP) dataset  (Hansen et al 2010, https ://data. giss. nasa. gov/GISTEMP/). This dataset offers a homogeneous reconstruction of global temperature based on historical station data back to 1880 with a 2 • × 2 • resolution. We use both the Land-Ocean Temperature Index which uses reconstructed SSTs (ERSSTv4) to fill in data gaps over the oceans (smoothing radius 1200 km) as well as the station-only dataset (smoothing radius 20 km).
Global climate model To assess the relative impact of internal and external variability, we compare reanalysis datasets with an ensemble model experiment. The ECMWF integrated an ensemble of ten Integrated Forecast System (IFS) atmospheric simulations for the years 1899-2009 at a horizontal resolution of 1 degree with 91 vertical levels reaching from the surface up to 1 Pa, known as the final ERA-20 cm version (ERA20CM). Specified sea ice concentration as well as sea-surface temperature boundary conditions come from HadISST. 2 (same as ERA20C) and the radiation scheme follows the CMIP5 protocol exactly, including aerosols, ozone and greenhouse gases  (Hersbach et al 2015). Both in the GCM and the reanalyses we use integrated heat-flux values, which are integrated over all model levels in the IFS model of the respective ECMWF dataset.
Sea ice data For recent sea ice data we use the ERA-Interim sea ice concentration (SIC), which is highly correlated to the NSIDC sea ice data. For instance, Sorokina et al  (2016)  reported no differences between these two datasets. For the ETCAW period we investigate five different SIC datasets, namely the three products used in the long-term reanalyses (see table  1 ) as well as the independent sea ice concentration data by Walsh et al (2017) plus the sea ice output from the centennial ocean reanalysis ORA-20C by the ECMWF  (De Boisséson et al 2017). Although there are considerable uncertainties regarding the Arctic sea ice conditions before 1950,  Connolly et al (2017)  recently provided a new reconstruction of historical Arctic sea ice which supported the melting period between 1910-1940.
Moreover,  Alekseev et al (2016)  found a substantial decrease of September Arctic sea ice between 1930-1940 in their assessment of Arctic sea ice in the 20th century. Thus, besides the reconstructions used in this study, other independent datasets suggest a sea ice reduction during the ETCAW period. We define the BKS as region bounded by 30-90 • E, 65-85 • N and focus on September sea ice. In September, sea ice reaches its annual minimum and open-water regions provide a strong heat and moisture release to the cold Arctic atmosphere  (Jaiser et al 2016). We checked using different autumn months between September and December and found that regression with September sea ice best represents the DJF temperature trend, especially so for the ETCAW (see supplementary figure 1 available at stacks.
Here, we investigate the impact of tropospheric heat transport associated with sea ice reduction. To investigate one possible driver behind these trends, figure  3  shows the same structure as figure  2 , but instead of trends over time, it shows linear regression of 2 m DJF temperature [K/std dev] anomalies projected onto the normalized BKS SIC time series (ERA-Interim September BKS sea ice 1987-2016 for the RAW, HadISST2 September BKS sea ice 1911-1940 for the ETCAW). In this analysis for the RAW period we used the ERA-Interim sea ice, whereas for the ETCAW period we used the HadISST2. 0 dataset. Interestingly, spatial patterns in figure  3  look nearly identical to the trend patterns (figure  2 ), suggesting that September BKS SIC is an important driver of the temperature distribution, showing a clear temperature decrease between the Eurasian Arctic and the midlatitudes.
Tropospheric dynamics associated with sea ice loss To investigate the tropospheric dynamics associated with the WACS pattern, figures 5(a)-(d) shows 500 hPa geopotential anomalies regressed onto the same BKS SIC time series that were used in previous figures. For the RAW, a strong positive anomaly dominates the Arctic, extending southward over western Russia and reaching the Caspian Sea. This high-pressure anomaly is flanked by two low-pressure systems leading to a zonal-to-meridional transition of the heat advection. For the ETCAW in ERA20C, a similar structure can be observed for Eurasia. Using the Walsh et al  (2017)  or ORA20C dataset, this transition becomes even more apparent (see supplementary figure  6 ). The geopotential height patterns shown figures 4(a)-(d) cause anomalous heat advection over the Eurasian domain.
Discussion We used a variety of gridded atmospheric datasets: AGCM simulations, reanalyses and reconstructions, to address some of the open questions regarding the cause and manifestation of the WACS surface temperature pattern-one of the most remarkable counterintuitive features associated with Arctic amplification. A key question in the ongoing discussion about the origin of WACS pattern is whether the observed temperature pattern is caused by a chain of physical processes  (Honda et al 2009 , Overland et al 2011 , Cohen et al 2014 , García-Serrano et al 2015 , Jaiser et al 2016) , or if it is part of internal variability and its occurrence results from the random sampling as revealed by the model sensitivity studies  (Sun et al 2016 , McCusker et al 2016 , Boland et al 2017 , Collow et al 2016).
We try to address this question by comparing the two Arctic warming periods (ETCAW and RAW), both characterized by decreasing BKS SIC using different reanalyses and comparing them with AGCM simulations. Using a re-analysis (ERA20C) and a model simulation (ERA20CM) using the same ECMWF atmospheric model allows us to directly infer the impact of assimilated data, since all boundary conditions are the same for both datasets. Our results support some of the aforementioned findings concerning the RAW: Striking similarities are found between the ETCAW and RAW periods, namely the manifestation of the WACS pattern and its dynamical background, the reduced atmospheric heat transport to central Eurasia. Our study suggests that the changes in the autumn SIC in the BKS largely explain the observed temperature trends by modulating the pressure patterns in the upper troposphere, which consequently impact the heat-flux convergence over the mid-latitudes  (Cohen et al 2014, Francis and Vavrus 2015).
Our results disagree with the findings of Sorokina et al (2016), who found that BKS sea ice reduction and the following turbulent heat flux over the ice-free areas is not enough to explain the WACS pattern. However, we want to underline the findings of  Gastineau et al (2017) , stating that the covariability of sea ice, Eurasian snow and hemispheric SSTs is important for the generation of the WACS pattern. Further research is needed to address this coupled ocean-cryosphere-atmosphere pattern, which is still a challenge for recent models. Our findings are qualitatively independent of the sea ice reconstruction at hand and it is mainly the significance of the resulting patterns that is impacted by the choice of SIC reconstruction. Interestingly, using the Walsh et al (2017) dataset for the ETCAW period resulted in the highest confidence for the regression analysis.
Moreover, the respective ETCAW 500 hPa GPH and heat flux anomalies show a very high similarity with those obtained for the RAW period, although the sea ice reduction trend between 1911-1940 in the Walsh et al (2017) dataset is the weakest among the investigated SIC reconstructions. Furthermore, our results support recent studies  (Cohen 2016 , Francis 2017 , Gastineau et al 2017)  which argue that current AGCMs lack the capability to properly reproduce climate feedbacks triggered by the Arctic sea ice decline. Although we found 1 out of 10 ensemble members which reproduced the WACS pattern relatively well for the analysis of trends, the regression analysis showed no connection to the underlying dynamics and disagreed with the findings from ERA20C. Additionally, the ERA20CM ensemble mean failed to reproduce the observed temperature and circulation fields for the ETCAW.
Conclusions Several simulated and reconstructed gridded datasets were used to examine the link between September SIC in the BKS, atmospheric conditions and the WACS temperature pattern in winter. We found evidence for a manifestation of this temperature pattern not only during the current ongoing Arctic warming period (RAW), but also for the Arctic warming between 1910-1940 (ETCAW). Regression analysis with normalized SIC from the BKS region indicates that the overall temperature and heat-flux trend is largely congruent to a reduction of September BKS sea ice. Upper troposphere conditions point towards a strengthened atmospheric blocking over the BKS, implying the advection of cold air from the polar regions to central Siberia on its eastern flank. Moreover, this blocking triggers a more meridional circulation, reducing the strength of the warm air advection by the westerlies.
Acknowledgments We thank the  ECMWF (Reading, UK)  for making available the  ERA-Interim ,  ERA-20C  and  CERA-20C  reanalyses as well as  ESRL NOAA (Boulder, USA)  for the provision of the 20CR reanalyses. This work was supported by the  BELMONT Fund ARCTIC-ERA  project funded by the  Agence Nationale de la Recherche (Paris, France)  and by the  EU -funded  FRAGERUS-MRCM  project. OZ was also supported by the  Russian Ministry of Education and Science  under Agreement # 14. 616. 21. 0035  (identification no. RFMEFI61615X0035 ). YOR is supported by the  Research Council of Norway  (Grant SNOWGLACE # 244166 ). The Authors are thankful to  Eric de Boisséson  for providing ORA20C SIC data, to  Stefan Brönnimann  for insightful comments and to  Morgan Gray  for editorial support.
For the GMM analysis we digitized 526 3D landmarks and semilandmarks on each ribcage using Viewbox 4 software (www. dhal. com), based on a recently published protocol for quantifying the ribcage of hominoids  68. Additionally, a metric analysis of selected linear measurements of T1-T10 vertebrae was performed using a different comparative sample (Supplementary Note 1). To assess the size and shape of the thorax using GMM, for ribs 1-10, 20 landmarks (7 fixed and 13 sliding); in ribs 11-12, 19 landmarks (6 fixed and 13 sliding) were captured in each rib, and four fixed landmarks at each thoracic vertebra  68. For ribs 1-10, fixed landmarks at the rib head were digitized at the most superior and most inferior points with another landmark at the most medial point of the rib head at the inter-articular crest.
In the case of the K2 thorax, we needed to estimate the missing landmarks, mainly at several proximal and distal ends due to the preservation of the fossils (Fig. 1 , Supplementary Note 2). Those landmarks were estimated following reference-based estimation through a thin-plate spline (TPS) method  71. According to this method, the coordinates of the reference individual, where all landmarks and are present, is used to calculate the missing data of the target specimen through an interpolation function using the reference information, so that the bending energy is minimized  71. In order to be conservative and due to the lack of other Neandertal rib cages to use as references, we used the coordinates of the mean H. sapiens male thorax as a reference specimen. Average H. sapiens coordinates were calculated using MorphoJ software  72.
The semilandmarks were slid twice and the full set of 3D coordinates was subjected to a generalized Procrustes analysis  44. Size was quantified as centroid size (CS) defined as the square root of the summed squared distances of each landmark to the centroid, with the centroid of a configuration calculated as the average coordinate (x, y, z) of the whole set of coordinates  44, 73. Morphological variability of the sample was explored by principal component analyses in shape space and form space  74  using EVAN toolkit software. Mean comparisons were also carried out between K2 and modern human males using a permutation test (n = 1000 permutations) in MorphoJ software  72.
Acknowledgements We thank the excavation and research team of Kebara for their work that has allowed for the recovery of the Kebara 2 individual and  B. Arensburg ,  Y. Rak ,  B. Vandermeersch ,  O. Bar-Yosef  and  I. Hershkowitz  their permission to study the K2 specimen and  V. Slon  for technical help. Thanks to our colleagues from  MNHN ,  TAU ,  EHU-UPV , MNCN,  UCM-ISCIII, UCAM , and especially  A. Balzeau ,  J. Stock ,  C. Shaw ,  T. Holliday ,  R. G. Franciscus ,  T. Durden ,  J. L. Arsuaga , and  J. M. Carretero  for their support and constructive discussion. We thank  J. Trueba  and  Madrid Scientific Films  for letting us to use one of their photographs. A. G. -O. had a  Marie Curie-IEF  ( FP7-PEOPLE-2012-IEF 327243 ) research fellowship during part of this work. A. G. -O. and M.
Superchambers are fabricated by coupling together two GE1/1 single chambers. Inside a cosmic stand, a high voltage scan is performed on each chamber and the relevant parameters such as detector gain, noise levels and cluster size are measured with the final detector electronics. Figure  5  shows the present cosmic stand at CERN, which allows to test up to 15 superchambers at once and features fully automated HV scans, measurements of cosmic muon tracks and a DAQ system comparable to the final setup to be used inside the CMS experiment. Throughout the entire detector assembly and quality control procedure, all detailed information and test results will be stored in an Oracle based database for future reference.
Analytical methods The yeast population was estimated after cell sonication using an electronic particle counter (Multisizer 3 Coulter Counter; Beckman Coulter) and by dry weight determination. The residual amino acid and ammonium concentrations were determined by HPLC. Samples were collected at 24-hour intervals after inoculation for the first 72 hours and then every 48 hours until the fermentation was completed. HPLC data are presented in S2 Table. For each sample, 1 mL of MS was centrifuged at 12,000 rpm for 10 min, and the supernatant was collected. A 20-μl aliquot of supernatant was injected into a Prominence HPLC system (Shimadzu, USA) equipped with a Biorad HPX-87H column according to Nissen et al. [54]. The concentrations of ammonium ion and the 14 amino acids were measured using the previously described HPLC analysis method  [55].
Bovine serum albumin (BSA) was used as the calibration standard. The different values obtained were compared using Fisher's LSD test and each mean value and standard error of the mean (SEM) were calculated from three replicates. The maximum uptake rate (μMax), i. e. the maximum flow of nitrogen consumed observed during fermentation process (mg N L -1 h -1 )was determined using GraphPad Prism version 6. 0 for Windows, GraphPad Software, La Jolla California USA, www. graphpad. com, according to the following equation: mMax ¼ Vmax Â X h Khalf h Â X h where the X parameter corresponds to the time value presented in abscissa, and the values of Vmax, h and Khal are provided by the software.
usadellab. org/cms/?page=trimmomatic). RNA-seq reads were then aligned to the S288c reference (S. cerevisiae genome obtained on 03/03/2016, from the Saccharomyces Genome Database, FTP SITE: http://downloads. yeastgenome. org/sequence/S288C_reference/genome_releases/ corresponding to a stable release from January 2015) using TopHat with default score settings  [62]. BAM files were sorted and indexed using Samtools with default score settings  [63]. Alignments were then processed, and gene counts were obtained using HTSeq  [64]  with the no-stranded and -gene counts configuration from the S288c gff file. Differential expression was assessed with edgeR utilizing the exactTest function  [65]. The results obtained from edgeR provided the differentially expressed genes between strains and/or conditions with FDR < 0.