dataesr/scientific-paragraphs-categorization huggingface dataset:
oup. com/gji/article/199/3/1554/616703 by CNRS -ISTO user on 08 November 2021   Torsvik et al. (2008b)  and in dark brown according to  Torsvik et al. (2012)  for the Oslo Graben (60 • N, 10 • E) and the error bounds (dashed grey/brown lines) based on the error in inclination ( I x ) derived from A 95. Dotted brown curve represents the recommended Laurussian spline path from  Torsvik et al. (2012). (b) Aitoff projection of the apparent polar wander path (APWP) in grey with corresponding A95 circles for the age interval 320-230 Ma (in 10 Ma increments) according to  Torsvik et al. (2008b; asterisks)  and  Torsvik et al. (2012; closed circles). Remaining symbols are the same as in (a). (e. g. Torsvik et al. 2006; Alvey 2009; Labails et al. (2010)  show that Pangaea A reconstructions are possible without the necessity of introducing non-dipolar fields.
The question remains however, if it is valid to introduce a uniform flattening factor for all sediments, as flattening factors have been shown to vary widely in both magnetite and hematite bearing rocks  (Bilardello & Kodama 2010). Applying a blanket flattening factor is however possibly the best approach currently at hand. Very recently, new studies have become available that present large high-quality data sets, often corrected for inclination shallowing whenever sediments were the subject of research. Studies on both the Laurussian  (Iosifidi et al. 2010; Meijers et al. 2010; Dominguez et al. 2011; Yuan et al. 2011)  and Gondwana part of Pangaea  (Domeier et al. 2011)  conclude in favour of Pangaea Atype reconstructions. The higher palaeolatitudes from our study and the study by  Dominguez et al.
(2011)  from the Oslo Graben would not necessitate a Pangaea B type reconstruction in the early Permian, when comparing the poles to the latest APWPs  (Torsvik et al. 2008b (Torsvik et al. , 2012;; Figs   10a  and b ). This is in line with recent studies carried out on volcanics and sediments (corrected for inclination shallowing) from other parts of Europe. Yuan et al. (2011)  present data from lower Permian trachytes from the Ukrainian shield, which is part of the East European Craton. On the basis of their data a Pangaea A-type reconstruction is favoured. Meijers et al. (2010)  present a large data set of Carboniferous and lowermost Permian sediments from the Donbas Foldbelt; an inverted rift basin that is part of the East European Craton. All sites, except for the earliest Permian site were corrected for inclination shallowing with the TK03.
GAD model  (Tauxe & Kent 2004) , and also conclude that there is no necessity for Pangaea B. In all these cases however, using the mean Downloaded from https://academic. oup. com/gji/article/199/3/1554/616703 by CNRS -ISTO user on 08 November 2021   2012 ) that assumes a uniform flattening factor of 0. 6 for all included sedimentary data sets that were not corrected for inclination shallowing in the original publications. Additional Laurussia reconstructions (red outlines) are shown based on the recommended spherical spline APWP for Laurussia as presented in table 5 of  Torsvik et al. (2012). (c) Gondwana reconstruction based on  Muttoni et al. (2013). The combination of our Laurussia reconstruction and the  Muttoni et al. (2013)  Gondwana reconstruction leads to a small overlap of the major continents (present-day North America and South America) and a more problematic fit of present-day Florida.
The position of Laurentia was reconstructed using an Euler pole at 78. 6 • N, 161. 9 • E (angle = 31. 0 • ) following  Torsvik et al. (2006)  and  Domeier et al. (2012). pole for Gondwana as presented by  Muttoni et al. (2013)  would lead to an overlap between the northern and southern continents, leading to a Pangaea B type reconstruction. Yuan et al. (2011)  therefore correctly conclude that one will have to omit the data from Adria in order to allow a Pangaea A reconstruction unless the position of Adria with respect to Africa is not rigid (cf. Van Hinsbergen et al. 2014). Pangaea B reconstructions are supported by two studies carried out in the Laurasian part of Pangaea (present-day eastern Canada;  Bilardello & Kodama 2009 , 2010; Kodama 2009 ). Sediments from those two studies corrected for inclination shallowing using the method of  Tan & Kodama (2003)  result in higher southerly latitudes for the portion of Laurussia located on the southern hemisphere in the Carboniferous, therefore increasing the overlap between Laurussia and Gondwana.
Interestingly, the vast majority of Laurussia was located on the southern hemisphere in the early Carboniferous. Its northward drift led to the position of most of the continental Laurussian landmass on the northern hemisphere by the late Carboniferous. Overall, correcting poles derived from sediments from both sides of the equator for inclination shallowing therefore inevitably leads to symmetric stretching of Laurussia around the equator, which poses an interesting issue. In concurrence with  Yuan et al. (2011)  we conclude that the crux in the current Pangaea A versus B debate lies in data selection and reliability criteria. The solution to the ongoing controversy is likely hidden in geological records from the southern continents that were assembled in Gondwana. Increasing the limited number of highquality data sets from Gondwana is however a challenging task since the records have often been affected by remagnetization (e.
C O N C L U S I O N S We have performed an extended palaeomagnetic study of the Oslo Graben volcanics, compared to the study of more than half a century ago by van Everdingen (1960), using modern techniques and a four times larger amount of sites (104 versus 27). We must conclude that the average direction (and palaeomagnetic pole) of the Krokskogen and Vestfold volcanics together are statistically identical to those of the earlier study. This gives confidence in the fact that older palaeomagnetic studies can be reliable and robust, even though methods have improved. Our larger number of sites, and better available age constraints, however, allow us to separate the data into two major intervals, of the on average younger Krokskogen area and the older Vestfold area. The results are a valuable addition to the Permian database of Laurussia and show firstly that palaeolatitudes are slightly higher-though within error-compared to most recent APWP of  Torsvik et al.
(2012). This implies that for both the early Permian and the late Permian  (Dominguez et al. 2011)  a Pangaea B configuration is not required, based on data from the Oslo Graben. The mean 280 Ma pole for Gondwana presented by  Muttoni et al. (2003 Muttoni et al. ( , 2009 Muttoni et al. ( , 2013) )  would lead to a slight overlap between Laurentia and South America, but is mostly problematic for Florida, therefore necessitating Pangaea B. The larger sample size also allows assessing the distributions of directions of the Oslo Graben-acquired during a superchron, the PCRS-in terms of geomagnetic field behaviour. In particular, the distributions show a significantly lower VGP scatter at the observed (low) latitudes than expected from a compilation of the last 5 Myr  (Johnson et al. 2008 ) but show excellent agreement with the scatter observed both during the CNS  (Biggin et al.
A C K N O W L E D G E M E N T S We thank  Martijn Deenen  and  Mark Dekkers  for their help in the field and for constructive discussions and the late  Tom Mullender  for technical support. This work was funded by the  Netherlands Organization for Scientific Research (NWO). MJMM acknowledges funding and support from the  Henri Poincaré Fellowship  ( Observatoire de la Côte d'Azur, Nice ) and the  College of Science and Engineering and the Department of Earth Sciences at the University of Minnesota. The manuscript benefited from the reviews of  Conall Mac Niocaill  and an anonymous reviewer.
Background: Inadvertent perioperative mild hypothermia is a common side effect of anesthesia and surgery. This complication has multiple origins. Hypothermia can negatively affect postoperative outcome. However, it might be prevented by specific measures. Objectives: As part of a quality assurance program, we assessed the incidence of severe intraoperative hypothermia in association with specific surgery and procedures. The next step consisted of a series of measures to decrease its incidence. Patients and Methods: After approval of our institutional review board, we used our computerized recording of anesthesia and postoperative care chart database (DEIO Archive Browser®) to extract the anesthetic files of patients who have experienced severe and mild hypothermia (< 34. 5°C) and (≥ 34. 5°C and < 35.
Patients and Methods Since 2001, our operating rooms are equipped with new computerized storing of anesthesia record software, DEIO Archive Browser that is linked to our anesthesia machine and the corresponding monitoring device AS5 (Datex-Ohmeda Inc. , Madison, WI, The USA). Therefore, this allows for an intra-hospital online recording of most data related to the anesthesia and postoperative care unit (PACU). Intraoperative vital parameters, respiratory machine parameters, target-controlled infusion (TCI) drug delivery systems, inhalational gas consumption, bispectral index, and electronic temperature probe connected to the anesthesia device are detected and values are stored automatically. Drug injection, regional procedures, and other events, including operation times, unexpected events (for example, rashes), and infrared temperature probing are entered manually into the system by anesthesia providers.
Once surgery and anesthesia are finished, and before leaving the operating room for the PACU, the patient file is saved. Upon arrival at the PACU, central temperature (tympanic), respiratory rate, (numerical) pain scores, nausea and vomiting, sedation scores (categorical variables), and other events, such as chills, are recorded manually by a nurse, while other vital signs are recorded automatically in the DEIO Archive Browser. All patients' data are also recorded as a PDF file (including the operating room and the PACU), which is printed and enclosed to his hospital file when leaving the PACU. The database is also available by keywords for system and quality assurance administrators. The interface is not intuitive and each data extraction needs several hours of specific trainings. We used descriptive statistics to present data.
5°C), specifically. Shortcomings of the present study are the fact that the preoperative (upon arrival in the operating room) temperature is not recorded in our database. According to the National Institute for Health and Care Excellence (NICE) guidelines CG65, warming should be initiated once the temperature is below 36°C in the ward and maintained throughout the intra-operative phase, while all patients should have their temperature monitored before induction of anesthesia. In addition, ambient room temperature was recorded neither in the anesthesia chart, nor in the operating chart. Ambient temperature has great influence on the anesthetized patient; hypothermia is significantly more frequent when the ambient temperature decreases to 21°C  (12). The other major tool to fight hypothermia is active warming; however, due to risk of burning, it cannot be used if intraoperative temperature is not monitored.
Our supposition is that in this setting, undersized tables, in addition to the small and less ergonomic environments, do not permit adequate prevention of hypothermia. We took several simple measures to decrease the incidence of sever hypothermia, including training and providing information to all operating room personnel. However, the measures should be sustainable and the results should be verified regularly, to be efficient. This approach is facilitated by the computerized recording of anesthesia chart and the database. Also, it can help to verify the indicator. Mild hypothermia is still a challenge and a special focus is necessary, especially in the preoperative setting. In fighting against inadvertent intraoperative mild hypothermia, which is much more difficult to prevent, simple measures, such an increasing room temperature, are generally very difficult to apply.
This is mainly due to surgeon's discomfort that can adversely affect the surgical outcome. Heat redistribution, after induction of anesthesia, is almost unavoidable, unless a pre-warming is performed. Unfortunately, this procedure is very difficult to perform in all patients. The anesthesia database recording system not only permitted us to detect these cases, but it also enabled us to follow the result of our actions. Obviously, the indicator is the annual rate of patients having mild to severe intraoperative hypothermia. This study provides a background for our future research plans, aiming to evaluate the specific short-term and long-term outcomes of hypothermia in patients undergoing cancer surgery.
This system only considers infected people who have been passively diagnosed within the framework of private and public health sectors. Regular data exchanges are carried out with bordering countries. The regional epidemiology unit of French Guiana reports any cases diagnosed along the Maroni and Oyapock rivers to Surinamese and Brazilian counterparts every week, including the putative contamination places. Data is received on a weekly basis from Suriname and once a month from Brazil. These exchanges show a large number of cases that most probably originated in French Guiana, but were diagnosed in Suriname or Brazil (Fig. 2 ). This number increases when illegal migrants do not have easy access to the healthcare system; this is particularly true in small villages where French military presence pushes them to stay in the forest and manage with locally available means.
We provide a detailed analysis of the impact of the newly measured optimised observables in the B → K * μ + μ -decay by the LHCb experiment. The analysis is performed in the MSSM, both in the context of the usual constrained scenarios and in the context of a more general set-up where the SUSY partner masses are independent. We show that the global agreement of the MSSM solutions with the data is still very good. Nevertheless, especially in the constrained scenarios, the limits from B → K * μ + μ -are now very strong and are at the same level as the well-known b → sγ constraints. We describe the implications of the B → K * μ + μ -measurements both on the Wilson coefficients and on the SUSY parameters.
g. Ref. [17] ). We have neglected the tensor 123 operators as they are highly suppressed both in the SM and in the Minimal Supersymmetric Standard Model (MSSM). The corresponding Wilson coefficients are well known in the SM  [18] [19] [20] [21] [22] [23] [24] , where C SM ), while their primed counterparts as well as the scalar and pseudo-scalar Wilson coefficients vanish to a good approximation. Global fits to all available b → sγ and b → sμ + μ -data constrain significantly the allowed values for the NP contributions δC i  [25] [26] [27] [28] , specially when the latest B → K * μ + μ -data is included  [1] [2] [3] [4]. It is precisely the decay B → K * (→ K π)μ + μ -that has attracted most of the attention recently. Its angular distribution  [29, 30]  (see also e. g. [17, 31, 32] ) provides a plethora of observables sensitive to different helicity structures in the decay amplitude.
[4]. Here we are not aiming to determine a preferred direction to which the current results with the observed anomalies would point to, but instead our goal is to examine the global agreement of the data with the MSSM predictions. Hence the use of the absolute χ 2 method is justified. Note, however, that if we had used the χ 2 method instead, we could still find solutions at the 1σ level, certainly different from the δC 9 ∼ -1 benchmark, because this method relies on finding a best fit point regardless of the goodness-of-fit of this point, and it depends on the model parameter ranges. On the contrary, in the absolute χ 2 method the allowed regions are defined directly from the value of the χ 2 as explained above, and they are independent of the parameter ranges.
Implications for MSSM parameters The results of the global fit can be used to put constraints on the supersymmetric parameters. This is done by computing such quantities from the input parameters within each MSSM scenario which satisfies the global constraints in the fit. First, in order to see the impact of the newly measured B → K * μ + μ -observables in a simple framework, in Fig. 4  we compare the constraining power of the fit to In very constrained scenarios such as the CMSSM, other observables are correlated to the ones studied in this paper. One example is the anomalous magnetic moment of the muon a μ. It is well known that the discrepancy in a μ is difficult to explain in the CMSSM, requiring low values for m 0 , m 1/2 virtually excluded by LHC data  [60, 61]. The corresponding constraints are shown in Fig.
In our analysis, the best fit values for the Wilson coefficients not only depend on the correlations among the different flavour observables but also on the correlations between the various Wilson coefficients in each specific MSSM scenario. Even though the small value of C 9 ∼ 3 favourable from model-independent analyses is not accessible within MSSM, there are regions of the MSSM parameter space that are still well compatible with the experimental data. Our results for the Wilson coefficients are consistent subsets of the solutions found in  [4]. We have translated the constraints on different SUSY parameters in the three aforementioned scenarios. These constraints provide complementary information to direct searches and show that the data on B → K * μ + μ -has become competitive with the traditional modes such as B → X s γ and B s → μ + μ -.
Finding Groups of Orthologous Genes We first identified groups of orthologous genes in a first set of commelinids species for which a complete genome sequence was available: seven grasses: maize (Zea mays), sorghum (Sorghum bicolor), foxtail millet (Setaria italica), bamboo (Phyllostachys heterocycla), rice (Oryza sativa), barley (Hordeum vulgare), purple false brome (Brachypodium distachyon), and two nongrass commelinids: banana (Musa acuminata) and palm tree (Elaeis guineensis). CDS and their corresponding protein sequences were downloaded from the Gramene database  (Monaco et al. 2014 ) using the BioMart interface  (Spooner et al. 2012 ) for all species with the exception of bamboo (http://202. 127. 18. 221/bamboo/, last accessed January 20, 2015,  Peng et al. 2013 ) and palm tree (http://genomsawit.
mpob. gov. my/genomsawit/auth/index. php?track=1, last accessed January 20, 2015,  Singh et al. 2013). For each gene, only one transcript corresponding to the longest protein was kept for further analyses. Groups of orthologous genes were determined by running the OrthoMCL software with default options  (Li et al. 2003). This software first does an all-against-all protein BLAST then clusters these BLAST results based on their scores. As groups can contain both orthologous and paralogous genes, groups containing only one-to-one orthologous genes (one gene per species, all species present) were kept for analyses. To study the evolution of GC3 deeper in the monocot phylogeny, we built a second data set by adding a basal monocot species. As no complete genome was available for species outside commelinids, we chose yam (Dioscorea alata) as the basal monocot with the largest expressed sequence tag (EST) data set currently available.
All the following steps were identical for both sets of species. Alignments and Phylogenetic Filtering CDS were aligned using the MACSE software  (Ranwez et al. 2011). Each aligned CDS was translated into aligned proteins. For each protein alignment we built a phylogenetic tree using PhyML (LG model, gamma distribution of rates,  Guindon et al. 2010). We then compared this tree to the reference tree: ((banana, palm tree),(((maize, sorghum), foxtail millet), (rice, (bamboo, (purple false brome, barley))))); for the first set of species and (((PACCMAD, BEP), banana), yam); for the second set of species using the ape package in R  (Popescu et al. 2012). We constructed our reference phylogenies from  Janssen and Bremer (2004)  for monocots and  Bouchenak-Khelladi et al. (2008)  for grasses. For the first data set we allowed multifurcations in the reference phylogeny for the positions of rice and bamboo because the corresponding internal branches are short and not very well supported.
Ancestral GC3 Gradient Reconstruction To study the evolution of GC3 gradient along genes, we first divided each CDS alignment into three classes based on the rice gene annotation for the first data set, and on either rice, barley, or purple false brome (depending of the sampled species, see above) for the second one. Annotations were retrieved from the Gramene database  (Monaco et al. 2014). Alignments positions overlapping the first exon were grouped as first exon, those overlapping the second exon were grouped together as second exon whereas all other positions were grouped as rest of gene. Ancestral base composition was estimated independently for each class in each gene using the same procedure as indicated above.
Simulations Because the extant species we used exhibit highly divergent GC3 for some genes, we performed simulations to test for the accuracy of the reconstruction method under conditions mimicking our data sets. We used the bppML software to simulate sequence evolution in the two phylogenies corresponding to the two data sets: ((banana, palm tree),(((maize, sorghum), foxtail millet), (rice, (bamboo, (purple false brome, barley))))); and (((PACCMAD, BEP), banana), yam); respectively. We first generated root sequences with fixed GC content, simulated evolution in each branch of the tree using a T92 model in each branch of the four species tree to generate sequences at terminal leaves (the k parameter was shared between all branches whereas the y was set for each branch according to the evolutionary scenario).
Data sets In this study, we chose to use two data sets. The first one includes all the commelinids species for which a genome sequence is available, namely seven grasses: maize (Ze. mays), sorghum (S. bicolor), foxtail millet (Se. italica), bamboo (P. heterocycla), rice (O. sativa), barley (H. vulgare), purple false brome (B. distachyon), and two nongrass commelinids: banana (M. acuminata), and palm tree (E. guineensis). Phylogenetic relationships between these species can be seen in figure  1A. Using the OrthoMCL software  (Li et al. 2003)  we identified 1,290 groups of orthologous genes, among which 1,032 were kept for subsequent analyses. In the second data set containing yam, banana, and two grasses (see phylogenetic relationships in fig. 1B ), we obtained 914 groups of orthologous genes after filtering.
We also performed an empirical test to verify that the inference of a GC-rich monocot ancestor is not driven by grass sequences. We analyzed a third data set that excluded grasses and included two nonmonocot GC3-poor and extremely homogeneous species Arabidopsis thaliana and Amborella trichopoda. To get enough orthologous sequences we only add banana in monocots. We first defined groups of orthologous genes by selecting banana protein-coding genes with a one-to-one orthologous gene in both A. thaliana and Am. trichopoda in the Gramene database  (Monaco et al. 2014)  using the BioMart interface  (Spooner et al. 2012). We then applied the same methodology as for other groups of species to reconstruct ancestral GC3 in different lineages. Ancestral sequences, both at the root and at the node, inferred from this trio of species are still GC3-rich (though not bimodally distributed) despite the absence of very GC3-rich grasses genes (supplementary fig.