dataesr/scientific-paragraphs-categorization huggingface dataset:
4 -29. 5 -26. 8 0. 5 0. 7 Rio Grande (S = 11,982 km 2 ) GSWP3 Princeton WATCH WFDEI All datasets -3. 6 -1. 0 -1. 3 -1. 5 -1. 9 -3. 0 -1. 2 -2. 0 -2. 0 -2. 1 -2. 6 -1. 1 -1. 4 -1. 3 -1. 6 -2. 7 -1. 1 -1. 1 -1. 0 -1. 5 -2. 9 -1. 1 -1. 5 -1. 45 -0. 04 0. 3 Susquehanna GSWP3 Princeton -0. 6 -0. 6 -0. 2 -0. 3 0. 04 -0. 1 -0. 1 -0. 3 -0. 22 -0. 33 (S = 67,313 km 2 ) WATCH WFDEI All datasets -1. 1 -1. 1 -0. 9 -0. 03 -0. 1 -0. 2 0. 2 0. 2 0. 1 -0. 1 -0. 2 -0. 2 -0. 26 -0. 3 0. 4 0. 4 NSE low flows-50% of lowest observed flows Baleine (S = 32,500 km 2 ) GSWP3 Princeton WATCH WFDEI All datasets -3. 5 -2. 2 -4. 0 -3. 2 -3. 2 -1. 1 -0. 4 -1. 6 -2. 3 -1. 4 -26. 6 -28. 5 -59. 0 -66. 3 -45. 1 -1. 4 -0. 3 -0. 5 -1. 9 -1 -8. 2 -7. 9 -16. 3 -18. 4 0. 2 0. 4 Liard (S = 275,000 GSWP3 Princeton WATCH -222. 8 -198.
2 -272. 4 -1043. 8 -1028. 8 -1112. 9 -3438. 1 -2095. 0 -3309. 3 -360. 6 -318. 2 -264. 0 -1266 -910 -1239 -12 -0. 8 km 2 ) WFDEI All datasets -292. 0 -246. 4 -1483. 4 -1167. 2 -3179. 2 -3005. 4 -412. 1 -338. 7 -1341 Rio Grande (S = 11,982 GSWP3 Princeton WATCH -21. 4 -12. 5 -9. 0 -8. 7 -6. 8 -8. 0 -22. 3 -8. 9 -9. 9 -23. 9 -16. 9 -19. 3 -19. 1 -11. 3 -11. 6 -6. 6 -6. 7 km 2 ) WFDEI All datasets -6. 7 -12. 4 -6. 8 -7. 6 -9. 2 -12. 6 -15. 0 -18. 8 -9. 4 Susquehanna GSWP3 Princeton -32. 6 -31. 4 -1. 1 -1. 3 -3. 0 -3. 3 -2. 1 -1. 6 -9. 7 -9. 4 (S = 67,313 km 2 ) WATCH WFDEI All datasets -57. 2 -47. 3 -42. 1 -2. 1 -2. 4 -1. 7 -6. 0 -5. 3 -4. 4 -2. 4 -4. 0 -2. 5 -16. 9 -14. 8 -1. 6 -2. 4.
Confocal Microscopy, Cell Counting and Statistical Analysis Tissues were imaged using confocal microscopy (Zeiss LSM 710 confocal microscope). Images of gut sections double immunostained with relevant tissue antibodies and DAPI. Equivalent fields of relevant sections were examined in controls, mutants and/or chimeras (minimum n = 3). Cells were counted and tissue thickness measured using the Image-J Fiji cell counter plugin and measuring tool, respectively. Data was plotted to a histogram or interval plot. The results for all samples were normalized to baseline. The outcome was set as the percentage of the difference between the baseline and the experimental conditions. Multiple linear regression was used to study the effects of tissue, species and gene mutation simultaneously  (Schneider et al.
, 2010; Lunt, 2013). This allowed us to assess the relative contribution of each predictor to the total variance that our model explained. Interaction terms were also considered with stricter p-value cut offs. The models were validated using the adjusted R squared and by performing residual analysis. To assess possible tissue rescue in the GFP > taplid 3 transplantation experiment, the non-parametric Spearman's Rho test was used to measure the correlation between control, chimera and mutant. All raw data and statistical analysis calculations are available here: https://datadryad. org/stash/share/ xNqbJ7teCQMoatb4CDKgsdbtIPGZ5QH1eGBvz_g-fVY.
ACKNOWLEDGMENTS This article is dedicated to the memory of RH. We would like to thank  Dagan Jenkins  for the IFT88 antibody,  Mark Turmaine  for assistance with SEM,  Kevin Lee  and  Erwin Pauws  for assistance with cartilage and bone staining as well as help with the phenotype analysis. We also thank  Jan Soetaert  and  Belén Martín-Martín  for help with microscopy. MD and the TALPID3 flock are supported by an  Institutional Strategic Grant (ISP)  to The  Roslin Institute  from the  BBSRC. NN was supported by a  Bolyai Fellowship and Hungarian Science Foundation   NKFI grant  ( 124740 ). CM was supported by  Guts UK (Derek Butler  Fellowship). We are grateful to the  French Society of Fetal Pathology (SoFFoet)  for participating in the study. We acknowledge the  NIHR Great Ormond Street Hospital Biomedical Research Centre  which supports all research at  Great Ormond Street Hospital NHS Foundation Trust  and  UCL Great Ormond Street Institute of Child Health.
X-ray absorption spectroscopy Speciation and mineralogy of the Fe fraction of TALDICE dust are investigated through X-ray absorption near-edge structure spectroscopy (XANES), performed at the Fe Kedge transition, that is the excitation energy of the innermost electrons of Fe. XANES relates the spectral features of X-ray absorption spectra to chemical and molecular characteristics of specific elements. The sample is irradiated with a monochromatic beam of photons whose energy finely changes with time. The response of the sample depends on features such as oxidation, coordination and mineralogy  (Calvin, 2013). For each sample three measurements are carried out, acquiring the fluorescence signal of the samples at steps of 0. 15 eV and considering the interval between 7000 and 7400 eV. Spectra are calibrated, normalized and averaged using the Athena software  (Ravel and Newville, 2005).
9) is selected to represent the sample. In some cases, the second best-fit combination has R 2 value close to the best fit, but the difference between the two combinations always regards the less abundant of the four selected references, with negligible effects on the interpretation. The combinatoric package of the Athena software is used  (Ravel and Newville, 2005). Following the procedure adopted by  Shoenfelt et al. (2018) , the relative abundance of Fe-bearing minerals is estimated considering the percentage of linear coefficients obtained from OLS. 3 Results and discussion.
Another possible implementation is the concurrent analysis of soluble and insoluble impurities, including elements other than Fe. This will help to investigate the dissolution and precipitation of primary and secondary minerals and identify the geochemical reactions responsible for these transformations, https://doi. org/10. 5194/tc-15-4807-2021 The Cryosphere, 15, 4807-4822, 2021 paving the way for the development of englacial geochemistry. Data availability. Data used to meet the aims of the present study are available in the Supplement. Full XANES spectra are found in the PANGAEA open repository, https://doi. org/10. 1594/PANGAEA. 924114 (Baccolo et al. , 2020). Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/tc-15-4807-2021-supplement.
Author contributions. GB conceived the idea of this work. GB, BD and EDS prepared the samples and performed Coulter counter analyses. GB, GC, DH and AM carried out X-ray absorption and fluorescence analyses. GB interpreted the data and wrote the manuscript with contributions from all the co-authors. Competing interests. The contact author has declared that neither they nor their co-authors have any competing interests. Disclaimer. Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Special issue statement. This article is part of the special issue "Oldest Ice: finding and interpreting climate proxies in ice older than 700 000 years (TC/CP/ESSD inter-journal SI)". It is not associated with a conference.
The Talos Dome Ice Core (TALDICE) project, a joint European program, is funded by national contributions from Italy, France, Germany, Switzerland and the United Kingdom. Primary logistical support was provided by PNRA at Talos Dome. This is TALDICE publication no. 62. This publication was generated in the frame of Beyond EPICA. The project has received funding from the European Union's Horizon 2020 Research And Innovation Programme under grant agreement no. 815384 (Oldest Ice Core). It is supported by national partners and funding agencies in Belgium, Denmark, France, Germany, Italy, Norway, Sweden, Switzerland, the Netherlands and the United Kingdom. Logistic support is mainly provided by PNRA and IPEV through the Concordia Station system. The opinions expressed and arguments employed herein do not necessarily reflect the official views of the European Union funding agency or other national funding bodies.
This is Beyond EPICA publication number 22. We thank Paolo Gentile for providing mineral standards and also Paul Niles and Tanya Peretyazhko for the fruitful discussions. Financial support. This research has been supported by the Ministero dell'Istruzione, dell'Università e della Ricerca (grant no. PNRA18-00098); Horizon 2020 (grant no. Beyond EPICA (815384)); and Diamond Light Source (grant nos. sp7314, sp8372 and sp9050). Review statement. This paper was edited by Florent Dominé and reviewed by Nicolas Stoll and one anonymous referee.
The term 'backpropagation' refers to the way that the gradient is computed for nonlinear multilayer networks  (Demuth and Beale, 2000). It is a generalization of the Widrow-Hoff learning rule to multiple-layer networks and nonlinear differentiable transfer functions. There are different types of backpropagation training algorithms such as  Levenberg-Marquardt, conjugate gradient and resilient backpropagation (De Villiers and Barnard, 1993). The Levenberg-Marquardt training algorithm works on loss functions that take the form of a sum of squared errors. The loss function is composed of an error and regularization. The error evaluates how a neural network fits the dataset and the regularization is used to prevent overfitting based on the effective complexity of the neural network. The Levenberg-Marquardt algorithm computes the gradient vector and the Jacobian matrix.
ANN architecture The performance of a neural network depends to a significant extent on how well it has been trained. Initially, a single layer linear network with the Widrow-Hoff least-square learning rule (also known as delta or least mean squared rule) was attempted. The training and testing showed unstable results on vector data (columns of input data). A multilayer feed-forward approach and standard Levenberg-Marquardt backpropagation training algorithm  (Levenberg, 1944; Marquardt, 1963)  were used in Matlab 2016b. The Levenberg-Marquardt method is an effective optimization algorithm and is preferred over steepest descent and Gauss-Newton methods in a wide variety of problems  (Wilson and Mantooth, 2013; Gavin, 2019). The Levenberg-Marquardt is a combination of the two optimization methods: the gradient descent method and the Gauss-Newton method.
ANN assessment ANN models were assessed using a k-fold cross-validation technique, multiple R values, and by comparing mean and maximum ice thicknesses. The evaluation of the developed ANN model was attempted based on k-fold cross-validation. This k-fold crossvalidation technique was applied to split the dataset for better generalization and to avoid the overfitting problem and provide a stable estimate of the model error  (Little and others, 2017). The first fold is kept for testing and the model is trained on k -1 folds. The process is repeated 10 times and each time different fold or a different group of data points are used for validation. Data points for training, testing and validation sets were taken randomly from all the available transects in both models and these points change depending on each fold.
Error analysis ANN modelled ice thickness data are continuous since they are based on the DEM. GPR-based ice depth observations are discontinuous, and data are often missing at the end of transects where the terrain is rough and GPR signals are weak due to steep valley walls  (Azam and others, 2012). Comparisons of observed and modelled ice thicknesses were made with datasets where (a) only the observed values were considered and (b) missing ice thickness observations were interpolated from nearby observations. Errors in modelled ice thickness are based on root mean squared error (RMSE) and the mean bias error. There are four sources of error: (1) error in GPR thickness, (2) mean average bias of ABT and AAT, (3) error in kriging interpolation for ABT and for AAT, and (4) error in DEM. To calculate the total errors in the resulting ice thickness estimation and total volume calculation, quadratic addition was applied based on the abovementioned four error sources  (Vanlooy and others, 2014).
The ABT model also showed greater deviations for all sections on the east side. The reason behind this shift may be the topographic pattern of the eastern sidewall of the glacier, given that the transverse section also included a portion of the glacier sidewall (300 m) in the input and training datasets. Another reason may be related to the interpolated values of GPR data at the sides due to the complexities of data collection. To understand the relationship of ABT and AAT data points with GPR data points, the Pearson R was used. The values of R for ABT and AAT are given in Table  S3. AAT performed better than ABT for cross sections 1-5, as suggested by the R values. ABT showed the highest correlation for cross section 3 whereas AAT showed the best correlation for cross section 2. The differences are probably due to the fact that AAT used partial GPR information of four points per profile randomly whereas ABT used only extrapolated values as targets.
74 ± 0. 25 km 3 derived by  Ramsankaran and others (2018). Vashisht and others (2017) used glacier flow mechanics, thickness-area scaling and volume-area scaling methods and estimated the volume of Chhota Shigri Glacier as 0. 55, 1. 40 and 7. 15 km 3 , respectively. Interestingly, the estimated volume using the volume-area scaling method was 13 times higher than the volume estimated from glacier flow mechanics. Singh and others (2012)  used the thickness-area scaling method and GPR data and computed a volume of 1. 20 km 3. Dobhal and others (1995)  used residual Bouger values to compute a volume of 0. 64 km 3.
Outlook Worthwhile directions for future study would be to investigate the potential of deep learning networks such as convolution neural networks (CNNs) with more number of parameters such as surface velocities (e. g. Dehecq and others, 2019)  and mass balance (e. g. Wu and others, 2019)  for more number of glaciers. The more the model is constrained with input predictors, the less dependency on data it will have. Another future scope of the present research will be to apply the ABT model on additional datasets for testing.
Acknowledgements. Mohd Anul Haq would like to thank the  Deanship of Scientific Research at Majmaah University  for supporting this work under Project No. R-2021-10. This work has also been supported by  IFCPAR/ CEFIPRA  within project no. 3900-W1  and by the French GLACIOCLIM observation service. MFA acknowledges the research grant provided by the  INSPIRE Faculty award  ( IFA-14-EAS-22 ) from the  Department of Science and Technology (DST, India). The authors thank three anonymous reviewers for their constructive comments and suggestions. The authors would like to warmly thanks Scientific Editor  Dr Joseph Shea  for numerous editorial revisions and suggestions. The authors are highly thankful to  Holger Frey  for sharing 90 m resolution GlabTop model data and  Raaj Ramsankaran  and  Ankur Pandit  for sharing GlabTop2 model data.
Highlights • Objectives To study the relation among Spike protein mutations, the emergence of relevant variants and the multi-wave pattern of the COVID-19 pandemic. • Setting Genomic sequencing of the SARS-CoV-2 Spike proteins in the UK nations (England, Scotland, Wales). Epidemiological data for the number of infections in the UK nations, South Africa, California and India. • Methodology We design a machine learning algorithm, based on the Levenshtein distance on the Spike protein sequences, that leads to a temporal clustering of the available dataset, from which we define emerging persistent variants. The above allows us to introduce the epidemiology of variants that we described via the Mutation epidemiological Renormalisation Group (MeRG) framework. • Results We show that: 1. Our approach, based only on the Spike protein sequence, allows to efficiently identify the variants of concern (VoCs) and of interest (VoIs), as well as other emerging variants occurring during the diffusion of the virus.
In this work, a mutation is a single change in the amino acid sequence of the Spike protein (substitution, addition, deletion), while a Spike variant, or simply variant, is a unique sequence of amino acids in the Spike protein that appears in clusters. Like other coronaviruses, the SARS-CoV-2 has relatively low mutation rates  2  , nevertheless the current COVID-19 pandemic has seen the emergence of several epidemiologically relevant variants. Efficient nucleotide sequencing has allowed to track sequence mutations along the genome of SARS-CoV-2, and to identify dangerous variants  3, 4  that appeared to increase the infectivity compared to the initial form that was sequenced from the outbreak in Wuhan, China  5  (GenBank: MN908947. 3). Since the second half of 2020, variants of concern (VoCs) and of interest (VoIs) have been identified in various regions of the world.
To combine branches that contain more than one sequence, we used Ward's method, after having checked that other choices do not significantly affect the results (more details in the supplementary material). The tree is completed when all sequences are grouped into a single branch. To define the clusters, we considered a cutoff in the distance so that branches whose Ward distance is larger than the cutoff are considered as separate clusters. We applied the same cutoff to all branches. As England has the largest available sequencing sample, with 646. 697 sequences as of the end of August 2021, we mainly focused on this dataset. This minimises statistical and sampling bias errors. After pruning, 461. 122 sequences were retained, out of which we identified 13. 887 distinct ones.
Considered altogether (see Fig. 4B ), this analysis can allow us to identify regions of the Spike protein that also provide hints for more efficient targeting in monitoring or pharmaceutical interventions. In the left column we show the total number of sequencing available on GISAID (in colour the ones associated to the relevant VoC or VoI); the middle column shows the number of new daily infected (per 100. 000 inhabitants); the right column shows the percentage of each VoC or VoI in the sequencing data. All plots show daily rates, with data smoothened over a period of 7 days. In the middle plots, the data are shown by dots, where blue corresponds to the total and the colours show the number of infected associated to each variant. The solid lines show the result of the fits to the MeRG model (note that only for the UK we fit the "standard variant" -in greenwith two logistic functions).
We saw empirically from Fig. 2  that persisting chains contain at least three clusters, hence an emerging variant could be defined only two months later (January 2021 for the Alpha VoC). To improve the performance of the ML algorithm in terms of prediction, we reanalysed the same data using a weekly-   2. Early warning for the Alpha VoC. Results of the ML analysis applied to weekly binned data after the first detection of the Alpha VoC in England. The columns contain the 2020 calendar week number, with the initial date (Monday), the total number of sequences in the dataset for each week and the percentage of Alpha VoC sequences identified a posteriori in the data. The fifth column contains the minimal number of clusters in the ML output that allows to isolate the Alpha VoC cases. When this indicator is equal to 1, an early warning can be issued (week 44).
We validated the method with both monthly and weekly time binning. We applied the algorithm to the sequencing data from England, which offers the largest dataset on the GISAID open-source genome repository. Among the emerging variants, the officially recognised VoCs (Alpha and Delta) were clearly identified and isolated. Similar results for Wales and Scotland (despite a more limited number of available sequences) confirmed the effectiveness of the algorithm, while comparison of our approach (that uses data of the Spike protein sequences only) to other informed methods based on the complete genome validated the algorithm. Furthermore, the temporal organisation of clusters into chains served as a tool not only to monitor the genetic evolution of the Spike protein but also to help shed light on its mechanisms.
Figure5. MeRG model for epidemiological data of variants. Results of the MeRG fitting of the number of infected associated to each relevant variant. Each row corresponds to a geographical region. In the left column we show the total number of sequencing available on GISAID (in colour the ones associated to the relevant VoC or VoI); the middle column shows the number of new daily infected (per 100. 000 inhabitants); the right column shows the percentage of each VoC or VoI in the sequencing data. All plots show daily rates, with data smoothened over a period of 7 days. In the middle plots, the data are shown by dots, where blue corresponds to the total and the colours show the number of infected associated to each variant. The solid lines show the result of the fits to the MeRG model (note that only for the UK we fit the "standard variant" -in greenwith two logistic functions).
For the astrophysical measurements of α we consider both the currently available data and a simulated dataset representative of Extremely Large Telescope measurements and expected to be available in the 2030s. Our parametric analysis shows that in the latter case the inclusion of astrophysical and local data improves the Euclid dark energy figure of merit by between 8% and 26%, depending on the correct fiducial model, with the improvements being larger in the null case where the fiducial coupling to the electromagnetic sector is vanishing. These improvements would be smaller with the current astrophysical data. Moreover, we illustrate how a genetic algorithms based reconstruction provides a null test for the presence of the coupling. Our results highlight the importance of complementing surveys like Euclid with external data products, in order to accurately test the wider parameter spaces of physically motivated paradigms.
Available and future data The purpose of this work is to constrain canonical scalarfield based dynamical dark energy models which allow for the possible variation of the fine structure constant, as detailed in Sect. 2, using both currently available data and the ones expected from future surveys. In particular, we will use data from observations of quasi-stellar object (QSO) spectral lines from archival datasets and dedicated measurements, complemented by laboratory constraints, detailed in Sect. 3. 1, as well as forecast data for the future measurements of ∆α/α from the Extremely Large Telescope (ELT) as discussed in  Leite et al. (2016) ; the assumptions made to generate mock datasets for this experiment are shown in Sect. 3. 2. However, as pointed out in  Calabrese et al. (2014)  and as can also be seen in Eqs.
Overall, this dataset includes measurements up to redshift z ∼ 4. 18. Additionally, the current drift rate of α is constrained by local comparison experiments between atomic clocks, with the most stringent bound being the one by  Lange et al. (2021) D| obs ≡ α α 0 = (1. 0 ± 1. 1) × 10 -18 yr -1. ( 12 ) Last but not least, we also use the recent MICRO-SCOPE bound on the Eötvös parameter of  Touboul et al. (2019) η = (-0. 1 ± 1. 3) × 10 -14 , (13) which, as previously discussed, constrains the model's coupling to the electromagnetic sector. In the rest of the paper, we refer to the combination of all these data as current α data, and we will show the constraints produced by such a combination. However, we note that the 293 archival data an the 26 dedicated ones are in slight tension with each other  (Martins 2017; Martins & Vila Miñana 2019).