dataesr/scientific-paragraphs-categorization huggingface dataset:
Fig. 2  Chemical and biological space (compound-target interaction space) of protease inhibitor dataset. The PCA analysis of chemical descriptors and amino acid descriptors shows an overlap of compound properties per target in PC1 and PC2 space. PC1 (34% variance) shows that the target descriptors influence the spread of data points, clustering the compounds belonging to FA10 and TRY closer to each other, with THRB forming an outer cluster (also shown in phylogram Fig. 1B ). PC2 explains the distinct clusters of seven different species (represented with different shapes) used in the study. The dataset has maximum information of human targets, however, the additional orthologous information is distinctly visible using binding pocket features. and biological space by addition of combined bioactivity data.
When the loadings of each principal component were investigated, it was found that maximum variance was explained by the binding pocket sequence descriptors in the biological space. PC1 is loaded mainly by first and second principal components of the Z-scales (34% variance), which summarises the lipophilicity (Z1) and steric bulk properties (Z2) of amino acids, whereas, PC2 (26% variance) is more related to Z-3 of the most varied amino acids in the pocket sequence (e. g. position 56, 140, 177 and 231), explaining the properties like electrophilicity and electronegativity of amino acids. 77 PCA also showed that the orthologs are within the targets space defined by FA10, THRB, and TRY. Hence we expected to be able to include them in our data set.
Here Z-scales (3) were selected in spite of better performance of Z-scales (5) (RMSE test = 0. 69 vs. 0. 70 pChEMBL units) because of a number of arguments. Firstly, Z-3 (162 bit vector) utilizes less variable space than Z-5 (270 bit vector). Secondly, the 3 primary Z-scales were derived from the properties of natural amino acids, whereas, Z-4 and Z-5 resulted in an extended PLS analysis required to expand the original dataset to 87 amino acids (including non-natural). The 4th and 5th are hence more difficult to interpret moreover they have been found to add little to performance in previous PCM models. 15, 77 Although there is room for improvement in binding site definitions for protease targets, e. g. by refining the description of the binding sites, these data indicate that the explicit introduction of binding site residue descriptors provide a signal on par with full sequence descriptors but provide better interpretability.
74 and RMSE = 0. 74 pChEMBL units. These results are in agreement with the study of Bender et al. ,  78  where the authors reported that 1D molecular descriptors (e. g. molecular weight or atom counts) play a significant role in enhancing the enrichment of active compounds in virtual screening. The high performance of models trained on binding site volume indicates that descriptors accounting for structural information of the binding site lead to models displaying slightly worse performance than those trained on more sophisticated Table  1  Performance analysis of the protein descriptors used in the PCM model. The complete dataset containing 20 213 data points on 67 protease targets were employed in a PCM model using structural fingerprints and protein descriptors. All protein descriptors showed an approximately similar performance.
Comparison of PCM with QSAR In order to assess whether the inclusion of explicit protein information improves model performance, we trained models on exclusively compound descriptors using the datapoints annotated on a given target (individual QSAR models), or all available datapoints, termed as global or Family QSAR (Table  2 ). 17 The global QSAR model exhibited significantly worse performance than PCM (RMSE QSAR = 1. 09, RMSE PCM = 0. 70 pChEMBL units). Similarly, PCM also outperformed per target QSAR models, with mean RMSE QSAR and RMSE PCM values on the test set of 1. 05 AE 0. 27, and 0. 66 AE 0. 20 pChEMBL units, respectively. This indicates that the explicit inclusion of target information in PCM improves the prediction of bioactivities of the compounds on this dataset.
This decrease in performance was expected, as 35% of the datapoints in the dataset are annotated on FA10 and THRB. Hence, removing either of them removes a large fraction of the compound-target interaction space and thus deteriorates model performance. On the other extreme, TMPS and CTRL are annotated with 0. 85% of the datapoints and present an average similarity to the other targets considered of B43% (Fig. 1B ), which is plausibly the reason for the poor performance of their corresponding LOTO models. The low performance of the LOTO model for the CTRL subfamily, with an RMSE equal to 1. 37 AE 0. 57, likely arises from the low similarity of the members of this family with respect to the other proteases considered here. This can be seen in Fig. 1A , where the CATG subfamily clusters along with HEPS, KLKB1 and other diverse member of TMPS.
Applicability domain The results from our applicability domain analysis are shown in Fig. 5 , which reports the compound similarity averaged over the 5 nearest neighbours against the RMSE values for the compounds in the test set. We obtained high RMSE values, up to 3 pChEMBL units, for compounds displaying a neighbouraveraged similarity value below 0. 92. RMSE values gradually decrease, to a minimum value of 0. 02 pChEMBL units, as the compound neighbour-averaged similarity increases. In practice, a new compound exhibiting a neighbour-averaged similarity value equal to or greater than 0. 90 is likely to be predicted with good accuracy. Nevertheless, we observed some outliers. For instance, two compounds (CHEMBL345710, CHEMBL109601) having a similarity value of B0. 94 were found to have an exceptionally high prediction error, namely 3.
21 and 3. 11 pChEMBL units on FA10 and CATG targets. One of these compounds (CHEMBL345710) displays pChEMBL values of 4. 20, 4. 67 and 6. 07 towards FA10, THRB and TRY, whereas the other compound (CHEMBL109601) exhibits bioactivity values of 4. 08, 6. 27, 8. 07 and 6. 36 pChEMBL units towards CATG, TRY, CEL and CMA. Together, these observations indicate that our models could not predict the selectivity of these two compounds towards FA10 and CATG, as the bioactivities of their closest neighbours are considerably higher, namely more than 2 pChEMBL units. In conclusion, these data indicate that estimating the error for individual predictions constitutes a valuable source of information in PCM-guided drug discovery campaigns. 80 e also performed an applicability domain analysis on the target space.
Absence of methylamino-2-butenoic acid was predicted to be beneficial for compound activity on average against FA10 but detrimental against THRB and TRY (Fig. 6c ). However, only one compound in our dataset exhibited this feature, and we could not find structural evidence in the literature for the interaction of this feature with THRB, TRY or FA10. Thus, the paucity of experimental data does not permit a complete interpretation of the general influence of this feature on the inhibition of these three proteases. In addition to these features, we predicted that absence of a prolinamide decreased the predicted bioactivity against THRB by on average 0. 10 AE 0. 66 pChEMBL units (Fig. 6d ), whereas the inverse picture was predicted for FA10 and TRY. When investigating the crystallographic structures available for THRB (PDB IDs: 1AE8 and 3RMM), we found that most of the THRB inhibitors containing this feature interact with GLY-216 and GLY-219.
However, the usage of binding site amino acid descriptors enabled a biologically meaningful interpretation of the models in agreement with the scientific literature. Similarly, the description of compounds with keyed fingerprints has permitted a chemically meaningful interpretation of the PCM models. This analysis has singled out compound substructures influencing compound potency and selectivity towards particular proteases, such as primary and tertiary sulphonamides for the selective inhibition of FA10, methylamino-2-butenoic acid for the inhibition of THRB and TRY, and prolinamide for the selective inhibition of THRB. Overall, the proteochemometric approaches applied on this dataset of serine proteases enabled us to interpret the target information in a meaningful way, which also shows the benefits/ strength of incorporating protein-related information in computational chemogenomics.
Acknowledgements The authors thank  Chad H. G. Allen  and  Lewis Mervin  for proof reading the manuscript. Q. U. A. thanks the  Islamic Development Bank and Cambridge Commonwealth Trust for Funding. O. M. L. is grateful to  CONACyT  (No. 217442/312933 ) and the  Cambridge Overseas Trust  for funding. G. v. W thanks EMBL (EIPOD) and  Marie Curie (COFUND)  for funding. A. B. thanks  Unilever  and the  ERC  (Starting Grant  ERC-2013-StG 336159.
In order to tackle the problem of the computational load in calculating the inverse of the FIM, a method based on the calculation of the local impulse response and the variance, in a single point, from a single row of the FIM, has been previously proposed for system design. However this approximation (circulant approximation) does not capture the global interdependence between the variables in shift-variant systems such as SPECT, and cannot account e. g. , for data truncation or missing data. Our new formulation relies on subsampling the FIM. The FIM is calculated over a subset of voxels arranged in a grid that covers the whole volume. Every element of the FIM at the grid points is calculated exactly, accounting for the acquisition geometry and for the object. This new formulation reduces the computational complexity in estimating the uncertainty, but nevertheless accounts for the global interdependence between the variables, enabling the exploration of design spaces hindered by the circulant approximation.
Every element of the FIM at the grid points is calculated exactly, accounting for the acquisition geometry and for the object, without further approximation. This new formulation, presented in Section II-C, reduces the computational complexity in inverting the FIM but nevertheless accounts for the global interdependence between the variables. The aim of this paper is to describe the new approximation and to explore its use for the optimization of SPECT systems; emphasizing how it enables us to explore the design of highly shift variant systems (as a result of distance dependent resolution, data truncation or adaptive data sampling). Such systems include the standard rotating camera with parallel hole collimator and an adaptive system for cardiac imaging, similar to the commercially available D-SPECT.
Section III describes several simulation studies, with the purpose of illustrating our novel methodology. We show how our new algorithm applies to the optimization of a parallel hole collimator for SPECT and how it can be employed to evaluate the reconstructed image quality in the case of truncated projection data and for different acquisition protocols for the D-SPECT system. In order to illustrate the reliability of our approximation, all the results presented in this paper are compared with the reference statistical method and with the circulant approximation method. Moreover, we evaluate the effect of the choice of the subsampling scheme for the optimization of the parameters of the aforementioned imaging systems. The results are presented in Section IV and discussion of the usefulness and limitations of the algorithm are presented in Section V.
2 ). Finally, the thread computes the integral (2-b-ii) in the intersection box. After completion of the partial FIM for a single camera, the process is repeated for another camera, accumulating the elements of the FIM, as, according to (9), information is additive. Computation times are reported in Table  I. The GPU-accelerated algorithm for the computation of the FIM has been integrated in the Niftyrec reconstruction software toolbox  [21]  and has MATLAB and Python interfaces which enable real time scripting interaction and full flexibility in the definition of the grid. ) for each Gamma camera (grey plane) position, the activity is resampled on a regular grid parallel to the camera plane and projected; 2) the grid points (yellow spheres) for the FIM are reinterpolated on the same parallel grid; 3) for each pair of points in the FIM grid, the FIM element is updated with the information relative to the current camera, which only depends on the region of the projection (black square) where the two PSFs (red square) and (blue square) intersect.
11 ). The pD-SPECT system parameterized with and the truncated single-camera SPECT system are both presenting a data truncation problem. However, the two systems differ substantially in that the SPECT camera rotates by 360 around the center of the imaging volume, acquiring information from all directions, while the nine cameras of the pD-SPECT system are essentially static for , acquiring information from only nine directions. The performance of the D-SPECT system increases abruptly as one moves away from due to the increased angular sampling.
In fact, the subsampled FIM method has shown to enable the exploration of design spaces previously precluded by the use of the circulant approximation, such as the evaluation of effects of data truncation in interior tomographic imaging. The recent development of adaptive SPECT systems has introduced a class of optimization problems where the parameters of the imaging system may be modified in order to image certain desired properties of the underlying object and in order to adapt, during acquisition, in response to the projection data. The D-SPECT is an example of such a system, where the acquisition protocol (in terms of the trajectory of the cameras) can be modified depending on the data acquired during the scan. In order to adapt the response of the system during acquisition, a set of different design parameters have to be compared in real time.
In this paper, we use the BDD algorithm proposed in  [32, 35]  to solve the linear system arising from a nonconforming discretization of DFN. The coarse level is defined following  [37]  and balancing is implemented as a preconditioning matrix  [21]. The algorithm is implemented in C++ in the parallel software SIDNUR  [35]. For DFN, choosing one subdomain given by one fracture, instead of a set of fractures has shown to be the most time saving decomposition  [35]. The paper is organized in four sections. Section 2 describes the flow model. Section 3 recalls the linear system derived from a nonconforming discretization of the DFN. Section 4 is the main contribution of this paper and presents the decomposition in local matrices. We apply the BDD method proposed in  [32, 35]  for networks satisfying some hypotheses on the mesh.
Conclusion This paper describes a Balancing Domain Decomposition method, implemented in the so-called SIDNUR solver, to simulate flow in DFN with a nonconforming mesh. DFN and local matrices are generated with the so-called MP FRAC software. Our current work is to extend the method to more general discretizations, which do not satisfy hypotheses (H1) -(H2), in the perspective of solving linear systems with several millions of unknowns. The parallelism of SIDNUR will be very helpful to reduce the time and memory costs. Moreover the very basic technic we use to coarsen the mesh could be improved by defining suitable a posteriori estimators.
Polar vortices on Mars provide case-studies to aid understanding of geophysical vortex dynamics and may help to resolve long-standing issues regarding polar vortices on Earth. Due to the recent development of the first publicly available Martian reanalysis dataset (MACDA), for the first time we are able to characterise thoroughly the structure and evolution of the Martian polar vortices, and hence perform a systematic comparison with the polar vortices on Earth. The winter atmospheric circulations of the two planets are compared, with a specific focus on the structure and evolution of the polar vortices. The Martian residual meridional overturning circulation is found to be very similar to the stratospheric residual circulation on Earth during winter. While on Earth this residual circulation is very different from the Eulerian circulation, on Mars it is found to be very similar.
, 2005; Newman and Nash, 2005) , with theories suggesting dominant roles for the interaction of eastward-travelling waves with quasi-stationary planetary waves forced in the troposphere  (Krüger et al. , 2005)  as well as enhanced tropospheric cyclogenesis  (A. O'Neill, 2013; personal communication). Importantly, when such unprecedented events occur, great advances in a field can be made. Due to the possible presence of SSW-like events on Mars, in this study we test whether Mars is a suitable analogue to Earth by considering both the mean state and variability of the vortices. Specific RPW case-studies will be addressed in future work. The article is organised as follows; section 2 gives an overview of the reanalysis datasets used for each planet and defines the various analysis diagnostics used.
Description of reanalyses Retrospective analyses (or reanalyses) combine state-of-the-art, high-resolution models with incomplete and noisy observations to produce a uniform-in-time, regularly sampled estimate of the atmospheric state. On Earth there are many such reanalyses to choose from. Here, we use the MERRA as it has high vertical resolution (72 vertical levels), a high model lid height (0. 01 hPa) and spans a period of more than 30 years from 1979 to 2011  (Rienecker et al. , 2011). Data are available at 6 h intervals on a 1/2 • latitude × 2/3 • longitude grid. We also used the ECMWF ERA-Interim reanalysis  (Dee et al. , 2011)  to confirm our results. The two reanalyses agree exceptionally well for all the diagnostics used here, so we display results only using MERRA. For Mars, only one reanalysis dataset is publicly available to date, although other reanalysis projects are ongoing (e.
g. Greybush et al. , 2012). We use the MACDA reanalysis for MGS/TES observations, spanning the period 1999-2004 (which corresponds to the period from late northern summer in MY 24 to late northern spring in MY 27) †. This dataset, publicly available online via the British Atmospheric Data Centre  (Montabone et al. , 2011) , is described in  Montabone et al. (2014). The MACDA reanalysis uses the Analysis Correction scheme  (Lorenc et al. , 1991)  in a MGCM based on the UK spectral version of an earlier version of the Laboratoire de Météorologie Dynamique MGCM  (Forget et al. , 1999). The assimilated observations consist of retrieved thermal profiles, and column dust optical depths from MGS/TES infrared atmospheric soundings in nadir view  (Smith, 2004). The Mars atmospheric reanalysis has 25 vertical levels in terrain-following (σ ) coordinates, approximately from 5 m up to 100 km altitude, corresponding to average pressures between ∼6.
, 2009)  on board NASA's Mars Reconnaissance Orbiter during  MYs 28, 29 and 30 (2006-2011)  show a peak at high latitudes in both winter hemispheres in the climatological PV profiles, more prominent around 350 K (not shown here). This is consistent with what is observed in the TES MACDA assimilation. MCS limb observations have higher vertical resolution and extension than TES nadir observations, allowing MCS thermal profiles to be retrieved up to 80 km altitude with twice the resolution of TES profiles. The fact that an annular shape for the Martian vortices is observed in the assimilation of two very different datasets at two different periods gives us some degree of confidence that this is a robust result. However, this may also mean that the reanalysis is more poorly constrained above 40 km; for that reason we primarily concentrate our analysis on levels below 40 km (except when analysing the full vertical structure of the vortex).
Polar warming events on Earth (SSWs) are often defined in terms of daily zonal mean zonal wind at 60 at 10 hPa, which is directly below the jet maximum. Figure  7(a, c ; grey lines) show this diagnostic for all years  in the Earth reanalysis dataset, where (b) and (d) show also the polar cap temperature at the same height. The black lines show the same diagnostics but for Mars, and at 1 hPa (also the lower flank of the jet). On Earth the zonal winds and polar cap averaged temperatures are far more variable in winter than in summer, and this is because Rossby waves cannot propagate in the summer easterly wind regime  (Charney and Drazin, 1961). The Martian zonal winds and cap temperatures (black lines) seem to be less variable than the corresponding terrestrial diagnostics during winter, although we note that it is hard to draw any strong conclusions as there are only three years of data available.
The annular nature of the PV is clearly observed up until L s = 318 • where the PV is weakened and shifted equatorward, much like a terrestrial SSW event. The vortex continues to weaken, reaching a minimum at L s = 323 • , from which point it recovers. The recovery of the polar vortex on Mars is much faster than on Earth, presumably due to the faster radiative time-scales. One clear difference between daily averaged PV maps on the two planets is the lack of PV filamentation on Mars. This may be an artifact of the resolution of the MACDA data, although similar resolution data of the terrestrial polar vortices still exhibit filamentation. On Earth, PV filaments are constantly stripped away from the main vortex air mass and mixed into the background flow, and this is due to the interaction of the vortex with large-scale Rossby waves, a process that seems to be absent in the MACDA reanalysis dataset.
Concluding remarks In this study we compared and contrasted the structure and evolution of the polar vortices on Mars with those on Earth. Using one of the first Martian reanalysis datasets, we have analyzed the mean state and variability of the Martian polar vortices to determine how similar in nature they are to the terrestrial polar vortices. In doing so, we have provided the first step to determine if useful analogies between SSWs on Earth and rapid polar warmings on Mars can be drawn. Specific dust storm events on Mars and their impact on the polar vortex structure will be addressed more comprehensively in future work. ls = 313, MY26 (a) (b) (c) (d) (e) (f) (g) (h The key differences and similarities between the terrestrial and Martian polar vortices are: 1. The residual meridional winter circulation is found to be very similar on both planets, and in both hemispheres.
Acknowledgements The authors would like to thank the two anonymous reviewers and  Darryn Waugh ,  Michael McIntyre  and  Ben Harvey  for useful comments on the manuscript. Thanks also to  Will Seviour  for providing TEM code for Earth,  Tao Ruan  for providing omega data for the Mars reanalysis, and  Peter Hitchcock  for providing code to work with Mars spectral model outputs. DMM is supported by a grant from the  UK Natural Environmental Research Council (NERC). LM's work on the Martian polar vortices is partly supported by the  USA National Aeronautics and Space Administration  under grant no. NNX13AK02G  issued through the Mars Data Analysis Program 2012. ST is supported by an  STFC  studentship.