dataesr/scientific-paragraphs-categorization huggingface dataset:
Finally, the PLUMED-ISDB  35  module has been contributed by a subset of PLUMED developers and enables different types of experimental data, such as measurements from nuclear magnetic resonance spectroscopy, small-angle X-ray scattering, and cryo-electron microscopy, to be integrated into MD simulations. All the collective variables and enhanced-sampling approaches contributed via these modules can be seamlessly combined with all the other functionalities implemented in PLUMED. Each module is accompanied by documentation and tutorials that are integrated in the online PLUMED documentation.
Box 3: PLUMED-NEST, the public repository of the PLUMED consortium A public repository has been created to collect contributions from members of the PLUMED consortium and from other PLUMED users. Dubbed PLUMED-NEST, it hosts the PLUMED input files and the links to all the data required to reproduce the results of enhanced-sampling simulations or analysis that have been carried out with PLUMED. Currently, PLUMED-NEST already contains more than 40 examples of applications and method developments in the areas of computational biology, chemistry, and material science.
Protein dynamics is pivotal to biological processes. However, experiments are very demanding and difficult to perform, and all-atom molecular dynamics simulations can still not provide all the answers. This motivates us to analyze protein dynamics in terms of different reduced coordinate representations. We then need to resolve how to reconstruct the full all-atom dynamics from its coarse grained approximation. Accordingly we scrutinize allatom molecular dynamics trajectories in terms of crystallographic Protein Data Bank (PDB) structures, and inquire to what extent is it possible to predict the dynamics of side chain Cβ atoms in terms of the static properties of backbone Cα and O atoms. Here we find that simulated Cβ dynamics at near physiological conditions can be reconstructed with very high precision, using the knowledge of the crystallographic backbone Cα and O positions.
Thus, these two atoms have a particularly important structural information content. Unexpectedly, it was found that the motions of the peptide plane O atoms and the side chain Cβ atoms could be reconstructed with high precision solely from the knowledge of the Cα atom dynamics, using a statistical analysis of static crystallographic protein structures in Protein Data Bank (PDB)  [23]. The results suggest that in an isolated dynamical protein, at near physiological conditions, the motions of its O and Cβ atoms remain strongly slaved to the Cα dynamics, to the extent that they are more or less only subject to modest thermal fluctuations. If this is indeed the case, the problem of protein dynamics at physiological conditions could be much simpler than expected: If the motions of different atoms are strongly correlated and in a systematic manner, many aspects of protein dynamics could be modelled by an effective coarse grained energy function, formulated in terms of a reduced sets of coordinates that describe only the Cα atoms  [24] [25] [26]  or a group of atoms including the Cα as effective interaction centers  [27] [28] [29].
• If we have more than one pair of matching amino acids in the PDB subset, we use the average values of the ensuing Cβ coordinates for our prediction. • If there are no PDB entries with matching amino acid pairs, we use the average value of Cβ coordinates (θ, ϕ) of all PDB structures in the given subset [Δκ;Δτ;Δψ] for our prediction. • Finally, if the PDB subset [Δκ;Δτ;Δψ] has no entries, we use a neighboring subset for our prediction. In selecting the neighboring subset, we move clockwise in along the strip of O atoms, then clockwise in terms of the torsion angle. In Step 2, the average latitude value κ ave is simply k ave ¼ 1 N X N i¼k k k where the summation is over all elements in the given subset [Δκ;Δτ;Δψ]. For the average longitude value τ ave we first define X ¼ 1 N X N i¼k cos t k & Y ¼ 1 N X N i¼k sin t k and R ¼ ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi X 2 þ Y 2 p The average value is then obtained from cos t ave ¼ X R & sin t ave ¼ Y R We compute the average longitude value ψ ave in the same way.
5 cm. Light was reflected by a dichroic mirror (FF458-Di02, Semrock) through a 60× 1. 0 NA water immersion objective (LUMPLFLN 60× W, Olympus) to the sample. Emission light was long-pass filtered with a cut-on wavelength of 455 nm. Exciting fura-2 at 420 nm reduced its dynamic range, but still allowed calibration. For Ca 2+ imaging we utilized a low resolution (80 × 80 pixel), but highly sensitive NeuroCCD camera (RedShirt Imaging, Decatur, GA, United States) mounted on the front port of U-TRU. This camera was controlled by Neuroplex software (RedShirt Imaging) and imaging time series were recorded in Neuroplex (. da file format). Post hoc separation of imaging channels was performed by a script available on github 1.
Software We used the Arduino IDE (Arduino CC) with the Teensyduino plugins installed 2  to program Teensy. PropellerIDE software (Parallax) was used to program the microcontroller for POPSAR. We used µManager (RRID:SCR_000415)  (Edelstein et al. , 2014)  for integrating microscope illumination and camera control with TEAMSTER. RedShirt Imaging NeuroCCD cameras were controlled by Neuroplex software (RRID:SCR_016193, v. 10. 1. 2 and v 10. 2. 1, RedShirt Imaging) and image acquisition trigger signals were detected by TEAMSTER and SLIDER.
Once triggered, the fluorescence LED is activated for the full defined frame duration. TEAMSTER is an affordable hardware interface (Supplementary Table  2 ) between the µManager software to control two LED drivers via TTL outputs based on pre-programmed and user selectable modes. The effectiveness of integrating TEAMSTER into patch-clamp experiments is demonstrated by two examples (Figure  2 ). We used a green LED (530 nm) for epifluorescence excitation and a near-IR LED (730 nm) for transmitted light illumination with either oblique illumination contrast or Dodt contrast. In combination with a long-pass emission filter, fluorescence and transmitted images can then be projected on the same camera. Timing is provided by controlling LEDs via programmed and user selectable modes in µManager.
We tested the functionality by targeting cut axon blebs  (Shu et al. , 2006; Kole et al. , 2007)  from pyramidal neurons in layer 2/3 and 5, which were filled with Alexa dyes during somatic whole-cell recordings  (Kole and Popovic, 2016). After ∼30 min neurons were sufficiently filled and the axon bleb could be clearly identified and targeted for electrical recording (Figures  2B, C ). To live overlay the epifluorescence image of the Alexa dye with a transmitted light image we ran an additional script in µManager. The script assigns look-up tables and continuously refreshes the combined image at up to 10 Hz. This approach allowed us to monitor the neuron morphology and the patch pipettes with a sufficiently high frame rate. We then targeted the fluorescence labeled axon bleb with a second recording pipette.
, 2012; Bosse et al. , 2015)  an affordable (<500 €) and versatile microscope illumination system can be achieved for a fraction of the price of commercially available systems. At the same time the end-user remains in full control to adjust software settings to experimental needs and has the flexibility to add and exchange components. Inspired by a system for rapid simultaneous imaging of two fluorescence probes  (Miyazaki and Ross, 2015)  we developed POPSAR and demonstrate its full functionality by simultaneous photo-stimulation and Ca 2+ imaging. Similar LED triggering paradigms were recently implemented in head mounted miniscope solutions  (Scott et al. , 2018; Stamatakis et al. , 2018). Stamatakis et al. (2018)  utilized genetically encoded GCaMP6 and a red-shifted opsin. However, in this study channelrhodopsin activation was still observed while the LED for Ca 2+ imaging was activated.
The results shows that Photometric PnP allows to determine the location of the light sources with better precision compared with classical methods, 1σ ≈ 0. 05 px, as it is shown in Figure  8. More accurate image coordinates translate in better determination of the relative target position with respect to the camera, LC. We show this comparing the position results with respect to a classical PnP algorithm. The reference for this comparison is obtained as follows. We exploit the fact that the target is static and we orient all the images with the well know bundle adjustment software Pix4D Mapper, with the scale being fixed by camera position priors from a GPS receiver and by ground control points. Such adjustment gives the reference camera poses and the target position with respect to a global frame W, Γ W C and L W , out of which the reference L C ref is computed.
Spatial extent and resolution The selection of areas for investment in GBI in the study area was based on planning units (PUs) that differed in size across the different aquatic ecosystems. According to the linear dependency along the river network  (Hermoso et al. , 2012) , for freshwater ecosystems we used river sub-catchments (i. e. , the river reach and its contributing area) derived from HydroSHEDS level 12 as PUs  (Lehner and Grill, 2013). Coastal PUs (i. e. 10 km buffer from the shoreline; EEA, 2015) and marine PUs were derived from two regular grids. The PU size in the coastal and the marine realm was a compromise between the resolution of the available data and the extent of the study area  (Lagabrielle et al. , 2018). PUs of the marine ecosystems were represented through a grid of 10 km × 10 km, whereas the coastal PU grid was 1 km × 1 km (Fig.
GBI spatial planning approach We propose a methodology to design a GBI network, based on stakeholder consultation, scenario development, management zones, related policies and EBM restoration measures (Fig. 2 ). We used Marxan with Zones software to identify a multifunctional GBI based on different prioritization features (i. e. , ES, biodiversity -species and habitat types-, ecosystem condition, protected sites and restoration measures). Marxan with Zones is an extension of Marxan software  (Watts et al. , 2009)  which applies a systematic conservation planning approach to optimize the allocation of different management zones. Marxan (Ball et al. , 2009)  was originally developed for designing protected site networks, but it has also used for establishing priority areas for restoration  (Adame et al.
Prioritization features For planning the GBI design we obtained accurate spatial data on the distribution of ecosystem services and the occurrence of biodiversity features (i. e. species and habitat types) over the study area. Ecosystem condition across the habitats guided the selection of restoration areas and was used as an additional criterion to prioritize habitat types among planning units. Existing protected areas and potential restoration sites were also considered as priority features to be included in the GBI design (restoration sites only included in the EBM scenario). 2. 3. 3. 1. Biodiversity. According to the data availability, we considered endangered species at the national level in Morocco and at the regional level in Spain (Andalusia region). Specifically, species occurrences of freshwater fishes, aquatic birds, amphibians, invertebrate species and characteristic plant species of aquatic and associated transitional ecotone habitats (dunes, sand and coastal cliffs) were included (Table  S1  and S2 in the Supplementary Material).
In order to achieve the representation of healthy ecosystems in the GBl, only habitat types at favourable ecosystem condition were included as prioritization features, but degraded ecosystems were used to identify the prioritization restoration areas (see below EBM restoration measures) where EBM measures should be implemented within the GBl (only included in the EBM scenario). 2. 3. 3. 4. Protected areas. In the northern section of the study area we considered Natura 2000 sites, namely the Special Conservation Areas (SCA), Sites of Community Interest (SCI) and Special Protection Areas (SPA), as well as the Coastal Areas of Special Protection as protected areas. In the southern section, we used the World Database on Protected Areas (UNEP-WCMC and IUCN, 2018), including the following sites: biological and ecological interest sites; marine protected areas (OSPAR); national parks; natural monuments, natural parks, nature places, nature reserves, peri-urban protected areas, permanent hunting reserves, Ramsar sites, wetlands of international importance; and specially protected areas of Mediterranean importance (Barcelona Convention).
Acknowledgments The designations employed and the presentation of the material in this publication do not imply the expression of any opinion whatsoever on the part of the Secretariats of  UNESCO  and  IOC  and the  European Commission , concerning the legal status of any country or territory, or its authorities, or concerning the delimitation of the frontiers of any country or territory. The ideas and opinions expressed in this publication are those of the authors, they are not necessarily those of UNESCO or the  European Commission  and do not commit them. This work was developed under the project  AQUACROSS Horizon 2020 Programme  -Grant Agreement no. 642317. We thank the  Regional Government of Andalusia , the  Department of Environment and Spatial Planning  and the  Network of Observatories and Environmental Information and Sustainable Development Networks of the Kingdom of Morocco  for their support during the project.
Funding This work was supported by the  European Union 's  Horizon 2020 Programme for Research, Technological Development and Demonstration  under Grant Agreement no. 642317project  AQUACROSS. SDL received funding from the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Skłodowska-Curie  grant agreement No. 748625. Thanks are also for the financial support, to CESAM ( UID/AMB/50017 -POCI-01-0145-FEDER-007638 ), to FCT/MEC through national funds (PIDDAC), and the co-funding by the  FEDER , within the  PT2020 Partnership Agreement and Compete 2020.
It is clear that this analysis bears high similarities to the STATIS method where the RV coefficient is used as a similarity index between the datasets  (Lavit, Escoufier, Sabatier & Traissac, 1994). A permutation test is performed in order to assess the significance of the overall agreement index. Following a similar strategy, we investigate the agreement of the respondents for each attribute taken separately. If the agreement index associated with a given attribute turns out to be non-significant, this means that this attribute has been misunderstood by the subjects or interpreted differently by them. Therefore, it is very likely that this inconsistency will result in a non-discrimination of the products. Drawing on selected case studies, we will compare the outcomes of the permutation tests regarding the consistency of the attributes with those of Cochran's test, which is commonly used to assess the discrimination of the attributes  (Meyners, Castura, Carr, 2013).
CLUSCATA while setting aside atypical respondents In a previous paper  (Llobell et al. , 2019) , a cluster analysis specially tailored for the CATA data -called CLUSCATA -was introduced. The rationale behind this method of analysis is to determine homogeneous clusters of respondents and, for each cluster, a group average dataset which is as close as possible to the datasets associated with the respondents in this cluster. From now on, the datasets % 1 = 1, … , associated with the various respondents are assumed to be scaled in order to have their norm equal to one. This is achieved by dividing the original binary datasets by their respective norms. This standardization makes it possible to account for the total number of checked attributes which may significantly differ from one respondent to another.
For what follows, we also need to extend the similarity index s to measure the similarity between two datasets which are not necessarily binary. This is given for two matrices = and > (say) by =, > = < =, > > = :?@AB => C D:?@AB == E :?@AB >> C. Obviously, this is the cosine between = and > considered as elements of the space of matrices of dimension ×. For more details, we refer to the paper by  Llobell et al. (2019). In that paper, we proposed an optimisation criterion that clearly highlights the rationale behind CLUSCATA. A new formulation of CLUSCATA which is more conducive to the 'noise cluster' idea is to seek to maximize the quantity: 4 = ∑ ∑ 8 % , F & 9 ∈H I J $)* 1 where F & = ∑. % ∈H I , with ∑. ∈H I = 1. F & is the group average dataset in cluster K $ , and the weight. in cluster K $ reflects the degree of agreement of respondent 1 with F &.
Clearly, this criterion reflects the idea that we are seeking clusters of respondents so that in each cluster, K $ , the datasets % are as much close (i. e. , high similarity) as possible to the group average dataset, F & associated with K $. As a matter of fact, we can show the following equality: ∑ || % , )* || = ∑ LF & L J $)* + ∑ ∑ || % - ∈H I J $)*. F & ||². Since ∑ LF & L J $)* ∑ || % , )* || P , which is equal to ∑ Q R S T SUR V , (i. e. , the overall homogeneity) also reflects how the W X are far removed from each others. The lower bound of this quantity, which is equal to Y/ , means there is no grounds for clustering (i. e. , between clusters variation negligible) and the upper bound, which is equal to 1, entails that we have perfectly separate clusters and within each cluster the datasets are similar (i.
e. , within clusters variation is equal to 0). In order to solve this maximization problem, a hierarchical algorithm followed by a partitioning algorithm was proposed  (Llobell et al. , 2019). We can also show that 4 = ∑ -$ J $)* where -$ is the largest eigenvalue of matrix S, which contains the pairwise similarities as measured by s between the respondents in group 3. As previously, we propose to assess the homogeneity in cluster 3 by / $ = 0 I , I where $ is the number of respondents in cluster 3. We will focus hereinafter on the partitioning algorithm, which is akin to the 3-means algorithm since the datasets are iteratively moved in and out of the clusters depending on their similarity with the average datasets of these clusters. In parallel, the average datasets are updated after each change (i.
e. , removal and inclusion of datasets in the clusters). The partitioning algorithm requires to fix the number of cluster and an initial partition of the respondents as a starting point. This can be achieved by examining the outcomes from the hierarchical strategy of clustering  (Everitt, Landau, Leese & Stahl, 2011; Llobell et al. , 2019). More precisely, the evolution of the aggregation criterion in the course of the clustering process is reflected by the lengths of branches of the dendrogram or hierarchical tree. Therefore, we can choose the number of clusters that corresponds to a significant jump of the aggregation criterion when passing from a partition with K clusters to a partition with K-1 clusters. The solution thus obtained is further improved by running the partitioning algorithm associated with CLUSCATA  (Llobell et al.
, 2019). We will discuss how the partitioning algorithm can be extended in order to identify and set aside the respondents who are atypical in that sense that they do not fit to the pattern of any cluster. Following Dave's idea  (Dave, 1991) , we introduce, besides the main Y clusters, an additional cluster called "noise cluster" or "K+1 cluster", which will contain the respondents who are deemed to be atypical. This strategy of analysis requires selecting a threshold value, [, between 0 and 1, below which the similarity between a dataset associated with a respondent and the average dataset associated with a given cluster is considered as weak. Naturally, the partitioning algorithm seeks to assign a respondent to the cluster for which this similarity is the largest but, if this similarity is smaller than the threshold value [, the respondent is assigned to the "K+1 cluster".
Formally, we seek to maximize the following criterion: 4 \ = ∑ ∑ 8] $ 8 % , F & 9 + ] J^* [ 9 , )* J $)* 2 where ] $ is the Kronecker symbol which is equal to 1 if the dataset % belongs to cluster 3 and 0 otherwise. We have the constraint ∑ ] $ = 1 J^* $)* , which stipulates that a subject belongs to one and only one cluster, including the noise cluster. To solve this maximization criterion, we run the following algorithm: Step 0 (initial partition). Choose a partition into Y clusters of datasets (i. e. , subjects) as a starting point. This can be done by a random assignment of the datasets at hand to Y clusters or by cutting the hierarchical tree at the level corresponding to Y clusters. Step 1 (group average datasets). In each cluster K $ , the group average F & is determined as a weighted average of the datasets in this cluster, where the weights are derived from the first eigenvector of the matrix which contains the pairwise similarities between the respondents in cluster K $.
Step 2 (changing clusters). New clusters of subjects are formed by moving each dataset, % , to the cluster K $ for which the quantity % , F & is the largest providing that this quantity is larger than [ otherwise, % is assigned to the cluster "K+1". Steps 1 and 2 are iterated until the datasets stop changing clusters. As a matter of fact, CLUSCATA is an adaptation of a clustering approach called CLUSTATIS to the particular case of CATA data  (Llobell et al. , 2019). Within the framework of CLUSTATIS, a strategy to select an appropriate threshold value was proposed and backed up by several considerations  (Llobell, Vigneau, Qannari, 2019). The same considerations could be applied to CLUSCATA. This leads us to select the following quantity: [ = ∑ `a % , F & % ^ a % , F 8& % b9 c d eUR , 3 where F & % is the weighted average dataset of the cluster to which the 1 :; dataset belongs, and F & % g is the weighted average dataset of the second nearest cluster to the 1 :; dataset, that is the cluster for which % , F & % g is the largest after % , F & % The rationale behind the selection of this parameter is to set up an intermediary value that delineates the boundary between the clusters, so much so that those subjects who are straddling two or more clusters are assigned to the noise cluster.
We have proposed an analytical expression for this parameter which, on the basis of the presented case studies, seems appropriate. It is clear that this parameter depends on the number of clusters, on the structure of the data (e. g. , within and between variation associated with the clusters), on the number of datasets in each cluster, etc. Further developments regarding these aspects are needed. This analytical strategy highlights an important issue related to the CATA experiment, that is, the number of attributes that are checked by each respondent for the various products. We proposed a standardization which consists in dividing each binary dataset by the square root of the total number of checked attributes for all the products. Notwithstanding, it still remains that those respondents who have a tendency to check more attributes will have a higher contribution to the analyses than those who check attributes more parsimoniously.