dataesr/scientific-paragraphs-categorization huggingface dataset:
095 s on S-waves. Figure4. NonLinLoc (v, w) , using the lower RMS/N origins (t, v) and using the strategy of  Bengoubou-Valérius et al. [2008] (u, w). The best origins overall location method and datasets are selected as preferred origins within the CDSA catalogue (x). Pink: 68% reduced ensemble (i. e. , ensemble of densest cells that include 68% of the origins). Red: slope of average RMS/N estimated in the 68% reduced ensemble (also given in annotation). The annotation provided the average RMS/N, the median and standard deviation for RMS and phase number, number of origins within (h) and outside (f) the histogram.
Error estimation on initial locations(a-c),locations made in yhis study(h-k)andoverall preferred origins (x). Each histogram represent the location RMS as a function of the number of P-and S-wave arrivals. Note that not all events have an origin and phase data in initial catalogue. Each histogram cell is coloured as a function of the number of included origins. There is an histogram for each location method and each dataset. The bottom two lines (i-s) give the error estimation on best origins considering individual datasets using the lower RMS/N origins (i-o) and using the strategy of Bengoubou-Valérius et al. [2008] (p-s). The two columns on the right side give the error estimation on best origins considering the individual location methods hypo71 (t, u) and In NonLinLoc, the uncertainties are estimated with the three-dimensional geometry of the PDF which is obtained from a systematic exploration of the cost function in the region of interest.
The catalogue produced by the CDSA by aggrega-tion and relocation includes 28,324 tectonic earth-quakes with improvements in RMS and NP+S com-pared to their initial origins. This sub-catalogue of improved origins include 21,343 origins with data from OVSG, 14,986 with data from OVSM, and 5010 with data from ISC. This means that the rest of the 46,703 earthquakes in the CDSA catalogue could not be improved by adding data nor by using another location method, and their preferred origins are kept as provided initially. The preferred magnitude for each preferred origin has been selected from the initial catalogues, either from the closest OVS-IPGP solution if available, or from the ISC. All the origins of each earthquake catalogue can be specifically re-quested on the CDSA website through the FDSN web services, and the preferred origin catalogue is pro-vided by default.
Earthquake catalogues in the Lesser Antilles area (10-20° N, 65-58° W) between 1972 and 2012 and earthquake catalogues provided by our study, exposed via the CDSA open-access web services (www. seismes-antilles. fr). Initial: numbers of earthquakes in initially available catalogues provided by ISC and IPGP. Note that not all earthquakes in initial catalogues have an origin and phase data, the number of origins are provided as index. CECM: number of earthquakes with automatic picks using data from the RAP-GIS provided by IPGP. Aggregation: numbers of earthquakes with expanded arrival times dataset using aggregation. Relocation: Numbers of earthquakes with improved location results. Earthquakes are the only event type considered, volcanic and other event types are not included.
Acknowledgements The framework for creating CDSA was funded by the  French government and Europe , the Guadeloupe re-gion, and the main collaborators IPGP, BRGM, and  UAG (FEDER Guadeloupe , Contrat de Projets Re-gion, 2007Re-gion, -2013)). We used data from IPGP obser-vatories, BRGM,  Resif-RAP , and  ISC. We are thank-ful for the continuous work provided by the teams of routine earthquake analysts from all observato-ries contributing to our study. We would like to thank  Mendy Bengoubou-Valérius ,  Daniel Amorese ,  Richard Robertson ,  Olivier Coutant ,  Mike Kendall ,  David Shlaphorst ,  Pascal Bernard ,  O'Leary Fernando Gonzalez Matos , and  Celine Dessert  for their benefi-cial discussion, as well as  Alexis Bosson , and  Xavier Beguin  for their contribution to the creation of the CDSA database and website.
Weng et al. , in a Retrospective Multi-Center Cohort in Central Taiwan evaluated the Combined Functional Assessment for Predicting Clinical Outcomes in Stroke Patients After PAC aiming to determine PAC assessment parameters, either alone or in combination, for predicting clinical outcomes. Data were collected on post-stroke patients' functional ability at baseline and after PAC stay. The assessment included the Modified Rankin Scale (MRS), Functional Oral Intake Scale (FOIS), Mini-Nutritional Assessment (MNA), Berg Balance Scale (BBS), Fugl-Meyer Assessment (FMA), Mini-Mental State Examination (MMSE), aphasia test, and quality of life. The above items were assessed first at baseline and again at discharge from PAC. Logistic regression was calculated to determine factors that were associated with PAC length of stay (LOS), 14-day hospital readmission, and 1-year mortality.
In a pilot study Sugimoto and Otake-Matsuura developed a tract-based Spatial Statistics Analysis of Diffusion Tensor Imaging in Older Adults After the Photo-Integrated Conversation Moderated by Robots (PICMOR) Intervention Program. This cognitive intervention program called PICMOR uses one of the most intellectual activities of daily life, conversations. To examine the effects of PICMOR on cognitive function in older adults, a randomized controlled trial was conducted and reported that verbal fluency task scores were improved by PICMOR. Based on these behavioral findings, the aim of the pilot study was to identify candidate structures for white matter microstructural changes induced by PICMOR. The results from tract-based spatial statistics analyses showed that the intervention group, who participated in PICMORbased conversations, had significantly higher FA values or lower MD, AD, or RD values across various fiber tracts, including the left anterior corona radiata, external capsule, and anterior limb of the internal capsule, compared to the control group, who participated in unstructured free conversations.
The CNNs module then extracted the spatial information in the new EEG data to obtain the spatial feature maps which were then sequentially inputted into a Bi-LSTM module to learn the bidirectional temporal information for emotion recognition. In conclusion, it was designed group experiments to demonstrate that the proposed CTA-CNN-Bi-LSTM framework outperforms the previous works. Moreover, the highest average recognition accuracy of the positive, negative, and neutral emotions achieved 98. 75%.
Acknowledgments All the editors of this Research Topic thank the Frontiers publisher for the confidence. A special thanks to all authors who contributed to this special issue of  Frontiers Front Aging Neurosciences entitled Interventional Strategies to Improve Quality of Life and Health Span in Older Adults. The authors contributed to the development of an impactful issue involving various approaches to improve and to aid in the development of  Interventional Strategies for Enhancing Quality of Life and Health Span in Older Adults.
The study of short association fibers is still an incomplete task due to their higher inter-subject variability and the smaller size of this kind of fibers in comparison to known long association bundles. However, their description is essential to understand human brain dysfunction and better characterize the human brain connectome. In this work, we present a multi-subject atlas of short association fibers, which was computed using a superficial white matter bundle identification method based on fiber clustering. To create the atlas, we used probabilistic tractography from one hundred subjects from the HCP database, aligned with non-linear registration. The method starts with an intra-subject clustering of short fibers (30-85 mm). Based on a cortical atlas, the intra-subject cluster centroids from all subjects are segmented to identify the centroids connecting each region of interest (ROI) of the atlas.
To reduce computational load, the centroids from each ROI group are randomly separated into ten subgroups. Then, an inter-subject hierarchical clustering is applied to each centroid subgroup, followed by a second level of clustering to select the most-reproducible clusters across subjects for each ROI group. Finally, the clusters are labeled according to the regions that they connect, and clustered to create the final bundle atlas. The resulting atlas is composed of 525 bundles of superficial short association fibers along the whole brain, with 384 bundles connecting pairs of different ROIs and 141 bundles connecting portions of the same ROI. The reproducibility of the bundles was verified using automatic segmentation on three different tractogram databases. Results for deterministic and probabilistic tractography data show high reproducibility, especially for probabilistic tractography in HCP data.
, 2021 ). Mainly, two approaches have been used to study the SWM structure. On one hand, the manual or automatic placement of regions of interest (ROI) enables the extraction of fiber bundles connecting two ROIs. One of the first works that employed this technique to SWM fibers used nonlinear warping of a gray-matter and white-matter ROI atlas to extract fibers connecting two gyri  ( Zhang et al. , 2010 ) , obtaining probability maps of 29 short fibers from 20 subjects of a DTI database. However, a more detailed analysis of fiber bundles can be obtained using manual positioning of ROIs by an expert. An example is the work proposed by  Catani et al. (2012)  , which performed a detailed study of the frontoparietal connections using small ROIs over twelve subjects from HARDI data. On the other hand, fiber clustering based on a distance metric between fibers can be used to find stable fascicles across subjects.
, 2021a ). Manual segmentation of DWM, on its side, presents high intra-rater and inter-rater variability and has unsolved challenges related to nomenclature and reproducibility  ( Rheault et al. , 2020a ). Fiber tractography bundle segmentation also depends on scanner effects, vendor effects, acquisition resolution, and diffusion sampling scheme and sensitization, being the acquisition resolution the factor with highest impact on reproducibility of tractography  ( Schilling et al. , 2021b ). Short association fibers, having a smaller size and higher inter-subject variability, require more stringent methods to disentangle short bundles that connect small regions of the cortex. Hence, unsupervised approach is a suitable strategy for their study over large databases. In the last decade, improvements in the quality of diffusion-weighted images and analysis algorithms have allowed the study of short association bundles.
One of the first works to study short fibers was done in  Guevara et al. (2012)  , where an atlas of short association fibers from the left hemisphere was created. The algorithm identified 47 short bundles with medium to high reproducibility between subjects. The results were preliminary and the study was performed with data from only twelve subjects. Recently, some works have been focused on the study of short bundles and the creation of atlases of superficial white matter. The work of  Guevara et al. (2017)  used a cortical parcellation to analyze deterministic tractography based on analytical Q-ball model  ( Descoteaux et al. , 2007 )  from 79 subjects of a HARDI database  ( Schmitt et al. , 2012 ). The authors extracted subtractograms composed of fibers with lengths between 20 and 80 mm, connecting two cortical ROIs.
Then, they applied intra-subject hierarchical clustering on each subtractogram, followed by inter-subject clustering, keeping only bundles present in most of the population. With the resulting clusters, a SWM bundle atlas was created, composed of 100 bundles for the whole brain, with a representativeness higher than or equal to 50% of the subjects. This work was the first to obtain well-defined whole-brain bundles from a high-quality database. However, this method initially depends on the cortical parcellation and did not include bundles connecting parts of the same ROI. Moreover, this work used affine transformations for the normalization of the subjects to a common space, which could negatively impact the identification of the bundles. In  Román et al. (2017)  , results for SWM fiber clustering using linear and non-linear registration were compared.
This work proposed an automatic method to identify reproducible short association bundles of the SWM, based on whole-brain inter-subject hierarchical clustering and a posterior automatic labeling of the representative bundles. The method was applied to 74 subjects of a HARDI database  ( Schmitt et al. , 2012 ) , using deterministic tractography based on the analytical Q-ball model. Bundles connecting different parts of the same ROI were also included in the results. Non-linear registration using DTI-TK  ( Zhang et al. , 2006 )  led to better results than affine registration, with the identification of a greater number of representative bundles, even when a more restrictive distance threshold was applied. The atlas contains 44 bundles in the left hemisphere and 49 in the right hemisphere. Bundles were validated using automatic segmentation of 78 subjects from a different HARDI database.
In  Zhang et al. (2018a)  , the authors applied a group-wise spectral clustering for the whole brain over 100 subjects from the Human Connectome Project (HCP) database  ( Glasser et al. , 2013 )  to obtain a curated WM bundle atlas. This work uses a two-tensor unscented Kalman filter method and non-linear registration. The resulting curated atlas is composed of 58 deep white matter tracts and 198 short short and medium range superficial fiber clusters organized into 16 categories according to the brain lobes they connect. Recently,  Guevara et al. (2020)  performed a comparison between these three atlases, finding a higher brain coverage for the the atlas proposed by  Zhang et al. (2018a)  , probably due to the higher quality of HCP data. Table  1  contains a summary of the main features of the three previous atlases and the proposed atlas, such as the dMRI database, the tractography and clustering methods, and the number of bundles.
In  Shinohara et al. (2020)  , the authors studied a pyramid-shape structure composed of U-fibers that connect different regions from the temporal lobe (the middle temporal gyrus with the inferior temporal gyrus and the middle temporal gyrus with the superior temporal gyrus). These studies are very valuable for the validation of short association fascicles found from tractography analysis. At the same time, it is necessary to improve the description of SWM bundles based on high-quality data, such as the HCP database, to obtain a better coverage of the brain while keeping well-defined bundles. This task needs methods adapted to the large number of fibers to be analyzed and the morphological complexity of short association fibers. Following the line of research discussed above, in this paper we propose an automatic method for the identification of a large number of representative superficial white matter bundles for the whole brain, based on inter-subject hierarchical clustering, applied to 100 subjects of the HCP database.
Diffusion MRI datasets Database for atlas construction. The Human Connectome Project (HCP) has made available a high-quality brain imaging database  ( Human Connectome Project, 2017 ). We used multi-shell diffusion MRI data of one hundred subjects from the 1200 Subjects Data Release (S1200 Data). Images were acquired on a Siemens Skyra scanner with a customized protocol  ( Sotiropoulos et al. , 2013 ). The multishell dMRI data was collected over three b-values (1000, 2000, 3000  ∕  2 ), with an isotropic voxel size of 1. 25. We worked with the HCP pre-processed data  ( Glasser et al. , 2013 ) , where diffusion imaging distortion corrections were applied  ( Andersson et al. , 2003; Ander-sson and Sotiropoulos, 2015 ). Diffusion data is aligned with the native structural space. Furthermore, the non-linear transformation from the structural space to MNI152 space, calculated with FNIRT software  ( Andersson et al.
, 2008 ) , is provided. All subjects signed an informed consent document at the beginning of the study (more information regarding the informed consent process available in  ( Van Essen et al. , 2013 ) ). Diffusion-weighted images (DWI) from HCP database were processed using MRtrix3 ( https://www. mrtrix. org )  ( Tournier et al. , 2012 ). The fiber orientation distributions (FODs) were estimated using constrained spherical deconvolution (CSD)  ( Tournier et al. , 2007 )  with default parameters. Probabilistic tractography was calculated using the secondorder Integration over FODs (iFOD2)  ( Tournier et al. , 2010 )  with the following parameters: step size = 0. 5 ×  _  , max length = 250  , maximum angle = 45 • , FOD amplitude threshold = 0. 06. For each subject, 30 million of fibers were generated through probabilistic tractography, where Anatomically-Constrained Tractography (ACT)  ( Smith et al.
, 2012 )  algorithm was used. Subsequently, tractography datasets were filtered using Spherical-deconvolution Informed Filtering of Tractograms (SIFT)  ( Smith et al. , 2013 ) , keeping 3 million of fibers per subject. The fibers were transformed to MNI space, by applying the nonlinear transformation provided by the HCP data. Database for reproducibility evaluation. We used three tractogram databases from two different image databases. The first one corresponds to a HARDI database, called CONNECT/ARCHI  ( Schmitt et al. , 2012 ). This database is composed of seventy-nine healthy subjects. All the subjects provided an informed written consent, and the data acquisition was performed with the approval of the local ethical committees. Images were acquired on a Tim Trio 3T MRI system with a 12-channel head coil (Siemens, Erlangen).
The MRI protocol included the acquisition of a T1-weighted dataset using an MPRAGE sequence, a B0 field map, and a SS-EPI single-shell HARDI dataset along 60 optimized diffusion-weighted directions, b = 1,500  ∕  2 , with voxel size = 1. 7187 × 1. 71875 × 1. 7  3. The data were preprocessed using BrainVISA/Connectomist-2. 0 software  ( Duclap et al. , 2012 ). They were preliminary corrected for artifacts (eddy currents, susceptibility effects, spikes and noise) and outliers were removed  ( Dubois et al. , 2014 ). Then, the analytical Q-ball model  ( Descoteaux et al. , 2007 )  was computed. For the fiber tractography, a T1-based propagation brain mask was calculated  ( Guevara et al. , 2011a ). The mask was transformed from T1 to Diffusion-Weighted (DW) space and used for seeding (one seed per voxel at T1 resolution) and to define the space where fibers were tracked.
Whole-brain regularized streamline deterministic tractography  ( Perrin et al. , 2005 )  was performed on diffusion-weighted native space, using the described mask (forward step = 0. 2  , maximum angle = 30 • ). This resulted in tractography datasets with an average of one million fibers per subject, and fiber length between 20 and 300 mm. The fibers are in each individual diffusion-weighted space. The database provides an affine normalization from DW space to Talairach referential. The second and third tractogram databases were calculated from 20 subjects of the HCP database, not used for the atlas creation. The second tractogram database was calculated using probabilistic tractography. This tracking is the same that was applied to the data used for the construction of the atlas. On the other hand, the third tractogram database was calculated using deterministic tractography based on Generalized Q-sampling Imaging  ( Yeh et al.
Reproducible bundle identification method This section describes the method used for the identification of reproducible short association bundles in the HCP database. We implemented a method inspired in our previous work  ( Román et al. , 2017 ) , which was applied to a HARDI database. The method was adapted to deal with the larger size and complexity of the HCP probabilistic tractography data. The proposed method is based on inter-subject hierarchical clustering (HC), and has the purpose of identifying short reproducible bundles and labeling them according to the ROIs that they connect. To handle the complex morphology and size of the data, the method is composed of several steps that include grouping the fibers based on the ROIs connected by them and the computation of different levels of clustering, without discarding fibers.
Reproducibility analysis of the atlas bundles The reproducibility of the atlas bundles was tested by applying automatic segmentation of the bundles to three tractogram databases from two different image databases, described in Section 2. 1. The first tractogram database is composed of deterministic tractography datasets of 78 subjects from a HARDI database (ARCHI  ( Schmitt et al. , 2012 ) ). The second and third tractogram databases were calculated from 20 subjects of the HCP database, using probabilistic and deterministic tractography. The segmentation method  ( Labra et al. , 2017 )  computes the    distance between a pair of fibers  and , where fiber  belongs to a bundle from the atlas and fiber  belongs to the subject tractography to be segmented. The    distance is the   distance penalized with the difference of lengths of fibers  (   ) and  (   ), as shown in Eq.
We used the dMRI images of 100 subjects from the HCP database and calculated the nQA maps using DSI Studio DSI over the HCP preprocessed DWI images. We performed an automatic segmentation of the bundles based on the probabilistic tractography of each subject  ( Labra et al. , 2017 ). We computed a binary mask for each bundle with the same resolution as the DWI images, labeling the voxels traversed by the bundle fibers. Then, we used the bundle masks to calculate the average nQA for each bundle of the 100 subjects. Finally, we computed the nQA asymmetry (nQAA) using the average nQA value of each pair of common bundles between hemispheres. The nQAA is defined in Eq. 3 as: 𝑛𝑄𝐴𝐴 = 𝑛𝑄𝐴 𝑅 -𝑛𝑄𝐴 𝐿 𝑛𝑄𝐴 𝑅 + 𝑛𝑄𝐴 𝐿 (3) where   and   are the average nQA values for the right and left hemispheres of a bundle, respectively.
Results We applied our bundle identification method to 100 subjects of the HCP database. After the pre-processing, about 1 million fibers per subject were preserved and sampled with 21 equidistant points. Then, we applied intra-subject clustering with FFClust to each subject, generat-ing about 40,000 compact fiber clusters per subject, and calculated a centroid for each intra-subject cluster (step 1). From the Segmentation of centroids into ROI groups (step 2), we obtained groups of centroids with a mean of 65,291 centroids per ROI, where the precentral ROI had the highest number of centroids  (188, 305). Centroids from each ROI were shuffled and divided into 10 subgroups of the same size. We verified that each group and subgroup contain intra-subject centroids from the 100 subjects. For each subgroup, we applied the two levels of average-link hierarchical clustering (step 3 and step 4), to obtain the common and representative clusters.
Reproducibility of the atlas bundles We used the bundles from our atlas to segment three tractogram databases, described in section 2. 1. The first tractogram database is composed of 79 subjects from the ARCHI database and the other two tractogram databases contain 20 subjects from the HCP database, in this case using deterministic and probabilistic tractography. Tables  1 to 6  of the Supplementary File 1 list the percentage of subjects where a bundle was correctly segmented for the HCP and ARCHI databases. Table  7  shows a summary of the percentage of segmented bundles for the three cases (ARCHI database with deterministic tractography, HCP data with probabilistic tractography and HCP data with deterministic tractography). The best results were obtained, as expected, for the HCP probabilistic tractography datasets, where 509 bundles were segmented in 100% of the subjects and the remaining bundles were segmented in more than 75% of the subjects.