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  • Neuroinformatics
  • 2019-2023
  • Publications
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Fischer, Svante; Hannes Graf, Martin; Fossurier, Carole; Châtelet, Madeleine; +1 Authors

    International audience; This article presents a Merovingian Period silver spoon that was recently discovered in an opulent female chamber grave in the “Niederfeld” row grave cemetery of Ichtratzheim (Bas-Rhin). The spoon has no less than three different inscriptions, one in seriffed Latin capitals and two in runes. The first contains a Latin male personal name, Matteus, the second a previously unattested runic lapela ‘spoon’, and the third a sequence abuda, presumably a female personal name. This makes it the second known example of an inscribed object with both runes and Latin from Merovingian Period Gaul. From a runological perspective, this is one of the most important discoveries in recent times because it contains the oldest known case of a linguistically meaningful runic inscription using the rare p-rune and some very archaic linguistic forms. From an archaeological perspective, this is one of the richest known Merovingian Period female burials in Alsace, and it is very likely that the buried woman may have been a leading member of the local elite.

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ The Journal of Archa...arrow_drop_down
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    The Journal of Archaeology and Ancient History
    Article . 2023 . Peer-reviewed
    License: CC BY
    Data sources: Crossref
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    Hyper Article en Ligne
    Other literature type . 2014
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Demnitz, N; Hulme, OJ; Siebner, HR; Krogsgaard, MR; +3 Authors

    Modifiable lifestyle factors have been shown to promote healthy brain ageing. However, studies have typically focused on a single factor at a time. Given that lifestyle factors do not occur in isolation, multivariable analyses provide a more realistic model of the lifestyle-brain relationship. Here, canonical correlation analyses (CCA) examined the relationship between nine lifestyle factors and seven MRI-derived indices of brain structure. The resulting covariance pattern was further explored with Bayesian regressions. CCA analyses were first conducted on a Danish cohort of older adults (n = 251) and then replicated in a British cohort (n = 668). In both cohorts, the latent factors of lifestyle and brain structure were positively correlated (UK: r = .37, p < 0.001; Denmark: r = .27, p < 0.001). In the cross-validation study, the correlation between lifestyle-brain latent factors was r = .10, p = 0.008. However, the pattern of associations differed between datasets. These findings suggest that baseline characterisation and tailoring towards the study sample may be beneficial for achieving targeted lifestyle interventions. Modifiable lifestyle factors have been shown to promote healthy brain ageing. However, studies have typically focused on a single factor at a time. Given that lifestyle factors do not occur in isolation, multivariable analyses provide a more realistic model of the lifestyle-brain relationship. Here, canonical correlation analyses (CCA) examined the relationship between nine lifestyle factors and seven MRI-derived indices of brain structure. The resulting covariance pattern was further explored with Bayesian regressions. CCA analyses were first conducted on a Danish cohort of older adults (n = 251) and then replicated in a British cohort (n = 668). In both cohorts, the latent factors of lifestyle and brain structure were positively correlated (UK: r =.37, p < 0.001; Denmark: r =.27, p < 0.001). In the cross-validation study, the correlation between lifestyle-brain latent factors was r =.10, p = 0.008. However, the pattern of associations differed between datasets. These findings suggest that baseline characterisation and tailoring towards the study sample may be beneficial for achieving targeted lifestyle interventions.

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ Oxford University Re...arrow_drop_down
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    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
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    Neurobiology of Aging; Oxford University Research Archive
    Other literature type . Article . 2023 . Peer-reviewed
    License: CC BY NC ND
    SSRN Electronic Journal
    Article . 2023 . Peer-reviewed
    Data sources: Crossref
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ Oxford University Re...arrow_drop_down
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      Neurobiology of Aging; Oxford University Research Archive
      Other literature type . Article . 2023 . Peer-reviewed
      License: CC BY NC ND
      SSRN Electronic Journal
      Article . 2023 . Peer-reviewed
      Data sources: Crossref
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: van den Heuvel, Henk; Crasborn, Onno; Sallandre, Marie-Anne; Efthimiou, Eleni; +2 Authors

    The CLARIN Resource family page for SL lists corpora and lexica for Sign Languages. A substantial number of these can be found in the CLARIN VLO but deserve a special podium in the spotlight. Sign language (SL) corpus resources contain transcriptions/annotations of spontaneous or elicited dialogues and narratives. All resources are in a video format because of the gestural/spatial-visual modality, a vital characteristic of signed languages (sign languages, used by Deaf-blind signers, can be received in tactile modality). SL corpora are crucial resources for various types of linguistic research, such as lexicography, phonology, syntax, and pragmatics, as well as for language typology. This page also provides access to SL lexical resources. Some of them are connected to SL corpora. There also are independent lexical resources that were primarily created for language learning and teaching.

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ Publikationer från S...arrow_drop_down
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    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Hyper Article en Ligne
    Other literature type . 2022
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    Conference object . 2022
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Elyas Mohammadi; Katarzyna Chojnowska; Michał Bieńkowski; Anna Kostecka; +12 Authors

    Abstract Background Visium Spatial Gene Expression (ST) is a method combining histological spatial information with transcriptomics profiles directly from tissue sections. The use of spatial information has made it possible to discover new modes of gene expression regulations. However, in the ST experiment, the nucleus size of cells may exceed the thickness of a tissue slice. This may, in turn, negatively affect comprehensive capturing the transcriptomics profile in a single slice, especially for tissues having large differences in the size of nuclei. Methods Here, we defined the effect of Consecutive Slices Data Integration (CSDI) on unveiling accurate spot clustering and deconvolution of spatial transcriptomic spots in human postmortem brains. By considering the histological information as reference, we assessed the improvement of unsupervised clustering and single nuclei RNA-seq and ST data integration before and after CSDI. Results Apart from the escalated number of defined clusters representing neuronal layers, the pattern of clusters in consecutive sections was concordant only after CSDI. Besides, the assigned cell labels to spots matches the histological pattern of tissue sections after CSDI. Conclusion CSDI can be applied to investigate consecutive sections studied with ST in the human cerebral cortex, avoiding misinterpretation of spot clustering and annotation, increasing accuracy of cell recognition as well as improvement in uncovering the layers of grey matter in the human brain.

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ Publikationer från U...arrow_drop_down
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    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Journal of Translational Medicine
    Article . 2023 . Peer-reviewed
    License: CC BY
    Data sources: Crossref
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    https://doi.org/10.21203/rs.3....
    Preprint . 2023
    License: CC BY
    Data sources: Crossref
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    DOAJ
    Article . 2023
    Data sources: DOAJ
    SSRN Electronic Journal
    Article . 2022 . Peer-reviewed
    Data sources: Crossref
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ Publikationer från U...arrow_drop_down
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      Journal of Translational Medicine
      Article . 2023 . Peer-reviewed
      License: CC BY
      Data sources: Crossref
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      https://doi.org/10.21203/rs.3....
      Preprint . 2023
      License: CC BY
      Data sources: Crossref
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      DOAJ
      Article . 2023
      Data sources: DOAJ
      SSRN Electronic Journal
      Article . 2022 . Peer-reviewed
      Data sources: Crossref
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Johan Eriksson; Lars Nyberg; Eva Elgh; Xiaolei Hu;

    Background and objective: We recently reported improvements of working memory across 10 years post stroke among middle-aged individuals. However, the mechanisms underlying working-memory recovery are largely unknown. This study investigated the associations between long-term improvement of working memory and resting-state functional connectivity in two frontoparietal networks: the frontoparietal network and the dorsal attention network. Methods: Working memory was repeatedly assessed by the Digit Span Backwards task in 21 persons, within 1 year after stroke onset and again 10 years post stroke onset. Brain functional connectivity was examined by resting state functional magnetic resonance imaging at the 10-year follow-up. Results: A significant improvement of working memory was found among 21 persons after stroke (median age = 64) at the 10-year follow-up compared to the within-one-year assessment. The magnitude of performance improvement on the Digit Span Backwards task was significantly positively correlated with stronger brain connectivity in the frontoparietal network (r = 0.51, p = 0.018) measured at the 10-year follow-up only. A similar association was observed in the dorsal attention network (r = 0.43, p = 0.052) but not in a visual network (r = -0.17, p = 0.46) that served as a control network. The association between functional connectivity within the above-mentioned networks and Digit Span Backwards scores at 10-year after stroke was in the same direction but did not reach significance. Conclusions: The present work relate stronger long-term performance improvement on the Digit Span Backwards task with higher integrity of frontoparietal network connectivity.

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ NeuroImage: Clinicalarrow_drop_down
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    NeuroImage: Clinical
    Article . 2023 . Peer-reviewed
    License: CC BY NC ND
    Data sources: Crossref
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    DOAJ
    Article . 2023
    Data sources: DOAJ
    SSRN Electronic Journal
    Article . 2022 . Peer-reviewed
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ NeuroImage: Clinicalarrow_drop_down
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      NeuroImage: Clinical
      Article . 2023 . Peer-reviewed
      License: CC BY NC ND
      Data sources: Crossref
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      DOAJ
      Article . 2023
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      SSRN Electronic Journal
      Article . 2022 . Peer-reviewed
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    Authors: Anton Borg; Martin Boldt; Oliver Rosander; Jim Ahlstrand;

    Classifying e-mails into distinct labels can have a great impact on customer support. By using machine learning to label e-mails, the system can set up queues containing e-mails of a specific category. This enables support personnel to handle request quicker and more easily by selecting a queue that match their expertise. This study aims to improve a manually defined rule-based algorithm, currently implemented at a large telecom company, by using machine learning. The proposed model should have higher F1-score and classification rate. Integrating or migrating from a manually defined rule-based model to a machine learning model should also reduce the administrative and maintenance work. It should also make the model more flexible. By using the frameworks, TensorFlow, Scikit-learn and Gensim, the authors conduct a number of experiments to test the performance of several common machine learning algorithms, text-representations, word embeddings to investigate how they work together. A long short-term memory network showed best classification performance with an F1-score of 0.91. The authors conclude that long short-term memory networks outperform other non-sequential models such as support vector machines and AdaBoost when predicting labels for e-mails. Further, the study also presents a Web-based interface that were implemented around the LSTM network, which can classify e-mails into 33 different labels. © 2020, The Author(s). Open access

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    Neural Computing and Applications
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    Neural Computing and Applications
    Article . 2020 . Peer-reviewed
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      Neural Computing and Applications
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Fischer, Svante; Hannes Graf, Martin; Fossurier, Carole; Châtelet, Madeleine; +1 Authors

    International audience; This article presents a Merovingian Period silver spoon that was recently discovered in an opulent female chamber grave in the “Niederfeld” row grave cemetery of Ichtratzheim (Bas-Rhin). The spoon has no less than three different inscriptions, one in seriffed Latin capitals and two in runes. The first contains a Latin male personal name, Matteus, the second a previously unattested runic lapela ‘spoon’, and the third a sequence abuda, presumably a female personal name. This makes it the second known example of an inscribed object with both runes and Latin from Merovingian Period Gaul. From a runological perspective, this is one of the most important discoveries in recent times because it contains the oldest known case of a linguistically meaningful runic inscription using the rare p-rune and some very archaic linguistic forms. From an archaeological perspective, this is one of the richest known Merovingian Period female burials in Alsace, and it is very likely that the buried woman may have been a leading member of the local elite.

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    The Journal of Archaeology and Ancient History
    Article . 2023 . Peer-reviewed
    License: CC BY
    Data sources: Crossref
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    Hyper Article en Ligne
    Other literature type . 2014
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    Authors: Demnitz, N; Hulme, OJ; Siebner, HR; Krogsgaard, MR; +3 Authors

    Modifiable lifestyle factors have been shown to promote healthy brain ageing. However, studies have typically focused on a single factor at a time. Given that lifestyle factors do not occur in isolation, multivariable analyses provide a more realistic model of the lifestyle-brain relationship. Here, canonical correlation analyses (CCA) examined the relationship between nine lifestyle factors and seven MRI-derived indices of brain structure. The resulting covariance pattern was further explored with Bayesian regressions. CCA analyses were first conducted on a Danish cohort of older adults (n = 251) and then replicated in a British cohort (n = 668). In both cohorts, the latent factors of lifestyle and brain structure were positively correlated (UK: r = .37, p < 0.001; Denmark: r = .27, p < 0.001). In the cross-validation study, the correlation between lifestyle-brain latent factors was r = .10, p = 0.008. However, the pattern of associations differed between datasets. These findings suggest that baseline characterisation and tailoring towards the study sample may be beneficial for achieving targeted lifestyle interventions. Modifiable lifestyle factors have been shown to promote healthy brain ageing. However, studies have typically focused on a single factor at a time. Given that lifestyle factors do not occur in isolation, multivariable analyses provide a more realistic model of the lifestyle-brain relationship. Here, canonical correlation analyses (CCA) examined the relationship between nine lifestyle factors and seven MRI-derived indices of brain structure. The resulting covariance pattern was further explored with Bayesian regressions. CCA analyses were first conducted on a Danish cohort of older adults (n = 251) and then replicated in a British cohort (n = 668). In both cohorts, the latent factors of lifestyle and brain structure were positively correlated (UK: r =.37, p < 0.001; Denmark: r =.27, p < 0.001). In the cross-validation study, the correlation between lifestyle-brain latent factors was r =.10, p = 0.008. However, the pattern of associations differed between datasets. These findings suggest that baseline characterisation and tailoring towards the study sample may be beneficial for achieving targeted lifestyle interventions.

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    Neurobiology of Aging; Oxford University Research Archive
    Other literature type . Article . 2023 . Peer-reviewed
    License: CC BY NC ND
    SSRN Electronic Journal
    Article . 2023 . Peer-reviewed
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      Neurobiology of Aging; Oxford University Research Archive
      Other literature type . Article . 2023 . Peer-reviewed
      License: CC BY NC ND
      SSRN Electronic Journal
      Article . 2023 . Peer-reviewed
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    Authors: van den Heuvel, Henk; Crasborn, Onno; Sallandre, Marie-Anne; Efthimiou, Eleni; +2 Authors

    The CLARIN Resource family page for SL lists corpora and lexica for Sign Languages. A substantial number of these can be found in the CLARIN VLO but deserve a special podium in the spotlight. Sign language (SL) corpus resources contain transcriptions/annotations of spontaneous or elicited dialogues and narratives. All resources are in a video format because of the gestural/spatial-visual modality, a vital characteristic of signed languages (sign languages, used by Deaf-blind signers, can be received in tactile modality). SL corpora are crucial resources for various types of linguistic research, such as lexicography, phonology, syntax, and pragmatics, as well as for language typology. This page also provides access to SL lexical resources. Some of them are connected to SL corpora. There also are independent lexical resources that were primarily created for language learning and teaching.

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    Hyper Article en Ligne
    Other literature type . 2022
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    Authors: Elyas Mohammadi; Katarzyna Chojnowska; Michał Bieńkowski; Anna Kostecka; +12 Authors

    Abstract Background Visium Spatial Gene Expression (ST) is a method combining histological spatial information with transcriptomics profiles directly from tissue sections. The use of spatial information has made it possible to discover new modes of gene expression regulations. However, in the ST experiment, the nucleus size of cells may exceed the thickness of a tissue slice. This may, in turn, negatively affect comprehensive capturing the transcriptomics profile in a single slice, especially for tissues having large differences in the size of nuclei. Methods Here, we defined the effect of Consecutive Slices Data Integration (CSDI) on unveiling accurate spot clustering and deconvolution of spatial transcriptomic spots in human postmortem brains. By considering the histological information as reference, we assessed the improvement of unsupervised clustering and single nuclei RNA-seq and ST data integration before and after CSDI. Results Apart from the escalated number of defined clusters representing neuronal layers, the pattern of clusters in consecutive sections was concordant only after CSDI. Besides, the assigned cell labels to spots matches the histological pattern of tissue sections after CSDI. Conclusion CSDI can be applied to investigate consecutive sections studied with ST in the human cerebral cortex, avoiding misinterpretation of spot clustering and annotation, increasing accuracy of cell recognition as well as improvement in uncovering the layers of grey matter in the human brain.

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    Journal of Translational Medicine
    Article . 2023 . Peer-reviewed
    License: CC BY
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    https://doi.org/10.21203/rs.3....
    Preprint . 2023
    License: CC BY
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    DOAJ
    Article . 2023
    Data sources: DOAJ
    SSRN Electronic Journal
    Article . 2022 . Peer-reviewed
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      Journal of Translational Medicine
      Article . 2023 . Peer-reviewed
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      https://doi.org/10.21203/rs.3....
      Preprint . 2023
      License: CC BY
      Data sources: Crossref
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      Article . 2023
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    Authors: Johan Eriksson; Lars Nyberg; Eva Elgh; Xiaolei Hu;

    Background and objective: We recently reported improvements of working memory across 10 years post stroke among middle-aged individuals. However, the mechanisms underlying working-memory recovery are largely unknown. This study investigated the associations between long-term improvement of working memory and resting-state functional connectivity in two frontoparietal networks: the frontoparietal network and the dorsal attention network. Methods: Working memory was repeatedly assessed by the Digit Span Backwards task in 21 persons, within 1 year after stroke onset and again 10 years post stroke onset. Brain functional connectivity was examined by resting state functional magnetic resonance imaging at the 10-year follow-up. Results: A significant improvement of working memory was found among 21 persons after stroke (median age = 64) at the 10-year follow-up compared to the within-one-year assessment. The magnitude of performance improvement on the Digit Span Backwards task was significantly positively correlated with stronger brain connectivity in the frontoparietal network (r = 0.51, p = 0.018) measured at the 10-year follow-up only. A similar association was observed in the dorsal attention network (r = 0.43, p = 0.052) but not in a visual network (r = -0.17, p = 0.46) that served as a control network. The association between functional connectivity within the above-mentioned networks and Digit Span Backwards scores at 10-year after stroke was in the same direction but did not reach significance. Conclusions: The present work relate stronger long-term performance improvement on the Digit Span Backwards task with higher integrity of frontoparietal network connectivity.

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    NeuroImage: Clinical
    Article . 2023 . Peer-reviewed
    License: CC BY NC ND
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    Authors: Anton Borg; Martin Boldt; Oliver Rosander; Jim Ahlstrand;

    Classifying e-mails into distinct labels can have a great impact on customer support. By using machine learning to label e-mails, the system can set up queues containing e-mails of a specific category. This enables support personnel to handle request quicker and more easily by selecting a queue that match their expertise. This study aims to improve a manually defined rule-based algorithm, currently implemented at a large telecom company, by using machine learning. The proposed model should have higher F1-score and classification rate. Integrating or migrating from a manually defined rule-based model to a machine learning model should also reduce the administrative and maintenance work. It should also make the model more flexible. By using the frameworks, TensorFlow, Scikit-learn and Gensim, the authors conduct a number of experiments to test the performance of several common machine learning algorithms, text-representations, word embeddings to investigate how they work together. A long short-term memory network showed best classification performance with an F1-score of 0.91. The authors conclude that long short-term memory networks outperform other non-sequential models such as support vector machines and AdaBoost when predicting labels for e-mails. Further, the study also presents a Web-based interface that were implemented around the LSTM network, which can classify e-mails into 33 different labels. © 2020, The Author(s). Open access

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    Neural Computing and Applications
    Article . 2020 . Peer-reviewed
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      Neural Computing and Applications
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