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\n\n \n \n \n \n \n \n A structurally precise mechanism links an epilepsy-associated KCNC2 potassium channel mutation to interneuron dysfunction.\n \n \n \n \n\n\n \n Clatot, J.; Currin, C. B.; Liang, Q.; Pipatpolkai, T.; Massey, S. L.; Helbig, I.; Delemotte, L.; Vogels, T. P.; Covarrubias, M.; and Goldberg, E. M.\n\n\n \n\n\n\n
Proceedings of the National Academy of Sciences, 121(3): e2307776121. January 2024.\n
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@article{clatot_structurally_2024,\n\ttitle = {A structurally precise mechanism links an epilepsy-associated \\textit{{KCNC2}} potassium channel mutation to interneuron dysfunction},\n\tvolume = {121},\n\tcopyright = {All rights reserved},\n\tissn = {0027-8424, 1091-6490},\n\turl = {https://pnas.org/doi/10.1073/pnas.2307776121},\n\tdoi = {10.1073/pnas.2307776121},\n\tabstract = {De novo heterozygous variants in\n KCNC2\n encoding the voltage-gated potassium (K\n +\n ) channel subunit Kv3.2 are a recently described cause of developmental and epileptic encephalopathy (DEE). A de novo variant in\n KCNC2\n c.374G {\\textgreater} A (p.Cys125Tyr) was identified via exome sequencing in a patient with DEE. Relative to wild-type Kv3.2, Kv3.2-p.Cys125Tyr induces K\n +\n currents exhibiting a large hyperpolarizing shift in the voltage dependence of activation, accelerated activation, and delayed deactivation consistent with a relative stabilization of the open conformation, along with increased current density. Leveraging the cryogenic electron microscopy (cryo-EM) structure of Kv3.1, molecular dynamic simulations suggest that a strong π-π stacking interaction between the variant Tyr125 and Tyr156 in the α-6 helix of the T1 domain promotes a relative stabilization of the open conformation of the channel, which underlies the observed gain of function. A multicompartment computational model of a Kv3-expressing parvalbumin-positive cerebral cortex fast-spiking γ-aminobutyric acidergic (GABAergic) interneuron (PV-IN) demonstrates how the Kv3.2-Cys125Tyr variant impairs neuronal excitability and dysregulates inhibition in cerebral cortex circuits to explain the resulting epilepsy.},\n\tlanguage = {en},\n\tnumber = {3},\n\turldate = {2024-01-10},\n\tjournal = {Proceedings of the National Academy of Sciences},\n\tauthor = {Clatot, Jerome and Currin, Christopher B. and Liang, Qiansheng and Pipatpolkai, Tanadet and Massey, Shavonne L. and Helbig, Ingo and Delemotte, Lucie and Vogels, Tim P. and Covarrubias, Manuel and Goldberg, Ethan M.},\n\tmonth = jan,\n\tyear = {2024},\n\tpages = {e2307776121},\n}\n\n\n
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\n De novo heterozygous variants in KCNC2 encoding the voltage-gated potassium (K + ) channel subunit Kv3.2 are a recently described cause of developmental and epileptic encephalopathy (DEE). A de novo variant in KCNC2 c.374G \\textgreater A (p.Cys125Tyr) was identified via exome sequencing in a patient with DEE. Relative to wild-type Kv3.2, Kv3.2-p.Cys125Tyr induces K + currents exhibiting a large hyperpolarizing shift in the voltage dependence of activation, accelerated activation, and delayed deactivation consistent with a relative stabilization of the open conformation, along with increased current density. Leveraging the cryogenic electron microscopy (cryo-EM) structure of Kv3.1, molecular dynamic simulations suggest that a strong π-π stacking interaction between the variant Tyr125 and Tyr156 in the α-6 helix of the T1 domain promotes a relative stabilization of the open conformation of the channel, which underlies the observed gain of function. A multicompartment computational model of a Kv3-expressing parvalbumin-positive cerebral cortex fast-spiking γ-aminobutyric acidergic (GABAergic) interneuron (PV-IN) demonstrates how the Kv3.2-Cys125Tyr variant impairs neuronal excitability and dysregulates inhibition in cerebral cortex circuits to explain the resulting epilepsy.\n
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\n\n \n \n \n \n \n \n Ten simple rules for pushing boundaries of inclusion at academic events.\n \n \n \n \n\n\n \n Hall, S. M.; Kochin, D.; Carne, C.; Herterich, P.; Lewers, K. L.; Abdelhack, M.; Ramasubramanian, A.; Alphonse, J. F. M.; Ung, V.; El-Gebali, S.; Currin, C. B.; Plomp, E.; Thompson, R.; and Sharan, M.\n\n\n \n\n\n\n
PLOS Computational Biology, 20(3): e1011797. March 2024.\n
Publisher: Public Library of Science\n\n
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\n\n \n \n Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n
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@article{hall_ten_2024,\n\ttitle = {Ten simple rules for pushing boundaries of inclusion at academic events},\n\tvolume = {20},\n\tcopyright = {Creative Commons Attribution 4.0 International License (CC-BY)},\n\tissn = {1553-7358},\n\turl = {https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1011797},\n\tdoi = {10.1371/journal.pcbi.1011797},\n\tabstract = {Inclusion at academic events is facing increased scrutiny as the communities these events serve raise their expectations for who can practically attend. Active efforts in recent years to bring more diversity to academic events have brought progress and created momentum. However, we must reflect on these efforts and determine which underrepresented groups are being disadvantaged. Inclusion at academic events is important to ensure diversity of discourse and opinion, to help build networks, and to avoid academic siloing. All of these contribute to the development of a robust and resilient academic field. We have developed these Ten Simple Rules both to amplify the voices that have been speaking out and to celebrate the progress of many Equity, Diversity, and Inclusivity practices that continue to drive the organisation of academic events. The Rules aim to raise awareness as well as provide actionable suggestions and tools to support these initiatives further. This aims to support academic organisations such as the Deep Learning Indaba, Neuromatch Academy, the IBRO-Simons Computational Neuroscience Imbizo, Biodiversity Information Standards (TDWG), Arabs in Neuroscience, FAIRPoints, and OLS (formerly Open Life Science). This article is a call to action for organisers to reevaluate the impact and reach of their inclusive practices.},\n\tlanguage = {en},\n\tnumber = {3},\n\turldate = {2024-03-04},\n\tjournal = {PLOS Computational Biology},\n\tauthor = {Hall, Siobhan Mackenzie and Kochin, Daniel and Carne, Carmel and Herterich, Patricia and Lewers, Kristen Lenay and Abdelhack, Mohamed and Ramasubramanian, Arun and Alphonse, Juno Felecia Michael and Ung, Visotheary and El-Gebali, Sara and Currin, Christopher Brian and Plomp, Esther and Thompson, Rachel and Sharan, Malvika},\n\tmonth = mar,\n\tyear = {2024},\n\tnote = {Publisher: Public Library of Science},\n\tpages = {e1011797},\n}\n\n\n
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\n Inclusion at academic events is facing increased scrutiny as the communities these events serve raise their expectations for who can practically attend. Active efforts in recent years to bring more diversity to academic events have brought progress and created momentum. However, we must reflect on these efforts and determine which underrepresented groups are being disadvantaged. Inclusion at academic events is important to ensure diversity of discourse and opinion, to help build networks, and to avoid academic siloing. All of these contribute to the development of a robust and resilient academic field. We have developed these Ten Simple Rules both to amplify the voices that have been speaking out and to celebrate the progress of many Equity, Diversity, and Inclusivity practices that continue to drive the organisation of academic events. The Rules aim to raise awareness as well as provide actionable suggestions and tools to support these initiatives further. This aims to support academic organisations such as the Deep Learning Indaba, Neuromatch Academy, the IBRO-Simons Computational Neuroscience Imbizo, Biodiversity Information Standards (TDWG), Arabs in Neuroscience, FAIRPoints, and OLS (formerly Open Life Science). This article is a call to action for organisers to reevaluate the impact and reach of their inclusive practices.\n
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\n\n \n \n \n \n \n \n Machine Learning for Healthcare: A Bibliometric Study of Contributions from Africa.\n \n \n \n \n\n\n \n Turki, H.; Pouris, A.; Ifeanyichukwu, F. M.; Namayega, C.; Taieb, M. A. H.; Adedayo, S. A.; Fourie, C.; Currin, C. B.; Asiedu, M. N.; Tonja, A. L.; Owodunni, A. T.; Dere, A.; Emezue, C. C.; Muhammad, S. H.; Isa, M. M.; and Aouicha, M. B.\n\n\n \n\n\n\n February 2023.\n
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@misc{turki_machine_2023,\n\ttitle = {Machine {Learning} for {Healthcare}: {A} {Bibliometric} {Study} of {Contributions} from {Africa}},\n\tcopyright = {All rights reserved},\n\tshorttitle = {Machine {Learning} for {Healthcare}},\n\turl = {https://www.preprints.org/manuscript/202302.0010/v1},\n\tdoi = {10.20944/preprints202302.0010.v1},\n\tabstract = {Machine learning has seen enormous growth in the last decade, with healthcare being a prime application for advanced diagnostics and improved patient care. The application of machine learning for healthcare is particularly pertinent in Africa, where many countries are resource-scarce. However, it is unclear how much research on this topic is arising from African institutes themselves, which is a crucial aspect for applications of machine learning to unique contexts and challenges on the continent. Here, we conduct a bibliometric study of African contributions to research publications related to machine learning for healthcare, as indexed in Scopus, between 1993 and 2022. We identified 3,772 research outputs, with most of these published since 2020. North African countries currently lead the way with 64.5\\% of publications for the reported period, yet Sub-Saharan Africa is rapidly increasing its output. We found that international support in the form of funding and collaborations is correlated with research output generally for the continent, with local support garnering less attention. Understanding African research contributions to machine learning for healthcare is a crucial first step in surveying the broader academic landscape, forming stronger research communities, and providing advanced and contextually aware biomedical access to Africa.},\n\tlanguage = {en},\n\turldate = {2023-03-03},\n\tpublisher = {Preprints},\n\tauthor = {Turki, Houcemeddine and Pouris, Anastassios and Ifeanyichukwu, Francis-Alfred Michaelangelo and Namayega, Catherine and Taieb, Mohamed Ali Hadj and Adedayo, Sadiq Adewale and Fourie, Chris and Currin, Christopher Brian and Asiedu, Mercy Nyamewaa and Tonja, Atnafu Lambebo and Owodunni, Abraham Toluwase and Dere, Abdulhameed and Emezue, Chris Chinenye and Muhammad, Shamsuddeen Hassan and Isa, Muhammad Musa and Aouicha, Mohamed Ben},\n\tmonth = feb,\n\tyear = {2023},\n}\n\n\n
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\n Machine learning has seen enormous growth in the last decade, with healthcare being a prime application for advanced diagnostics and improved patient care. The application of machine learning for healthcare is particularly pertinent in Africa, where many countries are resource-scarce. However, it is unclear how much research on this topic is arising from African institutes themselves, which is a crucial aspect for applications of machine learning to unique contexts and challenges on the continent. Here, we conduct a bibliometric study of African contributions to research publications related to machine learning for healthcare, as indexed in Scopus, between 1993 and 2022. We identified 3,772 research outputs, with most of these published since 2020. North African countries currently lead the way with 64.5% of publications for the reported period, yet Sub-Saharan Africa is rapidly increasing its output. We found that international support in the form of funding and collaborations is correlated with research output generally for the continent, with local support garnering less attention. Understanding African research contributions to machine learning for healthcare is a crucial first step in surveying the broader academic landscape, forming stronger research communities, and providing advanced and contextually aware biomedical access to Africa.\n
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\n\n \n \n \n \n \n \n A Framework for Grassroots Research Collaboration in Machine Learning and Global Health.\n \n \n \n \n\n\n \n Currin, C. B.; Asiedu, M. N.; Fourie, C.; Rosman, B.; Turki, H.; Siam, M.; Tonja, A. L.; Abbott, J.; Ajala, M.; Adedayo, S. A.; Emezue, C. C.; and Machangara, D.\n\n\n \n\n\n\n In
International Conference on Learning Representations 2023, 2023. \n
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\n\n \n \n Paper\n \n \n\n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n
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@inproceedings{currin_framework_2023,\n\ttitle = {A {Framework} for {Grassroots} {Research} {Collaboration} in {Machine} {Learning} and {Global} {Health}},\n\tcopyright = {Creative Commons Attribution-ShareAlike 4.0 International License (CC-BY-SA)},\n\turl = {https://openreview.net/forum?id=jHY_G91R880},\n\tabstract = {Traditional top-down approaches for global health have historically failed to achieve social progress (Hoffman et al., 2015; Hoffman \\& Røttingen, 2015). However, recently, a more holistic, multi-level approach, One Health (OH) (Osterhaus et al., 2020), is being adopted. Several challenges have been identified for the implementation of OH (dos S. Ribeiro et al., 2019), including policy and funding, education and training, and multi-actor, multi-domain, and multi-level collaborations. This is despite the increasing accessibility to knowledge and digital research tools through the internet. To address some of these challenges, we propose a general framework for grassroots community-based means of participatory research. Additionally, we present a specific roadmap to create a Machine Learning for Global Health community in Africa. The proposed framework aims to enable any small group of individuals with scarce resources to build and sustain an online community within approximately two years. We provide a discussion of the potential impact of the proposed framework on global health research collaborations.},\n\tlanguage = {en},\n\tbooktitle = {International {Conference} on {Learning} {Representations} 2023},\n\tauthor = {Currin, Christopher Brian and Asiedu, Mercy Nyamewaa and Fourie, Chris and Rosman, Benjamin and Turki, Houcemeddine and Siam, Mennatullah and Tonja, Atnafu Lambebo and Abbott, Jade and Ajala, Marvellous and Adedayo, Sadiq Adewale and Emezue, Chris Chinenye and Machangara, Daphne},\n\tyear = {2023},\n}\n\n\n
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\n Traditional top-down approaches for global health have historically failed to achieve social progress (Hoffman et al., 2015; Hoffman & Røttingen, 2015). However, recently, a more holistic, multi-level approach, One Health (OH) (Osterhaus et al., 2020), is being adopted. Several challenges have been identified for the implementation of OH (dos S. Ribeiro et al., 2019), including policy and funding, education and training, and multi-actor, multi-domain, and multi-level collaborations. This is despite the increasing accessibility to knowledge and digital research tools through the internet. To address some of these challenges, we propose a general framework for grassroots community-based means of participatory research. Additionally, we present a specific roadmap to create a Machine Learning for Global Health community in Africa. The proposed framework aims to enable any small group of individuals with scarce resources to build and sustain an online community within approximately two years. We provide a discussion of the potential impact of the proposed framework on global health research collaborations.\n
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