Molecular detection, risk factor and public awareness of avian bornavirus from captive and non-captive birds in Peninsular Malaysia by Mohd Lutpi, Syamsiah

“…Birds with PDD may show signs of gastrointestinal tract deficit (e.g., weight loss, regurgitation, passage of undigested food in faeces), or neurological dysfunction (e.g., ataxia, abnormal gait, proprioceptive defects) or even both. …”
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Using machine learning to discover candidate localised transcripts from microscopy and genome-wide bioinformatics data by Kiourlappou, M

“…Proper mRNA localisation is vital for the correct functioning of the entirety of a glial cell and dysregulation of mRNA localisation in glial cells has been implicated in neurodegenerative and neurological diseases and some types of cancer. Therefore, gaining a deeper understanding of the mechanisms that mediate mRNA localisation is of paramount value.…”

Genetic analysis of normal human phenotypes within the people of the British Isles by Meena, D

“…</p> <p>The last part of this thesis provides new insights into the field of olfactory perception and argues for the potential link between neurological disorders and olfactory impairment. Most, if not all, novel variants (suggestive genome-wide significant) presented in this research have strong functional biological support behind the preliminary statistical observations and will be helpful in understanding the visible pigmentation differences among humans as well as the genetics underlying food preferences.…”

A Digital Phenotypic Assessment in Neuro-Oncology (DANO): A Pilot Study on Sociability Changes in Patients Undergoing Treatment for Brain Malignancies by Siddi, Francesca, Emedom-Nnamdi, Patrick, Catalino, Michael P., Rana, Aakanksha, Boaro, Alessandro, Dawood, Hassan Y., Sala, Francesco, Onnela, Jukka-Pekka, Smith, Timothy R.

“…Abstract Background: The digital phenotyping tool has great potential for the deep characterization of neurological and quality-of-life assessments in brain tumor patients. …”
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Multi-site benchmark classification of major depressive disorder using machine learning on cortical and subcortical measures by Belov, Vladimir, Erwin-Grabner, Tracy, Aghajani, Moji, Aleman, Andre, Amod, Alyssa R., Basgoze, Zeynep, Benedetti, Francesco, Besteher, Bianca, Bülow, Robin, Ching, Christopher R. K., Connolly, Colm G., Cullen, Kathryn, Davey, Christopher G., Dima, Danai, Dols, Annemiek, Evans, Jennifer W., Fu, Cynthia H. Y., Gonul, Ali Saffet, Gotlib, Ian H., Grabe, Hans J., Groenewold, Nynke, Hamilton, J. Paul, Harrison, Ben J., Ho, Tiffany C., Mwangi, Benson, Jaworska, Natalia, Jahanshad, Neda, Klimes-Dougan, Bonnie, Koopowitz, Sheri-Michelle, Lancaster, Thomas, Li, Meng, Linden, David E. J., MacMaster, Frank P., Mehler, David M. A., Melloni, Elisa, Mueller, Bryon A., Ojha, Amar, Oudega, Mardien L., Penninx, Brenda W. J. H., Poletti, Sara, Pomarol-Clotet, Edith, Portella, Maria J., Pozzi, Elena, Reneman, Liesbeth, Sacchet, Matthew D., Sämann, Philipp G., Schrantee, Anouk, Sim, Kang, Soares, Jair C., Stein, Dan J., Thomopoulos, Sophia I., Uyar-Demir, Aslihan, van der Wee, Nic J. A., van der Werff, Steven J. A., Völzke, Henry, Whittle, Sarah, Wittfeld, Katharina, Wright, Margaret J., Wu, Mon-Ju, Yang, Tony T., Zarate, Carlos, Veltman, Dick J., Schmaal, Lianne, Thompson, Paul M., Goya-Maldonado, Roberto

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