Biology subjects

Green, C. S.

Publications and source records attributed to Green, C. S..

3 recordsLinked to original sources

bioRxiv2026

PLFest: A Multi-Site Validation of an Open Platform for Visual and Cognitive Assessment

PurposeTo evaluate the feasibility, validity, and scalability of PLFest, an open-source, Unity-based, cross-platform application designed for standardized, multi-site visual and cognitive assessment and training. MethodsTwo hundred sixty participants (mean age = 23 years) were recruited across four university sites in the United States. Participants completed a battery of five visual assessments administered through PLFest, including visual acuity, contrast sensitivity, spatial frequency cutoff, contrast sensitivity at spatial-frequency cutoff, and visual search. Five cognitive assessments measuring visuospatial working memory, verbal working memory, fluid reasoning, inhibitory control, and selective attention were also administered. Descriptive statistics and performance distributions were examined and compared with normative data. ResultsVisual acuity and contrast sensitivity measures closely matched previously reported normative values obtained using established clinical and psychophysical methods. Spatial frequency cutoff and visual search tasks produced stable threshold estimates while showing substantial inter-individual variability. Performance across all cognitive assessments was consistent with published validation studies of the corresponding tasks. Across the full battery, adaptive procedures demonstrated reliable convergence and generated well-distributed performance measures without evidence of substantial floor or ceiling effects. Importantly, these findings were observed across four geographically distributed testing sites using standardized consumer-grade tablet hardware. ConclusionsPLFest provides reliable and scalable assessment of visual and cognitive function using portable consumer devices. The platform supports standardized data collection across distributed research settings while maintaining performance characteristics consistent with established laboratory and clinical benchmarks. These findings support the use of PLFest as a reliable framework for large-scale studies of vision and cognition. Translational RelevanceBy reducing dependence on specialized laboratory infrastructure and trained personnel, PLFest may facilitate broader access to visual and cognitive assessment, enabling large-scale research, screening, and future rehabilitation applications.

neuroscience↗

bioRxiv2024

Antibody-dependent Intracellular neutralisation by TRIM21 is potentiated by HOIP and linear ubiquitin chains

Antibody-dependent intracellular neutralisation (ADIN) promotes the rapid proteasomal degradation of viruses and other large substrates in the cytosol. It is dependent on detection of intracellular virus-bound antibodies by the Fc receptor and E3 ligase TRIM21, followed by disassembly of the virus by the unfoldase VCP/p97. It is not known how VCP is recruited to TRIM21. We performed a limited siRNA knock-down screen of known VCP adaptors to determine their involvement in ADIN. Knock-down of HOIP, the only ubiquitin E3 ligase capable of generating linear ubiquitin chains, resulted in impaired virus neutralisation. HOIPIN-8, a HOIP inhibitor, showed concentration-dependent reduction in virus neutralisation. We found that the activity of HOIP in ADIN is dependent on its ubiquitin binding domains and its PUB domain which recruits VCP. Knock-down of OTULIN, the only deubiquitinating enzyme that exclusively cleaves linear ubiquitin chains, potentiated neutralisation of the virus. Our results expand the role of HOIP and linear ubiquitin chains in proteostasis.

immunology↗

bioRxiv2021

A mixture of generative models strategy helps humans generalize across tasks

What role do generative models play in generalization of learning in humans? Our novel multi-task prediction paradigm--where participants complete four sequence learning tasks, each being a different instance of a common generative family--allows the separate study of within-task learning (i.e., finding the solution to each of the tasks), and across-task learning (i.e., learning a task differently because of past experiences). The very first responses participants make in each task are not yet affected by within-task learning and thus reflect their priors. Our results show that these priors change across successive tasks, increasingly resembling the underlying generative family. We conceptualize multi-task learning as arising from a mixture-of-generative-models learning strategy, whereby participants simultaneously entertain multiple candidate models which compete against each other to explain the experienced sequences. This framework predicts specific error patterns, as well as a gating mechanism for learning, both of which are observed in the data.

neuroscience↗