Biology subjects

Ciccarelli, G.

Publications and source records attributed to Ciccarelli, G..

4 recordsLinked to original sources

bioRxiv2025

5'tRNA-derived fragments modulate β-cell homeostasis and islet macrophage activation in type 2 diabetes

During obesity and type 2 diabetes, pancreatic {beta}-cells face chronic environmental stress, while islet-resident macrophages (iMACs) undergo metabolic reprogramming that exacerbates {beta}-cell dysfunction. Stress-induced cleavage of transfer RNAs (tRNAs) generates tRNA-derived fragments (tRFs), whose role in this context is not fully understood. We identify elevated levels of 5tRFGlu(CTC) and 5tRFGly(GCC) in {beta}-cells and iMACs from db/db mice and in islets from type 2 diabetic patients. Notably, 5tRFGlu(CTC) is also induced under prediabetic conditions and inversely correlates with insulin secretion. Lipotoxic stress triggers their production via Angiogenin-mediated cleavage. Blocking 5tRFGlu(CTC) in islets protects against {beta}-cell apoptosis and restores insulin secretion under palmitate stress. Using a {beta}-cell/macrophage co-culture system, we show that {beta}-cell contact shapes a unique macrophage phenotype (iMAC-like) that shifts upon palmitate exposure--recapitulating in vivo observations. Inhibiting 5tRFGlu(CTC) in iMAC-like cells prevents this activation switch, reduces {beta}-cell stress, and improves insulin secretion. Mechanistically, 5tRFGlu(CTC) interacts with RNA-binding proteins to regulate transcriptional and post-transcriptional pathways linked to immune activation, extracellular matrex remodeling, neurogenesis, and oxidative stress. Our study identifies 5tRFs as key mediators of islet microenvironment remodeling in diabetes, offering new insights into intercellular stress signaling in metabolic disease.

cell biology↗

bioRxiv2018

Comparison of Two-Talker Attention Decoding from EEG with Nonlinear Neural Networks and Linear Methods

Auditory attention decoding (AAD) through a brain-computer interface has had a flowering of developments since it was first introduced by Mesgarani and Chang (2012) using electrocorticograph recordings. AAD has been pursued for its potential application to hearing-aid design in which an attention-guided algorithm selects, from multiple competing acoustic sources, which should be enhanced for the listener and which should be suppressed. Traditionally, researchers have separated the AAD problem into two stages: reconstruction of a representation of the attended audio from neural signals, followed by determining the similarity between the candidate audio streams and the reconstruction. In this work, we compare the traditional two-stage approach with a novel neural-network architecture that subsumes the explicit similarity step. We compare this new architecture against linear and non-linear (neural-network) baselines using both wet and dry electroencephalogram (EEG) systems. Our results indicate that the wet and dry systems can deliver comparable results despite the latter having one third as many EEG channels as the former, and that the new architecture outperforms the baseline stimulus-reconstruction methods for both EEG modalities. The 14-subject, wet-electrode AAD dataset for two competing, co-located talkers, the 11-subject, dry-electrode AAD dataset, and our software are available to download for further validation, experimentation, and modification.

neuroscience↗

bioRxiv2017

Detecting pathogen exposure during the non-symptomatic incubation period using physiological data

Early pathogen exposure detection allows better patient care and faster implementation of public health measures (patient isolation, contact tracing). Existing exposure detection most frequently relies on overt clinical symptoms, namely fever, during the infectious prodromal period. We have developed a robust machine learning based method to better detect asymptomatic states during the incubation period using subtle, sub-clinical physiological markers. Starting with high-resolution physiological waveform data from non-human primate studies of viral (Ebola, Marburg, Lassa, and Nipah viruses) and bacterial (Y. pestis) exposure, we processed the data to reduce short-term variability and normalize diurnal variations, then provided these to a supervised random forest classification algorithm and post-classifier declaration logic step to reduce false alarms. In most subjects detection is achieved well before the onset of fever; subject cross-validation across exposure studies (varying viruses, exposure routes, animal species, and target dose) lead to 51h mean early detection (at 0.93 area under the receiver-operating characteristic curve [AUCROC]). Evaluating the algorithm against entirely independent datasets for Lassa, Nipah, and Y. pestis exposures un-used in algorithm training and development yields a mean 51h early warning time (at AUCROC=0.95). We discuss which physiological indicators are most informative for early detection and options for extending this capability to limited datasets such as those available from wearable, non-invasive, ECG-based sensors.

physiology↗

bioRxiv2017

Current Speech Motor Control Models: An Overview of Architectures & Properties

This paper reviews the current state of several formal models of speech motor control with particular focus on the low level control of the speech articulators. Further development of speech motor control models may be aided by a comparison of model attributes. The review builds an understanding of existing models from first principles, before moving into a discussion of several models, showing how each is constructed out of the same basic domain-general ideas and components - e.g., generalized feedforward, feedback, and model predictive components. This approach allows for direct comparisons to be made in terms of where the models differ, and their points of agreement. Substantial differences among models can be observed in their use of feedforward control, process of estimating system state, and method of incorporating feedback signals into control. However, many commonalities exist among the models in terms of their reliance on higher-level motor planning, use of feedback signals, lack of time-variant adaptation, and focus on kinematic aspects of control and biomechanics. Ongoing research bridging hybrid feedforward/feedback pathways with forward dynamic control, as well as feedback/internal model-based state estimation is discussed.

neuroscience↗