Biology subjects

Cusimano, M.

Publications and source records attributed to Cusimano, M..

7 recordsLinked to original sources

bioRxiv2026

BirdCODE: Detecting bird communication at scale

Deep learning-based animal sound identification is regularly applied to large audio datasets for ecological monitoring and citizen science, but existing methods lack the fine temporal resolution required to extract insights into animal communication from these same datasets. Here we introduce Bird Communication Detector (BirdCODE), a deep learning model that detects and classifies the vocalizations of over 9000 bird species with precise temporal boundaries, a several hundredfold increase the number of species over previous bioacoustic sound event detection models. In extensive benchmarking, BirdCODE achieves state-of-the-art performance in detection and classification of bird sounds. Applying BirdCODE to 1.3M citizen-science recordings, we present four case studies of how BirdCODE-computed sound event boundaries can be used to carry out phylogenetic analyses, to describe geographic and temporal variation in acoustic communication, and to characterize cross-species interactions. Together, these demonstrate how BirdCODE can enable large-scale, data-driven studies of bird communication. Model code, weights, and predictions are publicly available.

bioinformatics↗

bioRxiv2026

Repertoire-wide contextual mapping reveals signal functions in cooperatively breeding crows

Communication structures society, and is likewise shaped by relationships and shared tasks; yet, for most socially complex species, we know little of their full vocal repertoire and its functions. We investigated how communication structures the who, what, and when of social interactions in cooperative carrion crows - group-living birds who rely on coordinated behaviors, as in chick care. Leveraging machine learning to integrate large-scale data from crow-borne audio-loggers and nest cameras, we charted the vocal repertoire across 24 cooperative groups and mapped all discovered call types to behaviors and social context. We found that crows used a rich repertoire across three domains of joint behavior - flocking, chick care, and territorial display. Relatively quiet call types were abundant and included close-range calls that may coordinate chick care by announcing nest visits. Our study demonstrates how combining continuous-capture data and machine learning can reveal a holistic understanding of how vocalizations function across contexts.

animal behavior and cognition↗

bioRxiv2023

Bayesian auditory scene synthesis explains human perception of illusions and everyday sounds

Perception has long been envisioned to use an internal model of the world to explain the causes of sensory signals. However, such accounts have historically not been testable, typically requiring intractable search through the space of possible explanations. Using auditory scenes as a case study, we leveraged contemporary computational tools to infer explanations of sounds in a candidate internal model of the auditory world (ecologically inspired audio synthesizers). Model inferences accounted for many classic illusions. Unlike traditional accounts of auditory illusions, the model is applicable to any sound, and exhibited human-like perceptual organization for real world sound mixtures. The combination of stimulus-computability and interpretable model structure enabled rich falsification, revealing additional assumptions about sound generation needed to account for perception. The results show how generative models can account for the perception of both classic illusions and everyday sensory signals, and provide the basis on which to build theories of perception.

neuroscience↗

bioRxiv2019

Meta-gene markers predict meningioma recurrence with high accuracy

BackgroundMeningiomas, the most common adult brain tumors, recur in up to half of cases[1-3]. This requires timely intervention and therefore accurate risk assessment of recurrence is essential. Our current practice relies heavily on histological grade and extent of surgical excision to predict meningioma recurrence. However, prediction accuracy can be as poor as 50% for low or intermediate grade tumors, which constitute more than 90% of all cases. Moreover, attempts to find molecular markers, particularly for predicting recurrence of intermediate tumors have been impeded by low[4] or heterogenous[5-8] genetic signal. We therefore sought to apply systems-biology approaches to transcriptomic data to better predict meningioma recurrence.\n\nMethodsWe apply gene co-expression networks to a cohort of 252 adult patients from the publicly-available genetic repository Gene Expression Omnibus. Resultant gene clusters (\"modules\") were represented by the first principle component of their expression levels, and their ability to predict recurrence assessed with a logistic regression model. External validation was done using an independent merged cohort of 108 patients using the same modules. We used the bioinformatics database DAVID[9] to examine the gene ontology associations of each module.\n\nResultsUsing gene co-expression analysis, we were able predict tumor recurrence with high accuracy using a single module which mapped to cell cycle-related functions (AUC of 0.81 {+/-} 0.09 in external validation). Further, the module maintained its accuracy with removal of up to 20 of its most individually predictive genes, suggesting robustness against noise at the single gene level.\n\nConclusionsWith the easy accessibility of gene panels in healthcare diagnostics, our results offer a basis for routine molecular testing in meningioma management.

bioinformatics↗

bioRxiv2019

Skull base meningiomas have a distinct immune landscape.

Modulation of tumor microenvironment is an emerging frontier for new therapeutics. However in meningiomas, the most frequent adult brain tumor, the correlation of microenvironment with tumor phenotype is scarcely studied. We applied a variety of systems biology approaches to bulk tumor transcriptomics to explore the immune environments of both skull base and convexity (hemispheric) meningiomas. We hypothesized that the more benign biology of skull base meningiomas parallels the relative composition and activity of immune cells that oppose tumor growth and/or survival. We firstly applied gene co-expression networks to tumor bulk transcriptomics from 107 meningiomas (derived from 3 independent studies) and found immune processes to be the sole biological mechanism correlated with anatomical location while correcting for tumour grade. We then derived tumor immune cell fractions from bulk transcriptomics data and examined the immune cell-cytokine interactions using a network-based approach. We demonstrate that oncolytic M1 macrophages dominate skull base meningiomas while mast cells, known to play a role in oncogenesis, show greater activity in convexity tumors. Our results are the first to suggest the importance of tumor microenvironment in meningioma biology in the context of anatomic location and immune landscape. These findings may help better inform surgical decision making and yield location-specific therapies through modulation of immune microenvironment.

cancer biology↗

bioRxiv2018

Homogenous subgroups of atypical meningiomas defined using oncogenic signatures: basis for a new grading system?

Meningiomas are the most common brain tumor with a prevalence of 3% in the population. Histological grading of meningiomas (1 through 3) has a major role in determining treatment choice and predicting outcome. While largely indolent grade 1 and the highly aggressive grade 3 meningiomas as considered mostly homogenous in clinical behavior, atypical or grade 2 meningiomas have highly diverse biological properties. Our aim was to identify homogenous subgroups of atypical meningiomas with the working hypothesis that these subgroups would share features with grade 1 and grade 3 counterparts. We carried out systems level analysis by gene module discovery using co-expression networks on the transcriptomics of 212 meningiomas. The newly identified subgroups were characterized in terms of recurrence rate and overlapping biological processes in gene ontology. We were able to reclassify 33 of 46 atypical meningiomas (72%) into a benign \"grade 1-like\" (14/46) and malignant \"grade 3-like\" (19/46) subgroup based on oncogenic signatures. Recurrence rates of \"Grade 1-like\" and \"grade 3-like\" tumors was 0% and 72% respectively. These two new subgroups showed similar recurrence rates and concordant biological processes with the respected grades. Our findings help resolve the heterogeneity/uncertainty around atypical meningioma biology and identify subgroups more homogenous than in prior studies. These results may help reshape prediction, follow-up planning, treatment decisions and recruitment protocols for future and ongoing clinical trials. The findings demonstrate the conceptual advantage of systems biology approaches and underpin the utility of molecular signatures as complements to the current histological grading system.

cancer biology↗

bioRxiv2018

Multimorbidity states with high sepsis-related deaths: a data-driven analysis in critical care

Sepsis remains a complex medical problem and a major challenge in healthcare. Diagnostics and outcome predictions are focused on physiological parameters with less consideration given to patients medical background. Given the aging population, not only are diseases becoming increasingly prevalent but occur more frequently in combinations (\"multimorbidity\"). Thus, it is imperative we incorporate morbidity state in our healthcare models.\n\nWe investigate effects of multimorbidity on the occurrence of sepsis and associated mortality in critical care (CC) through analysis of 36390 patients from the open source Medical Information Mart for Intensive Care III (MIMIC III) dataset. Morbidities were defined based on Elixhauser categories, a well-established scheme distinguishing 30 classes of chronic diseases. Using latent class analysis (LCA) we identified six clinically distinct subgroups based on demographics, admission type and morbidity compositions. Subgroup of middle-aged patients with health consequences of drug and alcohol addiction had the highest mortality rate, over 2-fold greater compared to other groups with older patients and complex multimorbid patterns. The findings promote incorporation of multimorbidity in healthcare models and the shift away from current single-disease paradigm in clinical practice, training and trial design.

bioinformatics↗