Biology subjects

Lefevre, N.

Publications and source records attributed to Lefevre, N..

4 recordsLinked to original sources

bioRxiv2026

From linear to nonlinear gait measures: validity of a multi-view markerless motion capture system

Markerless motion capture offers a practical alternative to marker-based optoelectronic systems, yet validation studies have focused almost exclusively on linear gait measures. Nonlinear measures of gait dynamics, sensitive to the fine temporal structure of locomotor signals, remain unvalidated in markerless systems. This study assessed the concurrent validity of a three-camera markerless system against a 15-camera optoelectronic system across four treadmill speeds in 22 healthy adults. Inter-system agreement was evaluated for spatiotemporal parameters (mean, variability, detrended fluctuation analysis [DFA] scaling exponents) and for joint angle and trunk acceleration time series (maximum Lyapunov exponents, sample entropy, Attractor Complexity Index [ACI]). Temporal measures reached near-perfect agreement, and spatial measures showed good to excellent agreement with small speed-dependent positive biases. Sagittal-plane joint kinematic waveforms were compared using statistical parametric mapping, with root-mean-square error (RMSE) reported for significant intervals. Agreement was best at the hip, with knee and ankle showing comparable, higher error (RMSE: 1.2-3.0{degrees} hip, 3.7-5.5{degrees} knee and 3.8-5.1{degrees} ankle). ACI demonstrated moderate to good agreement across all joints and good agreement across trunk acceleration directions. Most DFA scaling exponents for step-based series supported group-level comparisons: both measures are usable for markerless assessment of gait's nonlinear dynamics. Maximum Lyapunov exponents and sample entropy showed lower absolute agreement: at the hip and knee, they preserved inter-individual ranking and remained usable for within-system group comparisons, but agreement collapsed at the ankle and for trunk sample entropy, indicating these measures still need refinement. These findings define a tiered, measure-specific scope for markerless gait analysis, extending validation beyond spatiotemporal parameters.

bioengineering↗

bioRxiv2026

Beyond gait speed: a multidimensional motor signature of Motoric Cognitive Risk syndrome identified through domain-specific anomaly detection

Motoric Cognitive Risk (MCR) syndrome, defined by subjective cognitive complaints and slow gait speed, identifies older adults at increased risk of major neurocognitive disorders (NCDs). Yet, gait speed reflects a composite output shaped by heterogeneous neuromusculoskeletal and cognitive processes, limiting its clinical specificity. This study aimed to refine the motor signature of MCR by quantifying domain-specific gait deviations relative to a normative reference cohort using an anomaly detection approach. Ninety-seven adults ([≥] 55 years) completed two 3-minute treadmill walking bouts at their preferred speed. Participants were categorized into three groups: older adults with MCR (n = 20), healthy older adults with slow gait (sHOA; n = 20) matched to MCR for age and gait speed, and healthy older adults (HOA; n = 57). Linear spatiotemporal and nonlinear trunk acceleration-derived variables were organized into ten functional gait domains, conceptually grouped into gait pattern (pace, rhythm, phases, postural control, symmetry), fluctuation amplitude (variability), and temporal structure of fluctuations (regulation, signal complexity, divergence of movement trajectories, and attractor complexity). For each domain, a Gaussian mixture model trained on HOA data defined a normative reference space, from which individual anomaly scores quantified deviations across groups. Both sHOA and MCR showed higher deviations in gait pattern domains (pace and phases) than HOA, consistent with their slower gait speed. Only MCR exhibited additional deviations in domains related to fluctuation amplitude and temporal structure, reflected by increased step-to-step variability and trunk acceleration fluctuations that were more divergent, more predictable, and less complex. These findings reveal a multidimensional motor signature of MCR. Domain-specific anomaly scores may provide individualized, clinically interpretable biomarkers to support early detection and monitoring of older adults at increased risk of major NCDs. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=109 SRC="FIGDIR/small/716304v1_ufig1.gif" ALT="Figure 1"> View larger version (42K): org.highwire.dtl.DTLVardef@3b2e52org.highwire.dtl.DTLVardef@15e4101org.highwire.dtl.DTLVardef@fdb9c4org.highwire.dtl.DTLVardef@1af0d03_HPS_FORMAT_FIGEXP M_FIG C_FIG

neuroscience↗

bioRxiv2023

Employing a honeybee olfactory neural circuit as a novel gas sensor for the detection of human lung cancer biomarkers

Human breath contains biomarkers (odorants) that can be targeted for early disease detection. It is well known that honeybees have a keen sense of smell and can detect a wide variety of odors at low concentrations. Here, for the first time, we employ honeybee olfactory neuronal circuitry to classify human lung cancer volatile biomarkers and their mixtures at concentration ranges relevant to human breath, parts-per-billion to parts-per-trillion. Different lung cancer biomarkers evoked distinct spiking response dynamics in the honeybee antennal lobe neurons indicating that those neurons encoded biomarker-specific information. By investigating lung cancer biomarker-evoked population neuronal responses from the honeybee antennal lobe, we could classify individual human lung cancer biomarkers successfully (88% success rate). When we mixed six lung cancer biomarkers at different concentrations to create synthetic lung cancer vs. synthetic healthy breath, honeybee population neuronal responses were also able to classify those complex breath mixtures successfully (100% success rate with a leave-one-trial-out method). Finally, we used separate training and testing datasets containing responses to the synthetic lung cancer and healthy breath mixtures. We identified a simple metric, the peak response of the neuronal ensemble, with the ability to distinguish synthetic lung cancer breath from the healthy breath with 86.7% success rate. This study provides proof-of-concept results that a powerful biological gas sensor, the honeybee olfactory system, can be used to detect human lung cancer biomarkers and their complex mixtures at biological concentrations.

bioengineering↗

bioRxiv2022

Harnessing insect olfactory neural circuits for noninvasive detection of human cancer

There is overwhelming evidence that metabolic processes are altered in cancer cells and these changes are manifested in the volatile organic compound (VOC) composition of exhaled breath. Here, we take a novel approach of an insect olfactory neural circuit-based VOC sensor for cancer detection. We combined an in vivo antennae-attached insect brain with an electrophysiology platform and employed biological neural computation rules of antennal lobe circuitry for data analysis to achieve our goals. Our results demonstrate that three different human oral cancers can be robustly distinguished from each other and from a non-cancer oral cell line by analyzing individual cell culture VOC composition-evoked olfactory neural responses in the insect antennal lobe. By evaluating cancer vs. non-cancer VOC-evoked population neural responses, we show that olfactory neurons response-based classification of oral cancer is sensitive and reliable. Moreover, this brain-based cancer detection approach is very fast (detection time ~ 250 ms). We also demonstrate that this cancer detection technique is effective across changing chemical environments mimicking natural conditions. Our brain-based cancer detection system comprises a novel VOC sensing methodology that will spur the development of more forward engineering technologies for noninvasive detection of cancer.

bioengineering↗