Biology subjects

Gonzalez, M. C.

Publications and source records attributed to Gonzalez, M. C..

4 recordsLinked to original sources

bioRxiv2026

TARGETED MEMORY REACTIVATION DURING NON-RAPID EYE MOVEMENT SLEEP STRENGTHENS CONSOLIDATED DECLARATIVE MEMORIES

Targeted memory reactivation (TMR) during sleep can benefit recently acquired memories, but whether it can also influence memories after an initial period of consolidation remains unclear. Here, we tested whether auditory reactivation during non-rapid eye movement (NREM) sleep could strengthen declarative memories learned 24 h earlier. Twenty-six healthy young adults learned 30 sound word associations and returned the following day for a 90 min nap. During NREM sleep, participants in the Reactivation group received incomplete reminders consisting of the learned sound followed by the first syllable of the associated word, whereas the No-Reactivation group slept under the same conditions without memory related cues. Participants who received reminders showed significantly less forgetting, despite comparable training performance and sleep macroarchitecture. Across NREM sleep, reactivation was associated with greater slow oscillation and delta power, more slow oscillations and fast spindles, and greater slow oscillation spindle cooccurrence. The memory benefit remained significant after adjusting for NREM physiological measures and in sensitivity analyses restricted to overlapping physiological ranges between groups. Cue locked analyses revealed significant responses in the slow oscillation, delta, theta, and fast-spindle ranges, but the magnitude of these responses was not associated with memory change. These findings show that TMR during NREM sleep can benefit declarative memories after a 24 h consolidation interval and suggest that its effects extend beyond the immediate post-learning sleep period.

neuroscience↗

bioRxiv2026

Beyond Ground Truth in K-Complex Detection: A Waveform-Based SVM Classifier and the Limits of Expert Agreement

ObjectiveK-complexes (KCs) are large-amplitude EEG events that represent N2 sleep stage and have been linked to sensory gating, sleep protection, and memory consolidation. Their detection remains limited by inter-rater variability in visual scoring and by the reliance of detectors on features that discard temporal information. We propose a two-stage detector that combines a rule-based candidate localization algorithm with a Support Vector Machine (SVM) classifier operating directly on the raw 2-seconds waveform, and we evaluate it against an adjudicated expert consensus of two different datasets. MethodsPolysomnographic recordings from 10 healthy adults (Dataset 1) were independently annotated by two human scorers; discordant events were adjudicated by a senior expert, yielding 240 consensus KCs. The automatic classifier was evaluated using subject-level 10-fold Group K-Fold cross-validation and compared directly against the two human scorers under identical conditions. Cross-dataset generalization was further assessed on the public DREAMS database (Dataset 2) under both external and internal training criteria. ResultsThe SVM classifier achieved the highest F1-score (79.4%) and accuracy (78.8%) among all scorers, with balanced recall (81.7%) and specificity (75.8%). Of the 58 false positives, 42 originated from events both experts had rejected yet displayed canonical KC morphology and received high classifier confidence (P(KC)>0.7 in 45.2% of cases). This pattern was replicated on Dataset 2. ConclusionA waveform-based classifier matches expert performance and systematically flags morphologically valid KCs that fall outside conventional visual-scoring criteria. SignificanceThese findings question the existence of an unambiguous ground truth for KC detection and support a data-driven redefinition of the event boundary, with implications for sleep staging and memory-consolidation research.

bioengineering↗

bioRxiv2020

Dataset of the construction and characterization of stable biological nanoparticles

We suggest that the structural flexibility is key for certain proteins in order to fulfill functions that are required to interact with biological membranes, and that intra-chain chemical crosslinking may result in a different arrangement of protein with lipids. As interaction with biological membranes and lipids is a function attributed to many proteins in circulation, we intended to characterize an experimental design that helps in the study of many biological protein structures and their function. But in addition, by introducing intra-chain crosslinking, we obtained discoidal nano platforms that are stable under different conditions of temperate and time incubation. These platforms might be an excellent model to employ as biological carriers of intrinsic or external molecules. Thus, data shown here clearly strengthen the usefulness of an easy, accessible and inexpensive tool not only to study protein-lipid interactions, but to be used in different biological fields that require the transport of organic compounds.

biophysics↗

bioRxiv2019

Hand-hygiene mitigation strategies against globaldisease spreading through the air transportationnetwork

Hand hygiene is considered as an efficient and cost-effective way to limit the spread of diseases and, as such, it is recommended by both the World Health Organization (WHO) and the Centres for Disease Control and Prevention (CDC). While the effect of hand washing on individual transmissibility of a disease has been studied through medical and public-health research, its potential as a mitigation strategy against a global pandemic has not been fully explored yet. In this study, we investigate contagion dynamics through the world air transportation network and analyze the impact of hand-hygiene behavioural changes of airport population against the spread of infectious diseases worldwide. Using a granular dataset of the world air transportation traffic, we build a detailed individual mobility model that controls for the correlated and recurrent nature of human travel and the waiting-time distributions of individuals at different locations. We perform a Monte-Carlo simulation study to assess the impact of different hand-washing mitigation strategies at the early stages of a global epidemic. From the simulation results we find that increasing the hand cleanliness homogeneously at all airports in the world can inhibit the impact of a potential pandemic by 24 to 69%. By quantifying and ranking the contribution of the different airports to the mitigation of an epidemic outbreak, we identify ten key airports at the core of a cost-optimal deployment of the hand-washing strategy: increasing the engagement rate at those locations alone could potentially reduce a world pandemic by 8 to 37%. This research provides evidence of the effectiveness of hand hygiene in airports on the global spread of infectious diseases, and has important implications for the way public-health policymakers may design new effective strategies to enhance hand hygiene in airports through behavioral changes.

epidemiology↗