Percepção dos pais sobre os sinais e sintomas decorrentes da erupção dentária em bebês / Parents' perception about tooth eruption signs and symptoms in babies

A erupção dentária é definida como a movimentação dos dentes em desenvolvimento para emergir através dos tecidos moles da maxila e da mandíbula. O primeiro dente decíduo geralmente irrompe na cavidade bucal em um intervalo entre quatro e dez meses de idade e manifestações locais e sistêmicas associadas à erupção são observadas e relatadas por pais de bebês que passam pelo processo. Deste modo, este trabalho busca revisar e analisar a literatura em relação à percepção dos pais sobre os sinais e sintomas observados durante o processo de erupção dentária em bebês. Foi realizada uma revisão integrativa da literatura, buscando por artigos indexados nas bases eletrônicas de dados PubMed e Portal BVS. Ao final, foram selecionados 16 artigos científicos, sendo em sua maioria estudos com delineamento transversal (n: 11). Apenas dois estudos foram realizados no Brasil, sendo a Índia (n: 4) o país com maior número de artigos incluídos. Os sinais e sintomas mais relatados pelos pais foram febre (n: 16), perda de apetite (n: 13) e aumento da salivação (n: 12). Os estudos analisados apresentaram limitações, como a falta de padronização dos questionários direcionados aos pais. Compreende-se, desta forma, que mais estudos com populações variadas, amostras maiores e questionários padronizados são necessários.

Dental eruption is defined as the movement of developing teeth to emerge through the soft tissues of the maxilla and mandible. The first deciduous tooth usually erupts into the oral cavity between the fourth and tenth month of age. During this time, local and systemic manifestations are observed and reported by parents of babies who undergo the process. Thus, this stud seeks to review and analyze the literature regarding the perception of parents about the signs and symptoms observed during the process of tooth eruption in babies. An integrative literature review was performed, searching for articles indexed in PubMed and Portal BVS electronic databases. Sixteen papers were selected, mostly of which were cross-sectional studies (n: 11). Only two studies were carried out in Brazil, with India (n: 4) being the country with the highest number of articles included. The most reported signs and symptoms were fever (n: 16), loss of appetite (n: 13) and increased salivation (n: 12). The analyzed studies had limitations, such as the lack of standardization of the questionnaires addressed to parents. We conclude more studies with varied populations, larger samples and standardized questionnaires are needed.

Orthogonality of sensory and contextual categorical dynamics embedded in a continuum of responses from the second somatosensory cortex.

How does the brain simultaneously process signals that bring complementary information, like raw sensory signals and their transformed counterparts, without any disruptive interference? Contemporary research underscores the brain's adeptness in using decorrelated responses to reduce such interference. Both neurophysiological findings and artificial neural networks support the notion of orthogonal representation for signal differentiation and parallel processing. Yet, where, and how raw sensory signals are transformed into more abstract representations remains unclear. Using a temporal pattern discrimination task in trained monkeys, we revealed that the second somatosensory cortex (S2) efficiently segregates faithful and transformed neural responses into orthogonal subspaces. Importantly, S2 population encoding for transformed signals, but not for faithful ones, disappeared during a nondemanding version of this task, which suggests that signal transformation and their decoding from downstream areas are only active on-demand. A mechanistic computation model points to gain modulation as a possible biological mechanism for the observed context-dependent computation. Furthermore, individual neural activities that underlie the orthogonal population representations exhibited a continuum of responses, with no well-determined clusters. These findings advocate that the brain, while employing a continuum of heterogeneous neural responses, splits population signals into orthogonal subspaces in a context-dependent fashion to enhance robustness, performance, and improve coding efficiency.

Rényi entropy-complexity causality space: a novel neurocomputational tool for detecting scale-free features in EEG/iEEG data.

Scale-free brain activity, linked with learning, the integration of different time scales, and the formation of mental models, is correlated with a metastable cognitive basis. The spectral slope, a key aspect of scale-free dynamics, was proposed as a potential indicator to distinguish between different sleep stages. Studies suggest that brain networks maintain a consistent scale-free structure across wakefulness, anesthesia, and recovery. Although differences in anesthetic sensitivity between the sexes are recognized, these variations are not evident in clinical electroencephalographic recordings of the cortex. Recently, changes in the slope of the power law exponent of neural activity were found to correlate with changes in Rényi entropy, an extended concept of Shannon's information entropy. These findings establish quantifiers as a promising tool for the study of scale-free dynamics in the brain. Our study presents a novel visual representation called the Rényi entropy-complexity causality space, which encapsulates complexity, permutation entropy, and the Rényi parameter q. The main goal of this study is to define this space for classical dynamical systems within theoretical bounds. In addition, the study aims to investigate how well different time series mimicking scale-free activity can be discriminated. Finally, this tool is used to detect dynamic features in intracranial electroencephalography (iEEG) signals. To achieve these goals, the study implementse the Bandt and Pompe method for ordinal patterns. In this process, each signal is associated with a probability distribution, and the causal measures of Rényi entropy and complexity are computed based on the parameter q. This method is a valuable tool for analyzing simulated time series. It effectively distinguishes elements of correlated noise and provides a straightforward means of examining differences in behaviors, characteristics, and classifications. For the iEEG experimental data, the REM state showed a greater number of significant sex-based differences, while the supramarginal gyrus region showed the most variation across different modes and analyzes. Exploring scale-free brain activity with this framework could provide valuable insights into cognition and neurological disorders. The results may have implications for understanding differences in brain function between the sexes and their possible relevance to neurological disorders.

The social and defensive function of olfactory behaviors in a pair-living sexually monogamous primate.

Olfactory behaviors serve a wide variety of social functions in mammals. Odor may signal information about attributes of individuals important for mating and reproduction. Olfactory behaviors, such as scent-marking, may also function as part of home range or resource defense strategies. We assessed the potential social and home range defense functions of olfactory behavior in a pair-living and sexually monogamous primate, Azara's owl monkey (Aotus azarae), in the Argentinian Chaco. This is the most extensive investigation of owl monkey olfactory behaviors in the wild. Individuals regularly performed olfactory behaviors (group mean + SD = 1.3 + 0.5 per hour). The patterns were generally comparable to those observed in studies of captive owl monkeys, except that urine washing was the most common behavior in the wild, as opposed to scent-marking and genital inspections. Most olfactory behaviors were performed by adults, and there were striking sex differences in genital inspections: almost all consisted of an adult male inspecting the paired adult female. These findings suggest that olfactory behaviors play an important role in signaling and coordinating reproduction among owl monkeys, particularly during periods of female conception and pregnancy. Additionally, our research indicates that these behaviors may also serve as a defense strategy for maintaining the core area of their home ranges. This study offers the first assessment of the role of olfactory behaviors in reproductive contexts and home range defense in pair-living, monogamous platyrrhine primates.

Embedded Machine Learning System for Muscle Patterns Detection in a Patient with Shoulder Disarticulation.

The integration of artificial intelligence (AI) models in the classification of electromyographic (EMG) signals represents a significant advancement in the design of control systems for prostheses. This study explores the development of a portable system that classifies the electrical activity of three shoulder muscles in real time for actuator control, marking a milestone in the autonomy of prosthetic devices. Utilizing low-power microcontrollers, the system ensures continuous EMG signal recording, enhancing user mobility. Focusing on a case study-a 42-year-old man with left shoulder disarticulation-EMG activity was recorded over two days using a specifically designed electronic board. Data processing was performed using the Edge Impulse platform, renowned for its effectiveness in implementing AI on edge devices. The first day was dedicated to a training session with 150 repetitions spread across 30 trials and three different movements. Based on these data, the second day tested the AI model's ability to classify EMG signals in new movement executions in real time. The results demonstrate the potential of portable AI-based systems for prosthetic control, offering accurate and swift EMG signal classification that enhances prosthetic user functionality and experience. This study not only underscores the feasibility of real-time EMG signal classification but also paves the way for future research on practical applications and improvements in the quality of life for prosthetic users.

A library of electrophysiological responses in plants - a model of transversal education and open science.

Electrophysiology in plants is understudied, and, moreover, an ideal model for student inclusion at all levels of education. Here, we report on an investigation in open science, whereby scientists worked with high school students, faculty, and undergraduates from Chile, Germany, Serbia, South Korea, and the USA. The students recorded the electrophysiological signals of >15 plant species in response to a flame or tactile stimulus applied to the leaves. We observed that approximately 60% of the plants studied showed an electrophysiological response, with a delay of ~ 3-6 s after stimulus presentation. In preliminary conduction velocity experiments, we verified that observed signals are indeed biological in origin, with information transmission speeds of ~ 2-9 mm/s. Such easily replicable experiments can serve to include more investigators and students in contributing to our understanding of plant electrophysiology.

Implementation of a Real-Time Brain-to-Brain Synchrony Estimation Algorithm for Neuroeducation Applications.

This study centers on creating a real-time algorithm to estimate brain-to-brain synchronization during social interactions, specifically in collaborative and competitive scenarios. This type of algorithm can provide useful information in the educational context, for instance, during teacher-student or student-student interactions. Positioned within the context of neuroeducation and hyperscanning, this research addresses the need for biomarkers as metrics for feedback, a missing element in current teaching methods. Implementing the bispectrum technique with multiprocessing functions in Python, the algorithm effectively processes electroencephalography signals and estimates brain-to-brain synchronization between pairs of subjects during (competitive and collaborative) activities that imply specific cognitive processes. Noteworthy differences, such as higher bispectrum values in collaborative tasks compared to competitive ones, emerge with reliability, showing a total of 33.75% of significant results validated through a statistical test. While acknowledging progress, this study identifies areas of opportunity, including embedded operations, wider testing, and improved result visualization. Beyond academia, the algorithm's utility extends to classrooms, industries, and any setting involving human interactions. Moreover, the presented algorithm is shared openly, to facilitate implementations by other researchers, and is easily adjustable to other electroencephalography devices. This research not only bridges a technological gap but also contributes insights into the importance of interactions in educational contexts.

Method for phase space reconstruction to estimate the short-term future behavior of pressure signals in pipelines.

In this study, we propose a method based on phase space reconstruction to estimate the short-term future behavior of pressure signals in pipelines. The pressure time series data were obtained from an IoT experimental model conducted in the laboratory. The proposed hydraulic system demonstrated the presence of traces of weak chaos in the time series of the pressure signal. Fractal dimension analysis revealed a complex fractal structure in the data, indicating the existence of nonlinear dynamics. Similarly, Lyapunov coefficients, divergent trajectories, and autocorrelation analysis confirmed the presence of weak chaos in the time series. The results demonstrated the existence of apparently chaotic patterns that follow the theory proposed by Kolmogorov for deterministic dynamic systems that exhibit apparently random behaviors. Phase space reconstruction allowed us to show the dynamic characteristics of the signal so that short-term predictions were stable. Finally, the study of strange attractors in pipeline pressure time series can have significant contributions to anomaly detection.•A methodology is proposed for the reconstruction of the phase space to estimate the short-term future behavior of pressure signals in pipelines in real time.•The analysis of the proposed hydraulic system revealed some indications of weak chaos in the time series of the pressure signal obtained experimentally.•The methodology implemented and the results of this study showed that the short-term predictions were very accurate and consistent; Chaotic patterns were also identified that support the theory proposed by Kolmogorov.

Scale-Fractal Detrended Fluctuation Analysis for Fault Diagnosis of a Centrifugal Pump and a Reciprocating Compressor.

Reciprocating compressors and centrifugal pumps are rotating machines used in industry, where fault detection is crucial for avoiding unnecessary and costly downtime. A novel method for fault classification in reciprocating compressors and multi-stage centrifugal pumps is proposed. In the feature extraction stage, raw vibration signals are processed using multi-fractal detrended fluctuation analysis (MFDFA) to extract features indicative of different types of faults. Such MFDFA features enable the training of machine learning models for classifying faults. Several classical machine learning models and a deep learning model corresponding to the convolutional neural network (CNN) are compared with respect to their classification accuracy. The cross-validation results show that all models are highly accurate for classifying the 13 types of faults in the centrifugal pump, the 17 valve faults, and the 13 multi-faults in the reciprocating compressor. The random forest subspace discriminant (RFSD) and the CNN model achieved the best results using MFDFA features calculated with quadratic approximations. The proposed method is a promising approach for fault classification in reciprocating compressors and multi-stage centrifugal pumps.

Topological Data Analysis in Cardiovascular Signals: An Overview.

Topological data analysis (TDA) is a recent approach for analyzing and interpreting complex data sets based on ideas a branch of mathematics called algebraic topology. TDA has proven useful to disentangle non-trivial data structures in a broad range of data analytics problems including the study of cardiovascular signals. Here, we aim to provide an overview of the application of TDA to cardiovascular signals and its potential to enhance the understanding of cardiovascular diseases and their treatment in the form of a literature or narrative review. We first introduce the concept of TDA and its key techniques, including persistent homology, Mapper, and multidimensional scaling. We then discuss the use of TDA in analyzing various cardiovascular signals, including electrocardiography, photoplethysmography, and arterial stiffness. We also discuss the potential of TDA to improve the diagnosis and prognosis of cardiovascular diseases, as well as its limitations and challenges. Finally, we outline future directions for the use of TDA in cardiovascular signal analysis and its potential impact on clinical practice. Overall, TDA shows great promise as a powerful tool for the analysis of complex cardiovascular signals and may offer significant insights into the understanding and management of cardiovascular diseases.

Electrical signalling and plant response to herbivory: A short review.

For a long time, electrical signaling was neglected at the expense of signaling studies in plants being concentrated with chemical and hydraulic signals. Studies conducted in recent years have revealed that plants are capable of emitting, processing, and transmitting bioelectrical signals to regulate a wide variety of physiological functions. Many important biological and physiological phenomena are accompanied by these cellular electrical manifestations, which supports the hypothesis about the importance of bioelectricity as a fundamental 'model' for response the stresses environmental and for activities regeneration of these organisms. Electrical signals have also been characterized and discriminated against in genetically modified plants under stress mediated by sucking insects and/or by the application of systemic insecticides. Such results can guide future studies that aim to elucidate the factors involved in the processes of resistance to stress and plant defense, thus aiding in the development of successful strategies in integrated pest management. Therefore, this mini review includes the results of studies aimed at electrical signaling in response to biotic stress. We also demonstrated how the generation and propagation of electrical signals takes place and included a description of how these electrical potentials are measured.

Towards a more accurate quasi-static approximation of the electric potential for neurostimulation with kilohertz-frequency sources.

Objective.Our goal was to determine the conditions for which a more precise calculation of the electric potential than the quasi-static approximation may be needed in models of electrical neurostimulation, particularly for signals with kilohertz-frequency components.Approach.We conducted a comprehensive quantitative study of the differences in nerve fiber activation and conduction block when using the quasi-static and Helmholtz approximations for the electric potential in a model of electrical neurostimulation.Main results.We first show that the potentials generated by sources of unbalanced pulses exhibit different transients as compared to those of charge-balanced pulses, and this is disregarded by the quasi-static assumption. Secondly, relative errors for current-distance curves were below 3%, while for strength-duration curves these ranged between 1%-17%, but could be improved to less than 3% across the range of pulse duration by providing a corrected quasi-static conductivity. Third, we extended our analysis to trains of pulses and reported a 'congruence area' below 700 Hz, where the fidelity of fiber responses is maximal for supra-threshold stimulation. Further examination of waveforms and polarities revealed similar fidelities in the congruence area, but significant differences were observed beyond this area. However, the spike-train distance revealed differences in activation patterns when comparing the response generated by each model. Finally, in simulations of conduction-block, we found that block thresholds exhibited errors above 20% for repetition rates above 10 kHz. Yet, employing a corrected value of the conductivity improved the agreement between models, with errors no greater than 8%.Significance.Our results emphasize that the quasi-static approximation cannot be naively extended to electrical stimulation with high-frequency components, and notable differences can be observed in activation patterns. As well, we introduce a methodology to obtain more precise model responses using the quasi-static approach, retaining its simplicity, which can be a valuable resource in computational neuroengineering.

From the membrane to the nucleus: mechanical signals and transcription regulation.

Mechanical forces drive and modulate a wide variety of processes in eukaryotic cells including those occurring in the nucleus. Relevantly, forces are fundamental during development since they guide lineage specifications of embryonic stem cells. A sophisticated macromolecular machinery transduces mechanical stimuli received at the cell surface into a biochemical output; a key component in this mechanical communication is the cytoskeleton, a complex network of biofilaments in constant remodeling that links the cell membrane to the nuclear envelope. Recent evidence highlights that forces transmitted through the cytoskeleton directly affect the organization of chromatin and the accessibility of transcription-related molecules to their targets in the DNA. Consequently, mechanical forces can directly modulate transcription and change gene expression programs. Here, we will revise the biophysical toolbox involved in the mechanical communication with the cell nucleus and discuss how mechanical forces impact on the organization of this organelle and more specifically, on transcription. We will also discuss how live-cell fluorescence imaging is producing exquisite information to understand the mechanical response of cells and to quantify the landscape of interactions of transcription factors with chromatin in embryonic stem cells. These studies are building new biophysical insights that could be fundamental to achieve the goal of manipulating forces to guide cell differentiation in culture systems.

The lncRNA DLX6-AS1/miR-16-5p axis regulates autophagy and apoptosis in non-small cell lung cancer: A Boolean model of cell death.

Long non-coding RNA (lncRNA) distal-less homeobox 6 antisense RNA 1 (DLX6-AS1) is elevated in a variety of cancers, including non-small cell lung cancer (NSCLC) and cervical cancer. Although it was found that the microRNA-16-5p (miR-16), which is known to regulate autophagy and apoptosis, had been downregulated in similar cancers. Recent research has shown that in tumors with similar characteristics, DLX6-AS1 acts as a sponge for miR-16 expression. However, the cell death-related molecular mechanism of the DLX6-AS1/miR-16 axis has yet to be investigated. Therefore, we propose a dynamic Boolean model to investigate gene regulation in cell death processes via the DLX6-AS1/miR-16 axis. We found the finest concordance when we compared our model to many experimental investigations including gain-of-function genes in NSCLC and cervical cancer. A unique positive circuit involving BMI1/ATM/miR-16 is also something we predict. Our results suggest that this circuit is essential for regulating autophagy and apoptosis under stress signals. Thus, our Boolean network enables an evident cell-death process coupled with NSCLC and cervical cancer. Therefore, our results suggest that DLX6-AS1 targeting may boost miR-16 activity and thereby restrict tumor growth in these cancers by triggering autophagy and apoptosis.

Study of Polyvinyl Alcohol Hydrogels Applying Physical-Mechanical Methods and Dynamic Models of Photoacoustic Signals.

This study aims to analyze the physical-mechanical properties and dynamic models of tissue-simulating hydrogels, specifically the photoacoustic (PA) response signals, by varying the concentrations of polyvinyl alcohol (PVA) and molecular weight (MW). A state-space model (SSM) is proposed to study the PVA hydrogels to retrieve the PA-related signal's damping ratio and natural frequency. Nine box-shaped PVA hydrogels containing saline solution were used, with five concentrations of PVA (7, 9, 12, 15, 20%) for MW1 and four for MW2. The results indicated that the concentration of PVA and MW played an important role in the PA wave's amplitude, arrival time, and speed of sound over the hydrogels. The SSM parameters showed that increasing PVA and MW concentrations improved the hydrogels' ability to absorb and transfer energy under the PA effect. These parameters were also found to be correlated with density and modulus of elasticity. Additionally, the concentrations of PVA and MW affected the absorption and optical scattering coefficients. The physical-mechanical properties, including porosity, density, and modulus of elasticity, improved as the concentration of PVA and MW increased. The ultimate goal of this study is to develop hydrogels as phantoms that can be used for tissue simulation and imaging.

Lower Limb Exoskeleton for Rehabilitation with Flexible Joints and Movement Routines Commanded by Electromyography and Baropodometry Sensors.

This paper presents the development of an instrumented exoskeleton with baropodometry, electromyography, and torque sensors. The six degrees of freedom (Dof) exoskeleton has a human intention detection system based on a classifier of electromyographic signals coming from four sensors placed in the muscles of the lower extremity together with baropodometric signals from four resistive load sensors placed at the front and rear parts of both feet. In addition, the exoskeleton is instrumented with four flexible actuators coupled with torque sensors. The main objective of the paper was the development of a lower limb therapy exoskeleton, articulated at hip and knees to allow the performance of three types of motion depending on the detected user's intention: sitting to standing, standing to sitting, and standing to walking. In addition, the paper presents the development of a dynamical model and the implementation of a feedback control in the exoskeleton.

Opportune warning of COVID-19 in a Mexican health care worker cohort: Discrete beta distribution entropy of smartwatch physiological records.

We present a statistical study of heart rate, step cadence, and sleep stage registers of health care workers in the Hospital General de México "Dr. Eduardo Liceaga" (HGM), monitored continuously and non-invasively during the COVID-19 contingency from May to October 2020, using the Fitbit Charge 3® Smartwatch device. The HGM-COVID cohort consisted of 115 participants assigned to areas of COVID-19 exposure. We introduce a novel biomarker for an opportune signal for the likelihood of SARS-CoV-2 infection based on the Shannon Entropy of the Discrete Generalized Beta Distribution fit of rank ordered smartwatch registers. Our statistical test indicated infection for 94% of patients confirmed by positive polymer chain reaction (PCR+) test, 47% before the test, and 47% in coincidence. These results required innovative data preprocessing for the definition of a new biomarker index. The statistical method parameters are data-driven, confidence estimates were calibrated based on sensitivity tests using appropriately derived surrogate data as a benchmark. Our surrogate tests can also provide a benchmark for comparing results from other anomaly detection methods (ADMs). Biomarker comparison of the negative Immunoglobulin G Antibody (IgG-) subgroup with the PCR+ subgroup showed a statistically significant difference (p < 0.01, effect size = 1.44). The distribution of the uninfected population had a lower median and less dispersion than the PCR+ population. A retrospective study of our results confirmed that the biomarker index provides an early warning of the likelihood of COVID-19, even several days before the onset of symptoms or the PCR+ test request. The method can be calibrated for the analysis of different SARS-CoV-2 strains, the effect of vaccination, and previous infections. Furthermore, our biomarker screening could be implemented to provide general health profiles for other population sectors based on physiological signals from smartwatch wearable devices.

Volatile Constituents from Catasetum (Orchidaceae) Species with Occurrence in the Brazilian Amazon.

BACKGROUND: Catasetum Rich. ex Kunth is a genus of Neotropical orchids distributed in Central and South American regions. In the Brazilian Amazon, there are more than 60 species of Catasetum. The floral aromas of orchids are little known, particularly of Catasetum species. This work aimed to analyze the chemical constituents of the volatile concentrates of eight Catasetum specimens from the Amazon: C. alatum (1), C. albovirens (2), C. barbatum (1), C. ciliatum (2), C. galeritum (1), and C. gnomus (1). METHODS: Gas chromatography (GC) and gas chromatography-mass spectrometry (GC-MS) analyzed and identified the constituents of the volatile concentrates, and principal component analysis (PCA) and hierarchical cluster analysis (HCA) were used in the multivariate statistical analysis. RESULTS: The Catasetum main constituents in descending order and above 10% were trans-geranylgeraniol, 1,4-dimethoxybenzene, linalool, 2-phenylethyl acetate, geraniol, 7-epi-1,2-dehydro-sesquicineole, 1,8-cineole, benzyl acetate, limonene, methyl salicylate, (E)-ß-farnesene, anisyl butyrate, cis-carvone oxide, cadin-4-en-10-ol, indole, α-pinene, and δ-cadinene. CONCLUSIONS: Multivariate statistical analysis of Catasetum species showed that C. barbatum, C. albovirens, and C. gnomus are distinct from the other studied species, while C. alatum, C. ciliatum, and C. galeritum presented the same primary classes of compounds. These results contribute to a better understanding of the genus Catasetum chemotaxonomy.

Iridescent scales signal male fighting ability to access females in the jewelled splitfin Xenotoca variata.

Jewelled splitfin males (Xenotoca variata) possess multi-colour iridescent scales (speckles) on the flanks. This study tested the hypothesis that the number of speckles could be a good proxy for predicting fighting ability in contests for access to females. The experiments consisted in observing and recording males' agonistic behaviour, courtship displays and mating attempts in mixed-sex groups. The data were analysed and presented based on a Bayesian approach, which revealed that the density (cm-2 ) of speckles was positively correlated with the increase observed in the proportional frequency of attacks. Similarly, the density of speckles was positively associated with the increase observed in the frequency of courtship behaviour and with the frequency of mating attempts. Male-male aggressions drastically diminished (by eightfold) when females were removed from the observation tanks. These results indicate that the number of speckles is a consistent predictor of successful access to females and therefore, speckled males are likely to be able to gain more mating opportunities. This finding highlights the ubiquitous role iridescent colours play in visual signalling.

Temporal configuration and modality of components determine the performance of bumble bees during the learning of a multimodal signal.

Across communicative systems, the ability of compound signals to enhance receiver's perception and decoding is a potent explanation for the evolution of complexity. In nature, complex signaling involves spatiotemporal variation in perception of signal components; yet, how the synchrony between components affects performance of the receiver is much less understood. In the coevolution of plants and pollinators, bees are a model for understanding how visual and chemical components of floral displays may interact to influence performance. Understanding whether the temporal dimension of signal components impacts performance is central for evaluating hypotheses about the facilitation of information processing and for predicting how particular trait combinations function in nature. Here, I evaluated the role of the temporal dimension by testing the performance of bumble bees under restrained conditions while learning a bimodal (olfactory and visual) stimulus. I trained bumble bees under six different stimuli varying in their internal synchrony and structure. I also evaluated the acquisition of the individual components. I show that the temporal configuration and the identity of the components impact their combined and separate acquisition. Performance was favored by partial asynchrony and the initial presentation of the visual component, leading to higher acquisition of the olfactory component. This indicates that compound stimuli resembling the partially synchronous presentation of a floral display favor performance in a pollinator, thus highlighting the time dimension as crucial for the enhancement. Moreover, this supports the hypothesis that the evolution of multimodal floral signals may have been favored by the asynchrony perceived by the receiver during free flight.