Rhythmic Signals Research Articles

Decoding the rhythmic representation and communication of visual contents.

Rhythmic neural activity is considered essential for adaptively modulating responses in the visual system. In this opinion article we posit that visual brain rhythms also serve a key function in the representation and communication of visual contents. Collating a set of recent studies that used multivariate decoding methods on rhythmic brain signals, we highlight such rhythmic content representations in visual perception, imagery, and prediction. We argue that characterizing representations across frequency bands allows researchers to elegantly disentangle content transfer in feedforward and feedback directions. We further propose that alpha dynamics are central to content-specific feedback propagation in the visual system. We conclude that considering rhythmic content codes is pivotal for understanding information coding in vision and beyond.

Obstacle-enhanced spontaneous oscillation of confined active granules.

Spontaneous oscillation in particle numbers has been reported recently, in which two chambers connected by a narrow channel are alternately filled and emptied by self-propelled particles. The challenge in realizing the application of this oscillation lies in promotion of the oscillatory periodicity. By placing an asymmetric obstacle at an appropriate position near a channel opening, we can significantly improve the oscillation quality, which approaches the quality of an ideal oscillation. Additionally, we experimentally explore the relationship between the oscillation quality and various system parameters such as the obstacle position. Based on experimental observations, we incorporate a random noise into our previous model and properly reproduce the experimental results. The agreement between theory and experiment uncovers the mechanism of delicate competition between noise and unidirectional particle flow in influencing the oscillation quality. Our findings provide new insights for the optimization of the oscillation quality, expand the conventional rectification capability of the ratchet effect due to the obstacle, and make it possible for spontaneous oscillation to serve as a reliable source for rhythmic signals.

Reduced Cortical Excitability is Associated with Cognitive Symptoms in Concussed Adolescent Football Players.

American tackle football is associated with high rates of concussion, leading to neurophysiological disturbances and debilitating clinical symptoms. Previous investigations of the neurophysiological effects of concussion have largely ignored aperiodic neurophysiological activity, which is a marker of cortical excitability. We examined whether concussion during a season of high school football is related to changes in aperiodic and periodic neurophysiological activity and whether any such changes are associated with clinical outcomes. Pre- and post-season resting-state magnetoencephalography (MEG) data were collected from 91 high school football players over as many as four seasons of play, for a total of 278 data collections. During these seasons of football play, a cohort of 10 individuals were diagnosed with concussion. MEG data were source-imaged, frequency-transformed and parameterized, and linear mixed models were used to examine effects of concussion on pre-to-post-season changes in neurophysiological activity. Scores on the Post-Concussive Symptom Inventory were correlated with pre-to-post-season neurophysiological changes to determine their clinical relevance. Concussion was associated with increased aperiodic exponents in superior frontal cortices, indicating a relative reduction in cortical excitability. This slowing of aperiodic neurophysiology mediated concussion effects on raw delta and gamma power and was associated with worse cognitive concerns across participants. Pre-to-post-season changes in aperiodic-corrected alpha and theta rhythmic activity were also decreased in posterior cortices in concussed players. These findings indicate that concussion alters both the excitability and rhythmic signaling of the cortex, with differing spatial topographies and implications for clinical symptoms.

Dance displays in gibbons: biological and linguistic perspectives on structured, intentional, and rhythmic body movement.

Female crested gibbons (genus Nomascus)perform conspicuous sequences of twitching movements involving the rump and extremities. However, these dances have attracted little scientific attention and their structure and meaning remain largely obscure. Here we analyse close-range video recordings of captive crested gibbons, extracting descriptions of dance in four species (N. annamensis, N. gabriellae, N. leucogenys and N. siki). In addition, we report results from a survey amongst relevant professionals clarifying behavioural contexts of dance in captive and wild crested gibbons. Our results demonstrate that dances in Nomascus represent a common and intentional form of visual communication restricted to sexually mature females. Whilst primarily used as a proceptive signal to solicit copulation, dances occur in a wide range of contexts related to arousal and/or frustration in captivity. A linguistically informed view of this sequential behaviour demonstrates that movement withindances is organizedin groups andfollows an isochronous rhythm-patterns not described for visual displays in other non-human primates. We argue that applying the concept of dance to gibbons allows us to expand our understanding of communication in non-human primates and todevelop hypotheses on the rules and regularities characterising it. We propose that crestedgibbon dances likely evolved from less elaborate rhythmic proceptive signals, similar to those found in siamangs. Although dance displays in humans and crested gibbons share a number of key characteristics, they cannot be assumed to be homologous. Nevertheless, gibbon dances represent a strikingmodel behaviour to investigate the use of complex gestural signals in hominoid primates.

Electroencephalography Emotion Recognition Based on Rhythm Information Entropy Extraction

Electroencephalography (EEG) is a physiological signal directly generated by the central nervous system. Brain rhythm is closely related to a person’s emotional state and is widely used for EEG emotion recognition. In previous studies, the rhythm specificity between different brain channels was seldom explored. In this paper, the rhythm specificity of brain channels is studied to improve the accuracy of EEG emotion recognition. Variational mode decomposition is used to decompose rhythm signals and enhance features, and two kinds of information entropy, i.e., differential entropy (DE) and dispersion entropy (DispEn) are extracted. The rhythm being used to get the best result of single channel emotion recognition is selected as the representative rhythm, and the remove one method is employed to obtain rhythm information entropy feature. In the experiment, the DEAP database was used for EEG emotion recognition in valence-arousal space. The results showed that the best result of rhythm DE feature classification in the valence dimension is 77.04%, and the best result of rhythm DispEn feature classification in the arousal dimension is 79.25%.

Optimizing real-time phase detection in diverse rhythmic biological signals for phase-specific neuromodulation.

Closed-loop, phase-specific neurostimulation is a powerful method to modulate ongoing brain activity for clinical and research applications. Phase-specific stimulation relies on estimating the phase of an ongoing oscillation in real time and issuing a control command at a target phase. Phase detection algorithms based on Fast Fourier transform (FFT) are widely used due to their computational efficiency and robustness. However, it is unclear how algorithm performance depends on the spectral properties of the input signal and how algorithm parameters can be optimized. We used offline simulation to evaluate the performance of three algorithms (endpoint-corrected Hilbert Transform, Hilbert Transform and phase mapping) on three rhythmic biological signals with distinct spectral properties (rodent hippocampal theta potential, human EEG alpha and human essential tremor). First, we found that algorithm performance was more strongly influenced by signal amplitude and frequency variation compared with signal to noise ratio. Second, our simulations showed that the size of the data window for phase estimation was critical for the performance of FFT-based algorithms, where the optimal data window corresponds to the period of the oscillation. We validated this prediction with real time phase detection of hippocampal theta oscillations in freely behaving rats performing spatial navigation. Our findings define the relationship between signal properties and algorithm performance and provide a convenient method for optimizing FFT-based phase detection algorithms.

Model selection for spectral parameterization.

Neurophysiological brain activity comprises rhythmic (periodic) and arrhythmic (aperiodic) signal elements, which are increasingly studied in relation to behavioral traits and clinical symptoms. Current methods for spectral parameterization of neural recordings rely on user-dependent parameter selection, which challenges the replicability and robustness of findings. Here, we introduce a principled approach to model selection, relying on Bayesian information criterion, for static and time-resolved spectral parameterization of neurophysiological data. We present extensive tests of the approach with ground-truth and empirical magnetoencephalography recordings. Data-driven model selection enhances both the specificity and sensitivity of spectral and spectrogram decompositions, even in non-stationary contexts. Overall, the proposed spectral decomposition with data-driven model selection minimizes the reliance on user expertise and subjective choices, enabling more robust, reproducible, and interpretable research findings.

The rhythmic mind: brain functions of percussionists in improvisation.

Percussionists stand out for their expertise in rhythm, with the network for musical rhythm (NMR) serving a vital neurological function in their improvisation, which is deeply rooted in comprehensive musical knowledge. Our research examines the central representations of various improvisation tactics used by percussionists and investigates the interactions between the NMR and other relevant neural networks. Twenty-five percussionists participated in functional magnetic resonance imaging (fMRI) sessions, which included two cognitive strategies of improvisation. Structural improvisation (SIMP) emphasized rhythmic patterns, while free improvisation (FIMP) focused on musical spontaneity. Sight-reading scenario served as the reference condition. Paired t-tests were utilized for comparative analyses. The findings revealed a dynamic interplay characterized by increased activity in the executive control network and NMR, along with decreased activity in the default mode network during SIMP. During FIMP, heightened activity was observed in the executive control network, NMR, limbic, and memory systems. In both SIMP vs. sight-reading and FIMP vs. sight-reading comparisons, the visual network's activity decreased, a trend also observed in the comparative analysis of FIMP vs. SIMP. In SIMP, percussionists leverage external rhythmic signals, resulting in heightened NMR and ECN activity and reduced DMN activity. In contrast, FIMP is characterized by a rise in activity within the NMR, ECN, limbic system, memory system, and reward system, underscoring the vital roles of motivation and memory in the rapid production of spontaneous musical ideas within set frameworks. The diminished activity in the visual network during FIMP compared to SIMP suggests less reliance on visual stimuli in FIMP. These findings suggest that various improvisational tactics may engage different neural pathways.

New insights into the relationship between clock genes and ascorbic acid metabolism in spinach during pre- and postharvest periods

In this study, the effects of light and dark conditions on the expression of candidate clock genes (SoLHY, SoPRR5, and SoTOC1), photoreceptor genes (SoPHYA, SoPHYB, and SoZTL), ascorbic acid (AsA) synthesis genes (SoVTC2 and SoGLDH), an AsA degradation gene (SoAPX), an AsA regeneration gene (SoMDHAR), and AsA content of spinach during the pre- and postharvest periods were investigated. The AsA content decreased under constant dark storage, whereas the AsA content was efficiently maintained under a light/dark cycle. Gene expression with rhythmic signals confirmed by the JTK_CYCLE method (P ≤ 0.05) was used for the analysis of partial correlation coefficients. Strong correlations between SoLHY and SoVTC2 were observed under all investigated conditions. AsA synthesis regulation via the clock gene LHY was stable and likely not altered by harvesting stress and/or external light stimuli. After harvesting, SoPRR5 was negatively correlated with SoVTC2. This finding suggested that a reduction in the SoPRR5 expression level might be an alternative way to induce/maintain AsA content during storage. Additionally, the expression of the photoreceptor genes SoPHYA and SoZTL was moderately correlated with that of SoVTC2 during cultivation. Surprisingly, these correlations were not observed during storage even in the presence of light. Instead, SoPHYA expression was correlated with that of another AsA synthesis gene, SoGLDH, under light/dark storage. Moreover, under constant dark storage, SoZTL expression showed a strong correlation with SoVTC2. The considerable variation in the relationship between the expression levels of AsA-related genes and photoreceptor genes during cultivation and storage indicates the contribution of light regulators to maintaining AsA rhythm in spinach. In summary, this study is the first to suggest possible crosslinking of external signals (light/dark), photoreceptor genes, clock genes, and AsA metabolism genes, which are related to changes in the quality of spinach during the pre- and postharvest periods.

The neurophysiological brain-fingerprint of Parkinson’s disease

Research in healthy young adults shows that characteristic patterns of brain activity define individual "brain-fingerprints" that are unique to each person. However, variability in these brain-fingerprints increases in individuals with neurological conditions, challenging the clinical relevance and potential impact of the approach. Our study shows that brain-fingerprints derived from neurophysiological brain activity are associated with pathophysiological and clinical traits of individual patients with Parkinson's disease (PD). We created brain-fingerprints from task-free brain activity recorded through magnetoencephalography in 79 PD patients and compared them with those from two independent samples of age-matched healthy controls (N=424 total). We decomposed brain activity into arrhythmic and rhythmic components, defining distinct brain-fingerprints for each type from recording durations of up to 4min and as short as 30s. The arrhythmic spectral components of cortical activity in patients with Parkinson's disease are more variable over short periods, challenging the definition of a reliable brain-fingerprint. However, by isolating the rhythmic components of cortical activity, we derived brain-fingerprints that distinguished between patients and healthy controls with about 90% accuracy. The most prominent cortical features of the resulting Parkinson's brain-fingerprint are mapped to polyrhythmic activity in unimodal sensorimotor regions. Leveraging these features, we also demonstrate that Parkinson's symptom laterality can be decoded directly from cortical neurophysiological activity. Furthermore, our study reveals that the cortical topography of the Parkinson's brain-fingerprint aligns with that of neurotransmitter systems affected by the disease's pathophysiology. The increased moment-to-moment variability of arrhythmic brain-fingerprints challenges patient differentiation and explains previously published results. We outline patient-specific rhythmic brain signaling features that provide insights into both the neurophysiological signature and symptom laterality of Parkinson's disease. Thus, the proposed definition of a rhythmic brain-fingerprint of Parkinson's disease may contribute to novel, refined approaches to patient stratification. Symmetrically, we discuss how rhythmic brain-fingerprints may contribute to the improved identification and testing of therapeutic neurostimulation targets. Data collection and sharing for this project was provided by the Quebec Parkinson Network (QPN), the Pre-symptomatic Evaluation of Novel or Experimental Treatments for Alzheimer's Disease (PREVENT-AD; release 6.0) program, the Cambridge Centre for Aging Neuroscience (Cam-CAN), and the Open MEG Archives (OMEGA). The QPN is funded by a grant from Fonds de Recherche du Québec - Santé (FRQS). PREVENT-AD was launched in 2011 as a $13.5 million, 7-year public-private partnership using funds provided by McGill University, the FRQS, an unrestricted research grant from Pfizer Canada, the Levesque Foundation, the Douglas Hospital Research Centre and Foundation, the Government of Canada, and the Canada Fund for Innovation. The Brainstorm project is supported by funding to SB from the NIH (R01-EB026299-05). Further funding to SB for this study included a Discovery grant from the Natural Sciences and Engineering Research Council of Canada of Canada (436355-13), and the CIHR Canada research Chair in Neural Dynamics of Brain Systems (CRC-2017-00311).

Coupled Memristor Oscillators for Neuromorphic Locomotion Control: Modeling and Analysis.

The recent surge of interest in brain-inspired architectures along with the development of nonlinear dynamical electronic devices and circuits has enabled energy-efficient hardware realizations of several important neurobiological systems and features. Central pattern generator (CPG) is one such neural system underlying the control of various rhythmic motor behaviors in animals. A CPG can produce spontaneous coordinated rhythmic output signals without any feedback mechanism, ideally realizable by a system of coupled oscillators. Bio-inspired robotics aims to use this approach to control the limb movement for synchronized locomotion. Hence, devising a compact and energy-efficient hardware platform to implement neuromorphic CPGs would be of great benefit for bio-inspired robotics. In this work, we demonstrate that four capacitively coupled vanadium dioxide (VO2) memristor-based oscillators can produce spatiotemporal patterns corresponding to the primary quadruped gaits. The phase relationships underlying the gait patterns are governed by four tunable bias voltages (or four coupling strengths) making the network programmable, reducing the complex problem of gait selection and dynamic interleg coordination to the choice of four control parameters. To this end, we first introduce a dynamical model for the VO2 memristive nanodevice, then perform analytical and bifurcation analysis of a single oscillator, and finally demonstrate the dynamics of coupled oscillators through extensive numerical simulations. We also show that adopting the presented model for a VO2 memristor reveals a striking resemblance between VO2 memristor oscillators and conductance-based biological neuron models such as the Morris-Lecar (ML) model. This can inspire and guide further research on implementation of neuromorphic memristor circuits that emulate neurobiological phenomena.

Seasonal variation in D2/3 dopamine receptor availability in the human brain

PurposeBrain functional and physiological plasticity is essential to combat dynamic environmental challenges. The rhythmic dopamine signaling pathway, which regulates emotion, reward and learning, shows seasonal patterns with higher capacity of dopamine synthesis and lower number of dopamine transporters during dark seasons. However, seasonal variation of the dopamine receptor signaling remains to be characterized.MethodsBased on a historical database of healthy human brain [11C]raclopride PET scans (n = 291, 224 males and 67 females), we investigated the seasonal patterns of D2/3 dopamine receptor signaling. Daylength at the time of scanning was used as a predictor for brain regional non-displaceable binding of the radiotracer, while controlling for age and sex.ResultsDaylength was negatively correlated with availability of D2/3 dopamine receptors in the striatum. The largest effect was found in the left caudate, and based on the primary sample, every 4.26 h (i.e., one standard deviation) increase of daylength was associated with a mean 2.8% drop (95% CI -0.042 to -0.014) of the receptor availability.ConclusionsSeasonally varying D2/3 receptor signaling may also underlie the seasonality of mood, feeding, and motivational processes. Our finding suggests that in future studies of brain dopamine signaling, especially in high-latitude regions, the effect of seasonality should be considered.

Mathematical modeling of temperature-induced circadian rhythms

The central circadian pacemaker in the suprachiasmatic nuclei (SCN) aligns the phase and period of autonomous molecular oscillators in peripheral cells to daily light/dark cycles via physiological, neuronal, hormonal, and metabolic signals. Among different entrainment factors, temperature entrainment has been proposed as an essential alternative for inducing and sustaining circadian rhythms in vitro. While the synchronization mechanisms for hormones such as glucocorticoids have been widely studied, little is known about the crucial role of body temperature as a systemic cue. In this work, we develop a semi-mechanistic mathematical model describing the entrainment of peripheral clocks to temperature rhythms. The model incorporates a temperature sensing-transduction cascade involving a heat shock transcription factor-1 (HSF1) and heat shock response (HSR) pathway to simulate the entrainment of clock genes. The model is used to investigate the mammalian temperature entrainment and synchronization of cells subject to temperature oscillations of different amplitudes and magnitudes and examine the effects of transitioning between temperature schedules. Our computational analyses of the system’s dynamic responses reveal that 1) individual cells gradually synchronize to the rhythmic temperature signal by resetting their intrinsic phases to achieve coherent dynamics while oscillations are abolished in the absence of temperature rhythmicity; 2) alterations in the amplitude and period of temperature rhythms impact the peripheral synchronization behavior; 3) personalized synchronization strategies allow for differential, adaptive responses to temperature rhythms. Our results demonstrate that temperature can be a potent entrainer of circadian rhythms. Therefore, in vitro systems subjected to temperature modulation can serve as a potential tool for studying the adjustment or disruption of circadian rhythms.

Computational insights in cell physiology

Physiological processes are governed by intricate networks of transcriptional and post-translational regulations. Inter-cellular interactions and signaling pathways further modulate the response of the cells to environmental conditions. Understanding the dynamics of these systems in healthy conditions and their alterations in pathologic situations requires a “systems” approach. Computational models allow to formalize and to simulate the dynamics of complex networks. Here, we briefly illustrate, through a few selected examples, how modeling helps to answer non-trivial questions regarding rhythmic phenomena, signaling and decision-making in cellular systems. These examples relate to cell differentiation, metabolic regulation, chronopharmacology and calcium dynamics.

Contactless and short‐range vital signs detection with doppler radar millimetre‐wave (76–81 GHz) sensing firmware

AbstractVital signs such as heart rate (HR) and respiration rate (RR) are essential physiological parameters that are routinely used to monitor human health and bodily functions. They can be continuously monitored through contact or contactless measurements performed in the home or a hospital. In this study, a contactless Doppler radar W‐band sensing system was used for short‐range, contactless vital sign estimation. Frequency‐modulated continuous wave (FMCW) measurements were performed to reduce the influence of a patient's micromotion. Sensing software was developed that can process the received chirps to filter and extract heartbeat and breathing rhythm signals. The proposed contactless sensing system eliminates the need for the contact electrodes, electric patches, photoelectric sensors, and conductive wires used in typical physiological sensing methods. The system operates at 76–81 GHz in FMCW mode and can detect objects on the basis of changes in frequency and phase. The obtained signals are used to precisely monitor a patient's HR and RR with minimal noise interference. In a laboratory setting, the heartbeats and breathing rhythm signals of healthy young participants were measured, and their HR and RR were estimated through frequency‐ and time‐domain analyses. The experimental results confirmed the feasibility of the proposed W‐band mm‐wave radar for contactless and short‐range continuous detection of human vital signs.

A neurophysiological basis for aperiodic EEG and the background spectral trend

Electroencephalograms (EEGs) display a mixture of rhythmic and broadband fluctuations, the latter manifesting as an apparent 1/f spectral trend. While network oscillations are known to generate rhythmic EEG, the neural basis of broadband EEG remains unexplained. Here, we use biophysical modelling to show that aperiodic neural activity can generate detectable scalp potentials and shape broadband EEG features, but that these aperiodic signals do not significantly perturb brain rhythm quantification. Further model analysis demonstrated that rhythmic EEG signals are profoundly corrupted by shifts in synapse properties. To examine this scenario, we recorded EEGs of human subjects being administered propofol, a general anesthetic and GABA receptor agonist. Drug administration caused broadband EEG changes that quantitatively matched propofol’s known effects on GABA receptors. We used our model to correct for these confounding broadband changes, which revealed that delta power, uniquely, increased within seconds of individuals losing consciousness. Altogether, this work details how EEG signals are shaped by neurophysiological factors other than brain rhythms and elucidates how these signals can undermine traditional EEG interpretation.

Neurologically Motivated Simulation of Ensemble Performance

A “rhythmic agent” is simulated based on the foundation of a previously published behavioral sensorimotor synchronization (SMS) model. The model is adjustable to control the auditory and tactile modalities of the tap's feedback. In addition to the conventional mechanisms of phase and period error correction, as well as their activation conditions, the period is estimated by modeling a central timekeeper impacted by a novel short-term memory. Inspired by The ADaptation and Anticipation Model (ADAM), a mechanism for linearly extrapolating anticipation is also tested. To better match the perceptual and motor cognitive functions, the model's parameters have been tuned to observations from experimental neurosensory literature with an emphasis on transduction delays. The agent is programmed to synchronize with various external rhythmic input signals while accounting for both adaptive and predictive mechanisms. The definition of the agent is based on a minimal set of rules yet has successfully replicated results of real-world observations: against a metronome; it produces the well-known negative mean asynchrony. In a rhythmic joint action, the simulation of joint delayed coordination shows a behavior previously observed in human subjects: in a rhythmic collaboration, a moderate amount of delay is necessary to keep the tempo steady, and below that threshold, the rhythm tends to speed up. It is also shown that giving more weight to the tactile afferent feedback than the auditory intensifies this effect. Moreover, it is observed that including anticipation in addition to the reactive mechanism will decrease the effect. The proposed model as a rhythmic engine, combined with other standard modules such as a beat detection algorithm, can be used to implement musical co-performers that could improvise with a human rhythmically or perform a given score in a way that feels human-like.

The Post-Flight Rehabilitation Program for Astronauts in Health Resort: Case Report Series

INTRODUCTION. The prospects for studying deep space are associated with the development of methods and programs for restoring the body of astronauts after flights. In this case, an important role is given to the implementation of aspects of restoring the functions of the nervous system of astronauts at the 2nd stage of post-flight rehabilitation (PFR) in Health Resort. AIM. To develop a program of rehabilitation for cosmonauts in Health Resort (stage 2 of PFR), aimed at restoring the functions of the nervous system. MATERIALS AND METHODS. The study involved 5 cosmonauts. Diagnostics included assessment of psychological and psychophysiological parameters and properties of the nervous system, the functional state of the autonomic nervous system. The rehabilitation program included: intake of mineral water “Slavyanovsky”, carbon dioxide-hydrogen sulfide baths, baro-, halo-, inhalation and climate therapy, a course of 5 sessions of neurofeedback on the β-rhythm of the brain. The duration of PFR was 21 days, procedures were carried out every other day. RESULTS AND DISCUSSION. An assessment of heart rate variability revealed the predominance of the influence of the parasympathetic division. After the rehabilitation course, there was a decrease in the stiffness index. There was a positive trend towards normalization of indicators: blood saturation, reflection index, a marker of left ventricular function, cardiac output, blood pressure and oxygen absorption from the microcirculatory system. The dynamics of biofeedback training indicators based on the β-rhythm indicated the orderliness and synchronization of the electroencephalogram (EEG) rhythm signal; an increase in the efficiency of mental work was also revealed. CONCLUSION. The results obtained indicate the positive impact of the developed program of the 2nd stage of PFR, as a result of which a positive dynamics of indicators of autonomic regulation, central hemodynamics, improvement of mental performance, orderliness and synchronization of the EEG rhythm signal.

A chromosome-level genome assembly of Agave hybrid NO.11648 provides insights into the CAM photosynthesis

Abstract The subfamily Agavoideae comprises crassulacean acid metabolism (CAM), C3, and C4 plants with a young age of speciation and slower mutation accumulation, making it a model crop for studying CAM evolution. However, the genetic mechanism underlying CAM evolution remains unclear because of lacking genomic information. This study assembled the genome of Agave hybrid NO.11648, a constitutive CAM plant belonging to subfamily Agavoideae, at the chromosome level using data generated from high-throughput chromosome conformation capture, Nanopore, and Illumina techniques, resulting in 30 pseudo-chromosomes with a size of 4.87 Gb and scaffold N50 of 186.42 Mb. The genome annotation revealed 58 841 protein-coding genes and 76.91% repetitive sequences, with the dominant repetitive sequences being the I-type repeats (Copia and Gypsy accounting for 18.34% and 13.5% of the genome, respectively). Our findings also provide support for a whole genome duplication event in the lineage leading to A. hybrid, which occurred after its divergence from subfamily Asparagoideae. Moreover, we identified a gene duplication event in the phosphoenolpyruvate carboxylase kinase (PEPCK) gene family and revealed that three PEPCK genes (PEPCK3, PEPCK5, and PEPCK12) were involved in the CAM pathway. More importantly, we identified transcription factors enriched in the circadian rhythm, MAPK signaling, and plant hormone signal pathway that regulate the PEPCK3 expression by analysing the transcriptome and using yeast one-hybrid assays. Our results shed light on CAM evolution and offer an essential resource for the molecular breeding program of Agave spp.

EEG-based detection of driving fatigue using a novel electrode

In view of the serious harm caused by driving fatigue, this paper investigated driving fatigue detection based on electroencephalogram (EEG) features, designed a novel semi-dry electrode for acquisitioning drivers' EEG signals, and performed refined composite multiscale fluctuation dispersion entropy (RCMFDE) feature extraction of the θ and β bands rhythm signals in the acquisitioned EEG signals. The research results showed that the novel semi-dry electrode designed in this paper had the advantages of convenient replenishment of conductive liquid, early warning of insufficient conductive liquid, comfortable to wear compared with the traditional wet and dry electrodes under the premise of being able to collect EEG signals with qualified quality. And compared with traditional methods such as multiscale sample entropy (MSE), multiscale permutation entropy (MPE), multiscale symbolic dynamic entropy (MSDE), multiscale dispersion entropy (MDE), and refined composite multiscale dispersion entropy (RCMDE), the EEG features extracted by the RCMFDE method in this paper have a more obvious fatigue feature tendency, and thus can identify the driving fatigue state more effectively.