Search Results (123)

Oil and gas exploration conducted in the main fault zone of the Fuman Oilfield has yielded large-scale and high-production results. Against this background, the non-fault zone has emerged as a new domain for oil exploration endeavors. Nevertheless, the establishment of a unified sequence division scheme for the study area remains unachieved, primarily constrained by two key factors: first, the high costs associated with ultra-deep high-density coring operations; and second, the inconspicuous response characteristics exhibited by logging curves. This absence of a standardized scheme has further impeded the progress of oil and gas exploration in the non-main fault inter-region within the study area. Consequently, the present study is based on multi-source data, including seismic data, logging data, and field outcrop data. Magnetic susceptibility measurements from the cement plant section and natural gamma-ray logging data from the Yangjikan section were systematically analyzed to establish cyclostratigraphic frameworks. A sedimentary noise model (SNM) was employed to reconstruct Holocene sea-level fluctuations, enabling precise sequence stratigraphic subdivision within the Fuman Area. Results demonstrate that the Middle-Lower Ordovician Yijianfang–Penglaiba Formations retain robust astronomical cyclicity, validated by high-fidelity orbital forcing signals. Notably, the DYNOT (Dynamic Noise After Orbital Tuning) model effectively decouples orbital-driven sea-level oscillations from local depositional noise, offering a novel approach for sequence boundary identification. This methodology reveals a hierarchical sequence architecture comprising four third-order sequences and 11 fourth-order sequences within the Yijianfang–Penglaiba Formations. Such a framework provides critical insights into facies distribution patterns and non-fault-controlled exploration potential in the Fuman Basin. Full article

Background: Against the backdrop of global population aging, understanding the mechanisms of age-related cognitive decline has become crucial for improving the health and quality of life in older adults. Methods: This study employed a multimodal approach to investigate the neural modulations induced by a motor cognitive dual task and their relationship with age-related decline. By integrating behavioral assessments, electroencephalography (EEG), and body composition analysis, we comprehensively evaluated performance and neural correlates in 19 younger and 18 older adults. Specifically, EEG analyses focused on comparing pre-task and post-task resting-state recordings to investigate the immediate impact of a single acute cognitive-motor dual-task session on neural oscillations and brain network organization. Results: Key findings include: (1) older adults exhibited significantly inferior performance in task accuracy, reaction time, and composite performance score compared to younger adults (p < 0.001); (2) neural oscillatory analysis of resting-state data revealed a localized increase in gamma-band power at posterior-temporal sites (PO4/T6) in older adults following the dual-task, while younger adults exhibited widespread multi-band (delta to beta) power modulation across frontal, central, and temporal regions in younger adults; (3) brain network analysis demonstrated synergistic enhancement of multi-band (Theta, Alpha, Beta, Gamma) connectivity and optimized topological organization in younger adults post-task, contrasting with network rigidity and localized compensatory patterns in older adults; (4) correlation analyses indicated significant associations between dual-task performance and MoCA-B scores in older adults (r = 0.861, p < 0.001). Conclusions: This study innovatively elucidates the neurophysiological characteristics of brain aging. The motor-cognitive dual-task paradigm proves to be a sensitive tool for capturing early cognitive changes, holding significant promise for clinical screening. Full article

Background: Epilepsy is a network disorder, and network-based approaches to its diagnostics and therapies attract growing attention. Identification of prognostic markers of epileptogenesis and long-term risk for developing epilepsy after brain insults is an urgent, unresolved problem. We examined whether intracortical connectivity patterns reflect early epileptogenic changes in the cortex. Methods: We used the audiogenic kindling model, in which cortical epileptogenesis is initiated by repetition of reflex subcortically-driven seizures. Two measures of functional connectivity—mutual information and mean phase coherence—were applied to electrocorticographic recordings obtained from homotopical sites of parietal cortex during interictal and immediate postictal periods in awake rats. Interhemispheric connectivity and synchrony in non-kindled and slightly kindled rats were compared. Cortical spreading depolarization (SD), the first manifestation of growing cortical excitability in several models of epileptogenesis, was used as an electrographic marker of the earliest kindling stage. Results: In kindled animals, baseline levels of hemispheric connectivity and gamma band synchrony were significantly lower compared to seizure-naive rats. Before kindling, subcortical seizures elicited mild postictal depression of cortical gamma oscillations without changes in interhemispheric functional connectivity. Early in kindling, seizures produced wideband postictal depression of cortical activity and a striking drop in hemispheric connectivity. Conclusions: Primary network alterations during epileptogenesis involve hemispheric decoupling and reduced synchronization, both sustained (between seizures) and transient (postictal). Breakdown of long-range intracortical communication may reflect homeostatic plasticity and an active attempt to restrict epileptogenic reorganization of neural networks. We think that resting-state hemispheric hypocoupling could be an early marker of epileptogenesis. Seizure-induced SD contributes to the generation of postictal events. Full article

Background/Objectives: Deficits in auditory change detection are well-known in psychiatric disorders such as schizophrenia. An abrupt change in sound feature during periodic sounds elicits both evoked potentials and a transient change in neural oscillations. Both of these cerebral responses are thought to reflect the automatic change detection. However, the similarities and dissimilarities between these cerebral responses are unclear. To clarify them, we compared the temporal dynamics of evoked potentials and low gamma oscillations under a paired-pulse paradigm. Methods: Healthy adults (n = 21) participated. The stimulus was a 2 s sound consisting of a train of 25 ms pure tones. The sound pressure was increased by 15 dB twice within a 600 ms interval. Electroencephalographic signals were recorded from Fz and Cz electrodes referenced to linked mastoids. The peak (N100)-to-peak (P200) amplitude and the inter-trial phase coherence (ITPC) of low gamma oscillations were analyzed. Results: Auditory steady-state responses were evoked around 40 Hz. An abrupt change in sound pressure transiently decreased the ITPC of the oscillations at 40 Hz, whereas it increased the ITPC at the remaining frequencies. Unlike the change-related potentials, the degree of ITPC responses did not differ between the two changes. Conclusions: The synchrony of low gamma oscillations transiently responded to an abrupt increase in sound pressure but did not show paired-pulse suppression. This novel neurophysiological approach enables a focus on the neural change detection from multiple angles, which could be useful for investigations of psychiatric disorders. Full article

We propose a novel mathematical framework for understanding consciousness as a dynamical phenomenon governed by nonlinear integrable equations. The central hypothesis identifies conscious state dynamics with the Painlevé VI equation and its confluence limits, providing a unified description of stability, bifurcation, and collapse across cognitive regimes. In this approach, consciousness is modeled as an emergent phase sustained near criticality, where coherent quantum-like structures and classical decoherence coexist in a regulated balance. The theory is formulated in terms of isomonodromic deformations on $\mathrm{SL}(2,\mathbb{C})$ character varieties, allowing conscious states to be characterized by monodromy data and their controlled evolution. This geometric setting naturally encodes memory, attention, and transitions between conscious and unconscious phases, while confluence processes account for irreversible loss of coherence. A two-stage quantum-to-classical transition is identified, separating microscopic coherence from macroscopic stabilization. The framework yields universal signatures such as critical slowing down, scaling laws near transition points, and robustness under perturbations, linking consciousness dynamics to broader classes of critical phenomena observed in physics and complex systems. By replacing heuristic assumptions with a mathematically constrained dynamical structure, this work extends existing quantum consciousness models and provides a tractable platform for comparison with neural, biological, and informational data. Full article

Background: Autism Spectrum Disorder (ASD) is a neurodevelopmental condition characterized by impairments in social communication, reciprocity, and adaptive behavior. Converging neurobiological evidence suggests that these clinical features arise from aberrant connectivity and dysregulated neuronal oscillations across distributed brain networks. In particular, dysfunction within the mirror neuron regions, concentrated in the inferior frontal gyrus (IFG) and inferior parietal lobule (IPL), has been implicated in deficits of imitation, empathy, and social cognition in ASD. Non-invasive neuromodulation using repetitive transcranial magnetic stimulation (rTMS) has shown modest behavioral benefits in ASD. However, most studies apply the conventional protocols targeting the dorsolateral prefrontal cortex. The effects of intermittent theta-burst stimulation (iTBS), a potent excitatory rTMS protocol targeting the mirror neuron regions, on the oscillatory dynamics in ASD remain largely unexplored. Objective: To investigate whether iTBS targeting the bilateral IFG and IPL modulates EEG-derived oscillatory activity in adolescents with ASD and to explore the relationship between oscillatory changes and social reciprocity. Methods: Six adolescents with Level I or II ASD (ages 13–18) underwent bilateral iTBS targeting the IFG and IPL using a figure-of-eight coil and standardized theta-burst parameters. Participants were randomized to receive either 18 active iTBS sessions or a waitlist-controlled crossover design (9 sham followed by 9 active sessions). Standard 21-channel EEG recordings were obtained during the first (EEG-1) and final (EEG-2) active stimulation sessions, including pre- and post-stimulation epochs. Power spectral analyses were conducted across frequency bands (delta through gamma). Behavioral outcomes were assessed using the Childhood Autism Rating Scale, Second Edition (CARS2), administered pre- and post-intervention. Results: All participants tolerated the intervention without adverse effects. Behavioral analysis demonstrated a significant reduction in CARS2 scores following iTBS and is reported in detail in our prior clinical outcomes manuscript, consistent with improved social reciprocity (p < 0.001). EEG analysis revealed an immediate post-stimulation increase in gamma-band power during EEG-1 in five of six participants, whereas lower-frequency bands exhibited variable responses. In contrast, EEG-2 showed no consistent post-stimulation gamma enhancement. Net comparisons between EEG-1 and EEG-2 demonstrated attenuation of the initial gamma response in the same five participants. At the group level, gamma percent change did not reach statistical significance at EEG-1 (p = 0.12) or EEG-2 (p = 0.66), and exploratory comparisons between the 9-active versus 18-active arms did not reach statistical significance. While ipsi-directional changes in gamma power and CARS2 scores were observed in four participants, correlation was not identified in this pilot sample. Conclusions: Bilateral iTBS targeting the IFG and IPL induces a transient enhancement of gamma oscillations in adolescents with ASD that attenuates with repeated stimulation. This pattern is consistent with adaptive homeostatic plasticity (metaplasticity) within excitatory–inhibitory circuits, potentially mediated by GABAergic interneurons. These findings support the feasibility of EEG as an objective biomarker of neuromodulatory engagement in ASD and highlight the importance of network-level and oscillatory mechanisms in interpreting therapeutic responses. Larger, sham-controlled studies incorporating multimodal biomarkers are warranted to clarify clinical relevance and optimize personalized neuromodulation strategies. Full article

The para-positronium system $\left({}^{1}S_{0}Ps\right)$ is described by means of specially constructed coherent states (CSs) in the Klauder–Perelomov sense. It is analyzed from the physical point of view and from the geometry underlying the relevant symmetry group establishing the dynamics of the processes. In this new theoretical context, the possibility of a gamma-ray laser emission is investigated within a QFT context, showing explicitly that, in addition to the oscillator solution based only on a Bogoliubov approximation for the condensate, there is a second phase or “squeezed” stage by which physical features beyond the classical ones appear. Explicitly, while the generated photons are in the active medium (e.g., Ps-BEC), the evolution is described by a Heisenberg–Weyl coherent state with displacement operators dependent on the interaction time, which is related to the condensate shape. After the interaction time has elapsed, we explicitly demonstrate that the displacement operator of the ${}^{1}S_{0}Ps$ is transformed into a squeezed operator of the photonic fields modulated by the matrix element of the Positronium decay ${\mathcal{M}}_{{}^{1}S_{0}Ps}\to 2\gamma$. We also show that this squeezed operator (belonging to the Metaplectic group) generates a non-classical radiation state spanning only even (s = 1/4) levels in the number of photons. The implications in astrophysical systems of interest, considering gamma-ray coherent emission and the possibility of an ${}^{1}S_{0}Ps-BEC$ in the context of pulsars, blazars, and quasars, are briefly discussed. Full article

This study was focused on the assessment of fungal occurrence, mycotoxin dynamics, aflatoxin carry-over, and associated biochemical responses in dairy cattle. Moisture emerged as the dominant factor for fungal communities, promoting the co-proliferation of fungal genera adapted to high water activity conditions (a_w > 0.90) and antagonism against xerotolerant and xerophilic species. Aspergillus spp. dominated dry substrates (a_w < 0.75), Fusarium spp. showed strong positive associations with high-moisture matrices (a_w > 0.90), and Penicillium spp. exhibited intermediate, substrate-dependent behavior. Mycotoxin levels fluctuated non-linearly, independently of fungal counts: ochratoxin A (OTA) concentrations in corn silage increased from approximately 12 μg/kg at the onset of the ensiling period to >240 μg/kg at silo opening, indicating dynamic mycotoxin accumulation during storage, while zearalenone (ZEA) oscillated from 40 to 170 µg/kg. Despite the variation in total aflatoxins (AFLA-T) across feed matrices, aflatoxin M1 (AFM1) in milk remained low (0.0020–0.0093 μg/kg), confirming limited carry-over. Serum biochemical parameters—alanine aminotransferase (ALT), aspartate aminotransferase (AST), gamma-glutamyl transferase (GGT), alkaline phosphatase (ALP), total bilirubin (BIL-T), total protein (PROT-T)—remained within physiological limits, yet multivariate analyses revealed metabolic modulation linked to aflatoxin exposure. AFM1 explained >7% of the variance in serum biochemical profiles according to PERMANOVA (p = 0.002), showed significant MANOVA effect (Pillai = 0.198), and displayed a significant canonical association (p < 10⁻¹³). Linear discriminant analysis further separated Normal vs. Borderline hepatic profiles, indicating subclinical physiological adaptation to chronic low-dose exposure. Full article

Background/Objectives: The circadian regulator, circadian locomotor output cycles kaput (CLOCK), is well-established in maintaining sleep–wake rhythms, yet its cell-type-specific functions in sleep regulation remain largely unexplored. While ventromedial hypothalamic (VMH) prodynorphin (PDYN)-expressing (VMH^PDYN+) neurons are known to modulate homeostatic and motivational processes, their potential role in circadian sleep regulation has not been investigated. Methods: To address this, we developed mice with PDYN neuron-specific functional suppression of CLOCK activity (mClkΔ19) by interfering with their internal clock through Adeno-Associated Virus (AAV)-mediated overexpression of dominant-negative CLOCKΔ19 in PDYN-Cre mice. Results: We found that mClkΔ19 mice exhibited reduced locomotor activity during the dark phase, earlier activity peaks, and impaired rhythmicity of rapid eye movement (REM) and non-REM (NREM) sleep. Sleep analysis in mClkΔ19 mice showed selective reductions and fragmentation of light-phase REM sleep, more frequent sleep–wake transitions, and shorter REM cycles during the dark phase, indicating disrupted REM sleep timing. EEG spectral analysis in mClkΔ19 mice revealed decreased gamma activity during REM sleep in the light phase and an increase in delta activity coupled with decreased gamma during wakefulness in the dark phase. Conclusions: These findings suggest that the CLOCK activity in VMH^PDYN+ neurons is vital for circadian accuracy, REM sleep stability, and brain oscillations during sleep–wake cycles. Full article

The persistent Hubble tension—marked by a notable disparity between early- and late-universe determinations of the Hubble constant $H_0$—poses a serious challenge to the standard cosmological framework. Closely linked to this is the $H_0-r_d$ tension, which stems from the fact that BAO-based estimates of $H_0$ are intrinsically dependent on the assumed value of the sound horizon at the drag epoch, $r_d$. In this study, we construct a scalar field dark energy model within the framework of a spatially flat Friedmann–Lemaitre–Robertson–Walker model to explore the dynamics of cosmic acceleration. To solve the field equations, we introduce a generalized extension of the standard Lambda Cold Dark Matter model that allows for deviations in the expansion history. Employing advanced Markov Chain Monte Carlo techniques, we constrain the model parameters using a comprehensive combination of observational data, including Baryon Acoustic Oscillations, Cosmic Chronometers, and Standard Candle datasets from Pantheon, Quasars, and Gamma-Ray Bursts (GRBs). Our analysis reveals a transition redshift from deceleration to acceleration at $z_{\mathrm{tr}}=0.69$ and a present-day deceleration parameter value of $q_0=-0.64$. The model supports a dynamical scalar field interpretation, with an equation of state parameter satisfying $-1<{\omega }_0^{\varphi }<0$, consistent with quintessence behavior, and signaling a deviation from the $\mathrm{\Lambda }$. While the model aligns closely with the Lambda Cold Dark Matter scenario at lower redshifts ($z\lesssim 0.65$), notable departures emerge at higher redshifts ($z\gtrsim 0.65$), offering a potential window into modified early-time cosmology. Furthermore, the evolution of key cosmographic quantities such as energy density ${\rho }^{\varphi }$, pressure $p^{\varphi }$, and the scalar field equation of state highlights the robustness of scalar field frameworks in describing dark energy phenomenology. Importantly, our results indicate a slightly higher value of the Hubble constant $H_0$ for specific data combinations, suggesting that the model may provide a partial resolution of the current $H_0$ tension. Full article

We develop and analyze a distributed-delay model for nutrient–fish–mussel dynamics in multitrophic aquaculture systems. Extending the classical discrete-delay framework, we incorporate gamma-distributed kernels to capture the time-distributed nature of nutrient assimilation, yielding a more realistic and analytically tractable representation. These kernels introduce a form of temporal symmetry in the system’s memory, where past nutrient levels influence present dynamics in a balanced and structured way. Using the linear chain trick, we reformulate the integro-differential equations into ordinary differential systems for both weak and strong memory scenarios. We derive conditions for local stability and Hopf bifurcation, and establish global stability using Lyapunov-based methods. Numerical simulations confirm that increased delay can destabilize the system, leading to oscillations, while stronger memory mitigates this effect and enhances resilience. Bifurcation diagrams, time series, and phase portraits illustrate how memory strength governs the system’s dynamic response. This work highlights how symmetry in memory structures contributes to system robustness, offering theoretical insights and practical implications for the design and management of ecologically stable aquaculture systems. Full article

Background: Electroencephalography (EEG) offers millisecond-precision measurement of neural oscillations underlying human cognition and emotion. Despite extensive research, systematic frameworks mapping EEG metrics to psychological constructs remain fragmented. Objective: This interdisciplinary scoping review synthesizes current knowledge linking EEG signatures to affective and cognitive models from a neuroscience perspective. Methods: We examined empirical studies employing diverse EEG methodologies, from traditional spectral analysis to deep learning approaches, across laboratory and naturalistic settings. Results: Affective states manifest through distinct frequency-specific patterns: frontal alpha asymmetry (8–13 Hz) reliably indexes emotional valence with 75–85% classification accuracy, while arousal correlates with widespread beta/gamma power changes. Cognitive processes show characteristic signatures: frontal–midline theta (4–8 Hz) increases linearly with working memory load, alpha suppression marks attentional engagement, and theta/beta ratios provide robust cognitive load indices. Machine learning approaches achieve 85–98% accuracy for subject identification and 70–95% for state classification. However, significant challenges persist: spatial resolution remains limited (2–3 cm), inter-individual variability is substantial (alpha peak frequency: 7–14 Hz range), and overlapping signatures compromise diagnostic specificity across neuropsychiatric conditions. Evidence strongly supports integrated rather than segregated processing, with cross-frequency coupling mechanisms coordinating affective–cognitive interactions. Conclusions: While EEG-based assessment of mental states shows considerable promise for clinical diagnosis, brain–computer interfaces, and adaptive technologies, realizing this potential requires addressing technical limitations, standardizing methodologies, and establishing ethical frameworks for neural data privacy. Progress demands convergent approaches combining technological innovation with theoretical sophistication and ethical consideration. Full article

Background/Objectives: As the global population ages, the prevalence of disorders associated with memory dysfunction (e.g., Alzheimer’s disease) continues to increase. There is a need for novel interventions that can enhance memory and support affected individuals. Non-invasive brain stimulation provides a promising approach to engage circuits within the hippocampal network, a group of brain regions critical for episodic memory, and thereby improve cognition. Methods: Twenty healthy participants completed a single-blind, within-subject crossover study over four sessions. In each session, they received one of four interventions whilst viewing pictures of real-world objects: 40 Hz synchronised audiovisual stimulation (AVS), theta burst stimulation (TBS), a combination of synchronised 5 Hz repetitive transcranial magnetic stimulation with AVS (rTMS + AVS), or sham rTMS. Electroencephalography (EEG) was recorded to measure associated brain activity changes. Following each intervention, participants completed a recognition memory task. Results: Mixed-effect repeated measure models (MRMMs) revealed no significant differences in recognition memory performance or theta (5 Hz) activity across conditions. However, both TBS and rTMS + AVS significantly increased gamma (40 Hz) activity compared to sham rTMS, and TBS induced a widespread increase in theta-gamma phase-amplitude coupling during picture viewing. Conclusions: While the neuromodulatory interventions did not enhance memory performance, the observed increase in gamma activity, particularly following rTMS-based stimulation, suggests potential engagement of neural processes associated with memory. These findings warrant further investigation into the role of gamma oscillations in memory and cognitive enhancement. Full article

Background/Objectives: Dysregulated gamma oscillations are associated with cognitive dysfunction. Auditory stimulation at 40 Hz enhances neural activity in brain regions associated with learning, attention, and memory. This study assessed the safety and acceptability of 40 Hz amplitude-modulated auditory stimulation in healthy older people. Auditory stimuli were created using popular songs, where vocals and background music were separated and independently amplitude-modulated at 40 Hz with different modulation depths to generate periodic 40 Hz gamma waveforms. Methods: In this open-label, single-arm study, healthy participants aged ≥65 years received 40 Hz amplitude-modulated auditory stimulation daily via a smartphone for 28 days through earphones/headphones. Safety was assessed through adverse event (AE) monitoring and changes in clinical scores for depression, cognitive function, and hearing thresholds. Acceptability was evaluated by adherence rates, listening time, dropout reasons, volume levels, intent for future use, and subjective impressions of the sound source on a 7-point Likert scale. Results: Among 28 participants (mean age 69.1 years, 53.6% female), six reported 12 AEs, with six considered device-related (e.g., ear discomfort, jaw pain, musculoskeletal stiffness). The AEs observed were mild or moderate. Scores for cognitive function, depression, and hearing thresholds did not worsen during the study period. Adherence was observed in 96.4%, with 85.7% expressing interest in continuing. Most participants rated the sounds’ unnaturalness between 2 and 3 and discomfort between 1 and 3 on the 7-point Likert scale. Conclusions: The intervention was well tolerated and acceptable in study participants, with no major safety concerns identified. Auditory stimulation did not cause severe discomfort or reduce acceptability. Further studies should explore the long-term effects and broader clinical applications. Full article

Electroencephalography (EEG) provides a powerful means of capturing real-time neural activity, enabling the study of cognitive processes during complex learning tasks. This study explores the application of wearable EEG and advanced signal analysis to examine cognitive profiles of 30 sixth-grade students engaged in fraction learning. Using validated estimations alongside interactive digital tools such as Fraction Lab and the Diamond Paper task, EEG recordings were processed to evaluate spectral dynamics across delta, theta, alpha, and beta bands. Results revealed that lower-performing students exhibited elevated delta and theta power under cognitive load, whereas higher-performing students showed more stable beta activity linked to cognitive control. These findings highlight the utility of EEG-based signal analysis for identifying neurocognitive markers associated with conceptual and procedural knowledge (PK) in mathematics. The integration of such methodologies supports the development of precision-oriented educational strategies grounded in objective neural data. Clustering further revealed three learner profiles: Core Support Needed, Developing, and Advanced, while classification analyses confirmed that EEG features, especially gamma and beta oscillations, reliably distinguished among them, underscoring the potential of neurocognitive markers to guide adaptive instruction. Full article