Carbon-fixing bacteria in diverse groundwaters of karst area: Distribution patterns, ecological interactions, and driving factors.

The biological carbon pump in karst areas is of great significance for maintaining the effectiveness of karst carbon sinks. However, the spatial distribution and carbon-fixing potential of microorganisms in different aquifers within karst areas remain poorly understood. In this study, the distribution patterns, ecological roles, and environmental drivers of microbiota associated with CO2 fixation were investigated in karst groundwater (KW), porous groundwater (PW), fractured groundwater (FW), and surface water (SW) within a typical karst watershed, located in Guilin, southwest China. KW, PW, and FW displayed the similar community structure and indicative carbon-fixing bacteria composition, which were dominated by chemoautotrophic bacteria compared to SW. Higher abundances of indicative carbon-fixing bacteria and carbon-fixing genes, as well as richer proportions of microbial-derived DOC, indicated the more significant microbial carbon-fixing potential in KW and PW. At the profile of KW, a carbon-fixing hotspot was discovered at the depths of 0-50 m. Correlation analysis between carbon-fixing bacteria and DOC revealed that the chemoautotrophic process driven by nitrogen and sulfur oxidation predominated the microbial carbon fixation in groundwater. Co-occurrence network analysis demonstrated that carbon-fixing bacteria exhibited cooperation with other bacterial taxa in KW, while competition was the dominant interaction in PW. Moreover, carbon-fixing bacteria was found to lead bacterial assembly more deterministic in KW. The analysis of environmental factors and microbial diversity illustrated that inorganic carbon and redox state drove community variations across groundwaters. Structural equation model (SEM) further confirmed that ORP was the primary factor influencing the carbon fixation potential. This study provides a new insight into biological carbon fixation in karst aquatic systems, which holds significance in the accurate assessment of karst carbon sinks.

Insights into the enhanced mechanism of selenium-doped iron nitride carbon catalysts for elemental mercury removal in flue gas.

This study developed a novel selenium-doped metal nitride carbon, Fe-NC-Se, via pyrolysis and impregnated hydrothermal methods for elemental mercury removal from coal-fired flue gas. The Fe-NC-Se demonstrated a remarkable mercury removal performance, achieving an average efficiency of 96.98% within 60 min at an optimal Se/Fe ratio of 2:1 and temperature of 110 °C, which was 2.5 times higher than that of the pristine Fe-NC (iron nitride carbon). Notably, Fe-NC-Se maintained an 84% efficiency in a high SO2 environment (1600 ppm), indicating strong resistance to SO2 poisoning. Long-term testing over 24 h showed a consistent removal efficiency of 84.75%, suggesting potential for recyclability. Advanced characterization techniques, including TEM (transmission electron microscopy) and XPS (X-ray photoelectron spectrometer), along with Density Functional Theory calculations, were employed to explore the removal mechanism. Results indicated that selenium doping enhanced surface charge transfer and the reactivity of surface atoms, facilitating mercury oxidation and sequestration. The oxidized Hg2+ was anchored by Se and partially stabilized by C, N, and Fe atoms, enhancing the catalyst's effectiveness. This work not only advances the design of mercury abatement catalysts but also supports the industrial applicability of Fe-NC-Se in flue gas treatment.

State-dependent tACS effects reveal the potential causal role of prestimulus alpha traveling waves in visual contrast detection.

The intricate relationship between prestimulus alpha oscillations and visual contrast detection variability has been the focus of numerous studies. However, the causal impact of prestimulus alpha traveling waves on visual contrast detection remains largely unexplored. In our research, we sought to discern the causal link between prestimulus alpha traveling waves and visual contrast detection across different levels of mental fatigue. Using electroencephalography (EEG) alongside a visual detection task with 30 healthy adults (13 females; 17 males), we identified a robust negative correlation between prestimulus alpha forward traveling waves (FTW) and visual contrast threshold (VCT). Inspired by this correlation, we utilized 45°/-45° phase-shifted transcranial alternating current stimulation (tACS) in a sham-controlled, double-blind, within-subject experiment with 33 healthy adults (23 females; 10 males) to directly modulate these alpha traveling waves. After the application of 45° phase-shifted tACS, we observed a substantial decrease in FTW and an increase in backward traveling waves (BTW), along with a concurrent increase in VCT, compared with the sham condition. These changes were particularly pronounced under low fatigue state. The findings of state-dependent tACS effects reveal the potential causal role of prestimulus alpha traveling waves in visual contrast detection. Moreover, our study highlights the potential of 45°/-45° phase-shifted tACS in cognitive modulation and therapeutic applications.Significance Statement Visual contrast detection, despite consistent stimuli, frequently exhibits variability. This variability has been linked to prestimulus alpha neural oscillations in prior studies. Recently, there has been increased interest in exploring large-scale alpha traveling waves and their connection with visual processing. Yet, the role of these traveling waves in visual contrast detection remains unclarified. Through a combination of visual detection tasks, EEG data analysis, and 45°/-45° phase-shifted tACS, our study elucidates how prestimulus alpha traveling waves exert a potential causal influence on visual contrast detection.

Excitation-Inhibition Balance, Neural Criticality, and Activities in Neuronal Circuits.

Neural activities in local circuits exhibit complex and multilevel dynamic features. Individual neurons spike irregularly, which is believed to originate from receiving balanced amounts of excitatory and inhibitory inputs, known as the excitation-inhibition balance. The spatial-temporal cascades of clustered neuronal spikes occur in variable sizes and durations, manifested as neural avalanches with scale-free features. These may be explained by the neural criticality hypothesis, which posits that neural systems operate around the transition between distinct dynamic states. Here, we summarize the experimental evidence for and the underlying theory of excitation-inhibition balance and neural criticality. Furthermore, we review recent studies of excitatory-inhibitory networks with synaptic kinetics as a simple solution to reconcile these two apparently distinct theories in a single circuit model. This provides a more unified understanding of multilevel neural activities in local circuits, from spontaneous to stimulus-response dynamics.

Reshaping the Cortical Connectivity Gradient by Long-Term Cognitive Training During Development.

The organization of the brain follows a topological hierarchy that changes dynamically during development. However, it remains unknown whether and how cognitive training administered over multiple years during development can modify this hierarchical topology. By measuring the brain and behavior of school children who had carried out abacus-based mental calculation (AMC) training for five years (starting from 7 years to 12 years old) in pre-training and post-training, we revealed the reshaping effect of long-term AMC intervention during development on the brain hierarchical topology. We observed the development-induced emergence of the default network, AMC training-promoted shifting, and regional changes in cortical gradients. Moreover, the training-induced gradient changes were located in visual and somatomotor areas in association with the visuospatial/motor-imagery strategy. We found that gradient-based features can predict the math ability within groups. Our findings provide novel insights into the dynamic nature of network recruitment impacted by long-term cognitive training during development.

Multilevel and multifaceted brain response features in spiking, ERP and ERD: experimental observation and simultaneous generation in a neuronal network model with excitation-inhibition balance.

Brain as a dynamic system responds to stimulations with specific patterns affected by its inherent ongoing dynamics. The patterns are manifested across different levels of organization-from spiking activity of neurons to collective oscillations in local field potential (LFP) and electroencephalogram (EEG). The multilevel and multifaceted response activities show patterns seemingly distinct and non-comparable from each other, but they should be coherently related because they are generated from the same underlying neural dynamic system. A coherent understanding of the interrelationships between different levels/aspects of activity features is important for understanding the complex brain functions. Here, based on analysis of data from human EEG, monkey LFP and neuronal spiking, we demonstrated that the brain response activities from different levels of neural system are highly coherent: the external stimulus simultaneously generated event-related potentials, event-related desynchronization, and variation in neuronal spiking activities that precisely match with each other in the temporal unfolding. Based on a biologically plausible but generic network of conductance-based integrate-and-fire excitatory and inhibitory neurons with dense connections, we showed that the multiple key features can be simultaneously produced at critical dynamical regimes supported by excitation-inhibition (E-I) balance. The elucidation of the inherent coherency of various neural response activities and demonstration of a simple dynamical neural circuit system having the ability to simultaneously produce multiple features suggest the plausibility of understanding high-level brain function and cognition from elementary and generic neuronal dynamics. Supplementary Information: The online version contains supplementary material available at 10.1007/s11571-022-09889-w.

Exploiting Information in Event-Related Brain Potentials from Average Temporal Waveform, Time-Frequency Representation, and Phase Dynamics.

Characterizing the brain's dynamic pattern of response to an input in electroencephalography (EEG) is not a trivial task due to the entanglement of the complex spontaneous brain activity. In this context, the brain's response can be defined as (1) the additional neural activity components generated after the input or (2) the changes in the ongoing spontaneous activities induced by the input. Moreover, the response can be manifested in multiple features. Three commonly studied examples of features are (1) transient temporal waveform, (2) time-frequency representation, and (3) phase dynamics. The most extensively used method of average event-related potentials (ERPs) captures the first one, while the latter two and other more complex features are attracting increasing attention. However, there has not been much work providing a systematic illustration and guidance for how to effectively exploit multifaceted features in neural cognitive research. Based on a visual oddball ERPs dataset with 200 participants, this work demonstrates how the information from the above-mentioned features are complementary to each other and how they can be integrated based on stereotypical neural-network-based machine learning approaches to better exploit neural dynamic information in basic and applied cognitive research.

[Spatial Distribution, Source Analysis, and Health Risk Assessment of Metal Elements in Karst Water in Southeastern Chongqing].

Dispersed karst water is an important water supply source, or even the only water supply source, for some districts and counties in Chongqing City. It is particularly necessary to understand the distribution characteristics of metal elements in karst water and the health risks exposed. In this study, the scattered karst water in the southeastern part of Chongqing was taken as the main research object, and the concentrations of Al, Cu, Pb, Zn, Cr, Cd, Ni, Mn, As, and Hg in 42 groups of karst spring water samples were determined. The spatial distribution of metal elements with a high detection rate was revealed using the ordinary kriging interpolation method, and the spatial distribution characteristics, sources, and health risks of metal elements were analyzed using multivariate statistical methods and health risk models. The results showed that the quality of dispersed karst water in southeastern Chongqing was generally good, and the spatial scale variability in the occurrence of metal elements in karst water was strong, especially for Ni and As. The sources of Cu, Pb, As, Zn, and Cr were mainly affected by the regional geological background; Al and Mn were mainly affected by human industrial, agricultural, and mining activities; and Ni was affected by both the natural background and human activities. The total health risk of exposure through the drinking route was higher than that of the skin infiltration route, which was the main exposure route of the human body. The total health risk of children exposed through the drinking route was higher than that of adults, and the total health risk of adults exposed through the skin infiltration route was higher than that of children. It is worth noting that Cr was the determinant of total health risk. From the perspective of drinking water safety, local residents need to pay certain attention to water quality when drinking distributed karst groundwater, in order to reduce the health risk of the population.

The resin-supported iron-copper bimetallic composite as highly active heterogeneous Fenton-like catalysts for degradation of gaseous toluene.

In this study, a resin-supported iron-copper bimetallic heterogeneous Fenton catalyst with excellent removal performance, superior economy and outstanding recoverability was synthesized by an impregnation method and used to remove gaseous toluene. Experiments disclosed that 3-FeCu@LXQ-10 possessed extremely high catalytic capacity. At a temperature of 30 °C, an initial toluene concentration of 200 mg/m3 and H2O2 atomization amount of 3 mmol/h, the toluene removal efficiency of 3-FeCu@LXQ-10 was 97.50%. Experimental tests had revealed that the bimetallic supported catalysts exhibited higher catalytic activity than single metal-supported catalysts, owing to an interaction effect between iron and copper metal ions. Furthermore, electron paramagnetic resonance (EPR) and radical quenching tests were carried out, and the results indicated •OH radicals performed a key role in the Fenton-like process. In addition, the iron-copper bimetallic catalysts exhibited good reusability and stability characteristics during six degradation cycles. This study shows promising potential in using FeCu@LXQ-10 as a heterogeneous catalyst for removing toluene.

Commercial SCR catalyst modified with Cu metal to simultaneously efficiently remove NO and toluene in the fuel gas.

Developing an environmentally friendly selective catalytic reduction (SCR) catalyst to effectively eliminate both nitric oxides (NO) and toluene has garnered significant attention for regulating emissions from automobiles and the combustion of fossil fuels. This study synthesized a series of novel commercial V2O5-WO3/TiO2 catalysts modified with Cu through the wet impregnation method, which was employed to simultaneously remove NO and toluene from the fuel gas. The assessment of catalyst removal performance was conducted at a selective catalytic reduction system, and the experimental results showed a significant increase in the catalytic activity due to the modification of the copper metal. The 10% Cu/SCR catalyst showed a superior activity that the NO and toluene conversion reached 100% and 95.56% at 300 °C, respectively. Subsequently, various characterization techniques were employed to investigate the crystal phase, morphology, physical features, chemical states, and surface acidity properties of the synthesis catalysts. According to the characterization results, the presence of Cu metal did not have a noticeable impact on the physical property. However, the redox performance was enhanced, and the number of surface acidic sites was also increased after adding Cu to the SCR catalyst. Furthermore, the redox cycle of Cu metal and V species was facilitated to produce more active oxygen which helped to improve the NO and toluene conversion. This work offered a novel perspective into the synergistic oxidation of both NO and toluene, which was potentially relevant for improving the selective catalytic reduction process in coal-fired power plants.

Memory rescue and learning in synaptic impaired neuronal circuits.

Neuronal impairment is a characteristic of Alzheimer's disease (AD), but its effect on neural activity dynamics underlying memory deficits is unclear. Here, we studied the effects of synaptic impairment on neural activities associated with memory recall, memory rescue, and learning a new memory, in an integrate-and-fire neuronal network. Our results showed that reducing connectivity decreases the neuronal synchronization of memory neurons and impairs memory recall performance. Although, slow-gamma stimulation rescued memory recall and slow-gamma oscillations, the rescue caused a side effect of activating mixed memories. During the learning of a new memory, reducing connectivity caused impairment in storing the new memory, but did not affect previously stored memories. We also explored the effects of other types of impairments including neuronal loss and excitation-inhibition imbalance and the rescue by general increase of excitability. Our results reveal potential computational mechanisms underlying the memory deficits caused by impairment in AD.

Cognitive abilities are associated with specific conjunctions of structural and functional neural subnetworks.

Cognitive neuroscience assumes that different mental abilities correspond to at least partly separable brain subnetworks and strives to understand their relationships. However, single-task approaches typically revealed multiple brain subnetworks to be involved in performance. Here, we chose a bottom-up approach of investigating the association between structural and functional brain subnetworks, on the one hand, and domain-specific cognitive abilities, on the other. Structural network was identified using machine-learning graph neural network by clustering anatomical brain properties measured in 838 individuals enroled in the WU-Minn Young Adult Human Connectome Project. Functional network was adapted from seven Resting State Networks (7-RSN). We then analyzed the results of 15 cognitive tasks and estimated five latent abilities: fluid reasoning (Gf), crystallized intelligence (Gc), memory (Mem), executive functions (EF), and processing speed (Gs). In a final step we determined linear associations between these independently identified ability and brain entities. We found no one-to-one mapping between latent abilities and brain subnetworks. Analyses revealed that abilities are associated with properties of particular combinations of brain subnetworks. While some abilities are more strongly associated to within-subnetwork connections, others are related with connections between multiple subnetworks. Importantly, domain-specific abilities commonly rely on node(s) as hub(s) to connect with other subnetworks. To test the robustness of our findings, we ran the analyses through several defensible analytical decisions. Together, the present findings allow a novel perspective on the distinct nature of domain-specific cognitive abilities building upon unique combinations of associated brain subnetworks.

Fe(II)/LXQ-10 bifunctional resin materials for boosting synergistic adsorption/oxidation of benzene in industrial waste gas.

A series of adsorption/oxidation bifunctional material with different Fe(II) loading amounts was prepared by using ultrahigh crosslinking adsorption resin (LXQ-10) as a carrier and FeCl2 as an impregnating solution. The bifunctional material was characterized by BET, SEM, XRD, XPS, and EPR. The effects of Fe loading, reaction temperature, and space velocity on benzene adsorption efficiency were investigated using self-made experimental equipment to explore the optimal reaction condition. The adsorption results were fitted and analyzed by using four typical models: the quasi-first-order kinetic model, the quasi-second-order kinetic model, Elovich's kinetic model, and the Weber and Morris kinetic model. The quasi-first-order kinetic model had the highest R2 value (0.998) and the best applicability. The fitting effect of the Freundlich equation (R2 = 0.997) was better than that of the Langmuir equation (R2 = 0.919). Furthermore, the effects of Fe loading, H2O2 concentration, benzene inlet concentration, and temperature on the catalytic oxidation efficiency of benzene were studied. The catalytic oxidation efficiency of 3-Fe(II)/LXQ-10 was maintained at about 95% at a temperature of 303 K and an H2O2 concentration of 150 mmol/L. Compared with the adsorption efficiency, the catalytic oxidation efficiency of bifunctional resin materials in a heterogeneous Fenton system was remarkably improved and had excellent stability. A possible migration and transformation path during benzene removal was proposed according to the results of the analysis of GC-MS intermediates. This study provided a novel process for the adsorption and oxidative degradation of VOCs.

Enhanced intersubject similarity in functional connectivity by long-term abacus training.

The individual difference of intrinsic functional connectivity is increasingly acknowledged to be biologically informative and behaviorally relevant. However, such valuable information is still discounted as a stochastic variation in previous studies of cognitive training. Here, we explored the plasticity of intersubject similarity in functional connectivity (ISFC), induced by long-term abacus-based mental calculation (AMC) training. Using a longitudinal dataset (AMC: n = 40, 5-year training; Control: n = 43), we found robust training effect of enhanced ISFC, after accounting for the factor of development. Notably, the enhancement focused on selective subsets of FCs, or the "critical FCs," which predominantly impacted the default-mode and visual networks. Using a cross-sectional dataset with a larger sample (AMC: n = 93, 1/3/5-year training; Control: n = 110), we observed that the "critical FCs" and its intersubject similarity could predict mental calculation ability and its intersubject similarity, respectively, in the AMC group. However, such predictions cannot be generalized to the control group, suggesting that long-term training may be a prerequisite for establishing such brain-behavior relationships. Jointly, our findings implicated that the enhanced ISFC with profound impact on the default-mode network could be a plastic change that is associated with behavioral gains of training.

Complexity of cortical wave patterns of the wake mouse cortex.

Rich spatiotemporal dynamics of cortical activity, including complex and diverse wave patterns, have been identified during unconscious and conscious brain states. Yet, how these activity patterns emerge across different levels of wakefulness remain unclear. Here we study the evolution of wave patterns utilizing data from high spatiotemporal resolution optical voltage imaging of mice transitioning from barbiturate-induced anesthesia to wakefulness (N = 5) and awake mice (N = 4). We find that, as the brain transitions into wakefulness, there is a reduction in hemisphere-scale voltage waves, and an increase in local wave events and complexity. A neural mass model recapitulates the essential cellular-level features and shows how the dynamical competition between global and local spatiotemporal patterns and long-range connections can explain the experimental observations. These mechanisms possibly endow the awake cortex with enhanced integrative processing capabilities.

Performance and mechanisms of iron/copper-doped zirconium-based catalyst containing hydroxyl radicals for enhanced removal of gaseous benzene.

In the present study, novel copper-doped zirconium-based MOF (UIO-66) and copper-doped iron-based UIO-66 catalysts were prepared by hydrothermal synthesis method to improve the removal performance of gaseous benzene. The characteristics of the catalysts were analyzed by means of XRD, SEM, XPS, BET, and EPR. The copper loading catalyst had high crystallinity and irregular globular. The three kinds of catalysts with different Cu/Fe ratios had regular cubic shape. Compared with the catalyst supported with single copper, the bimetal Cu/Fe modification had a certain adjustment effect on the morphology, which specifically reflected in the uniform size and shape of catalyst particles with better dispersibility. The factors of different metal loading, dose of H2O2, and reaction temperature on benzene removal have been studied. It has been observed that in heterogeneous advanced oxidation removal of benzene, 3-Cu@UIO-66 and Cu1.5/Fe1.5@UIO-66 achieved the highest benzene removal efficiency of 81.2% and 94.6%, respectively. EPR results showed that the increase of Cu loading and different Cu/Fe ratios promoted the yield of hydroxyl radicals, thus promoted the benzene removal efficiency. The efficiency of heterogeneous oxidation removal of benzene first increased and then decreased with the increase of temperature due to H2O2 instability. DFT calculations exhibited that the Feoct-Cu-O site was a more effective activation site than the single Feoct-O site. Dissociative adsorption occurred with the O-O bond of H2O2 cracked, and the formed hydroxyls parallel adsorbed on the benzene surface. The combination of benzene and hydroxyls was strong chemisorption with the torsion angle of benzene ring obviously turned. The work was of great importance for identifying the roles of the novel catalyst for the removal of benzene pollutant from waste gases.

Resting-state brain signal complexity discriminates young healthy APOE e4 carriers from non-e4 carriers.

It is well established that the e4 allele of the APOE gene is associated with impaired brain functionality and cognitive decline in humans at elder age. However, it is controversial whether and how the APOE e4 allele is associated with superior brain function among young healthy individuals, thus indicates a case of antagonistic pleiotropy of APOE e4 allele. Signal complexity is a critical aspect of brain activity that has been associated with brain function. In this study, the multiscale entropy (MSE) of resting-state EEG signals among a sample of young healthy adults (N = 260) as an indicator of brain signal complexity was investigated. It was of interest whether MSE differs across APOE genotype groups while age and education level were controlled for and whether the APOE genotype effect on MSE interacts with MSE time scale, as well as EEG recording condition. Results of linear mixed models indicate overall larger MSE in APOE e4 carriers. This genotype-dependent difference is larger at high as compared with low time scales. The interaction effect between APOE genotype and recording condition indicates increased between-state MSE change in young healthy APOE e4 carriers as compared with non-carriers. Because higher complexity is commonly taken to be associated with better cognitive functioning, the present results complement previous findings and therefore point to a pleiotropic spectrum of the APOE gene polymorphism.

Spatiotemporal variability and control factors of NO3- in a polluted karst water system of an agricultural wetland in South China.

Nitrate (NO3-) pollution in karst water is an important environmental issue in intensive agricultural regions worldwide. The integrated understanding of the spatiotemporal variability and control factors of NO3- pollution in karst water is imperative for controlling the diffuse pollution caused by agricultural activities. In this study, 49 water samples were collected from surface water (SW) and groundwater (GW) in the Huixian karst wetland (HKW) and analyzed using hydrogeochemical and isotopic data (δ18O-NO3-, δ15N-NO3- and δ13CDIC) in combination with a Bayesian mixing model to investigate the spatiotemporal distribution and control factors in NO3--polluted karst water. The results showed that approximately 40.82% of the karst water samples exceeded the natural threshold value of 3 mg/L for NO3--N, and 32.14% of the GW samples exceeded the permissible limit for drinking water established by WHO (10 mg/L as NO3--N), indicating that high levels of NO3- were mainly found in GW samples from the agricultural core area, especially in the dry season. The NH4+-synthetic fertilizer (NHF) and soil organic nitrogen (SON) were the dominant factors controlling pollution sources in the HKW, accounting for 36.13% ± 4.66% and 28.68% ± 4.75% of the karst GW NO3- concentration, respectively. However, the seasonal differences in NO3- pollution sources were not significant in GW. Microbial nitrification was the main process affecting the NO3- levels in GW, whereas the occurrence of denitrification did not significantly affect NO3- concentration in the HKW due to the relatively low rate. Moreover, the HNO3 produced from NH4+ via microbial nitrification facilitated carbonate weathering, thereby controlling NO3- enrichment in karst GW. Our results suggest that NHF should be controlled to prevent further GW pollution in the HKW. Our study also provides a scientific basis for understanding the factors controlling the NO3- concentrations in karst water systems.

Cortex-wide spontaneous activity non-linearly steers propagating sensory-evoked activity in awake mice.

The brain responds highly variably to identical sensory inputs, but there is no consensus on the nature of this variability. We explore this question using cortex-wide optical voltage imaging and whisker stimulation in awake mice. Clustering analysis reveals that the sensory-evoked activity propagates over the cortex via distinct pathways associated with distinct behavioral states. The pathway taken by each trial is independent of the level of primary sensory-evoked activation but is partially predictable by the spatiotemporal features of the preceding cortical spontaneous activity patterns. The sensory inputs reduce trial-to-trial variability in brain activity and alter temporal autocorrelation in spatial activity pattern evolutions, suggesting non-linear interactions between evoked activities and spontaneous activities. Further, evoked activities and spontaneous activities occupy different positions in the state space, suggesting that sensory inputs can intricately interact with the internal state to generate large-scale evoked activity patterns not frequented by spontaneous brain states.

Visual cortex encodes timing information in humans and mice.

Despite the importance of timing in our daily lives, our understanding of how the human brain mediates second-scale time perception is limited. Here, we combined intracranial stereoelectroencephalography (SEEG) recordings in epileptic patients and circuit dissection in mice to show that visual cortex (VC) encodes timing information. We first asked human participants to perform an interval-timing task and found VC to be a key timing brain area. We then conducted optogenetic experiments in mice and showed that VC plays an important role in the interval-timing behavior. We further found that VC neurons fired in a time-keeping sequential manner and exhibited increased excitability in a timed manner. Finally, we used a computational model to illustrate a self-correcting learning process that generates interval-timed activities with scalar-timing property. Our work reveals how localized oscillations in VC occurring in the seconds to deca-seconds range relate timing information from the external world to guide behavior.