Record Of Events Research Articles

Learning-induced bidirectional enhancement of inhibitory synaptic metaplasticity.

Training rodents in a particularly difficult olfactory-discrimination (OD) task results in the acquisition of the ability to perform the task well, termed 'rule learning'. In addition to enhanced intrinsic excitability and synaptic excitation in piriform cortex pyramidal neurons, rule learning results in increased synaptic inhibition across the whole cortical network to the point where it precisely maintains the balance between inhibition and excitation. The mechanism underlying such precise inhibitory enhancement remains to be explored. Here, we use brain slices from transgenic mice (VGAT-ChR2-EYFP), enabling optogenetic stimulation of single GABAergic neurons and recordings of unitary synaptic events in pyramidal neurons. Quantal analysis revealed that learning-induced enhanced inhibition is mediated by increased quantal size of the evoked inhibitory events. Next, we examined the plasticity of synaptic inhibition induced by long-lasting, intrinsically evoked spike firing in post-synaptic neurons. Repetitive depolarizing current pulses from depolarized (-70mV) or hyperpolarized (-90mV) membrane potentials induced long-term depression (LTD) and long-term potentiation (LTP) of synaptic inhibition, respectively. We found a profound bidirectional increase in the ability to induce both LTD, mediated by L-type calcium channels, and LTP, mediated by R-type calcium channels after rule learning. Blocking the GABAB receptor reversed the effect of intrinsic stimulation at -90mV from LTP to LTD. We suggest that learning greatly enhances the ability to modify the strength of synaptic inhibition of principal neurons in both directions. Such plasticity of synaptic plasticity allows fine-tuning of inhibition on each particular neuron, thereby stabilizing the network while maintaining the memory of the rule. KEY POINTS: Olfactory discrimination rule learning results in long-lasting enhancement of synaptic inhibition on piriform cortex pyramidal neurons. Quantal analysis of unitary inhibitory synaptic events, evoked by optogenetic minimal stimulation, revealed that enhanced synaptic inhibition is mediated by increased quantal size. Surprisingly, metaplasticity of synaptic inhibition, induced by intrinsically evoked repetitive spike firing, is increased bidirectionally. The susceptibility to both long-term depression (LTD) and long-term potentiation (LTP) of inhibition is enhanced after learning. LTD of synaptic inhibition is mediated by L-type calcium channels and LTP by R-type calcium channels. LTP is also dependent on activation of GABAB receptors. We suggest that learning-induced changes in the metaplasticity of synaptic inhibition enable the fine-tuning of inhibition on each particular neuron, thereby stabilizing the network while maintaining the memory of the rule.

Utilization of Frequency Ratio and Logistic Regression Model for Landslide Susceptibility Mapping in Bogor Area

Java Island holds the highest record of landslide events in Indonesia. In 2021, the Bogor area, consisting of the city and regency of Bogor, recorded the highest number of landslides. These events further impact the fatalities, damage, and loss to society. Landslide mitigation should be considered to reduce the risk caused by landslide hazards. In this regard, a landslide susceptibility analysis is one of the fundamental steps in mitigation measures that can support policymakers in response to landslide disaster risk reduction. The location of landslide possibilities can be identified by mapping landslide susceptibility. Therefore, this study aims to produce a landslide susceptibility map (LSM) using a statistical frequency ratio method and logistic regression. The number of landslide inventories used in the model is about 822 events. To apply the model, the present study evaluates 13 influencing factors consisting of elevation, slope angle, slope aspect, slope curvature, Topographic Wetness Index (TWI), distance to river, lithological, distance to fault, soil type, annual rainfall, Normalized Difference Vegetation Index (NDVI), land use land cover (LULC), and road distance. The model performance is further evaluated using Area Under the ROC Curve (AUC). The frequency ratio (FR) and logistic regression (LR) models produce satisfactory results and have high predictions of future landslide occurrences with a score of 0.8317 and 0.8817, respectively.

A continuous 18.6–10 ka record of seismic events revealed by Xinmocun lacustrine sediments at Diexi, eastern Tibetan Plateau

Five Ms 7.0 earthquakes (Wenchuan, Yushu, Lushan, Jiuzhaigou and Maudo) have occurred on the eastern Tibetan Plateau since 2008, and > 20 historical earthquakes (Ms ≥ 5.0) have also been recorded, demonstrating the strong seismic activity prevalent in this region. Owing to the geomorphology of the region, consisting of alpine valleys with a lack of Quaternary deposits, it is difficult to conduct paleo-earthquake research through the excavation of trenches. Lacustrine sediments are regarded as the most suitable archives for paleo-earthquake studies because they often comprise a detailed, long-term, and continuous environmental record. To assess the seismic activity of the Diexi, we studied a well-exposed outcrop of Xinmocun lacustrine sediments that included three sections (XMCⅠ (11.5 m), XMCⅡ (5.6 m), and XMCⅢ (11 m)) near Diexi Lake, moving from a lake shore to lake center environment, at the intersection between the Minjiang fault and Songpinggou fault. Taking into account the geodynamic setting of the area and its known tectonic activity, soft-sediment deformation structures, including load and flame structures, injection structures, ball structures, pseudo-nodules, boudinage structures, plunged sediment mixture, liquefied breccia and micro-faults, discovered within the sections are interpreted as seismites. High-resolution grain-size and magnetic susceptibility indicators were used to obtain a continuous record of the changes in physical properties within the three sections enabling the recognition of additional seismic events that did not result in soft-sediment deformation structures. Rapidly deposited layers with underlying soft-sediment deformation structures in the sections record five (XMCI, XMCII and XMCIII) seismic events; these comprise siliciclastic-enriched sandy sediment fluxes from earthquake-triggered landslides or dust of siliciclastic-enriched clastic materials. Isolated rapidly deposited layers (without underlying soft-sediment deformation structures) record 18 (XMCII), 12 (XMCI), and 21 (XMCIII) additional earthquake-induced inputs of detrital material into the lake. Optically stimulated luminescence and 14C dating of the XMCⅠ, XMCⅡ, and XMCⅢ sections indicate continuous sediment deposition from 19.4–9.9 ka, 15.5–10.1 ka, and 18.7–10.6 ka, respectively, and the seismic events recorded in these sections can be cross-correlated within the age error range. Hence, there are a total of 30 seismic events recorded within the Xinmocun sediments during the period 18.6–10 ka, suggests a weakly periodic pattern with an apparent recurrence time of ∼ 280 years. The Minjiang fault and Songpinggou fault are Holocene active faults with a risk of causing strong earthquakes. Due to the Diexi area is located at the intersection of the Minjiang fault and Songpinggou fault, and has frequent seismic activity, suggest a high seismic risk in the future.

A 7000-year record of extreme flood events reconstructed from a threshold lake in southern Norway

Recent decades have witnessed a shift in the seasonality and frequency of river floods in Norway, primarily attributed to contemporary global warming. Such changes necessitate a more comprehensive understanding of climate-flood dynamics across river systems. A significant challenge in flood risk assessment is that instrumental data records cover only the last few decades and do not capture low-frequency, high-magnitude extremes. Another challenge is the non-stationarity of the leading atmospheric pattern over Scandinavia, the North Atlantic Oscillation (NAO), which requires a spatial understanding of extreme events through time. To address this issue, we use lacustrine sediments from southernmost Norway, aiming to extend flood records beyond the scope of instrumental and historical data by exploring a threshold lake that accumulates flood deposits only when river discharge exceeds a critical level. A multi-proxy approach, which includes X-ray fluorescence (XRF), computed tomography (CT), and magnetic susceptibility (MS), was applied to fingerprint the sediments. We detected thin layers of minerogenic sediments, which we interpreted as slackwater deposits accumulated in a lake environment during extreme flood events. These fine-grained minerogenic bands were quantified using XRF and CT data to reconstruct the frequency of major floods over the past ∼7000 years. The minimum water discharge necessary for transporting sediments into the lake was assessed with the HEC-RAS hydraulic simulation software. Our record shows a high frequency of extreme floods from 7000 to 5000 cal yr BP before a significant decrease from 5000 to 2300 cal yr BP. The frequency has been high since 2300 cal yr BP, with a peak frequency after 1600 CE, coinciding with the Little Ice Age. There is a strong co-variability between a positive NAO index, winter precipitation, and major flood events over the past 120 years. A comparison between our flood record and other palaeorecords suggests that this relationship also holds on multi-millennial time scales.

A DNA circuit that records molecular events.

Characterizing the relative onset time, strength, and duration of molecular signals is critical for understanding the operation of signal transduction and genetic regulatory networks. However, detecting multiple such molecules as they are produced and then quickly consumed is challenging. A MER can encode information about transient molecular events as stable DNA sequences and are amenable to downstream sequencing or other analysis. Here, we report the development of a de novo molecular event recorder that processes information using a strand displacement reaction network and encodes the information using the primer exchange reaction, which can be decoded and quantified by DNA sequencing. The event recorder was able to classify the order at which different molecular signals appeared in time with 88% accuracy, the concentrations with 100% accuracy, and the duration with 75% accuracy. This simultaneous and highly programmable multiparameter recording could enable the large-scale deciphering of molecular events such as within dynamic reaction environments, living cells, or tissues.

First high-resolution shallow-marine bulk-carbonate record of the Middle Oxfordian Event in the Sub-Boreal Realm, Lower Saxony Basin, Germany

First high-resolution shallow-marine bulk-carbonate record of the Middle Oxfordian Event in the Sub-Boreal Realm, Lower Saxony Basin, Germany

Machine learning based detection of T–wave alternans in real ambulatory conditions

Background and objectiveT-wave alternans (TWA) is a fluctuation in the repolarization morphology of the ECG. It is associated with cardiac instability and sudden cardiac death risk. Diverse methods have been proposed for TWA analysis. However, TWA detection in ambulatory settings remains a challenge due to the absence of standardized evaluation metrics and detection thresholds. MethodsIn this work we use traditional TWA analysis signal processing-based methods for feature extraction, and two machine learning (ML) methods, namely, K–nearest–neighbor (KNN) and random forest (RF), for TWA detection, addressing hyper–parameter tuning and feature selection. The final goal is the detection in ambulatory recordings of short, non-sustained and sparse TWA events. ResultsWe train ML methods to detect a wide variety of alternant voltage from 20 to 100 μV, i.e., ranging from non–visible micro–alternans to TWA of higher amplitudes, to recognize a wide range in concordance to risk stratification. In classification, RF outperforms significantly the recall in comparison with the signal processing methods, at the expense of a small lost in precision. Despite ambulatory detection stands for an imbalanced category context, the trained ML systems always outperform signal processing methods. ConclusionsWe propose a comprehensive integration of multiple variables inspired by TWA signal processing methods to fed learning-based methods. ML models consistently outperform the best signal processing methods, yielding superior recall scores.

An explainable long short-term memory network for surgical site infection identification

BackgroundCurrently, surgical site infection surveillance relies on labor-intensive manual chart review. Recently suggested solutions involve machine learning to identify surgical site infections directly from the medical record. Deep learning is a form of machine learning that has historically performed better than traditional methods while being harder to interpret. We propose a deep learning model, a long short-term memory network, for the identification of surgical site infection from the medical record with an attention layer for explainability. MethodsWe retrieved structured data and clinical notes from the University of Utah Health System’s electronic health care record for operative events randomly selected for manual chart review from January 2016 to June 2021. Surgical site infection occurring within 30 days of surgery was determined according to the National Surgical Quality Improvement Program definition. We trained the long short-term memory model along with traditional machine learning models for comparison. We calculated several performance metrics from a holdout test set and performed additional analyses to understand the performance of the long short-term memory, including an explainability analysis. ResultsSurgical site infection was present in 4.7% of the total 9,185 operative events. The area under the receiver operating characteristic curve and sensitivity of the long short-term memory was higher (area under the receiver operating characteristic curve: 0.954, sensitivity: 0.920) compared to the top traditional model (area under the receiver operating characteristic curve: 0.937, sensitivity: 0.736). The top 5 features of the long short-term memory included 2 procedure codes and 3 laboratory values. ConclusionSurgical site infection surveillance is vital for the reduction of surgical site infection rates. Our explainable long short-term memory achieved a comparable area under the receiver operating characteristic curve and greater sensitivity when compared to traditional machine learning methods. With explainable deep learning, automated surgical site infection surveillance could replace burdensome manual chart review processes.

THE CONTEXT OF THE SMO IN UKRAINE FROM THE PROSPECTIVE OF THE WORLD-SYSTEM AND MARXIST APPROACHES: A COMPARATIVE ANALYSIS

The article provides a structural and logical comparison of two theoretical approaches - Marxism and world-system approach. This comparison is applied to explain the current international situation. The comparison allows us to draw a conclusion about the difference in starting positions, which leads to different versions of the explanation of current processes in the world economy and world politics. At the same time, it reveals a number of similar points in the recording of certain events, social phenomena as key ones, as well as the typological isomorphism of both theoretical positions. The applied method of researching scientific texts allows us to design an expert system of institutional knowledge for the purpose of modeling international relations.

Praça do Derby, o pitoresco jardim recifense

Derby Square is a relevant public space in Recife, recognized as a historic garden and Brazilian cultural heritage due to Burle Marx’s intervention in the 1930s. However, its construction dates back to the 1920s, the period of modernization of the city, with scarce discussions, until now, about its initial design. This article aims to contribute to the historiographical process of Derby Square, focusing mainly on the 1920s and 1930s, ranging from its construction to Burle Marx’s remodeling. To this end, the technique of indirect documentation was adopted, using primary written and visual sources not yet discussed or published, allowing an investigation, recording, analysis, and interpretation of events and the formulation of a new narrative. With this research, it is understood that Derby Square can be interpreted as a palimpsest, in which the elements of the different periods that make up its history are also responsible for highlighting this garden as cultural heritage.

“Crosstalk between non-coding RNAs and transcription factor LRF in non-small cell lung cancer”

Epigenetic approaches in direct correlation with assessment of critical genetic mutations in non-small cell lung cancer (NSCLC) are currently very intensive, as the epigenetic components underlying NSCLC development and progression have attained high recognition. In this level of research, established human NSCLC cell lines as well as experimental animals are widely used to detect novel biomarkers and pharmacological targets to treat NSCLC. The epigenetic background holds a great potential for the identification of epi-biomarkers for treatment response however, it is highly complex and requires precise definition as these phenomena are variable between NSCLC subtypes and systems origin.We engaged an in-depth characterization of non-coding (nc)RNAs prevalent in human KRAS-mutant NSCLC cell lines A549 and H460 and mouse KRAS-mutant NSCLC tissue by Next Generation Sequencing (NGS) and quantitative Real Time PCRs (qPCRs). Also, the transcription factor (TF) LRF, a known epigenetic silencer, was examined as a modulator of non-coding RNAs expression. Finally, interacting networks underlying epigenetic variations in NSCLC subtypes were created. Data derived from our study highlights the divergent epigenetic profiles of NSCLC of human and mouse origin, as well as the significant contribution of 12qf1: 109,709,060–109,747,960 mouse chromosomal region to micro-RNA upregulated species. Furthermore, the novel epigenetic miR-148b-3p/lncPVT1/ZBTB7A axis was identified, which differentiates human cell line of lung adenocarcinoma from large cell lung carcinoma, two characteristic NSCLC subtypes.The detailed recording of epigenetic events in NSCLC and combinational studies including networking between ncRNAs and TFs validate the identification of significant epigenetic features, prevailing in NSCLC subtypes and among experimental models. Our results enrich knowledge in the field and empower research on the epigenetic prognostic biomarkers of the disease progression, NSCLC subtypes discrimination and advancement to patient-tailored treatments.

Anomaly detection in Smart Traffic Light system using blockchain: Securing through proof of stake and machine learning

<p>The Smart Traffic Light system plays a crucial role in smart cities, contributing significantly to enhancing the overall urban living standards, supporting sustainable practices, assuring public safety, and optimizing operational efficiency. Smart lighting systems leverage advanced technology such as the Internet of Things (IoT), data analytics, and networking to construct a complex and flexible lighting infrastructure. Anomalies within the context of a Smart Light system’s data pertain to patterns, events, or behaviors that are unexpected or uncommon, displaying a considerable deviation from the system’s typical operational state. The distributed and tamper-resistant ledger of blockchain technology renders it well-suited for maintaining an open and immutable record of data and events pertaining to smart lighting systems. The integration of blockchain technology and anomaly detection techniques enables the establishment of a resilient and reliable smart lighting system. The primary objective of this study is to investigate the application of the Isolation Forest algorithm for anomaly detection and its enhancement through the implementation of Proof of Stake for enhanced security measures. The Isolation Forest algorithm is utilized for anomaly prediction and evaluation of accuracy and precision. This is achieved by employing synthetic ground truth labels. Subsequently, the non-anomalous data is incorporated into the system as blocks using blockchain technology. The experimentation involves the use of synthetic numerical data derived from a dataset accessible on Kaggle. This process is conducted in two distinct phases. The first phase focuses on the analysis of synthetic numerical data, specifically targeting the identification of anomalies, prior to the implementation of blockchain technology. In the second phase, the same technique is applied to the synthetic numerical data following the integration of blockchain technology. Various machine learning (ML) models were utilized to analyze the data, resulting in improved accuracy from 48% to 94%, as evidenced by the validation of the obtained results. The empirical results indicate that the utilization of blockchain technology in data processing leads to an improvement in accuracy. Furthermore, the random forest algorithm exhibits robust performance when integrated with blockchain technology.</p>

Accountability as a service for robotics: Performance assessment of different accountability strategies for autonomous robots

Abstract An essential requirement for increasing human confidence in computer systems is knowing an event’s origin. Therefore, it is necessary to have an efficient method to record such information. It is especially challenging in robotics, where unexpected behaviours can have unpredictable consequences, endangering the interests of people or even their safety. Furthermore, to analyse an incident’s cause or anticipate future behaviours, we must identify the events that cause a specific action. Although it is common to use logging systems for such purposes, issues such as partial recording of events, unhelpful data or the impact on robot performance suggest conceiving new accountability solutions that assist when determining the responsible entities or the provenance of specific information. This paper presents a general-purpose approach to developing an accountability system for autonomous robots. It consists of four main components: a system event logger, a message producer, a distributed event streaming platform and a database. Our proposal is completely decoupled from the monitored system and allows real-time analysis, improving flexibility, besides system protection and transparency. Finally, the need to reduce the impact of the audit process and logging tasks on robot performance has promoted the development of different assessment scenarios to determine the best strategy for providing accounting services.

The role of social presence in live and recorded concert viewing: Effects on enjoyment and emotional well-being

In recent years livestream events have become increasingly popular, in part to cope with social isolation experienced during the COVID-19 pandemic. The potential of livestreams to mitigate the negative effects of isolation lacks empirical evidence. To address this gap, we compared the effect of watching a concert on livestream with watching a recording of the same event on viewer's social presence, enjoyment, social connectedness, and loneliness. Livestream viewing was expected to result in higher levels of enjoyment and social connectedness, and lower levels of loneliness compared to viewing the recording. The effect of concert viewing mode on viewers' experience was expected to be mediated by social presence. In line with expectations, participants reported higher social presence and enjoyment in the livestream condition compared to the recorded condition, although, participants reported similar levels of social connectedness and loneliness in both conditions. Moreover, we did not find support for social presence as a mediator. In sum, while livestreamed events can enhance feelings of social presence and be more enjoyable, further research is needed to understand whether and how this can be utilized to mitigate loneliness and lack of social connectedness.

Real-Time Human Activity Recognition on Embedded Equipment: A Comparative Study

As living standards improve, the growing demand for energy, comfort, and health monitoring drives the increased importance of innovative solutions. Real-time recognition of human activities (HAR) in smart homes is of significant relevance, offering varied applications to improve the quality of life of fragile individuals. These applications include facilitating autonomy at home for vulnerable people, early detection of deviations or disruptions in lifestyle habits, and immediate alerting in the event of critical situations. The first objective of this work is to develop a real-time HAR algorithm in embedded equipment. The proposed approach incorporates the event dynamic windowing based on space-temporal correlation and the knowledge of activity trigger sensors to recognize activities in the case of a record of new events. The second objective is to approach the HAR task from the perspective of edge computing. In concrete terms, this involves implementing a HAR algorithm in a “home box”, a low-power, low-cost computer, while guaranteeing performance in terms of accuracy and processing time. To achieve this goal, a HAR algorithm was first developed to perform these recognition tasks in real-time. Then, the proposed algorithm is ported on three hardware architectures to be compared: (i) a NUCLEO-H753ZI microcontroller from ST-Microelectronics using two programming languages, C language and MicroPython; (ii) an ESP32 microcontroller, often used for smart-home devices; and (iii) a Raspberry-PI, optimizing it to maintain accuracy of classification of activities with a requirement of processing time, memory resources, and energy consumption. The experimental results show that the proposed algorithm can be effectively implemented on a constrained resource hardware architecture. This could allow the design of an embedded system for real-time human activity recognition.

Applications of Blockchain in Tourism Industry

Tourism is a rapidly growing industry, even after seeing some drop back during the last few years due to COVID-19. The latest trend shows that it is gaining its position again to become a booming industry of the century, but as the industry gains pace, it faces several challenges that require modern solutions. With the coming up of new technologies, blockchain technology has gained rapid attention not only since it maintains a secure and decentralized record of transactions across the world but also due to its safe access to identifying data while enhancing accessibility for individuals in need in sectors like banking, travel, healthcare, and education. The current tourism industry fails the tourist by not providing a decentralized payment system for advance booking of hotels and vehicles, and the foreign tax required for such transactions increases the further cost for a tourist. With no real-time luggage tracking available, tourists are always on the lookout for their luggage. Blockchain technology improves the accuracy and reliability of baggage mishandling coding by providing a decentralized, tamper-proof record of all baggage handling events. If blockchain technology is incorporated into the tourism industry, many issues in the industry get solved, and it can grow more rapidly, helping economies all over the world by providing smart solutions to difficult problems and thus improving the experience of tourists by providing safety, a decentralized payment system, preventing fraud, and enhancing security by securing the data from unauthorized access. This research helps in further identifying the problem in the tourism industry and solving it by implementing blockchain technology, thus improving economies relying on the tourism industry.

Assessing Workplace Violence: Methodological Considerations.

The risk of violence is present in all workplaces. It must be accurately assessed to establish prevention and protection measures tailored to the features of each situation. The risk management process requires compliance in a sequential order: 1) risk identification, 2) quantitative risk assessment, and 3) impact assessment. Gathering workers' experiences using lists, focus groups, or participatory ergonomics groups is necessary to identify the phenomenon. For risk assessment, spontaneous reporting of events is often insufficient. It may be complemented with two methods: systematic recording of violent events that occurred in the past year during periodic medical examinations of workers and targeted surveys. The epidemiological analysis of data from individual interviews and surveys provides the phenomenon's prevalence, incidence, and evolution. Moreover, reporting the harm suffered by victims of violence allows constructing impact matrices to allocate resources where they are most needed.

Swept coded aperture real-time femtophotography

Single-shot real-time femtophotography is indispensable for imaging ultrafast dynamics during their times of occurrence. Despite their advantages over conventional multi-shot approaches, existing techniques confront restricted imaging speed or degraded data quality by the deployed optoelectronic devices and face challenges in the application scope and acquisition accuracy. They are also hindered by the limitations in the acquirable information imposed by the sensing models. Here, we overcome these challenges by developing swept coded aperture real-time femtophotography (SCARF). This computational imaging modality enables all-optical ultrafast sweeping of a static coded aperture during the recording of an ultrafast event, bringing full-sequence encoding of up to 156.3 THz to every pixel on a CCD camera. We demonstrate SCARF’s single-shot ultrafast imaging ability at tunable frame rates and spatial scales in both reflection and transmission modes. Using SCARF, we image ultrafast absorption in a semiconductor and ultrafast demagnetization of a metal alloy.

Exploring multi-decadal time series of temperature extremes in Australian coastal waters

Abstract. The intensity and frequency of extreme ocean temperature events, such as marine heatwaves (MHWs) and marine cold spells (MCSs), are expected to change as our oceans warm. Little is known about marine extremes in Australian coastal waters, particularly below the surface. Here we introduce a multi-decadal observational record of extreme ocean temperature events starting in the 1940s and 1950s between the surface and the bottom (50–100 m) at four long-term coastal sites around Australia: the Australian Multi-Decadal Ocean Time Series EXTreme (AMDOT-EXT) data products (https://doi.org/10.26198/wbc7-8h24, Hemming et al., 2024). The data products include indices indicating the timing of extreme warm and cold temperature events, their intensity and the corresponding temperature time series and climatology thresholds. We include MHWs, MCSs and shorter-duration heat spikes and cold spikes. For MHWs and MCSs, which are defined as anomalies above the daily varying 90th and 10th percentiles, respectively, and lasting more than 5 d, we also provide further event information, such as their category and onset and decline rates. The four data products are provided as CF-compliant NetCDF files, and it is our intention that they be updated periodically. It is advised that data users seek the latest data product version. Using these multi-decadal data products, we show the most intense and longest extreme temperature events at these sites, which have occurred below the surface. These data records highlight the value of long-term full water column ocean data for the identification of extreme temperature events below the surface.

Visuo-frontal interactions during social learning in freely moving macaques

Social interactions represent a ubiquitous aspect of our everyday life that we acquire by interpreting and responding to visual cues from conspecifics1. However, despite the general acceptance of this view, how visual information is used to guide the decision to cooperate is unknown. Here, we wirelessly recorded the spiking activity of populations of neurons in the visual and prefrontal cortex in conjunction with wireless recordings of oculomotor events while freely moving macaques engaged in social cooperation. As animals learned to cooperate, visual and executive areas refined the representation of social variables, such as the conspecific or reward, by distributing socially relevant information among neurons in each area. Decoding population activity showed that viewing social cues influences the decision to cooperate. Learning social events increased coordinated spiking between visual and prefrontal cortical neurons, which was associated with improved accuracy of neural populations to encode social cues and the decision to cooperate. These results indicate that the visual-frontal cortical network prioritizes relevant sensory information to facilitate learning social interactions while freely moving macaques interact in a naturalistic environment.