Historical Dataset Research Articles

Integrating LiDAR Sensor Data into Microsimulation Model Calibration for Proactive Safety Analysis.

Studies have shown that vehicle trajectory data are effective for calibrating microsimulation models. Light Detection and Ranging (LiDAR) technology offers high-resolution 3D data, allowing for detailed mapping of the surrounding environment, including road geometry, roadside infrastructures, and moving objects such as vehicles, cyclists, and pedestrians. Unlike other traditional methods of trajectory data collection, LiDAR's high-speed data processing, fine angular resolution, high measurement accuracy, and high performance in adverse weather and low-light conditions make it well suited for applications requiring real-time response, such as autonomous vehicles. This research presents a comprehensive framework for integrating LiDAR sensor data into simulation models and their accurate calibration strategies for proactive safety analysis. Vehicle trajectory data were extracted from LiDAR point clouds collected at six urban signalized intersections in Lubbock, Texas, in the USA. Each study intersection was modeled with PTV VISSIM and calibrated to replicate the observed field scenarios. The Directed Brute Force method was used to calibrate two car-following and two lane-change parameters of the Wiedemann 1999 model in VISSIM, resulting in an average accuracy of 92.7%. Rear-end conflicts extracted from the calibrated models combined with a ten-year historical crash dataset were fitted into a Negative Binomial (NB) model to estimate the model's parameters. In all the six intersections, rear-end conflict count is a statistically significant predictor (p-value < 0.05) of observed rear-end crash frequency. The outcome of this study provides a framework for the combined use of LiDAR-based vehicle trajectory data, microsimulation, and surrogate safety assessment tools to transportation professionals. This integration allows for more accurate and proactive safety evaluations, which are essential for designing safer transportation systems, effective traffic control strategies, and predicting future congestion problems.

Design of a Model Using Machine Learning and Deep Dyna Q Learning Integration for Improved Disease Prediction in Remote Healthcare

In the domain of proactive healthcare management, the imperative for remote health monitoring has escalated, the remote health care in this scenario specially means, the patient is seating at the remote location that is not in the hospital setting, and doctor or healthcare worker is monitoring the health parameters gathered using biomedical sensors and passed through the network. Conventional methodologies, while partially effective, encounter challenges in predictive precision, responsiveness to evolving health dynamics, and managing the vast array of patient data. These limitations underscore the demand for a sophisticated, holistic solution catering to diverse use cases. This work introduces a pioneering framework amalgamating traditional machine learning (ML) models with the advanced capabilities of Deep Dyna Q Learning process to overcome existing constraints. This framework strategically utilizes ensemble of traditional algorithms which amalgamates the strengths of these diverse models. Central to this model is the integration of Deep Dyna Q Learning, empowering the system with real-time adaptability and dynamic decision-making process through reinforcement learning principles, thereby deriving insights from historical and simulated datasets to foster more nuanced, patient-centric decisions. The impact of this comprehensive approach is profound, evidenced by preliminary results showcasing significant enhancements in the efficiency of remote health monitoring systems. Notably, the model achieves increase in precision, accuracy and recall for disease prediction. These improvements signify a paradigm shift towards proactive and efficient healthcare interventions, especially in remote settings. The fusion of traditional ML techniques with Deep Dyna Q Learning emerges as a potent solution, heralding a revolution in remote health monitoring and establishing a new benchmark for proactive healthcare delivery scenarios.

Has climate change over the last ten years caused a banalisation of diatom communities in Cypriot streams?

To unveil possible changes in diatom communities in Cypriot streams over the last ten years or so, we selected samples from the years 2020, 2021, and 2022 for the “recent” dataset (N = 119) and samples from the years 2010 and 2011 for the “historical” dataset (N = 108). Biotic homogenization has become a truly global phenomenon. Here we show that, over the last ten years, in response to increased water temperature, conductivity, and discharge variability due to climate-change, Cypriot stream diatom communities include a higher number of trivial (= widespread, tolerant, and opportunistic), aerial, and thermophilic species, have reduced β-diversity and increased nestedness. Moreover, IndVal analysis shows that indicator species from the historical dataset were characteristic, often relatively rare species, while the indicators of the recent dataset were a group of typical trivial, eutraphentic, and thermophilic species. As is almost always the case, the diatom communities we studied were subjected to multiple stressors, often affecting them in opposite ways. Besides the increase in trivial species, the reduction in β-diversity, and the rise in nestedness mentioned above, the diatom assemblages we studied also showed an increase in α-diversity that could be due to a moderate reduction in nutrients in several sites. High-ecological-integrity ecosystems, such as springs, waterfalls, and dripping rock-walls, in particular springs that were shown to be excellent hydrologic refugia in climates heavily affected by climate change, and the stream sites close to them should be carefully protected, as they can be refugia for sensitive and characteristic species that can recolonize the adjacent streams after adverse climatic events.

Practice of a Load Shifting Algorithm for Enhancing Community-Scale RES Utilization

Amidst the recent energy crisis, the pivotal roles of resource efficiency and renewable energy sources (RES) for sustainable development have become apparent. The transition to sustainability involves decentralized energy solutions empowering local communities to generate, store, and utilize their energy, diminishing the reliance on centralized systems and potentially transforming them into resources for power flexibility. Addressing the above necessitates, amongst other elements, the adoption of advanced demand-side management (DSM) strategies. In response, we introduce a versatile algorithm investigating the impact of DSM on the community scale, designed to maximize the utilization of renewable energy produced from local installations. Integrated as an ancillary module in a research data management platform, the algorithm underwent testing using historical datasets collected from end-consumers and a small-scale RES installation. This study not only offers insights for energy stakeholders, but also establishes theoretical parameters that can inform subsequent decision-making processes in the field.

Enriching Earth observation datasets through semantics for climate change applications: The EIFFEL ontology

Background Earth Observation (EO) datasets have become vital for decision support applications, particularly from open satellite portals that provide extensive historical datasets. These datasets can be integrated with in-situ data to power artificial intelligence mechanisms for accurate forecasting and trend analysis. However, researchers and data scientists face challenges in finding appropriate EO datasets due to inconsistent metadata structures and varied keyword descriptions. This misalignment hinders the discoverability and usability of EO data. Methods To address this challenge, the EIFFEL ontology (EIFF-O) is proposed. EIFF-O introduces taxonomies and ontologies to provide (i) global classification of EO data and (ii) linkage between different datasets through common concepts. The taxonomies specified by the European Association of Remote Sensing Companies (EARSC) have been formalized and implemented in EIFF-O. Additionally, EIFF-O incorporates: 1. An Essential Climate Variable (ECV) ontology, defined by the Global Climate Observing System (GCOS), is embedded and tailored for Climate Change (CC) applications. 2. The Sustainable Development Goals (SDG) ontology is included to facilitate linking datasets to specific targets. 3. The ontology extends schema.org vocabularies and promotes the use of JavaScript Object Notation for Linked Data (JSON-LD) formats for semantic web integration. Results EIFF-O provides a unified framework that enhances the discoverability, usability, and application of EO datasets. The implementation of EIFF-O allows data providers and users to bridge the gap between varied metadata descriptions and structured classification, thereby facilitating better linkage and integration of EO datasets. Conclusions The EIFFEL ontology represents a significant advancement in the organization and application of EO datasets. By embedding ECV and SDG ontologies and leveraging semantic web technologies, EIFF-O not only streamlines the data discovery process but also supports diverse applications, particularly in Climate Change monitoring and Sustainable Development Goals achievement. The open-source nature of the ontology and its associated tools promotes rapid adoption among developers

Unit information Dirichlet process prior.

Prior distributions, which represent one's belief in the distributions of unknown parameters before observing the data, impact Bayesian inference in a critical and fundamental way. With the ability to incorporate external information from expert opinions or historical datasets, the priors, if specified appropriately, can improve the statistical efficiency of Bayesian inference. In survival analysis, based on the concept of unit information (UI) under parametric models, we propose the unit information Dirichlet process (UIDP) as a new class of nonparametric priors for the underlying distribution of time-to-event data. By deriving the Fisher information in terms of the differential of the cumulative hazard function, the UIDP prior is formulated to match its prior UI with the weighted average of UI in historical datasets and thus can utilize both parametric and nonparametric information provided by historical datasets. With a Markov chain Monte Carlo algorithm, simulations and real data analysis demonstrate that the UIDP prior can adaptively borrow historical information and improve statistical efficiency in survival analysis.

Genetic trends in the Kenya Highland Maize Breeding Program between 1999 and 2020.

Optimization of a breeding program requires assessing and quantifying empirical genetic trends made through past efforts relative to the current breeding strategies, germplasm, technologies, and policy. To establish the genetic trends in the Kenyan Highland Maize Breeding Program (KHMP), a two-decade (1999-2020) historical dataset from the Preliminary Variety Trials (PVT) and Advanced Variety Trials (AVT) was analyzed. A mixed model analysis was used to compute the genetic gains for traits based on the best linear unbiased estimates in the PVT and AVT evaluation stages. A positive significant genetic gain estimate for grain yield of 88 kg ha-1 year-1 (1.94% year-1) and 26 kg ha-1 year-1 (0.42% year-1) was recorded for PVT and AVT, respectively. Root lodging, an important agronomic trait in the Kenya highlands, had a desired genetic gain of -2.65% year-1 for AVT. Results showed improvement in resistance to Turcicum Leaf Blight (TLB) with -1.19% and -0.27% year-1 for the PVT and AVT, respectively. Similarly, a significant genetic trend of -0.81% was noted for resistance to Gray Leaf Spot (GLS) in AVT. These findings highlight the good progress made by KHMP in developing adapted maize hybrids for Kenya's highland agroecology. Nevertheless, the study identified significant opportunities for the KHMP to make even greater genetic gains for key traits with introgression of favorable alleles for various traits, implementing a continuous improvement plan including marker-assisted forward breeding, sparse testing, and genomic selection, and doubled haploid technology for line development.

Conglomerates at the court: The political consequences of mergers &amp; acquisitions

Increasingly, corporations expand through the creation or acquisition of new subsidiary companies. Despite the commonality of the practice, little is known regarding how it influences corporations’ political behavior. This research note analyzes how subsidiaries shape corporations’ political interests and collaborations as they seek to influence the Supreme Court. To accomplish this, we construct a historical dataset of the acquisitions and mergers of a politically active sample of Fortune 500 corporations (spanning various industries and sizes) that we combine with their history of filing amicus curiae briefs to the Court. Through social network and longitudinal analyses, we analyze whether and how corporations change their targeted issue areas, collaborations, and political success following consolidation. While mergers and acquisitions have little effect on the quantity of actions or success before the Court, they expand the issues of political interest for corporations, and increase both their popularity and ability to broker information in their political network.

Increasing acceptance of AI-generated digital twins through clinical trial applications.

Today's approach to medicine requires extensive trial and error to determine the proper treatment path for each patient. While many fields have benefited from technological breakthroughs in computer science, such as artificial intelligence (AI), the task of developing effective treatments is actually getting slower and more costly. With the increased availability of rich historical datasets from previous clinical trials and real-world data sources, one can leverage AI models to create holistic forecasts of future health outcomes for an individual patient in the form of an AI-generated digital twin. This could support the rapid evaluation of intervention strategies in silico and could eventually be implemented in clinical practice to make personalized medicine a reality. In this work, we focus on uses for AI-generated digital twins of clinical trial participants and contend that the regulatory outlook for this technology within drug development makes it an ideal setting for the safe application of AI-generated digital twins in healthcare. With continued research and growing regulatory acceptance, this path will serve to increase trust in this technology and provide momentum for the widespread adoption of AI-generated digital twins in clinical practice.

Ricardo’s Law of Comparative Advantage in Bronze Age, Iron Age, and Early Historic Mainland Southeast Asian Metallurgy: Copper-base Metal Production, Exchange, and Consumption Behaviours at Khao Sai On, Nil Kham Haeng, Non Pa Wai, Phromthin Tai, Sab Champa, and Tha Kae in Central Thailand

Since its late 1970s to early 1980s discovery, the Khao Wong Prachan Valley (KWPV) of central Thailand has been assumed to have been a major supplier of copper in Bronze Age, Iron Age, and Early Historic Southeast Asia. KWPV was the first regional metal production system to be characterised by lead isotope (LI) analysis in the late 2000s, revealing a coherent signature easily distinguished from subsequent analyses of production systems at Phu Lon (northern Thailand) and the Vilabouly Complex (central Laos). Despite KWPV’s scale of production, its LI signature has scarcely been detected at Bronze Age metal consumption sites in nearby northeast Thailand and not at all in Iron Age assemblages. We study copper production and copper/bronze consumption behaviours of Iron Age and Early Historic sites in KWPV’s immediate vicinity: Khao Sai On, Phromthin Tai, Tha Kae, and Sab Champa. Khao Sai On and Phromthin Tai production signatures are highly consistent with those previously established, while the uranogenic tendency at Tha Kae explains outliers from Bronze Age Non Pa Wai and Nil Kham Haeng. Analysis of consumption assemblages reveals little to no consistency with local production signatures. This tendency was tested with LI Iron Age and Early Historic datasets for Ban Khu Muang, Ban Mai Chaimongkol, Ban Pong Manao, and Ban Pong Takhob. The pattern is clear: central Thai populations imported copper/bronze, potentially from the Vilabouly Complex or production loci with closely comparable signatures. We examine this counterintuitive behaviour inductively using Ricardo’s Law of Comparative Advantage combined with a technological appreciation of the relatively low productivity of central Thai primary copper production. This suggests central Thailand’s unattached copper producers freely exchanged copper for exotic goods (bronze, glass, semi-precious stone), with potentially poor terms of exchange, from their desire to participate in wider regional trends in conspicuous consumption.

Using an integrated hydro-economic model to determine the drought and energy relationship in the Upper Euphrates Basin

ABSTRACT Decreasing precipitation in the Upper Euphrates Basin and the negative impact of climate change directly affect water resources and hydroelectricity generation in the basin. This basin, which contains the largest dams in terms of hydroelectricity generation potential, requires research studies to assess and characterize drought for risk prevention and mitigation applicable to water resources management. To better assess drought in the upper Euphrates Basin due to recent warming, FEHEM is developed, a hydro-economic optimization model of the integrated reservoir system of the Upper Euphrates Basin. Using a historical hydrological dataset, water management and hydroelectric operations are evaluated with a linear programming model at monthly time steps. This paper uses two different drought indices: (1) the standardized precipitation index, which is based on precipitation alone; and (2) the reconnaissance drought index, which takes into account both evaporation and precipitation. These indices were used to evaluate the impact of temporal drought characteristics in the Upper Euphrates Basin on the hydropower generation of 10 dams with a total installed capacity of over 3255 MW in the basin, based on 45 years of precipitation data from more than a hundred measuring stations in the basin.

Single-instant spatial wave height forecast using machine learning: An image-to-image translation approach based on generative adversarial networks

An innovative single-instant approach for spatial significant wave height forecast based on a Generative Adversarial Networks (GANs) model is presented. The model's most distinctive feature is its ability to provide spatial wave height forecast with only a single snapshot of wind field input, eliminating the need for continuous wind field sequences while simultaneously maintaining high accuracy and enhanced efficiency. The proposed model architecture comprises an image-to-image translation generator and a patch-style discriminator, working in tandem to forecast high-quality wave height data. The model is trained using a 20-year ECMWF Reanalysis v5 (ERA5) wind and wave dataset. A vast sea area covering 15°-40°N, 105°-130°E in West Pacific is used for this study. The presented model is carefully tested against a 10-year period historical dataset and the results are discussed. The model exhibits outstanding overall performances with Mean Absolute Error (MAE) of 0.16 m, Root Mean Square Error (RMSE) of 0.24 m, and Mean Absolute Percentage Error (MAPE) of 13.4 %. The model proves particularly effective in forecasting extreme wave heights during tropical cyclones, capturing the structure of typhoon-driven waves and providing wave heights with favorable accuracy. It is also proficient at delivering satisfactory statistical wave height forecasts for long-term periods.

Historical Immigration Policies: Trends and Lessons

Abstract In recent years, scholars of migration have created several new immigration policy indexes, but most existing databases have limited temporal scope. They also focus, to a large extent, on the Global North. In this research note, we introduce the Historical Immigration Policy dataset (HIP), which begins to fill these gaps. We first provide an overview of the data and then describe how they offer new insights into immigration policy. We make three empirical observations. (1) On average, democracies are less open to immigration than authoritarian states but grant resident migrants more rights. (2) European states were open to immigration earlier than standard accounts of global migration assume. (3) Historically, openness to immigration and inclusive rights for resident migrants have often been complements, not substitutes.

Ground-penetrating radar scanning and historical interpretation of the location of the destroyed Epiphany Cathedral in Kyiv Brotherhood Monastery (Ukraine)

Background The article presents results of a ground penetrating radar (GPR) scanning carried out in a site of the Epiphany Cathedral of Brotherhood Monastery in Kyiv, Ukraine, destroyed in 1936 by the Soviets. The Cathedral is known as a burial site of Hetman Petro Sahaidachnyi (1582–1622), a Ukrainian Cossack political and civic leader, guardian and patron of Kyiv Brotherhood Monastery. The collection of archival documents, blueprints, stock sources, photographs and cartographic materials of the 17th-19th centuries, as well as historical works of the 19th-21st centuries, were thoroughly analyzed and used as a basis for the interpretation of geophysical results. The set of historical data covers the period from 1615, that is, from the beginning of the construction of the wooden Epiphany Church, the predecessor of the cathedral, to the present day. Methods Summarized information about the construction, restoration after the fire, functioning and destruction of the Cathedral, as well as about the construction on its site in the 20th century, archaeological research in the 20th-21st centuries, was used to clarify possible location of hidden foundations and target GPR measurements. In this context, written testimonies of archaeologists who personally observed the foundations of the cathedral became especially valuable. The shielded GPR antennas, with a central frequency of 300 MHz and 700 MHz, is used for non-invasive investigation. Results GPR scanning specified the site and showed the best-preserved group of foundations of the western wall of the Epiphany Cathedral. Conclusions An integrated historical and geophysical study provided the basis to certify the foundation of the Epiphany Cathedral as a protected object of cultural heritage and facilitated its archaeological research.

A stochastic-MILP dispatch optimization model for concentrated solar thermal under uncertainty

Concentrated Solar Thermal (CST) offers a promising solution for large-scale solar energy utilization as Thermal Energy Storage (TES) enables electricity generation independent of daily solar fluctuations, shifting to high-priced electricity intervals. The development of dispatch planning tools is mandatory to account for uncertainties associated with weather and electricity price forecasts. A Stochastic Mixed-Integer Linear Program (SMILP) is proposed to maximize Sample Average Approximation (SAA) of expected profit within a specified scenario space. The SMILP exhibits robust performance, yet its computational time poses a challenge. Three heuristic solutions are developed which run a set of deterministic optimizations on different historical weather profiles to generate candidate Dispatch Plans (DPs). Subsequently, the DP with the best average performance on all profiles is selected. The new methods are applied to a hypothetical 115 MW CST plant in South Australia. When the historical database has a limited set of historical weather profiles, the SMILP achieves 6–9 % higher profit than the closest heuristic when the DPs are applied to novel weather conditions. With a large historical weather dataset, the performance of the SMILP and closet heuristic becomes nearly identical since the SMILP can only utilize a limited number of trajectories for optimization without becoming computationally infeasible. In this case, the heuristic emerges a practical alternative, providing similar average profit in a reasonable time. Taken together, the results illustrate the importance of considering uncertainty in DP optimization and indicate that straightforward heuristics on a large database are a practical method for addressing uncertainty.

IMG-20. IDENTIFICATION OF PRE-OPERATIVE IMAGING FEATURES ASSOCIATED WITH COGNITIVE IMPAIRMENT IN CHILDREN WITH BRAIN TUMOURS USING A LANGUAGE MODELLING SYSTEM COMBINED WITH MACHINE LEARNING ANALYSIS

Abstract BACKGROUND Cognitive impairments caused by paediatric brain tumours negatively impact future quality-of-life. Impairments of processing speed and working memory are important contributors to challenges these children encounter in education. Inconsistent image acquisition and poor imaging quality can restrict advanced radiomic analysis in historical datasets. We developed and refined a language modelling human image-analysis support system, passing data to specific machine learning techniques that can utilise datasets with many data points in a limited population. METHODS Medical records of children treated for brain tumours with surgery and radiotherapy in Manchester between 2000-2020 were reviewed with imaging accessible from 2006-2020. Pre-operative MRI scans were reviewed for 52 patients along with their most recent processing speed and/or working memory test scores. 274 radiological variables analysing tumour features and damage to brain structures were reported per patient using a standardised reporting tool. Univariate analysis was performed using Linear Regression with time-to-test analysed as a covariate. Multivariate analysis used Elastic Net Regression. RESULTS Univariate analysis revealed risk factors for slower processing speed included midline shift, tumour in the corpus callosum, right temporal periventricular white matter oedema, diffusion changes in the left frontal lobe or amygdala and FLAIR changes in the left caudate nucleus (p&amp;lt;0.005). Risk factors for impaired working memory included midline shift, cortical tumour location, left periventricular white matter oedema and diffusion or FLAIR changes in the left frontal lobe (p&amp;lt;0.005). Despite the limited population size and review of only pre-operative features, multivariate analysis produced multiple predictive models able to outperform chance when predicting processing speed and working memory outcomes. CONCLUSIONS This study provides evidence that a radiological language modelling approach can be utilised for outcome prediction. It identified imaging features predictive of cognitive impairments prior to surgery and radiotherapy. Further work is needed to assess inter-observer variability and validate these findings.

IMG-21. HOW CAN WE BEST UTILISE HISTORICAL IMAGING DATASETS FOR RESEARCH IN PAEDIATRIC NEURO-ONCOLOGY? A REVIEW OF HISTORICAL PRE-OPERATIVE IMAGING PERFORMED FOR 92 PAEDIATRIC BRAIN TUMOUR PATIENTS WITH COMPARISON TO 2021 SIOPE GUIDELINES

Abstract BACKGROUND Utilisation of historical imaging is important for studying long-term outcomes in paediatric brain tumours often pooling data across multiple centres retrospectively over many years. Disparities in MRI sequence parameters, imaging planes, fields of view and resolution limit quantitative radiomic analysis and segmentation algorithms. We review historical imaging datasets, analysing the MRI scans performed assessing suitability for modern research techniques. METHODS Retrospective review of presenting MRI scans was performed in 92 paediatric brain tumour patients between 2006-2020. Whether imaging was performed internally at our paediatric neurosciences centre or externally at their referring hospitals was recorded. Image sequences, planes and slice thickness were compared with 2021 SIOPE guidelines for paediatric brain tumour imaging. T2 signal intensities for tumour, white and grey matter were compared for scans completed in different centres. Statistical testing was performed with T-Test and Paired T-Test. RESULTS 75% of patients had all SIOPE essential sequences and 60.9% were in the recommended planes. Only 1 patient had the desired slice thickness in all sequences recommended for 1.5T scanners. 38.5% had T1 and 27.8% had post-contrast T1 in the ≤1mm slice thickness recommended for 3T scanners. Comparison between 62 internal and 30 external scans revealed statistically significant differences in T2 signal intensities for regions of interest (ROI) within tumour, white and grey matter (p&amp;lt;0.01). Comparison of matched ROI for 12 patients imaged internally and externally demonstrated statistically significant differences in T2 signal intensities (p&amp;lt;0.05). Differences persisted despite normalisation of T2 signal intensity as ratios between tumour and the weighted mean of white and grey matter (p&amp;lt;0.01). CONCLUSIONS This study highlights the challenges of non-standardisation of imaging resolution and acquisition parameters in historical imaging. One approach to utilise these datasets is to use qualitative classifications where human experts label imaging features and encode data for future statistical or machine learning analysis.

The accurate digitization of historical sea level records

AbstractUnderstanding regional sea level variations is crucial for assessing coastal vulnerability, with accurate sea level data playing a pivotal role. Utilizing historical sea level marigrams can enhance datasets, but current digitization techniques face challenges such as bends and skews in paper charts, impacting sea level values. This study explores often‐overlooked issues during marigram digitization, focusing on the case study of Dún Laoghaire in Ireland (1925–1931). The methodology involves digitizing the original marigram trace and underlying grid to assess offsets at the nearest ft (foot) interval on the paper chart, corresponding to changes in the water level trace for each hour interval. Subtracting the digitized value from the known value (the actual measurement) allows for the determination of differences, which are then subtracted from each hourly trace value. After adjusting for offsets ranging from −3.962 to 13.716 mm (millimetres), the study improves the final accuracy of sea level data to approximately the 10 mm level. Notably, data from 1926 and 1931 exhibit modest offsets (&lt;7 mm), while other years show more substantial offsets (&gt;9–14 mm), emphasizing the importance of adjustments for accuracy. Such 10 mm accuracy is compatible with requirements of the Global Sea Level Observing System. Comparing the adjusted digitized data with other survey data shows similar amplitudes and phases for Dún Laoghaire in both the historical and modern datasets, and there is an overall mean sea level rise of 1.5 mm/year when combined with the available data from the Dublin region.

Temporal patterns of gut microbiota in lemurs (Eulemur rubriventer) living in intact and disturbed habitats in a novel sample type.

The gut microbiome is a plastic phenotype; gut microbial composition is highly variable across an individual host's lifetime and between host social groups, and this variation has consequences for host health. However, we do not yet fully understand how longitudinal microbial dynamics and their social drivers may be influenced by ecological stressors, such as habitat degradation. Answering these questions is difficult in most wild animal systems, as it requires long-term collections of matched host, microbiome, and environmental trait data. To test if temporal and social influences on microbiome composition differ by the history of human disturbance, we leveraged banked, desiccated fecal samples collected over 5 months in 2004 from two ecologically distinct populations of wild, red-bellied lemurs (Eulemur rubriventer) that are part of a long-term study system. We found that social group explained more variation in microbiome composition than host population membership did, and that temporal variation in common microbial taxa was similar between populations, despite differences in history of human disturbance. Furthermore, we found that social group membership and collection month were both more important than individual lemur identity. Taken together, our results suggest that synchronized environments use can lead to synchronized microbial dynamics over time, even between habitats of varying quality, and that desiccated samples could become a viable approach for studying primate gut microbiota. Our work opens the door for other projects to utilize historic biological sample data sets to answer novel temporal microbiome questions in an ecological context.

Adaptive Graph Spatial-Temporal Attention Networks for long lead ENSO prediction

El Niño-Southern Oscillation (ENSO) is a crucial factor in global climate change, which can lead to disasters such as floods, droughts, and heavy rainfall worldwide. Although accurate long-lead prediction of ENSO is essential, the intricate time-varying spatial correlations make it challenging. In this paper, we propose a novel deep learning architecture, called Adaptive Graph Spatial-Temporal Attention Network (AGSTAN), to model the extensive spatial–temporal interactions in ENSO. We first develop a dynamic spatial graph module with self-attention mechanism, wherein the structure is adapted to the characteristics of the evolving states. Furthermore, we introduce the long-term temporal sequence module to capture long-range dependency between time-series outputs and inputs, and generate accurate long lead predictions with only one forward step. To validate its efficacy and robustness, we conducted experiments on the simulated and historical climate datasets. Results showcase that AGSTAN possesses superior performance in ENSO prediction, outperforming contemporary deep learning models and boasting a 23-month forecasting horizon with heightened correlation and minimized bias.