Synthetic Developmental Biology: Understanding Through Reconstitution.

Reconstitution is an experimental strategy that seeks to recapitulate biological events outside their natural contexts using a reduced set of components. Classically, biochemical reconstitution has been extensively applied to identify the minimal set of molecules sufficient for recreating the basic chemistry of life. By analogy, reconstitution approaches to developmental biology recapitulate aspects of developmental events outside an embryo, with the goal of revealing the basic genetic circuits or physical cues sufficient for recreating developmental decisions. The rapidly growing repertoire of genetic, molecular, microscopic, and bioengineering tools is expanding the complexity and precision of reconstitution experiments. We review the emerging field of synthetic developmental biology, with a focus on the ways in which reconstitution strategies and new biological tools have enhanced our modern understanding of fundamental questions in developmental biology.

Brain-wide Electrical Spatiotemporal Dynamics Encode Depression Vulnerability.

Brain-wide fluctuations in local field potential oscillations reflect emergent network-level signals that mediate behavior. Cracking the code whereby these oscillations coordinate in time and space (spatiotemporal dynamics) to represent complex behaviors would provide fundamental insights into how the brain signals emotional pathology. Using machine learning, we discover a spatiotemporal dynamic network that predicts the emergence of major depressive disorder (MDD)-related behavioral dysfunction in mice subjected to chronic social defeat stress. Activity patterns in this network originate in prefrontal cortex and ventral striatum, relay through amygdala and ventral tegmental area, and converge in ventral hippocampus. This network is increased by acute threat, and it is also enhanced in three independent models of MDD vulnerability. Finally, we demonstrate that this vulnerability network is biologically distinct from the networks that encode dysfunction after stress. Thus, these findings reveal a convergent mechanism through which MDD vulnerability is mediated in the brain.

Signaling Microdomains in the Spotlight: Visualizing Compartmentalized Signaling Using Genetically Encoded Fluorescent Biosensors.

How cells muster a network of interlinking signaling pathways to faithfully convert diverse external cues to specific functional outcomes remains a central question in biology. Through their ability to convert dynamic biochemical activities to rapid and precise optical readouts, genetically encoded fluorescent biosensors have become instrumental in unraveling the molecular logic controlling the specificity of intracellular signaling. In this review, we discuss how the use of genetically encoded fluorescent biosensors to visualize dynamic signaling events within their native cellular context is elucidating the different strategies employed by cells to organize signaling activities into discrete compartments, or signaling microdomains, to ensure functional specificity.

A bright red fluorescent genetically encoded sensor for lactate imaging.

Lactate plays a crucial role in energy metabolism and greatly impacts protein activities, exerting diverse physiological and pathological effects. Therefore, convenient lactate assays for tracking spatiotemporal dynamics in living cells are desirable. In this paper, we engineered and optimized a red fluorescent protein sensor for l-lactate named FiLa-Red. This indicator exhibited a maximal fluorescence change of 730 % and an apparent dissociation constant (Kd) of approximately 460 µM. By utilizing FiLa-Red and other sensors, we monitored energy metabolism in a multiplex manner by simultaneously tracking lactate and NAD+/NADH abundance in the cytoplasm, nucleus, and mitochondria. The FiLa-Red sensor is expected to be a useful tool for performing metabolic analysis in vitro, in living cells and in vivo.

Optogenetic control of YAP cellular localisation and function.

YAP, an effector of the Hippo signalling pathway, promotes organ growth and regeneration. Prolonged YAP activation results in uncontrolled proliferation and cancer. Therefore, exogenous regulation of YAP activity has potential translational applications. We present a versatile optogenetic construct (optoYAP) for manipulating YAP localisation, and consequently its activity and function. We attach a LOV2 domain that photocages a nuclear localisation signal (NLS) to the N-terminus of YAP. In 488 nm light, the LOV2 domain unfolds, exposing the NLS, which shuttles optoYAP into the nucleus. Nuclear import of optoYAP is reversible and tuneable by light intensity. In cell culture, activated optoYAP promotes YAP target gene expression and cell proliferation. Similarly, optofYap can be used in zebrafish embryos to modulate target genes. We demonstrate that optoYAP can override a cell's response to substrate stiffness to generate anchorage-independent growth. OptoYAP is functional in both cell culture and in vivo, providing a powerful tool to address basic research questions and therapeutic applications in regeneration and disease.

Landsat observations of total suspended solids concentrations in the Pearl River Estuary, China, over the past 36 years.

Information on long-term trends in total suspended solids (TSS) is critical for assessing aquatic ecosystems. However, the long-term patterns of TSS concentration (CTSS) and its latent drivers have not been well investigated. In this study, we developed and validated three semi-analysis algorithms for deriving CTSS using Landsat images. Subsequently, the long-term trends in CTSS in the Pearl River Estuary (PRE) from 1987 to 2022 and the driving factors were clarified. The developed algorithms yielded excellent performance in estimating CTSS, with mean absolute percentage errors <25% and root mean square errors of <13 mg/L. Long-term Landsat observations showed an overall decreasing trend and significant spatiotemporal dynamics of the CTSS in the PRE from 1987 to 2022. The analysis of driving factors suggested that industrial sewage, cropland, forests and grasslands, and built-up land were the four potential driving forces that explained 87.81% of the long-term variation in CTSS. This study not only provides 36-year recorded datasets of CTSS in estuary water, but also offers new insights into the complex mechanisms that regulate CTSS spatiotemporal dynamics for water resource management.

Spatiotemporal dynamics of animal contests arise from effective forces between contestants.

Competition among animals for resources, notably food, territories, and mates, is ubiquitous at all scales of life. This competition is often resolved through contests among individuals, which are commonly understood according to their outcomes and in particular, how these outcomes depend on decision-making by the contestants. Because they are restricted to end-point predictions, these approaches cannot predict real-time or real-space dynamics of animal contest behavior. This limitation can be overcome by studying systems that feature typical contest behavior while being simple enough to track and model. Here, we propose to use such systems to construct a theoretical framework that describes real-time movements and behaviors of animal contestants. We study the spatiotemporal dynamics of contests in an orb-weaving spider, in which all the common elements of animal contests play out. The confined arena of the web, on which interactions are dominated by vibratory cues in a two-dimensional space, simplifies the analysis of interagent interactions. We ask whether these seemingly complex decision-makers can be modeled as interacting active particles responding only to effective forces of attraction and repulsion due to their interactions. By analyzing the emergent dynamics of "contestant particles," we provide mechanistic explanations for real-time dynamical aspects of animal contests, thereby explaining competitive advantages of larger competitors and demonstrating that complex decision-making need not be invoked in animal contests to achieve adaptive outcomes. Our results demonstrate that physics-based classification and modeling, in terms of effective rules of interaction, provide a powerful framework for understanding animal contest behaviors.

Enhancing Radar Echo Extrapolation by ConvLSTM2D for Precipitation Nowcasting.

Precipitation nowcasting in real-time is a challenging task that demands accurate and current data from multiple sources. Despite various approaches proposed by researchers to address this challenge, models such as the interaction-based dual attention LSTM (IDA-LSTM) face limitations, particularly in radar echo extrapolation. These limitations include higher computational costs and resource requirements. Moreover, the fixed kernel size across layers in these models restricts their ability to extract global features, focusing more on local representations. To address these issues, this study introduces an enhanced convolutional long short-term 2D (ConvLSTM2D) based architecture for precipitation nowcasting. The proposed approach includes time-distributed layers that enable parallel Conv2D operations on each image input, enabling effective analysis of spatial patterns. Following this, ConvLSTM2D is applied to capture spatiotemporal features, which improves the model's forecasting skills and computational efficacy. The performance evaluation employs a real-world weather dataset benchmarked against established techniques, with metrics including the Heidke skill score (HSS), critical success index (CSI), mean absolute error (MAE), and structural similarity index (SSIM). ConvLSTM2D demonstrates superior performance, achieving an HSS of 0.5493, a CSI of 0.5035, and an SSIM of 0.3847. Notably, a lower MAE of 11.16 further indicates the model's precision in predicting precipitation.

Terrestrial vegetation carbon sink analysis and driving mechanism identification in the Qinghai-Tibet Plateau.

The estimation of terrestrial carbon sinks in the Qinghai-Tibet Plateau (QTP) still faces significant uncertainties, and the spatiotemporal dynamics of terrestrial carbon sinks along altitudinal gradients remain unexplored. Moreover, the driving mechanisms of terrestrial carbon sinks at the watershed scale in the QTP continue to be lacking. To address these research gaps, based on multi-source remote sensing data and meteorological data, this study calculated the Net Ecosystem Productivity (NEP) in the QTP from 2000 to 2020 using the Modis NPP-soil respiration model. Through the coefficient of variation (CV), the Mann-Kendall test (MK), and the spatial autocorrelation methods, the spatial distribution pattern and spatiotemporal trends of NEP were investigated. Employing a pixel accumulation method, the variation of NEP along altitudinal gradients was explored. Grey relation analysis, Pearson correlation analysis, and Geographical detector (GD) were used to investigate the driving mechanisms of NEP at the watershed scale. Results showed that: (1) the terrestrial ecosystem in the QTP served as a carbon sink, which produced a total of 2.04 Pg C from 2000 to 2020, and the multi-year average of total carbon sinks was 96.92 Tg C; (2) the spatial distribution of NEP shows a decreasing change from southeast to northwest, and the clustering characteristic of NEP is significant at the watershed scale; (3) the elevation of 4507 m we proposed is likely to be a key threshold for biophysical processes of the terrestrial ecosystems in the QTP; (4) the fluctuation and change trend of carbon sources and carbon sinks show significant differences between the East and West; (5) at the watershed scale, precipitation and temperature play a dominant role in the variation of NEP, while the impact of human activities on NEP variation is weak. Our study aims to address the existing knowledge gaps and provide valuable insights into the management of terrestrial carbon sinks in QTP.

Urbanization and weather dynamics co-dominated the spatial-temporal variation in pCO2 and CO2 fluxes in small montanic rivers draining diverse landscapes.

Rivers have been widely reported as important CO2 emitters to the atmosphere. Rapid urbanization has a profound impact on the carbon biogeochemical cycle of rivers, leading to enhanced riverine CO2 evasions. However, it is still unclear whether the spatial-temporal patterns of CO2 emissions in the rivers draining diverse landscapes dominated by urbanization were stable, especially in mountainous areas. This study carried out a two-year investigation of water environmental hydrochemistry in three small mountainous rivers draining urban, suburban and rural landscapes in southwestern China, and CO2 partial pressure (pCO2) and fluxes (fCO2) in surface water were measured using headspace equilibrium method and classical thin boundary layer model. The average pCO2 and fCO2 in the highly urbanized river were of 4783.6 µatm and 700.0 mmol m-2 d-1, conspicuously higher than those in the rural river (1525.9 µatm and 123.2 mmol m-2 d-1), and the suburban river presented a moderate level (3114.2 µatm and 261.2 mmol m-2 d-1). It provided even clearer evidence that watershed urbanization could remarkably enhance riverine CO2 emissions. More importantly, the three rivers presented different longitudinal variations in pCO2, implying diversified spatial patterns of riverine CO2 emissions as a result of urbanization. The urban land can explain 49.6-69.1% of the total spatial variation in pCO2 at the reach scale, indicating that urban land distribution indirectly dominated the longitudinal pattern of riverine pCO2 and fCO2. pCO2 and fCO2 in the three rivers showed similar temporal variability with higher warm-rainy seasons and lower dry seasons, which are significantly controlled by weather dynamics, including monthly temperature and precipitation, but seem to be impervious to watershed urbanization. High temperature-stimulated microorganisms metabolism and riched-CO2 runoff input lead much higher pCO2 in warm-rainy seasons. However, it showed more sensitivity of pCO2 to monthly weather dynamics in urbanized rivers than that in rural rivers, and warm-rainy seasons showed hot moments of CO2 evasion for urban rivers. TOC, DOC, TN, pH and DO were the main controls on pCO2 in the urban and suburban rivers, while only pH and DO were connected with pCO2 in the rural rivers. This indicated differential controls and regulatory processes of pCO2 in the rivers draining diverse landscapes. Furthermore, it suggested that pCO2 calculated by the pH-total alkalinity method would obviously overestimate pCO2 in urban polluted rivers due to the inevitable influence of non-carbonate alkalinity, and thus, a relatively conservative headspace method should be recommended. We highlighted that urbanization and weather dynamics co-dominated the multiformity and uncertainty in spatial-temporal patterns of riverine CO2 evasions, which should be considered when modeling CO2 dynamics in urbanized rivers.

Disentangling the Ecological Processes and Driving Forces Shaping the Seasonal Pattern of Halobacteriovorax Communities in a Subtropical Estuary.

Halobacteriovorax are predatory bacteria that have a significant ecological role in marine environments. However, understanding of dynamics of populations, driving forces, and community composition of Halobacteriovorax groups in natural marine environments is still limited. Here, we used high-throughput sequencing to study the underlying mechanisms governing the diversity and assembly of the Halobacteriovorax community at spatiotemporal scales in a subtropical estuary. Phylogenetic analysis showed that 10 of 15 known Halobacteriovorax clusters were found in the studied estuary. Halobacteriovorax α-diversity and ß-diversity exhibited significant seasonal variation. Variation partitioning analysis showed that the effect of nutrients was greater than that of other measured water properties on Halobacteriovorax community distribution. The results of Spearman's and Mantel's tests indicated that the trophic pollutants dissolved inorganic phosphorus (DIP) and NH4+-N in the estuary were the key factors that significantly affected Halobacteriovorax species and community diversity. In addition, this work indicated that the biological stoichiometry (especially N/P) of nutrients exerted a significant influence on the Halobacteriovorax community. Furthermore, we found that both deterministic and stochastic processes contributed to the turnover of Halobacteriovorax communities, and environmental filtering dominated the assembly of estuarine Halobacteriovorax communities. Overall, we provide new insights into the mechanisms in the generation and maintenance of the Halobacteriovorax community in marine environments.

Distinct roles for the anterior temporal lobe and angular gyrus in the spatiotemporal cortical semantic network.

Semantic knowledge is supported by numerous brain regions, but the spatiotemporal configuration of the network that links these areas remains an open question. The hub-and-spokes model posits that a central semantic hub coordinates this network. In this study, we explored distinct aspects that define a semantic hub, as reflected in the spatiotemporal modulation of neural activity and connectivity by semantic variables, from the earliest stages of semantic processing. We used source-reconstructed electro/magnetoencephalography, and investigated the concreteness contrast across three tasks. In a whole-cortex analysis, the left anterior temporal lobe (ATL) was the only area that showed modulation of evoked brain activity from 100 ms post-stimulus. Furthermore, using Dynamic Causal Modeling of the evoked responses, we investigated effective connectivity amongst the candidate semantic hub regions, that is, left ATL, supramarginal/angular gyrus (SMG/AG), middle temporal gyrus, and inferior frontal gyrus. We found that models with a single semantic hub showed the highest Bayesian evidence, and the hub region was found to change from ATL (within 250 ms) to SMG/AG (within 450 ms) over time. Our results support a single semantic hub view, with ATL showing sustained modulation of neural activity by semantics, and both ATL and AG underlying connectivity depending on the stage of semantic processing.

Unveiling defect-mediated carrier dynamics in monolayer semiconductors by spatiotemporal microwave imaging.

The optoelectronic properties of atomically thin transition-metal dichalcogenides are strongly correlated with the presence of defects in the materials, which are not necessarily detrimental for certain applications. For instance, defects can lead to an enhanced photoconduction, a complicated process involving charge generation and recombination in the time domain and carrier transport in the spatial domain. Here, we report the simultaneous spatial and temporal photoconductivity imaging in two types of WS2 monolayers by laser-illuminated microwave impedance microscopy. The diffusion length and carrier lifetime were directly extracted from the spatial profile and temporal relaxation of microwave signals, respectively. Time-resolved experiments indicate that the critical process for photoexcited carriers is the escape of holes from trap states, which prolongs the apparent lifetime of mobile electrons in the conduction band. As a result, counterintuitively, the long-lived photoconductivity signal is higher in chemical-vapor deposited (CVD) samples than exfoliated monolayers due to the presence of traps that inhibits recombination. Our work reveals the intrinsic time and length scales of electrical response to photoexcitation in van der Waals materials, which is essential for their applications in optoelectronic devices.

Superresolution imaging reveals spatiotemporal propagation of human replication foci mediated by CTCF-organized chromatin structures.

Mammalian DNA replication is initiated at numerous replication origins, which are clustered into thousands of replication domains (RDs) across the genome. However, it remains unclear whether the replication origins within each RD are activated stochastically or preferentially near certain chromatin features. To understand how DNA replication in single human cells is regulated at the sub-RD level, we directly visualized and quantitatively characterized the spatiotemporal organization, morphology, and in situ epigenetic signatures of individual replication foci (RFi) across S-phase at superresolution using stochastic optical reconstruction microscopy. Importantly, we revealed a hierarchical radial pattern of RFi propagation dynamics that reverses directionality from early to late S-phase and is diminished upon caffeine treatment or CTCF knockdown. Together with simulation and bioinformatic analyses, our findings point to a "CTCF-organized REplication Propagation" (CoREP) model, which suggests a nonrandom selection mechanism for replication activation at the sub-RD level during early S-phase, mediated by CTCF-organized chromatin structures. Collectively, these findings offer critical insights into the key involvement of local epigenetic environment in coordinating DNA replication across the genome and have broad implications for our conceptualization of the role of multiscale chromatin architecture in regulating diverse cell nuclear dynamics in space and time.

Combining historical maps and landsat images to delineate the centennial-scale changes of lake wetlands in Taihu Lake Basin, China.

Lake wetlands (LWs) are essential components of the ecosystem and play an irreplaceable role in flood regulation, carbon fixation, and biodiversity maintenance. Continuous monitoring of LWs' change is necessary in the context of increased human disturbance and climate change, particularly in Taihu Lake Basin, China, an area exposed to early human exploitation. Yet, long-time series of LWs detection in this region is still unavailable due to the data limitation. To quantify the spatiotemporal dynamics of LWs and the associated driving forces, we combined 236 historical topographic maps and thousands of Landsat satellite images from the 1910s to 2021 to delineate the centennial-scale changes of lake wetlands for the first time in this region. We also applied land use transitions and statistical analyses to quantitively explore the climatic and anthropogenic factors behind LWs variations. Our results document a dramatic decline in the area and number of LWs in the Taihu Lake Basin over the last century and a shift in the 2000s: Taihu Lake Basin has seen a total of 89.15% loss in lake littoral wetlands and a decrease of 14.5% in the whole lake wetlands area, with a net reduction of 68 (from 156 in the 1910s to 88 in the 2021) lakes. This decrease has been especially predominant during the 1910s-2000s, because of the policy initiatives for reclamation and aquacultural industries. The area and number of LWs have gradually been recovered since the 2000s as the country strengthened concern on the ecological restoration and sustainable development. The statistical results suggested that human activities played a dominant role in the LWs changes, with GDP and population explained 80.74% of the changes, coupled with climatic contribution of only around 20%. This long-term investigation will provide baseline information for future lake wetlands monitoring. Our findings could also provide a guidance for decision makers regarding water resources management, environmental protection and land-use planning in urban areas.

Exploring spatiotemporal dynamics of flower visitor association pattern on two Avicennia mangroves: a network approach.

Plant-flower visitor interaction is one of the most important relationships regarding the co-existence of the floral and faunal communities. The implication of network approaches is an efficient way to understand the impact of community structure on ecosystem functionality. To understand the association pattern of flower visitors, we performed this study on Avicennia officinalis and Avicennia marina mangroves from the islands of Indian Sundarban over three consecutive years. We found that visiting time and sites (islands) influenced the abundance of visitors. The bipartite networks showed a significant generalized structure for both site-visitor and visiting time-visitor networks where the strength and specialization of visitor species showed a highly and moderately significant positive correlation between both networks respectively. All the site-wise visiting time-visitor networks and year-wise site-visitor networks were significantly modular in structure. For both the plants, most of the visitors showed a generalized association pattern among islands and also among visiting times. Additionally, the study of the foraging behavior of dominant visitors showed Apis dorsata and Apis mellifera as the potential visitors for these plants. Our results showed that flower visitor networks are spatiotemporally dynamic. The interactions of visitors with flowers at different times influence their contribution to the network for becoming a generalist or peripheral species in the context of their visiting time, which may subsequently change over islands. This approach will help to devise more precise plant species-specific conservation strategies by understanding the contribution of visitors through the spatiotemporal context.

Spatiotemporal dynamics and functional characteristics of the composition of the main fungal taxa in the root microhabitat of Calanthe sieboldii (Orchidaceae).

BACKGROUND: Endophytic fungi play a critical ecological role in the growth and development of orchids, but little is known about the spatial and temporal dynamics of fungal diversity or the ecological functions of fungi during orchid growth and reproduction. Calanthe sieboldii Decne. is listed in the Chinese National Key Protected Wild Plants as a class I protected wild plant. To understand the community characteristics of root and soil fungi of the orchid during its reproductive seasons, we investigated the community composition, spatial and temporal dynamics, and functional characteristics of the orchid microhabitat fungi by using diversity and ecological functional analyses. RESULTS: We discovered that there were three, seven, and four dominant fungal families in the orchid's roots, rhizoplane soil, and rhizosphere soil, respectively. Tulasnellaceae, Aspergillaceae, and Tricholomataceae were the dominant fungi in this endangered orchid's microhabitats. The closer the fungal community was to the orchid, the more stable and the less likely the community composition to change significantly over time. The fungal communities of this orchid's roots and rhizoplane soil varied seasonally, while those of the rhizosphere soil varied interannually. Saprophytic fungi were the most abundant in the orchid's fungal community, and the closer the distance to the orchid, the more symbiotic fungi were present. CONCLUSIONS: The fungi in different parts of the root microhabitat of C. sieboldii showed different spatiotemporal dynamic patterns. The fungal community near the orchid roots was relatively stable and displayed seasonal variation, while the community further away from the roots showed greater variation. In addition, compared with the soil fungi, the dominant endophytic fungi were more stable, and these may be key fungi influencing orchid growth and development. Our study on the spatiotemporal dynamics and functions of fungi provides a basis for the comprehensive understanding and utilization of orchid endophytic fungi.

Profile and dynamics of infectious diseases: a population-based observational study using multi-source big data.

BACKGROUND: The current surveillance system only focuses on notifiable infectious diseases in China. The arrival of the big-data era provides us a chance to elaborate on the full spectrum of infectious diseases. METHODS: In this population-based observational study, we used multiple health-related data extracted from the Shandong Multi-Center Healthcare Big Data Platform from January 2013 to June 2017 to estimate the incidence density and describe the epidemiological characteristics and dynamics of various infectious diseases in a population of 3,987,573 individuals in Shandong province, China. RESULTS: In total, 106,289 cases of 130 infectious diseases were diagnosed among the population, with an incidence density (ID) of 694.86 per 100,000 person-years. Besides 73,801 cases of 35 notifiable infectious diseases, 32,488 cases of 95 non-notifiable infectious diseases were identified. The overall ID continuously increased from 364.81 per 100,000 person-years in 2013 to 1071.80 per 100,000 person-years in 2017 (χ2 test for trend, P < 0.0001). Urban areas had a significantly higher ID than rural areas, with a relative risk of 1.25 (95% CI 1.23-1.27). Adolescents aged 10-19 years had the highest ID of varicella, women aged 20-39 years had significantly higher IDs of syphilis and trichomoniasis, and people aged ≥ 60 years had significantly higher IDs of zoster and viral conjunctivitis (all P < 0.05). CONCLUSIONS: Infectious diseases remain a substantial public health problem, and non-notifiable diseases should not be neglected. Multi-source-based big data are beneficial to better understand the profile and dynamics of infectious diseases.

Probing the Functional and Structural Connectivity Underlying EEG Traveling Waves.

Neural oscillations play an important role in the maintenance of brain function by regulating multi-scale neural activity. Characterizing the traveling properties of EEG is helpful for understanding the spatiotemporal dynamics of neural oscillations. However, traveling EEG based on non-invasive approach has little been investigated, and the relationship with brain intrinsic connectivity is not well known. In this study, traveling EEG of different frequency bands on the scalp in terms of the center of mass (EEG-CM) was examined. Then, two quantitative indexes describing the spatiotemporal features of EEG-CM were proposed, i.e., the traveling lateralization and velocity of EEG-CM. Further, based on simultaneous EEG-MRI approach, the relationship between traveling EEG-CM and the resting-state functional networks, as well as the microstructural connectivity of white matter was investigated. The results showed that there was similar spatial distribution of EEG-CM under different frequency bands, while the velocity of rhythmic EEG-CM increased in higher frequency bands. The lateralization of EEG-CM in low frequency bands (< 30 Hz) demonstrated negative relationship with the basal ganglia network (BGN). In addition, the velocity of the traveling EEG-CM was associated with the fractional anisotropy (FA) in corpus callosum and corona radiate. These results provided valid quantitative EEG index for understanding the spatiotemporal characteristics of the scalp EEG, and implied that the EEG dynamics were representations of functional and structural organization of cortical and subcortical structures.

Spatiotemporal dynamics and anthropologically dominated drivers of chlorophyll-a, TN and TP concentrations in the Pearl River Estuary based on retrieval algorithm and random forest regression.

Estimation of large-scale and high-precision water quality parameters is critical in explaining the spatiotemporal dynamics and the driving factors of water quality variability, especially in areas with environmental complexity (e.g., crisscrossing waterways, high flood risk in rainy season and seawater invasion). Thus, in this study, a retrieval algorithm was developed to predict chlorophyll-a (Chl-a), total nitrogen (TN) and total phosphorus (TP) concentrations in the Pearl River Estuary (PRE) based on a large amount of in situ measurements and Landsat 8 remote sensing images. Random Forest (RF) machine learning was conducted to identify the relationship between environmental indicators (pH, turbidity, conductivity, total dissolved solids and water temperature), Chl-a, TN and TP. The results showed that the NIR/R Binomial algorithm for Chl-a estimation presented appreciable reliability with R2 of 0.7429, root mean square error (RMSE) of 1.2089 and mean absolute percent error (MAPE) of 15.33%. The water quality variation in the PRE showed a characteristic of overall improvement and regional deterioration with average concentrations of 7.28 µg/L, 1.15 mg/L and 0.12 mg/L for Chl-a, TN, and TP respectively. Turbidity and pH were identified as the most important indicators to explain Chl-a (52.86%, 39.91%), TN (52.38%, 40.57%) and TP (55.23%, 40.03%) variation. Agricultural pollution was the main pollution source due to the intensive application of fertilizer and increased field size. Besides, land use patterns (e.g., increasing farmland but decreasing forest) greatly influenced water quality from 2010 to 2020. Moreover, light limitation caused by high turbidity reduced the algae productivity and further lowered the Chl-a concentration. The driving factors for regional water quality variations were anthropologically dominated and supplemented by climate change. This study improved the monitoring accuracy of regional water environment and provided quantitative early warning of water pollution events for environmental practitioners, so as to achieve long-term monitoring, precise pollution management and efficient water resources management.