Spatiotemporal Dynamics Research Articles

Macrophage-mediated heart repair and remodeling: A promising therapeutic target for post-myocardial infarction heart failure.

Heart failure (HF) remains prevalent in patients who survived myocardial infarction (MI). Despite the accessibility of the primary percutaneous coronary intervention and medications that alleviate ventricular remodeling with functional improvement, there is an urgent need for clinicians and basic scientists to further reveal the mechanisms behind post-MI HF as well as investigate earlier and more efficient treatment after MI. Growing numbers of studies have highlighted the crucial role of macrophages in cardiac repair and remodeling following MI, and timely intervention targeting the immune response via macrophages may represent a promising therapeutic avenue. Recently, technology such as single-cell sequencing has provided us with an updated and in-depth understanding of the role of macrophages in MI. Meanwhile, the development of biomaterials has made it possible for macrophage-targeted therapy. Thus, an overall and thorough understanding of the role of macrophages in post-MI HF and the current development status of macrophage-based therapy will assist in the further study and development of macrophage-targeted treatment for post-infarction cardiac remodeling. This review synthesizes the spatiotemporal dynamics, function, mechanism and signaling of macrophages in the process of HF after MI, as well as discusses the emerging bio-materials and possible therapeutic agents targeting macrophages for post-MI HF.

High-Accuracy Classification of Multiple Distinct Human Emotions Using EEG Differential Entropy Features and ResNet18

The high-accuracy detection of multiple distinct human emotions is crucial for advancing affective computing, mental health diagnostics, and human–computer interaction. The integration of deep learning networks with entropy measures holds significant potential in neuroscience and medicine, especially for analyzing EEG-based emotion states. This study proposes a method combining ResNet18 with differential entropy to identify five types of human emotions (happiness, sadness, fear, disgust, and neutral) from EEG signals. Our approach first calculates the differential entropy of EEG signals to capture the complexity and variability of the emotional states. Then, the ResNet18 network is employed to learn feature representations from the differential entropy measures, which effectively captures the intricate spatiotemporal dynamics inherent in emotional EEG patterns using residual connections. To validate the efficacy of our method, we conducted experiments on the SEED-V dataset, achieving an average accuracy of 95.61%. Our findings demonstrate that the combination of ResNet18 with differential entropy is highly effective in classifying multiple distinct human emotions from EEG signals. This method shows robust generalization and broad applicability, indicating its potential for extension to various pattern recognition tasks across different domains.

An exact mathematical description of computation with transient spatiotemporal dynamics in a complex-valued neural network

Networks throughout physics and biology leverage spatiotemporal dynamics for computation. However, the connection between structure and computation remains unclear. Here, we study a complex-valued neural network (cv-NN) with linear interactions and phase-delays. We report the cv-NN displays sophisticated spatiotemporal dynamics, which we then use, in combination with a nonlinear readout, for computation. The cv-NN can instantiate dynamics-based logic gates, encode short-term memories, and mediate secure message passing through a combination of interactions and phase-delays. The computations in this system can be fully described in an exact, closed-form mathematical expression. Finally, using direct intracellular recordings of neurons in slices from neocortex, we demonstrate that computations in the cv-NN are decodable by living biological neurons as the nonlinear readout. These results demonstrate that complex-valued linear systems can perform sophisticated computations, while also being exactly solvable. Taken together, these results open future avenues for design of highly adaptable, bio-hybrid computing systems that can interface seamlessly with other neural networks.

Unlocking opioid neuropeptide dynamics with genetically encoded biosensors

Neuropeptides are ubiquitous in the nervous system. Research into neuropeptides has been limited by a lack of experimental tools that allow for the precise dissection of their complex and diverse dynamics in a circuit-specific manner. Opioid peptides modulate pain, reward and aversion and as such have high clinical relevance. To illuminate the spatiotemporal dynamics of endogenous opioid signaling in the brain, we developed a class of genetically encoded fluorescence sensors based on kappa, delta and mu opioid receptors: κLight, δLight and µLight, respectively. We characterized the pharmacological profiles of these sensors in mammalian cells and in dissociated neurons. We used κLight to identify electrical stimulation parameters that trigger endogenous opioid release and the spatiotemporal scale of dynorphin volume transmission in brain slices. Using in vivo fiber photometry in mice, we demonstrated the utility of these sensors in detecting optogenetically driven opioid release and observed differential opioid release dynamics in response to fearful and rewarding conditions.

Ecosystem health assessment of East Kolkata Wetlands, India: Implications for environmental sustainability

The East Kolkata Wetlands (EKW) in Kolkata, India, span 12,500 ha and are a vital ecological zone providing several benefits, including water purification, flood control, and biodiversity support. This study investigated land use and land cover (LULC) alterations in the EKW from 1991 to 2023, using a random forest (RF) machine learning model. Significant LULC changes were observed over the 32 years, with wetland areas decreasing from 91.2 km2 in 1991 to 33.4 km2 in 2023, reflecting substantial habitat loss and reduced ecosystem services. Conversely, agricultural land expanded from 27.8 km2 to 58.7 km2, driven by economic and food production needs, and built-up areas increased dramatically from 0.2 km2 to 10.5 km2, indicating rapid urbanization. This study evaluated the health, resilience, and ecosystem functionality of EKW by analysing human-induced land use changes and using ecological indicators and landscape metrics. Landscape and class level metrics such as PLAND, largest patch index (LPI), total edge (TE), edge density (ED), number of patches (NP), and patch density (PD) were used to analyse the spatiotemporal dynamics of the wetlands. This study revealed a significant increase in fragmentation, with the number of patches increasing from 2689 in 1991 to 4532 in 2023, despite a consistent decrease in core wetland areas. Ecosystem health indicators, such as the ecosystem structure index (ESI) and landscape deviation degree (LDD), were used to assess landscape metrics and fragmentation changes. The ESI and other metrics revealed significant temporal fluctuations, providing insights into landscape structure, connectivity, and heterogeneity. The ESI improved from 0.87 in 1991 to 1.03 in 2023, indicating enhanced connectivity and diversity. Conversely, the LDD increased from 20.6% to 56.85%, indicating a shift towards impervious surfaces. The vegetation productivity and ecosystem health index (EHI) decreased, indicating biodiversity loss and reduced carbon sequestration. The EHI also dropped from 0.67 to 0.55, signifying ongoing environmental stress. This study emphasizes the need for conservation efforts to maintain the ecological integrity of the EKW amidst urbanization and land use changes and recommends a balanced approach for sustainable urban development and enhanced wetland resilience.

Full-Dimensional Proportional-Derivative Control Technique for Turing Pattern and Bifurcation of Delayed Reaction-Diffusion Bidirectional Ring Neural Networks

Abstract In neural networks, the states of neural networks often exhibit significant spatio-temporal heterogeneity due to the diffusion effect of electrons and differences in the concentration of neurotransmitters. One of the macroscopic reflections of this time-spatial inhomogeneity is Turing pattern. However, most current research in reaction-diffusion neural networks has focused only on one-dimensional location information, and the remaining results considering two-dimensional location information are still limited to the case of two neurons. In this paper, we conduct the dynamic analysis and optimal control of a delayed reaction-diffusion neural network model with bidirectional loop structure. First, several mathematical descriptions are given for the proposed neural network model and the full-dimensional partial differential proportional-derivative (PD) controller is introduced. Second, by analyzing the characteristic equation, the conditions for Hopf bifurcation and Turing instability of the controlled network model are obtained. Furthermore, the amplitude equation of the controlled neural network is obtained based on the multiscale analysis method. Subsequently, we determine the key parameters affecting the formation of Turing pattern depending on the amplitude equation. Finally, multiple sets of computer simulations are carried out to support our theoretical results. It is found that the diffusion coefficients and time delays have significant effects on spatio-temporal dynamics of neural networks. Moreover, after reasonable parameter proportioning, the full-dimensional PD control method can alleviate the spatial heterogeneity caused by diffusion projects and time delays.

Intentional binding for noninvasive BCI control.

Noninvasive brain-computer interfaces (BCIs) allow to interact with the external environment by naturally bypassing the musculoskeletal system.
Making BCIs efficient and accurate is paramount to improve the reliability of real-life and clinical applications, from open-loop device control to closed-loop neurorehabilitation. By promoting sense of agency and embodiment, realistic setups including multimodal channels of communication, such as eye-gaze, and robotic prostheses aim to improve BCI performance. However, how the mental imagery command should be integrated in those hybrid systems so as to ensure the best interaction is still poorly understood.
To address this question, we performed a hybrid EEG-based BCI training involving healthy volunteers enrolled in a reach-and-grasp action operated by a robotic arm. Main results showed that the hand grasping motor imagery timing significantly affects the BCI accuracy evolution as well as the spatiotemporal brain dynamics. Larger accuracy improvement was obtained when motor imagery is performed just after the robot reaching, as compared to before or during the movement. The proximity with the subsequent robot grasping favored intentional binding, led to stronger motor-related brain activity, and primed the ability of sensorimotor areas to integrate information from regions implicated in higher-order cognitive functions.
Taken together, these findings provided fresh evidence about the effects of intentional binding on human behavior and cortical network dynamics that can be exploited to design a new generation of efficient brain-machine interfaces.

Spatio-temporal changes of small protist and free-living bacterial communities in a temperate dimictic lake: insights from metabarcoding and machine learning.

Microbial communities, which include prokaryotes and protists, play an important role in aquatic ecosystems and influence ecological processes. To understand these communities, metabarcoding provides a powerful tool to assess their taxonomic composition and track spatio-temporal dynamics in both marine and freshwater environments. While marine ecosystems have been extensively studied, there is a notable research gap in understanding eukaryotic microbial communities in temperate lakes. Our study addresses this gap by investigating the free-living bacteria and small protist communities in Lake Roś (Poland), a dimictic temperate lake. Metabarcoding analysis revealed that both the bacterial and protist communities exhibit distinct seasonal patterns that are not necessarily shaped by dominant taxa. Furthermore, machine learning and statistical methods identified crucial amplicon sequence variants (ASVs) specific to each season. In addition, we identified a distinct community in the anoxic hypolimnion. We have also shown that the key factors shaping the composition of analysed community are temperature, oxygen, and silicon concentration. Understanding these community structures and the underlying factors is important in the context of climate change potentially impacting mixing patterns and leading to prolonged stratification.

ECOSENSE - Multi-scale quantification and modelling of spatio-temporal dynamics of ecosystem processes by smart autonomous sensor networks

Global climate change threatens ecosystem functioning worldwide. Forest ecosystems are particularly important for carbon sequestration, thereby buffering climate change and providing socio-economic services. However, recurrent stresses, such as heat waves, droughts and floods can affect forests with potential cascading effects on their carbon sink capacity, drought resilience and sustainability. Knowledge about the stress impact on the multitude of processes driving soil-plant-atmosphere interactions within these complex forest systems is widely lacking and uncertainty about future changes extremely high. Thus, forecasting forest response to climate change will require a dramatically improved process understanding of carbon and water cycling across various temporal (minutes to seasons) and spatial (leaf to ecosystem) scales covering atmosphere, biosphere, pedosphere and hydrosphere components. Many relevant processes controlling carbon and water exchange occur at small scales (e.g. rhizosphere, single leaf) with a high spatial and temporal variability, which is poorly constrained. However, interactions and feedback loops can be key players that amplify or dampen a system’s response to stress. Moreover, spatial and temporal scaling rules for these non-linear processes in structurally and functionally diverse ecosystems are unknown. Legacy effects, for example, altered response after previous stress and retarded recovery of forests after climate extremes, are not captured in state-of-the-art models. Currently, we are lacking the appropriate and interconnected measurement, data assimilation and modelling tools allowing for a comprehensive, real-time quantification of key processes at high spatio-temporal coverage in heterogeneous environments. Moreover, since climate impacts are highly unpredictable with respect to timing and location, future research will require novel mobile, easily deployable and cost-efficient approaches. ECOSENSE, therefore, assembles expertise from environmental and engineering sciences, both being excellently paired at the University of Freiburg. Our interdisciplinary research project will investigate all relevant scales in a next-generation ecosystem research assessment (ECOSENSE). Our vision is to detect and forecast critical changes in ecosystem functioning, based on the understanding of hierarchical process interaction. In the first phase, ECOSENSE will explore these process interactions by investigating pools and fluxes of water and carbon, i.e. CO2 exchange, isotope discrimination and volatile organic compounds (VOC), as well as stress indicators by remotely and in situ sensed chlorophyll fluorescence. To address these research tasks, ECOSENSE will develop, implement and test a distributed, autonomous, intelligent sensor network, based on novel microsensors tailored to the specific needs in remote and harsh forest environments. They will measure the spatio-temporal dynamics of ecosystem pools and fluxes in a naturally complex structured forest system with minimal physiological impact. Measured data will be transferred in real-time into a sophisticated database, which will be explored for process analysis, conducted by Artificial Intelligence and close to real-time process-based ecosystem models for now- and forecasting applications. Thereby, ECOSENSE will: i) break new ground for integrative ecosystem research by identifying hierarchies and interactions of abiotic and physiological processes of forest carbon and water exchange, ii) provide a profound understanding of complex ecosystem responses to environmental stressors and iii) enable the prediction of process-based alterations in ecosystem functioning and sustainability. Our novel ECOSENSE toolkit, tested and validated in controlled climate extreme experiments and our ECOSENSE Forest, will open new horizons for rapid assessment in vast and remote ecosystems. Thereby, ECOSENSE will allow for a unique avenue of data acquisition and, consequently, for unprecedented scale-crossing ecosystem understanding and modelling.

Disparities and similarities in the spatiotemporal dynamics of flash and slow droughts in China

Climate warming has induced significant transitions from slowly-developing droughts to rapidly-developing flash droughts in China, causing broad impacts on ecosystems, hydrological regimes, and society. To date, most studies focused on temporal evolution of flash droughts, while neglected the spatial expansion which is essential for understanding their origins and spatial propagations, especially for mega flash droughts. Based on the long-term (1940–2022) dataset of the 5th generation of the European ReAnalysis, here we use a three-dimensional drought identification method to analyze the disparities and similarities in the spatiotemporal dynamics of flash and slow droughts at the subseasonal time scale over China. Although half of the flash and slow droughts are characterized by small areas (<5000 km2), short durations (30–45 d) and short propagation distances of drought centroids (<50 km), the probability of large-scale (>30 000 km2) flash droughts with long propagation distances (>100 km) is twice of slow droughts. Moreover, global and local spatial autocorrelation analyses reveal that South China (SC) and North China are hotspots for large-scale flash and slow droughts, respectively, and they both show significant increasing trends (0.11–0.12 events/decade) during 1940–2022. Without these large-scale droughts, there is no obvious difference in spatial distributions of the frequency of flash and slow droughts. Despite disparities, both large-scale flash and slow droughts show a preferential westward propagation, with 60%–67% of the movements consistent with the pathways of atmospheric water vapor flux anomaly. Our study urges the understanding and prevention of large-scale flash drought events, especially in SC.

Integrating key ecosystem services to study the spatio-temporal dynamics and determinants of ecosystem health in Wuhan’s central urban area

The central areas of many Chinese cities have experienced large-scale urbanization in recent decades, a trend that is affecting the quality of life of residents and posing challenges to the sustainable development of urban ecosystems. Hence, it is vital to conduct assessments of urban ecosystems’ health conditions to ensure their sustainability. In this study, the spatial and temporal dynamics of ecosystem health in the central city of Wuhan were analyzed using the Vigor-Organization-Resilience-Service (VORS) model. The sensitivity of ecosystem health at different scales was also investigated using hierarchical health assessment delineation and random forest regression methods. The results showed that the composite index of ecosystem health has been declining since 2000, highlighting the negative impacts of shrinking ecological space, declining service capacity and urban expansion. Health zoning divides areas into five categories. Meanwhile, the health zones are located in areas of dense shrubs, cropland and water bodies on the edge of the central city, while the weak zones are mainly in the core of the city, which is 99.33 % impervious surface. The main factors influencing ecosystem health, including impervious surface area, water bodies and topography, vary from region to region. The study proposes targeted ecological management strategies for different health zones in Wuhan, emphasizing ecosystem protection and providing guidance for sustainable urban development.

Cell-intrinsic mechanical regulation of plasma membrane accumulation at the cytokinetic furrow

Cytokinesis is the process where the mother cell's cytoplasm separates into daughter cells. This is driven by an actomyosin contractile ring that produces cortical contractility and drives cleavage furrow ingression, resulting in the formation of a thin intercellular bridge. While cytoskeletal reorganization during cytokinesis has been extensively studied, less is known about the spatiotemporal dynamics of the plasma membrane. Here, we image and model plasma membrane lipid and protein dynamics on the cell surface during leukemia cell cytokinesis. We reveal an extensive accumulation and folding of the plasma membrane at the cleavage furrow and the intercellular bridge, accompanied by a depletion and unfolding of the plasma membrane at the cell poles. These membrane dynamics are caused by two actomyosin-driven biophysical mechanisms: the radial constriction of the cleavage furrow causes local compression of the apparent cell surface area and accumulation of the plasma membrane at the furrow, while actomyosin cortical flows drag the plasma membrane toward the cell division plane as the furrow ingresses. The magnitude of these effects depends on the plasma membrane fluidity, cortex adhesion, and cortical contractility. Overall, our work reveals cell-intrinsic mechanical regulation of plasma membrane accumulation at the cleavage furrow that is likely to generate localized differences in membrane tension across the cytokinetic cell. This may locally alter endocytosis, exocytosis, and mechanotransduction, while also serving as a self-protecting mechanism against cytokinesis failures that arise from high membrane tension at the intercellular bridge.

Spatiotemporal dynamics and similarity in soil moisture in shallow soils on karst slopes

Understanding the spatiotemporal dynamics and similarity of soil moisture on shallow slopes is important for inferring soil hydrological processes and for vegetation restoration. In karst areas, the spatial distribution patterns and dynamic changes in shallow soil moisture are particularly complex; however, whether there is spatiotemporal similarity in shallow soil moisture on karst slopes has not been fully determined. This work investigated the spatiotemporal dynamic variations in shallow soil moisture in three types of karst slope plots (abandoned land, peanut and Zanthoxylum bungeanum). Wavelet coherence analysis was used to evaluate the spatiotemporal similarity and factors influencing shallow soil moisture under different vegetation types and slope positions. The results indicate that the alternation between dry and wet conditions and spatiotemporal heterogeneity were significant in the shallow soils of slopes. Comparatively, the dry-wet cycle in the shallow soil of abandoned land occurred faster than that in the shallow soil, and the shallow soil for planting vegetation had a better water holding capacity. The terrain and slope position controlled the distribution and dynamic variations in soil moisture on shallow slopes. Compared with those in the middle and upper slopes, the soil moisture in the bottom slopes was greater, and the coefficient of variation was lower. The average coherence between the soil moisture in the different plots was greater than 0.90, and the significant area was greater than 93.0%. The coherence and significant area in the rainy season were significantly greater than those in the dry season. Due to the influence of vegetation types and meteorological conditions, the drought interval after rainfall events led to a decrease in the spatiotemporal similarity of soil moisture. This study indicates that shallow soil moisture in karst slopes exhibits temporal stability and dynamic similarity. The alternation of dry-wet conditions controls the similarity of shallow soil moisture on the slope.

Differences in spatiotemporal brain network dynamics of Montessori and traditionally schooled students

Across development, experience has a strong impact on the way we think and adapt. School experience affects academic and social-emotional outcomes, yet whether differences in pedagogical experience modulate underlying brain network development is still unknown. In this study, we compared the brain network dynamics of students with different pedagogical backgrounds. Specifically, we characterized the diversity and stability of brain activity at rest by combining both resting-state fMRI and diffusion-weighted structural imaging data of 87 4–18 years old students experiencing either the Montessori pedagogy (i.e., student-led, trial-and-error pedagogy) or the traditional pedagogy (i.e., teacher-led, test-based pedagogy). Our results revealed spatiotemporal brain dynamics differences between students as a function of schooling experience at the whole-brain level. Students from Montessori schools showed overall higher functional integration (higher system diversity) and neural stability (lower spatiotemporal diversity) compared to traditionally schooled students. Higher integration was explained mainly through the cerebellar (CBL) functional network. In contrast, higher temporal stability was observed in the ventral attention, dorsal attention, somatomotor, frontoparietal, and CBL functional networks. This study suggests a form of experience-dependent dynamic functional connectivity plasticity, in learning-related networks.

Incorporating Spatial Autocorrelation into GPP Estimation Using Eigenvector Spatial Filtering

Terrestrial gross primary productivity (GPP) is a critical part of land carbon fluxes. Accurately quantifying GPP in terrestrial ecosystems and understanding its spatiotemporal dynamics are essential for assessing the capability of vegetation to absorb carbon from the atmosphere. Nevertheless, traditional remote sensing estimation models often require complex parameters and data inputs, and they do not account for spatial effects resulting from the distribution of monitoring sites. This can lead to biased parameter estimation and unstable results. To address these challenges, we have raised a spatial autocorrelation light gradient boosting machine model (SA-LGBM) to enhance GPP estimation. SA-LGBM combines reflectance information from remote sensing observations with eigenvector spatial filtering (ESF) methods to create a set of variables that capture continuous spatiotemporal variations in plant functional types and GPP. SA-LGBM demonstrates promising results when compared to existing GPP products. With the inclusion of eigenvectors, we observed an 8.5% increase in R2 and a 20.8% decrease in RMSE. Furthermore, the residuals of the model became more random, reducing the inherent spatial effects within them. In summary, SA-LGBM represents the first attempt to quantify the impact of spatial autocorrelation and addresses the limitations of underestimation present in existing GPP products. Moreover, SA-LGBM exhibits favorable applicability across various vegetation types.

Hopf bifurcation and patterns formation in a diffusive two prey-one predator system with fear in preys and help

Recognizing the relationship between the spatial patterns in species concentrations and ecological heterogeneity is crucial for understanding demographics and species governance in a given domain, as ecological patterning processes are believed to be imitated in real ecosystems. In this present article, we have considered a two-prey-one-predator system with Holling type-II functional response where both the preys have fear of predator and compete in the absence of predator. Additionally, we have assumed that both the prey help each other when the predator attacks them. We have incorporated self-diffusion in this system under Neumann boundary conditions. The existence and stability conditions of different equilibria are discussed. We have studied the Hopf bifurcation around positive offset and derived the stability and direction of periodic solution. We have done a series of numerical simulations to ensure our theoretical finding. Diffusion-driven instability conditions are obtained. Different instability regions and fascinating patterns, such as spots, mixtures, and stripes, are depicted. Spatiotemporal dynamics also reflects that prey populations are located in isolated areas of low population concentration with increasing level of fear, whereas an increase in handling time forms lower-density stripes. But with increasing help, prey becomes more concentrated in some stripe regions. In the Hopf-Turing region, it is observed that the diffusion coefficient of predators can stabilize the system. Overall, pattern creation in predator-prey systems can help anticipate long-term system behaviour and analyze the influence of ecological factors on system dynamics.

Spatiotemporal dynamics and forecasting of ecological security pattern under the consideration of protecting habitat: a case study of the Poyang Lake ecoregion

ABSTRACT The continuous expansion and development of anthropogenic activities pose considerable threats to the habitats of myriad species. Therefore, exploring how to protect and optimize species’ habitats to achieve harmony between humans and nature is particularly important. As a paradise for wildlife, Poyang Lake is the habitat and breeding ground for birds, fish, and other aquatic organisms. In this study, adopting a multi-level perspective of ‘point-line-area’, we had an appliance for habitat conservation by constructing an ecological security pattern (ESP) in the Poyang Lake Ecoregion (PYLE). We produced land use and land cover from 2000 to 2040 using the random forest and cellular automata-Markov model. Combining morphological spatial pattern analysis, landscape pattern index, and Linkage Mapper, we constructed a long-term ESP in the PYLE considering habitat protection. The spatiotemporal dynamics of ESP were compared and analyzed. The results show that PYLE's rapid economic development led to extensive urban growth, encroaching on surrounding bare land, which could undermine local species’ survival. Based on PYLE's predicted ESP in 2040, we propose an optimized ‘one area, two zones, and multiple points’ scheme to prioritize habitat protection. This comprehensive study provides innovative ideas and novel insights for future research and policy formulation in habitat protection.

Primary Sjögren syndrome specific B cells induced aberrant surface aggregation of B cell receptors (BCRs) and signalling

Primary Sjögren’s syndrome (pSS) is a systematic autoimmune manifestation of the exocrine glands caused by impaired lymphocytic infiltration, leading to dry eyes and xerostomia. The pathologic hallmark of this disease is the dysfunction of the exocrine glands, which results in dry eyes and mouth. Clinical observations suggest that B cells contribute to the pathogenesis of pSS through the enhanced secretion of autoantibodies, hypergammaglobulinemia, reduced free light chains, and a higher B cell lymphoma. Despite increased evidence of B cell hyperactivation and dysregulated B cell interactions in pSS, detailed speculations into the mechanism by which pSS-specific B cells exhibited abnormal BCR signaling repertoires and the magnitude of downstream signaling responses remain limited. Upon encountering antigens, B lymphocytes produce neutralizing antibodies by phosphorylating immune receptor tyrosine activation motifs (ITAMs). Despite these advancements, we still do not understand how pSS-specific B cells influence the surface clustering of BCRs and subsequent downstream immune responses. This paper hypothesizes that pSS-specific B cells encountering surrogate antigens will undergo a coordinated series of events that initiate B cell activation and spatiotemporal dynamics of BCRs. Additionally, we assume that pSS individuals have aberrant BCR repertoire and clonal expansion. In the future, these studies can provide detailed therapeutic entanglements for B cell-linked autoimmune and other hypersensitive disorders.

Spatio-temporal dynamics of phytoplankton community structure in the coastal waters of the Southern Beibu Gulf.

This study was conducted during October 2021 (autumn) and April 2022 (spring) to explore the phytoplankton community structure, their distribution characteristics, and the influence of environmental factors in the coastal waters of the Southern Beibu Gulf. The 15 sampling sites were grouped based on the difference in offshore distance to analyze the temporal and spatial differences in community structure and environmental driving in the investigated sea area of the coastal waters of the Southern Beibu Gulf. Permutational multivariate analysis of variance was conducted on the sample data in time and space, revealing that there is no significant difference in space (p > 0.05), but there is significant difference in time (p < 0.05). Notably, water pressure, pH, chemical oxygen demand, nitrite, and labile phosphate were higher in autumn, while total ammonia nitrogen, dissolved oxygen, and suspended solids were significantly higher in spring. Additionally, the study identified 87 phytoplankton species belonging to 6 phyla, dominating by Bacillariophyta, followed by Dinophyta and Cyanophyta. The phytoplankton density, Shannon Weiner's diversity index (H'), Pielou's evenness index (J), and Margalef's richness index (D) ranged from 84.88 to 4675.33 cells L-1, 0.56 to 2.58, 0.26 to 0.89, and 1.21 to 3.64, respectively. Permutational multivariate analysis of variance showed non-significant spatial differences in phytoplankton composition (p > 0.05) but seasonal differences (p < 0.05). Furthermore, canonical correspondence analysis (CCA) identified pH, dissolved oxygen, suspended solids, chemical oxygen demand, nitrite, and labile phosphate as key environmental factors influencing the phytoplankton community structure (p < 0.05). In this study, the dynamic changes of phytoplankton community structure and environmental factors in the southern coastal waters of Beibu Gulf were analyzed in detail from two aspects of time and space. The key environmental factors to protect the ecological environment in the southern coastal area of Beibu Gulf were found out. It provides a reference method and theoretical basis for the management and protection of Beibu Gulf and other tropical marine environment.

Spatiotemporal Analysis of Nighttime Crimes in Vienna, Austria

Studying the spatiotemporal dynamics of crime is crucial for accurate crime geography research. While studies have examined crime patterns related to weekdays, seasons, and specific events, there is a noticeable gap in research on nighttime crimes. This study focuses on crimes occurring during the nighttime, investigating the temporal definition of nighttime crime and the correlation between nighttime lights and criminal activities. The study concentrates on four types of nighttime crimes, assault, theft, burglary, and robbery, conducting univariate and multivariate analyses. In the univariate analysis, correlations between nighttime crimes and nighttime light (NTL) values detected in satellite images and between streetlight density and nighttime crimes are explored. The results highlight that nighttime burglary strongly relates to NTL and streetlight density. The multivariate analysis delves into the relationships between each nighttime crime type and socioeconomic and urban infrastructure variables. Once again, nighttime burglary exhibits the highest correlation. For both univariate and multivariate regression models the geographically weighted regression (GWR) outperforms ordinary least squares (OLS) regression in explaining the relationships. This study underscores the importance of considering the location and offense time in crime geography research and emphasizes the potential of using NTL in nighttime crime analysis.